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The entire landscape of cancer management in primary care, from case identification to the management of people living with and beyond cancer, is evolving rapidly in the face of the coronavirus disease 2019 (COVID-19) pandemic.1 In a climate of fear and mandated avoidance of all but essential clinical services, delays in patient, population, and health-care system responses to suspected cancer symptoms seem inevitable. Patients might be reluctant to present because of fear of interacting with others, limited capacity to use video or teleconsultations, and concerns about wasting the doctor's time.6,7 For family doctors, the COVID-19 pandemic is affecting all aspects of normal working life, including a reduced workforce due to illness and self-isolation, and the reduced availability of appointments and investigations in primary and secondary care. The National Health Service guidelines state that patients will want to discuss whether the benefits of continuing active cancer treatment outweigh the risks of potentially being seriously unwell if they contract COVID-19, which is a role that could well fall to primary care.9 The UK cancer charity Macmillan Cancer Support reports that a quarter of calls to its support line are from patients with cancer who are anxious about COVID-19.10 Although cancer charities provide a vital support role, primary care needs to support the physical and mental health of patients for whom potentially lifesaving cancer treatments are being postponed.

Information from patient surveys can help to identify patient groups and cancers with the greatest potential for improvement in the experience and timeliness of cancer diagnosis. We aimed to examine variation in the number of pre-referral consultations with a general practitioner between patients with different cancers and sociodemographic characteristics. We analysed data from 41 299 patients with 24 different cancers who took part in the 2010 National Cancer Patient Experience Survey in England. We examined variation in the number of general practitioner consultations with cancer symptoms before hospital referral to diagnose cancer. Logistic regression was used to identify independent predictors of three or more pre-referral consultations, adjusting for cancer type, age, sex, deprivation quintile, and ethnic group. We identified wide variation between cancer types in the proportion of patients who had visited their general practitioner three or more times before hospital referral (7·4% [625 of 8408] for breast cancer and 10·1% [113 of 1124] for melanoma; 41·3% [193 of 467] for pancreatic cancer and 50·6% [939 of 1854] for multiple myeloma). In multivariable analysis, with patients with rectal cancer as the reference group, those with subsequent diagnosis of multiple myeloma (odds ratio [OR] 3·42, 95% CI 3·01–3·90), pancreatic cancer (2·35, 1·91–2·88), stomach cancer (1·96, 1·65–2·34), and lung cancer (1·68, 1·48–1·90) were more likely to have had three or more pre-referral consultations; conversely patients with subsequent diagnosis of breast cancer (0·19; 0·17–0·22), melanoma (0·34, 0·27–0·43), testicular cancer (0·47, 0·33–0·67), and endometrial cancer (0·59, 0·49–0·71) were more likely to have been referred to hospital after only one or two consultations. The probability of three or more pre-referral consultations was greater in young patients (OR for patients aged 16–24 years vs 65–74 years 2·12, 95% CI 1·63–2·75; p<0·0001), those from ethnic minorities (OR for Asian vs white 1·73, 1·45–2·08; p<0·0001; OR for black vs white 1·83, 1·51–2·23; p<0·0001), and women (OR for women vs men 1·28, 1·21–1·36; p<0·0001). We identified strong evidence of interactions between cancer type and age group and sex (p<0·0001 for both), and between age and ethnicity (p=0·0013). The model including these interactions showed a particularly strong sex effect for bladder cancer (OR for women vs men 2·31, 95% CI 1·98–2·69) and no apparent ethnic group differences in young patients aged 16–24 years, whilst the only cancers without an apparent age gradient were testicular cancer and mesothelioma. Our findings could help to prioritise and stratify early diagnosis initiatives and research, focusing on patients with cancers and sociodemographic characteristics with the largest potential for improvement. None.

