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A Cost-benefit Simulation Model of Coverage for Bariatric Surgery Among Full-time Employees
Objective: To use a simulation model to estimate the costs andbenefits of bariatric surgery among full-time employees.
Study Design: Multivariate regression analysis of nationally representativesurvey data sets to estimate the costs of obesity and asimulation model of the number of years until breakeven underalternate assumptions about the costs and benefits of bariatricsurgery.
Methods: We used a 2-part model to estimate medical costs ofobesity based on the 2000-2001 Medical Expenditure PanelSurvey. We estimated work loss with a negative binomial regressionbased on the 2002 National Health Interview Survey. Usingthese results, we simulated the expected number of years requiredfor a bariatric surgery procedure to become cost saving.
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Results: Nine percent of the full-time US workforce, or 29% ofthe obese workforce, is eligible for bariatric surgery. Obese workerseligible for bariatric surgery have 5.1 (< .01) additional daysof work loss and $2230 (in 2004 dollars) (< .01) higher annualmedical costs than persons of normal weight.
Conclusion: Although the cost implications of bariatric surgeryamong full-time employees depend on many factors, the simulationsreveal that 5 or more years of follow-up are most likely required forthese operations to become cost saving unless the employee bearsa significant fraction of the total costs of the surgery.
(Am J Manag Care. 2005;11:641-646)
The increased prevalence of obesity and obesity-relateddiseases among Americans is well documented.1-3 The overall rate of obesity in adultsgrew to 30.5% in 1999-2000, from 22.9% in 1988-1994and less than 15% in the 1970s.1 The prevalence of morbid,or severe, obesity has increased at a much fasterrate than obesity in general.4 A substantial body of literaturehas also shown large financial consequencesfrom obesity. For example, obese adults incur 36%greater annual medical expenditures than normal-weightpersons,5 and overweight and obesity account for9.1% of total annual medical expenditures.6
Increased prevalence of obesity among the workforcemay have several financial consequences for employers.7 As increased rates of obesity contribute to risingmedical costs, this will likely exacerbate health insurancecosts. Recent research has shown that 12% of therise in inflation-adjusted per capita medical spendingbetween 1987 and 2001 was attributable to theincreased prevalence of obesity.8 Obese employees havealso been shown to be absent from work more oftenthan their nonobese counterparts.7,9 Thompson et al7found that obese men were absent 2.7 more days peryear than normal-weight men, and obese women missed5.1 more days per year than normal-weight women.
With attention focused on the costs and prevalenceof obesity, bariatric surgery—in particular, Roux-en-Ygastric bypass and gastric banding—has recentlybecome a common form of treatment for severe obesity.10,11 Employers and insurers have, in turn, beenforced to make or revisit decisions about coverage forbariatric surgery and other obesity treatments.12
Although surgical operations have been shown to beeffective in reducing weight and resolving or reducingcomorbidities,13,14 few investigations have addressedpotential cost savings resulting from the surgery.15Three studies16-18 applied the conventional cost-effectivenessframework to compute the cost per quality-adjustedlife-year for surgery; results ranged from-$4000 per quality-adjusted life-year (net savings)18 to$35 600 per quality-adjusted life-year.16
Other studies19-22 looked at reductions in specificcost components resulting from the surgery, but only 1study23 reported the number of years before bariatricsurgery results in cost savings, and this study was limitedto medical costs in the Canadian healthcare system.The study reported that Roux-en-Y gastric bypass andvertical banded gastroplasty were cost saving after 3.5years. However, because the Canadian healthcare systemis different from the US healthcare system, resultsmay not be generalizable across borders.
In this study, we use nationally representative datafor the US full-time employed population to quantify theincrease in annual medical costs and work loss associatedwith obesity among the bariatric surgery-eligibleand bariatric surgery-ineligible obese populations. Wethen use these results in a simulation model to estimatethe potential benefits associated with coverage forbariatric surgery under various assumptions. We areaware of no available randomized controlled trial orquasiexperiment with sufficient data to permit adetailed cost-benefit analysis of bariatric surgery. In theabsence of such data, a simulation model is feasible andappropriate for providing base-case estimates.
