Data quality assessments stimulate improvements to health management information systems: evidence from five African countries
Bibliographic record
Abstract
BACKGROUND: Health service data are used to inform decisions about planning and implementation, as well as to evaluate performance and outcomes, and the quality of those data are important. Data quality assessments (DQA) afford the opportunity to collect information about health service data. Through its Rapid Access Expansion Programme (RAcE), the World Health Organization (WHO) funded non-governmental organizations (NGO) to support Ministries of Health (MOH) in implementing integrated community case management (iCCM) programs in the Democratic Republic of Congo, Malawi, Mozambique, Niger and Nigeria. WHO contracted ICF to support grantee monitoring and evaluation efforts, part of which was to conduct DQAs to enhance program monitoring and decision making. The contribution of DQAs to data-driven decision making has been documented and the purpose of this paper is to describe how DQAs contributed to health management information system (HMIS) strengthening and the findings of subsequent DQAs in those areas. METHODS: ICF created a mixed-methods DQA for iCCM data, comprising a review of the data collection and management system, a data tracing component and key informant interviews. The DQA was applied twice in each RAcE site, which enables a general comparison of system-level attributes before and after the first DQA application. For this qualitative assessment, we reviewed DQA reports to collate information about DQA recommendations and how they were addressed before a subsequent DQA, along with the findings of the second DQA. RESULTS: Findings from the first DQA in each RAcE site stimulated NGO and MOH efforts to strengthen different aspects of the HMIS in each country, including modifying data collection tools in the Democratic Republic of Congo; training community health workers (CHWs) and supervisors in Malawi; strengthening supervision in Mozambique; improving CHW registers and strengthening staff capacity at all levels to report data in Niger; establishing a data review system in Abia State, Nigeria; and, establishing processes to improve data use and quality in Niger State, Nigeria. CONCLUSION: Data quality assessments stimulated context-specific efforts by NGOs and MOHs to improve iCCM data quality. DQAs can serve as a collaborative and evidence-based activity to influence discussions of data quality and stimulate HMIS strengthening efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".