Evidence map›Paper›PMID 31158399›Full record

ArticlePreventive medicine2019

Predicting geographical variation in health-related quality of life.

Pallavi Dwivedi, Dina Huang, Weijun Yu, Quynh Nguyen

Abstract read
In one paragraph

Article in Preventive medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Evaluating the Impact of Social and Built Environments on Health-Related Quality of Life among Cancer Survivors.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
    Article
  5. Food, Big Data, and Decision-making: a Scoping Review-the 3-D Commission.Journal of urban health : bulletin of the New York Academy of Medicine · 2021
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Pallavi DwivediDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States. Electronic address: pdwived1@umd.edu.
Dina HuangDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.
Weijun YuDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.
Quynh NguyenDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.

Funding

Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects ResearchR01LM012849 · NLM · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, QUYNH · 2018 to 2021
$1.3M
HashtagHealth: A Social Media Big Data Resource for Neighborhood Effects ResearchK01ES025433 · NIEHS · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, QUYNH · 2014 to 2018
$816k
NIEHS NIH HHS K01 ES025433NLM NIH HHS R01 LM012849
6 · The paper itself

Abstract

Goods and services provided by businesses can either promote health or represent an additional risk factor. We assessed the association between business pattern indicators and the prevalence of adult obesity, diabetes, physical inactivity, fair or poor health and frequent physical and mental distress. Data on business types were obtained from the 2013 U.S. Census Bureau County Business Patterns. County health data were obtained from the Centers for Disease Control and Prevention Diabetes Interactive Atlas, Behavior Risk Factor Surveillance System and Fatality Analysis Reporting System. We explored the relationship at county level using the global (Ordinary Least Square regression) and local (Geographically Weighted Regression (GWR)) models in 3108 U.S. counties. Density of full service restaurants and fitness centers was associated with a significant decrease in adult obesity, diabetes, fair or poor health, physical inactivity, physical and mental distress. Conversely, density of payday loan centers was associated with an increase in these adverse health outcomes. However, our GWR models revealed substantial geographical variations in these relationships across the U.S. counties. Better understanding of the association between area-level structures and important health outcomes at the local level is important for developing targeted context-specific policy interventions. Full service restaurants and fitness centers may provide places for people to access higher quality food, socialize and exercise. Conversely, payday loans provide an expensive form of short-term credit and this debt may degrade an individual or family's ability to achieve or maintain health. Our study emphasizes the influence of local built environment characteristics on important health outcomes.

Indexed as

GeographyHealth BehaviorQuality of LifeAdultBehavioral Risk Factor Surveillance SystemChronic DiseaseCommerceDiabetes MellitusExerciseFemaleHumansMaleObesityPrevalenceRestaurantsRisk FactorsBusiness patternsChronic diseaseCountyGeographically weighted regressionGeographyHealthQuality of life

Identifiers

PMID31158399
PMCPMC6697589

What OpenQuestion holds

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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.