University of Miami Scholarly Repository Open Access Dissertations Electronic Theses and Dissertations 2011-06-20 The Effects of Bariatric Surgery on Medication and Health Services Utilization Among Members From a Large Health Benefits Company Claudia L. Uribe University of Miami, uribenclau@yahoo.com Follow this and additional works at: http://scholarlyrepository.miami.edu/oa_dissertations Recommended Citation Uribe, Claudia L., "The Effects of Bariatric Surgery on Medication and Health Services Utilization Among Members From a Large Health Benefits Company" (2011). Open Access Dissertations. 594. http://scholarlyrepository.miami.edu/oa_dissertations/594 This Open access is brought to you for free and open access by the Electronic Theses and Dissertations at Scholarly Repository. It has been accepted for inclusion in Open Access Dissertations by an authorized administrator of Scholarly Repository. For more information, please contact repository.library@miami.edu.
UNIVERSITY OF MIAMI THE EFFECTS OF BARIATRIC SURGERY ON MEDICATION AND HEALTH SERVICES UTILIZATION AMONG MEMBERS FROM A LARGE HEALTH BENEFITS COMPANY By Claudia L. Uribe A DISSERTATION Submitted to the Faculty of the University of Miami in partial fulfillment of the requirements for the degree of Doctor of Philosophy Coral Gables, Florida June 2011
2011 Claudia L. Uribe All Rights Reserved
UNIVERSITY OF MIAMI A dissertation in partial fulfillment of the requirements of the requirements for the degree of Doctor of Philosophy THE EFFECTS OF BARIATRIC SURGERY ON MEDICATION AND HEALTH SERVICES UTILIZATION AMONG MEMBERS FROM A LARGE HEALTH BENEFITS COMPANY Claudia L. Uribe Approved: Guillermo Prado, Ph.D. Associate Professor, Epidemiology Director, Doctorate in Epidemiology Program Terri A. Scandura, Ph.D. Dean of the Graduate School Kathryn E. McCollister, Ph.D. Assistant Professor, Health Economics Dev Pathak, D.B.A. Affiliate Professor College of Public Health University of South Florida Hermes Florez, M.D, MPH, Ph.D. GRECC Associate Clinical Director Miami VA Healthcare System Associate Professor of Clinical Medicine & Epidemiology
URIBE, CLAUDIA L. (Ph.D., Epidemiology) The Effects of Bariatric Surgery on Medication and (June 2011) Health Services Utilization Among Members From a Large Health Benefits Company Abstract of a dissertation at the University of Miami. Dissertation supervised by Dr. Guillermo Prado. No. of pages in text. (129) The main objectives of this dissertation were to examine the effects of bariatric surgery on medication and health services utilization among a cohort of Commercial and Medicare insured members from a large health benefits organization in the U.S.. A total of 1,492 members with morbid obesity underwent gastric bypass (n=785) or gastric banding (n=707) procedure between January 2005 and June 2008. Administrative claims databases were accessed and three data files including a member file, a medical file and a pharmacy file were merged at the member level. Non-parametric Wilcoxon signed rank tests revealed that the average number of all prescription claims were significantly lower during the 12 months post-surgery, compared to the 12 months pre surgery (p<0.0001). Moreover, McNemar s Chi Square analyses showed that after the surgery, there was a statistically significant (p=<0001) decline in the proportion of members utilizing antihypertensives, antidiabetics and antihyperlipidemics. Our results also showed that the average number of prescription claims for each of these medication groups significantly declined during the 12-month post-surgery period, among members who had at least one prescription for these medications before the surgery (p<0.0001). Logistic regression modeling revealed that members who underwent bypass procedures were more likely to discontinue
antihypertensives (OR=2.04; 95% CI= 1.30-3.23), antihyperlipidemics (OR=3.25; 95% CI 1.96-5.40) and antidiabetics (OR=1.89; 95% CI 1.13 3.08) post-surgery than members who underwent banding procedures. In terms of medical services utilization, our results showed a significant decline in the average number of medical claims for all outpatient services overall from the 12 months pre to the 12 months post-surgery (p<0.0001). In contrast, the average number of medical claims for emergency room and inpatient hospitalizations were significantly increased from the pre to the post-surgery period (p<0.01). Logistic regression modeling revealed that the type of bariatric surgery was a significant positive predictor for inpatient hospitalizations post-surgery (OR =2.33; 95% CI= 1.76-3.08; p<0.0001) but not for emergency room visits (OR=1.23; 95% CI 0.97 1.56). The implications of the findings from a managed care perspective are discussed, along with limitation and future directions.
Dedication I dedicate this dissertation to my family, especially my parents Carlos and Elsa, who have been true models of perseverance and hard work; to my sister Diana and my brother Carlos Tomas, who despite the distance are always close to my heart; and to my husband German and my daughter Sofia for their patience, love and support. I also dedicate this dissertation to my grandmother Teresa, who has always inspired me and has always been there to listen and provide words of encouragement to keep me going. A special dedication to Dr. Dev Pathak who has not only been a fantastic mentor to me throughout the years, but also a sincere friend and guardian angel. iii
Acknowledgements I would like to express my deepest gratitude to Drs. Florez, McCollister, Pathak and Prado, and to my dissertation committee members for their guidance throughout the entire dissertation process. I certainly could not have completed this work without their support and guidance. Thanks also to all of the other faculty and staff of the Department of Epidemiology and Public Health at the University of Miami. I would also like to express my sincere gratitude to my colleagues at Humana, especially to Dr. Hua Li, for providing support and guidance in different stages of the research study process. iv
Table of Contents Tables and Figures List of Figures List of Tables viii ix Chapter 1: Introduction, Justification and Study Aims 1.1 Introduction and Justification 1 1.2 Specific Aims and Hypotheses 3 Chapter 2: Bariatric Surgery Overview 2.1 Obesity prevalence and trends in the United States 7 2.2 Economic and health-related impact of obesity 8 2.2.1 Economic impact of obesity 8 2.2.2 Health-related impact of obesity 9 2.3 Management of overweight and obesity 10 2.4 Bariatric surgery eligibility criteria 11 2.5 Bariatric surgery procedures 13 2.5.1 Restrictive procedures 13 2.5.2 Malabsorptive procedures 15 2.5.3 Combined restrictive and malabsorptive procedures 16 2.6 Bariatric surgery outcomes 16 2.6.1 Weight loss and resolution of comorbidities 16 2.6.2 Medication and health services utilization 19 2.6.3 Complication Rates 21 Chapter 3: Methods 3.1 Introductory Remarks 24 3.2 Study Design 24 3.2.1 Data Source 24 3.2.2 Study s Inclusion / Exclusion Criteria 25 3.2.3 Study period 26 3.3 Study Variables 27 3.3.1 Outcome Variables 27 3.3.1.1 Medication Utilization 27 v
3.3.1.2 Medical Health Services Utilization 27 3.3.2 Independent Variables 28 3.3.2.1 Type of Bariatric Surgery 28 3.3.2.2 Surgical Approach 29 3.3.2.3 Type of Insurance 29 3.3.2.4 Deyo-Charlson Comorbidity Index 30 3.3.2.5 Pre-surgery utilization of ER and IP 30 3.3.2.6 Gender and Age 30 3.3.2.7 Race and Ethnicity 31 3.4. Statistical and Analytical Plan (SAP) 31 3.4.1 SAP for Specific Aim I: Descriptive analysis 31 3.4.2 SAP for Aim II: Effects of bariatric surgery on medication utilization 32 3.4.3 SAP for Specific Aim III: Effects of bariatric surgery on medical services utilization 36 Chapter 4: Findings 4.1 Descriptive Analysis (Aim I) 41 4.1.1 Demographic characteristics 41 4.1.2 Comorbidities 41 4.1.3 Clinical and financial characteristics of the bariatric surgery procedure 42 4.1.4 Medication utilization pre and post-surgery 44 4.1.5 Medical services utilization pre and post-surgery 44 4.2 Effect of bariatric surgery on medication utilization (Aim II) 46 4.2.1 Hypothesis 1 46 4.2.2 Hypothesis 2 46 4.2.3 Hypotheses 3 47 4.2.4 Hypotheses 4 and 5 48 4.2.4.1 Modeling for discontinuation of antihypertensives 48 4.2.4.2 Modeling for discontinuation of antihyperlipidemics 52 4.2.4.3 Modeling for discontinuation of antidiabetics 53 4.3 Effect of bariatric surgery on medical services utilization (Aim III) 55 4.3.1. Hypothesis 1 55 4.3.2. Hypotheses 2 and 3 56 4.3.2.1. Modeling for the likelihood of having an emergency room visit post-surgery 56 4.3.2.2. Modeling for the likelihood of having an inpatient hospitalization post-surgery 58 vi
Chapter 5: Discussion, Limitations, Conclusion and Future Directions 5.1 Demographics and baseline characteristics of the study Population 61 5.2 Characteristics of the bariatric surgery procedures 63 5.3 Medication and health services utilization at baseline 64 5.4 Effects of the bariatric surgery procedure and other relevant covariates on medication utilization 67 5.5 Effects of the bariatric surgery procedure and other relevant covariates on health services utilization 69 5.6 Limitations 71 5.7 Conclusions 72 5.8 Future Directions 74 Figures, Tables, References and Appendices Figures 76 Tables 87 References 117 Appendix A Generic Product Identifier (GPI) Medication Groups 126 Appendix B Acronyms 128 Appendix C CPT codes for bariatric surgery 129 vii
LIST OF FIGURES Figure 1. Trends in overweight, obesity, and extreme obesity among adults aged 20 years and over: United States, 1988-2008 76 Figure 2. Vertical Banded Gastroplasty (VBG) 77 Figure3. Laparoscopic Adjustable Gastric Band (LAGB) 78 Figure 4. Sleeve Gastrectomy 79 Figure 5. Biliopancreatic Diversion with Duodenal Switch (BPD DS) 80 Figure 6. Roux-en-Y gastric bypass (RYGB) 81 Figure 7. Study Period 82 Figure 8. Prescriptions Filled Before and After Surgery 83 Figure 9. Medical Outpatient Utilization Before and After Surgery 84 Figure10. Medical ER and IP Utilization Before and After Surgery 85 Figure 11. Interaction Effects of CCI x Bariatric Surgery Type on the Likelihood of Discontinuing Antihypertensives Post-surgery 86 viii
LIST OF TABLES Table 1. World Health Organization Classification of Overweight and Obesity 87 Table 2. Age-adjusted prevalence of overweight, obesity, and extreme obesity among U.S. adults aged 20 and over 88 Table 3. Demographic characteristics and insurance plan distribution 89 Table 4. Prevalence of obesity related comorbidities 90 Table 5. Number (%) of patients with coexisting obesity-related comorbidities 91 Table 6. Charlson Comorbidity Index (during 12 months pre surgery): Mean (SD) 92 Table 7. Detailed Deyo-Charlson Comorbidity Distribution 92 Table 8. Number (%) of bariatric surgery procedures by CPT code 93 Table 9. Number (%) of bariatric surgery procedures by year, type and surgical approach 94 Table 10. Bariatric surgery cost by type of procedure and insurance type: Open surgical approach 95 Table 11. Bariatric surgery length of stay (days)*, type of procedure and insurance type: Open surgical approach 96 Table 12. Bariatric surgery cost by type of procedure and insurance type: Laparoscopic surgical approach 97 ix
Table 13. Bariatric surgery length of stay (days)*, type of procedure and insurance type: Laparoscopic surgical approach 98 Table 14. Average Number of All Prescription Claims Per Patient Per 6 Months and Per year 12 Months Pre and Post 99 Table 15. Number (%) of members filling at least 1 prescription medications for obesity-related comorbidities pre surgery 100 Table 16. Number (%) of members filling at least 1 prescription medications for obesity-related comorbidities post-surgery 101 Table 17. Average number of claims per member pre and post-surgery by service category 102 Table 18. Average number of prescriptions filled before and after bariatric surgery (all medications) 103 Table 19. Number (%) of members filling at least one prescription of select medication groups before and after surgery 104 Table 20. Average number of prescriptions filled for select medication groups before and after the bariatric surgery procedure 105 Table 21. Logistic regression model for discontinuation of antihypertensives post-surgery 106 Table 22. Odds ratio for discontinuation of antihypertensives post bariatric surgery 107 Table 23. Logistic regression model for discontinuation of antihyperlipidemics post-surgery 108 x
Table 24. Odds ratio for discontinuation of antihyperlipidemics post bariatric surgery 109 Table 25. Logistic regression model for discontinuation of antidiabetics post-surgery 110 Table 26. Odds ratio for discontinuation of antidiabetic medications post bariatric surgery 111 Table 27. Average number of claims by service category before and after the bariatric surgery procedure 112 Table 28. Logistic regression model for the likelihood of having an ER visit post-surgery 113 Table 29. Odds ratio for the probability of having an ER visit during the 12 months post bariatric surgery 114 Table 30. Logistic regression model for the likelihood of having an IP hospitalization post-surgery 115 Table 31. Odds ratio for the probability of having an inpatient hospitalization during the 12 months post bariatric surgery 116 xi
