March 2015

Maternal, Newborn and Child Health

Working Paper

UNICEF Health Section, Programme Division unite for children

Common childhood infections and gender

inequalities: a systematic review

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Common childhood infections and gender inequalities: a systematic review

© United Nations Children’s Fund (UNICEF), New York, 2015 Knowledge Management and Implementation Research Unit, Health Section, Programme Division UNICEF 3 UN Plaza, New York, NY 10017 March 2015 This is a working document. It has been prepared to facilitate the exchange of knowledge and to stimulate discussion. The findings, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily reflect the policies or views of UNICEF or the United Nations. The text has not been edited to official publication standards, and UNICEF accepts no responsibility for errors. The designations in this publication do not imply an opinion on legal status of any country or territory, or of its authorities, or the delimitation of frontiers. The editors of the series are Alyssa Sharkey and David Hipgrave of UNICEF Programme Division. For more information on the series, or to submit a working paper, please contact asharkey@unicef.org or dhipgrave@unicef.org. COVER PHOTO: A child plays outside an Anganwadi center in the slums of Dharavi in the metropolis of Mumbai. © UNICEF/INDA2012-00133/Vishwanathan.

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Common childhood infections and

gender inequalities: a systematic review

__________________________________

Harish Nair, Harry Campbell, John Jungpa Park,

Luciana Brondi, Ting Shi, Sara Olsson, Stephanie

Hakim, Tan Hsi, Igor Rudan and Amir Kirolos

Centre for Population Health Sciences The University of Edinburgh Medical School, Teviot Place

Edinburgh EH8 9AG United Kingdom

Keywords: systematic review, child health, child mortality, childhood infections, pneumonia,

malaria, diarrhoea, gender, equity

Comments may be addressed by email to: Harish.Nair@ed.ac.uk cc: asharkey@unicef.org

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© UNICEF/INDA2009-00014/Khemka

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Acknowledgements

The authors wish to acknowledge Claire Parker of the University of Edinburgh for her valuable inputs. In addition, the authors wish to acknowledge the inputs and access to data provided by members of the Severe Acute Lower Respiratory Infections Working Group, listed below: Eric AF Simoes and Phyllis Carosone-Link (University of Colorado Denver and The Children’s Hospital, Denver, CO, USA); Eduardo Azziz-Baumgartner and Sonja J Olsen (Centers for Disease Control and Prevention, Atlanta, GA, USA); Jian Shayne F. Zhang (The University of Melbourne, Australia); Daniel R Feikin, Allan Audi, Gideon Emukule, Mark Katz and Robert F Breiman (Centers for Disease Control and Prevention-Kenya, Nairobi, Kenya); Grant A Mackenzie, Syed MA Zaman and Stephen RC Howie (Medical Research Council, The Gambia); Bradford D. Gessner (Agence de Médecine Préventive, Paris, France); Jennifer C Moisi, Patrick K Munywoki and J Anthony G Scott (Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya); Zulfiqar A Bhutta and Sajid Soofi (Aga Khan University, Karachi, Pakistan); Anna Roca and Betuel Lázaro Sigaúque (Universitat de Barcelona, Barcelona, Spain); Henry C Baggett, Susan A. Maloney and Kumnuan Ungchusak (Thailand MOPH -US CDC Collaboration, Thailand); Doli Goswami, Tanvir Mahmudul Huda, Aliya Naheed, Nusrat Homaira, Shams El Arifeen and W Abdullah Brooks (International Centre for Diarrhoeal Disease Research, Bangladesh); Rosalyn J Singleton and Dana Bruden (Arctic Investigations Program, CDC, Anchorage, AK, USA); Marilla G. Lucero (Research Institute for Tropical Medicine, Philippines); Aruna Chandran, Geoffrey Kahn and Katherine L O’Brien (Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA); Angela Gentile and Julia Bakir (Ricardo Gutierrez Children´s Hospital, Buenos Aires, Argentina); Cheryl Cohen, Marietjie Venter and Shabir A Madhi (National Institute for Communicable Diseases, South Africa); Anand Krishnan and Shobha Broor (All India Institute of Medical Sciences, New Delhi, India); Arturo Abdelnour and Adriano Arguedas (Universidad de CienciasMédicas de Centro América, San José, Costa Rica); Alexey Wilfrido Clara (CDC-Central American Region (CDC-CAR); Ana Lucia Andrade and Ruth Minamisava (Federal University of Goiás, Goiânia, Brazil); Cissy B. Kartasasmita and Kuswandewi Mutyara (The University of Padjadjaran, Bandung, Indonesia); Maurice Ope (East African Community Secretariat, Arusha, Tanzania); Raúl Oscar Ruvinsky (National University of Buenos Aires, Argentina); María Hortal (Program for Basic Sciences Development , National University / PNUD, Uruguay); Abdul Bari (Save the Children US, Pakistan Country Office, Islamabad, Pakistan); John P McCracken and María Reneé López (Universidad del Valle de Guatemala, Guatemala); Mukesh Dherani and Nigel Bruce (University of Liverpool, United Kingdom); Shama Parveen (Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi); Miguel Tregnaghi (CEDEPAP, Cordoba, Argentina); Hanna Nohynek (National Institute for Health and Welfare, Finland); Vivek Gupta (The INCLEN Trust International, New Delhi); Mahmudur Rahman (Institute of Epidemiology, Disease Control & Research, Dhaka, Bangladesh); Endang R. Sedyaningsih (Indonesian Ministry of Health, Jakarta, Indonesia); Julio Alberto Armero Guardado (Ministry of Health of El Salvador); Saul S Morris (Bill & Melinda Gates Foundation, Seattle, WA, USA).

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Executive Summary

Background

The 2011 UNICEF report “Boys and Girls Life Style: sex-disaggregated data on a selection of well-being indicators, from early childhood to young adulthood” reported that “under-five mortality is usually higher among boys, except in parts of Asia” and reported a male/ female mortality ratio of 0.96 globally. It concluded that there were “minimal gender disparities” among the health indicators in young children that were examined. The subsequent UNICEF Gender Action Plan 2014-17 stated that “addressing discrimination against girls in survival and care is essential.” However, it noted that the UNICEF analysis of child health indicators did not reveal “significant gender differences and the resulting Action Plan gave little priority to a consideration of gender-specific child survival (with only 2 of its 101 paragraphs of recommendations addressing this issue). Methods

Systematic Literature Review

We conducted a systematic review of published literature (January 1990- May 2014) to identify studies reporting data on rates of hospitalisation and in-hospital case fatality ratio (by gender). We conducted the literature search across English language databases (MEDLINE, Embase, Global Health, CINAHL, Web of Science, WHOLIS, IndMed, SIGLE); Chinese language databases (CNKI, Wanfang data, and Chongqing VIP); and Latin American language databases (LILACS). We included studies that met our eligibility criteria and reported data on hospital admissions and in-hospital case fatality rates (CFR) (stratified by gender) for pneumonia, diarrhoea and malaria in children aged below 5 years. Broadly, we included studies that were:

conducted in paediatric population and reporting data on pneumonia, diarrhoea and malaria;

reporting incidence rates or number of cases (and denominator population) (stratified by gender) for hospitalised pneumonia, diarrhoea and malaria.

describing hospitalisation admission and in-hospital mortality (by gender) for pneumonia, diarrhoea and malaria in children aged 0-59 months

Acquisition of unpublished data

We contacted members of the Severe ALRI Working Group (an investigator group that contributed unpublished data for the global estimates for hospitalised ALRI) to obtain unpublished data on pneumonia mortality (by gender)disaggregated by narrow age bands- 0-11 months, 12-23 months and 24-59 months. Analysis of DHS Data

We accessed the “Monitoring and Evaluation to Assess and Use Results Demographic and Health Surveys” (MEASURE DHS) database, a database containing household surveys from developing countries in Sub-Saharan Africa, North Africa, Europe, Australasia, Latin America, Caribbean and South-, South-East-, West- and Central Asia. We used the “Children’s recode” dataset for each country. This dataset was available for 67 countries and contains data on the prevalence and treatment of the common childhood illnesses diarrhoea, fever and acute respiratory infection in children aged 0 to 5 years. The sample was restricted to children having suffered from diarrhoea in the two weeks preceding the survey. To determine whether severity of pneumonia / diarrhoea impacted on the gender difference in care seeking behaviour, the sample was further analysed in two groups for each disease condition: “suspected pneumonia” and “suspected pneumonia with a known problem in the chest”; “all diarrhoea” and “bloody diarrhoea”. We analysed the proportion of children who sought treatment from a health-care facility (this includes the total proportion of children

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presenting to government hospitals, private health service, public health provider, shop and traditional practitioner) by gender for each country to see if a gender bias exists.

Results

Literature review and analysis of unpublished data

We identified 12744 articles for screening of titles and abstracts – of these 65 studies from LMIC satisfied our eligibility criteria and were included in the analysis. Forty four studies reported data on hospital admissions ─ of these, only 9 reported incidence data ─ five on pneumonia, four on diarrhoea, one on malaria and one reported on admissions for severe pneumonia, diarrhoea and fever. In general, the incidences of hospitalisations for all three illnesses are higher in boys compared to girls. For pneumonia, all studies (except one from Brazil) reported a significantly higher incidence of hospital admissions in boys (p<0.05), compared to girls. Similarly, studies reporting hospital admissions for diarrhoea and febrile illness reported a significantly higher incidence rate ratio (in boys compared to girls). We analysed unpublished data from 28 studies (obtained from the Severe ALRI Working Group). These studies reported marked and consistently higher rates of hospitalisation for severe pneumonia in boys compared to girls particularly in studies from Southeast Asia, where 2 to 3.5 fold differences were reported. However, if the data were not disaggregated by age range (i.e. children aged 0-59 months were considered as one group), then the majority of these differences are attenuated. Only seven studies (six from South and South East Asia) reported an incidence rate ratio (boys compared to girls) higher than 1.5. Further interpretation of these data should be informed by a systematic literature review to define the sex differential in risk of childhood illness so that an “expected” rate of hospitalisation by gender can be defined based on biological risk of disease. We identified 37 studies that reported data on hospital admissions for pneumonia, diarrhoea and malaria without a population denominator. We compared the gender ratio for hospital admissions with the sex ratio at the national level. Fifteen studies reported data for hospital admissions for pneumonia. All studies in general (except for one study from Malawi) revealed a higher admission ratio for boys compared to girls. However, in the case of studies from South Africa, South Asia and China, the gender ratio for hospital admission for pneumonia was substantially higher (greater than 1.5). Twenty studies reported data on hospital admissions (without denominator population) for diarrhoea. All studies (except one from Zambia) reported a higher admission ratio for boys. However, the gender ratio was substantially higher (greater than 1.5) in studies from India, Vietnam, China and Iraq. We also identified seven studies that reported data on hospital admissions (without denominator population) for malaria. In general, all studies reported a higher admission ratio in boys (compared to girls), with the study from India reporting a ratio of greater than 1.5. We identified 22 hospital-based studies that reported in-hospital case fatality rates (by gender) for pneumonia, diarrhoea and malaria in children aged below 5 years. Ten studies from 10 countries that included data from 7841 pneumonia cases in under-five children (of which 1036 died) reported CFR by gender. In seven of the 10 included studies, CFR for pneumonia was higher in girls compared to boys. The CFR ratio (boys compared to girls) was wide in 4 of the 10 studies, and there was no significant difference by gender in any of the studies. We identified seven studies that included 7037 diarrhoea cases in young children (with 354 deaths) that reported in-hospital CFR (by gender) - all studies except one from Zimbabwe reported a higher CFR in girls. In three of the studies, the CFR in girls was significantly higher compared to boys. Five studies that included 7368 malaria cases in young children (with 536 deaths) reported in-hospital CFR (by gender). Although all studies reported a higher CFR in girls, only one study from Yemen reported a significantly higher CFR. We also obtained unpublished data on pneumonia mortality (by gender) from the Severe ALRI Working Group. Twenty one sites provided data on 86254 pneumonia cases (and 2356 deaths) in under-five children

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disaggregated by narrow age bands. In general, all sites reported higher CFR in girls. However, since there were very few in-hospital pneumonia deaths in each age bands, the difference (by gender) was not statistically significant. Analysis of DHS data

Of the 67 countries that reported data on children with “suspected pneumonia”, a higher proportion of boys sought care from health care providers in 38 countries. In 6 countries - Egypt, Turkey, India, Malawi, Rwanda and Togo – a significantly higher proportion of boys sought care. In Namibia, a higher proportion of girls sought care for “suspected pneumonia”. In 4 countries - Bangladesh, Cambodia, India and the Democratic Republic of Congo a significantly higher number of boys were taken to a government hospital. In two countries- Namibia and Senegal there were significant differences by gender in the number of children seeking care from a pharmacy. In Namibia this was higher for boys, while in Senegal the reverse was true. In eight countries significantly higher numbers of boys than girls received treatment for “suspected pneumonia” from a private provider. These included Dominican Republic, Guyana, Egypt, Cambodia, India, Lesotho, Mozambique and Togo. Only 40 countries reported data on children suffering from a “suspected pneumonia with a known problem in the chest”; 22 had more boys than girls receiving treatment. However, we observed significant difference in only four countries. In Ghana a significantly higher number of boys than girls received treatment while in Namibia, Philippines and Zimbabwe a significantly higher number of girls received treatment for “suspected pneumonia with a known problem in the chest”. In India and Malawi, a significantly higher number of boys than girls presented to a government hospital while in Dominican Republic, Bangladesh and Burundi this difference was noted for a private provider. In the Philippines significantly higher number of girls than boys with “suspected pneumonia with a known problem in the chest” sought care from a pharmacy. Overall, in 40 out of the 67 countries a higher proportion of boys compared to girls were taken to a health-care facility. In two countries in Asia, Nepal and India a significantly higher proportion of boys compared to girls presented to a health-care provider. Additionally, a significantly higher proportion of boys were taken to private providers in India and government hospitals in Nepal, Congo (Brazzaville) and Senegal. On the other hand, significantly higher proportion of girls compared to boys were taken for treatment in four countries: in Guinea and Guyana (to a private provider); and in Uganda and Egypt (to a pharmacy). In 27 out of 54 countries a higher proportion of boys compared to girls were taken to a health care provider to receive treatment for bloody diarrhoea. In four countries Kenya, Madagascar, Pakistan and Armenia, a significantly higher proportion of boys compared to girls with bloody diarrhoea taken to receive treatment from a health-care provider. Two countries, Nigeria and Paraguay, revealed a statistically significantly higher proportion of boys compared to girls were taken to a pharmacy, whilst South Africa was the only country where a significantly higher proportion of boys compared to girls presented to the private sector. In two countries in Asia, Bangladesh and Cambodia, a significantly higher proportion of girls compared to boys were taken to government hospitals. Fifty six countries reported data on care-seeking (by gender) for fever. Of these, 28 countries reported a higher proportion of care seeking for boys compared to girls for febrile illnesses. In 3 countries- Nepal Yemen and Mali a significantly higher proportion of boys with fever sought health care. In 2 countries, a significantly higher proportion of boys sought treatment from government hospitals- Kenya and Yemen. A significantly higher proportion of boys with fever were taken to a private provider in 3 countries- Burkina Faso, Guinea and Timor Leste. While more girls with fever are seen in government hospitals in India, the proportion of boys with fever seeking care from the private health sector is significantly higher. In Cambodia a higher proportion of boys sought care for fever from a pharmacy.

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Conclusions and recommendations

In general, available gender-specific mortality data are consistent with reducing levels of disadvantage in young girls globally. However, against this general trend, there is evidence of substantial and increasing mortality differentials in some countries. It is important that these countries (and sub-national regions) are clearly identified and relevant data further explored so that these gender biases can be described in detail and tracked over time. Underlying causes need to be better understood so that effective UNICEF policy can be formed and appropriate action taken. A plan for identifying, assembling and analysing data to explore these issues should be formulated. This should include stratified analyses of MICS data by gender (e.g., by socio-economic and educational strata); on-going regular monitoring and reporting of gender-specific mortality data; analysis (by gender) of neonatal admissions in the sick newborn care units in India and other LMICs where such data are available with UNICEF; and assembling large datasets of gender-specific information on hospital admissions and deaths from low and middle income countries. Attention should be focused on countries and sub-national regions where significant gender biases exist. Actions should include awareness raising in globally, nationally and in local populations, establishment of key indicators which are monitored and reported and actions to address underlying causes. Interpreting and communicating gender specific mortality data is complex and somewhat challenging. Due to the biological advantages in female infants, equal infant mortality rates in boys and girls is a sign of significant disadvantage in girls. With the epidemiological transition accompanying economic development the level of female biological advantage changes over time making target setting for tackling gender discrimination problematic. UNICEF should form a communication plan to address these challenges so public and professional understanding of these issues is improved and clear and consistent messages are delivered. Gender discrimination, including gender disparity in care seeking and differential treatment by health services, is a continuing and growing problem in some areas of the world. It is an important cause of child health inequity and is limiting the effectiveness of some child heath interventions. This report presents some interim conclusions based on limited available data and proposes some next steps to investigate these issues.