DATA
We used 2 nationally representative data sets of thecivilian noninstitutionalized population to quantifyannual work loss and medical costs attributable to obesity.We used the 2002 National Health Interview Survey(NHIS) to analyze work loss due to illness or injury andthe 2000-2001 Medical Expenditure Panel Survey(MEPS) for medical costs. We applied common sampleselection criteria to both data sets. We restricted thesample to individuals aged 18 to 64 years who reportedworking full time (≥ 35 h/wk) for the entire year. We alsoexcluded pregnant women and individuals with missingbody mass index (BMI) data, calculated as weight inkilograms divided by the square of height in meters.
The NHIS is the principal source of information onthe health of the household population of the UnitedStates. Besides self-reported height, weight, and healthconditions, the NHIS includes information on workdaysmissed due to illness or injury and sociodemographiccharacteristics, including race and ethnicity, sex, age,education, family size, employment status, occupation,hours of work per week, and income. For analysis ofwork loss, we began with the 31 044 adults in the 2002NHIS. After applying the sample restrictions, the finaldata set included 12 019 full-time employed adults(6641 men and 5378 women) with sampling weights togenerate nationally representative estimates. Forty-sixpercent of the weighted regression population (41% ofmen and 53% of women) reported missing at least 1 dayof work due to illness or injury.
The MEPS sample is a subset of the NHIS participants.The MEPS provides additional details on healthconditions and annual medical expenditures, and eachindividual's data can be merged with his or her responsesto the NHIS survey. We used the MEPS and pooleddata from 2000-2001 to increase the sample size.Applying the sample selection criteria already detailedreduced the 41 217 (unweighted) adults (17 558 in 2000and 23 659 in 2001) to the final data set of 20 329 full-timeemployed adults (11 849 men and 8480 women).
Surgery-eligible obesity was defined as a BMI of 40 orgreater, or a BMI of 35 to less than 40 with angina, asthma,osteoarthritis, diabetes mellitus, or hypertension.This approximated the guidelines set by a NationalInstitutes of Health panel on bariatric surgery,24although the guidelines included other comorbiditiesthat we were unable to measure in our data. Surgery-ineligibleobesity was defined as a BMI of 30 to less than35, or a BMI of 35 to less than 40 without the comorbiditiesjust listed. All other BMI values were consideredto represent nonobesity. In the analyses, we includeddummy variables for overweight (BMI, 25 to < 30) andunderweight (BMI, < 18).
METHODS
Work Loss
Because the dependent variable (workdays missed)is discrete, we used a negative binomial regression witha log link to estimate work loss. We conducted separateanalyses for men and women when reporting results bysex and a pooled analysis (controlling for sex) whenreporting overall estimates. We included binary variablesfor BMI categories (underweight, overweight, surgeryineligible, and surgery eligible) to estimate theeffect of excess body weight on annual missed workdays;normal (BMI, 18 to < 25) weight constituted theomitted reference category. The regressions controlledfor other factors expected to affect the number ofmissed workdays, including race and ethnicity, age,education, family size, marital status, income, hourly orsalaried employee, class of occupation, years at currentjob, smoking status, alcohol use, and any functional limitationsnot self-reported as obesity related (eg, difficultywalking, standing, sitting, stooping, reaching, orgrasping). Our method allowed us to estimate theincrease in work loss associated with obesity among thesurgery-eligible and surgery-ineligible populations.
We used the regression estimates to predict annualmissed workdays for each employee. We then generateda second prediction after setting the 2 binary obesityvariables to zero, thus predicting the number of missedworkdays for a hypothetical nonobese employee with allother characteristics equivalent to those of an obeseemployee.25 The difference between these 2 predictionsreflects annual obesity-attributable missed workdays.
