Race and Health
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When Hurricane Katrina struck New Orleans a couple months ago the citizens of this country were bombarded with questions on race playing a responsibility of the survivors that were stranded for days to soon be plucked from their rooftops. These people who were living paycheck to paycheck did not have the same resources of the wealthy. They most likely had no insurance, no stable extended family that could lend them a house to stay or even a job to return to after the storm. The majority of New Orleans is African American and also in poverty and although it has been an uncomfortable subject to discuss it seems that if these people would have had the help to flee the city or been able to afford transportation to escape their health conditions might not have been so dire.
Behind it all there lays health, race and income. The person who earns the most, wins right? Socioeconomic status determines our place in life and if the rules are not broken, the path is fairly predictable. The factor of money seems to not only promote a better education, but also better-quality health care, neighborhoods and sanitation. To better determine how health, race and income are correlated I analyzed these three concepts. The independent variable used was combined race/ethnicity records. The dependent was their overall health conditions. Finally, I used income as a control variable to judge how much of a factor income plays on physical conditions.
Measurement
For this analysis my independent variable was race which included three groups of Hispanic, Non-Hispanic White and Non-Hispanic Black. The dependent variable I applied was overall health status with a rating of 4 being interpreted as fair health and a rating of 5 translating to poor health. The controlled variable analyzed was income of $20,000 or more and $20,000 or less. Each of these groupings varied in sample size. For the $20,000 or more category combined people measured for Hispanic was 1,246,519; for Non-Hispanic White it was 6,598,138 and finally for Non-Hispanic Black 1,185,431 were measured. For the controlled variable for $20,000 or less category combined people measured for Hispanic was 944,946; for Non-Hispanic White it was 3,220,455 and for Non-Hispanic Black 1,321,714 were questioned.
When examining these measurements it is observed that more whites were looked at then Hispanics or Blacks. When I give my interpretation I must then try to equal out the amount that was tested to be fair. To do so, I need to use a ratio to make sure that each sample is measured appropriately. For example, on the sample for the independent and dependent variables measured in the row total for Hispanics were 2,191,465. I then divided 80,000 by 2,191,465 to receive .037. To then get a percentage I multiplied this amount by 100 to receive a percentage of 3.6%. I had to proceed with this task in order to receive