Development and validation of the Cambridge Multimorbidity Score

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study

Background

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance.

Objectives

To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided.

Methods

Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests.

Results

We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead.

Conclusions

This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Adaptation of complex interventions for people with long-term conditions: a scoping review

Adaptation seeks to transfer and implement healthcare interventions developed and evaluated in one context to another. The aim of this scoping review was to understand current approaches to the adaptation of complex interventions for people with long-term conditions (LTCs) and to identify issues for studies performed in low- and middle-income countries (LMICs). Bibliographic databases were searched from 2000 to October 2022. This review involved five stages: (i) definition of the research question(s); (ii) identifying relevant studies; (iii) study selection; (iv) data charting; and (v) data synthesis. Extraction included an assessment of the: rationale for adaptation; stages and levels of adaptation; use of theoretical frameworks, and quality of reporting using a checklist based on the 2021 ADAPT guidance. Twenty-five studies were included from across 21 LTCs and a range of complex interventions. The majority (16 studies) focused on macro (national or international) level interventions. The rationale for adaptation included intervention transfer across geographical settings [high-income country (HIC) to LMIC: six studies, one HIC to another: eight studies, one LMIC to another: two studies], or transfer across socio-economic/racial groups (five studies), or transfer between different health settings within a single country (one study). Overall, studies were judged to be of moderate reporting quality (median score 23, maximum 46), and typically focused on early stages of adaptation (identification and development) with limited outcome evaluation or implementation assessment of the adapted version of the intervention. Improved reporting of the adaptation for complex interventions targeted at LTCs is needed. Development of future adaptation methods guidance needs to consider the needs and priorities of the LMIC context.

The diagnostic performance of CA-125 for the detection of ovarian cancer in women from different ethnic groups: a cohort study of English primary care data

BACKGROUND

CA-125 testing is a recommended first line investigation for women presenting with possible symptoms of ovarian cancer in English primary care, to help determine whether further investigation for ovarian cancer is needed. It is currently not known how well the CA-125 test performs in ovarian cancer detection for patients from different ethnic groups.

METHODS

A retrospective cohort study utilising English primary care data linked to the national cancer registry was undertaken. Women aged ≥ 40 years with a CA-125 test between 2010 and 2017 were included. Logistic regression predicted one-year ovarian cancer incidence by ethnicity, adjusting for age, deprivation status, and comorbidity score. The estimated incidence of ovarian cancer by CA-125 level was modelled for each ethnic group using restricted cubic splines.

RESULTS

The diagnostic performance of CA-125 differed for women from different ethnicities. In an unadjusted analysis, predicted CA-125 levels for Asian and Black women were higher than White women at corresponding probabilities of ovarian cancer. The higher PPVs for White women compared to Asian or Black women were eliminated by inclusion of covariates.

CONCLUSION

The introduction of ethnicity-specific thresholds may increase the specificity and PPVs of CA-125 in ovarian cancer detection at the expense of sensitivity, particularly for Asian and Black women. As such, we cannot recommend the use of ethnicity-specific thresholds for CA-125.

Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation

BACKGROUND

No single multimorbidity measure is validated for use in NHS (National Health Service) England's General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care data set created for COVID-19 pandemic research. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk, with 37 conditions defined by Read Codes. The GDPPR uses the more internationally used Systematized Nomenclature of Medicine clinical terms (SNOMED CT). We previously developed a modified version of the CMMS using SNOMED CT, but the number of terms for the GDPPR data set is limited making it impossible to use this version.

OBJECTIVE

We aimed to develop and validate a modified version of CMMS using the clinical terms available for the GDPPR.

METHODS

We used pseudonymized data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC), which has an extensive SNOMED CT list. From the 37 conditions in the original CMMS model, we selected conditions either with (1) high prevalence ratio (≥85%), calculated as the prevalence in the RSC data set but using the GDPPR set of SNOMED CT codes, divided by the prevalence included in the RSC SNOMED CT codes or (2) conditions with lower prevalence ratios but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development data set (n=500,000) and construct a new CMMS model, following the methods for the original CMMS study, with variable reduction and parsimony, achieved by backward elimination and the Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous data set (n=250,000) and 1-year and 5-year mortality estimates for an asynchronous data set (n=250,000). We compared the performance with that of the original CMMS and the modified CMMS that we previously developed using RSC data.

RESULTS

The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of the modified CMMS using the more extensive SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and 5-year mortality was slightly lower (Harrell C=0.90). Calibration was reasonable following an adjustment for overfitting. The performance was similar to that of both the original and previous modified CMMS models.

CONCLUSIONS

The new modified version of the CMMS can be used on the GDPPR, a nationwide primary care data set of 54 million people, to enable adjustment for multimorbidity in predicting mortality in people in real-world vaccine effectiveness, pandemic planning, and other research studies. It requires 17 variables to produce a comparable performance with our previous modification of CMMS to enable it to be used in routine data using SNOMED CT.

Impact of co-resident health and living alone on risk of hospital admission for people with Parkinson's disease

BACKGROUND

People with Parkinson's Disease (PwP) have a higher rate of hospitalisation compared to the general population. Little is known about the impact of having a co-resident and their health on hospitalisation rates of PwP.

METHODS

We utilised Clinical Practice Research Datalink (CPRD) GOLD data (2010-2015) to identify PwP and co-residents. We classed either the fittest or youngest adult as the primary caregiver in each household. Caregiver health was classified by the Cambridge Multimorbidity Score (CMS), primary care utilisation and prescriptions. We calculated the hospitalisation (elective, emergency) incidence rate ratios (IRRs) for PwP who lived alone compared to those with a caregiver using negative binomial regression, and whether worse caregiver health predicted higher risk of admissions.

RESULTS

We identified 3254 PwP and 4007 family members. PwP who lived alone were less likely to have an elective admission (0.79; 95 % CI 0.69-0.91) and more likely to have an emergency admission (1.40; 95 % CI 1.70-1.54). Worse caregiver health, as measured by the CMS, was associated with an increased risk of emergency admission (IRR 1.35; 95 % CI 1.17-1.57), but this attenuated and was consistent with chance in the fully adjusted model (1.04; 95 % CI 0.95-1.13). No strong associations were seen between caregiver health and elective admissions.

CONCLUSION

PwP who live alone are at increased risk of emergency and less likely to have elective hospital admissions. It is important that health care providers support such people and ensure they receive equitable access to the potential benefits of elective procedures.

Validity of the Chinese multimorbidity-weighted index in measuring disease burden using health check-ups data in primary care

BACKGROUND

As multimorbidity becomes common that imposes a considerable burden to patients, but the extent to which widely-used multimorbidity indexes can be applied to quantify disease burden using primary care data in China is not clear. We applied the Chinese Multimorbidity-Weighted Index (CMWI) to health check-ups data routinely collected among older adults by primary care, to examine its validity in measuring multimorbidity associated risks of disability and mortality in annual follow-ups.

METHODS

The study utilized data from annual health check-ups of older adults, which included information on individual age, sex, and 14 health conditions at primary care in a district of Guangzhou, Guangdong, China. The risk of CMWI for mortality was analysed in a total sample of 45,009 persons 65 years and older between 2014 and 2020 (average 2.70-year follow-up), and the risk for disability was in a subsample of 18,320 older adults free of physical impairment in 2019 and followed-up in 2020. Risk of death and disability were assessed with Cox proportional hazard regression and binary logistic regression, respectively, with both models adjusted for age and sex variables. The model fit was assessed by the Akaike information criterion (AIC), and C-statistic or the area under the receiver operating characteristic curve (AUC).

RESULTS

One unit increase in baseline-CMWI (Median= 1.70, IQR: 1.30-3.00) was associated with higher risk in subsequent disability (OR = 1.12, 95%CI = 1.05,1.20) and mortality (OR = 1.18, 95%CI = 1.14, 1.22). Participants in the top tertile of CMWI had 99% and 152% increased risks of disability and mortality than their counterparts in the bottom tertile. Model fit was satisfied with adequate AUC (0.84) or C-statistic (0.76) for both outcomes.

CONCLUSIONS

CMWI, calculated based on primary care's routine health check-ups data, provides valid estimates of disability and mortality risks in older adults. This validated tool can be used to quantity and monitor older patients' health risks in primary care.

Protocol for the development and validation of a Polypharmacy Assessment Score

BACKGROUND

An increasing number of people are using multiple medications each day, named polypharmacy. This is driven by an ageing population, increasing multimorbidity, and single disease-focussed guidelines. Medications carry obvious benefits, yet polypharmacy is also linked to adverse consequences including adverse drug events, drug-drug and drug-disease interactions, poor patient experience and wasted resources. Problematic polypharmacy is 'the prescribing of multiple medicines inappropriately, or where the intended benefits are not realised'. Identifying people with problematic polypharmacy is complex, as multiple medicines can be suitable for people with several chronic conditions requiring more treatment. Hence, polypharmacy is often potentially problematic, rather than always inappropriate, dependent on clinical context and individual benefit vs risk. There is a need to improve how we identify and evaluate these patients by extending beyond simple counts of medicines to include individual factors and long-term conditions.

AIM

To produce a Polypharmacy Assessment Score to identify a population with unusual levels of prescribing who may be at risk of potentially problematic polypharmacy.

METHODS

Analyses will be performed in three parts: 1. A prediction model will be constructed using observed medications count as the dependent variable, with age, gender and long-term conditions as independent variables. A 'Polypharmacy Assessment Score' will then be constructed through calculating the differences between the observed and expected count of prescribed medications, thereby highlighting people that have unexpected levels of prescribing. Parts 2 and 3 will examine different aspects of validity of the Polypharmacy Assessment Score: 2. To assess 'construct validity', cross-sectional analyses will evaluate high-risk prescribing within populations defined by a range of Polypharmacy Assessment Scores, using both explicit (STOPP/START criteria) and implicit (Medication Appropriateness Index) measures of inappropriate prescribing. 3. To assess 'predictive validity', a retrospective cohort study will explore differences in clinical outcomes (adverse drug reactions, unplanned hospitalisation and all-cause mortality) between differing scores.

DISCUSSION

Developing a cross-cutting measure of polypharmacy may allow healthcare professionals to prioritise and risk stratify patients with polypharmacy using unusual levels of prescribing. This would be an improvement from current approaches of either using simple cutoffs or narrow prescribing criteria.

10-year multimorbidity patterns among people with and without rheumatic and musculoskeletal diseases: an observational cohort study using linked electronic health records from Wales, UK

OBJECTIVES

To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019.

PARTICIPANTS

103 426 people with RMDs and 2.9 million comparators registered in 395 Wales general practices (GPs). Each patient with an RMD aged 0-100 years between January 2010 and December 2019 registered in Clinical Practice Research Welsh practices was matched with up to five comparators without an RMD, based on age, gender and GP code.

PRIMARY OUTCOME MEASURES

The prevalence of 29 Elixhauser-defined comorbidities in people with RMDs and comparators categorised by age, gender and GP practices. Conditional logistic regression models were fitted to calculate differences (OR, 95% CI) in associations with comorbidities between cohorts.

