ORIGINAL ARTICLE

Missing Variables: How Exclusion of Human Resources Policy Information Confounds Research Connecting Health and Business Outcomes Wendy D. Lynch, PhD and Bruce W. Sherman, MD

When corporate health researchers examine the effects of health on business outcomes or the effect of health interventions on health and business outcomes, results will necessarily be confounded by the corporate environment(s) in which they are studied. In this research setting, most studies control for factors traditionally identified in public health, such as demographics and health status. Nevertheless, often overlooked is the extent to which company policies can also independently impact health care cost, work attendance, and productivity outcomes. With changes in employment and benefits practices resulting from health care reform, including incentives and plan design options, consideration of these largely neglected variables in research design has become increasingly important. This commentary summarizes existing knowledge regarding the implications of policy variations in research outcomes and provides a framework for incorporating them into future employer-based research.

I

n all areas of quantitative research, investigators account for the effects of obvious confounders by keeping them constant or adjusting statistically for their influence. For example, in health research, it is standard practice to adjust for age and gender, which have known, strong associations with clinical and cost outcomes. In addition, studies of health interventions often adjust for levels of baseline illness to account for differences before the intervention began. Ignoring such differences in demographics or health can lead to misleading conclusions. For decades, occupational health and corporate health promotion research has focused heavily on the connection between workforce health status and business outcomes, such as medical costs, absenteeism, disability, and worker productivity. Hundreds of studies have investigated the association between specific diseases or health risk status and these outcomes. Nevertheless, very few have considered essential information about the corporate environment in which the investigations took place and, when they have, it is From Lynch Consulting, Ltd (Dr Lynch), Steamboat Springs, Colo; Altarum Institute (Dr Lynch), Ann Arbor, Mich; Employers Health Coalition, Inc (Dr Sherman), Canton, Ohio; and Department of Medicine (Dr Sherman), Case Western Reserve University School of Medicine, Cleveland, Ohio. Employers Health Coalition, Inc (EHCI) received funding from Johnson & Johnson, Inc, for research and manuscript preparation. Wendy Lynch (Lynch Consulting, Ltd) received payment from EHCI as an independent contractor for literature review and manuscript preparation. Bruce Sherman (Sherman Consulting Services) received payment from EHCI as an independent contractor for manuscript preparation while also serving as the EHCI medical director. Dr Lynch has received speaking honoraria from multiple pharmaceutical firms, employer coalitions, and health insurance companies in the past. She is currently receiving consulting fees from Teladoc and Eliza corporations. Dr Sherman is currently a member of the speaker bureaus for Abbott, Merck, and Pfizer. He has recently participated in advisory board meetings on behalf of Novo Nordisk, Merck, Eisai, Genentech, Bayer, and Allergan. He serves on the scientific advisory board for Humana and has received research funding from Sanofi and Pfizer. The authors declare no conflicts of interest. Address correspondence to: Wendy D. Lynch, PhD, 3175 Belvoir Blvd, Cleveland, OH 44122 ([email protected]). C 2014 by American College of Occupational and Environmental Copyright  Medicine DOI: 10.1097/JOM.0000000000000068

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usually limited to narrow aspects of medical plan design, such as cost-sharing.1 Similarly, efforts to identify and include corporate culture into research, that is, a “culture of health,” remain poorly defined and focused on health-related practices, not broader corporate policy. Thus, a significant question remains across the corporate health literature: How were results affected by site-to-site (or yearover-year) variation in policies that are known to strongly influence the same outcomes? At a minimum, the exclusion of critical information about such policies as health insurance design, paid time-off (PTO), and compensation structure confounds findings about the effects of workforce health. Failure to account for the impact of these policies could unknowingly contribute to a wide variation in estimates of the effects of health on costs and other outcomes. At worst, studies could be attributing business value to health factors when the observed business outcomes are actually a reflection of organizational policies. This article reviews the effects of quantifiable human resources (HR) policies on outcomes that corporate health researchers often measure. These include health care costs, absences, and worker productivity. First, the overview highlights how variation or changes in policies may have affected previous findings. Second, it provides corporate health and productivity researchers a list of variables that should be considered in future analyses. Last, and importantly, this review illustrates how the policies themselves, which researchers may consider control variables, can often have a greater influence on outcomes than health status.

