Burden of diseases: A micro level analysis for selected districts of Karnataka
Bibliographic record
Abstract
This paper presents a methodology for estimating the burden of diseases in monetary terms instead of a demographic indicator. The purpose was to ascertain the financial burden arising out of various categories of morbidity in the community. The disease-wise burden would act as a pointer for the policy maker to assign priority while allocating resources to different programmes within the health sector budget. The empirical study was carried out by CMDR, Dharwad under the sponsorship of IDRC, Canada, in four selected districts, viz. Belgaum, Bijapur, Dharwad and Dakshina Kannada of Karnataka with a sample size of 2039 households and 1156 patients. High share of unavoidable components of direct costs, viz. expenditure on medicines and doctors’ fees, makes morbidity all the more burdensome particularly for the poor people. Such results highlight the need for a specific policy about drug prices, charges for doctors’ services, discriminating pricing, etc. The study highlights that the nature of interventions about different permutations and combinations of preventive, promotive and curative care, or about the institutions of health care delivery (liberal, private, public, mix, on payment or free, etc), have to be consistent with the resource costs, incidence pattern of morbidity, socio-economic background of the morbid population and such other factors. Exercises of ranking of diseases according to total resource costs would aid adoption of an “eclectic cost-effectiveness approach”. Though the precision of the estimates from any empirical study may not be beyond dispute, the issues raised and new directions in thinking suggested, from the empirical study, deserve a serious consideration. The modest attempts made in this study to estimate resource costs should also be considered in that spirit, as issues of policy relevance in the health sector are raised from the present exercise.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".