The impact of income inequality and national wealth on child and adolescent mortality in low and middle-income countries.
Ontology highlight
ABSTRACT: Background
Income inequality and national wealth are strong determinants for health, but few studies have systematically investigated their influence on mortality across the early life-course, particularly outside the high-income world.Methods
We performed cross-sectional regression analyses of the relationship between income inequality (national Gini coefficient) and national wealth (Gross Domestic Product (GDP) averaged over previous decade), and all-cause and grouped cause national mortality rate amongst infants, 1-4, 5-9, 10-14, 15-19 and 20-24 year olds in low and middle-income countries (LMIC) in 2012. Gini models were adjusted for GDP.Results
Data were available for 103 (79%) countries. Gini was positively associated with increased all-cause and communicable disease mortality in both sexes across all age groups, after adjusting for national wealth. Gini was only positively associated with increased injury mortality amongst infants and 20-24 year olds, and increased non-communicable disease mortality amongst 20-24 year old females. The strength of these associations tended to increase during adolescence. Increasing GDP was negatively associated with all-cause, communicable and non-communicable disease mortality in males and females across all age groups. GDP was also associated with decreased injury mortality in all age groups except 15-19 year old females, and 15-24 year old males. GDP became a weaker predictor of mortality during adolescence.Conclusion
Policies to reduce income inequality, rather than prioritising economic growth at all costs, may be needed to improve adolescent mortality in low and middle-income countries, a key development priority.
SUBMITTER: Ward JL
PROVIDER: S-EPMC5425964 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.
| S-EPMC8784134 | biostudies-literature
Project description:BackgroundWealth quintiles derived from household asset indices are routinely used for measuring socioeconomic inequalities in the health of women and children in low and middle-income countries. We explore whether the use of wealth deciles rather than quintiles may be advantageous.MethodsWe selected 46 countries with available national surveys carried out between 2003 and 2013 and with a sample size of at least 3000 children. The outcomes were prevalence of under-five stunting and delivery by a skilled birth attendant (SBA). Differences and ratios between extreme groups for deciles (D1 and D10) and quintiles (Q1 and Q5) were calculated, as well as two summary measures: the slope index of inequality (SII) and concentration index (CIX).ResultsIn virtually all countries, stunting prevalence was highest among the poor, and there were larger differences between D1 and D10 than between Q1 and Q5. SBA coverage showed pro-rich patterns in all countries; in four countries the gap was greater than 80 pct points. With one exception, differences between extreme deciles were larger than between quintiles. Similar patterns emerged when using ratios instead of differences. The two summary measures provide very similar results for quintiles and deciles. Patterns of top or bottom inequality varied with national coverage levels.ConclusionResearchers and policymakers should consider breakdowns by wealth deciles, when sample sizes allow. Use of deciles may contribute to advocacy efforts, monitoring inequalities over time, and targeting health interventions. Summary indices of inequalities were unaffected by the use of quintiles or deciles in their calculation.
| S-EPMC5414946 | biostudies-literature
Project description:BackgroundThere has been growing interest in understanding the role of agricultural trade policies in diet and nutrition. This cross-country study examines associations between government policies on agricultural trade prices and child nutrition outcomes, particularly undernutrition.MethodsThis study links panel data on government distortions to agricultural incentives to data from 212,258 children aged 6 to 35?months participating in Demographic and Health Surveys from 22 countries between 1991 and 2010. Country fixed-effects regression models were used to examine the association between within-country changes in nominal rates of assistance to tradable agriculture (government price distortions as a percentage of original prices) and child nutritional outcomes (height-for-age, weight-for-age, and weight-for-height Z-scores) while controlling for a range of time-varying country covariates.ResultsFive-year average nominal rates of assistance to tradable agriculture ranged from -?72.0 to 45.5% with a mean of -?5.0% and standard deviation of 18.9 percentage points. A 10-percentage point increase in five-year average rates of assistance to tradable agriculture was associated with improved height-for-age (0.02, 95% CI: 0.00-0.05) and weight-for-age (0.05, 95% CI: 0.02-0.09) Z-scores. Improvements in nutritional status were greatest among children who had at least one parent earning wages in agriculture, and effects decreased as a country's proportion of tradable agriculture increased, particularly for weight-for-age Z-scores.ConclusionsGovernment assistance to tradable agriculture, such as through reduced taxation, was associated with small but significant improvements in child nutritional status, especially for children with a parent earning wages in agriculture when the share of tradable agriculture was not high.
| S-EPMC6420724 | biostudies-literature
Project description:Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0-59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards3-5. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization's median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99% of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40% and wasting to less than 5% by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications.
