Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland.

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

Multimorbidity, activity limitation and self-reported health all predict mortality risk, but better measures were required.

BACKGROUND AND OBJECTIVE: With an ageing population, morbidity and multiple health conditions are an increasing public health concern. This study aimed to investigate how morbidity as measured by the Charlson comorbidity index and two morbidity measures; activity limitation and self-reported health, individually and collectively explain the increased risk of mortality with age. METHODS: We used the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) wave one, dataset, a national representative sample of people over 50 years of age, with follow-up until May 31, 2020. Cox proportional hazard regression models were used to assess the associations between a modified Charlson comorbidity index (m-Charlson), self-reported health, activity limitation, and mortality in the Northern Irish cohort. RESULTS: m-Charlson showed a steeper relationship with age, the proportion of participants with a Charlson of two or more increase from 15.2% (aged 50-59) to 32.0% (aged 80+). All measures of morbidity exhibited a strong relationship with socio-economic status, though the gradients were more marked for self-reported health than for activity limitation or Charlson index. All three morbidity measures were independently related to mortality risk though the associations attenuated with age. However, even in models with all three measures of morbidity, most of the age-related increase in mortality risk remained unexplained. CONCLUSION: All three morbidity measures carry separate and independent information about the health status of older people and therefore about mortality risk; it is apparent that it is still worth asking patients how they feel and how their health is affecting their activity. Further research should aim to dissect health trajectories in dynamic prediction models in addition to other measures of health status.