This report details analyses and methodologies used to examine and visualize the spatial and nonspatial

Transcription

1 Analysis Summary: Acute Myocardial Infarction and Social Determinants of Health Acute Myocardial Infarction Study Summary March 2014 Project Summary :: Purpose This report details analyses and methodologies used to examine and visualize the spatial and nonspatial data relationships between Acute Myocardial Infarction (AMI) hospitalization rates, selected Social Determinants of Health (SDoH), and additional heart attack risk and behavioral factors (diabetes hospitalizations and adult cigarette smoking). This project investigated the premise that data analyses incorporating geo-spatial statistical methods can provide additional insight into understanding the distribution of data and interactions between data variables. This data analysis was completed in 2013 as a pilot project funded by the Environmental Public Health Tracking (EPHT) grant in which the Colorado Department of Public Health and Environment (CDPHE) has participated in, beginning in :: :: Goals Obtain and organize a consensus census-tract level database of Social Determinants of Health (SDoH) variables and additional risk factor variables of interest to examine community-level variation in the prevalence of Acute Myocardial Infarction hospitalizations Use selected geospatial statistical tools and spatial analysis methods to describe the socio-demographic characteristics of populations and risk factors in relation to the rate of Acute Myocardial Infarction hospitalizations at the census tract geography. Document the selected methods, tools, results, and maps in report format. :: Cardiovascular Disease and Acute Myocardial Infarction Cardiovascular disease including heart disease, stroke, and other vascular diseases is the leading cause of death in the United States. Each year, nearly 800,000 people die from cardiovascular disease, accounting for one in every three deaths. An estimated 67 million American adults have high blood pressure and 71 million American adults have high levels of low-density lipoprotein (LDL) cholesterol, the two leading risk factors for heart disease and stroke. About one of every six healthcare dollars in the United States is spent on treating cardiovascular disease. Annual US cardiovascular disease costs exceed $192.1 billion in direct medical expenses and $312.6 billion when indirect expenses are included. Lifestyle and genetics continue to be primary risk factors for cardiovascular disease. A heart attack, also called acute myocardial infarction, occurs when part of the heart muscle gets damaged or dies because it isn t getting enough blood. Heart attack hospitalization data can be used as an indicator of the cardiovascular disease burden, and to interpolate where disparities in cardiovascular disease exist, geographically. The 2011 Colorado statewide age-adjusted rate of heart attack hospitalizations was 21.3 per 10,000 persons. This report examined how population-level SDoH characteristics (e.g. poverty, race, educational attainment), diabetes hospitalization rates, and adult smoking prevalence influence the rate of heart attack hospitalizations, and provides additional insight into where these characteristics or risk factors may be more associated. Acute Myocardial Infarction Study Summary March 2014 page 1

2 :: Figure 1 Distribution of AMI Hospitalizations in Colorado Using census tract as our geographical unit, the map above (Figure 1) displays 5-Year age-adjusted hospitalization rates for Acute Myocardial Infarction (Data Source: Colorado Hospital Association), and illustrates the variation in heart attack hospitalizations across the state. The age-adjusted rate is grouped into four categories or quartiles based on its value and displayed on the map -- each quartile containing roughly the same number of census tracts. Census tracts in Colorado range in size from an entire county in rural areas to small neighborhoods in urban areas. Health outcomes and/or hospitalization data sets in Colorado are often aggregated to a county in order to display the data on a map or identify disparities across the state. This data analysis considers data at the census tract geography which can illustrate community-level patterns in the data. The above map depicts which census tracts rates have confidence intervals that are statistically higher than the state average by drawing a diagonal line across those census tract. Higher rates in census tracts that do not contain the diagonal line should be interpreted with caution as they may have been calculated using a small denominator and could be considered unstable. It should be noted that the hospitalization data used in this analysis was assigned a location based on the billing address of those patients admitted to participating hospitals with ICD-9 codes matching Acute Myocardial Infarction. Billing addresses were then geocoded to assign an accurate census tract ID. All of the addresses inside a census tract were then counted in order to calculate an age-adjusted hospitalization rate for each individual census tract. Acute Myocardial Infarction Study Summary March 2014 page 2

