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EXPLORING THE SOCIAL AND SPATIAL FACTORS FOR CHRONIC DISEASES IN TORONTO AND CHICAGO COMMUNITIES Patrycja Kolpak, MSA and Lu Wang, M.S., PhD1 1 Department of Geography, Ryerson University ABSTRACT This study combines the social determinants of health framework with potential spatial accessibility models to determine whether potential spatial accessibility to primary care physicians (PCPs) and hospitals and social factors relate to, or predict, poor health status, represented by diabetes, hypertension and stroke, among Toronto neighbourhood and Chicago community populations. The conventional health resource-to-population ratios and Two Step Floating Catchment Area (2SFCA) access scores to PCPs and hospitals revealed disparities between the downtown core and suburban fringes of both cities within 5 and 10km travel thresholds. However, the potential accessibility measures were not strongly correlated with the poor health measures. Statistical analyses showed that neighbourhoods and communities with high levels of poverty, unemployment, low education attainment (below bachelor degree) and certain ethnicities (South American and African) tended to be significantly positively correlated with the poor health measures. The multivariate linear regression models revealed that low education (high school) and ethnicity were significant predictors for the variance of the poor health measures. The findings were largely consistent with previous literature, reaffirming that social determinants of health framework to explain the chronic disease prevalence across neighbourhoods. This study also demonstrates ways to identify and quantify the relative importance of spatial and contextual factors with chronic conditions at the neighbourhood-level. BACKGROUND Social Determinants of Health: emphasizes that living conditions are the key determinants of health (Diez Roux and Mair, 2010; Mikkonen and Raphael, 2009). Living conditions are the social and economic conditions that individuals collectively experience on a daily basis, such as poverty and unemployment. These broader contextual factors can be conceptualized as feedback loops that potentially expand or limit the behavioural decisions and resources, whether health care-oriented or commercial, that individuals choose to adopt or use. Spatial Accessibility to Healthcare Resources: various spatial analytical techniques have been used to measure and visualize the spatial coverage and distribution of health care services. Techniques include gravity-based accessibility models (Crooks and Schuurman, 2012; Wang, 2012), 2SFCA model (Luo and Qi, 2009; McGrail, 2012) and hot spot analyses. Accessibility models are continually being refined and applied to health-based studies as valid methods for computing potential spatial accessibility patterns. Although there has been an extensive amount of research that has determined individual- level risk factors for chronic diseases, these traditional behavioural risk factors cannot entirely explain the prevalence of chronic conditions. By considering the social determinants of health framework, individual risk factors, including stress and coping mechanisms, constitute just one dimension for explaining disparities in health outcomes. The other dimensions acknowledge the significance of broader social and spatial factors that can potentially predispose certain population subgroups to experience greater risks for chronic disease development. Research on health care access and coverage has revealed differences in the availability and distribution of resources across regions. Collectively, these studies demonstrate that place and demography are intrinsically interrelated to health. OBJECTIVES Compare potential spatial accessibility models to identify disparities in healthcare resource access within urban neighbourhood/community contexts Quantify the impact and extent that contextual (social determinants) and spatial (physician and hospital accessibility) factors have on low health status, as represented by the following chronic conditions: diabetes, hypertension and stroke, among Toronto neighbourhood and Chicago community populations METHODS Accessibility Analysis: used to measure the potential spatial accessibility to PCPs and hospitals to compare conventional ratios and 2 Step Floating Catchment Area (2SFCA) method and identify disparities in health service allocation Bivariate Correlation Analysis: used to determine whether the contextual factors were associated with the poor health measures, and to quantify the strength of the relationships observed Multivariate Linear Regression: used to identify the contextual factors that predicted chronic disease prevalence for both cities Figure 2: Comparison between Traditional Hospital-to-Population Ratios and 2SFCA Access Scores for two catchment levels, 5 and 10km, for the City of Chicago, 2010 REFERENCES Crooks, V.A. and N. Schuurman. 2012. Interpreting the Results of a Modified Gravity Model: Examining Access to Primary Health Care Physicians in Five Canadian Provinces and Territories. BMC Health Services Research, 12:230, pp. 1-13. Diez Roux, A.V. and C. Mair. 2010. Neighbourhoods and Health. Annals of the New York Academy of Sciences, 1186, pp. 125-145. Grigsby-Toussaint, D., R. Lipton, N. Chavez, A. Handler, T.P. Johnson, and J. Kubo. 2010. Neighbourhood Socioeconomic Change and Diabetes Risk. Diabetes Care, 33:5, pp. 1065-69. Kim, I., C. Carrasco, C. Muntaner, K. McKenzie and S. Noh. 2013. Ethnicity and Postmigration Health Trajectory in New Immigrants to Canada. Research and Practice, 103:4, pp. 96-104. Koton, S., Y. Gerber, U. Goldbourt and Y. Drory. 