Philip Hurvitz Archives - Research Portal

Neighborhood greenspace and neighborhood income associated with white matter grade worsening: Cardiovascular Health Study

Besser, L. M., Lovasi, G. S., Zambrano, J. J., Camacho, S., Dhanekula, D., Michael, Y. L., Garg, P., Hirsch, J. A., Siscovick, D., Hurvitz, P. M., Biggs, M. L., Galvin, J. E., Bartz, T. M., & Longstreth, W. T. (2023). Neighborhood greenspace and neighborhood income associated with white matter grade worsening: Cardiovascular Health Study. Alzheimer’s & Dementia : Diagnosis, Assessment & Disease Monitoring, 15(4), e12484–e12484. https://doi.org/10.1002/dad2.12484

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Abstract

INTRODUCTION
We examined whether a combined measure of neighborhood greenspace and neighborhood median income was associated with white matter hyperintensity (WMH) and ventricle size changes.

METHODS
The sample included 1260 cognitively normal ≥ 65‐year‐olds with two magnetic resonance images (MRI; ≈ 5 years apart). WMH and ventricular size were graded from 0 (least) to 9 (most) abnormal (worsening = increase of ≥1 grade from initial to follow‐up MRI scans). The four‐category neighborhood greenspace–income measure was based on median neighborhood greenspace and income values at initial MRI. Multivariable logistic regression tested associations between neighborhood greenspace–income and MRI measures (worsening vs. not).

RESULTS
White matter grade worsening was more likely for those in lower greenspace–lower income neighborhoods than higher greenspace–higher income neighborhoods (odds ratio = 1.73; 95% confidence interval = 1.19–2.51).

DISCUSSION
The combination of lower neighborhood income and lower greenspace may be a risk factor for worsening white matter grade on MRI. However, findings need to be replicated in more diverse cohorts.

HIGHLIGHTS
Population‐based cohort of older adults (≥ 65 years) with greenspace and MRI data
Combined measure of neighborhood greenspace and neighborhood income at initial MRI
MRI outcomes included white matter hyperintensities (WMH) and ventricular size
Longitudinal change in MRI outcomes measured approximately 5 years apart
Worsening WMH over time more likely for lower greenspace‐lower income neighborhoods

Keywords

built environment; green space; magnetic resonance imaging; neighborhood; socioeconomic status

Association of Perceived Neighborhood Environments With Cognitive Function in Older Adults

Kim, B., Rosenberg, D. E., Dobra, A., Barrington, W. E., Hurvitz, P. M., & Belza, B. (2023). Association of Perceived Neighborhood Environments With Cognitive Function in Older Adults. Journal of Gerontological Nursing, 49(8), 35–41. https://doi.org/10.3928/00989134-20230707-04

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Abstract

The current study examined the associations between perceptions of the social and physical neighborhood environments and cognitive function in older adults. This cross-sectional study analyzed 821 adults aged & GE;65 years from the Adult Changes in Thought study. Perceived neighborhood attributes were measured by the Physical Activity Neighborhood Environment Scale. Cognitive function was assessed using the Cognitive Ability Screening Instrument. The associations were tested using multivariate linear regression. One point greater perceived access to public transit was associated with 0.56 points greater cognitive function score (95% confidence interval [CI] [0.25, 0.88]), and an additional one point of perceived sidewalk coverage was related to 0.22 points higher cognitive function score (95% CI [0.00, 0.45]) after controlling for sociodemographic factors. The perception of neighborhood attributes alongside physical infrastructure may play an important role in supporting older adults' cognitive function.

Keywords

Built environment; Physical-activity; Dementia; Reverse; Walking; Disease

Time-Varying Food Retail and Incident Disease in the Cardiovascular Health Study

Lovasi, G. S., Boise, S., Jogi, S., Hurvitz, P. M., Rundle, A. G., Diez, J., Hirsch, J. A., Fitzpatrick, A., Biggs, M. L., & Siscovick, D. S. (2023). Time-Varying Food Retail and Incident Disease in the Cardiovascular Health Study. American Journal of Preventive Medicine, 64(6), 877–887. https://doi.org/10.1016/j.amepre.2023.02.001

Clean Energy Justice: Different Adoption Characteristics of Underserved Communities in Rooftop Solar and Electric Vehicle Chargers in Seattle

Min, Yohan, Lee, Hyun Woo, & Hurvitz, Philip M. (2023). Clean Energy Justice: Different Adoption Characteristics of Underserved Communities in Rooftop Solar and Electric Vehicle Chargers in Seattle. Energy Research & Social Science, 96.