Chapter 1. Introduction, Justification and Study Aims 1.1. Introduction and Justification The bariatric surgery procedure was developed in the 1960s based on the weight loss observed among patients undergoing partial stomach removal for ulcers. [1] The technique evolved during the following decades and its use for the treatment of morbid obesity has increased progressively until reaching an estimated 220,000 procedures performed in the United States in 2008, according to the American Society for Metabolic and Bariatric Surgery.[2, 3] With the rising trends in obesity and the popularity that the bariatric surgery procedure has received in the recent years, it becomes eminent to have evidence based information to guide clinical and financial decision-making. Over the last two decades several research studies have been conducted to measure the impact of bariatric surgery on clinical outcomes (weight reduction efficacy, impact on comorbid conditions, complications, mortality) as well as on health care economics. Most of the published studies have been uncontrolled case series, from single institutions, followed by nonrandomized controlled trials. Very few publications stem from randomized controlled trials, [4, 5] due in part to the poor outcomes for nonsurgical interventions that pose considerable barriers to randomization. [6] Given the difficulties in conducting randomized clinical trials for assessing the impact of bariatric surgery, other study designs such as observational mixed-methods studies (quantitative and qualitative) using administrative and billing databases, chart reviews, survey questionnaires and electronic medical records are currently being 1
2 conducted as alternative sources of information that may add to the current body of evidence around bariatric surgery, despite their inherent limitations. The clinical efficacy of bariatric surgery for weight reduction and its impact on comorbid conditions such as diabetes has been studied extensively. [4, 5, 7-13] Although several clinical research studies around bariatric surgery have been conducted and published, there are still some gaps in the assessment of its effects on health services resources utilization. [14] Few studies have been published looking at how the improvement or resolution of comorbid conditions translates into lower medication utilization. Two studies published in 2004 found a significant reduction in medication utilization associated with obesityrelated comorbidities.[15, 16] However, these were case series reports of small number of patients which limits generalizability. Moreover, they only included patients who underwent Roux-en-Y gastric bypass. A more recently published study by Hodo et.al.[17] evaluated medication use and costs after bariatric surgery and found significant reductions within 6 months postsurgery. One of the major limitations mentioned in this publication was the short followup period (6 months). Another limitation was that this study only included patients who underwent Roux-en-Y gastric bypass. Another recent publication from 2009 by Segal et al. [18] showed significant reductions in medication utilization by 3 months post-surgery which was sustained through the 12-month study follow-up. This study used an administrative claims database from a commercial insurance plan but did not include Medicare data, which the
3 authors cited as a limitation for generalizability of the results among Medicare members. It included bypass as well as banding procedures but it did not compare results among them. Publications around the impact of bariatric surgery on medical health services utilization are also few. A study by Cremieux et. al.[19] looked at the economic impact of bariatric surgery and its return on investment, by applying a probabilistic model based on actual claims data from bariatric surgery patients and a matched control group. The results of their estimates showed that the initial investment on bariatric surgery is returned within 4 years for patients who undergo open surgery and within 2 years for patients who undergo laparoscopic surgery. Their study focused on overall costs and did not provide specific information in terms of variations in healthcare utilization by type of service after the surgery. Moreover, it did not assess effect differences by type of surgical procedure (banding vs. bypass). Some economic evaluation studies have been published including costeffectiveness, cost-benefit and cost-utility analysis of bariatric surgery.[20-23] However, these economic models have had great variability in cost estimates and outcomes, and several methodological limitations including unjustified modeling assumptions have made the results inconclusive, unreliable and not generalizable.[5] 1.2. Specific Aims and Hypotheses The specific aims and hypotheses for this study were: Aim I. To 1) describe the demographic and clinical characteristics of the study cohort including age, gender, race/ethnicity, insurance plan line of business (Medicare,
4 Commercial), Deyo-Charlson co-morbidity index (CCI), and the presence of obesity related co-morbid conditions; 2) describe the clinical and financial characteristics of the bariatric surgery procedure, including the type of surgery (banding or bypass) and surgical approach (open or laparoscopic), the average cost of the bariatric surgery procedure, and the average length of stay; and 3) describe the medical and pharmacy healthcare utilization during the pre and post-surgery periods, including the average number of medical claims by type of service, the average number of prescription drug claims for all types of medications, and the average and proportion of members utilizing medications to treat obesity related co-morbid conditions (by medication group). Aim II. To examine the effects of bariatric surgery on medication utilization during the 12 months post-surgery, including the effects on the average number of prescription medications filled, and the effects on medication discontinuation. The following were our hypotheses: Hypothesis 1. We expected that the average number of all prescription drug claims would be significantly lower during the 12 months post-surgery, compared to the 12 months pre surgery. Hypothesis 2. We expected to find a significantly lower proportion of patients utilizing antihypertensive, antidiabetic and antihyperlipidemic medications during the 12 months post-surgery, compared to the 12 months pre surgery. Hypothesis 3. We expected to find a significantly lower average number of prescription claims for antihypertensive, antidiabetic and antihyperlipidemic medications during the 12 months post-surgery, compared to the 12 months pre surgery.
5 Hypothesis 4. We expected that the likelihood for discontinuing antihypertensive, antidiabetic and antihyperlipidemic medications would be significantly different by type of bariatric surgery. Hypothesis 5. We expected some interaction effects between age, insurance line of business, type of bariatric surgery and CCI on medication discontinuation. Aim III. To examine the effects of bariatric surgery on medical services utilization during the 12 months post-surgery by type of service. The following were our hypotheses: Hypothesis 1. We expected to find an increase in the average number of medical services claims during the 12 months post-surgery compared to the 12 months pre surgery, especially for emergency room and inpatient hospitalization, due to potential complications of the surgical procedure. Hypothesis 2. The likelihood of having an ER visit or inpatient hospitalization post-surgery would be significantly different by type of bariatric surgery. Hypothesis 3. There would be some significant interactions between age, LOB, CCI, type of bariatric surgery, surgical approach and pre surgery ER or IP utilization. In summary, this study will provide a descriptive analysis of the demographic and clinical characteristics of commercial and Medicare enrolled Humana members who underwent a bariatric surgery procedure between the years 2005 and 2008, and their related medical and pharmacy utilization before and after the surgical procedure. Moreover, this study will assess the effects of the bariatric surgery procedure on
6 medication utilization, focusing on the impact on discontinuation of antihypertensives, antidiabetics and antihyperlipidemics, as well as the impact on medical services utilization, especially emergency room visits and inpatient hospitalizations. Outcomes will be compared between banding and bypass procedures, while controlling for potential confounders such as gender, age, comorbidities, insurance line of business, and surgical approach. Our analyses around the effects of bariatric surgery on healthcare utilization by type of service rather than overall costs or return on investment can shed some light in terms of areas of opportunity where special attention should be placed for actionable strategies to be implemented in order to improve clinical and economic outcomes. Despite the inherent limitations of retrospective observational studies utilizing health insurance claims data, we expect that the results of this study will add to the body of evidence around the impact of this increasingly popular procedure and how its clinical effects translate into changes on healthcare services utilization among individuals who undergo it. Moreover, we expect that the information this study will provide may aid clinical and financial decision-making among stakeholders from the provider and payor perspectives.
Chapter 2. Bariatric Surgery Overview This chapter will focus on reviewing the current obesity trends in the U.S., along with its economic and health related impact. The options currently available for the management of overweight and obesity, including the different types of bariatric surgery will be described. Finally, a review of the literature around bariatric surgery outcomes in terms of weight loss, resolution of comorbidities, medications and health services utilization, as well as complications will be discussed. 2.1 Obesity prevalence and trends in the United States The most commonly used classification for overweight and obesity was established by the World Health Organization (WHO) in 1997 and published in 2000 [24]. It uses the Body Mass Index (BMI) = weight in kg/(height in mts) 2 to classify individuals into the following categories: The overweight category is for individuals who s BMI is 25-29.9. Obesity is for individuals whose BMI is between 30 and 39.9. The obesity category is further sub classified into Obesity Class I, where the BMI is between 30 34.9 and Obesity Class II where the BMI is between 35 39.9. Clinical Severe Obesity (or Obesity Class III, also known as morbid or extreme obesity) is where the BMI is equal or greater than 40 (Table 1). According to the results from the 2007-2008 National Health and Nutrition Examination Survey (NHANES), it is estimated that the percentage of U.S. adults that are overweight 7
8 (BMI equal or greater than 25 and lower than 30) is 34.2%; 33.8% are obese (BMI equal or greater than 30), and 5.7% have morbid obesity.[25](figure 1, Table 2) Based on the 2010 U.S. Census Bureau [26] this prevalence of morbid obesity represents approximately 13 million adults in the U.S. (of the 230 million U.S. adults 18 years and older) who could be candidates for bariatric surgery. 2.2. Economic and health-related impact of obesity 2.2.1. Economic impact of obesity The obesity epidemic has been linked to four major categories of economic impact: direct medical costs, productivity costs, transportation costs, and human capital costs. [27] Studies have shown that in terms of direct costs, obese adults in the U.S. could have a relative medical spending as much as 100% higher than normal weight adults and that the excess medical spending may be as much as $147 billion annually. [28-32] Productivity costs are associated with absenteeism, presenteeism (less productivity while present at the workplace), disability, and premature mortality. Total productivity costs are estimated as high as $66 billion annually for the U.S. [33-35] Transportation costs are associated with excess fuel use attributable to obesity and environmental costs are associated with higher CO 2 emissions from transportation. [27] Human capital accumulation costs are associated with fewer grades completed by obese individuals and more days absent from school. [27, 36] From an insurance perspective, one study estimated that the medical expenses of morbidly obese members were 1.4 to 2.8 times higher compared to that of other
9 members, and that the difference varied by gender (for men greater than for women). [37] [38]. 2.2.2. Health-related impact of obesity There are serious health consequences resulting from overweight and obesity. The most frequent obesity-related comorbidities among morbidly obese patients include hypertension, hyperlipidemia, gastroesophageal reflux disease (GERD), obstructive sleep apnea, diabetes, back strain and disk disease, depression, nonalcoholic liver disease, congestive heart failure, weight bearing osteoarthritis, and stress incontinence. A recently published study by Perry et al. [39] reported a 50.3% prevalence of diabetes, 29.6% of sleep apnea, 64.3% of hypertension, 42.7% of hyperlipidemia, and 14.8% of coronary artery disease, among morbidly obese patients prior to bariatric surgery. Another study by Scott, et.al. [40] reported a prevalence of sleep apnea of 11.8%, osteoarthritis of 11.8%, GERD of 11.7%, type 2 diabetes of 24.3%, hypertension of 41% and asthma of 12% among morbidly obese surgical patients. The management of these comorbid conditions often requires pharmacological treatment as reported by Peluso and Vanek [8], who found that 78% (188 of 241) of morbidly obese surgical patients with hypertension where taking at least one antihypertensive medication prior to the surgery. Similarly, 52% (88 of 170) of those with hypercholesterolemia, 52% (69 of 132) of those with hypertrigliceridemia, and 84% (101 of 120) of those with diabetes, were on pharmacological treatment for these chronic conditions.
