the value proposition in creating and sustaining healthy...

16 IJHP • Volume 8, Number 1 • April 2016 www.IHPM.org

IJHP • INTERNATIONAL JOURNAL OF HEALTH & PRODUCTIVITY

ABSTRACT

The World Economic Forum (WEF) in its 2014-15 global competitiveness report identified the health of the workforce as a primary pillar of organizational competitiveness and pro-ductivity. Employees who are not at their optimum level of health cannot perform to their full potential and will be less productive. The current value proposition can only justify the effectiveness of workplace wellness programs (WWP) with regard to costs avoided by treat-ing the unhealthy. This study proposes an improved precision technique by using a simple three-category Markov mathematical model to predict population health dynamics. In addi-tion, it estimates the final distribution of health risk categories in terms of low, medium, and high health risk status.

Extending on the current body of work, these linear algebra relationships calculate directly the sensitivity of the steady state distribution. The result proposes an efficient method for estimating the sensitivity of direct costs to changes in each health risk category within the population. The direct costs in the population studied are 2.4 times more sensitive to changes in the low-risk retention rates compared with high-risk retention rates. This further suggests that incentivizing healthy employees to stay healthy has a far greater impact on direct costs, than simply attempting to reduce poor health status in a given population.

The outcome challenges the general critique of such a value proposition. This value proposi-tion suggests that, in applying sensitivity analysis, this maintenance effect could be more cost efficient and beneficial to organizations. Investing in healthy human capital is analogous to preventive maintenance programs for a company’s physical assets, suggesting to leadership that keeping a healthy workforce healthy is a profitable human capital investment.

2,@>69+:! Healthy Human Capital, Health Risk Migrations, Population Health Dynamics, Health & Productivity Manage-ment, Institute for Health & Productivity Management, Linear Algebra, Markov Models, Healthcare Costs, Prediction, Preventive Maintenance Programs, Sensitivity Analysis, Value Proposition, Leadership.

The Value Proposition in Creating and Sustaining Healthy Human CapitalSamantha Horseman, BPhty CWS CCP (Erg) MBA, DBL, Khalid Al-Dhubaib, Sean Sullivan, JD, Brent W. Mattson, PhD and Steve A. Seay, PhD

1.0 INTRODUCTIONWorkplace Wellness Programs (WWP) are designed to encourage people to create and sustain healthy lifestyles. These healthy hab-its include engaging in physical activity reg-ularly, maintaining a healthy body weight, selecting healthy eating options, quitting smoking, managing stress effectively, and being empowered to maintain current health status and reduce health risks (Chapman, 2012). The core objective of any WWP is to facilitate and help sustain healthy behav-iors toward a more meaningful and pro-ductive lifestyle (Goetzel & Ozminoskwi, 2008; Loeppke et al., 2009; Edington, 2009;

Baicker et al., 2010; Berry et al., 2010).Various schools of thought and theoreti-

cal models contribute to the development of WWP. The recognized “gold standard” and benchmark that organizations strive for, however, is the Health & Productivity Management (HPM) model (Chapman, 2005; Sullivan, 2006; Sullivan, 2008; Goetzel & Ozminoskwi, 2008; Loeppke et al., 2009; Edington, 2009; Baicker et al., 2010; Berry et al., 2010; World Economic Forum, 2013). HPM aligns and integrates both medical sci-ence and management science. As the devel-oper of this emergent model, the Institute for Health & Productivity Management

IJHP • Volume 8, Number 1 • April 2016 17www.IHPM.org

THE VALUE PROPOSITION IN CREATING AND SUSTAINING HEALTHY HUMAN CAPITAL

(IHPM) was established in 1997. IHPM is a global non-profit enterprise created to es-tablish the value of employee health as an investment in workplace productivity and business performance (Sullivan, 2005).

The World Economic Forum (WEF), in its 2014-15 global competitiveness report, states that the health of the workforce is a fundamental foundation for organizational competitiveness and productivity. Workers who are not at their optimum level of health cannot perform to their full potential and will be less productive. Ill health results in significant costs to business, as unhealthy employees are more often absent and less productive (WEF, 2014). The IHPM cham-pions the idea that health is human capital and the greatest untapped source of com-petitive advantage in the global marketplace (Sullivan, 2004; Sullivan, 2007).