Medical Costs
We used a standard 2-part model to estimate annualmedical costs attributable to obesity for the surgery-eligibleand surgery-ineligible populations.25,26 We conductedseparate analyses for men and women and apooled analysis (controlling for sex) when reportingoverall estimates. We used the same main set of independentvariables as for estimation of work loss, addinga categorical variable for census region and excludingvariables not directly captured in the MEPS, namely,family size, hourly or salaried employee, class of occupation,years at current job, and functional limitations.Predicted costs were generated as for work loss:we extrapolated one set of predictions using theobserved values and another with the binary obesityvariables set to zero so that the difference reflects estimatedobesity-attributable medical costs. Thesemethods followed those used in a recent study6 thatquantified national medical expenditures attributableto obesity. Medical costs were adjusted to 2004 dollarsusing the medical care component of the Bureau ofLabor Statistics Consumer Price Index.
Statistical Computation
All regressions were performed using Stata 8.2(StataCorp LP, College Station, Tex) with samplingweights that allowed for generating nationally representativeestimates of the full-time employed population.We computed standard errors by bootstrapping usingthe bsample procedure in Stata.
Bariatric Surgery Simulation
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Using the work loss and medical cost estimates, wesimulated the cost implications associated with coveragefor bariatric surgery from a self-insured employer'sperspective (ie, the employer bears all medical andwork loss costs). Using this perspective, a bariatricsurgery procedure would achieve breakeven when thesavings resulting from the procedure become equal tothe costs of the procedure. Algebraically, this occurswhen the following equation holds true: = + ,where indicates the number of years since the procedure;, the mean annual costs if the procedure is notperformed; , the fixed costs associated with the procedure;and , the mean annual costs after the procedurehas been performed. From a self-insured employer'sperspective, , , and include medical costs and workloss costs due to illness or injury (we assume that 100%of these costs accrue to the firm).
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Annual work loss and medical costs in the absence ofbariatric surgery, , were drawn from the 75th and 90thpercentiles of the predicted distribution of medicalcosts and work loss for the surgery-eligible populationas estimated using the NHIS and MEPS data. We alsoused the estimated mean values for this population inTable 1 and Table 2. However, the 75th and 90th percentilesare likely more representative of expectedcosts for those who elect surgery, as they have beenshown to have poorer health and presumably highercosts than the average of all surgery-eligible employees.13,19,27 For example, the mean BMI was 46.9 among16 944 patients in 105 extracted studies of bariatricsurgery.13 In contrast, the surgery-eligible population inthe MEPS sample used for our estimation had a meanBMI of 41.7; the 75th percentile of BMI was 44.1, andthe 90th percentile of BMI was 48.4.
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Fixed costs, , depend on the employer's share of themedical costs of the procedure and the value of daysmissed from work for employees who undergo the surgery.The employer's share of the medical costs willvary depending on the cost of the procedure and theemployee copayment. For our base case, we showresults using $20 000 for , based on a cost of $25 000for the bariatric surgery procedure16 and a 20% employeecopayment. We assumed that 15 workdays wererequired for recovery28 and multiplied the number ofmissed days by the mean hourly wage (including benefits)among full-time employees to calculate the value ofworkdays lost. The hourly wage, $26.50, was the nationalmean in 2004 for all US full-time employees29 andequals an annual salary of $42 140, before including thevalue (30.8%) of benefits.30 For our base case, weassume that bariatric surgery reduces obesity-attributablecosts for the surgery-eligible population by 75%. Arecent meta-analysis13 reported that patients lost amean of 61% of excess weight after bariatric surgery; asa result, 77% of diabetes mellitus cases were resolved,62% of hypertension cases were resolved, and 86% ofsleep apnea cases were resolved.
