Impact of the 2018-2020 Democratic Republic of Congo Ebola epidemic on health system utilization and health outcomes
Bibliographic record
Abstract
Background The 2018-2020 Ebola epidemic in the eastern Democratic Republic of Congo caused 3,481 infections and 2,299 deaths. The broader impact on health system utilization and health outcomes remains unclear. Methods From January to March 2020, a cross-sectional survey was administered to 3,631 households in Ebola-affected and non-affected health zones in North Kivu province to collect data on health behaviors and health status. Using linear models, we tested for associations between residence in an Ebola zone and multiple outcomes. Additionally, administrative data from 56 health facilities in Ebola zones was used to test for statistically significant changes in medical procedures (e.g. Cesarean sections) and disease rates before and during the epidemic. Results Comparing before the epidemic to during, we found no difference in monthly mean procedures per facility: measles vaccinations -58 (95% confidence interval, CI = -140, 24); Cesarean sections 1.4 (95% CI = -0.8, 3.6); laparotomy 0.2 (95% CI = -0.5, 0.9); open fractures 0.0 (95% CI = -0.1, 0.1); appendectomy 0.0 (95% CI = -0.3, 0.3); inguinal hernia 0.3 (95% CI = 0.0, 0.7). Households in Ebola zones were 16 percentage points (pp) (95% CI = 11, 21) more likely to report going to the hospital more often than normal because of free access, reported fewer measles vaccinations (-10pp 95% CI = -14, -5), and less willingness to vaccinate children (-6pp; 95% CI = -9, -3). However, administrative data showed no change in vaccination before and during Ebola in Ebola zones. Households in Ebola zones were 14pp less likely to report that a child had experienced measles (95% CI = -18, -11) and 8pp less likely to report that a child had experienced diarrhea (95% CI = -12, -4) since 1 Jan 2017. However, administrative data showed no change in either, comparing before-Ebola to during-Ebola in Ebola zones (difference in monthly mean procedures per facility: measles 5.6 (95% CI = -0.8, 12.0); diarrhea 41 (95% CI = -63, 145). Conclusions The Ebola epidemic did not have large effects on health system utilization or health outcomes (other than Ebola virus disease). This suggests that the Congolese and international response successfully maintained health system capacity during the epidemic.
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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 | 0.005 | 0.001 |
| 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 | 0.000 |
| 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".