Residential relocation to assess impact of changes in the living environment on cardio-respiratory health: A narrative literature review with considerations for exposome research
Bibliographic record
Abstract
Residential relocation studies have become increasingly valuable tools for evaluating the effects of changing living environments on human health, but little is known about their application to multiple aspects of the living environment and the most appropriate methodology. This narrative review explores the utility of residential relocation as a natural experiment for studying the impact of changing urban exposures on cardio-metabolic health in high-income settings. It provides a comprehensive overview of the use of residential relocation studies, evaluates their methodological approaches, and synthesizes findings related to health behaviors and cardio-metabolic outcomes. Our search identified 43 relevant studies published between January 1995 and February 2023, from eight countries, predominantly the USA, Canada, and Australia. The majority of eligible studies were published between 2012 and 2021 and examined changes in various domains of the living environment, such as walkability, the built and social environments, but rarely combinations of exposures. Included studies displayed heterogeneity in design and outcomes, 25 involving only movers and 18 considering both movers and non-movers. To mitigate the issue of residential self-selection bias, most studies employed a "change-in-change" design and adjusted for baseline covariates but only a fraction of them accounted for time-varying confounding. Relocation causes simultaneous changes in various features of the living environment, which presents an opportunity for exposome research to establish causal relationships, using large datasets with increased statistical power and a wide range of health outcomes, behaviors and biomarkers. Residential relocation is not a random process. Thus, studies focusing on living environment characteristics need to carefully select time-varying covariates and reference group. Overall, this review informs future research by guiding choices in study design, data requirements, and statistical methodologies. Ultimately, it contributes to the advancement of the urban exposome field and enhances our understanding of the complex relationship between urban environments and human health.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".