RESULTS

The most prevalent comorbidities were cardiovascular risk factors, hypertension and diabetes. Having an RMD diagnosis was associated with a significantly higher odds for many conditions including deficiency anaemia (OR 1.39, 95% CI (1.32 to 1.46)), hypothyroidism (OR 1.34, 95% CI (1.19 to 1.50)), pulmonary circulation disorders (OR 1.39, 95% CI 1.12 to 1.73) diabetes (OR 1.17, 95% CI (1.11 to 1.23)) and fluid and electrolyte disorders (OR 1.27, 95% CI (1.17 to 1.38)). RMDs have a higher proportion of multimorbidity (two or more conditions in addition to the RMD) compared with non-RMD group (81% and 73%, respectively in 2019) and the mean number of comorbidities was higher in women from the age of 25 and 50 in men than in non-RMDs group.

CONCLUSION

People with RMDs are approximately 1.5 times as likely to have multimorbidity as the general population and provide a high-risk group for targeted intervention studies. The individuals with RMDs experience a greater load of coexisting health conditions, which tend to manifest at earlier ages. This phenomenon is particularly pronounced among women. Additionally, there is an under-reporting of comorbidities in individuals with RMDs.

Rising to the challenge of defining and operationalising multimorbidity in a UK hospital setting: the ADMISSION research collaborative

PURPOSE

Greater transparency and consistency when defining multimorbidity in different settings is needed. We aimed to: (1) adapt published principles that can guide the selection of long-term conditions for inclusion in research studies of multimorbidity in hospitals; (2) apply these principles and identify a list of long-term conditions; (3) operationalise this list by mapping it to International Classification of Diseases 10th revision (ICD-10) codes.

METHODS

Review by independent assessors and ratification by an interdisciplinary programme management group.

RESULTS

Agreement was reached that when defining multimorbidity in hospitals for research purposes all conditions must meet the following four criteria: (1) medical diagnosis; (2) typically present for ≥ 12 months; (3) at least one of currently active; permanent in effect; requiring current treatment, care or therapy; requiring surveillance; remitting-relapsing and requiring ongoing treatment or care, and; (4) lead to at least one of: significantly increased risk of death; significantly reduced quality of life; frailty or physical disability; significantly worsened mental health; significantly increased treatment burden (indicated by an increased risk of hospital admission or increased length of hospital stay). Application of these principles to two existing lists of conditions led to the selection of 60 conditions that can be used when defining multimorbidity for research focused on hospitalised patients. ICD-10 codes were identified for each of these conditions to ensure consistency in their operationalisation.

CONCLUSIONS

This work contributes to achieving the goal of greater transparency and consistency in the approach to the study of multimorbidity, with a specific focus on the UK hospital setting.

Evidence for exercise-based interventions across 45 different long-term conditions: an overview of systematic reviews

Background

Almost half of the global population face significant challenges from long-term conditions (LTCs) resulting in substantive health and socioeconomic burden. Exercise is a potentially key intervention in effective LTC management.

Methods

In this overview of systematic reviews (SRs), we searched six electronic databases from January 2000 to October 2023 for SRs assessing health outcomes (mortality, hospitalisation, exercise capacity, disability, frailty, health-related quality of life (HRQoL), and physical activity) related to exercise-based interventions in adults (aged >18 years) diagnosed with one of 45 LTCs. Methodological quality was assessed using AMSTAR-2. International Prospective Resister of Systematic Reviews (PROSPERO) ID: CRD42022319214.

Findings

Forty-two SRs plus three supplementary RCTs were included, providing 990 RCTs in 936,825 people across 39 LTCs. No evidence was identified for six LTCs. Predominant outcome domains were HRQoL (82% of SRs/RCTs) and exercise capacity (66%); whereas disability, mortality, physical activity, and hospitalisation were less frequently reported (≤25%). Evidence supporting exercise-based interventions was identified in 25 LTCs, was unclear for 13 LTCs, and for one LTC suggested no effect. No SRs considered multimorbidity in the delivery of exercise. Methodological quality varied: critically-low (33%), low (26%), moderate (26%), and high (12%).

Interpretation

Exercise-based interventions improve HRQoL and exercise capacity across numerous LTCs. Key evidence gaps included limited mortality and hospitalisation data and consideration of multimorbidity impact on exercise-based interventions.

Funding

This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).

Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (PERFORM): protocol for a randomised feasibility trial

INTRODUCTION

Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (PERFORM) is a research programme that seeks to develop and evaluate a comprehensive exercise-based rehabilitation intervention designed for people with multimorbidity, the presence of multiple long-term conditions (MLTCs). This paper describes the protocol for a randomised trial to assess the feasibility and acceptability of the PERFORM intervention, study design and processes.

METHODS AND ANALYSIS

A multicentre, parallel two-group randomised trial with individual 2:1 allocation to the PERFORM exercise-based intervention plus usual care (intervention) or usual care alone (control). The primary outcome of this feasibility trial will be to assess whether prespecified progression criteria (recruitment, retention, intervention adherence) are met to progress to the full randomised trial. The trial will be conducted across three UK sites and 60 people with MLTCs, defined as two or more LTCs, with at least one having evidence of the beneficial effect of exercise. The PERFORM intervention comprises an 8-week (twice a week for 6 weeks and once a week for 2 weeks) supervised rehabilitation programme of personalised exercise training and self-management education delivered by trained healthcare professionals followed by two maintenance sessions. Trial participants will be recruited over a 4.5-month period, and outcomes assessed at baseline (prerandomisation) and 3 months postrandomisation and include health-related quality of life, psychological well-being, symptom burden, frailty, exercise capacity, physical activity, sleep, cognition and serious adverse events. A mixed-methods process evaluation will assess acceptability, feasibility and fidelity of intervention delivery and feasibility of trial processes. An economic evaluation will assess the feasibility of data collection and estimate the costs of the PERFORM intervention.

ETHICS AND DISSEMINATION

The trial has been given favourable opinion by the West Midlands, Edgbaston Research Ethics Service (Ref: 23/WM/0057). Participants will be asked to give full, written consent to take part by trained researchers. Findings will be disseminated via journals, presentations and targeted communications to clinicians, commissioners, service users and patients and the public.

TRIAL REGISTRATION NUMBER

ISRCTN68786622.

PROTOCOL VERSION

2.0 (16 May 2023).

AZD1222 effectiveness against severe COVID-19 in individuals with comorbidity or frailty: The RAVEN cohort study

OBJECTIVES

Despite being prioritized during initial COVID-19 vaccine rollout, vulnerable individuals at high risk of severe COVID-19 (hospitalization, intensive care unit admission, or death) remain underrepresented in vaccine effectiveness (VE) studies. The RAVEN cohort study (NCT05047822) assessed AZD1222 (ChAdOx1 nCov-19) two-dose primary series VE in vulnerable populations.

METHODS

Using the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub, linked to secondary care, death registration, and COVID-19 datasets in England, COVID-19 outcomes in 2021 were compared in vaccinated and unvaccinated individuals matched on age, sex, region, and multimorbidity.

RESULTS

Over 4.5 million AZD1222 recipients were matched (mean follow-up ∼5 months); 68% were ≥50 years, 57% had high multimorbidity. Overall, high VE against severe COVID-19 was demonstrated, with lower VE observed in vulnerable populations. VE against hospitalization was higher in the lowest multimorbidity quartile (91.1%; 95% CI: 90.1, 92.0) than the highest quartile (80.4%; 79.7, 81.1), and among individuals ≥65 years, higher in the 'fit' (86.2%; 84.5, 87.6) than the frailest (71.8%; 69.3, 74.2). VE against hospitalization was lowest in immunosuppressed individuals (64.6%; 60.7, 68.1).

CONCLUSIONS

Based on integrated and comprehensive UK health data, overall population-level VE with AZD1222 was high. VEs were notably lower in vulnerable groups, particularly the immunosuppressed.

Comparing the clinical practice and prescribing safety of locum and permanent doctors: observational study of primary care consultations in England

BACKGROUND

Temporary doctors, known as locums, are a key component of the medical workforce in the NHS but evidence on differences in quality and safety between locum and permanent doctors is limited. We aimed to examine differences in the clinical practice, and prescribing safety for locum and permanent doctors working in primary care in England.

METHODS

We accessed electronic health care records (EHRs) for 3.5 million patients from the CPRD GOLD database with linkage to Hospital Episode Statistics from 1st April 2010 to 31st March 2022. We used multi-level mixed effects logistic regression to compare consultations with locum and permanent GPs for several patient outcomes including general practice revisits; prescribing of antibiotics; strong opioids; hypnotics; A&E visits; emergency hospital admissions; admissions for ambulatory care sensitive conditions; test ordering; referrals; and prescribing safety indicators while controlling for patient and practice characteristics.

RESULTS

Consultations with locum GPs were 22% more likely to involve a prescription for an antibiotic (OR = 1.22 (1.21 to 1.22)), 8% more likely to involve a prescription for a strong opioid (OR = 1.08 (1.06 to 1.09)), 4% more likely to be followed by an A&E visit on the same day (OR = 1.04 (1.01 to 1.08)) and 5% more likely to be followed by an A&E visit within 1 to 7 days (OR = 1.05 (1.02 to 1.08)). Consultations with a locum were 12% less likely to lead to a practice revisit within 7 days (OR = 0.88 (0.87 to 0.88)), 4% less likely to involve a prescription for a hypnotic (OR = 0.96 (0.94 to 0.98)), 15% less likely to involve a referral (OR = 0.85 (0.84 to 0.86)) and 19% less likely to involve a test (OR = 0.81 (0.80 to 0.82)). We found no evidence that emergency admissions, ACSC admissions and eight out of the eleven prescribing safety indicators were different if patients were seen by a locum or a permanent GP.

CONCLUSIONS

Despite existing concerns, the clinical practice and performance of locum GPs did not appear to be systematically different from that of permanent GPs. The practice and performance of both locum and permanent GPs is likely shaped by the organisational setting and systems within which they work.

Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort

BACKGROUND

Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort.

METHODS

The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions.

RESULTS

For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis.

CONCLUSIONS

In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.

Association between patient ethnicity and prostate cancer diagnosis following a prostate-specific antigen test: a cohort study of 730,000 men in primary care in the UK

BACKGROUND

Black men have higher prostate-specific antigen (PSA) levels and higher prostate cancer incidence and mortality than White men, while Asian men tend to have lower prostate cancer incidence and mortality than White men. Much of the evidence comes from the USA, and information from UK populations is limited.

METHODS

This retrospective cohort study used data on patients registered at general practices in England contributing to the Clinical Practice Research Datalink (CPRD) Aurum dataset. Those eligible were men aged 40 and over with a record of ethnicity and a PSA test result recorded between 2010 and 2017 with no prior cancer diagnosis. The aim was to assess the incidence of prostate cancer following a raised PSA test result in men from different ethnic groups. Additionally, incidence of advanced prostate cancer was investigated. Cancer incidence was estimated from multi-level logistic regression models adjusting for potential confounding factors.

