Bibliographic record
Abstract
In a recent Perspective article in this journal (1), Roux and Donaldson criticize the usefulness of cost-of-illness (COI) (1) studies, which they suggest are fraught with methodological flaws.Furthermore, they contend that COI studies should not be used to set priorities for government investment in the prevention of disease and injury.We acknowledge the validity of some of their arguments and wholeheartedly agree that scientifically rigorous economic evaluation (such as cost-benefit analysis and cost-effectiveness analysis) should be used in decisions for funding interventions.However, as we recently noted (2), COI studies can be valuable tools for promoting attention to the economic burden imposed by specific illnesses and are a crucial first step in the economic evaluation of prevention interventions.As suggested by Roux and Donaldson, COI data may assist in mobilizing interest and resources to a particular public health problem.We argue that this is how many COI studies are being used in the government: not to explicitly set health policy but rather to spotlight burden of disease that moves beyond traditional epidemiological estimates of mortality and morbidity.For example, a recent COI study by Finkelstein et al. (3) showed that the government pays for Ͼ50% of the health care costs associated with persons who are obese or overweight.These findings clarify the debate about whether obesity is a personal or societal issue and provide a clear motivation for government to try to reduce the costs of obesity.Because economic evaluation is useful only after interventions have been developed and evaluated, COI studies are an important part of the process for priority-setting in research on prevention.Many of the methodological "flaws" that Roux and Donaldson contend to be inherent in COI studies, such as overestimating productivity losses and double-counting costs for chronic diseases, can be overcome.For example, concerns about using earnings to measure productivity have led many prevalence-based COI studies to focus on direct costs of care only.Furthermore, whereas the human capital approach has limitations to valuing lost productivity, this is not specific to COI studies.Indeed, cost-benefit analyses in the health care field often use the human capital approach to calculate the economic benefits from preventing death and disability.Many recent COI studies rely on individual level data that allow for directly modeling the relationship between a particular disease and expenditures (4) and, therefore, do not suffer from problems of double-counting ex-penditures that were most likely to occur in epidemiological COI studies.As stated in our own critique of COI studies, the potential for misuse of these, and many other, studies is ever-present, and, therefore, authors need to clearly state how the results should and should not be used.We also believe that COI studies will continue to benefit from better data and methods.Further debate on this subject should focus on how to advance the state of science of COI studies and how to transfer the results of these studies to policy makers required to allocate our scarce public health resources.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.034 | 0.055 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".