POLICIES AS COMPONENTS OF THE CORPORATE ENVIRONMENT Corporations influence worker behavior through formal and informal policies. How employees are trained and rewarded; how the organization communicates; what rules govern allowable work behaviors; and what managers emphasize and reinforce all influence business outcomes. A wide range of rules, traditions, and hierarchical structures create a company’s culture and environment. Although it is impossible to gather all aspects of business culture in a few variables, there are some policies that have a strong, consistent influence on health care costs, absence, and productivity. These policies will affect study results and cannot be ignored. Acknowledging that other authors2–4 have attempted to “tally” cultural practices related to corporate health, this review has a different focus. It will identify specific, objective, and quantifiable policies that can be used as variables in statistical models, including basic aspects of health insurance plan design, PTO policies, and elements of compensation. In addition, corporations with multiple business locations may further complicate interpretation of observed connections between health and business outcomes. At a workplace-specific level, some HR policies or practices may afford considerable flexibility in interpretation, while others may be decentralized and unique to specific sites. For example, staffing considerations may be left to the local operations manager, such that facility-specific use of overtime is variably or inconsistently implemented across an entire company. These location-specific practices also deserve consideration when evaluating the business impact of workforce health interventions. JOEM r Volume 56, Number 1, January 2014

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HEALTH INSURANCE PLAN DESIGN Health insurance benefit design can have a significant impact on overall health care expenditures, thereby influencing the interpretation of health care costs as a proxy for health status. The simplest way to describe the effect of plan design is that more cost-sharing on the part of the individual leads to lower overall utilization, independent of health status. Thus, higher copayments, higher deductibles, and higher out-of-pocket maximums result in less health care spending. Two different phenomena produce lower overall costs for the employer when cost-sharing is increased: cost avoidance from employees seeking fewer services and cost-shifting where employees pay a higher portion of the cost. The effects of each are difficult to separate, because patterns of care-seeking (avoidance) are simultaneous to and result from the increased financial burden (shifting). Nevertheless, even when employers cover funding a health savings account, mitigating the effects of cost-shifting, utilization decreases. A 4-year study of Indiana State employees after implementing a consumer-directed health plan found that more than 80% of employees experienced a net growth in their savings accounts each year, and both employer and employees saved money overall because of lower premiums and reduced utilization. The report found no indication that health status was negatively affected.5 The magnitude of the effect of greater cost-sharing is relatively large: compared with zero deductible and zero copayment, utilization when an individual has full responsibility for costs will be approximately 40% lower.6 In the original RAND Health Insurance Experiment (decades old, but nonetheless the only randomized, controlled trial of design),6 progressively higher levels of copayment resulted in progressively larger differences in utilization, compared with the free plan. Consumers reduced utilization of virtually all types of services, those deemed both necessary and unnecessary. Nevertheless, very few differences in health outcomes were found. Similar to the Indiana State study, a more recent RAND study found that introduction of a high-deductible plan from a traditional low-deductible plan resulted in 17% to 21% lower health care spending in the subsequent year. It was determined that two thirds of the reduction was because of fewer episodes of care, while one third was because of lower costs during the episode of care.7 Employers have the option of fully funding a health savings account, essentially mitigating the effects of cost-shifting and still achieving reductions in overall cost from care avoidance. It should be emphasized that the instant drop in utilization is not presumed to reflect changes in health status, but instead changes in one’s likelihood of seeking care. The reverse effect is also true. After reaching the deductible, specifically if subsequent cost-sharing is minimal, utilization will accelerate until the time when a new deductible is applied.8,9 Similarly, when a new plan, with higher cost-sharing or restrictions, is announced, utilization will accelerate in advance of the change.10 Once again, this reflects opportunistic consumption (moral hazard) where plan members choose to undergo a discretionary procedure while personal cost remains low, rather than an indication that the member’s health has worsened. In addition to broad levels of cost-sharing, targeted high copayments for specific items, such as emergency department use11–13 or brand-name medications,14–17 will generally reduce use of those services immediately following benefit redesign. Thus, differences in or changes to copayments and deductibles will influence health care costs and utilization patterns. Any newly implemented effort to encourage consumerism or value awareness will also have an impact on utilization. Reference-based pricing18 and consumer-directed health plan designs7,19 influence patterns of care. Similarly, mail-order20 or on-site pharmacy services21,22 can influence adherence patterns.

