Effect of power outages on the use of maternal health services: evidence from Maharashtra, India
Bibliographic record
Abstract
INTRODUCTION: Electricity outages are common in low/middle-income countries and have been shown to adversely affect the operation of health facilities; however, little is known about the effect of outages on the utilisation of health services. METHODS: Using data from the 2015-2016 India Demographic Health Survey, combined with information on electricity outages as reported by the state electricity provider, we explore the associations between outage duration and frequency and delivery in an institution, skilled birth attendance, and caesarean section delivery in Maharashtra State, India. We employ multivariable logistic regression, adjusting for individual and household-level covariates as well as month and district-level fixed effects. RESULTS: Power outage frequency was associated with a significantly lower odds of delivering in an institution (OR 0.98; 95% CI 0.96 to 0.99), and the average number of 8.5 electricity interruptions per month was found to yield a 2.08% lower likelihood of delivering in a facility, which translates to an almost 18% increase in home births. Both power outage frequency and duration were associated with a significantly lower odds of skilled birth attendance (OR 0.97; 95% CI 0.95 to 0.99, and OR 0.99; 95% CI 0.992 to 0.999, respectively), while neither power outage frequency nor duration was a significant predictor of caesarean section delivery. CONCLUSION: Power outage frequency and duration are important determinants of maternal health service usage in Maharashtra State, India. Improving electricity services may lead to improved maternal and newborn health outcomes.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".