A life satisfaction approach to valuing the impact of health behaviours on subjective well-being
Bibliographic record
Abstract
Abstract Background Increasingly, decision-makers are interested in understanding the returns on investments in programs and policies that promote health and prevent chronic diseases. While the costs of these programs are more easily quantified, many of the outcomes they aspire to achieve are intangible and lack obvious market values. The subjective well-being (SWB) method was developed to value a wide range of non-market goods, including health outcomes directly in monetary terms. This paper presents an application of the SWB approach to estimate the monetary value of health-promoting behaviours as the intermediate outcomes of health promotion and chronic disease prevention programs and policies. Methods Life satisfaction (LS) was used as a proxy of individuals’ SWB. Based on the combined Canadian Community Health Survey 2009–10 data, we modeled LS as a function of income and healthy behaviours, controlling for the socio-demographic factors associated with LS at the individual level using ordinary least squares regression. Equivalent effects of income and healthy behaviours on LS derived from the models allowed us to estimate the trade-off between income and healthy behaviours. Results We found that income and healthy behaviours were positively associated with LS. The values of increased physical activity, an additional daily serving of fruits/vegetables, and not smoking are respectively $631, $115 and $563 per week. These represent the amounts of additional weekly income required to maintain an individual at their level of LS in the absence of each of these behaviours. Conclusions The SWB method holds promise as a method to monetize the value of a range of non-market goods, including healthy behaviours for which market values do not exist. The SWB method can be applied efficiently and cost-effectively using readily available survey data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.002 | 0.001 |
| 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".