3 Data Included in the Analysis :: Social Determinants of Health Based on a literature review of research connecting specific socio-demographic and economic indicators to Acute Myocardial Infarction incidence, and through discussions with the CDPHE Chronic Disease prevention program, a core group of SDoH data variables were selected to include in this data analysis (see Table 2 below). Data for the 7 selected socio-demographic indicators were obtained directly from the 2010 U.S. Census and American Community Survey. These data were organized and grouped using 2010 Census Tract boundaries. :: Diabetes Hospitalization Data The age-adjusted Diabetes hospitalization rate (Data Source: Colorado Hospital Association) was also included in this data analysis as an additional risk factor for understanding the distribution of heart attack (AMI) hospitalizations. The ageadjusted Diabetes hospitalization rate was calculated for each individual census tract based on patient billing address. The hypothesis that this data closely models the geographic distribution and magnitude of heart attack hospitalization data was also examined as a component of this data analyses. :: Smoking Prevalence Also included in this data analysis, as an additional risk factor for understanding the distribution of heart attack (AMI) hospitalizations, was county-level adult smoking prevalence data, assigned to each census tract based on the county estimate. This 4-Year prevalence data represents the percent of adults aged 18+ years who currently smoke cigarettes and was assembled from the Colorado Behavioral Risk Factor Surveillance System (BRFSS) dataset. The inclusion of this data into the analysis assumed that every census tract inside of a county has the same adult smoking prevalence, whereas the other data included in this analysis represents an estimate specifically calculated for samples collected inside the census tract. Variables included in the Data Analysis: Health Outcome 01 Age-Adjusted Acute Myocardial Infarction Hospitalization Rate Social Determinants of Health 02 Percent of families living at or below poverty 03 Non-White, Percent of population 04 Percent of the population age 25 and over without a high school diploma 05 Percent of the population age 16 and over in the civilian labor force, Unemployed 06 Percent of the population living in urban areas 07 Percent of occupied housing units, With no vehicle available 08 Percent of occupied housing units, Renter occupied Diabetes Hospitalization Data 09 Age-Adjusted Diabetes Hospitalization Rates Smoking Prevalence 10 Percent of population, Smokes cigarettes (County-Level) Table 1 Acute Myocardial Infarction Study Summary March 2014 page 3

4 Methodology and Results :: Analysis Overview A data analysis framework was developed to understand the relationships and geographic relationships between the selected data variables (Social Determinants of Health, Diabetes Hospitalization Rate, and Adult Smoking Prevalence) and the age-adjusted AMI hospitalization rate. Specifically, the ArcGIS (ArcMap) 10.1 Spatial Statistics Toolset were used as data analysis tools. There were two goals to developing and applying this framework: 1) Describe the relationships between the outcome variable (AMI hospitalization rates) and independent variables (selected socio-demographic characteristics, diabetes hospitalization rates, and smoking prevalence estimates) and to describe and visualize local variation in these relationships between individual census tracts. 2) Document and communicate the applied methodologies and results from this analysis into a report including process flowcharts, maps, tables, and data visualizations. :: Spatial Analysis Steps The spatial analysis framework follows the five steps outlined below in Table 2: Spatial Data Analysis Steps Steps Exploratory Spatial Data Analysis Use What Does this Tell Us? Description Visualize patterns and understand the spatial distribution of all of the data variables included in the model (Moran s I, Cluster Analysis, Scatterplots) Independently, all of the socio-demographic, diabetes hospitalization rate, and smoking prevallence data exhibit clustering of similar values by census tract. Ordinary Least Squares Regression (OLS) Use What Does this Tell Us? This linear regression model (global) is used to assess the relationships between variables included in the model and to calculate coefficents representing the effect of each variable on the dependent variable. The results from the OLS Regression indicate that all of the variables included in the model are associated with the AMI hospitalization rate (p <0.05), except for these 3 variables: poverty status, vehicle access (mobility), and renter/owner occupancy. Coefficients representing the effect of each variable on the dependent variable were calculated. Identification of Hotspots Use The Getis-Ord Gi* statistic is used to understand where, and to what degree, clustering of high and low values are occurring for each variable in the model. Geographically Weighted Regression (GWR) What Does this Tell Us? Use What Does this Tell Us? There are significant hotspots of AMI hospitalization rates in the metropolitan Denver area, and northward along the front range into Larimer and Weld Counties. After identifying hotspots, the socio-demographic characteristics of hotspots vs. non-hotspots can be compared. The GWR model (local) describes relationships between the variables in the model and the AMI hospitalization rate, but unlike OLS, the GWR model fits a weighted regression equation to every census tract. GWR assess correlations between variables but also accounts for the spatial relationship between neighboring census tracts. Local coefficients were calculated for each census tract to describe the relationship between each variable and AMI hospitalization rate, after controlling for other variables. These coefficients were mapped to visualize the magnitude and geographic relationship between the age-adjusted AMI hospitalization rate, socio-demographics, diabetes hospitalizations, and adult smoking prevalence. Interpretation and Conclusion Using geo-statistical methods in conjunction with traditional statistics can provide Table specific 4 and informative results beyond traditional methods for visualizing and understanding the relationships between AMI hospitalization rates, selected SDoH data, diabetes hospitalization rates, and adult smoking prevalence. Table 2 Acute Myocardial Infarction Study Summary March 2014 page 4

