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Access to care

For children under‐five across high pneumonia mortality countries in sub‐Saharan Africa

Camielle Noordam

Promotor

Prof. dr. G.J. Dinant

Copromotor

Dr. J.W.L. Cals

Beoordelingscommissie

Prof. dr. C.J.P.A. Hoebe (voorzitter)

Dr. P. van den Hombergh

Dr. J.S.M. Krumeich

Prof. dr. J.F.M. Metsemakers

Prof. dr. S.S. Peterson

Contents

Part I Introduction 5 Chapter 1 General introduction 7 Part II The three phases of delay in care 17 Chapter 2 Associations between caregivers’ kowledge and care seeking 19 behaviour for children with suspected pneumonia in six sub‐Saharan African countries Submitted

Chapter 3 Care seeking behaviour for children with suspected pneumonia 33 in countries in sub‐Saharan Africa with high pneumonia mortality

PLoS One 2015;10:e0117919

Chapter 4 The use of counting beads to improve the classification of fast 53 breathing in low resource settings: A multi‐country review Health Policy and Planning 2015;30:696–704

Part III A potential solution to decrease delays 71 Chapter 5 Improvement of maternal health services through the use of 73 mobile phones

Tropical Medicine & International Health 2011;16:622–626

Chapter 6 Improving care‐seeking for facility‐based health services in a rural, 83 resource‐limited setting: Effects and potential of an mHealth project African Population Studies 2015;28:1643‐1662

Chapter 7 Assessing scale‐up of mHealth innovations based on intervention 103 complexity: Two case studies on child health programmes in Malawi and Zambia Journal of Health Communication 2015; 0: 1–11

Part IV Discussion 123 Chapter 8 General discussion 125

Summary 135

List of publications 139

5

PART I

Introduction

7

CHAPTER 1

General introduction

Chapter 1

8


9

CHILD MORTALITY

Over the past decades, child mortality has reduced significantly. Estimates from the

United Nations illustrate that there has been a global decline in the under‐five

mortality of 53 percent; from 91 deaths per 1,000 live births in 1990 to 43 in 2015.1‐2

Despite these changes, 5.9 million children died before their fifth birthday in 2015 (i.e.,

more than 16,000 deaths a day), mostly from preventable diseases.2

Infectious diseases, also known as transmissible or communicable diseases, accounted

globally for more than half of the under‐five deaths, followed by deaths during or

shortly after birth. Of the infectious diseases, pneumonia is the leading cause of the

under‐five mortality attributing to 16% of all child deaths, followed by diarrhoea (9%)

and malaria (5%). Nutritional status influences these outcomes; about 45 percent of the

under‐five mortality is attributable to under‐nutrition.1‐5 Of the children under the age

of five, the incidence of infectious diseases is the highest for those under the age of 2;

more specifically 81% of the deaths due to pneumonia occur within the first two years

of a child’s life.6 Figure 1.1 shows the differences in causes of under‐five mortality

between high‐ and low‐income countries, illustrating that as income levels within

countries decrease, the proportion of deaths due to infectious diseases increases.

Figure 1.1 Causes of under‐five mortality for high‐ and low income countries; a) high‐income countries, 1.4% of global under‐five mortality, and b) low‐income countries, 33% of global under‐five

mortality. Data from Committing to Child Survival: A Promise Renewed. Progress Report 2013.

© United Nations Children’s Fund (UNICEF) September 2013

Pneumonia17%

Diarrhoea10%

Malaria10%

AIDS2%

Pertussis, tetanus, measles, meningitis

7%

Sepsis5%

Other neonatal27%

Other 22%

Infectious diseases 51%

b

Chapter 1

10

Of all children, a child living in sub‐Saharan Africa is most likely to die before the age of

five, where on average 1 out of every 8 children dies before their fifth birthday.2 Huge

differences are seen in the chance of survival within and between these countries,

where Angola has the highest mortality rate (157 per 1,000 live births) and Seychelles

the lowest (14 per 1,000).1‐2 Figure 1.2 shows the differences in mortality by country.

Figure 1.2 The differences in under‐five mortality by country, with the highest rates found in sub‐Saharan

Africa. Printed with permission from Committing to Child Survival: A Promise Renewed.

Progress Report 2015. © United Nations Children’s Fund (UNICEF) September 2015

One of the reasons for child mortality to decline over the past decades is due to an

increase in coverage of effective health interventions. These interventions focus not

only on increasing access to care upon the onset of an illness (i.e., more effective and

affordable treatments), but also on measures which prevent children from becoming ill

in the first place (e.g., clean water, sanitation, education, improved nutrition and

vaccinations).2‐4 While improved access to these interventions has saved a lot of lives,

especially children living in isolated and marginalized settings still fail to reach them.7

Not only do these children fail to access preventive measures, but upon illness they also

often fail to reach acceptable, affordable and appropriate health care, in time.


11

ACCESS TO CARE

To know how to increase coverage of these interventions for children in sub‐Saharan

Africa, especially amongst those who currently fail to reach them in time, is important.

To do so, donors and policy makers need to understand the underlying determinants

which prevent children from accessing these interventions in the first place. The model

of ‘three delays’ has been used to help untangle challenges associated with care

seeking. The model focuses on delays in accessing care, as the understanding is that the

chance to survive is linked to the timelines in which care is received.8 In this thesis, the

model will be applied to assess the challenges associated with delays in accessing care

for children, more specifically those with symptoms of pneumonia (also referred to as

‘suspected pneumonia cases’). While the model was initially designed to categorize

factors affecting the onset of obstetric complications and its outcomes, it is not the first

time it is used to assess child health outcomes.9‐10 Analyses based on such modelling

help create a more comprehensive understanding of why care is – or is not sought in

time. This as it looks at challenges at household, as well as facility level. This is needed,

as the children accessing care represent only a subset of all the children actually

requiring health services. Hence delays which occur at home need to be examined too –

even more so in sub‐Saharan African settings, where sick children often fail to reach the

formal health system.10

How challenges in accessing care can be captured by three stages of delays

Thaddeus and Maine designed the model of three phases of delay, which was first

presented in 1994.8

The three phases are as followed;

1. The delay in deciding to seek care on the part of the individual, the family or both,

which can be influenced – amongst others – by the status of women; distance from

the health facility; costs; poor recognition and/or understanding of the illness (to

assess the complications and/ or risks); previous experiences; and perceived

quality of care.

2. The delay in reaching an adequate health care facility, which is mostly determined

by geographical aspects, such as the distribution of facilities and the conditions of

the road.

3. And, the delay in receiving adequate health care at the health facility, which can be

influenced by poor quality and lack of resources as a result of inadequately trained

and/ or motivated staff; out‐of‐stocks; inadequate referral systems; etc.

As the chance to survive is linked to the timelines in which care is received, it is evident

that in areas where mortality is high, coverage of effective interventions is insufficient.

Chapter 1

12

Therefore, the analyses of care seeking behaviour, based on the model of three delays,

can help improve programming to ensure more effective coverage of life‐saving health

interventions.11 To date, little is known on how these delays affect care seeking

behaviour across high mortality countries in sub‐Saharan African countries, to which

extent care seeking patterns are similar, and what lessons learned and potential best

practices are which should be shared across resource limited settings.

Leveraging mobile technology to reduce delays in accessing care

To improve coverage of effective programs, it is not only important to understand what

the existing challenges are, but also to identify ways in which these can be overcome.

With mobile phone users increasing almost a three‐fold between 2005 and 2010 and

reaching 367 million subscribers in mid‐2015 in sub‐Saharan Africa,12 there are high

expectations that this technology can help connect isolated communities and

healthcare services, thereby reducing delays in accessing care. The use of mobile

phones to improve access to health is referred to as mHealth. Over the past decade,

mHealth initiatives have focussed on addressing various aspects of the three phases of

delay; for example by focussing on increasing knowledge, providing financial support,

strengthening provider‐to‐provider communication systems, data collection methods,

and ‐ amongst others – strengthening the supply chain management.13 Nevertheless,

there is limited evidence on how mobile phones can most affectively address these

delays, with only few evaluating the effectiveness of these initiatives in resource limited

settings.14‐19 Finally, while the expectations are high, little is known on why these

initiatives fail to go to scale in rural settings in sub‐Saharan Africa.20

Problem statement

In summary, in sub‐Saharan African countries most children die of preventable and

treatable illnesses because they fail to reach acceptable, affordable and appropriate

health care in time. Pneumonia is responsible for most of these deaths. The extent to

which the three phases of delay ‐ (deciding to seek, as well as reaching and receiving

care) ‐ affect care seeking for children in sub‐Saharan African settings is yet unknown.

While mobile technology appears to present great opportunities to address these

delays, there is still little evidence on how this technology can best be used to improve

access to health care. A better understanding of these delays and the potential of

mHealth initiatives can help to identify programmatic coverage with their successes

and challenges, as well as to identify opportunities.21‐22


13

AIM, SCOPE AND OUTLINE OF THIS THESIS

Aim

The aim of this thesis is to expand the knowledge on the three phases of delays in

accessing health care, and to assess if and how the rapidly expanding field of mHealth

can decrease these delays in order to improve programmatic coverage of effective

health interventions across various settings in sub‐Saharan Africa.

The specific objectives, which this thesis aims to answer, are:

1. How do the delays in care (deciding to seek, as well as reaching and receiving)

affect care seeking behaviour across sub‐Saharan Africa? And, how can better

knowledge of these delays lead to improved programming? This thesis seeks to

improve the knowledge on these questions by focussing on the exemplary

condition of pneumonia.

2. What is the potential of mobile technology to improve health outcomes by

addressing the delays in accessing care, in resource limited settings? And, what are

programmatic challenges in implementing mHealth strategies?

Scope

To investigate the three phases of delays in all its complexity is far beyond the scope of

a single thesis. Nevertheless, all three delays are considered in this thesis as these can

adversely affect child health outcomes in sub‐Saharan Africa. A better understanding of

de magnitude of each of these delays is therefore relevant for policy makers and

programmers; especially those working in resource limited settings. To enable an

assessment of all three phases of delays, the complexity of each delay is reduced to

manageable proportions. In other words, in order not to state generalities, the strategy

of this thesis is to give exemplary case studies for each delay, to illustrate the

complexities at hand. For example, the first delay – deciding to seek care – is influenced

by more than just knowledge; nevertheless, this thesis only assesses the association

between knowledge and care seeking.

In general, the focus of this thesis is on children under the age of five, living in sub‐

Saharan Africa settings with high mortality rates. More specifically, the first three

chapters will focus on pneumonia; globally the main cause of childhood mortality.

Chapter 1

14

Outline

This thesis is built around two main parts, namely a section which addresses the three

phases of delay (Part II) and a section which assesses the potential of mobile phones as

a solution (Part III).

Part II, the section on the three phases of delay in care, is built around three chapters.

Each chapter focusses on one of the phases of delay. The first chapter analyses causes

of the delay in deciding to seek care (Chapter 2). This assessment is based on national

survey data from countries in sub‐Saharan Africa, and assesses the correlation between

knowledge of pneumonia symptoms and care seeking behaviour. The second chapter

analyses the causes of the delay in reaching care (Chapter 3). As there are many

reasons why care is not reached in time, this chapter starts with assessing to which care

providers caregivers take their child and what influences this. This assessment is also

based on national survey data, focussing on care seeking behaviour for children under

five with symptoms of pneumonia. The third, and last chapter of this section, focusses

on the delay in receiving adequate health care (Chapter 4). This delay can be caused by

a myriad of reasons often linked to a poor quality and lack of resources. As mentioned,

one of the reasons is inadequately trained staff. For this chapter, data were analysed

from five different international organizations working in resource limited settings in

sub‐Saharan Africa. The focus of this chapter is on the accuracy of community health

workers in classifying breathing rates and the effect of counting beads in helping them

to overcome key challenges related to classifying them.

Part III, the section a potential solution, is built around three chapters all three focusing

on the use of mobile phones. The first chapter is an assessment on the potential of

mobile phones to improve access to health services in resource‐limited settings

(Chapter 5). This assessment is based on a literature review and focusses on the

evidence regarding the impact of mobile phones to improved maternal and new‐born

health focussing on each phase of delay. The second chapter of part III investigates the

impact of a mHealth initiative in improving care seeking behaviour in Malawi. The

initiative is a toll‐free hotline and mobile messaging service, which connects caregivers

to health workers and health information (Chapter 6). The last chapter of this section

assesses what the key challenges are in relation to scaling‐up mHealth interventions

(Chapter 7). This assessment is based on two case studies, nutrition surveillance and

early infant diagnosis of HIV, in Malawi and Zambia.

The last part of this thesis is the general discussion, which will focus on how improved

knowledge of the delays and mHealth can help identify programmatic coverage with its

successes and challenges.


15

REFERENCES

1. United Nations Children's Fund (UNICEF) global databases 2015. Available: http://data.unicef.org/child‐mortality/under‐five.html. Accessed 2016, Jan 12.

2. UNICEF (2015) Levels & trends in child mortality. Report 2015. New York: UNICEF. 3. World Health Organization (WHO) and UNICEF (2013) Ending preventable child deaths for pneumonia

and diarrhoea by 2025: The integrated Global Action Plan for Pneumonia and Diarrhoea (GAPPD). Geneva: WHO.

4. UNICEF (2015) Committing to child survival: A Promise Renewed. Progress report: 2015.New York: UNICEF.

5. UNICEF (2014) Committing to child survival: A Promise Renewed. Progress report: 2014. New York: UNICEF.

6. Fischer Walker CL, Rudan I, Liu L, Nair H, Theodoratou E, et al. Global burden of childhood pneumonia and diarrhea. Lancet 2013;381:1405–1416.

7. UNICEF (2010) Narrowing the gaps to meet the goals. Available: http://www.unicef.org/nutrition/index_55927.html. Accessed 2016, Jan 12.

8. Thaddeus S, and Maine D. Too far to walk: Maternal mortality in context. Soc Sci Med 1994;38: 1091‐1110.

9. Waiswa P, Kallander K, Peterson S, Tomson G, Pariyo GW. Using the three delays model to understand why newborn babies die in eastern Uganda. Trop Med Int Health 2010;15:964–972.

10. Källander K, Hildenwall H, Waiswa P, Galiwango E, Peterson S, et al. Delayed care seeking for fatal pneumonia in children aged under five years in Uganda: a case‐series study. WHO Bulletin volume 2008;86:321‐416.

11. Gulliford M, Figueroa‐Munoz J, Morgan M, Hughes D, Gibson B, et al. What does ‘access to health care’ mean? J Health Serv Res Policy 2002;7:186‐188.

12. GSMA website. Available: http://www.gsma.com/. Accessed 2016, Jan 12. 13. Labrique AB, Vasudevan L, Kochi E, Fabricant R, Mehl G. mHealth innovations as health system

strengthening tools: 12 common applications and a visual framework. Glob Health Sci Pract 2013;1: 160‐171.

14. Tomlinson M, Rotheram‐Borus MJ, Swartz L, Tsai AC. Scaling up mHealth: Where is the evidence? PLoS Med 2013;10:e1001382.

15. Free C, Phillips G, Galli L, Watson L, Felix L, et al. The effectiveness of mobile‐health technology‐based health behaviour change or disease management interventions for health care consumers: A systematic review. PLoS Med 2013;10:e1001362.

16. Free C, Phillips G, Watson L, Galli L, Felix L, et al. The Effectiveness of Mobile‐Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta‐Analysis. PLoS Med 2013;10:e1001363.

17. Fotso JC, Robinson AL, Noordam AC, Crawford J. Fostering the use of quasi‐experimental designs for evaluating public health interventions: Insights from an mHealth project in Malawi. African Population Studies 2015;29:1607‐1627.

18. Medhanyie AA, Moser A, Spigt M, Yebyoa H, Little A, et al. Mobile health data collection at primary health care in Ethiopia: a feasible challenge. J Clin Epidemiol 2015;68:80‐86.

19. Medhanyie AA, Little A, Yebyo H, Spigt M, Tadesse K, et al. Health workers’ experiences, barriers, preferences and motivating factors in using mHealth forms in Ethiopia. Hum Resour Health. 2015;13:2.

20. Asiimwe C, Gelvin D, Lee E, Ben Amor Y, Quinto E, et al. Use of an innovative, affordable, and open‐source short message service–based tool to monitor malaria in remote areas of Uganda. Am J Trop Med Hyg 2011;85:26–33.

21. Chopra M, Mason E, Borrazzo J, Campbell H, Rudan I, et al. Ending of preventable deaths from pneumonia and diarrhoea: an achievable goal. Lancet 2013;381:1499‐1506.

22. Lavis JN, Posada FB, Haines A, Osei E. Use of research to inform public policymaking. 2004;364:1615‐21.

Chapter 1

16

17

PART II

The three phases of delay in care

19

CHAPTER 2

Associations between caregivers’ knowledge and care

seeking behaviour for children with suspected

pneumonia in six sub‐Saharan African countries

Noordam AC, Sharkey AB, Hinssen P, Dinant GJ, Cals JWL

Submitted

Chapter 2

20

ABSTRACT

Pneumonia is the main cause of child mortality world‐wide and most of these deaths

occur in sub‐Saharan Africa (SSA). Treatment with effective antibiotics is crucial to

prevent these deaths; nevertheless only 2 out of 5 children with symptoms of

pneumonia are taken to an appropriate care provider in SSA. While various factors

associated with care seeking have been identified, the relationship between caregivers’

knowledge of danger signs and actual care seeking for their child with symptoms of

pneumonia is not well researched. Based on data from Multiple Indicator Cluster

Surveys, we assessed the association between caregivers’ knowledge of symptoms

related to pneumonia – namely fast or difficulty breathing – and care seeking behaviour

for these symptoms. We analysed data of 4,163 children with symptoms of pneumonia

and their caregivers. Across all 6 countries only around 30% of caregivers were aware of

at least one of the two symptoms. We found no association between caregivers’

knowledge of danger signs and actual care seeking for their child with symptoms of

pneumonia in Central African Republic, Chad, Malawi, and Sierra Leone. Our study

shows that in the Democratic Republic of the Congo and Nigeria the association

between knowledge and care seeking was significant (P≤0.01), even after adjusting for

key variables (including wealth, residence, education). These findings reveal an urgent

need to increase community awareness of danger signs and symptoms of pneumonia,

while simultaneously designing context specific strategies to address the fundamental

challenges associated with timely care seeking.

Associations between knowledge and care seeking behaviour

21

INTRODUCTION

Pneumonia is responsible for more deaths among children under five years of age than

any other infectious disease. In 2015, pneumonia killed an estimated 922,000 children

under‐five globally; most of these deaths were in sub‐Saharan Africa.1 Timely treatment

with effective antibiotics is critical to prevent pneumonia‐related deaths.2

Unfortunately, early care seeking for childhood illnesses remains a challenge in

countries with high mortality rates.3‐6 Estimates from sub‐Saharan Africa indicate that

only 2 out of the 5 children with pneumonia specific symptoms are taken to an

appropriate provider for care.1 Key factors associated with care seeking include

household income,3,6‐7 education levels of the primary caregiver,7 limited caregivers’

recognition of the disease,8 younger age and sex of the child,7 geographical areas

(including rural locations)3 and religion.9 In addition, a systematic review of studies

conducted across sub‐Saharan Africa identified cultural beliefs and illness perceptions,

perceived severity of the illness, previous experience with health services, habit,

treatment costs and efficacy, and gender as key factors for care seeking.3

As failure to recognize an illness is expected to leading to delays in care seeking, the

first step to address pneumonia specific mortality is by ensuring that caregivers are

aware of pneumonia specific symptoms.10 To date, the relationship between specific

knowledge of these symptoms, recognition of them and related care seeking is not well

researched.8 And, only a few studies specifically focus on the association between

knowledge of symptoms related to pneumonia – namely fast or difficulty breathing –

and care seeking behavior.11,12 We hypothesize that knowledge of these specific

symptoms will enable caregivers to recognize an illness, consequently seeking timely

and appropriate care. Such information is critical to plan effective strategies to reduce

pneumonia mortality in high‐burden settings, particularly where rates of care seeking

are inadequate.

METHODOLOGY

Data sources

Our analyses are based on data from Multiple Indicator Cluster Surveys (MICS)

conducted in sub‐Saharan Africa. These nationally representative household surveys

are conducted by national implementing agencies with the support of the United

Nations Children’s Fund (UNICEF). MICS surveys collect statistically sound and

internationally comparable data on a variety of topics related to maternal and child

Chapter 2

22

health, including knowledge of symptoms (also referred to as danger signs) of

childhood illnesses and care seeking behaviour for children under the age of five

years.13.14 Sub‐Saharan African countries were selected for inclusion in this study if they

had a MICS conducted during or after 2010, the data was national representative, and

the datasets were available upon the start of our analyses (August 2015). Information

on knowledge of symptoms of childhood illnesses is obtained during interviews using

the ‘individual women’s’ questionnaire, which is administered to women age 15

through 49 years. Data on care seeking behaviour are obtained via the ‘children under‐

five’ questionnaires, which is administered primarily to mothers of children under the

age of five years. When the mother is deceased or is living elsewhere, the questionnaire

is administered to the child’s primary caregiver.

Survey methods and ethics

Sampling frames are usually based on the most recent national census and therefore do

not include non‐household populations; i.e. they exclude populations living in group

quarters (e.g. hospitals, military barracks) and those living on the street. Usually, a two‐

stage cluster sampling approach is used; the first stage: select enumeration areas and

do a listing of households, second stage: select households from list. While the surveys

are conducted in different countries ‐ and may therefore vary due to limitations in costs

and practical considerations (including security) ‐ all surveys ‘adhere to the

fundamentals of scientific sampling, including complete coverage of the targeted

population, use of suitable sample size, the need to conduct household listing and pre‐

selection of sample households’.13 Implementing agencies are required to obtain ethical

approval as abide by the laws of the country. Survey tools, datasets and more detailed

information on country specific survey methods can be found on the MICS website.14

Research questions

For each country, we first assessed the proportion of caregivers (either mothers, or

primary caregivers) of children under‐five that mentioned one or both symptoms of

childhood illnesses linked to pneumonia, i.e. fast and/ or difficulty breathing, as a

reason to seek care. The specific interview question asked to caregivers is the following

open‐ended question: “Sometimes children have severe illnesses and should be taken

immediately to a health facility. What types of symptoms would cause you to take a

child under the age of 5 to a health facility right away?” A subsequent probe question

asked: “Any other symptoms?” The responses were categorized as: child is not able to

drink or breastfeed; becomes sicker; develops a fever; has fast breathing; has difficulty

in breathing; has blood in stools; is drinking poorly; any other (unspecified); and some

countries included categories such as diarrhoea and/ or vomiting. We classified

caregivers who were able to identify either fast or difficulty breathing as having


23

knowledge of pneumonia symptoms. As responses were not categorized as “cough” or

“chest in‐drawing,” we were not able to include these symptoms in our analysis of

pneumonia specific knowledge.

Second, we calculated how many of these caregivers reported that their child had a

cough and fast or difficulty breathing due to a problem in the chest in the past two

weeks, as cases with symptoms of pneumonia. The interview questions related to these

cases are: “Has (name) had an illness with cough at any time in the last 2 weeks?”

“When (name) had an illness with cough, did he/she breathe faster than usual with

short rapid breaths or have difficulty breathing?” “Was the fast or difficult breathing

due to a problem in the chest or to a blocked or runny nose?” The children of whom the

caregiver reported that they had a cough with fast or difficulty in breathing, due to a

problem in the chest in the past two weeks, where considered as cases with symptoms

of pneumonia.

Third, we assessed which proportion of these caregivers brought their child to an

appropriate health provider. The interview questions related to care seeking are: “Did

you seek advice or treatment for the illness from any source?” “Where did you seek

advice or treatment?” A subsequent probe asked, “Anywhere else?” We defined

‘appropriate’ health care provider as one working at either a private or public hospital,

primary health care facility or any other government service, and who has undergone

formal training and received accreditation, authorizing them to treat children with signs

of acute respiratory tract infections.7 Once we knew which proportion sought

appropriate care, we assessed if the caregivers who mentioned at least one pneumonia

specific symptom as reasons to seek immediate care – i.e. fast or difficulty breathing –

were indeed more likely to seek care from these providers, as supposed to those who

did not mention one of these symptoms.

Data analysis

We conducted our analyses using SPSS version 21. The data analysis was conducted by

two independent researchers [CN and PH]. We first created a new dataset by merging

the two (i.e. the ‘individual women’s’ and ‘children under‐five’) data files, matching

eligible cases for both surveys based on cluster, household and line number. For

caregivers with more than one child under the age of five, we used the files of the

youngest child ‐ in the case of twins we kept the child that was mentioned last. We

weighted the data using the sample weight variables. Cases with missing data (i.e.

surveys which were party completed, as well as missing variables needed for these

analyses) were excluded from the analyses. We calculated the percentage of caregivers

included in the merged dataset who reported fast or difficulty breathing as reasons to

Chapter 2

24

seek immediate care from a health facility. We then calculated cross tabulations and

performed chi‐square tests to assess the association between care seeking and

knowledge of at least one symptom (i.e. fast or difficulty breathing).

Based on the literature on care seeking, we included the following variables in the

multivariate analyses: household wealth quintile (poorest, poorer, middle, richer and

richest); residence (rural, urban); caregiver’s age (15‐19, 20‐24, etc.) and their level of

education (none, primary, and secondary or higher); child age (< 2 years, 2‐5 years) and

their sex (boy, girl); and the total number of children ever born (<2, 2‐3 and 4+). When

possible we also adjusted for geographical location (regions) and religion. With the

categories for the last two variables being country specific, not always included in the

datasets, and – in some cases – sample size restrictions, we pre‐defined some

parameters for these variables when available in the dataset: 1) the sample has to be

large enough (≥10 cases per category); 2) geographical location has to refer to regions

e.g. the North, East, South or West and not to any other groupings (i.e. provinces, or

districts), and 3) we first assess if we can include geographical location (regions), after

which we conduct the same assessment for religion.

We performed a multivariate logistic regression for the dependent variable (care

seeking from an ‘appropriate’ health provider) and the independent variables, in order

to examine the association. We then calculated the adjusted odds ratios (ORs) with

corresponding 95% confidence intervals (CIs).

RESULTS

Of countries in sub‐Saharan Africa, in which a MICS was conducted during or after 2010

and for which survey results were available before the start of our analyses, we

selected the six countries with the largest sample sizes for analysis: Central African

Republic (CAR), Chad, Democratic Republic of the Congo (DRC), Malawi, Nigeria, and

Sierra Leone. Four surveys were from 2010 (CAR, Chad, DRC and Sierra Leone), Nigeria’s

survey was from 2011 and Malawi’s was from 2013‐14.

Background characteristics

Table 2.1 shows the samples per country, after merging the women’s and children’s

datasets, with Nigeria as the largest sample (N=16,242) and Sierra Leone as the smallest

(N=6,033). The vast majority of caregiver‐child combinations included in the analyses

across the six countries live in rural settings, and most of the caregivers have at least

four children.


25

Table 2.1

Background characteristics of all individuals included

in this analysis, in percen

tage (%) and numbers (n), and the total sam

ple size and rep

orted

cases of

children with sym

ptoms of pneu

monia in the past tw

o weeks.

