This document provides backup information on the Healthways Simulation Model and the Wellness App. It is organized in five sections.
The Wellness App is a tool that estimates:
- the costs of various chronic conditions in an employee population of certain characteristics
- the potential savings that various interventions would bring about by mitigating the key risk factors associated with certain chronic conditions
The results are based on profiles previously generated by the Healthways Simulation Model; they should not be considered exact, and are presented in ranges and subject to the data limitations and assumptions (see below). The calculations are based on average data, and therefore do not represent a specific employee population.
The main goal of the Wellness App is to suggest to senior executives and managers, in both private and public sectors, ways of thinking about wellness as a corporate strategic topic rather than just as an HR issue. Companies that are still in the early stages of wellness engagement can use the Wellness App to develop a high-level business case in order to kick-start a wellness programme. Companies that are already quite sophisticated in their wellness programmes can use the application to further engage their executives by presenting them with a robust business case for investing more deeply in wellness.
Think of the Wellness App as a powerful tool for triggering conversations with senior executives, rather than for providing specific solutions to workforce health issues. The Wellness App is not itself a wellness programme or strategy, and companies should not rely on the App alone when developing an optimal set of interventions as part of their overall wellness scheme. The App is a complement to rather than a substitute for a comprehensive health study – such as a Health Risk Assessment (HRA) – that can accurately profile an employee population.
Through 2006 and 2007, The Boston Consulting Group and Healthways (www.healthways.com) developed a model to assess the healthcare and productivity costs associated with the most costly chronic conditions (listed below), and also to show how the presence of a comprehensive wellness programme would affect healthcare costs and productivity in a given employee population. Over the following two years, the model was updated and tested on different organizations.
The model uses the quantitative modelling software Analytica as its user interface. The profile variables that can be adjusted are: location, prevalence of chronic conditions (low prevalence, average, high prevalence), prevalence of modifiable behaviours and risk factors (low prevalence, average, high prevalence), and interventions targeting eight key risk factors (listed below). To arrive at its cost estimates, the model uses average prevalence rates of different diseases and behaviours; costs of treating different conditions; and empirical evidence of the impact of diseases on absenteeism and presenteeism. The model also elucidates the relationships that various diseases have with various modifiable behaviours. Below is a schematic of the information architecture of the model.
The 15 most costly chronic conditions
- CAD (coronary artery disease)
- Heart failure
- COPD (chronic obstructive pulmonary disease)
- CKD (chronic kidney disease)
- Back pain.
The eight key risk factors
- Physical inactivity
- Poor diet
- Alcohol consumption levels
- Poor standard-of-care compliance
- Poor stress management
- Insufficient sleep
- Lack of health screening
After setting the four variables (location, prevalence of chronic conditions, prevalence of risks associated with modifiable behaviours and interventions), the epidemiological engine runs a Monte Carlo simulation to produce a series of data sets. For any given location-health-behaviour combination (27 in all), the model makes the following estimates: how many people have each disease at each level of severity, and how each segment of that population (10 segments in all – five age groups x two genders) is affected by each intervention. In the research, every location-health-behaviour combination was run multiple times to reduce data variability (20 times for the US, 10 times for Europe, 10 times for Asia).
To assess the cost of chronic conditions, the model uses the values from the Medical Expenditure Panel Survey (MEPS – http://www.meps.ahrq.gov/mepsweb) for the US, and from a range of studies for the EU, India and China – see Sources for details. For each chronic condition, the model then determines the healthcare and productivity implications for any given population. Healthcare costs are calculated as dollars per employee. Productivity costs are the sum of two types of costs – those due to absenteeism (absence from work) and those due to presenteeism (under-performance at work); each of these is calculated as a percentage of working hours lost per year, which is then multiplied by the average salary of the population in question.
As the final step in its costing exercise, the model develops a further data set that identifies healthcare costs, absenteeism costs and presenteeism costs per employee for each of the modelled 15 chronic conditions, for each of the 10 segments, for each of the 27 location-health-behaviour combinations, and for each of the runs completed. The results were then averaged among the multiple runs using a computer programme called Scilab (http://www.scilab.org/; it is an open-source version of Matlab – http://www.mathworks.com/) to create a steady-state population data set that is only minimally affected by data variation. The programme completes the data set by aggregating all the data into one file that contains all 27 location-health-behaviour combinations. (This data set is the master data set that underlies the Wellness App.)