RESULTS

730,515 men with a PSA test were included (88.9% White). Black men and men with mixed ethnicity had higher PSA values, particularly for those aged above 60 years. In the year following a raised PSA result (using age-specific thresholds), Black men had the highest prostate cancer incidence at 24.7% (95% CI 23.3%, 26.2%); Asian men had the lowest at 13.4% (12.2%, 14.7%); incidence for White men was 19.8% (19.4%, 20.2%). The peak incidence of prostate cancer for all groups was in men aged 70-79. Incidence of prostate cancer diagnosed at an advanced stage was similar between Black and White men.

CONCLUSIONS

More prostate cancer was diagnosed in Black men with a raised PSA result, but rates of advanced prostate cancer were not higher in this group. In this large primary care-based cohort, the incidence of prostate cancer in men with elevated PSA levels increases with increasing age, even when using age-adjusted thresholds, with Black men significantly more likely to be diagnosed compared to White or Asian men. The incidence of advanced stage prostate cancer at diagnosis was similar for Black and White men with a raised PSA result, but lower for Asian men.

Health of unpaid carers in Wales, UK: a population data linkage study

BACKGROUND

The population of unpaid carers in Wales increased to record. There is no systematic approach to record unpaid caring status, resulting in limited quantitative evidence on unpaid carers' health. The aim of this study is to: (i) create an e-cohort of unpaid carers by linking routinely collected health and administrative datasets in Wales, UK. (ii) investigate whether long-term health conditions and multimorbidity are more prevalent amongst unpaid carers than non-carers.

METHODS

Unpaid carers were identified by linking primary care dataset, National Survey for Wales data with demographic characteristics in the Secure Anonymise Information Linkage Databank. The clinical codes identified in Cambridge Multimorbidity Score were used to explore the prevalence of long-term health conditions.

RESULTS

A total of 91 220 unpaid carers in Wales were identified between 1 January 2010 and 1 March 2022. Unpaid carers were found at higher risk of managing 35 of 37 long-term health conditions and multimorbidity than non-carers, exacerbated amongst younger age groups and deprived communities.

CONCLUSIONS

The creation of the first e-cohort of unpaid carers in Wales provides opportunities to perform rapid analysis to systematically understand health needs and evaluate initiatives in future. To better support unpaid carers, flexible approaches focusing on early identification and prevention is crucial.

Prevalence of multimorbidity in survivors of 28 cancer sites: an English nationwide cross-sectional study

Multimorbidity, the presence of a chronic condition in addition to cancer, is of particular importance to cancer survivors. It has an impact on the progression, stage at diagnosis, prognosis, and treatment of cancer patients. Evidence is scarce on the prevalence of specific comorbidities in survivors of different cancers to inform prevention and management of multimorbidity. The objective of this study is to address this evidence gap by using large scale electronic health data from multiple linked UK healthcare databases to examine the prevalence of multimorbidity in 28 cancer sites. For this population-based cross-sectional study, we linked primary and secondary healthcare data from the UK Clinical Research Practice Datalink (CPRD) GOLD dataset and Hospital Episode Statistics (HES). We identified survivors of 28 common cancers aged 18 years or older at diagnosis who survived 2 years of cancer and compared their multimorbidity with matched controls without a history of cancer. To compare prevalence of individual comorbidity, multivariable logistic regression models, adjusted for confounding factors were used. Between January 1, 2010 and December 31, 2020, we identified 347,028 cancer survivors and 804,299 controls matched on age, sex and general practice. Cancer survivors had a higher prevalence of multimorbidity compared to non-cancer controls across all the cancer sites. Hypertension (56.2%), painful conditions (39.8%), osteoarthritis (38.0%), depression (31.8%) and constipation (31.4%) were the five most frequent chronic conditions reported. Compared to the controls, higher odds of constipation were found in survivors of 25 of the 28 cancer sites and higher odds of anaemia were found in 23 cancer sites. Prevalence of constipation, anaemia and painful conditions were higher after cancer diagnosis compared to before diagnosis. Since these comorbidities are not uniformly assessed as part of any of the comorbidity scales, they tend to be underreported among cancer survivors. The elevated risk of certain comorbidities in cancer survivors suggests the potential for preventative efforts in this population to lower disease burden and improve quality of life. Long-term conditions should not be viewed as the inevitable result of cancer diagnosis and treatment. We need to consider integrated management of chronic conditions tailored to specific cancers to improve cancer survivorship.

Using the Primary care Academic CollaboraTive to explore the characteristics and healthcare use of older housebound patients in England: protocol for a retrospective observational study and clinician survey (the CHiP study)

BACKGROUND

Older housebound people are an under-researched group for whom achieving good primary health care can be resource intensive.

AIMS

To describe the characteristics and healthcare use of older (≥65 years) housebound people; explore clinician views on delivery of care to housebound people; and assess the feasibility of using a new network of healthcare professionals to deliver high quality research.

DESIGN & SETTING

Retrospective observational study of electronic GP records and clinician survey in England.

METHOD

Clinical members of a new UK research network called the Primary care Academic CollaboraTive (PACT) will collect the data. For part A, around 20 GP practices will be recruited and clinicians will identify 20 housebound and 20 non-housebound people, matched by age and gender (around 400 total in each group). Anonymised data will be collected on characteristics (age, gender, ethnicity, deprivation decile), long-term conditions, prescribed medicines, quality of healthcare (via Quality Outcomes Framework targets), and continuity of care. Reports with benchmarked practice-level data will be provided to practices to identify areas for quality improvement and to enhance engagement. For part B, 2-4 clinicians will be recruited from around 50 practices in England (around 150 clinicians) to complete a survey about delivery of healthcare for housebound people. For part C, data will be collected to assess the feasibility of using the PACT network to deliver primary care research.

CONCLUSION

Older housebound people are a neglected group both in terms of research and clinical care. Understanding the characteristics and use of primary healthcare of housebound people will help identify how to improve their care.

Treatment burden in multiple long-term conditions: a mixed-methods study protocol

BACKGROUND

Treatment burden represents the work patients undertake because of their health care, and the impact of that effort on the patient. Most research has focused on older adults (aged >65 years) with multiple long-term conditions (multimorbidity) (MLTC-M), but there are now more younger adults (aged 18-65 years) living with MLTC-M and they may experience treatment burden differently. Understanding experiences of treatment burden, and identifying those most at risk of high treatment burden, are important for designing primary care services to meet their needs.

AIM

To understand the treatment burden associated with MLTC-M, for people aged 18-65 years, and how primary care services affect this burden.

DESIGN & SETTING

Mixed-methods study in up to 33 primary care practices in two UK regions.

METHOD

The following two approaches will be used: (i) in-depth qualitative interviews with adults living with MLTC-M (approximately 40 participants) to understand their experiences of treatment burden and the impact of primary care, with a think-aloud aspect to explore face validity of a novel short treatment burden questionnaire (STBQ) for routine clinical use in the initial 15 interviews; (ii) cross-sectional patient survey (approximately 1000 participants), with linked routine medical record data to examine the factors associated with treatment burden for people living with MLTC-M, and to test the validity of STBQ.

CONCLUSION

This study will generate in-depth understanding of the treatment burden experienced by people aged 18-65 years living with MLTC-M, and how primary care services affect this burden. This will inform further development and testing of interventions to reduce treatment burden, and potentially influence MLTC-M trajectories and improve health outcomes.

Continuity of care and consultation mode in general practice: a cross-sectional and longitudinal study using patient-level and practice-level data from before and during the COVID-19 pandemic in England

OBJECTIVES

Investigate trends in continuity of care with a general practitioner (GP) before and during the COVID-19 pandemic. Identify whether continuity of care is associated with consultation mode, controlling for other patient and practice characteristics.

DESIGN

Retrospective cross-sectional and longitudinal observational studies.

SETTING

Primary care records from 389 general practices participating in Clinical Practice Research Datalink Aurum in England.

PARTICIPANTS

In the descriptive analysis, 100 000+ patients were included each month between April 2018 and April 2021. Modelling of the association between continuity of care and consultation mode focused on 153 475 and 125 298 patients in index months of February 2020 (before the pandemic) and February 2021 (during the pandemic) respectively, and 76 281 patients in both index months.

PRIMARY AND SECONDARY OUTCOMES MEASURES

The primary outcome measure was the Usual Provider of Care index. Secondary outcomes included the Bice-Boxerman index and count of consultations with the most frequently seen GP.

RESULTS

Continuity of care was gradually declining before the pandemic but stabilised during it. There were consistent demographic, socioeconomic and regional differences in continuity of care. An average of 23% of consultations were delivered remotely in the year to February 2020 compared with 76% in February 2021. We found little evidence consultation mode was associated with continuity at the patient level, controlling for a range of covariates. In contrast, patient characteristics and practice-level supply and demand were associated with continuity.

CONCLUSIONS

We set out to examine the association of consultation mode with continuity of care but found that GP supply and patient demand were much more important. To improve continuity for patients, primary care capacity needs to increase. This requires sufficient, long-term investment in clinicians, staff, facilities and digital infrastructure. General practice also needs to transform ways of working to ensure continuity for those that need it, even in a capacity-constrained environment.

Progression of stroke risk in patients aged <65 years diagnosed with atrial fibrillation: a cohort study in general practice

BACKGROUND

As a result of new technologies, atrial fibrillation (AF) is more likely to be diagnosed in people aged <65 years.

AIM

To investigate the risk of someone diagnosed with AF aged <65 years developing an indication for anticoagulation before they reach 65 years.

DESIGN AND SETTING

Population-based cohort study of patients from English practices using the Clinical Practice Research Datalink, a primary care database of electronic medical records.

METHOD

The study included patients aged <65 years newly diagnosed with AF. The CHA2DS2-VASc score was derived at time of diagnosis based on patients' medical records. Patients not eligible for anticoagulation were followed up until they became eligible or turned 65 years old. The primary outcome of interest was development of a risk factor for stroke in AF.

RESULTS

Among 18 178 patients aged <65 years diagnosed with AF, 9188 (50.5%) were eligible for anticoagulation at the time of diagnosis. Among the 8990 patients not eligible for anticoagulation, 1688 (18.8%) developed a risk factor during follow-up before reaching 65 years of age or leaving the cohort for other reasons, at a rate of 6.1 per 100 patient-years. Hypertension and heart failure were the most common risk factors to occur, with rates of 2.65 (95% CI = 2.47 to 2.84) and 1.58 (95% CI = 1.45 to 1.72) per 100 patient-years, respectively. The rate of new diabetes was 0.95 (95% CI = 0.85 to 1.06) per 100 patient-years.

CONCLUSION

People aged <65 years with AF are at higher risk of developing hypertension, heart failure, and diabetes than the general population, so may warrant regular review to identify new occurrence of such risk factors.