Based on the premise of the HPM model, this new health management science looks beyond getting sick employees well or even back to work; it further expands the defi-nition of value to include better employee productivity and overall corporate perfor-mance (Allen & Bunn, 2003; Leutzinger, Sullivan & Chapman, 2004; Sullivan, 2004; Sullivan, 2007; Edington, 2009; Fabuis et al., 2013; Fabius et al., 2016). WWP based on the HPM model can improve function-al health, which can positively impact total labor costs (Allen & Bunn, 2003; Sullivan, 2005; Sullivan, 2007; Edington, 2009; Fabius et al., 2013; Fabuis et al., 2016) rath-er than just reduce health care costs. This new paradigm suggests that health is an asset rather than a cost (Bunn, et al, 2001; Bunn et al,2003; Sullivan, 2005; Sullivan, 2007; Edington, 2009). This study builds on the previous publications in the HPM field, in particular, the emergent work on developing an equation for Presenteeism (Horseman et al., 2013). The core objective of this study is to calculate the dollar value associated with keeping healthy employees healthy; through adopting a systems-thinking approach.

Healthy human capital investment aligns with preventive maintenance investment for physical assets (e.g. machines, special-ized equipment, etc.), integrating sensitiv-ity analysis and a Markov1 model transition

matrix to evaluate the impact on cost us-ing the methods of population dynamics (Horseman et al., 2013).

2.0 LITERATURE REVIEWEvidence suggests that WWP is cost-benefi-cial, saving companies money in health-care expenditures and producing a positive return on investment (ROI)2. Baicker et al., (2010), calculated an average return of $3.27 in med-ical costs for every dollar spent on workplace wellness programs. To support such findings, organizations that have reported cost sav-ings or positive ROI ratios include Johnson & Johnson, Citibank, Procter & Gamble, Chevron, California Public Retirement System, Bank of America, DuPont, Navistar, Duke University, and Highmark (Allen & Bunn, 2003; Bunn et al., 2003; Baicker et al., 2010; Anderko et al., 2012).

In the United States, The Prevention and Public Health Fund (PPHF) contained new provisions designed to improve public health and wellness. There are four key prevention areas: 1) community prevention, 2) clini-cal prevention, 3) public health infrastruc-ture and training, and 4) research and sur-veillance focused on workforce wellness. Understanding key issues that affect the American workforce is critical to improving prevention efforts (Healthy People 2020/CDC, 2010; Guidotti, 2011; Levi, Seigel & Kohn, 2011; Anderko et al., 2012). In re-cent years, it has been empirically proven that keeping a population healthy is a more cost-effective solution than addressing un-healthy conditions (Guidotti, 2011; WEF, 2013; Edington 2013; Horseman, et al. 2014). The concept of keeping healthy peo-ple healthy is not a new proposition. Since its creation in 1980, The Health Management Research Center (HMRC) at the University of Michigan has been a driving force in de-livering evidence of the economic impor-tance of sustaining a healthy population (Edington, 2009; Edington, 2013).

1 Markov Mathematical Model: A model that makes it

possible to study systems by establishing a state of the system

and transition to a new state. 2 ROI: A measure of the net in-

come that a company is able to earn with its assets.

Despite this evidence, the imbalance in health care budgeting still exists, and



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justif iably so. This imbalance will contin-ue until policymakers and business leaders can calculate actual returns on investments in healthy people (Horseman et al., 2013). In turn, this can only justify returns on in-vestment regarding costs avoided by treat-ing the unhealthy. Inspired by the elegance in representing health risk migrations in population as Markov models (Loeppke et al., 2008; Horseman et al., 2012), this study used a simple three-category Markov model for predicting population health dynamics and estimating the final distribution within health risk categories of low, medium, and high-risk status.

In extending the current body of work in HPM, however, the relationships from lin-ear algebra are used to calculate directly the sensitivity of the steady state distribution of the population. This proposition compares the perturbations in transition elements of the Markov model. The resulting sensitiv-ity matrices transform to a cost function. This application allows for the sensitivity of healthcare costs in a population, compared with perturbations in transition elements of the Markov model.

2.1 PREVENTIVE MAINTENANCE FOR PHYSICAL ASSETSThe recent WWP critique (Tu, & Mayrell, 2010; Hochart & Lang, 2011; Mattke, Schnyer & Busum, 2012) states that such programs only attract the healthy workforce population. Business leaders should consider that other company assets (i.e. physical as-sets, machinery, etc.) have well-established preventive maintenance programs. These physical assets include working operational machines registered in any organization’s preventive maintenance programs to main-tain the health and performance of machin-ery. Business Leaders also should acknowl-edge that the same should apply to healthy human capital investment, and that keeping the healthy workforce population healthy could be a significant investment in the long term.

This analogy between physical assets (preventive maintenance programs), and healthy human capital (preventive medi-cine programs – WWP) as shown in Table 1 should be verified through ROI. The cost

of maintenance for a physical asset annually should not exceed five percent of the asset value to remain profitable (Sondalini, 2011). Obviously, the total maintenance cost de-pends on the quality of the equipment and cost of maintenance. In physical assets, the best investment is the asset that requires little maintenance. In the chemical manufactur-ing industry, the world best practice mainte-nance costs are 1.8% to 2.0% of the replace-ment costs.