In 2009, BCG worked with Beehive Media to develop an html tool, The Wellness App, to present the results from the model in a user-friendly format. At a high level, the Wellness App allows you to create meaningful results for your company or any hypothetical company that you define; it does this by pulling only the relevant data from the modelís extensive master data set.
In using the Wellness App, there are six variables to adjust to create a chosen company profile: location, health, behaviour, age distribution, gender distribution and average salary by location. The specific location-health-behaviour combination will determine which section of the master data set will be used.
The next two variables – age and gender – will assign proper weighting to each segment (age x gender). The final variable – average salary – is used to calculate productivity costs; it is multiplied it by the percentage of working hours lost (as listed in the master data set).
Both the Healthways Simulation Model and the Wellness App incorporate various assumptions. Note that the lists below are not necessarily comprehensive. For additional details or questions on the model, please contact Michael F. Montijo, M.D., MPH, FACP, Solutions Lead, Vice President, Healthways at Michael.firstname.lastname@example.org or +1 (615) 614 4995, or the research group at email@example.com.
- All interventions are set at "medium" intervention strength, which reduces risk-factor severity in ~20-25% of the population in the prevalence bracket.
- Costs associated with the selected chronic conditions represent 40% of the total estimated health care costs for the US. Therefore, the model simulates only 40% of total healthcare costs. It is assumed that this cost ratio (cost of chronic conditions: total healthcare cost) also holds true for Europe and Asia (China, India).
- The App assumes the cost of interventions to be US$ 8 per member per month (PMPM) in Europe and the US, and US$ 1 PMPM in China/India. These estimates were guided by the Solutions and Science teams at Healthways.
- Wherever possible, cost and prevalence data for Europe were developed by using a population-weighted average of data for the following five EU countries: France, Germany, Italy, Spain, and the United Kingdom. It was not possible in some cases to find data for all five countries. In those cases, data for as many countries as possible was used (see Sources for details). Conversely, it was possible in some cases to find data for additional EU countries. In those cases (e.g. hypertension), the additional data were used.
- Using average data is one of the main limitations of the model.
- Cost data for Asia were developed by using a weighted average of India and China data (see Sources). It was not possible in some cases to find data for both India and China. In those cases, data for one country or the other was used as a proxy for both (see Sources for details).
- If a company already has an initiative in place targeting a specific risk factor, the assumed savings potential of a comprehensive intervention for that risk factor was reduced by 33%.
- The original model was built using US data only, but it has since been updated by incorporating data for the five EU countries (France, Germany, Italy, Spain and the United Kingdom), India and China. The European data come from different sources, and therefore do not have the level of robustness that a single source offers. Going forward, the model and the App will ideally be updated with truly global data, especially since many companies today have subsidiaries and therefore employees in multiple countries around the world.
- The healthcare cost ratio (cost of chronic conditions: total healthcare cost) is a major limitation of the App. Any subsequent version should seek to build specific healthcare cost ratios for European countries as well as for Asian and other countries.
The Wellness Application was developed by Rodrigo Martinez, Principal; Dave Matheson, Sr. Partner and Managing Director; Martin B. Silverstein, MD, Sr. Partner and Managing Director, Global Leader of the Healthcare Practice; from The Boston Consulting Group (BCG). We would like to acknowledge Gary Hall, Consultant, and Akifumi Kita, Associate Consultant; also from BCG, who were integral parts of this project. The entire team was based in BCG's Boston office. For queries, please contact Jeanine Kelly-Murphy, Health Care Knowledge Team Manager at Murphy.firstname.lastname@example.org.
We would also like to thank Michael F. Montijo, MD, MPH, FACP, Solutions Lead, Vice President; James E. Pope, MD, FACC, Vice President, Center for Health Research; and Jay Chyung, MD, PhD, Medical Director, Head of Translational Research and Analytics; of Healthways, Inc, for their support and input. For queries, please contact email@example.com.
In addition, we would like to thank Eva Jane-Llopis, PhD, Head Chronic Disease and Wellness, and Olivier Raynaud, MD, Senior Director, Global Health and Healthcare Sector, from the World Economic Forum, for their continuous support and engagement throughout this project. We would also like to thank the Project Board members for their guidance and input.