An Assessment of the Quality of Neonatal MortalityData

Margaret Mambori Mwaila Q50/76335/2009

University of NAIROBI Library

0435762 0

A Project submitted to the Population Studies and Research Institute in Partial Fulfilment for the award

of a Degree of Master of Arts at the University ofNairobi

DECLARATION

This research proposal is my original work and to the best of my knowledge has not been presented for a degree in any other university:

Name

Margaret M Mwaila

Signature Date

This research proposal has been submitted for examination with our approval as University Supervisors:

Supervisors Signature Date

Dr Alfred A Otieno

Mr Ben Obonyo Jarabi oU fj

DEDICATION

To: Karanja, Nyambura and MutwaWithout your unwavering support, this far I would not have come. Love.

ACKNOWLEDGEMENT

Foremost, I take this opportunity to sincerely thank my supervisors, Dr Alfred A Otieno and Mr

Ben O Jarabi for their assiduous effort in supporting and guiding my thought process and all the

work put forth in this study.

Second, all my lecturers through whose hands, I have been remodelled to view development

from the population and social perspective. My gratitude goes to Prof. John Oucho, Dr Lawrence

Ikamari (for the stewardship of the Institute and wise counsel), Dr Kimani Murungaru, Dr

Wanjiru Gichuhi (for the headstart in reviewing literature), Dr Anne Khasakhala, Mr George

Odipo and Mr Andrew Mutuku. Through the guidance of Prof. Oucho, the class was able to

work on a model national policy and programme - to be published. The work earned the class a

presentation at the UNFPA office in Gigiri.

This report will be incomplete without my acknowledging my colleagues - the Class of 2009!

There were moments of stress, but never falling short of comics and opportunities for a good

laugh! Victor Achieng will forever remain etched on our minds for providing unlimited reading

resources (5-10 pages of references even for a two-page writeup!); our very own ‘beloved Prof

Ohon for the sweeping statements - e.g. “the isomorphism of population landscapes, contours

and topologies....”. But that was not all about Ohon, he earned the title ‘Prof from being too

philosophical. Wait until he makes a presentation! Of course, Annette Aduda who ensured there

was a pinch of laughter even in the most absurd of situations and a good story to accompany her

reason for being late for anything. Gilbert Omedi and Sarafina Wanja (watu wa Uthiru) were

always very patient and willing to assist - I am ever so grateful for the support they gave me.

Oh! You could always tell when Inviolata Njeri was stressed (especially during exams) - she

would dance! Joseph Murage provided the IT support and fatherly care. Bernard Nyauchi and

Victor Esendi were always at hand to help. You could easily miss out Benjamin Kitavi - always

so calm and collected. Once he intimated to me, “Na Wanja anapenda kuongea” (Wanja likes

talking). And to complete the picture, I would like to thank Florence Rwamba, Faith Gichanga

and Lucy Njue.

IV

ABSTRACT

Although there has been a reduction in childhood mortality globally, sub Saharan Africa has

realized a very slow improvement. Neonatal mortality in Kenya has shown very little

improvement over the 20 year period. Over the years, health statistics measurement is gaining

currency in terms of reporting returns on investments, impact of interventions and planning.

Major challenges with regard to data quality still remain: first, lack of complete and function

vital registration system which ensures up to date collection of data. Second, due to cultural

attachments and ignorance of legal requirements, early neonatal deaths may go unreported. Total

omission of births and deaths of neonates is definite given the positive correlation between death

of a mother and her child which leads to underestimation of child mortality. Age heaping at day

7 and 14 are commonplace. This is especially important as it distorts estimates of early neonate

deaths. Generally, errors in non-reporting of neonate deaths have serious ramifications with

regard to socio-economic and development planning.

The main objective of this study is to assess the quality of neonatal mortality data from all

KDHS data (1989 - 2008-09) and document impacts and improvements realised. This study will

focus on errors in reporting neonate births and deaths, omissions of neonatal deaths,

displacement of events in time and documentation of trends over the study period.

The sample size comprises 39,560 respondents experiencing 28,654 births and 830 neonate

deaths. The information is compiled from responses from women or reproductive age

experiencing an event over a five-year period prior to a KDHS. Use was made of descriptive

statistics to show associations and differentials.

Using data from five year periods preceding a subsequent survey, this study found that

completeness of information on neonate births and deaths has been vibrant (over 95% reporting).

However, births are more likely to be reported than deaths. Sex ratios at death seem to be over­

reported especially for male neonates. Heaping at days 7 and 14 is evident while deaths are

generally misreported.

To achieve MDG 4 and indeed to ensure quality neonate data, Kenya needs to invest most

appropriately in quality data collection systems in order to inform interventions aimed at

reducing reporting error.

V

TABLE OF CONTENTS

DeclarationDedication

AcknowledgementsAbstract

List of Tables ...List of Figures...1. Introduction

1.1 Background1.2 Problem Statement1.3 Research Question

1.4 Objectives of Study

1.5 Study Rationale

1.6 Scope and Limitations of the Study2. Literature Review

2.1 Introduction

2.2 The Case of Neonatal Mortality in Kenya2.3 Data Quality Challenges ...

2.4 Age Pattern of Neonatal Deaths ...3. Methodology

3.1 Introduction

3.2 Data Source

3.3 Methods of Data Analysis

3.4 Description of Variables ...3.5 Displacement of Events in Time ...

3.6 Misreported Age at Death4. Data Quality Assessment

4.1 Introduction

4.2 Completeness of Information on Births and Deaths4.3 Omissions of Deaths

4.4 Displacement of Events in Time ...4.5 Misreported Age at Death

Pageiiiii

ivv

viiiix

12

3

3

34

6

7

79

10

10

10

11

13

15

16 16 17 20

21

VI

5. Data Quality Differentials

5.1 Introduction ... ... ... ... ... ... ... ... 235.2 Completeness of Information Regional Differentials ... ... ... 23

5.3 Ethnicity and Sex Ratio Differentials ... ... ... ... ... 23

6. Conclusions and Recommendations

6.1 Conclusions ... ... ... ... ... ... ... ... 25

6.2 Recommendations ... ... ... ... ... ... ... 25References ... ... ... ... ... ... ... ... ... ... 26

\

V II

LIST OF FIGURES

Page

Figure 2.1: Trends in Neonate Deaths, 1989-2008-09 ... ... ... ... 7

Figure 4.1: Proportion of Completeness of Information on Month and Yearof Neonate Births and Deaths, 1989 - 2008-09 ... ... ... ... 17