Table of Contents

Acknowledgements...................................................................................................................... v

Executive Summary ..................................................................................................................... vi

Abbreviations, definitions and acronyms .................................................................................... xii

Introduction ................................................................................................................................. 1

I. Data from a systematic literature review ................................................................................... 7

Methods .............................................................................................................................................................. 7

Results ................................................................................................................................................................. 9

II. Data from Demographic and Health Surveys ........................................................................... 27

Methods ............................................................................................................................................................ 27

Discussion .................................................................................................................................. 44

Hospital admissions in young children with pneumonia, diarrhoea and fever ................................................. 44

Case fatality ratios ............................................................................................................................................. 45

Summary of care-seeking data by gender from DHS and MICS surveys ........................................................... 45

Other important issues ...................................................................................................................................... 46

Final conclusions ................................................................................................................................................ 47

A proposal for a way forward ..................................................................................................... 48

Assembly of unpublished data through forming an international working group ........................................... 48

Explore the potential to assemble routine health services data from a large sample of health facilities in

collaboration with UNICEF country offices ....................................................................................................... 49

Stratified analysis on gender differentials in care seeking using DHS and MICS data ...................................... 49

References ................................................................................................................................. 50

Annexes ..................................................................................................................................... 56

Annex A: List of databases searched for the literature review ......................................................................... 56

Annex B: Medline and EMBASE search strategies to identify studies reporting gender differences in

hospitalisations and mortality for three leading causes of child mortality ....................................................... 57

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List of tables

Table 1: Incidence rates (by gender) of hospital admissions for pneumonia, diarrhea and malaria /

febrile illness ......................................................................................................................................... 11

Table 2: Studies reporting admissions for pneumonia by gender (without data on population

denominators) ...................................................................................................................................... 17

Table 3: Studies reporting admissions for diarrhoea by gender (without data on population

denominators) ...................................................................................................................................... 18

Table 4: Studies reporting admissions for malaria by gender (without data on population

denominators) ...................................................................................................................................... 20

Table 5: Studies reporting in-hospital case fatality rates (by gender) in children with pneumonia .... 22

Table 6: Studies reporting in-hospital case fatality rates (by gender) in children with diarrhoea ...... 23

Table 7: Studies reporting in-hospital case fatality rates (by gender) in children with malaria ......... 24

Table 8: In-hospital CFR in children aged 0-59 months with pneumonia (unpublished data) ............. 25

Table 9: Gender differences in care seeking for “suspected pneumonia” by various provider

categories ............................................................................................................................................. 28

Table 10: Gender differences in care seeking for “suspected pneumonia with a known problem

in the chest” by various provider categories ........................................................................................ 31

Table 11: Gender differences in care seeking for diarrhoea by various provider categories .............. 33

Table 12: Gender differences in care seeking for bloody diarrhoea by various provider categories .. 37

Table 13: Gender differences in care seeking for fever by various provider categories...................... 40

List of figures

Figure 1: Historical change in sex ratio of mortality as under-five mortality declined in selected

developed countries (from Hill and Upchurch) ...................................................................................... 3

Figure 2: Trends in sex ratio of child mortality in children 1-4 y by level of under-five mortality

(From Sawyer, 2012) .............................................................................................................................. 4

Figure 3. Illustration flowchart for data selection and extraction ......................................................... 8

Figure 4: Incidence of hospital admissions for pneumonia in boys compared to girls in children

aged 0-11 months, 12-23 months and 24-59 months (from Nair et. al, Lancet 2013) ........................ 14

Figure 5: Incidence of hospital admission for pneumonia (in boys compared to girls) in children

aged 0-59 months- reanalysis of data in Figure 4 ................................................................................ 15

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Abbreviations, definitions and acronyms

ALRI Acute Lower Respiratory Infections CFR Case Fatality Rate CI Confidence Interval DHS Demographic and Health Surveys EMRO WHO’s Regional Office for the Eastern Mediterranean GDD Global Disease Detection ICPD International Conference on Population and Development IEIP International Emerging Infections Program IGME Inter-Agency Group for Child Mortality Estimation IP Inpatient LMICs Low and middle income countries MICS Multiple Indicator Cluster Surveys MDG Millennium Development Goal OPD Outpatient department RSV GEN Respiratory Syncytial Virus Global Epidemiology Network Sex Ratio This is the ratio of males to females in the population (normalized to 100, although,

sometimes to 1000). At birth the sex ratio is fairly standard at around 105. UN United Nations UNICEF United Nations Children’s Fund WHO World Health Organisation

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Introduction

The Millennium Development Goal 4 (MDG4) aims to reduce under-5 child mortality rates by two thirds from 1990-2015. In 2012, rates of child mortality were reported to have halved globally from approximately 12 million to 6.6 million since MDG4 was implemented. Despite this significant improvement, concerns have been raised about the target being out of reach.[1, 2] While these gains are substantial, they are not uniform. There are large inequities between countries, and within a country across regions and by gender.[3] The lack of gender disaggregated data being collected in international child health outcomes, and consequently a poor understanding of the current gender inequity in child health indicate that efforts to improve child survival may be uneven and biased towards a particular sex with low levels of understanding of the importance of this issue. Therefore, it is important to consider gender equity in child survival and health planning and priority setting, and as the chapter on MDG4 closes in 2015, post-MDG efforts to improve child survival and health outcomes should incorporate gender equitable targets and related monitoring of appropriate indicators. Child health and gender: Within the context of international health

One of the first and most prominent international calls for gender and child health was the Programme of Action of the International Conference on Population and Development (ICPD) in 1994, which condemned the excess mortality among female children, and urged that:

“Leaders at all levels of the society must speak out and act forcefully against patterns of gender discrimination within the family, based on preference for sons. One of the aims should be to eliminate excess mortality of girls, wherever such a pattern exists” (paragraph 4.17).[4]

The UN General Assembly’s Special Session on Children, which produced the outcome document entitled World Fit for Children, at the twenty-seventh session in 2002, echoed these sentiments by committing to:

“Promote child health and survival and reduce disparities between and within developed and developing countries as quickly as possible, with particular attention to eliminating the pattern of excess and preventable mortality among girl infants and children” (A/RES/S-27/2. A World Fit for Children. Annex, para. 37.4).[5]

Important international reports which have dealt with this issue include that produced by the United Nations, Department of Economic and Social Affairs, Population Division, which in 2011 published a 254-page document entitled, Sex Differentials in Childhood Mortality.[3] Using data from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), and using statistical methodology to process data and estimate sex differentials in childhood mortality for 190 countries and all world regions between four decades from 1970 to 2010, this document was the first global review of sex differentials in child mortality published by the UN for over 10 years. The above UN division also published a less substantive work on sex differentials in childhood mortality, entitled Too Young to Die: Genes or Gender? which provided estimates of mortality by sex for the 1970s-80s for 82 developing countries.[6]

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Research issues in Gender and Child Health

Data Recording Issues

There are major challenges to determining whether improvements in child survival are seen to occur equally in males and females. The UN Sex Differentials in Childhood Mortality suggests that

This is due to the inadequate nature of birth and death statistics in most developing countries. In the absence of complete vital registration, mortality estimates for these countries are derived primarily from sample surveys and population censuses, through questions posed to women about the survival of their children. Such estimates can be subject to a great deal of uncertainty due to small sample sizes, as well as biases affecting the consistent reporting of all children.[3]

The problems of data recording and collection have been further complicated by use of different surveys over different time periods and non-systematic methodologies, making comparisons incredibly difficult. In order to address this problem, an Inter-Agency Group for Child Mortality Estimation (IGME) was formed in 1994, in order to provide a uniform source of estimation for child mortality, and has since the last UN’s Sex Differentials in Childhood Mortality (published in 2011), produced sex-disaggregated data. Issues around Biological Sex Differences

At present there is evidence in the literature to suggest that females have a biological advantage in survival over males due to being less vulnerable to congenital disease, infection, and perinatal illness including perinatal trauma, intrauterine hypoxia, birth asphyxia, prematurity, neonatal tetanus and acute respiratory distress syndrome.[7] Despite this advantage, females may be more vulnerable to infections such as measles, which are the primary causes of death in later infancy and childhood between 1-5 years old, and the advantage soon diminishes.[8] In developed countries, infectious diseases account for a lower number and proportion of causes of death and perinatal, congenital and external causes form a larger proportion of deaths between ages 1-5. Therefore, the female advantage in child mortality would increase assuming that there is no health discrimination based on sex.[9, 10] Figure 1 is from Hill and Upchurch, who describe the historical changes in sex ratios of infant, child and under-5 mortality for developed countries, which offered the same health resources for either sex of children. It was also shown that the female advantage can be diminished if nutritional or health care provisions were compromised by sex.

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Figure 1: Historical change in sex ratio of mortality as under-five mortality declined in selected developed countries (from Hill and Upchurch) Therefore, since females exhibit improved survival in the early years of life, an estimate of the baseline difference may need to be calculated in order to determine non-congenital causes of sex differentials on child health outcomes.[11] China and India: Global Spotlight on Gender and Child Health

Clear discrimination has been identified in certain countries in Asia, especially in India and China, where gender discrimination, son preference and selective abortion of female fetuses has resulted in excess female mortality.[12-26] Both countries have remained in the global spotlight for their poor record in excess female childhood mortality, and because the goal of MDG4 largely depends on their progress. China and India are not only the two most populous countries in the world, but they are also the only two countries known to have female infant mortality higher than male infant mortality in the 2000s.[11] In fact, for children aged less than 1, females have an advantage in survival in all countries except from China and India. The UN estimates that there are 163 million missing women in both countries due to sex gender bias.[27] It is notable that whilst child mortality has fallen in both countries, females have benefited far less compared to other countries and regions.[11]Figure 2 is from Sawyer, who describes the regional trends of male to female mortality ratios in children aged 1-4, against declines in overall under-five mortality.

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Figure 2: Trends in sex ratio of child mortality in children 1-4 years by level of under-five mortality (From Sawyer, 2012) It is important to highlight the key publications and policies that relate to gender and child health in these countries, particularly considering that they account for over 1/3 of all births between the 1970s to 2000s, which makes their situation relevant to global trends in child health and gender.[28] India

Globally, India has the largest number of child deaths and there are regional variations in under-five mortality.[29] According to WHO in 2006, sex-specific under-5 mortality rate of 98 per 1000 for males and 105 per 1000 for females, reveals a gender bias in survival against the girl child.[30] The 2011 Indian census estimated around 7.1 million fewer females than males aged 0-6 years, which was an increase from 6 million recorded in the 2001 census and 4.2 million in the 1991 census.[31] In fact, females in every region in India between 1-59 months had higher mortality compared to males.[32, 33]The Million Death study reveals that whilst neonatal mortality rates are lower for females (33.5) than for males (40.1), under-5 mortality rates of 90.2 per 1000 live births in females compared to 82.2 per 1000 for males shows a trend of bias in mortality in the post-natal period.[34] Therefore in India, (in contrast to China where female disadvantage in mortality is concentrated in the early days of life), the female mortality disadvantage in India begins slightly later and extends through the early childhood years. There is an extensive literature analyzing the gender bias in child mortality in India. Studies suggest that there is a correlation between the gender bias of excess female child mortality, and careseeking for leading infectious causes of child mortality (pneumonia, diarrhea, febrile illness), feeding practices and nutritional status, although they differ in the extent to which they contribute to excess mortality.[15, 21, 22, 25, 26] Oster suggests that sex differences in vaccinations could explain 20 to 30 percent of excess female mortality between ages of 1 to 4, whilst malnutrition for another 20%, whilst differences in care seeking for illness accounted for less.[24] Jha and colleagues used the National Family Health Surveys and birth cohort data to demonstrate

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that selective abortion of girls had increased substantially in the last two decades, and accounted for most of the growing gender imbalance in children aged 0-6 years.[31] They suggest that the most plausible explanation for the gap in the number of females and males aged 0-6 years is prenatal sex determination with subsequent abortion of female fetuses. This is consistent with the findings of the Million Death Study published in 2010, which demonstrated that more females than males die at ages 1-59 months. However, this is mostly offset by more males than females dying in the first month of life,[34] meaning that the excess 7.1 million males cannot be reasonably accounted for by any biological means but by prenatal determination. Jha and colleagues reported that from 1990 to 2005 the imbalance of sex ratio at births was greatest after a firstborn girl, whilst by contrast they did not change after a firstborn male, suggesting that selective abortion of female fetuses occurred usually after a firstborn girl.[31] This finding is consistent with other studies, which have demonstrated greater disadvantage of female children with older sisters. Two other studies have found that whilst differences in treatment and survival of first-born males and females are not large, girls with older sisters have poorer outcomes in terms of malnutrition, respiratory infection, and vaccination rates.[25, 26]Both studies also found disadvantages for boys with older brothers, but not as markedly as for females. It has been postulated that this is a result of larger families having limited resources.[35]Several studies point out that in recent years, child survival for the girl child has been poorest in the north and north central regions in India, but evidence exists for excess female mortality in children in all major states.[21] The Indian Government has implemented several policies to address the issue of gender bias in the country over the last two decades. These include prohibition of sex-selective abortion and programs to equalise male and female children.[36-39] However, studies suggest that the policies are limited by poor implementation at the state and local level.[25, 40]. China

In China, gender bias in child mortality was first widely recognised in the 1990s, after an analysis of survival statistics from 1950 to 1990 was published in the 1990s, which revealed an excess mortality in female children under-5 and high rates of female child abandonment.[41] Historically, whilst China had a sex ratio of infant mortality lower than that found in developed countries in the 1980s, the rapid decline in sex ratio was noted beginning with the 1990 census.[42, 43]There was a progressive decline in the sex ratio in the 1995 intercensal survey and 2000 census, until a slight improvement in the 2005 intercensal survey.[13] It is perhaps not surprising, and not dissimilar to India, that ultrasound technology was available widely in the 1980s, and sex-selective abortion may account for the excess female mortality. Bulte and colleagues have estimated 40 million missing female children, and attributed the ‘one-child policy’ to be the single most influential factor fuelling the gender gap. [44] Moreover, not dissimilar to India, infant mortality estimates in the 1990 census show that discrimination against females was greatest for those who had older female siblings, with a sex ratio of 96 with one older sister, and 81 with two older sisters.[42] This pattern of disadvantage for female children with older female siblings was evidenced again in subsequent studies.[19, 45, 46] These studies suggest that excess female mortality was associated with the early neonatal period, usually on the first day of life, and hence related to a failure to seek timely medical care for newborn female with life threatening conditions that occur shortly after birth. According to the National Health and Nutrition Survey, there was no significant difference in nutritional status between male and female children.[47] Attane assessed sex ratio trends of infant mortality and birth trends in the 1990 and 2000 censuses and found a linear correlation between excess female child mortality and sex ratio at

6

birth at the provincial level suggesting an increase in female selective abortion and neglect of female child health. [48] Several reasons have been postulated in the literature for excess female child mortality. Studies have attributed the nation’s one-child policy, prenatal diagnostic testing, cultural preference for male children, poor enforcement of the abortion laws on sex selection, and poor healthcare poor healthcare of female children.[14, 16, 41, 44, 48, 49] This is supported by the fact that on analyzing data by single years of age, excess mortality in females occurs mostly around age 1, whilst the child mortality sex ratio for ages 2-4 have improved for females since the 1990s.[18] However, in order to determine whether reports of gender bias in healthcare, particularly in rural areas of China, have any significant differences in outcome and accrue and apply nationally, require further detailed study of the data. The Chinese Government has recognised this problem and has implemented policies through the “Care for Girls” campaign in order to tackle the problem of gender imbalance in survival and female. It aims to equalise sex ratio at birth and reduce improve female child survival, and was piloted in 24 counties between 2003 and 2005. Part of the strategy included improving statistical reporting of health events by sex. The initial evaluation of the pilot stage, found that sex ratio at birth declined on average from 133.8 in 2000 to 119.6 in 2005, but analyses on mortality rates have either not been carried out or published as yet.[18] It is hoped that new policies will help guide better data quality, which has been highlighted by the UN’s Sex Differentials in Childhood Mortality as one of the ‘grand challenges’ in assessing sex differentials in childhood mortality in China. The report also highlights that there is no national system of vital registration to compare census and survey estimates creating uncertainties with regard to data verification.[3] Therefore, in order to better understand the role of gender in child health especially in the context of infectious diseases, we aimed to

1. Conduct a systematic literature review to estimate and report gender differences in rates of hospitalisation and in-hospital case fatality ratio for pneumonia, diarrhoea and malaria in children.

2. Summarise data, by gender, on care-seeking for fever, diarrhea and symptoms of acute respiratory infection from the Demographic and Health Surveys (with an intention to extend this to MICS data in future)

This would describe the problem (gender inequities in child health), complement the work done by IGME on gender differences in child mortality; and explore potential for indicators for health services to report gender bias / identify problem areas.

7

I. Data from a systematic literature review

Methods

We conducted a systematic review of published literature to identify studies reporting data on rates of hospitalisation and in-hospital case fatality ratio (by gender). We included all studies published since 1990. We conducted the literature search across English language databases (MEDLINE, Embase, Global Health, CINAHL, Web of Science, WHOLIS, IndMed, SIGLE); Chinese language databases (CNKI, Wanfang data, and Chongqing VIP); and Latin American language databases (LILACS) to identify studies meeting our eligibility criteria (See Annex A for details). We included studies that met our eligibility criteria and reported data on hospital admissions and in-hospital case fatality rates (CFR, stratified by gender) for pneumonia, diarrhoea and malaria in children aged below 5 years. Search strategy

We developed a search strategy for each database using a combination of MeSH terms and key word search to include at least the following terms (“Deaths”, “hospitalisation”, “care seeking”, “pneumonia”, “diarrhoea”, and “malaria”) (see Annex A for details). The search strategy tailored to the above-mentioned databases shall be developed in consultation with a Medical Librarian at the University of Edinburgh. Evidence from peer-reviewed published literature formed the core of this review. We also scanned the reference lists of the selected studies to ensure completeness of the literature search. Figure 3 summarizes the various steps involved in this literature review.

8

Figure 3. Illustration flowchart for data selection and extraction

* Manual search, website search and experts

Titles and abstracts screened (n= 12744)

Records identified through

database searching (n=13838)

Full text articles

assessed for eligibility

(n=1102) Main reasons for exclusion:

No relevant data reported

Adult population or all ages reported

No pneumonia, diarrhoea or malaria studied

No specific care seeking behaviours or deaths reported

No gender estimates reported

Poor quality

Small (100) sample size

Studies were done before 1990

Population mortality studies

Studies were not done in China (Chinese database)

No clear if outpatients or inpatients or both

Not hospital-based

No full text available

Final studies

selected and data

extracted (n=70)

Records identified through

other sources*(n=14)

Studies reporting

admissions only

(n=48)

Studies reporting

CFR (n=22)

LMIC

studies

(n= 65)

9

Eligibility Criteria

Inclusion Criteria:

Studies conducted in paediatric population and reporting data on pneumonia, diarrhoea and malaria;

Studies reporting incidence rates or number of cases (and denominator population) (stratified by gender) for hospitalised pneumonia, diarrhoea and malaria.

