Neighborhood-level measures of socioeconomic status are more correlated with individual-level measures in urban areas compared with less urban areas

Journal Pre-proof Neighborhood-level Measures of Socioeconomic Status are More Correlated with Individual-level Measures in Urban Areas Compared to Less Urban Areas Sherrie Xie, Rebecca A. Hubbard, Blanca E. Himes PII:

S1047-2797(19)30608-8

DOI:

https://doi.org/10.1016/j.annepidem.2020.01.012

Reference:

AEP 8789

To appear in:

Annals of Epidemiology

Received Date: 21 August 2019 Revised Date:

17 January 2020

Accepted Date: 31 January 2020

Please cite this article as: Xie S, Hubbard RA, Himes BE, Neighborhood-level Measures of Socioeconomic Status are More Correlated with Individual-level Measures in Urban Areas Compared to Less Urban Areas Annals of Epidemiology (2020), doi: https://doi.org/10.1016/j.annepidem.2020.01.012. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.

Neighborhood-level Measures of Socioeconomic Status are More Correlated with Individual-level Measures in Urban Areas Compared to Less Urban Areas Sherrie Xie, Rebecca A. Hubbard*, Blanca E. Himes* Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA *These authors contributed equally to this work. Correspondence to: Blanca E. Himes, Ph.D. 402 Blockley Hall 423 Guardian Drive Philadelphia, PA 19104 (215) 573-3282 Email: [email protected] Manuscript type: Original Article Abstract word count: 200 (max. 200) Main text word count: 2,998 (max. 3,000) Number of tables and figures: 6 (max. 6)

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Abstract Purpose: We tested the hypothesis that individual- and neighborhood-level measures of socioeconomic status (SES) are more concordant in urban than rural areas, and we used the previously established association between obesity and self-rated health to illustrate the effect of residual confounding by individual-level SES when only neighborhood-level SES is considered. Methods: Using data from two population-based surveys, we calculated Spearman’s rank correlations between household income and neighborhood socioeconomic advantage across eight Pennsylvania counties. We applied multivariable Poisson regression models with robust variance estimates to each county to estimate the degree that individual SES confounds the association between obesity and self-rated health when the analysis accounts for neighborhood SES only and examined how this confounding varied by county urbanicity. Results: Concordance between household income and neighborhood advantage increased with county urbanicity (ρ = 0.16-0.26 vs. 0.31-0.45 vs. 0.47 in medium metro/micropolitan, suburban, and large metro counties, respectively). Conversely, confounding by individual SES on the obesity and self-rated health association decreased with urbanicity (15-22% vs. 6-13% vs. 3% in medium metro/micropolitan, suburban, and large metro counties, respectively). Conclusion: Individual- and neighborhood-level SES measures are poorly correlated outside of urban areas, suggesting that neighborhood-level measures inadequately account for individual SES in rural settings. Keywords: socioeconomic status, urban-rural, neighborhood characteristics, research methods, confounding

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Introduction The socioeconomic status (SES) of individuals and their neighborhood environments are important social determinants of health that impact many health indicators and outcomes.1-5 Low SES has been associated with increased exposure to environmental hazards, pollutants and allergens, as well as negative life events and chronic stress.6-8 As a result, SES can confound exposure-outcome relationships, and SES-related measures are included as covariates in many epidemiological studies. The use of SES measures is often limited by data availability, and multilevel analyses that incorporate both individual- and neighborhood-level indicators are not always possible. For example, individual-level SES is seldom documented in large health datasets, including disease registries, claims databases, and electronic health records. A common approach for overcoming these data limitations is to use neighborhood-level measures to capture SES when individuallevel measures are unavailable.9-11 However, because individual- and neighborhood-level SES are distinct constructs with independent effects on health,3-5 use of neighborhood-level measures may not capture all relevant aspects of SES confounding exposure-outcome relationships. Figure 1 contains path diagrams illustrating possible relationships that individual and neighborhood SES may have with an exposure and an outcome. Adjusting for only neighborhood SES is appropriate when individual SES does not act as a confounder (Fig. 1b). However, when individual SES does act as a confounder (Fig. 1a and 1c), adjusting only for neighborhood SES leaves residual confounding by individual SES – especially absent a strong correlation between these variables. Although many studies use neighborhood-level measures of SES as the sole SES measure in analyses, the relationship between individual- and neighborhood-level SES is not always clear. Reported agreement between individual- and neighborhood-level SES measures has varied. Research in the U.S. found that agreement between individual- and neighborhood-level measures of SES was lower in a mixed urban-rural county in Minnesota than in an urban county in Missouri (Cohen’s κ = 0.15-0.22 vs. 0.26-0.36).12,13 Similarly, a study assessing the association between self-reported income and an area-based measure of income in a patient cohort from Alberta, Canada found little concordance (κ = 0.07) in this rural area,14 while agreement was higher in studies conducted in metropolitan Vancouver (Spearman’s ρ = 0.23-0.35)15 and Montreal (ρ = 0.31-0.39).16 Analysis of a pediatric asthma cohort in Rome, Italy found moderate agreement between parental education and neighborhood SES (ρ = 0.47-0.48).17 While these previous results suggest that neighborhood-level measures of SES are more correlated with individual-level measures in urban versus rural areas, the association between individual and neighborhood SES agreement and regional urbanicity has not been formally investigated. Here, we measured the correlation between individual- and neighborhood-level SES measures across eight counties in Pennsylvania with varying degrees of urbanization and determined whether the correlation between these measures increased with county urbanicity. We then assessed the extent to which the use of neighborhood-level SES measures results in

