Building integrated data systems for health and nutrition program evaluations: lessons learned from a multi-country implementation of a DHIS 2-based system
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.148 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".