CAR

Chad

DRC

Malaw

iNigeria

Sierra Leone

%

n

%

n

%

n

%

n

%

n

%

n

Wealth index

Poorest

21.6

1475

19.2

1897

22.5

1600

22.1

3079

22.8

3697

22.4

1349

Poor

21.7

1478

20.2

1992

20.8

1484

21.6

3014

20.8

3377

21.4

1292

Middle

20.4

1389

21.0

2078

20.5

1459

20.4

2851

18.6

3028

20.6

1245

Rich

19.5

1328

21.5

2123

19.6

1394

18.1

2522

18.9

3071

19.6

1185

Richest

16.9

1151

18.1

1784

16.6

1185

17.8

2486

18.9

3070

16.0

963

Residence

Rural

64.1

4371

78.5

7755

73.5

5238

87.0

12137

68.9

11188

72.1

4349

Urban

35.9

2450

21.5

2118

26.5

1884

13.0

1814

31.1

5054

27.9

1684

Education level

None

41.4

2823

73.8

7289

24.1

1715

12.0

1670

42.0

6828

71.2

4295

Primary

42.6

2905

18.4

1821

42.7

3044

69.6

9702

19.7

3202

13.5

813

Secondary +

16.0

1093

7.7

763

33.2

2363

18.4

2574

38.2

6212

15.3

925

Number of children

0‐1

18.5

1261

14.2

1403

16.9

1205

19.7

2754

15.7

2551

18.5

1119

2‐3

34.9

2378

27.6

2728

30.5

2173

35.5

4947

31.9

5189

34.6

2090

4+

46.6

3182

58.2

5742

52.6

3744

44.8

6250

52.3

8502

46.8

2824

Child age

<2

years

62.2

4241

58.9

5820

65.8

4687

50.3

7015

60.7

9858

50.7

3058

2‐5 years

37.8

2580

41.1

4053

34.2

2436

49.7

6936

39.3

6384

49.3

2975

Sex of the child

Male

50.6

3449

49.8

4920

50.2

3575

50.6

7058

51.4

8341

50.3

3031

Female

49.4

3372

50.2

4954

49.8

3548

49.4

6892

48.6

7901

49.7

2998

Total sam

ple size

6821

9873

7123

13951

16242

6033

Total reported

cases

7.2

492

9.6

947

6.6

473

7.8

1088

3.7

607

9.2

556

For all calculations, numbers and percentages are based

on w

eighted averages which are also adjusted

for missing data. For these reasons, m

anual re‐calculation

might show slight differences (i.e., the total sam

ple size will vary for each category as the missings within this category will differ).

Chapter 2

26

The largest differences across the countries are found in levels of education, with most

caregivers having had no education in Chad (73.8%), Sierra Leone (71.2%) and Nigeria

(42.0%) and at least primary school in Malawi (69.6%), DRC (42.7%) and CAR (42.6%).

The percentage of cases with symptoms of pneumonia (i.e. those for whom the mother

or caregiver mentioned that their child had a cough and fast and/or difficulty breathing

due to a problem in the chest in the past two weeks) ranged from 3.7% (n=607) in

Nigeria to 9.6% (n=947) in Chad (Table 2.1). Hence, in the total population we had 4163

cases with symptoms of pneumonia.

Knowledge of fast and/or difficulty in breathing

Of the two symptoms linked to pneumonia, caregivers were most aware of the

symptom ‘difficulty in breathing’, ranging from 17.4% of the sample in Chad to 24.0% in

CAR (Table 2.2). Across all 6 countries around 30% (ranging from 29.2‐32.9%) of

caregivers were aware of at least one of the two symptoms. We characterised these

caregivers as caregivers with appropriate knowledge of danger symptoms for

pneumonia, see Table 2.2. When assessing the percentage of caregivers who

mentioned both symptoms, this ranged from 4.5% in Chad to 11.2% in CAR.

Table 2.2 Percentage of caregivers surveyed who had knowledge of pneumonia specific danger signs.

CAR Chad DRC Malawi Nigeria Sierra Leone

Symptoms mentioned as reason

to seek care

Difficulty breathing 24.0% 17.4% 20.6% 19.9% 22.7% 21.7%

Fast breathing 17.0% 16.3% 16.0% 14.8% 19.5% 19.5% Fast and difficulty in breathing 11.2% 4.5% 6.8% 5.1% 10.3% 8.3%

Fast or difficulty in breathing (defined as

“knowledge” in this paper)

29.7% 29.2% 29.7% 29.6% 31.9% 32.9%

Total number of survey sample 6821 9873 7123 13951 16242 6033

Care seeking behaviour

Table 2.3 shows the associations between knowledge of at least one symptom for

pneumonia and care seeking from appropriate health care providers, also with

adjustments for the predefined variables in a multivariate logistic regression model. Of

children with symptoms of pneumonia in the previous two weeks, those living in Sierra

Leone and Malawi were most likely (73.2% and 68.8%, respectively) to be brought to an

appropriate provider. In Chad and CAR this was only 27.4% and 30.9%, respectively.

Care seeking was only moderately better in Nigeria (41.9%) and DRC (44.2%).


27

Table 2.3 Associations between knowledge of at least one symptom for pneumonia and care seeking

from appropriate health care providers.

Children with symptoms of pneumonia, taken to an appropriate provider

Total taken to provider and reported knowledge of danger signs

N (%) Unadjusted OR + (95% CI)

Adjusted1 OR + (95% CI)

Total 151/ 489 (30.9)

Knowledge ‐ No 104/ 357 (29.1) ref ref

CAR

Knowledge ‐ Yes 47/ 132 (35.6) 1.3 (0.9‐2.1) 1.3 (0.9 – 2.2)

Total 254/ 928 (27.4)


Chad

Knowledge ‐ Yes 88/ 298 (29.5) 1.2 (0.9 – 1.6) 1.1 (0.8 – 1.6)

Total 209/ 473 (44.2)


DRC

Knowledge ‐ Yes 89/ 164 (54.3) 1.9 (1.3 – 2.7)** 2.0 (1.3 – 3.0)**

Total 740/ 1076 (68.8)


Malawi

Knowledge ‐ Yes 221/ 333 (66.4) 0.9 (0.6 – 1.1) 0.8 (0.6 – 1.1)

Total 253/ 604 (41.9)


Nigeria

Knowledge ‐ Yes 98/ 199 (49.2) 1.6 (1.1‐2.2)** 1.9 (1.3 – 2.9)**

Total 402/ 549 (73.2)


Sierra Leone

Knowledge ‐ Yes 135/ 180 (75.0) 1.1 (0.8 – 1.7) 1.1 (0.8 – 1.9)

Knowledge is defined as those caregivers aware of at least one pneumonia symptom (i.e. fast or difficulty

breathing). All calculations: numbers (n), percentages (%), odds ratios (OR) and 95% confidence intervals (CI)

are based on weighted averages, adjusted for missing data. Numbers presented in this table are rounded.

Manual re‐calculation might therefore show slight differences. 1Adjusted for pre‐defined variables, namely; wealth, residence, maternal education, maternal age, child’s age,

child’s sex, and total number of children ever born. Based on the pre‐defined criteria geographical location

(defined as regions) were included in Malawi, Nigeria and Sierra Leone and religion in Chad, Malawi, and

Nigeria. Statistical significance: *p≤0.05, **p≤0.01

In DRC, a child whose caregiver mentioned at least one symptom related to pneumonia

as reasons to seek immediate care was 1.9 times more likely to have been brought to

an ‘appropriate’ health provider during his or her last illness than a child whose

caregiver did not mention one of the symptoms (95% CI = 1.3‐2.7, P≤0.01). The

association remained largely the same after adjusting for wealth, residence, maternal

age and education, the age and sex of the child, and number of children ever born

Chapter 2

28

(OR=2.0, 95% CI = 1.3‐3.0, P≤0.01). A significant association was also found in Nigeria,

where caregivers with knowledge of symptoms were 1.6 times more likely to seek care

from an ‘appropriate’ health provider (95% CI = 1.1‐2.2, p≤0.01). After adjusting for the

pre‐defined variables as mentioned for DRC, and for religion (Muslim, Christian) and

geographical location (6 regions; north‐east, north‐west, south‐south, south‐east,

south‐west, north‐centre) the OR increased to 1.9 (95% CI = 1.3‐2.9, p≤0.01). For the

other four countries, the analyses reveal no significant association between knowledge

and care seeking.

DISCUSSION

In this study, we found low levels of caregivers knowledge of symptoms related to

childhood pneumonia in sub‐Saharan Africa; on average only around 30% of caregivers

were aware of either fast or difficulty in breathing as a reason to seek care. These

caregivers, who were aware of at least one of these symptoms, were not necessarily

more likely to seek care from an appropriate health care provider. We found a

significant association between knowledge of at least one symptom and care seeking in

DRC and Nigeria. This association was evident even after adjusting for key background

characteristics. For the other four countries (CAR, Chad, Malawi and Sierra Leone) the

associations were not significant. Our analyses also reveal large differences in care

seeking for children with symptoms of pneumonia, ranging from 27% in Chad to 73% in

Sierra Leone.

Low levels of knowledge of pneumonia specific symptoms are confirmed by other

studies, even compared to knowledge of other illnesses such as malaria and

diarrhoea.15,16 Of the two symptoms, we found that caregivers more frequently

mentioned difficulty in breathing as compared to fast breathing. A study conducted in

Nigeria found that mothers were more likely to recognise pneumonia based on fever

and cough, than on either fast or difficulty breathing.17 This same study also found that

mothers were less likely to recognize pneumonia based on more severe symptoms such

as chest in‐drawing and central cyanosis.17 Studies conducted in Sierra Leone18 and

Nigeria11 also report a lack of knowledge on symptoms and that caregivers are often

not aware of ways to prevent or treat common childhood illnesses, including

pneumonia.

Other studies also conclude that care seeking patterns vary between and within

countries, as they are linked to a dynamic process influenced by a range of socio‐

economic, cultural and geographic access factors.3,7,19‐21 More specifically, a study from


29

Malawi confirms that wealth, urban‐rural residence, maternal education ‐ amongst

others ‐ were associated with care seeking.22 Studies also report that care seeking is

linked not only to the ability of caregivers to recognize an illness, but also to caregivers’

perception of the severity of the illness: the more severe caregivers’ perceived the

illness, the more likely they were to seek care.8,23 The complexity of factors that

influence care seeking behaviour could explain the fact that we found no significant

association between knowledge of symptoms and care seeking behaviour in most of

the countries included in these analyses. While this is concerning, other studies have

shown that knowledge alone is insufficient to change behaviour; e.g. knowledge of the

importance of using a condom to reduce the risk of HIV transmission, does not

necessarily translate to higher use of condoms amongst those most at risk.24 Other

studies also found that community based approaches such as provision of integrated

case management of childhood illnesses ‐ including pneumonia ‐ can be an important

strategy.25,26 A study from Nigeria27 showed that community information activities for

malaria lead to improved knowledge, home management and referral practices.

Limitations

There are some limitations related to these analyses. The children with symptoms of

pneumonia are based on caregivers’ perceptions of symptoms and their ability to recall

events, which could lead to incorrect estimates.28 In relation to this, we were not able

to assess if knowledge of pneumonia specific symptoms lead to more accuracy in the

recognition of pneumonia specific symptoms, subsequently leading to better

identification of suspected cases; or, as the cases are not clinically validated as those

requiring medical care, not seeking care might in some cases be justified. Also, we were

not able to disaggregate the data by severity of the illness, or by the severity of

symptoms (e.g. chest in‐drawing). And, as caregivers were not prompted about specific

symptoms, it is possible that pneumonia specific symptoms just did not come to mind

when interviewed, this could have led to under reporting of these specific symptoms.

Finally, we were not able to discern where caregivers learned about symptoms of

childhood illnesses and whether or not they learned this after care seeking for the

child’s last illness. A study from Nigeria29 showed that only 23% of the caregivers

received health information from the healthcare providers after being hospitalized.

Conclusion

We hypothesized that knowledge of disease specific symptoms improves the ability of

caregivers to recognize an illness and seek appropriate care and found low levels of

knowledge on pneumonia among caregivers in six high burden countries in sub‐Saharan

Africa. However, we found limited associations between knowledge and care seeking in

Chapter 2

30

this study, which suggests that knowledge alone is not sufficient to take action. With

low levels of knowledge of symptoms of pneumonia across these high pneumonia

mortality settings, emphasis should be put on education programs, which not only

focus on the primary caregivers, but on all those involved in decision‐making processes

and care seeking. In addition, factors other than knowledge (e.g. empowerment, costs)

should also be addressed to improve care seeking behaviour. In other words, there is a

need to increase community awareness of pneumonia, while simultaneously designing

context specific strategies to address the fundamental challenges associated with

timely care seeking.

Acknowledgement

The authors would like to express their gratitude to colleagues in UNICEF for reviewing

the manuscript.


31

REFERENCES

1. United Nations Children's Fund (UNICEF) global databases 2015. Available: http://data.unicef.org/child‐health/pneumonia.html. Accessed 2016, Jan 12.

2. Kallander K, Young M, Qazi S. Comment. Universal access to pneumonia prevention and care: a call for action. Lancet Respir Med 2014;2:950‐952.

3. Colvin CJ, Smith HJ, Swartz A, Ash JW, de Heer J, et al. Understanding careseeking for child illness in sub‐Saharan Africa: A systematic review and conceptual framework based on qualitative research of household recognition and response to child diarrhoea, pneumonia and malaria. Soc Sci Med 2013;86:66e78.

4. Mosites EM, Matheson AI, Kern E, Manhart LE, Morris SS, et al. Care‐seeking and appropriate treatment for childhood acute respiratory illness: an analysis of Demographic and Health Survey and Multiple Indicators Cluster Survey datasets for high mortality countries. BMC Public Health 2014;14:446.

5. Herbert HK, Lee ACC, Chandran A, Rudan I, Baqui AH. Care seeking for neonatal illness in low‐ and middle‐income countries: a systematic review. PLoS Med 2012;9:e1001183.

6. Barros AJD, Ronsmans C, Axelson H, Loaiza E, Bertoldi AD, et al. (2012) Equity in maternal, newborn, and child health interventions in Countdown to 2015: a retrospective review of survey data from 54 countries. Lancet 2012;379:1225‐1233.

7. Noordam AC, Carvajal‐Velez L, Sharkey AB, Young M, Cals JWL. Care Seeking Behaviour for Children with Suspected Pneumonia in Countries in Sub‐Saharan Africa with High Pneumonia Mortality. PLoS One 2015;10:e0117919.

8. Geldsetzer P, Williams TC, Kirolos A, Mitchell S, Ratcliffe LA, et al. The Recognition of and Care Seeking Behaviour for Childhood Illness in Developing Countries: A Systematic Review. PLoS One 2014;9:e93427.

9. Mebratie AD, Van de Poel E, Yilma Z, Abebaw D, Alemu G, et al. Healthcare‐seeking behaviour in rural Ethiopia: evidence from clinical vignettes. BMJ Open 2014;4:e004020.

10. UNICEF and World Health Organization (WHO) (2006) Pneumonia the forgotten killer of children. Available: http://www.childinfo.org/files/Pneumonia_The_Forgotten_Killer_of_Children.pdf. Accessed 2016 Feb 22.

11. Ndu IK, Ekwochi U, Osuorah CDI, Onah KS, Obuoha E, et al. Danger signs of childhood pneumonia: Caregiver awareness and care seeking behavior in a developing country. Int J Pediatr. 2015;2015:167261.

12. Tuhebwe D, Tumushabe E, Leontsini E, Wanyenze RK. Pneumonia among children under five in Uganda: symptom recognition and actions taken by caretakers. Afr Health Sci. 2014;14:993‐1000.

13. Hancioglu A, Arnold F. Measuring coverage in MNCH: tracking progress in health for women and children using DHS and MICS household surveys. PLoS Med 2013;10:e1001391.

14. UNICEF Statistics and Monitoring 2015. Multiple Indicator Cluster Survey (MICS) Available at: http://mics.unicef.org. Accessed 2016, Feb 2.

15. Bedford KJA, Sharkey AB. Local Barriers and Solutions to Improve Care‐Seeking for Childhood Pneumonia, Diarrhoea and Malaria in Kenya, Nigeria and Niger: A Qualitative Study. PLOS One 2014;9: e100038.

16. Ukwaja KN, Talabi AA, Aina OB. Pre‐hospital care seeking behaviour for childhood acute respiratory infections in south‐western Nigeria. International Health 2012;4:289‐294.

17. Uwaezuoke SN, Emodi IJ, Ibe BC. Maternal perception of pneumonia in children: a health facility survey in Enugu, eastern Nigeria. Ann Trop Paediatr 2002;22:281‐285.

18. Kanu JS, Tang Y, Liu Y. Assessment on the Knowledge and Reported Practices of Women on Maternal and Child Health in Rural Sierra Leone: A Cross‐Sectional Survey. PLoS One 2014;9: e105936.

19. Diaz T, George AS, Roa SR, Bangura PS, Baimba JB, et al. Healthcare seeking for diarhoea, malaria and pneumonia among children in four poor rural districts in Sierra Leone in the context of free health care: results of a cross‐sectional survey. BMC Public Health 2013;13:157

20. Hodgins S, Pullum T, Dougherty L. Understanding where parents take their sick children and why it matters: a multi‐country analysis. Glob Health Sci Pract 2013;1:328‐356.

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21. Prach LM, Treleven E, Isiguzo C, Liu, J. Care‐seeking at patent and proprietary medicine vendors in Nigeria. BMC Health Serv Res 2015;15:231.

22. Oyekale AS. Assessment of Malawian mothers’ malaria knowledge, healthcare preferences and timeliness of seeking fever treatments for children under five. Int J Environ Res Public Health 2015;12:521‐540.

23. Burton DC, Flannery B, Onyango B, Larson C, Alaii J, et al. Healthcare‐seeking behaviour for common infectious disease‐related illnesses in rural Kenya: a community‐based house‐to‐house survey. J Health Popul Nutr 2011;29:61‐70.

24. Olowookere SA, Adeleke NA, Fatiregun AA, Abioye‐Kuteyi EA. Pattern of condom use among clients at a Nigerian HIV Counseling and Testing Centre. BMC Research Notes 2013;6:289.

25. Das JK, Lassi ZS, Salam RA, Bhutta ZA. Effect of community based interventions on childhood diarrhea and pneumonia: uptake of treatment modalities and impact on mortality. BMC Public Health 2013;13:S29.

26. Bhutta ZA, Das JK, Walker N, Rizvi A, Campbell H, et al. Interventions to address deaths from childhood pneumonia and diarrhea equitably: what works and at what cost? Lancet 2013;381: 1417‐1429.

27. Okeke, TA. Improving malaria recognition, treatment and referral practices by training caretakers in rural Nigeria. J Biosoc Sci 2010;42:325‐339.

28. Hazir T, Begum K, el Arifeen S, Khan AM, Huque MH, et al. Measuring coverage in MNCH: A prospective validation study in Pakistan and Bangladesh on measuring correct treatment of childhood pneumonia. PLoS Med 2013;10:e1001422.

29. Nwaneri UD, Oviawe OO, Oviawe, O. Do caregivers receive health information on their children’s illnesses from healthcare providers while hospitalized? Niger Postgrad Med J 2014;21: 279‐284.

33

CHAPTER 3

Care seeking behaviour for children with suspected

pneumonia in countries in sub‐Saharan Africa with

high pneumonia mortality

Noordam AC, Carvajal‐Velez L, Sharkey AB, Young M, Cals JWL

PLoS One 2015;10:e0117919

Chapter 3

34

ABSTRACT

Pneumonia is the leading cause of childhood mortality in sub‐Saharan Africa (SSA).

Because effective antibiotic treatment exists, timely recognition of pneumonia and

subsequent care seeking for treatment can prevent deaths. For six high pneumonia

mortality countries in SSA we examined if children with suspected pneumonia were

taken for care, and if so, from which type of care providers, using national survey data

of 76530 children. We also assessed factors independently associated with care seeking

from health providers, also known as ‘appropriate’ providers. We report important

differences in care seeking patterns across these countries. In Tanzania 85% of children

with suspected pneumonia were taken for care, whereas this was only 30% in Ethiopia.

Most of the children living in these six countries were taken to a primary health care

facility; 86, 68 and 59% in Ethiopia, Tanzania and Burkina Faso respectively. In Uganda,

hospital care was sought for 60% of children. 16‐18% of children were taken to a

private pharmacy in Democratic Republic of Congo (DRC), Tanzania and Nigeria. In

Tanzania, children from the richest households were 9.5 times (CI 2.3‐39.3) more likely

to be brought for care than children from the poorest households, after controlling for

the child’s age, sex, caregiver’s education and urban‐rural residence. The influence of

the age of a child, when controlling for sex, urban‐rural residence, education and

wealth, shows that the youngest children (<2 years) were more likely to be brought to a

care provider in Nigeria, Ethiopia and DRC. Urban‐rural residence was not significantly

associated with care seeking, after controlling for the age and sex of the child,

caregivers education and wealth. The study suggests that it is crucial to understand

country‐specific care seeking patterns for children with suspected pneumonia and

related determinants using available data prior to planning programmatic responses.

Care seeking behaviour for children with suspected pneumonia

35

INTRODUCTION

Acute respiratory infections (ARIs) are the most common illnesses in childhood, of

which lower respiratory tract infections (LRTIs) are the most severe in developing

countries.1 Pneumonia, a common and severe LRTI, was responsible for 15% of all

deaths among children under‐five in sub‐Saharan Africa (SSA) in 2013 and most of these

deaths were concentrated in a few countries.2‐4 Cough and fast and/ or difficult

breathing (i.e. tachypnea and/or dyspnea) due to a problem in the chest are clinically

recognized as signs of childhood pneumonia.5

Effective antibiotic treatment for pneumonia exists, and therefore timely recognition of

these signs and symptoms by primary caregivers and subsequent care seeking for

treatment from ‘appropriate’ providers can prevent many of these deaths.6

Nevertheless, only 50% of children in SSA with suspected pneumonia were taken for

care in 2010.7 Caregivers may not seek care for myriad reasons: both financial (e.g., the

cost of services or treatment, transportation costs, loss of wages) and non‐financial

(e.g., gender and social norms, insufficient knowledge of danger signs and illness

severity, and previous experiences with health services).8‐11 Further analysis of care

seeking behaviours by primary caregivers, and on child, caregiver and household

characteristics associated with care seeking is needed to further optimise future

strategies within integrated approaches to prevent and treat childhood pneumonia.12

We examined care seeking behaviour by caregivers of children under‐five years of age

with suspected pneumonia in sub‐Saharan countries with high rates of childhood

pneumonia mortality, and examined to what extend caregivers and household

characteristics influenced care seeking.

METHODOLOGY

Data sources

We analysed data from the Demographic and Health Surveys (DHS) or Multiple

Indicator Cluster Surveys (MICS) of countries in sub‐Saharan Africa identified by the

Global Action Plan for Pneumonia and Diarrhoea (GAPPD) as being among those with

the highest burden for pneumonia.12 DHS and MICS surveys are typically conducted by

government statistics agencies every 3 to 5 years, with the support and technical

assistance of the United States Agency for International Development (USAID) and the

United Nations Children's Fund (UNICEF) respectively. Both surveys are relatively

similar in content and scope and have comparable results. DHS and MICS survey

programs enable low‐and middle‐income countries (LMICs) to produce estimates of a

range of indicators in the areas of health, education, child protection and HIV & AIDS.

Chapter 3

36

DHS and MICS work together to harmonize tools and methods to enable comparisons

of key indicators across countries and over time. Both surveys adhere to the

fundamentals of scientific sampling, including complete coverage, suitable sample sizes,

pre‐selection of sample households, and sample documentation. However, limitations

due to cost or other practical considerations, such as security, might result in some

inconsistencies.13 These survey data are available publically at www.dhsprogram.com

and www.data.unicef.org respectively.

Countries were considered eligible for inclusion in this analysis if: (1) a population‐

based survey was conducted during or after 2010, (2) the data was available at the start

of our analysis (June 2013) and (3) the survey included the standard question on care

seeking for children under‐five with suspected pneumonia (cough and rapid or difficulty

breathing due to a problem in the chest) and whether or not the caregiver sought care

and from where they sought care during the past two weeks.

Questions relating to child’s health are included in the women’s questionnaire in DHS

and in the questionnaire for under‐fives in MICS. In DHS, only mothers were

interviewed, while in MICS the under‐five questionnaire is administered to either

mothers or primary caregivers of children under‐five.

The following questions (similar in DHS and MICS) were used for the analysis:

1. Has (NAME) had an illness with a cough at any time in the last 2 weeks? (Yes/ No/

Don’t know)

2. When (NAME) had an illness with a cough, did he/she breathe faster than usual

with short, rapid breaths or have difficulty breathing? (Yes/ No/ Don’t know)

3. Was the fast or difficult breathing due to a problem in the chest or to a blocked or

runny nose? (Problem in chest only/ Blocked or runny nose only/ Both/ Other

(specify)/ Don’t know)

4. Did you seek advice or treatment for the illness from any source? (Yes/ No/ Don’t

know)

5. A. Where did you seek advice or treatment? B. Anywhere else? (Various services

which fall under the following categories: Public sector, Private sector, Other

sources and Other (specify) )

In order to be included in this analysis, the respondent had to answer “yes” to

questions 1, 2 and 4, and ‘a problem in the chest’ to question 3. Multiple answers were

possible for question 5 (sections A and B).

Method of analysis

We used recode manuals and guides from both DHS and MICS prior to analysis and

recoding, which was conducted using STATA 12.1. We weighted the data according to

sample size by using the samples weight variables (V005 in DHS and chweight in MICS)


37

provided in the dataset and explained in dataset guides. In case of missing data, the

cases were excluded from the analysis. Data analysis was cross‐checked by an

independent researcher.

To analyse the rate of care seeking for pneumonia, we first calculated how many

children under‐five had suspected pneumonia (cough and rapid or difficulty breathing

due to a problem in the chest), and then the percentage of those taken for care. For

those taken for care, we then categorised them into health providers, also known as

‘appropriate’ providers (i.e., accredited by that country’s government authorities) or

other provider also known as ‘non‐appropriate’ providers (i.e., those not accredited to

provide antibiotics). These other providers, for this analysis, include private

pharmacies, shops, and traditional healers amongst others, for a more detail see the

subsection below ‘categorisation of health care providers and facilities’. In both surveys

caregivers can report more than one source of care to which they brought their child

with suspected pneumonia during the past two weeks.

We calculated frequencies and cross tabulations and performed chi‐square (χ2) tests to

identify variables associated with the dependent variable ‘care seeking behaviour from

an ‘appropriate’ provider’. To determine the adjusted associations between child and

caregiver characteristics and care seeking behaviour, we predefined the following

groupings of independent variables: child’s age (grouped as <2 years and 2‐5 years),

child’s sex (male, female), residential setting (urban, rural), primary caregiver’s or

mother’s education (none, primary, secondary and higher) and household wealth

quintile (poorest, poorer, middle, richer and richest), which is a composite measure of a

household’s cumulative living standard used by DHS and other surveys in countries that

lack reliable data on income and expenditures.14 We selected ‘appropriate’ providers as

the dependent variable as they have undertaken formal training and are accredited to

provide antibiotics and for that reason, we wanted to assess which factors are

associated with seeking care from these providers. We performed a multivariate logistic

regression model for the dependent variable in order to examine independence of

associations (p≤0.05), with all predefined variables in the model. In addition, we

calculated odds ratios (ORs) with corresponding 95% confidence intervals (CIs).

Categorization of health care providers and facilities

When we refer to ‘any provider’, this includes all possible care providers (i.e. both

‘appropriate’ and ‘non‐appropriate’ providers).

The definition of health providers, also known as ‘appropriate’ providers (and therefore

referred to as such throughout this paper), varies among countries. However, this

category includes both private and public providers who have undergone formal

training and facilities that have received accreditation and are therefore authorized to

treat children with signs of ARI, for example:

‐ Hospitals: For all countries this includes private and government hospitals.

Chapter 3

38

‐ Primary health care (PHC) facilities: This category includes both private and public

health centres, clinics, dispensaries and posts and in some cases a private doctor or

other medical personal, village/ community health workers, mobile (outreach)

services as well as health facilities supported by non‐governmental organizations

(NGOs).

‐ Unidentified other services: These are other government led facilities; however, they

are not specified in the country specific databases.

Other providers, also known as ‘non‐appropriate’ providers (and therefore referred to

as such throughout this paper): include providers that are neither accredited nor

authorized to prescribe antibiotics for children with signs of ARI. This varies by country,

for this analysis, it includes private pharmacies, shops, and markets as well as relatives

and those practicing traditional medicine or faith healing. It also includes private

services which are unidentified in the country specific databases.

