Disruptions in maternal health service use during the COVID-19 pandemic in 2020: experiences from 37 health facilities in low-income and middle-income countries
Bibliographic record
Abstract
The COVID-19 pandemic has heterogeneously affected use of basic health services worldwide, with disruptions in some countries beginning in the early stages of the emergency in March 2020. These disruptions have occurred on both the supply and demand sides of healthcare, and have often been related to resource shortages to provide care and lower patient turnout associated with mobility restrictions and fear of contracting COVID-19 at facilities. In this paper, we assess the impact of the COVID-19 pandemic on the use of maternal health services using a time series modelling approach developed to monitor health service use during the pandemic using routinely collected health information systems data. We focus on data from 37 non-governmental organisation-supported health facilities in Haiti, Lesotho, Liberia, Malawi, Mexico and Sierra Leone. Overall, our analyses indicate significant declines in first antenatal care visits in Haiti (18% drop) and Sierra Leone (32% drop) and facility-based deliveries in all countries except Malawi from March to December 2020. Different strategies were adopted to maintain continuity of maternal health services, including communication campaigns, continuity of community health worker services, human resource capacity building to ensure compliance with international and national guidelines for front-line health workers, adapting spaces for safe distancing and ensuring the availability of personal protective equipment. We employ a local lens, providing prepandemic context and reporting results and strategies by country, to highlight the importance of developing context-specific interventions to design effective mitigation strategies.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".