Evaluation of the HIV Case-Based Surveillance System: A Pilot of the Electronic Health Record System in Mutare District, Zimbabwe, 2021
Bibliographic record
Abstract
Background: Zimbabwe started HIV case-based surveillance in April 2017. Rapid testing for HIV recent infection was introduced into routine HIV and testing services in 2019 along with the Impilo Electronic Health Record System. For the period January-June 2020, only 1 out of 13 health facilities in Mutare district reported seven newly diagnosed HIV patients through the electronic health record system compared to 483 in the District Health Information System (DHIS-2) recorded from paper-based registers. We evaluated the case-based surveillance system attributes, usefulness and reasons for under-reporting from January-December 2020. Methods: We conducted a descriptive cross-sectional study using updated Centres for Disease Control guidelines for evaluating public health surveillance systems. Questionnaires were administered to 36 health workers involved in HIV testing services. Facility checklists were used to collect data on knowledge, system attributes and usefulness of the system. Completed HIV case-based surveillance forms were assessed for completeness. Epi Info Version 7 was used to generate frequencies, means and proportions. Results: The reasons for under-reporting of patients in the electronic health record system were lack of reporting guidelines 26/36 (72%), limited coordination between technical staff and health facilities 24/36 (67%) and limited competency on the Electronic health record system 22/36 (61%). Timeliness, completeness, and validity were 88%, 82% and 100% respectively. The stability of the system was affected by the lack of standard operating procedures during system interruptions. Overall representativeness was 45% despite increasing from 3/226 (1%) to 224/303 (73%) between Quarter-1 and Quarter-4 of 2020. Acceptability was 100% due to reduced paperwork and the ability to generate simple reports. The information generated was used to identify new infection hotspots 28/36 (78%). Conclusion: The HIV cases based surveillance system was timely, acceptable with good data quality. Representativeness was poor due to limited competency on the electronic health record system. As a result, health workers received further training.
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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 | 0.025 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".