Covid-19 pandemic impact on maternal and child health services access in Nampula, Mozambique: a mixed methods research
Bibliographic record
Abstract
BACKGROUND: The Covid-19 pandemic has so far infected more than 30 million people in the world, having major impact on global health with collateral damage. In Mozambique, a public state of emergency was declared at the end of March 2020. This has limited people's movements and reduced public services, leading to a decrease in the number of people accessing health care facilities. An implementation research project, The Alert Community for a Prepared Hospital, has been promoting access to maternal and child health care, in Natikiri, Nampula, for the last four years. Nampula has the second highest incidence of Covid-19. The purpose of this study is to assess the impact of Covid-19 pandemic Government restrictions on access to maternal and child healthcare services. We compared health centres in Nampula city with healthcare centres in our research catchment area. We wanted to see if our previous research interventions have led to a more resilient response from the community. METHODS: Mixed-methods research, descriptive, cross-sectional, retrospective, using a review of patient visit documentation. We compared maternal and child health care unit statistical indicators from March-May 2019 to the same time-period in 2020. We tested for significant changes in access to maternal and child health services, using KrushKall Wallis, One-way Anova and mean and standard deviation tests. We compared interviews with health professionals, traditional birth attendants and patients in the two areas. We gathered data from a comparable city health centre and the main city referral hospital. The Marrere health centre and Marrere General Hospital were the two Alert Community for a Prepared Hospital intervention sites. RESULTS: Comparing 2019 quantitative maternal health services access indicators with those from 2020, showed decreases in most important indicators: family planning visits and elective C-sections dropped 28%; first antenatal visit occurring in the first trimester dropped 26%; hospital deliveries dropped a statistically significant 4% (p = 0.046), while home deliveries rose 74%; children vaccinated down 20%. CONCLUSION: Our results demonstrated the negative collateral effects of Covid-19 pandemic Government restrictions, on access to maternal and child healthcare services, and highlighted the need to improve the health information system in Mozambique.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".