Assessing the quality of routine data for the prevention of mother-to-child transmission of HIV: An analytical observational study in two health districts with high HIV prevalence in South Africa
Bibliographic record
Abstract
The prevention of mother-to-child transmission of HIV (PMTCT) is a key maternal and child-health intervention in the context of the HIV/AIDS pandemic in South Africa. Accordingly, the PMTCT programmes have been incorporated in the routine District Health Management Information System (DHMIS) which collects monthly facility-based data to support the management of public-health services. To date, there has been no comprehensive evaluation of the PMTCT information system. This study seeks to evaluate the quality of output indicators for monitoring PMTCT interventions in two health districts with high HIV prevalence. An analytical observational study was undertaken based on the Performance of Routine Information System Management (PRISM) framework and tools, including an assessment of the routine PMTCT data for quality in terms of accuracy and completeness. Data were collected from 57 public health facilities for six pre-defined PMTCT data elements by comparing the source registers with the routine monthly report (RMR), and the RMR with the DMHIS for January and April 2012. This was supplemented by the analysis of the monthly data reported routinely in the DMHIS for the period 2009–2012. Descriptive statistics, analysis of variance (ANOVA) and Bland Altman analysis were conducted using STATA® Version 13. Although completeness was relatively high at 91% (95% CI: 78–100%) at facility level and 96% (95% CI: 92–100%) at district level, the study revealed considerable data quality concerns for the PMTCT information with an average accuracy between the register and RMR of 51% (95% CI: 44–58%) and between the RMR and DHMIS database of 84% (95% CI: 78–91%). We observed differences in the data accuracy by organisational authority. The poor quality of the data was attributed partly to insufficient competencies of health information personnel. The study suggests that the primary point of departure for accurate data transfer is during the collation process. Institutional capacity to improve data quality at the facility level and ensure core competencies for routine health information system (RHIS)-related tasks are needed. Further exploration of the possible factors that may influence data accuracy, such as supervision, RHIS processes, training and leadership are needed. In particular understanding is needed about how individual actions can bring about changes in institutional routines.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".