Minimising multimorbidity clustering across the lifespan.

TLDR: The prevalence of multimorbidity increased from 15¢5% in the 2005 2010 study sample versus 25¢2% which supports previous data that the burden of disease and poor health is increasing in more recent years.

Abstract: While populations around the globe are living longer, so too have the number of years living in poor health [1]. Indeed, the burden of chronic health problems has steadily increased over previous decades, which are not exclusive to old age [2]. In consequence, there will be more individuals who can expect to live longer with multiple conditions, otherwise known as multimorbidity [3]. Against this background, there has been increasing interest in understanding nonrandom patterns, or clusters, of multimorbidity to understand the expression of chronic conditions in adulthood [4]. In The Lancet Regional Health Europe, Alessandra Bisquera and colleagues characterised multimorbidity clustering of chronic health conditions across the adult lifespan [5]. Here, they included 32 chronic health conditions, which were analysed from assembling 826,936 patient electronic health records (mean age 40 years old; 52% female) from 2005 to 2020 across 41 urban general practices in London. From patient record data, the authors used a statistical method, called multiple correspondence analysis, to reveal unique clusters of multimorbidity. In this cohort, 41% (n = 339,044) had at least one chronic health problem and 21% (n = 174,881) had multimorbidity. Females (23%) were more likely than men (20%) to have multimorbidity. The number of chronic conditions increased with age, where people aged 80 years or older had a median 5 chronic conditions versus those who were 18 39 years old (median 2 conditions). The prevalence of multimorbidity increased from 15¢5% in the 2005 2010 study sample versus 25¢2% which supports previous data that the burden of disease and poor health is increasing in more recent years [2]. The increase in multimorbidity prevalence shown here poses major challenges for health care and policy planning. In addition to demonstrating a rise in multimorbidity rates, the study by Bisquera et al. offer insights into commonly occurring multimorbidity clusters [5]. It is noteworthy that these clusters have