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Cancer Epidemiology, Biomarkers & Prevention
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CEBP Focus: Geospatial Approaches to Cancer Control and Population Sciences

Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)

Stephen J. Mooney, Spruha Joshi, Magdalena Cerdá, Gary J. Kennedy, John R. Beard and Andrew G. Rundle
Stephen J. Mooney
1Harborview Injury Prevention & Research Center, University of Washington, Seattle, Washington.
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  • For correspondence: sjm2186@u.washington.edu
Spruha Joshi
2Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota.
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Magdalena Cerdá
3Department of Emergency Medicine, University of California, Davis, Davis, California.
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Gary J. Kennedy
4Albert Einstein College of Medicine, New York, New York.
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John R. Beard
5Department of Ageing and Life Course, World Health Organization, Geneva, Switzerland.
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Andrew G. Rundle
6Department of Epidemiology, Mailman School of Public Health, New York, New York.
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DOI: 10.1158/1055-9965.EPI-16-0827 Published April 2017
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  • Figure 1.
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    Figure 1.

    Manhattan plots showing the strengths of associations between individual neighborhood variables and various physical activity outcomes as measured by the PASE after controlling for age, sex, race/ethnicity, educational attainment, income, and housing type.

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    Figure 2.

    Manhattan plots showing the strength of associations between individual neighborhood variables and total PASE score at smaller and larger buffer sizes after controlling for age, sex, race/ethnicity, educational attainment, income, and housing type.

Tables

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  • Table 1.

    Selected characteristics of the subjects included in this analysis

    Full cohort (N = 3,497)Fair or better health (N = 3,218)
    CharacteristicN (%)Sample weighted %N (%)Sample weighted %
    Age
     65–681,045 (33)34956 (33)33
     69–71664 (21)20608 (21)21
     72–751,442 (46)461,335 (46)47
    Sex
     Female2,094 (60581,907 (59)57
     Male1,403 (40)421,311 (41)43
    Race/Ethnicity
     Non-Hispanic white1,800 (51)471,701 (53)48
     Non-Hispanic black1,073 (31)26974 (30)26
     Hispanic245 (7)14209 (6)13
     Other379 (11)12334 (10)12
    Educational attainment
     Less than high school673 (19)32570 (18)30
     Completed high school949 (27)29870 (27)29
     Some college627 (18)15570 (18)15
     Completed college1,248 (36)241,208 (38)25
    Household income
     Less than $20,0001,279 (37)401,097 (34)37
     $20,000–40,000842 (24)24790 (25)24
     $40,000–80,000745 (21)21711 (22)22
     More than $80,000631 (18)16620 (19)17
    Health status
     Excellent645 (18)17645 (20)18
     Good1,523 (44)421,523 (47)46
     Fair1,050 (30)331,050 (33)36
     Poor279 (8)9– (–)–
    Activity measures
     Walked 5–7 days in the last week1,346 (38)421,154 (36)39
     Gardened in the last week710 (20)23686 (21)24
     Performed heavy housework in the last week1,872 (54)541,793 (56)57
  • Table 2.

    Summary of measures used in the NE-WAS

    DomainNumber of measuresData source(s)Examples
    Demographics and housing characteristics121American Community SurveyPopulation density, % white alone, % boys ages 10–14
    Education, employment, and income102American Community Survey% College grad, % in labor force, % in food prep sector
    Urban form and walkability50American Community Survey, New York State Accident Location Information Service Line Layer, NYC Transit Authority% walk to work, density of 4-way intersections, Bus stop density,% of roadbed covered by tree canopy
    Crime and disorder35Esri Crime Risk, Google Street View, New York Times Homicide Map, NYC Sanitation Department Report CardsWeighted average risk of larceny, Mean neighborhood disorder, % filthy streets
    Parks5New York City Department of Parks and Recreation% of land area in large parks
    Pedestrian safety24New York State Department of Transportation and New York City Police DepartmentCyclist injury density in the 1990s, Pedestrian fatality density in the 2000s
  • Table 3.

