To inform clinical decision-making for persons with multiple chronic conditions (MCC), we must compare the effects of treatments across conditions. In this study, we go beyond the question of comparative benefit of treatments for a single condition on single condition-specific outcomes to begin exploring the comparative effectiveness of treatments across multiple conditions on multiple universal health outcomes (e.g. function, symptoms, and survival) that are meaningful to patients and that are affected by all health conditions. Multiple universal outcomes are important to explore because treatments may affect outcomes differently and older adults with MCC vary in their health outcome priorities. While complex, this line of investigation is responsive to the RFA-AG-13-003 and of utmost relevance for older individuals with MCC. The project builds on recent work in which we have: 1) identified a set of universal health outcomes;2) determined the effect of several common conditions on these outcomes;3) developed a method for determining the % contribution to death of co-occurring conditions;4) identified the most common pairs of competing conditions;and 5) determined the effects of treatment for one condition on a potentially competing condition. In conducting this work, we have come to appreciate the methodological challenges that help explain the dearth of research in this area despite its clinical importance. The proposed methodology builds on our recent work in adapting joint modeling of longitudinal outcomes and developing the longitudinal extension of the average attributable fraction. Using innovative analytical methods, the Specific Aims are to: 1) estimate the effect of the 9 most commonly used guideline recommended medication classes for a set of common and morbid chronic conditions on 5 universal health outcomes;and 2) estimate the percent contribution of each medication to each universal outcome. Participants will be members of the large, well-characterized, nationally representative Medicare Current Beneficiary Survey (MCBS) cohort who have e 2 of the 9 study conditions. From a clinical perspective, this line of investigation eventually will allow an evidence-based approach to polypharmacy for patients with MCC by identifying medications with the best overall benefits. Results will inform future studies by introducing new methods, identifying promising treatments to compare in persons with MCC, and determining effect size and power estimates. Because MCBS represents the national older population with variable combinations of MCC, results will provide a population-level estimate of the relative effects of the study medications.

Public Health Relevance

Clinical decision-making focuses on effects of treatments for single conditions on condition-specific outcomes. For older adults with multiple conditions (MCC) and varying health outcome priorities, however, the key questions are what is the added benefit (or harm) of treatments at the patient, not the condition, level and how do these effects compare across treatments and conditions? The proposed project will open the door to answering these questions, ultimately improving decision-making for older adults with MCC.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21AG045148-02
Application #
8726278
Study Section
Special Emphasis Panel (ZAG1)
Program Officer
Salive, Marcel
Project Start
2013-09-01
Project End
2015-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Yale University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
New Haven
State
CT
Country
United States
Zip Code
06510
Allore, Heather (2016) Special Issue: Methods for Estimating Treatment Effects for Persons with Multiple Chronic Conditions. Int J Stat Med Res 5:1
Allore, Heather; McAvay, Gail; Vaz Fragoso, Carlos A et al. (2016) Individualized Absolute Risk Calculations for Persons with Multiple Chronic Conditions: Embracing Heterogeneity, Causality, and Competing Events. Int J Stat Med Res 5:48-55
Han, Ling; Pisani, M A; Araujo, K L B et al. (2016) Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients. Int J Stat Med Res 5:8-16
Allore, Heather G; Zhan, Yilei; Cohen, Andrew B et al. (2016) Methodology to Estimate the Longitudinal Average Attributable Fraction of Guideline-recommended Medications for Death in Older Adults With Multiple Chronic Conditions. J Gerontol A Biol Sci Med Sci 71:1113-6
Tinetti, Mary E; McAvay, Gail; Trentalange, Mark et al. (2015) Association between guideline recommended drugs and death in older adults with multiple chronic conditions: population based cohort study. BMJ 351:h4984
Bell, Susan P; Orr, Nicole M; Dodson, John A et al. (2015) What to Expect From the Evolving Field of Geriatric Cardiology. J Am Coll Cardiol 66:1286-99
Allore, Heather G; Zhan, Yilei; Tinetti, Mary et al. (2015) Longitudinal average attributable fraction as a method for studying time-varying conditions and treatments on recurrent self-rated health: the case of medications in older adults with multiple chronic conditions. Ann Epidemiol 25:681-686.e4