Evidence map›Paper›PMID 28767516›Full record

ArticleEpidemiology (Cambridge, Mass.)2017

The "Residential" Effect Fallacy in Neighborhood and Health Studies: Formal Definition, Empirical Identification, and Correction.

Basile Chaix, Dustin Duncan, Julie Vallée, Anne Vernez-Moudon, Tarik Benmarhnia, Yan Kestens

Abstract read
PubMed Publisher
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Basile ChaixFrom the aInserm, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris, France; bSorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris, France; cDepartment of Population Health, New York University School of Medicine, New York, NY; dUMR Géographie-Cités, CNRS, Paris, France; eDepartment of Urban Design and Planning, Urban Form Lab, University of Washington, Seattle, WA; fDepartment of Family Medicine and Public Health & Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA; and gDepartment of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada.
Dustin Duncan
Julie Vallée
Anne Vernez-Moudon
Tarik Benmarhnia
Yan Kestens

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBecause of confounding from the urban/rural and socioeconomic organizations of territories and resulting correlation between residential and nonresidential exposures, classically estimated residential neighborhood-outcome associations capture nonresidential environment effects, overestimating residential intervention effects. Our study diagnosed and corrected this "residential" effect fallacy bias applicable to a large fraction of neighborhood and health studies.

methodsOur empirical application investigated the effect that hypothetical interventions raising the residential number of services would have on the probability that a trip is walked. Using global positioning systems tracking and mobility surveys over 7 days (227 participants and 7440 trips), we employed a multilevel linear probability model to estimate the trip-level association between residential number of services and walking to derive a naïve intervention effect estimate and a corrected model accounting for numbers of services at the residence, trip origin, and trip destination to determine a corrected intervention effect estimate (true effect conditional on assumptions).

resultsThere was a strong correlation in service densities between the residential neighborhood and nonresidential places. From the naïve model, hypothetical interventions raising the residential number of services to 200, 500, and 1000 were associated with an increase by 0.020, 0.055, and 0.109 of the probability of walking in the intervention groups. Corrected estimates were of 0.007, 0.019, and 0.039. Thus, naïve estimates were overestimated by multiplicative factors of 3.0, 2.9, and 2.8.

conclusionsCommonly estimated residential intervention-outcome associations substantially overestimate true effects. Our somewhat paradoxical conclusion is that to estimate residential effects, investigators critically need information on nonresidential places visited.

Indexed as

WalkingAdultAgedAged, 80 and overConfounding Factors, EpidemiologicCoronary DiseaseFemaleFranceGeographic Information SystemsHousingHumansLinear ModelsMaleMiddle AgedResidence CharacteristicsSurveys and Questionnaires

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.