Studies describing hospitalisation admission and in-hospital mortality (by gender) for pneumonia, diarrhoea and malaria in children aged 0-59 months.

Exclusion Criteria:

Studies in individuals older than 5 years

Studies with sample size (number of cases) less than 100

Studies that do not specifically report data (by gender) on hospital admissions and in-hospital deaths due to pneumonia, diarrhoea or malaria

For the studies looking at case fatality rates the studies that do not specify the numbers or proportions of boys and girls who died

Studies that reported data for less than 12 consecutive months of hospitalisation or not reporting data at least for the first year of life.

Data appraisal

We critically appraised all papers eligible for full-text review for data quality specifically focusing on case definitions, study duration, sample size, completeness, accuracy and any specific study limitations. Unpublished data

We contacted members of the Severe ALRI Working Group (an investigator group that contributed unpublished data for the global estimates for hospitalised ALRI) to obtain unpublished data on pneumonia mortality (by gender)disaggregated by narrow age bands- 0-11 months, 12-23 months and 24-59 months. We developed a data extraction template which was used by the group members to provide us summary data for pneumonia mortality (by gender).

Results

A total of 72 studies with data on hospital admissions meeting our eligibility criteria were identified ─ of these 65 were from LMICs. Forty four studies reported data on hospital admissions ─of these, only 9 reported incidence data, the remainder were hospital based studies reporting admissions (by gender) without data on denominator population). We also identified 22 studies that reported data on case fatality rates (CFRs). Studies reporting incidence rates for hospital admissions

We identified 11 studies (seven from LMICs) that reported incidence rates (by gender) for hospital admissions for pneumonia, diarrhoea and malaria. [50-60] Of these, five studies reported incidence data on pneumonia, four on diarrhoea, one on malaria and one reported on admissions for severe pneumonia, diarrhoea and fever (Table 1). Most studies reported data for a single age range- either 0-59 months, or were restricted to narrower ranges- 0-11 or 0-23 months. In general, the incidences of hospitalisations for all three illnesses are higher in boys compared to girls. For pneumonia, all

10

studies (except one from Brazil) reported a significantly higher incidence of hospital admissions in boys (p<0.05), compared to girls. Similarly, studies reporting hospital admissions for diarrhoea and febrile illness reported a significantly higher incidence rate ratio (in boys compared to girls). One study from North India (Bhan, 2005) reported significantly higher hospital admissions for boys for all three illnesses (across the severity spectrum). They reported that in general, for every 100 boys admitted, only 49 to 67 girls were admitted. They conducted a verbal autopsy in the community which revealed higher mortality in girls.

11

Table 1: Incidence rates (by gender) of hospital admissions for pneumonia, diarrhea and malaria / febrile illness

Author, Year Country, setting Age group Population at risk (m)

Population at risk (f)

Hospital admission

[m]

Hospital admission

[f]

Incidence (per 1000 children

/ year) of hospitalisatio

n in males (95% CI)

Incidence (per 1000 children / year) of

hospitalisation in

females (95% CI)

Incidence rate ratio (M:F), p

value

Comments

Pneumonia

Moore, 2010

Australia, non-aboriginal children

0-23 mo

- - - - Not reported; but boys > girls

Not reported; but boys > girls

1.39 (1.34 - 1.45)

Australia, aboriginal children

- - - - Not reported; but boys > girls

Not reported; but boys > girls

1.35 (1.26 -1.45)

Clark, 2007 England, UK

0 - 59 mo 3.52 (3.14 – 3.95)

2.79 (2.44 - 3.18)

1.26 (1.24-1.29)

60 - 180 mo

0.56 (0.46 – 0.68)

0.46 (0.36 – 0.57)

1.22 (1.19-1.28)

Cesar,1997 Pelotas, Brazil, urban

1 mo -13 mo

2608 2696 - - 31 26 1.19 (0.86 - 1.65), p=0.29

Tornhein,2006

Bondo, Kenya, rural

0-59 mo NR NR 8.26 (7.58-8.93)

6.93 (6.37-7.49)

1.19 (1.05-1.33)

60-108 mo NR NR 0.88 (0.61-1.11)

0.85 (0.61-1.09)

1.02 (0.66-1.56)

Hasan, 2014 Thailand, rural 0- 59 mo - - 66.71 (65.74-67.69)

48.33 (47.48-49.19)

1.38 (1.35 -1.41)

12

Author, Year Country, setting Age group Population at risk (m)

Population at risk (f)

Hospital admission

[m]

Hospital admission

[f]

Incidence (per 1000 children

/ year) of hospitalisatio

n in males (95% CI)

Incidence (per 1000 children / year) of

hospitalisation in

females (95% CI)

Incidence rate ratio (M:F), p

value

Comments

Nair, 2013 (28 unpublished studies)

Global estimates

0-11 mo - - - - - -

1.53 (1.39 - 1.69)

12 - 23 mo - - - - - -

1.22 (1.15 - 1.29)

24 - 59 mo - - - - - -

1.28 (1.23 - 1.33)

Southeast Asia estimates

0-11 mo - - - - - -

1.88 (1.49 - 2.38)

12 - 23 mo - - - - - -

1.42 (1.22 - 1.65)

24 - 59 mo - - - - - -

1.9 (1.41 - 2.45)

Diarrhoea

Malek, 2006 USA 0-59 mo 83051 67354 8.5 (8.0 – 9.0) 7.2 (6.8-7.6) 1.18 (1.18-1.18)

Parashar, 1999

Connecticut 1 - 59 mo 6178 5146 5.24 (5.11-5.37)

4.60 (4.47-4.76)

1.14 (1.14-1.14)

He, L. et al 2010

Longhua area, Shenzhen, China 0 - 59 mo 11334 7856 174 101 15.35 12.85

1.19 (1.14-1.25)

Boccolini, 2012

25 cities Brazil, year 1999 0 -12 mo 365563 347015 4418 3341

12.09 (11.73-12.45)

9.63 (9.31-9.96)

1.26 (1.25-1.26)

25 cities Brazil, year 2008 0 -12 mo 315026 299763 1995 1513

6.33 (6.06-6.62)

5.05 (4.80-5.31)

1.25 (1.25 - 1.26)

13

Author, Year Country, setting Age group Population at risk (m)

Population at risk (f)

Hospital admission

[m]

Hospital admission

[f]

Incidence (per 1000 children

/ year) of hospitalisatio

n in males (95% CI)

Incidence (per 1000 children / year) of

hospitalisation in

females (95% CI)

Incidence rate ratio (M:F), p

value

Comments

Malaria / Febrile illness

Kazembe, 2006 Zomba, Malawi < 14y 201660 214626 2286 1683

11.34 (10.88-11.81)

7.8 (7.48-8.23)

1.45 (1.44-1.46)

Bhan, 2005 New Delhi, India 1 mo- 23 mo 45153 40480 2121 1694

46.97 (45.02-49.02)

41.85 (39.90-43.90)

1.12 (1.12-1.13)

Admissions for febrile illness

Others

Bhan, 2005 New Delhi, India 1 mo – 23 mo

45153 40480 2854 1564 63.20 (60.93-65.57)

38.64 (36.77-40.60)

1.64 (1.62-1.66) p<0.001

Admissions for pneumonia or diarrhoea

45153 40480 500 312 11.08 (10.14-12.09)

7.71 (6.90-8.61)

1.44 (1.40-1.47)

Admissions for severe pneumonia or severe diarrhoea.

14

Unpublished data

From the literature review, we identified only seven studies reporting incidence data on hospital admissions for pneumonia, of which six studies reported modest but significantly higher incidence in boys compared to girls. By contrast, a recent meta-analysis by Nair and colleagues [61] included hitherto unpublished data from 28 studies that reported marked and consistently higher rates of hospitalisation for severe pneumonia in boys compared to girls particularly in studies from Southeast Asia, where 2 to 3.5 fold differences were reported. They also reported the incidence rate ratio for pneumonia hospitalisations (in boys compared to girls) disaggregated by age bands (Figure 4). We reanalysed these data, and observed that if the data were not disaggregated by age range (i.e. children aged 0-59 months were considered as one group), then the majority of these differences are attenuated (Figure 3). Only seven studies (six from South and South East Asia) report an incidence rate ratio (boys compared to girls) higher than 1.5.

Figure 4: Incidence of hospital admissions for pneumonia in boys compared to girls in children aged 0-11 months, 12-23 months and 24-59 months (from Nair et. al, Lancet 2013)

15

Figure 5: Incidence of hospital admission for pneumonia (in boys compared to girls) in children aged 0-59 months- reanalysis of data in Figure 4.

16

Further interpretation of these data should be informed by a systematic literature review to define the sex differential in risk of childhood illness so that an “expected” rate of hospitalisation by gender can be defined based on biological risk of disease. Studies reporting hospital admissions without denominator data

We identified 37 studies that reported data on hospital admissions for pneumonia, diarrhoea and malaria without a population denominator. [62-98] We compared the gender ratio for hospital admissions with the sex ratio at the national level. Fifteen studies reported data for hospital admissions for pneumonia (Table 2). All studies in general (except for one study from Malawi) revealed a higher admission ratio for boys compared to girls. However, in the case of studies from South Africa, South Asia and China, the gender ratio for hospital admission for pneumonia was substantially higher (greater than 1.5). We identified 20 studies that reported data on hospital admissions (without denominator population) for diarrhoea (Table 3). All studies (except one from Zambia) reported a higher admission ratio for boys. However, the gender ratio was substantially higher (greater than 1.5) in studies from India, Vietnam, China and Iraq. We also identified seven studies that reported data on hospital admissions (without denominator population) for malaria (Table 4). In general, all studies reported a higher admission ratio in boys (compared to girls), with the study from India reporting a ratio of greater than 1.5.

17

Table 2: Studies reporting admissions for pneumonia by gender (without data on population denominators)

Author, Year Location/ Country/ setting Age group Male

admissions (n)

(%) male

Female admissions

(n )

(%) female

Gender ratio for hospital

admissions (M/F)*

Sex ratio in population (at national or

provincial level) ‡

Developed Countries

Lee, 2003 Western Australia 0-12 mo 282 54.9 232 45.1 1.21 1.05

Grant, 2012 New Zealand 0-59 mo 157 54 132 46 1.18 1.05

LMIC

Tall, 1994 Burkina Faso 0-30 mo 379 56.8 288 43.2 1.32 1

Hussey, 2000 South Africa < 12 mo 773 60.0 515 40.0 1.50 1.01

Nascimento-Carvalho, 2002

Salvador, Brazil, urban 0 mo - 15 y 54.1 45.9 1.18 1.04

Lesi, 2005 Lagos, Nigeria < 12 y 57 53.2 50 46.7 1.14 1.05

Accorsi, 2007 Northern Uganda <15 y 7480 55.4 6018 44.6 1.24 1.06

Tiewsoh, 2009 New Delhi, India, urban 2 - 60 mo 127 63.5 73 36.5 1.73 1.13

Lutala, 2009 Malawi 0-59 mo 207 44.6 257 55.4 0.81 0.99

Sawadhoki, 2007 Amirkola, Iran <12 y 237 58.7 167 41.3 1.42 1.05

Baqui, 2007 Bangladesh 0-59 mo 789 63.1 461 36.9 1.71 1.03

Banstola, 2013 Nepal 0-59 mo 463 60.0 309 40.0 1.50 1.04

Schimidt, 2012 Vietnam < 6 y - - - - 1.35 1.11

Yang, 2010 Chongqing, China 1 mo - 149 mo 189 83.0 2.28 < 1y =1.12;1-4y=1.19;5-9y=1.17

Zhang, 2012 Shanxi, China < 8 y 53 28.0 1.89 < 1y =1.16; 1-4y=1.12; 5-9y=1.09

* Admissions gender ratio = Male: Female ratio; ‡ Source: China: Zhu WX, Lu L, Hesketh T (2009), BMJ 338: b1211. For other countries: The Sex ratio is for 0-14 years and comes from The World Fact book, CIA available at https://www.cia.gov/library/publications/the-world-factbook/fields/2018.html#203.

18

Table 3: Studies reporting admissions for diarrhoea by gender (without data on population denominators)

Author, Year Location /country/

setting Age group

Male admissions

(n)

(% ) male

Female admissions

(n )

(% ) female

Gender ratio for hospital

admissions (M/F)*

Sex ratio in population (at national or

provincial level) ‡

Developed Countries

Lee, 2003 Western Australia < 12 mo 282 54.9 232 45.1 1.21 1.05

Ardern-Holmes, 1999

New Zealand 0 - 59 mo 2455 55.3 1981 44.6 1.16 1.05

LMIC

Awasthi, 2009

Multicountry: Brazil, Egypt, Nagpur, Lucknow, Kenya

2 mo-10 y 1328 56.6 1018 43.4 1.30 NA

Accorsi, 2007 Northern Uganda < 15 y 4427 56.7 3374 43.3 1.31 0.99

Amadi, 2001 Zambia 6-24 mo 94 47.0 106 53.0 0.89 1.01

Ali Z Ali, 1997 Basrah, Iraq < 5 y 344 57.1 258 42.9 1.33 1.04

Tawfeek, 2002 Iraq <5 y 12110 61.5 7570 38.5 1.60 1.04

Borade, 2010 Pune, India <5 y 150 61.0 96 39.0 1.56 1.13

Colombara, 2013 Bangladesh <5 y 8185 57.6 6033 42.4 1.36 1.03

Nguyen, 2004 Hanoi, Vietnam <5 y 181 66.1 93 33.9 1.95 1.11

Poo, 2007 Kuala Lumpur, Malaysia

2 - 139 mo 206 52.4 187 47.6 1.10 1.06

Chea, 2011 Sarwak, Borneo <12 y 132 56.4 102 43.6 1.29 1.04

Schimidt, 2012 Vietnam < 6 y - - - - 1.37 1.11

Shieh, 2013 Ho chi minh,

Vietnam <6 y 302 64.0 170 36.0 1.78 1.11 (Indonesia)

Yang, 2005 Xian, China 20 days-14

y 262 57.2 196 42.8 1.34

< 1y =1.34;1-4y=1.25;5-9y=1.23;10-14y=1.17

19

Author, Year Location /country/

setting Age group

Male admissions

(n)

(% ) male

Female admissions

(n )

(% ) female

Gender ratio for hospital

admissions (M/F)*

Sex ratio in population (at national or

provincial level) ‡

You, 2006 Guangdong, China 1 mo-12 y 298 58.2 214 41.8 1.39 < 1y =1.19;1-4y=1.33;5-9y=1.27;10-14y=1.15

Chen, 2009 Guangdong, China 22 days-93

mo - - - - 1.86

< 1y =1.19;1-4y=1.33;5-9y=1.27;10-14y=1.15

Wang, 2009 Shanghai, China 40 days-16

y 38 62.3 23 37.7 1.65

< 1y =117;1-4y=109;5-9y=111;10-14y=108

Wang, 2011 Henan, China 1-13 y 45 56.3 35 43.8 1.30 < 1y =122;1-4y=142;5-9y=131;10-14y=119

Li, 2012 Wuhan, China 1 day-5 y 1240 68.9 560 31.1 2.20 < 1y =122;1-4y=129

* Admissions gender ratio = Male: Female ratio; ‡ Source: China: Zhu WX, Lu L, Hesketh T (2009), BMJ 338: b1211. For other countries: The Sex ratio is for 0-14 years and comes from The World Fact book, CIA available at https://www.cia.gov/library/publications/the-world-factbook/fields/2018.html#203

20

Table 4: Studies reporting admissions for malaria by gender (without data on population denominators)

Author, Year Location /country/

setting Age group

Male admissions (n)

(%) male Female

admissions (n ) (%) female

Gender ratio for hospital

admissions (M/F)*

Sex ratio in population (at national or

provincial level) ‡

LMIC

Schellenberg, 1999

Ifakara, Tanzania, rural

< 15 y 1294 53.0 1137 46.7 1.14 1.02

Lesi, 2005 Lagos, Nigeria < 12 y 57 53.2 50 46.7 1.14 1.05

Accorsi, 2007 Uganda < 15 y 29002 53.1 25623 46.9 1.13 0.99

Arrieta, 2010 Ghana < 14y 209 54.3 176 45.7 1.19 1.01

Annobil, 1994 Asir, Saudi Arabia < 12 132 56.7 101 43.3 1.31 1.05

Ali Junejo, 2012 Larkana , Pakistan 3 mo - 12 y 117 58.5 83 41.5 1.41 1.06

Nanda, 2010 Rourkela, India not specified 195 63.9 110 36.1 1.77 1.13

* Admissions gender ratio = Male: Female ratio; ‡ Source: China: Zhu WX, Lu L, Hesketh T (2009), BMJ 338: b1211. For other countries: The Sex ratio is for 0-14 years and

comes from The World Fact book, CIA available at https://www.cia.gov/library/publications/the-world-factbook/fields/2018.html#203

21

Studies reporting case fatality rates

We identified 22 hospital-based studies that reported in-hospital case fatality rates (by gender) for pneumonia, diarrhoea and malaria in children aged below 5 years. [60, 99-118] The studies did not provide data by narrower age bands. Ten studies from 10 countries that included data from 7841 pneumonia cases in under-five children (of which 1036 died) reported CFR by gender (Table 5). Two of these studies reported CFR only in children aged 0-23 months. In seven of the 10 included studies, CFR for pneumonia was higher in girls compared to boys. The CFR ratio (boys compared to girls) was wide in 4 of the 10 studies, and there was no significant difference by gender in any of the studies. We identified seven studies that included 7037 diarrhoea cases in young children (with 354 deaths) that reported in-hospital CFR (by gender) (Table 6). All studies except one from Zimbabwe reported a higher CFR in girls. In three of the studies, the CFR in girls was significantly higher compared to boys. One study from Jamaica had very wide 95% confidence interval. We identified five studies that included 7368 malaria cases in young children (with 536 deaths) that reported in-hospital CFR (by gender) (Table 7). Although all studies reported a higher CFR in girls, only one study from Yemen reported a significantly higher CFR.