5 :: Exploratory Spatial Data Scatterplot Scatterplot Percent of Population age 25+ without a High School Diploma Percent Unemployed Age Adjusted Rate of AMI Hospitalizations Map of Families Living Below Poverty Map of Unemployment Age Adjusted Rate of AMI Hospitalizations Figure 2 Scatterplots are a way to visualize potential associations between data variables. The two scatterplots shown above in Figure 2 depict the relationship between two SDoH variables and the age-adjusted AMI hospitalization rate (for each census tract). A choropleth map for each independent variable was also created to show the distribution of the socio-demographic indicators, diabetes hospitalization rates, and estimated smoking prevalence. :: Ordinary Least Squares Regression OLS Resuts: AMI Hospitalizations and Socio-Demographic Indicators, Diabetes Hospitalizations and Smoking Prevalence Variable Coefficient Std Error Probability Robust Probability Intercept (01) Poverty (02) Minority Race/Ethnicity (03) Educational Attainment (04) Employment (05) Urban/Rural (06) Transportation/Mobility (07) Renter/Owner (08) Diabetes (09) Smoking Akaike s Information Criterion (AICc): Adjusted R-Squared: Koenker (BP) Statistic: (p> ) Jarque-Bera Statistic: (p> ) Table 3 The Ordinary Least Squares regression (Table 3) included the Age-adjusted AMI Hospitalization Rates as the dependent variable, and Poverty (01), Minority Race (02), Educational Attainment (03), Employment (04), Urban/Rural (05), Transportation/Mobility (06), Renter vs. Owner (07), Age-Adjusted Diabetes Hospitalizations (08), and Adult Smoking Prevalence (09) as the independent variables. Based on the OLS model, the percentage of the population that is of minority race/ethnicity (02), percentage of population (25 years and over) without a high school degree (03), percentage of the population unemployed age 16+ in the civilian labor force (04), percentage of the population Acute Myocardial Infarction Study Summary March 2014 page 5

