TECHNICAL APPENDIX WITH ADDITIONAL INFORMATION ON METHODS AND APPENDIX EXHIBITS. Ten health risks in this and the previous study were

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1 Goetzel RZ, Pei X, Tabrizi MJ, Henke RM, Kowlessar N, Nelson CF, Metz RD. Ten modifiable health risk factors are linked to more than one-fifth of employer-employee health care spending. Health Aff (Millwood). 2012;31(11). TECHNICAL APPENDIX WITH ADDITIONAL INFORMATION ON METHODS AND APPENDIX EXHIBITS Additional Information on Methods Health Risks Definition Ten health risks in this and the previous study were dichotomized as high versus lower risk using standard definitions documented in the original HERO analysis. Employees classified at high risk for physical inactivity exercised fewer than three days in a typical week. High risk for alcohol consumption meant, for men, consuming three or more drinks per day or 15 or more per week, and, for women, 2 or more drinks per day or 8 or more drinks per week. High-risk workers with for poor nutrition/eating habits consumed fewer than 5 fruits and vegetables a day. High-risk tobacco users were individuals who smoked cigarettes or used other tobacco products (i.e., pipe, cigar, snuff, or smokeless tobacco). Those classified as high risk for stress-related problems reported that their life was very stressful and that they were not effective in coping with that stress. Being Respondents were termed at high risk for depression if they felt unhappy or 1

2 hopeless and unwilling to ask for help, or if they reported being depressed almost always. Employees at high risk for biometric measures had BMI values of 30 and above; total cholesterol levels greater than 239 mg/dl; systolic blood pressure greater than 159 mg Hg, or diastolic blood pressure greater than 99 mg Hg; and blood glucose levels greater than 115 mg/liter. Minimum and maximum values for biometric measures were set by the researchers based on advice from clinicians on what constitute reasonable ranges for the values studied. Values outside those ranges were excluded from the analysis and set to missing. More Details about the Regression Model All the regression equations used a generalized linear model (GLM) to estimate risk-expenditure relationships. The outcome for all models was total medical expenditures post-health risk assessment. The predictor variables included indicators for each health risk and all confounding variables described above. A log dependent variable was used to accommodate the skewed nature of medical expenditure data. Differences between employees at high and lower risk levels were estimated by comparing adjusted costs from the model with and without having the risk, while holding all other variables constant at their 2

3 average value for the at-risk population. The incremental cost associated with being at high risk was calculated as a percent difference in costs compared to the reference category (lower risk). The software package employed in these studies was STATA. Confounders in the Regression Analyses The regression models in this study controlled for confounding variables that might also influence costs. Confounders included employee age; gender; type of health plan in which the employee was enrolled (fee for service [COMP], point of service [POS], health maintenance organization [HMO], preferred provider organization [PPO], consumer driven health plan [CDHP]); location (Northeast, North Central, South or West region of the country); industry type of employer (manufacturing durable goods, manufacturing non-durable goods, service, oil and gas); employment category (salaried or hourly); number of months followed in the database; and the specific employer contributing data (1-7). The employer indicator adjusted for employer-specific differences in benefit plan design, culture, or other factors not accounted for by the other variables in the model. Binary indicators were used to denote when information on health risks was missing, and these were controlled for in the analysis. Missing data 3

4 occurred when respondents failed to answer one or more of the HRA questions or when biometric screening data were not collected by the employer. Including the missing data indicators allowed the statistical tests to be completed without excluding any observations. Additional Information on Sensitivity Analyses We conducted several sensitivity analyses to control for having an illness at baseline, and for having varying degrees of disease comorbidities. Baseline disease severity was measured during the index year in which the health risk assessment was completed and reported using the Charlson Comorbidity Index (CCI) and Psychiatric Diagnosis Groups (PDG) values, respectively. These indices were entered into the regression models as part of the sensitivity analysis that controlled for baseline physical or mental illness an enhancement from the original HERO study methods. In the first sensitivity analysis, baseline CCI and PDG scores were added to the regression models, and in the second, outlier cases were excluded. Estimating Expenditures for Individuals with Multiple Risks In addition to calculating the incremental cost of each risk factor, estimates were developed for combinations of risk factors representing employees having multiple risks 4