The Missing Variables

Even small shifts in coverage rules, such as inclusion of chiropractic care23 or changes in state mandates,24 can change how workers seek and use health care, unrelated to any change in health status. Nuances regarding coverage and rules can also influence reported rates or costs of illness. For example, if plan coverage has high cost-sharing but workers’ compensation (WC) provides more substantial medical coverage, a larger number of health conditions will be reported and treated through the WC system.25,26 If an employer opens an on-site medical clinic27 or begins offering virtual, telephonic primary care,28 patterns of utilization will change and costs may also go down, unrelated to health status. Plus, employers may offer more than one health plan design option, which employees self-select, meaning that some will tend toward different levels of utilization than others.29 In addition, for companies with geographically dispersed workforces, differing availability of specific health plans in limited regions, notably health maintenance organizations, can further confound interpretation of findings. Unlike changes in health risk or conditions, the effects of health insurance policy change happen immediately and apply to all affected plan members. Consequently, plan design can dramatically and suddenly alter utilization patterns in claims data. Incorporation of plan design in analysis of the impact of program interventions on health and health care utilization is important. Because many employer-based health studies rely on claims data to infer prevalence and cost of illness, claims changes caused by plan design can be misinterpreted as a change in underlying health status. Furthermore, because interventions typically begin simultaneously with a new health plan–coverage year, changes in claims will reflect the effects of both. Unless all changes are controlled through study design or statistical adjustment, one cannot be estimated separately from the other. One example of simultaneous implementation of wellness and other health plan policies occurred in early days of corporate wellness. The city of Birmingham, Alabama, implemented a new wellness program along with several new plan design changes in the mid-1980s. Headlines and industry presentations touted the effects of wellness: “City of Birmingham nets cost savings from health risk intervention program,” focusing exclusively on the health improvement efforts made by the city. At the time, it was considered a strong example of wellness cost-effectiveness, credited with taking average employee costs from $300 more than the national average to $1200 less than the national average.30,31 Nevertheless, a subsequent look at these findings32 revealed other simultaneous changes in health plan design, coverage, and related policies. For example, employees were offered an entirely different set of health plans, shifting 90% of the population from indemnity to Health Maintenance Organization, copayment amounts changed, and programs such as medical utilization review and precertification were implemented. In this example, it remains unclear which interventions altered health care cost trends. Nevertheless, early claims that health risk management efforts were solely responsible clearly excluded important confounding factors.

PTO BENEFITS Sick Leave and PTO The primary determinant of absenteeism is PTO policy, which defines the maximum allowable number of days or hours that workers can miss with pay. Other things being equal, the more lenient the rules and the more generous the allowance, the more absences workers will have. Workers whose employers offer PTO miss much more work than those who do not.33 Categorization of PTO is one factor that influences absence rates. Employers who offer sick leave separately from vacation leave, as opposed to a combined bank of PTO days, will experience more worker absences classified as illness days.34–36 Also, workers who

