The risk of acute kidney injury in colorectal cancer survivors: an english population-based matched cohort study.

BACKGROUND: Colorectal cancer survival has improved in recent decades but there are concerns that survivors may develop kidney problems due to adverse effects of cancer treatment or complications of the cancer itself. We quantified the risk of acute kidney injury (AKI) in colorectal cancer survivors compared to people with no prior cancer. METHODS: Retrospective matched cohort study using electronic health record primary care data from the Clinical Practice Research Datalink GOLD linked to hospital data in England (HES-APC). Individuals with colorectal cancer between 1997-2018 were individually matched on age, sex, and GP practice to people with no prior cancer. We used Cox models to estimate hazard ratios for an incident hospital diagnosis of AKI in colorectal cancer survivors compared to individuals without cancer, overall and stratified by time since diagnosis adjusted for other individual-level factors (adj-HR). RESULTS: Twenty thousand three hundred forty colorectal cancer survivors were matched to 100,058 cancer-free individuals. Colorectal cancer survivors were at increased risk of developing AKI compared to people without cancer (adj-HR = 2.16; 95%CI 2.05-2.27). The HR was highest in the year after diagnosis (adj-HR 7.47, 6.66-8.37), and attenuated over time, but there was still increased AKI risk > 5 years after diagnosis (adj-HR = 1.26, 1.17-1.37). The association between colorectal cancer and AKI was greater for younger people, men, and those with pre-existing chronic kidney disease. CONCLUSIONS: Colorectal cancer survivors were at increased risk of AKI for several years after cancer diagnosis, suggesting a need to prioritise monitoring, prevention, and management of kidney problems in this group of cancer survivors.

Factors associated with excess all-cause mortality in the first wave of the COVID-19 pandemic in the UK: A time series analysis using the Clinical Practice Research Datalink.

BACKGROUND: Excess mortality captures the total effect of the Coronavirus Disease 2019 (COVID-19) pandemic on mortality and is not affected by misspecification of cause of death. We aimed to describe how health and demographic factors were associated with excess mortality during, compared to before, the pandemic. METHODS AND FINDINGS: We analysed a time series dataset including 9,635,613 adults (≥40 years old) registered at United Kingdom general practices contributing to the Clinical Practice Research Datalink. We extracted weekly numbers of deaths and numbers at risk between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during Wave 1 of the UK pandemic (5 March to 27 May 2020) compared to the prepandemic period was estimated using seasonally adjusted negative binomial regression models. Relative rates (RRs) of death for a range of factors were estimated before and during Wave 1 by including interaction terms. We found that all-cause mortality increased by 43% (95% CI 40% to 47%) during Wave 1 compared with prepandemic. Changes to the RR of death associated with most sociodemographic and clinical characteristics were small during Wave 1 compared with prepandemic. However, the mortality RR associated with dementia markedly increased (RR for dementia versus no dementia prepandemic: 3.5, 95% CI 3.4 to 3.5; RR during Wave 1: 5.1, 4.9 to 5.3); a similar pattern was seen for learning disabilities (RR prepandemic: 3.6, 3.4 to 3.5; during Wave 1: 4.8, 4.4 to 5.3), for black or South Asian ethnicity compared to white, and for London compared to other regions. Relative risks for morbidities were stable in multiple sensitivity analyses. However, a limitation of the study is that we cannot assume that the risks observed during Wave 1 would apply to other waves due to changes in population behaviour, virus transmission, and risk perception. CONCLUSIONS: The first wave of the UK COVID-19 pandemic appeared to amplify baseline mortality risk to approximately the same relative degree for most population subgroups. However, disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.

UK prevalence of underlying conditions which increase the risk of severe COVID-19 disease: a point prevalence study using electronic health records.