5 for heart disease, stroke, and psychosocial problems. This was accomplished by comparing predicted mean expenditures for employees with specific risk factors for a given health outcome to those without these risk factors. The multiple risk results were not simply the addition of costs for individual risks. Rather, high risk for multiple risk clusters was newly defined and the results produced showed the effect of having multiple risks simultaneously. The seven risk factors (risk cluster) used to predict the occurrence of heart disease were: 1) tobacco use, 2) high blood pressure, 3) high total cholesterol, 4) physical inactivity, 5) high blood glucose, 6) obesity, and 7) high stress. The cluster indicating being at high risk for a stroke meant the employee had the following risks 1) tobacco use, 2) high blood pressure, 3) high total cholesterol, and 4) high stress. Finally, having a psychosocial risk cluster was defined as having 1) high stress and 2) depression. These multiple risk analyses replicated the approach used in the original HERO study. 5

6 APPENDIX EXHIBIT 1 Description of the Study Sample Study Sample (n=92,486) Measure Value Percent Count Age , , , ,766 Gender Female ,746 Male ,740 Health plan COMP (fee for service) 8.3 7,656 HMO (health maintenance organization) ,273 POS (point of service) 3.8 3,470 PPO (preferred provider organization) ,302 CDHP (consumer driven health plan) 1.4 1,263 Unknown Employment Salary ,492 Hourly ,439 Other and Unknown Industry Oil & Gas Extraction 6.7 6,163 Manufacturing, Durable Goods ,400 Manufacturing, Nondurable Goods ,883 Services ,040 Region of Northeast 8.9 8,240 Residence North Central ,679 South ,569 West 7.5 6,893 Unknown/missing

7 CURRENT Percent (N=92,486) HERO Percent (N=46,026) Risk Category* Obesity High Total Cholesterol High Blood Glucose High Blood Pressure Poor Nutrition/Eating Habits Physical Inactivity Tobacco Use High Alcohol Consumption High Stress Depression Age, Severity Indicators, and Costs Mean Standard Error Age Total eligible years since HRA was taken Baseline Charlson Comorbidity Index (CCI) Baseline Number of Psychiatric Diagnosis Groups (PDGs) Total Medical and Drug Expenditures (2009 $) $ SOURCE Authors' analysis of data from 92,486 individuals from seven different organizations in the MarketScan Commercial Claims and Encounter Database. NOTES * The percentages in the Risk Category compare the values from the current study to the values from the 1998 HERO study (2). The Charlson Comorbidity Index predicts the one-year mortality for a patient who may have a range of co-morbid conditions. The Number of Psychiatric Diagnostic Groups measures the severity of mental illness using a group of identified mental disease diagnoses. Total Medical and Drug Expenditure, including employer and employee portions of medical and drug payments, was standardized to 2009 dollars, adjusted for inflation using the Medical Care Services Consumer Price Index (CPI) for medical care costs and Medical Care Commodities CPI for pharmaceuticals. 7

8 APPENDIX EXHIBIT 2 Adjusted Medical Expenditures (in 2009 Dollars) for High-Risk vs. Lower Risk Employees, Controlling for Charlson Comorbidity Index and the Number of Psychiatric Diagnostic Groups, and Excluding Outliers, and Differences between Each Risk Level with 95% Intervals Risk Measures Risk Level Controlling for CCI and NPDG 8 Excluding Outliers Depression High $12, $6, %Difference Controlling for CCI and NPDG %Difference Excluding Outliers Lower $9, $4, % 42.60% (24.9, Interval (23.7, 43.2) 36.0) Blood glucose High $12, $6, Lower $12, $4, % 34.09% Interval (-6.8,7.1) (24.2, 34.6) Blood pressure High $8, $5, Lower $6, $4, % 28.35% (19.5, Interval (17.6, 36.0) 30.0) Obesity High $7, $4, Lower $6, $3, % 27.18% (21.5, Interval (18.7, 27.4) 26.5) High $7, $3, Tobacco Use Lower $7, $3, % 16.13% Interval (1.1, 10.4) (4.3, 9.7) Physical inactivity High $7, $4, Lower $6, $3, % 13.05% Interval (3.2, 11.4) (2.6, 7.2) Stress High $8, $4, Lower $8, $4, % 7.54% Interval (-3.7, 8.9) (0.4, 7.8) Total Cholesterol High $7, $4, Lower $7, $4, % -1.08% Interval (-8.7, 6.0) (-5.9, 2.6) Nutrition/Eat ing Habits High $4, $3, Lower $4, $3, % -4.55% Interval (-5.5, 3.2) (-4.5, 0.7) Alcohol Consumption High $7, $3,706.36