RESULTS

Of the ten highest burden countries initially considered for the analysis; six met the

predefined inclusion criteria of having a DHS or MICS survey in 2010 or later (with data

publically available for analysis) and including the standard indicator on suspected

pneumonia and care seeking: Burkina Faso, Democratic Republic of Congo (DRC),

Ethiopia, Nigeria, Tanzania and Uganda, see Table 3.1. Together, these six countries

account for 297,000 deaths or 53% of childhood pneumonia mortality among children

under‐five in sub‐Saharan Africa, and 26% of global childhood pneumonia mortality.15

The remaining four countries were excluded for the following reasons: Angola did not

have a survey assessing the indicators of interest available, Kenya did not have a survey

in 2010 or later and relevant survey data for Niger and Mali were not available at the

time we started our analysis. Table 3.1 shows the under‐five mortality per 1,000 live

births based on the latest estimates developed by the UN Inter‐agency Group for Child

Mortality Estimates.4 The table also presents characteristics of the surveys in the

included countries. Data of 76530 children were available for analysis. Sample sizes per

country ranged from 7535 (Uganda) to 25192 (Nigeria).

Care seeking for suspected pneumonia

The number of children under‐five included in the survey with suspected pneumonia, as

reported by caregivers in the previous 2 weeks preceding the surveys, ranged from 267

in Burkina Faso to 1118 in Uganda in the adjusted country samples. In all countries,

except Ethiopia, care was sought for the majority of children with suspected

pneumonia.


39

Table 3.1

Countries included

in the analysis, data source and year, and sam

ple size of responden

ts and children with suspected pneu

monia.

Children in

cluded in

survey with suspected

pneumonia

Country

Data

source

Survey

Year

Under‐five

mortality per

1000 live

births

Child

ren

included in

the

survey

Number (m

issing)

Percentage

Children with suspected

pneumonia from whom

care was sough

t from an

‘appropriate’ p

rovider

Child

ren with suspected

pneumonia from whom

no care was sought

Burkina Faso DHS

2010

98

14001

261 + (6)

1.9%

57.1%

29.9%

DRC

MICS

2010

119

11093

700 + (0)

6.3%

40.3%

34.7%

Ethiopia

DHS

2011

64

11042

767 + (6)

7.0%

27.2%

70.5%

Nigeria

MICS

2011

117

25192

890 + (0)

3.5%

39.7%

37.7%

Tanzania

DHS

2010

52

7667

326 + (6)

4.3%

72.0%

15.0%

Uganda

DHS

2011

66

7535

1115 + (3)

14.8%

78.9%

16.4%

Data on the under‐five m

ortality rate are deaths per 1000 live births, and based

on the 2013 estim

ates developed

by the UN Inter‐agen

cy Group for Child

Mortality

Estimation; these modelled estim

ates rely on the quality of the underlying data.

4 For the numbers from the surveys, all numbers are rounded

, adjusted

for sample size

(weighted) and m

issing data. For care seeking, all children are included

who had

suspected

pneu

monia and who sought care at least once.

Chapter 3

40

Figure 3.1 shows the large variation in care seeking patterns for suspected pneumonia

found across the six countries, juxtaposed with overall levels of pneumonia mortality. In

Tanzania 85% of children with suspected pneumonia were taken to a provider by their

caregiver, as opposed to only 30% in Ethiopia.

Figure 3.1 Care seeking for signs of childhood ARI by provider type, and overall level of pneumonia specific

child mortality by country

Sources: Care seeking data comes from DHS and MICS, mortality data comes from the United Nations Inter‐agency Group for Child Mortality Estimation (2013)

Differences are also seen across the countries in overall levels of care seeking from

‘appropriate’ providers (i.e. health care providers), ranging from 27% in Ethiopia to 79%

in Uganda. In both these countries, when people do seek care they most often use

‘appropriate’ providers. Uganda and Tanzania were the only two countries where more

than 70% of children were brought to an ‘appropriate’ provider. In Nigeria and DRC,

overall care seeking is just over 60%, but care seeking from ‘appropriate’ providers

stands at or just below 40%. All six countries present relatively high levels of

pneumonia specific mortality.

Among only those children for whom care was sought, Table 3.2 shows to which

specific type of health care facilities and providers these children were brought. In

Ethiopia, Tanzania and Burkina Faso children were most often taken to a primary health

care (PHC) facility, which includes health centres, clinics and posts as well as community

based and outreach services. Hospitals were also frequently accessed for care in Nigeria

and Uganda; in fact, in Uganda children were more likely to be taken to a hospital than

a PHC facility. With respect to ‘non‐appropriate’ or other providers, private pharmacies

are frequently accessed in DRC, Tanzania and Nigeria (18, 18 and 16% respectively).

Traditional practitioners and other providers (e.g. vendors, shops, churches, relatives or

friends) are also often consulted in DRC and Nigeria in particular.


41

Table 3.2

Total number of visits to each facilities and/or providers within the six countries (%

).

Burkina Faso

DRC

Ethiopia

Nigeria

Tanzania

Uganda

n

%

n

%

n

%

n

%

n

%

n

%

Total %

of child

ren brough

t for care

183

70.1

457

65.3

226

29.5

555

62.3

277

85.0

932

83.6

Total visits to any provider

188

100

520

100

245

100

596

100

296

100

999

100

Total visits ‘appropriate’ care (i.e.

health) provider

149

79

294

57

225

92

369

62

244

82

938

94

Hospital

38

20

62

12

14

6

174

29

43

15

596

60

PHC facility

111

59

175

34

211

86

152

26

201

68

339

34

‐Health cen

tre

110

59

103

20

113

46

66

11

59

20

‐ ‐

‐Clinic

‐ ‐

‐ ‐

71

29

‐ ‐

‐ ‐

301

30

‐Post

‐ ‐

52

10

20

8

23

4

‐ ‐

‐ ‐

‐Dispensary

‐ ‐

‐ ‐

‐ ‐

‐ ‐

142

48

‐ ‐

‐Private m

edic

‐ ‐

4

1

‐ ‐

11

2

‐ ‐

12

1

‐Community health worker

1

1

15

3

0

0

33

6

‐ ‐

18

2

‐Mobile (outreach) clinic

‐ ‐

1

0

‐ ‐

19

3

‐ ‐

0

0

‐NGO services

‐ ‐

‐ ‐

7

3

‐ ‐

‐ ‐

‐ ‐

‐Uniden

tified

other

‐ ‐

57

11

0

0

43

7

‐ ‐

11

1

Total visits ‘non‐appropriate’ (i.e.

other) providers

39

21

226

43

20

8

227

38

52

18

61

6

Private Pharmacy

13

7

93

18

14

6

94

16

52

18

19

2

Traditional Practitioner

15

8

31

6

2

1

40

7

‐ ‐

7

1

Any othera

11

6

102

20

4

2

93

16

‐ ‐

35

4

As some of the children were taken to m

ore than

one care provider, each visit is included

sep

arately (e.g. if a child

was taken

to a private and governmen

t hospital,

both visits are included

under hospital), the numbers are therefore higher than

as the number of children brought for care. N

umbers are rounded

. Data is adjusted

for

sample size. a Includes all visits to any other ‘non‐appropriate’ provider such as a vendor, shop, church, relative or friend and any other un‐iden

tified

‘non‐

appropriate’ provider.

Chapter 3

42

Table 3.3 shows the results of the multivariate logistic regression to examine potential

associations between child or caregiver characteristics and care seeking behaviour from

‘appropriate’ providers. In Nigeria, the influence of the age of a child, when controlling

for the other variables (i.e., sex, urban‐rural residence, education and wealth) shows

that younger children (<2 years) were 1.7 times more likely to be brought to a care

provider than children between 2 and 5 years of age (95% CI 1.1‐2.5, p<0.01). This

significant association between younger age of the child and care seeking from

‘appropriate’ providers was also found for Ethiopia and DRC. In general, the influence

of a caregiver’s education, when controlling for other variables shown in Table 3.3

shows that higher educated caregivers and higher wealth quintiles were also associated

with higher levels of care seeking from ‘appropriate’ providers in this group of

countries. In Uganda having at least a primary education, when controlling for age and

sex of the child, urban‐rural residence and wealth, was associated with higher

likelihood to seek care. In Tanzania, Ethiopia, Nigeria and Burkina Faso, caregivers from

the richest quintile were much more likely as those from the poorest quintile to seek

care for their child with suspected pneumonia, after controlling for age and sex of the

child, urban‐rural residence and education, with odds ratios ranging from 4.7 (95% CI

1.5‐15.1) to 9.4 (95% CI 2.3‐39.3). Similar patterns did not emerge in the DRC and

Uganda. With regard to the sex of a child, controlling for the child’s age, urban‐rural

residence, education and wealth, girls were 1.7 times more likely to be brought to seek

care in Uganda than boys (95% CI 1.2‐2.4, p<0.05). We found no statistically significant

differences in care seeking patterns between rural and urban settings after controlling

for the age and sex of a child, education and wealth.


43

Table 3.3

Associations am

ong key child

and caregiver characteristics and care seeking from any ‘appropriate’ provider.

Country

Burkina Faso

DRC

Ethiopia

Nigeria

Tan

zania

Uganda

Variable

N (%)

OR + (95% CI)

N %

OR + (95% CI)

N %

OR + (95% CI)

N %

OR + (95% CI)

N %

OR + (95% CI)

N %

OR + (95% CI)

Age:

<2

years

83/135 (61.2)

1.4 (0.8‐2.4)

172/379 (45.3)

1.5 (1.0‐2.3)*

117/361 (32.5)

1.8 (1.1‐2.8)*

186/418 (44.6)

1.7 (1.1‐2.5)**

118/153 (77.1)

1.5 (0.8‐3.0)

435/553 (78.7)

1.0 (0.7‐1.4)

2‐5 years

66/126 (52.7)

ref

111/321 (34.5)

ref

91/406 (22.5)

ref

167/473 (35.4)

ref

117/172 (67.6)

ref

445/562 (79.1)

ref

Sex of child

:

Male

78/146 (53.3)

0.8 (0.5‐1.4)

143/386 (36.9)

0.8 (0.5‐1.2)

100/393 (25.4)

0.8 (0.5‐1.2)

184/474 (38.8)

0.9 (0.6‐1.4)

128/170 (74.9)

1.4 (0.7‐2.6)

433/578 (74.9)

0.6 (0.4‐0.8) **

Female

71/114 (62.0)

ref

140/314 (44.6)

ref

109/375 (29.1)

ref

170/416 (40.8)

ref

107/155 (69.0)

ref

447/537 (83.3)

ref

Residence:

Urban

46/71 (65.5)

0.8 (0.3‐1.8)

66/160 (41.4)

0.8 (0.4‐1.6)

32/69 (46.9)

0.8 (0.3‐2.0)

101/191 (53.0)

1.1 (0.6‐2.0)

74/85 (86.1)

1.2 (0.4‐3.2)

114/141 (80.8)

0.9 (0.4‐1.9)

Rural

103/190 (54.0)

ref

216/540 (40.0)

ref

176/698 (25.2)

ref

252/699 (36.1)

ref

161/240 (67.1)

ref

766/974 (78.7)

ref

Education:

Secondary +

15/22 ( ‐ )

87/206 (42.1)

1.2 (0.6‐2.2)

18/22 ( ‐ )

124/214 (57.9)

1.6 (0.9‐2.7)

22/26 (83.4)

1.0 (0.2‐6.0)

164/201 (81.6)

1.9 (1.0‐3.7)*

Primary

25/37 .(67.0)

1.3 (0.5‐3.3)

131/320 (40.8)

1.1 (0.7‐1.9)

55/198 (27.9)

0.9 (0.5‐1.6)

57/140 (40.8)

1.2 (0.8‐2.0)

173/ 238

(72.8)

1.2 (0.6‐2.7)

603/752 (80.2)

1.8 (1.1‐2.8)*

No education 109/202 (54.2)

ref

65/174 (37.4)

ref

136/547 (24.8)

ref

173/535 (32.3)

ref

40/62 (64.7)

ref

113/162 (69.7)

ref

Wealth index quintile:

Richest

46/66 (70.3)

4.7 (1.5‐15.1)**

47/105 (44.7)

1.8 (0.7‐4.5)

42/67 (61.8)

9.2 (3.1‐27.0)**

67/90 (74.5)

6.0 (2.3‐15.7)**

54/58 (93.4)

9.5 (2.3‐39.3)** 140/170 (82.3)

1.2 (0.6‐2.8)

Richer

37/65 (56.7)

2.4 (0.9‐5.9)

48/115 (40.0)

1.3 (0.6‐2.8)

57/173 (33.2)

2.7 (1.3‐5.6)**

58/129 (45.0)

1.7 (0.8‐3.5)

59/76 (77.6)

2.3 (0.8‐7.0)

124/161 (77.2)

0.8 (0.5‐1.5)

Middle

30/46 (64.7)

3.0 (1.2‐7.9)*

63/164 (38.5)

1.3 (0.7‐2.4)

43/191 (22.7)

1.7 (0.8‐3.3)

68/144 (47.2)

2.2 (1.2‐3.8)**

48/83 (57.8)

1.0 (0.4‐2.5)

148/190 (78.1)

0.9 (0.5‐1.5)

Poorer

23/48 (47.2)

1.5 (0.6‐4.0)

74/153 (48.1)

1.8 (1.0‐3.3)

37/148 (25.2)

1.9 (0.9‐3.9)

79/233 (33.9)

1.4 (0.8‐2.3)

40/48 (81.6)

3.2 (1.2‐8.5)*

207/261 (79.2)

1.0 (0.6‐1.6)

Poorest

13/35 .(36.8)

ref

53/164 (32.4)

ref

29/188 (15.5)

ref

82/294 (27.8)

ref

35/61 (57.0)

ref

261/334 (78.3)

ref

Total <5 years

149/261 (57.1)

282/700 (40.3)

209/767 (27.2)

354/ 890 (39.7)

235/326 (72.1)

880/1115 (79.0)

All calculations: numbers (n), percentages (%), odds ratio’s (OR) and 95% confidence interval (CI) are based

on weighted averages which are also adjusted

for missing data. Numbers presented in

this table are rounded, w

e

used STA

TA for these analysis. M

anually re‐calculation m

ight, therefore show slight differences. N ( ‐ ) less than

25 un‐w

eighted cases. N

(%) based

on 25‐49 un‐w

eighted cases. Statistical significance: *p<0.05, **p

<0.01.

Chapter 3

44

DISCUSSION

We found considerable variation in care seeking behaviour for suspected pneumonia

across six high pneumonia mortality countries in sub‐Saharan Africa. The three

countries found to have the lowest levels of care seeking from ‘appropriate’ providers

(i.e. health facility/ provider) were Ethiopia, Nigeria and DRC.

Overall care seeking

Country variations in care seeking for childhood infections, including pneumonia, have

been reported previously.16‐17 We concur with the conclusions of the Hodgins study16

that programs should be adjusted to country specific needs based on identified

barriers. Our data clearly show that both care seeking from ‘appropriate’ providers and

childhood pneumonia mortality is high in Uganda, suggesting that the quality of care

available to these children may be sub‐standard or that children present for care late,

as has been reported in other studies.18‐20

In Ethiopia, where we found care seeking to be the lowest among the six countries,

strategies that investigate what the key challenges are related to accessing care need to

be prioritized. Previous studies conducted in Ethiopia indicate that lack of knowledge

and delay in recognition of the severity of an illness are important factors that predict

care seeking,21‐22 as are religion23 and household wealth.24 Further, vast distances to

facilities within the country were the impetus for the health extension worker program,

which has made important strides in increasing coverage of treatment for childhood

illnesses, although some challenges remain.25 Our analysis confirms that there is a

strong association between wealth and care seeking in Ethiopia, which we also found in

Tanzania, Nigeria and Burkina Faso.

Other studies report that the associations between wealth and care seeking are not

only linked to whether care is sought or not, but also from which facility. Studies from

Nigeria and Tanzania reported that poorer women are more likely to utilize facilities

which provide poor quality services.26‐27 In Tanzania, women living in rural areas tend to

visit primary health care (PHC) facilities more often, whereas richer and higher

educated women visit hospitals or better equipped health facilities. The main reasons

for bypassing PHC facilities are related to the lack of diagnostic aids and drugs.28

In each setting, once solutions to locally specific barriers in care seeking are identified,

it will be critical to ensure that demand generation efforts are not jeopardized by sub‐

standard service and treatment availability.29‐30 An earlier study that reported higher

treatment rates in countries with well‐established private sector services suggested

that the appropriateness of treatments provided may be a challenge.17

Our study also found high use of ‘non‐appropriate’ providers (e.g. private pharmacies,

traditional practitioners and other services, such as shops, churches, relatives, etc.) in


45

both DRC and Nigeria, 43 and 38% of the total care seeking respectively (see Table 3.2).

Another recent study from DRC indicates that while ‘formal care’ (in this paper referred

to as health providers or ‘appropriate’ providers) is valued, the cost of services creates

barriers and results in families either self‐medicating or using traditional provider

options.31 Another study from DRC also concluded that costs were a barrier, but

suggested that distrust of government health services is also a problem.32 Similarly,

studies of care seeking for childhood illnesses in Nigeria have reported high use of

home care, drug vendors and private clinics due to financial constraints, wishing to try

home management first, and poor recognition of the severity of the illness or waiting

for the child to improve.33‐35 As Quinley & Govindasamy have reported, drug shops do

often function as de facto clinics.36 Strategies for improving the quality of care in these

service delivery points may be an important program strategy to consider.16

The only association between sex of the child and care seeking we found was in

Uganda, where girls were actually more likely to be brought for care than boys. Earlier

published findings from Asia indicate preferential care seeking for male children,37‐38

and previous African studies of the association between the sex of a child and care

seeking in Tanzania, where no association was found.39,40 In addition, while studies

have reported associations between better health outcomes and higher levels of

caregiver education,34‐41 for associations between caregiver education and care seeking

‐ hence increasing the likelihood of receiving correct treatment ‐ we only observed an

association in Uganda.

We found differences between care seeking in rural versus urban areas in Burkina Faso,

Ethiopia, Nigeria and Tanzania, as has been reported in previous studies.21,42 For

example, in Ethiopia the percentage seeking care from an ‘appropriate’ provider in

rural areas is 25, in contrast to 47% in urban areas. However, when we controlled for

wealth, education, sex and the age of a child, there was no independent association of

urban‐rural residence. This may be (partly) explained by lower education and wealth

status of those living in rural areas. In relation to this, our analysis shows that lower

wealth quintiles were associated with lower levels of care seeking from ‘appropriate’

providers in Burkina, Ethiopia, Tanzania and Nigeria, independent of the actual

residency of caregivers. In other words, the poorest are the least likely to seek care,

independent of where they live (i.e. in urban or rural settings). This implies that those

living in rural areas are more disadvantaged due to poverty and access to health

services. These associations were not found in DRC and Uganda.

Our study indicates that care seeking for children under the age of 2 with suspected

pneumonia was more frequent than for children between 2‐5 years of age, particularly

in Nigeria, Ethiopia and DRC, this relationship was significant. Although similar findings

were reported in previous studies from Nairobi43 and Nigeria,44 studies from Uganda19

and Ethiopia24 reported no association between care seeking and age of the child.

Seeking treatment for younger children with suspected pneumonia is especially critical

Chapter 3

46

to decrease childhood mortality due to pneumonia, not only because the incidence of

pneumonia is highest amongst these children, but also because 81% of child deaths due

to pneumonia occur within this age group.45 Demand generation efforts should take

this into account and should focus on preventive measures as well, e.g. increasing

coverage of immunization, and improving breastfeeding practices and nutritional

status.46‐47

One of the strategies to improve the quality of care is through Integrated Management

of Childhood Illnesses (IMCI) – a strategy designed to reduce child mortality and

morbidity due to common illnesses. IMCI has been implemented in all 6 countries

included in these analyses. The strategy aims to improve family and community health

practices, case management of health staff and the overall health system,48 although

the extent to which this is achieved in any particular setting will vary. The level of

implementation, quality of training and supervision will have an impact on care seeking

behaviour and the quality of care.49,50 Moreover, the quality of care is not merely

affected by the capacity of health workers, but also on the availability of essential

resources, inkling appropriate medicines.49 The IMCI protocol guides health workers to

classify a child as having pneumonia when s/he presents with a cough and fast and/ or

difficult breathing due to a problem in the chest. Despite the protocol, health workers

are often challenged to classify and prescribe treatment for these suspected

pneumonia cases.19,51

Limitations

There are limitations related to these analyses. Pneumonia prevalence, which is

collected in household surveys primarily for use as a denominator for indicators relating

to pneumonia, should be interpreted with caution as it depends on caregivers’

perception of the signs and symptoms (which may or may not be accurate) and their

capacity to recall the events (which may be prone to recall bias), leading to incorrect

estimates.52 Moreover, the prevalence of suspected pneumonia varies seasonally,

which also influences care seeking (i.e., it may be more difficult to take a child for care

during harvest and rainy seasons). In relation to this, caregivers may identify signs and

symptoms such as cough and difficulty or rapid breathing due to a problem in the chest,

while, clinically these may refer to another acute respiratory tract (ARI) infection, rather

than to pneumonia specifically.

Secondly, survey data do not allow us to determine the specific pathways of care taken

(i.e. which care provider – either ‘appropriate’ or ‘inappropriate’ ‐ was visited first), if

the same provider was visited multiple times or if the same health worker assessed a

sick child in a health centre and again later in his or her capacity as a private

pharmacist. Having this additional information would allow for more nuanced analyses

of care seeking behaviours in these settings.


47

Finally, survey data also do not provide information on severity of illness, and it is

therefore not possible to distinguish whether or not seeking care from PHC facility was

appropriate, or if the child should have gone directly to hospital. In relation to this, it

would be interesting to assess if a child received treatment, however, as Hazir et al

(2013) concluded ‘…data in its current format from DHS/MICS surveys should not be

used for the purpose of monitoring antibiotic treatment rates in children with

pneumonia at the present time as their quality is jeopardized’.52 This is because the

identified cases depend on the caregiver’s interpretation of signs and symptoms; the

validity of receiving antibiotics is therefore dependent on the accuracy of this

interpretation.

Further research areas

In this analysis we included (when applicable) multiple providers/facilities per child,

which could include both ‘appropriate’ and ‘inappropriate’ provider categories. Further

research could assess the percentage of care sought from more than one type of

provider and why. A better understanding of the complexity of care seeking and

associated delays including the timing of care‐seeking could reveal additional

information about quality of care and user preferences. Further research should aim to

understand the correlation between mortality and care seeking, and assess if there are

differences in this association between any care seeking and care seeking from

‘appropriate’ providers. These analyses should focus not merely on pneumonia, but

also on the other main causes of illnesses (e.g. diarrhoea and fever).53‐54 Finally, we

need to know why overall care seeking is unacceptably low in some countries, even for

the youngest children (who benefitted from slightly higher levels of care seeking in this

study but who are also the most likely to die from pneumonia). It is critical to identify

strategies to improve the quality of services visited most often by caregivers, including

the ‘informal’ sector, e.g. private pharmacies.

Conclusion

In conclusion, this study illustrates that prior to planning strategies to decrease

pneumonia mortality, it is crucial to understand care seeking patterns (and the related

determinants) between countries and within countries using available data, such as

national survey data. Further research is needed to better understand the reason

behind these findings by conducting more systematic analyses at national and sub‐

national level, including the assessment of socioeconomic, knowledge and information,

cultural and health system factors that influence care seeking. Locally specific research

is also needed to understand why families choose the providers and facilities they

choose, and then programmatic strategies should be developed that engage local

community members to identify relevant, feasible and acceptable solutions.

Chapter 3

48

Acknowledgement

Special thanks to Paddy Hinssen for cross‐checking our analysis.


49

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53

CHAPTER 4

The use of counting beads to improve the

classification of fast breathing in low resource settings:

A multi‐country review

Noordam AC, Barberá Laínez Y, Sadruddin S, van Heck PM, Chono AO, Acaye GL,

Lara V, Nanyonjo A, Ocan C, Källander K

Health Policy and Planning 2015;30:696–704

Chapter 4

54

ABSTRACT

To decrease child mortality due to common but life threatening illnesses, community

health workers (CHWs) are trained to assess, classify and treat sick children. For

pneumonia, CHWs are trained to count the respiratory rate of a child with cough and/

or difficulty breathing, and determine whether the child has fast breathing or not based

on how the child’s breath count relates to age‐specific respiratory rate cut‐off points.

International organisations training CHWs to classify fast breathing realized that many

of them faced challenges counting and determining how the respiratory rate relates to

age specific cut‐off points. Counting beads were designed to overcome these

challenges. This paper presents findings from different studies on the utility of these

beads, in conjunction with a timer, as a tool to improve classification of fast breathing.

Studies conducted by the International Rescue Committee and Save the Children

among illiterate CHWs assessed the effectiveness of counting beads to improve both

counting and classifying respiratory rate against age specific cut‐off points. These

studies found that the use of counting beads enabled and improved the assessment

and classification of fast breathing. However, a Malaria Consortium study found that

the use of counting beads decreased the accuracy of counting breaths among literate

CHWs. Qualitative findings from these studies and two additional studies by UNICEF

suggest that design of the beads is crucial: beads should move comfortably and a

separate bead string, with color‐coding, is required for the age‐groups with different

cut‐off thresholds – eliminating more complicated calculations. Further research, using

standardised protocols and gold standard comparisons, is needed to understand the

accuracy of beads in comparison to other tools used for classifying pneumonia, which

CHWs benefit most from each different tool (i.e. disaggregating data by levels of

literacy and numeracy) and what the impact is on improving appropriate treatment for

pneumonia.

The use of counting beads to improve the classification of fast breathing

55

INTRODUCTION

Because of the overall complexity of diagnosis, the still staggering mortality, lack of

diagnostic aids and the growing problem of antibiotic resistance for pneumonia, there

is an urgent need for more robust data on tools for pneumonia diagnosis. Pneumonia is

the leading cause of childhood mortality, accounting for 17% of global deaths of

children under five.1 Timely recognition of pneumonia signs and symptoms, appropriate

care seeking and access to antibiotic treatment can prevent many of these deaths. The

Integrated Management for Childhood Illness (IMCI) protocol for ‘Caring for Newborns

and Children in the Community’ guides community health workers (CHWs) to assess

fast breathing as an indicator of non‐severe pneumonia in children with cough and/ or

difficulty in breathing.2

There are two steps in detecting if a child has fast breathing or not: first, a CHW needs

to visually count a child’s breath for one minute, second, the CHW has to determine

how the child’s breath count relates to age specific respiratory cut off points.2

International organisations training CHWs with various degrees of literacy and

numeracy realized that many of them faced challenges in counting and relating the

breath count to age‐specific cut‐off points. However, accurate assessment of fast

breathing is crucial as selected children (children 2‐11 months of age with a respiratory

rate (RR) of 50 or more breaths per minute and children 12‐59 months of age with a RR

of 40 or more) are classified as having pneumonia based on their breathing rate and

require immediate treatment with antibiotics.3‐4

Watches and timers have been used as timing aids to facilitate one minute respiratory

rate counting. Box 4.1 shows an example of an acute respiratory infection (ARI) timer

that is distributed by UNICEF. Until now, there is limited evidence on counting devices

and other affordable tools to help CHWs in resource poor settings improve

classification of fast breathing. One study evaluating the effectiveness of an abacus

with a built‐in sandglass concluded that CHWs were better able to correctly classify fast

breathing with the breath counter.5

The potential of the use of counting devices, such as beads, is unknown due to lack of

information regarding their effectiveness and utility. However, several organizations

(including International Rescue Committee (IRC), Save the Children, Malaria

Consortium, UNICEF and Population Services International (PSI)) have conducted small

scale studies among CHWs with differing levels of literacy and numeracy, using

counting beads, which contribute to the knowledge base.

In this review we compile the current evidence‐base of the effectiveness of counting

beads to assess and classify breathing rates to guide pneumonia diagnosis. These

findings are essential to further guide integrated Community Case Management (iCCM)

programing aiming to decrease pneumonia deaths in young children.

Chapter 4

56

Box 4.1 The ARI timer.

METHODS

The findings presented are based on studies conducted by IRC, Save the Children,

Malaria Consortium and UNICEF to improve iCCM programming in South Sudan,

Uganda and Ghana. All CHWs in these studies were trained in iCCM using the

WHO/UNICEF IMCI ‘Caring for newborns and children in the community’ protocol.

CHWs are named differently in the various countries, for example in South Sudan they

are called community based distributors; however, in this paper, we refer to them as

CHWs.