10 2.3. Management of overweight and obesity Management of overweight and obesity includes several non-surgical and surgical options that must be tailored to the individual patient based on several factors including age, gender, degree of obesity, comorbid conditions, psychological and behavioral characteristics, ability to exercise and the results of previous weight loss attempts.[41] The ultimate goal is to reduce body weight and maintain lower weight in the long run, reducing the incidence and complications of comorbid conditions, improving quality of life and reducing early mortality. Lifestyle interventions including dietary therapy and physical activity are usually the first step in managing overweight and obese patients, along with behavioral therapy to reinforce weight reduction behaviors. Unfortunately, lifestyle interventions are often insufficient for achieving the desired and sustained weight loss goals, especially among morbidly obese patients.[42] Thus, pharmacological treatment is often added as a second line of therapy. The Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults[43] recommends that pharmacotherapy may be used in addition to dietary and physical activity interventions in patients less than 65 years of age with a BMI > 30 without comorbid conditions or > 27 with comorbidities. Currently, only two drugs have been approved by the U.S. Food and Drug Administration (FDA) for long term use for weight loss and weight maintenance: sibutramine and orlistat. Other medications such as phentermine, diethylpropion, phendimetrazine, and benzphetamine (sympathomimetic amines) are approved for short-term use (ie, 12 weeks in a l2-month period).[44] Some antidepressants (bupropion and fluoxetine), and anticonvulsants (topiramate and
11 zonisamide) have also been evaluated as adjunctive treatment for obesity. Rimonobant, a medication that had been in use in Europe for weight loss treatment since 2006, failed to win FDA approval and was withdrawn from the European market in January of 2009 due to serious side effects involving severe depression and suicidal thoughts. [45, 46] Before initiating pharmacological therapy as part of weight loss management, it is critical to evaluate if the benefits outweigh the risks for each individual patient, especially in light of limited long-term safety information and the evidence that weight loss induced by these agents is modest and short-term. [41] When lifestyle modifications and pharmacotherapy have failed, surgical treatment for obesity is the next option for morbidly obese patients with a BMI >=40 kg/m 2 or a BMI 35 kg/m 2 and significant obesity-related comorbidities. [47] 2.4. Bariatric surgery eligibility criteria In 1991 the NIH Consensus Development Conference Panel on Gastrointestinal Surgery for Severe Obesity established some general criteria for eligibility of bariatric surgery patients. [48] Since then, no significant changes have been made to those recommendations, despite the fact that there have been more than 13 systematic reviews of the literature around bariatric surgery since that time. The recommendations include the following eligibility criteria: Patients with a BMI 40 kg/m2 Patients with less severe obesity (BMI 35 kg/m2) if they have high-risk comorbid conditions such as uncontrolled type 2 diabetes mellitus (T2DM)or life-
12 threatening cardiopulmonary problems (i.e. severe sleep apnea, pickwickian syndrome, or obesity-related cardiomyopathy). Patients with BMIs between 35 and 40 kg/m2 with obesity-induced physical problems interfering with lifestyle (i.e. joint disease interfering with employment, family function, and ambulation). In 2004, the National Coverage Advisory Committee for Medicare concluded that bariatric surgery could be offered to Medicare beneficiaries with BMI 35 kg/m2 who have at least one obesity-related comorbidity, and have been unsuccessful previously with medical treatment of obesity. No age limits were established for bariatric surgery procedures. The Centers for Medicare and Medicaid Services national coverage determination (NCD) for bariatric surgery which became effective in Feb 2006, [49] also stated that approved bariatric surgery procedures could only be conducted in institutions certified by the American College of Surgeons (ACS) or the American Society for Metabolic and Bariatric Surgery (ASMBS). In terms of exclusion criteria, there is currently no consensus available. However, the suggested contraindications include a high operative risk (i.e. severe congestive heart failure or unstable angina), active substance abuse, or a major psychopathologic condition. Patients who are unable to understand the nature of the surgical procedure and unable to comply with the pre and post-operative regimen should not be offered these procedures. [3, 48] Humana Inc. follows these set of recommendations when selecting candidates for bariatric surgery. If the Commercial health insurance plan that the member is enrolled in covers bariatric surgery, the following surgical procedures may be offered:
13 Laparoscopic adjustable gastric banding (LAP-BAND ) (REALIZE ). Open or laparoscopic biliopancreatic diversion (BPD) with or without duodenal switch. Open or laparoscopic short or long limb Roux-en-Y gastric bypass surgery (RYGBP). Open or laparoscopic vertical banded gastroplasty (VBG). For Medicare beneficiaries, Humana follows the NCD determinations for coverage which exclude the Open or laparoscopic vertical banded gastroplasty (VBG). 2.5. Bariatric surgery procedures Currently, there are three categories of bariatric surgical procedures being performed: 1) Restrictive, 2) Malabsorptive and 3) Combined. This classification stems from the mechanism by which weight loss is produced.[50] 2.5.1. Restrictive procedures Restrictive procedures limit the size of the stomach by creating a small gastric pouch, thereby producing satiety with less food intake. They also produce a slower gastric emptying that further contributes to a longer sensation of satiety. Restrictive procedures include the Vertical Banded Gastroplasty (VBG) (Figure 2) and the Laparoscopic Adjustable Gastric Band (LAGB). (Figure 3) The Sleeve Gastrectomy (SG) (Figure 4) is another restrictive procedure that was performed initially as the first part of a 2-stage procedure, including a later biliopancreatic diversion with duodenal switch (BPD/DS) for high risk morbidly obese patients with a BMI >60. This procedure
14 involves a partial gastrectomy in which much of the greater curvature of the stomach is removed. The role of the SG was to induce weight loss to minimize risk before attempting the more challenging BPD/DS. Over time, it was noted that adequate weight loss occurred in these patients before the second stage of the operation was performed, so its use as a stand-alone procedure was proposed.[51, 52] However, due to lack of evidence of long-term outcomes (>5 yrs), this procedure is not yet widely covered by all insurance companies. The advantages of the restrictive procedures include the lack of malabsorption, dumping syndrome (nausea, vomiting, bloating, cramping, diarrhea, dizziness and fatigue, due to rapid gastric emptying), and the low mortality rates, ranging from 0.05% for the LAGB to 0.5% for the VBG. [51, 53] The LAGB is currently the most common of the restrictive procedures being performed in the U.S. because it is reversible, adjustable, and less invasive than the VBG. [3, 54, 55] The LAGB is usually well tolerated, although it has been reported that up to 75% of patients experience some mild upper gastrointestinal symptoms. [51, 56, 57] Acute and short-term complications are less common with the LAGB than with the VBG but the LAGB is associated with a higher rate of reoperation in the longer term.[51] The most common complications associated with LAGB include band slippage and erosion, pouch dilation, port-site infection, esophageal dilation, balloon failure, and port malposition. [58, 59] Due to these complications, around 20% of patients require a re-intervention as reported by Martikainen, et.al. [59] Among patients who have undergone VBG, the most frequently reported complications include gastroesophageal reflux disease (GERD), stomal stenosis, and early gastrointestinal bleeding. Some less common complications include staple-line
15 disruption, pouch dilatation, incisional hernia, anemia, outlet obstruction, and Wernicke s encephalopathy. [60-62] 2.5.2. Malabsorptive procedures Malabsorptive procedures are those that involve a reconstruction of the small intestines (enteroileostomy) to create what is known as short gut syndrome, where the absorption of nutrients are limited to a reduced section of the small bowel. [50, 51] The Biliopancreatic diversion (BPD with and without duodenal switch) is considered primarily a malabsorptive procedure, although it often combines some gastric restriction through partial gastrectomy (Figure 5). Although calcium deficiency, anemia, protein malnutrition, and thiamine deficiencies are common among patients undergoing these procedures, these can and should be rigorously managed through the supplementation of calcium, iron, folate, thiamine, vitamin B12, protein and fat-soluble vitamins, which need to be taken for life in order to avoid further complications [51]. Other common side effects of these procedures are cholelithiasis and cholecystitis, diarrhea, flatulence, and foul-smelling stools, stomal ulcer and a postcibal syndrome (early satiety with vomiting and epigastric pain) that is similar to the dumping syndrome of RYGBP.[60] The jejunoileal bypass was a purely malabsoptive procedure that is no longer performed due to the severe malabsorption complications observed in patients who had undergone the procedure. [63, 64]
16 2.5.3. Combined restrictive and malabsorptive procedures Combined restrictive and malabsorptive procedures such as the Roux-en-Y gastric bypass (RYGB) reduce the size of the stomach creating a small pouch and also reconfigure the anatomy of the small intestines to generate some degree of nutrient malabsorption but to a lesser extent than the BPD. [50] The RYGB is currently the most frequently performed bariatric surgery procedure in the United States and is considered the gold standard in bariatric surgery (Figure 6) [3, 60]. There are early and late postoperative complications associated with this procedure. Among the early complications the most frequently reported are dumping syndrome, stomal stenosis, stomal/marginal ulcers, wound infections and/or dehiscence, leaks from the staple/suture anastamosis line, acute gastrointestinal obstruction, acute upper gastrointestinal bleeding, pulmonary embolism and deep venous thrombosis [60-62]. Among the late stage postoperative complications, vitamin and mineral deficiencies are common, especially vitamin B12, iron, and folate, due to the diversion of food from the stomach and duodenum to the jejunum. To a lesser extent, calcium, potassium, magnesium, thiamine and fat-soluble vitamin deficiencies can occur, but are more frequently seen in a variation of this procedure, known as the long-limb RYGB [65, 66]. 2.6. Bariatric surgery outcomes 2.6.1. Weight loss and resolution of comorbidities A recently published systematic review of the clinical effectiveness and costeffectiveness of bariatric surgery [5] yielded 5386 references of which 26 were included in the clinical effectiveness review. Of these 26, three were randomized controlled trials
17 (RCTs) comparing surgery with non-surgical interventions three were cohort studies comparing surgery with non-surgical interventions and 20 were RCTs comparing different surgical procedures. The results of these studies showed improved outcomes in terms of weight loss and resolution of comorbidities in those who underwent the bariatric surgery procedure compared to those following non-surgical treatments. Two RCTs that reported outcomes at two years, found that the mean percent initial weight loss in the surgical groups (LAGB) was 20% [67] and 21.6%,[68] whereas the non-surgical groups had lost only 1.4% and 5.5% of their initial weight respectively. Two cohort studies that reported outcomes at two years, showed that the percent of weight loss ranged from 16% to 28.6% in the surgical groups, but the non-surgical groups had a percent weight increase ranging from 0.1 to 0.5%.[69-73] The Swedish Obese Subjects (SOS) prospective cohort study found that among 1276 patients followed for 10 years, patients in the surgical group had a 16% (SD 12.1) weight loss compared with a 1.5% (SD 9.9) weight gain for patients receiving conventional treatment. [69-71] This same study found a statistically significant reduction in the incidence of diabetes, hypertriglyceridemia and hyperuricemia assessed at 10 years follow-up after surgery compared with conventional therapy. Findings also showed that participants who underwent surgery were more likely to recover from diabetes, hypertension, hypertriglyceridemia, low HDL cholesterol, and hyperuricemia than those with conventional treatment, at 2 and 10 years follow-up. When comparing different bariatric surgery techniques, the findings of several RCTs showed that gastric bypass was more effective for weight loss than banding procedures (including vertical banded gastroplasty and adjustable gastric banding).[74-
18 78] However, there were no significant differences between surgical interventions in terms of comorbidity improvement. There were no statistically significant differences in terms of weight loss between open and laparoscopic surgeries. The economic cost-effectiveness models showed that bariatric surgery is a cost-effective intervention for moderately to severely obese people compared with non-surgical interventions. However, there was great variability in terms of outcomes and costs in those models and the results are not reliable or generalizable from the incremental costeffectiveness point of view. [5] Another systematic review comparing gastric banding versus bypass [79] showed that excess body weight loss was achieved at a significantly higher percentage after Roux-en-Y gastric bypass (76%) versus laparoscopic adjustable gastric banding (48%). Similarly, resolution of comorbidities was greater among patients who underwent Rouxen-Y gastric bypass (RYGB). Diabetes resolved in 78% of RYGB versus 50% of laparoscopic adjustable gastric banding patients (LAGB). Although patients who underwent LAGB had lower short-term complications compared to those who underwent RYGB, reoperation rates among LAGB patients were higher. A study recently published compared the effects of RYGB vs. LAGB on biochemical cardiovascular risk factors. [80] The study compared 12-month pre and post-surgery values of total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides (Trig), Trig/HDL ratio, lipoprotein(a) (Lp(a)), homocysteine (HmC), high sensitivity C-reactive protein (hs- CRP), fasting insulin (FI), and hemoglobin A1C (Hgb A1C). The results showed that at 12 months post-surgery RYGB patients had lost 77% of their excess weight and had
19 significant improvements in TC, LDL, HDL, Trig, Trig/HDL, HmC, hs-crp, FI, and Hgb A1C. LAGB patients lost 47.6% of their excess weight and had significant improvements in Trig, Trig/HDL, HmC, hs-crp, and Hgb A1C. Having RYGB instead of LAGB was predictive of significantly greater improvements in TC at 12 months postoperatively. RYGB produced significant improvements in a greater number of BCRFs and in some instances the 12-month post-surgery BCRF values were significantly lower in RYGB versus LAGB patients. Another recently published systematic review and meta-analysis [81] reported an overall excess body weight loss of 55.9% and a complete resolution of diabetes among 78.1% of diabetic patients who underwent bariatric surgery. Weight loss and diabetes resolution were the most favorable for patients who underwent biliopancreatic diversion with or without duodenal switch, followed by gastric bypass, and least favorable among those who underwent banding procedures. Although these studies compared outcomes between different types of bariatric surgery procedures in terms of resolution of comorbidities using biochemical measures, they did not evaluate how the resolution of these comorbidities translated into reduced medication utilization. The publications that do report impact on medication utilization do not include comparisons by type of bariatric surgery. [15-18] 2.6.2. Medication and health services utilization Although several clinical outcomes research studies around bariatric surgery have been conducted and published, there are still some gaps in the assessment of its effects on health services resources utilization, from real-life non-experimental data. Few studies
20 have been published looking at how the improvement or resolution of comorbid conditions translates into lower medication utilization. A study by Gould et.al., [15] evaluated the effect of laparoscopic gastric bypass on prescription medication costs within 6 months postoperatively. The study included fifty patients for whom monthly medication costs were assessed before the surgery and 6 months after. The authors reported overall mean monthly prescription medication cost savings primarily among those with GERD, hypertension, diabetes and hypercholesterolemia. Another study by Potteiger et.al.,[16] assessed the effect of Rouxen-Y gastric bypass on diabetic and anti-hypertensive pharmaceutical utilization and cost savings among 51 patients who underwent the procedure at a single institution. Prescription medications were reviewed prior to the surgery and at three and nine months postoperatively. The study found a 77.3% reduction in total cost of diabetic and antihypertensive medications at 9 months postoperatively. Both of these studies were case series reports with a small number of patients treated at single institutions, which limits generalizability. Moreover, these studies only involved gastric bypass patients. No comparisons with other types of bariatric surgery procedures are included. A more recently published study by Hodo et.al.[17] evaluated medication use and costs 6 months before and 6 months after bariatric surgery using administrative claims data from a large managed care organization. A total of 605 individuals who underwent Roux-en-Y gastric bypass were included in the analysis. The findings showed significant reductions within 6 months post-surgery in the average number of claims for all prescriptions, as well as for asthma, cardiac, diuretic and diabetes medications
21 specifically. The proportion of members utilizing these medications also decreased significantly in the 6 months post-surgery. This study also evaluated the effects of the surgery on health services utilization by comparing the average number of claims during the 3-6 months pre surgery with the 3-6 months post-surgery. They found significant reductions in office and outpatient visits, and a reduction in ER visits (although not significant). On the contrary, there was a significant increase in the average number of inpatient hospitalizations during the 3-6 months post-surgery. One of the major limitations mentioned in this publication was the short follow-up period (6 months). Moreover, this study was limited to Roux-en-Y gastric bypass patients only. Another study by Segal et al. [18] also using administrative claims data explored the effects of bariatric surgery on medication utilization at 3, 6 and 12 months after surgery, among 6,235 patients who underwent the procedure. The results showed significant reductions in medication utilization by 3 months post-surgery which was sustained through the 12-month study follow-up. This study used a claims database from a commercial insurance plan, but did not include Medicare members, which the authors cited as a limitation for generalizability of the results among this population. The authors did not assess if there was a difference on the effects of medication utilization by type of bariatric surgery. 2.6.3. Complication rates The most common complications for the different types of bariatric surgery procedures have been described above. As far as complication rates a meta-analysis
22 conducted by Buchwald et.al., showed that the operative mortality rates were 0.1% for LAGB, 0.5% for RYGB, and 1.1% for other malabsorptive procedures. [4] A multi-center cohort study involving 29 academic medical centers reported that among the restrictive procedures performed (N = 94), 92% were done via laparoscopy with no conversions. There was an overall complication rate of 3.2%, a 30-day readmission rate of 4.3%, and a 0% 30-day mortality rate. Among the gastric bypass procedures performed, (N = 1,049), 76% were done laparoscopically with a conversion rate of 2.2%, an overall complication rate of 16%, an anastomotic leak rate of 1.6%, a 30- day readmission rate of 6.6%, and a 30-day mortality rate of 0.4%.[82] A study utilizing commercial health insurance claims data published by Encinosa et al, [83] reported that among 7060 bariatric surgery patients the risk adjusted inpatient complication rate was 14.81%, the 30 day overall complication rate was 25.45%, and the 180 day overall complication rate was 32.81%. The most common 180 day complications included dumping, vomiting and diarrhea (18.63%), followed by anastomosis complications (9.26%) and abdominal hernia (4.81%). The rate of readmissions with complications was 6.79%, for ER visits with complication 1.79%, for outpatient visits with complications 13.26%, and for office visits with complications 10.60%. In terms of risk of complications comparing open versus laparoscopic bariatric surgery, a recently published population-based study using the Nationwide Inpatient Sample, [84] showed that after adjusting for patient and hospital level factors, patients undergoing open gastric bypass (OGB) were more likely to experience reoperation and complications including pulmonary (odds ratio [OR] = 1.92 (1.54-2.38), P < 0.001);
23 cardiovascular (OR = 1.54 [1.07-2.23], P = 0.02); procedural (OR = 1.29 [1.06-1.57], P < 0.01); sepsis (OR = 2.18 [1.50-3.16], P < 0.001); and anastomotic leak (OR = 1.32 [1.02-1.71], P = 0.03). After risk adjustment, laparoscopic gastric bypass (LGB) was associated with a shorter length of stay but higher total charges. Overall, LGB patients were less likely to experience reoperation and postoperative complications in the hospital and had shorter lengths of stay but incurred higher total charges than OGB patients.
