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Targeting High-Risk Employees May Reduce Cardiovascular Racial Disparities
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Targeting High-Risk Employees May Reduce Cardiovascular Racial Disparities

James F. Burke, MD, MS; Sandeep Vijan, MD; Lynette A. Chekan, MBA; Ted M. Makowiec, MBA; Laurita Thomas, MEd; and Lewis B. Morgenstern, MD
Targeting cardiovascular risk reduction interventions to high-risk patients has the potential to reduce cardiovascular racial disparities, improve health, and reduce costs.
Objectives
A possible remedy for health disparities is for employers to promote cardiovascular health among minority employees. We sought to quantify the financial return to employers of interventions to improve minority health, and to determine whether a race- or risk-targeted strategy was better.

Study Design
Retrospective claims-based cohort analysis.

Methods
Unconditional per-person costs attributable to stroke and myocardial infarction (MI) were estimated for University of Michigan employees from 2006 to 2009 using a 2-part model. The model was then used to predict the costs of cardiovascular disease to the University for 2 subgroups of employees—minorities and high-risk patients—and to calculate cost-savings thresholds: the point at which the costs of hypothetical interventions (eg, workplace fitness programs) would equal the cost savings from stroke/ MI prevention.

Results
Of the 38,314 enrollees, 10% were African American. Estimated unconditional payments for stroke/MI were almost the same in African Americans ($128 per employee per year; 95% CI, $79-$177) and whites ($128 per employee per year; 95% CI, $101- $156), including higher event rates and lower payments per event in African Americans. Targeting the highest risk decile with interventions to reduce stroke/MI would result in a substantially higher cost-savings threshold ($81) compared with targeting African Americans ($13). An unanticipated consequence of risk-based targeting is that African Americans would substantially benefit: an intervention targeted at the top risk decile would prevent 75% of the events in African Americans, just as would an intervention that exclusively targeted African Americans.

Conclusions
Targeting all high-risk employees for cardiovascular risk reduction may be a win-win-win situation for employers: improving health, decreasing costs, and reducing disparities.

Am J Manag Care. 2014;20(9):725-733
Disparities in cardiovascular disease incidence create a potential financial incentive for employers and insurers to improve the health of minorities. While the magnitude of this incentive is small for race-targeted strategies, risk-targeted strategies have the potential of generating a unique win-win-win situation for employers and insurers: improving health, decreasing costs, and reducing disparities.

• 75% of all events that would be prevented by targeting African Americans would be prevented by targeting the highest risk decile of employees.

• Even in the low-risk population studied, the cost-savings threshold is substantial ($198) for even modestly effective interventions targeting the highest risk decile.
Racial and ethnic disparities in the incidence, quality of care, and outcomes of cardiovascular disease and stroke have been widely documented.1-3 These disorders are potentially preventable and appropriate treatment can reduce morbidity and mortality. Thus, there is the hope that targeted interventions can reduce or even eliminate health disparities.4 Government programs to expand healthcare access have reduced access disparities,5,6 but significant outcome disparities persist even in insured populations.7,8

The potentially wider role of organizations outside of government, including employers, in funding programs to eliminate heath disparities deserves consideration. Employers benefit from a diverse, healthy, and productive workforce that minimizes the cost of insurance, employee absenteeism, and turnover. Further, some health promotion activities may be cost saving for employers.9,10 Are there incentives for employers to fund projects to improve minority health and reduce disparities for their employees?

We sought to understand the potential financial incentives for employers to implement interventions to improve minority health. We first estimated the cost differential for cardiovascular disease and stroke in African Americans compared with whites, hypothesizing that average costs would be higher in African Americans, per capita, due to increased disease incidence and severity. We also explored whether the best strategy would be to focus health improvement programs on minority employees, or instead on all employees with risk factors for heart disease and stroke. We then estimated the cost-savings threshold, the break-even point, for interventions to reduce cardiovascular risk targeted at either a) all African American employees, or b) all employees at high risk, defined as employees, white or African American, with an average annual risk of stroke or myocardial infarction (MI) greater than 1.5%. For employers, any intervention that could generate net cost savings represents a rare win-win situation: decreased disease burden and decreased net costs.

METHODS

Study Population

We analyzed all health insurance claims for University of Michigan employees from 2006 to 2009. The University of Michigan, like many large employers, is self-insured and thus assumes the risk associated with health insurance claims from their employees. The study population included all continuously insured employees and their dependents under the age of 65 years. De-identified demographic and employment information for employees and dependents was obtained through university employment records. Race and ethnicity of employees was obtained from employment records in which it was declared by self-report. The university does not record race and ethnicity of dependents, thus we made a limiting assumption that dependent race and ethnicity was the same as that of the related employee.

The University of Michigan Institutional Review Board reviewed the study protocol and determined it was exempt from formal review as it relied on de-identified data.

