1 Preventive Care Use in Males with Multiple Sclerosis Sherri L. LaVela, MPH, MBA Department of Veterans Affairs, University of Illinois at Chicago, School of Public Health Academy Health Meeting/June 10, 2008

2 Background MS – chronic, potentially debilitating disease ~400,000 individuals in the US Normal life expectancies

3 Problem/significance Premature aging? Affected by chronic diseases (+ MS) Health promotion/disease prevention lacking

4 Study Aims To compare the use of preventive health services by male veterans w/MS with that of non-MS male cohorts To identify variables associated with preventive service use by males with MS.

5 Methods Design. Cross-sectional survey. Data Sources: 1) Multiple Sclerosis Health Care Questionnaire (MS-HCQ), (created w/BRFSS items) & conducted with MS veteran cohort. 2) Behavioral Risk Factor Surveillance System (BRFSS) survey data, downloadable from Centers for Disease Control and Prevention (CDC) to use for comparison. Sample: Three cohorts of males include: veterans with MS general veteran population general population

6 Methods/Analyses Demographics and Health Behaviors (age, race, education, marital status, employment status, geographic area of residence, health behaviors, tobacco use, alcohol consumption) Descriptive, (chi-square or t-tests) comparisons between groups MS vs. general veteran MS vs. general population

7 Methods/Analyses Use of preventive services (cholesterol checked, routine dental visit, influenza vaccination, pneumonia vaccination, colon screening, prostate screening) Bivariate analyses: Proportions of users: MS vs non-MS groups Chi-square or t-tests Multivariate analyses: Variables associated with use of preventive service (dichotomized 1=received, 0=did not) Separate logistic regression models (adjusted for potential confounders: demographics, health behaviors, & physiological conditions)

8 Characteristics of Males 50 Years of Age and Older MS (n=962) General veteran c (n=25055) P value a General population c (n=21316) P value b White (%) <0.001* <0.001* Mean age (years) <0.001* <0.001* Did not graduate high school (%) <0.001* <0.001* Employed for wages (%) <0.001* <0.001* Married (%) Region of residence (% South) <0.001* <0.001* Current smoker (%) * <0.001* Chronic drinker (%) * * Mean age at diagnosis (years) yrs n/a Mean duration of MS (years) yrs n/a a P values for male veterans with MS versus males in the general veteran population. *0.01; **0.05 b P values for male veterans with MS versus males in the general population. c Frequencies and p values calculated using CDCs weighted formula.

9 Use of Preventive Health Care Services by Male Participants a MS % (n) General veteran population b % (n)P value c General population b % (n)P value d e Cholesterol checked within the past 5 years : Men aged > 20 years Men 35 and older (1128) (1122) (31359) (29816) <0.001* (66051) (47488) <0.001* f Dental care within past year68.64 (1142)68.56 (5904) (12281)0.31 g Influenza vaccination > 50 years; within past year (962)57.83 (25055)<0.001*42.07 (21316)<0.001* h Pneumonia shot > 65 years; ever received80.93 (386)67.20 (13528)<0.001*51.49 (6078)<0.001* i Colon screening in persons > 50 years within past 5 years within past 10 years (962) (962) (4740) (4740) <0.001* (4084) (4084) <0.001* j Prostate screening > [45-70 for African American men] PSA past year DRE past year PSA and DRE past year (717) (717) (717) (1977) (1977) (1977) 0.03** 0.05** <0.001* (2475) (2475) (2475) 0.004* 0.009* 0.39 a Item response rates for each variable in this table were 93% or greater. *0.01; **0.05 b Frequencies and p values calculated using CDCs weighted formula. c P values for male veterans with MS versus males in the general veteran population. d P values for male veterans with MS versus males in the general population.

