Background Age of onset of multimorbidity and its prevalence are well documented. However, its contribution to inequalities in life expectancy has yet to be quantified. Methods A cohort of 1.1 million English people aged 45 and older were followed up from 2001 to 2010. Multimorbidity was defined as having 2 or more of 30 major chronic diseases. Multi-state models were used to estimate years spent healthy and with multimorbidity, stratified by sex, smoking status and quintiles of small-area deprivation. Results Unequal rates of multimorbidity onset and subsequent survival contributed to higher life expectancy at age 65 for the least (Q1) compared with most (Q5) deprived: there was a 2-year gap in healthy life expectancy for men [Q1: 7.7 years (95% confidence interval: 6.4–8.5) vs Q5: 5.4 (4.4–6.0)] and a 3-year gap for women [Q1: 8.6 (7.5–9.4) vs Q5: 5.9 (4.8–6.4)]; a 1-year gap in life expectancy with multimorbidity for men [Q1: 10.4 (9.9–11.2) vs Q5: 9.1 (8.7–9.6)] but none for women [Q1: 11.6 (11.1–12.4) vs Q5: 11.5 (11.1–12.2)]. Inequalities were attenuated but not fully attributable to socio-economic differences in smoking prevalence: multimorbidity onset was latest for never smokers and subsequent survival was longer for never and ex smokers. Conclusions The association between social disadvantage and multimorbidity is complex. By quantifying socio-demographic and smoking-related contributions to multimorbidity onset and subsequent survival, we provide evidence for more equitable allocation of prevention and health-care resources to meet local needs.

Summary Background Non-communicable diseases (NCDs) have been highlighted as important risk factors for COVID-19 mortality. However, insufficient data exist on the wider context of infectious diseases in people with NCDs. We aimed to investigate the association between NCDs and the risk of death from any infection before the COVID-19 pandemic (up to Dec 31, 2019). Methods For this observational study, we used data from the UK Biobank observational cohort study to explore factors associated with infection death. We excluded participants if data were missing for comorbidities, body-mass index, smoking status, ethnicity, and socioeconomic deprivation, and if they were lost to follow-up or withdrew consent. Deaths were censored up to Dec 31, 2019. We used Poisson regression models including NCDs present at recruitment to the UK Biobank (obesity [defined by use of body-mass index] and self-reported hypertension, chronic heart disease, chronic respiratory disease, diabetes, cancer, chronic liver disease, chronic kidney disease, previous stroke or transient ischaemic attack, other neurological disease, psychiatric disorder, and chronic inflammatory and autoimmune rheumatological disease), age, sex, ethnicity, smoking status, and socioeconomic deprivation. Separate models were constructed with individual NCDs replaced by the total number of prevalent NCDs to define associations with multimorbidity. All analyses were repeated with non-infection-related death as an alternate outcome measure to establish differential associations of infection death and non-infection death. Associations are reported as incidence rate ratios (IRR) accompanied by 95% CIs. Findings After exclusion of 9210 (1·8%) of the 502 505 participants in the UK Biobank cohort, our study sample comprised 493 295 individuals. During 5 273 731 person-years of follow-up (median 10·9 years [IQR 10·1–11·6] per participant), 27 729 deaths occurred, of which 1385 (5%) were related to infection. Advancing age, male sex, smoking, socioeconomic deprivation, and all studied NCDs were independently associated with the rate of both infection death and non-infection death. Compared with White ethnicity, a pooled Black, Asian, and minority ethnicity group was associated with a reduced risk of infection death (IRR 0·64, 95% CI 0·46–0·87) and non-infection death (0·80, 0·75–0·86). Stronger associations with infection death than with non-infection death were observed for advancing age (age 65 years vs 45 years: 7·59, 95% CI 5·92–9·73, for infection death vs 5·21, 4·97–5·48, for non-infection death), current smoking ( vs never smoking: 3·69, 3·19–4·26, vs 2·52, 2·44–2·61), socioeconomic deprivation (most vs least deprived quintile: 2·13, 1·78–2·56, vs 1·38, 1·33–1·43), class 3 obesity ( vs non-obese: 2·2...

