Factors contributing to household wealth inequality in under-five deaths in low- and middle-income countries: decomposition analysis

Background: The burden of under-5 deaths is disproportionately high among poor households relative to economically viable ones in developing countries. Despite this, the factors driving this inequality has not been well explored. This study decomposed the contributions of the factors associated with wealth inequalities in under-5 deaths in low- and middle-income countries (LMICs). Methods: We analysed data of 856,987 children from 66,495 neighbourhoods across 59 LMICs spanning recent Demographic and Health Surveys (2010-2018). Under-5 mortality was described as deaths among live births within 0 to 59 months of birth and it was treated as a dichotomous variable (dead or alive). The prevalence of under-five deaths was stratified using household wealth status. A Fairlie decomposition analysis was utilized to investigate the relative contribution of the factors associated with household wealth inequality in under-5 deaths at p<0.05. The WHO health equity assessment toolkit Plus was used to assess the differences (D) ratios (R), population attributable risk (PAR), and population attributable fraction (PAF) in household wealth inequalities across the countries. Results: The proportion of children from poor households was 45%. The prevalence of under-5 deaths in all samples was 51 per 1000 children, with 60 per 1000 and 44 per 1000 among children from poor and non-poor households (p<0.001). The prevalence of under-5 deaths was higher among children from poor households than those from non-poor households in all countries except in Ethiopia, Tanzania, Zambia, Lesotho, Gambia and Sierra Leone, and in the Maldives. Thirty-four of the 59 countries showed significantly higher under-5 deaths in poor households than in non-poor households (pro-non-poor inequality) and no significant pro-poor inequality. Rural-urban contexts, maternal education, neighborhood socioeconomic status, sex of the child, toilet kinds, birth weight and preceding birth intervals, and sources of drinking water are the most significant drivers of pro-poor inequities in under-5 deaths in these countries. Conclusions: Individual-level and neighbourhood-level factors were associated with a high prevalence of under-5 deaths among poor households in LMICs. Interventions in countries should focus on reducing the gap between the poor and the rich as well as improve the education and livelihood of disadvantaged people. The routinely collected Demographic and Health Surveys (DHS) data was used for this analysis. Every five years, the DHS is conducted in each of the participating LMICs. The ICF Macro, the USA in conjunction with the designated organizations such as Statistics boards, Universities, Ministry of Health, etc. in the participating countries collect the data. The surveys nationally representative, population-based and cross-sectional. We merged the DHS conducted between 2010 and 2018 and was released on September 10th, 2020. This study comprised a total of 59 LMICs that matched the inclusion criteria. Data from 856,987 U5M from 66,495 neighborhoods in 59 LMICs was used in the analysis. In each of the countries where the surveys were conducted, similar clustered multi-stage sampling methodologies were utilised. The sampling frames were mostly based on each country's most recent census figures. The number of levels and their stratification are determined by each country's current geographical and administrative structures. In some of these countries, the multi-stage sampling used regions/states/divisions as the first stage, districts as the second level, and clusters as the final stage. Clusters are defined in the same way in all countries and are also referred to as primary sample units (PSU) [15, 16]. At the last stage, the households were selected from the PSUs. As a result of uneven population sizes among states/regions/districts within a country, DHS generated and provided sampling weights with the data for each country. The sampling weights were calculated using a multi-stage sampling technique to ensure that the estimates from the samples were representative of the general population. Each country employed a similar set of standardized questionnaires, research protocols, interviewer training procedures, supervision, and execution. The entire specifics of the sample procedures, as well as other data has been reported elsewhere [17]. The dependent variable is U5D, which is defined as deaths in the first five years of life among live births [18–20]. It is defined as death occurring between the ages of 0 and 59 months after birth [21]. To ensure the study's accuracy, correctness, and completeness, women were questioned if they had given birth to a child within the previous five years. Those who said yes were then asked to recall the dates of their children's births, followed by a question about whether each of those children was living or deceased at the time of the interview. U5D was calculated using the dates of death or ages at death of the deceased children. As a result, U5D was a binary variable: Dead or Alive before 5th birthday. The burden of U5Ds was stratified using household wealth status. The DHS survey did not capture participants’ expenditures and incomes but captured an array of assets owned by participants’ households. These assets were used to compute household wealth status within each country by the DHS and released with the data. The quintiles of household wealth were determined using a composite score of assets possessed by households [22]. Detailed report that can be found at dhsprogram.com has further information on the techniques and country-specific assets used to calculate the wealth quintiles in each of the countries. The DHS recommended that the household wealth status derived from asset ownership be used as a proxy for participants' wealth, which may subsequently be interpreted as a measure of their economic or poverty status. The calculated household wealth scores were divided into 20 percent household wealth quintiles. In this study, we re-categorized household wealth quintile into two categories: the first two 20% as poor and the upper three 20% as non-poor. This was to enable the comparison of U5Ds among children from poor and non-poor households. A similar categorization has been used in the literature [23–26]. Individual-level and neighborhood-level factors were employed as independent variables in the study. These factors have been linked to poverty and childhood mortality in the literature. [6, 8–10, 27]. Children's characteristics, mothers' profiles, and household variables are the individual-level factors. Weight at birth (very small/small/average), sex (female/male), birth order (1/2/3/4+), birth interval (firstborn/36 months/>=36 months), and if a child is a twin (single/multiple (2+) are all factors considered. Educational level of mother, age of mother, marital status, maternal and paternal employment status, and health insurance status, are among the characteristics of mothers. The characteristics of households include household’s head gender (female/male), access to the media (captured using ownership or access to television, newspaper or radio, at least of these), drinking water sources (either improved/unimproved), toilet type (either improved/unimproved), cooking fuel (clean fuel/biomass), and housing materials (either improved/unimproved) [28–30], and, locations where mothers live (rural or urban). The categorizations of drinking water sources, housing materials, toilet type and cooking fuel as improved or not have been reported in previous studies [18–20, 28–35]. The “neighbourhood” is the clustering of children as used in the sampling frames for the surveys. The DHS referred to “cluster” as a common geographical area that contains people that share similar contextual factors [15, 16, 18]. Children in the same cluster were referred to as “neighbours.” As a community-level variable, we looked at neighbourhood socioeconomic status (SES). It was a composite variable made up of community education, access to the media, and unemployment rates calculated using the principal component factor approach. In this study, descriptive and inferential statistics were used. The country, regions, U5Ds, and other significant features of the children by U5D was depicted using basic descriptive statistics such as maps, graphs, tables, and proportions. Table ​Table11 shows the results of tests of equality in proportions of U5Ds among children from poor and non-poor homes in each country and region. The distribution of the background characteristics of the children by the prevalence of U5Ds among children from poor and non-poor households was reported in Table ​Table2.2. The spatial distribution of under-five deaths per 1000 live births among children in poor and non-poor households are shown in Fig. ​Fig.1(a)1(a) and (b) respectively. The maps were built in Microsoft Projects 2020. Also, to further examine household wealth inequality in U5Ds, absolute and relative measures of inequality recommended in the WHO Health Equity Assessment Toolkit Plus (HEAT Plus) were utilised [36]. These measures include Difference (D), Ratio (R), Population Attributable Fraction (PAF) and Population Attributable Risk (PAR). The R and D show the relative ratio and absolute difference between two categories within a dimension of inequality (highest and lowest wealth quintile). For D, a positive value indicates that there is pro-non-poor U5Ds and vice versa. The R statistic shows the relative inequality between poor and non-poor households. For an adverse indicator as U5Ds, R values equal to 1 indicate that there exists no inequality and values greater than one represent a pro-non-poor U5Ds. The higher this value is, the larger the gap between the poor and non-poor. The PAR is the difference between the most-advantaged subgroup (lowest wealth quintile) and the national average, while PAF is computed by ascertaining the ratio of the national average (μ) and the PAR, multiplied by 100, i.e. PAF = [PAR / μ] * 100. Unlike the R measure of inequality, the PAR and PAF take only negative values for adverse outcomes with higher values reflecting a wider gap between population subgroups. Comprehensive details regarding the computation of these measures have been reported [25]. The R, D. PAF and PAR estimate from household wealth inequality in U5Ds across LMIC using the WHO HEAT Plus are reported in Table ​Table3.3. The graphical illustrations of the estimates are provided in Fig. ​Fig.33. Distribution of sample characteristics by countries, regions and prevalence of under-five deaths by household wealth inequality in LMIC, 2010–2018 *significant at 5% test of equality of proportion Summary of pooled background characteristics of the studied children and prevalence of under-five deaths by household wealth inequality in LMIC, 2010–2018. ++insignificant at 5% test of equality of proportion Household wealth inequality in U5Ds in LMIC, 2010–2018 using WHO HEAT Plus Spatial distribution of under-five deaths among children in poor and non-poor households in the LMIC studied (Source: Authors Drawings) The