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residual confounding by individual-level SES, using the association between obesity and selfrated health as an illustrative example. Materials and Methods Study population and survey implementation We obtained data from two population-based community health surveys administered by the Public Health Management Corporation (PHMC) in eight contiguous counties of Southeastern Pennsylvania: Southeastern Pennsylvania Household Health Survey (SEPA-HHS) and BerksLancaster-Schuylkill Household Health Survey (BLS-HHS). SEPA-HHS and BLS-HHS were approved by the PHMC Institutional Review Board, and PHMC obtained informed consent from all participating individuals. SEPA-HHS was administered in 2010 and 2012 to residents of urban Philadelphia County, which is coterminous with the City of Philadelphia, as well as residents of the neighboring suburban counties of Bucks, Chester, Delaware, and Montgomery (Supplemental Fig. 1). BLS-HHS was administered to residents of these eponymous outlying counties in 2011. SEPA-HHS and BLS-HHS were administered using similar survey instruments and procedures.18-20 Both surveys aimed to capture the health status and personal health behaviors of local residents through telephone interviews with people 18 years or older. Sampling in all cycles was stratified by geographic subareas (54 for SEPA-HHS and 13 for BLSHHS), and balancing weights were provided to account for differences in response rates and ensure respondents were demographically representative of their areas of residence. To understand the demographic and socioeconomic characteristics of the eight counties surveyed, we obtained county-aggregated summary measures from the American Community Survey 5-year estimates for 2009-2013.21 The level of urbanization for each county was determined using the 2006 National Center for Health Statistics (NHCS) Urban-Rural Classification Scheme for Counties, which divides U.S. counties into six classes: large central metro (1), large fringe metro (2), medium metro (3), small metro (4), micropolitan (5), and noncore (6).22 The eight surveyed counties fell into NCHS classes 1, 2, 3 and 5, which we’ve labeled large metro, suburban, medium metro and micropolitan, respectively. Individual-level SES measures Individual-level SES measures captured by the surveys included self-reported household income and educational attainment. Household income was an ordinal variable with 24-29 levels (i.e., income categories), which we transformed to a continuous variable by averaging upper and lower bounds for middle income categories and assigning the single boundary value for highest and lowest income categories. Educational attainment was an ordinal variable with the following levels: less than high school (equivalent to 0-11 years of education), high school graduate (12 years), some college (13-15 years), and college graduate or higher (≥16 years). We excluded respondents less than 25 years old to reduce the risk of exposure misclassification, as individuals in this age group were least likely to have completed their education. Unadjusted household income may be an unreliable measure of SES because a family’s financial resource requirements vary substantially by household size and composition. To allow income 4

to be more readily compared between households, we determined the household-specific poverty threshold for each respondent based on: (1) the number of related adults and children reported to be living in the household by the respondent and (2) federal poverty threshold tables released by the U.S. Census Bureau corresponding to the years of survey administration.23 We then calculated each household’s relative income by dividing unadjusted income by the household-specific poverty threshold. Neighborhood-level SES measure The Area Deprivation Index (ADI) is a composite measure of neighborhood socioeconomic disadvantage that captures multiple socioeconomic domains, including education, income, employment, and housing quality (Supplemental Table 1). First formulated by Singh in 2003,24 the ADI has been validated for detecting socioeconomic disparities in a number of health outcomes, including all-cause, cardiovascular, cancer, and childhood mortality,24-28 hospital readmission risk29,30 and cytomegalovirus seropositivity during pregnancy.31 We sourced 2013 ADI (calculated using variables from the American Community Survey 5-year estimates for 2009-2013) for all block groups in our study area from the Neighborhood Atlas online portal.32,33 Because the surveys reported residence at the level of census tracts, rather than block groups, we derived census tract-level ADI as the median ADI of nested block groups and assigned values to respondents according to their residential census tracts. To facilitate comparison with relative income and education, where higher values represent greater advantage, we transformed ADI (reported as a percentile ranking taking integer values 1-100) by subtracting it from 101 to derive a measure of neighborhood advantage. Evaluating agreement between individual- and neighborhood-level SES measures We calculated Spearman’s rank correlation coefficients (ρ) between individual-level SES measures (i.e., relative income and education) and neighborhood advantage, while stratifying respondents by their county of residence. To evaluate whether individual and neighborhood SES agreement differed according to regional urbanicity, we plotted county-wide agreement (ρ) against log-transformed county population density (a proxy for urbanicity) and computed Spearman’s rank correlation coefficients. We similarly assessed trends in correlation between individual and neighborhood SES versus NCHS class. Weighted correlations were obtained with the wCorr R package using survey weights provided by PHMC, and statistical significance was assessed via permutation tests.34 Residual confounding by individual-level SES after adjustment for neighborhood-level SES To illustrate residual confounding by individual-level SES that may remain when only neighborhood-level SES is accounted for, we estimated the degree to which individual SES confounded the relationship between obesity and self-rated health. We chose self-rated health as our outcome variable because it is a validated measure of general health status35-37 and has been shown to be independently associated with both obesity and individual SES.38-42 Given the evidence linking low income and educational attainment to obesity,43-45 we reasoned that individual SES is likely to confound the association between obesity and self-rated health. SEPAHHS and BLS-HHS asked respondents to rate their health as excellent, good, fair, or poor, and we dichotomized this variable as excellent/good or fair/poor. Body mass index (BMI) was 5

