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Janaína Calu Costa, Fernando C Wehrmeister, Aluísio JD Barros, and Cesar G Victora

Abstract

Background

Preventive and curative medical interventions can reduce child mortality. It is important to assess whether there is gender bias in access to these interventions, which can lead to preferential treatment of children of a given sex.

Methods

Data from Demographic and Health Surveys carried out in 57 low– and middle–income countries were used. The outcome variable was a composite careseeking indicator, which represents the proportion of children with common childhood symptoms or illnesses (diarrhea, fever, or suspected pneumonia) who were taken to an appropriate provider. Results were stratified by sex at the national level and within each wealth quintile. Ecological analyses were carried out to assess if sex ratios varied by world region, religion, national income and its distribution, and gender inequality indices. Linear multilevel regression models were used to estimate time trends in careseeking by sex between 1994 and 2014.

Findings

Eight out of 57 countries showed significant differences in careseeking; in six countries, girls were less likely to receive care (Colombia, Egypt, India, Liberia, Senegal and Yemen). Seven countries had significant interactions between sex and wealth quintile, but the patterns varied from country to country. In the ecological analyses, lower careseeking for girls tended to be more common in countries with higher income concentration (P = 0.039) and higher Muslim population (P = 0.006). Coverage increased for both sexes; 0.95 percent points (pp) a year among girls (32.9% to 51.9%), and 0.91 pp (34.8% to 52.9%) among boys.

Conclusion

The overall frequency of careseeking is similar for girls and boys, but not in all countries, where there is evidence of gender bias. A gender perspective should be an integral part of monitoring, accountability and programming. Countries where bias is present need renewed attention by national and international initiatives, in order to ensure that girls receive adequate care and protection.


Disaggregation of child health statistics by sex is important in order to identify gender bias in health intervention coverage, and in outcomes such as morbidity, mortality and nutritional status among children under the age of five years. Gender bias is a multidimensional social construct, in which different values are attributed to men and women in a given society, which can lead to preferential treatment of children of a given sex [1,2]; the use of this concept refers to a system of relations including sex, but goes beyond biological differences [3]. The study of gender bias in child health is affected by the greater biological vulnerability of boys compared to girls; in societies where there is no evidence of discrimination, boys show higher mortality rates than girls [4,5].

Two recent analyses assessed gender bias in the coverage of essential child health interventions in low– and middle–income countries (LMICs) [6,7]. Essential interventions may be classified as preventive (for example, measles vaccination, early initiation of breastfeeding, exclusive breastfeeding from 0–5 months, and use of insecticide treated bednets) or curative (use of antimalarials, careseeking for pneumonia, oral rehydration therapy, etc.) A UNICEF report showed no difference between girls and boys in terms of the seven interventions listed above [6]. There were also no differences in undernutrition (stunting, wasting or underweight). The numbers of countries included in these analyses ranged from 23 to 80 [6]. However, in spite of the lack of gender bias at national level, differences may exist at the subnational level, particularly among disadvantaged groups [8].

A recent systematic review investigated sex differences in hospitalizations for diarrhea, pneumonia and malaria in LMICs, and showed higher admission rates for boys, and higher case–fatality rates for girls [7]. However, hospital admissions are not a good indicator to study gender bias, because admission depends both on severity of the illness – which is likely to be greater for boys – and on careseeking by the caregivers [7]. Comparing careseeking rates among boys and girls for all cases of defined diseases or conditions is likely to be more useful in terms of detecting gender bias. In the same study, the authors analyzed data from 67 Demographic and Health Surveys (DHS) to investigate sex differences in careseeking by type of provider for diarrhea, fever, and pneumonia. Overall, more boys were taken to a health–care facility compared to girls [7].

Careseeking indicators are based on children who presented symptoms for each illness, usually in the two weeks before a survey. In these cases, the number of children is small, which leads to wide confidence intervals for these indicators, and may fail to detect differences between boys and girls as statistically significant due to low power [9].