Understanding covid-19 outcomes among people with intellectual disabilities in England

BACKGROUND

Evidence from the UK from the early stages of the covid-19 pandemic showed that people with Intellectual Disabilities (ID) had higher rates of covid-19 mortality than people without ID. However, estimates of the magnitude of risk vary widely; different studies used different time periods; and only early stages of the pandemic have been analysed. Existing analyses of risk factors have also been limited. The objective of this study was to investigate covid-19 mortality rates, hospitalisation rates, and risk factors in people with ID in England up to the end of 2021.

METHODS

Retrospective cohort study of all people with a laboratory-confirmed SARS-CoV-2 infection or death involving covid-19. Datasets covering primary care, secondary care, covid-19 tests and vaccinations, prescriptions, and deaths were linked at individual level.

RESULTS

Covid-19 carries a disproportionately higher risk of death for people with ID, above their already higher risk of dying from other causes, in comparison to those without ID. Around 2,000 people with ID had a death involving covid-19 in England up to the end of 2021; approximately 1 in 180. The covid-19 standardized mortality ratio was 5.6 [95% CI 5.4, 5.9]. People with ID were also more likely to be hospitalised for covid-19 than people without ID. The main determinants of severe covid-19 outcomes (deaths and/or hospitalisations) in both populations were age, multimorbidity and vaccination status. The key factor responsible for the higher risk of severe covid-19 in the ID population was a much higher prevalence of multimorbidity in this population. AstraZeneca vaccine was slightly less effective in preventing severe covid-19 outcomes among people with ID than among people without ID.

CONCLUSIONS

People with ID should be considered a priority group in future pandemics, such as shielding and vaccinations.

Hepatitis A Vaccination Coverage Among People With Chronic Liver Disease in England (HEALD): Protocol for a Retrospective Cohort Study

BACKGROUND

Hepatitis A outbreaks in the United Kingdom are uncommon. Most people develop mild to moderate symptoms that resolve, without sequelae, within months. However, in high-risk groups, including those with underlying chronic liver disease (CLD), hepatitis A infection can be severe, with a higher risk of mortality and morbidity. The Health Security Agency and the National Institute of Health and Care Excellence recommend preexposure hepatitis A vaccination given in 2 doses to people with CLD, regardless of its cause. There are currently no published reports of vaccination coverage for people with CLD in England or internationally.

OBJECTIVE

This study aims to describe hepatitis A vaccination coverage in adults with CLD in a UK primary care setting and compare liver disease etiology, sociodemographic characteristics, and comorbidities in people who are and are not exposed to the hepatitis A vaccine.

METHODS

We will conduct a retrospective cohort study with data from the Primary Care Sentinel Cohort of the Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub database, which is nationally representative of the English population. We will include people aged 18 years and older who have been registered in general practices in the Research and Surveillance Centre network and have a record of CLD between January 1, 2012, and December 31, 2022, including those with alcohol-related liver disease, chronic hepatitis B, chronic hepatitis C, nonalcohol fatty liver disease, Wilson disease, hemochromatosis, and autoimmune hepatitis. We will carefully curate variables using the Systematized Nomenclature of Medicine Clinical Terms. We will report the sociodemographic characteristics of those who are vaccinated. These include age, gender, ethnicity, population density, region, socioeconomic status (measured using the index of multiple deprivation), obesity, alcohol consumption, and smoking. Hepatitis A vaccination coverage for 1 and 2 doses will be calculated using an estimate of the CLD population as the denominator. We will analyze the baseline characteristics using descriptive statistics, including measures of dispersion. Pairwise comparisons of case-mix characteristics, comorbidities, and complications will be reported according to vaccination status. A multistate survival model will be fitted to estimate the transition probabilities among four states: (1) diagnosed with CLD, (2) first dose of hepatitis A vaccination, (3) second dose of hepatitis A vaccination, and (4) death. This will identify any potential disparities in how people with CLD get vaccinated.

RESULTS

The Research and Surveillance Centre population comprises over 8 million people. The reported incidence of CLD is 20.7 cases per 100,000. International estimates of hepatitis A vaccine coverage vary between 10% and 50% in this group.

CONCLUSIONS

This study will describe the uptake of the hepatitis A vaccine in people with CLD and report any disparities or differences in the characteristics of the vaccinated population.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/51861.

Reynolds N, R. Barnes M, Hull S, Finer S, Mathur R. Ethnic differences in early onset multimorbidity and associations with health service use, long-term prescribing, years of life lost, and mortality: A cross-sectional study using clustering in the UK Clinical Practice Research Datalink

BACKGROUND

The population prevalence of multimorbidity (the existence of at least 2 or more long-term conditions [LTCs] in an individual) is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. In this study, we applied a data-driven approach to identify clusters of individuals who had an early onset multimorbidity in an ethnically and socioeconomically diverse population. We identified associations between clusters and a range of health outcomes.

METHODS AND FINDINGS

Using linked primary and secondary care data from the Clinical Practice Research Datalink GOLD (CPRD GOLD), we conducted a cross-sectional study of 837,869 individuals with early onset multimorbidity (aged between 16 and 39 years old when the second LTC was recorded) registered with an English general practice between 2010 and 2020. The study population included 777,906 people of White ethnicity (93%), 33,915 people of South Asian ethnicity (4%), and 26,048 people of Black African/Caribbean ethnicity (3%). A total of 204 LTCs were considered. Latent class analysis stratified by ethnicity identified 4 clusters of multimorbidity in White groups and 3 clusters in South Asian and Black groups. We found that early onset multimorbidity was more common among South Asian (59%, 33,915) and Black (56% 26,048) groups compared to the White population (42%, 777,906). Latent class analysis revealed physical and mental health conditions that were common across all ethnic groups (i.e., hypertension, depression, and painful conditions). However, each ethnic group also presented exclusive LTCs and different sociodemographic profiles: In White groups, the cluster with the highest rates/odds of the outcomes was predominantly male (54%, 44,150) and more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. On the other hand, South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. At the end of the study, 4% (34,922) of the White early onset multimorbidity population had died compared to 2% of the South Asian and Black early onset multimorbidity populations (535 and 570, respectively); however, the latter groups died younger and lost more years of life. The 3 ethnic groups each displayed a cluster of individuals with increased rates of primary care consultations, hospitalisations, long-term prescribing, and odds of mortality. Study limitations include the exclusion of individuals with missing ethnicity information, the age of diagnosis not reflecting the actual age of onset, and the exclusion of people from Mixed, Chinese, and other ethnic groups due to insufficient power to investigate associations between multimorbidity and health-related outcomes in these groups.

CONCLUSIONS

These findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work provides additional insights into the excess burden of early onset multimorbidity in those from socioeconomically deprived and diverse groups who are disproportionately and more severely affected by multimorbidity and highlights the need to ensure healthcare improvements are equitable.

Vertical integration of general practices with acute hospitals in England: rapid impact evaluation

Background

Vertical integration means merging organisations that operate at different stages along the patient pathway. We focus on acute hospitals running primary care medical practices. Evidence is scarce concerning the impact on use of health-care services and patient experience.

Objectives

To assess the impact of vertical integration on use of hospital services, service delivery and patient experience and whether patients with multiple long-term conditions are affected differently from others.

Design

Rapid, mixed methods evaluation with four work packages: (1) review of NHS trust annual reports and other sources to understand the scale of vertical integration across England; (2) development of the statistical analysis; (3) analysis of national survey data on patient experience, and national data on use of hospital services over the 2 years preceding and following vertical integration, comparing vertically integrated practices with a variety of control practices; and (4) focus groups and interviews with staff and patients across three case study sites to explore the impact of vertical integration on patient experience of care.

Results

At 31 March 2021, 26 NHS trusts were in vertically integrated organisations, running 85 general practices across 116 practice sites. The earliest vertical integration between trusts and general practices was in 2015; a mean of 3.3 practices run by each trust (range 1-12). On average, integrated practices have fewer patients, are slightly more likely to be in the most deprived decile of areas, are more likely to hold an alternative provider medical services contract and have worse Quality and Outcomes Framework scores compared with non-integrated practices. Vertical integration is associated with statistically significant, modest reductions in rates of accident and emergency department attendances: 2% reduction (incidence rate ratio 0.98, 95% confidence interval 0.96 to 0.99; p < 0.0001); outpatient attendances: 1% reduction (incidence rate ratio 0.99, 95% confidence interval 0.99 to 1.00; p = 0.0061), emergency inpatient admissions: 3% reduction (incidence rate ratio 0.97, 95% confidence interval 0.95 to 0.99; p = 0.0062) and emergency readmissions: 5% reduction (incidence rate ratio 0.95, 95% confidence interval 0.91 to 1.00; p = 0.039), with no impact on length of stay, overall inpatient admissions or inpatient admissions for ambulatory care sensitive conditions. The falls in accident and emergency department and outpatient attendance rates are temporary. Focus groups and interviews with staff (N = 22) and interviews with patients (N = 14) showed that with vertical integration, health service improvements are introduced following a period of cultural interchange. Patients with multiple long-term conditions continue to encounter 'navigation work' choosing and accessing health-care provision, with diminishing continuity of care.

Limitations

In the quantitative analysis, we could not replicate the counterfactual of what would have happened in those specific locations had practices not merged with trusts. There was imbalance across three case study sites with regard to staff and patients recruited for interview, and the latter were drawn from patient participation groups who may not be representative of local populations.

Conclusions

Vertical integration can lead to modest reductions in use of hospital services and has minor or no impact on patient experience of care. Our analysis does not reveal a case for widespread roll-out of the approach.

Future research

Further quantitative follow-up of the longer-term impact of vertical integration on hospital usage and more extensive interviewing of patients and their carers about patient experiences of navigating care.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (BRACE Project no. 16/138/31) and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 17. See the NIHR Journals Library website for further project information.

Educational inequality in multimorbidity: causality and causal pathways. A mendelian randomisation study in UK Biobank

BACKGROUND

Multimorbidity, typically defined as having two or more long-term health conditions, is associated with reduced wellbeing and life expectancy. Understanding the determinants of multimorbidity, including whether they are causal, may help with the design and prioritisation of prevention interventions. This study seeks to assess the causality of education, BMI, smoking and alcohol as determinants of multimorbidity, and the degree to which BMI, smoking and alcohol mediate differences in multimorbidity by level of education.

METHODS

Participants were 181,214 females and 155,677 males, mean ages 56.7 and 57.1 years respectively, from UK Biobank. We used a Mendelian randomization design; an approach that uses genetic variants as instrumental variables to interrogate causality.

RESULTS

The prevalence of multimorbidity was 55.1%. Mendelian randomization suggests that lower education, higher BMI and higher levels of smoking causally increase the risk of multimorbidity. For example, one standard deviation (equivalent to 5.1 years) increase in genetically-predicted years of education decreases the risk of multimorbidity by 9.0% (95% CI: 6.5 to 11.4%). A 5 kg/m2 increase in genetically-predicted BMI increases the risk of multimorbidity by 9.2% (95% CI: 8.1 to 10.3%) and a one SD higher lifetime smoking index increases the risk of multimorbidity by 6.8% (95% CI: 3.3 to 10.4%). Evidence for a causal effect of genetically-predicted alcohol consumption on multimorbidity was less strong; an increase of 5 units of alcohol per week increases the risk of multimorbidity by 1.3% (95% CI: 0.2 to 2.5%). The proportions of the association between education and multimorbidity explained by BMI and smoking are 20.4% and 17.6% respectively. Collectively, BMI and smoking account for 31.8% of the educational inequality in multimorbidity.