Figure 4.2: The Sex Ratios at Birth and Neonate Deaths ... ... ... ... 20

Figure 4.3: The Heaping Ratio, 1989 - 2008-09 ... ... ... ... ... 21

ix

1. INTRODUCTION

1.1 Background

Neonatal deaths refer to deaths of babies within the first 28 days after a live birth. Neonatal

mortality is the death of a product of a live birth within 28 days of life. Early neonatal

mortality, on the other hand, refers to death of newborns aged 0 to 6 days among born alive

infants (KNBS and ICF Macro, 2010; WHO, 2006; CDC, 2002).

Globally, more than 4 million neonates die out of the 130 million babies born annually. 99

percent of these deaths occur in developing countries. On average, developing countries

record a neonate mortality rate of 39 deaths per 1000 live births (WHO, 2006). Neonatal

deaths account for over 40 percent of all childhood deaths, globally, yet historically, neonatal

mortality has received limited attention (Oestergaard, et al, 2011). “Neonatal mortality is

increasingly important because the proportion of under-five deaths that occur during the

neonatal period is increasing as under-five mortality declines’' (UNICEF, 2011).

In Kenya, most of the deaths are experienced within less than 24 hours upon a live birth.

KNBS (2010) observes that “high maternal and neonatal deaths occur during the first 48

hours after delivery” also observed by UNDP (2010). A neonatal and perinatal mortality

study done in Kilifi district in the Coast Province of Kenya using 2004-05 Demographic

Surveillance Systems (DSS) data found that neonatal death was the highest contributor to

infant deaths (65 percent) (Bauni, et al, 2005). A situation acknowledged by the United

Nations Millennium Development Goals (MDGs) report of 2010 for Kenya.

Mortality is an important demographic variable required for development planning.

Specifically, child mortality is an important indicator of the impact of child survival

interventions and broadly the socio-economic development of a country (UNICEF, 2011). In

recent times, there has been a growing need for implementers and donors to show returns on

investments, track progress/improvements and overall planning. All these require quality

health statistics, therefore the need to set up health information management and vital

registration systems.

Data quality, is an integral component for ensuring proper estimates of mortality are made.

Data quality can almost be assured in countries with functional vital registration systems.

However, developing countries rely more on indirect methods to derive mortality estimates

(US Census Bureau, 2011; WHO, 2005). “The reliability of the neonatal mortality estimates

depends on accuracy and completeness of reporting and recording of births and deaths.

Underreporting and misclassification are common, especially for deaths occurring early on in

life” (WHO, 2006). Under reporting, especially in countries recording high out of hospital

childbirths, has been noted (Global Health Action, 2011).

African population censuses and surveys are bedevilled by age measurement, not just in

terms of vagueness or digit preference, but also due to “complete ignorance” (Magadi, 1990).

Magadi (1990) further notes that age misreporting and variations in coverage distort the

relation between two age distributions leading to biased estimates.

In her study to estimate the age distribution, census coverage and death registration

completeness in Kenya, Magadi (1990) conceded that data quality is influenced by

demographic, environmental, socio-economic and cultural factors. And errors in data affect

“demographic estimates derived from use of direct or indirect estimation techniques”. The

derivation of estimates using wrong data causes problems when used to model intervention

because such interventions may fail to yield impact. Additionally, Wamai (2004) noted that

poor data quality leads to poor estimates that affect socio-economic planning, health

interventions (with impact on fertility and mortality, sex ratio, planning, etc), in effect a

country’s development.

1.2 Problem Statement

In Kenya, neonate deaths make a significant contribution to infant mortality (60%) compared

to 40 percent globally (UNICEF, 2009; WHO, 2006; WHO, 2009; KNBS and ICF Macro,

2010; UN, 2010; Oestergaard, et al, 2011). In spite of improvements in child health, early

neonatal mortality has been rising over the years contributing to more than three quarters

(82%) of all deaths experienced at infancy in Kenya (KNBS and ICF Macro, 2010). Yet a lot

of emphasis has been laid on studies and programming around infant and child mortality with

little or no consideration of neonate deaths (Oestergaard, et al, 2011).

Functional vital registration systems and Health Management Information Systems (HMIS)

are the best sources of data needed for direct estimations of neonatal mortality. However, due

to their non-existence or dysfunction over the years, the demographic and health surveys are

becoming the major source of data for estimation of vital events in developing countries

(Lancet, 2010; Oestergaard, et al, 2011). Additionally, while the UN interagency Group for

Child mortality, makes annual estimates of infant and under five mortality, it has never done

for neonatal mortality (Oestergaard, et al, 2011) therefore, the need to check the quality of

such data. Despite numerous studies indicating uncertainty with regard to data quality, little

attempt has been made at assessing the data to detect and innovate means of reducing such

errors. It then follows that the quality of neonatal mortality data cannot be assured. This

study attempts to assess data quality of neonatal data collected over five DHS’ (1989 till

2009).

1.3 Research Question

What is the extent of errors in neonatal mortality data and what are the implications of such

error in estimating neonatal mortality.