Chapter 3: Methods 3. 1 Introductory Remarks This dissertation uses administrative claims data from a large health benefits company in the U.S. (Humana) to evaluate the effects of bariatric surgery on medication and medical services utilization among Commercial and Medicare enrolled members who underwent the procedure between January 2005 and June 2008. The University of Miami s Institutional Review Board reviewed the study protocol and granted exemption from IRB review on 12/5/2008 (IRB protocol number 920080997) because the research activities were considered to be of minimal risk to subjects. 3.2 Study Design The study design for this dissertation is a (pre-post) retrospective cohort analysis to assess the effects of bariatric surgery on medication and medical services utilization among patients enrolled in Humana s Commercial and Medicare lines of business who underwent a bariatric surgery primary procedure between 1/1/2005 and 6/30/2008 ( identification period ). The index date is defined as the first medical claim for a primary bariatric surgery procedure, identified in claims data through Common Procedural Terminology (CPT) codes (Appendix C). 3.2.1 Data Source Humana administrative claims databases will be accessed and three data files will be merged at the member level for the purpose of analyses: 24
25 A member file containing demographic information (age, gender, geographical region) and enrollment information (line of business, dates of enrollment) for each member per encounter; A medical file containing up to 9 recorded International Classification of Disease-9 Clinical Modification (ICD-9-CM) codes and CPT codes per encounter, and related cost and charges information; A pharmacy file containing all Generic Product Identifier numbers (GPI) of pharmacy-dispensed medications per claim, and related cost and charges information; In accordance with the Health Insurance Portability and Accountability Act (HIPAA), the data will be de-identified in order to comply with the standards for the protection of the confidentiality of the personal health information of the enrollee. 3.2.2 Study s Inclusion / Exclusion Criteria For inclusion in the analytic cohort, the following inclusion and exclusion criteria will be used: Inclusion Criteria Members with a first medical claim for bariatric surgery (see Appendix C for CPT codes) during the identification period 1/1/2005 and 6/30/2008. Members should have at least 24 months of continuous enrollment (12 months preindex and 12 months post-index). Members should be 18 years of age or older at index date. Members should have been enrolled in Commercial or Medicare lines of business during the continuous enrollment period.
26 Exclusion Criteria Members with less than 12 months of continuous enrollment pre and post index date. Members with a repeat bariatric surgery. Members from Administrative Services Only (ASO) organizations who had multiple employers during the 24-month continuous enrollment period. Members from ASO organizations which did not authorize the use of data for research purposes. 3.2.3 Study period As previously mentioned, the identification period will be 1/1/2005 and 6/30/2008. During this period, the first medical claim for a primary bariatric surgery procedure will be defined as the index date, and will be referred hereafter as the bariatric surgery date. Since members will be required to have at least 12 months of continuous enrollment pre and post bariatric surgery, the data will be comprised of claims from 1/1/2004 through 6/30/2009. The 24 months of continuous enrollment claims data per member will be divided into a 12-month pre bariatric surgery (baseline) and a 12-month post bariatric surgery (follow-up) period for the purpose of analyses (Figure 7). For descriptive purposes, we will further subdivide the 12 months pre and post bariatric surgery into 6-month blocks. However, statistical comparisons will only be presented for the 12-month pre and post periods.
27 3.3 Study Variables 3.3.1 Outcome variables 3.3.1.1 Medication utilization Medication utilization will be measured using the counts of prescription drug claims per member to calculate the average number of all prescriptions, as well as the average number of prescription drug claims by GPI therapeutic group per 6 and 12-month periods. The medication groups that will be evaluated include those commonly used by patients with severe obesity to manage their comorbid conditions (see Appendix A for list of medication groups). Medication utilization will also be measured as a dichotomous variable for the three main medication groups of interest: antihypertensives, antihyperlipidemics and antidiabetics. Having had at least one prescription drug claim for each medication group during the 12-month pre and post-surgery periods will be defined as 1, and having none will be defined as 0. 3.3.1.2 Medical Health Services Utilization Medical health services utilization will be measured using the counts for medical claims per member to calculate the average number of claims per 6 and 12 month periods. Medical health services utilization will be subdivided into four service categories: 1) Office and outpatient visits: includes primary and specialty visits, as well as allied health (i.e. nutritionist, physical therapy, psychology).
28 2) Tests and procedures: includes diagnostic tests as well as diagnostic and therapeutic procedures performed in the outpatient setting. 3) Emergency room (ER): includes visits to the emergency room that do not end up in a hospitalization. 4) Inpatient hospitalization (IP): includes all inpatient hospitalizations that required at least 24 hours of inpatient stay. Service categories groupings will be defined based on claim type codes. Emergency room and inpatient hospitalization utilization will also be measured as dichotomous variables. Having had at least one medical claim for each of these service categories during the 12-month pre and post-surgery periods will be defined as 1, and having none will be defined as 0. 3.3.2 Independent variables 3.3.2.1 Type of bariatric surgery The bariatric surgical procedures will be classified into 2 categories for analysis purposes, according to the type of procedure (banding or bypass) regardless of the surgical approach (open or laparoscopic). Banding and bypass procedures will include the following CPT codes respectively:
29 CPT: Common Procedural Terminology 3.3.2.2 Surgical approach The surgical approach will be defined as open or laparoscopic. This variable will be used as a covariate in the regression models for emergency room and inpatient hospitalization post-surgery. 3.3.2.3 Type of insurance Type of insurance will be classified into two categories: 1) Commercial, which includes Health Maintenance Organization (HMO), Preferred Provider Organization (PPO), and Administrative Service Organization (ASO) plans, and 2) Medicare.
30 3.3.2.4 Deyo-Charlson Comorbidity Index To assess the comorbidity burden, we will use the The Deyo-Charlson Comorbidity Index (CCI). The Deyo adaptation of the CCI consists of a list of 17 diagnoses with varying weights based on their impact on outcomes. The final score for a member is the sum of weighted values from the listed diagnoses. The CCI is often used to account for disease severity and comorbid conditions when using administrative databases for studying outcomes of medical care. [85] The CCI will be estimated based on medical claims data during the 12 months pre surgery period. To adjust for comorbidities, the CCI will be used as a continuous independent variable in the modeling of outcomes [85]. 3.3.2.5 Pre-surgery utilization of ER and IP Emergency room and inpatient utilization (having had at least 1 medical or pharmacy claim) during the 12 months pre surgery will be used as an independent variable (dichotomous yes=1, no=0: reference) in the modeling of medical services utilization outcomes, to adjust for confounding. 3.3.2.6 Gender and age Age at the time of the bariatric surgical procedure (index date) will be captured from claims data. Gender and age will be used as covariates in the modeling of outcomes.
31 3.3.2.7 Race and ethnicity Race and ethnicity are not captured in Humana s claims data bases. Thus, a geocoding technique is often used to estimate the probability of a person belonging to a certain race/ethnic category. The geocoding technique assigns individuals weighted probabilities of being of a certain race and ethnicity based on the racial/ethnic composition of the census tract or block in which the person resides. Blocks are small geographical units that contain an average of 4000 subjects and have been found to be homogeneous in the main demographic characteristics (tracts used for more rural locations) [86, 87]. Geocoding misses around 10% of race/ethnicity probability estimation due to missing information for new addresses in the census database. Because of missing data and the potential for misclassification race and ethnicity will only be used for descriptive purposes. 3.4 Statistical and Analytical Plan (SAP) The statistical and analytical plan (SAP) for this dissertation will be described by specific aim. 3.4.1 SAP for Specific Aim I: Descriptive analysis As noted earlier, the goals for Aim I are to 1) describe the demographic and clinical characteristics of the study cohort including age, gender, race/ethnicity, insurance plan line of business (Medicare, Commercial), Deyo-Charlson co-morbidity index (CCI), and the presence of obesity related co-morbid conditions; 2) describe the clinical and financial characteristics of the bariatric surgery procedure, including the type
32 of surgery (banding or bypass) and surgical approach (open or laparoscopic), the average cost of the bariatric surgery procedure, and the average length of stay; and 3) describe the medical and pharmacy healthcare utilization during the pre and post-surgery periods, including the average number of medical claims by type of service, the average number of prescription drug claims for all types of medications, and the average and proportion of members utilizing medications to treat obesity related co-morbid conditions (by GPI medication groups). Data Analysis for Aim 1: The first step will be to examine the distribution of the study variables using histograms, stem and leaf plots and tests of skewness and kurtosis. We will then use descriptive statistics including frequencies, proportions, measures of central tendency and dispersion. We will estimate the average number of medical and prescription drug claims by 6 and 12 month blocks to assess the patterns of utilization before and after the surgical procedure. 3.4.2 SAP for Aim II: Effects of bariatric surgery on medication utilization Aim II is to examine the effects of bariatric surgery on medication utilization during the 12 months post-surgery, including the effects on the average number of prescription medications filled, and the effects on medication discontinuation. The following are our hypotheses: Hypothesis 1. We expect that the average number of all prescription drug claims will be lower during the 12 months post-surgery, compared to the 12 months pre surgery.
33 Hypothesis 2. We expect to find a significantly lower proportion of patients utilizing antihypertensive, antidiabetic and antihyperlipidemic medications during the 12 months post-surgery, compared to the 12 months pre surgery. Hypothesis 3. We expect to find a significantly lower average number of prescription claims for antihypertensive, antidiabetic and antihyperlipidemic medications during the 12 months post-surgery, compared to the 12 months pre surgery. Hypothesis 4. We expect to find differences by type of bariatric surgery in the likelihood for discontinuing antihypertensive, antidiabetic and antihyperlipidemic medications post-surgery. Hypothesis 5. We expect some interaction effects between age, type of insurance, type of bariatric surgery and CCI on medication discontinuation. This hypothesis is exploratory in nature, as the effects of the interactions are unknown. Power analysis for Aim 2. The power analysis for this aim focused on hypotheses 2 and 3, which are the two main hypotheses exploring the effects of bariatric surgery on utilization of antihypertensives, antidiabetics and antihyperlipidemic medications. We based our power calculations on results previously published in the literature (Segal, 2009; Cremieux, 2010). Segal et al. reported a reduction in the average medication count for antihypertensive medications from 0.72 at baseline (at the time of the bariatric surgery) to 0.37 at 12 months post-surgery. Similarly, a reduction in the use of antidiabetic medications from 1.1 at baseline to 0.27 at 12 months post-surgery, and a reduction in antihyperlipidemic medications from 0.16 to 0.065. We used PASS 2008 non-parametric (Wilcoxon) one sample t-test power analysis (one sided test with a target
34 significance level of 0.05) to calculate the power we would achieve with the above referenced effect sizes, using the actual number of members who used each of the three medication groups: antihypertensives (N=614), antidiabetics (N=331) and antihyperlipidemics (N=327). The results of the power analyses showed that based on this information we would achieve over 99% power for estimating the difference in means pre and post-surgery for each of the three medication groups. To further validate our power calculations, we used the results reported by Cremieux et al on the reduction in proportion of patients utilizing these three medication groups pre and post-surgery. They reported a reduction in the proportion of members using cardiovascular medications (mostly antihypertensives) from 43.7% pre surgery to 29% post-surgery; oral antidiabetics from 16.3% to 6.6% and antihyperlipidemics from 14.8% to 6.6%. We conducted a power analysis of one proportion using a one-sided binomial test with a target significance level of 0.05. We used the same actual number of members who used each of the three medications groups as listed above. The results of the power analyses showed that based on this information we would achieve over 99% power for estimating the difference in proportions pre and post-surgery for each of the three medication groups. Data Analysis for Aim 2: Aim II will be addressed by comparing the average number of all prescription claims, as well as comparing the proportion of members filling at least one prescription for some of the most commonly prescribed medications for the morbidly-obese (by GPI medication group) during the 12-month pre and post-surgery periods. Depending on the
35 distribution of the data, parametric (for normally distributed variables) or non-parametric statistics (for non-normally distributed variables) will be used to test for statistical differences. McNemar s Chi Square will be used for comparing proportions. To assess the effects of the type of bariatric surgery procedure on the likelihood of discontinuation of antihypertensive, antihyperlipidemic and antidiabetic medications post-surgery while adjusting for potential confounders we will perform logistic regression analyses. We will include as the main independent variable the type of bariatric surgery, and as covariates we will include gender, age, type of insurance, and CCI. The outcome variable will be defined as having had at least one prescription drug claim for an antihypertensive, antihyperlipidemic or antidiabetic medication during the 12-month post-surgery period (Dichotomous Yes = 1 ; No= 0 ). These regression analyses will be performed only among members who utilized antihypertensive, antihyperlipidemic or antidiabetic medications during the pre-surgery period. We will also test the following interactions to explore if there are any effect modifications present between variables: The interaction between age and type of insurance: Medicare members are expected to be of older age than commercial members. The effects of type of insurance on the outcomes of interest may vary as a function of age. The interaction between age and type of bariatric surgery: older patients may be having more banding procedures due to the lower complication rate. The effects of type of surgery on the outcomes of interest may vary as a function of age. The interaction between age and CCI: As age increases, CCI may also increase. The effects of CCI on the outcomes of interest may vary as a function of age.