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must take leave without pay for a waiting period before receiving paid sick leave will use fewer days than those with no waiting period.35 Employers who combine sick leave and vacation time into one allotment of PTO may not reduce overall absence from work, because employees do value time-off but do alter the likelihood of incidental, unplanned absences.37 Employees are less likely to use a sick day for minor illness when it lessens the number of available vacation days later in the year. Because PTO and vacation are often linked to tenure in an organization, it also may be important to understand how long workers have been employed and more systematically incorporate this as a research variable Other aspects of policy that influence rates of absence are rules regarding unused time-off at the end of the year. Workers who can “cash in” unused time38 for additional pay will use fewer of their allowed absence days. Workers faced with a use-it-or-lose-it scenario will use more time-off39 as the time of loss (year end or termination) approaches. Finally, methods of reporting absence also influence use of time-off. Those who have to speak directly to a supervisor,40,41 rather than report through an impersonal system, are less inclined to use sick leave for other purposes. Under the same policy structure, health status has been shown to influence absenteeism. Nevertheless, changes in policy, such as implementation of waiting periods and cash-in options, have been shown to have immediate, wide-ranging effects on absenteeism42 that exceed the effects of all but the most serious illnesses. Indeed, a meta-analysis of 35 absence studies42 concluded that the effect of such policies are much stronger than the effects of demographics or work characteristics.

Disability Disability rates and durations are also strongly influenced by disability insurance design. Although serious medical events do increase the rate of extended absences, likelihood of disability is less attributable to severity of illness than most people assume. Indeed, the level of salary replacement during an episode of short-term disability influences the likelihood of a disability claim significantly. In a study of the effects of salary replacement on rates of disability in a large population, those receiving 100% of pay during the first 180 days of absence were almost four times as likely to file a disability claim than those receiving 60% of pay.43 These effects were most marked for more-subjective diagnoses such as pain and mental health issues. Conversely, there was no salary-replacement effect for rates of disability for broken bones or pregnancy.43 Similar to rates of disability, duration of a disability event is also associated with rules regarding salary replacement. Workers receiving higher rates of salary replacement remain out of work significantly longer than those receiving lower replacement.34,44 As an example, for workers absent for a musculoskeletal issue in one study, those receiving 75% to 100% salary replacement remained absent 25 days longer on average than those receiving 25% to 50%.44 Not all companies provide a standard disability benefit to both part-time and full-time employees, and those that do may also offer a “buy-up” option to increase the wage replacement from the baseline value. Although not studied in detail, it makes sense that self-selection of the available buy-up disability benefit and the reimbursement level contained therein may also impact lost time duration.

Workers’ Compensation Workers’ compensation policies are mostly dictated by state regulations, which vary from state to state. Nevertheless, the largest determinants of rate and duration of WC claims are the type of industry (ie, physical jobs),45,46 culture regarding claim filing,47–50 and internal policies regarding safety and return-to-work efforts.51 Similar companies with similar jobs can vary dramatically in rates of WC claims,52 some of which is not attributable to underlying rates of injury or speed of recovery. 30

As examples, companies where grievances are not handled effectively often experience higher rates of WC claims.53 Similarly, when mechanisms for handling termination of poor-performing workers are ineffective or cumbersome, there is a higher likelihood of WC claims.54

Return to Work One of the areas with perhaps the greatest variability among employers is the implementation of return-to-work practices. Some employers have policies only for WC, while others have implemented return-to-work programs for all employees, irrespective of whether the injury or illness claim originates through the health plan or WC.55 In addition, for individual employers, location-specific interpretation and implementation may vary despite standardized organizational return-to-work policies. Systematic implementation of return-to-work policies may help minimize variation and reduce the duration of disability and WC absences.