calculated using self-reported height and weight measures and dichotomized as obese or not obese, where obesity was defined as BMI ≥ 30. Confounding of the association between obesity and self-rated health by individual SES was assessed as follows. First, we determined whether individual-level SES was independently associated with both obesity and self-rated health in each county: after stratifying respondents by county of residence, we used multivariable Poisson regression models with robust variance estimates46 to estimate the relative risk of fair/poor health associated with education and relative income, while adjusting for age, sex, race/ethnicity and neighborhood advantage. Relative income and neighborhood advantage were categorized by quartiles, and age was categorized to seven ordinal levels (25-34, 35-44, 45-54, …, ≥85). We repeated this analysis with obesity as the dependent variable. Only counties for which individual-level SES was independently associated with obesity and self-rated health (according to two-sided alpha = 0.05 level) were included in the next step, as absence of these associations indicated that individual SES did not confound the obesity/self-rated health association. Second, we created multivariable Poisson regression models with robust variance estimates to estimate the relative risk of fair/poor health associated with obesity, stratified by county. Two relative risk estimates were derived for each county: model 1 included covariate adjustment for demographic variables (i.e., age, sex, race/ethnicity) and neighborhood-level SES (i.e. neighborhood advantage), while model 2 additionally included adjustment for two individual-level SES variables (i.e. relative income and respondent education). The magnitude of residual confounding by individual-level SES was quantified for each county as the percentage difference between the two relative risk estimates for the obesity/self-rated health association: 100*(RR1 – RR2)/RR2, where RR1 and RR2 were the relative risks estimated from models 1 and 2, respectively. Results Characteristics of counties based on ACS 5-year estimates for 2009-2013 are provided in Table 1. Philadelphia was a large metro characterized by high population density and roughly equal proportions of black and white residents. Compared to Philadelphia, the neighboring suburban counties of Bucks, Chester, Delaware, and Montgomery were less populated, predominantly white, and more socioeconomically affluent. The medium metro/micropolitan counties of Berks, Lancaster, and Schuylkill were predominantly white, more sparsely populated than the suburban counties, and had median income and poverty rates between those of the suburban counties and Philadelphia. A total of 23,024 respondents were surveyed by SEPA-HHS and BLS-HHS in 2010-2012. Of these, 1,289 (5.6%) respondents were excluded for being less than 25 years old, 4,764 (21.9%) were excluded for missing income information, 1,690 (10.0%) were excluded for missing address or geocode information, and 147 (0.6%) were excluded for other missingness, leaving 15,134 (65.7%) complete cases for assessing individual and neighborhood SES agreement. Respondents who were excluded due to missing income were generally older and less educated but did not differ substantially by race/ethnicity compared to those with complete variable information 6

(Supplemental Table 1), while respondents who were excluded due to incomplete address information did not differ substantially from those with complete variable information (Supplemental Table 2). Characteristics of the 15,134 respondents included in analyses are provided in Table 2. Respondent characteristics generally reflected county characteristics although females were over-represented. Respondents from Philadelphia were more likely to be black and low-income than those of other counties, while respondents from suburban counties tended to have higher levels of educational attainment and higher household incomes. The medium metro/micropolitan counties had similarly high proportions of white respondents as suburban counties, although the average educational attainment of these respondents was lower and followed distributions similar to those of Philadelphia. Spearman’s rank correlations between individual-level SES measures and neighborhood advantage are provided in Table 3. Agreement (ρ) between household income and neighborhood advantage generally increased with county urbanicity (Fig. 2). Agreement was low in micropolitan Schuylkill county (ρ = 0.26) and medium metro Berks and Lancaster counties (ρ = 0.16-0.31), low to moderate in suburban Bucks, Chester, Delaware, and Montgomery counties (ρ = 0.33-0.45), and moderate in the large metro of Philadelphia (ρ = 0.47). The correlation between relative income-neighborhood advantage agreement and county urbanicity was high, whether urbanization was represented by county population density (Spearman’s ρ = 0.86) or NCHS class (ρ = -0.89). The correlations between respondent education-neighborhood advantage agreement and county urbanicity were similar to those obtained for relative income (Supplemental Fig. 2; ρ = 0.83 for county population density and ρ = -0.89 for NCHS class). However, the magnitude of the agreement between individual-level SES and neighborhood advantage was lower for all counties when education, rather than income, served as the individual-level SES indicator (ρ = 0.05-0.39 vs. 0.16-0.47; Table 3). With the exception of the correlation between education and neighborhood advantage in Lancaster (p = 0.16), all correlations were significant with p < 0.001. Individual-level SES measures (relative income and education) were associated with fair/poor health and obesity in all counties (p < 0.05), with the exception of Schuylkill county, where individual-level SES measures were not associated with obesity (Supplemental Table 3 and Supplemental Table 4). Schuylkill county residents were thus excluded from the analysis of residual confounding. Fair/poor health was significantly associated with obesity in all models for the remaining counties (p<0.05), and point estimates from models that only adjusted for neighborhood-level SES were larger than those that additionally adjusted for individual-level SES (RR1 > RR2; Fig. 3a and Supplemental Table 5). Furthermore, the estimated magnitude of residual confounding by individual-level SES decreased with county urbanicity: it was highest in the medium metro counties (15-22%), intermediate in the suburban counties (6-13%), and lowest in Philadelphia (3%; Fig. 3b). Discussion

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When individual-level measures of SES are unavailable for the secondary analysis of health datasets (such as data derived from administrative databases9,10 or electronic health records11), a common approach is to use neighborhood-level measures alone to represent SES. Our results suggest that when individual SES acts as a confounder, adjusting only for neighborhood-level SES can lead to significant residual confounding by individual-level SES, particularly outside of urban areas where correlations between individual- and neighborhood-level SES tend to be low. The greater agreement between individual and neighborhood SES measures in urban areas may be expected, as Census tracts drawn by the U.S. Census Bureau, which aim to capture ~4,000 inhabitants that are “as homogeneous as possible” with respect to demographic and socioeconomic characteristics,47 tend to be smaller and more uniform in urban areas. Summary measures of neighborhood environments are relatively good proxies for individual-level measures in urban areas, and thus neighborhood-level SES measures may be able to account for the confounding effects of individual SES in urban centers like Philadelphia. In less urbanized areas, however, the use of neighborhood-level measures to represent individual SES is likely to result in considerable measurement error. Because controlling for mis-measured variables results in incomplete covariate adjustment,48 adjusting only for neighborhood-level SES measures may result in significant residual confounding by individual SES when individual- and neighborhood-level measures are weakly correlated. These findings carry implications for multi-site studies or analyses of state- or nation-wide databases that encompass multiple areas spanning the urban-rural spectrum. If individual SES confounds the exposure-outcome relationship under study, adjustment for neighborhood-level SES measures only will result in varying degrees of residual confounding by individual SES, with rural areas generally exhibiting more confounding. If this residual confounding biases results in a systematic way (e.g., away from the null as in the present analysis), then stratified analyses may spuriously demonstrate effect modification by regional urbanicity. Using the present study as an example, one might conclude from analyses using only neighborhood-level SES measures that the link between obesity and fair/poor health weakens with increased urbanization when this trend can be attributed to the greater effects of residual confounding in rural areas. Our results are subject to limitations. The analysis was restricted to eight counties in Southeastern Pennsylvania, and the extent that results can be generalized to other regions of the United States is unknown. The surveys captured only one rural (i.e., non-metro) county, and the trend between urbanicity grade and individual-neighborhood SES agreement was determined across mostly metro areas rather than areas ranging the full urban-rural spectrum. However, agreement is likely minimal in very remote areas, where census tract boundaries encompass large, disparate areas; thus, we expect the trend between urbanicity and individualneighborhood SES agreement to be even more pronounced across a full urban-rural spectrum. Neighborhood SES was derived at the census tract level, and our results are likely to depend on the choice of neighborhood aggregation boundary (an issue known as the modifiable areal unit problem49). Nevertheless, the urban-rural gradient in individual vs. neighborhood SES agreement was still evident in analyses where Zip Code Tabulation Areas formed the 8

