Article Figures & Data
Figures
- 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.
- 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
- Table 1.
Selected characteristics of the subjects included in this analysis
Full cohort (N = 3,497) Fair or better health (N = 3,218) Characteristic N (%) Sample weighted % N (%) Sample weighted % Age 65–68 1,045 (33) 34 956 (33) 33 69–71 664 (21) 20 608 (21) 21 72–75 1,442 (46) 46 1,335 (46) 47 Sex Female 2,094 (60 58 1,907 (59) 57 Male 1,403 (40) 42 1,311 (41) 43 Race/Ethnicity Non-Hispanic white 1,800 (51) 47 1,701 (53) 48 Non-Hispanic black 1,073 (31) 26 974 (30) 26 Hispanic 245 (7) 14 209 (6) 13 Other 379 (11) 12 334 (10) 12 Educational attainment Less than high school 673 (19) 32 570 (18) 30 Completed high school 949 (27) 29 870 (27) 29 Some college 627 (18) 15 570 (18) 15 Completed college 1,248 (36) 24 1,208 (38) 25 Household income Less than $20,000 1,279 (37) 40 1,097 (34) 37 $20,000–40,000 842 (24) 24 790 (25) 24 $40,000–80,000 745 (21) 21 711 (22) 22 More than $80,000 631 (18) 16 620 (19) 17 Health status Excellent 645 (18) 17 645 (20) 18 Good 1,523 (44) 42 1,523 (47) 46 Fair 1,050 (30) 33 1,050 (33) 36 Poor 279 (8) 9 – (–) – Activity measures Walked 5–7 days in the last week 1,346 (38) 42 1,154 (36) 39 Gardened in the last week 710 (20) 23 686 (21) 24 Performed heavy housework in the last week 1,872 (54) 54 1,793 (56) 57 - Table 2.
Summary of measures used in the NE-WAS
Domain Number of measures Data source(s) Examples Demographics and housing characteristics 121 American Community Survey Population density, % white alone, % boys ages 10–14 Education, employment, and income 102 American Community Survey % College grad, % in labor force, % in food prep sector Urban form and walkability 50 American 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 disorder 35 Esri Crime Risk, Google Street View, New York Times Homicide Map, NYC Sanitation Department Report Cards Weighted average risk of larceny, Mean neighborhood disorder, % filthy streets Parks 5 New York City Department of Parks and Recreation % of land area in large parks Pedestrian safety 24 New York State Department of Transportation and New York City Police Department Cyclist 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 Score Gardening Walking daily Heavy housework Count of measures that remained significant after Bonferroni correction 5 (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
Measure Difference in –log10P value Better at 0.25-km scale Proportion 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 scale Proportion 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 years 2.42 NOTE: Plus or minus indicates the direction of association between the neighborhood measure and PASE score.
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 plots� 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.
April 2017
Volume 26, Issue 4
Volume 26, Issue 4
Contextual Correlates of Physical Activity among Older Adults: A Neighborhood Environment-Wide Association Study (NE-WAS)
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