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The American Journal of Managed Care October 2016
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Cost-Effectiveness of a Statewide Falls Prevention Program in Pennsylvania: Healthy Steps for Older Adults
Steven M. Albert, PhD; Jonathan Raviotta, MPH; Chyongchiou J. Lin, PhD; Offer Edelstein, PhD; and Kenneth J. Smith, MD
Benchmarking Health-Related Quality-of-Life Data From a Clinical Setting
Janel Hanmer, MD, PhD; Rachel Hess, MD, MS; Sarah Sullivan, BS; Lan Yu, PhD; Winifred Teuteberg, MD; Jeffrey Teuteberg, MD; and Dio Kavalieratos, PhD
Patients' Success in Negotiating Out-of-Network Bills
Kelly A. Kyanko, MD, MHS, and Susan H. Busch, PhD
Connected Care: Improving Outcomes for Adults With Serious Mental Illness
James M. Schuster, MD, MBA; Suzanne M. Kinsky, MPH, PhD; Jung Y. Kim, MPH; Jane N. Kogan, PhD; Allison Hamblin, MSPH; Cara Nikolajski, MPH; and John Lovelace, MS
A Call for a Statewide Medication Reconciliation Program
Elisabeth Askin, MD, and David Margolius, MD
Postdischarge Telephone Calls by Hospitalists as a Transitional Care Strategy
Sarah A. Stella, MD; Angela Keniston, MSPH; Maria G. Frank, MD; Dan Heppe, MD; Katarzyna Mastalerz, MD; Jason Lones, BA; David Brody, MD; Richard K. Albert, MD; and Marisha Burden, MD
Mortality Following Hip Fracture in Chinese, Japanese, and Filipina Women
Minal C. Patel, MD; Malini Chandra, MS, MBA; and Joan C. Lo, MD
Estimating the Social Value of G-CSF Therapies in the United States
Jacqueline Vanderpuye-Orgle, PhD; Alison Sexton Ward, PhD; Caroline Huber, MPH; Chelsey Kamson, BS; and Anupam B. Jena, MD, PhD
Periodic Health Examinations and Missed Opportunities Among Patients Likely Needing Mental Health Care
Ming Tai-Seale, PhD; Laura A. Hatfield, PhD; Caroline J. Wilson, MSc; Cheryl D. Stults, PhD; Thomas G. McGuire, PhD; Lisa C. Diamond, MD; Richard M. Frankel, PhD; Lisa MacLean, MD; Ashley Stone, MPH; and Jennifer Elston Lafata, PhD
Does Medicare Managed Care Reduce Racial/Ethnic Disparities in Diabetes Preventive Care and Healthcare Expenditures?
Elham Mahmoudi, PhD; Wassim Tarraf, PhD; Brianna L. Maroukis, BS; and Helen G. Levy, PhD

Cost-Effectiveness of a Statewide Falls Prevention Program in Pennsylvania: Healthy Steps for Older Adults

Steven M. Albert, PhD; Jonathan Raviotta, MPH; Chyongchiou J. Lin, PhD; Offer Edelstein, PhD; and Kenneth J. Smith, MD
“Healthy Steps for Older Adults,” the Pennsylvania Department of Aging’s falls prevention program, resulted in savings of $718 to $840 per person.
ABSTRACT

Objectives: Pennsylvania’s Department of Aging has offered a falls prevention program, “Healthy Steps for Older Adults” (HSOA), since 2005, with about 40,000 older adults screened for falls risk. In 2010 to 2011, older adults 50 years or older who completed HSOA (n = 814) had an 18% reduction in falls incidence compared with a comparison group that attended the same senior centers (n = 1019). We examined the effect of HSOA on hospitalization and emergency department (ED) treatment, and estimated the potential cost savings.

Study Design: Decision-tree analysis.

Methods: The following were included in a decision-tree model based on a prior longitudinal cohort study: costs of the intervention, number of falls, frequency and costs of ED visits and hospitalizations, and self-reported quality of life of individuals in each outcome condition. A Monte Carlo probabilistic sensitivity analysis assigned appropriate distributions to all input parameters and evaluated model results over 500 iterations. The model included all ED and hospitalization episodes rather than just episodes linked to falls.

Results: Over 12 months of follow-up, 11.3% of the HSOA arm and 14.8% of the comparison group experienced 1 or more hospitalizations (P = .04). HSOA participants had less hospital care when matched for falls status. Observed values suggest expected costs per participant of $3013 in the HSOA arm and $3853 in the comparison condition, an average savings of $840 per person. Results were confirmed in Monte Carlo simulations ($3164 vs $3882, savings of $718).

Conclusions: The savings of $718 to $840 per person is comparable to reports from other falls prevention economic evaluations. The advantages of HSOA include its statewide reach and integration with county aging services.