2012. Socioeconomic Risk Factor Aggregation and Long-term Incidence of Ischemic Stroke in Patients after First Acute Myocardial Infarction. International Journal of Cardiology, 157, pp. 324-329. Luo, W. and Y. Qi. 2009. An Enhanced Two-Step Floating Catchment Area (E2SFCA) Method for Measuring Spatial Accessibility to Primary Care Physicians. Health & Place, 15, pp. 1100-1107. McGrail, M.R. 2012. Spatial Accessibility of Primary Health Care Utilising the Two-Step Floating Catchment Area Method: An Assessment of Recent Improvements. International Journal of Health Geographics, 11:50, pp. 1-12. Mikkonen, J. and D. Raphael. 2009. Social Determinants of Health: The Canadian Facts. Toronto: York University School of Health Policy and Management. Retrieved from http://www.thecanadianfacts.org/ Sims, M., A.V. Diez Roux, S. Boykin, D. Sarpong, S.Y. Gebreab, S.B. Wyatt, D. Hickson, M. Payton, L. Ekunwe and H.A. Taylor. 2011. The Socioeconomic Gradient of Diabetes Prevalence, Awareness, Treatment, and Control Among African Americans in the Jackson Heart Study. Annual Epidemiology, 21, pp. 892-898. Wang, F. 2012. Measurement, Optimization, and Impact of Health Care Accessibility: A Methodological Review. Annals of the Association of American Geographers, 102:5, pp. 1104-1112. Robbins, J.M., V. Vaccarino, H. Zang and S.V. Kasl. 2005. Socioeconomic Status and Diagnosed Diabetes Incidence. Diabetes Research and Clinical Practice, 6, pp. 230-236. RESULTS & DISCUSSION CONCLUSIONS The spatial accessibility measures were both able to detect disparities in PCP and hospital coverage within specified catchment areas of 5, 10, 15 and 20 km Bivariate correlation and multivariate linear regression models showed that broader social determinants for certain chronic conditions can be identified at the neighbourhood and community-level The methods used can potentially help identify medically underserved and disadvantaged neighbourhoods or communities Figure 1: Comparison between Traditional Hospital-to-Population Ratios and 2SFCA Access Scores for two catchment levels, 5 and 10km, for the City of Toronto, 2010 Table 1: Multivariate Linear Regression Model Summary Results for the Poor Health Measures for Toronto Neighbourhoods, 2006-2007, and Chicago Communities, 2008-2012 * Significance at the 0.05 level ** Significance at the 0.01 level City of Toronto City of Chicago Diabetes Prevalence Independent Variables Β SE p Value Independent Variables Β SE p Value High School Diploma .359** .032 <.000 African Ancestry .874** .190 <.000 Households Below Poverty .091** .011 <.000 Households Below Poverty .346** .119 .005 South American Origin .318** .044 <.000 .827** .473* High Blood Pressure Stroke Independent Variables B SE p Value Independent Variables B SE p Value High School Diploma .438** .032 <.000 High School (No Diploma) 1.450** .234 <.000 South American Origin .343** .048 <.000 African Origin .255** .069 <.000 .784** .470** N = 140 N = 77 Accessibility Analysis: Highest potential spatial access scores found within the downtown cores, whereas the lowest potential spatial access scores found in the fringes of both cities Both methods identified many of the same neighbourhoods/communities with the best and worst access Both methods also identified a potential spatial access gradient between the highly urban cores and the surrounding suburban areas Statistical Analysis: Neighbourhoods and communities with higher levels of unemployment, households below poverty, deprivation and lower education attainment were associated with greater prevalence rates of diabetes, hypertension and stroke Private insurance coverage negatively associated with the poor health measures, while public health coverage positively associated with the poor health measures for Chicago For Toronto neighbourhood populations, native-born and North American ancestry were negatively correlated with the poor health measures, while foreign-born, Asian origins and ancestry were positively associated with the poor health measures Opposite patterns were observed for Chicago communities: native-born and North American ancestry were positively related to the poor health measures, and foreign- born, Asian origins and ancestry were negatively associated with the poor health measures South American and African origins and ancestry were positively related to the poor health measures; both cities also had negative relationships between European ancestry (including subgroups) and the poor health measures The general relationships between the ethic variables and the poor health measures are consistent with the latest immigration trends for both cities Several contextual social factors were identified as significant correlates and predictors for the poor health measures for Toronto neighbourhoods and Chicago communities, which have been acknowledged as significant social determinants in previous studies (Grigsby-Toussaint et al., 2010; Koton et al., 2012; Robbins et al., 2005; Sims et al., 2011)