Background: There is evidence of higher prevalence of asthma in populations of lower socioeconomic status in affluent societies, and the prevalence of asthma is also very high in some Latin American countries, where societies are characterized by a marked inequality in wealth. This study aimed to examine the relationship between estimates of asthma prevalence based on surveys conducted in children in Brazilian cities and health and socioeconomic indicators measured at the population level in the same cities.

ObjectivesTo assess changes in ankylosing spondylitis (AS) incidence, prevalence and time to diagnosis, between 1998 and 2017.MethodsUsing UK GP data from the Clinical Practice Research Datalink, we identified patients diagnosed with AS between 1998 and 2017. We estimated the annual AS incidence, prevalence and length of time from first recorded symptom of back pain to rheumatology referral and diagnosis.ResultsWe identified 12 333 patients with AS. The incidence declined from 0.72 (±0.14) per 10 000 patient-years in 1998 to 0.39 (±0.06) in 2007, with this decline significant only in men, then incidence rose to 0.57 (±0.11) in 2017. By contrast, prevalence increased between 1998 and 2017 (from 0.13%±0.006 to 0.18%±0.006), rising steeply among women (from 0.06%±0.05 to 0.10%±0.06) and patients aged ≥60 (from 0.14%±0.01 to 0.26%±0.01). The overall median time from first symptom to rheumatology referral was 4.87 years (IQR=1.42–10.23). The median time from first symptom to diagnosis rose between 1998 and 2017 (from 3.62 years (IQR=1.14–7.07) to 8.31 (IQR=3.77–15.89)) and was longer in women (6.71 (IQR=2.30–12.36)) than men (5.65 (IQR=1.66–11.20)).ConclusionAS incidence declined significantly between 1998 and 2007, with an increase between 2007 and 2017 that may be explained by an improvement in the recognition of AS or confidence in diagnosing AS over time, stemming from increased awareness of inflammatory back pain and the importance of early treatment. The rising AS prevalence may indicate improved patient survival. The persisting delay in rheumatology referral and diagnosis remains of concern, particularly in women.

Background We aimed to identify clinical, socio-demographic and genetic risk factors for severe COVID-19 (hospitalization, critical care admission or death) in the general population. Methods In this observational study, we identified 9560 UK Biobank participants diagnosed with COVID-19 during 2020. A polygenic risk score (PRS) for severe COVID-19 was derived and optimized using publicly available European and trans-ethnic COVID-19 genome-wide summary statistics. We estimated the risk of hospital or critical care admission within 28 days or death within 100 days following COVID-19 diagnosis, and assessed associations with socio-demographic factors, immunosuppressant use and morbidities reported at UK Biobank enrolment (2006–2010) and the PRS. To improve biological understanding, pathway analysis was performed using genetic variants comprising the PRS. Results We included 9560 patients followed for a median of 61 (interquartile range = 34–88) days since COVID-19 diagnosis. The risk of severe COVID-19 increased with age and obesity, and was higher in men, current smokers, those living in socio-economically deprived areas, those with historic immunosuppressant use and individuals with morbidities and higher co-morbidity count. An optimized PRS, enriched for single-nucleotide polymorphisms in multiple immune-related pathways, including the ‘oligoadenylate synthetase antiviral response’ and ‘interleukin-10 signalling’ pathways, was associated with severe COVID-19 (adjusted odds ratio 1.32, 95% CI 1.11–1.58 for the highest compared with the lowest PRS quintile). Conclusion This study conducted in the pre-SARS-CoV-2-vaccination era, emphasizes the novel insights to be gained from using genetic data alongside commonly considered clinical and socio-demographic factors to develop greater biological understanding of severe COVID-19 outcomes.

