To develop a risk-scoring tool to identify in a base year patients likely to have high medical spending in the subsequent year and to understand the role obesity and obesity reduction may play in mitigating this risk.
Design.
Cross-sectional analysis, using commercial claims and health risk assessment data.
Setting.
United States, 2004–2009.
Subjects.
Panel of 192,750 person-year observations from 116,868 unique working-age employees of large companies.
Measures.
Probability of high medical expenses (80th percentile or above) in the following year; adjusted body mass index (BMI).
Analysis.
Generate risk scores by modeling the likelihood of high next-year expenses as a function of base-year age, sex, medical utilization, comorbidities, and BMI. Estimate the effect of simulated bariatric intervention on patient risk scores.
Results.
Individuals with higher BMI were more likely to be categorized in the very high risk group, in which the average annual medical expense was $8621. A weight-loss intervention transitioning a patient to the next lower obesity class was predicted to reduce this risk by 1.5% to 27.4%—comparable to hypothetically curing a patient of depression or type 2 diabetes.
Conclusion.
A logistic model was used to capture the effect of BMI on the risk of high future medical spending. Weight-loss interventions for obese patients may generate significant savings by reducing this risk.
OgdenCLCarrollMDKitBKFlegalKM. Prevalence of Obesity in the United States, 2009–2010. Hyattsville, Md: National Center for Health Statistics; 2012.
2.
FlegalKMCarrollMDOgdenCLJohnsonCL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002;288:1723–1727.
3.
FieldAECoakleyEHMustA. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med.2001;161:1581–1586.
4.
MustASpadanoJCoakleyEH. The disease burden associated with overweight and obesity. JAMA. 1999;282:1523–1529.
5.
ArenaVCPadiyarKRBurtonWNSchwerhaJJ. The impact of body mass index on short-term disability in the workplace. J Occup Environ Med.2006;48:1118–1124.
6.
BungumTSatterwhiteMJacksonAWMorrowJRJr. The relationship of body mass index, medical costs, and job absenteeism. Am J Health Behav. 2003;27:456–462.
7.
RicciJACheeE. Lost productive time associated with excess weight in the U.S. workforce. J Occup Environ Med.2005;47:1227–1234.
8.
FinkelsteinEAFiebelkornICWangG. National medical spending attributable to overweight and obesity: How much, and who's paying?Health Aff (Millwood). 2003;(suppl 2003):W3–W219.
9.
FinkelsteinEATrogdonJGCohenJWDietzW. Annual medical spending attributable to obesity: Payer-and service-specific estimates. Health Aff (Millwood). 2009;28:w822–w831.
10.
ThorpeKEFlorenceCSHowardDHJoskiP. Trends: The impact of obesity on rising medical spending. Health Aff (Millwood). 2004;SUPP(2004):283–283.
11.
FarleyJFHarleyCRDevineJW. A comparison of comorbidity measurements to predict healthcare expenditures. Am J Manag Care. 2006;12:110–119.
12.
FishmanPAGoodmanMJHornbrookMC. Risk adjustment using automated ambulatory pharmacy data: The RxRisk model. Med Care. 2003;41:84–99.
13.
FleishmanJACohenJW. Using information on clinical conditions to predict high-cost patients. Health Serv Res.2010;45:532–552.
14.
FleishmanJACohenJWManningWGKosinskiM. Using the SF-12 health status measure to improve predictions of medical expenditures. Med Care. 2006;44(5 suppl):I54–I63.
15.
MeenanRTGoodmanMJFishmanPA. Using risk-adjustment models to identify high-cost risks. Med Care. 2003;41:1301–1312.
16.
PerkinsAJKroenkeKUnutzerJ. Common comorbidity scales were similar in their ability to predict health care costs and mortality. J Clin Epidemiol. 2004;57:1040–1048.
17.
SchneeweissSSeegerJDMaclureM. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol. 2001;154:854–864.
18.
GoldmanDPZhengYGirosiF. The benefits of risk factor prevention in Americans aged 51 years and older. Am J Public Health. 2009;99:2096–2101.
19.
RothmanKJ. BMI-related errors in the measurement of obesity. Int J Obes (Lond). 2008;32(suppl 3):S56–S59.
20.
NawazHChanWAbdulrahmanM. Self-reported weight and height: Implications for obesity research. Am J Prev Med.2001;20:294–298.
21.
SpencerEAApplebyPNDaveyGKKeyTJ. Validity of self-reported height and weight in 4808 EPIC-Oxford participants. Public Health Nutr.2002;5:561–565.
22.
VillanuevaEV. The validity of self-reported weight in US adults: A population based cross-sectional study. BMC Public Health. 2001;1:11.
23.
BurkhauserRVCawleyJ. Beyond BMI: The value of more accurate measures of fatness and obesity in social science research. J Health Econ.2008;27:519–529.
24.
StommelMSchoenbornCA. Accuracy and usefulness of BMI measures based on self-reported weight and height: Findings from the NHANES & NHIS 2001–2006. BMC Public Health. 2009;9:421.
25.
ElixhauserASteinerCHarrisDRCoffeyRM. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27.