Overall Although health status does influence an individual’s ability to attend work, policies in the everyday work environment may have more influence on the overall rate of absence42 across a workforce than illness. Variations in policies within an organization, for example, union and nonunionized groups, or location-specific implementation, between organizations, or over time will directly influence rates and durations of absence in ways that may conceal or distort studies of health, absence, and performance. One vivid example of how policy affects absence rates is depicted in a longitudinal study of public policy in Sweden. Although routinely recognized as one of the healthiest nations,56 rates of absence in Sweden remain among the highest in the world, at 26 days per person annually,57 and 10% of the working age population receives disability benefits.58 In a detailed analysis of earlier policy changes, economists selected a random sample of workers and tracked the effects of documented policy changes in 1987 and 1991 for which detailed data were available. Sweden offers governmentpaid sick leave, and in 1986, workers could receive 80% of pay from the government for each day they were sick, even on nonworking days, after a 1-day waiting period. Nevertheless, the waiting period could be avoided if a call was made before midnight the evening before. Beginning in 1987, the waiting period was waived, but pay was only allowed on working days, leading to a notable increase in short-term absences along with a huge drop in the number of absence episodes ending on Sunday. In 1991, payment for days absent was decreased to 65% from 80% for the first few days of an episode, which led to a decrease in rate of short-term absences.59 Overall, the rate of pay reimbursement had more of an impact on duration of absence than the presence of any specific illness. Had scientists been correlating health status with absence rates during these periods, changes in health would have been largely obscured by changes in policy. More recent policy changes have been implemented in Sweden in attempts to reverse the continued trend. Nevertheless, it remains a serious threat to their national economy.60

COMPENSATION Compensation is a factor sometimes considered in studies of health and business outcomes, most often considered as a statistical control in the form of salary level. Indeed, base salary can be an important covariate in analyses, because it corresponds positively to both health status and likelihood of practicing healthier lifestyle.61,62 Although the specific mechanism connecting salary to health is not certain, some evidence suggests that higher-paid individuals have more discretionary funds to participate in healthier activities (healthy food and fitness),63,64 hire others to handle basic tasks, have more discretionary time to participate in health-producing activities, or value their human capital more because of a higher net

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JOEM r Volume 56, Number 1, January 2014

The relative percentage of full-time and part-time workers has particular significance in terms of benefits eligibility and enrollment. Depending on the number of work hours and organizational policies, part-time employees may not be eligible for health or disability benefits. In contrast, wellness program participation may be offered to the entire workforce. Failure to account for meaningful differences in workforce employment status has the potential to affect interpretation of research outcomes. This may become particularly more significant with health care reform legislation where employers have financial incentives to reduce the number of full-time personnel to lessen the cost burden associated with mandated health benefits.

OVERLAP AMONG HR POLICIES Not surprisingly, policies in one area of HR also indirectly influence business outcomes in other areas. For example, when given the opportunity to earn more through performance bonuses, incidental absenteeism often decreases because employees have a greater incentive to attend work.34,70,75 Similarly, companies offering stock ownership or profit sharing76 also have been shown to have lower average absence rates. In another example, simply having health coverage increases both health care utilization and absenteeism as employees seek care during work hours.33 Correspondingly, employees eligible for disability coverage experience higher health care costs than those not eligible.

RELATIVE CONTRIBUTION OF POLICIES VERSUS HEALTH STATUS Few comparisons have been done to assess the relative contribution of certain categories of variables versus others in predicting cost. One such analysis took the approach of dividing a list of variables into four categories.34,77,78 Two categories were considered nonmodifiable (base cost and bad luck, labor characteristics); two were considered modifiable (health status and corporate policies). The outcome of interest was a combined cost of health care and PTO across tens of thousands of employees from multiple organizations. Base costs were calculated as those incurred by young, single workers, having no children, with high pay, living in a low-cost region of the country. Other variables were fixed at the lowest-cost option. Labor characteristics are the region, job type, industry, exempt status, Medical and absence costs 100% 90% 80% 70%

Sample averages

(28%)

