Child mortality in the Democratic Republic of Congo: cross-sectional evidence of the effect of geographic location and prolonged conflict from a national household survey

Background The child mortality rate is a good indicator of development. High levels of infectious diseases and high child mortality make the Democratic Republic of Congo (DRC) one of the most challenging environments for health development in Sub-Saharan Africa (SSA). Recent conflicts in the eastern part of the country and bad governance have compounded the problem. This study aimed to examine province-level geographic variation in under-five mortality (U5M), accounting for individual- and household-level risk factors including environmental factors such as conflict. Methods Our analysis used the nationally representative cross-sectional household sample of 8,992 children under five in the 2007 DRC Demographic and Health Survey. In the survey year, 1,005 deaths among this group were observed. Information on U5M was aggregated to the 11 provinces, and a Bayesian geo-additive discrete-time survival mixed model was used to map the geographic distribution of under-five mortality rates (U5MRs) at the province level, accounting for observable and unobservable risk factors. Results The overall U5MR was 159 per 1,000 live births. Significant associations with risk of U5M were found for < 24 month birth interval [posterior odds ratio and 95% credible region: 1.14 (1.04, 1.26)], home birth [1.13 (1.01, 1.27)] and living with a single mother [1.16 (1.03, 1.33)]. Striking variation was also noted in the risk of U5M by province of residence, with the highest risk in Kasaï-Oriental, a non-conflict area of the DRC, and the lowest in the conflict area of North Kivu. Conclusion This study reveals clear geographic patterns in rates of U5M in the DRC and shows the potential role of individual child, household and environmental factors, which are unexplained by the ongoing conflict. The displacement of mothers to safer areas may explain the lower U5MR observed at the epicentre of the conflict in North Kivu, compared with rates in conflict-free areas. Overall, the U5M maps point to a lack of progress towards the Millennium Development Goal of reducing U5M by half by 2015

Diarrhoea, acute respiratory infection, and fever among children in the Democratic Republic of Congo

Several years of war have created a humanitarian crisis in the Democratic Republic of Congo (DRC) with extensive disruption of civil society, the economy and provision of basic services including health care. Health policy and planning in the DRC are constrained by a lack of reliable and accessible population data. Thus there is currently a need for primary research to guide programme and policy development for reconstruction and to measure attainment of the Millennium Development Goals (MDGs). This study uses the 2001 Multiple Indicators Cluster Survey to disentangle children's health inequalities by mapping the impact of geographical distribution of childhood morbidity stemming from diarrhoea, acute respiratory infection, and fever. We observe a low prevalence of childhood diarrhoea, acute respiratory infection and fever in the western provinces (Kinshasa, Bas-Congo and Bandundu), and a relatively higher prevalence in the south-eastern provinces (Sud-Kivu and Katanga). However, each disease has a distinct geographical pattern of variation. Among covariate factors, child age had a significant association with disease prevalence. The risk of the three ailments increased in the first 8–10 months after birth, with a gradual improvement thereafter. The effects of socioeconomic factors vary according to the disease. Accounting for the effects of the geographical location, our analysis was able to explain a significant share of the pronounced residual geographical effects. Using large scale household survey data, we have produced for the first time spatial residual maps in the DRC and in so doing we have undertaken a comprehensive analysis of geographical variation at province level of childhood diarrhoea, acute respiratory infection, and fever prevalence. Understanding these complex relationships through disease prevalence maps can facilitate design of targeted intervention programs for reconstruction and achievement of the MDGs

Monitoring of Health and Demographic Outcomes in Poor Urban Settlements: Evidence from the Nairobi Urban Health and Demographic Surveillance System

The Nairobi Urban Health and Demographic Surveillance System (NUHDSS) was set up in Korogocho and Viwandani slum settlements to provide a platform for investigating linkages between urban poverty, health, and demographic and other socioeconomic outcomes, and to facilitate the evaluation of interventions to improve the wellbeing of the urban poor. Data from the NUHDSS confirm the high level of population mobility in slum settlements, and also demonstrate that slum settlements are long-term homes for many people. Research and intervention programs should take account of the duality of slum residency. Consistent with the trends observed countrywide, the data show substantial improvements in measures of child mortality, while there has been limited decline in fertility in slum settlements. The NUHDSS experience has shown that it is feasible to set up and implement long-term health and demographic surveillance system in urban slum settlements and to generate vital data for guiding policy and actions aimed at improving the wellbeing of the urban poor