We compiled all research findings on the use of counting beads within these programs.

While most findings presented are part of larger research initiatives, in this review we

focused on the following questions:

1. What is known regarding primarily illiterate CHWs’ ability to assess and classify fast

breathing without the use of counting beads?

2. Does the use of beads improve the ability of CHWs, particularly those with limited

or no literacy and numeracy, to correctly classify fast breathing (hence, including

tracking the breaths and classifying the breath count based on IMCI age‐specific

respiratory rate cut‐off points)?

3. Does the use of beads improve the ability of literate CHWs to correctly assess the

breath count?

4. What are CHWs’ perceptions of these tools, and does this differ by literacy level?

A brief description of the studies is provided below and a summary of the key

methodological elements of the different studies can be found in Table 4.1. All studies

used the ARI timer explained in Box 4.1 and the design of beads used by the various

organizations can be found in Box 4.2. The results are separated by qualitative and

quantitative research methods, as well as by organization. In addition, anecdotal

evidence from two rapid assessments, from PSI in Democratic Republic of Congo (DRC)

and from IRC in Sierra Leone, is not included in the methods or findings, but is used to

strengthen the discussion.

The ARI timer makes a ticking sound every second and has an alarm

after 30 seconds as well as a final alarm to inform the user that 1

minute has passed. The user must press the button to start the 1

minute timing, during which a child’s breath is counted.


57

Table 4.1

Summary of the key methodological elements of the different studies.

International Rescue Committee (IRC)

Save the Child

ren US

Malaria Consortium

UNICEF

Country

South Sudan

Uganda

South Sudan

Uganda

Uganda

Ghana

Geographic location

North Bahr el G

hazal: 1

sub‐county in Aweil East

Karam

oja: 2

sub‐counties

in M

oroto district

Kapoeta North County,

Eastern Equatoria

Bukomero, sub‐county

Kibonga

Kyankw

anzi and M

pigi

district

Tolon/Kumbungu

and

Cen

tral Gonja district

Year research

2011

2011

2013

2012

2011

2012

Literacy level:

100% illiterate

66% illiterate

100% illiterate

0% illiterate

0% illiterate

mainly illiterate

Training iCCM:

6 months prior

6 months prior

Part of research

Since 2010

> 3 months prior

During 2007 ‐ 2009

Use of ARI tim

er:

6 months prior

6 months prior

Part of research

Since 2010

> 3 months prior

During 2010 ‐ 2012

Use of beads:

Part of research

Part of research

Part of research

Part of research

Part of research

During 2010 ‐ 2012

Background

Characteristics

Type beads:

Age‐specific + color codedAge‐specific + color coded Age‐specific + color coded

Age‐specific + color coded

Age‐specific + color

coded

Non‐age specific and

not color coded

Sample size:

32 cases / 32 CHWs

33*1 cases / 33 CHWs

69 cases / 27 CHWs

282 cases / 94 CHWs

Sample method:

Random sam

pling

Random sam

pling

All CHWs attending the

training

All CHWs included

Assessing the

impact of:

Timer vs tim

er & beads

Timer vs tim

er & beads

Timer & beads

Timer vs timer & beads vs

mobile application

Type and place of

assessment:

A m

ock visit at home of

which the CHW was not

aware, the children were

not necessarily sick

children

A mock visit at home of

which the CHW was not

aware, the children were

not necessarily sick

children

Selection of sick children

at an out‐patient

dep

artm

ent in a hospital

Video case scenarios (2

with fast breathing and

one without)

Correct using timer

alone:

Count within ± 3 breaths

of gold standard + knew

the cut‐off point


of gold standard + knew

the cut‐off point

NA


of gold standard

Quantitative research component

Correct using timer

& beads:

CHWs finger within ± 3

beads of gold standard

CHWs finger was within ±

3 beads of gold standard

CHWs finger in sam

e

classification area as gold

standard


of gold standard

Not applicable for this study (NA)

Sample size

32 cases / 32 CHWs

33*1 cases / 33 CHWs

94 CHWs

87 CHWs

± 45 CHWs

Sample method

Random sam

pling

Random sam

pling

All CHWs included

Purpose sam

pling +

snowball technique

Purpose sam

pling +

snowball technique

Key research focus

Perceptions and potential

improvemen

ts

Perceptions and potential

improvements

Perceptions, user

experience and opinions

on communication of

results

Percep

tions and ideas

for diagnostic aids

Perceptions, users

experience and

potential

improvements

Qualitative research

component

Method

Interviews

Interviews

NA

Focus group discussions

Focus‐groups, in‐depth interviews and

observations

*1 46 were initially selected, but due to tim

e constraints and the eviden

t advantage of counting beads the team stopped

after assessing 33 CHWs

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58

Box 4.2 Overview of devices used by the various organizations.

Quantitative research

International Rescue Committee study in Uganda and South Sudan

As part of a larger assessment evaluating the quality of care provided by CHWs, IRC

compared the CHWs’ ability to assess fast breathing using the ARI timer alone versus

using the timer and beads. The geographical areas (payams) were selected on the basis

of their geographical accessibility to the evaluation team and recent start‐up of the

program.

Picture Description of the device Used by:

1 set of 2 ‘age‐specific and color‐coded’ strands of beads, with 1 bead counted per breath. The two strands can be

distinguished because the beads of 1 age group have

different colours and sizes than the beads of the other strand. Several tiny beads are used to create space between the

beads and the strand is tightly tied to hold the beads in place.

At the end of age cut‐offs for each strand, there are 10 red/ pink beads which, if counted, indicate fast breathing. Strands

have a clasp so they can be used open (straight) or closed

(like a necklace). Beads are eclipse shaped and made from newspapers and glue.

Save the Children

1 strand of beads, non‐specific for children ages 0 to 5 years)

and color‐coded per 10 beads to ease counting. The strand is

necklace shaped and has a protruding start/ end bead. 1 bead is counted per breath. Beads are made from plastic. CHWs

are using these beads in conjunction with the ARI Timer.

Ministry of

Health in

Ghana and UNICEF

1 set of 2 ‘age‐specific and color‐coded’ strains of beads, with

1 bead counted per breath. The two strands can be distinguished because the 10 fast breathing beads of one age

group are red and other green (matching the amoxicillin

packaging in Uganda). There are no separator beads and they are tied so that there is space for moving the beads across

the string. The beads are made from plastic and round

shaped. Strands have a clasp so they can be used open (straight) or closed (like a necklace). The white beads are

16mm in diameter, whereas the coloured beads are 12mm in

diameter.

IRC and

Malaria Consortium

1 set of 2 ‘age‐specific and color‐coded’ strains of beads, with 1 bead per breath. The two strands can be distinguished

because the fast breathing beads of one age group are pink

and other green. There are no separator beads. The beads are made from plastic and round shaped. The strand is

straight, with a small orange bead at the beginning and end.

PSI


59

First, CHWs were asked to describe the cut‐off points for fast breathing for the two age

groups (2‐11 and 12‐59 months) and to count the child’s respiratory rate (RR) using the

ARI timer. A trained clinician who was part of the evaluation team counted

simultaneously with the CHW. The RR from the clinician was written down and the

CHW was asked to say his/her count. Afterwards, CHWs were given the right counting

bead string for the age of the child being assessed and were shown how to move their

fingers along the beads and to stop when the timer beeped. Then CHWs were asked to

repeat the assessment with the same child using the timer and the beads. The clinician

used the counting beads simultaneously with, but out of sight of, the CHW. The beads

were counted back and recorded for both the CHW and the trained clinician. Microsoft

Excel was used to analyse the data.

Save the Children study in South Sudan

The use of beads by CHWs with limited numeracy was part of an operational research

lead by Save the Children. The research was conducted to assess the effectiveness of

simulation based training of CHWs using video technology.

During the training, CHWs were shown video clips of cases with danger signs (including

case scenarios of malaria, pneumonia and diarrhoea) and the interactions between

CHWs and caretakers. The video also showed the assessment, classification, treatment

and advice on home care. CHWs were given 1 set (two strings) of beads to count and

classify the breath count along with the ARI timer acting as a stop watch. CHWs were

taken to a hospital outpatient department for post training skills assessment, where

they assessed sick children (40 out of the 69 cases had a history of cough). Each CHW

managed (assessing, classifying and deciding to treat) 2‐3 sick children aged

2‐59 months. Each CHW also completed a knowledge questionnaire. As these particular

CHWs were illiterate, one clinician trained in IMCI observed the CHWs’ management of

sick children and recorded the assessment, classification and treatment findings on a

study form. A senior evaluator who was also an IMCI trainer independently assessed

the children at the same time and recorded his findings on a similar form. The RR was

measured for all 69 children by the CHWs and the evaluator. To assess fast breathing

the CHW had to choose the correct bead string for the age group. The observer marked

the case as having fast breathing if the CHW reached the red beads (fast breathing for

2‐11 month and 12‐59 month age groups) within the minute. The evaluator used the

ARI timer to count the child breaths and noted the actual breath count in the form. The

data was entered in CSPro, and imported and analysed in Microsoft Excel.

Malaria Consortium study in Uganda

Malaria Consortium assessed if there was a difference in accuracy in counting RR

among CHWs using 1) the ARI timer alone versus 2) using the timer & beads and

3) using a mobile phone application, where the centre button on the phone was

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60

pressed for every breath observed, and which beeped after one minute, after which the

count was displayed. The sub‐county was selected on the basis of its geographical

location and mixture of CHWs with different age, sex and literacy levels (varying from

being able to read and write well in any of the local languages (78%) to fairly well

(22%)).

First, each CHW received a detailed explanation of how to count the RR, while

simultaneously using the ARI timer, the same timer with beads as well as the mobile

phone application. The CHWs then had the opportunity to practise the three different

methods of counting the RR on videos of children with different breath counts. After

familiarizing themselves with the three methods, each CHW was observed counting

using the three options respectively, on children with different breath counts. The

video case scenarios were displayed on laptop screens, enabling one CHW to assess a

child at a time. The final RR was recorded for the three tools as follows: 1) when using

the timer alone, the CHW was asked for the final RR count, 2) when using both the

timer and beads, the beads were counted back by a research assistant who provided

the final RR count, and 3) for the mobile phone application, the result was read from

the screen. The breathing rate of the children in the video was known and was used as

the gold standard. STATA 12 was used to analyse the data. The research only assessed

the impact on counting the RR and did not assess the impact of these devices on

classifying the breath count against the age‐specific cut‐off points.

Qualitative research

Along with their quantitative research efforts, IRC and Malaria Consortium assessed

CHWs’ perceptions about the beads, focussing on their opinions and proposed

improvements. Data were collected through interviews. Malaria Consortium also

assessed CHWs’ perceptions about how the use of counting beads helped them

communicate the test results to caregivers.

Other qualitative research was initiated by UNICEF, as part of a broader effort to

identify CHWs’ unmet needs regarding tools to support the assessment and

classification of RRs. In Uganda the focus of the research was on CHWs’ experiences

assessing RR using the ARI timer and their ideas regarding tools that might be useful to

help them improve their assessment and the classification of RRs. The CHWs were not

familiar with the use of beads. Building on these findings, a subsequent study was

conducted in Northern Ghana, where the CHWs had been trained to use both the ARI

timer and beads. The main objective of this study was to help UNICEF improve the

design of diagnostic aids based on the challenges CHWs indicated they face while

assessing, classifying and identifying treatment needs for children under‐five with rapid

breathing.


61

FINDINGS

Table 4.2 summarizes the key findings of the various studies. In addition, an overview of

the two different types of counting beads (a non‐age specific type and an age‐specific

type with color‐coded beads) can be found in Box 4.3.

Box 4.3 Overview of the two types of counting beads intended for use in conjunction with an ARI timer.

Non‐age specific counting beads These counting beads are designed to help the CHW keep track of the amount of breaths taken. The CHW

counts moves bead for each breath. When one minute has passed the CHW counts back the beads to

determine the respiratory rate. Due to the colour‐coding of a number of beads (e.g. every set of 10), the CHW can count back the beads per colour: e.g., 1 colour = 10 breaths, 2 colours = 20 breaths, etc. Based on

the respiratory rate the CHW compares the result against the IMCI guideline, correctly remembering the age

specific cut‐off rate.

Age‐specific beads with color‐coding

These counting beads support the CHW not only with counting, but also with interpreting the RR against the IMCI guidelines. These beads remove the need to count by the CHWs to assess pneumonia (unless the actual

RR is required for reporting purposes) because they consist of a set of two strands or rows of beads that are

color‐coded to match the thresholds for the two different age groups. Depending on the age of the child, the CHW selects the matching bead strand and moves the beads for one minute. Once the minute has passed,

the CHW can identify if the child has pneumonia or not, depending on the colour of the bead s/he is holding

between his/ her fingers.

Primarily illiterate CHWs’ ability to count breaths and their knowledge of age‐specific

cut‐off rates

The IRC assessed what the key challenges were for primarily illiterate CHWs while

classifying fast breathing. Findings indicated that 46% (21/46) of CHWs in Uganda were

not able to apply the age‐specific cut‐off points and 33% (15/46) made an incorrect

count using the ARI timer. In South Sudan, 59% (19/32) of CHWs were not able to apply

the age‐specific cut‐off points and 72% (23/32) of CHWs made an incorrect count using

the ARI timer.

Primarily illiterate CHWs’ ability to classify fast breathing using counting beads

In South Sudan 13% (4 out of 32) of the CHWs were able to classify fast breathing using

only the ARI timer, whereas this number increased to 63% (20/32) (OR=11.7, p=0.002)

when they used the beads together with the timer. Findings were similar in Uganda,

where the ability to classify fast breathing increased from 37% (17/46), using only the

timer, to 73% (24/33) (OR= 4.4, p<0.005), using both tools. Combined the data from

both countries; the ability to classify fast breathing increased from 27% (21/78) to 68%

(44/65) (OR= 5.7, p<0.005), see Figure 4.1.

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62

Table 4.2

Summary table of key findings related to the 4 research questions

International R

escue Committee (IRC)

Save the Children US

Malaria Consortium

UNICEF

South Sudan

Uganda

South Sudan

Uganda

Uganda

Ghana

1: W

hat is known regarding

primarily illiterate CHWs

ability to assess fast without

the use of counting beads?

59% were not able

to apply the age‐

specific cut‐off

points

72% m

ade an

incorrect count

using the timer

46% w

ere not able

to apply the age‐

specific cut‐off

points

33% m

ade an

incorrect count

using the timer

Prior to the use of

beads CHWs were

not able to assess

fastbreathing as they

could not count

beyond 10 and do

simple arithmetic

2: D

oes the use of beads

improve the ability of CHWs,

particularly those with limited

or no literacy and numeracy,

to correctly classify fast

breathing

The use of beads

increased the

correct classification

from 13% to 63%

The use of beads

increased

the

correct classification

from 37% to 73%

The use of beads

enabled 60% of the

CHWs to correctly

classify fast breathing

No specific data for this research question (NA)

3: D

oes the use of beads

improve the ability of literate

CHWs to correctly assess the

breath count?

NA

CHWs were 5.6 tim

es

more likely to count

the respiratory rate

correctly using the

timer alone compared

to when

it is combined

with beads

NA

4: W

hat are CHWs’

perceptions of these tools,

and does this differ by literacy

level?

Beads simplifies the classification of fast

breathing

Color‐coded beads match the locally

available amoxicillin package, w

hich

helped

CHWs iden

tify appropriate strings

There was a req

uest for more training and

practice

NA

Use of beads in m

ade

it easier to

communicate the

results to the

caretaker

Combining the tim

er

and beads is m

ore

tasking, with a slow

bead m

ovemen

t process which red

uces

the accuracy

CHWs who only used

the timer revealed

that it would be

useful to have a

device to support

them

with classifying

and communicating

results to caregivers

Beads were

perceived

as easily

confused with a toy,

but useful

CHWs used ‘non‐age

specific beads’ and

were still challenged

in counting and

remem

bering cut‐

off points

When

shown age‐

specific & color‐

coded

beads used by

Save the Children

and IR

C, these were

preferred


63

Figure 4.1 Percentage of CHWs able to classify fast breathing correctly, based on findings from IRC.

Save the Children’s research did not have a component of assessing a sick child without

the use of beads, as they identified that CHWs had limited numeracy, and were not

able to count beyond 10. A total of 69 sick children were assessed by the 27 CHWs.

Using the ‘age‐specified and color‐coded’ beads and ARI timer, the CHWs classified

25 cases as suffering from fast breathing. The senior evaluator who only used the ARI

timer classified 23 as fast breathers. Of the 25 CHW classified fast breathing cases,

15 (60%) matched with the evaluator classified fast breathing cases.

CHWs were also administered a knowledge questionnaire which included questions on

use of beads appropriate for age. All 27 CHWs picked the beads appropriate for the

2 age groups. With respect to classifying a child as having fast breathing, 26 out of

27 picked the right colour (red) for the 2‐11 months group and 27 out of 27 for the

12‐59 months age group.

Literate CHWs’ ability to correctly count breaths using counting beads

In Uganda, Malaria Consortium assessed if the use of tools, including counting beads,

improved the ability of literate CHWs to count the respiratory rate. CHWs were

5.6 times more likely to count (not classify) respiratory rate correctly (i.e. ±2 breaths)

using the timer alone compared to when it is combined with beads (OR=5.6, p<0.001).

There was no significant difference between the ARI timer and mobile phone

application (OR=1.1, p=0.08), implying that CHWs have a similar capacity to correctly

count respiratory rate using either of the assessment methods (i.e. counting

themselves versus pressing a button for every breath observed).

Overall, the median difference between the “true rate” and the rate observed with the

three methods was ‐1 (IQR ‐5−2) for the UNICEF mer, ‐1 (IQR ‐7−2) for the mobile

phone application and ‐5 (‐12−2) for the ARI mer with beads. Using the sign test for

non‐parametric data on matched pairs, the differences in rates observed using the ARI

timer compared to the true rate was not significantly different from 0 (p=0.01),

13

3727

6373

68

South Sudan Uganda Total

Using only the ARI timer

Using both the ARI timer & 'age‐specific and color‐coded' counting beads

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64

whereas for the mobile phone application and counting beads, the difference between

the rates observed and the true rate were significantly different from 0 (p=0.001 and

p<0.0001, respectively). Using the same test, the median difference observed between

the ARI timer and mobile phone application was not significantly different (p=0.179),

whereas the difference was significantly different between the ARI timer and the

counting beads (p<0.001) as well as between the mobile phone timer and the counting

beads and timer (p=0.001).

When analysing the accuracy of the three different methods by the characteristic of the

rate of the child in the video (i.e. normal or fast), it was demonstrated that all three

methods performed much better on the slower breathing rates (i.e. 40 breaths/min)

than the fast rates (i.e. 65 and 66 breaths/min). All three methods tended to

overestimate the rate in the slow breathing scenario whereas they all under estimated

the rate in the fast breathing scenarios.

CHWs’ perceptions on the use and design of age‐specific and color‐coded beads

The color‐coded beads designed by IRC in Uganda match the colour of the locally

available amoxicillin packages for specific age groups and according to (mainly illiterate)

CHWs this helped them to identify the correct string needed for the child, eliminating

the need to recall the cut‐off points for the different age groups. Regarding the use of

counting beads, CHWs interviewed by IRC were most likely to say that: 1) the use of

beads eliminated the need to count or worry about forgetting the number or making

mistakes while counting; 2) it was easy to move hands along the beads; 3) it was easy

to know when to give the treatment; and 4) it was easy to explain to the mother that

her child did not need medicine. The need for more training and practice regarding the

use of the beads in combination with the ARI timer was mentioned by CHWs in South

Sudan and Uganda.

In the study amongst literate CHWs by Malaria Consortium in Uganda, CHWs expressed

their support for alternative methods to count the RR other than the timer which they

were familiar with. Using the beads, in addition to the timer, was perceived as being

advantageous because each breath being represented by movement of a bead and

then counting of the beads could be done afterwards, thus giving more accurate

results.

“What I have liked about this method is that I don’t have to count the beads as I

move them, counting usually comes last and this gives me more concentration on

observing the child and moving the beads.”


65

The method was acknowledged as one that could give a quick picture of the diagnosis

by identifying the colour of the bead where the hand has stopped after the alarm has

gone off (i.e. whether white or red/green bead), the next steps could follow later:

“From what I have learnt today, the combination of both the timer and beads is

very helpful because it helps me to immediately know whether the child has fast

breathing by looking at the colour of beads where I have stopped and then, I

count the beads to confirm what I have seen, after which I write in my book.”

The use of the beads also made it easier to communicate the results to the caretaker,

as the colours visually flagged if the child had a high breath rate (indicating pneumonia)

or not.

However, the beads were also perceived by some as disadvantageous because

combining the timer and beads was considered to be more tasking, with a slow bead

movement process and therefore reduced accuracy. During observations, it was noted

that CHWs often found it difficult to start moving the beads immediately after starting

the timer.

“For me, I find this method very challenging because I have to observe three

things at the same time: I have to look at the child, start the timer and also move

the beads at the same time, which is a bit tasking. That is why you have seen that

I have been forgetting to put on the timer as I move the beads.”

Related to the design, CHWs often mentioned that the space between individual beads

can affect the outcome of the count. The space between the beads should be small, as

the bigger the distance was between the beads, the more difficult it was to move them.

The CHWs also suggested that the beads should be light and not attractive in order not

to be mistaken for accessories and have a strong string, which would not break easily.

Data from the assessment in Uganda by UNICEF, where mainly illiterate CHWs only

used the ARI timer, revealed that CHWs would find it useful to have a device that

supports them with the classification of fast breathing and that would help them

communicate the findings to the caregivers. When the CHWs in Uganda were shown

counting beads they mentioned that beads might be confused with a toy; however, it

would help them with the assessment and the classification of fast breathing.

“I would have preferred the beads because each time I count, I hold a bead so I

will be able to know how many beads I left behind in case I forget where I was

when counting”

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“Green is OK. Red is for danger.”

CHWs perceptions on the use and design of non‐age specific beads

The main concern among mainly illiterate CHWs regarding the use of non‐age specific

beads used in the Northern region of Ghana, was that these beads still required

counting and remembering the cut off rate. The beads also have ‘separator bead’ which

the CHWs thought creates confusion since “we move the beads without looking at

them”. Nevertheless, the use of prayer beads in the region is common; CHWs are used

to counting with beads and are at ease with this. However, the CHWs reported that this

similarity could reduce the acceptance by caretakers as the beads are perceived as a

tool for prayer and not as a healthcare related tool.

When shown the ‘age‐specific and color‐coded’ beads used by IRC and Save the

Children, the CHWs and their supervisors in Ghana showed a strong preference to this

design as it eliminated the need to count and remember the cut‐off rate.

“This one has only two colours and the colours easily tell if the child has

pneumonia or not. I think that this one will be more convenient to use than the

current one we have.”

DISCUSSION

This review of studies on the utility of counting beads as a tool to improve classification

of fast breathing in children with cough and/ or difficulty breathing to guide pneumonia

diagnosis, shows that the introduction of ‘age‐specific and color‐coded’ counting beads,

in addition to an accurate timer, can help CHWs with limited numeracy and literacy to

more accurately assess fast breathing. While CHWs also expressed concerns on the

task‐intensity of the method, in general it was acceptable and applicable across

different settings.

There are several limitations associated with the studies included in this review. First,

the studies used several different research approaches, methods (including gold

standards) and research questions. Moreover, programmatic settings differed as well as

the levels of literacy and numeracy of the CHWs. Second, the case scenarios by the

various organizations assessed children in different settings (at home, hospital or on a

video screen) which resulted in different breathings patterns, e.g. the children assessed

at home were unlikely to have fast breathing, although a few of them happened to

have it. This review suggests that these factors influence the utility of beads.


67

Data from IRC, Save the Children and anecdotal evidence from PSI and IRC suggest that

‘age‐specific and color‐coded’ beads enable and improve accuracy in the classification

of fast breathing by CHWs with limited literacy and numeracy. However, if literacy and

numeracy is not an issue, findings from Malaria Consortium show that the use of beads

complicates the assessment, as it resulted in more in‐accurate counts (i.e. CHWs were

5.6 times less likely to count correctly using the beads and timer). A reason for the

breathing count inaccuracy by literate CHWs was that some perceived the use of beads

as more task‐intensive. While for CHWs with limited literacy and numeracy (e.g. those

not able to count beyond 10), the use of beads enabled them to track the breathing

rates, as well as classifying the count against the age‐specific cut‐off points, which

would not be possible without these beads.

It was also found that the rate of breathing influences the accuracy in the respiratory

rate count. The research conducted by Malaria Consortium shows that, regardless of

the tool CHWs used, they tended to overestimate the rate in the slow breathing

scenario whereas they all under estimated the rate in the fast breathing scenarios.

Recommendations

Due to the overall complexity of diagnosis, the still staggering mortality, lack of

diagnostic aids and the growing problem of antibiotic resistance for pneumonia, there

is an urgent need for more robust data on tools for pneumonia diagnosis. Regarding the

use of counting beads, these data show that it is premature to conclude to which

degree the beads, in addition to a timer, would improve the ability of trained CHWs to

correctly classify fast breathing. A more conclusive assessment is needed amongst sick

children, disaggregating data by intensity of training and supervision, levels of literacy,

numeracy and comparing the final breath count to a gold standard.

Previously conducted studies on pneumonia diagnosis by CHWs indicate that even if

CHWs are good in counting, they still often make mistakes in classifying the breath

count.6‐7 Here, the use of beads could help in classification and it should become clearer

how for literate CHWs this potential positive effect is affected by the inaccuracy in

counting when using beads. Is this because of the lack of familiarity in using the beads,

or is the use of beads actually more complex and task‐intensive?

Concurrent to the need of more evidence regarding the utility of beads, there is a need

for more research assessing the effectiveness of other devices, such as automated

respiratory rate counters.

Conclusion

Given the overall paucity of data, this review of recent studies provides insights on a

range of issues to consider when implementing counting beads in iCCM programs. This

emerging evidence suggests that the introduction of well‐designed ‘age‐specific and

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68

color‐coded’ beads in addition to an accurate timer can help CHWs who have difficulty

counting breaths and remembering age‐specific cut‐off rates to more accurately assess

and classify fast breathing. It also has potential to improve communication with the

child’s caretakers – particularly regarding appropriate treatment options. However,

more research is needed on these and other devices to decrease the inaccuracy in

pneumonia diagnosis.

Acknowledgement

Special thanks to Alyssa Sharkey for reviewing and editing this article. The authors also

thank all who supported the various research components, including people from local

governments, colleagues from country offices and partners from various research

institutes. Authors like to thank Mark Young (UNICEF) and Shamim Ahmad Qazi (WHO)

for reviewing this article.


69

REFERENCES

1. United Nations Children’s Fund (UNICEF). Committing to Child Survival: A Promise Renewed Progress Report 2013 New York: UNICEF.

2. WHO and UNICEF. 2011. Integrated Management of Childhood Illnesses Caring for Newborns and

Children in the Community. Geneva: WHO. 3. World Health Organization (WHO) and UNICEF. 2005. Handbook IMCI Integrated Management of

Childhood Illness. Geneva: WHO.

4. Pio A. Standard case management of pneumonia in children in developing countries: the cornerstone of the acute respiratory infection programme. Bulletin of the World Health Organization 2003;81:298‐300.

5. Bang AT and RA Bang. Breath Counter: A new device for household diagnosis of childhood pneumonia.

Indian J Paediatr 1992;59:79‐84. 6. Kallander K, Tomson G, Nsabagasani X et al. Can community health workers and caretakers recognise

pneumonia in children? Experiences from western Uganda. Trans R Soc Trop Med Hyg 2006;100:

956‐963. 7. Mukanga D, Babirye R, Peterson S et al. Can lay community health workers be trained to use

diagnostics to distinguish and treat malaria and pneumonia in children? Lessons from rural Uganda.

Trop Med Int Health 2011;16:1234‐1242.

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71

PART III

A potential solution to decrease delays

73

CHAPTER 5

Improvement of maternal health services through the

use of mobile phones

Noordam AC, Kuepper BM, Stekelenburg J, Milen A

Tropical Medicine & International Health 2011;16:622–626

Chapter 5

74

ABSTRACT

OBJECTIVE: To analyse, on the basis of the literature, the potential of mobile phones to

improve maternal health services in Low and Middle Income Countries (LMIC).

