The HPAfrica protocol: Assessment of health behaviour and population-based socioeconomic, hygiene behavioural factors – a standardised repeated cross-sectional study in multiple cohorts in sub-Saharan Africa


Gi Deok Pak, Andrea Haekyung Haselbeck, Hyeong Won Seo, Isaac Osei, John Amuasi, Robert F Breiman, Ligia Maria Cruz Espinosa, Marianne Holm, Justin Im, Geun Hyeog Jang, Hyon Jin Jeon, Stephen P Luby, Octavie Lunguya-Metila, William MacWright, Ondari Daniel Mogeni, Iruka N Okeke, Ellis Owusu-Dabo, Jin Kyung Park, Se Eun Park, Oluwafemi Popoola, Hye Jin Seo, Abdramane Bassiahi Soura, Mekonnen Teferi, Trevor Toy, Yun Chon, Mathilde Rafindrakalia, Raphaël Rakotozandrindrainy, Christian G Meyer, Florian Marks, Ursula Panzner


The objective of the Health Population Africa (HPAfrica) study is to determine health behaviour and population-based factors, including socioeconomic, ethnographic, hygiene and sanitation factors, at sites of the Severe Typhoid Fever in Africa (SETA) programme. SETA aims to investigate healthcare facility-based fever surveillance in Burkina Faso, the Democratic Republic of the Congo, Ethiopia, Ghana, Madagascar and Nigeria. Meaningful disease burden estimates require adjustment for health behaviour patterns, which are assumed to vary among a study population.

For the minimum sample size of household interviews required, the assumptions of an infinite population, a design effect and age-stratification and sex-stratification are considered. In the absence of a population sampling frame or household list, a spatial approach will be used to generate geographic random points with an Aeronautical Reconnaissance Coverage Geographic Information System tool. Printouts of Google Earth Pro satellite imagery visualise these points. Data of interest will be assessed in different seasons by applying population-weighted stratified sampling. An Android-based application and a web service will be developed for electronic data capturing and synchronisation with the database server in real time. Sampling weights will be computed to adjust for possible differences in selection probabilities. Descriptive data analyses will be performed in order to assess baseline information of each study population and age-stratified and sex-stratified health behaviour. This will allow adjusting disease burden estimates. In addition, multivariate analyses will be applied to look into associations between health behaviour, population-based factors and the disease burden as determined in the SETA study.

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