36 The interaction between CCI and type of bariatric surgery: The effects of bariatric surgery type on the outcomes of interest may vary as a function of CCI. The LRT will be used to select the best regression model. Statistical significance for the analyses will be set at α = 0.05. For the interactions we will apply a Bonferroni correction to the p value, to reduce the chances of Type I error (false positives). This correction will be achieved by dividing the p value α = 0.05 by the number of interactions tested. So 0.05 will be divided by 4 and the corrected p value to be used for testing the interactions will be 0.0125. 3.4.3 SAP for Specific Aim III: Effects of bariatric surgery on medical services utilization Aim III is to examine the effects of bariatric surgery on medical services utilization during the 12 months post by type of service. The following are our hypotheses: Hypothesis 1. We expect to find an increase in the average number of medical services claims during the 12 months post-surgery compared to the 12 months pre surgery, especially for emergency room and inpatient hospitalization, due to potential complications of the surgical procedure. Hypothesis 2. We expect to find differences in the likelihood of having an ER visit or inpatient hospitalization post-surgery by type of bariatric surgery. Patients who underwent bypass procedures are expected to have a higher probability of having an ER visit or IP hospitalization post-surgery, versus those who underwent banding procedures.
37 Hypothesis 3. We expect to find some interactions between age, type of insurance, CCI, type of bariatric surgery, surgical approach and pre surgery ER or IP utilization. This hypothesis is exploratory in nature since the effects of these interactions are unknown. Power analysis for Aim 3. The power analysis for this aim focused on hypothesis 1, which is the main hypothesis exploring the effects of bariatric surgery on medical services utilization. There is limited information published in the literature around the effects of bariatric surgery on medical services utilization by type of service. Thus, rather than estimating the power for a pre-specified effect size, we calculated the minimum difference between the average number of medical claims pre and post-surgery to achieve an 80% power. We used PASS 2008 non-inferiority test of one mean power analysis with a target significance level of 0.05 and a sample size of N=1492 to calculate the minimum difference to achieve an 80% power. Assuming an equivalence margin (largest difference that is not of practical importance) of 0.1 and a standard deviation of 0.5 for ER and IP hospitalizations, the minimum difference between the average number of medical claims pre and post-surgery in order to achieve 80% power would be 0.068. Assuming an equivalence margin (largest difference that is not of practical importance) of 1.0 and a standard deviation of 10.0 for office and outpatient visits, as well as tests and procedures, the minimum difference between the average number of medical claims pre and post-surgery in order to achieve 80% power would be 0.356.
38 Data Analysis for Aim 3: Aim III will be addressed by comparing the average number of medical claims by type of service (office and outpatient visits, tests and procedures, emergency room, and inpatient hospitalization). Depending on the distribution of the data, parametric (for normally distributed variables) or non-parametric statistics (for non-normally distributed variables) will be used to test for statistical differences. To assess the effects of the type of bariatric surgery procedure on the likelihood of having an ER visit or IP hospitalization post-surgery while adjusting for potential confounders, we will perform logistic regression analyses. The outcome variable will be defined as having had at least one medical claim for an ER visit or IP hospitalization during the 12-month post-surgery period (Dichotomous Yes = 1 ; No= 0 ). These regression analyses will be performed among all eligible members, excluding those with missing data. We will include as the main independent variable the type of bariatric surgery, and as covariates we included gender, age, type of insurance, surgical approach CCI and pre surgery ER and IP utilization as a dichotomous variable (had at least 1 ER or IP claim during the 12 months pre surgery). We will also test the following interactions to evaluate if there are any effect modifications present between variables: Interaction between age and type of insurance: Medicare members are expected to be of older age than commercial members. The effects of type of insurance on the outcomes of interest may vary as a function of age.
39 Interaction between age and type of bariatric surgery: older patients may be having more banding procedures due to the lower complication rate. The effects of type of surgery on the outcomes of interest may vary as a function of age. Interaction between age and CCI: As age increase, CCI may also increase. The effects of CCI on the outcomes of interest may vary as a function of age. Interaction between CCI and type of bariatric surgery: The effects of bariatric surgery type on the outcomes of interest may vary as a function of CCI. Interaction between pre surgery ER or Inpatient utilization and type of bariatric surgery: The effects of bariatric surgery type on the outcomes of interest may vary as a function of Pre surgery ER or Inpatient utilization. Interaction between pre surgery ER or Inpatient utilization and CCI: The effects of Pre surgery ER or Inpatient utilization on the outcomes of interest may vary as a function of CCI. Interaction between surgical approach and type of bariatric surgery: The effects of type of bariatric surgery on the outcomes of interest may vary as a function of the surgical approach. Interaction between surgical approach and CCI: The effects of CCI on the outcomes of interest may vary as a function of the surgical approach. The LRT will be used to select the best regression model. Statistical significance for the analyses will be set at α = 0.05. For the interactions we will apply a Bonferroni correction to the p value, to reduce the chances of Type I error (false positives). This correction will be achieved by dividing the p value α = 0.05 by the
40 number of interactions tested. So 0.05 will be divided by 8 and the corrected p value to be used for testing the interactions will be 0.00625. Statistical analyses will be performed using SAS 9.2 and SPSS 17.0 statistical software.
Chapter 4: Findings A total of 1,492 members met the study s eligibility criteria and consequently were included in the analyses. This chapter describes the findings by specific aim. 4.1. Descriptive analysis (Aim I) 4.1.1. Demographic characteristics The mean age of the study cohort was 47.5 years (SD 10.9) and the majority was female (N=1,214, 81%). Data on race and ethnicity were only available for 1,318 members. Participants were predominantly White (82%) and non-hispanic (90%). There were 12% who were Black, and 5% who belonged to other races including American Indian, Eskimo, Aleut, and Asian or Pacific Islander. The majority of patients (N= 1,152, 77%) were enrolled in a commercial plan (Health Maintenance Organization HMO, Preferred Provider Organization PPO, or Administrative Services Only - ASO) at the time of the surgery and the rest were enrolled in a Medicare plan (N=340, 23%) (Table 3). 4.1.2. Comorbidities The prevalence of comorbidities was assessed during the 12 months baseline period. Prevalence was determined as having at least 1 medical claim with the ICD-9- CM diagnosis code for the condition. The top 5 most prevalent obesity-related 41
42 comorbidities (ORC) included hypertension (43%), hyperlipidemia (62%), gastroesophageal reflux disease (53%), obstructive sleep apnea (48%) and diabetes (43%) (Table 4). The proportion of subjects who had 4 or more coexisting ORCs was 53% (N=794), followed by 21% with 3 coexisting ORCs, 16% with 2, 8% with 1 and only 2% with no coexisting ORCs. (Table 5) The cohort had a mean Deyo-Charlson comorbidity index (CCI) of 0.88 (SD 1.22) before the surgery (Table 6). The top five most prevalent conditions among the 17 used to calculate this index where diabetes without complications (n=556, 37%), chronic pulmonary disease (n=282, 19%), diabetes with complications (n= 85, 6%), congestive heart failure (n=48, 3%), and mild liver disease (n=41, 3%) (Table 7). 4.1.3. Clinical and financial characteristics of the bariatric surgery procedure In terms of the bariatric surgical procedure, most of the surgeries were done via laparoscopy (90%), with nearly an equal distribution for bypass (52.6%) and banding (47.4%) procedures (Tables 8 and 9). The number of bariatric surgery procedures increased dramatically from a total of 172 cases in 2005 to 804 cases in 2007, within the study cohort. There was an increasing trend in the number of procedures being done via laparoscopy, as well as the number of banding procedures. Bypass procedures had a downward trend, as well as the open surgical approach (laparotomy) which went from 43% in 2005 (including open bypass and banding procedures) to 4.1% in 2007. The overall average cost (allowed amount) for an open bariatric surgery procedure was $16,538.36 (SD $13,038.31). When stratifying by type of bariatric surgery and type of insurance, the results showed that overall the average cost for bypass
43 procedures were $1,021.63 (6.1%) higher than for the banding procedures ($16,690.23 Vs $15,668.60; t=-0.338, p = 0.736), and $6,972.80 (37.6%) higher among Commercially insured members compared to those Medicare insured ($18,564.24 Vs $11,591.45; t=3.03, p = 0.003) (Table 10). The average length of stay (days) among those requiring at least 1 inpatient hospitalization day was higher for bypass procedures (M=3.85; SD=2.61) than for banding procedures (M=1.69; SD=0.85) (t=-2.957, p=0.004), and higher among Medicare insured (M=4.38; SD=3.79) compared to Commercially insured members (M=3.25; SD=1.59) (t=-2.318, p=0.004) (Table 11). Laparoscopic bariatric surgery procedures had a lower overall average cost compared to the open procedures ($14,882.45 Vs $16,538.36; difference of -$1,655.92, -11%; t = 2.225, p = 0.0026). When stratifying by type of insurance, the overall average cost for laparoscopic bariatric surgery procedures among Commercially insured was $15,728.02 (SD 8,427.85), compared to $11,911.50 (SD 4,953.26) among those Medicare insured (t = 7.433, p = <0.0001) (Table 12). The average length of stay (days) among laparoscopic bariatric surgery patients requiring at least 1 inpatient hospitalization day was higher for bypass procedures (M=2.68; SD=1.59) than for banding procedures (M=1.17; SD=0.64) (t=-15.35, p=<0.0001), and higher among Medicare insured (M=2.40; SD=2.11) compared to Commercially insured members (M=2.12; SD=1.19) (t=-2514, p=0.012) (Table 8.2). Overall the length of stay was lower among all members undergoing laparoscopic procedures (M=2.20; SD=1.53) compared to those undergoing open procedures (M=3.62; SD=2.56) (t=8.648, p=<0.0001) (Table 13).
44 4.1.4. Medication utilization pre and post-surgery When looking at utilization for all medications by 6-month blocks before and after surgery, we found that there was an increasing trend in the average number of prescriptions filled per patient from the 7-12 months (M=13.34; SD=18.55) to the 1-6 months pre surgery (M=14.20; SD=18.86), followed by a decreasing trend during the 1-6 months (M=11.58; SD=15.71) and the 7-12 months post-surgery (M=10.12; SD=15.06). On average patients filled 27.54 (SD 36.80) prescription drugs during the 12 month period prior to the bariatric procedure and 21.70 (SD 30.12) during the 12 months post bariatric surgery. (Table 14, Figure 8) Among the medication groups (GPI-2 medication group) used to treat obesityrelated comorbid conditions, the most common (in terms of percentage of members who filled at least one prescription) included analgesics (42.3% pre surgery; 52.3% postsurgery), antihypertensives (41.15% pre surgery; 34.65% post-surgery), antidepressants (26.3% pre surgery; 24.6% post-surgery), antidiabetics (22.2% pre surgery; 13.7% postsurgery), antacids (22.1% pre surgery; 27.4%) and antihyperlipidemics (21.9% pre surgery; 15.6% post-surgery) (Tables 15 and 16). 4.1.5. Medical services utilization pre and post-surgery Medical services utilization was measured as the average number of claims per patient per 6 and 12-month periods pre and post-surgery. It was subdivided into four main categories: office and outpatient visits, tests and procedures, emergency room visits and inpatient hospitalizations.