1.4 Objectives of the Study

This study aims at:

■ Assessing the quality of all KDHS data (1989 - 2008-09) and documenting

improvements realised in neonatal mortality data quality

Specifically, the study will:

a. Assess completeness of information on neonate births and deaths

b. Assess the extent of omissions of neonatal deaths

c. Assess the extent of displacement of events in time

d. Assess the extent of misreporting of age at death

e. Establish completeness of information on neonate births and deaths and

f. Sex ratio at birth and ethnic group differentials

1.5 Study Rationale

As global infant and under-five mortality begin to decline, neonatal mortality emerges as an

increasingly crucial component of overall under-five mortality thus, receiving additional

attention. Therefore, information on neonatal mortality at international level is in great

demand (WHO, 2006). Again, government, policy makers and programme implementers

need reliable data to model interventions and make crucial investment decisions.

Hall (2005) notes a number of challenges developing countries face with regard to making

accurate measurement of neonatal mortality. Among these are: incomplete or non-vibrant to

non-existent vital registration system, home deliveries not captured by the health

information system, lack of common understanding and policies on death classification

between neonatal and stillbirth. The extreme can be misreporting by healthcare providers - a

public relations gimmick to attract clientele.

It is important to carry out a data quality check to ensure that errors are not the culprit in the

“unstable” trend that is emerging in Kenya’s neonatal mortality. While studying the stall in

mortality decline in Ghana, Johnson, et al (2005) noted the importance of assessing data

quality before concluding on the validity of mortality rates - the report noted: data changes

over time and sampling variability are important aspects requiring quality checks. Period

mortality rates (such as those collected by DHS’) can be affected by the following reporting

errors: incomplete information on the date of birth or death, omissions of births and deaths,

displacement of events in time and age at death misreporting. Sullivan (2008), while

assessing child mortality estimates and credibility of mortality declines using 22 DHS’,

noted that the most important quality errors of DHS birth histories are: those in recorded

dates of birth, underreported deaths, sampling errors and misreporting of age at death.

This study lays emphasis on the importance of data quality in coming up with good estimates

in order to model impactful interventions aimed at reducing neonate and childhood mortality.

Such a study has not been carried out in Kenya before.

1.6 Scope and Limitations of Study

This study limits its scope to the data quality assessment in neonatal mortality in Kenya.

Given that Kenya lacks a functional vital registration system, reporting on deaths of children,

especially those who die at infancy can be riddled with “socio-cultural beliefs and taboos”

(Muganzi in Oucho, et al, 2000; Warnai, 2004; Magadi, 1990). Further, Muganzi brought to

the fore the retrospective nature of the DHS which opens it to age heaping and under

reporting (especially in the case of maternal and neonate death). Such retrospective nature

also makes the DHS susceptible to misreporting, especially for neonatal mortality given

memory recall lapse (Brass, 1968; Kpedekpo, 1977 in Kenyi, 1993). For these reasons,

Muganzi in Oucho, et al (2000) cautioned researchers to bear such errors in mind during data

interpretation and estimates derivation.

The KDHS is a sample survey whose results are generalised to the whole population. A case

is noted of North Eastern region where data was collected only in Garissa town, whose

population can hardly offer a true reflection of the greater pastoralist population of the region

yet that was the case (coverage error). Until 2003, earlier DHS’ have excluded the North

Eastern province despite its unique characteristics.

Use of secondary data is usually a limitation in itself given that the data cannot be specific to

any other study apart from the one data was collected for. Elowever, this study takes

advantage of the consistency of the KDHS data to document data quality errors and show

improvements of the same over the years.

2. LITERATURE REVIEW

2.1 Introduction

This chapter presents the desk review on neonatal mortality especially, its concomitant data

quality issues. Apart from briefly providing the topology of neonatal mortality in Kenya, this

chapter discusses data quality challenges and the age structure of neonate deaths.

According to the 2010 Millennium Development Goals (MDGs) Progress Report, the highest

rates of child mortality were experienced in sub-Saharan Africa. In 2008, the region

contributed 33 out of the 34 countries with high under-five mortality rate (over 100 deaths

per 1000 live births).

UNICEF (2011) notes a global increase of 10 percent in neonatal deaths between 1990 and

2010. The risk of neonatal death is six times higher in developing countries compared to

developed countries with sub Saharan Africa bearing the highest risk and making the least

progress (UNICEF, 2011). While sub-Saharan Africa bears the highest burden with 41

neonatal deaths per 1000 live births (WHO, 2006), a developed country such as Sweden has

a neonatal mortality of 2-3 per 1000 live births. As Kenya grapples with 82 percent early

neonate deaths (KNBS, 2010), Singapore boasts of 0 deaths of neonates (Global Health

Action 2011). Developing countries are home to 99 percent of the four million neonatal

deaths experienced annually at the global level. Moreover, of these, three million are early

neonate deaths (WHO, 2006; Lancet, 2005).

The WHO defines a live birth as

the complete expulsion or extraction from its mother o f a product o f conception, irrespective o f the duration o f the pregnancy, which, after such separation, breathes or shows any other evidence o f life — e.g. beating o f the heart, pulsation o f the umbilical cord or definite movement o f voluntary muscles - whether or not the umbilical cord has been cut or the placenta is attached. Each product o f such a birth is considered live born

2.2 The Case of Neonatal Mortality in Kenya

Neonatal deaths have not significantly dropped in the past 20 years. Figure 1 below traces the

trend in neonate mortality over the 20 year period (KDHS history). In 1989, there were 204

neonate deaths. These reduced to 157 by 1993 and further to 98 by 1998. However, the 2003

KDHS observed an increase to 196 which reduced to 175 by 2008-09 - realising only an 11

percent reduction. Using a line of best fit, it can be observed that neonate deaths reduced

from 204 to 175 over the 20 year period. The trend is unpredictable as it is unstable.

Figure 2.1: Trends in Neonate Deaths, 1989-2008-09

2.3 Data Quality Challenges

One important objective of the recently launched National Road Map for Maternal and

Newborn Health (MNH) MoPHS (2010) is “to strengthen data management and utilisation

for improved MNH”. This will help improve reporting of neonatal mortality thereby improve

data quality.