BACKGROUND: Characterising the size and distribution of the population at risk of severe COVID-19 is vital for effective policy and planning. Older age, and underlying health conditions, are associated with higher risk of death from COVID-19. This study aimed to describe the population at risk of severe COVID-19 due to underlying health conditions across the United Kingdom. METHODS: We used anonymised electronic health records from the Clinical Practice Research Datalink GOLD to estimate the point prevalence on 5 March 2019 of the at-risk population following national guidance. Prevalence for any risk condition and for each individual condition is given overall and stratified by age and region with binomial exact confidence intervals. We repeated the analysis on 5 March 2014 for full regional representation and to describe prevalence of underlying health conditions in pregnancy. We additionally described the population of cancer survivors, and assessed the value of linked secondary care records for ascertaining COVID-19 at-risk status. RESULTS: On 5 March 2019, 24.4% of the UK population were at risk due to a record of at least one underlying health condition, including 8.3% of school-aged children, 19.6% of working-aged adults, and 66.2% of individuals aged 70 years or more. 7.1% of the population had multimorbidity. The size of the at-risk population was stable over time comparing 2014 to 2019, despite increases in chronic liver disease and diabetes and decreases in chronic kidney disease and current asthma. Separately, 1.6% of the population had a new diagnosis of cancer in the past 5 y. CONCLUSIONS: The population at risk of severe COVID-19 (defined as either aged ≥70 years, or younger with an underlying health condition) comprises 18.5 million individuals in the UK, including a considerable proportion of school-aged and working-aged individuals. Our national estimates broadly support the use of Global Burden of Disease modelled estimates in other countries. We provide age- and region- stratified prevalence for each condition to support effective modelling of public health interventions and planning of vaccine resource allocation. The high prevalence of health conditions among older age groups suggests that age-targeted vaccination strategies may efficiently target individuals at higher risk of severe COVID-19.

Medium and long-term risks of specific cardiovascular diseases in survivors of 20 adult cancers: a population-based cohort study using multiple linked UK electronic health records databases.

BACKGROUND: The past few decades have seen substantial improvements in cancer survival, but concerns exist about long-term cardiovascular disease risk in survivors. Evidence is scarce on the risks of specific cardiovascular diseases in survivors of a wide range of cancers to inform prevention and management. In this study, we used large-scale electronic health records data from multiple linked UK databases to address these evidence gaps. METHODS: For this population-based cohort study, we used linked primary care, hospital, and cancer registry data from the UK Clinical Practice Research Datalink to identify cohorts of survivors of the 20 most common cancers who were 18 years or older and alive 12 months after diagnosis and controls without history of cancer, matched for age, sex, and general practice. We compared risks for a range of cardiovascular disease outcomes using crude and adjusted Cox models. We fitted interactions to investigate effect modification, and flexible parametric survival models to estimate absolute excess risks over time. FINDINGS: Between Jan 1, 1990, and Dec 31, 2015, 126 120 individuals with a diagnosis of a cancer of interest still being followed up at least 1 year later were identified and matched to 630 144 controls. After exclusions, 108 215 cancer survivors and 523 541 controls were included in the main analyses. Venous thromboembolism risk was elevated in survivors of 18 of 20 site-specific cancers compared with that of controls; adjusted hazard ratios (HRs) ranged from 1·72 (95% CI 1·57-1·89) in patients after prostate cancer to 9·72 (5·50-17·18) after pancreatic cancer. HRs decreased over time, but remained elevated more than 5 years after diagnosis. We observed increased risks of heart failure or cardiomyopathy in patients after ten of 20 cancers, including haematological (adjusted HR 1·94, 1·66-2·25, with non-Hodgkin lymphoma; 1·77, 1·50-2·09, with leukaemia; and 3·29, 2·59-4·18, with multiple myeloma), oesophageal (1·96, 1·46-2·64), lung (1·82, 1·52-2·17) kidney (1·73, 1·38-2·17) and ovarian (1·59, 1·19-2·12). Elevated risks of arrhythmia, pericarditis, coronary artery disease, stroke, and valvular heart disease were also observed for multiple cancers, including haematological malignancies. HRs for heart failure or cardiomyopathy and venous thromboembolism were greater in patients without previous cardiovascular disease and in younger patients. However, absolute excess risks were generally greater with increasing age. Increased risks of these outcomes seemed most pronounced in patients who had received chemotherapy. INTERPRETATION: Survivors of most site-specific cancers had increased medium-term to long-term risk for one or more cardiovascular diseases compared with that for the general population, with substantial variations between cancer sites. FUNDING: Wellcome Trust and Royal Society.

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility.

PURPOSE: To provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiology and healthcare research. Planning a new data linkage requires careful evaluation to weigh the resources required with the potential overall benefits. METHODS: In response to an International Society for Pharmacoepidemiology (ISPE) call for manuscripts, a working group comprised of members from academic, industry, and government determined priority content areas; appropriateness and feasibility of data linkage was selected. Within this topic, scientific and operational considerations were determined, reviewed, and formulated into key areas, and translated into 12 consensus recommendations. RESULTS: Guidance for feasibility assessment was categorized into five key areas: (1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. Within these key areas, recommendations to consider prior to initiation were developed to evaluate suitability of the linkage to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections. When creating novel linked datasets, researchers must assess the feasibility of both scientific (data quality and linkage methods) and operational (access, data use and transfer, governance, and cost) aspects. CONCLUSIONS: The data linkage feasibility assessment considerations outlined can be used as a guide when designing sustainable linked data resources to generate actionable evidence in healthcare research. These recommendations were constructed for wide applicability and can be adapted depending on the geographic, structural, and data components of the linkage.