For each 1% of asset replacement value spent annually on maintenance over a twen-ty year period, $75,000 of every $1,000,000 of original capital will not return any divi-dend on the investment (Sodalini, 2011; Koo & Hoy, 2012). Determining the economic value and performance output of preventive maintenance (PM) on physical assets, com-pared with the value of preventive medicine for human capital, needs further consider-ation.

Specific financial factors must be consid-ered to sustain performance and longevity through the extension of expected asset life, and avoid future repair costs. The finan-cial ratios demonstrate that PM generates a steady rate of return through risk mitigation and asset protection (Koo & Hoy, 2012). A comparative analysis of PM for physical as-sets with PM for human capital provides a particular logical assumption. The most sig-nificant factor to consider, however, is that the best physical asset investments require little maintenance. In human capital terms, this means the healthy and productive work-ers are in the low-risk category.

Koo & Hoy, (2012) tested various scenar-ios that evaluated the ROI on PM of physi-cal assets, (operational working machines) over a financial year. The analysis compar-ing Scenario 1 with Scenario 3 (1= no PM, 3 = industry benchmark PM) demonstrates positive returns from performing preven-tive maintenance, as highlighted in Table 1. The methodology calculates the ROI by subtracting PM Scenario (3) from the non – PM Scenario (1), if the result is positive then performing PM makes viable financial success and economic sense. If the value is negative then performing PM is financially unsuccessful and not justified economically.

The outcome of the analysis demonstrates



a powerful value proposition for PM, with a high ROI. In the current study, the PM of physical assets is all about keeping equip-ment and machinery healthy. The analysis at portfolio level indicates a net present value (NPV) of $2 billion over a twenty-five-year period for a $39 million-per-year ($0.33/sf ) PM Program; he ROI is 545%. The most significant return came from increasing the useful life of the physical assets (equipment), compared with improved energy efficiency calculated at seven percent of the return.

Applying the same logic, the same ap-proach in this study also suggests that the strength of WWP is in keeping human capi-tal healthy. WWP maintains the health sta-tus of human capital, thus preventing upward health risk migration, and improving absen-teeism and presenteeism in a given work-force population (Horseman et al., 2013). In effect, WWPs are preventive maintenance for human capital assets. Markov math-ematical models suggest that such a main-tenance effect could be more cost effective

and beneficial to organizations. This study examines that hypothesis.

3.0 METHODOLOGYA sample population of 875 male (mean age 41.7±0.7) and 228 female (mean age 40.4±0.8) employees was enrolled in a workplace wellness program (WWP). This sample was selected and tracked over the course of a six-year period from May 2005 to November 2011 (Horseman et al., 2013). The employees were of diverse nationalities, but had similar socioeconomic status and lived in the same community. The method-ology evolved into a business solution, so it was important to create a process that han-dled large amounts of information quickly.

An efficient algorithm computed the re-sults that scales O (n log(n)) with an input of size n (see Figure 1 and Appendix A).

Figure 1 highlights (1-2) Employee health risk classification was determined by as-sessing body mass index (BMI), body fat percent, and both systolic and diastolic

4.Construct

Time-VaryingTransition

Matrix

5.Calculate

Sensitivity ofCost to

TransitionMatrix

Elements

6.ValidateResults

Figure 1. Overview Algorithm Components: Input to Validation

1.Biometric

HealthStatusData

2.Identify HealthRisk Category

Based onBiometricCriteria

3.Determine

ShiftBetween Risk

CategoriesUsing SortingAlgorithms

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Physical Capital: Preventive Maintenance (Sodalini, 2011; Koo& Hoy, 2012)

Human Capital: Preventative Medicine (Chapman, 2008; Sullivan, 2007; WEF, 2013)

The equipment will perform betterEmployees are more productive at work (i.e., they experience less presenteeism)

Repair costs will fall Employees incur lower direct medical costs

Downtime will be reduced Reduced absenteeism

Equipment life will be extended Employees live longer “work lives”

Tenant satisfaction will be increased )QTPS]IIW�LEZI�E�LMKLIV�NSF�WEXMWJEGXMSR

The manufacturer says we need to do itLegislation in some countries are (e.g. Affordable Care Act, in the US).WorkPlace Centers (IHPM) are advancing this work with the World Economic Forum – strategic partnership

Table 1: Alignment of Preventive Maintenance of Physical Assets and Preventive Medicine of Human Capital

Preventive Maintenance Aligned with Preventive Medicine



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blood pressure according to criteria set by Horseman, et al., (2013). (3-4) This input was used to determine the health risk distri-bution of the population over time, as well as to construct the time-varying transition ma-trix. (5) The time- variance of the transition matrix was simple, remaining constant and only changing once per year when enough data were available for a given year. (6) The final model was cross - validated by random-ly excluding half of the sample data from the algorithm and comparing the resulting two predictions. If the error was small, the mod-els were averaged and the final model con-firmed.

