Impact of the COVID-19 pandemic and response on the utilisation of health services during the first wave in Kinshasa, the Democratic Republic of the Congo
Bibliographic record
Abstract
Abstract Introduction Health service use among the general public can decline during infectious disease outbreaks and has been predicted among low and middle-income countries during the COVID-19 pandemic. In March 2020, the government of the Democratic Republic of the Congo (DRC) implemented public health measures across Kinshasa, including strict lockdown measures in the Gombe health zone, to mitigate impact of the pandemic. Methods Using data from the Health Management Information System (January 2018 - December 2020), we evaluated the impact of the pandemic on the use of essential health services (total visits, maternal health, vaccinations, visits for common infectious diseases, and diagnosis of non-communicable diseases) using interrupted time series with mixed effects segmented Poisson regression models during the first wave of the pandemic. Analyses were stratified by age, sex, health facility, and neighbourhood. Results Health service use dropped rapidly following the start of the pandemic and ranged from 16% for hypertension diagnoses to 39% for diabetes diagnoses. However, reductions were highly concentrated in Gombe (81% decline in total visits) relative to health zones without lockdown. When the lockdown was lifted, total visits, visits for infectious diseases, and diagnoses for non-communicable diseases increased approximately two-fold. Hospitals were more affected than health centres. Overall, the use of maternal health services and vaccinations was not significantly affected. Conclusion The COVID-19 pandemic resulted in important reductions in health service utilisation in Kinshasa, particularly Gombe. Lifting of lockdown led to a rebound in the level of health service use but it remained lower than pre-pandemic levels. Summary Box What is already known about this subject Substantial declines in the use of health services among the general public have been well-documented during previous outbreaks of infectious diseases. Modelled studies predicted substantial increases in morbidity and mortality in many low- and middle-income countries (LMICs) mainly due to expected declines in the use of health services among the general public. Only a small number of studies have so far evaluated the impact of the COVID-19 pandemic on the use of health services in LMICs and none have also evaluated both the implementation and lifting of lockdown measures. What are the new findings This study found that overall use of health services declined in Kinshasa but was most pronounced in the Gombe health zone which was subject to strict lockdown measures. Some health services were more affected than others, most notably visits and tests for malaria and visits for new diagnoses of non-communicable diseases. Maternal and child health services were relatively unaffected. When the lockdown measures were lifted, health service utilization rebounded but remained at levels lower than those observed pre-pandemic. What do the new findings imply The COVID-19 pandemic has likely had important effects on the use of health services among the general public throughout LMICs. However, evidence from Kinshasa suggests the effects may not be as widespread as previously assumed. The impact of strict social distancing measures needs on COVID-19 outcomes needs to be weighed off against the potential population-level health effects of these policies in various international contexts.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".