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.

Figure 1

Mapping of diarrhoea incidence among children younger than 5 years in low-income and middle-income countries by second administrative-level unit, 2017 Estimated mean incidence rate per 1000 children in 2017 (A). Absolute deviation from mean incidence rate by country in 2017 (B). Annualised decrease in diarrhoea incidence rate from 2000 to 2017 (C). Estimated mean number of cases of diarrhoea among children in 2017 (D). All panels are aggregated to the second administrative-level unit. Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as barren or sparsely vegetated and had fewer than ten people per 1 × 1 km grid cell, or were not included in these analyses., , , , ,

Figure 2

Mapping of diarrhoeal mortality among children younger than 5 years in low-income and middle-income countries by second administrative-level unit, 2017 Estimated mean mortality rate per 1000 children in 2017 (A). Absolute deviation from the mean mortality rate by country in 2017 (B). Annualised decrease in diarrhoeal mortality rate from 2000 to 2017 (C). Estimated mean number of diarrhoeal deaths among children in 2017 (D). All panels are aggregated to the second administrative-level unit. Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as barren or sparsely vegetated and had fewer than ten people per 1 × 1 km grid cell, or were not included in these analyses., , , , ,

Figure 3

Relative geographical inequality of childhood diarrhoeal mortality in Indonesia and Peru in 2000 and 2017 Relative deviation of second administrative-level units from country mean for Indonesia in 2000 (A), Indonesia in 2017 (B), Peru in 2000 (C), and Peru in 2017 (D). Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as barren or sparsely vegetated and had fewer than ten people per 1 × 1 km grid cell, or were not included in these analyses., , , , ,

Figure 4

Geographical inequality of childhood diarrhoeal mortality at the second administrative-level unit The left panel shows the range of relative deviation from the country mean diarrhoea mortality rate for each country in 2000 (upper bar) and 2017 (lower bar, coloured by GBD super-region). Each bar represents the range from the lowest to highest second administrative-level unit deviation for each country. The right panel shows LMICs with at least one death from diarrhoea per 10 000 children at the second administrative-level unit ranked by childhood diarrhoea mortality rate in 2017. Mean mortality rates are shown as dark grey dots and are national-level aggregations that correspond to the results shown in figure 3. Each bar represents the range from the lowest to highest second administrative-level unit childhood diarrhoeal mortality rate for each country in 2000 (upper bar) and 2017 (lower bar, coloured by GBD super-region). Country names in both panels are the designated ISO 3166-1 alpha-3 codes. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. LMICs=low-income and middle-income countries.

Figure 5

Averted diarrhoeal deaths in 2017 attributable to improvements in key risk factors implemented from 2000 to 2017 Number of deaths averted per 1000 children (A). Number of total deaths averted (B). Number of deaths averted per 1000 children with colour scale driven by dominant driver (C). Number of total deaths averted with colour scale driven by dominant driver (D). The risk factor contributing most of the reduction is indicated as either WASH (blue), CGF (purple), and none (gold), in which none represents locations where neither risk factor is dominant. Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as barren or sparsely vegetated and had fewer than ten people per 1 × 1 km grid cell, or were not included in these analyses., , , , , WASH water, sanitation, and hygiene. CGF=child growth failure.

Figure 6

Second administrative-level units in sub-Saharan Africa with childhood mortality rates in the lower 20% Second administrative units are coloured according to where children are most likely to die of diarrhoea, or the lower 20% (A). Scatter plots of mortality rates against ORS coverage (B), access to improved sanitation (C), and childhood stunting prevalence (D). The left axes are based on 2000 values whereas the right axes are based on 2017 values. The scale change in the y axis is due to substantial decline in mortality rates across most of sub-Saharan Africa. Because lower 20% is itself a relative distinction, scales are adjusted accordingly. Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as barren or sparsely vegetated and had fewer than ten people per 1 × 1 km grid cell, or were not included in these analyses., , , , , ORS=oral rehydration solution.

Figure 6

Second administrative-level units in sub-Saharan Africa with childhood mortality rates in the lower 20% Second administrative units are coloured according to where children are most likely to die of diarrhoea, or the lower 20% (A). Scatter plots of mortality rates against ORS coverage (B), access to improved sanitation (C), and childhood stunting prevalence (D). The left axes are based on 2000 values whereas the right axes are based on 2017 values. The scale change in the y axis is due to substantial decline in mortality rates across most of sub-Saharan Africa. Because lower 20% is itself a relative distinction, scales are adjusted accordingly. Maps reflect administrative boundaries, land cover, lakes, and population; grey-coloured grid cells were classified as barren or sparsely vegetated and had fewer than ten people per 1 × 1 km grid cell, or were not included in these analyses., , , , , ORS=oral rehydration solution.