    Specific neighborhood measures identified as most predictive for several physical activity outcomes using regression models

    PASE ScoreGardeningWalking dailyHeavy housework
    Count of measures that remained significant after Bonferroni correction5 (1.5%)33 (9.8%)49 (14.4%)0 (0.0%)
    Top 5 statistically significant neighborhood measures (by P value of coefficient)People living in households with incomes less than half the poverty level (−)People living in households with incomes less than half the poverty level (−)Proportion of residents with 60- to 90-minute travel time to work (−)—
    People living in households with incomes below the poverty line (−)Neighborhood Physical Disorder (−)Broken windows in HVS survey (−)—
    No problems with windows in HVS survey (+)People living in households with incomes below the poverty line (−)Proportion of adult population with at least some college education (+)—
    People living in households with incomes more than twice the poverty level (+)People living in households above twice the poverty line (+)Proportion of working adult population commuting by car, truck, or van (−)—
    People living in households with incomes between half and three-quarters of the poverty level (−)People living in households with any interest, dividend, or rental income (+)Proportion of adult population working in professional or management industries (+)—
    • NOTE: All analyses control for subject age, race/ethnicity, educational attainment, household income, gender, and home type.

    • Abbreviation: HVS, New York City Housing and Vacancy Survey

  • Table 4.

    The top 5 measures for each neighborhood definition that were more significant predictors of total PASE score than using the alternate neighborhood definition

    MeasureDifference in –log10P value
    Better at 0.25-km scaleProportion of population living in households (+)2.24
    Proportion of population who are naturalized citizens (−)1.68
    Proportion of population living in households with income below half the poverty level (−)1.57
    Density of 3-way intersections (+)1.28
    Proportion of vacant housing units offered for rent (−)1.27
    Better at 1.0-km scaleProportion of households with incomes between 25K and 30K (−)2.77
    Proportion of adult residents with a professional degree or more (+)2.56
    Proportion of households with incomes between 30K and 35K (−)2.56
    Proportion of family households living below the poverty line with a male householder and no children under age 18 (−)2.45
    Proportion of total population aged 10 to 14 years2.42
    • NOTE: Plus or minus indicates the direction of association between the neighborhood measure and PASE score.

Additional Files

  • Figures
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  • Supplementary Data

    • Supplementary Data - Supplementary Figure 1. Using Shapiro-Wilk Test Statistic to Decide Whether to Log-Transform Predictor Variable. Supplementary Table 1. Measures used in the NE-WAS. Supplementary Figure 2: Manhattan Plots showing the strengths of correlations between individual neighborhood variables and various physical activity outcomes as measured by the Physical Activity Scale for the Elderly (PASE) after controlling for age, sex, race/ethnicity, educational attainment, income, and housing type. Supplementary Figure 3. Manhattan Plots showing the statistical significance of correlations between individual neighborhood variables total Physical Activity Scale for the Elderly (PASE) score at smaller and larger buffer sizes. Supplementary Figure 4. “Volcano plotsâ€� relating log p-value to regression coefficient for each variable. Supplementary Table 2. Specific neighborhood measures identified as most predictive for several physical activity outcomes using regression modes in the full cohort. Supplementary Brief Report: LASSO and Random Forest Approaches for NE-WAS. Supplementary Figure 5. Regularization path graph for the LASSO regression on PASE score. Supplementary Figure 6: Example of a decision tree computed using a recursive partitioning algorithm to predict probability of reporting gardening as a function of neighborhood-level and individual-level covariates among subjects in NYCNAMES-II who reported fair or better health. Supplementary Table 3. Specific neighborhood measures identified as most predictive for several physical activity outcomes using Least Absolute Shrinkage and Selection Operator (‘LASSO’). Supplementary Table 4. Specific neighborhood measures identified as most important by the Random Forest algorithm for several physical activity outcomes. Supplementary Table 5. Specific neighborhood measures identified as most predictive for several physical activity outcomes using LASSO on cohort created by selecting a random imputation for each subject.
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Cancer Epidemiology Biomarkers & Prevention: 26 (4)
April 2017
Volume 26, Issue 4
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Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)
Stephen J. Mooney, Spruha Joshi, Magdalena Cerdá, Gary J. Kennedy, John R. Beard and Andrew G. Rundle
Cancer Epidemiol Biomarkers Prev April 1 2017 (26) (4) 495-504; DOI: 10.1158/1055-9965.EPI-16-0827

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Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)
Stephen J. Mooney, Spruha Joshi, Magdalena Cerdá, Gary J. Kennedy, John R. Beard and Andrew G. Rundle
Cancer Epidemiol Biomarkers Prev April 1 2017 (26) (4) 495-504; DOI: 10.1158/1055-9965.EPI-16-0827
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