Body mass index and Hodgkin's lymphoma: UK population-based cohort study of 5.8 million individuals.

Previous epidemiological studies describe a positive association between body mass index (BMI) and Hodgkin's lymphoma, mainly in obese vs. normal weight individuals. We examined the shape of this relationship in individuals aged 16 years or older, using primary care data from the United Kingdom's Clinical Practice Research Datalink. Cox models were fitted with linear, non-linear (spline) and categorical BMI. Models were adjusted for potential confounders and effect modification was investigated. Five point eight two million patients were included, 927 of whom developed Hodgkin's lymphoma during 41.6 million years of follow-up. Each 5 kg/m2 increase in BMI was associated with a 10% increase in Hodgkin's lymphoma (95% confidence intervals: 2-19). Analysis of non-linearity suggested a J-shaped association with incidence increasing with BMI above 24.2 kg/m2. Seven point four per cent of adult Hodgkin's lymphoma cases were estimated to be attributable to excess weight. Our findings suggest a pattern of increasing risk beyond the World Health Organisation healthy weight category in the general population.

Approach to record linkage of primary care data from Clinical Practice Research Datalink to other health-related patient data: overview and implications.

Record linkage is increasingly used to expand the information available for public health research. An understanding of record linkage methods and the relevant strengths and limitations is important for robust analysis and interpretation of linked data. Here, we describe the approach used by Clinical Practice Research Datalink (CPRD) to link primary care data to other patient level datasets, and the potential implications of this approach for CPRD data analysis. General practice electronic health record software providers separately submit de-identified data to CPRD and patient identifiers to NHS Digital, excluding patients who have opted-out from contributing data. Data custodians for external datasets also send patient identifiers to NHS Digital. NHS Digital uses identifiers to link the datasets using an 8-stage deterministic methodology. CPRD subsequently receives a de-identified linked cohort file and provides researchers with anonymised linked data and metadata detailing the linkage process. This methodology has been used to generate routine primary care linked datasets, including data from Hospital Episode Statistics, Office for National Statistics and National Cancer Registration and Analysis Service. 10.6 million (M) patients from 411 English general practices were included in record linkage in June 2018. 9.1M (86%) patients were of research quality, of which 8.0M (88%) had a valid NHS number and were eligible for linkage in the CPRD standard linked dataset release. Linking CPRD data to other sources improves the range and validity of research studies. This manuscript, together with metadata generated on match strength and linkage eligibility, can be used to inform study design and explore potential linkage-related selection and misclassification biases.

Limitations for health research with restricted data collection from UK primary care.

PURPOSE: UK primary care provides a rich data source for research. The impact of proposed data collection restrictions is unknown. This study aimed to assess the impact of restricting the scope of electronic health record (EHR) data collection on the ability to conduct research. The study estimated the consequences of restricted data collection on published Clinical Practice Research Datalink studies from high impact journals or referenced in clinical guidelines. METHODS: A structured form was used to systematically analyse the extent to which individual studies would have been possible using a database with data collection restrictions in place: (1) retrospective collection of specified diseases only; (2) retrospective collection restricted to a 6- or 12-year period; (3) prospective and retrospective collection restricted to non-sensitive data. Outcomes were categorised as unfeasible (not reproducible without major bias); compromised (feasible with design modification); or unaffected. RESULTS: Overall, 91% studies were compromised with all restrictions in place; 56% studies were unfeasible even with design modification. With restrictions on diseases alone, 74% studies were compromised; 51% were unfeasible. Restricting collection to 6/12 years had a major impact, with 67 and 22% of studies compromised, respectively. Restricting collection of sensitive data had a lesser but marked impact with 10% studies compromised. CONCLUSION: EHR data collection restrictions can profoundly reduce the capacity for public health research that underpins evidence-based medicine and clinical guidance. National initiatives seeking to collect EHRs should consider the implications of restricting data collection on the ability to address vital public health questions.

Statin use in cancer survivors versus the general population: cohort study using primary care data from the UK clinical practice research datalink.