differences (D) ratios (R), population attributable risk (PAR), and population attributable fraction (PAF) in household wealth inequalities across the LMIC using the WHO HEAT Plus (Abbreviations of the country names are provided in Table ​Table33) We obtained the risk difference (RD) between the risk of U5Ds among children from poor and non-poor households for each country and showed the meta-analysis of these RDs in Fig. ​Fig.2.2. We calculated the risk difference in U5D between poor and non-poor households and displayed the results in Fig. ​Fig.22 as a country-level meta-analysis of U5D prevalence in each of the countries. A random-effects meta-analysis was used based on the assumption that each trial calculates a study-specific actual effect. Using the “metabin” tool in R, the meta-analysis was carried out by identifying the summary measure (SM) as risk difference (RD), the number of fatalities in poor and non-poor households, and the total number of participants for each country, stratified by regions [18]. Scatter and ordered balloon charts were used to show the distributions of the RDs viz-a-viz the prevalence of U5Ds in each country in Figs. ​Figs.33 and ​and4.4. We defined pro-poor inequality as situations in which the RD in U5D is significantly lower among children from poor households than those from non-poor households and pro-non-poor inequality as situations in which the RD in U5D is significantly higher among children children from poor poor households than those from non-poor households [18, 19]. The countries formed 3 groups based on the RDs: countries with pro-poor, insignificant and pro-non-poor inequalities. The “pro-non-poor inequality” and “pro-poor inequality” countries are countries with higher U5D in poor households than in non-poor households and vice versa. Lastly, we fitted adjusted binary logistic regression to the risk of U5Ds among all the pro-poor countries and applied a Fairlie decomposition analysis (FDA) to the inequality in the U5Ds among children from poor and non-poor households and the results were presented in Fig. ​Fig.55. Forest plot of the risk difference in the prevalence of under-five deaths by household wealth inequality in LMIC (Source: Authors Drawings) Risk difference in the prevalence of under-five deaths between children from poor and non-poor households in LMICs (Source: Authors Drawings) Scatter plot of rate of under-five deaths and risk difference by household wealth inequality in LMICs (Source: Authors Drawings) We applied sampling weights to all the analyses to control for different cluster sizes and stratifications, as well as to guarantee that our results accurately reflect the target population The “colin” tool in Stata version 16 was used to test for multicollinearity among the independent variables. The variable inflation factor was specified by the command (VIF). The VIF is around 1/(1-R2) and ranges from 1 to Regressing the jth independent variable on other independent variables yields the R2-value. All variables with a VIF greater than 2.5 were eliminated from the regression [37]. In several countries, insurance coverage, the employment status of father, access to media, cooking fuel type, and housing material were not reported and were excluded from the decomposition analysis. Prior to performing the decomposition analysis, we conducted a test of heterogeneity of U5D chances across all nations to confirm the presence of heterogeneity. We computed and presented the I-squared and the Mantel-Haenszel (MH) pooled estimate of the odds ratio (OR). We selected the pro-non-poor countries, conducted a homogeneity test among them, and provided the I-squared and MH pooled odds ratio (OR) estimates. Several studies on the understanding of factors associated with inequalities in a wide range of health outcomes have adopted the technique of multivariable decomposition analysis [24, 26, 38–40]. Multivariable decomposition analysis is ideal for the quantifications of the contributions of different factors to gaps in an outcome of interest between two groups [41]. It constrains the predicted probability of U5Ds to between 0 and 1. The difference between the predicted probability for one group (say, Group A – poor) using the regression coefficients of the other group (say, Group B – non-poor) and the expected probability for the non-poor group using its regression coefficients is measured in the decomposition analysis [42]. According to Fairlie et al., the decomposition of a nonlinear model Y=F(X) can be written as: Where NA is the sample size for group J. Other model details have been reported [18, 19, 31, 33, 43]. The independent contribution of X1 and X2 to the gap are expressed as follows: and respectively. Further numerical details have been documented in the literature [42, 44–47]. In this study, the FDA was implemented in STATA version 16 (StataCorp, College Station, Texas, United States of America) using the “Fairlie” command. However, Fairlie’s sequential decomposition has issues with path dependence [42, 44–47], whereby different ordering of variables in the decomposition analysis produces different results. To address this, we checked the robustness of the sensitivity analysis of variable re-ordering randomization. First, we conducted and assessed the performance of 10 different ordering of the variables and tested the sensitivity of decomposition estimates. Secondly, we invoked the “random” option with the “Fairlie” Stata command used in conducting the Fairlie decomposition. In this study, the FDA was implemented in STATA version 16 (StataCorp, College Station, Texas, United States of America).