Background: multimorbidity analysis provides essential information to support health policy in the field of prevention, clinical management, and resources allocation in order to guarantee personalized and adequate strategies for patients with multiple chronic pathologies.
Objectives: to present the application of a methodology based upon data retrieved in healthcare administrative databases to investigate the extent of multimorbidity (coexistence of two or more chronic condition), evaluating its epidemiology, its impact on healthcare resources, and identifying patterns of associative multimorbidity, based on non-random association among chronic diseases. DESIGN: observational study based on regional healthcare data record linkage.
Setting and participants: all people aged 18 years or older permanently or temporarily resident in Emilia-Romagna Region (Northern Italy) during 2017 (amounting to 3,901,252 persons) were included.
Main outcome measures: period prevalence and incidence of 32 chronic diseases; identification of patients affected by two or more concurrent chronic diseases (multimorbid patients), and evaluation of their period prevalence, incidence, healthcare resources use, and costs. Factorial analysis was applied to assess association among chronic diseases and to estimate groups of chronic conditions non-randomly coexisting (patterns of multimorbidity) among the elderly (people aged 65+ years).
Results: the multimorbidity incidence rate in 2017 was 2.7% (4.9% in the elderly) and the multimorbidity period prevalence, evaluated on the 3,901,252 adult residents, was 25.2%, ranging from 2.8% in people aged <40 years to 72.5% in octogenarians, with no major difference by gender. Sixty one percent of the elderly suffered from two or more concurrent chronic diseases and, among these, four groups of chronic condition non-randomly coexisting were recognized (cardiovascular, neuropsychiatric, metabolic, and pain pattern). These four multimorbidity patterns affected 39.6% of over 65. The impact on healthcare resources use was considerable: about 70% of all provided healthcare services and 72% of the costs incurred by Regional Health Service was allocated to multimorbid patients (81% and 86.7%, respectively, among the elderly).
Conclusions: healthcare administrative databases are a valuable tool to assess the frequency of multimorbidity and its impact on healthcare resources. Patients belonging to the four common patterns of multimorbidity identified in this study explained a high proportion of multimorbidity prevalence and healthcare resources use.
Keywords: multimorbidity; prevalence; incidence; factor analysis; healthcare resource consumption.