Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000–17: analysis for the Global Burden of Disease Study 2017

Abstract

Background Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood diarrhoea.Methods We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor estimates.Findings The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54•0% (95% uncertainty interval [UI] 38•1-65•8), 17•4% (7•7-28•4), and 59•5% (34•2-86•9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy coverage.Interpretation By co-analysing geospatial trends in diarrhoeal burden and its key risk factors, we could assess candidate drivers of subnational death reduction. Further, by doing a counterfactual analysis of the remaining disease burden using key risk factors, we identified potential intervention strategies for vulnerable populations. In view of the demands for limited resources in LMICs, accurately quantifying the burden of diarrhoea and its drivers is important for precision public health.Funding Bill & Melinda Gates Foundation.

Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys

“…All ten studies reporting on all-cause childhood mortality estimated under-five mortality rate, while a few studies also presented estimates for neonatal [ 30 , 90 ] and infant [ 90 , 91 ] mortality rates. For cause-specific mortality, deaths by malaria [ 38 ], diarrhea [ 32 , 35 ] and lower respiratory infection [ 33 ] were studied.…”

“…Most studies, 12 out of 14, assessed changes over time—a major focus for mortality—most likely relating to monitoring development goals. In terms of resolution, six studies aimed at reaching smaller administrative units such as districts or counties [ 35 , 55 , 92 – 96 ], six presented gridded estimates [ 25 , 30 , 32 , 33 , 38 , 91 ] and one employed both approaches [ 90 ]. As with all outcomes, Bayesian models were predominant, used in 10 out of 14 studies.…”

“…The use of multiple modeling techniques allows any complex non-linear effects of the covariates to be captured, while the final predictions are estimated using a robust, consolidated modeling technique. All 11 studies following this approach [27][28][29][30][31][32][33][34][35][36][37] used a Bayesian hierarchical model fitted using INLA for the final predictions. Only one study relied on ensemble models and did not perform stacked generalization [38].…”

Mapping geographical inequalities in access to drinking water and sanitation facilities in low-income and middle-income countries, 2000–17

“…To assess the effect of changes in water and sanitation access on diarrhoeal disease mortality in children younger than 5 years, we used a comparative risk assessment framework to construct counterfactuals and assess child deaths averted due to increased access. 2 We used estimates of diarrhoeal mortality in children younger than 5 years available at the same spatial scales from the geospatial analysis described by Reiner and colleagues 36 for this counterfactual analysis; as such, only countries with data available from Reiner and colleagues were included. We combined these mortality estimates with risk ratios estimated in GBD 2017, 2 which associated different types of water and sanitation facilities with varied risks of diarrhoeal disease.…”

“… 2 , 26 We then used access estimates in 2000 to calculate a counterfactual population attributable fraction. Using these population attributable fractions in conjunction with the Reiner and colleagues estimates of child diarrhoeal disease mortality across units, 36 we calculated attributable under-5 deaths for water and sanitation in 2017, as well as the number of averted child deaths in 2017 due to changes in water and sanitation access since 2000. We propagated uncertainty by repeating the calculation for values from each of the 250 draws of the posterior from our model.…”