Time trends in socio-economic and geographic-based inequalities in childhood wasting in Guinea over 2 decades: a cross-sectional study

Abstract

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.

Trends of socioeconomic and geographic inequalities in severe wasting among under-five children in Ethiopia from 2000 to 2019: using the WHO Health Equity Assessment Toolkit

“…This might be taken as a good track record to achieve the 2025 and 2030 targets to reduce the prevalence of wasting to less than 5% and 3%, respectively 5 . The rate of decline in severe wasting is also higher than in a study conducted in Guinea where the proportion of childhood wasting decreased by less than half from 10.1% in 1999 to 8.1% in 2016 37 . This might be due to the extensive application of maternal and child health services in primary health care units through the health extension program in the health systems of Ethiopia.…”

Wasting, Stunting, and Anemia in Angolan Children after Deworming with Albendazole or a Test-and-Treat Approach for Intestinal Parasites: Binary Longitudinal Models with Temporal Structure in a Four-Arm Randomized Trial

“…Regardless of our study’s results, given the diversity of factors leading to malnutrition, we believe that there may have been indirect health benefits for all participants. For example, in our study it was possible to verify a high percentage of mothers with no level of education (9.5%), which has been reported to impact on childhood wasting in other African countries, since the absence of knowledge on child health promotion can negatively influence the nutritional status of children ([ 43 , 47 ]. From this point of view, nutritional education targeting mothers with no formal education can be interesting for future interventions in child nutrition.…”

Socioeconomic and residence-based inequalities in adolescent fertility in 39 African countries

“…Both educational attainment and wealth quintile were considered as measures of socioeconomic status. Also, previous studies that utilized the HEAT software included these stratifiers in examining inequalities in several health indicators [30,38,39]. The categories of the stratifiers consisted of the following: place of residence (rural, and urban), level of education (no education, primary, secondary, and higher), and wealth quintile (poorest, poorer, middle, richer, and richest).…”