We attempt to overcome this limitation by measuring sex differences using a composite careseeking indicator for three common childhood illnesses or symptoms. In addition, given the conflicting results of the two above–mentioned analyses, we expand our investigation to also assess whether these differences vary by wealth quintile, and whether sex differences in careseeking are associated with country characteristics such as income, religion and gender inequality indices. By doing so, we test the hypotheses that socioeconomic and related factors may modify the extent of gender bias in careseeking.

METHODS

We analyzed data from nationally–representative Demographic and Health Surveys (DHS) conducted in low– and middle–income countries. We included all surveys with public–domain datasets available on the DHS website (http://dhsprogram.com/) as of May 2016, which had all the variables required for the analyses.

DHS asks mothers or caretakers of children under five years of age about diarrhea, fever, and symptoms of pneumonia (see Table S1 in Online Supplementary Document(Online Supplementary Document) ). We used a composite careseeking indicator; the numerator was the number of children in a survey who were taken to an appropriate health care provider (defined by each country), during recent episodes of diarrhea, fever or suspected pneumonia, and the denominator was the number of children for which such an episode was reported during the two weeks preceding the interview. Pharmacies, shops and traditional practitioners were not considered appropriate providers.

The outcome variable was the proportion of children with symptoms who were taken to an appropriate provider. This was calculated separately for boys and girls in each survey, both at the national level and within each wealth quintile. Wealth indices were calculated for each survey through principal component analysis of household assets and building characteristics [1012]. The first component resulting from the analysis was divided into quintiles, with Q1 representing the poorest, and Q5 the wealthiest, 20% of all families.

For the descriptive analyses, we selected the most recent survey from each country, from 2005 to 2014. Differences between the sexes in each country were assessed using chi–squared tests. Sex ratios were calculated for each survey by dividing careseeking proportions in girls and in boys, with values below 1.0 indicating gender bias against girls. The 95% confidence intervals for sex ratios were calculated using a jackknife approach based on repeated sub–sampling within the full survey sample. Interactions between wealth quintiles and sex of the child were assessed using Poisson regression with careseeking as the outcome.

Countries with more than one survey were included in the analyses of global time trends in careseeking between 1994 and 2014, using linear multilevel regression models with surveys as level one units and countries as level two units. We fitted separate trends for boys and for girls.

Ecological analyses were carried out with careseeking sex ratios as the outcome, based on the most recent survey for each country. The following explanatory variables were selected: region of the world according to UNICEF classification; religion (predominant and percentage in the population); country income groups; Gross Domestic Product per capita in USD; Gini coefficient of income inequality; and three indices related to gender equity (Gender Inequality Index, Gender Development Index, and Global Gender Gap Index) (see Table S2 in Online Supplementary Document(Online Supplementary Document) for full definitions and data sources) [1320]. Associations between careseeking sex ratios and categorical explanatory variables were analyzed using analysis of variance (ANOVA), and those with continuous explanatory variables using Pearson´s correlation.

All analyses were carried out using Stata version 13.1 (StataCorp LP, College Station, Texas, USA), and considered the complex sampling structure of the surveys and the sampling weights.

RESULTS

A total of 57 countries had DHS data sets since 2005 with the required variables. The median survey year was 2012. Sample sizes ranged from 1450 (Armenia) to 48 679 (India) children under five years ( Table 1 ). The median sample size was 7526 children and the interquartile range was 5054 to 10 935.