geographic unit used to derive neighborhood SES (Supplemental Table 7, Supplemental Fig. 3), suggesting this trend was not solely driven by the use of census tracts as the neighborhood aggregation boundary. Income was subject to self-reporting bias and missing for 22% of respondents, consistent with income nonresponse rates noted in other health surveys.50,51 Respondents excluded for income nonresponse were generally older and had less education than those who reported their income, and this selective nonresponse may have biased our results. In conclusion, neighborhood-level measures of SES were poorly correlated with individual-level measures outside of large metro or suburban areas, suggesting that analyses that include only neighborhood-level measures may result in significant residual confounding by individual-level SES in less urban settings. When SES is suspected to confound an exposure-outcome relationship, the results of regression models that include covariate adjustment for neighborhood-, but not individual-, level SES measures should be interpreted cautiously, particularly for studies of rural populations or analyses of state-or nation-wide databases that cover a spectrum of rural and urban environments. Acknowledgements: We would like to thank Gary Klein, Senior Data Analyst at PHMC, for helping us obtain and understand data from the SEPA-HHS and BLS-HHS. This work was supported by National Institutes of Health grants F31-HL142153 (SX), R21-CA227613 (RAH), R01-HL133433 (BEH), and R01-HL141992 (BEH). Additionally, this work was supported in part by the National Institute of Environmental Health Sciences under award number P30ES013508.

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Legends Table 1. Characteristics of counties included in the Southeastern Pennsylvania Household Health Survey and Berks-Lancaster-Schuylkill Household Health Survey Table 2. Characteristics of respondents with non-missing data by county residence, the Southeastern Pennsylvania Household Health Survey 2010, 2012 (SEPA-HHS) and BerksLancaster-Schuylkill Household Health Survey 2011 (N = 15,134) Table 3. Agreement (ρ) between individual SES indicators (indicated by column name) and neighborhood advantage for counties surveyed by SEPA-HHS and BLS-HHS. Bolded values are significant with p < 0.001. Fig. 1. Path diagrams illustrating hypothetical situations where an exposure-outcome relationship is confounded by individual-level SES only (a), neighborhood-level SES only (b), or both individual- and neighborhood-level SES (c). Fig. 2. County-level agreement (Spearman’s ρ) between respondents’ relative household income and neighborhood advantage. PHI = Philadelphia, BUC = Bucks, CHE = Chester, DEL = Delaware, MON = Montgomery, BER = Berks, LAN = Lancaster, and SCH = Schuylkill. Fig. 3. Estimated magnitude of residual confounding by individual-level SES when only neighborhood-level SES was accounted for. Fig. 3a shows the relative risk (RR) of self-rated fair/poor health associated with obesity according to multivariable Poisson regression estimated using model 1, which included demographic variables and neighborhood advantage and model 2, which included demographic variables, neighborhood advantage, relative household income and respondent education. Fig. 3b shows the magnitude of confounding by individual-level SES estimated as 100*(RR1 – RR2)/RR2, where RR1 and RR2 were estimated using models 1 and 2, respectively. PHI = Philadelphia, BUC = Bucks, CHE = Chester, DEL = Delaware, MON = Montgomery, BER = Berks, LAN = Lancaster, and SCH = Schuylkill.

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Tables Table 1 Characteristics of counties included in the Southeastern Pennsylvania Household Health Survey and Berks-Lancaster-Schuylkill Household Health Survey Survey region

Population densitya,b

NCHS classc

Percentage whiteb

Percentage blackb

Median incomed

Poverty rated

Philadelphia

SEPA

4,393.6

1

41.0

43.4

37,192

26.5

Bucks

SEPA

399.5

2

89.2

3.6

76,555

5.4

Chester

SEPA

256.6

2

85.5

6.1

86,050

6.9

Delaware

SEPA

1,173.9

2

72.5

19.7

64,041

10.3

Montgomery

SEPA

639.3

2

81.1

8.7

79,183

6.1

Berks

BLS

185.5

3

83.2

4.9

55,170

13.9

Lancaster

BLS

212.5

3

88.6

3.7

56,483

10.5

Schuylkill

BLS

73.5

5

94.4

2.7

45,012

12.8

Pennsylvania

--

109.6

--

81.9

10.8

52,548

13.3

United States

--

33.7

--

72.4

12.6

53,046

15.4

County

a

b

Population density is expressed as population per square kilometer of land area; County-, state- and nationalc level population density and racial composition are taken from the 2010 Decennial Census; 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties categories are: 1 - large central metro, 2 - large fringe metro, 3 - medium metro, 4 - small metro, 5 - micropolitan, and 6 – noncore; dCounty-, state- and national-level median income, expressed in 2013 inflation-adjusted dollars, and poverty rate, expressed as the percent population below poverty level, are taken from the 2013 ACS 5-year estimates.