Sensitivity Analysis
In addition to the base case, we simulated a 100%elimination of obesity-attributable costs for the surgery-eligiblepopulation, as well as a 50% elimination. We alsosimulated results in which the employer pays only 50%of the costs of the surgery (perhaps through a large coinsurancerate), as well as results in which those whoundergo the procedure have about 21/2 times the meanwage rate, or a $100 000 annual salary, before benefits.Higher wage rates will reduce the time to breakevenbecause of the higher costs that accrue to the firm as aresult of work loss. Last, we simulated the results if individualsare able to return to work in 5 workdays, asopposed to 15 workdays. All scenarios use a 3% annualdiscount rate to convert future savings to present value.The simulations were performed using Microsoft Excel2002 (Microsoft Corp, Redmond, Wash).
RESULTS
Work Loss Estimates
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Table 1 gives predicted annual work loss attributableto obesity among full-time employees. The difference inwork loss between surgery-eligible and surgery-ineligibleemployees was striking. Overall, less severe, surgery-ineligible obesity increased work loss by 0.8 days(< .01) over normal weight, 1.8 days for women (<.01) and 0.4 days for men (= .08). Surgery-eligibleobese employees, on the other hand, missed 5.1 moredays of work on average than employees of normalweight, 4.1 more days among men and 5.5 more daysamong women (< .01 for all). When both sexes wereevaluated together, the difference between surgery-eligibleand surgery-ineligible employees was statisticallysignificant (= .02). When the sexes were evaluatedseparately, the difference between surgery-eligible andsurgery-ineligible employees was insignificant for men(= .22) and women (= .10).
Medical Cost Estimates
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Predicted annual per capita obesity-attributablemedical costs are given in Table 2. Obesity increasedmedical costs substantially relative to normal-weightemployees; all comparison values in the table were statisticallysignificant (< .01). Obese employees ineligiblefor bariatric surgery had, on average, $550 (< .01)higher medical costs than normal-weight employees,while employees in the surgery-eligible group incurredan estimated $2230 (< .01) in additional medical costs.The difference in costs between surgery-eligible andsurgery-ineligible employees was significant (< .01)within each sex group and for overall obesity.
Bariatric Surgery Simulation
In Table 3, we report the number of years until thebenefits of bariatric surgery become equal to theemployer's medical and work loss costs. Our base caseassumes that 75% of obesity-attributable medical andwork loss costs would be reduced as a result of bariatricsurgery. For an eligible employee in the 90th percentileof the cost distribution, 5.0 years were required tobreak even, and focusing solely on medical costs, 6.6years were required; for an eligible employee in the75th percentile of the cost distribution, these numbersincrease to 7.1 and 8.9 years, respectively. For a surgery-eligible employee with average medical costs andwork loss, 10.3 years were required to break even (13.5years for medical costs only) (Tables 2 and 3).
In Table 4, we report the results of the sensitivityanalyses. In the best-case scenario in which 100% ofobesity-attributable costs are eliminated, 3.7 years wererequired to break even (4.9 years for medical costsonly). In a less optimistic case, 7.9 to 17.0 years wererequired when only 50% of costs are reduced (10.5-23.0years for medical costs only), depending on what levelsof costs were assumed before surgery. A 50% coinsurancerate would reduce the break-even period in thebase case from 5.0 to 3.3 years for an individual in the90th percentile and from 7.1 to 4.6 years for an individualin the 75th percentile.
Increasing wages from the base-case mean of$42 140 to $100 000 (before benefits) reduces thenumber of years to breakeven in the base case from5.0 to 4.0 years for an individual in the 90th percentileand from 7.1 to 5.9 years for an individual inthe 75th percentile. If individuals are able to returnto work in 5 (as opposed to 15) days, this reducesthe number of years to breakeven in the base casefrom 5.0 to 4.6 years for an individual in the 90thpercentile and from 7.1 to 6.4 years for an individualin the 75th percentile. Although increases inthe wage rate and reductions in work loss due tosurgery reduce the break-even period, even largechanges in these inputs result in small changes inthe time required to break even.