6 living in urban areas (05), age-adjusted diabetes hospitalization rates (08), and estimated county smoking prevalence (09) are all significantly associated with age-adjusted AMI hospitalization rates. This model has an adjusted R² of 0.455, which tells us that it accounts for 45.50% of the variability in the age-adjusted AMI hospitalization rate. The Koenker and Jarque-Bera Statistic were significant (p<0.0000) indicating the normal probability should be used, and that the residuals are not normally distributed, detecting spatial dependency in the OLS model. Based on these characteristics, the Geographically Weighted Regression (GWR) model may help explain the spatial dependency occurring in the OLS model. The coefficients resulting from the OLS regression model can be used directly in reporting on the burden of this health outcome. A 1.0% increase in the percentage of the population of minority race/ethnicity was associated with a 1.0 per 100,000 persons decrease in the AMI hospitalization rate. A 1.0% increase in the percentage of population (25 years and over) without a high school degree is associated with a 1.3 per 100,000 persons increase in the AMI hospitalization rate. A 1.0% increase in the percentage of population unemployed (age16+ in the civilian labor force) is associated with a 2.7 per 100,000 persons increase in the AMI hospitalization rate. Finally, a 1.0% increase in estimated adult smoking prevalence is associated with a 1.8 per 100,000 persons increase in the AMI hospitalization rate. These coefficients represent effects for all of the census tracts in Colorado, and are not specific to one indiviudal census tract or community. After interpreting the OLS regression output, a Moran s I Test for Spatial Autocorrelation was performed on the residuals from the OLS model, in order to detect if the residuals are clustered. Clustering of the residuals was detected (Figure 3), which indicates that moving to a Geographic Weighted Regression (GWR) may be beneficial in analyzing the data. Test for Spatial Autocorrelation Ordinary Least Squares Regression: Moran s I , z-score 6.92, p< A positive value for Moran s I indicates that a feature has neighboring features with similarly high or low attribute values; this feature is part of a cluster. :: Getis-Ord Gi* Statistic Cluster Analysis Figure 3 Cluster analysis using the Getis-Ord Gi* statistic in ArcGIS 10.1 was able to identify census tracts with statistically significant clusters of higher and lower AMI hospitalization rates. The resultant z-scores and p-values define where features with either high or low values cluster spatially. This tool takes into account each census tract within the context of neighboring census tracts. A census tract with a high AMI hospitalization rate may not be a statistically significant hot spot. To be a statistically significant hot spot, a census tract assigned a high AMI hospitalization rate must also be surrounded by neighboring census tracts with high values. Acute Myocardial Infarction Study Summary March 2014 page 6

7 Significant Clusters of High and Low AMI Rates in Colorado (Census Tract) Figure 4 The map above (Figure 4) displays the result of the Hotspot Analysis Rendering tool (Getis-Ord Gi* Statistic). Given the location of census tracts with their age-adjusted AMI hospitalization rates, the tool calculated the Getis-Ord Gi* statistic which was used to identify statistically significant hot spots and cold spots. The map displays in red the census tracts that are identified as hot spots (Z-Scores of 1.65 or higher). No cold spots were identified. A Moran s I test for Spatial Autocorrelation can also be performed on the AMI hospitalization rates, and the results are shown at right in Figure 5. Given the location of census tracts and their age-adjusted AMI hospitalization rates, this statistic found the pattern of age-adjusted AMI hospitalization rates across Colorado to be clustered. Figure 5 Acute Myocardial Infarction Study Summary March 2014 page 7

8 Comparison of Variables in AMI Hot Spots vs. Non-Hot Spot (Census Tracts) Variables Statistically Significant Hot Spots of AMI Rates Not a Statistically Significant Hot Spots of AMI Rates Not Included in Study Area Crude Values No. of Census Tracts 24 1, AMI Hospitalizations , Population (2010) 96,463 4,826, ,651 Percent by Census Tract Families Below Poverty Minority Population without a High School Degree Unemployment Urban Population Transportation/Mobility Renter/Owner Adult Smoking Prevalence Diabetes Hosp. Rate Table 4 ::SDoH, Diabetes Hospitalization Rate, and Adult Smoking Prevalence of Hot-Spots One way of examining the characteristics of those who live in areas of high AMI hospitalization rates, is to spatially define the AMI hotspots which are census tracts that age-adjusted AMI hospitalization rates that are statistically higher than the state average AND have a Getis-Ord Gi z-score in the 95th percentile. Based on the AMI hospitalization data used in this analysis, here were 24 census tracts in Colorado (out of 1249) that were identified as hotspots. Within these hotspot census tracts, trends in the SDoH, diabetes hosp. rate, and adult smoking prevalence variables can be identified and compared to census tracts that are outside of defined hotspots. Table 4 above compares the socio-demographic characteristics of hotspots to non-hotspots. Comparing these characteristics helps us to contrast and understand the disparities in socio-demographics, diabetes hospitalization rates, and adult smoking prevalence within communities known to have higher AMI hospitalizaton rates. It is evident that the hotspot population is defined by lower socio-demographic characteristics, higher diabetes hospitalization rates, and higher adult smoking prevalence. Acute Myocardial Infarction Study Summary March 2014 page 8