22

Table 5: Studies reporting in-hospital case fatality rates (by gender) in children with pneumonia

Author, Year Location/

Country/setting Age group

Admissions (males)

Admissions (females)

In-hospital deaths (males)

In-hospital deaths

(females)

In-hospital CFR males

(%)

In-hospital CFR females

(%)

In-hospital CFR ratio (95%

CI) (M:F) Comments

ASIA

Suwanjutha, 1994

Bangkok, Thailand 0-59 mo 151 116 5 4 3.3 3.4 0.96 (0.25-3.66)

Djelantik, 2003 Lombok, Indonesia

0-23 mo NA NA 293 212 11 12 0.95 (0.80-1.1) Measles excluded, 60% < 12 mo

Lupisan, 2007 Bohol, Philippines/rural

2-59 mo 728 521 17 13 2.3 2.5 0.93 (0.45-1.94); p=0.855

Kumar, 2011 Kolkata, India 0-59 mo 56 44 7 3 12.5 6.8 1.82 (0.49-6.83)

AFRICA

Bahwere,2004 DRC, Congo, rural NR 447 398 50 61 11.2 15.3 0.73 (0.54-0.99)

Measles excluded, 60% < 12 mo

Johnson, 2008 Ibadan, South West Nigeria

0-59 mo 177 146 22 13 12.4 8.9 1.40 (0.81-2.41)

Sigauque,2009 Manhica, Mocambique/rural

0 -23 mo 440 317 42 34 11.51 13.93 0.83 (0.51 - 1.33)

Ayieko, 2012 Kilifi, Kenya/rural 2 - 59 mo 1762 1443 97 90 5.5 6.7 0.88 (0.71-1.1)

EASTERN MEDITERRANEAN

Banajeh, 1997 Yemen 0-59 mo 354 175 31 21 8.7 12 0.73 (0.48-1.12)

LATIN AMERICA

McCarthy, 2009 Western Jamaica 0-59 mo 320 246 14 7 4.4 2.9 1.54 (0.7-3.39)

23

Table 6: Studies reporting in-hospital case fatality rates (by gender) in children with diarrhoea

Author, Year Location/ Country/setting Age

Hospital admissions

(males)

Hospital admissions (females)

In-hospital deaths (males)

In-hospital deaths (females)

In-hospital CFR males (%)

In-hospital CFR females (%)

In-hospital CFR ratio (95% CI) (M:F) Comments

ASIA

Lee, 1999 Kuala Lumpur, Malaysia, urban

< 16 y 2763 1926 2 8 0.07 0.42 0.17 (0.03-0.89)

All deaths in < 12 mo old. 15 years of admissions

Mitra, 2000 Bangladesh, Dhaka, ICDDR,B

< 5 y 296 170 111 88 37.5 51.8 0.72 (0.60-0.88)

AFRICA

Nathoo, 1998 Harare, Zimbabwe, urban

<12 y 178 134 NR NR 35.40 23.90 1.48 (1.02-2.15)

Dysentery during Shigella outbreak; In multivariate analysis male sex was not a predictor of death

Ravelomana Madagascar, urban

6-35 mo 89 59 9 25 10.1 42.3 0.24 (0.12-0.47)

Tornheim, 2010 Bondo district, Kenya, rural

0 - 59 mo

1.65 (1.00 - 2.73)

O'Reilly, 2012 Bondo and Siaya, Kenya, rural

0 - 59 mo

638 508 55 52 8.6 10.2 0.84 (0.63-1.13)

LATIN AMERICA

McCarthy*, 2009

Western Jamaica

0-59 mo 159 117 2 2 1.26 1.71 0.74 (0.07-7.61)

*Hospital Registry based study

24

Table 7: Studies reporting in-hospital case fatality rates (by gender) in children with malaria Author, Year Location/

Country/ setting Age group Hospital

admissions (males)

Hospital admissions (females)

In-hospital deaths (males)

In-hospital deaths (females)

In-hospital CFR males (%)

In-hospital CFR females (%)

CFR ratio

AFRICA

Gellert, 1998

Maiduguri, Borno state, Nigeria, urban 2 - 144 mo 368 255 33 32 8.97 12.5 0.71 (0.48-1.05)

Kazembe, 2006 Zomba, Malawi 0 mo - 14 y 2286 1683 173 129 7.6 7.7 0.99 (0.84-1.16)

Orimadegun, 2007

Ibadan, Nigeria, urban 6 mo - 15 y 946 860 64 61 6.7 7.0 0.95 (0.73-1.25)

Camara, 2011 Dakar, Senegal, urban 2 - 180 mo 95 67 10 8 11.7* 13.5* 0.88 (0.39-1.99)

EASTERN MEDITERRANEAN

Al-Taiar, 2006

Taiz and Hodeidah, Yemen, mixed rural urban 6 mo- 10 y 484 324 9 17 1.9 5.2 0.35 (0.17-0.76)

*CFR calculated for all patients where outcome was known (not all admitted)

25

Unpublished data

We also obtained unpublished data on pneumonia mortality (by gender) from the Severe ALRI Working Group (an investigator group that contributed unpublished data for the global estimates for hospitalised ALRI. Twenty one sites provided data on 86254 pneumonia cases (and 2356 deaths) in under-five children disaggregated by narrow age bands (Table 8). In general, all sites reported higher CFR in girls. However, since there were very few in-hospital pneumonia deaths in each age bands (two studies- one in urban Dhaka and one multicentre in rural India had zero in-hospital mortality in the 0-59 month age group), the difference (by gender) was not statistically significant. Table 8: In-hospital CFR in children aged 0-59 months with pneumonia (unpublished data)

Author Location Study period

CFR ratio (M:F) (95% CI)

0-11 mo 12-23 mo 24-59 mo

Asia

Naheed (unpublished)

Multicentre, Bangladesh

2004-08 0.83 (0.74-0.93)

0.93 (0.40-2.19)

1.15 (0.59-2.39)

Krishnan (unpublished)

Ballabgarh, India 2009-10 0.24 (0.01-7.39)

* *

Gessner (unpublished)

Lombok, Indonesia 1999-2002

0.89 (0.80-1.00)

1.27 (0.71-2.28)

N/A

Brooks (unpublished)

Kamalapur, Bangladesh 2008-10 * * *

Chandran (unpublished)

Multicentre, India 2005-07 * * *

Baggett (unpublished)

Sa Kaeo and Nakhon Phanom, Thailand

2004-08 0.87 (0.57-1.31)

1.20 (0.35-4.12)

0.76 (0.25-2.35)

Arifeen (unpublished)

Mirzapur, Bangladesh 2004-08 0.55 (0.21-1.46)

* *

Africa

Ope (unpublished)

Bondo, Kenya 2007-2009

0.81 (0.41-1.58)

0.62 (0.24-1.6)

0.16 (0.01-1.52)

Madhi (unpublished)

Soweto, S Africa 1998-2005

0.76 (0.58-0.99)

0.88 (0.36-2.13)

1.16 (0.35-3.92)

Roca (unpublished)

Manhica, Mozambique 2004-06 0.59 (0.33-1.07)

1.02 (0.40-1.58)

N/A

Breiman (unpublished)

Kibera, Kenya 2008-2010

1.62 (0.06-44.93)

* *

Breiman (unpublished)

Lwak, Kenya 2008-2010

* 0.75 (0.15-36.58)

0.87 (0.15-5.03)

Cohen (unpublished)

Soweto, S Africa 2009 0.86 (0.3-2.49)

* *

Moisi (unpublished)

Kilifi, Kenya 2003-08 0.97 (0.84-1.12)

0.83 (0.50-1.4)

1.27 (0.85-1.87)

Howie (unpublished)

Basse, The Gambia 2008-09 1.06 (0.31-3.57)

0.26 (0.02-3.44)

1.38 (0.05-37.73)

Americas

Andrade (unpublished)

Goiania, Brazil 2007-09 0.45 (0.1-2.05)

* 0.78 (0.02 -38.81)

26

Author Location Study period

CFR ratio (M:F) (95% CI)

0-11 mo 12-23 mo 24-59 mo

Gentile (unpublished)

Pilar, Argentina 2003-05 0.66 (0.01-32.68)

* *

Bruce (unpublished)

San Lorenzo & Comitancillo

2002-04 0.9 (0.24-3.41)

* *

McCracken (unpublished)

Santa Rosa and Quetzaltenango

2007-10 0.49 (0.29-0.86)

0.50 (0.14-1.8)

1.09 (0.22-5.35)

Simoes (unpublished)

Colorado, USA 2004-08 0.75 (0.55-1.01)

0.90 (0.36-2.24)

1.31 (0.69-2.49)

Western Pacific

Lucero (unpublished)

Bohol, Philippines 2000-2004

0.73 (0.23-2.37)

* *

* No deaths were observed in this age group

27

II. Data from Demographic and Health Surveys

Methods

We accessed the “Monitoring and Evaluation to Assess and Use Results Demographic and Health Surveys” (MEASURE DHS) database, a database containing household surveys from developing countries in Sub-Saharan Africa, North Africa, Europe, Australasia, Latin America, Caribbean and South-, South-East-, West- and Central Asia. (6) MEASURE DHS has been collecting data since 1984 and the project is implemented by the International Classification of Functioning, Disability and Health (ICF International) and funded by the United States Agency for International Development (USAID). (7) The “Standard DHS Surveys”, are conducted on average every 5 years and have a sample size of between 5,000 and 30,000 households. (8) They include households in different regions in each country, both urban and rural residents. (9) We used the “Children’s recode” dataset for each country. This dataset was available for 67 countries and contains data on the prevalence and treatment of the common childhood illnesses diarrhoea, fever and acute respiratory infection in children aged 0 to 5 years. (10) Using SPSS, variables from the children’s dataset relating to pneumonia, diarrhoea and febrile illness were selected and analysed. The sample was restricted to children having suffered from pneumonia / diarrhoea in the two weeks preceding the survey. To determine whether severity of pneumonia / diarrhoea impacted on the gender difference in care seeking behaviour, the sample was further analysed in two groups for each disease condition: “suspected pneumonia” and “suspected pneumonia with a known problem in the chest”; “all diarrhoea” and “bloody diarrhoea”. Through cross tabulation of data, a set of tables were formed for each country which by gender, showed the proportion of children who sought treatment from a health-care facility (this includes the total proportion of children presenting to government hospitals, private health service, public health provider, shop and traditional practitioner). In addition, the proportion of children taken to the following health services specifically was analysed: government hospital, private health service and pharmacy. Using these tables, a comparison was made between the proportion of boys and girls taken to health-care facilities for each country, to see if a gender bias exists. Pneumonia

Of the 67 countries that reported data on children with suspected pneumonia, a higher proportion of boys sought care from health care providers in 38 countries (Table 9). In 6 countries - Egypt, Turkey, India, Malawi, Rwanda and Togo – a significantly higher proportion of boys sought care. In Namibia, a higher proportion of girls sought care for “suspected pneumonia”. In 4 countries - Bangladesh, Cambodia, India and the Democratic Republic of Congo a significantly higher number of boys were taken to a government hospital. In two countries- Namibia and Senegal there were significant differences by gender in the number of children seeking care from a pharmacy. In Namibia this was higher for boys, while in Senegal the reverse was true. In eight countries significantly higher numbers of boys than girls received treatment for “suspected pneumonia” from a private provider. These included Dominican Republic, Guyana, Egypt, Cambodia, India, Lesotho, Mozambique and Togo.

28

Table 9: Gender differences in care seeking for “suspected pneumonia” by various provider categories

Government Hospital Private Hospital/Clinic/Doctor Pharmacy Received any treatment

Boys Girls Boys Girls Boys Girls Boys Girls

Central Asia

Kazakhstan 0 (0%) 1 (5.0%) 0 (0%) 0 (0%) 0 (0%) 1 (5.0%) 12 (63.2%) 10 (50.0%)

Kyrgyz Republic 2 (9.5%) 2 (12.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 11 (52.4%) 8 (50.0%)

Latin America and Caribbean

Bolivia 107 (11.9%) 100 (13.2%) 55 (6.1%) 41 (5.4%) 66 (7.3%) 75 (9.9%) 573 (63.7%) 504 (66.8%)

Brazil 78 (13.1%) 65 (12.1%) 49 (8.2%) 44 (8.2%) 50 (8.4%) 46 (8.5%) 341 (57.1%) 299 (55.5%)

Colombia 371 (59.7%) 321 (61.5%) 17 (2.7%) 17 (3.3%) 24 (3.9%) 18 (3.4%) 439 (70.7%) 371 (71.1%)

Dominican Republic 355 (45.5%) 300 (44.2%) * 99 (12.7%) * 66 (9.1%) 13 (1.7%) 10 (1.5%) 578 (74.2%) 483 (71.3%)

Guatemala 16 (1.6%) 11 (1.2%) 137 (13.6%) 118 (12.5%) 106 (10.5%) 94 (9.9%) 499 (49.6%) 492 (52%)

Guyana 36 (31.3%) 26 (27.4%) * 10 (8.7%) * 2 (2.1%) 5 (4.3%) 6 (6.3%) 88 (76.5%) 65 (68.4%)

Haiti 21 (2.8%) 27 (3.6%) 50 (6.7%) 40 (5.3%) 6 (0.8%) 10 (1.3%) 276 (37.2%) 269 (35.4%)

Honduras 41 (4.2%) 27 (3%) 111 (11.4%) 88 (9.8%) 21 (2.2%) 15 (1.7%) 554 (57.0%) 506 (56.4%)

Nicaragua 68 (6.5%) 44 (4.6%) 93 (8.9%) 89 (9.3%) 57 (5.5%) 53 (5.6%) 690 (66.3%) 631 (66.3%)

Paraguay 5 (1.3%) 2 (0.6%) 81 (21.3%) 57 (17.8%) 48 (12.6%) 42 (13.1%) 253 (66.4%) 212 (66.0%)

Peru 104 (6.2%) 104 (6.6%) 120 (7.1%) 127 (8.1%) 110 (6.5%) 109 (7.0%) 1252 (74.5%) 1203 (76.9%)

North Africa/West Asia/Europe

Albania 14 (17.5%) 7 (11.5%) 2 (2.5%) 1 (1.6%) 3 (3.8%) 6 (9.8%) 51 (63.7%) 40 (65.6%)

Armenia 7 (11.9%) 8 (17%) 0 (0%) 1 (2.1%) 0 (0%) 0 (0%) 31 (52.5%) 27 (57.4%)

Azerbaijan 9 (15%) 4 (10.8%) 4 (6.7%) 1 (2.7%) 0 (0%) 0 (0%) 26 (43.3%) 12 (32.4%)

Egypt 64 (11.1%) 54 (11%) *279 (48.3%) *199 (40.7%) 40 (6.9%) 39 (8%) *456 (78.9%) * 357 (73%)

Jordan 46 (12.8%) 30 (10.3%) 82 (22.8%) 64 (22.0%) 4 (1.1%) 4 (1.4%) 273 (75.8%) 223 (76.6%)

Moldova 8 (13.1%) 9 (16.4%) 0 (0%) 3 (5.5%) 2 (3.3%) 1 (1.8%) 39 (63.9%) 35 (63.6%)

Morocco 21 (5.7%) 10 (3.1%) 39 (10.5%) 33 (10.2%) 94 (25.3%) 86 (26.7%) 268 (72.2%) 214 (66.5%)

Turkey 93 (13.6%) 74 (12.1%) 69 (10.1%) 47 (7.7%) 6 (0.9%) 7 (1.1%) * 327 (47.7%) * 256 (41.8%)

Yemen

N/A N/A 47 (6.2%) 44 (6.7%) 112 (14.8%) 100 (15.2%) 360 (47.4%) 288 (43.8%)

South and Southeast Asia

29

Bangladesh * 27 (3.7%) * 12 (1.8%) 180 (24.8%) 133 (20.5%) 134 (18.4%) 140 (21.6%) 605 (83.2%) 529 (81.5%)

Cambodia * 17 (3.8%) * 5 (1.3%) * 93 (20.9%) * 60 (15.6%) 61 (13.7%) 49 (12.7%) 384 (86.3%) 322 (83.6%)

India *** 204 (9.1%)

*** 114 (5.9%)

* 1008 (45.1%) * 800 (41.6%) 129 (5.8%) 103 (5.4%) *** 1701 (76.1%)

*** 1356 (70.6%)

Indonesia 36 (3.0%) 32 (3.3%) 202 (16.9%) 163 (16.6%) 134 (11.2%) 92 (9.4%) 1100 (92.3%) 885 (90.4%)

Maldives 15 (5.6%) 18 (7.4%) 26 (9.8%) 32 (13.2%) N/A N/A 217 (81.6%) 200 (82.6%)

Nepal 19 (7.3%) 14 (6.2%) 64 (24.5%) 48 (21.2%) 73 (28%) 59 (26.1%) 200 (76.6%) 160 (70.8%)

Pakistan 84 (9.8%) 64 (9.0%) 485 (56.4%) 407 (56.9%) 2 (0.2%) 2 (0.3%) 696 (80.9%) 582 (81.4%)

Philippines 18 (4.8%) 17 (6.1%) 41 (10.9%) 43 (15.5%) 9 (2.4%) 12 (4.3%) 207 (54.9%) 161 (58.1%)

Timor-Leste 5 (2.5%) 6 (3.2%) 8 (4.0%) 12 (6.4%) 1 (0.5%) 1 (0.5%) 142 (70.6%) 142 (75.5%)

Vietnam 13 (8.5%) 10 (9.8%) 50 (32.7%) 27 (26.5%) 35 (22.9%) 16 (15.7%) 138 (90.2%) 84 (82.4%)

Sub-Saharan Africa

Benin 17 (2.4%) 16 (2.4%) 36 (5%) 46 (6.8%) 25 (3.5%) 22 (3.2%) 483 (66.9%) 476 (70%)

Burkina Faso 36 (11.6%) 31 (11.1%) 5 (1.6%) 2 (0.7%) 11 (3.5%) 9 (3.2%) 207 (66.6%) 173 (61.8%)

