Building integrated data systems for health and nutrition program evaluations
Lessons learned from a multi-country implementation of a DHIS 2-based system
Bibliographic Data
| ID | 19552140 |
|---|---|
| Authors | Elizabeth Hazel (0000-0002-9176-3278, Johns Hopkins University), Emily Wilson (0000-0003-4350-6012, Johns Hopkins University), Adebusoye Anifalaje, Adebusoye A Anifalaje (Georgetown University), Talata Sawadogo‐Lewis (0000-0002-0541-1859, Johns Hopkins University), Rebecca Heidkamp (0000-0003-3146-1320, Johns Hopkins University) |
| Year | 2018 |
| Volume | 8 |
| Issue | 2 |
| Pages | 020307-020307 |
| Publication date | 2018-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Global Health (JOURNAL) |
| Journal identifiers | ISSN: 2047-2978 • E-ISSN: 2047-2986 |
| Publisher | International Society of Global Health (PUBLISHER • GB) |
| DOI | 10.7189/jogh.08.020307 |
| PMID | 30356499 |
| OpenAlex | W2897590787 |
| Language | EN |
| Citations received | 3 |
| References cited | 3 |
G lobally, we have more data on population health than ever before.In the era of "big data", the challenge is not only to collect data but also to make use and sense of what is already there.Countries have a wealth of information on maternal, newborn and child health and nutrition (MNCH&N) programs and impact but it is dispersed through a variety of sources, and sometimes difficult for policy and program decision-makers to access, analyze and develop informative outputs.The National Evaluation Platform (NEP) is an approach to large-scale program evaluation of complex, multi-faceted MNCH&N programs [1].A key component the original NEP concept involved developing an integrated set of data organized by geographic area that draws from a variety of data sources available in the country including household surveys (Demographic Health Surveys, Multiple Indicator Cluster Surveys, etc), facility surveys (Service Provision Assessment, etc) and administrative sources [2].The data include core indicators from MNCH&N program impact pathways to be updated as new data are released, facilitating rapid analysis in response to time-sensitive questions from MNCH&N stakeholders [3].In 2013, the Government of Canada funded the real-world implementation of NEP by public-sector stakeholders in four countries in sub-Saharan Africa: Malawi, Mali, Mozambique, and Tanzania.The Institute for International Programs at the Johns Hopkins Bloomberg School of Public Health (IIP) provided technical support.In early 2014, the IIP team began to explore options for operationalizing an integrated district-level data set referred to as the "NEP data system".In this viewpoint, we describe development, implementation challenges and lessons learned from design and deployment of a data system built on District Health Information System 2 (DHIS 2) platform in four countries in sub-Saharan Africa.As the "big data" era advances and use of DHIS 2 becomes more widespread, we hope this documentation will be useful to software developers and others aiming to build data systems that support evidence-based decision-making
Data science · Environmental health · Health care · Health data · Healthcare system · Political science · Computer Science · demographic modeling and climate adaptation · Medical Coding and Health Information · Medicine · Primary Care and Health Outcomes
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| Citations per year | 0.5 |
|---|---|
| Citation span | 2020 - 2025 (6) |
| Highly cited | No |