A model and cost estimates are presented for an exercise program for elderly in small, local communities. This may be an efficient way to avert health care costs. The investment-to- return ratio is 1:1.8 with returns beginning the first year. One key point of health economics is that savings to society as a whole are considered first, irrespective of who pays and who saves. Are there “true gains” to be made by doing things more efficiently? In contrast, merely transferring funds from one group to another may create a false impression, at least among the receivers of funds, that “progress” is being made. This paper does not focus on who would/should make the investment and who reaps the savings. During the current economic and health crisis one should realize that most health funds eventually can be traced back to the same source. Thus, the concept of efficiency should become a main strategy rather than each agency merely seeking a larger slice of a pie of finite size. Increasing exercise is a key component of our public health response to the obesity epidemic. Although this recommendation is made almost reflexively, evaluations of exercise programs need to take into account adherence, since this is a key factor correlating to benefits.7–10
If one replicates evidence based programs and adjusts for local variations in adherence and program costs, one can estimate the economic impact, in terms of cost effectiveness or cost benefit, of such programs.
One assumes that evidence-based exercise interventions, when adhered to, will reduce baseline health costs for the elderly by an average of x%. There may be wide variations in baseline costs correlating with one's level of fitness, which in turn might correlate with one's ability to adhere to an exercise program. One weakness of this approach is that those who do manage to adhere are a self-selecting group. Their health care costs may not be similar to that of the general elderly population, for whom national data is tracked. While it is tempting to think that those who adhere have a “healthy worker” effect with lower baseline health costs, it is also possible that they are indeed less healthy with higher baseline costs, and their better adherence reflects their physicians' stronger recommendation to exercise. Therefore it may be better to simply measure health costs pre- and post-intervention, rather than try to predict adjustments to averted costs. In this study, averted costs are not actually measured. A valid measurement calls for a large rigorous study with cost standardization across different insurance programs. Small communities, such as Kaua‘i, with an estimated population of 63,000, may not support this type of research. In addition, considering the rapid rise in obesity, there may be no time for such detailed studies prior to making general recommendations for exercise. Communities starting such programs before they know the costs and benefits should at least measure these parameters as they conduct their programs. In addition it is important to gather cost data, despite small numbers, on self-selecting groups.
Low attendance/adherence rates will usually be the weak link of behavior modification programs. This will affect the I:R ratio. Only 67% of study clients met attendance criteria to be considered “successful.” While this is somewhat better than the 50% rate seen in the Washington study,13
other exercise programs fail because of lower participation. To address low attendance, some of the elderly in the present study requested more classes per week, for the sake of convenience. On the other hand, funders argue that having classes three times a week might already be excessive when the criterion for success is a minimum attendance of once a week. At some point, increasing the number of classes leads to diminishing returns. If the total number enrolled had all been successful, attending at least once a week, the I:R ratio would have been 1:2.5. One way to improve attendance without scheduling more classes is to increase enrollment beyond the 22 clients that the center can accommodate, to overbook classes - planning for a 33% absenteeism. When the actual attendance exceeds the ceiling of 22 participants, a non-supervised activity could be offered to those who had already come at least once that week.
Another way to improve adherence might be to interview clients to determine what motivates them to attend. In the current study, since sessions were scheduled away from senior center meal times, socialization, health, and exercise are the main reported reasons for attending. Other programs might consider meals, snacks, and gifts as incentives. Alternatively, it is suggested that asking participants to pay a nominal amount at the beginning of the program would result in more commitment to attend, to get one's “money's worth.” These kinds of indirect incentives could backfire and flood the classes with too many clients if over-enrollment is used. One can not over-emphasize the need to study factors which affect adherence,18,19
bearing in mind that this study only reports adherence during the first year of the program. Participants may need additional motivation as the novelty wears off, especially because health benefits do continue beyond the first year (personal communication, Dr. Ackerman).
There are other adjustments which this analysis overlooks. The average US elderly health care costs were used in this analysis. Presumably this represents urban and rural populations. On the neighbor islands if more complex medical treatment requires interisland travel, then adjustments to averted costs need to account for travel and lodging. Thus, it may be that preventive programs on neighbor islands might be more cost effective than those run in Honolulu. The exercise program operates with trained, but non-medical, staff. As long as medical costs continue to rise faster than general wages of the staff, the I:R ratio will show rising returns. With costs rising so quickly, the study's I:R ratios probably are outdated by the time this article is printed. But even under 2009 values, this analysis shows that there are costs to be averted, savings to be made.
As explained in the introduction, estimation of attendance is based on data from only one of the authors initial sites. Although it might have been better to wait for a year's worth of data from all six sites, stakeholders wanted this interim analysis since funding priorities are important in theses difficult economic times. Health officials and those who fund prevention programs are often forced to act with less than perfect information, especially when so many are calling for more physical activity. One has to balance waiting for rigorous proof of new methods to enhance adherence or taking action now. Furthermore, in today's cost conscious economy, funders are asking for best estimates of cost:benefit, the “bang for buck.” This paper is submitted as an example of a methodology to approach both crises of rising obesity rates and rising health care costs. Weighed against alternative approaches one can now begin to argue if such programs should terminate, continue or expand.