Considerations for an integrated population health databank in Africa: lessons from global best practices

Perceptions of human movement researchers and clinicians on the barriers and facilitators to health research data sharing in Africa

INTRODUCTION

The benefits of research data sharing abound in the literature. However, some factors define how researchers and clinicians approach the challenges surrounding sharing human movement health research data.

PURPOSE

To describe the perceptions of human movement researchers and clinicians on the barriers and facilitators to research data sharing in Africa.

METHOD

A qualitative descriptive design with a purposive sampling method was used. In-depth interviews with human movement researchers and clinicians across Africa were conducted online via Microsoft Teams. Sixteen (n = 16) participants took part in this study. This sample size was representative of East, West, Northern, and Southern Africa. Efforts made to engage with participants in Central Africa were unsuccessful.

RESULT

Five themes emerged: 1) the researcher-clinician gap; 2) technological pros and cons in Africa; 3) cost matters; 4) bureaucracy and ethical factors; and 5) the unique African perspective. Mainly, barriers rather than facilitators to data sharing exist among African human movement researchers and clinicians.

CONCLUSION

There needs to be a societal and psychological shift through reorientation to encourage data sharing among African human movement researchers and clinicians.

Use of Population-Level Administrative Data in Developmental Science

Population-level administrative data-data on individuals' interactions with administrative systems (e.g., health, criminal justice, and education)-have substantially advanced our understanding of life-course development. In this review, we focus on five areas where research using these data has made significant contributions to developmental science: (a) understanding small or difficult-to-study populations, (b) evaluating intergenerational and family influences, (c) enabling estimation of causal effects through natural experiments and regional comparisons, (d) identifying individuals at risk for negative developmental outcomes, and (e) assessing neighborhood and environmental influences. Further advances will be made by linking prospective surveys to administrative data to expand the range of developmental questions that can be tested; supporting efforts to establish new linked administrative data resources, including in developing countries; and conducting cross-national comparisons to test findings' generalizability. New administrative data initiatives should involve consultation with population subgroups including vulnerable groups, efforts to obtain social license, and strong ethical oversight and governance arrangements.