Chronic pain and COVID-19 hospitalisation and mortality: a UK Biobank cohort study.

ABSTRACT: The risk of COVID-19 in those with chronic pain is unknown. We investigated whether self-reported chronic pain was associated with COVID-19 hospitalisation or mortality. UK Biobank recruited 502,624 participants aged 37 to 73 years between 2006 and 2010. Baseline exposure data, including chronic pain (>3 months, in at least 1 of 7 prespecified body sites) and chronic widespread pain (>3 months, all over body), were linked to COVID-19 hospitalisations or mortality. Univariable or multivariable Poisson regression analyses were performed on the association between chronic pain and COVID-19 hospitalisation and Cox regression analyses of the associations with COVID-19 mortality. Multivariable analyses adjusted incrementally for sociodemographic confounders, then lifestyle risk factors, and finally long-term condition count. Of 441,403 UK Biobank participants with complete data, 3180 (0.7%) were hospitalised for COVID-19 and 1040 (0.2%) died from COVID-19. Chronic pain was associated with hospital admission for COVID-19 even after adjustment for all covariates (incidence rate ratio 1.16; 95% confidence interval [CI] 1.08-1.24; P < 0.001), as was chronic widespread pain (incidence rate ratio 1.33; 95% CI 1.06-1.66; P = 0.012). There was clear evidence of a dose-response relationship with number of pain sites (fully adjusted global P-value < 0.001). After adjustment for all covariates, there was no association between chronic pain (HR 1.01; 95% CI 0.89-1.15; P = 0.834) but attenuated association with chronic widespread pain (HR 1.50, 95% CI 1.04-2.16, P-value = 0.032) and COVID-19 mortality. Chronic pain is associated with higher risk of hospitalisation for COVID-19, but the association with mortality is unclear. Future research is required to investigate these findings further and determine whether pain is associated with long COVID.

Multimorbidity.

Multimorbidity (two or more coexisting conditions in an individual) is a growing global challenge with substantial effects on individuals, carers and society. Multimorbidity occurs a decade earlier in socioeconomically deprived communities and is associated with premature death, poorer function and quality of life and increased health-care utilization. Mechanisms underlying the development of multimorbidity are complex, interrelated and multilevel, but are related to ageing and underlying biological mechanisms and broader determinants of health such as socioeconomic deprivation. Little is known about prevention of multimorbidity, but focusing on psychosocial and behavioural factors, particularly population level interventions and structural changes, is likely to be beneficial. Most clinical practice guidelines and health-care training and delivery focus on single diseases, leading to care that is sometimes inadequate and potentially harmful. Multimorbidity requires person-centred care, prioritizing what matters most to the individual and the individual's carers, ensuring care that is effectively coordinated and minimally disruptive, and aligns with the patient's values. Interventions are likely to be complex and multifaceted. Although an increasing number of studies have examined multimorbidity interventions, there is still limited evidence to support any approach. Greater investment in multimorbidity research and training along with reconfiguration of health care supporting the management of multimorbidity is urgently needed.

Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country.

Diagnostics for COVID-19 detection are limited in many settings. Syndromic surveillance is often the only means to identify cases but lacks specificity. Rapid antigen testing is inexpensive and easy-to-deploy but can lack sensitivity. We examine how combining these approaches can improve surveillance for guiding interventions in low-income communities in Dhaka, Bangladesh. Rapid-antigen-testing with PCR validation was performed on 1172 symptomatically-identified individuals in their homes. Statistical models were fitted to predict PCR-status using rapid-antigen-test results, syndromic data, and their combination. Under contrasting epidemiological scenarios, the models' predictive and classification performance was evaluated. Models combining rapid-antigen-testing and syndromic data yielded equal-to-better performance to rapid-antigen-test-only models across all scenarios with their best performance in the epidemic growth scenario. These results show that drawing on complementary strengths across rapid diagnostics, improves COVID-19 detection, and reduces false-positive and -negative diagnoses to match local requirements; improvements achievable without additional expense, or changes for patients or practitioners.

The association between a lifestyle score, socioeconomic status, and COVID-19 outcomes within the UK Biobank cohort.

