Micro-Economic Impact of Congenital Heart Surgery: Results of a Prospective Study from a Limited-Resource Setting
Bibliographic record
Abstract
INTRODUCTION: The microeconomic impact of surgery for congenital heart disease is unexplored, particularly in resource limited environments. We sought to understand the direct and indirect costs related to congenital heart surgery and its impact on Indian households from a family perspective. METHODS: Baseline and first follow-up data of 644 consecutive children admitted for surgery for congenital heart disease (March 2013 - July 2014) in a tertiary referral hospital in Central Kerala, South India was collected prospectivelyfrom parents through questionnaires using a semi-structured interview schedule. RESULTS: The median age was 8.2 months (IQR: 3.0- 36.0 months). Most families belonged to upper middle (43.0%) and lower middle (35.7%) socioeconomic class. Only 3.9% of families had some form of health insurance. The median expense for the admission and surgery was INR 201898 (IQR: 163287-266139) [I$ 11989 (IQR: 9696-15804)], which was 0.93 (IQR: 0.52-1.49) times the annual family income of affected patients. Median loss of man-days was 35 (IQR: 24-50) and job-days was 15 (IQR: 11-24). Surgical risk category and hospital stay duration significantly predicted higher costs. One in two families reported overwhelming to high financial stress during admission period for surgery. Approximately half of the families borrowed money during the follow up period after surgery. CONCLUSION: Surgery for congenital heart disease results in significant financial burden for majority of families studied. Efforts should be directed at further reductions in treatment costs without compromising the quality of care together with generating financial support for affected families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".