9 :: Geographically Weighted Regression Geographically Weighted Regression (GWR) is a linear regression model used to develop local coefficients. GWR creates local statistics that generally reduce spatial autocorrelation, increase model fit, increase R2,, and allow parameter estimates to vary geographically. Whereas the OLS model assigned one coefficient for each variable representing its effect on the AMI hospitalization rate across the state, the GWR model assigned local coefficients for each census tract vary based on the weighted values of neighboring census tracts. GWR coefficients are estimated based on a weighting scheme that considers neighboring observations more of an influence than those further away. Similar to a least squares regression, the parameter estimate represented a one unit change in the independent variable that will lead to a corresponding change in the dependent variable. Only those variables that were identified as statistically significant from the OLS Regression model output are examined in the Geographic Weighted Regression model. Geographic Weighted Regression Results: Bandwidth: Up to 743 Neighbors Variable Coefficient Range (02) % of the Population that is of Minority Race/Ethnicity (03) % of the Population Without a High School Degree (25+) (04) % of the Population Unemployed (16+ in Civ. Labor Force) (05) % of the Population that Lives in Urban Areas (08) Age-Adjusted Rate of Diabetes Hospitalizations (09) % of Adults who Currently Smoke Cigarettes Akaike s Information Criterion (AICc): Adjusted R-Squared: 0.48 *AICc values indicates improved model fit versus OLS Model Spatial Autocorrelation Results: Geographically Weighted Regression: Ordinary Least Squares Regression: Moran s I , z-score 2.09, p< Moran s I , z-score 6.92, p< Table 5 The GWR results, shown above in Table 5, indicate that the location of the census tract in Colorado also affects the magnitude association of the variables with age-adjusted AMI hospitalization rates. The GWR model indicated an improved model fit versus the OLS Model as the Adjusted R-Square value improved from 0.45 to Although these models both indicate significant clustering of residuals, there is still a reduction in spatial clustering compared to the OLS model. This clustering in the residuals from the GWR indicates that there may be variables not included in the model that account for the spatial autocorrelation in the residuals. GWR coefficients indicated a protective factor for some variables that are known not to be protective of cardiovascular disease and AMI hospitalizations. This is observed above in the coefficients for adult smoking prevalence. Based on what is known about cigarette use as a risk factor for cardiovascular disease, it is unlikely that smoking is protective. As observed in many GWR models, one variable can exhibit such a strong association with the outcome, that other variables in the model will appear to have an effect opposite of what is expected. This is especially the case when the variable with a weaker association may have little presence in a census tract where the variable with a stronger Acute Myocardial Infarction Study Summary March 2014 page 9

10 :: Mapping the Coefficients from the GWR The maps below depict the range of coefficients for each independent variable included in the GWR model, showing the association between independent variables and the age-adjusted AMI hospitalization rates, adjusted for the other independent variables in the GWR model. Areas of darker color represent a greater association between the variable and AMI hospitalization rate. These maps can be used in targeting specific risk factors for AMI depending on location of a census tract within the state of Colorado. For every one unit change in the variables listed in Table 5, there is a corresponding change in the AMI hospitalization rate, as indicated by the coefficient value. 02 Minority Race/Ethnicity The census tracts or areas with darker color indicate where the minority race/ethnicity percent of population value has a greater association with the age-adjusted AMI hospitalization rate, after adjusting for the other 5 independent variables (educational attainment, unemployment, urban/rural, diabetes hospitalizations, and estimated smoking prevalence). 03 Educational Attainment The census tracts or areas with darker color indicate where the percent of the population with a high school degree value has a greater association with the age-adjusted AMI hospitalization rate, after adjusting for the other 5 independent variables (minority/ race, unemployment, urban/rural, diabetes hospitalizations, and estimated smoking prevalence). 04 Employment The census tracts or areas with darker color indicate where the percent of the population unemployed value has a greater association with the age-adjusted AMI hospitalization rate, after adjusting for the other 5 independent variables (minority/race, educational attainment, urban/rural, diabetes hospitalizations, and estimated smoking prevalence) Acute Myocardial Infarction Study Summary March 2014 page 10