Numerous studies across the world have noted challenges in data quality. UNICEF (2011)

notes a profound challenge with regard to developing countries’ lack of complete vital

registration systems and health management information systems - both of which are

valuable in generating accurate child mortality estimates. Magadi (1990) pointed out that

African population censuses and surveys are bedevilled by errors due to illiteracy. Among

the problems noted were lack of records (such as birth and death certificates) and societal

attachments to such events. Kichamu (1986), in his thesis on Mortality Estimation in Kenya

with a special Case Study of Vital Registration in Central Province, observed that censuses

and vital registration data reported errors and biases. For example, he noted that it is common

practice to underreport dead children and infants, include stillbirths in number of dead

children (also noted by WHO, 2006), underreport children ever born and ages of children and

mothers. Magadi (1990) also conceded that age reporting, especially age heaping or digit

preference and systematic transfer of ages across age boundaries are common sources of

errors. Hill and Choi (2006), in their study of Neonatal Mortality in the Developing World,

conceded that there are two types of errors that could have profound impacts on early

neonatal and overall neonatal mortality measurements: first, “omissions of live births that die

in the first few days of life” and second, heaping of deaths at day seven.

Omission of child deaths in the birth history is a serious response error for the calculation of

childhood mortality rates. Such error may be deliberate or due to problems of recall. This

problem is common in neonate deaths especially early neonatal deaths. It leads to under­

estimation of neonate mortality and distorts age patterns of mortality (Curtis, 1995).

Curtis (1995) pointed to two critical structural biases in neonatal mortality data collection:

one, selection bias where only surviving women are surveyed. This locks out child mortality

experiences of women who died during the study period. As noted by Curtis (1995), this bias

reduces estimation of neonate, infant and child mortality, especially given the premise that

death of a mother increases the risk of child death. The magnitude of selection bias can be

deepened by HIV/AIDS deaths and longer periods between surveys. According to WHO

(2006) in high mortality countries, maternal deaths lead to 5 percent of neonate death under­

reporting. Second, demographic and health surveys target women up to 49 years at the time

of the survey. This means that neonate deaths are reported only for women aged 44 and

below at the 5 year study period. On a similar point, Garenne (2003), while reviewing survey

data to study sex ratios in African populations noted that due to targeting of women of

reproductive age (15-49 years), there tends to be over-representation of young women - a

bias in itself.

Hall (2005) noted the following challenges that developing countries face in making accurate

measurements of neonatal mortality: incomplete to non-existence thereof of vital registration

systems, misunderstanding and misclassification of death between stillbirth and neonate (also

noted by Kichamu (1986); Aleshina, et al (2003); Sullivan, (2008) and WHO (2006)) and

deliberate misreporting of death by healthcare workers (also noted by Mwale (undated) while

analysing DHS data in Malawi). Ignorance of the legal requirements for reporting births and

deaths may also lead to underreporting.

2.4 Age Pattern of Neonate Deaths

Neonatal mortality records sharp declines during early neonate and neonate stages then

continue to decline gradually through late infancy and early childhood. Underreporting of

early childhood deaths in retrospective years leads to abnormally low neonate to infant

mortality ratio. In a study by Curtis (1995), Kenya recorded an unusually low neonate to

infant mortality ratio since 1989. Table 2.1 below adds credence to the above observation.

Table 2.1: Age/Sex Pattern of Neonate Deaths, 1989 - 2008-09

Age at Death (Days)

1989 1993 1998 2003 2008-09Sex of child Sex of child Sex of child Sex of child Sex of child

TotalMale Female Total Male Female Total Male Female Total Male Female Total Male Female

0-6 87 54 141 58 53 111 43 34 77 94 67 161 90 58 1487-14 27 25 52 21 18 39 8 9 17 14 9 23 12 9 2115-21 6 2 8 3 1 4 1 0 1 2 3 5 3 1 422-28 2 1 3 1 2 3 3 0 3 2 5 7 1 1 2Total 122 82 204 83 74 157 55 43 98 112 84 196 106 69 175

Source: Computed from 1989- 2008-09 DHS Datasets

This study will analyse data collected by the Kenya Demographic and Health Surveys

(KDHS) since 1989 when the first KDHS was conducted. The Kenya Demographic and

Health Surveys are five-year period sample surveys. That is, they collect, analyse and report

demographic and health histories at five year intervals. They are important because they

provide estimates and other information relevant for crafting interventions, decisions,

approaches, strategies and policies important for development. In recent years, the KDHS has

been acknowledged at the major source of baseline data for other studies in population and

health.

This study is especially interested in the following questions asked of respondents about their

newborns: sex of child, month and year of birth and age at death (in days). The responses are

crucial for the estimation of neonatal mortality, calculation of sex ratios as well as

ascertaining completeness of information on neonate births and deaths. The variable on

region of residence is added to help come up with the regional differentials as concerns the

quality of neonatal mortality data.

3. METHODOLOGY

3.1 Introduction

This section discusses the data source and methods of data analysis to be used. SPSS was

used to run frequencies to confirm the data quality. Cross tabulations and frequencies were

used for analysis.

3.2 Data Source

This study used secondary data from the Kenya Demographic and Health Surveys (KDHS’)

from 1989 to 2008-09. The study will use the child files of the five DHS’ (covering 1989

through to 2008-09). The sample size comprises 39,560 respondents experiencing 28,654

births and 830 neonate deaths. The information is compiled from responses from women of

reproductive age (15-49) experiencing an event during a five-year period prior to a KDHS.

3.3 Methods of Data Analysis

This study uses neonate deaths as the unit of analysis. The KDHS uses an already existing

sampling frame constructed by the Kenya National Bureau of Statistics (KNBS) known as

the National Sample Survey and Evaluation Programme (NASSEP). NASSEP uses a two-

stage sampling design: one involves selection of data clusters from the national master

sample frame and the other involves the systematic sampling of households using an updated

list of households. KDHS collects data from 10,000 households (KNBS and ICF Macro,

2010).