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Table 2 Characteristics of respondents with non-missing data by county residence, the Southeastern Pennsylvania Household Health Survey 2010, 2012 (SEPA-HHS) and Berks-Lancaster-Schuylkill Household Health Survey 2011 (N = 15,134) Survey cycle(s)

SEPA-HHS 2010 & 2012

BLS-HHS 2011

Philadelphia (n = 5,212)

Bucks (n = 1,656)

Chester (n = 1,792)

Delaware (n = 1,937)

Montgomery

Berks (n = 704)

Lancaster (n = 705)

Schuylkill (n = 741)

53 (42-63)

52 (44-62)

52 (43-61)

53 (43-62)

52 (43-62)

54 (45-63)

52 (42-63)

54 (44-64)

3,483 (66.8)

1,009 (60.9)

1,069 (59.7)

1,233 (63.7)

1,471 (61.6)

389 (55.3)

347 (49.2)

413 (55.7)

Non-Hispanic white

2,326 (44.6)

1,543 (93.2)

1,593 (88.9)

1,530 (79.0)

2,087 (87.4)

628 (89.2)

631 (89.5)

715 (96.5)

Non-Hispanic black

2,253 (43.2) 405 (7.8)

36 (2.2)

99 (5.5)

299 (15.4)

166 (7.0)

21 (3.0)

15 (2.1)

4 (0.5)

31 (1.9)

39 (2.2)

35 (1.8)

40 (1.7)

44 (6.2)

41 (5.8)

9 (1.2)

228 (4.4)

46 (2.8)

61 (3.4)

73 (3.8)

94 (3.9)

11 (1.6)

18 (2.6)

13 (1.8)

High (>5)

1,027 (19.7)

698 (42.1)

885 (49.4)

671 (34.6)

1,001 (41.9)

159 (22.6)

150 (21.3)

103 (13.9)

Middle (2-5)

1,928 (37.0)

670 (40.5)

645 (36.0)

814 (42.0)

1,039 (43.5)

354 (50.3)

371 (52.6)

363 (49.0)

Low (<2)

2,257 (43.3)

288 (17.4)

262 (14.6)

452 (23.2)

347 (14.5)

191 (27.1)

184 (26.1)

275 (37.1)

College +

1,619 (31.1)

754 (45.5)

1,038 (57.9)

856 (44.2)

1,295 (54.3)

202 (28.7)

216 (30.6)

136 (18.4)

Some college

1,168 (22.4)

390 (23.6)

308 (17.2)

429 (22.1)

454 (19.0)

146 (20.7)

122 (17.3)

168 (22.7)

High school

1,862 (35.7)

458 (27.7)

386 (21.5)

585 (30.2)

562 (23.5)

275 (39.1)

279 (39.6)

368 (49.7)

563 (10.8)

54 (3.3)

60 (3.3)

67 (3.5)

76 (3.2)

81 (11.5)

88 (12.5)

69 (9.3)

County Age, median (IQR), y Female, No. (%)

(n = 2,387)

Race/ethnicity, No. (%)

Hispanic Other a

b

Education , No. (%)

< High school a

Rel. income (relative income) refers to the factor by which reported household income is above or below the federal poverty line determined for that b household; Education refers to the respondent’s educational attainment

12

Rel. income , No. (%)

Table 3 Agreement (ρ) between individual SES indicators (indicated by column name) and neighborhood advantage for counties surveyed by SEPA-HHS and BLS-HHS. Bolded values are significant with p < 0.001. Urbanicity a (NCHS class )

Rel. income

Large metro (1)

0.472

0.385

Bucks

Suburban (2)

0.332

0.311

Chester

Suburban (2)

0.390

0.320

Delaware

Suburban (2)

0.447

0.354

Montgomery

Suburban (2)

0.356

0.283

Berks

Medium metro (3)

0.308

0.239

Lancaster

Medium metro (3)

0.156

0.045

Schuylkill

Micropolitan (5)

0.256

0.154

County Philadelphia

a

b

Education

c

2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme for Counties categories are: 1 - large central metro, 2 - large fringe metro, 3 - medium metro, 4 - small metro, 5 - micropolitan, and 6 – noncore; b Rel. income (relative income) refers to the factor by which reported household income is above or below the c federal poverty line determined for that household; Not significant at alpha = 0.05 level

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Figures

a

b

Neighborhood SES Individual SES Exposure

c

Neighborhood SES

Neighborhood SES Individual SES

Individual SES Outcome

Outcome

Exposure

Exposure

Outcome

Fig. 1. Path diagrams illustrating hypothetical situations where an exposure-outcome relationship is confounded by individual-level SES only (a), neighborhood-level SES only (b), or both individual- and neighborhood-level SES (c).

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0.6

Agreement (ρ)

0.5

PHI

DEL 0.4

Large metro (1)

CHE BER

0.3

Urbanization level (NCHS class)

MON BUC

Suburban (2) Medium metro (3)

SCH

Micropolitan (5)

0.2

LAN 0.1

5

6

7

8

Log population density Fig. 2. County-level agreement (Spearman’s ρ) between respondents’ relative household income and neighborhood advantage. PHI = Philadelphia, BUC = Bucks, CHE = Chester, DEL = Delaware, MON = Montgomery, BER = Berks, LAN = Lancaster, and SCH = Schuylkill.

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Fig. 3. Estimated magnitude of residual confounding by individual-level SES when only neighborhood-level SES was accounted for. Fig. 3a shows the relative risk (RR) of self-rated fair or poor health associated with obesity according to multivariable Poisson regression estimated using model 1, which included demographic variables and neighborhood advantage and model 2, which included demographic variables, neighborhood advantage, relative household income and respondent education. Fig. 3b shows the magnitude of confounding by individual-level SES estimated as 100*(RR1 – RR2)/RR2, where RR1 and RR2 were estimated using models 1 and 2, respectively. PHI = Philadelphia, BUC = Bucks, CHE = Chester, DEL = Delaware, MON = Montgomery, BER = Berks, LAN = Lancaster, and SCH = Schuylkill.

