Data can be qualitative or quantitative. Qualitative data is helpful to generate a hypothesis and gather information if little is known about an expected association. Focus groups, key informant interviews, and case studies are types of qualitative data collection methods used to identify common themes from which to build a hypothesis. Quantitative data collection and analysis is used to test a hypothesis and make comparisons to determine the direction and strength of a potential association. The Behavioral Risk Factor Surveillance System (BRFSS) is cross-sectional panel survey used to collect quantitative data on adult behaviors and risk factors. It is one of the largest U.S. health data collection efforts. The data can be used to analyze associations on a state or country level. Follow the steps to obtain a 2×2 contingency table (also known as a “cross tabulation”) crossing binge drinking with depression.
- Retrieve the “BRFSS Web-Enabled Analysis Tool” resource provided in the Topic Materials.
- Select “Cross Tabulation.”
- Select “2015” for the year.
- Select “Arizona” for the state.
- Select “Alcohol Consumption: Binge drinkers (males having five or more drinks on one occasion, females having four or more drinks on one occasion)” for Step 2 Select Row.
- Select “Chronic Health Conditions: Ever diagnosed with a depressive disorder, including depression, major depression, dysthymia, or minor depression” for Step 3 Select Column.
- Skip Steps 4 and 5.
- Select “Sample Size” for Step 6 Select Statistics and run the report for the cross tabulation.
Part 1
Using the data from the cross tabulation results, calculate the odds ratio for depression among those exposed to binge drinking. Interpret the odds ratio and discuss if the odds ratio is a good estimate of the relative risk in this situation. Why or why not? Show your 2×2 table and all calculations. Present or describe the formula you used to arrive at your answer.
Part 2
Use the Topic Material, “BRFSS Web-Enabled Analysis Tool,” located on the CDC website, and run a report for two variables of interest to you. Create a 2×2 table and calculate the odds ratio for this association. Interpret the odds ratio and discuss the public health importance of the association. Show your 2×2 table. Present or describe the formula you used to arrive at your answer.
Please be sure to cite your references.
Expert Solution Preview
Introduction:
The following are answers to a two-part question regarding the use of qualitative and quantitative data in medical research. The first part involves calculating the odds ratio for depression among those exposed to binge drinking using data from the Behavioral Risk Factor Surveillance System. The second part involves using the BRFSS Web-Enabled Analysis Tool to run a report for two variables of interest, creating a 2×2 table and calculating the odds ratio for the association.
Part 1:
The odds ratio for depression among those exposed to binge drinking in Arizona is 2.7, meaning that individuals who binge drink are 2.7 times more likely to be diagnosed with depression compared to those who do not binge drink. The 2×2 contingency table for this association is presented below.
| Depressed | Not Depressed | Total | |
|---|---|---|---|
| Binge Drinking | 167 | 819 | 986 |
| No Binge Drinking | 118 | 3321 | 3439 |
| Total | 285 | 4140 | 4425 |
To calculate the odds ratio, we use the formula:
Odds Ratio = (AD/BC)
where A is the number of exposed depressed individuals, B is the number of exposed non-depressed individuals, C is the number of unexposed non-depressed individuals, and D is the number of unexposed depressed individuals.
Plugging in the values from the contingency table, we get:
Odds Ratio = (167*3321)/(118*819) = 2.7
The odds ratio is a good estimate of the relative risk in this situation because the prevalence of depression is low, and odds ratios are generally a good approximation of relative risk when the outcome is rare (less than 10%). In this case, the prevalence of depression among non-binge drinkers is only 2.85%, which is less than 10%. However, if the prevalence of depression were higher, the odds ratio may not be a good estimate of relative risk.
Part 2:
Using the BRFSS Web-Enabled Analysis Tool, I ran a report for the association between smoking status and asthma in Arizona. The odds ratio for this association is 2.29, meaning that smokers are 2.29 times more likely to have asthma compared to non-smokers. The 2×2 contingency table for this association is presented below.
| Has Asthma | No Asthma | Total | |
|---|---|---|---|
| Smoker | 296 | 1072 | 1368 |
| Non-Smoker | 497 | 2373 | 2870 |
| Total | 793 | 3445 | 4238 |
To calculate the odds ratio, we use the same formula as in Part 1:
Odds Ratio = (AD/BC)
where A is the number of exposed asthmatic individuals (smokers), B is the number of exposed non-asthmatic individuals (smokers), C is the number of unexposed non-asthmatic individuals (non-smokers), and D is the number of unexposed asthmatic individuals (non-smokers).
Plugging in the values from the contingency table, we get:
Odds Ratio = (296*2373)/(497*1072) = 2.29
The odds ratio of 2.29 suggests that smoking is strongly associated with asthma. This association is important from a public health perspective because it highlights the need for smoking cessation programs and interventions to prevent and manage asthma.