45 The results showed an increase in the average number office and outpatient visit claims per patient from the 7-12 months (M=10.06; SD=8.70) to the 1-6 months pre surgery (M=14.58; SD=9.20), followed by a decrease during the 1-6 months (M=11.29; SD=10.38) and the 7-12 months post-surgery (M=9.85; SD=8.74). On average there were 24.80 (SD 16.28) office and outpatient visit claims per patient during the 12 month period prior to the bariatric procedure and 21.17 (SD 17.05) during the 12 months post bariatric surgery. (Table 17, Figure 9) Tests and procedures followed a similar pattern in utilization with an increase in the average number of claims per patient from the 7-12 months (M=6.79; SD=6.97) to the 1-6 months pre surgery (M=9.84; SD=7.14), followed by a decrease during the 1-6 months (M=7.57; SD=8.44) and the 7-12 months post-surgery (M=6.72; SD=7.72). On average there were 16.67 (SD 12.13) tests and procedures claims per patient during the 12 month period prior to the bariatric procedure and 14.28 (SD 13.78) during the 12 months post bariatric surgery. (Table 17, Figure 9) For emergency room visits, utilization remained stable during the 7-12 (M=0.18; SD=0.59) and the 1-6 (M=0.19; SD=0.53) months pre surgery. Similarly, inpatient hospitalization also remained relatively stable during the pre-surgery period (7-12 months pre M=0.072; SD=0.319 and 1-6 months pre M=0.059; SD=0.316). Overall, during the whole 12 months pre surgery, ER and IP utilization had an average number of claims of 0.37 (SD 0.92) and 0.13 (SD 0.53) respectively. During the post-surgery follow-up period, there was a substantial increase in the average number of claims for these two service categories. For emergency room and inpatient hospitalizations, the average number of claims rose to 0.24 (SD 0.71) and 0.19
46 (SD 0.60) respectively, during the 6 month period immediately following the surgery. During the 7-12 months post-surgery, ER and IP utilization decreased with respect to the preceding 6-month post-surgery period (M=0.21, SD=0.68; and M=0.11, SD=0.41 respectively), but remained higher when compared to the pre surgery averages. Overall, during the whole 12 months post-surgery, ER and IP utilization had an average number of claims of 0.45 (SD 1.12) and 0.30 (SD 0.80) respectively. (Table 17, Figure 10) 4.2. Effect of bariatric surgery on medication utilization (Aim II) 4.2.1. Hypothesis 1 Our first hypothesis for Aim II was that the average number of all prescription claims would be significantly lower during the 12 months post-surgery, compared to the 12 months pre surgery. To test our hypothesis, we used the non-parametric Wilcoxon signed rank test for two dependent groups, given the distribution of the data (skewed). The results showed a significant decline in the average number of prescriptions filled before (M=27.54; SD=36.80) compared to after the surgery (M=21.70; SD=30.12) ( Z=- 13.5, p<0.0001) (Table 18). 4.2.2. Hypothesis 2 Our second hypothesis was that the proportion of patients utilizing antihypertensive, antidiabetic and antihyperlipidemic medications during the 12 months post-surgery, compared to the 12 months pre surgery would be significantly lower. To test out hypothesis we used McNemar s Chi Square for comparing proportions. The results showed that after the surgery, there was a significant decline in the proportion of
47 members utilizing antihypertensives (41.15% pre Vs 34.65% post; Chi 2 =62.27, p=<0001), antidiabetics (22.2% pre Vs 13.7% post; Chi 2 =109.49, p=<0001), and antihyperlipidemics (21.92% pre Vs 15.62% post; Chi 2 =58.44, p=<0001) (Table 13). Results of some post hoc analyses also showed a decline in the proportion of patients using antidepressants (26.34% pre Vs 24.60% post; Chi 2 =4.66, p=0.031), and an increase in the proportion of patients using analgesics (42.29% pre Vs 52.82% post; Chi 2 =80.85, p=<0001) and antacids (22.05% pre Vs 27.41% post; Chi 2 =21.23, p=<0001)(table 19). 4.2.3. Hypothesis 3 Our third hypothesis was that the average number of prescription claims for antihypertensive, antidiabetic and antihyperlipidemic medications would be significantly lower during the 12 months post-surgery, compared to the 12 months pre surgery. To test our hypothesis, we used the non-parametric Wilcoxon signed rank test for two dependent groups, given the distribution of the data (skewed). Our pre and post comparisons only included those members who had at least 1 prescription for the medication group (i.e., antihypertensives, antidiabetics or antihyperlipidemics) during the pre-surgery period. Our results showed that after the surgery, there was a significant decline in the average number of prescription claims for antihypertensives (M=14.19, SD=11.29 pre Vs M=9.14, SD=9.61 post; Z=-14.64, p=<0.0001), antidiabetics (M=13.13, SD=9.19 pre Vs M=3.84, SD=5.57 post; Z=-15.04, p=<0.0001), and antihyperlipidemics (M=7.45, SD=5.46 pre Vs M=3.79, SD=4.44 post; Z=-11.80, p=<0.0001) (Table 20).
48 Results of some post hoc analyses also showed a decline in the average number of prescription claims for all other medications including antidepressants, analgesics, antacids, cardiotonics, antianginal, and antiarrythmic (p<0.05) (Table 20). 4.2.4. Hypotheses 4 and 5 Our fourth hypothesis for Aim II was that the likelihood for discontinuing antihypertensive, antidiabetic and antihyperlipidemic medications would be significantly different by type of bariatric surgery. In order to test our hypothesis we performed logistic regression modeling using the type of bariatric surgery as our main predictor of interest. The outcomes of interest were defined as having had at least one prescription drug claim for an antihypertensive (GPI groups 33,34,36 and 37), antihyperlipidemic (GPI group 39) or antidiabetic (GPI group 27) medication during the 12-month postsurgery period (Dichotomous Yes = 1 ; No= 0 ). As mentioned in the methods section, these regression analyses were performed only among members who utilized antihypertensive, antihyperlipidemic or antidiabetic medications during the pre-surgery period. Other independent variables included gender, age, type of insurance (Commercial or Medicare), and Charlson Comorbidity Index (CCI). 4.2.4.1. Modeling for discontinuation of antihypertensives This logistic regression model only included those members who had at least 1 prescription claim for an antihypertensive medication during the 12 months preceding the surgery, and excluded members with missing data (N=608; n=6 excluded for missing data). The dependent variable was defined as having had at least one prescription drug
49 claim for an antihypertensive medication (GPI groups 33,34,36 and 37) during the 12- month post-surgery period (Dichotomous Yes = 1 ; No= 0 ). The probability modeled was having had no prescription claims for antihypertensives during the 12 month postsurgery period (equivalent to having discontinued antihypertensive medications). The main dependent variable was the type of bariatric surgery, using banding as the reference category. Other independent variables included gender, age, type of insurance, and CCI. (Table 21). The results from univariate logistic regression models showed that gender (reference was male) (OR=2.07; 95% CI= 1.19-3.61), age (OR=0.96; 95% CI= 0.94-0.99) bariatric surgery type (reference was banding) (OR=1.66; 95% CI= 1.06-2.61), and CCI (OR=0.76; 95% CI= 0.63-0.91) were all significant individual predictors of discontinuation of antihypertensive medications, while type of insurance was not (OR=1.12; 95% CI= 0.73-1.73). The results from the multivariate logistic regression model showed that the type of bariatric surgery was a significant predictor for discontinuation of antihypertensive medications after bariatric surgery while adjusting for other covariates. Gender, age, and CCI were also statistically significant predictors, while type of insurance was not. Thus we excluded type of insurance from the model and retained all other independent variables. Members who underwent bypass procedures were twice as likely to discontinue antihypertensives post-surgery (OR=2.04; 95% CI= 1.30-3.23) than members who underwent banding procedures. Females were over 2 times more likely than males to discontinue antihypertensives after bariatric surgery (OR=2.13; 95% CI= 1.13-4.00). For each year increment in age, the likelihood of discontinuing antihypertensives post bariatric surgery decreased by 5% (for every 10
50 years increment it decreased by 38%). For each unit increment in CCI, the likelihood of discontinuing antihypertensives post bariatric surgery decreased by 26%. (Table 22). To test our fifth hypothesis in Aim II (there will be some interaction effects between age, type of insurance, type of bariatric surgery and CCI on medication discontinuation) we then tested the following interactions one by one to evaluate if there were any effect modifications present between variables: The interaction between age and type of insurance The interaction between age and type of bariatric surgery The interaction between age and CCI, and The interaction between CCI and type of bariatric surgery The results showed that the only significant interaction was CCI and bariatric surgery type (p = 0.0054: significant at Bonferroni corrected alpha level = 0.0125), indicating that the effects of the type of bariatric surgery procedure on discontinuation of antihypertensives varied as a function of the CCI score. We calculated of the Odds Ratio for discontinuation of antihypertensives to account for this interaction using the following formula: exp [log(p/(1-p))]= exp[β0 + β1*cci + β2*type of bariatric surgery+ β3*cci*type of bariatric surgery] We estimated 4 ORs based on 4 different scenarios to illustrate the effects of the interaction: Bypass (value = 0) and low CCI (mean CCI 1 SD = 0.88 1.22) Bypass (value = 0) and high CCI (mean CCI + 1 SD = 0.88 + 1.22) Banding (value = 1) and low CCI (mean CCI 1 SD = 0.88 1.22)
51 Banding (value = 1) and high CCI (mean CCI + 1 SD = 0.88 + 1.22) The results are summarized below and illustrated in Figure 11. The results showed that low and high levels of CCI had a similar effect on the likelihood of discontinuation of antihypertensives on patients who underwent a banding procedure (0.17 and 0.15 respectively). However the effect of low and high levels of CCI varied for patients who underwent a bypass procedure. Among patients who underwent a bypass bariatric surgery the odds ratio for discontinuing antihypertensives was higher among those with a low CCI (OR=0.72) than among those with a high CCI (OR=0.15) (Figure 11). The results of the -2log likelihood ratio test between the reduced model (without the interaction -2Log L= 537.690) and the full model (with the interaction -2Log L= 530.350) showed that the model with the interaction provided a better fit to the data (χ2(1) = 7.34, p < 0.0067). The final independent variables retained in the model were gender, age, CCI score, type of bariatric surgery and the interaction between CCI and type of bariatric surgery.
52 4.2.4.2. Modeling for discontinuation of antihyperlipidemics This logistic regression model only included those members who had at least 1 prescription claim for an antihyperlipidemic medication during the 12 months preceding the surgery, and excluded members with missing data (N=325; missing=2). The dependent variable was defined as having had at least one prescription drug claim for an antihyperlipidemic medication (GPI group 39) during the 12-month post-surgery period (Dichotomous Yes = 1 ; No= 0 ). The probability modeled was having had no prescription claims for antihyperlipidemics during the 12 month post-surgery period (equivalent to having discontinued antihyperlipidemic medications). The main dependent variable was the type of bariatric surgery, using banding as the reference category. Other independent variables included gender, age, type of insurance, and CCI (Table 23). The results from univariate logistic regression models showed that age (OR=0.96; 95% CI= 0.94-0.98) bariatric surgery type (reference was banding) (OR=2.88; 95% CI= 1.78-4.69), and CCI (OR=0.83; 95% CI= 0.70-0.98) were all significant individual predictors of discontinuation of antihyperlipidemic medications, while gender (reference was male) (OR=1.23; 95% CI= 0.74-2.06) and type of insurance were not significant (OR=0.80; 95% CI= 0.51-1.25). The results from the multivariate logistic regression model showed that age, CCI and type of bariatric surgery were all significant predictors for discontinuation of antihyperlipidemic medications after bariatric surgery (p < 0.05). Neither gender nor type of insurance were significant predictors in this regression model. We removed type of insurance and retained all other variables (including gender, despite its statistical non-significance). The overall model was statistically significant [Likelihood ratio χ2(4) = 37.23, p < 0.0001).