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20

Neighborhood-level Measures of Socioeconomic Status are More Correlated with Individual-level Measures in Urban Areas Compared to Less Urban Areas Supplementary Materials Table of Contents Supplemental Table 1. Census data variables and factor score coefficients used to calculate the 2013 Area Deprivation Index (ADI) Supplemental Table 2. Characteristics of respondents missing household income by county, SEPA-HHS 2010, 2012 and BLS-HHS 2011 (N = 4,764) Supplemental Table 3. Characteristics of respondents missing address or geocode information by county, SEPA-HHS 2010, 2012 and BLS-HHS 2011 (N = 1,673) Supplemental Table 4. Adjusted relative risk (RR) of fair/poor health associated with individual-level SES measures Supplemental Table 5. Adjusted relative risk (RR) of obesity associated with individual-level SES measures Supplemental Table 6. Relative risk (RR) of fair/poor health associated with obesity and the magnitude of confounding by individuallevel SES. Supplemental Table 7. Comparison of the agreement (Spearman’s ρ) between relative income and neighborhood advantage aggregated at the level of census tracts and Zip Code Tabulation Areas (ZCTAs) Supplemental Fig. 1. Counties surveyed by SEPA-HHS and BLS-HHS shaded by urban/rural classification Supplemental Fig. 2. County-level agreement (Spearman’s ρ) between educational attainment and tract-level neighborhood advantage Supplemental Fig. 3. County-level agreement (Spearman’s ρ) between relative income and ZCTA-level neighborhood advantage

Supplemental Table 1. Census data variables and factor score coefficients used to calculate 2013 Area Deprivation Index (ADI)a Domain

Variable

Coefficient

Education

% Population aged 25 years or older with less than 9 years of education

0.0849

% Population aged 25 years or older with at least a high school diploma

-0.0970

Median family income in US dollars

-0.0977

Income/ employment

Housing

Household characteristics

a

b

Income disparity

0.0936

% Families below federal poverty level

0.0977

% Population below 150% of federal poverty level

0.1037

% Civilian labor force population aged 16 years or older who are unemployed

0.0806

% Employed population aged 16 years or older in white-collar occupations

-0.0874

Median home value in US dollars

-0.0688

Median gross rent in US dollars

-0.0781

Median monthly mortgage in US dollars

-0.0770

% Owner-occupied housing units

-0.0615

% Occupied housing units without complete plumbing (log)

0.0510

% Single-parent households with children younger than 18

0.0719

% Households without a motor vehicle

0.0694

% Households without a telephone

0.0877

% Households with more than one person per room

0.0556

2013 ADI was calculated using data from the American Community Survey 5-year estimates from 2009-2013 by summing variables weighted by their respective factor score b coefficients; Income disparity defined as the log of 100*ratio of households with <$10,000 income to number of households with ≥$50,000 income

Supplemental Table 2. Characteristics of respondents missing household income by county, SEPA-HHS 2010, 2012 and BLS-HHS 2011 (N = 4,764) Survey cycle(s)

SEPA-HHS 2010 & 2012

BLS-HHS 2011

Philadelphia (n = 1,694)

Bucks (n = 523)

Chester (n = 605)

Delaware (n = 621)

Montgomery (n = 830)

Berks (n = 175)

Lancaster (n = 172)

Schuylkill (n = 144)

58 (48-71)

58 (49-71)

57 (49-69)

58 (49-72)

58 (48-71)

61 (48-73)

60 (49-72)

62 (49-75)

1,167 (68.9)

362 (69.2)

398 (65.8)

425 (68.4)

568 (68.4)

108 (61.7)

98 (57)

93 (64.6)

White

701 (41.4)

455 (87)

512 (84.6)

489 (78.7)

706 (85.1)

156 (89.1)

153 (89.0)

134 (93.1)

Black

637 (37.6)

6 (1.1)

31 (5.1)

72 (11.6)

36 (4.3)

2 (1.1)

3 (1.7)

1 (0.7)

Hispanic

148 (8.7)

14 (2.7)

14 (2.3)

5 (0.8)

15 (1.8)

7 (4.0)

3 (1.7)

3 (2.1)

Other

87 (5.1)

16 (3.1)

18 (3.0)

17 (2.7)

26 (3.1)

2 (1.1)

2 (1.2)

1 (0.7)

121 (7.1)

32 (6.1)

30 (5.0)

38 (6.1)

47 (5.7)

8 (4.6)

11 (6.4)

5 (3.5)

375 (22.1)

219 (41.9)

316 (52.2)

264 (42.5)

428 (51.6)

39 (22.3)

42 (24.4)

19 (13.2)

County Age, median (IQR), y Female, No. (%) a

Race , No. (%)

Missing b

Education , No. (%) College +

a

Some college

341 (20.1)

110 (21.0)

109 (18.0)

127 (20.5)

154 (18.6)

35 (20.0)

30 (17.4)

31 (21.5)

High school

669 (39.5)

153 (29.3)

140 (23.1)

193 (31.1)

195 (23.5)

73 (41.7)

75 (43.6)

75 (52.1)

< High school

270 (15.9)

35 (6.7)

35 (5.8)

25 (4.0)

37 (4.5)

25 (14.3)

23 (13.4)

17 (11.8)

b

White refers to Non-Hispanic white race/ethnicity and black refers to Non-Hispanic black race/ethnicity; Education refers to respondent’s educational attainment

Supplemental Table 3. Characteristics of respondents missing address or geocode information by county, SEPA-HHS 2010, 2012 and BLS-HHS 2011 (N = 1,673) Survey cycle(s)

