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Get Help Now!A generation ago, people used to see their doctor only when they were sick or dying. Today, preventative health care is becoming commonplace as people become more educated and empowered about their own health. Regular, routine medical check-ups can help Xnd potential health issues before they become a problem. Early detection of problems gives the best chance for getting the right treatment quickly, avoiding any complications.
You have been employed as part of an active public health campaign that is aiming to increase routine 12- monthly check-ups. Your job is to identify groups of people with lower rates of check-ups in the last 12 months where a targeted campaign would be of most beneXt.
The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project between all of the states in the United States (US) and participating US territories and the Centers for Disease Control and Prevention (CDC). The BRFSS is a system of ongoing health-related telephone surveys designed to collect data on health-related risk behaviours, chronic health conditions and use of preventive services from the non-institutionalised adult population (?18 years)residing in the United States. Using the prepared BRFSS dataset (found on the Assessment page) identify demographic, social and behavioural factors that are associated with routine check- up.
The BRFSS:
A short summary of the study design of the BRFSS and a brief discussion of its limitations
Description of the population:
- Create a table of 5 demographic factors by routine check-up attendance of a range of data types.
- Each cell should contain the appropriate summary measures and 95% confidence interval. The final column in the table should contain the p-value for a hypothesis test of difference or independence. Footnotes should be used to indicate which statistical tests were used.
- Analysis of social and behavioural factors:
- In an appropriate manner, present the results of analysis into the effect of social and/or behavioural factors on attendance.
For each factor you should report:
- Variable name and data type
- Name of measure calculated
- Results of statistical analysis performed
Statistical interpretation:
For one of the identifies factors, you should explore the possibility of confounding by at least one other factor.
- Create a diagram to justify your choice of potential confounder
- Perform appropriate analysis
- Interpret your result
- Statistical justifcation:
For one of each type of measure / test statistic calculated provide:
a. Justifcation for use of that measure / test statistic
b. An overview of the inherent assumptions
c. The Stata output
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