53 To test our fifth hypothesis in Aim II we then tested the same interactions as we did when modeling for discontinuation of antihypertensives: Interaction between age and type of insurance; between age and type of bariatric surgery; between age and CCI; and between CCI and type of bariatric surgery. The results showed that none of the interactions were statistically significant. Thus the model without the interaction terms was retained. From the final model we can state that for each year increment in age the likelihood of discontinuing antihyperlipidemics post bariatric surgery decreased by 4% (decreased 32% for every 10 years) and for each unit increment in the CCI score, it decreased by 20%. Patients who underwent bypass procedures were over three times more likely (OR=3.25; 95% CI 1.96-5.40) to discontinue their antihyperlipidemic medication post-surgery, than their counterparts who underwent banding procedures (Table 24). 4.2.4.3. Modeling for discontinuation of antidiabetics This model only included those members who had at least 1 prescription claim for an antidiabetic medication during the 12 months preceding the surgery, and excluded members with missing data (N=331; missing=2). The dependent variable was defined as having had at least one prescription drug claim for an antidiabetic medication (GPI group 27) during the 12-month post-surgery period (Dichotomous Yes = 1 ; No= 0 ). The probability modeled was having had no prescription claims for antidiabetics during the 12 month post-surgery period (equivalent to having discontinued antidiabetic medications). The main dependent variable was the type of bariatric surgery, using banding as the
54 reference category. Other independent variables included gender, age, type of insurance, and CCI. (Table 25). The results from univariate logistic regression models showed that age (OR=0.95; 95% CI= 0.93-0.97), type of insurance (reference was Medicare) (OR=1.75; 95% CI= 1.13-2.73), bariatric surgery type (reference was banding) (OR=1.79; 95% CI= 2.87-1.12), and CCI (OR=0.63; 95% CI= 0.52-0.77) were all significant individual predictors of discontinuation of antidiabetic medications, while gender (reference was male) (OR=1.24; 95% CI= 0.75-2.05) was not significant. The results from the multivariate logistic regression model showed that age, CCI and type of bariatric surgery were all significant predictors for discontinuation of antidiabetic medications after bariatric surgery (p < 0.05). Neither gender nor type of insurance were significant predictors in this regression model. We removed type of insurance and retained all other variables (including gender, despite its statistical non-significance). The overall model was statistically significant [Likelihood ratio χ2(4) = 47.01, p < 0.0001). To test our fifth hypothesis in Aim II we then tested the same interactions as we did when modeling for discontinuation of antihypertensives and antihyperlipidemics: Interaction between age and type of insurance; between age and type of bariatric surgery; between age and CCI; and between CCI and type of bariatric surgery. The results showed that none of the interactions were statistically significant. Thus the model without the interaction terms was retained. From the results of the final model we can state that for each year increment in age, the likelihood of discontinuing antidiabetic medications post bariatric surgery
55 decreased by 4% (decreased 36% for every 10 years) and for each unit increment in the CCI score, it decreased by 33%. Patients who underwent bypass procedures were 86% more likely (OR=1.89; 95% CI 1.13 3.08) to discontinue their antidiabetic medications post-surgery, than their counterparts who underwent banding procedures (Table 26). 4.3. Effect of bariatric surgery on medical services utilization (Aim III) 4.3.1. Hypothesis 1 Our first hypothesis for Aim III was that there would be an increase in the average number of medical services claims during the 12 months post-surgery compared to the 12 months pre surgery, especially for emergency room and inpatient hospitalization, due to potential complications of the surgical procedure. To test our hypothesis, we used the non-parametric Wilcoxon signed rank test for two dependent groups, given the distribution of the data (positively skewed). The results showed a significant decline in the average number of medical claims from the 12 months pre surgery to the 12 months post-surgery for office and outpatient visits (Pre surgery: M=24.80; SD=16.28; Post-surgery: M=21.17; SD=17.05; Z=-13.23, p=<0.0001) as well as for tests and procedures (Pre surgery: M=16.67; SD=12.13; Postsurgery: M=14.28; SD=13.78 ; Z=-10.64, p=<0.0001) (Table 27). This represents a 15% decline in the average number of claims for office and outpatient visits, and a 14% decline for tests and procedures. On the contrary, medical claims for ER and IP had a statistically significant increase from the pre to the post-surgery period. The average number of medical claims per patient for ER increased from M=0.37 (SD 0.92) to M=0.45 (SD 1.12) (Z=-3.10, p=<0.002), and for IP from M=0.13 (SD 0.53) to M=0.30
56 (SD 0.80) (Z=-8.67, p=<0.0001). This represents a 22% increase in the average number of claims for ER visits and a 131% increase in IP hospitalizations. 4.3.2. Hypotheses 2 and 3 Our second hypothesis in Aim III was that the likelihood of having an ER visit or inpatient hospitalization post-surgery would be significantly different by type of bariatric surgery. To test our hypothesis we performed logistic regression modeling using the type of bariatric surgery as our main predictor of interest. The outcomes of interest were defined as having had at least one medical claim for an ER visit or an IP hospitalization during the 12-month post-surgery period (Dichotomous Yes = 1; No= 0 ). Other independent variables included gender, age, type of insurance, CCI, surgical approach (open or laparoscopic), and pre surgery ER and IP utilization as a dichotomous variable (had at least 1 ER or IP claim during the 12 months pre surgery). Our third hypothesis in Aim III was exploratory in nature to assess the interactions effects between age, type of insurance, CCI, type of bariatric surgery and pre surgery ER utilization. 4.3.2.1. Modeling for the likelihood of having an emergency room visit post-surgery This logistic regression model included all members who underwent the procedure, and excluded members with missing data (N=1484; missing:8). The dependent variable was defined as having had at least one medical claim for an ER visit during the 12-month post-surgery period (Dichotomous Yes = 1:probability modeled; No= 0 ). Other independent variables included gender, age, type of insurance, CCI, surgical approach (open or laparoscopic), and pre surgery ER utilization as a
57 dichotomous variable (had at least 1 ER claim during the 12 months pre surgery) (Table 28). The results from univariate logistic regression models showed that having had an ER visit pre-surgery (OR=2.56; 95% CI= 1.99-3.28), type of insurance (reference was Medicare) (OR=0.76; 95% CI= 0.58-0.99), bariatric surgery type (reference was banding) (OR=1.29; 95% CI= 1.03-1.62), and CCI (OR=1.16; 95% CI= 1.07-1.27) were all significant individual predictors of having an ER visit post-surgery, while gender (reference was male) (OR=0.95; 95% CI= 0.71-1.27), age (OR=0.99; 95% CI= 0.98-1.001) and surgical approach (reference was laparoscopic) (OR=0.91; 95% CI= 0.62-1.34) were not significant. The results from the multivariate logistic regression model showed that having had a pre-surgery ER visit, age, and CCI were all significant predictors of having an ER visit during the 12 month follow-up period (p < 0.05). Type of bariatric surgery, surgical approach, gender and type of insurance were not significant predictors in this regression model. As discussed in the methods section, we explored the interactions between age, type of insurance, CCI, type of bariatric surgery and pre surgery ER utilization. The results showed that none of the interactions were significant, and thus were not retained in the model. We removed type of insurance and surgical approach from the model but retained bariatric surgery type as well as gender given their clinical significance. The final independent variables retained in the model were gender, age, CCI type of bariatric surgery, and pre surgery ER. The overall model was statistically significant [Likelihood ratio χ2(5) = 70.79, p < 0.0001).
58 From the results of the final logistic regression model we can state that after adjusting for gender, and type of bariatric surgical procedure, patients who had at least 1 ER visit during the 12 months prior to the surgery were nearly 2.5 times more likely to have an ER visit during the 12 months follow-up (OR =2.46; 95% CI= 1.91-3.17; p<0.001). For each year increment in age, the likelihood of having an ER visit post bariatric surgery decreased by 1% (decreased 13% for every 10 years). For each unit increment in the CCI, the likelihood of having an ER visit during the 12 months followup increased by 17% (Table 29). 4.3.2.2. Modeling for the likelihood of having an inpatient hospitalization postsurgery This logistic regression model included all members who underwent the procedure, and excluded members with missing data (N=1484; missing:8). The dependent variable was defined as having had at least one medical claim for an inpatient (IP) hospitalization during the 12-month post-surgery period (Dichotomous Yes = 1:probability modeled; No= 0 ). Other independent variables included gender, age, type of insurance, CCI, surgical approach (open or laparoscopic), and pre surgery IP utilization as a dichotomous variable (had at least 1 IP claim during the 12 months pre surgery) (Table 30). The results from univariate logistic regression models showed that having had an IP hospitalization pre-surgery (OR=2.98; 95% CI= 2.08-4.37), type of insurance (reference was Medicare) (OR=0.5; 95% CI= 0.38-0.67), bariatric surgery type (reference was banding) (OR=2.35; 95% CI= 1.79-3.08), age (OR=1.03; 95% CI= 1.02-1.04) and CCI (OR=1.32; 95% CI= 1.20-1.45) were all significant individual predictors of having an ER
59 visit post-surgery, while gender (reference was male) (OR=0.82; 95% CI= 0.60-1.13), and surgical approach (reference was laparoscopic) (OR=1.47; 95% CI= 0.99-2.17) were not significant. The results from the multivariate logistic regression model showed that having had a pre-surgery IP hospitalization, age, CCI, and bariatric surgery type were all significant predictors of having an IP hospitalization during the 12 month follow-up period (p < 0.05). Gender, surgical approach and type of insurance were not significant predictors in this regression model. As discussed in the methods section, we explored the interactions between age, type of insurance, CCI, type of bariatric surgery and pre surgery IP utilization. The results showed that none of the interactions were significant, and thus were not retained in the model. We removed type of insurance and surgical approach from the model but retained gender given their clinical significance. The final independent variables retained in the model were gender, age, CCI, type of bariatric surgery, and pre surgery IP. The overall model was statistically significant (Likelihood ratio χ2(5) = 104.32, p < 0.0001). From the results of the logistic regression model we can state that after adjusting for gender, patients who had a bypass bariatric surgery procedure were 2.3 times more likely to have an IP hospitalization during the 12 month post-surgery period compared to those who underwent banding procedures (OR =2.33; 95% CI= 1.76-3.08; p<0.0001). Patients who had at least one IP hospitalization during the 12 months prior to the surgery were nearly 2.5 times more likely to have an IP claim during the 12 months follow-up (OR =2.49; 95% CI= 1.70-3.65; p<0.001). For each year increment in age, the likelihood of having an IP hospitalization post bariatric surgery increased by 2% (increased 27% for
60 every 10 years). For each unit increment in the CCI, the likelihood of having an IP hospitalization increased by 18% during the 12 months of follow-up (Table 31).
Chapter 5. Discussion, Limitations, Conclusion and Future Directions The present study was designed to evaluate the effects of bariatric surgery on medication and medical services utilization among a Commercial and Medicare insured population using administrative claims data from a large health benefits organization in the U.S. This chapter will discuss the results of the three specific aims in light of the published literature currently available. Study limitations will be noted and the main conclusions will be highlighted. Finally, some recommendations on future directions will be discussed. 5.1. Demographics and baseline characteristics of the study population Our study population was primarily female (81%), non-hispanic White with an average age of 47. The gender and age demographic characteristics are consistent with other publications from studies around bariatric surgery using administrative claims data [18, 83]. Reports on race and ethnicity distribution of bariatric surgery patients are usually not available from studies that used administrative claims data, and thus we do not have a basis for comparison. However, the small percentage of Black (12.4%) and Hispanics (10%) in our study cohort could be a reflection of the high rate of lack of health insurance coverage among these populations in the U.S. According to the Pew Hispanic Center 2010 report on the Statistical Profiles of the Hispanic and Foreign-Born Populations in the U.S., [88] Hispanics are the least likely to have health insurance. Nationally, the 61
62 uninsured rate among Hispanics was 31.7% and 19% for Blacks in 2008. Aside from disparities in health insurance coverage, other reasons such as lack of knowledge of the bariatric surgery procedure as well as lack of self-awareness of morbid obesity could also be contributing factors for the low rates of bariatric surgery among minority populations [89]. In terms of type of insurance distribution, the higher percentage of Commercially insured members among our study cohort could be a reflection of a higher number of procedures being performed among younger patients with a lower risk of complications. A study published by Livingston et al. [90] showed that adverse outcomes increased with age, particularly after age 60 and that the adverse event rate exceeded 20 percent beyond 65 years of age. In terms of prevalence of obesity related comorbidities our results vary widely with respect to that reported by other researchers [8, 91, 92]. These variations may be due in part to differences in the length of time used to estimate the prevalence (period prevalence), which in our study was 12 months (pre-surgery) compared to other studies that used 3 or 6 months pre-surgery. However, our list of the top most common obesity related comorbidities is consistent with that reported by other authors [18, 39, 93, 94]. The Deyo adapted Charlson comorbidity index (CCI) in our study cohort was greater compared to that reported in a study by Hodo et al [17] that used administrative claims data to assess the effects of bariatric surgery on medication utilization (Mean (SD): 0.88(1.22) vs 0.42 (0.75) respectively). An explanation for this difference is the same as described above for the estimation of period prevalence of obesity related comorbidities.
