A space-time analysis of recurrent malnutrition-related hospitalisations in Kilifi, Kenya for children under-5 years

Mapping wasted children using data from the Ethiopia Demographic and Health Surveys between 2000 and 2019: A bayesian geospatial analysis

OBJECTIVES

Undernutrition, particularly wasting, is continuing to be a major challenge in developing countries like Ethiopia. However, data on the geographic variations over time are limited. We aimed to investigate the spatiotemporal variation of wasting in Ethiopia using two decades of Ethiopia Demographic and Health Surveys data, from 2000 to 2019.

METHODS

Trend and geospatial regression analysis using a bayesian framework were used to predict wasting in Ethiopia among children aged <5 y. The primary outcomes (wasting) were obtained from the Ethiopia Demographic and Health Surveys conducted between 2000 and 2019. Covariates were accessed from different publicly available credible sources at a high resolution. The spatial binomial regression model through the bayesian framework was fitted to identify drivers of wasting among children in Ethiopia.

RESULTS

The overall national prevalence of wasting among children aged <5 y in Ethiopia was 11.9% in 2000, 11.1% in 2005, 10.2% in 2011, 12.3% in 2016, and 9.4% in 2019, with substantial spatial variation across subnational and local levels over time. Spatial clustering of wasting was observed in eastern Ethiopia (Afar and Somali regions). Altitude (mean regression coefficient = -0.38; 95% credible interval, -0.69 to -0.07) and population density (mean regression coefficient = -0.02; 95% credible interval, -0.03 to -0.01) were negatively associated with wasting, whereas distance to health facilities (mean regression coefficient = 0.13; 95% credible interval, 0.03-0.23) was positively associated with wasting.

CONCLUSIONS

The reduction in the national prevalence of wasting among children was not as expected. Spatial clustering of wasting was observed in the northern, northeastern, eastern, and western parts of Ethiopia. Spatial clustering of wasting was associated with altitude, precipitation, population density, distance to health facilities, travel time to the nearest cities, and distance to a water body. Early screening and treatment of wasted children should be strengthened. Furthermore, outreach community awareness, especially in rural parts of the country, should be recommended through community health extension workers.

Mortality in rural coastal Kenya measured using the Kilifi Health and Demographic Surveillance System: a 16-year descriptive analysis

Background: The Kilifi Health and Demographic Surveillance System (KHDSS) was established in 2000 to define the incidence and prevalence of local diseases and evaluate the impact of community-based interventions. KHDSS morbidity data have been reported comprehensively but mortality has not been described. This analysis describes mortality in the KHDSS over 16 years. Methods: We calculated mortality rates from 2003-2018 in four intervals of equal duration and assessed differences in mortality across these intervals by age and sex. We calculated the period survival function and median survival using the Kaplan-Meier method and mean life expectancies using abridged life tables. We estimated trend and seasonality by decomposing a time series of monthly mortality rates. We used choropleth maps and random-effects Poisson regression to investigate geographical heterogeneity. Results: Mortality declined by 36% overall between 2003-2018 and by 59% in children aged <5 years. Most of the decline occurred between 2003 and 2006. Among adults, the greatest decline (49%) was observed in those aged 15-54 years. Life expectancy at birth increased by 12 years. Females outlived males by 6 years. Seasonality was only evident in the 1-4 year age group in the first four years. Geographical variation in mortality was ±10% of the median value and did not change over time. Conclusions: Between 2003 and 2018, mortality among children and young adults has improved substantially. The steep decline in 2003-2006 followed by a much slower reduction thereafter suggests improvements in health and wellbeing have plateaued in the last 12 years. However, there is substantial inequality in mortality experience by geographical location.

Mortality in rural coastal Kenya measured using the Kilifi Health and Demographic Surveillance System: a 16-year descriptive analysis

Background: The Kilifi Health and Demographic Surveillance System (KHDSS) was established in 2000 to define the incidence and prevalence of local diseases and evaluate the impact of community-based interventions. KHDSS morbidity data have been reported comprehensively but mortality has not been described. This analysis describes mortality in the KHDSS over 16 years. Methods: We calculated mortality rates from 2003–2018 in four intervals of equal duration and assessed differences in mortality across these intervals by age and sex. We calculated the period survival function and median survival using the Kaplan–Meier method and mean life expectancies using abridged life tables. We estimated trend and seasonality by decomposing a time series of monthly mortality rates. We used choropleth maps and random-effects Poisson regression to investigate geographical heterogeneity. Results: Mortality declined by 36% overall between 2003–2018 and by 59% in children aged <5 years. Most of the decline occurred between 2003 and 2006. Among adults, the greatest decline (49%) was observed in those aged 15–54 years. Life expectancy at birth increased by 12 years. Females outlived males by 6 years. Seasonality was only evident in the 1–4 year age group in the first four years. Geographical variation in mortality was ±10% of the median value and did not change over time. Conclusions: Between 2003 and 2018, mortality among children and young adults has improved substantially. The steep decline in 2003–2006 followed by a much slower reduction thereafter suggests improvements in health and wellbeing have plateaued in the last 12 years. However, there is substantial inequality in mortality experience by geographical location.