Consistency Between Administrative Health Records and Self-Reported Health Status and Health Care Use Among Indigenous Wayuu Health Insurance Enrollees: La Guajira, Colombia
Bibliographic record
Abstract
The objective of the study was to assess the consistency between self-reported demographic characteristics, health conditions, and healthcare use, and administrative healthcare records, in a sample of enrollees of an Indigenous health organization in Colombia. We conducted a phone survey of a random sample of 2113 enrollees September-2020/February-2021. Administrative health records were obtained for the sample. Using ICD-10 diagnostic codes, we identified individuals who had healthcare visits for diabetes, hypertension, and/or pregnancy. Using unique identifiers, we linked their survey data to the administrative dataset. Agreement percentages and Cohen's Kappa coefficients were calculated. Logistic regressions were performed for each health condition/state. Results showed high degree of agreement between data sources for sex and age, similar rates for diabetes and hypertension, 10% variation for pregnancy. Kappa statistics were in the moderate range. Age was significantly associated with agreement between data sources. Sex, language, and self-rated health were significant for diabetes. This is the first study with data from an Indigenous population assessing the consistency between self-reported data and administrative health records. Survey and administrative data produced similar results, suggesting that Anas Wauu can be confident in using their data for planning and research purposes, as part of the movement toward data sovereignty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".