Table 1.  Characterization of 57 countries with available DHS surveys post–2005 according to region, income group, sample size and careseeking indicator
Country Year World Region (UNICEF) Income group (World Bank) Children under five years (n) Children with diarrhea, fever or suspect pneumonia (n) Careseeking sex ratio (CI 95%) P–value
Total Boys Girls
Albania 2008 CEE & CIS Upper middle 1586 267 145 122 0.9 (0.72–1.07) 0.292
Armenia 2010 CEE & CIS Lower middle 1450 290 153 137 0.92 (0.66–1.19) 0.599
Azerbaijan 2006 CEE & CIS Upper middle 2196 405 227 178 0.82 (0.57–1.06) 0.183
Bangladesh 2014 South Asia Low 7567 3089 1614 1475 0.99 (0.84–1.15) 0.984
Benin 2011 West & Central Africa Low 12 679 1857 954 903 0.96 (0.83–1.08) 0.521
Burkina Faso 2010 West & Central Africa Low 13 716 4175 2143 2032 0.94 (0.88–1.00) 0.099
Burundi 2010 Eastern & Southern Africa Low 7231 3713 1864 1849 0.96 (0.90–1.02) 0.302
Cambodia 2014 East Asia & Pacific Low 6971 2248 1182 1066 1.09 (0.98–1.19) 0.076
Cameroon 2011 West & Central Africa Lower middle 10 734 4443 2231 2212 0.93 (0.81–1.04) 0.263
Colombia* 2010 LAC Upper middle 17 443 8669 4522 4147 0.93 (0.88–0.98) 0.020
Comoros 2012 Eastern & Southern Africa Low 3022 951 489 462 0.95 (0.72–1.18) 0.683
Congo (Brazzaville) 2011 West & Central Africa Lower middle 8857 3398 1733 1665 0.93 (0.82–1.04) 0.257
Congo D.R. 2013 West & Central Africa Low 17 228 7292 3657 3635 1.01 (0.92–1.09) 0.781
Cote d’Ivoire 2011 West & Central Africa Lower middle 7093 2453 1233 1220 1.03 (0.86–1.20) 0.687
Dominican Republic 2013 LAC Upper middle 3606 1412 724 688 1.03 (0.91–1.15) 0.572
Egypt* 2014 Middle East & North Africa Lower middle 15 466 5262 2867 2395 0.93 (0.89–0.97) 0.004
Ethiopia 2011 Eastern & Southern Africa Low 10 808 3161 1621 1540 1.01 (0.85–1.17) 0.848
Gabon 2012 West & Central Africa Upper middle 5747 2258 1135 1123 0.87 (0.72–1.02) 0.126
Gambia 2013 West & Central Africa Low 7788 2127 1112 1015 0.96 (0.88–1.04) 0.370
Ghana 2014 West & Central Africa Lower middle 5595 1396 767 629 1.03 (0.92–1.13) 0.513
Guinea 2012 West & Central Africa Low 6424 2547 1311 1236 0.94 (0.83–1.05) 0.311
Guyana 2009 LAC Lower middle 2105 600 315 285 1.06 (0.89–1.23) 0.427
Haiti* 2012 LAC Low 6744 3650 1840 1810 1.11 (0.99–1.22) 0.044
Honduras 2011 LAC Lower middle 10 592 4379 2335 2044 1.00 (0.93–1.06) 0.978
India* 2005 South Asia Lower middle 48 679 11,336 6089 5247 0.93 (0.90–0.96) 0.000
Indonesia 2012 East Asia & Pacific Lower middle 17 367 7029 3787 3242 0.96 (0.92–1.00) 0.068
Jordan 2012 Middle East & North Africa Upper middle 10 128 3017 1595 1422 0.97 (0.88–1.07) 0.649
