1. Predictors of non-compliance in primary care of patients with chronic disease Roger Zoorob, MD, MPH, FAAFP; Mohamad Sidani, MD, MS; Medhat Kalliny, MD, PhD; Kristy M. Durkin, MSW, LCSW; and Robert Levine, MD Department of Family and Community Medicine, Meharry Medical College Nashville, Tennessee

2. Presenter Disclosures The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months: Mohamad Sidani, MD, MS “No relationships to disclose”

3. OJECTIVES: 1) What are the barriers of compliance among patients with chronic Diseases ? Scope of the Issue  Compliance is a key concept in health care and affects all areas of health care including chronic diseases.  Non-compliance has previously been a label attached to many patients without much thought having been given to the causes of poor compliance.  A better understanding of the factors affecting compliance, is imperative in order to improved outcomes. Objectives/Issue

4. Study Purpose  The purpose of our study was to investigate the predictors of non-compliance among patients living with chronic diseases (n=267) who were seen at our two Family Medicine Residency-based clinics. Our Study

5. Participant Criteria  Diagnosed with Type 2 diabetes, hypertension, hyperlipidemia, or obesity  Patients seen at either of our two Family Health Clinics  Meharry Family Clinic (49.2%) in a metropolitan area of Nashville  Madison Family Clinic (50.8%) in a suburban area half an hour outside Nashville city. Our Study

6. Design Method  Data was collected from the patient's electronic health record from 2008 to 2010.  We compared the effect of gender, age, ethnicity, marital status, employment, insurance, tobacco or alcohol use, clinic location, and co-morbidity on compliance.  Used Chi-Square, t-tests, and logistic regression to analyze results. Our Study

7. Patients were deemed compliant if they had a minimum of two regular check-ups per year. Compliance

8. Gender was not found to be a predictor of non-compliance (p=.379). Gender

9. Ethnicity was not found to be a predictor of non- compliance (p=.379) Ethnicity

10. Age was not found to be a predictor of non-compliance (p=.930) AGE

11. Marital Status was not found to be a predictor of non- compliance (p=.721) Marital Status

12. Tobacco (p=.375) was not found to be a predictor of non- compliance Substance Use

13. Alcohol use (p=.535) was not found to be a predictor of non-compliance. Substance Use

14. Location of residence (p=.117) was not found to be a predictor of non-compliance.

15. clinic location was not found to be a predictor of non- compliance (p<.001)

16. Being Unemployed/Disabled was not found to be a predictor of non-compliance (p=.290) Economics

17. Patients having insurance were significantly more compliant (53.5%) than those without (5.7%) (p<.000). Economics

18.  Having Comorbidity was not found to be a predictor of non-compliance (p=.168) 31.7% With and 43.2% Without were compliant  Having Hypertension was not found to be a predictor of non-compliance (p=.671) 32.7% With and 35.7% Without were compliant  Having Hyperlipidemia was not found to be a predictor of non-compliance (p=.209) 36.1% With and 28.6% Without were compliant  Those having Obesity were significantly more compliant than those who did not (40.1% versus 10%) (p<.000). Comorbidity

19. Co-morbidity

20.  Patients who received Diet Counseling (n=185) from the clinic’s nutritionist were significantly more complaint than those who did not (n=82) (43.8% verses 9.8%) (p<.000).  patients who received Exercise Counseling (n=156) from the clinic’s nutritionist were significantly more complaint than those who did not (n=111) (44.9% verses 17.1%) (p<.000). Counseling

21. Predictors of Non-Compliance Clinic Location  patients seen in the suburban clinic (Madison) were more likely to be compliant than those from the urban clinic (Meharry) (44.2% versus 24.8%)(p<.01).  When both insurance and clinic were entered into a logistic regression model, insurance status was a significant predictor of compliance while clinic location was not.  A follow-up chi-square revealed that patients from the suburban clinic (Madison) were significantly more likely to have insurance than the urban clinic (72% versus 48%) (p<.01). Findings

22. Predictors of Non-Compliance Obesity  Our findings suggest that those with obesity make more office visits.  Obese were significantly more compliant than those who were not (40.1% vs.10%)(p<.000).  Almost all of the patients who were compliant were also obese (93.3%) compared to patients who were compliant and not obese (6.7%)(p<.01). Findings

23. Predictors of Non-Compliance Insurance  Not having insurance is a significant risk factor for noncompliance among patients with chronic diseases.  Patients having insurance were significantly more compliant (53.5%) than those without (5.7%) (p<.000).  Almost all patients with insurance (93.3%) were more compliant than those without (36%)(p<.01)  Those with insurance were also less likely to drop out after the first visit compared to those without (22% versus 73%)(p<.01). Findings

24. Any Questions or Comments?