CONCLUSIONS

Education, BMI, smoking and alcohol consumption are intervenable causal risk factors for multimorbidity. Furthermore, BMI and lifetime smoking make a considerable contribution to the generation of educational inequalities in multimorbidity. Public health interventions that improve population-wide levels of these risk factors are likely to reduce multimorbidity and inequalities in its occurrence.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service

BACKGROUND

Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources.

METHODS

A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality.

RESULTS

Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%).

CONCLUSIONS

The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Multi-morbidity and its association with common cancer diagnoses: a UK Biobank prospective study

BACKGROUND

Whilst multi-morbidity is known to be a concern in people with cancer, very little is known about the risk of cancer in multi-morbid patients. This study aims to investigate the risk of being diagnosed with lung, colorectal, breast and prostate cancer associated with multi-morbidity.

METHODS

We investigated the association between multi-morbidity and subsequent risk of cancer diagnosis in UK Biobank. Cox models were used to estimate the relative risks of each cancer of interest in multi-morbid participants, using the Cambridge Multimorbidity Score. The extent to which reverse causation, residual confounding and ascertainment bias may have impacted on the findings was robustly investigated.

RESULTS

Of the 436,990 participants included in the study who were cancer-free at baseline, 21.6% (99,965) were multi-morbid (≥ 2 diseases). Over a median follow-up time of 10.9 [IQR 10.0-11.7] years, 9,019 prostate, 7,994 breast, 5,241 colorectal, and 3,591 lung cancers were diagnosed. After exclusion of the first year of follow-up, there was no clear association between multi-morbidity and risk of colorectal, prostate or breast cancer diagnosis. Those with ≥ 4 diseases at recruitment had double the risk of a subsequent lung cancer diagnosis compared to those with no diseases (HR 2.00 [95% CI 1.70-2.35] p for trend < 0.001). These findings were robust to sensitivity analyses aimed at reducing the impact of reverse causation, residual confounding from known cancer risk factors and ascertainment bias.

CONCLUSIONS

Individuals with multi-morbidity are at an increased risk of lung cancer diagnosis. While this association did not appear to be due to common sources of bias in observational studies, further research is needed to understand what underlies this association.

Are There Ethnic Differences in Recorded Features among Patients Subsequently Diagnosed with Cancer? An English Longitudinal Data-Linked Study

We investigated ethnic differences in the presenting features recorded in primary care before cancer diagnosis.

METHODS

English population-based cancer-registry-linked primary care data were analysed. We identified the coded features of six cancers (breast, lung, prostate, colorectal, oesophagogastric, and myeloma) in the year pre-diagnosis. Logistic regression models investigated ethnic differences in first-incident cancer features, adjusted for age, sex, smoking status, deprivation, and comorbidity.

RESULTS

Of 130,944 patients, 92% were White. In total, 188,487 incident features were recorded in the year pre-diagnosis, with 48% (89,531) as sole features. Compared with White patients, Asian and Black patients with breast, colorectal, and prostate cancer were more likely than White patients to have multiple features; the opposite was seen for the Black and Other ethnic groups with lung or prostate cancer. The proportion with relevant recorded features was broadly similar by ethnicity, with notable cancer-specific exceptions. Asian and Black patients were more likely to have low-risk features (e.g., cough, upper abdominal pain) recorded. Non-White patients were less likely to have alarm features.

CONCLUSION

The degree to which these differences reflect disease, patient or healthcare factors is unclear. Further research examining the predictive value of cancer features in ethnic minority groups and their association with cancer outcomes is needed.

Impact of multimorbidity on mortality in heart failure with reduced ejection fraction: which comorbidities matter most? An analysis of PARADIGM-HF and ATMOSPHERE

AIMS

Multimorbidity, the coexistence of two or more chronic conditions, is synonymous with heart failure (HF). How risk related to comorbidities compares at individual and population levels is unknown. The aim of this study is to examine the risk related to comorbidities, alone and in combination, both at individual and population levels.

METHODS AND RESULTS

Using two clinical trials in HF - the Prospective comparison of ARNI (Angiotensin Receptor-Neprilysin Inhibitor) with ACEI (Angiotensin-Converting Enzyme Inhibitor) to Determine Impact on Global Mortality and morbidity in HF trial (PARADIGM-HF) and the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure trials (ATMOSPHERE) - we identified the 10 most common comorbidities and examined 45 possible pairs. We calculated population attributable fractions (PAF) for all-cause death and relative excess risk due to interaction with Cox proportional hazard models. Of 15 066 patients in the study, 14 133 (93.7%) had at least one and 11 867 (78.8%) had at least two of the 10 most prevalent comorbidities. The greatest individual risk among pairs was associated with peripheral artery disease (PAD) in combination with stroke (hazard ratio [HR] 1.73; 95% confidence interval [CI] 1.28-2.33) and anaemia (HR 1.71; 95% CI 1.39-2.11). The combination of chronic kidney disease (CKD) and hypertension had the highest PAF (5.65%; 95% CI 3.66-7.61). Two pairs demonstrated significant synergistic interaction (atrial fibrillation with CKD and coronary artery disease, respectively) and one an antagonistic interaction (anaemia and obesity).

CONCLUSIONS

In HF, the impact of multimorbidity differed at the individual patient and population level, depending on the prevalence of and the risk related to each comorbidity, and the interaction between individual comorbidities. Patients with coexistent PAD and stroke were at greatest individual risk whereas, from a population perspective, coexistent CKD and hypertension mattered most.

5α-Reductase Inhibitors and Risk of Kidney and Bladder Cancers in Men with Benign Prostatic Hyperplasia: A Population-Based Cohort Study

BACKGROUND

Preclinical evidence suggests that 5α-reductase inhibitors (5ARi), commonly used to treat benign prostatic hyperplasia (BPH), are associated with reduced incidence of certain urologic cancers, yet epidemiologic studies are conflicting. This study aimed to determine whether 5ARi's are associated with a reduced risk of kidney and bladder cancers.

METHODS

We conducted a new-user active-comparator cohort study in the United Kingdom Clinical Practice Research Datalink. From a base cohort of patients with incident BPH, new users of 5ARi's and α-blockers were identified. Patients were followed up until a first ever diagnosis of kidney or bladder cancer, death from any cause, end of registration, or December 31, 2017. Cox proportional hazards models were used to calculate HRs and 95% confidence intervals (CI) for incident kidney and bladder cancer.

RESULTS

There were 5,414 and 37,681 new users of 5ARi's and α-blockers, respectively. During a mean follow-up of 6.3 years, we found no association between the use of 5ARi's and kidney (adjusted HR, 1.26; 95% CI, 0.74-2.12; n = 23) or bladder (adjusted HR, 0.89; 95% CI, 0.64-1.23; n = 57) cancer risk compared with α-blockers. Similar results were observed across sensitivity analyses.

CONCLUSIONS

In this study, we found no association between the use of 5ARi's and kidney or bladder cancer incidence in men with BPH when compared with α-blocker use.

IMPACT

The findings of this study indicate that 5ARi's are unlikely to reduce kidney or bladder cancer risk.

Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: An exemplar applied to oesophagogastric cancer

BACKGROUND

Current methods for estimating the timeliness of cancer diagnosis are not robust because dates of key defining milestones, for example first presentation, are uncertain. This is exacerbated when patients have other conditions (multimorbidity), particularly those that share symptoms with cancer. Methods independent of this uncertainty are needed for accurate estimates of the timeliness of cancer diagnosis, and to understand how multimorbidity impacts the diagnostic process.

METHODS

Participants were diagnosed with oesophagogastric cancer between 2010 and 2019. Controls were matched on year of birth, sex, general practice and multimorbidity burden calculated using the Cambridge Multimorbidity Score. Primary care data (Clinical Practice Research Datalink) was used to explore population-level consultation rates for up to two years before diagnosis across different multimorbidity burdens. Five approaches were compared on the timing of the consultation frequency increase, the inflection point for different multimorbidity burdens, different aggregated time-periods and sample sizes.

RESULTS

We included 15,410 participants, of which 13,328 (86.5 %) had a measurable multimorbidity burden. Our new maximum likelihood estimation method found evidence that the inflection point in consultation frequency varied with multimorbidity burden, from 154 days (95 %CI 131.8-176.2) before diagnosis for patients with no multimorbidity, to 126 days (108.5-143.5) for patients with the greatest multimorbidity burden. Inflection points identified using alternative methods were closer to diagnosis for up to three burden groups. Sample size reduction and changing the aggregation period resulted in inflection points closer to diagnosis, with the smallest change for the maximum likelihood method.

DISCUSSION

Existing methods to identify changes in consultation rates can introduce substantial bias which depends on sample size and aggregation period. The direct maximum likelihood method was less prone to this bias than other methods and offers a robust, population-level alternative for estimating the timeliness of cancer diagnosis.

Representativeness, Vaccination Uptake, and COVID-19 Clinical Outcomes 2020-2021 in the UK Oxford-Royal College of General Practitioners Research and Surveillance Network: Cohort Profile Summary

BACKGROUND

The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe's oldest sentinel systems, working with the UK Health Security Agency (UKHSA) and its predecessor bodies for 55 years. Its surveillance report now runs twice weekly, supplemented by online observatories. In addition to conducting sentinel surveillance from a nationally representative group of practices, the RSC is now also providing data for syndromic surveillance.

OBJECTIVE

The aim of this study was to describe the cohort profile at the start of the 2021-2022 surveillance season and recent changes to our surveillance practice.

METHODS

The RSC's pseudonymized primary care data, linked to hospital and other data, are held in the Oxford-RCGP Clinical Informatics Digital Hub, a Trusted Research Environment. We describe the RSC's cohort profile as of September 2021, divided into a Primary Care Sentinel Cohort (PCSC)-collecting virological and serological specimens-and a larger group of syndromic surveillance general practices (SSGPs). We report changes to our sampling strategy that brings the RSC into alignment with European Centre for Disease Control guidance and then compare our cohort's sociodemographic characteristics with Office for National Statistics data. We further describe influenza and COVID-19 vaccine coverage for the 2020-2021 season (week 40 of 2020 to week 39 of 2021), with the latter differentiated by vaccine brand. Finally, we report COVID-19-related outcomes in terms of hospitalization, intensive care unit (ICU) admission, and death.