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Abstract

Concerns over global climate change have led to energy transition to clean energy systems with the development of various clean energy policies. However, social equity issues have emerged in association with the rapid transition of energy systems related to distributed energy resources (DERs), evidenced by disparities in clean energy access. While most existing studies have focused on several variables impacting the adoption of DERs, there is a dearth of studies concerning distributional and recognition justice specifically aimed at investigating: (1) which DER adoption variable is the most important among several variables identified in the literature; and (2) how adoption patterns vary by technologies and communities. The objective of the present study is to answer the two questions by examining the geographic distribution of rooftop solar and electric vehicle (EV) chargers and the related community attributes. Also, the study involves identifying latent variables by addressing inter-correlations among several adoption determinants. The results show that rooftop solar and EV charger adoptions in Seattle present disparities associated with geographic locations and community attributes. In particular, housing variables are the main indicators for rooftop solar adoption and even stronger in communities with low adoption rates. EV charger adoptions are strongly associated with economic variables. Furthermore, spatial inequality of rooftop solar adoption is higher than that of EV charger adoption. The study suggests housing-related support may increase the adoption of both technologies, particularly in communities with low adoption rates. Considering that the installations of rooftop solar and EV chargers were concentrated in particular communities, the study results imply that policies aimed at increasing the adoption of DERs should be tailored to local community characteristics.

Mediating Role of Walking between Perceived and Objective Walkability and Cognitive Function in Older Adults

Kim, Boeun, Barrington, Wendy E., Dobra, Adrian, Rosenberg, Dori, Hurvitz, Philip, & Belza, Basia. (2022). Mediating Role of Walking between Perceived and Objective Walkability and Cognitive Function in Older Adults. Health & Place, 79.

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Abstract

The aim of this study was to examine the role of walking in explaining associations between perceived and objective measures of walkability and cognitive function among older adults. The study employed a cross-sectional design analyzing existing data. Data were obtained from the Adult Changes in Thought Activity Monitor study. Cognitive function and perceived walkability were measured by a survey. Objective walkability was measured using geographic information systems (GIS). Walking was measured using an accelerometer. We tested the mediating relationship based on 1,000 bootstrapped samples. Perceived walkability was associated with a 0.04 point higher cognitive function score through walking (p = 0.006). The mediating relationship accounted for 34% of the total relationship between perceived walkability and cognitive function. Walking did not have a significant indirect relationship on the association between objective walkability and cognitive function. Perceived walkability may be more relevant to walking behavior than objective walkability among older adults. Greater levels of perceived walkability may encourage older adults to undertake more walking, and more walking may in turn improve cognitive function in older adults.

Keywords

Built environment; Cognitive function; Walking; Mediation analysis; Older adults

Impact of a Light Rail Transit Line on Physical Activity: Findings from the Longitudinal Travel Assessment and Community (TRAC) Study

Saelens, Brian E., Hurvitz, Philip M., Zhou, C., Colburn, T., Marchese, A., & Moudon, Anne Vernez (2022). Impact of a Light Rail Transit Line on Physical Activity: Findings from the Longitudinal Travel Assessment and Community (TRAC) Study. Journal of Transport & Health, 27.

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Abstract

Increasing transit infrastructure could increase transit use and result in higher physical activity if users actively travel to access transit. Few studies have rigorously examined transit use and physical activity change from before to years after among residents living close versus farther away from new transit options.Methods: An initial sample (n = 722) of residents living either close (1 mile network distance; unexposed) from future new light rail transit (LRT) stops in the Seattle/King County area were recruited and assessed prior to LRT opening and again 1-2 and 3-4 years later. At each assessment timepoint, residents wore an accelerometer and GPS data logger for 7 days and completed a 7-day travel log and demographic and attitudinal survey. Difference-in-difference analyses examined longitudinal change between those exposed versus unexposed to LRT in physical activity, walking (both utilitarian and recreational), and transit-related walking, and transit use.Results: There was no differential change by LRT exposure in overall physical activity (including or not including light intensity physical activity), recreational walking, or utilitarian walking, with most decreasing significantly in both exposure conditions through follow-ups. There was a differential change in transit-related walking, with those exposed to LRT slightly increasing such physical activity to the most distal follow-up, but the difference from the unexposed condition was modest (<2 min/day). There was no substantial differential change over time in transit use by LRT exposure.Conclusions: Exposure to a new light rail line did not markedly change the frequency of transit use of nearby residents, but did result in a small increase in transit-related walking relative to those unexposed. This did not differentially change the amount of overall physical activity or time spent walking compared to residents living farther away from the new LRT.

Keywords

Public Transit; Accelerometer Data; Built Environment; Behavior; Walking; Transportation; Neighborhood; Time; GPS

Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III

Buszkiewicz, James H.; Rose, Chelsea M.; Ko, Linda K.; Mou, Jin; Moudon, Anne Vernez; Hurvitz, Philip M.; Cook, Andrea J.; Drewnowski, Adam. (2022). Associations between Neighborhood Built Environment, Residential Property Values, and Adult BMI Change: The Seattle Obesity Study III. SSM-Population Health, 19.

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Abstract

Objective: To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1-and 2-year changes in body mass index (BMI). Methods: The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1-3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results: Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion: Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1-and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

Keywords

Body-mass Index; Physical-activity; Food Environment; Socioeconomic-status; Weight-gain; Health; Quality

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

Cruz, Maricela; Drewnowski, Adam; Bobb, Jennifer F.; Hurvitz, Philip M.; Moudon, Anne Vernez; Cook, Andrea; Mooney, Stephen J.; Buszkiewicz, James H.; Lozano, Paula; Rosenberg, Dori E.; Kapos, Flavia; Theis, Mary Kay; Anau, Jane; Arterburn, David. (2022). Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study. Epidemiology, 33(5), 747-755.

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Abstract

Background: Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight. Methods: Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight. Results: In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]). Conclusions: Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Keywords

Body-mass Index; Neighborhood Socioeconomic-status; New-york-city; Built Environment; Physical-activity; Food Environment; Urban Sprawl; Risk-factors; Obesity; Walking; Electronic Medical Records; Fast Foods; Population Density; Residential Density; Residential Moves; Supermarkets

Home Versus Nonhome Neighborhood: Quantifying Differences in Exposure to the Built Environment

Hurvitz, Philip M.; Moudon, Anne Vernez. (2012). Home Versus Nonhome Neighborhood: Quantifying Differences in Exposure to the Built Environment. American Journal Of Preventive Medicine, 42(4), 411 – 417.

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Abstract

Background: Built environment and health research have focused on characteristics of home neighborhoods, whereas overall environmental exposures occur over larger spatial ranges. Purpose: Differences in built environment characteristics were analyzed for home and nonhome locations using GPS data. Methods: GPS data collected in 2007-2008 were analyzed for 41 subjects in the Seattle area in 2010. Environmental characteristics for 3.8 million locations were measured using novel GIS data sets called SmartMaps, representing spatially continuous values of local built environment variables in the domains of neighborhood composition, utilitarian destinations, transportation infrastructure, and traffic conditions. Using bootstrap sampling, CIs were estimated for differences in built environment values for home (1666 m) GPS locations. Results: Home and nonhome built environment values were significantly different for more than 90% of variables across subjects (p < 0.001). Only 51% of subjects had higher counts of supermarkets near than away from home. Different measures of neighborhood parks yielded varying results. Conclusions: SmartMaps helped measure local built environment characteristics for a large set of GPS locations. Most subjects had significantly different home and nonhome built environment exposures. Considering the full range of individuals' environmental exposures may improve understanding of effects of the built environment on behavior and health outcomes. (Am J Prev Med 2012;42(4):411-417) (C) 2012 American Journal of Preventive Medicine

Keywords

Built Environment; Public Health Research; Individual Differences; Neighborhoods; Environmental Exposure; Health Of Homeless People; Global Positioning System; Data Analysis; Quantitative Research; Seattle (wash.); Washington (state); Geographic Information-systems; Global Positioning Systems; Physical-activity; Health Research; Urban Form; Land-use; Associations; Transportation; Availability; Walkability

Obesity, Diet Quality, Physical Activity, and the Built Environment: The Need for Behavioral Pathways

Drewnowski, Adam; Aggarwal, Anju; Tang, Wesley; Hurvitz, Philip M.; Scully, Jason; Stewart, Orion; Moudon, Anne Vernez. (2016). Obesity, Diet Quality, Physical Activity, and the Built Environment: The Need for Behavioral Pathways. BMC Public Health, 16.

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Abstract

Background: The built environment ( BE) is said to influence local obesity rates. Few studies have explored causal pathways between home-neighborhood BE variables and health outcomes such as obesity. Such pathways are likely to involve both physical activity and diet. Methods: The Seattle Obesity Study ( SOS II) was a longitudinal cohort of 440 adult residents of King Co, WA. Home addresses were geocoded. Home-neighborhood BE measures were framed as counts and densities of food sources and physical activity locations. Tax parcel property values were obtained from County tax assessor. Healthy Eating Index ( HEI 2010) scores were constructed using data from food frequency questionnaires. Physical activity ( PA) was obtained by self-report. Weights and heights were measured at baseline and following 12 months' exposure. Multivariable regressions examined the associations among BE measures at baseline, health behaviors ( HEI-2010 and physical activity) at baseline, and health outcome both cross-sectionally and longitudinally. Results: None of the conventional neighborhood BE metrics were associated either with diet quality, or with meeting PA guidelines. Only higher property values did predict better diets and more physical activity. Better diets and more physical activity were associated with lower obesity prevalence at baseline and 12 mo, but did not predict weight change. Conclusion: Any links between the BE and health outcomes critically depend on establishing appropriate behavioral pathways. In this study, home-centric BE measures, were not related to physical activity or to diet. Further studies will need to consider a broader range of BE attributes that may be related to diets and health.

Keywords

Body-mass Index; Local Food Environment; Residential Property-values; Supermarket Accessibility; Park Proximity; Neighborhood Walkability; Vegetable Consumption; Atherosclerosis Risk; Restaurant Food; Associations; Built Environment; Physical Activity; Obesity; Diet Quality