26.
BerkMLMonheitAC. The concentration of health care expenditures, revisited. Health Aff (Millwood). 2001;20:9–18.
27.
MohosESchmaldienstEPragerM. Quality of life parameters, weight change and improvement of co-morbidities after laparoscopic Roux Y gastric bypass and laparoscopic gastric sleeve resection—comparative study. Obes Surg.2011;21:288–294.
SjostromLPeltonenMJacobsonP. Bariatric surgery and long-term cardiovascular events. JAMA. 2012;307:56–65.
32.
PoirierPCornierMAMazzoneT. Bariatric surgery and cardiovascular risk factors: A scientific statement from the American Heart Association. Circulation. 2011;123:1683–1701.
33.
AndreyevaTSturmRRingelJS. Moderate and severe obesity have large differences in health care costs. Obes Res.2004;12:1936–1943.
34.
BertakisKDAzariR. Obesity and the use of health care services. Obes Res.2005;13:372–379.
35.
AshASZhaoYEllisRPKramerSchlein M. Finding future high-cost cases: Comparing prior cost versus diagnosis-based methods. Health Serv Res.2001;36(6, pt 2):194–206.
36.
CousinsMSShickleLMBanderJA. An introduction to predictive modeling for disease management risk stratification. Dis Manag. 2002;5:157–167.
37.
BaumeisterSEDorrMRadkeD. Predictive modeling of health care costs: Do cardiovascular risk markers improve prediction?Eur J Cardiovasc Prev Rehabil. 2010;17:355–362.
38.
AdamsTDDavidsonLELitwinSE. Health benefits of gastric bypass surgery after 6 years. JAMA. 2012;308:1122–1131.
39.
ShieldsMCarrollMDOgdenCL. Adult Obesity Prevalence in Canada and the United States. Hyattsville, Md: National Center for Health Statistics; 2011.
40.
National Institute of Diabetes and Digestive and Kidney Diseases. Gastrointestinal Surgery for Severe Obesity. Washington, DC: NIDDK; 2004. NIH publication 04–4006.
41.
ChebliJESchindlerR. The results of a surgical complication protection program (BLIS, Inc.) for private pay bariatric patients in the U.S.: 2006–2011. Obes Surg.2012;22:1798–1801.
42.
DormanRBRasmusNFal-HaddadBJ. Benefits and complications of the duodenal switch/biliopancreatic diversion compared to the Roux-en-Y gastric bypass. Surgery. 2012;152:758–765; discussion 765–757.
43.
JacksonTDHutterMM. Morbidity and effectiveness of laparoscopic sleeve gastrectomy, adjustable gastric band, and gastric bypass for morbid obesity. Adv Surg.2012;46:255–268.
44.
WorniMOstbyeTShahA. High risks for adverse outcomes after gastric bypass surgery following failed gastric banding: A population-based trend analysis of the United States. Ann Surg.2013;257:279–286.
45.
MichaudP-CGoldmanDPLakdawallaDN. The value of medical and pharmaceutical interventions for reducing obesity. J Health Econ.2012;31:630–643.
46.
Barlow SE, and Expert Committee. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: Summary report. Pediatrics. 2007;120(suppl 4):S164–S192.
47.
GeeLAgrasWS. A randomized pilot study of a brief outpatient problem-solving intervention to promote healthy eating and activity habits in adolescents. Clin Pediatr. 2013.
ArterburnDNoelPH. Effects of drug treatment for obesity in adults. Clin Evid.2001;00:412–419.
50.
Economic Research Service. Access to Affordable and Nutritious Food. Washington, DC: USDA; 2009.
51.
JetterKCassadyD. The availability and cost of healthier food alternatives. Am J Prev Med.2006;30:38–44.
52.
DrenowskiA. The cost of U.S. foods as related to their nutritive value. Am J Clin Nutr.2010;92:1181–1188.
53.
DubowitzTHeronMBirdC. Neighborhood socioeconomic status and fruit and vegetable intake among whites, blacks, and Mexican Americans in the United States. Am J Clin Nutr.2008;87:1883–1891.
54.
ZenkSSchulzAIsraelB. Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit. Am J Public Health. 2005;95:660–667.
55.
DarmonNDrewnoskiA. Does social class predict diet quality?Am J Clin Nutr.2008;87:1107–1117.
56.
LeeRECubbinC. Neighborhood context and youth cardiovascular health behaviors. Am J Public Health. 2002;92:428–436.
57.
SanbonmatsuLLudwigJKatzLF. Moving to Opportunity for Fair Housing Demonstration Program: Final Impacts Evaluation. Washington, DC: HUD; 2011.
58.
Federal Trade Commission. Marketing Food to Children and Adolescents: A Review of Industry Expenditures, Activities, and Self-Regulation—A Report to Congress. Washington, DC: FTC; 2008.
59.
Institute of Medicine. Food Marketing to Children and Youth: Threat or Opportunity?Washington, DC: IOM; 2005.
60.
Institute of Medicine. Challenges and Opportunities for Change in Food Marketing for Children. Washington, DC: IOM; 2013.
