Measuring socioeconomic and health financing inequality in maternal mortality in Colombia: a mixed methods approach
Bibliographic record
Abstract
BACKGROUND: Understanding health financing reforms and means is key to evaluate how maternal health has improved. Problems related to health financing policies are contributing to inadequate quality of care and inequitable use of healthcare by pregnant women, resulting in poor maternal health outcomes. The purpose of the study was to measure socioeconomic and health financing related inequality in maternal mortality in Colombia as well as identifying potential epicenters of this inequality. METHODS: The data used was obtained from National Information of Social Protection (Sispro), the Department of Planning and National Statistics Department. Maternal mortality ratios were calculated by health insurance scheme and disaggregated by health spending per capita quintiles to allow for closer examination of inequality. The Slope Index of Inequality and Concentration Index were estimated to express absolute and relative inequality. We conducted interviews with key informants involved in the implementation of health financing and maternal health policies. RESULTS: The main finding shows inequality in maternal mortality across regions and in particular in the subsidized health insurance. The contributory health insurance scheme is closing gaps over time, but inequality in the subsidized scheme is significantly widening, which impacts the severity of overall measurements of inequality. 20% of territories with the lowest health spending per capita have reached 35% of maternal mortality, and it such rates are worsening. This means that there is a marginal exclusion in which most of maternal deaths still occur in the regions with lowest resources. CONCLUSIONS: Beyond the key issues in health financing, issues of quality of care must be addressed. The country must define its own approach to financing for maternal health coverage given its unique situation and starting point. Potential policy implications that emerged are: i) afro-Colombian, indigenous, poorer and migrant women must be put at the center of the maternal health care services; ii) better skills, Reproductive, Maternal, Newborn and Child Health RMNCH training and health worker retention strategies and training in rural, insular and remote geographical areas; ii) a better understanding of provider payment mechanisms and the incentives that influence provider behaviors; and iv) inequality prompt calls for a targeted approach, whereby care is directed toward the most disadvantaged regions.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".