Burundi 90 (8.9%) 82 (7.6%) 50 (5%) 44 (4.1%) 29 (2.9%) 30 (2.8%) 570 (56.5%) 598 (55.5%)

Cameroon 66 (7.8%) 61 (7.5%) 20 (2.4%) 17 (2.1%) 63 (7.4%) 74 (9.1%) 492 (58.2%) 477 (58.8%)

Central African Republic

43 (11.9%) 37 (10.9%) 3 (0.8%) 2 (0.6%) 1 (0.3%) 1 (0.3%) 168 (46.4%) 150 (44%)

Chad 4 (4.2%) 7 (6.2%) 4 (4.2%) 2 (1.8%) 0 (0%) 2 (1.8%) 72 (75.8%) 86 (76.1%)

Comoros 33 (27.3%) 37 (32.5%) 7 (5.8%) 5 (4.4%) 5 (4.1%) 11 (9.6%) 84 (69.4%) 80 (70.2%)

Congo (Brazzaville) 59 (33.9%) 57 (39.3%) 11 (6.3%) 6 (4.1%) 13 (7.5%) 11 (7.6%) 126 (72.4%) 106 (73.1%)

Congo Democratic Republic

* 38 (6.2%) * 19 (3.2%) 74 (12.1%) 66 (11%) 101 (16.5%) 93 (15.5%) 403 (65.7%) 368 (61.3%)

Cote d’Ivoire 21 (16.9%) 19 (13%) 3 (2.4%) 5 (3.4%) 12 (9.7%) 14 (9.6%) 80 (64.5%) 101 (69.2%)

Ethiopia 18 (2.9%) 19 (3.1%) 3 (0.5%) 3 (0.5%) 12 (1.9%) 13 (2.1%) 206 (33.3%) 203 (32.8%)

Gabon 82 (31.3%) 67 (27.1%) 9 (3.4%) 5 (2%) 38 (14.5%) 35 (14.2%) 174 (66.4%) 153 (61.9%)

Ghana 31 (19.5%) 27 (19%) 11 (6.9%) 8 (5.6%) 18 (11.3%) 21 (14.8%) 113 (71.1%) 86 (60.6%)

Guinea 11 (4.8%) 13 (7%) 6 (2.6%) 5 (2.7%) 4 (1.8%) 5 (2.7%) 154 (67.8%) 130 (69.5%)

Kenya 50 (13.2%) 52 (15.7%) 30 (7.9%) 26 (7.9%) 38 (10.0%) 28 (8.5%) 269 (71.0%) 230 (69.5%)

Lesotho 22 (10.3%) 19 (7.8%) * 17 (7.9%) * 7 (2.9%) 2 (0.9%) 9 (3.7%) 148 (69.2%) 161 (66.0%)

Liberia 41 (11.0%) 31 (9.7%) 68 (18.2%) 44 (13.7%) 33 (8.8%) 34 (10.6%) 305 (81.6%) 251 (78.2%)

Madagascar 5 (1.6%) 6 (2.0%) 32 (10.4%) 20 (6.8%) 3 (1.0%) 3 (1.0%) 152 (49.4%) 155 (52.4%)

Malawi 231 (16.5%) 197 (14.4%) 68 (4.9%) 54 (4.0%) 0 (0%) 1 (0.1%) ** 1110 (79.3%) ** 1018 (74.6%)

30

Mali 7 (1.9%) 5 (1.5%) * 4 (1.1%) * 11 (3.3%) 12 (3.3%) 16 (4.9%) 219 (59.8%) 198 (60.2%)

Mozambique 44 (9.2%) 42 (9.4%) * 5 (1.0%) * 0 (0%) 6 (1.3%) 4 (0.9%) 315 (65.9%) 291 (65.1%)

Namibia 38 (19.9%) 44 (23.2%) 13 (6.8%) 16 (8.4%) ** 7 (3.7%) ** 0 (0%) ** 119 (62.3%) ** 145 (76.3%)

Niger 14 (2.4%) 11 (2.0%) 15 (2.5%) 9 (1.6%) 12 (2.0%) 15 (2.7%) 446 (75.3%) 398 (70.9%)

Nigeria 55 (9.4%) 61 (10.6%) 34 (5.8%) 36 (6.2%) 83 (14.1%) 89 (15.4%) 411 (69.9%) 423 (73.2%)

Rwanda 0 (0%) 1 (0.3%) 10 (2.2%) 3 (0.8%) 20 (4.5%) 19 (4.9%) * 235 (52.8%) * 176 (45.1%)

Sao Tome and Principe

14 (12.5%) 7 (7.7%) 9 (8.0%) 9 (9.9%) 1 (0.9%) 4 (4.4%) 78 (69.6%) 73 (80.2%)

Senegal 38 (5.5%) 32 (5.5%) 8 (1.2%) 9 (1.6%) ** 19 (2.8%) ** 35 (6.1%) 327 (47.7%) 299 (51.8%)

Sierra Leone 16 (6.6%) 20 (8.4%) 10 (4.1.%) 15 (6.3%) 8 (3.3%) 8 (3.3%) 143 (58.8%) 139 (58.2%)

South Africa 87 (20.4%) 80 (18.7%) 97 (22.7%) 102 (23.8%) 5 (1.2%) 10 (2.3%) 324 (75.9%) 336 (78.5%)

Swaziland 6 (3.0%) 7 (4.1%) 15 (7.6%) 7 (4.1%) 11 (5.6%) 10 (5.9%) 148 (75.1%) 132 (78.1%)

Tanzania 25 (9.0%) 21 (8.4%) 0 (0%) 3 (1.2%) 48 (17.2%) 40 (16.1%) 233 (83.5%) 200 (80.3%)

Togo 31 (8.8%) 23 (6.3%) * 9 (2.5%) * 2 (0.6%) 0 (0%) 3 (0.8%) * 192 (54.2%) * 169 (46.6%)

Uganda 60 (7.0%) 50 (6.4%) 369 (42.9%) 343 (43.9%) 23 (2.7%) 23 (2.9%) 690 (80.2%) 642 (82.2%)

Zambia 19 (7.0%) 16 (6.4%) 7 (2.6%) 3 (1.2%) 12 (4.4%) 9 (3.6%) 209 (76.8%) 180 (71.7%)

Zimbabwe 34 (12.4%) 26 (10.2%) 2 (0.7%) 4 (1.6%) 9 (3.3%) 7 (2.7%) 142 (51.6%) 133 (52.0%)

* p<0.05, **p<0.01; ***p<0.005

Only 40 countries reported data on children suffering from a “suspected pneumonia with a known problem in the chest” (Table 10); 22 had more boys than girls receiving treatment. However, we observed significant difference in only four countries. In Ghana a significantly higher number of boys than girls received treatment while in Namibia, Philippines and Zimbabwe a significantly higher number of girls received treatment for “suspected pneumonia with a known problem in the chest”. In India and Malawi, a significantly higher number of boys than girls presented to a government hospital while in Dominican Republic, Bangladesh and Burundi this difference was noted for a private provider. In the Philippines significantly higher number of girls than boys with “suspected pneumonia with a known problem in the chest” sought care from a pharmacy.

31

Table 10: Gender differences in care seeking for “suspected pneumonia with a known problem in the chest” by various provider categories

Region Government Hospital Private Hospital/Clinic/Doctor

Pharmacy Received any treatment

Boys Girls Boys Girls Boys Girls Boys Girls

Latin America and Caribbean

Colombia 356 (60.1%) 307 (61.6%) 15 (2.5%) 17 (3.4%) 23 (3.9%) 17 (3.4%) 419 (70.8%) 355 (71.3%)

Dominican Republic 128 (50.0%) 108 (50.2%) * 35 (13.7%) * 14 (6.5%) 4 (1.6%) 1 (0.5%) 198 (77.3%) 151 (70.2%)

Guyana 8 (23.5%) 12 (36.4%) 6 (17.6%) 2 (6.1%) 1 (2.9%) 0 (0%) 25 (73.5%) 21 (63.6%)

Haiti 0 (0%) 0 (0%) 3 (11.5%) 1 (4.3%) 0 (0%) 1 (4.3%) 12 (46.2%) 9 (39.1%)

Honduras 21 (5.6%) 11 (3.5%) 47 (12.6%) 39 (12.3%) 2 (0.5%) 4 (1.3%) 230 (61.8%) 175 (55.2%)

Peru 27 (6.1%) 30 (7.3%) 37 (8.3%) 40 (9.8%) 36 (8.1%) 24 (5.9%) 338 (76.0%) 327 (80.0%)

North Africa/West Asia/Europe

Albania 1 (7.1%) 3 (23.1%) 0 (0%) 0 (0%) 0 (0%) 2 (15.4%) 7 (50%) 10 (76.9%)

Armenia 3 (18.8%) 4 (21.1%) 0 (0%) 1 (5.3%) 0 (0%) 0 (0%) 8 (50%) 12 (63.2%)

Azerbaijan 7 (18.4%) 2 (9.5%) 1 (2.6%) 0 (0%) 0 (0%) 0 (0%) 14 (36.8%) 6 (28.6%)

Egypt 23 (11.2%) 15 (10.1%) 110 (53.4%) 67 (45.0%) 8 (3.9%) 12 (8.1%) 162 (78.6%) 114 (76.5%)

Jordan 24 (14.0%) 11 (9.4%) 39 (22.7%) 28 (23.9%) 3 (1.7%) 3 (2.6%) 131 (76.2%) 92 (78.6%)

South and Southeast Asia

Bangladesh 12 (6.1%) 3 (2.3%) * 61 (31.1%) * 27 (20.8%) 37 (18.9%) 31 (23.8%) 171 (87.2%) 108 (83.1%)

Cambodia 3 (2.0%) 2 (2.0%) 44 (29.5%) 24 (23.8%) 17 (11.4%) 14 (13.9%) 131 (87.9%) 85 (84.2%)

India * 39 (9.9%) * 22 (5.6%) 191 (48.4%) 163 (41.5%) 18 (4.6%) 15 (3.8%) 295 (74.7%) 269 (68.4%)

Maldives 0 (0%) 3 (27.3%) 1 (9.1%) 1 (9.1%) N/A N/A 10 (90.9%) 8 (72.7%)

Nepal 5 (6.0%) 8 (11.3%) 23 (27.7%) 19 (26.8%) 29 (34.9%) 23 (32.4%) 66 (79.5%) 59 (83.1%)

Pakistan 48 (11.2%) 31 (8.6%) 243 (56.8%) 205 (56.6%) 0 (0%) 0 (0%) 350 (81.8%) 301 (83.1%)

Philippines 9 (5.6%) 8 (8.7%) 18 (11.3%) 11 (12.0%) ** 2 (1.3%) ** 7 (7.6%) * 85 (53.1%) * 62 (67.4%)

Timor-Leste 2 (3.5%) 4 (7.3%) 3 (5.3%) 1 (1.8%) 0 (0%) 0 (0%) 42 (73.7%) 37 (67.3%)

Sub-Saharan Africa

32

Burkina Faso 8 (20.0%) 2 (7.4%) 0 (0%) 0 (0%) 1 (2.5%) 1 (3.7%) 30 (75.0%) 22 (81.5%)

Burundi 16 (7.2%) 18 (6.9%) * 15 (6.8%) * 6 (2.3%) 6 (2.7%) 6 (2.3%) 144 (64.9%) 159 (61.2%)

Cameroon 13 (9.7%) 14 (10.6%) 4 (3.0%) 2 (1.5%) 12 (9.0%) 14 (10.6%) 86 (64.2%) 82 (62.1%)

Ethiopia 9 (3.6%) 7 (2.7%) 1 (0.4%) 1 (0.4%) 7 (2.8%) 7 (2.7%) 103 (40.7%) 95 (37.1%)

Ghana 9 (20.0%) 9 (19.1%) 5 (11.1%) 2 (4.3%) 8 (17.8%) 5 (10.6%) * 35 (77.8%) * 27 (57.4%)

Kenya 16 (10.9%) 21 (14.3%) 14 (9.5%) 17 (11.6%) 14 (9.5%) 12 (8.2%) 107 (72.8%) 110 (74.8%)

Lesotho 9 (12.5%) 10 (11.9%) 6 (8.3%) 4 (4.8%) 1 (1.4%) 3 (3.6%) 55 (76.4%) 59 (70.2%)

Liberia 18 (12.6%) 15 (11.6%) 28 (19.6%) 16 (12.4%) 13 (9.1%) 16 (12.4%) 121 (84.6%) 103 (79.8%)

Madagascar 2 (1.7%) 2 (1.7%) 8 (7.0%) 9 (7.5%) 2 (1.7%) 1 (0.8%) 59 (51.3%) 63 (52.5%)

Malawi * 93 (23.2%) * 62 (16.1%) 29 (7.2%) 19 (4.9%) 0 (0%) 1 (0.3%) 334 (83.3%) 307 (79.7%)

Namibia 16 (22.2%) 22 (31.0%) 7 (9.7%) 10 (14.1%) 2 (2.8%) 0 (0%) * 48 (66.7%) * 60 (84.5%)

Nigeria 12 (8.6%) 23 (15.8%) 8 (5.7%) 5 (3.4%) 19 (13.6%) 25 (17.1%) 101 (72.1%) 105 (71.9%)

Rwanda 0 (0%) 0 (0%) 1 (0.8%) 1 (1.1%) 6 (5.0%) 3 (3.2%) 72 (59.5%) 59 (62.8%)

Sao Tome and Principe 7 (15.9%) 2 (6.1%) 4 (9.1%) 4 (12.1%) 0 (0%) 0 (0%) 35 (79.5%) 26 (78.8%)

Senegal 13 (10.3%) 7 (5.8%) 2 (1.6%) 2 (1.7%) 3 (2.4%) 5 (4.2%) 76 (60.3%) 65 (54.2%)

Sierra Leone 4 (5.7%) 6 (8.6%) 2 (2.9%) 7 (10.0%) 2 (2.9%) 1 (1.4%) 38 (54.3%) 40 (57.1%)

Swaziland 3 (4.5%) 3 (9.1%) 5 (7.5%) 2 (6.1%) 1 (1.5%) 0 (0%) 48 (71.6%) 27 (81.8%)

Tanzania 11 (11.0%) 10 (11.6%) 0 (0%) 1 (1.2%) 11 (11.0%) 12 (14.0%) 83 (83.0%) 70 (81.4%)

Uganda 19 (6.6%) 20 (7.5%) 115 (40.1%) 128 (47.9%) 7 (2.4%) 6 (2.2%) 228 (79.4%) 227 (85.0%)

Zambia 7 (7.1%) 9 (10.8%) 3 (3.1%) 1 (1.2%) 4 (4.1%) 3 (3.6%) 78 (79.6%) 68 (81.9%)

Zimbabwe 9 (12.3%) 9 (13.0%) 0 (0%) 1 (1.4%) 1 (1.4%) 3 (4.3%) * 37 (50.7%) * 47 (68.1%)

*p<0.05; **p<0.001; ***p<0.0005 Diarrhoea

Overall, in 40 out of the 67 countries a higher proportion of boys compared to girls were taken to a health-care facility (Table 11). In two countries in Asia, Nepal and India a significantly higher proportion of boys compared to girls presented to a health-care provider. Additionally, a significantly higher proportion of boys were taken to private providers in India and government hospitals in Nepal, Congo (Brazzaville) and Senegal. On the other hand,

33

significantly higher proportion of girls compared to boys were taken for treatment in four countries: in Guinea and Guyana (to a private provider); and in Uganda and Egypt (to a pharmacy). Table 11: Gender differences in care seeking for diarrhoea by various provider categories

Total number of children

presenting with diarrhoea

Sought Treatment from a health-care facility

Government hospital Private Hospital/ Doctor/Clinic

Pharmacy

Male Female Male Female Male Female Male Female Male Female

Central Asia

Kazakhstan 78 87 17 (21.8%) 25 (28.7%) 2 (2.6%) 3 (3.4%) 0 (0%) 2 (2.3%) 0 (0%) 0 (0%)

Kyrgyz Republic 106 79 43 (40.6%) 32 (40.5%) 10 (9.4%) 10 (12.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Latin America and Caribbean

Bolivia 1089 963 660 (60.6%) 612 (63.6%) 127 (11.7%) 125 (13.0%) 65 (6.0%) 45 (4.7%) 52 (4.8%) 63 (6.5%)

Brazil 355 344 126 (35.5%) 138 (40.1%) 30 (8.5%) 30 (8.7%) 18 (5.1%) 20 (5.8%) 11 (3.1%) 17 (4.9%)

Colombia 1344 1151 628 (46.7%) 551 (47.9%) 523 (38.9%) 443 (38.5%) 26 (1.9%) 33 (2.9%) 30 (2.2%) 32 (2.8%)

Dominican Republic 944 787 565 (59.5%) 454 (57.7%) 345 (36.5%) 270 (34.3%) 117 (12.4%)

88 (11.2%) 9 (1.0%) 4 (0.5%)

Guatemala 939 895 381 (40.6%) 362 (40.4%) 10 (1.1%) 8 (0.9%) 90 (9.6%) 75 (8.4%) 73 (7.8%) 70 (7.8%)

Guyana 99 103 68 (68.7%) 76 (73.8%) 32 (32.3%) 24 (23.3%) 0 (0%) * 5 (4.9%) * 3 (3.0%) 5 (4.9%)

Haiti 615 581 224 (36.4%) 221 (38.0%) 17 (2.8%) 18 (3.1%) 28 (4.6%) 24 (4.1%) 17 (2.8%) 12 (2.1%)

Honduras 942 851 481 (51.1%) 422 (49.6%) 41(4.4%) 41 (4.8%) 4 (0.4%) 6 (0.7%) 12 (1.3%) 7 (0.8%)

Nicaragua 438 405 219 (50%) 200 (49.4%) 16 (3.7%) 16 (4.0%) 31 (7.1%) 26 (6.4%) 6 (1.4%) 13 (3.2%)

Paraguay 165 169 77 (46.7%) 79 (46.7%) 0 (0%) 3 (1.8%) 16 (9.7%) 20 (11.8%) 15 (9.1%) 11 (6.5%)