| S-EPMC7015855 | biostudies-literature
Project description:Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005-2007 and the 2013-2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers.
| S-EPMC7908191 | biostudies-literature
Project description:Material wealth is a key factor shaping human development and well-being. Every year, hundreds of studies in social science and policy fields assess material wealth in low- and middle-income countries assuming that there is a single dimension by which households can move from poverty to prosperity. However, a one-dimensional model may miss important kinds of prosperity, particularly in countries where traditional subsistence-based livelihoods coexist with modern cash economies. Using multiple correspondence analysis to analyze representative household data from six countries-Nepal, Bangladesh, Ethiopia, Kenya, Tanzania and Guatemala-across three world regions, we identify a number of independent dimension of wealth, each with a clear link to locally relevant pathways to success in cash and agricultural economies. In all cases, the first dimension identified by this approach replicates standard one-dimensional estimates and captures success in cash economies. The novel dimensions we identify reflect success in different agricultural sectors and are independently associated with key benchmarks of food security and human growth, such as adult body mass index and child height. The multidimensional models of wealth we describe here provide new opportunities for examining the causes and consequences of wealth inequality that go beyond success in cash economies, for tracing the emergence of hybrid pathways to prosperity, and for assessing how these different pathways to economic success carry different health risks and social opportunities.
| S-EPMC5590995 | biostudies-literature
Project description:ObjectivesTo assess the magnitude and pattern of socioeconomic inequality in current smoking in low and middle income countries.MethodsWe used data from the World Health Survey [WHS] in 48 low-income and middle-income countries to estimate the crude prevalence of current smoking according to household wealth quintile. A Poisson regression model with a robust variance was used to generate the Relative Index of Inequality [RII] according to wealth within each of the countries studied.ResultsIn males, smoking was disproportionately prevalent in the poor in the majority of countries. In numerous countries the poorest men were over 2.5 times more likely to smoke than the richest men. Socioeconomic inequality in women was more varied showing patterns of both pro-rich and pro-poor inequality. In 20 countries pro-rich relative socioeconomic inequality was statistically significant: the poorest women had a higher prevalence of smoking compared to the richest women. Conversely, in 9 countries women in the richest population groups had a statistically significant greater risk of smoking compared to the poorest groups.ConclusionBoth the pattern and magnitude of relative inequality may vary greatly between countries. Prevention measures should address the specific pattern of smoking inequality observed within a population.
| S-EPMC3430638 | biostudies-literature
Project description:BackgroundThe progress to achieve the fourth Millennium Development Goal in reducing mortality rate in children younger than 5 years since 1990 has been remarkable. However, work remains to be done in the Sustainable Development Goal era. Estimates of under-5 mortality rates at the national level can hide disparities within countries. We assessed disparities in under-5 mortality rates by household economic status in low-income and middle-income countries (LMICs).MethodWe estimated country-year-specific under-5 mortality rates by wealth quintile on the basis of household wealth indices for 137 LMICs from 1990 to 2016, using a Bayesian statistical model. We estimated the association between quintile-specific and national-level under-5 mortality rates. We assessed the levels and trends of absolute and relative disparity in under-5 mortality rate between the poorest and richest quintiles, and among all quintiles.FindingsIn 2016, for all LMICs (excluding China), the aggregated under-5 mortality rate was 64·6 (90% uncertainty interval [UI] 61·1-70·1) deaths per 1000 livebirths in the poorest households (first quintile), 31·3 (29·5-34·2) deaths per 1000 livebirths in the richest households (fifth quintile), and in between those outcomes for the middle quintiles. Between 1990 and 2016, the largest absolute decline in under-5 mortality rate occurred in the two poorest quintiles: 77·6 (90% UI 71·2-82·6) deaths per 1000 livebirths in the poorest quintile and 77·9 (72·0-82·2) deaths per 1000 livebirths in the second poorest quintile. The difference in under-5 mortality rate between the poorest and richest quintiles decreased significantly by 38·8 (90% UI 32·9-43·8) deaths per 1000 livebirths between 1990 and 2016. The poorest to richest under-5 mortality rate ratio, however, remained similar (2·03 [90% UI 1·94-2·11] in 1990, 1·99 [1·91-2·08] in 2000, and 2·06 [1·92-2·20] in 2016). During 1990-2016, around half of the total under-5 deaths occurred in the poorest two quintiles (48·5% in 1990 and 2000, 49·5% in 2016) and less than a third were in the richest two quintiles (30·4% in 1990, 30·5% in 2000, 29·9% in 2016). For all regions, differences in the under-5 mortality rate between the first and fifth quintiles decreased significantly, ranging from 20·6 (90% UI 15·9-25·1) deaths per 1000 livebirths in eastern Europe and central Asia to 59·5 (48·5-70·4) deaths per 1000 livebirths in south Asia. In 2016, the ratios of under-5 mortality rate in the first quintile to under-5 mortality rate in the fifth quintile were significantly above 2·00 in two regions, with 2·49 (90% UI 2·15-2·87) in east Asia and Pacific (excluding China) and 2·41 (2·05-2·80) in south Asia. Eastern and southern Africa had the smallest ratio in 2016 at 1·62 (90% UI 1·48-1·76). Our model suggested that the expected ratio of under-5 mortality rate in the first quintile to under-5 mortality rate in the fifth quintile increases as national-level under-5 mortality rate decreases.InterpretationFor all LMICs (excluding China) combined, the absolute disparities in under-5 mortality rate between the poorest and richest households have narrowed significantly since 1990, whereas the relative differences have remained stable. To further narrow the rich-and-poor gap in under-5 mortality rate on the relative scale, targeted interventions that focus on the poorest populations are needed.FundingNational University of Singapore, UN Children's Fund, United States Agency for International Development, and the Bill & Melinda Gates Foundation.