3.1 MATHEMATICAL BACKGROUNDInspired by previous research (Edington, 2009; Edington, 2013; Horseman et al., 2013; Horseman et al., 2014) the Markov model for risk dynamics in a population over time can be constructed from the transition matrix. This further describes the probabil-ity of each individual in the population of transitioning from their current category. The model considered in this study consists of three categories: low risk, medium risk and high risk and can be represented by the transition matrix:

Where Phm is the proportion of individ-uals transitioning from category h (high) to category m (medium) for a given year t. The selection of this model is logical since it is easy to classify and track a population in terms of low, medium, and high health risk status, as demonstrated by Loeppke et al., (2008), Edington (2009), and Horseman et al., (2012). Another interesting feature of Markov models is the ease with which they can be represented graphically as demon-strated in Figure 2 below. For a more de-tailed mathematical description of the transi-tion matrix, see Appendix A.

The output of the Markov model is a three-component vector representing the proportions of the population that are clas-sified as low risk PL , medium risk PM , and high risk PH , at year t.

Instead of studying the f luctuations of indi-vidual health risk categories, it is useful to study the total economic impact of health status in a population. The output from equation (2) can be transformed by the cost function suggested by Horseman et al., (2013) to give the total healthcare expenditure of each grouping.

Equation 1.

Equation 2.

Figure 2: The Markov model and elements of the transition matrix from expression (1) as above.

Figure 2. Graphical Representation of Markov Modeling

Phh

High Risk

Low Risk

Medium Risk

Pmh Phm

Pml

Plm

Pmm

Pu

Plh Phl


Where İ is the ratio of total cost impact to direct healthcare costs, µ is the average med-ical cost per health risk, Ȍ is the total num-ber of individuals in the population, and R is the average number of health risks per year for an individual in the health risk category. The motivation for this study was calculat-ing the dollar value associated with keeping healthy people healthy. Moreover, the sen-sitivity analysis was used to identify which elements of the transition matrix ultimately had the most impact on cost. (Horseman et al., 2013). The derivation by Caswell (1980) based on the sensitivity of steady-state popu-lation proportions to changes in a transition matrix was applied. The following equation demonstrates the sensitivity of the steady state population healthcare cost to changes in elements of the transition matrix. The ei-genvalues (i.e. when an equation has a non – zero solution under specific conditions) and left and right eigenvectors (i.e. a vector that gives a scalar multiple of itself ).

Where wm(k) the kth component of the mth

right eigenvector, vm is the mth left eigenvec-tor, Rk is the average number of health risks

for the kth health risk category, ࠴m is the mth eigenvalue, c1 is the first element of a vec-tor of constants determined from initial con-ditions, and Qij is a vector of proportional changes in the column of the transition ma-trix containing the element of Pij.

Appendix A illustrates the details on the derivation of this equation (Caswell, 1980) and the sensitivity analysis. General condi-tions identify which cost is more sensitive to changes in low risk. The sensitivity analysis iteration describes a general population with only two categories; healthy and unhealthy. Similar to Figure 2, the Markov model rep-resenting this group is graphically depicted in Figure 3, above with these simplifications the model is easier to understand.

Since the columns of the stochastic matri-ces sum to one, in this specific case this can be expressed by two independent variables. Therefore the transition matrix can be ex-pressed as:

To compute the sensitivity of cost to changes in low-risk retention and high-risk retention, this simplified system was signifi-cantly easier to study analytically and result-ed in the following simplification of equa-tion (4), expressed in equation (6) below.


Figure 3.�7IRWMXMZMX]�EREP]WMW�GSQTPIXIH�[MXL�SRP]�X[S�TSTYPEXMSR�GEXIKSVMIW��LIEPXL]�SV�YRLIEPXL]��8LMW�WMQTPM½GEXMSR�MQTPMIW�that individuals each year either remain in their current category (healthy or unhealthy) and the remaining become either unhealthy or vice versa.

Figure 3. Graphical Representation of Markov Model

UnhealthyPopulation (U)

Percent healthy thatremain healthy (Phh)

Percent healthy that become unhealthy(Phu) Percent unhealthy that

remain unhealthy (Puu)

Percent unhealthy that become healthy(Puh)

HealthyPopulation (H)

(5)

Equation 3.