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

This online publication has been corrected. The corrected version first appeared at thelancet.com on September 28, 2023BACKGROUND : Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. METHODS : Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. FINDINGS : In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world’s highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. INTERPRETATION : Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers.Bill & Melinda Gates Foundation.http://www.thelancet.comam2024School of Health Systems and Public Health (SHSPH)SDG-03:Good heatlh and well-bein

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Profiling malaria infection among under-five children in the Democratic Republic of Congo.

IntroductionIn 2018, Malaria accounted for 38% of the overall morbidity and 36% of the overall mortality in the Democratic Republic of Congo (DRC). This study aimed to identify malaria socioeconomic predictors among children aged 6-59 months in DRC and to describe a socioeconomic profile of the most-at-risk children aged 6-59 months for malaria infection.Materials and methodsThis study used data from the 2013 DRC Demographic and Health Survey. The sample included 8,547 children aged 6-59 months who were tested for malaria by microscopy. Malaria infection status, the dependent variable, is a dummy variable characterized as a positive or negative test. The independent variables were child's sex, age, and living arrangement; mother's education; household's socioeconomic variables; province of residence; and type of place of residence. Statistical analyses used the chi-square automatic interaction detector (CHAID) model and logistic regression.ResultsOf the 8,547 children included in the sample, 25% had malaria infection. Four variables-child's age, mother's education, province, and wealth index-were statistically associated with the prevalence of malaria infection in bivariate analysis and multivariate analysis (CHAID and logistic regression). The prevalence of malaria infection increases with child's age and decreases significantly with mother's education and the household wealth index. These findings suggest that the prevalence of malaria infection is driven by interactions among environmental factors, socioeconomic characteristics, and probably differences in the implementation of malaria programs across the country. The effect of mother's education on malaria infection was only significant among under-five children living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, and Maniema provinces, and the effect of wealth index was significant in Mai-Ndombe, Tshopo, and Haut-Katanga provinces.ConclusionFindings from this study could be used for targeting malaria interventions in DRC. Although malaria infection is common across the country, the prevalence of children at high risk for malaria infection varies by province and other background characteristics, including age, mother's education, wealth index, and place of residence. In light of these findings, designing provincial and multisectoral interventions could be an effective strategy to achieve zero malaria infection in DRC

Diarrhoea, acute respiratory infection, and fever among children in the Democratic Republic of Congo

Several years of war have created a humanitarian crisis in the Democratic Republic of Congo (DRC) with extensive disruption of civil society, the economy and provision of basic services including health care. Health policy and planning in the DRC are constrained by a lack of reliable and accessible population data. Thus there is currently a need for primary research to guide programme and policy development for reconstruction and to measure attainment of the Millennium Development Goals (MDGs). This study uses the 2001 Multiple Indicators Cluster Survey to disentangle children's health inequalities by mapping the impact of geographical distribution of childhood morbidity stemming from diarrhoea, acute respiratory infection, and fever. We observe a low prevalence of childhood diarrhoea, acute respiratory infection and fever in the western provinces (Kinshasa, Bas-Congo and Bandundu), and a relatively higher prevalence in the south-eastern provinces (Sud-Kivu and Katanga). However, each disease has a distinct geographical pattern of variation. Among covariate factors, child age had a significant association with disease prevalence. The risk of the three ailments increased in the first 8-10 months after birth, with a gradual improvement thereafter. The effects of socioeconomic factors vary according to the disease. Accounting for the effects of the geographical location, our analysis was able to explain a significant share of the pronounced residual geographical effects. Using large scale household survey data, we have produced for the first time spatial residual maps in the DRC and in so doing we have undertaken a comprehensive analysis of geographical variation at province level of childhood diarrhoea, acute respiratory infection, and fever prevalence. Understanding these complex relationships through disease prevalence maps can facilitate design of targeted intervention programs for reconstruction and achievement of the MDGs.Diarrhoea Acute respiratory infection Fever Millennium development goals (MDGs) Child health Democratic Republic of Congo Disease prevalence maps