Active Lives South Australia health economic analysis: an evidence base for the potential of health promotion strategies supporting physical activity guidelines to reduce public health costs while improving wellbeing
Bibliographic record
Abstract
Aim: The COVID-19 pandemic has threatened individual and population wellbeing and strategies to jointly address these challenges within budget constraints are required. The aim of our research is to analyse evidence from the Active Lives South Australia study to consider the potential of physical activity (PA) health promotion strategies to be health-system cost saving while addressing wellbeing challenges. Methods: The Active Lives South Australia study compares adult populations who meet and do not meet physical activity (PA) guidelines (150+ minutes of weekly physical activity) with respect to their subjective wellbeing and health care utilisation. Subject and results: Adults who met PA guidelines had better wellbeing across all aspects with and without adjustment for age, sex and income covariates. Analysis showed significant associations between meeting guidelines and lower probabilities of visiting and utilisation of GPs, specialist doctors, other health professionals, hospital inpatient admissions, outpatient clinic and emergency department visits, and an overall A$1760 lower cost per person annually. Controlling for age, sex and income, health expenditure for adults who met PA guidelines was significantly lower by A$1393 per person annually. That translated to A$804 million potential annual SA health system cost saving by shifting all adults to meeting PA guidelines. Conclusion: There is significant potential for effective health promotion strategies to be net cost saving while addressing wellbeing challenges of COVID-19 recovery where they can shift target populations from not meeting to meeting PA guidelines.
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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 | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".