Premature Mortality

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April 24, 2012 Article Summary Geographic and Racial Variation in Premature Mortality in the U.S.: Analyzing the Disparities Author: Cullen R. M, Cummins C,; Fuchs R. V Purpose The article, “Geographic and Racial Variation in Premature Mortality in the U.S.: Analyzing the Disparities” was written to identity the high mortality rates for African American ( men and women) and the decreased mortality rates amongst Caucasians ( men and women). An important key element for the study was the use of an ecologic model of premature mortality- death before the age of 70. Researchers of this study have discovered that racial and geographical living created various reasons for excess mortality: education, income, employment, climate change,…show more content…
Estimations for mortality rates are based on five- and ten-year interval ranging from birth to age 70. Since life expectancy ( LE) represents events very early or late in life, S70, represents age of death, and main measurement for mortality rates in the 40's, 50's and 60's. Researchers designed S70 for each sex-race group in every county, however Compressed Mortality Files (CMF) requires a minimum of 2000 total sub population in each area. Due to rules and regulations for privacy the Census defines Public Use Microdata Areas (PUMAs) are intended to capture 100,000+ total population areas. The low density areas of the study were clumped together while high-density counties were sub-divided. In order to maxim coverage researchers created their own area units that matched the CMF and Census geographic definitions. Researchers of the study used single counties representing a possible or matching groups because contiguous counties were already grouped into Public Use Microdata Areas( PUMAs). The result is 510 areas covering 73 percent of the white and 96 percent of the black populations. They include 268 single counties and 242 groups of contiguous…show more content…
Black-white differences are greater for males than females, and accordingly, male-female differences are greater for blacks than whites. 4.) There are 22 predictor variables( Prof & Mgr, income, inpov, propvalue, gini prop, b/w income, single, noncitizen, current smoker, former smoker, obesity, uninsured, fruits & veg, physical active, chol checked, Chol Chk & Obes, Metro, Partmetro, South, Growth, Fast Food, Betablocker, JanTemp, July Temp, PM 2.5 % Black, % Stateblack, Intercept ) for the four sex-race groups in each of the 510 counties. 5.) Similarities can be seen between men and women of each race, but race differs. 6.) Use of regression model to examine results and the race differences relating to differences in the predictor variables 7.) Race differences in S70 for county level: −2.4% (+/−2.4) for men, −3.7% (+/−2.3) for women. 8.) If the study were reversed, the result is the same; black values for the predictor variables are substituted for white values in the white regression model, the curves for predicted white males (or females) resemble their black
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