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- Amouzou, A, et al.
(författare)
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Health service utilisation during the COVID-19 pandemic in sub-Saharan Africa in 2020: a multicountry empirical assessment with a focus on maternal, newborn and child health services
- 2022
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Ingår i: BMJ global health. - : BMJ. - 2059-7908. ; 7:5
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Tidskriftsartikel (refereegranskat)abstract
- There are concerns about the impact of the COVID-19 pandemic on the continuation of essential health services in sub-Saharan Africa. Through the Countdown to 2030 for Women’s, Children’s and Adolescents’ Health country collaborations, analysts from country and global public health institutions and ministries of health assessed the trends in selected services for maternal, newborn and child health, general service utilisation.MethodsMonthly routine health facility data by district for the period 2017–2020 were compiled by 12 country teams and adjusted after extensive quality assessments. Mixed effects linear regressions were used to estimate the size of any change in service utilisation for each month from March to December 2020 and for the whole COVID-19 period in 2020.ResultsThe completeness of reporting of health facilities was high in 2020 (median of 12 countries, 96% national and 91% of districts ≥90%), higher than in the preceding years and extreme outliers were few. The country median reduction in utilisation of nine health services for the whole period March–December 2020 was 3.9% (range: −8.2 to 2.4). The greatest reductions were observed for inpatient admissions (median=−17.0%) and outpatient admissions (median=−7.1%), while antenatal, delivery care and immunisation services generally had smaller reductions (median from −2% to −6%). Eastern African countries had greater reductions than those in West Africa, and rural districts were slightly more affected than urban districts. The greatest drop in services was observed for March–June 2020 for general services, when the response was strongest as measured by a stringency index.ConclusionThe district health facility reports provide a solid basis for trend assessment after extensive data quality assessment and adjustment. Even the modest negative impact on service utilisation observed in most countries will require major efforts, supported by the international partners, to maintain progress towards the SDG health targets by 2030.
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| 2. |
- Bosco, C, et al.
(författare)
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Exploring the high-resolution mapping of gender-disaggregated development indicators
- 2017
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Ingår i: Journal of the Royal Society, Interface. - : The Royal Society. - 1742-5662 .- 1742-5689. ; 14:129
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Tidskriftsartikel (refereegranskat)abstract
- Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.
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