METHODS: Wide search for scientific and grey literature using various terms linked to:

maternal health, mobile telecommunication and LMIC. Applications requiring an

internet connection were excluded as this is not widely available in LMIC yet.

RESULTS: Few projects exist in this field and little evidence is available as yet on the

impact of mobile phones on the quality of maternal health services. Projects focus

mainly on the delay in recognizing the need and making the decision to seek care, and

the delay in arriving at the health facility. This is achieved by connecting lesser trained

health workers to specialists, and the coordination of referrals. Ongoing projects focus

on empowering women to seek health care.

DISCUSSION: There is broad agreement that access to communication is one of several

essential components to improve maternal health services and hence the use of mobile

phones has much potential. However, there is a need for robust evidence on

constraints and impacts, especially when financial and human resources will be

invested. Concurrently, other ways in which mobile phones can be used to benefit

maternal health services need to be further explored, taking into consideration privacy

and confidentiality.

Improvement of maternal health services through the use of mobile phones

75

INTRODUCTION

Progress in achieving Millennium Development Goal (MDG) 5, to improve maternal

health by reducing maternal mortality and improving access to reproductive health, is

lagging behind the targets. New impulses are needed to attain the goals. Two recent

international initiatives recommend mobile phones as a means to improve maternal

health services.1‐2

Maternal health

Every 90 seconds a woman dies of complications related to pregnancy and childbirth,

resulting in more than 340 000 maternal deaths a year.3 Millions of women suffer from

pregnancy‐related illnesses or experience other severe consequences such as infertility,

fistula and incontinence.4 Delay is considered the key factor leading to women not

accessing health services.

There are three phases of delay: (i) recognizing the need for health care and in the

decision‐making process; (ii) arrival at a health facility; and (iii) receiving appropriate

and adequate care at the health facility.5 Underlying determinants that cause the

delays are the position of women in society, large geographical distances, weak health

systems, poverty and lack of education.4,6

Mobile phones

MDG 8 addresses the need to make benefits of new technologies available, especially

those related to information and communication. The fastest growing new technology

worldwide is the mobile phone. In Africa and Asia, where the burden of maternal

mortality is greatest,7 the expectations are that by 2012, 50% of the people will have

access to a mobile phone.8 The uptake of mobile phones varies; it is inversely

proportional to poverty rates, but also influenced by the competitiveness and thus the

price levels of the relevant markets.9 The use of mobile phones in health systems is

called mHealth. This article discusses the potential of mobile phones to improve

maternal health services in LMIC by strengthening communication throughout different

levels of the health system.

METHODS

Our literature search limited to English publications combined terms linked to:

maternal health, mobile telecommunication, and LMIC. Only publications considering

the basic use of mobile devices (without requiring internet access) were included, as

poor internet coverage, high illiteracy rates and low levels of experience in using

technology make more advanced use of mobile technology difficult in LMIC.

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76

Searches initiated in PubMed, Embase, Cochrane Library, Scopus, Science Direct and

African Journals Online retrieved a large amount of mHealth‐related publications, of

which only eight were relevant; these articles address maternal health services in LMIC,

the use of mobile devices and reported preliminary results. The search was

subsequently expanded to grey literature, and reference lists were also screened for

further relevant sources.

LITERATURE FINDINGS

A recently published paper on mobile phone technology for health care in LMIC10

reviewed literature on mHealth, such as treatment compliance, data collection and

disease prevention. The authors see great potential for mHealth; however, there is not

much evidence of actual and wide‐scale impacts yet. We analysed resources for the

particular area of maternal mHealth and confirmed a lack of evidence‐based studies

focusing on the efficacy and effectiveness of interventions. Most documentation

referred to pilot studies and often lacked baseline data, a control group and clear

outcome indicators.

Accessing emergency obstetric care

Before the wider use of mobile phones, several project publications considered

improved communication through radio systems as one component among several

aimed at improving access to emergency obstetric care and referral systems. These

projects mainly focused on reducing the second phase of delay. Traditional Birth

Attendants (TBAs) and ⁄ or midwives were equipped with walkie‐talkies, enabling them

to contact supervisors and ambulances when facing difficult situations. Concurrently,

other components such as the overall quality of the health services were improved

through more reliable transport means, increased capacity, medical equipment and

reduction of financial barriers.

Projects in Mali, Uganda, Malawi, Sierra Leone and Ghana, which implemented the

above mentioned components, noted a significant reduction in maternal deaths and an

increase in supervised births when comparing the situation before and after the

interventions. Faster modes of communication and transport were named as important

factors in improving access to emergency obstetric care.11‐16 The projects in Uganda and

Ghana additionally considered the first phase of delay by connecting traditional health

providers to the biomedical health system. As TBAs are frequently at the homes of

pregnant women, they can speed up the process there.

Krasovec (2004) concluded that studies provided only weak empirical evidence

regarding the actual impact of communication systems and that access to tools of

communication is not the solution for decreasing maternal deaths in isolated areas.17


77

The tight timeframe in which a woman requires emergency obstetric care (due to e.g.

severe bleeding) implies that quality services need to be accessible at short notice and

supported by effective infrastructure management. In a more recent review, Lee et al.

(2009) confirm the need for more rigorous assessments.18

Information regarding plans for scaling‐up projects that use radio systems was only

found for the pilot project in Uganda. These plans were not realized due to high costs,

inability to maintain equipment and lack of integration into the health system.

However, in this project the radio system was later replaced by mobile phones, which

were found to be a cheaper and a more practical solution.19

Improving the capacity of lesser trained health workers

More recent projects introduced mobile phones to improve the capacity of lesser

trained health workers by connecting them to better trained medical staff, thus aiming

to reduce the third phase of delay. In Indonesia, Chib et al. (2008) selected 15 health

facilities through random sampling; midwives in eight of the facilities received a mobile

phone.20 Perceived benefits reported were that: (i) mobile phones made it easier to

contact patients, midwives and supervisors, (ii) time efficiency increased due to the

ability to coordinate visits, and (iii) if complications occurred assistance was only a call

away. Despite these advantages, constraints included the costs, poor mobile phone

network infrastructure in rural areas, increased demand for consultation, difficulties in

uptake of higher technology programmes for data analysis, and hesitation in contacting

supervisors due to organizational hierarchy.20‐21

A recently launched project in Rwanda went a step further by using text messaging to

facilitate and coordinate the communication as well as data exchange between

community health workers, health centres and hospitals. Preliminary data suggested a

positive effect on access to maternal health services and consequently lower death

rates.22

An initiative in Tanzania designed a phone‐based application that contained forms and

protocols meant to support pregnant women before, during and after delivery.23 The

results of a pilot project seemed positive; however, the authors mentioned the need to

further assess the impact of the project.

Empowering women to contact health services and access information

To decrease the first phase of delay, several programmes aimed to empower women to

contact health services and access information; however, data was still being processed

at the time this article was written. In Zanzibar, a study following 2,500 women

investigated the impact of both voice and text messages on maternal health.24‐25 Text

messages were sent to pregnant women containing basic health education and

reminders for routine health care appointments. Expectant mothers received vouchers

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78

and phone numbers that they could use to contact services for questions and

emergencies. The study assessed the impact on quality of services, health seeking

behaviour and maternal morbidity and mortality. The data was being processed at the

time of writing this article; the study promised to yield useful information.26

MoTECH is an ongoing project in Ghana aiming to determine how mobile phones can

best be used to increase the quantity and quality of antenatal care.27 Results from

randomized treatment and control groups were not yet available.28

Gender discrepancies in access to and use of the technology

The analysis of the potential of mobile phones for maternal health requires examining

how mobile phones may relate to the root cause of poor maternal health, namely the

position of women in society.4 Globally, a woman is 21% less likely to own a mobile

phone than a man.29 This discrepancy in the uptake of mobile phones is highest in

South Asia, followed by Sub‐Saharan Africa.

Women who do have access to a mobile phone often use it for business, banking and

employment opportunities29‐31 and thus to make themselves more independent.

Several projects use mobile phones to improve access to basic education for women,

for example text message‐based literacy programmes.29

The main reason for not owning a mobile phone lies in the associated costs, illiteracy

and lack of electricity.29,31 Being practical, especially women in Africa are likely to

borrow a phone if they do not own one.30 Other discrepancies in the ownership of

mobile phones exist between countries and in rural areas versus urban areas, mainly

due to poor network coverage.32

DISCUSSION

Robust studies providing evidence on the impact of introducing mobile phones to

improve the quality or increase the use of maternal health services are lacking.

However, there is broad agreement that access to communication is an essential

component of improving the use and quality of maternal health services. The mobile

phone has a high potential as it is small, portable, widely used, relatively cheap and the

extending network coverage increasingly enables communication with rural and

isolated areas.

The extremely quick uptake of mobile phones worldwide can shorten delays in seeking

and receiving health care. The available literature suggests great potential in

connecting traditional and biomedical health care, as well as connecting the different

levels within a health care system, provided that women are not restricted due to their

position in society, lack of finance or means of transport.


79

To fully realize the benefits of mobile communication, research needs to generate the

evidence‐basis for scaling up mHealth and enabling informed mHealth policy‐making,

and to analyse its benefit in ensuring timely delivery of medical equipment, provide

health education and improve access to reproductive health services, e.g. for family

planning.

So far, projects mainly focus on acute, life threatening situations, but mobile phones

can also be used to deliver mass health messages to pregnant women, recalling women

with risk factors to present themselves at an antenatal clinic or referring women who

suffer from complications such as fistula, incontinence and infertility. Possibilities

related to connecting them to specialized hospitals need to be integrated into research

and project designs. In addition, all the different applications, best practices,

constraints and lessons learned need to be documented.

The quick uptake of the mobile phone and its use in health care requires policies and

guidance of governments, especially related to issues such as privacy and

confidentiality.

An overuse of text messaging by the private and public sector will soon be regarded as

spam, making it lose its effectiveness. In addition to privacy, governments need to

ensure confidentiality of sensitive information.

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REFERENCES

1. International Telecommunication Union (ITU) (2010) Committed to Connecting the World ITU is the UN Agency for Information and Communication Technology. Available at: http://www.itu.int.

2. mHealth Alliance (2010) Maternal and Newborn mHealth Initiative. Available at:

www.mhealthalliance.org. 3. Hogan MC, Foreman KJ, Naghavi M et al. Maternal mortality for 181 countries, 1980–2008: a

systematic analysis of progress towards Millennium Development Goal 5. Lancet 2010;375:1609–1623.

4. United Nations Children’s Fund (UNICEF) (2009) The state of the world’s children 2009. New York: UNICEF.

5. Thaddeus S, Maine D. Too far to walk: maternal mortality in context. Soc Sci Med 1994;38:1091–1110.

6. Ronsmans C, Graham WJ. Maternal mortality: who, when, where, and why. Lancet 2006;368: 1189–1200.

7. World Health Organization & UNICEF (2010) Countdown to 2015 Decade Report (2000–2010): Taking

Stock of Maternal, Newborn and Child Survival. Countdown to 2015 Coordination Committee, Geneva: WHO.

8. International Telecommunication Union (ITU) (2009) Information Society Statistical Profiles 2009.

Africa. ITU, Genève. Available at: http://www.itu.int/ITU‐D/ict/material/ISSP09‐AFR_finalen.pdf. 9. United Nations Conference on Trade and Development (UNCTAD) (2010) Information Economy Report

2010 – ICTs, enterprises and poverty alleviation. United Nations, New York and Geneva. Available at:

http://www.unctad.org/en/docs/ier2010_embargo2010_en.pdf. 10. Mechael PN, Batavia H, Kaonga N et al. (2010) Barriers and Gaps Affecting mHealth in Low and Middle

Income Countries: Policy White Paper. Center for Global Health and Economic Development Earth

Institute, Columbia University, New York. 11. Samai O & Sengeh P. Facilitating emergency obstetric care through transportation and communication,

Bo, Sierra Leone. Int J Gynecol Obstet 1997;59:S157–S164.

12. Musoke MGN. Some Information and Communication Technologies and Their Effect on Maternal Health in Rural Uganda. A summary of research findings prepared for the African Development Forum

1999, Addis Abeba.

13. Musoke MGN (2002) Maternal Health Care in Rural Uganda: Leveraging Traditional and Modern Knowledge Systems. IK Notes No.40, World Bank, Washington DC.

14. Matthews MK, Walley RL. Working with midwives to improve maternal health in rural Ghana. Canadian

Journal of Midwifery Research and Practice 2005;3:24–33. 15. Lungu K, Ratsma YEC. Does the upgrading of the radio communications network in health facilities

reduce the delay in the referral of obstetric emergencies in Southern Malawi? Malawi Med J

2007;19:1–8. 16. Fournier P, Dumont A, Tourigny C, Dunkley G, DraméS. Improved access to comprehensive emergency

obstetric care and its effect on institutional maternal mortality in rural Mali. Bulletin of the World

Health Organisation 2009;87:30–38. 17. Krasovec K. Auxiliary technologies related to transport and communication for obstetric emergencies.

International Journal of Gynecology & Obstetrics 2004;85(Suppl. 1):S14–S23.

18. Lee ACC, Lawn JE, Cousens S et al. Linking families and facilities for care at birth: what works to avert intrapartumrelated births? International Journal of Gynecology and Obstetrics 2009;107(Suppl. 1):

S65‐S85.

19. UNFPA (2007) Report on the regional conference on obstetric fistula and maternal health. UNFPA regional conference on obstetric fistula and maternal health, 10–13 December 2007, Nouakchott.

Available at: http://www.fistulanetwork.org/FistulaNetwork/user/ReportNKKT_Final‐Version.pdf.

20. Chib A, Lwin MO, Ang J, Lin H, Santoso F. Midwives and mobiles: using ICTs to improve healthcare in Aceh Besar, Indonesia. Asian Journal of Communication 2008;18:348–364.

21. Chib A. The Aceh Besar midwives with mobile phones project: Design and evaluation perspective using

the information and communication technologies for healthcare development model. Journal of Computer‐Mediated Communication 2010;15:500–525.

22. Holmes D. Rwanda: an injection of hope. Lancet 2010;376:945–946.


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23. Svoronos T, Mjungu D, Dhadialla P et al. (2010) CommCare: Automated Quality Improvement To

Strengthen Community‐Based Health. Available at: http://d‐tree.org/wp‐content/uploads/2010/05/Svoronos‐Medinfo‐CommCare‐safepregnancy1.pdf.

24. Lund S (2009) Mobile Phones can Save Lives. Profile ⁄ Global Health, University of Copenhagen,

Copenhagen, 18–19. Available at: http://www.e‐pages.dk/ku/307/18. 25. Lund S (2010a) Wired Mothers – Use of Mobile Phones to Improve Maternal and Neonatal Health in

Zanzibar’. Enreca Health. Available at: http://www.enrecahealth.dk/archive/ wiredmothers/.

26. Lund S (2010b) Personal communication via email, 10 June 2010. 27. Mechael PN (2009) MoTECH: mHealth Ethnography Report. Dodowa Health Research Center for The

Grameen Foundation, Washington DC.

28. Mailman School of Public Health (2010) MoTeCH: A Comprehensive Overview. Colombia University, New York.

29. GSMA Development Fund, Cherie Blair Foundation for Women&Vital Wave Consulting (2010) Women

& Mobile: A Global Opportunity. A Study on the Mobile Phone Gender Gap in Low and Middle‐Income Countries. GSMA, London.

30. Macueve G, Mandlate J, Ginger L, Gaster P & Macome E. Women’s use of information and

communication technologies in Mozambique: a tool for empowerment? In: African Women & ICTs, 1st edn (eds Buskens I & Webb A) Zed Books Ltd, London, 2009: 21–32.

31. Hellström J (2010) The innovative use of mobile applications in East Africa. Sida Review 2010:12,

Stockholm. Available at: www.upgraid.files.wordpress.com/2010/06/sr2010‐12_sida_hellstrom.pdf. 32 Comfort K, Dada J. Rural women’s use of cell phones to meet their communication needs: a study from

northern Nigeria. In: African Women & ICTs, 1st edn (eds Buskens I & Webb A) Zed Books Ltd, London,

2009:44–55.

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83

CHAPTER 6

Improving care‐seeking for facility‐based health

services in a rural, resource‐limited setting: Effects and

potential of an mHealth project

Higgins‐Steele A, Noordam AC, Crawford J, Fotso JC

African Population Studies 2015;28:1643‐1662

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ABSTRACT

The aim of this paper was to investigate the impact of a toll‐free hotline and mobile

messaging service on care‐seeking behaviours. Due to the low uptake of the services,

the treatment on the treated estimate is used. For maternal health, the intervention

had a strong, positive impact on antenatal care initiation and skilled birth attendance.

No effect was observed for postnatal check‐ups, receiving the recommended four

antenatal care visits and vitamin A uptake. A negative effect was observed on tetanus

toxoid coverage. For child health, no change was seen in child immunization, and a

significant decrease was observed for care‐seeking for children with fever. Different

factors are associated with care‐seeking, which may explain in part the variations seen

across care‐seeking behaviours and possible influence of exogenous factors.

Introduction of mHealth services for demand generation require attention to local

health systems to ensure adequate supply and quality are available.

Improving care‐seeking for facility‐based health services

85

INTRODUCTION

In Malawi, despite a consistent reduction over the last two decades, infant and under‐

five mortality rates remain high.1,2 Under‐five mortality in Malawi was 71 per 1,000 live

births in 2012, down from 112 in 2010 and 234 in 1992.2 Maternal mortality on the

other hand dropped only minimally, with the Millennium Development Goal target still

about six times lower than the current level of around 675 maternal deaths per 100,000

live births.3‐5 While a package of effective, health facility and community‐based,

interventions could significantly improve maternal and child health (MCH) outcomes,

uptake of these services remains low.6 For example, less than half of pregnant women

in Malawi receive the four recommended antenatal care (ANC) visits and only 12% of

women attend ANC within the first trimester of pregnancy. Also, nearly 30% of

deliveries are not attended by a skilled birth attendant (SBA) and almost half of women

do not receive postnatal care (PNC). And, nearly one third of children with symptoms of

malaria or acute respiratory infections (ARI) are not taken to a health facility for advice

or treatment.1

To accelerate progress towards improved MCH outcomes, global commitments and

national efforts triggered the implementation of new strategies and innovative

solutions in low‐resource contexts.7 One such strategy is the use of mobile phones to

improve the delivery of health services, also referred to as mHealth. As mobile phone

ownership in low‐ and middle‐income countries has grown substantially in the last

decade, its potential to improve health services by enabling faster modes of

communication is widely recognized.8‐9 Use of mobile phones – for example through

two‐way communication, voice messages and/or short message services (SMS) – can

overcome persistent health system constraints across the continuum of care.10 In low‐

resource settings, mHealth interventions targeting groups in the general population

have shown the potential to improve adherence to treatment protocols, promote

healthy behaviour, increase utilization of health services, and increase access to health

information.11‐13

To adopt mHealth strategies to increase the utilization of facility‐based services

requires measuring and understanding health‐seeking behaviour, while also

acknowledging the challenges and shortcomings of health care delivery in resource‐

limited settings.14 Appropriate utilization of facility‐based MCH services is not only

linked to demand‐side barriers, such as lack of knowledge and cost of those services,

but also to supply‐side barriers such as human resources and availability of medicines.15

For example, care may be sought but appropriate treatment may not be provided.16 In

Malawi, among other barriers, challenges linked to accessing services include long

distances to facilities, poor perceptions of quality, and lack of human resources.17‐18

Moreover, women and caregivers of young children lack access to health information

for decision‐making which leads to delays in seeking care.19‐20

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Previous studies have highlighted ways in which mHealth and health promotion can be

successful in improving healthy behaviours, positively influencing timely care‐

seeking.21‐22 Strategies such as SMS to deliver health‐related messages23 and hotline

systems to enable two‐way communication between individuals and health workers

have helped overcome some of the challenges surrounding utilization of health facility‐

based services.24‐25 More specifically for Malawi, mHealth solutions have been limited

largely to strengthening provider‐to‐provider communications and data reporting. In

two districts in Malawi, an intervention aimed at connecting community health workers

and district level officials found that mHealth can aid in dissemination of new

information down to the health worker level, improve reporting on service delivery

data, and save time and money on transportation costs.26 Another study in Malawi

reported on a mHealth pilot project designed to facilitate communications between

community health workers and their supervisors through SMS. This study

demonstrated that the SMS project improved communication systems for several

common applications such as reporting patient adherence, queries to supervisors on

complicated symptoms or other issues, requests for medicines, and emergency

transport referrals.27 MHealth services to share information between the health worker

or health system and the individual have only been documented, yet not studied

extensively in Malawi.14 Despite its potential, recent reviews of mHealth projects in

low‐resource settings highlight poor evaluations for desired outcomes and impact of

these projects,21,28‐30 which includes effects on care‐seeking behaviours.

This paper analyzes data from a study using a pre‐post‐test design with a comparison

group to examine changes in care‐seeking behaviours among women and caregivers of

young children after the implementation of an mHealth initiative. We investigate the

impact of the intervention on facility‐based care‐seeking for MCH. These findings are

important to further guide the integration of mobile technology into health services.

The paper is part of a series analyzing the effectiveness of a hotline and text messaging

service named Chipatala Cha Pa Foni (CCPF) or “health centre by phone”, implemented

between 2011 and 2013 in the catchment areas of four health centre’s in Balaka

district, Malawi. The purpose of these services was to inform women and caregivers on

essential care and encourage appropriate care‐seeking during pregnancy, childbirth and

infancy.

DATA AND METHODS

Data

This paper draws on the evaluation data of the CCPF project in Balaka district, Malawi.

The project aimed to increase knowledge and behaviour relating to recommended MCH

home‐based and facility‐based care. The CCPF project included a toll‐free hotline


87

service providing protocol‐based health information, advice and referrals. Users could

access this service to seek advice and information regarding the illness of their child

under the age of five. The project also included an automated and personalized tips &

reminders mobile messaging service, for which subscribers could opt‐in, with a choice

of two local languages. For those community members who did not have a mobile

telephone, volunteers were selected from communities and provided with a mobile

telephone as a way to provide access to them. Box 6.1 provides some examples of

automated tips & reminders messages sent to CCPF subscribers, which include women

who signed up and received messages via a volunteer equipped with a phone for

community use. The project was implemented between July 2011 and June 2013 in

Balaka district, an area with some of the poorest MCH indicators in the country.23 The

project is described in more detail in the second paper in this series by Larsen Cooper et

al, under review.31

The evaluation used a quasi‐experimental design, with catchment areas of two health

centre’s in the contiguous Ntcheu district as the control site. The control district was

selected based on similar characteristics with the intervention site and since other

districts either had dissimilar characteristics or had other MCH projects ongoing that

may influence responses of participants. Once the health centre’s in the intervention

and control sites were selected for the study, GIS information was used to map

catchment areas of the health facilities and create a comprehensive list of villages in

each of the catchment area. GIS information also provided mean distance from each

village to the nearest health center.32

The core of the evaluation data consisted of cross‐sectional baseline and end line

household surveys conducted in June‐July 2011 and April‐May 2013, respectively. Three

questionnaires (household, woman and under‐five) were developed, covering more

than 30 MCH indicators largely drawn from the Multiple Indicators Cluster Survey

(MICS).32 As it is the rule, the same questionnaires were used at both baseline and end

line, with the exception of an additional exposure module administered at end line only

with questions referring the CCPF project which began after baseline data was

collected. At baseline, a total of 2,840 women (1,119 in the control site and 1,721 in the

intervention site) and 3,605 children under‐five (1,385 in the control and 2,220 in the

intervention) were surveyed. At end line, a total of 3,853 women (2,509 in the

intervention) and 3,261 children were surveyed. Based on the population size in the

catchment area, it was determined during planning for the baseline that for statistical

significance a minimum of 1,600 subjects was needed per group for the intervention

area and a minimum of 1,200 per group for the control.

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Box 6.1 Examples of automated tips and reminders messages.

Messages for pregnant women

6 weeks: “When you and your family know that you are pregnant, a visit to ANC will help you understand

everything you need to do to keep the baby healthy.” 10 weeks: “The ANC is your partner in the pregnancy. It is important to go to all 4 of your visits to use the

tablets that they have given to you.”

20 weeks: “At ANC visits, you will have a Tetanus Toxoid (TT) vaccine during pregnancy, to stop you or your baby from getting tetanus, which is a serious infection.”

32 weeks: “When you deliver at the hospital, the baby will get everything he needs to start life healthy.

Make sure you have a plan to get to the hospital when it is time” “Baby can come anytime now. Do you have all you need for delivery packed in a bag? Pack napkins, cloths,

baby soap, towels, basin and clothes for you.”

41 weeks: “Be sure to take your baby to the health centre 6 weeks after delivery. Your baby will get checked and receive more immunizations.”

Messages for mothers for their infant 1 week: “Make sure your baby has its vaccination. In the first week, your baby should get polio vaccine by

mouth and the BCG vaccine against TB by injection.”

6 weeks: “This week your baby needs to receive 2 vaccines; OPV and DTP‐HepB‐Hib. She will receive these vaccines 2 more times. It very important to protect your baby.”

11 weeks: “Remember to have your baby sleep under a treated mosquito net every night to prevent malaria

and take him to the clinic straight away if he has fever.” 14 weeks: “It is now time for the third and final dose of OPV and DTP‐HepB‐Hib vaccines for your baby. It is

important that these are given 4 weeks apart.”

17 weeks: “Prevent pneumonia by protecting your baby from breathing smoke from rubbish or cooking fires and tobacco. Get treatment immediately if baby has fast breathing”

Variables of interest

In this paper, we analyze the following aggregate and individual variables related to

facility‐based care for MCH:

Facility‐based maternal health care (for women who had a live birth in the last

18 months)

‐ Received the correct dosage of the tetanus toxoid (TT) vaccine during the last

pregnancy

‐ Received a Vitamin A dose during the last pregnancy

‐ Received the recommended four antenatal care (ANC) consultations during the

last pregnancy

‐ Started ANC in first trimester during the last pregnancy

‐ Gave the last birth under the supervision of a skilled birth attendant

‐ Received one postnatal care (PNC) check‐up within 2 days of the last birth

Facility‐based child health care

‐ Child was fully immunized by first birthday (children aged 12‐23 months)

‐ Child with symptoms of acute respiratory illness (ARI) in the last two weeks

preceding the survey sought care at facility


89

‐ Child with fever in the last two weeks preceding the survey sought care at

facility

The control variables included well known confounders at the community, household,

woman and child levels, the selection of which was guided by literature reviews. These

variables, as can be seen in Tables 6.1 and 6.2, include at the household level, wealth

status, number of under‐five children, and ethnicity and religion of the head of

household. At the woman level, these are age, marital status, education, and access to

a personal phone. Child characteristics are age and sex. Finally, at the community

(village) level, the analyses control for the mean distance to the nearest health centre.

Table 6.1 Percentage distribution of mothers/caretakers of children under 5 and pregnant women

Baseline

Control Intervention

Endline

Community level covariates

Mean distance to the health centre (km) 5.7 4.4 4.8

Household level covariates Household wealth

Poor (lowest 50%) 56.6 53.0 55.3

Rich (highest 50%) 43.4 47.0 44.7 No. of children under the age of 5 yrs.