BACKGROUND: Infection with SARS-CoV-2 virus (COVID-19) impacts disadvantaged groups most. Lifestyle factors are also associated with adverse COVID-19 outcomes. To inform COVID-19 policy and interventions, we explored effect modification of socioeconomic-status (SES) on associations between lifestyle and COVID-19 outcomes. METHODS: Using data from UK-Biobank, a large prospective cohort of 502,536 participants aged 37-73 years recruited between 2006 and 2010, we assigned participants a lifestyle score comprising nine factors. Poisson regression models with penalised splines were used to analyse associations between lifestyle score, deprivation (Townsend), and COVID-19 mortality and severe COVID-19. Associations between each exposure and outcome were examined independently before participants were dichotomised by deprivation to examine exposures jointly. Models were adjusted for sociodemographic/health factors. RESULTS: Of 343,850 participants (mean age > 60 years) with complete data, 707 (0.21%) died from COVID-19 and 2506 (0.76%) had severe COVID-19. There was evidence of a nonlinear association between lifestyle score and COVID-19 mortality but limited evidence for nonlinearity between lifestyle score and severe COVID-19 and between deprivation and COVID-19 outcomes. Compared with low deprivation, participants in the high deprivation group had higher risk of COVID-19 outcomes across the lifestyle score. There was evidence for an additive interaction between lifestyle score and deprivation. Compared with participants with the healthiest lifestyle score in the low deprivation group, COVID-19 mortality risk ratios (95% CIs) for those with less healthy scores in low versus high deprivation groups were 5.09 (1.39-25.20) and 9.60 (4.70-21.44), respectively. Equivalent figures for severe COVID-19 were 5.17 (2.46-12.01) and 6.02 (4.72-7.71). Alternative SES measures produced similar results. CONCLUSIONS: Unhealthy lifestyles are associated with higher risk of adverse COVID-19, but risks are highest in the most disadvantaged, suggesting an additive influence between SES and lifestyle. COVID-19 policy and interventions should consider both lifestyle and SES. The greatest public health benefit from lifestyle focussed COVID-19 policy and interventions is likely to be seen when greatest support for healthy living is provided to the most disadvantaged groups.

Ethnic and socioeconomic differences in SARS-CoV-2 infection: prospective cohort study using UK Biobank.

BACKGROUND: Understanding of the role of ethnicity and socioeconomic position in the risk of developing SARS-CoV-2 infection is limited. We investigated this in the UK Biobank study. METHODS: The UK Biobank study recruited 40-70-year-olds in 2006-2010 from the general population, collecting information about self-defined ethnicity and socioeconomic variables (including area-level socioeconomic deprivation and educational attainment). SARS-CoV-2 test results from Public Health England were linked to baseline UK Biobank data. Poisson regression with robust standard errors was used to assess risk ratios (RRs) between the exposures and dichotomous variables for being tested, having a positive test and testing positive in hospital. We also investigated whether ethnicity and socioeconomic position were associated with having a positive test amongst those tested. We adjusted for covariates including age, sex, social variables (including healthcare work and household size), behavioural risk factors and baseline health. RESULTS: Amongst 392,116 participants in England, 2658 had been tested for SARS-CoV-2 and 948 tested positive (726 in hospital) between 16 March and 3 May 2020. Black and south Asian groups were more likely to test positive (RR 3.35 (95% CI 2.48-4.53) and RR 2.42 (95% CI 1.75-3.36) respectively), with Pakistani ethnicity at highest risk within the south Asian group (RR 3.24 (95% CI 1.73-6.07)). These ethnic groups were more likely to be hospital cases compared to the white British. Adjustment for baseline health and behavioural risk factors led to little change, with only modest attenuation when accounting for socioeconomic variables. Socioeconomic deprivation and having no qualifications were consistently associated with a higher risk of confirmed infection (RR 2.19 for most deprived quartile vs least (95% CI 1.80-2.66) and RR 2.00 for no qualifications vs degree (95% CI 1.66-2.42)). CONCLUSIONS: Some minority ethnic groups have a higher risk of confirmed SARS-CoV-2 infection in the UK Biobank study, which was not accounted for by differences in socioeconomic conditions, baseline self-reported health or behavioural risk factors. An urgent response to addressing these elevated risks is required.

Comparison of COVID-19 outcomes among shielded and non-shielded populations.

Many western countries used shielding (extended self-isolation) of people presumed to be at high-risk from COVID-19 to protect them and reduce healthcare demand. To investigate the effectiveness of this strategy, we linked family practitioner, prescribing, laboratory, hospital and death records and compared COVID-19 outcomes among shielded and non-shielded individuals in the West of Scotland. Of the 1.3 million population, 27,747 (2.03%) were advised to shield, and 353,085 (26.85%) were classified a priori as moderate risk. COVID-19 testing was more common in the shielded (7.01%) and moderate risk (2.03%) groups, than low risk (0.73%). Referent to low-risk, the shielded group had higher confirmed infections (RR 8.45, 95% 7.44-9.59), case-fatality (RR 5.62, 95% CI 4.47-7.07) and population mortality (RR 57.56, 95% 44.06-75.19). The moderate-risk had intermediate confirmed infections (RR 4.11, 95% CI 3.82-4.42) and population mortality (RR 25.41, 95% CI 20.36-31.71) but, due to their higher prevalence, made the largest contribution to deaths (PAF 75.30%). Age ≥ 70 years accounted for 49.55% of deaths. In conclusion, in spite of the shielding strategy, high risk individuals were at increased risk of death. Furthermore, to be effective as a population strategy, shielding criteria would have needed to be widely expanded to include other criteria, such as the elderly.