| S-EPMC5905403 | biostudies-literature
Project description:OBJECTIVE:Using estimates from the Global Burden of Disease (GBD) study, we examined differences in unintentional occupational injury mortality rates from 1990 to 2016 between high-income countries (HICs) and low- and middle-income countries (LMICs). METHODS:Unintentional occupational injury mortality rates were obtained through the GBD online visualization tool. We quantified mortality changes over time for common external causes of injury for ages 15?49 years and 50?69 years separately in HICs and LMICs using negative binomial regression models. RESULTS:In 2016, there were 24,396 and 303,999 unintentional occupational injury deaths among individuals aged 15 to 69 years in HICs and LMICs, respectively, corresponding to 3.1 and 7.0 per 100,000 people. Between 1990 and 2016, unintentional occupational injury mortality for people aged 15?69 years dropped 46% (from 5.7 to 3.1 per 100,000 people) in HICs and 42% in LMICs (from 13.2 to 7.0 per 100,000 people). Sustained and large disparities existed between HICs and LMICs for both sexes and both age groups during 1990?2016 (mortality rate ratio: 2.2?2.4). All unintentional occupational injury causes of death displayed significant reduction with one exception (ages 15?49 years in HICs). Country-specific analysis revealed large variations in unintentional occupational injury mortality and changes in occupational injury mortality between 1990 and 2016. CONCLUSIONS:Despite substantial decreases in mortality between 1990 and 2016 for both HICs and LMICs, a large disparity continues to exist between HICs and LMICs. Multifaceted efforts are needed to reduce the disparity.
| S-EPMC6210857 | biostudies-other
Project description:IntroductionLow levels of household access to basic environmental health assets (EHAs)-including technologies such as clean cookstoves and bed nets or infrastructure such as piped water and electricity-in low- and middle-income countries (LMICs) are known to contribute significantly to the global burden of disease. This low access persists despite decades of promotion of many low-cost, life-saving technologies, and is particularly pronounced among poor households. This study aims to characterize variation in access to EHAs among LMIC households as a function of wealth, as defined by ownership of various assets.MethodsDemographic and Health Survey (DHS) data from 41 low- and middle-income countries were used to assess household-level access to the following EHAs: 1) improved water supply; 2) piped water supply; 3) improved sanitation; 4) modern cooking fuels; 5) electricity; and 6) bed nets. For comparison, we included access to mobile phones, which is considered a highly successful technology in terms of its penetration into poor households within LMICs. Ownership levels were compared across country-specific wealth quintiles constructed from household assets using bivariate analysis and multivariable linear regression models.ResultsAccess to EHAs was low among the households in the bottom three quintiles of wealth. Access to piped water, modern cooking fuels, electricity and improved sanitation, for example, were all below 50% for households in the bottom three wealth quintiles. Access to certain EHAs such as improved water supply and bed nets increased only slowly with concomitant increases in wealth, while gaps in access to other EHAs varied to a greater degree by wealth quintile. For example, disparities in access between the richest and poorest quintiles were greatest for electricity and improved sanitation. Rural households in all wealth quintiles had much lower levels of access to EHAs, except for bed nets, relative to urban households.ConclusionsThe findings of this study provide a basis for understanding how EHAs are distributed among poor households in LMICs, elucidate where inequalities in access are particularly pronounced, and point to a need for strategies that better reach the poor, if the global environmental burden of disease is to be reduced.
| S-EPMC6239312 | biostudies-literature