DISCUSSION
Results from the NHIS and MEPS analysesconfirm the substantial medical and work losscosts incurred by obese individuals, especiallyby the bariatric surgery-eligible population.Although no previous articles, to our knowledge,reported costs separately by eligibility statusfor bariatric surgery, our estimates aresimilar to those in the existing literature. Wefound that obesity caused an additional 5.1 daysof work loss for persons eligible for surgery and0.8 days for persons ineligible for surgery.Thompson et al7 reported that obese men aged25 to 54 years missed 0.2 to 2.7 more days peryear than normal-weight men and that obesewomen aged 25 to 64 years missed 2.3 to 5.1more days per year than normal-weight women.Finkelstein et al6 reported that among privatelyinsured individuals obesity increased costs byapproximately $540 (in 2004 dollars). This isnearly identical to our estimate for the surgery-ineligiblepopulation but less than our combined estimate for surgery-eligible and surgery-ineligible individuals. However,because their estimate includes all individuals with privateinsurance and ours is limited to full-time employees(with or without insurance), the estimates are not directlycomparable.
Because of the high prevalence of severe obesityamong full-time employees, the aggregate costs ofsevere obesity are substantial. We estimate that 9% of allfull-time employees, or 29% of obese (BMI, ³30)employees, are eligible for bariatric surgery (calculatedfrom the 1999-2000 National Health and NutritionExamination Survey). Although surgery-eligible obeseindividuals make up only about one third of the fulltimeemployed obese population, approximately twothirds of all medical and work loss costs attributable toobesity are incurred by those eligible for bariatric surgery.This is particularly concerning for employersbecause survey data continue to show an increase in therates of severe obesity among full-time employees.1,4
Bariatric surgery is one proposed solution for treatingsevere obesity. We use a simulation model to assessthe financial effect of an employer's decision to offercoverage for bariatric surgery. Based on the simulationresults, 5 to 10 years were required in the base case forbariatric surgery to break even. Although some scenariosresulted in breakeven in less than 4 years, theserequired complete resolution of obesity-attributablecosts or substantial cost-sharing arrangements.
Using Canadian medical cost data, Sampalis et al23reported that 3.5 years were required to break evenwhen focusing only on medical costs. In our base case,a break-even period of 3.5 years for medical costs wouldrequire the employer's cost of bariatric surgery to be$8500. Costs in their study are not directly comparableto ours because costs in the Canadian healthcare systemdo not reflect US market prices.
There are several limitations to our study. We stroveto base the assumptions of our model on findings fromthe MEPS and NHIS analyses and those in the literature,but the results of the simulation model are based largelyon assumptions of the medical and work loss costsincurred with or without the bariatric surgery procedure.We used sensitivity analyses to gauge the effect ofthese assumptions on our results; however, the experiencefor any particular individual undergoing the proceduremay be substantially different from the resultsreported herein.
Our base case of a 75% reduction in obesity-attributablecosts as a result of undergoing a bariatric surgeryprocedure was based on estimates of the reduction incomorbidities reported in the medical literature.13 Evenif the surgery were to reduce excess weight by 75%, itmay reduce costs by a greater or lesser degree. Severalmarket factors relating to coverage and costs forbariatric surgery procedures will also affect the results.We assumed the price of the procedure to be $25 000. Areduction in this price, or an increase in employee cost-sharing,would reduce the time to breakeven.
The simulations are also restricted to medical andwork loss costs among full-time employees. Costs thatwe were unable to include because of lack of data werethose associated with "presenteeism" (ie, reduced productivity) and others (eg, disability costs) that mayaccrue to employers as a result of obesity.7,31 Inclusionof these costs could reduce the time to breakeven associatedwith bariatric surgery.
We used self-reported height and weight data toderive the costs of obesity from the NHIS and MEPSdata sets. Other researchers showed self-reportedweight to be underestimated and self-reported height tobe somewhat overestimated.32,33 We also assumed thatthe difference in costs between those who do and do notundergo bariatric surgery is constant over time. If costsescalate faster among those who do not undergo the surgery,then the cost savings would be greater than ourmodel suggests.