Objectives: Non-communicable diseases (NCDs) have recently been highlighted as important risk factors for COVID-19 fatality. We set out to investigate the association between NCDs and the risk of death from any infection in the pre-COVID-19 era. Design: Prospective population-based study Setting: UK Biobank Participants: 493,295 participants Main outcome measures: Infection death prior to December 31st 2019. Results: During 5,277,344 participant-years of follow-up, 1,385 infection deaths occurred, accounting for 5% of all deaths. Competing risks regression revealed that advancing age, male sex, smoking, socio-economic deprivation and all studied NCDs were independently associated with both the risk of infection death and non-infection death; ethnicity was associated with neither. Only smoking, socio-economic deprivation, hypertension, respiratory disease, chronic kidney disease, psychiatric disease and rheumatological disease were associated with greater hazard ratios for infection than non-infection death. Accrual of multimorbidity was also associated with a greater increases in the risk of infection death (HR 9.03 [95% confidence interval 6.61 to 12.34] for 5+ comorbidities versus none; p<0.001), than non-infection death (HR 5.68 [95% confidence interval 5.22 to 6.17] for 5+ comorbidities versus none; p<0.001). Conclusions: Diverse NCDs are associated with increased risk of infection death, suggesting that recently reported associations with COVID-19 death may be non-specific. Moreover, only a subset of NCDs, together with the accrual of multimorbidity, smoking and socio-economic deprivation, are associated with greater relative risks of infection death than other causes of death. Further research is needed to define why these risk factors are biased toward infection death so that more effective preventative strategies can be targeted to high-risk groups.

Introduction:We report dementia incidence, comorbidities, reasons for health-care visits, mortality, causes of death, and examined dementia patterns by relative deprivation in the UK. Method:A longitudinal cohort analysis of linked electronic health records from 4.3 million people in the UK was conducted to investigate dementia incidence and mortality.Reasons for hospitalization and causes of death were compared in individuals with and without dementia. Results: From 1998 to 2016 we observed 145,319 (3.1%) individuals with incident dementia. Repeated hospitalizations among senior adults for infection, unknown morbidity, and multiple primary care visits for chronic pain were observed prior to dementia diagnosis. Multiple long-term conditions are present in half of the individuals at the time of diagnosis. Individuals living in high deprivation areas had higher dementia incidence and high fatality.Discussion: There is a considerable disparity of dementia that informs priorities of prevention and provision of patient care.

Cardiovascular disease (CVD) mortality has substantially improved over recent decades. Some evidence indicates this has been paralleled by an increasing proportion of non-cardiovascular mortality in people with CVD. However, the contemporary causes of death across a broad spectrum of CVDs, either alone or in combination, remains unclear. We analysed cardiovascular, infection, cancer and other causes of death prior to the COVID-19 pandemic in 493,280 participants in the prospective UK Biobank study. Studied CVDs included baseline: abdominal aortic aneurysm, atrial fibrillation, coronary artery disease, heart failure, hypertension, peripheral vascular disease, stroke, valvular heart disease and venous thromboembolic disease; we separately considered cardiovascular multimorbidity defined as the total number of these baseline CVDs. Crude mortality rates and Poisson regression analysis were used to quantify the absolute and relative risk of cause-specific death. Associations are reported as incidence rate ratios (IRR) with 95% CIs. During a median follow-up of 10.9 [IQR 10.1-11.6] years per participant, there were 27,729 deaths (20.4% primarily attributed to CVD, 53.6% to cancer, 5.0% to infection and 21.0% to other causes). As the number of co-morbid CVDs increased, the proportion of cardiovascular and infection-related deaths increased, whereas cancer and other deaths decreased. Accrual of multiple CVDs was associated with marked increases in relative risk of infection and cardiovascular death; versus those without CVD, people with three or more CVDs, the relative risk of cardiovascular death increased most (IRR 3.89; 95%CI 3.59-4.21), followed by infection (4.41; 3.44-5.64), with other (2.01; 1.72-2.35) and cancer (1.52; 1.35-1.72) being substantially less increased. All studied CVDs except atrial fibrillation were independently associated with increased risk of infection death, with heart failure (2.73; 1.60-4.66) and valvular heart disease (3.09; 2.38-4.00) posing the greatest risk. In conclusion, causes of death vary substantially between differing baseline CVDs, and according to the number of baseline CVDs, with non-cardiovascular deaths due to cancer and infection making an important contribution. Holistic and personalized care are likely to be important tools for continuing to improve outcomes in people with CVD.