Socioecologically informed use of remote sensing data to predict rural household poverty.

Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.

Strengthening immunization programs through innovative sub-national public-private partnerships in selected provinces in the Democratic Republic of the Congo.

BACKGROUND: Low immunization coverage rates in the Democratic Republic of Congo (DRC) have been reflective of challenges with vaccine access, support and delivery in the country. Motivated by measles and vaccine-derived polio virus (VDPV) outbreaks in 2016-17 and low vaccination rates, the provinces of Haut Lomami and Tanganyika were identified as pilot locations for an innovative approach focused on establishing a consortium of partners supporting local government. This approach was formalized through Memorandums of Understanding (MoUs) between the Bill and Melinda Gates Foundation and Provincial governments in 2018. A third province, Lualaba, established an MoU in 2021. MOU IMPLEMENTATION: These MoUs were 5-year partnerships designed to aid provinces in meeting four key objectives: 80 % immunization coverage, management/elimination of polio/cVDPV outbreaks, improvement of vaccine accessibility, and transfer of immunization service management to provincial leadership. OUTCOMES: During the MoU period, Haut-Lomami saw an increase in full immunization coverage, from 35.7 % (MICS 2018) to 88.9 % (VCS 2021-22), the highest in country. A sharp drop in percentage of zero-dose children was observed in the 3 provinces, confirming improved access to immunization services. Tanganyika saw initial improvement in full immunization coverage, followed by a drop in the VCS 2021-22 due to COVID-19 and healthcare worker strikes. Coverage improved in Tanganyika in the 2023 VCS. The 3 provinces increased their financial contributions to routine immunization and are now the top contributing provinces. While no cVDPV cases were recorded in 2020 and 2021, cVDPV1 and cVDPV2 outbreaks are afflicting the 3 provinces since 2022. CONCLUSIONS: Ultimately, the provincial MoUs were successful in bolstering provincial autonomy and capacity building with the biggest success being a drop in zero-dose children. While not all objectives have been met, the MoU approach served as an innovative program for key aspects of strengthening routine immunization in the DRC.

High-resolution population estimation using household survey data and building footprints.

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa.

Background: Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels. Methods: Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min). Results: Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels. Conclusions: The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.