0 5.7 4.5 29.5

1 66.6 60.8 52.3 2+ 27.7 34.7 18.1

Ethnicity of the head of household

Lomwe 6.0 21.0 16.3 Ngoni 77.3 20.6 41.1

Yao 6.8 38.9 28.7

Other 9.9 19.5 13.9 Religion of the head of household

Catholic 16.1 17.5 18.3

Other Christian 70.1 41.0 50.9 Muslim 5.5 37.0 25.7

Other/ No Religion 8.4 4.5 4.8

Women‐level covariates Cell phone in Household

No 77.6 68.0 68.5

Yes 22.4 32.0 31.5 Education

None 16.5 15.4 13.2

Primary 73.7 74.0 73.8 Secondary+ 9.7 10.6 13.1

Marital status

Not in union 11.0 17.0 24.4 In union 89.0 83.0 75.6

Age in years

<20 10.6 11.3 16.0 20‐29 57.2 53.9 41.7

30+ 32.2 34.7 42.3

N 1,119 1,721 3,853

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90

Table 6.2 Percentage distribution of children under 5 Baseline

Control Intervention

Endline

Age in months

< 12 25.4 23.2 24.2

12‐23 21.2 19.7 22.7

24+ 53.4 57.0 53.2 Sex

Male 48.4 51.3 51.5

Female 51.6 48.7 48.5 N 1,365 2,220 3,261

Data analysis

Descriptive and multivariate analyses are employed to quantity the impact of the

intervention on the outcomes of interest. Firstly, we estimate the simple difference‐in‐

difference (DID) as follows:

Where and represent the average outcome at endline in the intervention site

and control area, respectively, and and the average outcome at baseline in the

intervention site and control area, respectively. Since this estimate compares the

intervention and the control sites regardless of the use of the services offered by the

project, it is to be interpreted as intention‐to‐treat (ITT) effect.33

Secondly, to adjust for potential confounding variables we conduct multivariate DID

defined as follows:

Yivt = 0 + 1 Tv + 2 Pt + 3 (T * P)vt + Wivt + Xv + ivt

where Yivt is the outcome measure for woman/child i, in village v, at time t. Tv is a

dummy variable taking the value 1 for individuals in treatment areas and 0 for

individuals in control areas; Pt is a dummy variable taking the value 0 for the baseline

data and 1 for the endline data; Wivt is a vector of the controls at the household, women and child levels; X is a village‐level control variable; and ivt is the idiosyncratic error, clustered by health centre catchment area.

Thirdly, we further analyze the data to assess the effect of the program on its users,

applying the so‐called “treatment effect on the treated” (TOT) model. This analysis is of

specific importance, especially if the uptake of the intervention is not very high. The

method uses instrumental variable analyses to construct a proper counterfactual – the

women who would have used the services in control communities had they been

offered.34 More detailed information on the research methodologies including study

limitations can be found in the first paper of this series.35


91

Ethical clearance

Ethical approval for the study was granted by the National Health Sciences Research

Committee, Malawi Ministry of Health.

RESULTS

Sample characteristics

Table 6.1 shows the characteristics of women interviewed at baseline in the

intervention and comparison areas. The distribution of women by household wealth,

number of children under the age of five years, education, marital status and age

appears similar across the intervention and control communities. As can be seen, a

majority of women were from households with one child under‐five years of age; about

three‐quarters of women had completed primary education; and more than 80% were

in union. The mean distance to the nearest health facility was slightly shorter in the

intervention site compared with the control area (4.4 km versus 5.7 km). The

distribution by ethnicity, religion and access to a mobile phone exhibits differences,

with the control area dominated by the Ngoni ethnic group (77.3%) and non‐Catholic

Christians (70.1%), while the intervention site shows a seemingly more balanced

distribution across the ethnic and religious groups. Access to phone was higher in the

intervention area (32%) than in control communities (22%).

Table 6.1 also shows that the overall baseline and endline samples had similar

characteristics. There are two noticeable exceptions. The proportion of women in union

dropped slightly from an average of 86% at baseline to 75.6% at end line. Access to

phone on the other hand, improved from around 28% to 31.5% during the same period.

The sex and age of the sample of children under the age of five are presented in Table

6.2. As can be seen, the distribution is similar across the intervention and control

groups at baseline, and between the baseline and end line samples.

Table 6.3 shows that at end line, the awareness of the hotline was high in the

intervention area, at around 77% among the total sample of 2,509 women. Among

individuals who heard about the hotline (N=1,929), less than 24% used the services, a

proportion which represent about 18% of the total sample of women at end line.

Awareness of the mobile messaging system was substantially lower, at 33.3%, and use

was estimated at 22.6%.

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92

Table 6.3 Awareness and use of the services among women of child bearing age at endline.

Intervention area Control area

% N % N

Awareness and use of the hotline services

Heard about the services 76.9 2,509 2.8 1,344

Used the services 23.8 1,929 3.5 38

Awareness and use of the Mobile messaging services Heard about the services 33.3 2,509 0.4 1,344

Used the services 22.6 835 0.0 5

Effect of the intervention on care‐seeking behavior for maternal health

Table 6.4 shows the levels of the selected maternal health indicators at baseline and

endline and across the intervention and control areas, as well as the resulted

difference‐in‐difference, adjusted and unadjusted. Three variables – TT vaccine, skilled

birth attendance (SBA) and vitamin A – had high coverage in both sites, while PNC, and

to a lesser extent, ANC initiation in the first trimester of pregnancy, displayed a low

coverage. The proportion of pregnancies which received the recommended four ANC

visits, on the other hand, ranged from 55% to 64%.

The unadjusted and adjusted DID results indicate that the intervention did not have any

ITT effect on either of the indicators for facility‐based maternal care, and as a result, did

not affect the aggregate maternal health indicator. Given the low use of services (only

18% of women used the services), the TOT model is a more appropriate approach, as it

adjusts for the fact that some individuals in the intervention area did not use the

services, and some others in the control area would not use the services even if

offered. As Table 6.4 shows, the TOT model reveals a markedly different pattern. The

intervention had a strong, positive impact on ANC initiation during the first trimester of

pregnancy. While the increase between baseline and end line did not vary across the

two areas (about 12 percentage points increase) hence a negligible DID, the focus on

women who used the services reveals a strong impact of the intervention. The project

also had a positive effect on SBA, despite a similar margin of increase (about 5

percentage points) across the control and the intervention sites as observed for ANC

initiation.

At the other end of the scale, there is a negative TOT effect of CCPF on TT vaccine

during pregnancy. The indicator remained almost unchanged in the intervention site,

but declined by about 4.5 percentage points in the control area, yielding a positive

though not statistically significant DID. Table 6.4 also shows an absence of TOT effect

on PNC check‐up, receiving the recommended four ANC visits, and vitamin A.

Effect of the intervention on care‐seeking behavior for child health

The content of Table 6.5 which presents the results on child health is similar to that of

Table 6.4.


93

Table 6.4

Intention to treat (ITT) and treatmen

t on the treated

(TO

T) effects of the interventions on facility‐based

care for maternal health

Descriptive analysis

Multivariate analysis

BaselineEndlineITT effect (Difference

in difference)

ITT effect (Difference

in difference)

TOT effect

(Difference in

difference)

Corresponding sub‐sam

ple

Interven

tion

86.0%

85.8%

1. Received the correct dosage of

the TT vaccine during pregnancy

Control

93.0%

88.4%

0.044

0.040

‐0.104**

Same

Interven

tion

72.9%

71.5%

2. Received a Vitam

in A dose during

last pregnancy

Control

63.2%

54.9%

0.068

0.069

0.083

Same

Interven

tion

61.7%

55.4%

3. Received the recommen

ded

4

ANC consultations

Control

61.9%

63.8%

0.085

0.079

0.055

Same

Interven

tion

26.4%

38.9%

4. Started ANC in

first trimester

Control

20.6%

32.3%

0.009

0.008

0.444***

Same

Interven

tion

91.9%

96.1%

5. Gave birth under the supervision

of a skilled birth atten

dant

Control

90.8%

96.1%

‐0.010

‐0.012

0.110**

Same

Interven

tion

3.9%

5.3%

6. Received one PNC check‐up

within 2 days of birth

Control

5.7%

5.4%

0.017

0.016

0.022

Same

Interven

tion

0.4%

9.1%

Overall facility‐based

care for

maternal health

Control

0.0%

‐0.5%

0.092

0.085

0.239**

Had

a live birth in

last 18

months (N=2,813)

Statistical significance: *p<0.10, **p

<0.05, ***p<0.01

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94

Table 6.5

Intention to treat (ITT) and treatmen

t on the treated

(TO

T) effects of the interventions on facility‐based

care for child

health.

Descriptive analysis

Multivariate analysis

BaselineEndlineITT effect (Difference

in difference)

ITT effect (Difference

in difference)

TOT effect

(Difference in

difference)

Corresponding sub‐sam

ple

Interven

tion

0.788

0.781

1. Child

was fully im

munized

by

first birthday

Control

0.777

0.758

0.013

0.009

0.012

Aged 12‐23 m

onths (N=1,610)

Interven

tion

0.639

0.641

2. Child

with sym

ptoms of ARI

in last 2 weeks who sought

care

Control

0.676

0.708

‐0.030

‐0.025

‐0.083

Had

sym

ptoms of ARI

(N=1,895)

Interven

tion

0.675

0.521

3. Child

with fever in

last 2

weeks who sought care

Control

0.591

0.627

‐0.189***

‐0.181***

‐0.499***

Had

fever in

last 2 weeks

(N=2,194)

Interven

tion

0.045

‐0.090

Overall facility‐based

care for

child

health

Control

0.000

0.037

‐0.172***

‐0.171***

‐0.499***

Aged 12‐23, or had

fever or

symptoms of ARI (N=4,068)

Statistical significance: *p<0.10, **p

<0.05, ***p<0.01


95

Large, negative TOT effects are seen for the aggregate facility‐based child health

indicator (p<0.01). This negative effect results exclusively from, and reflects, a

reduction in the rate at which children with fever in the intervention communities

sought treatment at a health facility. Indeed, the proportion of children with fever who

visited a health facility dropped by 15.4 percentage points in the intervention site (from

67.5% to 52.1%), and by contrast, increased by 3.6 percentage points in the control

area (from 59.1% to 62.7%), hence a DID of about 19 percentage points (p<0.01).

The project did not have any ITT or TOT effect on the full immunization. Its coverage did

not change noticeably over time, remaining at around 78% in the intervention group

and at 77% in the control area. A similar pattern was observed for facility care for ARI

symptoms.

DISCUSSION

Characteristics between intervention and control samples are similar for household‐

and women‐level covariates with the exceptions of ethnicity, religion and access to a

mobile phone which show differences between groups sampled in Balaka and Ntcheu

districts. It is noted elsewhere that differences in ethnicity and religion do not appear to

affect MCH outcomes in Malawi.32,36

Utilization and access

The overall use of the CCPF services was low; only 18% of the targeted population or

less than 24% of those who were aware of the service used the hotline and the use of

the tips & reminders service was lower. A possible reason for the low uptake of the

service may be access to a mobile phone in the household was less than one‐third. For

tips & reminders, women may have been more inclined to use the hotline service via a

community volunteer phone than to sign up for tips & reminders on one of these

shared phones. Importantly, the rural location of Malawi was selected, not in relation

to mobile phone penetration, but because of poor MCH indicators that this pilot would

seek to address. The pilot illustrates that even with enablers introduced by the pilot to

increase access to the mHealth services – namely through community activities to

enhance knowledge of the services and volunteers to facilitate access – use of the

services was not widespread in the target groups.

Other studies confirm that uptake of new and potentially successful solutions aiming to

improve access to health care and information can be low. Moreover, there is

insufficient evidence on facilitators and barriers to the use of mobile phones for health‐

related needs.37 Besides inadequate knowledge of how the new service works and what

benefits are of using it,38 reasons for non‐use of mobile phones have been linked to

privacy concerns and network coverage.39‐40

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96

Due to the low uptake of the CCPF project, its benefits can only be measured by

assessing the impact on those who actually used the service, i.e., through a TOT model,

a notable finding with implications for measurement of other mHealth projects.

Maternal health

The TOT model showed that, among the women who used the service, there was a

significant increase in ANC initiation within the first trimester and SBA during delivery.

ANC as well as SBA are crucial indicators for monitoring the wellbeing of the mother

and child and ensuring timely identification of danger signs, birth preparedness as well

as access to life‐saving interventions when needed.41 Yet, according to Malawi’s

Demographic and Health Survey (DHS 2010), the majority of women start ANC late,

only 12% nationally1 and 21‐26% in our study area, attended ANC in the first trimester.

SBA is much higher, nationally at around 71% and 91‐92% in our study area. Studies

conducted in Malawi suggest that delays in ANC are (often) due to superstitious beliefs

regarding the consequences of disclosing pregnancy in the first trimester.42‐43 Access to

information through the hotline and tips & reminders service may therefore have

resulted in an increase in timely utilization of these services, either by providing

information or facilitating referrals.

The intervention had no TOT effects on maternal indicators such as the use of Vitamin

A, attending ANC at least four times during pregnancy and receiving PNC. In the long

run, the increase in timely access to ANC might have a positive effect on these

indicators, especially the use of Vitamin A and attending all four ANC visits. This

assumes regular and adequate stock of Vitamin A and health workers administering the

supplement during an ANC visit. For PNC, national coverage in Malawi is 43%, yet in our

study area this was as low as 4‐6%. This large difference may be explained by variations

in question phrasing between the DHS questionnaire and the Multiple Indicator Cluster

Survey (MICS) indicator selected as part of this study. The PNC question for this study

did not include home‐based PNC visits, for example, and focused on how long after

birth PNC was received. Even with these questionnaire differences, further research is

needed to better understand why coverage of PNC is so low in the study area, whether

this is due to structural reasons related to the local health system (e.g., lack of health

resources) or associated with demand side barriers.

Finally, a negative effect was observed for women receiving TT vaccines. A decrease in

TT coverage was found in both the intervention and control areas. While not specifically

examined in this study, this could be related to limited availability of TT vaccines or

health worker reluctance to vaccinate pregnant women. A study in Kenya suggests that

improving access to ANC services is a necessary condition but not sufficient for

improving uptake of TT immunization and suggests areas of future research to include

the quality of provider‐client interaction, availability of stocks of TT and women’s

perception of TT immunizations.44


97

Overall, the CCPF project had a positive impact on maternal health indicators, which is

important, as a combination of these interventions – and specifically increase in SBA –

are needed to reduce the still high mortality rate in Malawi. If an increased proportion

of women started ANC earlier, the health system could be strengthened to deliver

other pregnancy‐related interventions, such as to encourage repeat ANC visits to fulfil

recommended four visits, Vitamin A supplementation and TT vaccination, and

importantly encourage SBA. A multi‐country study found significant and positive effect

on the number of antenatal consultations on SBA during child birth [45]. Effective

strategies, which not only include mobile phone use, need to be identified to ensure

timely access to and supply of these and other essential services. Adjiwanou and

LeGrand suggest SBA can be enhanced through improvements in quality of ANC,

notably adequate information conveyed on the importance of SBA by the health care

provider and provision of services closer to populations in need.45

Child health

There was no TOT effect of the CCPF project on coverage of immunization and care‐

seeking for children with signs of ARI presumably because baseline values for both

variables were relatively high. The percentage of children fully immunized in the study

area was estimated at around 78%, a figure comparable to the national estimate from

the 2010 DHS. For care‐seeking for ARI, the national coverage was around 70% as

opposed to 65% in our study area. While vaccine coverage in Malawi has improved,

children are often vaccinated at a later age. Reasons for this include that caregivers

believe children were too young to be vaccinated,46 poor recognition of danger signs

and illness severity, and financial considerations limiting timely access to care.47 For

both the increase in coverage of vaccinations as well as the recognition of ARI, SMS

services could inform caregivers about appropriate timing of vaccinations. Moreover,

hotline services could potentially ensure timely referral for ARI, although this was an

aim of CCPF and demonstrated negative effects.

Our findings show a negative effect of facility‐based care‐seeking for fever. Other

studies show that children with fever in Malawi are often treated at home.48‐49

According to Ministry of Health protocols, hotline workers only refer children with a

fever to the nearest village clinic or health centre for diagnosis and treatment if the

fever is being presented as a danger sign and has persisted for seven days or if the fever

is accompanied by other symptoms. It is possible that some children with a fever were

appropriately treated at home and did not require a visit to a health facility. This is

confirmed by findings on home‐based care for the CCPF project, published in the third

paper of this series,50 suggesting that caregivers were well equipped to handle

conditions like fever at home and avoid unnecessary trips to the facility.

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Conclusion

When used by the targeted population, mHealth services providing the user options of

how to access information on when and where to seek facility‐based care have

potential for behaviour change. Changes were seen in a relatively short duration of this

pilot, evaluated after less than two years after its introduction. Different factors are

associated with care‐seeking for outcome variables measured, which may explain in

part the variations seen across care‐seeking behaviours and possible influence of

exogenous factors. MHealth can be a useful channel to improve care‐seeking, though

reasonable expectations are required in terms of uptake and use by target groups,

which can be low even with demand generation activities for the service, as well as of

effects on demand for MCH services. Introduction of mHealth services for demand

generation and information on care‐seeking require attention to local health systems to

ensure adequate supply and quality are available.

Acknowledgement

The CCPF project is part of Innovations for Maternal, Newborn & Child Health, an

initiative of Concern Worldwide U.S. funded through a multi‐year grant from the Bill &

Melinda Gates Foundation. The Government of Norway and the United Nations

Foundation also supported the Malawi mHealth project (CCPF) through the Innovation

Working Group Catalytic mHealth Grants program as part of the UN Secretary General’s

Every Women Every Child strategy. The project was implemented by VillageReach, an

international NGO headquartered in Seattle, USA. We would like to give special thanks

to the Reproductive Health Unit and its Director, Mrs. Fannie Kachale, and the Balaka

District Health Office for their support of CCPF. The evaluation was conducted by Invest

in Knowledge Initiative (IKI), a Malawi‐based research institution, with the leadership of

Professor Susan Watkins of University of Pennsylvania and Dr. Amanda Robinson of

Ohio State University. The authors would like to thank Dr. Amanda Robinson for her

contribution to data analysis, Dr. Linda Vesel of Concern Worldwide US for reviewing

the manuscript, and the anonymous reviewers for their comments.


99

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CHAPTER 7

Assessing scale up of mHealth innovations based on

intervention complexity: Two case studies of child

health programmes in Malawi and Zambia

Noordam AC, George A, Sharkey AB, Jafarli A, Bakshi SS, Kim JC

Journal of Health Communication 2015; 0: 1–11

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ABSTRACT

As interest in mHealth (including Short Message Services or SMS) increases, it is

important to assess potential benefits and limitations of this technology in improving

interventions in resource‐poor settings. We analysed two case studies (early infant

diagnosis of HIV and nutrition surveillance) of three projects in Malawi and Zambia

using a conceptual framework that assesses the technical complexity of the

programmes, with and without the use of SMS technology. We based our findings on

literature and discussions with key informants involved in the programmes. For both

interventions, introducing SMS reduced barriers to effective and timely delivery of

services by simplifying the tracking and analysis of data and improving communication

between healthcare providers. However, the primary implementation challenges for

both interventions were related to broader programme delivery characteristics (e.g.

human resource needs and transportation requirements), which are not easily

addressed by the addition of SMS. The addition of SMS technology itself introduced

new layers of complexity. This article points the reader to technological complexities to

consider when implementing health‐related SMS interventions so as to help encourage

programmes that can be successful and sustainable. We argue that before deciding

whether and how to introduce SMS, it is important to understand the underlying

challenges within the existing programme, the potential contribution of SMS, where it

might introduce new challenges, and how these can be addressed. Conceptual

frameworks that analyse technical complexities can be useful to help reveal strategic

needs regarding scale up in existing health systems.

Assessing scale up of mHealth innovations based on intervention complexity

105

INTRODUCTION

There is increasing interest in the use of information and communications technology

(ICT) for health (eHealth) in low‐ and middle‐ income countries (LMIC), especially in the

use of mobile phone technology for health (mHealth). Emerging evidence suggests that

mHealth in the form of Short Message Service (SMS) or text messages can have positive

implications in LMIC for improving routine service delivery in terms of timely data

collection1 and quality case management.2 In addition, it has been argued that SMS

technology presents an opportunity to reduce the complexity of health interventions

and reduce inequities in service delivery.3 However, the evidence that is available is

based on implementation of pilot projects that were not brought to scale.4

As more resources are invested in the specific use of SMS, there is an urgent need to

better understand the implications of pairing this technology with existing

interventions,5 including assessing the implications that this technology has for

intervention coverage and the impact it has on health outcomes.6 In addition, it is

important to better understand why most of these projects stay at a ‘small pilot project

level’,4 with few progressing to large‐scale coverage in rural Africa.1 Such analysis is

critical, especially in ensuring that investments effectively address the challenges of

operating at scale in disadvantaged settings in LMIC.

Using a previously developed framework to assess intervention technical complexity,7

we examine mHealth interventions implemented as part of three maternal, newborn

and child health (MNCH) ‐related projects in Malawi and Zambia and use this evidence

to explore why mHealth projects face constraints in going to scale.

METHODS

Conceptual framework to analyze the challenges of scaling up health programs

A number of conceptual frameworks have been proposed to help analyse the

challenges of bringing interventions to scale.7‐11 Although economic considerations are

important, they are not the only limiting factor, as feasibility analyses can make an

important contribution to increasing the likelihood of successfully scaling up

interventions.12‐13 In 2005, Gericke et al presented a framework to assess the feasibility

of scaling up an intervention.7 In addition to financial resources, this framework asserts

that feasibility depends on the degree of the intervention’s technical complexity in four

domains: 1) intervention characteristics, 2) delivery characteristics, 3) government

capacity requirements and 4) usage characteristics (described in Table 7.1). This

framework has previously been used to assess complexity of a range of health

interventions including condom promotion,7 tuberculosis DOTS programs,7 diet

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106

improvement,14 and food poisoning risk reduction strategies,12 as well as identifying

strategies to improve nutrition mainstreaming.15

Table 7.1 Four domains of technical complexity that can impact scale‐up of health interventions (Gericke

et al, 20057).

The domain Category Criteria

Basic product design* Stability, standardizability, safety profile, ease of storage, ease

of transport

Supplies Need for regular supplies

Intervention

characteristics

Equipment The need for high‐technology equipment and infrastructure,

number of different types of equipment and maintenance

Facilities Retail sector, outreach services, first‐level care and hospital care

Human resources Skill level required for service provision and supervision,

intensity of professional services in terms of frequency or

duration and management and planning requirements

Delivery Characteristics

Communication and Transport

Dependence of delivery on communication and transport infrastructures

Regulation/ legislation Need for regulation, monitoring and regulatory measures and

regulation enforcement

Management systems Need for sophisticated management systems

Government

capacity

requirements

Collaborative action Need for intersectoral action within government, partnership

between government and civil society and partnership between government and external funding agencies

Ease of usage Need for information/ education and supervision

Pre‐existing demand Need for promotion

Usage

characteristics

Black‐market risk Need to prevent resale/ counterfeiting

* The basic product design for the assessment of adding RapidSMS to the project is the mobile phone.

Selection of case studies

We selected three projects supported by UNICEF (two in Malawi, one in Zambia) which

combine an SMS platform called RapidSMS, a free and open‐source framework for

dynamic data collection, logistics coordination and communication, with MNCH

interventions.

These projects were selected based on three criteria:

1. Representation of different public health challenges (early infant diagnosis (EID) of

HIV and child malnutrition) of relevance to the Millennium Development Goals

2. Representation of diverse applications of SMS technology with different

stakeholders (e.g. improving reporting of HIV test results and follow‐up with

healthcare workers and mothers, and strengthening nutritional data surveillance)

3. Availability of data relevant to the four domains of technical complexity in the

Gericke et al framework.7


107

Methods used to apply framework

First a review of both published and unpublished literature was conducted to identify

any existing evidence relating to the selected case studies as well as issues relating to

implementation of EID and nutrition surveillance projects. This search was limited to

English publications combining terms linked to: EID, HIV, nutrition surveillance,

including linking these terms to Zambia and Malawi. Searches were initiated in PubMed

and expanded to grey literature and reference lists. Literature was gathered to

understand why the project was implemented and to assess the program’s complexity

with respect to the four domains described by Gerick et al.

This search was then expanded to include programs incorporating RapidSMS

technology. Combined with the search terms above, the following terms were added:

SMS, mHealth, mobile phones and RapidSMS. Because the existing evidence was

limited, additional data were collected through discussions with two key informants:

those individuals responsible for implementation of the RapidSMS component in the

Malawi and Zambia programmes. In particular, these informants were asked an open‐

ended question regarding what they perceived to be the main strengths and challenges

they faced in implementation. Following application of the framework, the informants

were also asked to review and provide their inputs on the preliminary findings.

The literature search was conducted by two independent researchers. To improve

validity of the analysis, preliminary findings were shared and discussed with UNICEF

technical experts and country‐level program managers and implementers in Malawi

and Zambia. These inputs were incorporated into the final analysis.

With this information, we then applied Gericke et al’s conceptual framework7 in order

to:

1. Assess the interventions (prior to the addition of the RapidSMS technology) for

their degree of technical complexity, the extent to which they were able to scale‐

up, and, as relevant, the most significant constraints to scale up and

2. Assess the interventions (following the addition of the RapidSMS technology) for

their degree of technical complexity, making note of whether SMS technology had

addressed any key constraints to scale‐up identified earlier, and whether any

additional constraints had been introduced.

RESULTS

See the boxes at the end of the text for overviews of the case study projects, before

and after the integration of RapidSMS, box 7.1 and 7.2.

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108

Case Study 1: Early Infant Diagnosis

Basic program intervention (without SMS)

As illustrated in Table 7.2, Column 1 and based on application of four domains of the

framework, the main sources of technical complexity for the basic EID program in both

Malawi and Zambia are linked to the lack of human resources and poor communication

and transportation infrastructures. Although Dried Blood Spot (DBS) tests themselves

are stable and safe to transport, complexities are linked to supplies and maintenance of

laboratories, diagnostic equipment and computers. EID requires outreach services to

ensure follow‐up of HIV positive mothers and their infants, a network of laboratories

with regular quality control for DBS analysis, trained nurses, adequate care

management and couriers to transport DBS samples and test results. In Malawi and

Zambia a critical shortage of skilled health workers has resulted in overstressed staff

and inadequate supervision. At the level of providing guidance, EID requires a strong

national HIV control strategy and coordinating actions across national and local

governments, different tiers of the health sector, as well as NGOs and other actors. For

example, in Malawi the frequent changes in HIV policies have restricted full

implementation of these strategies. Finally, at community level, awareness and

demand for PMTCT services need to be stimulated in order to identify HIV positive

mothers and promote EID. Given the stigma surrounding HIV and AIDS, this can be

difficult.

Intervention with SMS

When the analytical framework was applied to the EID projects in Malawi and Zambia

following introduction of RapidSMS technology, the reductions in technical complexity

are primarily found in reducing communication and transportation needs. The main

benefits of SMS technology are seen in its ability to simplify the tracking of DBS tests,

potentially reducing transport needs, improving efficiency of communication of test

results to health workers and their ability to remind mothers/caretakers to return to

the clinic for test results, by contacting them via phone.

However, applying the framework also suggests that this innovation can add

complexities to the existing program. For example, despite the fact that RapidSMS can

be used on any mobile device, unreliable network coverage and limited access to

electricity (which influence the connectivity) have constrained the adaptation of the

programme to other geographic areas. In addition securing privacy of data, when using

personal mobile phones, is a potential concern. Complexities increase as the use of

RapidSMS requires technical expertise. Moreover, the application requires national

eHealth policies and government leadership for coordination with public sectors and

implementing organizations to ensure sustainability. For example, in Malawi, delays in

scalability of the project were linked to government ownership and prioritization.


109

Widespread use of mobile phones in both Zambia and Malawi, however, suggest that

given adequate training, accessibility of phones should not increase program

complexity.

Case Study 2: Nutrition Surveillance

Basic program intervention (without SMS)

The application of the framework to nutrition surveillance (Table 7.3, Column 1)

indicates that the main source of technical complexity includes the lack of human

resources, supervision and training of health workers, which results in poor data

quality.. In contrast to this, the basic product design (survey tool) is relatively simple,

although the paper‐based system is reliant on couriers to reach national level. Other

challenges are linked to the lack of capacity in data analysis at national level and

utilizing the collected data. Similarly, there are challenges in raising community

awareness regarding the importance of nutrition surveillance and ensuring that

mothers and their children enrolled return on a monthly basis for monitoring.

Intervention with SMS

When applying the framework to nutrition surveillance with SMS intervention, (Table

7.3, Column 2), the main reduction in complexity is found in simplifying constraints

linked to communication and transportation of survey data, by reducing the delay in

data transmission and improving data quality and analysis as well as bypassing the

labour‐intensive paper based system.