BACKGROUND: Cancer survivors may be at increased risk of cardiovascular diseases, but little is known about whether prescribing guidelines for the primary prevention of cardiovascular disease are adequately implemented in these patients. We compared levels of statin initiation and cessation among cancer survivors compared to the general population to determine differences in uptake of pharmaceutical cardiovascular risk prevention measures in these groups. METHODS: The study population included individuals aged ≥40 during 2005-13 within the UK Clinical Practice Research Datalink primary care database. Within this population we identified cancer survivors who were alive and under follow-up at least 1 year after diagnosis, and controls with no cancer history. Follow-up time prior to cancer diagnosis was included in the control cohort. Using logistic regression, we compared these groups with respect to uptake of statins within 1 month of a first high recorded cardiovascular risk score. Then, we used Cox modelling to compare persistence on statin therapy (time to statin cessation) between cancer survivors and controls from the main study population who had initiated on a statin. RESULTS: Among 4202 cancer survivors and 113,035 controls with a record indicating a high cardiovascular risk score, 23.0% and 23.5% respectively initiated a statin within 1 month (adjusted odds ratio 0.98 [91.8-1.05], p = 0.626). Cancer survivors appeared more likely to discontinue statin treatment than controls (adjusted hazard ratio 1.07 [1.01-1.12], p = 0.02). This greater risk of discontinuing was only evident after the first year of therapy (p-interaction < 0.001). INTERPRETATION: Although cardiovascular risk is thought to be higher in cancer survivors compared to the general population, cancer survivors were no more likely to receive statins, and marginally more likely to cease long-term therapy, than general population controls. There may be an opportunity to mitigate the suspected higher cardiovascular risk in the growing population of cancer survivors by improving uptake of lipid-lowering treatment and persistence on therapy.

NHS National Data Opt-Outs: trends and potential consequences for health data research.

BACKGROUND: The English National Health Service (NHS) data opt-out allows people to prevent use of their health data for purposes other than direct care. In 2021, the number of opt-outs increased in response to government-led proposals to create a centralised pseudonymised primary care record database. AIM: To describe the potential impact of NHS National Data Opt-outs in 2021 on health data research. DESIGN & SETTING: We conducted a descriptive analysis of opt-outs using publicly available data and discuss the potential consequences on research. METHOD: Trends in opt-outs in England were described by age, sex and region. Using a hypothetical study, we explored statistical and epidemiological implications of opt-outs. RESULTS: During the lead up to a key government-led deadline for registering opt-outs (from 31 May 2021 to 30 June 2021), 1,339,862 national data opt-outs were recorded; increasing the percentage of opt-outs in England from 2.77% to 4.97% of the population. Amongst females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared to 75% in males (2.51%-4.41%). Across age-groups, the highest relative increase was among people aged 40-49 years which rose from 2.89% to 6.04%. Considerable geographical variation was not clearly related to deprivation. Key research consequences of opt-outs include reductions in sample size and unpredictable distortion of observed measures of the frequency of health events or associations between these events. CONCLUSIONS: Opt-out rates varied by age, sex and place. The impact of this and variation by other characteristics on research is not quantifiable. Potential effects of opt-outs on research and consequences for health policies based on this research must be considered when creating future opt-out solutions.

Risk of fractures in half a million survivors of 20 cancers: a population-based matched cohort study using linked English electronic health records.

BACKGROUND: A history of multiple myeloma, prostate cancer, and breast cancer has been associated with adverse bone health, but associations across a broader range of cancers are unclear. We aimed to compare the risk of any bone fracture and major osteoporotic fractures in survivors of a wide range of cancers versus cancer-free individuals. METHODS: In this population-based matched cohort study, we used electronic health records from the UK Clinical Practice Research Datalink linked to hospital data. We included adults (aged ≥18 years) eligible for linkage, and we restricted the study start to Jan 2, 1998, onwards and applied administrative censoring on Jan 31, 2020. The cancer survivor group included survivors of the 20 most common cancers. Each individual with cancer was matched (age, sex, and general practice) to up to five controls (1:5) who were cancer-free. The primary outcomes were any bone fracture and any major osteoporotic fracture (pelvic, hip, wrist, spine, or proximal humeral fractures) occurring more than 1 year after index date (ie, the diagnosis date of the matched individual with cancer). We used Cox regression models, adjusted for shared risk factors, to estimate associations between cancer survivorship and bone fractures. FINDINGS: 578 160 adults with cancer diagnosed in 1998-2020 were matched to 3 226 404 cancer-free individuals. Crude incidence rates of fractures in cancer survivors ranged between 8·39 cases (95% CI 7·45-9·46) per 1000 person-years for thyroid cancer and 21·62 cases (20·18-23·18) per 1000 person-years for multiple myeloma. Compared with cancer-free individuals, the risk of any bone fracture was increased in 15 of 20 cancers, and of major osteoporotic fractures in 17 of 20 cancers. Effect sizes varied: adjusted hazard ratios (HRs) were largest for multiple myeloma (1·94, 95% CI 1·77-2·13) and prostate cancer (1·43, 1·39-1·47); HRs in the range 1·20-1·50 were seen for stomach, liver, pancreas, lung, breast, kidney, and CNS cancers; smaller associations (HR <1·20) were observed for malignant melanoma, non-Hodgkin lymphoma, leukaemia, and oesophageal, colorectal, and cervical cancers. Increased risks of major osteoporotic fracture were noted most substantially in multiple myeloma (2·25, 1·96-2·58) and CNS (2·12, 1·56-2·87), liver (1·62, 1·01-2·61), prostate (1·60, 1·53-1·67), and lung cancers (1·60, 1·44-1·77). Effect sizes tended to reduce over time since diagnosis but remained elevated for more than 5 years in several cancers, such as multiple myeloma and stomach, lung, breast, prostate, and CNS cancers. INTERPRETATION: Survivors of most types of cancer were at increased risk of bone fracture for several years after cancer, with variation by cancer type. These findings can help to inform mitigation and prevention strategies. FUNDING: Wellcome Trust.

