PART 1: EXAMINE RELATIONSHIPS BETWEEN HEALTH CARE UTILIZATION & HEALTH

1. Use the Polit2SetB dataset to answer the following research question:
   Are there relationships between health care utilization (# OF VISITS TO MD IN PAST 12 MONTHS), weight (BODY MASS INDEX), physical health (SF-12 PHYSICAL HEALTH SCORE) and mental health (SF-12 MENTAL HEALTH SCORE) among low-income women?
   2 In the dataset, identify the four variables that you will need to answer the question. Look carefully at the variable names and variable labels to select the correct variables. Refer to the codebook (Canvas, under Class databases) for details about the variables and their characteristics as needed.
   3 Note: The SF-12 is a brief, widely used measure of health status based on participants’ self-report. For both the SF-12 physical health and SF-12 mental health subscales, higher scores represent better health.
   4 Run a correlation analysis to examine the relationship between these 4 variables. Put all 4 variables into the analysis at the same time. Then, answer the following questions.

QUESTION ANSWER: FILL IN THE BLANKS
1a. Which two variables have the strongest correlation in the matrix? Interpret this correlation. There is a (statistically significant/not statistically significant) correlation between VAR1 and VAR2 (r= ?). Higher values of VAR1 is correlated with (higher or lower) scores on VAR2.

2a. Which two variables have the weakest correlation in the matrix? Interpret this correlation. There is a (statistically significant/not statistically significant) correlation between VAR1 and VAR2 (r= ?). Higher values of VAR1 is correlated with (higher or lower) scores on VAR2.

PART 2: ANALYZE RELATIONSHIPS BETWEEN EMPLOYMENT STATUS, HEALTH-RELATED CHARACTERISTICS, AND HEALTH

1. Use the Polit2SetB database to answer the following research question:
   In this sample of low-income women, are there relationships between women’s current employment status [WORKNOW] and their health-related characteristics, such as having no health insurance [NOINSUR], health condition limits ability to work [HLTHLIMIT], and self-report of being in fair or poor health [HEALTH]?
2. Examine the level of measurement for these variables. Run the appropriate statistical test to examine the relationships between these variables. Put all four variables into the analysis at the same time. Identify the statistics from your SPSS output that are needed to complete the following table.
3. Fill in Table 2 on this worksheet (type in the blank numbers in the body of the table) with the results. Then summarize and interpret the results by filling in the blanks in the paragraph template below.
4. Tips:
   a. n (%) indicates that you need to provide number and percentage, not just percentage.
   b. Correctly choose the row percentage or the column percentage to complete this table. The question that you are answering is this: How many and what percent of non-working women and of working women had the health-related characteristic listed in the left column.

Table 2. Relationship between Women’s Employment Status and Health Characteristics (N=1,000 a)

Health-related Characteristic Total Number of Women
n (%) Current Employment Status
Non-working
n (%) Working
n (%)
Having no health insurance 178 (17.9) n (%) 101 (22.4%)
Health condition limits ability to work 312 (31.3) 231 (42.4%) n (%)
Fair or poor health 286 (29.8) n (%) 95 (21.9%)
a The overall sample was N=1,000. However, the total number of women in each separate analysis varied from 960 to 998, reflecting missing data for some variables.

INTERPRETATION OF TABLE 2 USING THE TEMPLATE PROVIDED BELOW:
Women who worked outside the home reported (greater or fewer) health problems than those who were not employed. Non-working women were more than (how many) times as likely as employed women to say they had a health condition that limited their ability to work, (insert percentage for non-working women and percentage for working women, respectively. Non-working women were (more or less) likely to report their health as fair/poor than working women (insert percentage for non-working women) and (insert percentage for working women), respectively. Non-working women were, however, (more or less) likely to say they had no health insurance (insert percentage) than employed women (insert percentage), perhaps reflecting the fact that women who worked outside the home (provide possible explanation related to health insurance).

PART 3: GENERATE CROSS TABULATION AND CALCULATE RISK INDICES IN SPSS

1. Use the Polit2SetB database to answer the following research question:
   In a sample of low- income women, does age at first birth increase the risk of being in poverty?
2. Use the following variables:
   poverty (categorical variable: 1= below the poverty line, 2= above poverty line)
   age1bir (continuous variable: age at first birth, in years).
3. In order to compute the crosstabulation table and risk indices, both variables must be categorical. Recode the age1bir to convert it to a categorical variable with the correct 0-1 codes. Create a new variable called age1bir_rec.
4. Use SPSS to create a 2 X 2 crosstabs table to evaluate this relationship. Be sure to set up the crosstabs correctly. Poverty is the outcome variable – being below the poverty line is the negative outcome. Age at first birth (age1bir_rec) is the risk exposure variable – giving birth before age 18 is the “at risk/exposed to risk” category.
   ANSWER THE FOLLOWING QUESTIONS. WHERE APPROPRIATE, ROUND YOUR ANSWERS TO 2 DECIMAL PLACES.
   QUESTION ANSWER
   3a. What is the absolute risk of living below poverty level for women who gave birth before age 18?

3b. What is the absolute risk of living below poverty level for women who gave birth when they were 18 years of age or older?

3c. What is the relative risk (RR) of being in poverty for women who gave birth < 18 years versus > 18 years?

3d. Write one sentence to interpret the RR of being in poverty in this sample.

PART 4: INTERPRET RESULTS IN PUBLISHED ARTICLE
Below is an excerpt from the Aiken article referenced in the lecture. Reference is posted in Canvas. Focus on the Failure to Rescue portion of the table.

USE THE COLUMN LABELED “ESTIMATED JOINTLY AND ADJUSTED OR” TO ANSWER THE FOLLOWING QUESTIONS.

QUESTION ANSWER
4a. Which odds ratio(s) for the effects of nurse and physician variables on failure to rescue were not statistically significant (assuming statistical significance when p < .05)?
4b. Interpret the odds ratio for the relationship between having a board-certified surgeon on staff on failure to rescue, using the provided template. Having a board-certified surgeon on staff reduced the odds of failure to rescue by (what percentage). For 95% of samples, having a board-certified surgeon will be associated with (higher/lower) odds of failure to rescue by 0.68 – 0.94. This means a (what percent) reduction in odds of death at best, and a (what percent) reduction in odds of failure to rescue at worst.

SUBMIT YOUR ANSWERS:

1. COMPLETE the assignment, using this document as a worksheet.
2. SAVE THIS COMPLETED WORKSHEET AS A WORD FILE, using the class file naming convention: YOURLASTNAME_NGR 6840_assignment_3.doc
3. SAVE THE OUTPUT FILE, using the class file naming convention:
   YOURLASTNAME_NGR 6840_assignment_3.spv
4. Answer questions in the Assignment 3 link about your results.
5. UPLOAD BOTH THE SPSS OUTPUT AND THE COMPLETED Word document in the Assignment 3 link.