The nature of cancer control is changing, with an increasing emphasis, fuelled by public and political demand, on prevention, early diagnosis, and patient experience during and after treatment. At the same time, primary care is increasingly promoted, by governments and health funders worldwide, as the preferred setting for most health care for reasons of increasing need, to stabilise health-care costs, and to accommodate patient preference for care close to home. It is timely, then, to consider how this expanding role for primary care can work for cancer control, which has long been dominated by highly technical interventions centred on treatment, and in which the contribution of primary care has been largely perceived as marginal. In this Commission, expert opinion from primary care and public health professionals with academic and clinical cancer expertise—from epidemiologists, psychologists, policy makers, and cancer specialists—has contributed to a detailed consideration of the evidence for cancer control provided in primary care and community care settings. Ranging from primary prevention to end-of-life care, the scope for new models of care is explored, and the actions needed to effect change are outlined. The strengths of primary care—its continuous, coordinated, and comprehensive care for individuals and families—are particularly evident in prevention and diagnosis, in shared follow-up and survivorship care, and in end-of-life care. A strong theme of integration of care runs throughout, and its elements (clinical, vertical, and functional) and the tools needed for integrated working are described in detail. All of this change, as it evolves, will need to be underpinned by new research and by continuing and shared multiprofessional development.

ObjectivesTo estimate the impact of the COVID-19 pandemic on cancer care services and overall (direct and indirect) excess deaths in people with cancer.MethodsWe employed near real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services. We also used these data, together with national death registrations until June 2020 to model deaths, in excess of background (pre-COVID-19) mortality, in people with cancer. Background mortality risks for 24 cancers with and without COVID-19-relevant comorbidities were obtained from population-based primary care cohort (Clinical Practice Research Datalink) on 3 862 012 adults in England.ResultsDeclines in urgent referrals (median=−70.4%) and chemotherapy attendances (median=−41.5%) to a nadir (lowest point) in the pandemic were observed. By 31 May, these declines have only partially recovered; urgent referrals (median=−44.5%) and chemotherapy attendances (median=−31.2%). There were short-term excess death registrations for cancer (without COVID-19), with peak relative risk (RR) of 1.17 at week ending on 3 April. The peak RR for all-cause deaths was 2.1 from week ending on 17 April. Based on these findings and recent literature, we modelled 40% and 80% of cancer patients being affected by the pandemic in the long-term. At 40% affected, we estimated 1-year total (direct and indirect) excess deaths in people with cancer as between 7165 and 17 910, using RRs of 1.2 and 1.5, respectively, where 78% of excess deaths occured in patients with ≥1 comorbidity.ConclusionsDramatic reductions were detected in the demand for, and supply of, cancer services which have not fully recovered with lockdown easing. These may contribute, over a 1-year time horizon, to substantial excess mortality among people with cancer and multimorbidity. It is urgent to understand how the recovery of general practitioner, oncology and other hospital services might best mitigate these long-term excess mortality risks.