The scope of our analysis is relevant for the full-timeemployed population but should not be extendedto the population at large. Obese individuals with full-timeemployment may be healthier than the generalpopulation and thus may have lower healthcare costsin the absence of bariatric surgery. In fact, differencesin the population scope could be an additional reasonwhy our results deviate from the findings of Sampaliset al,23 whose data are not restricted to full-timeemployees.
We estimated the net benefits of bariatric surgery byconsidering a hypothetical obese employee. Beforemaking a decision on whether to provide coverage forbariatric surgery, firms ideally would also have informationon the number of bariatric surgery proceduresthat would be required by employees at specific cost-sharingarrangements and the cost profile of theseemployees before surgery. Moreover, we acknowledgethat clinical guidelines and medical necessity oftendrive coverage decisions and that financial considerationsmay be of secondary importance. Nevertheless,our results suggest 3 important findings. First, the costsof obesity among full-time employees are disproportionatelyconcentrated among those eligible forbariatric surgery, even though they make up only aboutone third of the obese working population. Second,increased work loss among the surgery-eligible populationrepresents a significant cost of obesity to employers.Last, while bariatric surgery may pay for itself inless than 4 years, as suggested by Sampalis et al,23 oursimulations reveal that the most likely breakevenoccurs 5 or more years after surgery. Future studiesbased on a randomized controlled trial or quasi-experimentand actual cost and outcome data will ultimatelydetermine the accuracy of our simulation results forthe US employed population.
Acknowledgment
We thank Olga Khavjou, MA, for excellent assistance with the dataanalysis and programming.
From RTI International, Research Triangle Park, NC.
This study was funded by an internal grant from RTI International, an independent nonprofitresearch corporation. The authors had sole responsibility for the collection, analysis,and interpretation of the data.
Address correspondence to: Eric A. Finkelstein, PhD, RTI International, 3040Cornwallis Road, PO Box 12194, Research Triangle Park, NC 27709-2194. E-mail:finkelse@rti.org.
JAMA.
1. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesityamong US adults, 1999-2000. 2002;288:1723-1727.
N Engl J
Med.
2. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity,and mortality from cancer in a prospectively studied cohort of US adults. 2003;348:1625-1638.
JAMA.
3. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The diseaseburden associated with overweight and obesity. 1999;282:1523-1529.
Arch Intern Med.
4. Sturm R. Increases in clinically severe obesity in the United States, 1986-2000.2003;163:2146-2148.
Health Aff (Millwood).
5. Sturm R. The effects of obesity, smoking, and drinking on medical problems andcosts: obesity outranks both smoking and drinking in its deleterious effects onhealth and health costs. 2002;21(2):245-253.
Health Aff (Millwood).
6. Finkelstein EA, Fiebelkom IC, Wang G. National medical spending attributableto overweight and obesity: how much, and who's paying? 2003;Jan-Jun(suppl Web exclusives):W3-219-226.
Am J Health Promot.
7. Thompson D, Edelsberg J, Kinsey KL, Oster G. Estimated economic costs ofobesity to U.S. business. 1998;13:120-127.
Health Aff (Millwood).
8. Thorpe KE, Florence CS, Howard DH, Joski P. The impact of obesity on risingmedical spending. 2004;Jul-Dec(suppl Web exclusives):W4-480-486.
Am J Health Promot.
9. Tucker LA, Friedman GM. Obesity and absenteeism: an epidemiologic study of10 825 employed adults. 1998;12:202-207.
N Engl J Med.
10. Steinbrook R. Surgery for severe obesity. 2004;350:1075-1079.
J Gastrointest Surg.
11. Pope GD, Birkmeyer JD, Finlayson SR. National trends in utilization and in-hospitaloutcomes of bariatric surgery. 2002;6:855-861.
Washington
Post.
12. Stein R. As obesity surgeries soar, so do safety, cost concerns. April 11, 2004:A1.
JAMA.
13. Buchwald H, Avidor Y, Braunwald E, et al. Bariatric surgery: a systematicreview and meta-analysis. 2004;292:1724-1737.