Descriptive statistics (frequencies and cross tabulations) have been used to bring out the

association and relationship/differentials in the background characteristics under study over a

20 year period.

Cross tabulations are good in establishing relationships between independent variables. They

help make a quick comparison between variables and are therefore most appropriate to

analyse and illustrate distributions and relationships (Misati, 2003). They are also useful in

establishing trends and differentials over periods of time, which this study seeks to do.

li

3.4 Description of Variables

According to Sullivan (2008), there are four types of quality concerns in mortality data

collected by DHS. These are: “errors in the recorded dates of birth of children,

underreporting of deceased children, sampling problems (e.g. unrepresentativeness of the

selected sample) and misreporting of age at death”. However, this study will not look at

sampling problems as these were noted earlier for North Eastern region. Additionally, issues

related to sampling frames are beyond the scope of this study.

This section gives a description of the variables used in the study to assess errors in Kenya's

neonatal mortality data such as: completeness of information on births and deaths, omissions

of deaths, displacement of events in time and misreported age at death. It also further

explains how the variables were measured and their importance in detecting data quality.

3.4.1 Age at Neonate Death

The variable was recoded to provide information on age at death of neonates from 0 to 28

days. Through recoding it was possible to identify early neonate and neonate deaths. Age at

death is the base variable for estimations of neonatal mortality, sex ratio at death, comparing

early neonate, neonate and infant mortality. All the above measurements have been used to

assess data quality of neonatal mortality.

3.4.2 Completeness of Information on Births and Deaths

Data reliability depends a lot on reliable reporting and recording of births and deaths both by

a mother and the available statistical recording system (WHO, 2006).

The DHS collects information of when births and deaths occur. The questionnaire asks

respondents to give information on month and year of such events for completeness of

records. However, given the retrospective nature of the DHS, it is almost impossible for a

woman to have information on the timing of birth and/or death events in as much as she may

have a recollection of such events. As noted by Magadi (1990), lack of record keeping (in

terms of birth and death certificates) may make recall even more difficult.

According to Becker (2005), Johnson, et al (2005) month of birth is the most likely to be

missing. However, if the year is correctly reported, “imputing a month makes virtually no

difference in mortality estimates for multi-year periods” (Johnson, et al, 2005). The Kenyan

DHS’ combined month and year making the data more complete.

This variable is important for ensuring that events are captured as they have occurred. Any

error could lead to displacement of events in time or complete omissions of births and/or

deaths. Sullivan, et al (1990) noted a positive correlation between completeness of

information and women’s education.

3.4.3 Omissions of Death

These refer to complete exclusion of events from birth and death histories. Omissions of

deaths can be detected using three indirect approaches: first, by calculating the ratio of early

neonate mortality against all neonate mortality, second by getting the ratio of neonate to all

infant mortality and third, by using the sex ratios at birth and death.

3.4.3.1 Ratio of Early Neonate to Neonate Death

Early neonate death refers to deaths that occur within the first seven days after a live birth

while neonate deaths are those that happen within 28 days upon a live birth. The ratio is

calculated by dividing the total reported early neonate deaths in a given year against the total

reported neonate deaths in the same year multiplied by 100 (to be expressed as a proportion)

Sullivan, et al (1990) observed that generally, “approximately 70 percent of neonatal deaths

are expected to occur during the first seven days of life”. Should this not be the case, it could

be an indication of omissions of neonate deaths.

3.4.3.2 Ratio of Neonate to Infant Death

While neonate deaths are those deaths of babies occurring within 28 days upon a live birth,

infant deaths occur within 11 months after a live birth. This is presented as a proportion of

neonate deaths occurring in a given year over infant deaths experienced in the same year.

The ratio of neonatal mortality to infant mortality is expected to follow a steady increase

with declining overall mortality. Converse of such a trend could indicate underreporting. A

study by the Research Institute of Obstetrics and Pediatrics and Macro International Inc

(1998) observed that the ratio of neonatal mortality to infant mortality over a 10 year period

preceding the survey gives a good indicator of the quality of data. Kenya’s data for 1998

and 2008 shows a ratio of 56 which is considered vibrant. Low ratios of neonate to infant

mortality are an indication of misreporting (KNBS & ICF Macro, 2010).

3.4.3.3 Sex Ratios at Birth and Death

Sex ratio at birth is the ratio of the number of male births to those of females. Sex ratio at

birth is the most stable population parameter. According to Garenne (2003) the global sex

ratio at birth is approximately 1.055. Sex ratios may vary over time, however, sex

fluctuations from time to time occur only in cases of significant migrations. Sex ratio can be

expressed as a percentage. It is calculated by taking the total male births/deaths in a given

period against total female births/deaths in that period. The value is then multiplied by 100

to give a proportion.

The sex ratio is a tell-tale for omissions of deaths on the basis of sex. According to James

(1987) sex ratio at birth should be 105. And Johnson (2005), normal human sex ratio at birth

ranges between 103 and 107 males per 100 females. WHO (2006) agrees that more males a

re born compared with females. Therefore a sex ratio at birth should be 105 to 106 boys per

100 girls. Consequently, ratios significantly lower or higher than the given range suggest

misreporting. However, Garenne (2003) noted that sex ratios among Africans and especially

Bantu speaking ethnic groups are generally low. In a study of sex ratios in African

Populations, Garenne (2003) observed that in the United States of America (USA) and

Europe, African Americas and people with African origins experienced lower sex ratios

compared with those from other parts of the world. And in Africa, Bantu speaking

communities of East and Southern Africa were observed to have low sex ratios.

3.5 Displacement of Events in Time

Displacement of events in time refers to reporting events outside the time they actually

occurred.