RESULTS

As a response to COVID-19, the RSC grew from just over 500 PCSC practices in 2019 to 1879 practices in 2021 (PCSC, n=938; SSGP, n=1203). This represents 28.6% of English general practices and 30.59% (17,299,780/56,550,136) of the population. In the reporting period, the PCSC collected >8000 virology and >23,000 serology samples. The RSC population was broadly representative of the national population in terms of age, gender, ethnicity, National Health Service Region, socioeconomic status, obesity, and smoking habit. The RSC captured vaccine coverage data for influenza (n=5.4 million) and COVID-19, reporting dose one (n=11.9 million), two (n=11 million), and three (n=0.4 million) for the latter as well as brand-specific uptake data (AstraZeneca vaccine, n=11.6 million; Pfizer, n=10.8 million; and Moderna, n=0.7 million). The median (IQR) number of COVID-19 hospitalizations and ICU admissions was 1181 (559-1559) and 115 (50-174) per week, respectively.

CONCLUSIONS

The RSC is broadly representative of the national population; its PCSC is geographically representative and its SSGPs are newly supporting UKHSA syndromic surveillance efforts. The network captures vaccine coverage and has expanded from reporting primary care attendances to providing data on onward hospital outcomes and deaths. The challenge remains to increase virological and serological sampling to monitor the effectiveness and waning of all vaccines available in a timely manner.

Associations between air pollution and multimorbidity in the UK Biobank: A cross-sectional study

Background

Long-term exposure to air pollution concentrations is known to be adversely associated with a broad range of single non-communicable diseases, but its role in multimorbidity has not been investigated in the UK. We aimed to assess associations between long-term air pollution exposure and multimorbidity status, severity, and patterns using the UK Biobank cohort.

Methods

Multimorbidity status was calculated based on 41 physical and mental conditions. We assessed cross-sectional associations between annual modeled particulate matter (PM)2.5, PMcoarse, PM10, and nitrogen dioxide (NO2) concentrations (μg/m3-modeled to residential address) and multimorbidity status at the baseline assessment (2006-2010) in 364,144 people (mean age: 52.2 ± 8.1 years, 52.6% female). Air pollutants were categorized into quartiles to assess dose-response associations. Among those with multimorbidity (≥2 conditions; n = 156,395) we assessed associations between air pollutant exposure levels and multimorbidity severity and multimorbidity patterns, which were identified using exploratory factor analysis. Associations were explored using generalized linear models adjusted for sociodemographic, behavioral, and environmental indicators.

Results

Higher exposures to PM2.5, and NO2 were associated with multimorbidity status in a dose-dependent manner. These associations were strongest when we compared the highest air pollution quartile (quartile 4: Q4) with the lowest quartile (Q1) [PM2.5: adjusted odds ratio (adjOR) = 1.21 (95% CI = 1.18, 1.24); NO2: adjOR = 1.19 (95 % CI = 1.16, 1.23)]. We also observed dose-response associations between air pollutant exposures and multimorbidity severity scores. We identified 11 multimorbidity patterns. Air pollution was associated with several multimorbidity patterns with strongest associations (Q4 vs. Q1) observed for neurological (stroke, epilepsy, alcohol/substance dependency) [PM2.5: adjOR = 1.31 (95% CI = 1.14, 1.51); NO2: adjOR = 1.33 (95% CI = 1.11, 1.60)] and respiratory patterns (COPD, asthma) [PM2.5: adjOR = 1.24 (95% CI = 1.16, 1.33); NO2: adjOR = 1.26 (95% CI = 1.15, 1.38)].

Conclusions

This cross-sectional study provides evidence that exposure to air pollution might be associated with having multimorbid, multi-organ conditions. Longitudinal studies are needed to further explore these associations.

Compression and Expansion of Morbidity

BACKGROUND

Morbidity is said to be compressed when the interval from the onset of a disease or disability to death becomes shorter over time, or when the incidence of the disease or disability declines over time. In the reverse situation, morbidity is said to be expanded.

METHODS

This review is based on national and international studies retrieved by a selective literature search on secular trends in morbidity and mortality. The findings were derived from data from surveys and registries, and from the routine data of health insurance carriers.

RESULTS

Three different types of secular trends in morbidity were seen. For some diseases (e.g., lung cancer, stroke, and dementia), morbidity among the elderly was compressed over time. On the other hand, for multimorbidity and type 2 diabetes including comorbidities, morbidity expanded over time. Unexpectedly, a double development was seen in certain other conditions, with both compression among the elderly and expansion among the middle-aged: this was particularly so for myo - cardial infarction, grip strength, and indicators of general health.

CONCLUSION

The notion of morbidity being reduced by compression seems less tenable in view of the double development just mentioned. The findings suggest that the observed secular trend toward better health among the elderly has not persisted among the more recently born cohorts. This can have negative effects on social security systems, particularly with respect to retirement ages being deferred or made more flexible, as well as the cost of health care.

Assessing trial representativeness using serious adverse events: an observational analysis using aggregate and individual-level data from clinical trials and routine healthcare data

BACKGROUND

The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. We explore an approach assessing trial representativeness by comparing rates of trial serious adverse events (SAE) to rates of hospitalisation/death in routine care.

METHODS

This was an observational analysis of individual (125 trials, n=122,069) and aggregate-level drug trial data (483 trials, n=636,267) for 21 index conditions compared to population-based routine healthcare data (routine care). Trials were identified from ClinicalTrials.gov . Routine care comparison from linked primary care and hospital data from Wales, UK (n=2.3M). Our outcome of interest was SAEs (routinely reported in trials). In routine care, SAEs were based on hospitalisations and deaths (which are SAEs by definition). We compared trial SAEs in trials to expected SAEs based on age/sex standardised routine care populations with the same index condition. Using IPD, we assessed the relationship between multimorbidity count and SAEs in both trials and routine care and assessed the impact on the observed/expected SAE ratio additionally accounting for multimorbidity.

RESULTS

For 12/21 index conditions, the pooled observed/expected SAE ratio was <1, indicating fewer SAEs in trial participants than in routine care. A further 6/21 had point estimates <1 but the 95% CI included the null. The median pooled estimate of observed/expected SAE ratio was 0.60 (95% CI 0.55-0.64; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.87 (0.58-1.29; inflammatory bowel disease). Higher multimorbidity count was associated with SAEs across all index conditions in both routine care and trials. For most trials, the observed/expected SAE ratio moved closer to 1 after additionally accounting for multimorbidity count, but it nonetheless remained below 1 for most.

CONCLUSIONS

Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess the applicability of trial findings to older populations in whom multimorbidity and frailty are common.

Mental Health Morbidities and Time to Cancer Diagnosis Among Adults With Colon Cancer in England

Importance

Mental health morbidity (MHM) in patients presenting with possible cancer symptoms may be associated with prediagnostic care and time to cancer diagnosis.

Objective

To compare the length of intervals to cancer diagnosis by preexisting MHM status in patients who presented with symptoms of as-yet-undiagnosed colon cancer and evaluate their risk of emergency cancer diagnosis.

Design, Setting, and Participants

This cohort study was conducted using linked primary care data obtained from the population-based Clinical Practice Research Datalink, which includes primary care practices in England, linked to cancer registry and hospital data. Included participants were 3766 patients diagnosed with colon cancer between 2011 and 2015 presenting with cancer-relevant symptoms up to 24 months before their diagnosis. Data analysis was performed in January 2021 to April 2022.

Exposures

Mental health conditions recorded in primary care before cancer diagnosis, including anxiety, depression, schizophrenia, bipolar disorder, alcohol addiction, anorexia, and bulimia.

Main Outcomes and Measures

Fast-track (also termed 2-week wait) specialist referral for investigations, time to colonoscopy and cancer diagnosis, and risk of emergency cancer diagnosis.

Results

Among 3766 patients with colon cancer (median [IQR] age, 75 [65-82] years; 1911 [50.7%] women ), 623 patients [16.5%] had preexisting MHM recorded in primary care the year before cancer diagnosis, including 562 patients (14.9%) with preexisting anxiety or depression (accounting for 90.2% of patients with preexisting MHM) and 61 patients (1.6%) with other MHM; 3143 patients (83.5%) did not have MHM. Patients with MHM had records of red-flag symptoms or signs (ie, rectal bleeding, change in bowel habit, or anemia) in the 24 months before cancer diagnosis in a smaller proportion compared with patients without MHM (308 patients [49.4%] vs 1807 patients [57.5%]; P < .001). Even when red-flag symptoms were recorded, patients with MHM had lower odds of fast-track specialist referral (adjusted odds ratio [OR] = 0.72; 95% CI, 0.55-0.94; P = .01). Among 2115 patients with red-flag symptoms or signs, 308 patients with MHM experienced a more than 2-fold longer median (IQR) time to cancer diagnosis (326 [75-552] days vs 133 [47-422] days) and higher odds of emergency diagnosis (90 patients [29.2%] vs 327 patients [18.1%]; adjusted OR = 1.63; 95% CI, 1.23-2.24; P < .001) compared with 1807 patients without MHM.

Conclusions and Relevance

This study found that patients with MHM experienced large and prognostically consequential disparities in diagnostic care before a colon cancer diagnosis. These findings suggest that appropriate pathways and follow-up strategies after symptomatic presentation are needed for earlier cancer diagnoses and improved health outcomes in this large patient group.

Association between continuity of primary care and both prescribing and adherence of common cardiovascular medications: a cohort study among patients in England

OBJECTIVES

To investigate whether better continuity of care is associated with increased prescribing of clinically relevant medication and improved medication adherence.

SETTING

Random sample of 300 000 patients aged 30+ in 2017 within 83 English general practitioner (GP) practices from the Clinical Practice Research Datalink.

DESIGN

Patients were assigned to a randomly selected index date in 2017 on which medication use and continuity of care were determined. Adjusted associations between continuity of care and the prescribing and adherence of five cardiovascular medication groups were examined using logistic regression.

PARTICIPANTS

Continuity of Care Index was calculated for 173 993 patients with 4+ GP consultations 2 years prior to their index date and divided into five categories: absence of continuity, below-average continuity, average, above-average continuity and perfect continuity.

MAIN OUTCOME MEASURES

(A) Prescription for statins (primary or secondary prevention separately), anticoagulants, antiplatelet agents and antihypertensives covering the patient's index date. (B) Adherence (>80%) estimated using medication possession ratio.

RESULTS

There was strong evidence (p<0.01) that prescription of all five cardiovascular medication groups increased with greater continuity of care. Patients with absence of continuity were less likely to be prescribed cardiovascular medications than patients with above-average continuity (statins primary prevention OR 0.73, 95% CI 0.59 to 0.85; statins secondary prevention 0.77, 95% CI 0.57 to 1.03; antiplatelets 0.55, 95% CI 0.33 to 0.92; antihypertensives 0.51, 95% CI 0.39 to 0.65). Furthermore, patients with perfect continuity were more likely to be prescribed cardiovascular medications than those with above-average continuity (statins primary prevention OR 1.23, 95% CI 1.01 to 1.49; statins secondary prevention 1.37, 95% CI 1.10 to 1.71; antiplatelets 1.37, 95% CI 1.08 to 1.74; antihypertensives 1.10, 95% CI 0.99 to 1.23). Continuity was generally not associated with medication adherence, except for adherence to statins for secondary prevention (OR 0.75, 95% CI 0.60 to 0.94 for average compared with above-average continuity).