Am J Manag Care. 2016;22(10):638-644
Take-Away Points

Since 2005, Pennsylvania’s Department of Aging has offered a falls prevention program, “Healthy Steps for Older Adults” with about 40,000 older adults screened for falls risk. In an evaluation, the program was associated with an 18% reduction in falls incidence. The cost-effectiveness of the program is evident in:  
  • Over 12 months of follow-up, 11.3% of individuals in the program experienced 1 or more hospitalizations versus 14.8% in a matched comparison group.
  • Program participants had less hospital care when matched for falls status. 
  • Expected costs of hospital and emergency department care averaged $3013 per participant in the program arm and $3853 in the comparison condition—a savings of $840.
The public health significance of falls among adults 50 years or older is clear. In 2011, the rate of nonfatal fall-related injuries requiring emergency department (ED) care was 2301 per 100,000 among individuals aged 50 to 54; however, it was 14,159 per 100,000 among individuals 85 years or older.1 Self-report measures from health surveys confirm the high risk of falls (30%-40% annually in individuals 65 years or older), and it only increases with age (40%-50% of older adults age 80 or older). Even noninjurious falls are disabling in that they are associated with activity restriction, isolation, deconditioning, and depression. In 2010, medical care costs associated with nonfatal falls in the United States for individuals 50 years or older totaled about $40 billion.1

Pennsylvania’s Department of Aging has offered a falls prevention program in its network of senior centers and related sites since 2007, with about 40,000 older adults completing the program to date. “Healthy Steps for Older Adults” (HSOA) offers screening for falls risk and education regarding falls prevention using this statewide aging services infrastructure. Senior centers and allied sites host the program, and older adults interested in the program may complete it as part of their normal attendance at senior center events or specifically because of an interest in falls prevention. This program is voluntary and available to all adults residing in the state 50 years or older.

HSOA includes the following elements: physical performance assessments of balance and mobility conducted by staff or trained volunteers (Timed Get Up and Go, 1-legged stand, 60-second chair stand), referrals for physician care and home safety for participants scoring below age- and gender-based norms on performance assessments, and a 2-hour falls prevention class involving recognition of home hazards and falls risk situations, as well as demonstrations of exercises designed to improve balance and mobility. The PrimeTime Health office of the PA Department of Aging assures program fidelity by training staff at sites, monitoring data entry, conducting brief follow-up interviews with a random 10% sample of participants after programs, and hosting monthly conference calls with the county Area Agencies on Aging. HSOA recently received certification as an evidence-based falls prevention program by the federal Administration for Community Living.

Quasi-experimental evidence for the effectiveness of this short-term, low-cost, populationwide program is now available and suggests that the program reduced falls incidence by 18% over a median of 7.5 months of follow-up.2 Details of the evaluation study design and use of monthly interactive voice response (IVR) calls to assess falls have been reported.3,4

Briefly, from 2010 to 2011, older adults who completed participation in HSOA (n = 814), or who did not but attended the same senior center sites (n = 1019), were enrolled and followed monthly for up to 12 months. Falls were defined as any occasion when an individual ended up on the floor or ground without being able to stop or prevent it. Although participants were not randomly allocated to study conditions, the 2 groups did not differ in demography, self-reported health status, falls risk at baseline, or attrition over follow-up. We ascertained falls each month using a telephone IVR system. In multivariate models, adjusted falls incidence rate ratios among HSOA participants were lower than in the comparator group for both total (incidence rate ratio [IRR], 0.83; 95% CI, 0.72-0.96) and activity-adjusted (IRR, 0.81; 95% CI, 0.70-0.93) months of follow-up.2

In prior research, we also examined process indicators to assess the uptake of the program.2,3 Of HSOA participants, 84.1% reported they were told how well they did on the mobility and balance screening. Among participants who were told by staff that they were at a high risk of falling (21.3%), 21.5% reported they saw a physician to discuss their HSOA assessment. Most HSOA participants (92.1%) at a high risk of falling reported they were given a home safety guide; 78.6% reported use of the guide to conduct a home safety assessment, and 32% reported a change in the home environment as a result of this effort.

Although the falls reduction benefit is important in its own right, it would also be valuable to see if HSOA lowers healthcare utilization by keeping individuals out of the ED and hospital. This benefit might accrue if having fewer falls results in less of a need for healthcare services, or if the program lowers the risk of high-intensity healthcare utilization among those who do fall. Because our monthly follow-up telephone contact also collected information from participants about use of the ED and hospitalization, we were able to examine the cost-effectiveness of the program for these outcomes.

For this analysis, we used all ED and hospitalization episodes, rather than just episodes linked to falls. We chose this approach because monthly calls did not establish whether use of medical care was definitively the result of a fall, nor did we have access to diagnostic information about the reason for treatment or procedures performed. We used the more conservative approach of examining all ED and hospital events by falls status and intervention arm.

