Clinical Epidemiology (May 2024)
10-Year Multimorbidity Trajectories in Older People Have Limited Benefit in Predicting Short-Term Health Outcomes in Comparison to Standard Multimorbidity Thresholds: A Population-Based Study
Abstract
Marc Simard,1– 4 Elham Rahme,5 Marjolaine Dubé,1 Véronique Boiteau,1 Denis Talbot,2,3 Miceline Mésidor,2,3 Yohann Moanahere Chiu,1,4,6 Caroline Sirois1,3,4,6 1Institut national de santé publique du Québec, Québec, QC, Canada; 2Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada; 3Centre de recherche du CHU de Québec, Québec, QC, Canada; 4VITAM-Centre de recherche en santé durable, Québec, QC, Canada; 5Department of Medicine, Division of Clinical Epidemiology, McGill University, and Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, QC, Canada; 6Faculty of de Pharmacy, Université Laval, Québec, QC, CanadaCorrespondence: Marc Simard, Institut national de santé publique du Québec, 945, Wolfe, 5e étage, Québec, QC, G1V 5B3, Canada, Tel +1 418 650 5115 #5522, Fax +1 418 643 5099, Email [email protected]: To identify multimorbidity trajectories among older adults and to compare their health outcome predictive performance with that of cross-sectional multimorbidity thresholds (eg, ≥ 2 chronic conditions (CCs)).Patients and Methods: We performed a population-based longitudinal study with a random sample of 99,411 individuals aged > 65 years on April 1, 2019. Using health administrative data, we calculated for each individual the yearly CCs number from 2010 to 2019 and constructed the trajectories with latent class growth analysis. We used logistic regression to determine the increase in predictive capacity (c-statistic) of multimorbidity trajectories and traditional cross-sectional indicators (≥ 2, ≥ 3, or ≥ 4 CCs, assessed in April 2019) over that of a baseline model (including age, sex, and deprivation). We predicted 1-year mortality, hospitalization, polypharmacy, and frequent general practitioner, specialist, or emergency department visits.Results: We identified eight multimorbidity trajectories, each representing between 3% and 25% of the population. These trajectories exhibited trends of increasing, stable, or decreasing number of CCs. When predicting mortality, the 95% CI for the increase in the c-statistic for multimorbidity trajectories [0.032– 0.044] overlapped with that of the ≥ 3 indicator [0.037– 0.050]. Similar results were observed when predicting other health outcomes and with other cross-sectional indicators.Conclusion: Multimorbidity trajectories displayed comparable health outcome predictive capacity to those of traditional cross-sectional multimorbidity indicators. Given its ease of calculation, continued use of traditional multimorbidity thresholds remains relevant for population-based multimorbidity surveillance and clinical practice.Keywords: multimorbidity, trajectories, prevalence, health outcome prediction, population-based
