Available online at www.sciencedirect.com R Preventive Medicine 37 (2003) 319–326 www.elsevier.com/locate/ypmed Changes in physical activity and short-term changes in health care charges: a prospective cohort study of older adults Brian C. Martinson, Ph.D.,a,* A. Lauren Crain, Ph.D.,a Nicolaas P. Pronk, Ph.D.,a,b Patrick J. O’Connor, M.D., MPH,a and Michael V. Maciosek, Ph.D.a a HealthPartners Research Foundation, 3105 East 80th Street, Mod C, Suite 299, Bloomington, MN 55425, USA b HealthPartners Center for Health Promotion, 3105 East 80th Street, Bloomington, MN 55425, USA Abstract Background. Better understanding the impact of changes in physical activity behavior on short-term health care charges may inform resource allocation decisions to increase population levels of physical activity. This study examines the prospective relationship of changes in physical activity status on short-term changes in health care charges for older adults. Methods. A prospective cohort study was done on a stratified random sample of 2,393 adults aged 50 and older enrolled in a Minnesota health plan, predicting changes in resource use between two periods (September 1994 to August 1995 and September 1996 to August 1997) based on billed health care charges. Results. After adjustment for age, gender, comorbidity, smoking status, and body mass index, all physical activity states had declining health care charges, relative to those who were consistently inactive. Subjects who increased their physical activity from 0–1 to 3ϩ days/week had significant declines in their mean annualized total charges (Ϫ$2,202, P Ͻ 0.01) relative to those who remained inactive. Conclusions. Increased physical activity among older adults is associated with lower health care charges within 2 years, relative to charges for those who were persistently inactive. These cost savings may justify investments in effective interventions to increase physical activity in older adults. © 2003 American Health Foundation and Elsevier Inc. All rights reserved. Keywords: Physical activity; Health surveys; Costs and cost analysis Introduction increasing levels of physical activity can be shown to lower health care charges, or if decreasing activity levels predict Low levels of physical activity are consistently associ- higher charges, then strategic investments in programs to ated with adverse health outcomes, and a sedentary lifestyle support active lifestyles may be justified from the economic characterized by inadequate levels of physical activity is as well as the clinical point of view. endemic among U.S. adults, particularly among older adults Most previous studies of physical activity and health care [1]. Moreover, older adults experience a disproportionate costs have relied on a single baseline assessment of physical burden of illness associated with sedentary behavior and activity, with measurement of subsequent health care low cardiorespiratory fitness [1–3]. Low levels of physical charges [4,5]. To further test the hypothesis that low levels activity have been associated with high short-term health of physical activity and higher health care costs are causally care charges in prospective [4] and retrospective analyses related, analyses need to extend beyond single-exposure [5]. However, there are few data that relate change in assessments and measure change in physical activity over physical activity level to change in health care costs. If time, preferably using multiple measures from individuals. Such analyses would permit estimation of the health care costs that may be averted by interventions that increase * Corresponding author. Fax: ϩ1-952-967-5022. E-mail address: [email protected] (B.C. Mar- physical activity. tinson). In this study, we examined the impact of changes in 0091-7435/$ – see front matter © 2003 American Health Foundation and Elsevier Inc. All rights reserved. doi:10.1016/S0091-7435(03)00139-7 320 B.C. Martinson et al. / Preventive Medicine 37 (2003) 319–326 physical activity over the course of a single year with Sample exclusions short-term changes in health care charges incurred in a randomly selected cohort of health plan members ages 50 Of 8,000 initial sample subjects, 533 were unable to years or older. complete the first survey due to death, disenrollment, mail- ing address problems, language, or other problems, leaving 7,467 eligible. Of these, 5,975 responded (80% response rate) to the first survey. Of these, 4,067 also responded to Methods the Year 2 survey, representing roughly 55% of the eligible sample and 68% of the first survey respondents. We limited The study was conducted at HealthPartners, a Minnesota the analysis to those 50 years of age or older at baseline, health plan with 700,000 members in either owned or con- because younger subjects (n ϭ 963) have relatively low and tracted clinics. We surveyed a stratified random sample of stable health care charges, thus contributing little informa- 8,000 health