11 05 Urban/Rural The census tracts or areas with darker color indicate where the percent of the population living in an urban area value has a greater association with the age-adjusted AMI hospitalization rate, after adjusting for the other 5 independent variables (minority/ race, educational attainment, unemployment, diabetes hospitalizations, and estimated smoking prevalence). 08 Diabetes Hospitalizations The census tracts or areas with darker color indicate where the age-adjusted diabetes hospitalization rate has a greater association with the age-adjusted AMI hospitalization rate, after adjusting for the other 5 independent variables (minority/race, educational attainment, unemployment, urban/rural, and estimated smoking prevalence). 09 Smoking The census tracts or areas with darker color indicate where the estimated percent of adults who smoke value has a greater association with the age-adjusted AMI hospitalization rate, after adjusting for the other 5 independent variables (minority/ race, educational attainment, unemployment, urban/rural, and diabetes hospitalizations). 08 Acute Myocardial Infarction Study Summary March 2014 page 11

12 Conclusion The results of this analysis indicate that race, educational attainment, unemployment, and urban/rural population are all significantly associated with the age-adjusted AMI hospitalization rate, along with diabetes hospitalization rate and adult smoking prevalance. Based on the results of the GWR and associated spatial analyses, these results vary in influence across Colorado. The utility of analyzing age-adjusted AMI hospitalization rates at the sub-county geography, using these geospatial statistical methods, is important in that it provides program professionals within our health department information on community-level disparities, and it increased awareness and visualization that not all SDoH and other risk factors (diabetes hospitalizations and smoking) are distributed evenly across the landscape of higher and lower AMI hospitalization rates in Colorado. Furthermore, this analysis and map products helps to increase the visualization and utility of Colorado Hospitalization Data in that a model for tying together health outcome with socio-demographic and risk factor data using geospatial statistical methods tools now exists. These results also indicate spatial clustering of age-adjusted AMI hospitalization rates as well as clustering of variables known to be risk factors for AMI. This data analyses and mapping project demonstrated the ability to analyze and visualize SDoH and health outcome data using accessible methodologies and tools through mainstream GIS software that many individuals, private sector, non-profit, and government agencies have access to. Geospatial methodologies in conjunction with Ordinary Least Squares (OLS) models can provide specific and informative results beyond non-spatial methods for social determinants of health data (SDoH). These results can indicate spatial clustering of health outcomes as well as clustering of variables known to effect health outcomes. This geographic information can provide substantial insight on the magnitude SDoH can have on health outcomes and the way they can interact across geography. When data and data relationships are visualized at the subcounty level, the results can provide a much more compelling story about a health outcome and the populations that are affected. This is partly because finer resolution geography (census tract) can better capture the true variation in SDoH, risk factor, or health outcome. Displaying data at the census tract level captures communities and populations in a way that allows people to engage more with the data because viewers are better able to identify trends within communities and neighborhoods. Maps allow individuals to instantly connect with the distribution, magnitude, and scale of a problem in a way that is more difficult with traditional data dissemination methods. These types of analyses begin to incorporate what all major health organizations have acknowledged for years: that social determinants of health are significant predictors of health outcomes. This project begins to address the top tier constructs in most health equity models in trying to understanding a health outcome by examining the influence of socio-demographics and/or risk factors. These types of data analyses can confirm the idea that where we live and our social/economic status can significantly influence our health behaviors and overall wellbeing, and that our healthy lifestyles and behaviors are linked to much broader ideas related to our environment, culture and social characteristics. Finally, the use of spatial analysis methods can help resolve many of the statistical issues that arise with data exhibiting spatial dependency. The use of GWR allows for spatial variation in the parameter estimates of the model, therefore reducing the spatial dependency in the model. In allowing these results to vary by census tract, we can get more accurate estimates of the variables that account for regional differences and variation in the population. Acute Myocardial Infarction Study Summary March 2014 page 12

13 Acute Myocardial Infarction Study Summary March 2014 page 13