Advanced Research Methods in Health Services

Lauren Silver

HPM 587 Fall 2009

PREDICTORS OF PARTICIPATION IN A DIABETES DISEASE MANAGEMENT PROGRAM

INTRODUCTION In a survey of medical directors of 65 health maintenance organizations, the three diseases most often targeted by disease management programs were diabetes, asthma, and congestive heart failure.1 Diabetes disease management programs, in particular, are on the rise.2 This should come as no surprise given that diabetes affects approximately 18 million Americans and contributes substantially to rising health care costs.3 Thus, most diabetes disease management programs focus on how to improve diabetes outcomes, particularly by teaching participants how to manage their own condition via self-care techniques.4 Indeed, a recent analysis of the impact of a national managed care organization’s diabetes disease management program—entailing repeated telephone outreach by nurses, dietitians, and health educators as well as web- and mail-based reminders and education—found that the program was associated with 1) lower overall costs of care within one year at participating sites (22-30% decrease in hospitalizations), 2) a slight decline in physician office visits, and 3) improved diabetes-related HEDIS (Health Employer Data and Information Set) and non-HEDIS quality measures.5 Given the potential for diabetes disease management programs to improve diabetes outcomes, this study seeks to identify predictors of participation in these programs—who is most likely and least likely to participate? More specifically, both demographic and clinical correlates of participation in a diabetes disease management program are examined. By identifying predictors of participation, health plan outreach activities can be tailored so as to increase the

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Lauren Silver

HPM 587 Fall 2009

rates of participation among those least likely to partake, thereby narrowing disparities in diabetes health outcomes and improving outcomes, overall.

BACKGROUND A number of demographic and clinical characteristics are associated with diabetes patients’ level of self-care, including race, age, health literacy, disease duration, and use of medication, among others. Prior research has demonstrated that diabetes patients with the highest risk for complications are those least likely to self-monitor blood glucose levels.6 In a study of 4,565 adult, diabetic enrollees in an eastern Massachusetts managed care organization, Adams et al. found that the elderly, minorities, and those living in low socioeconomic neighborhoods were significantly less likely to self-monitor than their younger, Caucasian, and more well-to-do counterparts. Additionally, even among those taking insulin, the effect of socioeconomic status and age on self-monitoring persisted, however, minorities were more likely to self-monitor than whites if taking insulin.7 It also has been established that health literacy influences the ability of those with chronic conditions to self-monitor by posing barriers to educating patients about their condition. In a survey of 402 patients with hypertension and 114 patients with diabetes, Williams et al. found that “almost half of the patients…had inadequate functional health literacy, and these patients had significantly less knowledge of their disease, important lifestyle modifications, and essential self-management skills.”8 Thus, it follows that education level likely plays a significant role in whether diabetics will foresee as well as reap the benefit of participating in a disease management program. This study seeks to further confirm as well as expand on the literature

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Lauren Silver

HPM 587 Fall 2009

regarding predictors of diabetes self-management by examining participation in a diabetes disease management program. DATA SOURCE Data for this study came from the Centers for Disease Control and Prevention’s (CDC) 2008 Behavioral Risk Factor Surveillance System (BRFSS) survey, a cross-sectional telephone survey conducted by state health departments with technical assistance provided by the CDC. Only one adult—18 years or older—per household is interviewed in all 50 states, the District of Columbia, Puerto Rico, the U.S. Virgin Islands, and Guam.. Information is collected on health risk behaviors (e.g., diet and exercise), preventive health practices, health care access, chronic conditions, and sociodemographic characteristics. In the 2008 BRFSS, there was a total of 414,509 observations.

METHODS Dependent Variable The dependent variable examined in this study is whether a BRFSS respondent attended a class in managing diabetes, which was operationalized through the following survey question: “Have you ever taken a course or class in how to manage your diabetes yourself?” For the present study, this variable is a binary categorical variable coded as 0 for “no” and 1 for “yes.” In 2008, a total of 32,795 respondents provided either a “yes” or “no” answer to this question—17,933 responded “yes” (54.68%) while 14,862 responded “no” (45.32%). Thus, more than half of the respondents to which diabetes-related questions applied reported ever attending a diabetes disease management program in 2008.