As observed by Magadi (1990) and Kichamu (1986), there is usually a tendency to misreport

age especially due to ignorance. On the other hand, interviewers may choose to record births

outside the eligible time frame so as to escape the task of completing the long and tedious

questionnaire sections dealing with maternal and child health (Johnson, et al, 2005; WHO,

2006; Hill and Choi, 2006; Sullivan, 2008). Many DHS reports worldwide have reported

evidence of age shifting or transference to years outside the required cut-off year to avoid

administering the lengthy birth-history-related questions. (Sullivan, 2008; WHO, 2006).

13

The section also calls for probing in cases where the respondent does not have a recollection

of the time the event occurred (Becker and Suliman, 2005). This could lead to reporting

events outside the inter-DHS period.

Displacement of births beyond the reference period can be checked by obtaining the ratio of

all births (regardless of survival status) in year x and those before and after year x. The

formula to be applied: 100*(2Bx/(Bx.i+Bx+i)).

Where:

Bx = Births in period x

Bx+i= Births in the next period

Bx.i- Births in the previous period

Ratio should be close to 100; a value less than 100 is an indication of fewer births than

expected in year x (Johnson, et al, 2005).

Sullivan (2008) offers a methodology for testing the extent of age transference by using a

transference index. The index gives a “ratio of number of births in the year prior to the

health cut-off to the number in the year after the health cut-off.” A ratio significantly greater

than 1 indicates birth transference. However, for 5-year periods before a survey has potential

for a negative bias. Health cut-off date refers to the period usually “January of the fifth year

before the year of the interview” noted Sullivan (1990). At this cut-off period a respondent

is required to answer the lengthy health questionnaire should she have experienced any

demographic event.

According to Kichamu (1986) misreporting can cause displacement of deaths to either

earlier or later than the actual occurrence. This can lead to distortion of age pattern of death

and subsequent understatement of mortality and fertility rates (also noted by Republic of

Marshall Islands DHS, 2007 (EPPSO, 2007)). KNBS and ICF Macro (2010) note that

displacement of deaths distorts the mortality trend thus a potential data quality issue. A birth

transference has greater effect on neonate mortality because of its potential to transfer both

birth and death events “out of the last estimation period” (Sullivan, 2008).

3.6 Misreported Age at Death

“Heaping refers to the tendency for reports or measurements of interval-level variables to be

concentrated at certain values”, intimates Pullum (2008) in his assessment of quality of data

on health and nutrition in DHS. Age heaping refers to the preference for reporting deaths at

specific days. According to Hall (2005), Becker (2005) and Johnson, et al (2005), heaping

occurs at 7days, 30days, 12 and 24months. In their study to analyse the depth of heaping at

day 7, Hill and Choi (2004) (cited in Hall, 2005) found that 40 percent of neonate deaths

occurring between days four and nine were reported as day 7 deaths.

Using the Dx to represent the number of deaths at age x, the following formula is applied for

neonatal mortality:

3*Dx/(Dx_i+Dx+Dx+i)

Where Dx = deaths at day x

Dx_i = deaths on previous day

Dx+i = deaths on the next day

Johnson, et al (2005), explain that a heaping ratio can be calculated to estimate the

magnitude of the problem. A ratio close to 1 indicates data vibrancy.

Hill and Choi (2006) documented another method of quantifying the magnitude of heaping.

They proposed a “simple heaping index” by: Dy/i^Dj-Dg). The value should be close to 1 in

case of no error.

While heaping can have an effect on rates and/or percentages if the values occur at interval

boundaries, it may also cast doubts on the quality of the data collection process - especially

when it happens at variables such as “missing”, “don’t know” or “not stated”.

Heaping at 7 days affects the calculation of early neonatal mortality by underestimation. It

leads to lower estimation of early neonatal and perinatal mortality which further affects

policy and planning decisions (EPPSO, 2007). Deaths occurring later in the month or

between months could also be reported as infant deaths thereby interfering with infant

mortality estimates.

4. QUALITY OF NEONATAL DATA

4.1 Introduction

This chapter gives an analysis of errors in estimating neonatal mortality by methods of

detection.

An assessment of the quality of DHS data is necessary in order to detect possible estimation

errors occasioned by misreporting due to relapse and recall problems. The methodology used

in collecting DHS data actually opens it up for bias (e.g. in reporting neonate death by sex)

and incompleteness of information. DHS targets surviving women aged 15-49 . Especially

in countries ravaged by HIV and AIDS, this already excludes information on births and

deaths of neonates from women who are dead (Hill & Choi, 2006). Such births and deaths go

unreported. Another area of concern necessitating data quality assessment is the fact that the

targeted respondents exclude women who are over 49 years but have experienced births or

deaths of neonates (Mwale, undated). Women outside the age bracket may overstate or

underreport their age so as to participate in the study.

This study endeavours to use the following approaches to assess the data quality for all the

DHS’ ever done in Kenya:

4.2 Completeness of Information on Births and Deaths

Figure 4.1 below displays the completeness of information on neonate births and deaths. As

can be seen, information on births by month and year has been well documented and more

complete than that on neonate deaths. However, in 1998 and 2008-09, completeness of death

reporting was universal. In Kenya, completeness of reports by month and year has never

gone below 90 percent. The requirement for interviewers to check records such as birth

certificates and immunisation cards ensured completeness of reporting (Sullivan, et al,

1990). This could indicate some aspect of reliability. However, Curtis (1995) cautions that

completeness of information is not necessarily an indicator for data quality.

It

Figure 4.1: Proportion of completeness of information on month and year of neonate birthand death

Co’■MruE0H-c01 V Q.E0 u *-> c01

102100989694929088

86

1989 1993 1998 2003 2008-09

Births by Month and Year 98.5 97.3 99.6 99.8 99.9

— — Deaths by Month and Year 95.1 90.5 100 97.4 100

Source: Computed from 1989 - 2008-09 DHS Datasets

Similar to Curtis (1995), this study does not support the hypothesis of recall error - that

respondents are unlikely to remember events that occurred further in time. However, it is

clear that births are much remembered compared to deaths. This may also be due to cultural

beliefs. According to WHO (2006), it is acknowledged that many societies do not consider

neonate deaths a problem maybe because they happen too often. In fact, according to the

report, some cultures delay newborn naming ceremonies until such babies are beyond a

specific age. The same report acknowledges that births are more likely to be reported than

neonate deaths.