SEPA-HHS 2010 & 2012

BLS-HHS 2011

Philadelphia (n = 633)

Bucks (n = 158)

Chester (n = 175)

Delaware (n = 239)

Montgomery (n = 213)

Berks (n = 82)

Lancaster (n = 82)

Schuylkill (n = 91)

Age, median (IQR), y

50 (40-59)

50 (42-61)

52 (45-62)

52 (41-60)

50 (41-59)

52 (42-62)

50 (41-61)

56 (48-67)

Female, No. (%)

442 (69.8)

84 (53.2)

102 (58.3)

152 (63.6)

134 (62.9)

43 (52.4)

39 (47.6)

37 (40.7)

White

218 (34.4)

133 (84.2)

139 (79.4)

160 (66.9)

168 (78.9)

65 (79.3)

69 (84.1)

86 (94.5)

Black

316 (49.9)

11 (7.9)

16 (9.1)

58 (24.3)

23 (10.8)

2 (2.4)

2 (2.4)

0

County

a

Race , No. (%)

Hispanic

54 (8.5)

7 (4.4)

12 (6.9)

10 (4.2)

10 (4.7)

10 (12.2)

8 (9.8)

1 (1.1)

Other

45 (7.1)

7 (4.4)

8 (4.6)

11 (4.6)

12 (5.6)

5 (6.1)

3 (3.7)

4 (4.4)

High (>5)

117 (18.5)

64 (40.5)

83 (47.4)

89 (37.2)

92 (43.2)

17 (20.7)

20 (24.4)

12 (13.2)

Middle (2-5)

236 (37.3)

57 (36.1)

58 (33.1)

87 (36.4)

94 (44.1)

43 (52.4)

36 (43.9)

52 (57.1)

Low (<2)

280 (44.2)

37 (23.4)

34 (19.4)

63 (26.4)

27 (12.7)

22 (26.8)

26 (31.7)

27 (29.7)

College +

194 (30.6)

63 (39.9)

100 (57.1)

105 (43.9)

117 (54.9)

27 (32.9)

27 (32.9)

10 (11.0)

Some college

137 (21.6)

41 (25.9)

27 (15.4)

54 (22.6)

49 (23.0)

10 (12.2)

17 (20.7)

18 (19.8)

High school

221 (34.9)

47 (29.7)

39 (22.3)

67 (28.0)

41 (19.2)

27 (32.9)

25 (30.5)

53 (58.2)

< High school

81 (12.8)

7 (4.4)

9 (5.1)

13 (5.4)

6 (2.8)

18 (22.0)

13 (15.9)

10 (11.0)

b

Rel. income , No. (%)

c

Education , No. (%)

a

b

White refers to Non-Hispanic white race/ethnicity and black refers to Non-Hispanic black race/ethnicity; Rel. income (relative income) refers to the factor by which reported household income is above or below the federal poverty line determined for that household; cEducation refers to respondent’s educational attainment

Supplemental Table 4. Adjusteda relative risk (RR) of fair/poor health associated with individual-level SES measures. Significant results (p < 0.05) are bolded. Individual-level SES indicator

Philadelphia

Bucks

Chester

Delaware

Montgomery

Berks

Lancaster

Schuylkill

β (95% CI )

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Q2

0.75 (0.67, 0.83)

0.42 (0.31, 0.58)

0.48 (0.34, 0.67)

0.51 (0.39, 0.67)

0.67 (0.52, 0.86)

0.54 (0.37, 0.78)

0.48 (0.31, 0.76)

0.49 (0.35, 0.69)

Q3

0.50 (0.44, 0.58)

0.41 (0.29, 0.59)

0.30 (0.19, 0.47)

0.44 (0.32, 0.61)

0.32 (0.22, 0.46)

0.43 (0.28, 0.67)

0.46 (0.28, 0.75)

0.31 (0.20, 0.47)

Q4 (highest)

0.35 (0.28, 0.43)

0.35 (0.23, 0.54)

0.28 (0.16, 0.48)

0.28 (0.18, 0.45)

0.24 (0.15, 0.38)

0.16 (0.07, 0.33)

0.30 (0.15, 0.59)

0.28 (0.17, 0.47)

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Some college

1.37 (1.15, 1.64)

1.63 (1.16, 2.28)

1.36 (0.95, 1.94)

2.03 (1.43, 2.89)

1.24 (0.90, 1.69)

1.09 (0.61, 1.94)

1.59 (0.82, 3.1)

0.91 (0.53, 1.57)

High school

1.48 (1.25, 1.75)

1.65 (1.17, 2.32)

1.34 (0.95, 1.87)

2.15 (1.52, 3.03)

1.34 (1.01, 1.77)

1.54 (0.96, 2.47)

1.73 (0.98, 3.06)

1.06 (0.66, 1.70)

< High school

2.07 (1.72, 2.49)

1.75 (0.99, 3.1)

1.40 (0.85, 2.32)

3.03 (1.96, 4.68)

1.59 (1.04, 2.42)

1.91 (1.14, 3.19)

1.88 (0.95, 3.72)

1.42 (0.83, 2.43)

b

Relative income Q1 (lowest)

Education College +

a

b

All models adjusted for age, sex, race/ethnicity, neighborhood advantage, relative income, and education; CI denotes confidence interval

Supplemental Table 5. Adjusteda relative risk (RR) of obesity associated with individual-level SES measures. Significant results (p < 0.05) are bolded. Individual-level SES indicator

Philadelphia

Bucks

Chester

Delaware

Montgomery

Berks

Lancaster

Schuylkill

β (95% CI )

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

β (95% CI)

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Q2

1.00 (0.91, 1.09)

0.77 (0.61, 0.98)

0.81 (0.65, 1.02)

0.95 (0.80, 1.14)

0.81 (0.67, 0.97)

0.68 (0.50, 0.93)

0.90 (0.67, 1.22)

0.93 (0.70, 1.24)

Q3

0.97 (0.87, 1.07)

0.96 (0.76, 1.20)