Peru 1203 1062 646 (53.7%) 552 (52.0%) 43 (3.6%) 42 (4.0%) 42 (3.5%) 45 (4.2%) 81 (6.7%) 57 (5.4%)

North Africa/West Asia/Europe

Albania 48 33 28 (58.3%) 17 (51.5%) 13 (27.1%) 4 (12.1%) 1 (2.1%) 1 (3.0%) 2 (4.2%) 1 (3.0%)

Armenia 61 65 31 (50.8%) 22 (33.8%) 10 (16.4%) 4 (6.2%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Azerbaijan 124 102 50 (40.3%) 40 (39.2%) 15 (12.1%) 16 (15.7%) 4 (3.2%) 0 (0%) 2 (1.6%) 1 (1.0%)

34

Total number of children

presenting with diarrhoea

Sought Treatment from a health-care facility

Government hospital Private Hospital/ Doctor/Clinic

Pharmacy

Male Female Male Female Male Female Male Female Male Female

Egypt 519 458 318 (61.3%) 288 (62.9%) 51 (9.8%) 30 (6.6%) 181 (34.9%)

152 (33.2%)

29 (5.6%) ** 47 (10.3%) **

Jordan 837 822 508 (60.7%) 491 (59.7%) 93 (11.1%) 76 (9.2%) 148 (17.7%)

144 (17.5%)

16 (1.9%) 14 (1.7%)

Moldova 56 75 22 (39.3%) 31 (41.3%) 4 (7.1%) 8 (10.7%) 1 (1.8%) 1 (1.3%) 0 (0%) 0 (0%)

Morocco 355 368 129 (36.3%) 135 (36.5%) 7 (2.0%) 10 (2.7%) 12 (3.4%) 14 (3.8%) 26 (7.3%) 39 (10.6%)

Turkey 584 457 257 (44%) 180 (39.4%) 65 (11.1%) 39 (8.5%) 88 (15.1%)

72 (15.8%) 8 (1.4%) 8 (1.8%)

Yemen 1055 934 406 (38.4%) 321 (34.3%) N/A N/A 61 (5.8%) 51 (5.5%) 69 (6.5%) 58 (6.2%)

South and South East Asia

Bangladesh 209 174 161 (77.0%) 128 (73.6%) 5 (2.4%) 6 (3.4%) 30 (14.4%)

29 (16.7%) 47 (22.5%) 39 (22.4%)

Cambodia 611 522 447 (73.2%) 396 (75.9%) 18 (2.9%) 13 (2.5%) 17 (28%) 11(2.1%) 76 (12.4%) 58 (11.1%)

India 2417 2003 1654(68.4%) *

1314 (65.6%) * 191 (7.9%) 132 (6.6%) 954 (39.5%) *

731 (36.5%) *

147 (6.1%) 148 (7.4%)

Indonesia 1417 1119 1102 (78.0%) 865 (76.7%) 37 (2.6%) 22 (2.0%) 172 (12.1%)

129 (11.5%)

89 (6.3%) 76 (6.8%)

Maldives 97 88 77 (79.4%) 77 (87.5%) 9 (9.3%) 3 (3.4%) 4 (4.1%) 5 (5.7%) N/A N/A

Nepal 396 283 266 (67.2%) * 164 (58.0%) * 23 (5.8%) ** 4 (1.4%) ** 64 (16.2%)

43 (15.2%) 88 (22.2%) 58 (20.5%)

Pakistan 999 870 709 (71.0%) 599 (68.9%) 84 (8.4%) 79 (9.1%) 474 (47.4%)

393 (45.2%)

7 (0.7%) 6 (0.7%)

Philippines 314 257 118 (37.6%) 113 (44.0%) 17 (5.4%) 8 (3.1%) 30 (9.6%) 29 (11.3%) 2 (0.6%) 3 (1.2%)

Timor-Leste 670 718 500 (74.6%) 520 (72.4%) 8 (1.2%) 12 (1.7%) 33 (4.9%) 28 (3.9%) 1 (0.1%) 4 (0.6%)

Vietnam 80 57 55 (68.8%) 41 (71.9%) 7 (8.8%) 5 (8.8%) 12 (15%) 7 (12.3%) 17 (21.3%) 11 (19.3%)

Sub-Saharan Africa

Benin 744 642 305 (41.0%) 254 (39.6%) 12 (1.6%) 8 (1.2%) 17 (2.3%) 19 (3.0%) 10 (1.3%) 14 (2.2%)

35

Total number of children

presenting with diarrhoea

Sought Treatment from a health-care facility

Government hospital Private Hospital/ Doctor/Clinic

Pharmacy

Male Female Male Female Male Female Male Female Male Female

Burkina Faso 970 941 618 (63.7%) 562 (59.7%) 83 (8.6%) 82 (8.7%) 10 (1.0%) 6 (0.6%) 13 (1.3%) 15 (1.6%)

Burundi 871 881 529 (60.7%) 533 (60.5%) 77 (8.8%) 74 (8.4%) 41 (4.7%) 34 (3.9%) 35 (4.0%) 25 (2.8%)

Cameroon 1041 1002 622 (59.8%) 576 (57.5%) 68 (6.5%) 66 (6.6%) 19 (1.8%) 10 (1.0%) 41 (3.9%) 50 (5.0%)

Central African Republic

299 277 110 (36.8%) 94 (33.9%) 19 (6.4%) 16 (5.8%) 4 (1.3%) 4 (1.4%) 1 (0.3%) 1 (0.4%)

Chad 620 566 346 (55.8%) 308 (54.4%) 25 (4.0%) 14 (2.5%) 5 (0.8%) 10 (1.8%) 10 (1.6%) 7 (1.2%)

Comoros 119 117 63 (52.9%) 66 (56.4%) 19 (16.0%) 23 (19.7%) 6 (5.0%) 4 (3.4%) 3 (2.5%) 9 (7.7%)

Congo (Brazzaville) 330 295 158 (47.9%) 123 (41.7%) 68 (20.6%) * 43 (14.6%) * 10 (3.0%) 4 (1.4%) 13 (3.9%) 6 (2.0%)

Congo Democratic Republic

648 630 247 (38.1%) 240 (38.1%) 19 (2.9%) 17 (2.7%) 1 (0.2%) 1 (0.2%) 20 (3.1%) 26 (4.1%)

Cote d'Ivoire 170 165 92 (54.1%) 92 (55.8%) 23 (13.5%) 18 (10.9%) 1 (0.6%) 3 (1.8%) 8 (10.6%) 9 (5.5%)

Ethiopia 793 700 310 (39.1%) 282 (40.3%) 17 (2.1%) 15 (2.1%) 2 (0.3%) 3 (0.4%) 24 (3.0%) 21 (3.0%)

Gabon 293 295 148 (50.5%) 135 (45.8%) 68 (23.2%) 66 (22.4%) 6 (2.0%) 5 (1.7%) 29 (9.9%) 35 (11.9%)

Ghana 278 270 182 (65.5%) 171 (63.3%) 49 (17.6%) 47 (17.4%) 13 (4.7%) 17 (6.3%) 44 (15.8%) 42(15.6%)

Guinea 445 377 211 (47.4%) 154 (40.8%) 10 (2.2%) 12 (3.2%) 2 (0.4%) *

8 (2.1%) * 6 (1.3%) 3 (0.8%)

Kenya 498 436 306 (61.4%) 263 (60.3%) 71 (14.3%) 48 (11.0%) 29 (5.8%) 26 (6.0%) 24 (4.8%) 31 (7.1%)

Lesotho 208 209 125 (60.1%) 122 (58.4%) 25 (12.0%) 14 (6.7%) 12 (5.8%) 17 (8.1%) 0 (0%) 1 (0.5%)

Liberia 548 498 446 (81.4%) 396 (79.5%) 64 (11.7%) 50 (10%) 74 (13.5%)

66 (13.3%) 79 (14.4%) 58 (11.6%)

Madagascar 521 470 225 (43.2%) 207 (44.0%) 5 (1.0%) 5 (1.1%) 31 (6.0%) 36 (7.7%) 7 (1.3%) 3 (0.6%)

Malawi 1522 1474 1132 (74.4%) 1089 (73.9%) 218 (14.3%) 227 (15.4%) 43 (2.8%) 47 (3.2%) 0 (0%) 0 (0%)

Mali 752 696 241 (32.0%) 203 (29.1%) 9 (1.2%) 5 (0.7%) 4 (0.5%) 9 (1.3%) 1 (0.1%) 0 (0%)

Mozambique 632 567 347 (54.9%) 318 (56.1%) 45 (7.1%) 36 (6.3%) 1 (0.2%) 1 (0.2%) 5 (0.8%) 3 (0.5%)

Namibia 287 282 187 (65.2%) 174 (61.7%) 54 (18.8%) 53 (18.8%) 8 (2.8%) 15 (5.3%) 6 (2.1%) 3 (1.1%)

Niger 802 803 281 (35%) 280 (34.9%) 8 (1.0%) 12 (1.5%) 11 (1.4%) 20 (2.5%) 2 (0.2%) 0 (0%)

36

Total number of children

presenting with diarrhoea

Sought Treatment from a health-care facility

Government hospital Private Hospital/ Doctor/Clinic

Pharmacy

Male Female Male Female Male Female Male Female Male Female

Nigeria 1283 1115 868 (67.7%) 745 (66.8%) 129 (10.1%) 117 (10.5%) 65 (5.1%) 44 (3.9%) 167 (13%) 140 (12.6%)

Rwanda 577 495 297 (51.5%) 239 (48.3%) 1 (0.2%) 0 (0%) 3 (0.5%) 0 (0%) 18 (3.1%) 26 (5.3%)

Sao Tome and Principe

122 102 70 (57.4%) 62 (60.8%) 7 (5.7%) 3 (2.9%) 4 (3.3%) 8 (7.8%) 2 (1.6%) 0 (0%)

Senegal 1173 1023 579 (49.4%) 494 (48.3%) 67 (5.7%) * 32 (3.1%) * 9 (0.8%) 9 (0.9%) 21 (1.8%) 31 (3.0%)

Sierra Leone 302 272 181 (59.9%) 144 (52.9%) 15 (5.0%) 14 (5.1%) 13 (4.3%) 15 (5.5%) 16 (5.3%) 15 (5.5%)

South Africa 339 275 215 (63.4%) 178 (64.7%) 59 (17.4%) 54 (19.6%) 52 (15.3%)

46 (16.7%) 4 (1.2%) 2 (0.7%)

Swaziland 197 149 148 (75.1%) 115 (77.2%) 7 (3.6%) 9 (6.0%) 14 (7.1%) 10 (6.7%) 7 (3.6%) 2 (1.3%)

Tanzania 509 471 340 (66.8%) 310 (65.8%) 35 (6.9%) 24 (5.1%) 8 (1.6%) 7 (1.5%) 53 (10.4%) 59 (12.5%)

Togo 583 588 223 (38.3%) 228 (38.8%) 26 (4.5%) 30 (5.1%) 5 (0.9%) 1 (0.2%) 1 (0.2%) 5 (0.9%)

Uganda 855 817 659 (77.1%) 646 (79.1%) 57 (6.7%) 40 (4.9%) 340 (39.8%)

337 (41.2%)

9 (1.1%) * 22 (2.7%) *

Zambia 443 451 296 (66.8%) 305 (67.6%) 20 (4.5%) 22 (4.9%) 9 (2.0%) 7 (1.6%) 4 (0.9%) 8 (1.8%)

Zimbabwe 357 317 144 (40.3%) 131 (41.3%) 40 (11.2%) 24 (7.6%) 2 (0.6%) 1 (0.3%) 5 (1.4%) 3 (0.9%)

In 27 out of 54 countries a higher proportion of boys compared to girls were taken to a health care provider to receive treatment for bloody diarrhoea (Table 12). In four countries Kenya, Madagascar, Pakistan and Armenia, a significantly higher proportion of boys compared to girls with bloody diarrhoea taken to receive treatment from a health-care provider. Two countries, Nigeria and Paraguay, revealed a statistically significantly higher proportion of boys compared to girls were taken to a pharmacy, whilst South Africa was the only country where a significantly higher proportion of boys compared to girls presented to the private sector. In two countries in Asia, Bangladesh and Cambodia, a significantly higher proportion of girls compared to boys were taken to government hospitals.

37

Table 12: Gender differences in care seeking for bloody diarrhoea by various provider categories

Total number of children presenting

with diarrhoea

Treatment Sought from a health-care facility

Government hospital Private Hospital/ Doctor/Clinic

Pharmacy

Male Female Male Female Male Female Male Female Male Female

Central Asia

Kyrgyz Republic 4 2 1 (25.0%) 2 (100%) 0 (0%) 1 (50%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Latin America

Bolivia 224 196 153 (68.3%) 141 (71.9%) 31 (13.8%) 28 (14.3%) 12 (5.4%) 7 (3.6%) 11 (4.9%) 15 (7.7%)

Brazil 30 29 13 (43.3%) 15 (51.7%) 2 (6.7%) 4 (13.8%) 1 (3.3%) 1 (3.4%) 2 (6.7%) 1 (3.4%)

Colombia 285 256 173 (60.7%) 142 (55.5%) 147 (51.6%) 119 (36.5%) 8 (2.8%) 7 (2.7%) 3 (1.1%) 7 (2.7%)

Dominican Republic 82 58 64 (78.0%) 44 (75.9%) 44 (53.7%) 30 (51.7%) 10 (12.2%) 9 (15.5%) 0 (0%) 1 (1.7%)

Guatemala 154 170 74 (48.1%) 80 (47.1%) 3 (1.9%) 0 (0%) 17 (11%) 17 (10%) 16 (10.4%)

11 (6.5%)

Guyana 10 13 9 (90.0%) 10 (76.9%) 5 (50.0%) 4 (30.8%) 0 (0%) 0 (0%) 1 (10.0%) 0 (0%)

Haiti 132 112 51 (38.6%) 49 (43.8%) 6 (4.5%) 5 (4.5%) 6 (4.5%) 3 (2.7%) 2 (1.5%) 1 (0.9%)

Honduras 87 94 56 (64.4%) 50 (53.2%) 7 (8.0%) 3 (3.2%) 2 (2.3%) 0 (0%) 1 (1.1%) 0 (0%)

Paraguay 17 22 10 (58.8%) 11 (50.0%) 0 (0%) 0 (0%) 2 (11.8%) 3 (13.6%) 4 (23.5%) *

0 (0%) *

Peru 190 158 124 (65.3%) 103 (65.2%) 5 (2.6%) 5 (3.2%) 5 (2.6%) 4 (2.5%) 12 (6.3%) 10 (6.3%)

North Africa/West Asia/Europe

Albania 4 0 2 (50.0%) N/A 2 (50.0%) N/A 0 (0%) N/A 0 (0%) N/A

Armenia 5 3 5 (100%) ***

0 (0%) *** 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Azerbaijan 8 5 4 (50.0%) 3 (60.0%) 2 (25%) 1 (20%) 1 (12.5%) 0 (0%) 0 (0%) 0 (0%)

Egypt 25 20 18 (72.0%) 15 (75.0%) 2 (8.0%) 1 (5.0%) 8 (32.0%) 5 (25.0%) 1 (4.0%) 3 (15.0%)

Jordan 52 58 42 (80.8%) 43 (74.1%) 10 (19.2%) 10 (17.2%) 10 (19.2%) 12 (20.7%) 2 (3.8%) 1 (1.7%)

Turkey 61 60 37 (60.7%) 32 (53.3%) 12 (19.7%) 12 (20%) 14 (23.0%) 11 (18.3%) 0 (0%) 1 (1.7%)

Yemen 243 199 90 (36.7%) 63 (31.7%) N/A N/A 17 (7.0%) 9 (4.5%) 10 (4.1%) 13 (6.5%)

38

Total number of children presenting

with diarrhoea

Treatment Sought from a health-care facility

Government hospital Private Hospital/ Doctor/Clinic

Pharmacy

Male Female Male Female Male Female Male Female Male Female

South & Southeast Asia

Bangladesh 33 22 27 (81.8%) 18 (81.8%) 0 (0%) * 3 (13.6%) * 6 (18.2%) 3 (13.6%) 6 (18.2%) 6 (27.3%)

Cambodia 72 57 80.6% (58) 78.9% (45) 0 (0%) * 3 (5.3%) * 4 (5.6%) 2 (3.5%) 9 (12.5%) 5 (8.8%)

India 196 205 145 (74.0%) 157 (76.6%) 20 (10.2%) 16 (7.8%) 74 (37.8%) 78 (38.0%) 22(11.2%) 15 (7.3%)

Maldives 7 3 7 (100%) 3 (100%) 0 (0%) 0 (0%) 1 (14.3%) 0 (0% Empty empty

Nepal 73 49 51 (69.9%) 33 (67.3%) 4 (5.5%) 2 (4.1%) 10 (13.7%) 13 (26.5%) 16 (21.9%)

10 (20.4%)

Pakistan 121 115 106 (87.6%) *

88 (76.5%) * 15 (12.4%) 16 (13.9%) 70 (57.9%) 57 (49.6%) 0 (0%) 1 (0.9%)

Philippines 23 14 11 (47.8%) 8 (57.1%) 2 (8.7%) 0 (0%) 4 (17.4%) 1 (7.1%) 0 (0%) 0(0%)

Timor-Leste 58 53 46 (79.3%) 39 (73.6%) 2 (3.4%) 2 (3.8%) 4 (6.9%) 1 (1.9%) 0 (0%) 1 (1.9%)

Vietnam 5 5 5 (100%) 5 (100%) 1 (20%) 1 (20%) 0 (0%) 0 (0%) 2 (40%) 1 (20%)

Sub-Saharan Africa

Burkina Faso 116 132 87 (75%) 88 (66.7%) 8 (6.9%) 14 (10.6%) (0) 0% 1 (0.8%) 1 (0.9%) 2 (1.5%)

Burundi 137 132 102 (74.5%) 103 (78%) 21 (15.3%) 15 (11.4 %( 6 (4.4%) 8 (6.1%) 5 (3.6%) 3 (2.3%)