Modifiable

TRANSITIONS AND TURNOVER One additional set of variables that helps describe a corporate environment is the rate of migration (hiring, transfers, promotion, and terminations) within an organization. Although not a policy, per se, transitions are objective indicators that reflect stability and mobility within and across a workforce. Divisions with a high transfer or voluntary turnover rate often experience higher medical costs and absenteeism unrelated to health or health status, as employees often miss more work and use more health care in advance of quitting.39 Furthermore, higher rates of voluntary turnover can reflect low morale, which also increases absenteeism and lowers productivity both because of lower motivation and disruption among team members. When studying the effects of health on business outcomes, one presumes a cause-and-effect relationship. Nevertheless, in business units experiencing disruption or upheaval that produce high turnover rates, it is possible that other factors will overshadow the influence of health.71–73 A difficult work setting74 can affect health, absence rates, and performance, independent from any actual mechanisms of disease. By including voluntary termination rates at the business-unit level, researchers can identify potential confounding from turnover. Even better, hiring and other work transition rates may be useful in understanding other forces affecting health, absence, and performance. To date, few studies of health and work performance account for turnover, except to limit the analysis to those continuously employed. This limits the population and overlooks departing employees on whom resources were spent.

WORKFORCE FULL-TIME AND PART-TIME EMPLOYMENT STATUS

$1,389

$547 (11%)

60% (35%)

50% 40%

30% 20% 10%

Nonmodifiable

worth. Regardless of the mechanism, researchers have reason to note and consider controlling for differences in salary. Beyond salary, variable pay (performance bonus) components of compensation also influence business outcomes, sometimes dramatically. A vivid example is the conversion of an hourly-paid workforce to an outcomes-paid workforce, which resulted in a 44% increase in productivity and up to a 28% increase in pay.65 Other examples include increasing productivity by 5% to 9% by instituting profit sharing,66,67 and improving driving safety68 by adjusting compensation and offering incentives.69 By attaching at least some pay to performance, companies can increase the quality and quantity of work performed. One example of variable pay as a core business philosophy is the approach of NuCor Steel.70 Its first guiding principle is that employees have an opportunity to earn more for higher performance. Despite a near collapse of the US Steel industry as a whole in the 1990s, NuCor remains competitive globally because of its high production and low labor costs, and has had no layoffs in three decades. Not unrelated, the firm experiences extremely low absenteeism rates, less than 2%, because an absence results in forfeited bonus. Although NuCor has a broad set of policies and practices that influence its outcomes, compensation is the centerpiece. Similarly, in one small firm where a department implemented a bonus program based on performance, not only did productivity increase but the number of sick days decreased by almost 75% in the first month.34 Although not without limitation, measures of variable compensation can serve as a proxy for an organization’s attention to, measurement of, and rewards for worker performance. Companies that give specific feedback about productivity and place a defined financial value on achievement will, by definition, have better information about productivity. They also can expect higher performance levels, which are often outcomes in health-related studies. Differences in bonus eligibility or discretionary implementation of variable pay incentives across departments or locations have the potential to influence productivity, absenteeism, and other health-related outcomes in one group versus another.

The Missing Variables

$1,749

(26%)

$1,278

0%

FIGURE 1. Portion of employee health and absence costs attributable to different factors. Dashed horizontal, base cost (lowest possible); dots, workforce demographics; outlined diamond, improved employee health status; upward diagonal, improved employer business practices.

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TABLE 1. Policy Variables Contributing to Change in Health–Business Research Policies Health care plan design

Absence/sick leave

Disability

Workers’ compensation

Plans offered Plan characteristics: Deductible Cost-sharing OOP maximum New inclusions/exclusions Eligibility rules Structure (PTO/sick leave) Days allowed Cash-in policy/amounts FMLA Short-term coverage Reimbursement level Eligibility rules Long-term coverage Reimbursement level Eligibility rules Rules Salary reimbursement

