When it rains, it pours: detecting seasonal patterns in utilization of maternal healthcare in Mozambique using routine data
Bibliographic record
Abstract
BACKGROUND: Climatic conditions and seasonal trends can affect population health, but typically, we consider the effect of climate on the epidemiology of communicable diseases. However, climate can also have an effect on access to care, particularly in remote rural areas of low- and middle-income countries. In this study, we investigate associations between the rainy season and the utilization of maternal health services in Mozambique. METHODS: We examined patterns in the number of women receiving antenatal care (ANC) and delivering at a health facility for 2012-2019, using data from Mozambique's Health Management Information Systems. We investigated the association between seasonality (rainfall) and maternal health service utilization (ANC and institutional delivery) at national and provincial level. We fit a negative binomial regression model for institutional delivery and used it to estimate the yearly reduction in institutional deliveries due to the rainy season, with other factors held constant. We used the Lives Saved Tool (LiST) to model increases in mortality due to this estimated decrease in institutional delivery associated with the rainy season. RESULTS: In our national analysis, the rate of ANC visits was 1% lower during the rainy season, adjusting for year and province (IRR = 0.99, 95% CI: 0.96-1.03). The rate of institutional deliveries was 6% lower during the rainy season than the dry season, after adjusting for time and province (IRR = 0.94, 95% CI: 0.92-0.96). In provincial analyses, all provinces except for Maputo-Cidade, Maputo-Province, Nampula, and Niassa showed a statistically significantly lower rate of institutional deliveries in the rainy season. None were statistically significantly lower for ANC. We estimate that, due to reductions in institutional delivery attributable only to the rainy season, there were 74 additional maternal deaths and 726 additional deaths of children under the age of 1 month in 2021, that would not have died if the mothers had instead delivered at a facility. CONCLUSION: Fewer women deliver at a health facility during the rainy season in Mozambique than during the dry season. Barriers to receiving care during pregnancy and childbirth must be addressed using a multisectoral approach, considering the impact of geographical inequities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".