Equation 4.

Equation 5.

Equation 6.

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As demonstrated, the total sensitivity of cost to changes in the elements of the transi-tion matrix corresponded to the simplified model and is dependent on one variable, all else remaining constant. This supports the proposition that investing in healthy people always will be more cost-effective than in-vesting in unhealthy people as long as the current retention rate of healthy people is higher than the current retention rate of un-healthy people. More formally:

Due to the scope of this article and nature of this IHPM publication, descriptions and formulas are described further in Appendix A. For the mathematician readership, more details on the derivation of the above solu-tion and general condition are provided in Appendix A.

4.0 RESULTSThe predictions of proportions of the popu-lation in each of the health risk categories as estimated by equation (2) correlated sig-nificantly with the proportions of the group-ing in each of the health risk categories. Specifically, the correlation constants and p-values were represented as three-component vectors, p = 19.5%, 49.8%, 15.2% and p = 0.005, < 0.001, 0.030 respectively, starting with the values for low risk and ending with the values for high risk.

Given the significant p-values, these were described as high correlation values despite the considerable variance in the data. The variation in the data and its relationship to the predictions estimated by the Markov model are discussed more in the discussion section. In further support of the correlation values obtained, Figure 4 graphically compares the predictions estimated by the Markov and the population data. Moreover, the cost function as given by equation (3) was used to count the number of risks and estimate the costs avoided each year - as a result of the changes in health risk migrations. As demonstrated in Figure 4, even though there is an increase in data f luctuation, it clearly follows the general trend predicted by the Markov model. The

bottom left plots the output of the Markov model as a cumulative distribution, extend-ing the predictions into the future.

For more details on how future values were predicted using the Markov model see Appendix A. The bottom right pane is the result of the cost function as applied to the Markov model (Figure 4). This provided the estimate of the direct and total cost avoid-ance since the start of the study in 2006. The cost function was used assuming an av-erage medical cost per risk of $1,503 USD, a population size of 10,000, and a ratio of 4.7 total cost to direct cost (Edington, 2009; Horseman, et al., 2013; Horseman, et al., 2014).

The cost sensitivity matrix of the popula-tion studied as determined by equation (4) was:

The two values in this matrix reveal much about the nature of the population, corre-sponding to the elements Pll and Phh. The comparison in the sensitivity of cost to per-turbations in Pll with the sensitivity of cost to perturbations in Phh is relevant, further dem-onstrating that increasing low-risk retention rates has a higher negative impact on cost than decreasing high-risk retention rates. This outcome further supports that keeping healthy employees healthy and preventing migrations to higher health risk categories was 2.48 times more effective in cost mitiga-tion. This suggests that investing in healthy human capital is advantageous to organiza-tions. To further investigate this finding, a simplified model with only two categories and phase-plane plots was generated. The objective was to demonstrate the relation-ship between the sensitivity of cost and the model parameters, represented in Figure 5.

A. Phase plane plots of the sensitivity of cost to changes in healthy employees (left) and unhealthy employees (right); they clear-ly demonstrate the two distinct regions where cost sensitivity begins increasing rap-idly in both phase plane plots, and the point of the corresponding high values for reten-tion rates. The numerical values shown in

Equation 7.



the phase plane plots are unique to the pa-rameters selected for average medical cost per risk, population size, ratio of total cost to direct cost and an average number of risks per category.

B. On the left is a phase plane of the rela-tive sensitivity of value to changes in healthy employees compared with changes in un-healthy employees. Highlighted in this plot is the critical boundary at which cost is equally sensitive to changes in healthy and unhealthy employees; note the cross section deviated toward the right. The choice of cross sec-tion corresponds to the expected steady-state proportion of the population that is healthy on the x-axis. As demonstrated, the

more biased a population is to be healthy, the more sensitive the cost is to changes in the healthy population. Conversely, the more biased a population is to be unhealthy, the more sensitive the raw value is to changes in the unhealthy population. Unlike the phase plane plots in A, the ratio of sensitivity does not depend on any model parameters. Consequently, this will remain constant for any population regardless of the definition of health categories, population size and aver-age medical costs.

Because the simplified model contained only two independent variables (As dem-onstrated in the mathematical background section), the phase plane plots were easy

Figure 4. Markov Model Predictions of Population Health Status and Cost Estimates

PredictedActual

Low Risk PopulationPe

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50

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Medium Risk Population

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PredictedActual

High Risk Population

Perc

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PredictedActual

30

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20

15

10

Low RiskMedium RiskHigh Risk

Cumulative Population Predictions

Perc

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ion

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Annual Risk Avoidance

Risk

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Annual Cost Avoidance

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(M

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to visualize in two dimensions, as seen in Figure 5. It is easy to see that cost becomes more sensitive to changes in the healthy pop-ulation. This relationship is proportional to the retention rate of healthy employees and inversely proportional to the retention rate of unhealthy employees. To further high-light this logical link, the opposite is true for the sensitivity of cost in response to health changes.

