Abstract
BackgroundThe prevalence of multimorbidity varies widely due to the lack of consensus in defining multimorbidity. This study aimed to measure the prevalence of multimorbidity in a primary care setting using two definitions of multimorbidity with two different lists of chronic conditions.MethodsWe conducted a cross-sectional study of 787,446 patients, aged 0 to 99 years, who consulted a family physician between July 2015 to June 2016. Multimorbidity was defined as ‘two or more’ (MM2+) or ‘three or more’ (MM3+) chronic conditions using the Fortin list and Chronic Disease Management Program (CDMP) list of chronic conditions. Crude and standardised prevalence rates were reported, and the corresponding age, sex or ethnic-stratified standardised prevalence rates were adjusted to the local population census.ResultsThe number of patients with multimorbidity increased with age. Age-sex-ethnicity standardised prevalence rates of multimorbidity using MM2+ and MM3+ for Fortin list (25.9, 17.2%) were higher than those for CDMP list (22.0%; 12.4%). Sex-stratified, age-ethnicity standardised prevalence rates for MM2+ and MM3+ were consistently higher in males compared to females for both lists. Chinese and Indians have the highest standardised prevalence rates among the four ethnicities using MM2+ and MM3+ respectively.ConclusionsMM3+ was better at identifying a smaller number of patients with multimorbidity requiring higher needs compared to MM2+. Using the Fortin list seemed more appropriate than the CDMP list because the chronic conditions in Fortin’s list were more commonly seen in primary care. A consistent definition of multimorbidity will help researchers and clinicians to understand the epidemiology of multimorbidity better.
Highlights
The prevalence of multimorbidity varies widely due to the lack of consensus in defining multimorbidity
Our study showed that using different definitions and lists of conditions for multimorbidity measurement resulted in different prevalence rates which were statistically different from each other when we adjusted for age, sex, and ethnicity for the same population
The major strength of the study was the rigorous mapping of International Classification of Diseases (ICD-10) diagnostic codes available in the primary care EMR to two multimorbidity lists (CDMP and Fortin) with two operational definitions of multimorbidity (MM2+ and MM3+). In conclusion, this is the first study describing the prevalence of multimorbidity using a large electronic medical record database in a Singapore primary care setting
Summary
The prevalence of multimorbidity varies widely due to the lack of consensus in defining multimorbidity. This study aimed to measure the prevalence of multimorbidity in a primary care setting using two definitions of multimorbidity with two different lists of chronic conditions. Estimates for the prevalence of multimorbidity in primary care vary widely (12.9 to 95.1%) due to the inconsistencies in the definition of multimorbidity [2]. The lack of reporting or consensus on the five components have made comparisons between prevalence rates found in different studies difficult, preventing reliable estimations of disease burden and hinder resource distribution for effective disease management. Our recent work has proposed that an ideal operational definition of multimorbidity should comprise at least 12 chronic diseases, each with high burden and clinically relevant to the particular healthcare setting of interest [6]
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