The COVID-19 pandemic’s impact on health service utilization among pregnant women in three Nigerian States: a mixed methods study
Bibliographic record
Abstract
Abstract Background COVID-19 disrupted health service delivery and weakened global and national health systems. The objective of this study was to describe the changes in health service utilization in three local government areas in three Nigerian states and examine factors involved. Methods A cross-sectional mixed-methods approach was used to examine changes in service utilization during the first nine months of the COVID-19 pandemic and associated factors in three Nigerian states; Ebonyi, Niger and Ondo. A total of 315 pregnant women seen for antenatal care in 80 health facilities between October 1 and November 30, 2020, participated in exit interviews; 93 women participated in focus group discussions (FGDs). Descriptive analyses and a multivariable logistic analysis were conducted to examine associations between characteristics and decreased service utilization. Content analysis was used to identify the emerging themes for factors that impacted health service utilization during the pandemic. Results One quarter of women surveyed reported that they reduced or ceased health service utilization during this initial period of the pandemic. The biggest reported changes in visits were for immunization (47% pre-pandemic versus 30% during the pandemic, p<0.001) and a small but statistically significant decline in antenatal care (99% to 94%, p<0.001) was observed.State of residence was significantly associated with reduced or ceased utilization during the pandemic (p<0.01); other sociodemographic characteristics were not. Qualitative findings show that lockdowns, transportation issues, increased costs and fear of contracting COVID-19 or being labeled as COVID-positive were the most common reasons mentioned for not seeking care during this period of the pandemic. Conclusion s The pandemic from March to November 2020 negatively impacted health service utilization amongst pregnant women in Nigeria. A better understanding of differences in the pandemic and state response could help inform future actions. The FGDs findings highlight the need for health systems to consider how to facilitate service utilization during a pandemic, such as providing safe transport or increasing outreach, and to minimize stigma for those seeking care.
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 | 0.051 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".