Risk Factor and Diagnosis Assignment. An employee was classified as having stroke or MI if that employee had any claim between 2006 and 2009 in which one of the first 3 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes represented stroke or MI. MI was identified through ICD-9-CM codes classified as “Acute MI” (Single-Level Diagnosis Category 100) by the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification Software (CCS).11 Stroke was identified using ICD-9-CM codes 433.x1, 434.x1, and 436.12 Vascular risk factors were classified using CCS categories. Modified Charlson Comorbidity Index (CCI) scores were calculated for each patient.13

Payments. Direct payments for medical care were abstracted from claims. Indirect costs, costs borne by the employer as a consequence of illness that are separate from direct medical expenditures, were estimated by summing 3 sources of employer cost: missed work, long-term disability, and worker replacement. The cost of missed work was estimated by multiplying the days of work lost to outpatient visits or hospitalizations14 by the employee’s daily salary. Long-term disability costs were calculated by multiplying an individual’s total compensation (1.3 times salary to account for benefits) by 0.66 (the fraction of total benefits paid out for long-term disability) for all individuals on long-term disability for the time spent on disability during the study. Costs of worker replacement were estimated for individuals who went onto long-term disability as 0.3 times the individual’s salary.15 All cost data were inflation-adjusted to 2009 dollars using the gross domestic product deflator.16 Because the available mortality data was incomplete (no data was available on employees who died in the first 15 months of the study), we did not account for patients who died. However, this would likely only have a small effect on our estimates because only a very small proportion of the population died; 195 of the entire study population (N = 38,314) died during the last 33 months of the study, including 12 of 502 of the employees with stroke or MI.

Statistical Analysis. Characteristics of the population were summarized with descriptive statistics. Comparisons between racial and ethnic groups were made using Analysis of Variance (ANOVA) for normally distributed continuous variables, χ2 tests for categorical variables, and Kruskal-Wallis tests for non-normally distributed variables.

Modeling Rationale and Approach. To quantify the difference in payments for stroke/MI in minorities, we sought to determine the relationship between race/ethnicity and payments independently of other factors that predict payments (eg, age). Two features of the claims data posed challenges for simple modeling approaches: the large number of patients with zero payments and the heavily right-skewed distribution of payments. To address these issues, we developed a 2-part model. This approach relies on estimating 2 separate quantities for each individual: 1) the probability of having a stroke or MI in a given year, and 2) the estimated cost of stroke/MI, conditional on that particular individual having a stroke or MI.17 By multiplying these 2 estimates, we can estimate the unconditional costs due to stroke or MI in a given year—the predicted costs for stroke and MI for a given employee in a given year.

To implement this approach, we first used logistic regression to estimate an individual employee’s risk of stroke or MI. Specifically, we estimated the probability of an employee having a stroke or MI in a given year (Part 1 of the model; logit [probability of stroke or MI] = β0 + β1 X age + β2 X sex + β3 X race/ethnicity + β4 X hypertension + β5 X diabetes + β6 X hyperlipidemia + β7 X salary decile + β8 X job type + β9 X CCI). We then defined risk deciles based on this model and categorized the number of actual events and race of employees across risk deciles. Second, we estimated payments in employees who had a stroke or MI in a given year. Specifically, ordinary least squares (OLS) regression was used to predict log-transformed annual payments in individuals who were diagnosed with stroke or MI in that year (Part 2 of model; log (annual payments) = β1 X age + β2 X sex + β3 X race/ethnicity + β4 X hypertension + β5 X diabetes + β6 X hyperlipidemia + β7 X salary decile + β8 X job type + β9 X CCI + Ɛ1). By multiplying an individual employee’s risk of stroke/MI by the predicted payments for stroke/MI in patients as if they had an event, we were able to estimate payments for each employee in each year prior to knowing whether they would have an event—unconditional annual payments attributable to stroke or MI for an individual employee. Duan-smear detransformation was used to detransform Part 2 of the model.18 Differences in payments between African Americans and whites were characterized using mean unconditional estimated payments by race. Standard errors were estimated using bootstrapping. Both parts of the model were adjusted for age, sex, vascular risk factors, salary, job category, and CCI.

We explored alternate modeling strategies including both 1- and 2-part generalized linear modeling approaches following the approach of Buntin and Zaslavsky.19 We chose the 2-part logistic-OLS model described above because this model predicted most accurately over the range of payments, had the lowest root mean squared error, and had relatively normally distributed residuals.

Estimating Cost-Savings Thresholds

To determine the cost implications of potential interventions to prevent stroke/MI in targeted groups, we estimated the number of events prevented and racial/ethnic distribution of event prevention for hypothetical interventions of varying efficacy, assuming equal relative treatment effects across strata of risk and race. We then determined the cost-savings threshold of these hypothetical interventions—the point at which the cost of a hypothetical intervention would be equal to the cost savings generated by stroke/MI prevention, estimated by our 2-part model. In contrast to a conventional cost analysis, which specifies the estimated cost of a specific intervention up front, this approach works backwards by first estimating the cost savings associated with event prevention and then dividing by the number of targeted enrollees to estimate the cost per enrollee that would break even, from the perspective of the employer. We generated cost-savings thresholds by solving a linear equation where the cost of a hypothetical intervention was set equal to cost savings attributable to event prevention: intervention cost = cost per event X expected events X (1 – Relative Risk [RR]) (Number of enrollees in target population). Using this equation, we were able to vary the RR of the hypothetical intervention and estimate the intervention costs that would break even. Relative risk was conceptualized as an estimate of intervention effectiveness—ie, efficacy after accounting for imperfect enrollment and compliance with an intervention—rather than pure efficacy. All analyses were performed with Stata, version 12 (Stata Corp, College Station, Texas).

RESULTS

Population Characteristics. The study included a total of 38,314 individuals, 16,179 (42%) of whom were male. Eighty-eight percent of the study population was white, 10% was African American, and 3% was Hispanic American. Mean average annual medical payments were $3355 (SD $7825). Both the African American and Hispanic American populations were younger than the white population, although the burden of vascular risk factors was increased in African Americans relative to whites (Table 1).

 
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