CONCLUSION

Better continuity of care is associated with improved prescribing of medication to patients at higher risk of cardiovascular disease but does not appear to be related to patient's medication adherence.

Ethnic inequalities in routes to diagnosis of cancer: a population-based UK cohort study

BACKGROUND

UK Asian and Black ethnic groups have poorer outcomes for some cancers and are less likely to report a positive care experience than their White counterparts. This study investigated ethnic differences in the route to diagnosis (RTD) to identify areas in patients' cancer journeys where inequalities lie, and targeted intervention might have optimum impact.

METHODS

We analysed data of 243,825 patients with 10 cancers (2006-2016) from the RTD project linked to primary care data. Crude and adjusted proportions of patients diagnosed via six routes (emergency, elective GP referral, two-week wait (2WW), screen-detected, hospital, and Other routes) were calculated by ethnicity. Adjusted odds ratios (including two-way interactions between cancer and age, sex, IMD, and ethnicity) determined cancer-specific differences in RTD by ethnicity.

RESULTS

Across the 10 cancers studied, most patients were diagnosed via 2WW (36.4%), elective GP referral (23.2%), emergency (18.2%), hospital routes (10.3%), and screening (8.61%). Patients of Other ethnic group had the highest proportion of diagnosis via the emergency route, followed by White patients. Asian and Black group were more likely to be GP-referred, with the Black and Mixed groups also more likely to follow the 2WW route. However, there were notable cancer-specific differences in the RTD by ethnicity.

CONCLUSION

Our findings suggest that, where inequalities exist, the adverse cancer outcomes among Asian and Black patients are unlikely to be arising solely from a poorer diagnostic process.

Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study

BACKGROUND

Most studies of long COVID (symptoms of COVID-19 infection beyond 4 weeks) have focused on people hospitalized in their initial illness. Long COVID is thought to be underrecorded in UK primary care electronic records.

OBJECTIVE

We sought to determine which symptoms people present to primary care after COVID-19 infection and whether presentation differs in people who were not hospitalized, as well as post-long COVID mortality rates.

METHODS

We used routine data from the nationally representative primary care sentinel cohort of the Oxford-Royal College of General Practitioners Research and Surveillance Centre (N=7,396,702), applying a predefined long COVID phenotype and grouped by whether the index infection occurred in hospital or in the community. We included COVID-19 infection cases from March 1, 2020, to April 1, 2021. We conducted a before-and-after analysis of long COVID symptoms prespecified by the Office of National Statistics, comparing symptoms presented between 1 and 6 months after the index infection matched with the same months 1 year previously. We conducted logistic regression analysis, quoting odds ratios (ORs) with 95% CIs.

RESULTS

In total, 5.63% (416,505/7,396,702) and 1.83% (7623/416,505) of the patients had received a coded diagnosis of COVID-19 infection and diagnosis of, or referral for, long COVID, respectively. People with diagnosis or referral of long COVID had higher odds of presenting the prespecified symptoms after versus before COVID-19 infection (OR 2.66, 95% CI 2.46-2.88, for those with index community infection and OR 2.42, 95% CI 2.03-2.89, for those hospitalized). After an index community infection, patients were more likely to present with nonspecific symptoms (OR 3.44, 95% CI 3.00-3.95; P<.001) compared with after a hospital admission (OR 2.09, 95% CI 1.56-2.80; P<.001). Mental health sequelae were more strongly associated with index hospital infections (OR 2.21, 95% CI 1.64-2.96) than with index community infections (OR 1.36, 95% CI 1.21-1.53; P<.001). People presenting to primary care after hospital infection were more likely to be men (OR 1.43, 95% CI 1.25-1.64; P<.001), more socioeconomically deprived (OR 1.42, 95% CI 1.24-1.63; P<.001), and with higher multimorbidity scores (OR 1.41, 95% CI 1.26-1.57; P<.001) than those presenting after an index community infection. All-cause mortality in people with long COVID was associated with increasing age, male sex (OR 3.32, 95% CI 1.34-9.24; P=.01), and higher multimorbidity score (OR 2.11, 95% CI 1.34-3.29; P<.001). Vaccination was associated with reduced odds of mortality (OR 0.10, 95% CI 0.03-0.35; P<.001).

CONCLUSIONS

The low percentage of people recorded as having long COVID after COVID-19 infection reflects either low prevalence or underrecording. The characteristics and comorbidities of those presenting with long COVID after a community infection are different from those hospitalized. This study provides insights into the presentation of long COVID in primary care and implications for workload.

Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis

BACKGROUND

Following COVID-19, up to 40% of people have ongoing health problems, referred to as postacute COVID-19 or long COVID (LC). LC varies from a single persisting symptom to a complex multisystem disease. Research has flagged that this condition is underrecorded in primary care records, and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine-processable. An LC phenotype can underpin research into this condition.

OBJECTIVE

This study aims to develop a phenotype for LC to inform the epidemiology and future research into this condition. We compared clinical symptoms in people with LC before and after their index infection, recorded from March 1, 2020, to April 1, 2021. We also compared people recorded as having acute infection with those with LC who were hospitalized and those who were not.

METHODS

We used data from the Primary Care Sentinel Cohort (PCSC) of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. This network was recruited to be nationally representative of the English population. We developed an LC phenotype using our established 3-step ontological method: (1) ontological step (defining the reasoning process underpinning the phenotype, (2) coding step (exploring what clinical terms are available, and (3) logical extract model (testing performance). We created a version of this phenotype using Protégé in the ontology web language for BioPortal and using PhenoFlow. Next, we used the phenotype to compare people with LC (1) with regard to their symptoms in the year prior to acquiring COVID-19 and (2) with people with acute COVID-19. We also compared hospitalized people with LC with those not hospitalized. We compared sociodemographic details, comorbidities, and Office of National Statistics-defined LC symptoms between groups. We used descriptive statistics and logistic regression.

RESULTS

The long-COVID phenotype differentiated people hospitalized with LC from people who were not and where no index infection was identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7471 (1.74%, 95% CI 1.70-1.78) people were coded as having LC, 1009 (13.5%, 95% CI 12.7-14.3) had a hospital admission related to acute COVID-19, and 6462 (86.5%, 95% CI 85.7-87.3) were not hospitalized, of whom 2728 (42.2%) had no COVID-19 index date recorded. In addition, 1009 (13.5%, 95% CI 12.73-14.28) people with LC were hospitalized compared to 17,993 (4.5%, 95% CI 4.48-4.61; P<.001) with uncomplicated COVID-19.

CONCLUSIONS

Our LC phenotype enables the identification of individuals with the condition in routine data sets, facilitating their comparison with unaffected people through retrospective research. This phenotype and study protocol to explore its face validity contributes to a better understanding of LC.

Revising the Suspected-Cancer Guidelines: Impacts on Patients' Primary Care Contacts and Costs

OBJECTIVES

This study aimed to explore the impact of revising suspected-cancer referral guidelines on primary care contacts and costs.

METHODS

Participants had incident cancer (colorectal, n = 2000; ovary, n = 763; and pancreas, n = 597) codes in the Clinical Practice Research Datalink or England cancer registry. Difference-in-differences analyses explored guideline impacts on contact days and nonzero costs between the first cancer feature and diagnosis. Participants were controls ("old National Institute for Health and Care Excellence [NICE]") or "new NICE" if their index feature was introduced during guideline revision. Model assumptions were inspected visually and by falsification tests. Sensitivity analyses reclassified participants who subsequently presented with features in the original guidelines as "old NICE." For colorectal cancer, sensitivity analysis (n = 3481) adjusted for multimorbidity burden.

RESULTS

Median contact days and costs were, respectively, 4 (interquartile range [IQR] 2-7) and £117.69 (IQR £53.23-£206.65) for colorectal, 5 (IQR 3-9) and £156.92 (IQR £78.46-£272.29) for ovary, and 7 (IQR 4-13) and £230.64 (IQR £120.78-£408.34) for pancreas. Revising ovary guidelines may have decreased contact days (incidence rate ratio [IRR] 0.74; 95% confidence interval 0.55-1.00; P = .05) with unchanged costs, but parallel trends assumptions were violated. Costs decreased by 13% (equivalent to -£28.05, -£50.43 to -£5.67) after colorectal guidance revision but only in sensitivity analyses adjusting for multimorbidity. Contact days and costs remained unchanged after pancreas guidance revision.

CONCLUSIONS

The main analyses of symptomatic patients suggested that prediagnosis primary care costs remained unchanged after guidance revision for pancreatic cancer. For colorectal cancer, contact days and costs decreased in analyses adjusting for multimorbidity. Revising ovarian cancer guidelines may have decreased primary care contact days but not costs, suggesting increased resource-use intensity; nevertheless, there is evidence of confounding.

Interprofessional collaboration to support patients with social problems in general practice-a qualitative focus group study

BACKGROUND

Social problems of patients such as family or work-related conflicts as well as financial difficulties affect the individual health situation and the treatment of diseases in general practice. General practitioners (GPs) would like to have direct access to professionals in social care services. In Germany, there are many different social care facilities for people with a wide range of social problems. As the social and health care systems hardly interact collaborations between social professionals (SPs) and GPs are rare exceptions. This study explored perspectives of GPs regarding their patients with social problems in combination with the perspectives of SPs. Aim of this study was to explore how a systematic interprofessional collaboration between GPs and SPs could be realised.

METHODS

We carried out a participatory sequential qualitative study design consisting of two focus groups with GPs, two with SPs and two mixed-professional focus groups with GPs and SPs. The focus groups were conducted with semi-structured moderating guidelines and analysed with a qualitative content analysis approach using inductive and deductive categories.

RESULTS

GPs view themselves as the first point of contact for their patients' social problems. For persistent social problems, they expressed a desire for support and SPs were willing to provide this. We developed a stepped care implementation model for a systematic cooperation consisting of nine collaboration strategies. These strategies included: index or website of social care services, referrals to the social care system, using flyers and posters of social care services, direct contact/hotline to local social care services, participation in meetings of social care facilities, involving physician assistants, external social care advice service in GP rooms, implementation in education and training and access to volunteers.

CONCLUSIONS

Our stepped care implementation model for a systematic cooperation of GPs and SPs could be a feasible need- and resource-oriented approach for the collaborative care of patients with social problems to improve their medical treatment in most western healthcare systems. GPs and SPs are ready to generate the necessary evidence for policy makers in high quality RCTs.

Assessing Ethnic Inequalities in Diagnostic Interval of Common Cancers: A Population-Based UK Cohort Study

BACKGROUND

This study investigated ethnic differences in diagnostic interval (DI)-the period between initial primary care presentation and diagnosis.

METHODS

We analysed the primary care-linked data of patients who reported features of seven cancers (breast, lung, prostate, colorectal, oesophagogastric, myeloma, and ovarian) one year before diagnosis. Accelerated failure time (AFT) models investigated the association between DI and ethnicity, adjusting for age, sex, deprivation, and morbidity.