Although many cost analyses are available, these largely involve hospital,5 nursing home,6,7 or emergency medical service8 samples. Community-based assessments are less common, and these have mostly been limited to evaluations of clinical interventions,9,10 rather than the broad-based community-level intervention assessed in our study.

The goal of this study was to determine cost savings associated with the HSOA falls prevention program. We examined the frequency of falls and episodes of ED, as well as hospital, use in the intervention and comparison arms. We also elicited health utilities for respondents in groups defined by falling and medical use categories and used these quality-of-life (QoL) utility values to examine the incremental cost-effectiveness ratio (ICER) of the program.

METHODS

We examined outcomes associated with implementing HSOA. To determine the cost-effectiveness of the intervention, observations from the statewide evaluation were used to construct a decision tree using TreeAge Pro 2015 (TreeAge Software, Inc, Williamstown, Massachusetts). The model inputs were the per-person costs of the intervention, the number of falls, the frequency and costs of ED visits and in-patient hospitalizations, and self-reported QoL of individuals in each outcome condition of the decision tree. All participants completed signed informed consent, and the Institutional Review Board of the University of Pittsburgh approved the research protocol.

Ascertainment of Outcome States

Falls and medical care utilization, as mentioned earlier, were assessed with monthly telephone calls using IVR technology. Respondents were registered into an Internet-based system and were automatically dialed every 30 days to complete a 6-question report on falls, physical activity, hospitalization, and ED use over the prior month. A recording posed questions, which respondents answered by pressing buttons on the telephone; for example, the instructions were “press 1 for yes, 2 for no.” Although one-third of the sample switched from IVR to in-person calls over follow-up, the proportion switching did not differ between study arms.4

IVR reports of falls over the follow-up period were significantly correlated with both self-reported and performance assessment of balance.2 We were unable to validate reports of ED and hospital treatment elicited in telephone follow-up, but the absence of differences across study arms in baseline characteristics and in attrition over follow-up reduced the likelihood of differential reporting of healthcare utilization.

To elicit QoL ratings, respondents completed the EuroQol 5-Dimensions Questionnaire (EQ-5D).11 In the EQ-5D, respondents report level of difficulty with mobility, self-care, usual activities, pain, and mental health. Each of the 5 domains has 3 levels: no problems, some problems, extreme problems. Each combination of problems reported for the 5 domains has a utility value ranging from 0 to 1.0. We used the EQ-5D scoring algorithm described in a US replication study.12 EQ-5D values were elicited at a 6-month follow-up interview.

Model

The primary outcomes were the expected per-person direct medical costs and individual-level utility of each condition. As mentioned earlier, the HSOA intervention significantly reduced the incidence of number of falls among participants compared with a comparison group.1 The present model compared the total per-person costs of the intervention, ED visits, and hospitalizations between HSOA participants and nonparticipants; that is, we did not limit outcomes to ED or hospital episodes associated with falls, but rather, we considered all events over the observation period. For this analysis, we extended the 7.5-month follow-up period reported in earlier analyses to 12 months. The model also examined self-reported QoL utility values of individuals within each outcome condition. Interpretation of the model is driven by the assumption that: participation in the intervention will result in fewer falls, ED visits, and hospitalizations; the total costs of these episodes will be lower; and the QoL of HSOA participants in any outcome condition will be higher than that of nonparticipants.

Decision Tree

The decision tree was constructed as a series of binary chance nodes branching from an intervention exposure decision node. Logic for the treatment and comparison groups was identical and included all permutations of falls and treatments ranging from no falls and no treatments to multiple falls, multiple ED visits, and multiple hospitalizations. Figure 1 is a simplified illustration of the tree for 1 branch. Available data were categorized in the following way: HSOA = yes or no; falls = 0, 1, ≥2; ED visits = 0, 1, ≥2; hospitalizations = 0, 1, ≥2. Branch utilities were calculated by averaging QoL utility values for all individuals within each outcome condition.

Costs for ED and hospital treatment were represented by the mean cost of the medical events in Pennsylvania during the study period based on state averages from the PA Health Care Cost Containment Council: $1100 for each ED treatment and $18,083 for each hospitalization. In the categories of ≥2 ED episodes or ≥2 hospitalizations (or combinations of a single event of the one type plus ≥2 of the other), we averaged costs. Hence, average costs for the category of ≥2 hospitalizations vary, for example, because individuals in the category often had more than 2 hospitalizations (eg, respondents in the ≥2 hospitalization group had 2 to 6 hospitalizations over the study period). The cost of the HSOA program is $70 per participant, the amount senior center sites are reimbursed for the program.

Because of the many permutations of outcome states, some nodes required estimation of values for missing data. The Table reports the number of respondents in each outcome state by intervention status. Missing utility values were populated with the value of the less extreme adjacent condition to reduce risk of bias from potentially spurious treatment effects. All probabilities not explicitly provided by the study results were derived using Bayesian transformations of the available data.

 
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