Time from symptomatic presentation to cancer diagnosis (diagnostic interval) is an important, and modifiable, part of the patient's cancer pathway, and can be affected by various factors such as age, gender and type of presenting symptoms. The aim of this study was to quantify the relationships of diagnostic interval with these variables in 15 cancers diagnosed between 2007 and 2010 using routinely collected data from the Clinical Practice Research Datalink (CPRD) in the UK. Symptom lists for each cancer were prepared from the literature and by consensus amongst the clinician researchers, which were then categorised into either NICE qualifying (NICE) or not (non-NICE) based on NICE Urgent Referral Guidelines for Suspected Cancer criteria. Multivariable linear regression models were fitted to examine the relationship between diagnostic interval (outcome) and the predictors: age, gender and symptom type. 18,618 newly diagnosed cancer patients aged ≥40 who had a recorded symptom in the preceding year were included in the analysis. Mean diagnostic interval was greater for older patients in four disease sites (difference in days per 10 year increase in age; 95% CI): bladder (10.3; 5.5 to 15.1; P<0.001), kidney (11.0; 3.4 to 18.6; P=0.004), leukaemia (18.5; 8.8 to 28.1; P<0.001) and lung (10.1; 6.7 to 13.4; P<0.001). There was also evidence of longer diagnostic interval in older patients with colorectal cancer (P<0.001). However, we found that mean diagnostic interval was shorter with increasing age in two cancers: gastric (-5.9; -11.7 to -0.2; P=0.04) and pancreatic (-6.0; -11.2 to -0.7; P=0.03). Diagnostic interval was longer for females in six of the gender non-specific cancers (mean difference in days; 95% CI): bladder (12.2; 0.8 to 23.6; P=0.04), colorectal (10.4; 4.3 to 16.5; P=0.001), gastric (14.3; 1.1 to 27.6; P=0.03), head and neck (31.3; 6.2 to 56.5; P=0.02), lung (8.0; 1.2 to 14.9; P=0.02), and lymphoma (19.2; 3.8 to 34.7; P=0.01). Evidence of longer diagnostic interval was found for patients presenting with non-NICE symptoms in 10 of 15 cancers (mean difference in days; 95% CI): bladder (62.9; 48.7 to 77.2; P<0.001), breast (115.1; 105.9 to 124.3; P<0.001), cervical (60.3; 31.6 to 89.0; P<0.001), colorectal (25.8; 19.6 to 31.9; P<0.001), gastric (24.1; 3.4 to 44.8; P=0.02), kidney (22.1; 4.5 to 39.7; P=0.01), oesophageal (67.0; 42.1 to 92.0; P<0.001), pancreatic (48.6; 28.1 to 69.1; P<0.001), testicular (36.7; 17.0 to 56.4; P< 0.001), and endometrial (73.8; 60.3 to 87.3; P<0.001). Pooled analysis across all cancers demonstrated highly significant evidence of differences overall showing longer diagnostic intervals with increasing age (7.8 days; 6.4 to 9.1; P<0.001); for females (8.9 days; 5.5 to 12.2; P<0.001); and in non-NICE symptoms (27.7 days; 23.9 to 31.5; P<0.001). We found age and gender-specific inequalities in time to diagnosis for some but not all cancer sites studied. Whilst these need further explanation, these findings can inform the development and evaluation of interventions intended to achieve timely diagnosis and improved cancer outcomes, such as to provide equity across all age and gender groupings.

A comprehensive evaluation of the total burden of morbidity endured by cancer survivors remains unavailable. This study quantified the burden of 144 health conditions and critical care admissions across 26 adult cancers and treatment modalities in 243,767 adults. By age 60, top conditions ranked by fold difference (cumulative burden in survivors divided by cumulative burden in controls) were haematology, immunology/infection and pulmonary conditions. Patients who had all three forms of treatment (chemotherapy, radiotherapy and surgery) experienced a high cumulative burden of late morbidities compared with patients who received radiotherapy alone. The top five cancers with the highest cumulative burden of critical care admissions by age 60 were bone (12.4 events per 100 individuals [CI: 11.6-13.1]), brain (9.0 [7.5-10.5]), spinal cord and nervous system (7.2 [6.7-7.8]), testis (6.7 [4.9-8.4]) and Hodgkin lymphoma (4.4 [3.6-5.1]). Conditions that were associated with high excess years-of-life-lost were haematological conditions (9.6 years), pulmonary conditions (8.6 years) and immunological conditions or infections (7.8 years). As the population of cancer survivors continues to grow, our results indicate that it is important to tackle long-term health consequences through enacting data-driven policies. Here the authors evaluate the burden of 144 health conditions in adult cancer survivors, and show that the magnitude of late morbidities experienced by survivors varies according to the type of cancer and treatment, highlighting opportunities for optimising patient care