5.0 DISCUSSIONThe core conclusions of this study suggest that it does make econometric sense that the value proposition and the rationale for keeping employees healthy must be that do-ing so is less costly than just focusing on treating employees with chronic conditions. This logic, however, is challenged in the recent literature. The WWP critique (Tu, & Mayrell, 2010; Hochart & Lang, 2011; Mattke, Schnyer & Busum, 2012) states that most preventive approaches are delivered to large populations, a good proportion of which would never acquire the targeted dis-ease even in the absence of the intervention. This study suggests that keeping healthy

employees healthy is more cost - effective and is a value-added approach to health management.

Whether or not a preventive intervention is cost-effective or even cost-saving de-pends on several factors: f irst is the cost of the intervention, followed by the probabil-ity of disease onset in the targeted popula-tion. Another factor is the effectiveness of the intervention, and the cost of the man-ifestation of illness (Tu, & Mayrell, 2010; Hochart & Lang, 2011; Mattke, Schnyer & Busum, 2012).

Applying more sophisticated mathemati-cal Markov transition matrices, phase plane plots and sensitivity analysis provides a dif-ferent perspective on population dynamics. The critique of the emerging field of WWP is that the currently available evidence pro-vides only proof of concept and that more research is required to determine the impact of WWP in real-world settings in order to adequately inform policy decisions at a glob-al level (Osilla, Van Busum, Schnyer, Larkin, Eibner, & Mattke, 2003; Mattke, Schnyer, & Van Busum, 2012; Serxner, Alberti, & Weinberger, 2012; Horwitz, Kelly &

0 0.2 0.4 0.6 0.8 1.0

Figure 5. Phase Plane Plots of Cost Sensitivity

A

B

Sensitivity of Cost to Changes in Healthy People

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$800 M USD

$600 M USD

$400 M USD

$200 M USD

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Healthy Retention Rate

Relative of Sensitivity of Cost to Changes in Healthy People vs Unhealthy People

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Cross Section from Neighboring Graph

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DiNardo, 2013). Moreover, the critique suggests that there is insufficient objective evidence to evaluate the impact of WWP on health outcomes and cost (Osilla, Van Busum, Schnyer, Larkin, Eibner, & Mattke, 2003; Mattke, Schnyer & Van Busum, 2012; Serxner, Alberti, & Weinberger, 2012; Horwitz, Kelly & DiNardo, 2013). Contrary to this critique, this sensitivity analysis pro-vides objective support for applying such integrative scientific approaches as Markov mathematical models, transition matrices, phase plane plots, and sensitivity analysis.

This provides the foundation for future research and inquiry. Although the phase-plane plots from the simplified system shown in Figure 5 clearly identify critical regions where cost is more sensitive to changes in the healthy proportion of the population, it is not clear how these relationships would generalize to higher dimensions with the three-category model including low, me-dium and high health risk categories. The dynamics would be much more complicated and harder to visualize, however, the un-derlying principle would be the same. The more biased a population is toward being healthy; the more sensitive costs will be to changes in healthy employees – but further research is required here.

One of the research challenges faced in this study was reconciling the large f luctu-ations in the proportions of the population in each of the health risk categories. A next step would be to evaluate the data based on the predictions from the Markov model. Part of these f luctuations may be explained by white noise in the data due to using a rela-tively small sample size, and the remaining changes may have resulted from seasonal trends (Al-Dhubaib and Horseman, 2014). Given a sample population of more than n = 1,100 employees, however, if f luctuations of the magnitude seen in Figure 2 are a re-sult of small sample size, then this has seri-ous implications for the applicability of this method for small to mid-sized businesses and organizations.

This would mean that small to mid-sized businesses would require very rich and fre-quently collected information regarding the health status of their employees to com-pensate for the smaller population sizes. Additionally, more sophisticated methods

need further development for measuring the goodness of fit of the Markov model. The use of Fourier analysis and studying the low-frequency trends of the population data may assure further validity. This also would allow comparing the long-term trends of the pop-ulation data on the proportions of the work-force population in low, medium and high health risk categories with the predictions of the Markov model, while ignoring high-frequency f luctuations and recurring trends.

