Strengthening routine data reporting in private hospitals in Lagos, Nigeria
Bibliographic record
Abstract
The availability of routine health information is critical for effective health planning, especially in resource-limited countries. Nigeria adopted the web-based District Health Information System (DHIS) to harmonize the collection, analysis and storage of data for informed decision-making. However, only 44% of all private hospitals in Lagos State reported to the DHIS despite constituting 90% of all health facilities in the state. To bridge this gap, this study implemented targeted interventions. This paper describes (1) the implemented interventions, (2) the effects of the interventions on data reporting on DHIS during the intervention period and (3) the evaluation of data reporting on DHIS after the intervention period in select private hospitals in Lagos State. A five-pronged intervention was implemented in 55 private hospitals (intervention hospitals), which entailed stakeholder engagement, on-the-job training, in-facility mentoring and the provision of data tools and job aids, to improve data reporting on DHIS from 2014 to 2017. A controlled before-and-after study design was employed to assess the effectiveness of the implemented interventions. A comparable cohort of 55 non-intervention private hospitals was selected, and data were extracted from both groups. Data analysis was conducted using paired and independent t-tests to assess the effect and measure the difference between both groups of hospitals, respectively. An average increase of 65.28% (P < 0.01) in reporting rate and 50.31% (P < 0.01) in the timeliness of reporting on DHIS was seen among intervention hospitals. Similarly, the difference between intervention and non-intervention hospitals post-intervention was significantly different for both data reporting (mean difference = -22.38, P < 0.01) and timeliness (mean difference = -18.81, P < 0.01), respectively. Furthermore, a sustained improvement in data reporting and timeliness of reporting on DHIS was observed among intervention hospitals 24 months after interventions. Thus, implementing targeted interventions can strengthen routine data reporting for better performance and informed decision-making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".