Impact of a free care policy on the utilisation of health services during an Ebola outbreak in the Democratic Republic of Congo: an interrupted time-series analysis
Bibliographic record
Abstract
BACKGROUND: During past outbreaks of Ebola virus disease (EVD) and other infectious diseases, health service utilisation declined among the general public, delaying health seeking behaviour and affecting population health. From May to July 2018, the Democratic Republic of Congo experienced an outbreak of EVD in Equateur province. The Ministry of Public Health introduced a free care policy (FCP) in both affected and neighbouring health zones. We evaluated the impact of this policy on health service utilisation. METHODS: Using monthly data from the national Health Management Information System from January 2017 to January 2019, we examined rates of the use of nine health services at primary health facilities: total visits; first and fourth antenatal care visits; institutional deliveries; postnatal care visits; diphtheria, pertussis and tetanus (DTP) vaccinations and visits for uncomplicated malaria, pneumonia and diarrhoea. We used controlled interrupted time series analysis with a mixed effects model to estimate changes in the rates of services use during the policy (June-September 2018) and afterwards. FINDINGS: Overall, use of most services increased compared to control health zones, including EVD affected areas. Total visits and visits for pneumonia and diarrhoea initially increased more than two-fold relative to the control areas (p<0.001), while institutional deliveries and first antenatal care increased between 20% and 50% (p<0.01). Visits for DTP, fourth antenatal care visits and postnatal care visits were not significantly affected. During the FCP period, visit rates followed a downward trend. Most increases did not persist after the policy ended. INTERPRETATION: The FCP was effective at rapidly increasing the use of some health services both EVD affected and not affected health zones, but this effect was not sustained post FCP. Such policies may mitigate the adverse impact of infectious disease outbreaks on population health.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".