Kenya 2014 Eastern & Southern Africa Low 20 093 7690 3922 3768 0.98 (0.94–1.03) 0.601
Kyrgyzstan 2012 CEE & CIS Low 4247 392 200 192 0.88 (0.66–1.10) 0.320
Lesotho 2009 Eastern & Southern Africa Lower middle 3606 1033 505 528 1.00 (0.89–1.12) 0.872
Liberia* 2013 West & Central Africa Low 7058 3219 1659 1560 0.91 (0.83–0.98) 0.029
Madagascar 2008 Eastern & Southern Africa Low 11 750 2029 1027 1002 0.92 (0.80–1.04) 0.244
Malawi 2010 Eastern & Southern Africa Low 18 360 8227 4174 4053 0.99 (0.95–1.02) 0.634
Maldives 2009 South Asia Upper middle 3761 1353 689 664 1.03 (0.96–1.09) 0.350
Mali 2012 West & Central Africa Low 9582 1619 870 749 1.01 (0.83–1.19) 0.861
Moldova 2005 CEE & CIS Lower middle 1533 368 172 196 0.96 (0.75–1.17) 0.723
Mozambique 2011 Eastern & Southern Africa Low 10 291 2224 1131 1093 1.02 (0.93–1.10) 0.622
Namibia 2013 Eastern & Southern Africa Upper middle 4818 1699 855 844 0.94 (0.86–1.03) 0.260
Nepal 2011 South Asia Low 5054 1416 793 623 0.88 (0.75–1.01) 0.085
Niger 2012 West & Central Africa Low 11 602 2852 1418 1434 0.93 (0.85–1.02) 0.165
Nigeria 2013 West & Central Africa Lower middle 28 596 5787 2965 2822 1.00 (0.91–1.09) 0.897
Pakistan 2012 South Asia Lower middle 10 935 5213 2750 2463 0.96 (0.93–1.00) 0.095
Peru 2012 LAC Upper middle 9445 3134 1617 1517 1.02 (0.93–1.12) 0.543
Philippines 2013 East Asia & Pacific Lower middle 7012 2413 1263 1150 1.01 (0.93–1.09) 0.707
Rwanda 2014 Eastern & Southern Africa Low 7558 2190 1105 1085 0.99 (0.89–1.08) 0.903
Sao Tome and Principe 2008 West & Central Africa Lower middle 1851 504 269 235 1.08 (0.90–1.26) 0.329
Senegal* 2014 West & Central Africa Lower middle 6526 1633 865 768 0.76 (0.65–0.87) 0.000
Sierra Leone 2013 West & Central Africa Low 10 618 3602 1797 1805 0.97 (0.91–1.02) 0.338
Swaziland 2006 Eastern & Southern Africa Lower middle 2537 946 524 422 1.03 (0.90–1.16) 0.563
Tajikistan 2012 CEE & CIS Low 4838 922 511 411 1.08 (0.93–1.22) 0.265
Tanzania 2010 Eastern & Southern Africa Low 7526 2290 1163 1127 1.04 (0.96–1.12) 0.269
Timor–Leste 2009 East Asia & Pacific Lower middle 9294 2661 1308 1353 0.98 (0.93–1.03) 0.473
Togo 2013 West & Central Africa Low 6535 2262 1155 1107 1.07 (0.95–1.20) 0.221
Uganda* 2011 Eastern & Southern Africa Low 7355 3946 2007 1939 1.04 (1.01–1.08) 0.008
Yemen* 2013 Middle East & North Africa Lower middle 15 383 7345 3875 3470 0.85 (0.78–0.92) 0.000
Zambia 2013 Eastern & Southern Africa Lower middle 12 714 4238 2139 2099 1.01 (0.95–1.06) 0.656
Zimbabwe 2010 Eastern & Southern Africa Low 5203 1358 686 672 1.06 (0.91–1.21) 0.405

