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BACKGROUND: Today, an estimated 7.3% (50 million) of all children <5 y of age suffer from wasting, with more burden in African countries including Guinea. Investigating inequalities in childhood wasting is essential for designing efficient programs and interventions, but no related evidence exists in Guinea. This study aimed to examine the trends in the prevalence of childhood wasting and the extent of sex, socio-economic and geographic-based disparities in Guinea. METHODS: Data from the 1999, 2005 and 2012 Guinea Demographic and Health Surveys and the 2016 Guinea Multiple Indicator Cluster Survey, with a total of 16 137 children <5 y of age were included for analysis. For inequality analysis, we used the 2019 updated World Health Organization Health Equity Assessment Toolkit (HEAT) software. Inequality was measured using four summary measures (difference [D], population attributable risk [PAR], ratio [R] and population attributable fraction [PAF]) for five equity stratifiers (economic status, education, place of residence, sex and subnational region). We computed 95% confidence intervals (CIs) around the points estimates to measure statistical significance. RESULTS: The findings revealed a pro-rich (R=1.68 [95% CI 1.11 to 2.24]), pro-urban (PAR=-1.04 [95% CI -1.90 to -0.18]) and subnational region (D=8.11 [95% CI 4.85 to 11.36]) inequalities in childhood wasting across all surveys. Except in 2005, education-based disparities (PAF=-18.2 [95% CI -36.10 to -0.26]) were observed across all survey years, but not sex-based disparities. An approximately constant inequality pattern was seen across all dimensions. CONCLUSIONS: This study showed inequalities in childhood wasting in Guinea with a disproportionately higher risk of wasting among children from disadvantaged subpopulations/mothers, including uneducated, poorest/poor, rural residents and regions. Policies that target disadvantaged populations need to be considered in order to ensure social protection, access to a wholesome diet and universal and quality health services.
The data for this study were from three waves of the Guinea Demographic and Health Survey (GDHS; 1999, 2005 and 2012) and one wave of the Guinea Multiple Indicator Cluster Survey (GMICS; 2016). The GDHS and GMICS were conducted by the National Institute of Statistics of the Ministry of Planning in collaboration with the United States Agency for International Development (USAID) and the United Nations Children's Fund, respectively, with technical assistance from Inner-City Fund International. GDHS and GMICS are highly comparable nationally representative data sources that permit direct comparison between them25–27 and samples of men and women in their reproductive age, and they provide an adequate representation of urban and rural settings. The surveys also covered all eight administrative regions (Boké, Conakry, Faranah, Kankan, Kindia, Labé, Mamou and Nzérékoré). Both the GDHS and GMICS employed a two-stage stratified cluster sampling technique. First, clusters or enumeration areas (EAs) were selected across the entire nation from a list of EAs established in the most recent census. The second stage involved household sampling, where 25–30 households were selected in each cluster.28–30 The analysis was carried out on 16 137 children <5 y of age preceding the respective surveys. Wasting was the outcome variable and was measured as the weight-for-height z-score (WHZ) <−2 standard deviations (SDs) from the median of the World Health Organization (WHO) child growth standard.3,4 For children <5 y of age, a WHZ <−2 SDs from the WHO reference population was coded 1 and a WHZ between −2 SDs and +5 SDs was coded as 0.3,4 Children with WHZ +5 SDs were considered as having invalid data and were excluded from the analysis. Children who were not weighed and measured and children whose values for weight and height were not recorded were excluded. Children whose month or year of birth was missing or unknown were flagged and excluded. Children whose day of birth was missing or unknown were assigned day 15. Children who were flagged for out-of-range z-scores or invalid z-scores were excluded.31,32 Inequality in wasting was measured using five equity stratifiers: economic status, education, place of residence, sex and subnational region. Economic status was approximated by a wealth index.33 The selection of these five dimensions of inequality (equity stratifiers) was because these equity stratifiers represent common sources of discrimination and can be widely applied to populations in low- and middle-income countries.34 In the GDHS and GMICS, the wealth index is usually computed using durable goods, household characteristics and basic services, following the methodology explained elsewhere.34,35 The constructed wealth index was further categorized into five quintiles: from poorest (quintile 1) to richest (quintile 5). Maternal education status was classified as no education, primary education and secondary education or more. Place of residence was classified as urban or rural and child sex was categorized as male or female. The subnational region included the eight regions in the country (Boké, Conakry, Faranah, Kankan, Kindia, Labé, Mamou and Nzérékoré). The analysis was conducted with the latest version of the WHO Health Equity Assessment Toolkit (HEAT) software. This software is used to investigate health inequalities within and between countries for >30 reproductive, maternal, newborn and child health indicators.36,37 A detailed discussion of the software is available elsewhere.36,37 We used this equity assessment toolkit to examine socio-economic and geographic inequalities in childhood wasting following two steps. First, the prevalence of wasting was disaggregated by five equity stratifiers (economic status, education status, place of residence, sex and subnational region) across different subpopulations. Second, inequality was assessed using four measures of inequality: difference (D), population attributable risk (PAR), population attributable fraction (PAF) and ratio (R). R and PAF are relative measures, while D and PAR are absolute summary measures. The selection of these simple and complex summary measures was based on evidence that supports the scientific significance of using both absolute and relative measures in studies involving a single health inequality.38 This is deemed essential because of the likelihood of obtaining different and even contrasting conclusions,38 which can lead to bias-informed decisions.38 Details about summary measures and the methods for calculating the summary measures and subsequent interpretation adopted in this study have been described elsewhere38,39 and are available in Supplementary file 1. Regarding the interpretation of summary measures, if there is no inequality, D takes the value zero. Greater absolute values of D indicate higher levels of wasting inequality. Positive values of R indicate a higher concentration of wasting among the disadvantaged and negative values indicate a higher concentration among the advantaged. If there is no inequality, R takes the value one. It takes only positive values (>1 or <1). The further the value of R from 1, the higher the level of inequality. PAR and PAF take negative values for adverse health outcome indicators such as wasting. The larger the absolute value of PAR, the higher the level of inequality. PAR is zero if no further improvement can be achieved, i.e. if all subgroups have reached the same level of wasting prevalence as the reference subgroup. The trend of inequality for each summary measure was assessed by referring to the 95% confidence intervals (CIs) for the different survey years. Inequalities exist if the UIs do not overlap.40 Ethical approval was not required because the study used publicly available GDHS and GMICS data. All DHS and MICS surveys are approved by Inner City Fund (ICF) International as well as an institutional review board (IRB) in the respective country to ensure that the protocols are in compliance with the US Department of Health and Human Services regulations for the protection of human subjects.