Severe COVID-19 outcomes by cardiovascular risk profile in England in 2020: a population-based cohort study.

Background: While cardiovascular disease (CVD) is a risk factor for severe COVID-19, the association between predicted cardiovascular risk and severe COVID-19 among people without diagnosed CVD is unclear. Methods: We carried out historical, population-based cohort studies among adults aged 40-84 years in England using linked data from the Clinical Practice Research Datalink. Individuals were categorized into: existing CVD, raised cardiovascular risk (defined using QRISK3 score ≥10%) and low risk (QRISK3 score <10%) at 12/03/2020. We described incidence and severe outcomes of COVID-19 (deaths, intensive care unit [ICU] admissions, hospitalisations, major adverse cardiovascular events [MACE]) for each group. Among those with a COVID-19 record to 31/12/2020, we re-classified cardiovascular risk at infection and assessed the risk of severe outcomes using multivariable Cox regression with complete case analysis. We repeated analyses using hypertension to define raised cardiovascular risk. Findings: Among 6,059,055 individuals, 741,913 (12.2%) had established CVD, 1,929,627 (31.8%) had a QRISK3 score ≥10% and 3,387,515 (55.9%) had a QRISK3 score <10%. Marked gradients were seen in the incidence of all severe COVID-19 outcomes by cardiovascular risk profile. Among those with COVID-19 (N = 146,760), there was a strong association between raised QRISK3 score and death: adjusted hazard ratio [aHR] 8.77 (7.62-10.10), N = 97,725, which remained present, though attenuated in age-stratified results. Risks of other outcomes were also higher among those with raised QRISK3 score: aHR 3.66 (3.18-4.21) for ICU admissions, 3.38 (3.22-3.56) for hospitalisations, 5.43 (4.44-6.64) for MACE. When raised cardiovascular risk was redefined by hypertension status, only the association with MACE remained: aHR 1.49 (1.20-1.85), N = 57,264. Interpretation: Individuals without pre-existing CVD but with raised cardiovascular risk (by QRISK3 score) were more likely to experience severe COVID-19 outcomes and should be prioritised for prevention and treatment. Addressing cardiovascular risk factors could improve COVID-19 outcomes. Funding: BMA Foundation for Medical Research/Rosetrees Trust, Wellcome, BHF.

Acute Cardiovascular Events After COVID-19 in England in 2020: A Self-Controlled Case Series Study.

Purpose: To assess the risk of incident cardiovascular outcomes after COVID-19 by level of cardiovascular risk in waves one and two of the pandemic in England in 2020. Patients and methods: We conducted a self-controlled case-series study among adults aged 40-84 years with no pre-existing cardiovascular disease using linked data from the Clinical Practice Research Datalink. We generated season-adjusted incidence ratios (IRs) for first acute cardiovascular event after SARS-CoV-2 infection compared with baseline time before and >91 days after infection. We used composite and individual acute cardiovascular event outcomes including myocardial infarction, major ventricular arrhythmia, left ventricular heart failure, and ischemic stroke. We stratified by cardiovascular risk, using diagnosed hypertension and QRISK3 predicted risk, and by wave one and two of the pandemic. Results: We included 1762 individuals, 76.6% had a QRISK3 score ≥10% and 59.4% had hypertension. The risk of any cardiovascular event was elevated in the 1-7 days after infection (IR 7.14 [95% CI 6.06-8.41]) and, while the effect size tapered, the risk remained for 15-28 days after infection (1.74 [1.33-2.26]). Risks were similar for individual event type, differing by level of cardiovascular risk, and in wave one and two of the pandemic. . Conclusion: SARS-CoV-2 infection is associated with early elevations in the risk of first acute cardiovascular event, across cardiovascular risk levels and in both wave one and two of the pandemic. Prevention of COVID-19 is important to avert cardiovascular complications.

