Pierre-Yves Cremieux, PhD; Henry Buchwald, MD, PhD; Scott A. Shikora, MD; Arindam Ghosh, PhD; Haixia Elaine Yang, PhD; and Marric Buessing, BA
The prevalence of obesity among the US adult population has increased steadily to reach one third of the US adult population.1
More alarming yet, the trend in morbid obesity outpaces that of nonmorbid obesity. From 2000 through 2005, the US obesity rate increased by 24%, while the rate of morbid obesity (body mass index [BMI], calculated as weight in kilograms divided by height in meters squared >40) grew by 50%, and the rate of patients with a BMI exceeding 50 grew by 75%.2,3
This trend in morbid obesity results in increased healthcare utilization and costs, as healthcare costs for the morbidly obese are 81% above those for the nonobese population and 47% above costs for the non–morbidly obese population.4,5
Morbid obesity is associated with a myriad of serious comorbid conditions, including hypertension, type 2 diabetes mellitus, dyslipidemia, osteoarthritis, and gallbladder disease.6,7
Bariatric surgery has been demonstrated to be an effective weight-loss alternative for the morbidly obese8-10
and is associated with marked resolution of comorbidities.9
have found similar results, with reductions in morbidity, cardiovascular risk, healthcare utilization, and costs in bariatric surgery patients compared with control subjects. Although most of the current literature examines health benefits associated with bariatric surgery,14
studies have also documented quality-of-life improvements,15,16
and reduced work loss20
associated with bariatric surgery.
Despite the extensive literature on the clinical effects of bariatric surgery, little research has been published on the economic impact of the procedure. This represents a growing gap in the literature as the clinical outcomes become better known and the procedure becomes more commonplace (>170,000 surgical procedures in 2005), while its economic costs or benefits remain unclear.21
The present analysis is unique in its use of actual patient-level cost data for 3651 patients who underwent the procedure. The resulting return on investment is calculated based on up to 5 years of postoperative cost data.
This study quantifies the effect of bariatric surgery on direct medical costs. We focus on the time required for third-party payers to recover the initial investment associated with bariatric surgery (ie, the return on investment).8
Using the Ingenix private insurer claims database and a matched cohort method and focusing only on costs incurred and saved by the private insurer, we build on findings of a previous study20
that suggested a 9-year period to recoup the cost of bariatric surgery. We further examine changes in return on investment over time as bariatric surgery techniques have improved and focus on laparoscopic surgery outcomes.22
This analysis should help evaluate the cost-benefit implications of bariatric surgery.
We used a privately insured administrative claims database containing medical and drug claims from 1999 through 2005 covering more than 5 million lives from 31 large companies that provided extensive health insurance coverage, including mental health. These companies have operations nationwide in a broad array of industries and job classifications (eg, financial services, manufacturing, telecommunications, energy, and food and beverage). The data contain deidentified information on patients’ demographics (eg, age and sex) and monthly enrollment history, as well as medical and pharmacy claims. Specifically, patients’ utilization of medical services is recorded with the date of service, place of service, associated diagnoses, performed procedures, billed charges, and actual amount of payments. Patients’ pharmacy claims contain prescribed medications identified by National Drug Code, the date a prescription was filled, days of supply, quantity, and actual payment amount. The study sample for this analysis included claimants having a diagnosis of morbid obesity (International Classification of Diseases, Ninth Revision, Clinical Modification code 278.01). Patients 18 years or older who underwent bariatric surgery were identified using Health Care Financing Administration Common Procedural Coding System and Current Procedural Terminology codes 43644, 43645, 43842, 43843, 43845, 43846, 43847, S2085, S2082, and S2083. Of these procedures, 73% were gastric restrictions with bypass (codes 43845, 43846, and 43847), 11% were gastric restrictions without bypass (codes 43842 and 43843), 12% were laparoscopic surgical procedures with bypass (codes 43644, 43645, and S2085), and 4% were laparoscopic surgical procedures without bypass (codes S2082 and S2083).Analysis
The initial date of bariatric surgery was defined as the index date for the relevant patient, as well as his or her control. All claimants in the study sample were required to have at least 6 months of continuous enrollment before the index date and 1 month following the index date.
Because patients with a morbid obesity claim may be sicker, on average, than patients with no such claim recorded, surgery-eligible controls (morbidly obese patients with no bariatric surgery procedure code) were matched to bariatric surgery patients based on age group, sex, state of residence, comorbidities, and 5-month presurgery direct costs (months −6 to −2, excluding month −1 immediately before surgery, which is often characterized by increased costs associated with preparation for surgery). Each bariatric surgery patient was matched to a specific control drawn from the morbidly obese control population that never underwent bariatric surgery.
For each bariatric surgery patient, a control was considered a match if (1) the control's age was within the same 10-year age range as that of the bariatric surgery patient, (2) the control was of the same sex, (3) the control resided in the same state as the bariatric surgery patient, (4) the control had the same 10 comorbidities as the bariatric surgery patient (Table 1
), and (5) the control’s healthcare costs fell within 1 SD of the cumulative costs (during months −6 to −2) incurred by the bariatric surgery patient. The matching of bariatric surgery patients with their controls is performed based on 10 comorbidities, although findings in a review of the existing literature23
and the guidelines of the American Society for Bariatric Surgery24
suggest that 18 comorbidities could cause imbalance between the 2 samples. However, not every patient could be matched on the demographics and on all 18 comorbidities because patients with the corresponding combination of comorbidities may not be observed in the control group. Hence, patients were matched to controls using a subset of the following 10 comorbidities: asthma, coronary artery disease, diabetes mellitus, dyslipidemia, gallstones, gastroesophageal reflux, hypertension, nonalcoholic steatohepatitis or nonalcoholic fatty liver disease, sleep apnea, and urinary incontinence. Multivariate analysis was used to account for the remaining 8 comorbidities, thereby addressing any remaining imbalance across matched samples. These 8 comorbidities are breast cancer, congestive heart failure, lymphedema, major depression, osteoarthritis, polycystic ovary syndrome, pseudotumor cerebri, and venous stasis or leg ulcers.
Because calculating a return on investment requires a comparison of costs for the bariatric surgery and control patients during multiple years, 2 adjustments were made. First, costs were inflation adjusted to 2005 US dollars using the Consumer Price Index for Medical Care because a dollar spent today would purchase more goods and services than a dollar spent in 2 years (as long as inflation is positive). Second, cost savings were discounted by a 3.07% interest rate, the mean return on a 3-month Treasury bill, because a dollar saved today would, if invested in a risk-free Treasury bill, be worth more than a dollar in 2 years (roughly $1.06 at the stated rate). However, the shorter the time horizon to recoup costs, the less effect discounting will have on the estimated return on investment. The multivariate analysis modeled normalized monthly costs as a function of bariatric surgery interacting with discrete indicators of time from surgery. A positive coefficient indicates that costs incurred by the bariatric surgery patients are higher; a negative coefficient indicates that costs are lower relative to their controls. Therefore, positive coefficients indicate incremental third-party payer costs associated with bariatric surgery (the “investment”), and negative coefficients indicate savings from bariatric surgery (the “return”). The return on investment calculations combine these estimates to determine the number of months necessary for cumulative savings associated with improved comorbidity outcomes following surgery to cover the initial investments. The point estimates are reported with 95% confidence intervals (CIs). Indicator variables in 6-month increments are included to allow for nonlinear savings. In addition to the indicator variables, the multivariate model controlled for age and the 8 comorbidities already mentioned. The comorbidities were tracked at 3-month intervals to record changes in prevalence.
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