Despite these challenges, the results con-vincingly support the long-standing logical perspective that keeping healthy employ-ees healthy or investing in healthy human capital is a more cost efficient solution for an integrated HPM strategy. The tools de-veloped in this study could be applied to a robust business intelligence platform based on a progressive investment strategy plan. To demonstrate this further, Figure 6 is an envisioned business intelligence interface constructed from the databases developed in Appendix A. It would further provide intui-tive business intelligence planning tools for business leaders and policymakers regarding the cost sensitivity matrix, as well as costs in relationship to the transition matrix and pre-dicted health expenditure and performance over time.

The example detailed in figure 4 is based on the cost sensitivity matrix given in the re-sults section. Based on equation (4), the esti-mated change in steady state cost in response to a five percent increase in Phh is:

ǻC = 0.05 x (-4.67 x 108) = -$23.4 million USD

Similarly, a five percent decrease in Phh re-sults in an estimated impact on steady state cost of:

ǻC = -0.05 x (1.88 x 108) = -$9.6 million USD

Figure 6 clearly demonstrates the impli-cations and applicability of this current re-search for business leaders and policymakers. The scenario analysis in Figure 6 illustrates the envisioned business intelligence interface and compares the impacts of a timeframe ex-tending to 2018. This outcome demonstrates that increasing the low-risk retention rate (Pll) by five percent has the effect of decreas-ing the high-risk retention rate (Phh) by five



www.IHPM.org

percent to the steady state cost reached by 2018. These results are manifested through multiplying the relevant values of the sen-sitivity matrix, multiplying planned change in each of the two categories (±5%) and comparing them with each other. This five percent value is insightful, considering the findings from PM of machinery that the cost of maintenance of physical assets annually should not exceed five percent higher than the tangible asset value to remain profitable (Sondalini, 2011).

6.0 CONCLUSIONThis study supports the proposition that in-vesting in the maintenance of the health of a workforce is a sound business strategy. There is significant value from investing in creating and sustaining healthy human capi-tal. Furthermore, this study provides orga-nizational applications for policymakers and leaders to assist in calculating the absolute return on investment (ROI) in keeping em-ployees healthy.

These findings contribute to the growing

argument for the effectiveness of workplace wellness programs. This study proposed an improved precision process using a simple three-category Markov mathematical mod-el to predict population health dynamics. It also estimated the final workforce distribu-tion among health risk categories of low, me-dium, and high risk status. Keeping healthy employees healthy is not a new concept; equipped with the tools developed in this study, however, business leaders now can as-sign a dollar value to returns on investing in healthy human capital just as physical assets are in good working condition as the result of a comprehensive preventive maintenance program. Having such a tool may help show the importance of investing in and sustain-ing healthy human capital, i.e., healthy em-ployees.

This is only a starting point. In addition to addressing concerns regarding the good-ness of fit and sample size, future expan-sions of this work may include predicting the long-term trends of the time-varying transi-tion matrix. This would further prove the

Figure 6. Application: Business Intelligence

Figure 6. Scenario analysis shown in this envisioned business intelligence interface compares the impacts to 2018.

Investment Strategy Scenario Analysis

Target Low Risk Retention Change:

Target High Risk Retention Change:

+5%

-5%

Current Health Risk Cost Distribution Low Risk Cost Medium Risk Cost High Risk Cost

$250

$200

$150

$100

$50

$0

Mill

ion

USD

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

$36

0.828

0.156

0.0159

-4.67e8

4.11e8

7.95e8

0.280

0.565

0.155

-3.02e8

3.34e8

3.34e8

0.8280.025

0.242

0.733

-2.99e8

1.18e8

1.18e8

A 5 percent in-crease in retention of low risk indi-viduals can reduce annual aggregate costs by $23.4 mil-lion USD per year by 2018. This return is 2.4 times greater than the return on investment from re-ducing the high risk retention rate in the same population by the same amount. These values are calculated from the sensitivity matrix elements shown in blue on the left in relationship to the corresponding transition matrix elements in black.

$109$89 $74 $63 $55$57 $60 $64 $66 $68 $70 $71 $72

$57 $55 $53 $50 $54 $63 $66 $68 $69 $69 $69 $70 $70

$33 $37 $41 $44 $44 $41 $40 $39 $38 $37 $37 $36



dynamics of the time- variance. Because the transition matrix inevitably changes from year to year, the steady state solution is not expected to be constant. It is important to in-vestigate how these research insights would impact the sensitivity calculations in various time sequences.

Additionally, the Business Intelligence Application could also integrate various oth-er databases to show how improved health status improves performance. These data-bases could include absenteeism and accident and injury rates, in addition to health risks and health status. Furthermore, this applica-tion needs to be in real time to be meaning-ful and persuasive as a value proposition; this requires further study and development. The future objective is also to develop an integra-tive sensory system to capture these data in real time, by applying a systematic environ-mental and wearable technology platform (Horseman Patent Portfolio, 2015).