Cancer history as a predictor in cardiovascular risk scores: a primary care cohort study.

BACKGROUND: Cardiovascular risks are raised in cancer survivors but cancer history is not included in cardiovascular risk scores that inform preventive decisions. AIM: To assess whether cancer diagnosis should be included in cardiovascular risk scores. DESIGN AND SETTING: Cohort study using data from English general practices linked to hospital, cancer registration, and death registration data from 1990 to 2015. METHOD: Adults alive 1 year after a first cancer diagnosis and age, sex, general practice, and calendar- time matched cancer-free individuals were included. Individuals with <2 years of follow-up before index, recent statin prescriptions, or pre-existing coronary heart or cerebrovascular disease were excluded. Cox proportional hazard models used to develop QRISK3 scores were replicated with added cancer history variables. Whether independent hazard ratios for these variables met thresholds for inclusion in QRISK3 (>10% relative difference with P<0.01) was assessed. RESULTS: In total, 81 420 cancer survivors and 413 547 cancer-free individuals were followed for a median 5.2 years (interquartile range [IQR] 2.8- 9.1) and 6.3 years (IQR 3.5-10.2), respectively. Including a 1-year cancer survivorship variable in a QRISK3-based model met the threshold for inclusion for males (independent hazard ratio [iHR] 1.16, 95% confidence interval [CI] = 1.11 to 1.20, P<0.001) but not females (iHR 1.07, 95% CI = 1.01 to 1.14, P = 0.02). When including cancer type, the threshold was met for both sexes with history of haematological cancer (males: iHR 1.27, 95% CI = 1.16 to 1.40, P <0.001; females: iHR 1.59, 95% CI = 1.32 to 1.91, P<0.001) and for males but not females with history of solid cancers (males: iHR 1.13, 95% CI = 1.08 to 1.18, P <0.001; females: iHR 1.04, 95% CI = 0.98 to 1.10, P = 0.19). CONCLUSION: Developers should consider including cancer history variables in future cardiovascular risk models.

Does Cardiovascular Mortality Overtake Cancer Mortality During Cancer Survivorship?: An English Retrospective Cohort Study.

Background: Cancer survivors have a higher risk for developing cardiovascular diseases than the general population. Objectives: The aim of this study was to investigate whether cardiovascular mortality overtakes cancer-specific mortality during cancer survivorship and, if so, at what point cardiovascular disease becomes the dominant cause of death. Methods: This cohort study used linked English electronic health records, including death registration data. The study population included 104,028 adults ≥40 years of age whose first cancer diagnosis was for 1 of 9 common cancers and who were alive and followed up at least 1 year after diagnosis. Age-stratified mortality rates were estimated from cardiovascular disease or cancer by predicting from Poisson models incorporating categorical age at diagnosis and time since diagnosis. Where cardiovascular disease mortality overtook cancer mortality, the crossover point was estimated using interpolation. Results: Mortality from cardiovascular causes overtook mortality due to the primary cancer at 2 to 11 years after cancer diagnosis in survivors of all 9 cancer types ≥80 years of age at diagnosis and after 5 to 17 years in survivors of 7 cancer types 60 to 79 years of age at diagnosis. Cardiovascular mortality overtook all cancer mortality for 6 and 2 cancer sites in the ≥80-year and 60- to 79-year age groups, respectively, over a longer time period. Cardiovascular mortality did not overtake cancer mortality during the observation period in patients aged 40 to 59 years, except among survivors of uterine cancer. Conclusions: In older survivors of 9 common cancers, cardiovascular mortality becomes dominant over mortality from the primary cancer, though not always over total cancer mortality, as time passes since cancer diagnosis.

The importance of blood pressure thresholds versus predicted cardiovascular risk on subsequent rates of cardiovascular disease: a cohort study in English primary care.