Correction: Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort.

[This corrects the article DOI: 10.1371/journal.pone.0238091.].

Family history of diabetes and risk of SARS-COV-2 in UK Biobank: A prospective cohort study.

INTRODUCTION: The aim of this study was to determine risk of being SARS-CoV-2 positive and severe infection (associated with hospitalization/mortality) in those with family history of diabetes. METHODS: We used UK Biobank, an observational cohort recruited between 2006 and 2010. We compared the risk of being SARS-CoV-2 positive and severe infection for those with family history of diabetes (mother/father/sibling) against those without. RESULTS: Of 401,268 participants in total, 13,331 tested positive for SARS-CoV-2 and 2282 had severe infection by end of January 2021. In unadjusted models, participants with ≥2 family members with diabetes were more likely to be SARS-CoV-2 positive (risk ratio-RR 1.35; 95% confidence interval-CI 1.24-1.47) and severe infection (RR 1.30; 95% CI 1.04-1.59), compared to those without. The excess risk of being tested positive for SARS-CoV-2 was attenuated but significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions. The excess risk for severe infection was no longer significant after adjusting for demographics, lifestyle factors, multimorbidity and presence of cardiometabolic conditions, and was absent when excluding incident diabetes. CONCLUSION: The totality of the results suggests that good lifestyle and not developing incident diabetes may lessen risks of severe infections in people with a strong family of diabetes.

COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study.

Background: COVID-19 is responsible for increasing deaths globally. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some speculate that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs, using the limited data available early in the pandemic. Methods: We first estimated YLL from COVID-19 using WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs in a Bayesian model to estimate likely combinations of LTCs among people dying with COVID-19. We used routine UK healthcare data from Scotland and Wales to estimate life expectancy based on age/sex/these combinations of LTCs using Gompertz models from which we then estimate YLL. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (11.6 and 9.4 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was >10 years for people with 0 LTCs, and <3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data (including LTC type, severity, and potential confounders such as socioeconomic-deprivation and care-home status) is needed to optimise YLL estimates for specific populations, and to understand the global burden of COVID-19, and guide policy-making and interventions.

COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.

BACKGROUND: Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. OBJECTIVE: This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities. METHODS: We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system-independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks. RESULTS: Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225). CONCLUSIONS: The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.

Comparison of two different frailty measurements and risk of hospitalisation or death from COVID-19: findings from UK Biobank.

BACKGROUND: Frailty has been associated with worse prognosis following COVID-19 infection. While several studies have reported the association between frailty and COVID-19 mortality or length of hospital stay, there have been no community-based studies on the association between frailty and risk of severe infection. Considering that different definitions have been identified to assess frailty, this study aimed to compare the association between frailty and severe COVID-19 infection in UK Biobank using two frailty classifications: the frailty phenotype and the frailty index. METHODS: A total of 383,845 UK Biobank participants recruited 2006-2010 in England (211,310 [55.1%] women, baseline age 37-73 years) were included. COVID-19 test data were provided by Public Health England (available up to 28 June 2020). An adapted version of the frailty phenotype derived by Fried et al. was used to define frailty phenotype (robust, pre-frail, or frail). A previously validated frailty index was derived from 49 self-reported questionnaire items related to health, disease and disability, and mental wellbeing (robust, mild frailty, and moderate/severe frailty). Both classifications were derived from baseline data (2006-2010). Poisson regression models with robust standard errors were used to analyse the associations between both frailty classifications and severe COVID-19 infection (resulting in hospital admission or death), adjusted for sociodemographic and lifestyle factors. RESULTS: Of UK Biobank participants included, 802 were admitted to hospital with and/or died from COVID19 (323 deaths and 479 hospitalisations). After analyses were adjusted for sociodemographic and lifestyle factors, a higher risk of COVID-19 was observed for pre-frail (risk ratio (RR) 1.47 [95% CI 1.26; 1.71]) and frail (RR 2.66 [95% CI 2.04; 3.47]) individuals compared to those classified as robust using the frailty phenotype. Similar results were observed when the frailty index was used (RR mildly frail 1.46 [95% CI 1.26; 1.71] and RR moderate/severe frailty 2.43 [95% CI 1.91; 3.10]). CONCLUSIONS: Frailty was associated with a higher risk of severe COVID-19 infection resulting in hospital admission or death, irrespective of how it was measured and independent of sociodemographic and lifestyle factors. Public health strategies need to consider the additional risk that COVID-19 poses in individuals with frailty, including which additional preventive measures might be required.

COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study [version 1;peer review: awaiting peer review]

Background: The COVID-19 pandemic is responsible for increasing deaths globally. Most estimates have focused on numbers of deaths, with little direct quantification of years of life lost (YLL) through COVID-19. As most people dying with COVID-19 are older with underlying long-term conditions (LTCs), some have speculated that YLL are low. We aim to estimate YLL attributable to COVID-19, before and after adjustment for number/type of LTCs. Methods: We first estimated YLL from COVID-19 using standard WHO life tables, based on published age/sex data from COVID-19 deaths in Italy. We then used aggregate data on number/type of LTCs to model likely combinations of LTCs among people dying with COVID-19. From these, we used routine UK healthcare data to estimate life expectancy based on age/sex/different combinations of LTCs. We then calculated YLL based on age, sex and type of LTCs and multimorbidity count. Results: Using the standard WHO life tables, YLL per COVID-19 death was 14 for men and 12 for women. After adjustment for number and type of LTCs, the mean YLL was slightly lower, but remained high (13 and 11 years for men and women, respectively). The number and type of LTCs led to wide variability in the estimated YLL at a given age (e.g. at ≥80 years, YLL was 10 years for people with 0 LTCs, and 3 years for people with ≥6). Conclusions: Deaths from COVID-19 represent a substantial burden in terms of per-person YLL, more than a decade, even after adjusting for the typical number and type of LTCs found in people dying of COVID-19. The extent of multimorbidity heavily influences the estimated YLL at a given age. More comprehensive and standardised collection of data on LTCs is needed to better understand and quantify the global burden of COVID-19 and to guide policy-making and interventions.

Recording COVID-19 consultations: review of symptoms, risk factors, and proposed SNOMED CT terms.

BACKGROUND: There is an urgent need for epidemiological research in primary care to develop risk assessment processes for patients presenting with COVID-19, but lack of a standardised approach to data collection is a significant barrier to implementation. AIM: To collate a list of relevant symptoms, assessment items, demographics, and lifestyle and health conditions associated with COVID-19, and match these data items with corresponding SNOMED CT clinical terms to support the development and implementation of consultation templates. DESIGN & SETTING: Published and preprint literature for systematic reviews, meta-analyses, and clinical guidelines describing the symptoms, assessment items, demographics, and/or lifestyle and health conditions associated with COVID-19 and its complications were reviewed. Corresponding clinical concepts from SNOMED CT, a widely used structured clinical vocabulary for electronic primary care health records, were identified. METHOD: Guidelines and published and unpublished reviews (N = 61) were utilised to collate a list of relevant data items for COVID-19 consultations. The NHS Digital SNOMED CT Browser was used to identify concept and descriptive identifiers. Key implementation challenges were conceptualised through a Normalisation Process Theory (NPT) lens. RESULTS: In total, 32 symptoms, eight demographic and lifestyle features, 25 health conditions, and 20 assessment items relevant to COVID-19 were identified, with proposed corresponding SNOMED CT concepts. These data items can be adapted into a consultation template for COVID-19. Key implementation challenges include: 1) engaging with key stakeholders to achieve 'buy in'; and 2) ensuring any template is usable within practice settings. CONCLUSION: Consultation templates for COVID-19 are needed to standardise data collection, facilitate research and learning, and potentially improve quality of care for COVID-19.

Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort.

BACKGROUND: It is now well recognised that the risk of severe COVID-19 increases with some long-term conditions (LTCs). However, prior research primarily focuses on individual LTCs and there is a lack of data on the influence of multimorbidity (≥2 LTCs) on the risk of COVID-19. Given the high prevalence of multimorbidity, more detailed understanding of the associations with multimorbidity and COVID-19 would improve risk stratification and help protect those most vulnerable to severe COVID-19. Here we examine the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors. METHODS AND FINDINGS: We studied data from UK Biobank (428,199 participants; aged 37-73; recruited 2006-2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data. Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function). 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19. Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96-1.30)), whereas those with ≥2 LTCs had 48% higher risk; RR 1.48 (1.28-1.71). Compared with no cardiometabolic LTCs, having 1 and ≥2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12-1.46) and 1.77 (1.46-2.15), respectively. Polypharmacy was associated with a dose response higher risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI ≥40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09-3.78); 2.79 (2.00-3.90); 2.66 (1.88-3.76); 2.13 (1.46-3.12), respectively. No multiplicative interaction between multimorbidity and prognostic factors was identified. Important limitations include the low proportion of UK Biobank participants with COVID-19 test data (1.05%) and UK Biobank participants being more affluent, healthier and less ethnically diverse than the general population. CONCLUSIONS: Increasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19.