Evidence Report/Technology
Assessment Number 103: Pharmacological and Surgical Treatment of Obesity.
14. Shekelle PG, Morton SC, Maglione M, et al. Rockville, Md: Agency for Healthcare Research and Quality; 2004. AHRQ publication04-E028-1.
J Am Coll Surg.
15. Salem L, Jensen CC, Flum DR. Are bariatric surgical outcomes worth theircost? a systematic review. 2005;200:270-278.
Am
J Med.
16. Craig BM, Tseng DS. Cost-effectiveness of gastric bypass for severe obesity. 2002;113:491-498.
Int
J Obes Relat Metab Disord.
17. Clegg A, Colquitt J, Sidhu M, Royle P, Walker A. Clinical and cost effectivenessof surgery for morbid obesity: a systematic review and economic evaluation. 2003;27:1167-1177.
Obes Surg.
18. van Gemert WG, Adang EM, Kop M, Vos G, Greve JW, Soeters PB. Aprospective cost-effectiveness analysis of vertical banded gastroplasty for the treatmentof morbid obesity. 1999;9:484-491.
Ann Surg.
19. Christou NV, Sampalis JS, Liberman M, et al. Surgery decreases long-termmortality, morbidity, and health care use in morbidly obese patients. 2004;240:416-424.
Obes Surg.
20. Monk JS Jr, Dia Nagib N, Stehr W. Pharmaceutical savings after gastric bypasssurgery. 2004;14:13-15.
Obes Surg.
21. Snow LL, Weinstein LS, Hannon JK, et al. The effect of Roux-en-Y gastricbypass on prescription drug costs. 2004;14:1031-1035.
Obes Surg.
22. Potteiger CE, Paragi PR, Inverso NA, et al. Bariatric surgery: shedding the monetaryweight of prescription costs in the managed care arena. 2004;14:725-730.
Obes Surg.
23. Sampalis JS, Liberman M, Auger S, Christou NV. Impact of weight reduction surgeryon health-care costs in morbidly obese patients. 2004;14:939-947.
Ann Intern Med.
24. Consensus Development Conference Panel. Gastrointestinal surgery for severeobesity. 1991;115:956-961.
Am Econ Rev.
25. Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz A, Marquis MS.Health insurance and the demand for medical care: evidence from a randomizedexperiment. 1987;77:251-277.
J Health Econ.
26. Manning WG. The logged dependent variable, heteroscedasticity, and theretransformation problem. 1998;17:283-295.
Surgery.
27. Livingston EH, Ko CY. Socioeconomic characteristics of the population eligiblefor obesity surgery. 2004;135:288-296.
Obes Surg.
28. Fisher BL. Comparison of recovery time after open and laparoscopic gastricbypass and laparoscopic adjustable banding. 2004;14:67-72.
Statistical Abstract of the United States, 2004-2005.
29. US Bureau of the Census. Table 628, employer costs for employee compensationper hour worked: 2004. In: Washington, DC: US Bureau of the Census; 2004.
Prevention Effectiveness: A Guide to Decision Analysis and Economic
Evaluation.
30. Grosse S. Productivity loss tables (appendix I). In: Haddix A, Teutsch S, CorsoP, eds. 2nd ed. London, England: Oxford University Press; 2003:245-257.
J Occup Environ Med.
31. Burton WN, Conti DJ, Chen CY, Schultz AB, Edington DW. The role of healthrisk factors and disease on worker productivity. 1999;41:863-877.
Am J Epidemiol.
32. Stewart AW, Jackson RT, Ford MA, Beaglehole R. Underestimation of relativeweight by use of self-reported height and weight. 1987;125:122-126.
J Am Diet Assoc.
33. Kuczmarski MF, Kuczmarski RJ, Najjar M. Effects of age on validity of self-reportedheight, weight, and body mass index: findings from the Third National Healthand Nutrition Examination Survey, 1988-1994. 2001;101:28-34.
