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.

Objective: To investigate the clustering of physical health multimorbidity in people with severe mental illness (SMI) compared to matched comparators. Design: A cohort-nested analysis of lifetime diagnoses of physical health conditions. Setting: Over 1,800 UK general practices (GP) contributing to Clinical Practice Research DataLink (CPRD) Gold or Aurum databases. Participants: 68,392 adult patients with a diagnosis of SMI between 2000 and 2018, with at least one year of follow up data, matched 1:4 to patients without an SMI diagnosis, on age, sex, GP, and year of GP registration. Main outcome measures: Odds ratios for 24 physical health conditions derived using Elixhauser and Charlson comorbidity indices. We controlled for age, sex, region, and ethnicity; and then additionally for smoking status, alcohol and drug misuse and body mass index. We defined multimorbidity clusters using Multiple Correspondence Analysis and K-Means cluster analysis and described them based on the observed/expected ratio. Results: Patients with a diagnosis of SMI had an increased odds of 19 of 24 physical health conditions and had a higher prevalence of multimorbidity at a younger age compared to comparators (aOR: 2.47; 95%CI: 2.25 to 2.72 in patients aged 20-29). Smoking, obesity, alcohol, and drug misuse were more prevalent in the SMI group and adjusting for these reduced the odds ratio of all comorbid conditions. In patients with multimorbidity (SMI cohort: n=22,843, comparators: n=68,856), we identified six multimorbidity clusters in the SMI cohort, and five in the comparator cohort. Five profiles were common to both. The "hypertension and varied multimorbidity" cluster was most common: 49.8% in the SMI cohort, and 56.7% in comparators. 41.5% of the SMI cohort were in a "respiratory and neurological disease" cluster, compared to 28.7% of comparators. Conclusions: Physical health multimorbidity clusters similarly in people with and without SMI, though patients with SMI develop multimorbidity earlier and a greater proportion fall into a "respiratory and neurological disease" cluster. There is a need for interventions aimed at younger-age multimorbidity in those with SMI.

Background People with severe mental illness (SMI) are at higher risk of physical health conditions compared to the general population, however, the impact of specific underlying health conditions on the use of secondary care by people with SMI is unknown. We investigated hospital use in people managed in the community with SMI and five common physical long-term conditions: cardiovascular diseases, COPD, cancers, diabetes and liver disease. Methods We performed a systematic review and meta-analysis (Prospero: CRD42020176251) using terms for SMI, physical health conditions and hospitalisation. We included observational studies in adults under the age of 75 with a diagnosis of SMI who were managed in the community and had one of the physical conditions of interest. The primary outcomes were hospital use for all causes, physical health causes and related to the physical condition under study. We performed random-effects meta-analyses, stratified by physical condition. Results We identified 5,129 studies, of which 50 were included: focusing on diabetes (n = 21), cardiovascular disease (n = 19), COPD (n = 4), cancer (n = 3), liver disease (n = 1), and multiple physical health conditions (n = 2). The pooled odds ratio (pOR) of any hospital use in patients with diabetes and SMI was 1.28 (95%CI:1.15–1.44) compared to patients with diabetes alone and pooled hazard ratio was 1.19 (95%CI:1.08–1.31). The risk of 30-day readmissions was raised in patients with SMI and diabetes (pOR: 1.18, 95%CI:1.08–1.29), SMI and cardiovascular disease (pOR: 1.27, 95%CI:1.06–1.53) and SMI and COPD (pOR:1.18, 95%CI: 1.14–1.22) compared to patients with those conditions but no SMI. Conclusion People with SMI and five physical conditions are at higher risk of hospitalisation compared to people with that physical condition alone. Further research is warranted into the combined effects of SMI and physical conditions on longer-term hospital use to better target interventions aimed at reducing inappropriate hospital use and improving disease management and outcomes.

Background People with severe mental illness (SMI) are 2.5 times more likely to die prematurely from cancer in England. Lower participation in screening may be a contributing factor. Methods Clinical Practice Research Datalink data for 1.71 million, 1.34 million and 2.50 million adults were assessed (using multivariate logistic regression) for possible associations between SMI and participation in bowel, breast and cervical screening, respectively. Results Screening participation was lower among adults with SMI, than without, for bowel (42.11% vs. 58.89%), breast (48.33% vs. 60.44%) and cervical screening (64.15% vs. 69.72%; all p < 0.001). Participation was lowest in those with schizophrenia (bowel, breast, cervical: 33.50%, 42.02%, 54.88%), then other psychoses (41.97%, 45.57%, 61.98%), then bipolar disorder (49.94%, 54.35%, 69.69%; all p-values < 0.001, except cervical screening in bipolar disorder; p-value > 0.05). Participation was lowest among people with SMI who live in the most deprived quintile of areas (bowel, breast, cervical: 36.17%, 40.23%, 61.47%), or are of a Black ethnicity (34.68%, 38.68%, 64.80%). Higher levels of deprivation and diversity, associated with SMI, did not explain the lower participation in screening. Conclusions In England, participation in cancer screening is low among people with SMI. Support should be targeted to ethnically diverse and socioeconomically deprived areas, where SMI prevalence is greatest.

In 1995 the Calman–Hine report provided a policy framework for commissioning high-quality cancer services in England and Wales. 1 A comprehensive NHS cancer plan followed in 2000. 2 Both documents recommended that cancer care should be centralised around site-specific multidisciplinary teams. These reforms aimed to improve outcomes and reduce inequalities in NHS cancer care through service reconfiguration rather than the introduction of new health technologies. A body of evidence that high-workload 3 or specialist 4,5 teams had better outcomes than their low-workload or generalist peers supported the recommendations. In contrast, some studies produced inconclusive results 6 and not all health services adopted this site-specific approach. 7