This research paper examines the association between race and coronary heart disease using retrospective analysis of the National Inpatient Sample database from 2019-2020. The study employs statistical methods including one-way ANOVA to compare cholesterol levels across racial groups and linear regression to assess the predictive strength of race on cardiac disease risk. The methodology demonstrates rigorous epidemiological approaches to investigating health disparities in cardiovascular outcomes among diverse populations.
12 January, 2024.
RQ: Is there an association between race and coronary heart disease?
Null Hypothesis: There is no association between race and ethnicity and coronary heart disease.
Alternate Hypothesis: There is an association between race and ethnicity and coronary heart disease.
Statistical Test: To be determined.
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The study seeks to determine whether there is a significant association between one’s race and their risk of getting coronary heart disease. The target population is adults (aged 18 and over) living in the United States. The study participants are adults admitted to US community hospitals, including general, short-term, non-federal, and other specialty hospitals, for diverse health-related complications between 2019 and 2020. The researcher will conduct a retrospective analysis of secondary data obtained from the National Inpatient Sample (NIS) for years 2019 to 2020. The NIS is the largest inpatient care database in the US, which incorporates all payers and draws from state inpatient databases to cover over 7 million in-patient hospital stays across 48 states and the District of Columbia. This makes the data highly representative of the US population’s public health status.
Studies have found high cholesterol levels to be a significant risk factor for coronary heart disease. Thus, total blood cholesterol will be used to predict the risk of coronary heart disease in this study. A one-way analysis of variance will be used to compare means in total blood cholesterol levels among racial categories. The analysis of variance is appropriate for comparing means across groups when the independent variable is categorical, while the dependent variable is continuous. The dependent variable, total blood cholesterol, is measured as a ratio variable, where values 240 represent high levels. The independent variable, race, is represented by six categories as follows: 1 (white), 2 (Black), 3 (Hispanic), 4(Asian or Pacific Islander), 5(Native American), and 6 (Other). Invalid responses will be eliminated from the study. Simple linear regression will be used to test the strength of the association, or how well race predicts the risk of coronary heart disease. Linear regression is appropriate when the dependent variable is continuous in nature, while the independent variable is either continuous or categorical as is the case with the variables in this study. The variable race will be coded as a dummy variable in the regression, in which case non-weighted numerical values will be assigned to represent different racial categories.
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