Clustering of physical health multimorbidity in people with severe mental illness: An accumulated prevalence analysis of United Kingdom primary care data

Abstract

Background People with severe mental illness (SMI) have higher rates of a range of physical health conditions, yet little is known regarding the clustering of physical health conditions in this population. We aimed to investigate the prevalence and clustering of chronic physical health conditions in people with SMI, compared to people without SMI. Methods and findings We performed a cohort-nested accumulated prevalence study, using primary care data from the Clinical Practice Research Datalink (CPRD), which holds details of 39 million patients in the United Kingdom. We identified 68,783 adults with a primary care diagnosis of SMI (schizophrenia, bipolar disorder, or other psychoses) from 2000 to 2018, matched up to 1:4 to 274,684 patients without an SMI diagnosis, on age, sex, primary care practice, and year of registration at the practice. Patients had a median of 28.85 (IQR: 19.10 to 41.37) years of primary care observations. Patients with SMI had higher prevalence of smoking (27.65% versus 46.08%), obesity (24.91% versus 38.09%), alcohol misuse (3.66% versus 13.47%), and drug misuse (2.08% versus 12.84%) than comparators. We defined 24 physical health conditions derived from the Elixhauser and Charlson comorbidity indices and used logistic regression to investigate individual conditions and multimorbidity. We controlled for age, sex, region, and ethnicity and then additionally for health risk factors: smoking status, alcohol misuse, drug misuse, and body mass index (BMI). We defined multimorbidity clusters using multiple correspondence analysis (MCA) and K-means cluster analysis and described them based on the observed/expected ratio. Patients with SMI had higher odds of 19 of 24 conditions and a higher prevalence of multimorbidity (odds ratio (OR): 1.84; 95% confidence interval [CI]: 1.80 to 1.88, p < 0.001) compared to those without SMI, particularly in younger age groups (males aged 30 to 39: OR: 2.49; 95% CI: 2.27 to 2.73; p < 0.001; females aged 18 to 30: OR: 2.69; 95% CI: 2.36 to 3.07; p < 0.001). Adjusting for health risk factors reduced the OR of all conditions. We identified 7 multimorbidity clusters in those with SMI and 7 in those without SMI. A total of 4 clusters were common to those with and without SMI; 1, heart disease, appeared as one cluster in those with SMI and 3 distinct clusters in comparators; and 2 small clusters were unique to the SMI cohort. Limitations to this study include missing data, which may have led to residual confounding, and an inability to investigate the temporal associations between SMI and physical health conditions. Conclusions In this study, we observed that physical health conditions cluster similarly in people with and without SMI, although patients with SMI had higher burden of multimorbidity, particularly in younger age groups. While interventions aimed at the general population may also be appropriate for those with SMI, there is a need for interventions aimed at better management of younger-age multimorbidity, and preventative measures focusing on diseases of younger age, and reduction of health risk factors.

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System

“…In fact, it has been reported in another study of 74220 hospitalized patients in Spain that such characteristics cluster in a community of chronic comorbidities [ 47 ]. Another investigation that included adults with mental disorders (e.g., depression) reported a comorbidity group with a higher prevalence of heart disease, respiratory disease, arthritis, kidney disease, and hypertension [ 23 ]. The present study reports that these chronic conditions present cross-network loadings on all three factors and are associated to a greater degree, indicating certain comorbidity pathways in the systemic representation of multimorbidity in American adults with depression.…”

“…Additionally, other possible biomarkers including anemia, C-reactive protein, and cystatin C have been identified [85]. Other studies that have reported similar comorbidity patterns have evidenced a high burden of mental illness [23]. In particular, previous studies have found strong correlations between cardiovascular disease and depression [86].…”

“…It should be recognized that mental health diagnoses have been recognized as an important component of multimorbidity in patients [ 22 ]; however, there are few studies in adults with depression that have documented the network structure of chronic comorbidity. In fact, there is a lack of evidence regarding the clustering of physical comorbidities in individuals with depression [ 23 ]. The paucity of studies, the increasing prevalence of comorbid illness, and the burden of depression in adults evidence the need to identify latent comorbidity patterns and explore the network structure of chronic comorbidity.…”

Identifying latent comorbidity patterns in adults with perceived cognitive impairment: Network findings from the behavioral risk factor surveillance system

“…While diabetes, hypertension, and respiratory diseases presented cross-network loadings with the third latent factor [cardiovascular (CVD) and renal diseases]. Another investigation that considered older adults with neurological problems in the United Kingdom reported a cardiovascular comorbidity group with a higher prevalence that included such conditions ( 23 ). In addition, another investigation conducted in the Chinese population using the factor analysis method to group comorbidity patterns reported that diabetes and hypertension share a common underlying cause with CVD and cerebrovascular disease ( 24 ), such non-communicable diseases guide certain comorbidity pathways in the systemic representation of multimorbidity in American adults with perceived cognitive impairment.…”

Research protocol for a preliminary evaluation of a gamified tool for adult wellbeing

“…There are multiple risks associated with mental illness which include, but are not limited to, a lack of social connectedness which manifests as isolation or loneliness (Kiely et al , 2021), decreased quality of life (Radicke et al , 2021) and shorter life expectancy than those not suffering from mental illness (Stevenson et al , 2022). Fundamentally, the disadvantage of mental illness is that it not only predisposes individuals to dying by suicide, with almost 90% of those dying from suicide having suffered from a mental illness (Ghossoub et al , 2021), but it also increases their susceptibility to a range of physical health conditions (Launders et al , 2022). The aforementioned dichotomous presentation of wellbeing and mental illness, consequences of experience of mental illness and increased risk of poor mental wellbeing triggered by the COVID-19 pandemic (Campion et al , 2020), suggests the potential benefit in exploring interventions aimed at improving mental wellbeing and strengthening its protective capabilities.…”