Cost-effectiveness of investing in sidewalks as a means of increasing physical activity: a RESIDE modelling study
Bibliographic record
Abstract
BACKGROUND: Studies consistently find that supportive neighbourhood built environments increase physical activity by encouraging walking and cycling. However, evidence on the cost-effectiveness of investing in built environment interventions as a means of promoting physical activity is lacking. In this study, we assess the cost-effectiveness of increasing sidewalk availability as one means of encouraging walking. METHODS: Using data from the RESIDE study in Perth, Australia, we modelled the cost impact and change in health-adjusted life years (HALYs) of installing additional sidewalks in established neighbourhoods. Estimates of the relationship between sidewalk availability and walking were taken from a previous study. Multistate life table models were used to estimate HALYs associated with changes in walking frequency and duration. Sensitivity analyses were used to explore the impact of variations in population density, discount rates, sidewalk costs and the inclusion of unrelated healthcare costs in added life years. RESULTS: Installing and maintaining an additional 10 km of sidewalk in an average neighbourhood with 19 000 adult residents was estimated to cost A$4.2 million over 30 years and gain 24 HALYs over the lifetime of an average neighbourhood adult resident population. The incremental cost-effectiveness ratio was A$176 000/HALY. However, sensitivity results indicated that increasing population densities improves cost-effectiveness. CONCLUSIONS: In low-density cities such as in Australia, installing sidewalks in established neighbourhoods as a single intervention is unlikely to cost-effectively improve health. Sidewalks must be considered alongside other complementary elements of walkability, such as density, land use mix and street connectivity. Population density is particularly important because at higher densities, more residents are exposed and this improves the cost-effectiveness. Health gain is one of many benefits of enhancing neighbourhood walkability and future studies might consider a more comprehensive assessment of its social value (eg, social cohesion, safety and air quality).
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".