The next exciting step toward future re-search and continuation of this integrat-ed science approach is already in action. Moreover, this current work could contrib-ute to developing sound business intelligence platforms to support the value proposition for a healthy workforce. These business in-telligence platforms are essential tools for leaders. This HPM system also could assist in highlighting low, medium and high-risk population dynamics across any organiza-tion. These metrics could further support and justify HPM value and sustainable man-agement investment.

Additionally, future work already is un-derway to operationalize this study in de-veloping HPM dashboards for management. These business scorecards will provide real-time data streaming for calculating the re-quired human capital investment to maintain the health and performance of a workforce. At a strategic level, these metrics could be an essential organizational health key per-formance indicator, presented monthly at the C- Suite level. Machine - learning algo-rithms could add further value in predictive analytics for the health and productivity of any given workforce.

This body of work already is in progress in what the authors refer to as the leadWELL program (Mattson, 2015). Development of the business methodologies and processes

previously mentioned as real-time applica-tions will strengthen business leader buy-in to investing in HPM. This study adds sup-port to the argument that a healthy and safe workforce aligns with a company’s perfor-mance (Allen & Bunn, 2003; Chapman, 2015; Berry et al., 2012) and its ability to provide positive returns to shareholders (Fabius et al., 2013; Fabuis et al., 2016). This study also has contributed to the growing knowledge that investing in healthy human capital makes good business sense. In support of this value proposition it only seems fitting to add this original quote from Sean Sullivan (2007); which is often quoted by others “ If we oiled and greased our people the way we do our machines, they would not break down as often and cost us so much in repairs and lost productivity”. The continuation of such research and future projects will further support this important value proposition for organizations.

APPENDIX A

AN EFFICIENT ALGORITHM TO CONSTRUCT THE TIME-VARYING TRANSITION MATRIX FOR HEALTH & PRODUCTIVITY MANAGEMENT - ROI.The following section will describe an effi-cient algorithm that scales O (n log(n )) with input size. The data used to construct the matrix was a cohort consisting of biometric information on n =1,157 employees enrolled in a WWP (Horseman et al. 2014). The al-gorithm consists of three modules that pro-cess the input and pass on new output to the next module in the following sequence:

1) Health Risk Assessment Module: Based on the biometric criteria set by Horseman et al., (2014), the biometric records are eval-uated, and each subject is assigned a health risk category. A typical record includes a time-stamp, employee identification num-ber, body mass index, percent fat, systolic and diastolic blood pressure, and informa-tion on non-communicable disease and in-juries. This module iterates through the da-tabase once and thus has a runtime that scales O (n) with input.

2) Sorting Module: Quicksort is used to sort the biometric profile by employee identification

28 IJHP • Volume 8, Number 1 • April 2016 www.IHPM.orgwww.IHPM.org


number to efficiently identify previous health risk category assignments by time-stamp if any. Next, quicksort is repeated to sort the group by time-stamp and prepare for the next module. The component with the longest runtime in this module is quicksort, which has a runtime that scales O (n log(n)) with input.

3) Transition Matrix Module: The biomet-ric profiles sorted into year-long segments based on the first time-stamp available. Next, each health risk assignment is com-pared to the previous health risk group, and the corresponding element of the transition matrix incremented if appropriate. Once the end of a year-long segment is reached, the columns of the transition matrix are normal-ized to have a sum of one, and a new transi-tion matrix evolves.

Since this module only requires one it-eration through the database, it scales O (n) with input. This algorithm has a run-time T (n) that is a linear sum of each of the run-times of its components, such that:

T (n) = O (n) + O (n log(n)) + O (n) (1)

T (n) = O (n log(n) + 2n) (2)

T (n) = O (n log(n)) (3)

is the asymptotic complexity of quicksort. The rationale behind this algorithm con-struction depends on the complexity of the data mechanism. The complexity of the sort-ing algorithm further guarantees fast perfor-mance in delivering the solutions required.

Acknowledgements

The authors would like to thank Dr. Robin Snyder and Todd

Fennimore, Case Western Reserve University for review-

ing the sensitivity analysis, and the Institute for Health and

Productivity Management (IHPM) for supporting devel-

opment of this proposed HPM methodology and ongoing

HPM research. During this study timeframe, a new strate-

gic business unit was also developed called Human Energy

Management, (HEM), responsible for creating and sustaining

a healthy and productive Saudi Aramco workforce. Without

this great strategic investment and commitment from the

Saudi Aramco Management, this HPM leadership work

could not have reached such levels of research excellence.

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