Publication

National and sub-national variation in patterns of febrile case management in sub-Saharan Africa

Given national healthcare coverage gaps, understanding treatment-seeking behaviour for fever is crucial for the management of childhood illness and to reduce deaths. Here, we conduct a modelling study triangulating household survey data for fever in children under the age of five years with georeferenced public health facility databases (n = 86,442 facilities) in 29 countries across sub-Saharan Africa, to estimate the probability of seeking treatment for fever at public facilities. A Bayesian item response theory framework is used to estimate this probability based on reported fever episodes, treatment choice, residence, and estimated travel-time to the nearest public-sector health facility. Findings show inter- and intra-country variation, with the likelihood of seeking treatment for fever less than 50% in 16 countries. Results highlight the need to invest in public healthcare and related databases. The variation in public sector use illustrates the need to include such modelling in future infectious disease burden estimation.

Authors V. Alegana, et al.
Source Nature Communications
Published 2018
Full publication

More publications

COVID-19: Supporting the Government of Namibia with mobility data

Anonymised and aggregated data from Mobile Network Operators is a key data source for understanding the mobility patterns of populations, and improving decision-making and scenario planning during the COVID-19 epidemic. This data can be analysed in near real-time and provide an overview of mobility patterns across all of Namibia

GRID3 Mapping for Health – Brochure

Supported by Gavi through its INFUSE initiative, GRID3 Mapping for Health in the Democratic Republic of the Congo (DRC) is a Ministry of Health initiative, delivered in partnership with Flowminder and the Center for International Earth Science Information Network at […]

Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot

Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries.