Associations of street-view greenspace with Parkinson's disease hospitalizations in an open cohort of elderly US Medicare beneficiaries

Abstract

Introduction: Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization).

Methods: We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007-2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization.

Results: There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density.

Conclusion: Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas.

Keywords: Built Environment; Neurological Disorders; Parkinson’s Disease; Street-View Greenspace; Visual Exposure.

Fig. 1.. GSV images with different coverage scores for trees, grass and other green.

a a For trees Q1: <12.5 %; Q2: 12.5 %−19.4 %; Q3: 19.4 %−26.2 %; Q4: 26.2 %<. For grass Q1: <4.5 %; Q2: 4.5 %−8.9 %; Q3: 8.9 %−11.9 %; Q4: 11.9 %<. For other green Q1: <1.3 %; Q2: 1.3 %−2.0 %; Q3: 2.0 %−3.5 %; Q4: 3.5 %<. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2.. Associations of ZIP code-level GSV-derived greenspace measures with PD-involved hospitalization stratified by Medicaid eligibility and race/ethnicity.

^{a a} The models included Trees, Other green, Grass simultaneously and Total green individually. The models included calendar year, region, ZIP code-level percent Hispanic, percent Black, population density, median home value, median household income, percent of the population with less than a high school degree, percent below the poverty level, percent of owner-occupied housing units, county-level smoking status, an offset for total person-time and strata for all possible combinations of sex, race, Medicaid Eligibility, age at study entry (2-year categories), and follow-up year. Effect modification models did not include a strata for the specific effect modifier. Associations are expressed per IQR increase of the full cohort (IQR Trees = 13.7 %, IQR Other green = 2.3 %, IQR Grass = 7.4 %, IQR Total green = 15.9 %).

Fig. 3.. Associations of ZIP code-level GSV-derived greenspace measures with PD-involved hospitalization stratified by ZIP code-level neighborhood and environmental indicators.

^{a, ba} The models included Trees, Other green and Grass simultaneously and Total green individually. The models included calendar year, region, ZIP code-level percent Hispanic, percent Black, population density, median home value, median household income, percent of the population with less than a high school degree, percent below the poverty level, percent of owner-occupied housing units, county-level smoking status, an offset for total person-time and strata for all possible combinations of sex, race, Medicaid Eligibility, age at study entry (2-year categories), and follow-up year. For effect modification by ZIP code-level median household income, models did not include ZIP code-level median home value, median household income, percent of the population with less than a high school degree, percent below the poverty level, percent of owner-occupied housing units. For effect modification by ZIP code-level population density, models did not include ZIP code-level population density. Associations are expressed per IQR increase of the full cohort (IQR Trees = 13.7 %, IQR Other green = 2.3 %, IQR Grass = 7.4 %, IQR Total green = 15.9 %).^b Inc = ZIP code-level median household income, Popd = ZIP code-level population density, NO₂ = ZIP code-level NO₂, PM_2.5 = ZIP code-level PM_2.5, Temp = ZIP code-level temperature.