The Health and economic effects of light rail lines: design, methods, and protocol for a natural experiment
Bibliographic record
Abstract
BACKGROUND: The health impacts of community design have been studied extensively over the past two decades. In particular, public transportation use is associated with more walking between transit stops and shops, work, home and other destinations. Change in transit access has been linked with physical activity and obesity but seldom to health outcomes and associated costs, especially within a causal framework. Health related fiscal impacts of transit investment should be a key consideration in major transit investment decisions. METHODS: The Rails & Health study is a natural experiment evaluating changes in clinical measures, health care utilization and health care costs among Kaiser Permanente Northwest (KPNW) members following the opening of a new light rail transit (LRT) line in Portland, Oregon. The study is prospectively following 3036 adults exposed to the new LRT line and a similar cohort of 4386 adults who do not live close to the new line. Individual-level outcomes and covariates are extracted from the electronic medical record at KPNW, including member demographics and comorbidities, blood pressure, body mass index, lipids, glycosylated hemoglobin, and health care utilization and costs. In addition, participants are surveyed about additional demographics, travel patterns, physical activity (PA), and perceived neighborhood walkability. In a subsample of the study population, we are collecting direct measures of travel-related behavior-physical activity (accelerometry), global positioning system (GPS) tracking, and travel diaries-to document mechanisms responsible for observed changes in health outcomes and cost. Comprehensive measures of the built environment at baseline and after rail construction are also collected. Statistical analyses will (1) examine the effects of opening a new LRT line on chronic disease indicators, health care utilization, and health care costs and (2) evaluate the degree to which observed effects of the LRT line on health measures and costs are mediated by changes in total and transportation-associated PA. DISCUSSION: The results of the Rails & Health study will provide urban planners, transportation engineers, health practitioners, developers, and decision makers with critical information needed to document how transit investments impact population health and related costs.
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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 | 0.047 | 0.044 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.064 | 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".