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PREDICTORS OF OBESITY MEDICATION USE IN AMBULATORY SETTING: NAMCS 2006-07 ANALYSIS Hemalkumar B. Mehta*, Jeetvan G. Patel, Rohan C. Parikh, and Susan S. Abughosh, PhD. Department of Clinical Sciences & Administration, College of Pharmacy, University of Houston, Houston, Texas, USA. BACKGROUND & SIGNIFICANCE United States has the highest prevalence of overweight (66.3%) and obese (33.2%) individuals among all developed countries.1 Approximately 9.1% of the annual US medical expenditure ($117 billion) is attributed to obesity.2 Cawley et. al. demonstrated disparities in anti-obesity medication use across factors such as race, ethnicity, gender etc.; however important factors like physician specialty, counseling and co-morbidity status are yet to be explored.3 To our knowledge there is no study which describes anti-obesity drug use in ambulatory setting. RESEARCH OBJECTIVE To determine predictors affecting obesity medication use amongst adults who are diagnosed with obesity in ambulatory setting. METHODS ♦Data Source: NAMCS ♦Identification: Obesity Diagnosis ♦Years: 2006-07 Study Design: National probability sample survey of visits to office-based physician. Inclusion Criteria: All patients ≥ 18 years of age and diagnosed with obesity (ICD-9-CM: 278.00). Confounders: Race, sex, region, MSA, counseling, physician type, type of insurance, and co-morbidity status. Medications: Only US-FDA approved medications for obesity treatment were included in the analysis. Multum Lexicon codes were used to identify the drugs. Unit of Analysis: Patient visit level and prescription level. Statistical Analyses: Descriptive analysis and chi-square tests were performed on the demographic variables. Domain analysis was performed to select adult visits with a diagnosis of obesity . A multivariate logistic regression model was constructed to test the association of drug use with the independent variables. SAS v9.2 was used with the survey procedures to perform statistical analysis. Table1: US-FDA approved anti-obesity medications Generic name Trade name Drug type Orlistat Xenical Lipase Inhibitor Sibutramine Meridia Appetite Suppressant Diethylpropion Tenuate Appetite Suppressant Phendimetrazine Bontril Appetite Suppressant Phentermine Adipex-P Appetite Suppressant For further information contact at : [email protected] RESULTS Table 2: Patient and Provider characteristics for obese adults with outcome variable of receiving a prescription or not. Visits No Characteristics Prescription p-value (thousands) Prescription All Adult Visits 112,964 6,550 106,414 NA (Percent) (5.79%) (94.21%) Sex Female 73,817 89.61 63.85 <0.0001* Male 39,147 10.39 36.15 Race Whites 92,775 86.37 81.87 0.2743 Non - Whites 20,189 13.63 18.13 Region South 47,343 67.00 40.37 0.0308* Others 65.621 33.00 59.63 Paytype Private Insurance 59,276 32.33 57.15 Public Insurance 38,960 12.22 38.13 <0.0001* No Insurance 8,181 55.45 4.72 Provider PCP 62,468 32.98 56.57 0.0201* Other 50,496 67.02 43.33 Counseling Yes 52,619 78.19 44.63 <0.0001* No 60,345 21.81 55.37 Comorbidity Very-high or High 99,008 67.32 88.89 <0.001* Low or No 13,956 32.68 11.11 MSA region MSA 95,413 81.09 84.67 0.6663 Non-MSA 17,551 18.91 15.32 Table 3: Patient and Provider characteristics and associations with prescription of at least one anti-obesity. (Multivariate Logistic Model). Odds ratio Confidence Characteristics p-value β (e ) Interval (95%) Sex Female Reference Reference <0.0001* Male 0.244 0.128-0.463 Race Whites Reference Reference 0.2201 Non - Whites 0.686 0.375-1.325 Age (1 yr. increments) 0.976 0.962 - 0.990 <0.001* Region South Reference Reference 0.3195 Other 0.628 0.251-1.570 Paytype Private Insurance 0.056 0.021-0.146 <0.0001* Public Insurance 0.080 0.034-0.189 <0.0001* No Insurance Reference Reference Provider PCP 0.597 0.253-1.408 0.2384 Other Reference Reference Counseling Yes 3.730 1.878-7.407 <0.001* No Reference Reference Comorbidity Low or No Reference Reference 0.3717 Very High or High 0.718 0.347-1.486 MSA region MSA Reference Reference 0.2162 Non-MSA 1.638 0.749-3.579 DISCUSSION Obese women are more likely to be associated with social stigma and lower self esteem as compared to obese men and thus, utilization of drugs in women is expected to be higher among women compared to men.3 Obesity is known to be a harbinger for other chronic diseases like CHF, diabetes, hypertension, arthritis and some cancers. Reducing access to anti-obesity medications might eventually lead to an overall increase in healthcare expenditure in United States through long term complications. Obesity related counseling increases awareness about obesity epidemic and thus could lead to better management of obesity.4 LIMITATIONS Differences across different socio-economic status in adults could not be assessed. Quality of obesity counseling could not be assessed. About 6% visits with an obesity diagnosis received an anti-obesity medication prescription. Almost 90% visits where an antiobesity medication was prescribed were by females and 86% visits were by whites. Unadjusted analysis showed that females, uninsured and people from south region had significantly higher chance of getting a drug prescription. In adjusted analysis, males were about 75% less likely to receive a prescription. There was no difference amongst different races and visits at different region. Chances of an anti-obesity medication prescription progressively with age. Patients visits with private or public insurance were less likely to receive an anti-obesity medication prescription as compared to uninsured. Visits where obesity counseling was given were almost 4 times more likely to get an anti-obesity medication prescription. CONCLUSIONS Study findings suggest that adequate coverage for anti-obesity medications might not be available. Patients who received counseling were more likely to receive an anti-obesity medication prescription. Further research to evaluate prescribing practices across people from different socio-economic status is warranted. REFERENCES 1. Low S et al. Review on Epidemic of obesity. Annals Academy of Medicine. 2009;38(1):57-65. 2. Powers KA et al. Financial impact of obesity and bariatric surgery. The Medical Clinics of North America. 2007;91(1):321-38. 3. Cawley et. al. One pill makes you smaller: The demand for anti-obesity drugs. Advances in Health Economics and Health Services Research. 2006;17:149-183. 4. Dastani et al. Combating the obesity epidemic: Community Pharmacists’ counseling on obesity management. The Annals of Pharmacotherapy. 2004;38:1800-1804.