Cameroon 214 213 149 (69.6%) 140 (65.7%) 14 (6.5%) 10 (4.7%) 5 (2.3%) 2 (0.9%) 15 (7.0%) 16 (7.5%)

Central African Republic 52 38 21 (40.4%) 16 (42.1%) 3 (5.8%) 4 (10.5%) 0 (0%) 2 (5.3%) 0 (0%) (0) 0%

Comoros 16 15 11 (68.8%) 10 (66.7%) 3 (18.8%) 0 (0%) 1 (6.3%) 1 (6.7%) 0 (0%) 2 (13.3%)

Cote d'Ivoire 33 35 20 (60.6%) 22 (62.9%) 3 (9.1%) 3 (8.6%) 0 (0%) 0 (0%) 5 (15.2%) 2 (5.7%)

Ethiopia 206 198 87 (42.2%) 85 (42.9%) 6 (2.9%) 1 (0.5%) 0 (0%) 0 (0%) 9 (4.4%) 8 (4.0%)

Gabon 29 33 17 (58.6%) 18 (54.5%) 8 (27.6%) 11 (33.3%) 0 (0%) 0 (0%) 3 (10.3%) 6 (18.2%)

Ghana 50 43 44 (88.0%) * 29 (67.4%) * 13 (26%) 5 (11.6%) 4 (8.0%) 6 (14.0%) 9 (18.0%) 4 (9.3%)

Kenya 104 74 77 (74.0%) 53 (71.6%) 13 (12.5%) 9 (12.2%) 5 (4.8%) 4 (5.4%) 10 (9.6%) 7 (9.5%)

Lesotho 46 41 30 (65.2%) 26 (63.4%) 6 (13%) 4 (9.8%) 3 (6.5%) 2 (4.9%) 0 (0%) 0 (0%)

39

Total number of children presenting

with diarrhoea

Treatment Sought from a health-care facility

Government hospital Private Hospital/ Doctor/Clinic

Pharmacy

Male Female Male Female Male Female Male Female Male Female

Liberia 127 147 104 (81.9%) 119 (81.0%) 15 (11.8%) 10 (6.8%) 25 (19.7%) 19 (12.9%) 15 (11.8%)

14 (9.5%)

Madagascar 62 52 41 (66.1%) * 24 (46.2%) * 0 (0%) 1 (1.9%) 7 (11.3%) 5 (9.6%) 1 (1.6%) 0 (0%)

Malawi 227 213 174 (76.7%) 170 (79.8%) 37 (16.3%) 42 (19.7%) 8 (3.5%) 4 (1.9%) 0 (0%) 0 (0%)

Namibia 41 44 32 (78%) 31 (70.5%) 12 (29.3%) 10 (22.7%) 2 (4.9%) 5 (11.4%) 1 (2.4%) 1 (2.3%)

Nigeria 264 268 203 (76.9%) 191 (71.3%) 31 (11.7%) 27 (10.1%) 9 (3.4%) 13 (4.9%) 49 (18.6%)*

33 (12.3%) *

Rwanda 92 73 61 (66.3%) 50 (68.5%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 5 (5.4%) 5 (6.8%)

Sao Tome and Principe 9 10 4 (44.4%) 6 (60.0%) 0 (0%) 0 (0%) 0 (0%) 1 (10%) 0 (0%) 0 (0%)

Senegal 122 114 75 (61.5%) 71 (62.3%) 11 (9.0%) 4 (3.5%) 0 (0%) 0 (0%) 1 (0.8%) 5 (4.4%)

Sierra Leone 69 61 39 (56.5%) 40 (65.6%) 4 (5.8%) 4 (6.6%) 3 (4.3%) 3 (4.9%) 4 (5.8%) 4 (6.6%)

South Africa 48 44 39 (81.3%) 31 (70.5%) 12 (25%) 10 (22.7%) 16 (33.3%) *

5 (11.4%) * 1 (2.1%) 0 (0%)

Swaziland 43 37 32 (74.4%) 32 (86.5%) 3 (7.0%) 3 (8.1%) 2 (4.7%) 4 (10.8%) 1 (2.3%) 0 (0%)

Tanzania 73 50 57 (78.1%) 41 (82.0%) 2 (2.7%) 1 (2.0%) 1 (1.4%) 0 (0%) 15 (20.5%)

10 (20.0%)

Togo 115 118 50 (43.7%) 57 (48.3%) 2 (1.7%) 7 (5.9%) 0 (0%) 0 (0%) 0 (0%) 2 (1.7%)

Uganda 165 138 136 (82.4%) 119 (86.2%) 10 (6.1%) 6 (4.3%) 77 (46.7%) 56 (40.6%) 3 (1.8%) 1 (0.7%)

Zambia 57 57 42 (73.7%) 43 (75.4%) 1 (1.8%) 2 (3.5%) 0 (0%) 0 (0%) 1 (1.8%) 2 (3.5%)

Zimbabwe 37 24 19 (51.4%) 13 (54.2%) 8 (21.6%) 4 (16.7%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

*p<0.05; **p<0.01; ***p<0.005

Febrile illness

Fifty six countries reported data on care-seeking (by gender) for fever (Table 13). Of these, 28 countries reported a higher proportion of care seeking for boys compared to girls for febrile illnesses. In 3 countries- Nepal Yemen and Mali a significantly higher proportion of boys with fever sought health care. In 2 countries, a significantly higher proportion of boys sought treatment from government hospitals- Kenya and Yemen. A significantly higher proportion

40

of boys with fever were taken to a private provider in 3 countries- Burkina Faso, Guinea and Timor Leste. While more girls with fever are seen in government hospitals in India, the proportion of boys with fever seeking care from the private health sector is significantly higher. In Cambodia a higher proportion of boys sought care for fever from a pharmacy. Table 13: Gender differences in care seeking for fever by various provider categories

Total number of children with fever

Sought treatment/advice overall

Government hospitals Private health sector Pharmacy

Male Female Male Female Male Female Male Female Male Female

Central Asia

Kazakhstan 35 40 12 (34.3%) 16 (40.0%) 2 (5.7%) 2 (5.0%) 0 (0.0%) 1 (2.5%) 0 (0.0%) 0 (0.0%)

Latin America and Caribbean

Columbia 701 652 353 (50.4%) 303 (46.5%) 281 (40.1%) 243 (37.3%) 17 (2.4%) 20 (3.1%) 26 (3.7%) 24 (3.7%)

Dominican Republic 367 329 128 (34.9%) 107 (32.5%) 145 (39.5%) 128 (38.9%) 44 (12.0%) 42 (12.8%) 2 (0.5%) 5 (1.5%)

Guyana 58 56 32 (55.2%) 36 (64.3%) 14 (24.1%) 12 (21.4%) 3 (5.2%) 2 (3.6%) 2 (3.4%) 3 (5.4%)

Nicaragua 135 154 82 (60.7%) 95 (61.7%) 9 (6.7%) 6 (3.9%) 14 (10.4%) 16 (10.4%) 11 (8.1%) 11 (7.1%)

Paraguay 85 118 43 (50.6%) 74 (62.7%) 0 (0.0%) 0 (0.0%) 20 (23.5%) 34 (28.8%) 6 (7.1%) 6 (5.1%)

Peru 383 348 243 (63.4%) 227 (65.2%) 13 (3.4%) 21 (6.0%) 29 (7.6%) 22 (6.3%) 22 (5.7%) 24 (6.9%)

North Africa/West Asia/Europe

Albania 22 23 17 (77.3%) 13 (56.5%) 5 (22.7%) 1 (4.3%) 0 (0.0%) 0 (0.0%) 2 (9.1%) 2 (8.7%)

Armenia 34 21 17 (50.0%) 11 (52.4%) 6 (17.6%) 3 (14.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%)

Azerbaijan 57 48 27 (47.4%) 25 (52.1%) 9 (15.8%) 11 (22.9%) 0 (0.0%) 1 (2.1%) 0 (0.0%) 0 (0.0%)

Egypt 203 193 108 (53.2%) 106 (54.9%) 11 (5.4%) 10 (5.2%) 69 (34.0%) 53 (27.5%) 13 (6.4%) 15 (7.8%)

Jordan 283 314 199 (70.3%) 212 (67.5%) 33 (11.7%) 34 (10.8%) 55 (19.4%) 70 (22.3%) 5 (1.8%) 6 (1.9%)

Moldova 52 66 29 (55.8%) 32 (48.5%) 8 (15.4%) 8 (12.1%) 1 (1.9%) 2 (3.0%) 0 (0.0%) 3 (4.5%)

Morocco 164 168 102 (62.2%) 98 (58.3%) 10 (6.1%) 4 (2.4%) 11 (6.7%) 9 (5.4%) 51 (31.1%)

44 (26.2%)

Turkey 144 147 40 (27.8%) 44 (29.9%) 11 (7.6%) 12 (8.2%) 9 (6.3%) 6 (4.1%) 2 (1.4%) 3 (2.0%)

41

Total number of children with fever

Sought treatment/advice overall

Government hospitals Private health sector Pharmacy

Male Female Male Female Male Female Male Female Male Female

Yemen 339 308 182 (53.7%)***

122 (39.6%)***

90 (26.5%) 55 (18.0%) 20 (5.9%) 14 (4.6%) 44 (13.0%)

41 (13.4%)

South and South East Asia

Bangladesh 397 394 264 (66.5%) 251 (63.7%) 0 (0.0%) 0 (0.0%) 51 (12.8%) 64 (16.2%) 81 (20.4%)

86 (21.8%)

Cambodia 415 369 317 (76.4%) 281 (76.2%) 14 (3.4%) 7 (1.9%) 69 (16.6%) 62 (16.8%) 72 (17.3%)*

42 (11.4%)*

India 1117 974 818 (73.2%) 678 (69.6%) 63 (5.6%)* 79 (8.1%)* 535 (47.9%)***

393 (40.3%)***

49 (4.4%) 56 (5.7%)

Indonesia 771 676 674 (87.4%) 575 (85.1%) 18 (2.3%) 13 (1.9%) 102 (13.2%) 92 (13.6%) 86 (11.2%)

64 (9.5%)

Maldives 201 191 163 (81.1%) 161 (84.3%) 9 (4.5%) 11 (5.8%) 15 (7.5%) 14 (7.3%) N/A N/A

Nepal 160 108 106 (66.3%)* 57 (52.8%)* 6 (3.8%) 3 (2.8%) 34 (21.3%) 24 (22.2%) 42 (26.3%)

18 (16.7%)

Pakistan 351 296 263 (74.9%) 210 (70.9%) 0 (0.0%) 0 (0.0%) 186 (53.0%) 137 (46.3%)

4 (1.1%) 3 (1.0%)

Philippines 247 197 99 (40.1%) 67 (34.0%) 13 (5.3%) 5 (2.5%) 20 (8.1%) 15 (7.6%) 6 (2.4%) 4 (2.0%)

Timor-Leste 324 319 234 (72.2%) 217 (68.0%) 0 (0.0%) 3 (0.9%) 24 (7.4%)* 10 (3.1%)* 0 (0.0%) 0 (0.0%)

Sub-Saharan Africa

Angola 1328 1317 781 (58.8%) 813 (61.7%) 245 (18.4%) 240 (18.2%) 62 (4.7%) 79 (6.0%) 23 (1.7%) 20 (1.5%)

Benin 1029 990 655 (63.7%) 628 (63.4%) 34 (3.3%) 29 (2.9%) 57 (5.5%) 53 (5.4%) 25 (2.4%) 19 (1.9%)

Burkina Faso 1060 1000 687 (64.8%) 646 (64.6%) 82 (7.7%) 72 (7.2%) 8 (0.8%)* 1 (0.1%)* 20 (1.9%) 29 (2.9%)

Burundi 408 310 259 (63.5%) 205 (66.1%) 35 (8.6%) 25 (8.1%) 13 (3.2%) 18 (5.8%) 12 (2.9%) 6 (1.9%)

Cameroon 488 475 276 (56.6%) 270 (56.8%) 38 (7.8%) 40 (8.4%) 9 (1.8%) 11 (2.3%) 25 (5.1%) 33 (6.9%)

Chad 467 440 304 (65.1%) 279 (63.4%) 21 (4.5%) 18 (4.1%) 9 (1.9%) 9 (2.0%) 5 (1.1%) 7 (1.6%)

Congo (Brazzaville) 208 163 142 (68.3%) 108 (66.3%) 81 (38.9%) 51 (31.3%) 4 (1.9%) 3 (1.8%) 11 (5.3%) 8 (4.9%)

Congo Democratic Republic

372 363 221 (59.4%) 213 (58.7%) 18 (4.8%) 19 (5.2%) 51 (13.7%) 42 (11.6%) 42 (11.3%)

54 (14.9%)

42

Total number of children with fever

Sought treatment/advice overall

Government hospitals Private health sector Pharmacy

Male Female Male Female Male Female Male Female Male Female

Ethiopia 384 339 126 (32.8%) 106 (31.3%) 6 (1.6%) 8 (2.4%) 36 (9.4%) 25 (7.4%) 11 (2.9%) 9 (2.7%)

Ghana 127 100 86 (67.7%) 68 (68.0%) 26 (20.5%) 22 (22.0%) 8 (6.3%) 8 (8.0%) 20 (15.7%)

10 (10.0%)

Guinea 382 380 213 (55.8%) 196 (51.6%) 15 (3.9%) 9 (2.4%) 12 (3.1%)* 3 (0.8%)* 9 (2.4%) 5 (1.3%)

Kenya 216 180 128 (59.3%) 91 (50.6%) 34 (15.7%)* 14 (7.8%)* 14 (6.5%) 14 (7.8%) 8 (3.7%) 10 (5.6%)

Lesotho 86 99 48 (55.8%) 50 (50.5%) 10 (11.6%) 5 (5.1%) 5 (5.8%) 6 (6.1%) 2 (2.3%) 0 (0.0%)

Liberia 241 219 187 (77.6%) 177 (80.8%) 29 (12.0%) 35 (16.0%) 34 (14.1%) 29 (13.2%) 26 (10.8%)

18 (8.2%)

Madagascar 168 170 80 (47.6%) 75 (44.1%) 5 (3.0%) 7 (4.1%) 14 (8.3%) 15 (8.8%) 1 (0.6%) 4 (2.4%)

Malawi 1466 1400 1063 (72.5%) 1009 (72.1%)

228 (15.6%) 188 (13.4%) 48 (3.3%) 47 (3.4%) 0 (0.0%) 2 (0.1%)

Mali 593 544 351 (59.2%)**

274 (50.4%)**

5 (0.9%) 9 (1.7%) 6 (1.0%) 2 (0.4%) 16 (2.8%) 11 (2.1%)

Mozambique 602 624 373 (62.0%) 409 (65.5%) 30 (5.0%) 32 (5.1%) 2 (0.3%) 5 (0.8%) 1 (0.2%) 5 (0.8%)

Namibia 132 109 76 (57.6%) 65 (59.6%) 15 (11.4%) 14 (12.8%) 15 (11.4%) 6 (5.5%) 3 (2.3%) 0 (0.0%)

Niger 363 374 220 (60.6%) 230 (61.5%) 8 (2.2%) 6 (1.6%) 2 (0.6%)* 9 (2.4%)* 7 (1.9%) 5 (1.3%)

Nigeria 1050 856 739 (70.4%) 603 (70.4%) 127 (12.1%) 89 (10.4%) 58 (5.5%) 36 (4.2%) 79 (7.5%)*

87 (10.2%)*

Rwanda 236 222 119 (50.4%) 105 (47.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (0.5%) 11 (4.7%) 4 (1.8%)

Sao Tome and Principe

31 27 22 (71.0%) 21 (77.8%) 6 (19.4%) 1 (3.7%) 0 (0.0%) 2 (7.4%) 0 (0.0%) 0 (0.0%)

Senegal 578 494 282 (48.8%) 238 (48.2%) 34 (5.9%) 21 (4.3%) 4 (0.7%) 4 (0.8%) 19 (3.3%) 25 (5.1%)

Sierra Leone 232 212 139 (59.9%) 109 (51.4%) 15 (6.5%) 9 (4.2%) 14 (6.0%) 19 (9.0%) 17 (7.3%) 12 (5.7%)

Swaziland 81 55 31 (38.3%) 20 (36.4%) 3 (3.7%) 4 (7.3%) 6 (7.4%) 2 (3.6%) 3 (3.7%) 5 (9.1%)

Tanzania 293 309 231 (78.8%)* 264 (85.4%)*

18 (6.1%) 27 (8.7%) 3 (1.0%) 0 (0.0%) 54 (18.4%)

51 (16.5%)

Uganda 470 467 385 (81.9%) 399 (85.4%) 22 (4.7%) 25 (5.4%) 189 (40.2%) 201 (43.0%)

10 (2.1%) 8 (1.7%)

43

Total number of children with fever

Sought treatment/advice overall

Government hospitals Private health sector Pharmacy

Male Female Male Female Male Female Male Female Male Female

Zambia 169 169 120 (71.0%) 123 (72.8%) 14 (8.3%) 8 (4.7%) 3 (1.8%) 1 (0.6%) 2 (1.2%) 3 (1.8%)

Zimbabwe 109 118 40 (36.7 %) 56 (47.5%) 5 (4.6%) 6 (5.1%) 0 (0.0%) 3 (2.5%) 6 (5.5%) 7 (5.9%)

*p<0.05; **p<0.01; ***p<0.005

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Discussion

Our systematic literature review aimed to estimate and report on gender differences in rates of hospitalisation and in-hospital case fatality ratio for pneumonia, diarrhoea and malaria in children. We expected to report the ratio of hospital admissions and in-hospital case fatality rates for boys compared to girls for these three diseases. This would help us identify areas where gender discrimination in care-seeking or health service care exists (after adjusting for the (biological) risk that can be attributed to gender. [50] We also hoped to then conduct a meta-analysis and report the meta-estimates of relative risk (with 95% CI) by WHO regions as was done by Nair and colleagues. The data retrieved does not allow such an analysis, apart from the description of the general comparison of hospital admissions and case fatality ratios. Our review highlights a paucity of gender specific data on hospital admissions and case fatality ratios, in the published literature.

