Identifying residual hotspots and mapping lower respiratory infection morbidity and mortality in African children from 2000 to 2017

Abstract

Lower respiratory infections (LRIs) are the leading cause of death in children under the age of 5, despite the existence of vaccines against many of their aetiologies. Furthermore, more than half of these deaths occur in Africa. Geospatial models can provide highly detailed estimates of trends subnationally, at the level where implementation of health policies has the greatest impact. We used Bayesian geostatistical modelling to estimate LRI incidence, prevalence and mortality in children under 5 subnationally in Africa for 2000–2017, using surveys covering 1.46 million children and 9,215,000 cases of LRI. Our model reveals large within-country variation in both health burden and its change over time. While reductions in childhood morbidity and mortality due to LRI were estimated for almost every country, we expose a cluster of residual high risk across seven countries, which averages 5.5 LRI deaths per 1,000 children per year. The preventable nature of the vast majority of LRI deaths mandates focused health system efforts in specific locations with the highest burden.

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].…”

Predictive value of pulse oximetry for mortality in infants and children presenting to primary care with clinical pneumonia in rural Malawi: A data linkage study

“…Pneumonia remains a leading cause of death in children under 5, especially in low-income and middle-income countries (LMICs), with around 800,000 pneumonia-related deaths a year globally [ 1 ]. Incidence of clinical pneumonia is estimated to be as high as 500 episodes per 1,000 child-years in some regions, with an average of 122 episodes per 1,000 child-years in Africa in 2017 [ 2 ]. Although hospitalisation rates are increasing and hospital case fatality rates are decreasing, case fatality rates are still typically around 3%–5% in LMICs [ 3 ].…”

Life Expectancy in Areas around Subway Stations in the Seoul Metropolitan Area in Korea, 2008–2017

“…As part of the Global Burden of Disease Study, researchers are working to identify hotspots with high burdens of disease in low- and middle-income countries, including those in Africa. 1 2 3 4 In the United States, life expectancy and mortality trends are reported on the level of census tracts, which are small areas that roughly correspond to neighborhoods. 5 6 In the United Kingdom, the distribution of mortality indicators is also reported by electoral wards.…”