BACKGROUND: For five decades, blood pressure lowering treatment has been recommended for patients with hypertension (currently defined as blood pressure of ≥140/90 mm Hg). In the past 20 years, guidelines for treatment began incorporating predicted absolute cardiovascular disease risk (predicted risk) and reducing blood pressure thresholds. The blood pressure threshold at which to start treatment has become a secondary consideration in some countries. We aimed to provide descriptive data to assess the relative importance of blood pressure thresholds versus predicted risk on the subsequent rate of cardiovascular disease to inform treatment decisions. METHODS: In this English population-based cohort study, we used linked data from the Clinical Practice Research Datalink (CPRD) GOLD, Hospital Episode Statistics Admitted Patient Care, and the Office for National Statistics mortality data, and area-based deprivation indices (Townsend scores). Eligible patients were aged 30-79 years on Jan 1, 2011 (cohort entry date) and could be linked to hospital, mortality, and deprivation data. Patients were followed up until death, end of CPRD follow-up, or Nov 31, 2018. We examined three outcomes: cardiovascular disease, markers of potential target organ damage, and incident dementia without a known cause. The rate of each outcome was estimated and stratified by systolic blood pressure and predicted 10-year risk of cardiovascular disease (QRISK2 algorithm). FINDINGS: Between Jan 1, 2011, and Nov 31, 2018, 1 098 991 patients were included in the cohort and followed up for a median of 4·3 years (IQR 2·6-6·0; total follow-up of 4·6 million person-years). Median age at entry was 52 years (IQR 42-62) and 629 711 (57·3%) patients were female. There were 51 996 cardiovascular disease events and the overall rate of cardiovascular disease was 11·2 per 1000 person-years (95% CI 11·1-11·3). Median QRISK2 10-year predicted risk was 4·6% (IQR 1·4-12·0) and mean systolic blood pressure before cohort entry was 129·1 mm Hg (SD 15·7). Within strata of predicted risk, the effect of increasing systolic blood pressure on outcomes was small. For example, in the group with 10·0-19·9% predicted risk, rates of all cardiovascular disease rose from 20·1 to 23·6 per 1000 person-years between systolic blood pressures less than 110 mm Hg and 180 and higher mm Hg. But among patients with systolic blood pressure 140·0-149·9 mm Hg, rates rose from 6·9 to 52·3 per 1000 person-years between those with less than 10·0% risk and those with 30·0% or higher predicted risk. INTERPRETATION: For a wide range of blood pressures, the rate of cardiovascular disease and effectiveness of blood pressure drug treatment was mainly determined by predicted risk, with blood pressure thresholds 140/90 mm Hg or 160/100 mm Hg-ubiquitous in most countries-adding little useful information. When medium-term predicted risk is low, there is no urgency to initiate drug treatment, allowing time to attempt non-pharmacological blood pressure reduction. FUNDING: National Institute for Health Research.

Indirect acute effects of the COVID-19 pandemic on physical and mental health in the UK: a population-based study.

BACKGROUND: There are concerns that the response to the COVID-19 pandemic in the UK might have worsened physical and mental health, and reduced use of health services. However, the scale of the problem is unquantified, impeding development of effective mitigations. We aimed to ascertain what has happened to general practice contacts for acute physical and mental health outcomes during the pandemic. METHODS: Using de-identified electronic health records from the Clinical Research Practice Datalink (CPRD) Aurum (covering 13% of the UK population), between 2017 and 2020, we calculated weekly primary care contacts for selected acute physical and mental health conditions: anxiety, depression, self-harm (fatal and non-fatal), severe mental illness, eating disorder, obsessive-compulsive disorder, acute alcohol-related events, asthma exacerbation, chronic obstructive pulmonary disease exacerbation, acute cardiovascular events (cerebrovascular accident, heart failure, myocardial infarction, transient ischaemic attacks, unstable angina, and venous thromboembolism), and diabetic emergency. Primary care contacts included remote and face-to-face consultations, diagnoses from hospital discharge letters, and secondary care referrals, and conditions were identified through primary care records for diagnoses, symptoms, and prescribing. Our overall study population included individuals aged 11 years or older who had at least 1 year of registration with practices contributing to CPRD Aurum in the specified period, but denominator populations varied depending on the condition being analysed. We used an interrupted time-series analysis to formally quantify changes in conditions after the introduction of population-wide restrictions (defined as March 29, 2020) compared with the period before their introduction (defined as Jan 1, 2017 to March 7, 2020), with data excluded for an adjustment-to-restrictions period (March 8-28). FINDINGS: The overall population included 9 863 903 individuals on Jan 1, 2017, and increased to 10 226 939 by Jan 1, 2020. Primary care contacts for almost all conditions dropped considerably after the introduction of population-wide restrictions. The largest reductions were observed for contacts for diabetic emergencies (odds ratio 0·35 [95% CI 0·25-0·50]), depression (0·53 [0·52-0·53]), and self-harm (0·56 [0·54-0·58]). In the interrupted time-series analysis, with the exception of acute alcohol-related events (0·98 [0·89-1·10]), there was evidence of a reduction in contacts for all conditions (anxiety 0·67 [0·66-0·67], eating disorders 0·62 [0·59-0·66], obsessive-compulsive disorder [0·69 [0·64-0·74]], self-harm 0·56 [0·54-0·58], severe mental illness 0·80 [0·78-0·83], stroke 0·59 [0·56-0·62], transient ischaemic attack 0·63 [0·58-0·67], heart failure 0·62 [0·60-0·64], myocardial infarction 0·72 [0·68-0·77], unstable angina 0·72 [0·60-0·87], venous thromboembolism 0·94 [0·90-0·99], and asthma exacerbation 0·88 [0·86-0·90]). By July, 2020, except for unstable angina and acute alcohol-related events, contacts for all conditions had not recovered to pre-lockdown levels. INTERPRETATION: There were substantial reductions in primary care contacts for acute physical and mental conditions following the introduction of restrictions, with limited recovery by July, 2020. Further research is needed to ascertain whether these reductions reflect changes in disease frequency or missed opportunities for care. Maintaining health-care access should be a key priority in future public health planning, including further restrictions. The conditions we studied are sufficiently severe that any unmet need will have substantial ramifications for the people with the conditions as well as health-care provision. FUNDING: Wellcome Trust Senior Fellowship, Health Data Research UK.