As with the EID case study, similar complexities relating to mobile phone connectivity

are also identified. However, for nutrition surveillance in Malawi, a lack of available

computers and internet, (required for data collection at districts and national level) is

also a challenge. Although SMS simplifies the labour intensive paper‐based survey

process (which was kept in place after adding the SMS component), capacity to

maintain the software is lacking, thus limiting full utilization of the potentials offered by

the program. Advantages for the use of SMS are that it limits errors which can occur

when aggregating the data to national level using paper based systems, as it allows raw

data to directly enter the central server at national government level. Errors made by

health workers will, remain (e.g. incorrectly taking a child measurement, sample bias),

unless the entered data is physically impossible and an automated SMS response

prompts the health worker to correct the data entered. The use of SMS reduces delays

in data transmission and manpower requirements for data entry and analysis. In regard

to providing guidance, there is a need for mHealth policies and strong leadership. A key

constraint prior to the addition of SMS was that survey data needs to be analysed at

central level government ensuring timely action – a constraint which Rapid SMS does

not alleviate.

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110

Case study 1: Early infant diagnosis (Table 7.2)

Table 7.2

Analysis of EID of with and without the use of rapidsm

s in M

alaw

i and Zam

bia.

Table 7.2 applies the fram

ework to assess interven

tion complexity for EID (Column 1) and EID with Rapid SMS (Column 2). In Column 2, green indicates

areas where complexity was red

uced, and red

where complexity was increased

. Eviden

ce regarding the effectiven

ess of using mobile phones in

health is

still lim

ited

and less robust. Th

e level of evidence beh

ind the claims in column 2 is categorized w

ith a 1: if there is eviden

ce (e.g. from evaluations or

reflected in various review

s) and the eviden

ce is consisten

t and a 2: if the claim

is speculated/potential, but not documented or evaluated

or if the

findings are contradicted in

other reviews.

Colomn 1: B

asic EID program

Column 2: EID with addition of rapidsm

s

Interven

tion Characteristics: includes 1) basic product design, 2) supplies, 3) equipmen

t

For the basic product design the dried blood spot tests (DBS) are heat stable,

simple to use in

a standardized

manner and safe for health workers to use if

regular HIV precautions are taken [39 ]. The DBS tests can be transported

via

mail or courier, to a lab where the specim

ens are tested

for HIV using PCR

technology.

Facilities often face logistical constraints related to supplies needed

for EID [41].

And, privacy and confiden

tiality are key issues

Same + Mobile phones are safe to use, easily transportable and widely available

1.

Some geo

graphic areas can

experience delays and localized

system outages due to

insufficient mobile network coverage

1 [23] or experien

ce unreliable services from

local m

obile network operators

1 (Sharpey‐Schafer, K, personal communication,

2011) and/ or lim

ited access to electricity

1

RapidSM

S can be used on any mobile phone, but men

us may differ1

Confidentiality of patient data can be an issue when

using SM

S based

systems and

personal m

obile phones2

[5,25]

Regular supplies of DBS tests are essential and in

relation to ART, health

facilities often

face drug stock‐outs [41 ]

Same + phones need battery chargers (e.g. solar chargers) and cellphone airtim

e1

[24]

Equipmen

t needed are laboratory equipmen

t for DBS analysis, computers for

database and m

aintenance [41]

Same + phones an

d internet, however, health care workers use personal phones

and are therefore responsible for the rep

airs, recharging and replacements

2 [24].

Additional equipment is need

ed to bridge database and SMS servers

1

Delivery Characteristics: includes 1) facility, 2) human

resources, 3) transport and communication

For facilities, outreach services are need

ed for tracing lost to follow‐up [42], and

first‐level health care services for diagnosis and treatmen

t managem

ent. And,

a network of laboratories for DBS analysis with regular quality control [

39]

Same, but prelim

inary data suggests that Rapid SMS could sim

plified the nature of

outreach services, for exam

ple by contacting caregivers by mobile phone to collect

their results2 [16 ].

However, there is a need to contract service providers and m

obile phone providers1

[25]

In regard to human

resources, there is a critical shortage of skilled

health

workers, m

ainly in

sub‐Sahara Africa [21], including Zambia and M

alaw

i [41 ].

Resulting in overstressed staff and lack of supervision. For EID, nurses are

needed for DBS tests and laboratory personnel for DBS analysis, due to lack of

health personal this m

ay be challenging. Other personal is needed

for the

managem

ent and planning of regular drug supply

Same + requires RapidSM

S technicians (&

designers), in addition to the human

resources for health

1 [25 ]. There is also a need for locally‐based

project m

anagers to

manage the quality and delivery of the project, w

hich are highly constrained

.1 The

lack of technical capacity lead

to challenges in scaling up the project (Sharpey‐

Schafer, K, personal communication, 2011)

Constraints were linked to the availability of local software consultants to m

anage

the RapidSM

S platform

2 , however, as part of the scale‐up in

Zam

bia, partners have

committed to support the capacity developmen

t at both national and district level2


111

Regular drug supply req

uires functional transport infrastructure; as couriers

needed to transport DBS tests/ results to and from labs. And, communication is

needed between the HW, labs, and caregiver regarding test results and

treatm

ent [42]

Same, but SM

S has im

proved tracking of DBS tests to lab, reduce transport needs

(DBS results sent by SM

S), and im

proved speed and reliability of communication of

test results to facility/ health worker

1, and potentially the caregiver2 [16 ]

Governmen

t Capacity: includes 1) legislative and regulatory capacity, 2) managem

ent system

s, 3) dep

enden

ce on collaborative action

Regarding legislative and regulatory capacity, there is a need for;a national HIV

control strategy, to regulate licensing for HIV drugs and standard setting and

quality monitoring. Frequen

t change in

policy have restricted

implementation

in M

alaw

i [41]

Same + Need for national m

‐ and eHealth policy, need to regulate legal and ethical

fram

eworks regarding eH

ealth

1 [28 ]

Managem

ent system

s are need

ed for government financing and stewardship

of a national HIV program

providing training, drugs, supplies, epidemiological

surveillance activities and quality assurance

Same, but SM

S can im

prove m

anagemen

t by making transport and communication

about DBS tests and results more transparen

t1 [16]

However, needs strong coordination with public sector and partners1

(e.g. Schaefer,

M, personal communication), resulting in delays in scalability for Malaw

i.

Dep

enden

ce and collaborative action req

uired between

national and local

governmen

t, between different tiers of the health sector, between the form

al

health sector and private providers, NGOs and supervisors

Same + partnership within the governmen

t (e.g. M

inistries of Health and

Telecommunication) and m

obile network providers need

ed and need for long term

financial commitment and national level ownership

1 In Zam

bia, there is strong ownership by national governmen

t2 [16]

Usage Characteristics: includes 1) ease of usage, 2) dem

and, 3) the risk of dim

inished

efficacy and efficiency due to factors such as black m

arkets

To increase usage, there is a need to create community aw

aren

ess and

dem

and regarding EID, w

hich can

be challenging given the stigm

a surrounding

HIV and AIDS. Supervision of nurses during counselling, testing and treatmen

t is need

ed.

Same + The health care workers need to be trained

to use the m

obile phone

applications, however, from field experience, this is m

entioned

not to be too

complex as identified users often already know how to use the phone and literacy

rates am

ong them is high2 (Sharpey‐Schafer, K, personal communication, 2011)

Need to increase awaren

ess and utilization (dem

and) of EID, specifically

enrollm

ent of pregnant women

in HIV testing which req

uires community

mobilization with various stakeh

olders

HIV positive m

others need to enroll in EID ensuring that they receive the

results of their child

[42 ]

Same + Mobile phones are used widely1, however, its use for data collection is still in

an early stage

The risk for dim

inished

efficiency and efficiency due to black m

arkets is not

applicable

Same

Chapter 7

112

Case Study 2: N

utrition Surveillance (Table 7.3)

Table 7.3

Analysis of nutrition surveillance with and without the use of rapidsm

s in M

alaw

i.

Table 7.3 applies the fram

ework to assess interven

tion complexity for Nutrition Surveillance (Column 1) and N

utrition Surveillance w

ith Rapid SMS

(Column 2). In C

olumn 2, green indicates areas where complexity was reduced, and red, where complexity increased. Evidence regarding the

effectiveness of using mobile phones in

health is still lim

ited

and less robust. Th

e level of eviden

ce beh

ind the claims in column 2 is categorized with a 1:

if there is eviden

ce (e.g. from evaluations or reflected

in various review

s) and the eviden

ce is consisten

t and a 2: if the claim is speculated/potential, but

not documented or evaluated

or if the findings are contradicted in

other review

s.

Column 1: B

asic nutrition surveillance

program

Column2: N

utrition surveillance

with addition of rapidsm

s

Interven

tion Characteristics: includes: 1) basic product design, and 2) supplies and 3) eq

uipmen

t For the basic product design, the pap

er based

system is standardized

, easy

to store and procure; h

owever, paper‐based

rep

orting can lead

to increase

errors. Poor quality of data includes sam

ple bias, errors linked to illegibility

of handwriting, wrong measuremen

ts entered, incorrect child

ID numbers

written

and/ or data with m

issing values [23,43]

Paper based

system req

uires m

any form

s and couriers to reach national

level and these can cau

se delays.

Finally, once the data reaches the national level, it need

s to be entered into

an excel sheet.

Same + Mobile phones are safe to use, easily transportab

le and widely available

1. Some

areas can experience delays and localized

system outages due to insufficient mobile

network coverage (resulting in rep

eated sen

t SM

S messages as well as delays in

receiving confirm

ation and feedback) [23] or experience unreliable services from local

mobile network operators (Sharpey‐Schafer, K, personal communication, 2011) and/ or

limited

access to electricity

1

When

data entered is physically im

possible, an automated

SMS response prompts the

health worker to correct the data en

tered, partly im

proving data quality

RapidSM

S can be used on any mobile phone, m

enus may differ1

Confiden

tiality of patient data can be an

issue when

using SM

S based

systems and

personal m

obile phones

2 [5,25 ]

Supplies needed

are regular supply of form

s and tools for growth

monitoring, which are gen

erally easy to m

aintain

Same + phones need battery chargers (e.g. solar chargers) and cellphone airtime1 [24]

Equipmen

t needed

are computers for en

tering and analysing data at

national level entered by database servers.

As the INFSSS system is owned

by various ministries, the division of roles

and responsibilities for maintaining the equipmen

t are unclear [23]

Same + phone and internet. Access to computers and internet is sometim

es lacking. For

the use of phones, health care workers use their personal phones and are therefore

them

selves responsible for the repairs, recharging and rep

lacements

2 [16 ]. A

dditional

equipment is needed

to bridge datab

ase and SMS servers1

Delivery Characteristics: includes 1) facility, 2) human

resources and 3) tran

sport and communication

As for facilities needed

, for active case finding outreach services are

needed, ensuring follow‐up of enrolled children

Same + contract service providers an

d m

obile phone providers

In regard to human

resources: q

ualified health workers are needed

to

perform

growth m

onitoring. In

addition, database servers are needed

for

entering and analysing data at national level, supervision of data collectors

is needed

to ensure accurate m

onitoring. In

Malaw

i there is a lack of

capacity for data entry and analysis [23 ]

Personal is needed

for managem

ent and planning for regular supplies and

sending collected

data to national level

Same +requires RapidSM

S technicians (&

designers) and public health experts to closely

work together. In M

alaw

i there is a lack of local software consultants to m

anage the

RapidSM

S platform

and a need for locally‐based

project m

anagers to m

anage the

quality and delivery of the project

1 (Sharpey‐Schafer, K, personal communication,

2011). For exam

ple, the fact that one of the sites failed to rep

ort data for tw

o weeks,

was identified

by UNICEF workers in New

York, however it was not detected by local

consultants in M

alaw

i However, using SM

S and RapidSM

S platform

enabled inputs and

analyses to happen

much faster1[23]


113

Table 7.3

(continued

)

Form

s must be transported

from local, to district and then

to national level

and poor quality of data is a significant challenge. To im

prove quality

communications infrastructure is needed

Regular supplies require functional transport infrastructure [43]

As before, except elim

ination of costs related to transporting paper form

s1 and

manually entering data1; real‐time data access and analysis1 , increased accuracy of

reporting (automatic calculation)1, and increased accuracy of inform

ation given

to

caregivers regarding the health of their children2 [23]

Curren

tly pap

er based

system continues

Governmen

t Capacity: includes 1) legislative and regulatory capacity, 2) managem

ent system

s and 3) depen

den

ce on collaborative action

As for legislative and regulatory capacity: policies around nutrition m

ust be

enforced

[23 ] and nationally rep

resentative surveillance systems such as the

Nutrition Surveillance Program

mes (NSP) and National Nutrition

Program

mes (NNP) should be in place [44]

Same + Need for national m

‐ and eHealth policy, need to regulate legal and ethical

fram

eworks regarding eH

ealth1 [28]

Managem

ent system

s, as lack of governmen

t ownership and human

resources result in

constraints related to fully utilizing ben

efits of data

system

s and analysis in

order to iden

tify crises in nutrition and food [23]

Same + Need for strong coordination with private public partnership (PPP), for

exam

ple to negotiate free

air tim

e or lower SMS costs1 [19]

The RapidSM

S Platform

helps to stream

line data collection, data analysis still needs to

be sufficiently monitored to better assess trends and increase the effectiveness of

program

ming. Such analysis is not being done on a regular basis and proves to be a key

challenge in

terms of effectiveness [23] .

The ability to provide real‐tim

e aggregated feedback can potentially increase efficiency

in program

managem

ent2

Collaborative action is req

uired

between national and local governmen

t,

civil society organizations and the government on initiatives regarding

nutrition and food security [23 ]

An existing challenge for INFSSS is linked to governmen

t ownership

Same + partnership within the governmen

t (e.g. M

in of Health and

telecommunication) and m

obile network providers needed

[19] and need for long term

(financial) commitmen

t and national level ownership

1

In M

alaw

i challenges are faced in

taking full ownership of the system

by the

government1 (Sharpey‐Schafer, K, personal communication, 2011)

Usage Characteristics: these are based on 1) ease of usage, 2) dem

and and 3) the risk of dim

inished

efficacy an

d efficiency due to black m

arkets

For usage there is a need to create community aw

aren

ess regarding the

importance of nutrition surveillance and child

monitoring. M

alaw

i faced

challenges due to loss of follow‐up of participants (as children are enrolled

for a year) [

23]

Same + HW can

use m

obile phones and are literate

2 (Sharpey‐Schafer, K, personal

communication, 2011)

Skills to use computer‐and internet differs and for some users can

still be challenging2

Need to increase awareness regarding nutrition surveillance

Same + promoting use of mobile phones for data collection

The risk for dim

inished

efficiency and efficiency due to black m

arkets is not

applicable

Same

Chapter 7

114

DISCUSSION

Interest in incorporating and improving technology within health systems in resource

poor settings is increasing, and therefore it is essential that we first critically analyse

existing systems and programmes in order to understand how and where such

technology may make a meaningful contribution. If communication and flow of

information are identified to be a weak link, for example, SMS or other forms of

mHealth technology can have a positive effect on the system or programme. However,

if after analysing the existing programme, the main weaknesses are not related to

exchanging or improving the quality of information and data, the benefits of SMS

technology, for example RapidSMS may be limited and other solutions may need to be

prioritized. Further, the application of a new technology itself may create additional

(perhaps even negative) effects that influence the ability of the program to be

sustained or scaled up.

In this paper, we applied an analytical framework to assess the technical complexity of

three projects prior to adding SMS (two on EID and one on nutrition surveillance) and

conclude that each contained varying levels of complexity that posed significant

challenges to operating at scale. Within both EID and nutrition surveillance, the key

challenges identified were linked to human resources and infrastructure requirements.

For both interventions, the addition of RapidSMS proved to be effective in helping to

overcome constraints relating to communication and transportation infrastructure. For

EID, the use of SMS decreased the turnaround time for receipt of results by the facility.

For nutrition surveillance, the use of RapidSMS replaced the labour intensive paper

based process with a simplified one, and improved data quality, enabling more

sophisticated analysis and decreased delays in data transmission. Nevertheless, some

key challenges remained and new constraints emerged when adding RapidSMS,

including some that have implications for sustainability and scale‐up of the projects.

This analysis shows that in regard to implementing RapidSMS, an important constraint

was the shortage of local technical staff to maintain the associated data and database

in both countries. For the initial phase, Zambia and Malawi depended on external

consultants to design and oversee the software programs which added additional

management complexities. Another key constraint found in this analysis was the

difficulty created when the government did not take full ownership of the system. This

was a problem in Malawi due to competing government priorities; in Zambia

government ownership only occurred when maintenance of the RapidSMS service was

incorporated into the Ministry of Health‘s information technology infrastructure.16

Second, for both case studies there were difficulties implementing RapidSMS in some

geographic areas, a phenomenon that has been found in other health‐related SMS


115

programmes as well.17‐20 For example, an assessment in Ethiopia identified poor

network coverage as the number one obstacle in relation to implementing RapidSMS.20

Third, in both case studies, key challenges were linked to a critical shortage of skilled

health workers.21 Given that this was a key operating challenge prior to the

introduction of RapidSMS, it would be critical to identify a strategy to address the

needs of these (often) overstressed health workers, ensuring that any new technology

introduced had a positive impact on their workload.22 The effectiveness of using

technology itself to address this particular problem is still under debate. For example,

the use of RapidSMS for nutrition surveillance suggests that it can have a positive

impact by decreasing the workload,23 however preliminary data on health workers

using SMS to trace mothers/ caregivers in other contexts suggests that this may not

always be the case.24 A related point is that all health workers and their supervisors

must be trained to ensure they are able to use the newly available data in a meaningful

way so that it contributes to better programming and health outcomes.

In addition to these constraints highlighted in our analysis, other constraints that

influence the effectiveness and scalability of mHealth projects have been identified in

the literature. These include price levels of relevant products,25‐26 the frequency in

change of mobile phone numbers of individuals,25,1,27 data security25,5 and lack of

regulations, for example to protect personal identifiable data.28

The literature shows that many of these challenges are not only linked to the use of

RapidSMS, or the broader concept of SMS technology, in LMIC. Findings from a study

conducted for the European Commission on the use of eHealth in high income

countries (HIC) argued that key challenges were linked to political, legal and practical

obstacles and that almost everywhere the use of ICT was ‘proven to be much more

complex and time‐consuming than initially anticipated’.29 In addition, a study assessing

the effects of using mobile phones for diabetes care in the USA concluded that the use

of mobile phones can have great potential, however, it is a complex process and the

technology itself ‘is not sufficient to make a difference’.30 It is therefore important to

carefully plan the use of technology, establish technical working groups that involve all

partners implementing eHealth initiatives and exchanging experiences and lessons

learned between LMIC, as well as from HIC. At a local level, mHealth projects should be

designed from the beginning with local partners, governments and users, as they can

best identify challenges which may be influential for the success of the project.9,27‐28,31

Efficiencies could be gained by 'upgrading' the technological infrastructure. Even

though these may not target the specific identified program challenges, they hold

potential for creating efficiencies that will benefit multiple programs using a common

mHealth surveillance or workflow management system. Improving the technological

infrastructure is crucial to overcome the need for maintaining the phone and paper

based parallel systems due to challenges mentioned earlier due to possible failures in

electronic systems. Further, upgrading the infrastructure may encourage increased

Chapter 7

116

availability and use of diverse types of technology. This will in turn enable utilization of

more complex technological tools such as smartphones or laptops that can support

more complex programs, rather than basic mobile phones. However, this also has

important implications for scalability, sustainability and cost which should be taken into

consideration during planning.

Limitations

The key limitation of this analysis is that there was a dearth of relevant project level

data available. For this reason, project data were supplemented by key informant

interviews. A second limitation is that the analytic framework used does not take

economic considerations and cost effectiveness into consideration. Addressing costs‐

effectiveness is essential as some mHealth projects may be based on very expensive

technology which may not be scalable due to that reason. And, as one of the key

informants involved in the mHealth projects mentioned, funding sources (and

limitations) can have an important impact: “Barring connectivity and other logistic

issues, communication and information flow are real bottlenecks in a country like

Malawi. However, community level health information systems as well as mHealth

initiatives almost entirely are partner‐initiate driven and most come to an abrupt halt at

the end of many a project's lifecycle.”

Another limitation is that our analysis does not address contextual issues such as social

and gender dynamics (e.g. who has the right to use a phone in a household), cultural

issues (e.g. how is health care perceived), economic aspects (e.g. price of a phone and

calls, repair) and usability (e.g. who can use the phone and for which purposes).

Despite high penetration of mobile phones world‐wide, women, the elderly, and the

poorest populations32 remain those most likely to not have access to technology,

raising important considerations regarding equity.

Conclusion

In conclusion, scaling up existing health programmes in which use of mobile phone

applications such as RapidSMS have been piloted is not a simple issue. SMS technology

has the potential to facilitate implementation of MNCH interventions by simplifying

elements of their technical complexity. However, in deciding whether and how to

introduce SMS technology to MNCH programmes, it is critical to first understand what

the underlying implementation challenges are, what SMS technology can contribute,

where it may introduce new challenges, and how these can be addressed within the

local context. Conceptual frameworks that describe and analyse technical complexity

can be useful for clarifying these factors. In this paper, we applied a framework by

Gericke et al. (2005) to analyse the underlying system complexity of three MNCH

programmes before assessing the contribution (positive and negative) of SMS

technology.7 We argue that such analysis is an important first step in assessing the


117

potential contribution of SMS (and eHealth more broadly), a second step would be to

select mHealth strategies which are worthy of scale as part of a strength, weakness,

opportunities and threats (SWOT) analysis or other strategies used by governments and

implementing agencies to make these decisions. With such complexities identified and

strategies in place to attempt to manage some complexities, robust monitoring and

evaluating frameworks are needed to ensure that SMS investments effectively address

identified challenges and equip key actors to handle existing and emerging complexity –

capacities which are critical for sustaining and scaling up interventions in resource poor

settings.33‐36 To date, rigorous evaluation of programmes using SMS technology has

been limited, and further research in this area will be vital to ensure that potential

benefits of mHealth innovations reduce health inequities and reach those most in

need.3

Chapter 7

118

Box 7.1 Rapidsms and early infant diagnosis of HIV in Malawi and Zambia.

Without timely diagnosis and treatment, about one third of HIV positive children will die in their first year

and almost 50% by their second year of life.37 Efforts to strengthen early infant diagnosis (EID) of HIV are

integrated with prevention of mother to child transmission (PMTCT) programmes.

In Malawi and Zambia, EID programmes begin by offering counselling and HIV testing to pregnant women

through routine antenatal care services. Those who are HIV‐positive are enrolled into the PMTCT program.

They are then counselled and instructed to return to the clinic to test their newborn 4‐6 weeks after birth.

38 The advent of dried blood spot (DBS) tests has advanced the field of EID by allowing healthcare

workers to more easily obtain blood samples from infants (via heel or finger prick) for diagnosis.39 For EID,

the health worker sends the DBS tests to a laboratory by courier, where the specimens are tested; a process that can take up to 2 weeks. At laboratory, lab technicians process the tests and update the

records in an excel database, which can also take 2 weeks. The laboratory then sends the paper‐based

results back to the health facility, by courier, which can take up to 3 weeks.16,24

Subsequently, the mother or caregiver is traced and brought back to the clinic for counselling so that both mother and infant can be

enrolled for appropriate treatment and care. At every step of the EID process the loss to follow‐up can be

high.

Incorporation of RapidSMS into the program

In 2010, a RapidSMS platform was added to EID programmes in both Malawi and Zambia with the aim of

facilitating bi‐directional communication around DBS tests and results between health workers and laboratories using the ‘Result160’ application and to simplify follow‐up of mothers/ caregivers and their

newborns through the ‘RemindMi’ application.24 Within this program, health workers and volunteers send

and receive messages free of charge using their personal phones.

After sending DBS samples to the laboratory, health workers send an SMS (text message), to a central

database reporting the number of samples that have been sent to the laboratory. If the samples haven’t

reached the laboratory within a specific timeframe, the central database system sends a SMS to the health worker, informing him or her that the samples have not yet arrived. Once the lab technician enters the

test results into the computer at the laboratory, the system sends a SMS to all health workers in the

health facility informing them that the results are ready. The health worker designated to collect the data then logs in with a pin code and receives the results on his or her phone. Finally, all health workers in the

facility receive a SMS informing them that their colleague collected the results successfully.

Through RemindMi, the health worker sends a SMS to a designated community health volunteer or a traditional birth attendant to trace the caregiver and newborn, in order to refer them to the health

facility. The central database system continues to send reminders for tracing caregivers, until the

designated volunteer successfully refers the caregiver and stops the automatized reminders by sending a deactivation code to the central database.


119

Box 7.2 Rapidsms and nutrition and foor security surveillance (INFSS) in Malawi.

Chronic malnutrition and growth stunting affects close to 50% of the overall population of Malawi, and

these high rates of malnutrition are compounded by persistent food shortages and high disease burdens,

including HIV and AIDS. Of all children under five in Malawi, 46% suffer from moderate to severe stunting and 25% are moderately to severely underweight.

40

After a famine in 2002, the Integrated Nutrition and Food Security Surveillance System (INFSSS) was set up

to address chronic malnutrition in Malawi. INFSSS monitors the nutritional status for approximately 9100 children through five district growth monitoring clinics (GMCs) on a monthly basis for one year.

23 The

children that are monitored are randomly selected from those visiting GMCs; as a result the sample

includes healthy, malnourished, and ill children. Health Surveillance Assistants (HSAs) collect and record malnutrition data from these children, and the GMCs sends the nutrition data to a local district office each

month. The local district then forwards this information to the national level for data entry and analysis.

By monitoring child malnutrition through children attending GMCs, the Government of Malawi is thus able to assess and subsequently respond to trends in malnutrition levels among those attending GMCs.

Incorporation of RapidSMS into the program

In 2009, a RapidSMS platform was added to INFSSS to streamline and improve the quality, speed, and accuracy of data collection.

23 Using SMS, HSAs are trained to enter and send child nutrition data with

mobile phones. Health workers receive immediate confirmation that their information was received. A

central server analyses the data based on automated algorithms to screen for child malnutrition, with follow up directions provided to health workers if the data indicates child malnutrition. A website created

by INFSSS provides the Malawian government and other stakeholder’s real‐time access to the data and its

analysis, allowing timely analysis and follow‐up to changes in malnutrition trends. Data sent by the HSA is then directly analysed by the central server and feedback is sent back to the HSA.

Acknowledgement

Many individuals reviewed drafts of this work and provided helpful feedback. We thank Christian Salazar, Mickey Chopra, Theresa Diaz, Khassoum Diallo, Kumanan Rasanathan, Ariel Higgins‐Steele, and Ahmet Afsar. The manuscript was also reviewed by colleagues from the UNICEF office in Zambia; we thank Nilda Lambo and Lastone Chitembo. From the office in Malawi we thank Luula Mariano. For the inputs regarding the use of SMS technology for both projects, we thank Erica Kochi, Merrick Schaefer, and Kieran Sharpey‐Schafer.

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120

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12. Wu F, Khlangwiset P. Evaluating the Technical Feasibility of aflatoxin risk reduction strategies in Africa. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 2010;27:658‐676.

13. Ross‐Degnan D, Backes‐Kozhimannil K, Payson A, Aupont O, LeCates R, Briggs J, Chalke J. Improving

Community Use of Medicines in the Management of Child Illness: A Guide to Developing Interventions. Submitted to the U.S. Agency for International Development by the Rational Pharmaceutical

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14. Snowdon W, Potter JL, Swinburn B, Schultz J, Lawrence M. Prioritizing Policy Interventions to improve diets? Will it work, can it happen, will it do harm? Health Promot Int 2010;25:123‐133.

15. Menon P, Frongillo EA, Pelletier DL, Stoltzfus RJ, Ahmed AM, Ahmed T. Assessment of epidemiologic,

operational, and sociopolitical domains for mainstreaming nutrition. Food Nutr Bull 2011;32(2 Suppl):S105‐14.

16. Seidenberg P, Nicholson S, Schaefer M, Semrau K, Bweupe M, Masese N, Bonawitz R, Chitembo L,

Goggin C, Thea DM. Early infant diagnosis of HIV infection in Zambia through mobile phone texting of blood results Bull World Health Organ 2012;90:348‐356.