RESULTS

Of 126,627 eligible participants, 92.1% were White, 1.99% Black, 1.71% Asian, 1.83% Mixed, and 2.36% were of Other ethnic backgrounds. Considering all cancer sites combined, the median (interquartile range) DI was 55 (20-175) days, longest in lung [127, (42-265) days], and shortest in breast cancer [13 (13, 8-18) days]. DI for the Black and Asian groups was 10% (AFT ratio, 95%CI 1.10, 1.05-1.14) and 16% (1.16, 1.10-1.22), respectively, longer than for the White group. Site-specific analyses revealed evidence of longer DI in Asian and Black patients with prostate, colorectal, and oesophagogastric cancer, plus Black patients with breast cancer and myeloma, and the Mixed group with lung cancer compared with White patients. DI was shorter for the Other group with lung, prostate, myeloma, and oesophagogastric cancer than the White group.

CONCLUSION

We found limited and inconsistent evidence of ethnic differences in DI among patients who reported cancer features in primary care before diagnosis. Our findings suggest that inequalities in diagnostic intervals, where present, are unlikely to be the sole explanation for ethnic variations in cancer outcomes.

Construction and validation of a morbidity index based on the International Classification of Primary Care

OBJECTIVES

In epidemiological studies it is often necessary to describe morbidity. The aim of the present study is to construct and validate a morbidity index based on the International Classification of Primary Care (ICPC-2).

DESIGN AND SETTING

This is a cohort study based on linked data from national registries. An ICPC morbidity index was constructed based on a list of longstanding health problems in earlier published Scottish data from general practice and adapted to diagnostic ICPC-2 codes recorded in Norwegian general practice 2015 - 2017.

SUBJECTS

The index was constructed among Norwegian born people only (N = 4 509 382) and validated in a different population, foreign-born people living in Norway (N = 959 496).

MAIN OUTCOME MEASURES

Predictive ability for death in 2018 in these populations was compared with the Charlson index. Multiple logistic regression was used to identify morbidities with the highest odds ratios (OR) for death and predictive ability for different combinations of morbidities was estimated by the area under receiver operating characteristic curves (AUC).

RESULTS

An index based on 18 morbidities was found to be optimal, predicting mortality with an AUC of 0.78, slightly better than the Charlson index (AUC 0.77). External validation in a foreign-born population yielded an AUC of 0.76 for the ICPC morbidity index and 0.77 for the Charlson index.

CONCLUSIONS

The ICPC morbidity index performs equal to the Charlson index and can be recommended for use in data materials collected in primary health care.Key pointsThis is the first morbidity index based on the International Classification of Primary Care, 2^nd edition (ICPC-2)It predicted mortality equal to the Charlson index and validated acceptably in a different populationThe ICPC morbidity index can be used as an adjustment variable in epidemiological research in primary care databases.

Associations between multiple long-term conditions and mortality in diverse ethnic groups

BACKGROUND

Multiple conditions are more prevalent in some minoritised ethnic groups and are associated with higher mortality rate but studies examining differential mortality once conditions are established is US-based. Our study tested whether the association between multiple conditions and mortality varies across ethnic groups in England.

METHODS AND FINDINGS

A random sample of primary care patients from Clinical Practice Research Datalink (CPRD) was followed from 1st January 2015 until 31st December 2019. Ethnicity, usually self-ascribed, was obtained from primary care records if present or from hospital records. Long-term conditions were counted from a list of 32 that have previously been associated with greater primary care, hospital admissions, or mortality risk. Cox regression models were used to estimate mortality by count of conditions, ethnicity and their interaction, with adjustment for age and sex for 532,059 patients with complete data. During five years of follow-up, 5.9% of patients died. Each additional condition at baseline was associated with increased mortality. The direction of the interaction of number of conditions with ethnicity showed a statistically higher mortality rate associated with long-term conditions in Pakistani, Black African, Black Caribbean and Other Black ethnic groups. In ethnicity-stratified models, the mortality rate per additional condition at age 50 was 1.33 (95% CI 1.31,1.35) for White ethnicity, 1.43 (95% CI 1.26,1.61) for Black Caribbean ethnicity and 1.78 (95% CI 1.41,2.24) for Other Black ethnicity.

CONCLUSIONS

The higher mortality rate associated with having multiple conditions is greater in minoritised compared with White ethnic groups. Research is now needed to identify factors that contribute to these inequalities. Within the health care setting, there may be opportunities to target clinical and self-management support for people with multiple conditions from minoritised ethnic groups.

Do presenting symptoms, use of pre-diagnostic endoscopy and risk of emergency cancer diagnosis vary by comorbidity burden and type in patients with colorectal cancer?

BACKGROUND

Cancer patients often have pre-existing comorbidities, which can influence timeliness of cancer diagnosis. We examined symptoms, investigations and emergency presentation (EP) risk among colorectal cancer (CRC) patients by comorbidity status.

METHODS

Using linked cancer registration, primary care and hospital records of 4836 CRC patients (2011-2015), and multivariate quantile and logistic regression, we examined variations in specialist investigations, diagnostic intervals and EP risk.

RESULTS

Among colon cancer patients, 46% had at least one pre-existing hospital-recorded comorbidity, most frequently cardiovascular disease (CVD, 18%). Comorbid versus non-comorbid cancer patients more frequently had records of anaemia (43% vs 38%), less frequently rectal bleeding/change in bowel habit (20% vs 27%), and longer intervals from symptom-to-first relevant test (median 136 vs 74 days). Comorbid patients were less likely investigated with colonoscopy/sigmoidoscopy, independently of symptoms (adjusted OR = 0.7[0.6, 0.9] for Charlson comorbidity score 1-2 and OR = 0.5 [0.4-0.7] for score 3+ versus 0. EP risk increased with comorbidity score 0, 1, 2, 3+: 23%, 35%, 33%, 47%; adjusted OR = 1.8 [1.4, 2.2]; 1.7 [1.3, 2.3]; 3.0 [2.3, 4.0]) and for patients with CVD (adjusted OR = 2.0 [1.5, 2.5]).

CONCLUSIONS

Comorbid individuals with as-yet-undiagnosed CRC often present with general rather than localising symptoms and are less likely promptly investigated with colonoscopy/sigmoidoscopy. Comorbidity is a risk factor for diagnostic delay and has potential, additionally to symptoms, as risk-stratifier for prioritising patients needing prompt assessment to reduce EP.

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies

BACKGROUND

Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample.

METHODS AND FINDINGS

These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included.

CONCLUSIONS

In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Background

Multimorbidity represents a global health challenge, which requires a more global understanding of multimorbidity patterns and trends. However, the majority of studies completed to date have often relied on self-reported conditions, and a simultaneous assessment of the entire spectrum of chronic disease co-occurrence, especially in developing regions, has not yet been performed.

Objective

We attempted to provide a multidimensional approach to understand the full spectrum of chronic disease co-occurrence among general inpatients in southwest China, in order to investigate multimorbidity patterns and temporal trends, and assess their age and sex differences.

Methods

We conducted a retrospective cohort analysis based on 8.8 million hospital discharge records of about 5.0 million individuals of all ages from 2015 to 2019 in a megacity in southwest China. We examined all chronic diagnoses using the ICD-10 (International Classification of Diseases, 10th revision) codes at 3 digits and focused on chronic diseases with ≥1% prevalence for each of the age and sex strata, which resulted in a total of 149 and 145 chronic diseases in males and females, respectively. We constructed multimorbidity networks in the general population based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. Then, we divided the networks into communities and assessed their temporal trends.

Results

The results showed complex interactions among chronic diseases, with more intensive connections among males and inpatients ≥40 years old. A total of 9 chronic diseases were simultaneously classified as central diseases, hubs, and bursts in the multimorbidity networks. Among them, 5 diseases were common to both males and females, including hypertension, chronic ischemic heart disease, cerebral infarction, other cerebrovascular diseases, and atherosclerosis. The earliest leaps (degree leaps ≥6) appeared at a disorder of glycoprotein metabolism that happened at 25-29 years in males, about 15 years earlier than in females. The number of chronic diseases in the community increased over time, but the new entrants did not replace the root of the community.

Conclusions

Our multimorbidity network analysis identified specific differences in the co-occurrence of chronic diagnoses by sex and age, which could help in the design of clinical interventions for inpatient multimorbidity.

Developing the Total Health Profile, a Generalizable Unified Set of Multimorbidity Risk Scores Derived From Machine Learning for Broad Patient Populations: Retrospective Cohort Study

BACKGROUND

Multimorbidity clinical risk scores allow clinicians to quickly assess their patients' health for decision making, often for recommendation to care management programs. However, these scores are limited by several issues: existing multimorbidity scores (1) are generally limited to one data group (eg, diagnoses, labs) and may be missing vital information, (2) are usually limited to specific demographic groups (eg, age), and (3) do not formally provide any granularity in the form of more nuanced multimorbidity risk scores to direct clinician attention.

OBJECTIVE

Using diagnosis, lab, prescription, procedure, and demographic data from electronic health records (EHRs), we developed a physiologically diverse and generalizable set of multimorbidity risk scores.

METHODS

Using EHR data from a nationwide cohort of patients, we developed the total health profile, a set of six integrated risk scores reflecting five distinct organ systems and overall health. We selected the occurrence of an inpatient hospital visitation over a 2-year follow-up window, attributable to specific organ systems, as our risk endpoint. Using a physician-curated set of features, we trained six machine learning models on 794,294 patients to predict the calibrated probability of the aforementioned endpoint, producing risk scores for heart, lung, neuro, kidney, and digestive functions and a sixth score for combined risk. We evaluated the scores using a held-out test cohort of 198,574 patients.

RESULTS

Study patients closely matched national census averages, with a median age of 41 years, a median income of $66,829, and racial averages by zip code of 73.8% White, 5.9% Asian, and 11.9% African American. All models were well calibrated and demonstrated strong performance with areas under the receiver operating curve (AUROCs) of 0.83 for the total health score (THS), 0.89 for heart, 0.86 for lung, 0.84 for neuro, 0.90 for kidney, and 0.83 for digestive functions. There was consistent performance of this scoring system across sexes, diverse patient ages, and zip code income levels. Each model learned to generate predictions by focusing on appropriate clinically relevant patient features, such as heart-related hospitalizations and chronic hypertension diagnosis for the heart model. The THS outperformed the other commonly used multimorbidity scoring systems, specifically the Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Index (ECI) overall (AUROCs: THS=0.823, CCI=0.735, ECI=0.649) as well as for every age, sex, and income bracket. Performance improvements were most pronounced for middle-aged and lower-income subgroups. Ablation tests using only diagnosis, prescription, social determinants of health, and lab feature groups, while retaining procedure-related features, showed that the combination of feature groups has the best predictive performance, though only marginally better than the diagnosis-only model on at-risk groups.

CONCLUSIONS

Massive retrospective EHR data sets have made it possible to use machine learning to build practical multimorbidity risk scores that are highly predictive, personalizable, intuitive to explain, and generalizable across diverse patient populations.