Out-of-pocket expenditure and drug adherence of patients with diabetes in Odisha
Bibliographic record
Abstract
INTRODUCTION: The burden of diabetes mellitus (DM) is increasing in India and across states. Given the chronic and progressive nature of the disease, it implicates huge financial burden on patients. Given this, the objectives of this study are to estimate the out-of-pocket (OOP) expenditure on diabetes care and assess the magnitude of medication adherence among patients in a public hospital. MATERIALS AND METHODS: A cross-sectional survey was conducted among 206 patients with age ≥25 years visiting the outpatient department of a tertiary care hospital in Odisha. Cost data were collected from April to June 2016 using a structured questionnaire, and drug adherence was assessed using the Morisky Medication Adherence Scale. RESULTS: The average total expenditure per patient per month was INR 1265 (95% confidence interval 1178-1351), of which medical expenditure was INR 993 (95 confidence interval 912-1075) and that of nonmedical expenditure was INR 271 (95 confidence interval 251-292). Expenditure on medicine constituted around 65% of total medical expenditure. The other drivers of medical expenditure were diagnostics services constituting 13.2% and transportation (11.8%). Overall, only 15% of the patients reported high adherence to medication. DISCUSSION: This study generated evidence on OOP expenditure on diabetics in Odisha which are comparable to many Indian studies. One of the critical findings of this study was that a majority of patients visiting public hospitals had to spend OOP on medicine and diagnostic services. These findings could be used to design appropriate financing strategies to protect the interest of the poor who largely use public health facility in Odisha.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".