0.73 (0.58, 0.93)

0.83 (0.67, 1.02)

0.78 (0.64, 0.95)

0.87 (0.65, 1.16)

0.82 (0.60, 1.13)

0.97 (0.73, 1.29)

Q4 (highest)

0.84 (0.74, 0.95)

0.89 (0.69, 1.14)

0.77 (0.58, 1.02)

0.78 (0.61, 0.98)

0.72 (0.58, 0.90)

0.63 (0.44, 0.91)

0.84 (0.59, 1.20)

0.79 (0.57, 1.10)

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Reference

Some college

1.28 (1.15, 1.43)

1.18 (0.96, 1.46)

1.08 (0.86, 1.36)

1.15 (0.96, 1.39)

1.16 (0.96, 1.39)

1.09 (0.77, 1.55)

1.26 (0.87, 1.80)

0.88 (0.63, 1.23)

High school

1.16 (1.04, 1.29)

1.03 (0.83, 1.27)

1.06 (0.85, 1.33)

1.20 (1.00, 1.44)

1.24 (1.04, 1.48)

1.26 (0.94, 1.71)

1.26 (0.92, 1.74)

0.94 (0.70, 1.26)

< High school

1.12 (0.97, 1.30)

1.93 (1.38, 2.70)

1.02 (0.68, 1.55)

1.13 (0.79, 1.63)

1.44 (1.04, 1.99)

1.68 (1.16, 2.43)

1.56 (1.04, 2.35)

0.74 (0.46, 1.21)

b

Relative income Q1 (lowest)

Education College +

a

b

All models adjusted for age, sex, race/ethnicity, neighborhood advantage, relative income, and education; CI denotes confidence interval

Supplemental Table 6. Relative risk (RR) of fair/poor health associated with obesity and the magnitude of confounding by individuallevel SES. Model 1 , RR1 (95% CI )

Model 2, RR2 (95% CI)

Magnitude of c confounding (%)

Urban (1)

1.60 (1.45, 1.76)

1.55 (1.41, 1.70)

3.2

Bucks

Suburban (2)

2.07 (1.64, 2.62)

1.96 (1.55, 2.48)

5.6

Chester

Suburban (2)

2.40 (1.85, 3.10)

2.19 (1.7, 2.83)

9.6

Delaware

Suburban (2)

1.74 (1.40, 2.16)

1.60 (1.30, 1.97)

8.8

Montgomery

Suburban (2)

1.95 (1.57, 2.44)

1.73 (1.39, 2.15)

12.7

Berks

Medium metro (3)

2.05 (1.52, 2.76)

1.68 (1.25, 2.25)

22.0

Lancaster

Medium metro (3)

1.85 (1.32, 2.61)

1.61 (1.14, 2.27)

14.9

Philadelphia

a

RR of fair/poor health associated with obesity

Urbanicity (NCHS class)

County

a

b

Model 1 was a Poisson regression model with robust variance estimates with covariate adjustments for respondent sex, age, race/ethnicity, and neighborhood advantage b categorized into quartiles. Model 2 included all covariates included in Model 1 with additional adjustment for relative income quartiles and respondent education. CI indicates c confidence interval. Magnitude of confounding by individual-level SES when only neighborhood-level measures were accounted for, estimated as a percentage: 100*(RR1 – RR2)/RR2, where RR1 and RR2 were estimated from models 1 and 2, respectively.

Supplemental Table 7. Comparison of the agreement (Spearman’s ρ) between relative income and census tract-level vs. ZIP Code Tabulation Area (ZCTA)-level neighborhood advantagea County

a

Census tracts

ZCTAs

Philadelphia

0.472

0.414

Bucks

0.332

0.313

Chester

0.390

0.303

Delaware

0.447

0.453

Montgomery

0.356

0.277

Berks

0.308

0.266

Lancaster

0.156

0.058

Schuylkill

0.255

0.232

ZCTA-level neighborhood advantage was calculated as follows: (1) variables listed in Supplemental Table 1 were extracted at the ZCTA level from the American Community Survey 5-year estimates for 2009-2013; (2) ZCTA-level ADI were derived as the linear combination of variables using factor coefficients given in Supplemental Table 1 and transformed to a percentile ranking (taking values 0-99); (3) ZCTA-level neighborhood advantage was calculated by subtracting the ADI percentile from 100

Supplemental Fig. 1. Counties surveyed by SEPA-HHS and BLS-HHS shaded by urban/rural classification. Counties are abbreviated as follows: PHI = Philadelphia, BUC = Bucks, CHE = Chester, DEL = Delaware, MON = Montgomery, BER = Berks, LAN = Lancaster, and SCH = Schuylkill. The inset map in the bottom left-hand corner shows the state of Pennsylvania with the study area shaded in color.

Agreement (ρ)

0.6

0.4

DEL

PHI

Urbanization level (NCHS class) Large metro (1)

CHE BUC MON BER

Suburban (2)

0.2

Medium metro (3)

SCH

Micropolitan (5) LAN 0.0

5

6

7

8

Log population density

Supplemental Fig. 2. County-level agreement (Spearman’s ρ) between educational attainment and tract-level neighborhood advantage. PHI = Philadelphia, BUC = Bucks, CHE = Chester, DEL = Delaware, MON = Montgomery, BER = Berks, LAN = Lancaster, and SCH = Schuylkill.

Agreement (ρ)

0.6

DEL PHI

0.4

Urbanization level (NCHS class) Large metro (1)

0.2

SCH

BUC CHE MON BER

Suburban (2) Medium metro (3) Micropolitan (5)

LAN 5

6

7

8

Log population density Supplemental Fig. 3. County-level agreement (Spearman’s ρ) between relative income and ZCTA-level neighborhood advantage. PHI = Philadelphia, BUC = Bucks, CHE = Chester, DEL = Delaware, MON = Montgomery, BER = Berks, LAN = Lancaster, and SCH = Schuylkill.