4.3 Omissions of Deaths

Omission of death refers to total lack of acknowledgement of a death event. In health

facilities, this may happen if the health workers report a live birth as ‘stillbirth’. Three

measurements are used to detect omissions of deaths: one early neonate to neonate ratio;

neonate to infant ratio and sex ratio both at birth and death.

^35)es_ '■ -H i r-is-

r ,

. J0\ w m t H 8 I T Y OF u im ii!

U 8 R A R V

i

17

4.3.1 Proportion of Early Neonate to Neonate Deaths

Table 4.1: Proportion of Early Neonate and Neonate Deaths, 1989 - 2008-09

DHSYear

EarlyNeonateDeath

NeonateDeath

Ratio(%)

1989 141 204 69.12

1993 111 157 70.70

1998 77 98 78.57

2003 161 196 82.14

2008-09 148 175 84.57

Source: Computed from 1989 - 2008-09KDHS Datasets

Overall mortality declines, especially those in neonate and infant, translate to increasing

early neonatal and neonatal ratio. A ratio around 70 percent is considered vibrant while

anything way beyond or below the mark can be considered misreporting. From Table 4.1

above, apart from 1989 when Kenya’s data did not meet the 70 percent threshold, Kenya

displays a trend of steady increase in the early neonate to all neonate ratio. The result is

palatable.

4.3.2 Proportion of Neonate to Infant Deaths

Table 4.2: Proportion of Neonate and Infant Deaths, 1989 - 2008-09

DHSYear

NeonatalDeaths

InfantDeaths

Ratio(%)

1989 204 487 41.91993 157 381 41.21998 98 237 41.42003 196 427 45.9

2008-09 175 326 53.7

Source: computed from 1989 - 2008-09 KDHS Datasets

Neonatal to infant mortality ratio is expected to increase against overall declines in

mortality. Table 4.2 above shows a rather stagnant ratio between 1989 and 1998. This could

be an indication of omissions in death reporting. A similar observation of omissions was

made by Curtis (1995) who noted: “...unusually low ratio of neonatal to infant mortality,

particularly in the periods 5-9 and 10-14 years” suggestive of omissions of neonatal deaths.

However, the ratio improved in 2003 making a steady increase from 46 percent to 54

percent by 2008-09.

4.3.3 Sex Ratios at Neonate Births and Deaths

Sex Ratio at Birth

Table 4.3: Kenya's Sex Ratio at Birth, 1989-2008-09

DHSYear Male Female Total

SexRatio(%)

1989 3491 3489 6980 100.11993 3054 3061 6115 99.81998 1779 1752 3531 101.52003 3015 2934 5949 102.8

2008-09 3134 2945 6079 106.4Source: Computed from 1989-2008-09 DHS Datasets

According to Johnson et al (2005), sex ratio at birth should range from 103 to 107 males per

100 females. Ranges significantly above or below the stated ratio indicate omissions. In the

case of Kenya (per Table 4.3 above), over-reporting of female births might have occurred

with the 1993 KDHS. However, the sex ratio is within the prescribed global range of 1.055.

Sex Ratio at Death

Sex ratio at death is expected to favour females since despite high male births, their survival

rate is usually lower especially at early neonate. Sullivan, et al (1990) noted that biological

factors predispose the male neonate to early death.

Table 4.4: Kenya’s Sex Ratio at Neonate Death, 1989 - 2008-09

DHSYear Male Female Total

SexRatio(%)

1989 122 82 204 148.81993 83 74 157 112.21998 55 43 98 127.92003 112 84 196 133.3

2008-09 106 69 175 153.6Source: Computed from 1989 - 2008-09 DHS Datasets

19

figure 4.2: Sex Ratios at Birth and Neonate Deaths, 1989 - 2008-09170.0

160.0

80.0 ----------------------------------------------------------------------------------------------------------------1989 1993 1998 2003 2008-09

" ♦ Sex Ratio at Birth (%) B Sex Ratio at Death (%)

Source: Computed from 1989 - 2008-09 DHS Datasets

Figure 4.2 above compares both the sex ratio at birth and that at neonate death. It is observed

that sex ratios at birth are almost accurately reported while at death, males seem to be over­

reported. At neonate stage, more females than males survive. According to demographic

studies, more males than females die within the first 7 and 28 days of life.

4.4 Displacement of Events in Time

Table 4.5: Birth Ratios, 1993 -2003

YearNo. o f B irths Ratio

1989 69801993 6115 116.41998 3531 58.52003 5949 123.8

2008-09 6079Source: Computed from 1989 - 2008-09 DHS Datasets

According to Johnson, et al (2005) the ratio of births should be close to 100. However, from

Table 4.5 above, only the 1993 ratio was close to 100 (116). While the ratio for 1998 of 58.5

indicates much fewer births than expected, 2003 is the converse. The study, therefore, notes

misreporting of births throughout the study period. This observation gives credence to

Magadi (1990) and Sullivan (2008) on their observation of transference of age across age

borders.

2 0

The birth transference index proposed by Sullivan (2008) is applicable in situations where

vital registration system can be used as a data source. Kenya’s vital registration system is

only approximately 50 percent functional therefore not able to provide previous and post

cut-off period data on births required to calculate the index.

4.5 Misreported Age at Death

As if to corroborate the case of heaping in Kenya’s data, Figure 4.3 below gives evidence of

heaping of age at death of neonates. The problem was at its height in 1989 (2.4) and lowest

in 2003 (approx.2). However, it increased to 2.1 in 2008-09. Heaping ratio should be 1,

therefore, there is misreporting of age at death.

