School of Public Health

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

Note: Prior to July 1, 2007, the School of Public Health was named the College of Health & Human Performance.

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Subject: search.filters.subject.Census Tract Level

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    Being overburdened and medically underserved: assessment of this double disparity for populations in the state of Maryland
    (Springer Nature, 2014-04-04) Wilson, Sacoby; Zhang, Hongmei; Jiang, Chengsheng; Burwell, Kristen; Rehr, Rebecca; Murray, Rianna; Dalemarre, Laura; Naney, Charles
    Environmental justice research has shown that many communities of color and low-income persons are differentially burdened by noxious land uses including Toxic Release Inventory (TRI) facilities. However, limited work has been performed to assess how these populations tend to be both overburdened and medically underserved. We explored this “double disparity” for the first time in Maryland. We assessed spatial disparities in the distribution of TRI facilities in Maryland across varying levels of sociodemographic composition using 2010 US Census Health Professional Shortage Area (HPSA) data. Univariate and multivariate regression in addition to geographic information systems (GIS) were used to examine relationships between sociodemographic measures and location of TRI facilities. Buffer analysis was also used to assess spatial disparities. Four buffer categories included: 1) census tracts hosting one or more TRI facilities; 2) tracts located more than 0 and up to 0.5 km from the closest TRI facility; 3) tracts located more than 0.5 km and up to 1 km from a TRI facility; and 4) tracts located more than 1 km and up to 5 km from a TRI facility. We found that tracts with higher proportions of non-white residents and people living in poverty were more likely to be closer to TRI facilities. A significant increase in income was observed with an increase in distance between a census tract and the closest TRI facility. In general, percent non-white was higher in HPSA tracts that host at least one TRI facility than in non-HPSA tracts that host at least one TRI facility. Additionally, percent poverty, unemployment, less than high school education, and homes built pre-1950 were higher in HPSA tracts hosting TRI facilities than in non-HPSA tracts hosting TRI facilities. We found that people of color and low-income groups are differentially burdened by TRI facilities in Maryland. We also found that both low-income groups and persons without a high school education are both overburdened and medically underserved. The results of this study provide insight into how state agencies can better address the double disparity of disproportionate environmental hazards and limited access to health care resources facing vulnerable communities in Maryland.
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    Spatial clustering of physical activity and obesity in relation to built environment factors among older women in three U.S. states
    (Springer Nature, 2014-12-24) Tamura, Kosuke; Puett, Robin C; Hart, Jaime E; Starnes, Heather A; Laden, Francine; Troped, Philip J
    Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study’s aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters. In 2004, Nurses’ Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared. Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband’s education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters. Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.