10 Multivariate Logistic Regression: Variables Associated With Receipt of Cholesterol Check in Male Veterans with MS Aged 35 Years or Older (n=1069) OR 95% CISEP value Age ** White <0.001* Did not complete high school Married Employed for wages Living in the south ** Current smoker Chronic drinker Presence of physiological conditions <0.001* -2 Log L=478.26; chi-square=38.69, p< * 0.01 ** 0.05 a Reference groups: age is continuous; white (vs. non-white); did not complete high school (vs. completed GED, high school or more education); married (vs. not married); employed for wages (vs. not employed); living in the South (vs. West, Midwest, or Northeast); current smoker (vs. past/never smoker ); chronic drinker (vs. non-drinker or occasional drinker); and physiological conditions (# of conditions present of asthma, diabetes, hypertension, high cholesterol, coronary heart disease, stroke, and prostate cancer).

11 Multivariate Logistic Regression: Variables Associated With Receipt of Influenza Vaccination in Male Veterans with MS Aged 50 Years or Older (n=922) * 0.01 ** 0.05 a Reference groups: age is continuous; white (vs. non-white); did not complete high school (vs. completed GED, high school or more education); married (vs. not married); employed for wages (vs. not employed); living in the South (vs. West, Midwest, or Northeast); current smoker (vs. past/never smoker ); chronic drinker (vs. non-drinker or occasional drinker); and physiological conditions (# of conditions present of asthma, diabetes, hypertension, high cholesterol, coronary heart disease, stroke, and prostate cancer). OR 95% CISEP value Age * White Did not complete high school Married Employed for wages Living in the south Current smoker * Chronic drinker Presence of physiological conditions Log L= ; chi-square=37.54, p<0.001

12 Multivariate Logistic Regression: Variables Associated with Receipt of Pneumococcal Polysaccharide Vaccination in Male Veterans with MS Aged 50 Years or Older (n=356) OR 95% CISEP value Employed for wages ** -2 Log L=348.96; chi-square=3.98, p=0.05 * 0.01 ** 0.05 a Reference group: employed for wages (vs. not employed)

13 Multivariate Logistic Regression: Variables Associated With Receipt of Colon Screening In Male Veterans With MS Aged 50 Years or Older (n=914) * 0.01 ** 0.05 Reference groups: age is continuous; employed for wages (vs. not employed); current smoker (vs. past/never smoker ); chronic drinker (vs. non-drinker or occasional drinker); and physiological conditions (# of conditions present of asthma, diabetes, hypertension, high cholesterol, coronary heart disease, stroke, and prostate cancer). OR 95% CISEP value Age * Employed for wages Current smoker Chronic drinker Presence of physiological conditions Log L= ; chi-square=13.53, p=0.02

14 Multivariate Logistic Regression: Variables Associated with Receipt of Prostate Cancer Screening in Male Veterans with MS Aged (45-70 AA) (n=677) * 0.01 ** 0.05 a Reference groups: age is continuous; white (vs. non-white); did not complete high school (vs. completed GED, high school or more education); married (vs. not married); employed for wages (vs. not employed); living in the South (vs. West, Midwest, or Northeast); current smoker (vs. past/never smoker ); chronic drinker (vs. non-drinker or occasional drinker); and physiological conditions (# of conditions present of asthma, diabetes, hypertension, high cholesterol, coronary heart disease, stroke, and prostate cancer). OR 95% CISEP value Age * White Did not complete high school Married Employed for wages Living in the south Current smoker * Chronic drinker Presence of physiological conditions Log L= ; chi-square=37.54, p<0.001

15 Limitations Unable to identify MS course, severity Samples - not mutually exclusive MS may exist in CDC sample, but small Response rate low for MS group Mode effects Self report, recall bias Other factors may have influenced utilization of preventive services

16 Discussion Male veterans with MS are doing well in receipt of many preventive services. Healthy People 2010 target goals Prostate screening Targeted intervention: Cholesterol check (older, non-white, less morbidity) Influenza vaccination (younger, smokers) Colon (younger) Prostate (younger, not currently employed)

17 Next steps These findings will help to bridge an existing gap in the MS literature. Data may be used to prioritize prevention efforts and to reduce disparities in receipt (to be addressed in future interventions). Foundation for a longitudinal study aimed at identifying associations between preventive service use and chronic disease prevalence.