Figure 4.3: The Heaping Ratio, 1989 - 2008-09

2.5 2.4

2.3 2.2 2.1

2 1.9 1.8

■ Heaping Ratio

Source: Computed from 1989 - 2008-09 KDHS Datasets

1989 1993 1998 2003 2008

2:

Hill and Choi (2006) proposed a simple heaping index to assess age heaping in neonatal

mortality having observed that most of the neonate deaths occurring in days 5 to 9 are

reported as day 7.

Table 4.6 below shows the heaping index. A ratio of 1 indicates no errors

Table 4.6: Heaping Index 1989 - 2008-09

DHSYear

HeapingIndex

1989 4.08

1993 3.95

1998 3.33

2003 2.37

2008-09 2.65Source: Computed from 1989 - 2008-09 KDHS Datasets

The index gives a ratio way beyond the stipulated threshold. Therefore, there is an indication of heaping.

E

5. DATA QUALITY DIFFERENTIALS

5.1 Introduction

This section intends to note regional differentials in neonatal mortality estimates. This

information will be critical in informing health interventions while noting challenges on the

ground. Specifically, the study will assess differentials in terms of completeness of

information to take note of which regions have improved in this area. Another point for

differential is Garenne’s hypothesis regarding sex ratios and ethnicity.

5.2 Completeness of Information Regional Differentials

Table 5.1: Proportion of Completeness of Information by Month and Year, 1989 - 2008-09

%Completeness of Information by Month and Year

Region 1989 1993 1998 2003 2008-9

Nairobi 99.4 98.0 99.2 99.6 100Central 98.1 99.4 98.9 99.9 99.8Coast 97.3 97.6 99.8 99.3 100Eastern 98.6 97.8 99.8 100.0 100Nyanza 98.8 98.0 99.5 100.0 100Rift Valley 99.0 94.6 99.3 99.8 100Western 98.4 98.3 100 100.0 100

North Eastern - - - 100.0 100

Source: Computed from 1989 - 2008-09 DHS Datasets

Table 5.1 observes that the proportion of completeness of information by month and year in

Kenya is very high (99.2% on average) with the 2008-09 KDHS reporting 100 percent

completeness. On average, all regions reported information completeness above 99.2 percent

except Coast and Rift Valley reporting 98.8 percent and 98.5 percent, respectively. Central

is the only region that did not record a 100 percent completeness in 2008-09.

5.3 Ethnicity and Sex Ratio Differentials

Following on the hypothesis by Garenne (2003) that populations of African origin,

especially those of Bantu ethnic origin have low sex ratios at birth, this study sought to

check the sex ratio at birth by ethnicity. From Table 5.2 below, the major Bantu speaking

ethnic groups in Kenya are Kamba, Kikuyu, Kisii, Luhya, Meru and Embu, Mijikenda and

Swahili and finally the Taita and Taveta. Considering the 103 - 107 sex ratios at birth, it can

be observed from Table 5.2 below that Bantu speaking groups display low sex ratios. They

have more females than males at birth. Over-reporting of male births can be observed among

the Meru/Embu in 2003 (170.6), the Somali in 1989 and 2008-09 (133) while serious under

reporting occurred among the Somali in 1993 posting a ratio of 25. Of the Bantu ethnic

groups, only Mijikenda/Swahili seemed to meet the threshold throughout the study period.

Table 5.2: Sex Ratio at Birth by Ethnic Groups in Kenya, 1989 - 2008-09

Ethnic Sex Ratio at BirthGroup 1989 1993 1998 2003 2008-09Kalenjin 107 110.2 134.6 99 112.4Kamba 115 100 101.1 101.4 88.3Kikuyu 97.7 93.1 93.7 100.4 107.5Kisii 107.1 90.5 95 91.9 110.9Luhya 94.8 103.6 98.6 98.4 102Luo 92 104.1 91.9 100.9 102.2

Maasai — — 70 81.1 112.1Meru /Embu 103.6 96.8 84.2 170.6 107.9Mijikenda/Swahili 107.2 102.5 100.7 109.9 103.4SomaliTaita

133.3 25 100 113.5 133.6

/Taveta . . . . 91.2 103 74.4Other 113.7 66.0 82.8 119.3 104.0

Source: Computed from 1989 - 2008-09 DHS Datasets

24

6.0 CONCLUSIONS AND RECOMMENDATIONS

6.1 Conclusions

Completeness of neonate births and deaths information was reported to be vibrant with all

period studies reporting over 90 percent completeness. Reporting of month and year for

births was the most complete and even indicated an improving trend. However,

completeness of month and year for neonate deaths showed an unstable trend.

Sex ratios at birth were normal - within the modelled threshold. However, while it is true

that more neonate males die compared to females, the ratios were rather high, e.g. 1.54 in

2008-09 - an indication of over-reporting of male neonate deaths. The sex ratio at birth and

ethnicity differential agreed with Garenne’s hypothesis that generally, Bantu ethnic group

has low sex ratios at birth.

Heaping at days that are multiples of seven is evident across the study period. The most

commonly reported occurring on day seven. Throughout the study period, the threshold of a

ratio of 1 was not achieved.

Displacement of events in time was also noted during the study period. The ratio of 100

could not be achieved.

While the quality of neonatal mortality data shows improvement, errors still exist. However,

inaccuracies in this report are not severe enough to warrant invalidity. It is also important to

note that in Kenya the DHS provides the most reliable periodic data for estimations

compared to the Health Information management and Vital Registration Systems.

6.2 Recommendations

National governments and international health bodies need to invest in improved methods

for data collection and measurement of neonatal deaths. For all data sources (both routine -

e.g. HMIS and Vital Registration and non routine - e.g. surveys), there is need to identify

and train qualified staff on data collection, analysis, supervision and reporting so as to

ensure quality.

Reducing the health questions asked at the cut-off period will also improve misreporting and

displacement of events in time as proposed by Sullivan, et al (1990).

25

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