CI – confidence interval, CEE – Central and Eastern Europe, CIS – Commonwealth of Independent States, LAC – Latin America & the Caribbean

*Countries with significant sex differences in careseeking (P < 0.05).



Sex ratios for careseeking (girls/boys) ranged from 0.76 (0.68–0.85) in Senegal to 1.11 (0.99–1.24) in Haiti ( Figure 1 ). The average value for all countries was 0.97 (0.96–1.00).

Figure 1.  Careseeking sex ratios (95% confidence interval), by country.
jogh-07-010418-F1


Eight countries showed statistical evidence of gender bias. In six of these (Senegal, Yemen, Liberia, Egypt, Colombia and India) girls were less likely to be taken to a provider, with sex ratios ranging from 0.76 to 0.94. In the other two countries, Haiti and Uganda (sex ratios of 1.11 and 1.05, respectively), girls were more likely to receive care. Further results at country level including 95% confidence intervals and p values are shown in Table 1 .

We also examined interactions between wealth and sex in careseeking coverage. Of the 57 countries, significant interactions (P < 0.05) were found in three. In Gabon and Lesotho, higher socioeconomic position was associated with greater careseeking for boys but not for girls; in Niger, the trend was in the opposite direction ( Figure 2 ). Another four countries had interactions with p levels between 0.05 and 0.1: Burkina Faso, Congo Brazzaville, Dominican Republic and Senegal. Figure S1 in Online Supplementary Document(Online Supplementary Document) shows that interaction patterns were also inconsistent in these countries.

Figure 2.  Careseeking for common childhood symptoms or illnesses by sex in countries with significant (P < 0.05) interactions between child sex and wealth quintile.
jogh-07-010418-F2


Time trends analysis showed that global careseeking coverage increased by 0.93 percent point (pp) a year between 1994 and 2014 (from 33.9% to 52.4%) ( Figure 3 ). Coverage increased for both sexes (P < 0.001): among girls the increase was 0.95 pp a year (32.9% to 51.9%), and among boys, 0.91 pp (34.8% to 52.9%).

Figure 3.  Gender differences in child health: evidences from Demographic and Health Surveys. Regression lines for changes in careseeking over time (1994–2014) by sex, for all countries combined.
jogh-07-010418-F3


Ecological analyses showed a lack of association between the careseeking sex ratio and most explanatory variables ( Table 2 and Table 3 ). There was no evidence of difference between the world regions. However, it should be noted that there are few surveys available for countries in South Asia and in Middle East & North Africa; most surveys are from countries in Eastern & Southern Africa, and in West & Central Africa.

Table 2.  Ecological analyses of careseeking sex ratio and selected categorical exposure variables at national level
Variables Categories Number of countries Mean Standard deviation P–value*
World region CEE & CIS† 6 0.93 0.09
East Asia & Pacific 4 1.01 0.06
Eastern & Southern Africa 15 1.00 0.04
Latin America & Caribbean 6 1.03 0.06 0.055
Middle East & North Africa 3 0.92 0.06
South Asia 5 0.96 0.06
West & Central Africa 18 0.97 0.08
Country income group Low 28 0.98 0.07
Lower–middle 20 0.98 0.06 0.398
Upper–middle 9 0.95 0.08
Gender Development Index groups‡ 1. High equality 7 0.98 0.05
2. Medium to high equality 3 0.96 0.07
3. Medium equality 9 0.98 0.08 0.995
4. Medium to low equality 8 0.98 0.05
5. Low equality 28 0.98 0.07
Predominant religion Christian 35 1.00 0.05
Muslim 19 0.94 0.07 0.011
Other 3 0.97 0.11

*P–value based on ANOVA.

†CEE & CIS: Central and Eastern Europe and the Commonwealth of Independent States.

‡Gender Development Index groups: Countries are divided into five groups by absolute deviation from gender parity in HDI values. Group 1: countries with high equality in HDI achievements between women and men (absolute deviation of less than 2.5%); Group 2: medium to high equality (absolute deviation of 2.5–5%); Group 3: medium equality (absolute deviation of 5–7.5%); Group 4: medium to low equality (absolute deviation of 7.5–10%); and Group 5: low equality (absolute deviation from gender parity of more than 10%) [20].



Table 3.  Ecological analyses of careseeking sex ratio and selected continuous exposure variables at national level
Variables Number of countries Correlation (95% CI) P–value*
GDP per capita (2012) 57 –0.257 (–0.485; 0.003) 0.053
GDP per capita – log (2012) 57 –0.190 (–0.429; 0.156) 0.157
Gini coefficient for income inequality 46 0.306 (0.018; 0.547) 0.039
Gender Inequality Index (2013) 50 0.074 (–0.208; 0.345) 0.607
Gender Development Index (2013) 50 0.090 (–0.193; 0.359) 0.531
Global Gender Gap Index (2014) 41 0.190 (–0.124; 0.470) 0.231
Muslim (% population) 57 –0.361 (–0.568; 0.111) 0.006
Christian (% population) 57 0.305 (0.049; 0.523) 0.021

CI – confidence interval

*P–value based on Pearson's correlation coefficient.