Hospital admissions in young children with pneumonia, diarrhoea and fever

For the comparison of rates of hospitalisation by gender for pneumonia, the bulk of evidence has been identified through our previous international working group (on hospitalised severe pneumonia) (Nair et al Lancet 2013) which was based on data from a substantial number (28) of unpublished studies. For pneumonia, an excess admission rates for boys was expected given their increased biological susceptibility, especially in the first year of life. However, our meta-analysis showed a much greater difference in admission rates for boys than expected, especially in South Asia. This gender difference persists after infancy and cannot be explained solely by increased biological susceptibility for pneumonia in boys. In this current systematic review we identified 2 additional studies (not reported in Nair et al.) from South Asia which reported on admission incidence rates for pneumonia [52] and for pneumonia and diarrhoea and fever [50]. Both these studies also report similar levels of differential hospitalisation rates by gender. In addition, the study in the outskirts of New Delhi [50] reports a higher odds of dying for girls (community study) and highlights the fact that gender bias against girls appears to be greater among more educated mothers in this study population. As for pneumonia we found only a very small number of published studies reporting on admission rates for malaria and diarrhoea. In the case of diarrhoea, all four studies reported a significantly higher admission rate for boys (but with the gender differential less than that reported in the pneumonia studies). Only one study reported admission rates for malaria and no difference by gender was reported. This again demonstrates that the published literature reporting on paediatric admission rates by gender in LMIC is very limited. To draw meaningful conclusions about differential hospital admission rates by gender it is crucial to identify unpublished sources of data to complement the limited published data. This is particularly important from countries where child gender ratios are skewed (such as in several countries in Asia) and where gender discrimination against girls is reported to be prevalent. Our experience from an international working group on hospital studies on pneumonia has been that this approach can be very successful at identifying good quality unpublished data. For example, some of the published hospital based studies that reported admissions by gender were reported but without denominator data may have unreported catchment area child population data by gender. We also describe in our results those hospital-based studies reporting on admissions without any population denominator data. We then compared the admission gender ratio in each study with the child population gender ratio for the province (or when not available, country) where the study was conducted. In table 6 all but two studies from Africa (one on pneumonia and one on diarrhoea) describe an admission gender ratio greater than the child population gender ratio. The size of this

45

difference tends to be greater in South Asian, EMRO region and Chinese studies suggesting preferential care seeking for boys in these countries. In conclusion, there is clear evidence for gender discrimination (against girls) in hospital admission for pneumonia in some countries from our previous meta-analysis (Nair Lancet 2013). However, it is important to continue this work by making further efforts to identify more LMIC studies, especially from the EMRO region, South Asia and China. This will allow a more detailed analysis of differences by age group and region. For diarrhoea and malaria, the published evidence is insufficient to produce estimates and to draw final conclusions. Several malaria studies which we identified did not fulfil the section criteria adopted by the review, suggesting a need for better studies to be conducted. The higher hospital admission gender ratio than gender ratio in the child population in South Asia, EMRO region and China (for the studies with no denominator) suggests that there is a gender bias in hospital admission rates in these regions. This could reflect gender differences in care-seeking and/ or in health service care and it is important that these initial data are confirmed with data from studies reporting on population-based rates.

Case fatality ratios

Few studies reported CFRs and admissions by gender for specific diseases. If male child preferential treatment exists in a community, we would expect the studies to describe a lower rate of admissions for girls compared to boys and a higher than expected CFR for girls. It is important to note that for pneumonia, because boys are biologically more vulnerable to pneumonia and death by pneumonia in the first year of life, even if the CFRs were similar in both sexes, this could also suggest gender bias against girls. In our review, for pneumonia, two thirds of the studies reporting CFRs reported a higher CFRs for girls compared to boys and in one (in Madagascar) a significant difference was reported [112]. For diarrhoea, three Asian studies report significantly higher CFRs for girls. Of the three African studies reporting on diarrhoea, one reported a significantly (borderline)higher CFR for infant boys, two others no significant difference. One Latin American study reported no difference in CFRs per gender for diarrhoea. For Malaria, only one study conducted in Yemen[118] reported a significant difference with girls three times more likely to die from severe malaria than boys. However, many of these studies typically report on small numbers of child deaths and more data are required. To produce more reliable estimates of differences in CFR by gender we would need more studies per disease (since CFRs are disease specific) and larger studies reporting data diaggregated by age group (to assess the role of biological vulnerability of boys in the first year of life). We also need to collate more extensive data from developed countries where gender bias is not prevalent to be able to compare our results to those in LMICs. Therefore, although our review suggest that CFRs for these threee diseases is affected by gender discrimination against girls in some LMICs, it does not allow us to produce reliable estimates because of the scarcity and quality of data reported.

Summary of care-seeking data by gender from DHS and MICS surveys

We recently conducted a recent systematic review into general care seeking for common childhood illnesses (cough, diarrhoea, fever) in children [119] and highlighted the scarcity of published data by gender. We therefore conducted an initial analysis of care-seeking by child gender based on available DHS data. Care seeking from healthcare providers was generally higher in boys- most of this perhaps could be due to increased biological susceptibility in boys. However, in some countries significant differences were noted in overall care-seeking for an episode of pneumonia, diarrhoea or fever; or specifically for a category of service provider. In six of the seven countries (Egypt, Turkey, India, Malawi, Rwanda

46

and Togo) that reported significant differences in care seeking for “suspected pneumonia” – a higher proportion of boys sought care. In 4 countries - Bangladesh, Cambodia, India and the Democratic Republic of Congo a significantly higher number of boys were taken to a government hospital. In eight countries (Dominican Republic, Guyana, Egypt, Cambodia, India, Lesotho, Mozambique and Togo) significantly higher numbers of boys than girls received treatment for “suspected pneumonia” from a private provider. For diarrhoea, two countries in Asia, Nepal and India reported that a significantly higher proportion of boys compared to girls presented to a health-care provider. Additionally, a significantly higher proportion of boys were taken to private providers in India and government hospitals in Nepal, Congo (Brazzaville) and Senegal. We observed that for febrile illnesses, three countries- Nepal, Yemen and Mali reported that a significantly higher proportion of boys with fever sought health care. In 2 countries, a significantly higher proportion of boys sought treatment from government hospitals- Kenya and Yemen. A significantly higher proportion of boys with fever were taken to a private provider in 4 countries- India, Burkina Faso, Guinea and Timor Leste. This work should be extended to include MICS data so that a comprehensive report on reported (2 week period) prevalence of fever, diarrhoea and ARI symptoms; and of care-seeking for these conditions can be reported by gender. Data on care-seeking by provider should also be reported as absolute rates (related to the gender specific population denominator data from the surveys) as well as proportions. This could then be brought together with the published data (and any related data from an international working group on hospital admissions by gender) in order to prepare a report on this issue. Ways of presenting these data graphically need to be explored further.

Other important issues

During the systematic review we identified many studies where sex /gender data or issues were not properly reported and this led to the exclusion of a high number of studies:

Study design: gender was noted in the study design but not reported for all study outcomes and /or subgroups analysed

Data collection: where data for admissions was collected by gender but no analysis of outcome by gender was reported

Discussion: data on gender was analysed but there was no discussion of these data (even when differences by gender were reported; possible reasons underlying gender differences were rarely discussed

Conclusions and recommendations; very few studies highlighted this issue as important. Some studies lacked sufficient power to show significant differences by sex. This is the case for some diarrhoea studies reporting small numbers of deaths. If data are routinely presented by gender, study power is an important consideration in study / survey design. Studies exploring both gender and other socio-economic determinants in South Asia [120] [121] [50] [122] reported a persistent gender bias against girls which was even more apparent in more educated and wealthy families, compared to poorer and less educated ones. This suggests that these relationships are complex and increased access to education, health or social care services may not necessarily decrease gender inequalities.

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Final conclusions

The 2011 UNICEF report “Boys and Girls Life Style: sex-disaggregated data on a selection of well-being indicators, from early childhood to young adulthood” reported that “under-five mortality is usually higher among boys, except in parts of Asia” and reported a male/female mortality ratio of 0.96 globally. It concluded that there were “minimal gender disparities” among the health indicators in young children that were examined. Specifically, it found that there were little or no clear differences in birth registration rates, measles immunization coverage (except in South Asia), breastfeeding practices, nutritional status, and intervention coverage (against malaria, suspected pneumonia and diarrhoea). The subsequent UNICEF Gender Action Plan 2014-17 noted that “data increasingly reveal child survival gaps between girls and boys beyond biological sex differences, and as evidence is emerging on patterns of differential care, addressing discrimination against girls in survival and care is essential” and that the UNICEF analysis of child health indicators did not reveal “significant gender differences. However, these differences may exist at the subnational level, particularly among disadvantaged groups”. The resulting Action Plan give little priority to a consideration of gender-specific child survival (with only 2 of its 101 paragraphs of recommendations addressing this issue). This is somewhat in contrast with:

1. The UN report 2011 on Sex Differentials on Child hood Mortality. This identifies countries with apparent female mortality disadvantage and those in which advances in child survival are inequitable between girls and boys (in relation to expected sex differentials expected given changing cause-of-death patterns that accompany mortality decline). It also notes that “female survival advantage in infancy in most of the developing world is not as great as would be expected based on the historical experience of some developed countries at similar levels of mortality”. In some large countries, notably India and China, female mortality disadvantage would appear to be substantial and this level of inequity may exist in some regions of other countries.

2. The work conducted by WHO (Colin Mathers and Leontine Alkema) which is drawing attention to substantial female disadvantage in infant mortality (work currently submitted for publication). They have constructed global average sex ratios against which to compare country-specific sex ratios to identify countries where there was substantial female disadvantage. For example, for infant deaths the global average ratio is about 1.2 M/F so countries like some in Asia where the ratio is approximately 1 actually have a female disadvantage of around 20%.

3. Our analysis of gender-specific hospitalisation rates for childhood pneumonia (Lancet 2013) which shows marked differentials in some (mainly Asian) countries.

Media and policy attention on child gender issues have tended to concentrate on sex-selective abortion / prenatal discrimination in recent years and have given little attention to gender differentials in child survival and their causes. There are scarce data in the published literature reporting on levels and patterns of care seeking and health services (including hospital) treatment and outcomes by gender for the leading causes of child mortality. However, our prior experience with child pneumonia suggests that such data can be identified and analysed through an international working group of investigators sharing unpublished data. It may also be possible, in collaboration with UNICEF country offices, to assemble routine health services data on outpatient department (OPD) attendances and inpatient (IP) admission by diagnosis, age and sex from a large number of health services in order to build up a picture of

48

possible gender differentials in treatment which would add significantly to our understanding of this problem. Although in general the studies we identified report a higher care seeking for boys (also demonstrated in the DHS data), many of the studies included in this review had small sample sizes (which was reflected in the wide 95% CIs). Most of the studies reporting in-hospital CFRs also report a higher CFR for girls. Therefore, to achieve further reductions in child mortality, it is necessary to identify and overcome specific barriers to achieving MDG 4 – including gender disparity in care seeking and differential treatment by health services. This appears to be more prevalent in LMICs, especially in Asia and parts of Africa. More and better quality data are required for a better understanding of this problem so that awareness can be raised and child health programmes can appropriately address these issues.

A proposal for a way forward

As noted by the 2011 UN report on Sex Differentials in Child Mortality there is a need for “further study to see if differential treatment is an issue”. We think this could have 2 main elements: assembly of unpublished data through forming an international working group and collaborating with UNICEF country offices to obtain routine health services data from a large number of health facilities in a wide range of countries / regions. We propose this work should aim to:

1. provide data on gender- specific data on health services utilisation and health services treatment

2. explore the extent to which useful data could be obtained from routine health services (perhaps through a sample of “indicator health facilities” and

3. propose some indicators that could be monitored and reported on to track progress.

Assembly of unpublished data through forming an international working group

As anticipated in our review protocol, there is a lack of studies providing good quality data on hospitalisation and in-hospital case fatality ratio estimates (by gender and age group) in the published literature. To produce appropriate gender specific estimates, methodological and other issues highlighted in the previous sections of this report need to be dealt with. In order to address the scarcity of published data, we propose forming a Common Childhood Infections and Gender Working Group. We believe that a number of studies would have collected data on hospital admission and mortality by gender, even though they may not have published these data. This would allow us to obtain detailed data (by narrow age range and across severity spectrum) for the three diseases using common case definitions and analysis protocols. We would build on our recent experience in setting up investigator groups (consisting of investigators from developing countries) and collecting unpublished data using common case definitions and data collection template including analytical approaches. We have successfully led three such investigator groups (consisting of approximately 30 investigators each) in the past (to estimate disease burden due to RSV associated ALRI, and influenza associated ALRI and severe pneumonia hospitalisations) and these efforts have resulted in publications in the Lancet, are extensively cited and have infirmed and influenced global policy. We also have excellent collaborative links with Global Disease Detection (GDD)/ International Emerging Infections Programme (IEIP) sites run by US Centres for Disease Control and Prevention (US CDC) that have good quality data in numerous developing countries sites and have previously contributed to our analysis. We believe that setting up of such a working group would be of immense value to this initiative funded by the UNICEF. Besides our existing collaborating pneumonia investigators (as part of the RSV GEN initiative), we also identified other leading investigators in childhood diarrhoea and

49

malaria through this review. Therefore we would like to organise a meeting of the Common Childhood Infections and Gender working group (at supplementary cost) to be able to request relevant unpublished data from them. Alternatively, if UNICEF were to consider organising this meeting, we would be happy provide all necessary assistance to UNICEF at no additional cost.

Explore the potential to assemble routine health services data from a large sample of health facilities in collaboration with UNICEF country offices

As noted above this would aim to provide data on gender- specific data on health services utilisation and health services treatment and explore the extent to which useful data could be obtained from routine health services (perhaps through a sample of “indicator health facilities”). It would focus initially on assembling large datasets of gender-specific information on hospital admissions and deaths from low and middle income countries and sub-national regions where there is existing evidence of substantial gender differentials in infant and child mortality and explore that extent to which differentials in health care utilisation and treatment may contribute to this. Attention should be focused on countries and where significant gender biases exist. This would also involve analysis of neonatal data from sick newborn care units in India and LMICs (where such data are available with UNICEF). The scope of this work and the interaction with UNICEF country offices would need to be planned in discussion with UNICEF.

Stratified analysis on gender differentials in care seeking using DHS and MICS data

A third element to this work could include an analysis of DHS and MICs data focusing on gender differentials in care-seeking. Considerable high quality analysis of these data has already been conducted by UNICEF / DHS staff. However, it may be useful to conduct further stratified analyses (at country level) which explore differences by socio-economic strata, and educational status of parents and to bring together all DHS and MICS datasets into a comprehensive report which would make all these findings readily available. The final report would set these findings in the overall context of gender differentials in child survival. This would require further discussions with the data managers in UNICEF (and MEASURE DHS) to define a way forward.

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Annexes

Annex A: List of databases searched for the literature review

Database Website

PubMed/Medline http://www.ncbi.nlm.nih.gov/pubmed

Embase http://www.embase.com/

CINAHL http://www.ebscohost.com/biomedical-libraries/cinahl-complete

AIM (AFRO), LILACS (AMRO/PAHO), IMEMR (EMRO), IMSEAR (SEARO), WPRIM (WPRO)

http://pesquisa.bvsalud.org/ghl/index.php

WHOLIS( mainly WHO documents and Bulletin of the World Health Organization and Weekly Epidemiological Record)

http://dosei.who.int/uhtbin/webcat

Global Health CABI http://www.cabi.org/publishing-products/online-information-resources/global-health/

IndMed http://indmed.nic.in/

SIGLE http://www.opengrey.eu/

Web of Science http://wok.mimas.ac.uk.ezproxy.is.ed.ac.uk/

CNKI http://www.cnki.net/

Wanfang data http://g.wanfangdata.com.cn/

Chongqing VIP http://vip.calis.edu.cn/index.asp

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Annex B: Medline and EMBASE search strategies to identify studies reporting gender differences in hospitalisations and mortality for three leading causes of child mortality

Medline 1. exp "Cause of Death"/ or exp Death/ 2. mortality/ or "cause of death"/ or exp child mortality/ or fatal outcome/ or hospital mortality/ or exp infant mortality/ or mortality, premature.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier] 3. (care seeking or care-seeking).mp. or ambulatory care/ or hospitalization/ or "length of stay"/ or patient admission/ or burden.mp. or exp Morbidity/sn, td [Statistics & Numerical Data, Trends] 4. (girl* or boy* or gender or female or male).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier] 5. exp pneumonia/ or *pneumonia/ or acute lower respiratory infections.mp. 6. exp diarrhea/ or exp diarrhea, infantile/ or diarrhoea.mp. 7. exp malaria/ 8. 1 or 2 or 3 9. 5 or 6 or 7 10. 4 and 8 and 9 11. limit 10 to (humans and yr="1990 -Current" and ("infant (1 to 23 months)" or "preschool child (2 to 5 years)" or "child (6 to 12 years)")) EMBASE 1. mortality/ or exp childhood mortality/ or exp infant mortality/ or exp child death/ or exp Cause of Death/ 2. exp incidence/ or exp prevalence/ or morbidity/ or hospitalized child/ or hospitalized infant/ or (care seeking or care-seeking).mp. or burden.mp. 3. gender/ or gender bias/ or boy/ or girl/ or sex factors.mp. or boy.mp. or girl.mp. or gender.mp. or sex.mp. 4. exp lower respiratory tract infection/ or exp respiratory tract infection/ or pneumonia/ or acute chest syndrome/ or bronchopneumonia/ or infectious pneumonia/ 5. exp malaria/ 6. exp diarrhea/ or diarrhoea.mp. 7. 1 or 2 8. 4 or 5 or 6 9. 3 and 7 and 8 10. limit 9 to (human and yr="1990 -Current" and (preschool child <1 to 6 years> or school child <7 to 12 years> or adolescent <13 to 17 years>)) BACK COVER PHOTO: Conakry, Guinea. © UNICEF/NYHQ2015-0078/UNMEER Martine Perret