Multiple risk-prediction models are used in clinical practice to triage patients as being at low risk or high risk of ovarian cancer. In the ROCkeTS study, we aimed to identify the best diagnostic test for ovarian cancer in symptomatic patients, through head-to-head comparisons of risk-prediction models, in a real-world setting. Here, we report the results for the postmenopausal cohort. In this multicentre, prospective diagnostic accuracy study, we recruited newly presenting female patients aged 16–90 years with non-specific symptoms and raised CA125 or abnormal ultrasound results (or both) who had been referred via rapid access, elective clinics, or emergency presentations from 23 hospitals in the UK. Patients with normal CA125 and simple ovarian cysts of smaller than 5 cm in diameter, active non-ovarian malignancy, or previous ovarian malignancy, or those who were pregnant or declined a transvaginal scan, were ineligible. In this analysis, only postmenopausal participants were included. Participants completed a symptom questionnaire, gave a blood sample, and had transabdominal and transvaginal ultrasounds performed by International Ovarian Tumour Analysis consortium (IOTA)-certified sonographers. Index tests were Risk of Malignancy 1 (RMI1) at a threshold of 200, Risk of Malignancy Algorithm (ROMA) at multiple thresholds, IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX) at thresholds of 3% and 10%, IOTA SRRisk model at thresholds of 3% and 10%, IOTA Simple Rules (malignant vs benign, or inconclusive), and CA125 at 35 IU/mL. In a post-hoc analysis, the Ovarian Adnexal and Reporting Data System (ORADS) at 10% was derived from IOTA ultrasound variables using established methods since ORADS was described after completion of recruitment. Index tests were conducted by study staff masked to the results of the reference standard. The comparator was RMI1 at the 250 threshold (the current UK National Health Service standard of care). The reference standard was surgical or biopsy tissue histology or cytology within 3 months, or a self-reported diagnosis of ovarian cancer at 12 month follow-up. The primary outcome was diagnostic accuracy at predicting primary invasive ovarian cancer versus benign or normal histology, assessed by analysing the sensitivity, specificity, C-index, area under receiver operating characteristic curve, positive and negative predictive values, and calibration plots in participants with conclusive reference standard results and available index test data. This study is registered with the International Standard Randomised Controlled Trial Number registry (ISRCTN17160843). Between July 13, 2015, and Nov 30, 2018, 1242 postmenopausal patients were recruited, of whom 215 (17%) had primary ovarian cancer. 166 participants had missing, inconclusive, or other reference standard results; therefore, data from a maximum of 1076 participants were used to assess the index tests for the primary outcome. Compared with RMI1 at 250 (sensitivity 82·9% [95% CI 76·7 to 88·0], specificity 87·4% [84·9 to 89·6]), IOTA ADNEX at 10% was more sensitive (difference of –13·9% [–20·2 to –7·6], p<0·0001) but less specific (difference of 28·5% [24·7 to 32·3], p<0·0001). ROMA at 29·9 had similar sensitivity (difference of –3·6% [–9·1 to 1·9], p=0·24) but lower specificity (difference of 5·2% [2·5 to 8·0], p=0·0001). RMI1 at 200 had similar sensitivity (difference of –2·1% [–4·7 to 0·5], p=0·13) but lower specificity (difference of 3·0% [1·7 to 4·3], p<0·0001). IOTA SRRisk model at 10% had similar sensitivity (difference of –4·3% [–11·0 to –2·3], p=0·23) but lower specificity (difference of 16·2% [12·6 to 19·8], p<0·0001). IOTA Simple Rules had similar sensitivity (difference of –1·6% [–9·3 to 6·2], p=0·82) and specificity (difference of –2·2% [–5·1 to 0·6], p=0·14). CA125 at 35 IU/mL had similar sensitivity (difference of –2·1% [–6·6 to 2·3], p=0·42) but higher specificity (difference of 6·7% [4·3 to 9·1], p<0·0001). In a post-hoc analysis, when compared with RMI1 at 250, ORADS achieved similar sensitivity (difference of –2·1%, 95% CI –8·6 to 4·3, p=0·60) and lower specificity (difference of 10·2%, 95% CI 6·8 to 13·6, p<0·0001). In view of its higher sensitivity than RMI1 at 250, despite some loss in specificity, we recommend that IOTA ADNEX at 10% should be considered as the new standard-of-care diagnostic in ovarian cancer for postmenopausal patients. UK National Institute for Health and Care Research.

IntroductionLung cancer is the world’s leading cause of cancer death. Low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20% in the US National Lung Screening Trial. Here, we present the Yorkshire Lung Screening Trial (YLST), which will address key questions of relevance for screening implementation.Methods and analysisUsing a single-consent Zelen’s design, ever-smokers aged 55–80 years registered with a general practice in Leeds will be randomised (1:1) to invitation to a telephone-based risk-assessment for a Lung Health Check or to usual care. The anticipated number randomised by household is 62 980 individuals. Responders at high risk will be invited for LDCT scanning for lung cancer on a mobile van in the community. There will be two rounds of screening at an interval of 2 years. Primary objectives are (1) measure participation rates, (2) compare the performance of PLCOM2012 (threshold ≥1.51%), Liverpool Lung Project (V.2) (threshold ≥5%) and US Preventive Services Task Force eligibility criteria for screening population selection and (3) assess lung cancer outcomes in the intervention and usual care arms. Secondary evaluations include health economics, quality of life, smoking rates according to intervention arm, screening programme performance with ancillary biomarker and smoking cessation studies.Ethics and disseminationThe study has been approved by the Greater Manchester West research ethics committee (18-NW-0012) and the Health Research Authority following review by the Confidentiality Advisory Group. The results will be disseminated through publication in peer-reviewed scientific journals, presentation at conferences and on the YLST website.Trial registration numbersISRCTN42704678 and NCT03750110.