Regarding income levels, most of the countries surveyed are in the low– and lower–middle income groups, and no association was found between the level and careseeking sex ratios ( Table 2 ).

There was a negative correlation, which was not statistically significant (P = 0.053) between continuous GDP per capita and the sex ratio, but not for log GDP per capita (P = 0.157).

None of the gender inequality indices were associated with the careseeking sex ratio ( Table 2 and Table 3 ). The Gender Development Index was tested both as a categorical variable, as recommended by its developers, and as a continuous index.

The religion variables were expressed both as categories of the predominant religion in each country ( Table 2 ) and as the percent of Christians and Muslims in the population ( Table 3 ). In both sets of ecological analyses, Christian religion was associated with improved care for girls, and Muslim religion with preferential careseeking for boys. These associations remained virtually unchanged after adjustment of the religion variables by GDP per capita (partial correlation coefficients of –0.351 for percent Muslim and –0.307 for percent Christian). Figure S2 in Online Supplementary Document(Online Supplementary Document) shows the careseeking sex ratios and 95% confidence intervals, for countries ranked according to the percentage of Muslim population.

We opted not to carry out extensive multivariable analyses because several explanatory variables are highly collinear (eg, GDI and income per capita, etc.) and because the gender indices also included socioeconomic variables in their construction.

DISCUSSION

The analysis of the Demographic and Health Surveys, conducted in low– and middle–income countries, explored the magnitude of gender bias against girls, investigating whether families would be less likely to seek care from appropriate providers for girls with symptoms of fever, diarrhea or pneumonia, compared to boys. We expand upon the existing literature on this topic by calculating a new composite careseeking index encompassing three conditions – diarrhea, fever and suspected pneumonia – and therefore increasing the statistical power relative to earlier analyses in which each condition was treated separately.

We found evidence of gender bias in a limited number of countries. In contrast to the pervasive socioeconomic inequalities in careseeking and coverage, gender inequalities in careseeking are modest or even absent in most countries.

A systematic review explored studies on the recognition of signs and symptoms of, and/or careseeking for pneumonia, diarrhea or malaria in low– and middle–income countries. The authors identified seven publications that evaluated careseeking by sex; four which did not find significant differences between girls and boys, two reporting higher prevalence of careseeking for boys (in Burkina Faso and Indonesia), and one showing higher careseeking for girls, but only for malaria episodes [21]. The mixed results from this review are consistent with our analyses, which do not show a clear pattern of gender bias throughout the world.

At regional levels, we did not identify evidence of gender bias; however, in six countries careseeking was significantly higher for boys, and in two for girls. At the 5% P level, one would expect 1–2 significant pro–boy differences, and another 1–2 pro–girl differences, simply due to chance. We sought interactions between sex and wealth quintiles in careseeking for all 57 countries, but only detected significant interactions (with P < 0.10) in seven countries, which could have arisen by chance. In addition, interaction patterns were not consistent, sometimes with greater gender gaps in the wealthy, and for other countries with greater gaps among the poor.

The use of a composite careseeking indicator for three common conditions, using data from nationally representative surveys avoid small denominators – a frequent problem in analyses of careseeking – and thus increases statistical power [9]. Nevertheless, in our analyses sample size varied widely between surveys, and countries with the largest surveys such as India, results can be statistically significant even when absolute differences are small.

When comparing our results with the UNICEF analyses on careseeking for separate conditions, we found that three of the six countries we identified as presenting gender bias in the combined careseeking indicator had also been identified as such by UNICEF: Yemen (fever), Egypt (suspected pneumonia) and India (suspected pneumonia and diarrhea) [7]. It is important to highlight that the UNICEF report includes some unofficial health care providers that we did not include (such as shops and traditional practitioners), and that the year of the surveys may not be the same.