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EXPLORING THE SOCIAL AND SPATIAL FACTORS FOR CHRONIC DISEASES IN TORONTO AND CHICAGO COMMUNITIES

Patrycja Kolpak, MSA and Lu Wang, M.S., PhD11 Department of Geography, Ryerson University

ABSTRACTThis study combines the social determinants of health framework with potential spatial accessibility models to determine whether potential spatial accessibility to primary care physicians (PCPs) and hospitals and social factors relate to, or predict, poor health status, represented by diabetes, hypertension and stroke, among Toronto neighbourhood and Chicago community populations. The conventional health resource-to-population ratios and Two Step Floating Catchment Area (2SFCA) access scores to PCPs and hospitals revealed disparities between the downtown core and suburban fringes of both cities within 5 and 10km travel thresholds. However, the potential accessibility measures were not strongly correlated with the poor health measures. Statistical analyses showed that neighbourhoodsand communities with high levels of poverty, unemployment, low education attainment (below bachelor degree) and certain ethnicities (South American and African) tended to be significantly positively correlated with the poor health measures. The multivariate linear regression models revealed that low education (high school) and ethnicity were significant predictors for the variance of the poor health measures. The findings were largely consistent with previous literature, reaffirming that social determinants of health framework to explain the chronic disease prevalence across neighbourhoods. This study also demonstrates ways to identify and quantify the relative importance of spatial and contextual factors with chronic conditions at the neighbourhood-level.

BACKGROUND

Social Determinants of Health: emphasizes that living conditions are the key determinants of health (Diez Roux and Mair, 2010; Mikkonen and Raphael, 2009). Living conditions are the social and economic conditions that individuals collectively experience on a daily basis, such as poverty and unemployment. These broader contextual factors can be conceptualized as feedback loops that potentially expand or limit the behavioural decisions and resources, whether health care-oriented or commercial, that individuals choose to adopt or use.

Spatial Accessibility to Healthcare Resources: various spatial analytical techniques have been used to measure and visualize the spatial coverage and distribution of health care services. Techniques include gravity-based accessibility models (Crooks and Schuurman, 2012; Wang, 2012), 2SFCA model (Luo and Qi, 2009; McGrail, 2012) and hot spot analyses. Accessibility models are continually being refined and applied to health-based studies as valid methods for computing potential spatial accessibility patterns.

Although there has been an extensive amount of research that has determined individual-level risk factors for chronic diseases, these traditional behavioural risk factors cannot entirely explain the prevalence of chronic conditions. By considering the social determinants of health framework, individual risk factors, including stress and coping mechanisms, constitute just one dimension for explaining disparities in health outcomes. The other dimensions acknowledge the significance of broader social and spatial factors that can potentially predispose certain population subgroups to experience greater risks for chronic disease development. Research on health care access and coverage has revealed differences in the availability and distribution of resources across regions. Collectively, these studies demonstrate that place and demography are intrinsically interrelated to health.

OBJECTIVES• Compare potential spatial accessibility models to identify disparities in healthcare

resource access within urban neighbourhood/community contexts

• Quantify the impact and extent that contextual (social determinants) and spatial (physician and hospital accessibility) factors have on low health status, as represented by the following chronic conditions: diabetes, hypertension and stroke, among Toronto neighbourhood and Chicago community populations

METHODS• Accessibility Analysis: used to measure the potential spatial accessibility to PCPs

and hospitals to compare conventional ratios and 2 Step Floating Catchment Area (2SFCA) method and identify disparities in health service allocation

• Bivariate Correlation Analysis: used to determine whether the contextual factors were associated with the poor health measures, and to quantify the strength of the relationships observed

• Multivariate Linear Regression: used to identify the contextual factors that predicted chronic disease prevalence for both cities

Figure 2: Comparison between Traditional Hospital-to-Population Ratios and 2SFCA Access Scores for two catchment levels, 5 and 10km, for the City of Chicago, 2010

REFERENCESCrooks, V.A. and N. Schuurman. 2012. Interpreting the Results of a Modified Gravity Model: Examining Access to Primary Health Care Physicians in Five Canadian Provinces and Territories. BMC Health

Services Research, 12:230, pp. 1-13.Diez Roux, A.V. and C. Mair. 2010. Neighbourhoods and Health. Annals of the New York Academy of Sciences, 1186, pp. 125-145.Grigsby-Toussaint, D., R. Lipton, N. Chavez, A. Handler, T.P. Johnson, and J. Kubo. 2010. Neighbourhood Socioeconomic Change and Diabetes Risk. Diabetes Care, 33:5, pp. 1065-69.Kim, I., C. Carrasco, C. Muntaner, K. McKenzie and S. Noh. 2013. Ethnicity and Postmigration Health Trajectory in New Immigrants to Canada. Research and Practice, 103:4, pp. 96-104.Koton, S., Y. Gerber, U. Goldbourt and Y. Drory. 2012. Socioeconomic Risk Factor Aggregation and Long-term Incidence of Ischemic Stroke in Patients after First Acute Myocardial Infarction.