Background The early detection and diagnosis of cancer to reduce avoidable mortality and morbidity is a challenging task in primary health care. There is a growing evidence base on how to enable earlier cancer diagnosis, but well-recognised gaps and delays exist around the translation of new research findings into routine clinical practice. Implementation research aims to accelerate the uptake of evidence by health care systems and professionals. We aimed to identify priorities for implementation research in early cancer diagnosis in primary care. Methods We used a RAND/UCLA modified Delphi consensus process to identify and rank research priorities. We asked primary care physicians, patients and researchers to complete an online survey suggesting priorities for implementation research in cancer detection and diagnosis. We summarised and presented these suggestions to an 11-member consensus panel comprising nine primary care physicians and two patients. Panellists independently rated the importance of suggestions on a 1–9 scale (9 = very high priority; 1 = very low priority) before and after a structured group discussion. We ranked suggestions using median ratings. Results We received a total of 115 suggested priorities for implementation research from 32 survey respondents (including 16 primary care professionals, 11 researchers, and 4 patient and public representatives; 88% of respondents were UK-based). After removing duplicates and ineligible suggestions, we presented 37 suggestions grouped within 17 categories to the consensus panel. Following two rounds of rating, 27 suggestions were highly supported (median rating 7–9). The most highly rated suggestions concerned diagnostic support (e.g., access to imaging) interventions (e.g., professional or patient education), organisation of the delivery of care (e.g., communication within and between teams) and understanding variations in care and outcomes. Conclusions We have identified a set of priorities for implementation research on the early diagnosis of cancer, ranked in importance by primary care physicians and patients. We suggest that researchers and research funders consider these in directing further efforts and resources to improve population outcomes.

Background Breast cancer clinics across the UK have long been struggling to cope with high demand. Novel risk prediction tools – such as the PinPoint test – could help to reduce unnecessary clinic referrals. Using early data on the expected accuracy of the test, we explore the potential impact of PinPoint on: (a) the percentage of patients meeting the two-week referral target, and (b) the number of clinic ‘overspill’ appointments generated (i.e. patients having to return to the clinic to complete their required investigations). Methods A simulation model was built to reflect the annual flow of patients through a single UK clinic. Due to current uncertainty around the exact impact of PinPoint testing on standard care, two primary scenarios were assessed. Scenario 1 assumed complete GP adherence to testing, with only non-referred cancerous cases returning for delayed referral. Scenario 2 assumed GPs would overrule 20% of low-risk results, and that 10% of non-referred non-cancerous cases would also return for delayed referral. A range of sensitivity analyses were conducted to explore the impact of key uncertainties on the model results. Service reconfiguration scenarios, removing individual weekly clinics from the clinic schedule, were also explored. Results Under standard care, 66.3% (95% CI: 66.0 to 66.5) of patients met the referral target, with 1,685 (1,648 to 1,722) overspill appointments. Under both PinPoint scenarios, > 98% of patients met the referral target, with overspill appointments reduced to between 727 (707 to 746) [Scenario 1] and 886 (861 to 911) [Scenario 2]. The reduced clinic demand was sufficient to allow removal of one weekly low-capacity clinic [N = 10], and the results were robust to sensitivity analyses. Conclusion The findings from this early analysis indicate that risk prediction tools could have the potential to alleviate pressure on cancer clinics, and are expected to have increased utility in the wake of heightened pressures resulting from the COVID-19 pandemic. Further research is required to validate these findings with real world evidence; evaluate the broader clinical and economic impact of the test; and to determine outcomes and risks for patients deemed to be low-risk on the PinPoint test and therefore not initially referred.