17. Kallander K. Landscape analysis of mHealth approaches which can increase performance and retention

of community based agents. InScale Innovations at Scale for Community Access and Lasting Effects. Kampala, Uganda: Malaria Consortium Resource Centre, 2010.

18. Buys P, Dasgupta S, Thomas TS. Determinants of a Digital Divide in Sub‐Sahara Africa: A Spatial

Econometric analysis of Cell Phone Coverage. Elsevier Ltd. World Development 2009;37:1494‐1505. 19. Schackleton SJ. Rapid Assessment of Cell Phones for Development. UNICEF. Women’s Net, 2007.

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20. UNICEF, RapidSMS Ethiopia Assessment: Improved Nutrition and RUTF Monitoring. Unpublished, UNICEF, (no date).

21. Christopher JB, Le May A, Lewin S, Ross DA. Thirty years after Alma‐Ata: a systematic review of the

impact of community health workers delivering curative interventions against malaria, pneumonia and diarrhoea on child mortality and morbidity in sub‐Saharan Africa. Hum Resour Health 2011;9:27.

22. Keeton C. Measuring the impact of e‐Health Bull World Health Organ 2012;90:326‐327.


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23. Blaschke S, Bokenkamp K, Cosmaciuc R, Denby M, Hailu B, Short R. Using Mobile Phones to Improve

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and Child Health in High HIV‐burden Areas Through Mobile Technology. UNICEF Malawi and UNICEF Innovations and SIPA, Columbia University School of International and Public Affairs, New York, 2010.

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26. UNCTAD. Information Economy Report 2010 ‐ ICTs, enterprises and proverty alleviation. New York and Geneva: United Nations. [http://www.unctad.org/en/docs/ier2010_en.pdf], 2010.

27. Coomes CM, Lewis MA, Uhrig JD, Furberg RD, Harris JL, Bann CM. Beyond reminders: a conceptual

framework for using short message service to promote prevention and improve healthcare quality and clinical outcomes for people living with HIV. AIDS Care, 2011.

28. WHO. Atlas eHealth Country Profiles Global Observatory for eHealth series ‐ Volume 1. Geneva,

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29. Watson R. European Union leads way on e‐health but obstacles remain. BMJ 2010;341:c5195.

30. Katz R, Mesfin T, Barr K. Lessons from a community‐based mHealth diabetes self‐management program: “It's not just about the cell phone”, J Health Commun 2012;17:sup1:67‐72.

31. Levine D, McCright J, Dobkin L, Woodruff AJ, Klausner JD. SEXINFO: a sexual health text messaging

service for San Francisco youth. Am J Public Health 2008;98:393‐395. 32. GSMA. Women and mobile: A global opportunity. A study on the mobile phone gender gap in low and

middle‐income countries. GSMA Development Fund, Cherie Blair Foundation for Women and Vital

Wave Consulting, 2010. 33. Paina L, Peters DH. Understanding patways for scaling up health services through the lens of complex

adaptive systems. Health Policy Plan 2012;27:365‐373.

34. Subramanian S, Naimoli J, Matsubayashi T, Peters DH. Do we have the right models for scaling up health services to achieve the Millennium Development Goals? BMC Health Serv Res 2011;11:336.

35. Leon N, Schneider H, Daviaud E. Applying a framework for assessing the health system challenges to

scaling up mHealth in South Africa. BMC Medical Informatics and Decision Making 2012;12:123 36. Mair FS, May C, O’Donnell C, Finch T, Sullivan F, Murray E. Factors that promote or inhibit the

implementation of e‐health systems: an explanatory systematic review Bulletin of the World Health

Organization 2012;90:357‐365. 37. Newell ML, Coovadia H, Cortina‐Borja M, Rollins N, Gaillard P, Dabis F.Mortality of Infected and

Uninfected Infants Born to HIV‐infected Mothers in Africa: A pooled Analysis. Lancet 2004;364:

1236‐1243. 38. UNICEF. Scaling up Early Infant Diagnosis and Linkage to Care and Treatment. A Briefing Paper.

[http://www.unicef.org/aids/files/EIDWorkingPaperJune02.pdf], 2009.

39. Ciaranello AL, Park J, Ramirez‐Avila L, Freedberg KA, Walensky RP, Leroy V. Early infant HIV‐1 diagnosis programs in resource‐limited settings: opportunities for improved outcomes and more cost‐effective

interventions. BMC Med 2011;9:59.

40. UNICEF. The Situation of Women and Children Retrieved May 2012 from: [http://www.unicef.org/malawi/children], 2012.

41. UNGASS. Malawi HIV and AIDS Monitoring and Evaluation Report 2008‐2009, 2010.

42. Braun M, Kabue MM, McCollum ED, Ahmed S, Kim M, Aertker L, Chirwa M, Eliya M, Mofolo I, Hoffman I, Kazembe PN, van der Horst C, Kline MW, Hosseinipour MC. Inadequate coordination of maternal and

infant HIV services detrimentally affects early infant diagnosis outcomes in Lilongwe, Malawi. J Acquir

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44. Akhter N, Haselow N. Using data from a nationally representative nutrition surveillance system to assess

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123

PART IV

Discussion

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CHAPTER 8

General discussion

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General discussion

127

GENERAL DISCUSSION

To ensure equitable access to health care, we need to know more about a) the key

determinants in the delays that prevent a child from receiving adequate care, and b)

how these delays can be addressed. The exemplary case studies in this thesis are based

on children with symptoms of pneumonia (of which most occur in sub‐Saharan Africa1),

and mHealth (of which sub‐Saharan Africa is the fastest growing mobile region2). This

discussion aims to address these issues based on the findings from the individual

studies in this thesis, in order to optimize programmatic coverage focussed on delays in

care seeking, possibly facilitated by mHealth, with its successes and challenges.

To better understand the key determinants which delay care seeking, we first need to

know which child with symptoms of pneumonia is currently delayed in accessing care

Sub‐Saharan Africa has the highest under‐five mortality rates, including those due to

pneumonia, and the lowest levels of care seeking.1,3‐4 It is estimated that only 2 out of

every 5 children with pneumonia specific symptoms are taken to an appropriate care

provider,4 hence knowing which child is delayed in accessing care, is crucial. The use of

large national household surveys is specifically useful in resource limited settings for

several reasons. This thesis shows that the USAID‐supported Demographic and Health

Surveys (DHS) and the UNICEF‐supported Multiple Indicator Cluster Surveys (MICS), are

helpful in identifying marginalized children. For all countries, these surveys are

sufficient to produce reliable estimates at national, urban‐rural and regional levels.5

Depending on the sample, additional sub‐national or local level surveys are required to

help identify poor and marginalized communities at (sub‐) district level; e.g. those living

in urban slums, religious and ethnic minorities, and/ or those living in conflict zones.

Such analyses are essential to improve coverage of effective health interventions, as

national averages often conceal broad disparities.6 With both DHS and MICS conducted

on a routine basis (every 3‐5 years), these surveys can also be used to monitor if

programs have been successful in decreasing inequities in coverage, or not. Another

reason to better use these existing datasets in resource limited settings is because

routine health management information systems5 and other program level data7 are

often of poor quality, lack periodicity to track changes and trends, or are simply not

available. Finally, while these surveys consist of various interviews, including women’s,

men’s and children’s questionnaires; Chapter 2 shows that merging these datasets

enabled us to conduct analyses on both caregivers’ characteristics as well as child

health outcomes.

Based on both DHS and MICS, the two chapters show that, even between neighbouring

countries in sub‐Saharan Africa, care seeking patterns vary widely. In Uganda almost

80% of the children with symptoms of pneumonia are taken to an appropriate care

provider, whereas this is less than 30% in Ethiopia and Chad. We also found variations

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within the countries, for example between urban‐rural residence, geographical

locations, religious groups, caregivers’ levels of education, number of children ever

born, the child’s age and sex, and others. In spite of these differences, the main

similarity is that a child belonging to the poorest household is the least likely to obtain

adequate care, regardless of where he or she lives. Still, knowing where these children

live is crucial to leverage resources and to ensure that they are not lagging behind when

attempting to improve coverage of health interventions overall.

After having identified the child which is delayed in accessing care, we need to know

why this child with symptoms of pneumonia is not receiving antibiotics (or other

required treatment) in time

To untangle delays which ultimately lead to high pneumonia mortality rates, the ‘three

delays’ model by Thaddeus and Maine8 is useful. The first phase is the decision to seek

care on the part of the individual, the family or both (deciding to seek care). The second

phase deals with reaching an adequate health care facility (reaching adequate care),

while the third phase presents if adequate health care is received at the health facility

(receiving adequate care). While the model was initially designed to recognise the

barriers women face when trying to reach adequate care in time, it is not the first time

it is applied more broadly.9‐10 Based on the model, Chapter 3 shows that for Ethiopia

and Chad the main causes of delays occur at household level, while in Uganda delays

are most likely due to quality of care being sub‐standard. Despite these variations,

causes of delays often affect more than one phase of delay; e.g. wealth is linked to

whether care is sought or not, if care can be reached, but also from which quality.11‐12

Therefore, researchers should not aim to identify one specific phase in which most

delays occur, but rather unravel the complete chain of challenges associated with care

seeking; starting at home, the delay‐model serving as a way to structure the inventory

of the chain.

For children with symptoms of pneumonia, the first phase of delay (deciding to seek

care) was the most complex to measure, as the decision of caregivers to seek

appropriate care or not is influenced by a range of factors such as their ability to

recognise symptoms, socio‐economic aspects, beliefs, and the perceived quality of care

provided by health workers.8 Based on the model of three delays, Chapter 2 shows

that, while pneumonia is the main cause of childhood mortality; caregivers in high

mortality settings are often unaware of its symptoms. Chapters 2 and 3 also reveal

large differences in the reported prevalence of pneumonia (this ranged from 1.9% in

Burkina Faso to 14.8% in Uganda). As causes of child death,13 including those due to

pneumonia,14 differ substantially from one country to another, it is not possible to

reveal to which extent the range in reported cases is due to caregivers’ (in‐) ability to

recognize symptoms. One of the ways to further assess the accuracy in which

caregivers recognize symptoms is by interviewing caregivers at hospital level, where

children can be clinically classified as having pneumonia or not. Yet, again, this

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approach would miss those caregivers’ perspectives of children who were never

brought to receive care. Hence, the main challenges of assessing this within the

formalized health structure is that it will exclude the poor and marginalized child, as

they are more likely to seek care elsewhere, e.g. from the informal sector (e.g. private

pharmacies) or at home.15 A way to include these children could be done by conducting

verbal autopsies at community level.16

To develop effective programs for multiple countries, we need to identify common

challenges across these high pneumonia mortality settings, as revealed by multi‐country

reviews

Chapter 4 illustrates the most concerning cause of the third phase of delay (receiving

adequate treatment), namely; a health worker is too often not able to correctly

distinguish if a child has pneumonia or not. With no other cross‐country reviews on the

ability (or inability) of community health workers to correctly count and classify

respiratory rates in children with a cough or difficulty breathing being published before,

our findings illustrate the challenges they face; especially those with limited training,

literacy and numeracy. Failure to properly classify a sick child is especially problematic;

also as it influences caregivers’ decision to seek care upon a next illness episode. These

challenges reveal an urgent need to design, test and scale‐up potential pneumonia

diagnostic devices.

While counting the respiratory rate is still the WHO/UNICEF criteria for classifying

pneumonia in resource limited settings, more emphasis is currently put on the need to

develop point‐of‐care diagnostic test which can differentiate between viral and

bacterial pneumonia.17‐18 One of the reasons behind the need for more advanced

diagnostic devices is the changes in the epidemiology of pneumonia; with an increase in

coverage of the pneumococcal conjugate vaccine (PCV),19 amongst other reasons, it is

expected that the proportion of pneumonia cases attributed to viral infections will

increase.20

With the fast uptake of mobile phones, programmers need to know why mobile

technology based interventions succeed or fail; in order to rank such interventions in

their effectivity to decrease delays in accessing care

Chapter 5 illustrates that mobile phones have great potential to overcome fundamental

communication challenges, shortening delays in deciding to seek care, reaching and

receiving health care. The expectations are especially high in sub‐Saharan Africa; where

many communities live in isolation and the uptake of mobile phones has been

surprisingly fast.

Nevertheless, various studies have underlined important challenges associated with the

use of mobile phones to improve health outcomes (i.e. mHealth), this as most of the

mHealth initiatives piloted in sub‐Saharan Africa fail to go to scale.21 Simultaneously,

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little is known on what’s working, what’s not and why not.22 Aiming to build on this

evidence gap, Chapter 6 illustrates the effectiveness of a hotline and messaging service

based on the “treatment effect on the treated” (TOT) model. The TOT model was used

as the uptake of the initiative was low; only 18% of the targeted population actually

used the toll‐free hotline service. The use of the SMS messaging service was even

lower. Low uptake of mHealth initiatives has been reported elsewhere.23 Having a

phone or not (despite the fact that caregivers could borrow a phone), is likely to be

associated with the low uptake of the initiative; only 32% of the caregivers in the

intervention area had a phone. The GSMA report on gaps in mobile phone ownership24

found that the most important reason for not owning a phone is related to its costs.

The report also reveals that women are less likely to own a phone, as compared to

men. In sub‐Saharan Africa the gender discrepancy is 13%; of the three countries

included in the GSMA report this difference was the largest in Niger (45%), followed by

DRC (33%) and Kenya (7%). In Kenya the gender gap widened to 16% amongst the

poorest households.24 Hence, programmers should be careful when designing projects

that use these devices as a strategy to overcome delays in accessing health care,

especially since women are the caregivers primarily responsible for taking their child for

care.

With access to phones, network coverage, and other factors playing a significant role in

the success of mHealth project; Chapter 7 illustrates that the intervention complexity is

what determines scalability of the initiative. We found that primary implementation

challenges before adding the SMS component were related to broad programmatic

challenges (e.g. resources and governance), and these are not (easily) addressed by text

messaging or faster communication systems. Nevertheless, the mHealth applications

did address barriers related to effectiveness and timeliness as it enabled faster

communication of results amongst health workers and data collection. In other words,

the addition of SMS is not the solution, but part of a strategy to overcome

communication barriers. Translating this to care seeking for pneumonia; ultimately the

sick child still requires antibiotics, therefore the use of mobile phones can only help

overcome part of the inefficiency and timeliness in reaching the required antibiotics.

This can be achieved by using mobile phones to support caregivers to recognize the

illness (delay in deciding to seek care), coordinate referral (delay in reaching care) and

by facilitating consultation with more senior health staff (delay in receiving adequate

care).

Despite the expectations, the TOT ‐ as explained in Chapter 6 ‐ revealed no effect on

care‐seeking for children with symptoms of pneumonia. While these findings may not

be as we anticipated, they are in‐line with our findings presented in Chapter 2, where

we found a lack of significant associations between knowledge and care seeking. Both

studies highlight the complexities surrounding care seeking behaviour and they

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illustrate a need for integrated health system and community based approaches; as

they go beyond targeting individuals and the challenges they face.

Recommendations

To conclude, this thesis aimed to answer the two main research questions which were

posed in the introduction, namely: 1) How do the delays in care affect care seeking

behaviour, and how can better knowledge of these delays lead to improved

programming? And, 2) what is the potential of mobile technology to improve health

outcomes by addressing these delays, and what are programmatic challenges in

implementing mHealth strategies?

The first step to increase coverage of health interventions is by identifying the child

which is most likely to die due to pneumonia, as he or she fails to reach acceptable,

affordable and appropriate health care in time. While national surveys are essential,

local level research is required to identify why these children fail to reach adequate

care. As the model of three delays is shown to be helpful to untangle determinants

which affect care seeking, verbal autopsies at community level, based on this

framework can help reveal causes of delays, starting with those which occur at

household level. This thesis illustrates that care seeking behaviour is very much context

specific, hence programmers (and donors) need to be careful when generalizing or

duplicating effective strategies from one context to another.

Despite the large differences, this thesis highlights that there are similarities across high

pneumonia mortality settings. The main similarity is that caregivers and health workers

fail to know and recognize pneumonia specific symptoms. Further research should

focus on improving caregivers and health workers knowledge of pneumonia specific

danger signs. Better knowledge on how to recognize pneumonia is crucial, especially in

remote areas where the risk of dying due to pneumonia is the highest and health

workers are the least equipped to classifying illnesses correctly. Such research should

also focus on how knowledge, illness perception and other caregivers’ characteristics

affect the accuracy in which caregivers are able to recognize pneumonia specific

symptoms. In regard to pneumonia diagnosis, simple solutions ‐ such as counting beads

‐ need to be recognized and implemented more broadly, while waiting for other

diagnostic aids which are preferably also low‐tech and more robust. To overcome

challenges in timely accessing care, the focus should be on children under the age of

two, as the incidence is higher amongst this age group. Finally, with timely care seeking

being so complex, and child mortality due to pneumonia being so high, research needs

to focus on preventing a child from getting ill in the first place, for example by

increasing vaccine coverage, breastfeeding practices and improved nutrition.

To finish, while the fast uptake of mobile phones has potential to shorten delays in

seeking and receiving health care; programmers, donors and policy need to critically

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assess when and how such intervention is most likely to be successful. And, while

further research should be robust and well documented aiming to build on the

evidence‐base of mHealth initiatives, resources simultaneously need to be leveraged to

test and scale‐up other interventions which are not (necessarily) technology driven.

Especially equity focussed programs should be careful in technology driven

interventions, since, despite the impressive uptake of these devices in sub‐Saharan

Africa, (high) technology will stay out of reach for the poorest and most marginalized

for still some time.

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REFERENCES

1. United Nations Children’s Fund (UNICEF). Levels & trends in child mortality. Report 2015. New York:

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2. GSMA. Mobile for development. Available: http://www.gsma.com/mobilefordevelopment/ Accessed 2016, 2015.

3. UNICEF. Committing to child survival: A Promise Renewed. Progress report 2015. New York: UNICEF.

4. United Nations Children's Fund (UNICEF) global databases 2015. Available: http://data.unicef.org/child‐health/pneumonia.html. Accessed 2016, Jan 12.

5. Hancioglu A, Arnold F. Measuring coverage in MNCH: tracking progress in health for women and

children using DHS and MICS household surveys. PLoS Med 2013;10:e1001391. 6. UNICEF (2010) Narrowing the gaps to meet the goals. New York: UNICEF.

7. Noordam AC, George A, Sharkey AB, Jafarli A, Bakshi SS, Kim JC. Assessing scale up of mHealth

innovations based on intervention complexity: Two case studies of child health programmes in Malawi and Zambia. Journal of Health Communication: International Perspectives 2014;20:1‐11

8. Thaddeus S, Maine D. Too far to walk: Maternal mortality in context. Soc Sci Med 1994;38:1091‐1110

9. Mbaruku G, van Roosmalen J, Kimondo I, Bilango F, Bergstrom S. Perinatal audit using the 3‐delays model in western Tanzania. Int J Gynaecol Obstet 2009;106:85‐88.

10. Waiswa P, Kallander K, Peterson S, Tomson G, Pariyo GW. Using the three delays model to understand

why newborn babies die in eastern Uganda. Tropical Medicine and International Health volume 2010;15:964–972

11. Kahabuka C, Kvåle G, Hinderaker SG. Care‐seeking and management of common childhood illnesses in

Tanzania—Results from the 2010 Demographic and Health Survey. PLoS One 2013;8:e58789. 12. Onwujekwe O, Hanson K, Uzochukwu B. Do poor people use poor quality providers? Evidence from the

treatment of presumptive malaria in Nigeria. Tropical Medicine & International Health 2011;16:1087‐

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2003;361:2226–2234.

14. Rudan I, Boschi‐Pinto C, Biloglav Z, Mulhollandd K, Campbelle H. Epidemiology and etiology of childhood pneumonia. Bulletin of the World Health Organization 2008;86:408–416

15. Kerber KJ, de Graft‐Johnson JE, Bhutta ZA, Okong P, Starrs A, et al. Continuum of care for maternal,

newborn, and child health: from slogan to service delivery. Lancet 2007;370:1358–1369 16. Serina P, Riley I, Stewart A, Flaxman AD, Lozano R. A shortened verbal autopsy instrument for use in

routine mortality surveillance systems BMC Medicine 2015;13:302.

17. Rambaud‐Althaus C, Althaus F, Genton B, D'Acremont V. Clinical features for diagnosis of pneumonia in children younger than 5 years: a systematic review and meta‐analysis. Lancet Infectious Diseases

2015;15:439‐50.

18. Qazi S, Were W. Improving diagnosis of childhood pneumonia. Lancet Infectious Diseases 2015;15: 372‐373.

19. Global Alliance for Vaccines and Immunizations (GAVI). Saving children’s lives and protecting people’s

health by increasing access to immunization in poor countries. The 2014 annual progress report. Available at: http://www.gavi.org/progress‐report, 2015.

20. Usuf E, Bottomley C, Adegbola RA, Hall A. Pneumococcal carriage in sub‐Saharan Africa—A systematic

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challenges to scaling up mHealth in South Africa. BMC Medical Informatics and Decision Making

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23. Chib A, Wilkin H, Hoefman B. Vulnerabilities in mHealth implementation: a Ugandan HIV/AIDS SMS

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Summary

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Summary / Samenvatting

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SUMMARY

Despite an increase in coverage of effective interventions to improve child health

outcomes, millions of children still die before their fifth birthday each year. Children

living in sub‐Saharan Africa are most likely to die, mainly due to infectious diseases ‐ of

which most are attributed to pneumonia. With health outcomes most adversely

affected by a delay in treatment, as described in Chapter 1, the aim of this thesis is to

better understand what needs to be done to ensure timely access to adequate care for

children with symptoms of pneumonia. Firstly, by examining the causes of delays based

on three phases: the delay in deciding to seek care (phase 1), and the delay in reaching

(phase 2) and receiving (phase 3) care. Secondly, this thesis examines what the

potential is of mHealth strategies to overcome these delays.

The three phases of delay in care

While there are a lot of factors which influence if a caregiver seeks care, we only

examined the association between knowledge and care seeking. While pneumonia is

the main cause of childhood mortality, Chapter 2 shows that only 30% of the caregivers

living in sub‐Saharan African are aware of its symptoms (i.e. fast or difficulty in

breathing). This chapter also shows that, the caregivers who are aware of these

symptoms are not necessarily more likely to seek care. This illustrates the complexities

surrounding care seeking, as it is interlinked to various aspects (including ability to

recognize symptoms, empowerment, distance, etc.) and for that reason, knowledge of

symptoms alone is insufficient to address the delay in deciding to seek care (phase 1).

Linked to this, Chapter 3 illustrates that care seeking behaviour varies widely across

high pneumonia mortality countries. For example, in Tanzania 85% of the children with

symptoms of pneumonia are taken to a care provider, whereas this is less than 30% in

Ethiopia. Despite the majority attending to primary health care facilities, many

caregivers visit ‘non‐appropriate’ (or unauthorized) services, such as private pharmacies

and traditional practitioners ‐ especially in Democratic Republic of Congo (DRC) and

Nigeria. This chapter also illustrates that ‐ with an exception for DRC and Uganda ‐

caregivers from poor households are more often delayed in reaching care (phase 2), as

compared to those from wealthier households. Our analyses illustrate that care seeking

is lower in rural settings, which may (partly) be explained by lower education and

income levels of those living in these areas. Finally, to ensure that children with

symptoms of pneumonia receive adequate care (phase 3), especially in rural areas,

governments ‐ in collaboration with partners ‐ train community health workers (CHWs)

to assess, classify and treat these children. However, Chapter 4 shows that these health

workers are often challenged in counting and determining how the respiratory rate

relates to the age‐specific cut‐off points. More specifically, Save the Children found that

the CHWs had limited numeracy, and were not able to count beyond 10. For these

reasons, various organizations tested the utility of counting beads to help overcome the

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challenges these health workers face while determining if a child has fast breathing or

not. We found that beads enable and improve the assessment and classification of

breathing rates for illiterate CHWs. This study illustrates that there is an urgent need to

better equip (community) health workers, especially those working in remote settings

with high childhood mortality rates.

A potential solution to decrease delays

To decrease delays in accessing care, Chapter 5 of this thesis shows that mobile

phones, as a way to strengthen communication systems, have a lot of potential. The

literature review illustrates that mobile phones are mainly used to reach and receive

care in a timelier manner, this by coordinating referrals and supervision. While access

to communication is one of the essential components to improve health services,

fundamental challenges associated with care remain, e.g., gender discrepancies,

organizational hierarchies, and remoteness. Therefore, while it is evident that

connecting health workers within the health system is crucial, robust evidence on

constraints and the impact of mHealth projects is limited. To build on the evidence

base, Chapter 6 evaluates the impact of a toll‐free hotline and mobile messaging

service on care seeking behaviour. Due to low uptake of the services, the impact could

only be measured by assessing the effect of the service on those who actually used it,

rather than comparing the differences between intervention and control area. The

assessment shows an increase in facility‐based care for maternal health, yet an overall

decrease of facility‐based care for children. For children with symptoms of pneumonia,

there was no observed difference. One of the possible explanations could be that –

after consulting with a health worker by phone – caregivers were able to treat the child

at home and did not require a visit to a facility. Finally, as the interest in Short Message

Services (or SMS) is increasing, we assessed the potential benefits and limitation of this

specific technology in improving three projects. The assessment is based on a

conceptual framework which assesses the technical complexities of programs. We

assessed the complexities of the programs before and after adding the SMS

component. Chapter 7 shows that despite the potential of mHealth projects, the

primary implementation challenges for the existing programs can be thus complex that

mHealth – or more specifically the use of Short Message Services (SMS) – may not

necessarily be the best solution. In addition, there is a challenge that the technology

itself may introduce new layers of complexities.

Finally, the discussion presented in Chapter 8 aims to address the delays and potential

of mHealth, based on the individual studies presented in this thesis. The discussion

focusses on the importance of utilizing existing data, as well as the context specific

challenges in accessing adequate care. Lastly, while communication means are needed

to overcome delays, it highlights the need to assess how adding a technological layer is

going to affect the intervention complexity of the existing program.

139

List of publications

Chapter 9

140

List of publications

141

LIST OF PUBLICATIONS

Part of dissertation

Noordam AC, Kuepper BM, Stekelenburg J, and Milen A. Improvement of maternal

health services through the use of mobile phones. Trop Med Int Health

2011;16:622–626.

Noordam AC, George A, Sharkey AB, Jafarli A, Bakshi SS, and Kim JC. Assessing scale up

of mHealth innovations based on intervention complexity: Two case studies of

child health programmes in Malawi and Zambia. J Health Commun 2015;0: 1–11.

Noordam AC, Barberá Laínez Y, Sadruddin S, van Heck PM, Chono AO, Acaye GL, Lara V,

Nanyonjo A, Ocan C, and Kallander K. The use of counting beads to improve the

classification of fast breathing in low‐resource settings: a multi‐country review.

Health Policy and Planning 2015;30:696–704.

Higgins‐Steele A, Noordam AC, Crawford J, and Fotso JC. Improving care‐seeking for

facility‐based health services in a rural, resource‐limited setting: Effects and

potential of an mHealth project. African Population Studies 2015;29:1634‐1654.

Noordam AC, Carvajal‐Velez L, Sharkey AB, Young M, and Cals JWL. Care Seeking

Behaviour for Children with Suspected Pneumonia in Countries in Sub‐Saharan

Africa with High Pneumonia Mortality. PLoS One 2015;10:e0117919.

Noordam AC, Sharkey AB, Hinssen P, Dinant GJ, Cals JWL. Associations between

Knowledge and Care Seeking Behaviour for Children with symptoms of

Pneumonia in six sub‐Saharan African Countries. Submitted.

Additional writing activities

Palmer AC, Diaz T, Noordam AC. and Dalmiya, N. Evolution of the child health day

strategy for the integrated delivery of child health and nutrition services. Food

and Nutrition Bulletin 2013;34:412‐419.

Fotso JC, Robinson AL, Noordam AC, and Crawford J. Fostering the use of quasi‐

experimental designs for evaluating public health interventions: insights from an

mHealth project in Malawi. African Population Studies 2015;29:1597‐1615.

Chapter 9

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