In managing the health of populations, there is emerging a growing consensus that managing diseases is only part of the “solution” for controlling healthcare costs. Managing the population’s overall health and reducing their risks can be far more rewarding in the long run at least. Moreover, when the population is a workforce, managing not merely risks but “productivity/performance-impairment factors” can end up saving employers far more in labor costs than they can realize in healthcare costs alone, when employee health management (EHM) is provided.

When addressing health risks, there is a substantial body of research that suggests that by reducing the sheer number of risks that members of the population of the population have can significantly reduce both healthcare costs and productivity/performance impairment. I have 33 articles in my EHM files on the subject that report either the cost impact of the numbers of risks or the benefit of reducing them, sometimes both. And I have at least three examples, all from the same source, reflecting the productivity impairment levels associated with both numbers of health risks and numbers of diseases affecting employee populations, and reporting both the costs of having them and the benefits of reducing them.

Unfortunately, there is little uniformity in: 1) deciding what health risks will be included in research; 2) choosing how many risks constitute a “low”, “medium” and “high” risk status; or 3) choosing which and how many costs of risks will be included in research. As a result, there are limited opportunities to compare results from different studies, or determine anything like average results across multiple studies.

For example, dollar costs for various individual risks, and for the numbers of risks affecting individuals, vary from a few hundred to one or two thousand dollars per person affected per year, in healthcare costs alone. In general, the costs per risk added, when only the numbers of risks were counted, usually range in the one to a few hundred dollars each, though the addition of risks tends to increase total costs per person affected more when the numbers are above five or six than when only zero to three.

The vast majority of “risks” measured have been conditions or behaviors that increase the probability of a particular chronic disease, e.g. high blood pressure and cholesterol as risks for heart disease, stroke, etc. and high blood sugar as a risk for diabetes. Obesity is a risk factor for all these diseases, as well as for arthritis and other diseases. But as a growing number of health risk assessments focus on employee populations, vs. insured lives, and on labor costs and contributions, vs. just healthcare costs, a whole new set of “productivity impairment factors” and costs associated therewith are being measured.

Productivity impairment typically identifies conditions such as high stress, emotional disorders, lack of fitness, and obesity as impairment factors, not merely disease risks. And smoking, for example, as well as poor nutrition and lack of physical activity, are behaviors that are linked to reduced productivity. Smoking, for example, is linked to the immediate loss of work time as smokers take frequent smoke breaks away from their work station and often outside their workplace, entirely. Stress is also a major impairment factor, as are emotional disorders, such as depressed and anxious feelings (mostly undiagnosed and untreated).

The costs of individual risks or impairment factors are almost impossible to determine, because people almost always have more than one affecting them simultaneously, and only the total effect of all of them, on health care costs or productivity, can be calculated or estimated. For this reason, counting the costs or impairment across a population by the number of risks is helpful, since every individual has just one number of risks, so multiple counting of separate risks is avoided.

The impact of the number of risks on both expenses and productivity can be calculated quite easily, though these are often counted separately, with employers or insurers counting expenses, based on claims costs, and EHM providers estimating impairment, based on employee self-reporting. After all, employees would be very reluctant to report their impairment to their employer, lest they be penalized for it. By contrast, such reports are kept confidential relative to individuals, with only overall impairment totals and improvements gauged by providers.

One EHM provider that has reported the productivity impairment of over 200,000 employees in its database is HealthMedia® ,Inc. Ann Arbor, Michigan. It does not have the healthcare/disability/WC costs of these employees, however, so its data understate the total costs of the chronic diseases and risk conditions, as well as risk behaviors of those in its database. And while it reports the total impairment costs associated with individual diseases and risks, it also has reported the amount of impairment linked to specific numbers of each, avoiding double counting except among those who have both chronic conditions and other impairment factors.

Its most recent report notes that the productivity impairment linked to different numbers of the seven impairment conditions analyzed ranged from an average of 6.03% for the 14.01% of the population who had less than three risks, to 8.33% for the 26.47% with three, and 11.53% for the 59.53% with four or more. Across the seven chronic risk and disease conditions analyzed, impairment started at a low of 8.2% among those with no chronic condition (they would still have impairment factors, of course), then 10.19% for those with one, 12.4% for those with two, 12.10% for those with three, 17.95% for those with four, 19.95% for those with five, and 24.63% for those with six (though only 0.22% of those in the database, roughly 440 employees, had six).

This certainly suggests that chronic risk and disease conditions are more significant impairment factors for those who are affected, but with only a minority of employees having even one chronic condition, and an even smaller minority (6.25%) having three or more, the impact of the two kinds of impairment factors was far greater for numbers of health risks, since the entire population was included in that count. The total impairment for all employees based on their impairment factors was 9.66% for example, while that linked to chronic conditions alone was only 8.51%, when the effect was spread over the entire population, since only a minority had one or more. But the average impairment of those who had at least one condition was 11.73%, where the average for those who had at least one impairment factor was only 9.50%.

Individual risk factors varied widely in their individual “effects”, i.e. the amount of impairment reported by those who reported having them, along with any others they also had. Impairment due to coronary disease, asthma and diabetes were all in the 14-15% range, while high blood pressure and cholesterol, were 11-12%, and congestive heart failure was 20.71%. Those who were obese reported 11.3% impairment, while those extremely obese reported 16.35%. Smokers were 11.86% impaired, compared to non-smokers’ 9.35%. Those troubled by high stress “fairly often” reported impairment of 14.01%, while those troubled “very often” reported 19.74%.

Among workers who reported being troubled by depressed feelings “sometimes”, impairment was 13.63%; if “occasionally”, 17.99%; if most of the time 22.78%. Even those who did not answer this question, 7.82% of the population had impairment of 19.02%.Those who slept six hours per night or less had impairment of 12.03%, while those who slept nine or more hours, and those who failed to answer the question averaged 12.23%. Those with low nutrition scores ranted from 10.32% to 18.20% as their scores got worse, while those with less than recommended levels of physical activity ranted from as little as 6% to as much as 13.54%.

Because HealthMedia only reported the number of impairment factors across a range of zero to four+, there is no real way to determine how much the addition of a single risk factor added to the impairment linked to employees with different numbers. The impairment linked to number of factors did not topped the overall average impairment of the entire population of 9.56% until the number reached four, and while impairment most likely increased from there up to seven, if anyone had all seven, the only thing reported was that with four or more such factors, average impairment was 11.53%. In any case, the key to assessing the problems for investment decisions is which impairment factors or chronic conditions, once managed, yielded the greatest overall impact.

The sheer number of conditions and factors offers some insights for use in planning EHM investments. But it is the prevalence of particular conditions and factors, together with the predicted improvement potential of each, in terms of reduced healthcare, workers compensation, and disability expenses, plus improved productivity, which should guide investment decisions. Neither the number nor the type of risks is an accurate or reliable basis for fully understanding the problem or predicting potential gains, nor even the two together.