What are the implications of using individual and combined sources of routinely collected data to identify and characterise incident site-specific cancers? a concordance and validation study using linked English electronic health records data.

OBJECTIVES: To describe the benefits and limitations of using individual and combinations of linked English electronic health data to identify incident cancers. DESIGN AND SETTING: Our descriptive study uses linked English Clinical Practice Research Datalink primary care; cancer registration; hospitalisation and death registration data. PARTICIPANTS AND MEASURES: We implemented case definitions to identify first site-specific cancers at the 20 most common sites, based on the first ever cancer diagnosis recorded in each individual or commonly used combination of data sources between 2000 and 2014. We calculated positive predictive values and sensitivities of each definition, compared with a gold standard algorithm that used information from all linked data sets to identify first cancers. We described completeness of grade and stage information in the cancer registration data set. RESULTS: 165 953 gold standard cancers were identified. Positive predictive values of all case definitions were ≥80% and ≥94% for the four most common cancers (breast, lung, colorectal and prostate). Sensitivity for case definitions that used cancer registration alone or in combination was ≥92% for the four most common cancers and ≥80% across all cancer sites except bladder cancer (65% using cancer registration alone). For case definitions using linked primary care, hospitalisation and death registration data, sensitivity was ≥89% for the four most common cancers, and ≥80% for all cancer sites except kidney (69%), oral cavity (76%) and ovarian cancer (78%). When primary care or hospitalisation data were used alone, sensitivities were generally lower and diagnosis dates were delayed. Completeness of staging data in cancer registration data was high from 2012 (minimum 76.0% in 2012 and 86.4% in 2014 for the four most common cancers). CONCLUSIONS: Ascertainment of incident cancers was good when using cancer registration data alone or in combination with other data sets, and for the majority of cancers when using a combination of primary care, hospitalisation and death registration data.

Prevalence of COVID-19-related risk factors and risk of severe influenza outcomes in cancer survivors: A matched cohort study using linked English electronic health records data.

BACKGROUND: People with active cancer are recognised as at risk of COVID-19 complications, but it is unclear whether the much larger population of cancer survivors is at elevated risk. We aimed to address this by comparing cancer survivors and cancer-free controls for (i) prevalence of comorbidities considered risk factors for COVID-19; and (ii) risk of severe influenza, as a marker of susceptibility to severe outcomes from epidemic respiratory viruses. METHODS: We included survivors (≥1 year) of the 20 most common cancers, and age, sex and general practice-matched cancer-free controls, derived from English primary care data linked to cancer registrations, hospital admissions and death registrations. Comorbidity prevalences were calculated 1 and 5 years from cancer diagnosis. Risk of hospitalisation or death due to influenza was compared using Cox models adjusted for baseline demographics and comorbidities. FINDINGS: 108,215 cancer survivors and 523,541 cancer-free controls were included. Cancer survivors had more diabetes, asthma, other respiratory, cardiac, neurological, renal, and liver diseases, and less obesity, compared with controls, but there was variation by cancer site. There were 205 influenza hospitalisations/deaths, with cancer survivors at higher risk than controls (adjusted HR 2.78, 95% CI 2.04-3.80). Haematological cancer survivors had large elevated risks persisting for >10 years (HR overall 15.17, 7.84-29.35; HR >10 years from cancer diagnosis 10.06, 2.47-40.93). Survivors of other cancers had evidence of raised risk up to 5 years from cancer diagnosis only (HR >5 years 2.22, 1.31-3.74). INTERPRETATION: Risks of severe COVID-19 outcomes are likely to be elevated in cancer survivors. This should be taken into account in policies targeted at clinical risk groups, and vaccination for both influenza, and, when available, COVID-19, should be encouraged in cancer survivors.