plan members by mail in August 1995, and tion to the dependent variable. To avoid potential confound- sent postcard reminders 1 week after the initial survey ing between our primary outcome of interest (changes in mailing. A replacement survey was mailed to nonrespon- annual health care charges between 1995 and 1997) and dents 3 weeks after initial mailing, with telephone follow-up changes in the capacity to be physically active, we excluded of those not responding to the replacement survey. In Sep- from the analysis subjects who reported any level of phys- tember 1996, the same procedures were followed to send a ical impairment at either survey (n ϭ 710). Exclusion of second survey to members who responded to the first sur- individuals who were not continuously enrolled throughout vey. The study was approved in advance and monitored by the 3-year study period (6%) did not materially alter our the HealthPartners Institutional Review Board. results. Thus, we have retained these subjects in the analy- ses presented below. Based on examination of regression residuals for excessively influential cases, we excluded one Study subjects subject from the analysis due to a very large Cook D statistic (Ͼ1.5). After these exclusions, our analytic sample All members aged 40 and over who were enrolled on consisted of N ϭ 2,393 subjects. December 15, 1994 were potential subjects for the study. The sample comprised three strata of members who had Data definitions been diagnosed with none, one, or two or more of four chronic conditions, respectively. Diagnoses of diabetes, Similar survey instruments (approximately 60 questions heart disease, hypertension, and dyslipidemia were assigned each) were administered in 1995 and 1996, and included to members based on 1994 administrative data using Inter- items on demographics, health status, use of preventive national Classification of Diseases, 9th Revision, Clinical services, modifiable health risks, and readiness to change Modification (ICD-9-CM) coding and pharmacotherapy da- modifiable health risks. The core of the survey items was tabases. adapted from the Centers for Disease Control and Preven- A diagnosis of diabetes was assigned if the member had tion’s Behavioral Risk Factor Surveillance System, which two or more ICD-9 codes 250.xx, or a filled prescription for has reliability coefficients for behavioral risk factors above a diabetes-specific drug such as insulin, a sulfonylurea, or a 0.70 [7]. biguanide. Heart disease was assigned if the member had one or more ICD-9 codes 412, 413.9, 429.2, or 428.0. Dependent variable Hypertension was assigned if the member had one or more The primary outcome variable in the analysis was the ICD-9 codes 401, 401.1, or 401.9. Dyslipidemia was as- total of health care charges gathered from the HealthPart- ners claims system. Each encounter in either an owned or signed if the member had an ICD-9 code of 272.4. A more contracted clinic generated a claim/charge that included detailed description of the identification of members with inpatient and outpatient claims for both facilities and pro- specific conditions and the sensitivity, specificity, and pos- fessional fees, exclusive of pharmaceuticals. All charges itive predictive value of this method has been previously were adjusted to 1997 dollars using the Consumer Price published [6]. Index and then difference scores were computed by sub- A random sample of 3,000 members (1.89%) were se- tracting total charges billed during Year 0 of the study (1 lected from 158,415 members with none of the four chronic September 1994 to 31 August 1995, the period on which conditions; 2,500 members (7.3%) were selected from the subjects reported in the first survey) from total charges 34,159 members who had one condition; and 2,500 (33%) billed in Year 2 (1 September 1996 to 31 August 1997, the were selected from the 7,571 members who had two or more year after the second survey). Charges for Year 1 (1 Sep- conditions. Hence, the total study population included a tember 1995 to 31 August 1996, the period reported on by stratified random sample of 8,000 individuals aged 40 and subjects in the second survey) are not considered in the older. construction of the dependent variable. Adjustment for re- B.C. Martinson et al. / Preventive Medicine 37 (2003) 319–326 321 gression to the mean was accomplished using the method Covariates proposed by Chuang-Stein and Tong [8]. This method es- Covariates included in the analysis were age, gender, sentially reduces an individual’s raw difference score by an chronic disease status, smoking status, and body mass index amount determined by the product of (1) the distance of (BMI). Prior research has shown that health care charges are their charges at Year 0 from the grand mean of Year 0 associated with these variables before and after adjustment charges, and (2) the correlation between their Year 0 and for functional health status and other factors [11,12].