We used ecological analyses in an attempt to identify national characteristics associated with gender bias. Surprisingly, we did not detect correlations between careseeking sex ratios and gender inequality indices. A recent study reported a positive association between the Gender Inequality Index with under–five mortality rate for both sexes combined; this association remained after adjustment for GDP per capita, but separate associations with mortality rates for boys and girls were not investigated [22]. The authors speculate that if gender inequality is linked to maternal health, then mortality of boys and girls would be equally affected.

National levels of wealth were not associated with gender bias in careseeking, but bias was more likely in countries with unequal income distributions. We also found that religion was a cultural characteristic that explained part of the variability, with improved careseeking for boys in countries with a higher Muslim population. More research is needed to better understand the effects of religion and culture on careseeking, including whether the ecological association we report here is also found at individual level analyses within a given country, or whether it is due to an ecological fallacy.

Other limitations in the data should be recognized. Differences in careseeking could be due to increased severity of infectious diseases among boys [7], but our results showing similar careseeking rates in most countries suggest that this did not bias the results. Also, information on the incidence of illness and on careseeking patterns is based on maternal recall, which may or may not vary systematically according to child’s sex [21].

In addition, a composite index for careseeking does not reflect how different illnesses may be perceived along the spectrum of severity; more detailed analyses might consider only severe cases (such as bloody diarrhea, for example) but this would further reduce the denominator and analyses would only be possible for very large sample sizes.

Lastly, our analyses are limited to the most recent survey per country, so that results on time trends must be interpreted with caution as for some countries the most recent publicly available survey was carried out a decade or more ago, as is the case for India.

CONCLUSIONS

Our results suggest that, with a few exceptions, the overall frequency of careseeking for common health conditions is similar for boys and girls in most, but not in all countries. Similar results are available for under–five mortality [4,7,23]. Countries where there is evidence of gender bias in careseeking need renewed attention of national and international initiatives, in order to ensure that girls receive adequate care and protection. In addition, more research is needed to understand the reasons behind the different treatment for girls and boys in these circumstances, including a mixture of qualitative and quantitative methods.

Acknowledgements

Funding: This article was made possible with funds from the Wellcome Trust [Grant Number 101815/Z/13/Z], Bill & Melinda Gates Foundation [Grant: OPP1135522] and Associação Brasileira de Saúde Coletiva (ABRASCO). The funders had no role in the writing of this article.

Ethics approval and consent to participate: The analyses were based on publicly available data and ethical clearance was the responsibility of the institutions that administered the surveys.

Disclaimer: The views expressed in this manuscript are those of the authors and do not represent an official position or the institutions or funder.

Authors’ contributions: JCC and CGV designed the study, analyzed and interpreted the data, and were the major contributors in writing the manuscript. FCW and AJDB supervised the analyses. All authors read and approved the final manuscript.

Competing interests: All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/coi_disclosure.pdf (available upon request from the corresponding author) and declare no conflict of interest.

REFERENCES

[1] N Krieger. Gender, sexes, and health: what are the connections – and why does it matter? Int J Epidemiol. 2003; 32: 652 -7. DOI: 10.1093/ije/dyg156. [12913047]

[2] A Sen. Missing women. BMJ. 1992; 304: 587 -8. DOI: 10.1136/bmj.304.6827.587. [1559085]

[3] MTA Olinto. Reflexões sobre o uso do conceito de gênero e/ou sexo na epidemiologia: um exemplo nos modelos hierarquizados de análise. Rev Bras Epidemiol. 1998; 1: 161 -9. DOI: 10.1590/S1415-790X1998000200006

[4] L Alkema, F Chao, D You, J Pedersen, and CC Sawyer. National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. Lancet Glob Health. 2014; 2: e521 -30. DOI: 10.1016/S2214-109X(14)70280-3. [25304419]

[5] K Hill and DM Upchurch. Gender differences in child health: evidences from Demographic and Health Surveys. Popul Dev Rev. 1995; 21: 127 -51. DOI: 10.2307/2137416

[6] UNICEF. Boys and girls in the life cycle. 2011. Available: https://www.unicef.org/lac/boys_and_girls_life_cycle.pdf. Accessed: 13 October 2015.

