Options for Optimal Coverage of Free C-Section Services for Poor Mothers in Indian State of Gujarat: Location Allocation Analysis Using GIS
Bibliographic record
Abstract
BACKGROUND: Gujarat, a western state of India, has seen a steep rise in the proportion of institutional deliveries over the last decade. However, there has been a limited access to cesarean section (C-Section) deliveries for complicated obstetric cases especially for poor rural women. C-section is a lifesaving intervention that can prevent both maternal and perinatal mortality. Poor women bear a disproportionate burden of maternal mortality, and lack of access to C-section, especially for these women, is an important contributor for high maternal and perinatal mortality in resource limited settings. To improve access for this underserved population in the context of inadequate public provision of emergency obstetric services, the state government of Gujarat initiated a public private partnership program called "Chiranjeevi Yojana" (CY) in 2005 to increase the number of facilities providing free C-section services. This study aimed to analyze the current availability of these services in three districts of Gujarat and to identify the best locations for additional service centres to optimize access to free C-section services using Geographic Information System technology. METHODOLOGY: Supply and demand for obstetric care were calculated using secondary data from sources such as Census and primary data from cross-sectional facility survey. The study is unique in using primary data from facilities, which was collected in 2012-13. Information on obstetric beds and functionality of facilities to calculate supply was collected using pretested questionnaire by trained researchers after obtaining written consent from the participating facilities. Census data of population and birth rates for the study districts was used for demand calculations. Location-allocation model of ArcGIS 10 was used for analyses. RESULTS: Currently, about 50 to 84% of populations in all three study districts have access to free C-section facilities within a 20km radius. The model suggests that about 80-96% of the population can be covered for free C-section services with addition of 4-6 centres in critical but underserved regions. It was also suggested that upgrading of public sector facilities with minimal investment can improve the services. CONCLUSION: This study highlights utility of Geographic Information System technology for planning service centres to optimize access to vital lifesaving procedure such as C-section. Although the location allocation methodology has been available for decades, it has been used sparsely by public health professionals. This paper makes an important contribution to the literature for use of the method for planning in resource limited settings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".