Objective: The aim was to clarify which pairs or clusters of diseases predict the hospital-related events and death in a population of patients with complex health care needs (PCHCN). Method: Subjects classified in 2012 as PCHCN in a local health unit by ACG® (Adjusted Clinical Groups) System were linked with hospital discharge records in 2013 to identify those who experienced any of a series of hospital admission events and death. Number of comorbidities, comorbidities dyads, and latent classes were used as exposure variable. Regression analyses were applied to examine the associations between dependent and exposure variables. Results: Besides the fact that larger number of chronic conditions is associated with higher odds of hospital admission or death, we showed that certain dyads and classes of diseases have a particularly strong association with these outcomes. Discussion: Unlike morbidity counts, analyzing morbidity clusters and dyads reveals which combinations of morbidities are associated with the highest hospitalization rates or death.
This article reviews the Adjusted Clinical Group Case-Mix System and describes how it is being applied in the management of physician services in British Columbia. Developed in the United States for management and research, adjusted clinical groups are used to measure the illness burden and health service needs of individuals and, when aggregated, of populations, by grouping the range of conditions coded on physician claims and hospital care records over a defined time period, typically one year. In Canadian and United States settings, adjusted clinical groups are up to five times more predictive of ambulatory resource use than are age and sex groups alone. The article describes how adjusted clinical groups are being applied to adjust capitation payments for physician groups in British Columbia's Primary Care Demonstration Project and profiles of physician practice activity.