Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda

Abstract Background To reduce the under-five mortality (U5M), fine-gained spatial assessment of the effects of health interventions is critical because national averages can obscure important sub-national disparities. In turn, sub-national estimates can guide control programmes for spatial targeting...

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Main Authors: Betty B. Nambuusi, Julius Ssempiira, Fredrick E. Makumbi, Jürg Utzinger, Simon Kasasa, Penelope Vounatsou
Format: Article
Language:English
Published: BMC 2019-10-01
Series:BMC Public Health
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12889-019-7636-x
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spelling doaj-94338cd6ff02461ea8c045d0b9a325d42020-11-25T03:56:18ZengBMCBMC Public Health1471-24582019-10-0119111710.1186/s12889-019-7636-xGeographical variations of the associations between health interventions and all-cause under-five mortality in UgandaBetty B. Nambuusi0Julius Ssempiira1Fredrick E. Makumbi2Jürg Utzinger3Simon Kasasa4Penelope Vounatsou5Swiss Tropical and Public Health InstituteSwiss Tropical and Public Health InstituteMakerere University School of Public Health, New Mulago Hospital ComplexSwiss Tropical and Public Health InstituteMakerere University School of Public Health, New Mulago Hospital ComplexSwiss Tropical and Public Health InstituteAbstract Background To reduce the under-five mortality (U5M), fine-gained spatial assessment of the effects of health interventions is critical because national averages can obscure important sub-national disparities. In turn, sub-national estimates can guide control programmes for spatial targeting. The purpose of our study is to quantify associations of interventions with U5M rate at national and sub-national scales in Uganda and to identify interventions associated with the largest reductions in U5M rate at the sub-national scale. Methods Spatially explicit data on U5M, interventions and sociodemographic indicators were obtained from the 2011 Uganda Demographic and Health Survey (DHS). Climatic data were extracted from remote sensing sources. Bayesian geostatistical Weibull proportional hazards models with spatially varying effects at sub-national scales were utilized to quantify associations between all-cause U5M and interventions at national and regional levels. Bayesian variable selection was employed to select the most important determinants of U5M. Results At the national level, interventions associated with the highest reduction in U5M were artemisinin-based combination therapy (hazard rate ratio (HRR) = 0.60; 95% Bayesian credible interval (BCI): 0.11, 0.79), initiation of breastfeeding within 1 h of birth (HR = 0.70; 95% BCI: 0.51, 0.86), intermittent preventive treatment (IPTp) (HRR = 0.74; 95% BCI: 0.67, 0.97) and access to insecticide-treated nets (ITN) (HRR = 0.75; 95% BCI: 0.63, 0.84). In Central 2, Mid-Western and South-West, largest reduction in U5M was associated with access to ITNs. In Mid-North and West-Nile, improved source of drinking water explained most of the U5M reduction. In North-East, improved sanitation facilities were associated with the highest decline in U5M. In Kampala and Mid-Eastern, IPTp had the largest associated with U5M. In Central1 and East-Central, oral rehydration solution and postnatal care were associated with highest decreases in U5M respectively. Conclusion Sub-national estimates of the associations between U5M and interventions can guide control programmes for spatial targeting and accelerate progress towards mortality-related Sustainable Development Goals.http://link.springer.com/article/10.1186/s12889-019-7636-xBayesian proportional hazards geostatistical modelsDemographic and health surveyGeographical variationsInterventionsSub-national scaleUganda
collection DOAJ
language English
format Article
sources DOAJ
author Betty B. Nambuusi
Julius Ssempiira
Fredrick E. Makumbi
Jürg Utzinger
Simon Kasasa
Penelope Vounatsou
spellingShingle Betty B. Nambuusi
Julius Ssempiira
Fredrick E. Makumbi
Jürg Utzinger
Simon Kasasa
Penelope Vounatsou
Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda
BMC Public Health
Bayesian proportional hazards geostatistical models
Demographic and health survey
Geographical variations
Interventions
Sub-national scale
Uganda
author_facet Betty B. Nambuusi
Julius Ssempiira
Fredrick E. Makumbi
Jürg Utzinger
Simon Kasasa
Penelope Vounatsou
author_sort Betty B. Nambuusi
title Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda
title_short Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda
title_full Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda
title_fullStr Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda
title_full_unstemmed Geographical variations of the associations between health interventions and all-cause under-five mortality in Uganda
title_sort geographical variations of the associations between health interventions and all-cause under-five mortality in uganda
publisher BMC
series BMC Public Health
issn 1471-2458
publishDate 2019-10-01
description Abstract Background To reduce the under-five mortality (U5M), fine-gained spatial assessment of the effects of health interventions is critical because national averages can obscure important sub-national disparities. In turn, sub-national estimates can guide control programmes for spatial targeting. The purpose of our study is to quantify associations of interventions with U5M rate at national and sub-national scales in Uganda and to identify interventions associated with the largest reductions in U5M rate at the sub-national scale. Methods Spatially explicit data on U5M, interventions and sociodemographic indicators were obtained from the 2011 Uganda Demographic and Health Survey (DHS). Climatic data were extracted from remote sensing sources. Bayesian geostatistical Weibull proportional hazards models with spatially varying effects at sub-national scales were utilized to quantify associations between all-cause U5M and interventions at national and regional levels. Bayesian variable selection was employed to select the most important determinants of U5M. Results At the national level, interventions associated with the highest reduction in U5M were artemisinin-based combination therapy (hazard rate ratio (HRR) = 0.60; 95% Bayesian credible interval (BCI): 0.11, 0.79), initiation of breastfeeding within 1 h of birth (HR = 0.70; 95% BCI: 0.51, 0.86), intermittent preventive treatment (IPTp) (HRR = 0.74; 95% BCI: 0.67, 0.97) and access to insecticide-treated nets (ITN) (HRR = 0.75; 95% BCI: 0.63, 0.84). In Central 2, Mid-Western and South-West, largest reduction in U5M was associated with access to ITNs. In Mid-North and West-Nile, improved source of drinking water explained most of the U5M reduction. In North-East, improved sanitation facilities were associated with the highest decline in U5M. In Kampala and Mid-Eastern, IPTp had the largest associated with U5M. In Central1 and East-Central, oral rehydration solution and postnatal care were associated with highest decreases in U5M respectively. Conclusion Sub-national estimates of the associations between U5M and interventions can guide control programmes for spatial targeting and accelerate progress towards mortality-related Sustainable Development Goals.
topic Bayesian proportional hazards geostatistical models
Demographic and health survey
Geographical variations
Interventions
Sub-national scale
Uganda
url http://link.springer.com/article/10.1186/s12889-019-7636-x
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