International Journal of Cardiology, 157, pp. 324-329.Luo, W. and Y. Qi. 2009. An Enhanced Two-Step Floating Catchment Area (E2SFCA) Method for Measuring Spatial Accessibility to Primary Care Physicians. Health & Place, 15, pp. 1100-1107.McGrail, M.R. 2012. Spatial Accessibility of Primary Health Care Utilising the Two-Step Floating Catchment Area Method: An Assessment of Recent Improvements. International Journal of Health

Geographics, 11:50, pp. 1-12.Mikkonen, J. and D. Raphael. 2009. Social Determinants of Health: The Canadian Facts. Toronto: York University School of Health Policy and Management. Retrieved from

http://www.thecanadianfacts.org/Sims, M., A.V. Diez Roux, S. Boykin, D. Sarpong, S.Y. Gebreab, S.B. Wyatt, D. Hickson, M. Payton, L. Ekunwe and H.A. Taylor. 2011. The Socioeconomic Gradient of Diabetes Prevalence, Awareness,

Treatment, and Control Among African Americans in the Jackson Heart Study. Annual Epidemiology, 21, pp. 892-898.Wang, F. 2012. Measurement, Optimization, and Impact of Health Care Accessibility: A Methodological Review. Annals of the Association of American Geographers, 102:5, pp. 1104-1112. Robbins, J.M., V. Vaccarino, H. Zang and S.V. Kasl. 2005. Socioeconomic Status and Diagnosed Diabetes Incidence. Diabetes Research and Clinical Practice, 6, pp. 230-236.

RESULTS & DISCUSSION

CONCLUSIONS• The spatial accessibility measures were both able to detect disparities in PCP and

hospital coverage within specified catchment areas of 5, 10, 15 and 20 km

• Bivariate correlation and multivariate linear regression models showed that broader social determinants for certain chronic conditions can be identified at the neighbourhood and community-level

• The methods used can potentially help identify medically underserved and disadvantaged neighbourhoods or communities

Figure 1: Comparison between Traditional Hospital-to-Population Ratios and 2SFCA Access Scores for two catchment levels, 5 and 10km, for the City of Toronto, 2010

Table 1: Multivariate Linear Regression Model Summary Results for the Poor Health Measures for Toronto Neighbourhoods, 2006-2007, and Chicago Communities, 2008-2012

* Significance at the 0.05 level ** Significance at the 0.01 level

City of Toronto City of ChicagoDiabetes Prevalence

Independent Variables Β SE p Value Independent Variables Β SE p ValueHigh School Diploma .359** .032 <.000 African Ancestry .874** .190 <.000Households Below Poverty .091** .011 <.000 Households Below Poverty .346** .119 .005South American Origin .318** .044 <.000R² .827** R² .473*

High Blood Pressure StrokeIndependent Variables B SE p Value Independent Variables B SE p ValueHigh School Diploma .438** .032 <.000 High School (No Diploma) 1.450** .234 <.000South American Origin .343** .048 <.000 African Origin .255** .069 <.000R² .784** R² .470**

N = 140 N = 77

Accessibility Analysis:

• Highest potential spatial access scores found within the downtown cores, whereas the lowest potential spatial access scores found in the fringes of both cities

• Both methods identified many of the same neighbourhoods/communities with the best and worst access

• Both methods also identified a potential spatial access gradient between the highly urban cores and the surrounding suburban areas

Statistical Analysis:

• Neighbourhoods and communities with higher levels of unemployment, households below poverty, deprivation and lower education attainment were associated with greater prevalence rates of diabetes, hypertension and stroke

• Private insurance coverage negatively associated with the poor health measures, while public health coverage positively associated with the poor health measures for Chicago

• For Toronto neighbourhood populations, native-born and North American ancestry were negatively correlated with the poor health measures, while foreign-born, Asian origins and ancestry were positively associated with the poor health measures

• Opposite patterns were observed for Chicago communities: native-born and North American ancestry were positively related to the poor health measures, and foreign-born, Asian origins and ancestry were negatively associated with the poor health measures

• South American and African origins and ancestry were positively related to the poor health measures; both cities also had negative relationships between European ancestry (including subgroups) and the poor health measures

• The general relationships between the ethic variables and the poor health measures are consistent with the latest immigration trends for both cities

• Several contextual social factors were identified as significant correlates and predictors for the poor health measures for Toronto neighbourhoods and Chicago communities, which have been acknowledged as significant social determinants in previous studies (Grigsby-Toussaint et al., 2010; Koton et al., 2012; Robbins et al., 2005; Sims et al., 2011)