Person

Business Units

Plan eligibility Plan selected Changes since last time period

Differences in eligibility or plan types

Dates and nature of plan changes

PTO eligibility Days taken Days cashed-in Days accrued/lost Changes since last time period Program enrollment Plan selected Claims made Claim reason Claim duration Changes since last time period Changes since last period of time

Differences in eligibility or rules Rates

Dates and nature of changes

Differences in eligibility or rules Rates of claims Duration of claims

Dates and nature of changes

Rates of claims Duration of claims Relevant state-level differences in policy

Dates and nature of changes

Productivity Measures Compensation

Variable pay rules Criteria

Work transitions

Longitudinal Events

Eligible for variable pay Range/maximum eligible Tenure

Group-level eligibility and individual eligibility Turnover

Current position Previous position(s) Level/reporting structure Part-time or full-time

Transitions Hires

Dates and nature of changes Dates and nature of changes Dates of hire, position change, and termination

FMLA, Family Medical Leave Act; OOP, out-of-pocket; PTO, paid time-off.

education, and demographics of a workforce. These are considered nonmodifiable unless the firm moves, changes industry or worker type. Health status was measured as the number of unique diagnoses and the number of risk factors listed on a health risk appraisal. Finally, policies that were measured included the use of a high deductible in a health plan, availability of a health savings account, percentage of pay received during short-term disability, PTO policies, and percentage of pay received as variable pay. To evaluate the relative effects of each category of variable, models were run with the least and most expensive options, while holding other sets of variables constant. The results are shown in Figure 1. It was estimated that 61% of combined medical and absence costs were nonmodifiable. Of the 39% that were modifiable, almost three quarters could be attributed to policies. Only 11% of overall costs were modifiable by changing health status by 10% (10% fewer illnesses or 10% fewer risk factors), an aggressive target. Considering only modifiable costs, the ratio of impact of these policies versus health status was 2.5:1.34

describe the policy environment. First, there should be a separate policy data section that identifies critical data elements. For example, in health plans, this includes considerations like the level of deductible and out-of-pocket maximum. For absence policy, this includes the number of paid days allowed and whether sick leave is separate from vacation. Second, there should be a person-level database indicating eligibility for and use of specific aspects of the identified policies. This should include both current selections for a time period and changes made in utilization or selection. Third, there should be some summary indicators at the business unit or location level to detect whether the individual’s unit or location has unique utilization patterns that might indicate significant variation in use or application of the policy. Last, there should be a section where dates and nature of changes in policies are kept. This allows researchers to identify any significant alteration in some policy that might affect cost, absence, or productivity outcomes.

METRICS

It is not uncommon for researchers to focus exclusively on their own disciplines when designing studies and analyzing results. In the case of corporate health, most studies control for factors traditionally identified in public health, such as demographics and health status. What many studies overlook in the corporate setting is

SUMMARY Table 1 identifies the types of information required to account for the above-described policy differences that may confound health–business research. Policy areas are shown in seven rows. In each policy area, there are several levels of information required to 32

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JOEM r Volume 56, Number 1, January 2014

how significantly business outcomes, namely cost, attendance, and productivity, are influenced by company policy. When researchers examine the effects of health on business outcomes or the effect of health interventions on health and business outcomes, results will necessarily be confounded by the corporate environment(s) in which they are studied. At minimum, researchers should document that important policies have not changed and do not vary among the study population. Better yet, the analysis should control for and quantify how policies have influenced the results within study subgroups or compared with other research. Because these policies have been largely ignored as potentially confounding variables, the industry cannot determine the degree to which the connection between health and business has been under- or overestimated. With changes in employment and benefit practices resulting from health care reform, consideration of these largely neglected variables has become increasingly important. In future research, by including these policies as variables, researchers and practitioners can better understand the full array of factors that optimize health and business outcomes.

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The Missing Variables

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Missing variables: how exclusion of human resources policy information confounds research connecting health and business outcomes.

When corporate health researchers examine the effects of health on business outcomes or the effect of health interventions on health and business outc...
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