Evidence of resiliency in maternal health services and outcomes in Kono District, Sierra Leone during the COVID-19 pandemic: an observational study
Bibliographic record
Abstract
BACKGROUND: Following the Ebola outbreak in Sierra Leone, the post-Ebola recovery investments in Wellbody Clinic and Koidu Government Hospital provide an opportunity to conduct a more focused examination of facility-level maternal health services and outcomes in the context of COVID-19 pandemic. This study aimed to describe the use of maternal healthcare services and outcomes in these health facilities before, during, and after the COVID-19 pandemic. METHODS: The study involved analysis of routine programme data (March 2019 to February 2022) from two public health facilities supported by Partners In Health Sierra Leone: Koidu Government Hospital and Wellbody Clinic. Aggregated and de-identified secondary data was abstracted using a standardized tool. Descriptive statistics and bivariable negative binomial regression were used to assess the association between time periods ( pre-COVID-19 period [March 2019 to February 2020], during COVID-19 emergency period [March 2020 to February 2021], after COVID-19 emergency period [March 2021 to February 2022) and outcomes (antenatal care visit and facility deliveries). RESULTS: The study analyzed 3,204 fourth antenatal care visits and 7,369 deliveries over 36 months at both health facilities. The fourth antenatal care visits (from 947 to 920) and facility deliveries (from 2309 to 2221) decreased during COVID-19 compared to pre-COVID-19. However, maternal (from 32 to 23) and neonatal (36 to 26) deaths declined during COVID-19 compared to the pre-COVID-19 period at Koidu Government Hospital. Regression analysis showed that relative to the period of COVID-19 emergency period, there were no observable difference in the rate of fourth antenatal care visits in the pre-COVID-19 period [IRR = 1.02, 95%CI: 0.61, 1.72] and during the post-COVID-19 emergency period [IRR = 1.45, 95%CI: 0.87, 2.42]. Relative to the COVID-19 emergency period, there was also no difference observed in maternal deliveries: pre-COVID-19 [IRR = 1.03, 95%CI: 0.69, 1.56] and post-COVID-19 emergency [IRR = 1.28, 95%CI: 0.85, 1.92]. CONCLUSION: In Sierra Leone, the resources and efforts directed to the post-Ebola recovery strategy were tested during and after the COVID-19 pandemic. Our study demonstrates the resilience of maternal and neonatal services in two healthcare facilities in a less-affected region of Sierra Leone, to the anticipated disruptions due to the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".