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Lauren Silver

HPM 587 Fall 2009

Independent Variables The independent variables included in the model (Table 1) were chosen based on findings from the literature regarding the association between diabetes self-care and demographic and clinical characteristics. More specifically, predictors were chosen based on whether research to date has demonstrated a consistent association with participating in a disease management program and whether research to date has not fully established a relationship between an explanatory variable and participation (e.g., marital status as a proxy for social support). Below is the model examined.

DIABEDUi = f(RACE i, INCi, EDUi, AGE i, SEXi, EYEi, MARi, INSi, HPi) + ε, where RACEi = a five-category dummy variable representing the ith respondent’s reported preferred race (“Black or African American,” “Asian,” “Native Hawaiian or other Pacific Islander,” “American Indian or Alaskan Native,” compared to “White only” as the reference group). INCi = an eight-category dummy variable representing the ith respondent’s reported annual income (“less than $10,000,” “$10,000 to less than $15,000,” “$15,000 to less than $20,000,” $20,000 to less than $25,000,” “$25,000 to less than $35,000,” “$35,000 to less than $50,000,” and “$50,000 to less than $75,000” compared to “greater than or equal to $75,000” as the reference group) EDUi = a four-category dummy variable representing the ith respondent’s reported education level (“did not graduate high school,” “graduated high school,” and “attended college or technical school,” compared to “graduated college or technical school” as the reference group) AGEi = a six-category dummy variable representing the ith respondent’s age in years (“18 to 24,” “25 to 34,” “35 to 44,” “45 to 54,” and “55 to 64” compared to “65 or older” as the reference group) SEXi = a dummy variable equal to 1 if the ith respondent’s sex was male, 0 otherwise, with male as the reference group EYEi = a dummy variable equal to 1 if the ith respondent reported ever being told by a doctor that diabetes has affected his or her eyes or that he or she had retinopathy, 0 otherwise, with no as the reference group

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Lauren Silver

HPM 587 Fall 2009

MARi = a dummy variable representing the ith respondent’s marital status (“divorced,” “widowed,” “separated,” “never married,” and “member of unmarried couple” compared to “married” as the reference group) INSi = a dummy variable equal to 1 if the ith respondent reported he or she currently is taking insulin, 0 otherwise, with not taking insulin as the reference group HPi = a dummy variable equal to 1 if the ith respondent reported having current health insurance coverage, 0 otherwise, with having health insurance coverage as the reference group Statistical Analysis Given that the outcome variable of interest is binary (0,1), a probit analysis was performed using the dprobit command in STATA in order to calculate average/partial effects for each of the categorical independent variables. Thus, partial effects were calculated for each independent variable while all other independent variables were held at their sample mean. Hypothesized Signs of the Coefficients Prior to conducting the analysis, the directions of the estimated coefficients for each independent variable were hypothesized. For the demographic characteristics, it was hypothesized that females, whites, and those who are married or younger would be more likely to participate in a diabetes disease management program. Thus, relative to their respective reference groups, a negative coefficient should appear for the various categories of RACEi and MARi while a positive coefficient should appear for AGEi and SEXi. Furthermore, higher levels of income, higher levels of education, and health insurance coverage were expected to correspond to a higher likelihood of participation. Relative to their respective references groups, EDUi, INCi and HPi should exhibit negative coefficients. For the clinical characteristics, those taking insulin and those who had been told by a doctor that their diabetes has affected their eyes or contributed to retinopathy were expected to participate more. Thus, relative to their respective reference groups, positive coefficients should emerge for EYEi and INSi.

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Lauren Silver

HPM 587 Fall 2009

RESULTS Descriptive Statistics Table 1 presents the frequency and percentage of respondents falling into the various categories of the independent variables examined. Table 1: Descriptive Statistics for Demographic and Clinical Predictors, BRFSS 2008 Independent Variable (n) Frequency (%) Race (31.252) White Black or African American Asian Native Hawaiian or other Pacific Islander American Indian or Alaskan Native Income (28,105)