9 Lower $7, $4, % -8.95% Interval (-9.6, 8.4) (-9.2, 0.8) SOURCE Results for % difference controlling for Charlson Comorbidity Index and the Number of Psychiatric Diagnostic Groups are from the authors' analysis of data from 92,486 individuals from seven different organizations in the MarketScan Commercial Claims and Encounter Database. Results for % difference excluding outliers are from the authors' analysis of data from 87,862 individuals from seven different organizations in the MarketScan Commercial Claims and Encounter Database. NOTES Medical expenditures include employer and employee portions of medical payments. Outlier costs are defined as greater than or equal to 95 percentile of the total medical cost. C.I. stands for confidence intervals. 9

10 APPENDIX EXHIBIT 3 Estimated Annual Medical Expenditures (in 2009 $) for Employees With and Without Selected Multiple Risk Factors Coexisting Multiple Risk Factors Leading to: With multiple risk factors Without any of the risk factors %Difference High risk for heart disease $10,134 $3, % High risk for stroke $6,137 $3, % High risk for psychosocial problems $6,165 $3, % SOURCE Authors' analysis of data from 92,486 individuals from seven different organizations in the MarketScan Commercial Claims and Encounter Database. NOTES Medical expenditures include employer and employee portions of medical payments. Risk-free individual is estimated to have medical expenditures of $3,207. The estimated annual medical expenditures were estimated using regression models assuming average values for other risk categories and covariates. High risk for heart disease is defined as tobacco use, or high blood pressure, or high blood glucose, or high total cholesterol, or physical inactivity, or obesity, or high stress. High risk for stroke is defined as tobacco use, or high blood pressure, or high total cholesterol, or high stress. High risk for psychosocial problems is defined as high stress or depression. 10

11 APPENDIX EXHIBIT 4 Regression Model Estimates Corresponding to Exhibit 1 in the Manuscript Dependent Variable: Total Medical Expenditure Independent Variable Coefficient Relative Risk Std. Err. z P>z [95% Conf. Interval] Obesity Total cholesterol Blood pressure Blood glucose Tobacco use Alcohol consumption Nutrition/Eating habits Physical inactivity Stress Depression Age (35-44) Age (45-54) Age (55-64) Female Months Salary Hourly Northeast North central South West Capitated plan Employer Employer Employer Employer Employer Employer Missing bprisk Missing chrisk Missing bgrisk Missing alrisk Missing tbrisk

12 Missing exrisk Missing nurisk Missing strisk Missing derisk SOURCE Authors' analysis of data from 92,486 individuals from seven companies in the MarketScan Commercial Claims and Encounter Database. Nine missing indicators were added in the model to indicate whether each health risk was missing. 12

13 APPENDIX EXHIBIT 5 Regression Model Estimates After Controlling for Charlson Comorbidity Index (CCI) and Number of Psychiatric Diagnosis Groups (PDG) Dependent Variable: Total Medical Expenditure Independent Coefficient Relative Risk Variables Std. Err. z P>z [95% Conf. Interval] Obesity High total cholesterol High blood pressure High blood glucose Tobacco use High alcohol consumption Poor nutrition/eating habits Physical inactivity High stress Depression Age (35-44) Age (45-54) Age (55-64) Female Months Salary Hourly Northeast North central South West Capitated Plan CCI NPDG employer employer employer employer employer employer Missing bprisk Missing chrisk

14 Missing bgrisk Missing alrisk Missing tbrisk Missing exrisk Missing nurisk Missing strisk Missing derisk SOURCE Authors' analysis of data from 92,486 individuals from seven companies in the MarketScan Commercial Claims and Encounter Database. Nine missing indicators were added to the model to indicate whether each health risk was missing. 14

15 APPENDIX EXHIBIT 6 Regression Model Results After Excluding Outliers Dependent Variable: Total Medical Expenditure Independent Variables Coefficients Relative Risk Std. Err. z P>z [95% Conf. Interval] Obesity High total cholesterol High blood pressure High blood glucose Tobacco use High alcohol consumption Poor nutrition/eating habits Physical inactivity High stress Depression Age (35-44) Age (45-54) Age (55-64) Female Months Salary Hourly Northeast North central South West Capitated Plan Employer Employer Employer Employer Employer Employer Missing bprisk Missing chrisk Missing bgrisk Missing alrisk Missing tbrisk Missing exrisk Missing nurisk

16 Missing strisk Missing derisk SOURCE Authors' analysis of data from 92,486 individuals from seven companies in the MarketScan Commercial Claims and Encounter Database. Nine missing indicators were added in the model to indicate whether each health risk was missing. 16