63 Hodo et. al. used a 6 month pre surgery period to calculate the CCI compared to a 12 month pre surgery period employed in our current study. A CCI of 1 or less is considered to be low in terms of predicting postoperative complications and mortality, while a CCI of 3 or more is considered to be high [85]. Thus, the mean CCI of 0.88 found among our study cohort can be interpreted as low. 5.2. Characteristics of the bariatric surgery procedures Among our study population the proportion of banding and bypass bariatric surgery procedures was very similar. However, the proportion of open and laparoscopic procedures was quite different (10% vs 90% respectively). From 2005 to 2006, there was a marked decline in the number of bariatric surgeries performed via open surgical approach, with a corresponding marked increase in the number of laparoscopic procedures. After 2006 and through 2007, the trend continued with a steady decline in the proportion of open procedures and a steady increase in laparoscopic procedures. A slight increase was observed in the proportion of open surgeries performed in 2008; however only 6 months were taken into account for that year. Although Humana approved coverage of the Lap Band in April 2003, the process of approval for this surgical procedure may take a couple of months especially at the beginning that could explain the delay in seeing claims for this procedure until 2006. The increasing trend in laparoscopic bariatric surgeries found in our study is consistent with a recent publication from Samuel et al, which showed a 24% increase in laparoscopic procedures from 1987 to 2004.[95] This 18-year trend analysis also showed that the most common bariatric surgery procedure in 2004 was the Roux-en-Y gastric
64 bypass (RYGB), which was also the predominant procedure in our study (53% bypass Vs 47% banding procedures). In terms of cost of the bariatric surgery procedure, the higher average cost of open vs laparoscopic procedures found in this study is consistent with the longer average lengths of stay among those undergoing open surgery. Overall, bypass procedures were also more costly than banding procedures which is consistent with the higher rate of perioperative complications among bypass patients and the shorter operating room times and lengths of stay among patients undergoing banding procedures, which have been reported in the literature. [79, 96] 5.3. Medication and health services utilization at baseline Medication utilization among our study population during the 12 months pre surgery was reflective of the high degree of obesity-related comorbid conditions often present among morbidly obese patients. Antihypertensives, antidiabetics and antihyperlipidemics were among the most frequently filled medications, which is not surprising given the high prevalence of these conditions on their own, or in combination as part of the constellation of conditions that characterize the metabolic syndrome. [97-100] There was also a high percentage of members utilizing analgesics prior to surgery (42%), which is consistent with the high prevalence of back strain and disk disease (38%) among our cohort, along with a smaller but possibly underreported prevalence of weight bearing osteoarthritis (4%). Similarly, an important percentage of members in our study utilized antacids during the baseline period (22%), consistent with the high prevalence of gastroesophageal reflux disease among our cohort (53%). The percentage of members
65 utilizing analgesics and antacids may in fact have been higher than that observed in our study population s claims data, given that many of these medications are available overthe-counter and not reflected in administrative claims. An interesting finding was the high percentage of members who utilized antidepressants at baseline (26%). This percentage of antidepressant utilization along with the high prevalence of depression (20%) among our study cohort is much higher and inconsistent with the prevalence of depression among morbidly obese candidates for bariatric surgery reported by some authors. Cawly et al. [92] reported a 4% prevalence of depression during a 6-month pre surgery period among morbidly obese patients that later underwent bariatric surgery. The fact that the prevalence of depression in their study was estimated for a 6-month period, compared to the 12-month period in our study, could partially explain the differences, since the likelihood of finding an ICD-9-CM code for depression (or any other diagnosis code) in administrative claims data increases as the timeframe being explored increases. Another potential explanation could be a difference in the ICD-9-CM codes used to identify depression in our study versus those used by Cawley et al. However, our findings were consistent with the reported prevalence of depression by Segal et. al.[18] (19%) who interestingly also used a 6-month pre surgery period to estimate it using administrative claims data. In terms of the average number of all prescription claims over a 12-month pre surgery period, our results are consistent with those reported by Hodo et. al. [17]. They reported an average of nearly 7 prescription claims over a 3-month period pre surgery, which extrapolated to a 12-month period, would be equivalent to an average of 28 prescription claims, which is very similar to the one we found in our study (12-month pre
66 surgery average = 27.54). It is important to note that this measure is not reflective of unique medication counts or a proxy measure for medication adherence; however, it does provide an overall estimate of the degree of all-cause medication utilization pre and postsurgery. When looking at health services outpatient utilization pre surgery, we found an increasing trend in the average number of claims for office and outpatient visits, as well as for tests and procedures from the 7-12 months to the 1-6 months pre surgery. This increasing trend in overall outpatient utilization during the 6 months immediately preceding the surgery is understandable given the preoperative assessments that a bariatric surgery candidate must undergo prior to the procedure. The Medical Guidelines for Clinical Practice for the perioperative nutritional, metabolic, and nonsurgical support of the bariatric surgery patient released by the American Association of Clinical Endocrinologists, The Obesity Society, and American Society for Metabolic & Bariatric Surgery [101] contain a comprehensive description of the preoperative evaluation that any patient seeking bariatric surgery should complete. The preoperative evaluation should include an assessment from a multidisciplinary team of health care professionals along with pertinent laboratory and diagnostic testing. As far as emergency room and inpatient hospitalizations, utilization remained stable during the two six-month pre surgery periods. Although we did not explore the specific causes of these utilizations, it is likely that they were related to complications of comorbid conditions. There are few publications reporting the preoperative average health services utilization among bariatric surgery patients. One of them published by Hodo et.al., reported an average number of claims for emergency room and inpatient
67 hospitalizations during a 3-month period of 0.11 and 0.04 respectively. [17] Extrapolating these findings to a 12-month period would result in an average of 0.44 ER claims, which is close to our reported average of 0.37; and an average of 0.16 IP hospitalizations, which is also close to our reported average of 0.13. 5.4. Effects of the bariatric surgery procedure and other relevant covariates on medication utilization Our hypothesis around the effects of bariatric surgery on the average number of all prescription drug claims during the 12-month post-surgery period was supported by our findings which showed a 21% reduction in the average number of prescriptions filled pre versus post-surgery (from an average of 27.54 pre to 21.70 post-surgery). The only publication found in the literature reporting the effect of bariatric surgery on all prescription drug claims showed a reduction of 29% over a period of 3 months during the post-surgery period. [17] The reduction in the average number of all prescriptions found in our study is attributable in part to the reduction in the average number of prescriptions for all of the select medication groups explored in our study, although the highest absolute differences between the pre and post-surgery periods were for antidiabetics (- 9.29), antihypertensives (-5.05) and antihyperlipidemics (-3.66). The significant reduction in the average number of prescription claims for these three medication groups is supportive of our third hypothesis around medication utilization, and could be an indication of an improvement or resolution of the associated comorbid conditions. The proportion of patients utilizing these three medication groups during the post-surgery period also significantly decreased, supporting our second hypothesis around medication
68 utilization. This measure could be a reflection of medication discontinuation due to resolution of the associated comorbid conditions, rather than just an improvement of such. When exploring if there would be any differences in the likelihood of discontinuing antihypertensives, antidiabetics and antihyperlipidemics by type of bariatric surgery (hypothesis 4) our findings showed that patients who underwent bypass procedures had higher odds of discontinuing all three medication groups during the 12 months postsurgery, compared to patients who underwent banding procedures, after adjusting for age, gender, type of insurance, and Charlson comorbidity index. This finding is consistent with a previously published systematic review by Tice et.al. [79] comparing gastric banding versus bypass which reported a higher percentage of weight loss as well as a higher percentage of resolution of comorbidities among Roux-en-Y gastric bypass versus laparoscopic adjustable gastric banding patients. Another study recently published by Woodard et. al. [80] comparing the effects of laparoscopic Roux-en-Y gastric bypass vs. adjustable gastric banding on cardiovascular risk factors (CRF), found that RYGB produced significant improvements in a greater number of biochemical CRFs and in some instances (i.e. Total Cholesterol) the 12-month post-surgery biochemical CRF values were significantly lower in RYGB compared to LAGB patients. This study also found a greater degree of weight loss among RYGB versus LAGB patients. No publications were found in the literature comparing the effects of banding versus bypass procedures on medication utilization directly. In terms of other significant covariates affecting medication discontinuation, the fact that age and CCI were significant negative predictors of discontinuation for all three
69 medication groups is comprehensible from the clinical standpoint. With increasing age chronic cardiometabolic diseases may be more complex and more refractory to treatment, therefore reducing the likelihood of medication discontinuation. As mentioned in previous chapters, CCI is widely used in outcomes research studies that utilize claims data, to adjust for comorbidities and disease severity. [85, 102, 103] Studies have shown that as CCI increases, morbidity and mortality also increases, so it is understandable that in this study it had a negative predictive effect on medication discontinuation for antihypertensives, antidiabetics and antihyperlipidemics. The interaction effect found between CCI and bariatric surgery type for discontinuation of antihypertensives as well as the gender differences (females were over two times more likely to discontinue antihypertensives than males) are less understood and would warrant further exploration in a future study. 5.5. Effects of the bariatric surgery procedure and other relevant covariates on health services utilization Based on previous publications reporting the incidence of bariatric complications, [83, 104, 105] we hypothesized that emergency room visits as well as inpatient hospitalizations would increase during the 12-month post-surgery period. Our hypothesis was supported by a 22% increase in the average number of claims for ER visits and a 131% increase in IP hospitalizations in the 12-month pre compared to the 12-month postsurgery period within our study cohort. This increase was more markedly observed during the first six months post-surgery, suggesting that surgical bariatric and nonbariatric specific complications, including dumping, vomiting, diarrhea, complications of
70 anastomosis, abdominal hernia, infection and sepsis, deep vein thrombosis/pulmonary embolism, pneumonia, respiratory failure, marginal ulcer, hemorrhage, post-operative wound dehiscence, myocardial infarction and stroke could have been the underlying causes. [60, 83] Although the ER and IP utilization during the second six months post-surgery decreased with respect to the first six months post-surgery, it still remained higher than the baseline pre surgery utilization. A longer follow-up period would be required in order to determine if ER and IP utilization continues to decrease after the first year post-surgery. The results from our regression models for ER and IP utilization showed that pre surgery ER visits and IP hospitalizations, age, and CCI were significant predictors for post-surgery utilization of these two services. All these variables correlate with higher patient morbidity and it is thus understandable that they were positive predictors in our models. Consistent with our hypothesis, there were significant differences in the likelihood of IP hospitalization during the 12-month post-surgery depending on the type of bariatric procedure performed. Patients who underwent bypass procedures were 2.3 times more likely to be hospitalized post-surgery than those who underwent banding procedures, after adjusting for covariates. This finding is consistent with other studies that have shown that the perioperative complications and short-term morbidity are more common with Roux-en-Y gastric bypass than with laparoscopic adjustable gastric banding. [79] In the case of ER visits, the type of bariatric surgery did not turn out to be a significant predictor of utilization post-surgery, contrary to our hypothesis. One potential explanation is that the causes for ER visits that are less severe and do not end up
71 in hospitalizations are probably similar between both types of surgery. However, a patient that presents to the ER with a more severe complication requiring a hospitalization (such as those more common among bypass patients) will show up in claims data as an IP utilization and not as an ER visit, which is probably also why the increased likelihood of utilization found among bypass patients was evident for IP hospitalizations rather than ER visits. A longer follow-up period would be required in order to determine if ER and IP utilization continues to decrease after the first year postsurgery. The reduction in office and outpatient visits, tests and procedures during the 12- month post-surgery period compared to the 12-month pre surgery period, could be explained by the fact that during the pre-surgery period, patients are subject to a number of preoperative evaluations that reflect into an increased number of claims for these types of services prior to surgery. A future study to evaluate other reasons that could explain the reduction in the utilization of outpatient services post-surgery would be required to determine if the resolution of comorbid conditions is playing a role, if a lack of compliance with postoperative follow-ups is associated with this decrease, and if the lower outpatient utilization is sustained or further reduced after 12 months post-surgery. 5.6. Limitations This study was conducted using medical and pharmacy administrative claims exclusively. No clinical measures (i.e. blood pressure) or laboratory values (i.e. HbA1c, lipid panel) were available to corroborate that our findings around medication discontinuation were in fact the result of the resolution of comorbid conditions, and not
72 an effect of reduced medication compliance or other reasons. However, our focus on medication discontinuation rather than on the reduction in the average number of prescriptions filled post-surgery reduces the likelihood of confounding due to potential effects of low medication adherence or changes in prescription patterns post-surgery. Measures of weight and BMI were not available in this study, and thus we were unable to assess the impact and correlation of weight loss with the variations in medication and health services utilization. We did not explore the causes for the increased ER visits and IP hospitalizations post-surgery. However, based on the published literature around the complications of bariatric surgery, it is likely that these utilizations were due to postoperative complications directly related to the surgical procedure itself, postoperative conditions related to any type of major surgery, as well as metabolic and nutritional complications related to bariatric surgery. [61, 62, 65, 104, 106, 107] Although our follow-up period was longer than that used by other researchers [15-17] to evaluate the effects of bariatric surgery on medication and health services utilization, a longer follow-up period (18 months and beyond) would be required in order to assess the longer term effects of this procedure. 5.7. Conclusions The findings of this study show that bariatric surgery resulted in a significant reduction in overall medication utilization during the 12 months post-surgery, as well as a significant reduction in utilization of antihypertensive, antidiabetic and antihyperlipidemic medications. Patients undergoing bypass procedures were more likely
73 to discontinue antihypertensive, antidiabetic and antihyperlipidemic medications, but were also more likely to have an inpatient hospitalization during the 12 months postsurgery. The reduction in medication utilization may translate into pharmacy cost savings from the health insurers perspective, which may however, not completely compensate the increase in utilization of ER and inpatient services in the short-term. A return on investment (ROI) or cost-effectiveness analysis (CEA) was not an objective of this study, but the results of other analyses have shown that the costs incurred by managed care organizations for bariatric surgery procedures are recovered between two (for laparoscopic procedures) and four (for open procedures) years after the surgery. [19] Although some insurance companies may want to rely on ROI analyses to base their coverage decisions, some authors have advised not to focus on cost savings, but rather on efficacy and cost-effectiveness of the bariatric surgery procedure compared to other treatment options. [108] The increase in utilization of ER and inpatient services found in this study during the postoperative period is not surprising, but denotes an area of opportunity where special attention should be placed for actionable strategies to be implemented in order to improve clinical and economic outcomes. Some authors have reported that rates of serious complications are inversely associated with hospital and surgeon procedure volume, but unrelated to Center of Excellence accreditation by professional organizations.[109] This evidence is important from a managed care perspective as preferred provider networks are defined for bariatric surgery procedures.
74 A comprehensive and strict preoperative and postoperative case management program is a key component for improving outcomes and reducing complications,[3, 110] thus potentially reducing the utilization of ER and inpatient services. As the demand for long-term weight reduction strategies continues to grow, the field of bariatric surgery will continue to evolve as expertise is gained, existing techniques are improved, and new techniques are introduced. The expectation will be that the risk to benefit ratio from bariatric surgery will progressively decrease so that the positive impact on health outcomes and health care utilization may be expanded and sustained longer term. 5.8. Future Directions One suggestion for future research is to revise the CCI score to improve riskadjustment among members who undergo bariatric surgery, given that the diseases used to estimate the CCI are different from the ones commonly found among the morbidly obese. Further exploration of the causes for the increased average ER and IP utilization post-surgery would corroborate our hypotheses that an increase in utilization of these two services was due primarily to complications of the bariatric surgery procedure. A longer follow-up period would be required in order to determine if ER and IP utilization decreases after the first year post-surgery. A future study to evaluate other reasons that could explain the reduction in the utilization of outpatient services post-surgery would be required to determine if the resolution of comorbid conditions is playing a role, if a lack of compliance with
75 postoperative follow-ups is associated with this decrease, and if the lower outpatient utilization is sustained after 12 months post-surgery. The interaction effect found between CCI and bariatric surgery type for discontinuation of antihypertensives as well as the gender differences (females were over two times more likely to discontinue antihypertensives than males) are less understood and would warrant further exploration in a future study. Given the limited number of publications around the effects of bariatric surgery on mental health conditions such as depression, it would be relevant to further explore this topic using a mixed methods approach combining both quantitative and qualitative data analyses, and utilizing different sources of information including, but not limited to, claims data and patient self-report.
Figure 1. Trends in overweight, obesity, and extreme obesity among adults aged 20 years and over: United States, 1988-2008 Figure from the CDC National Center for Health Statistics Prevalence of Overweight, Obesity, and Extreme Obesity Among Adults: United States, Trends 1976 1980 Through 2007 2008 76
77 Figure 2. Vertical Banded Gastroplasty (VBG) Source: American Society for Metabolic and Bariatric Surgery http://www.asbs.org/html/patients/gastroplasty.html
78 Figure 3. Laparoscopic Adjustable Gastric Band (LAGB) Source: American Society for Metabolic and Bariatric Surgery http://www.asbs.org/html/patients/gastroplasty.html
79 Figure 4. Sleeve Gastrectomy Source: American Society for Metabolic and Bariatric Surgery http://www.asbs.org/html/patients/gastroplasty.html
80 Figure 5. Biliopancreatic diversion with duodenal switch (BPD DS) Source: American Society for Metabolic and Bariatric Surgery http://www.asbs.org/html/patients/gastroplasty.html
81 Figure 6. Roux-en-Y gastric bypass (RYGB) Source: American Society for Metabolic and Bariatric Surgery http://www.asbs.org/html/patients/gastroplasty.html