[7] UNICEF. Common childhood infections and gender inequalities: a systematic review. 2015. Available: https://www.unicef.org/health/files/Systematic_review_of_childhood_infections_and_gender_FINAL.pdf. Accessed: 28 September 2015.

[8] UNICEF. UNICEF Gender Action Plan 2014-2017. 2014. Available: https://www.unicef.org/esaro/UNICEF_Gender_Action_Plan_2014-2017.pdf. Accessed: 6 May 2016.

[9] A Amouzou, M Kanyuka, E Hazel, R Heidkamp, A Marsh, and T Mleme. Independent evaluation of the Integrated Community Case Management of Childhood Illness Strategy in Malawi using a National Evaluation Platform Design. Am J Trop Med Hyg. 2016; 94: 1434 -5. DOI: 10.4269/ajtmh.16-0110b. [27252480]

[10] AJ Barros and CG Victora. Measuring coverage in MNCH: determining and interpreting inequalities in coverage of maternal, newborn, and child health interventions. PLoS Med. 2013; 10: e1001390 DOI: 10.1371/journal.pmed.1001390. [23667332]

[11] Filmer D, Pritchett L. Estimating wealth effects without expenditure data—or tears: with an application to educational enrollments in states of India. Washington DC: The World Bank; 1998.

[12] Rutsein S, Johnson K. The DHS Wealth Index. DHS Comparative Reports n.6. Calverton, Maryland: ORC Macro; 2004. Available: http://dhsprogram.com/publications/publication-cr6-comparative-reports.cfm#sthash.IU5jGDFE.dpuf. Accessed: 23 June 2015.

[13] Central Intelligence Agency. The World Factbook. 2015. Available: https://www.cia.gov/library/publications/the-world-factbook/docs/guidetowfbook.html. Accessed: 23 June 2015.

[14] United Nations Development Programme. Human Development Reports. Gender Inequality Index 2014. Available: http://hdr.undp.org/en/content/gender-inequality-index-gii. Accessed: 16 January 2016.

[15] UNICEF. Committing to Child Survival: A Promise Renewed - Progress Report 2015. 2015. Available: https://www.unicef.org/publications/index_83078.html. Accessed: 13 September 2015.

[16] World Bank. World Bank Country and Lending Groups. Available: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. Accessed: 16 January 2016.

[17] World Bank. GDP per capita (current US$). 2015. Available: http://data.worldbank.org/indicator/NY.GDP.PCAP.CD. Accessed: 16 January 2016.

[18] World Bank. GINI Index (World Bank estimate). 2015. Available: http://data.worldbank.org/indicator/SI.POV.GINI. Accessed: 16 January 2016.

[19] World Economic Forum. The Global Gender Gap Report 2013. Geneva: World Economic Forum; 2013.

[20] United Nations Development Programme. Human Development Reports 2013. Table 4: Gender Development Index. Available: http://hdr.undp.org/en/content/gender-development-index-gdi. Accessed: 16 January 2016.

[21] P Geldsetzer, TC Williams, A Kirolos, S Mitchell, LA Ratcliffe, and MK Kohli-Lynch. The recognition of and care seeking behaviour for childhood illness in developing countries: a systematic review. PLoS One. 2014; 9: e93427 DOI: 10.1371/journal.pone.0093427. [24718483]

[22] EM Brinda, AP Rajkumar, and U Enemark. Association between gender inequality index and child mortality rates: a cross-national study of 138 countries. BMC Public Health. 2015; 15: 97 DOI: 10.1186/s12889-015-1449-3. [25886485]

[23] CC Sawyer. Child mortality estimation: Estimating sex differences in childhood mortality since the 1970s. PLoS Med. 2012; 9: e1001287 DOI: 10.1371/journal.pmed.1001287. [22952433]

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Journal of Global Health (ISSN 2047-2986), Edinburgh University Global Health Society
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