There are few primary care studies of the COVID-19 pandemic. We aimed to identify demographic and clinical risk factors for testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre primary care network. We analysed routinely collected, pseudonymised data for patients in the RCGP Research and Surveillance Centre primary care sentinel network who were tested for SARS-CoV-2 between Jan 28 and April 4, 2020. We used multivariable logistic regression models with multiple imputation to identify risk factors for positive SARS-CoV-2 tests within this surveillance network. We identified 3802 SARS-CoV-2 test results, of which 587 were positive. In multivariable analysis, male sex was independently associated with testing positive for SARS-CoV-2 (296 [18·4%] of 1612 men vs 291 [13·3%] of 2190 women; adjusted odds ratio [OR] 1·55, 95% CI 1·27-1·89). Adults were at increased risk of testing positive for SARS-CoV-2 compared with children, and people aged 40-64 years were at greatest risk in the multivariable model (243 [18·5%] of 1316 adults aged 40-64 years vs 23 [4·6%] of 499 children; adjusted OR 5·36, 95% CI 3·28-8·76). Compared with white people, the adjusted odds of a positive test were greater in black people (388 [15·5%] of 2497 white people vs 36 [62·1%] of 58 black people; adjusted OR 4·75, 95% CI 2·65-8·51). People living in urban areas versus rural areas (476 [26·2%] of 1816 in urban areas vs 111 [5·6%] of 1986 in rural areas; adjusted OR 4·59, 95% CI 3·57-5·90) and in more deprived areas (197 [29·5%] of 668 in most deprived vs 143 [7·7%] of 1855 in least deprived; adjusted OR 2·03, 95% CI 1·51-2·71) were more likely to test positive. People with chronic kidney disease were more likely to test positive in the adjusted analysis (68 [32·9%] of 207 with chronic kidney disease vs 519 [14·4%] of 3595 without; adjusted OR 1·91, 95% CI 1·31-2·78), but there was no significant association with other chronic conditions in that analysis. We found increased odds of a positive test among people who are obese (142 [20·9%] of 680 people with obesity vs 171 [13·2%] of 1296 normal-weight people; adjusted OR 1·41, 95% CI 1·04-1·91). Notably, active smoking was linked with decreased odds of a positive test result (47 [11·4%] of 413 active smokers vs 201 [17·9%] of 1125 non-smokers; adjusted OR 0·49, 95% CI 0·34-0·71). A positive SARS-CoV-2 test result in this primary care cohort was associated with similar risk factors as observed for severe outcomes of COVID-19 in hospital settings, except for smoking. We provide evidence of potential sociodemographic factors associated with a positive test, including deprivation, population density, ethnicity, and chronic kidney disease. Wellcome Trust.

UK Biobank is a UK-wide cohort of 502,655 people aged 40–69, recruited from National Health Service registrants between 2006–10, with healthcare data linkage. Type 2 diabetes is a key exposure and outcome. We developed algorithms to define prevalent and incident diabetes for UK Biobank. The algorithms will be implemented by UK Biobank and their results made available to researchers on request. We used UK Biobank self-reported medical history and medication to assign prevalent diabetes and type, and tested this against linked primary and secondary care data in Welsh UK Biobank participants. Additionally, we derived and tested algorithms for incident diabetes using linked primary and secondary care data in the English Clinical Practice Research Datalink, and ran these on secondary care data in UK Biobank. For prevalent diabetes, 0.001% and 0.002% of people classified as “diabetes unlikely” in UK Biobank had evidence of diabetes in their primary or secondary care record respectively. Of those classified as “probable” type 2 diabetes, 75% and 96% had specific type 2 diabetes codes in their primary and secondary care records. For incidence, 95% of people with the type 2 diabetes-specific C10F Read code in primary care had corroborative evidence of diabetes from medications, blood testing or diabetes specific process of care codes. Only 41% of people identified with type 2 diabetes in primary care had secondary care evidence of type 2 diabetes. In contrast, of incident cases using ICD-10 type 2 diabetes specific codes in secondary care, 77% had corroborative evidence of diabetes in primary care. We suggest our definition of prevalent diabetes from UK Biobank baseline data has external validity, and recommend that specific primary care Read codes should be used for incident diabetes to ensure precision. Secondary care data should be used for incident diabetes with caution, as around half of all cases are missed, and a quarter have no corroborative evidence of diabetes in primary care.

Early identification of patients with chronic kidney disease (CKD) may allow health-care systems to implement interventions aimed at decreasing disease progression and eventual morbidity and mortality. Primary care in the United Kingdom is computerized suggesting a separate screening program for CKD may not be necessary because identifying data already populates primary care databases. Our study utilized a data set of 163 demographic, laboratory, diagnosis, and prescription variables from 130 226 adults in the regions of Kent, Manchester, and Surrey. The patients were 18 years of age and older in a 5-year study period culminating in November 2003. Estimated glomerular filtration rate was calculated from the four-variable Modification of Diet in Renal Disease equation using calibrated creatinine levels. A valid creatinine value was recorded in almost 30% of this cohort. The age-standardized prevalence of stage 3–5 CKD was 10.6% for females and 5.8% for males. In these patients, the odds ratio for hypertension was 2.1, for diabetes 1.33, and for cardiovascular disease 1.69. Only 20% of the diabetic people with stage 3–5 CKD had a blood pressure less than or equal to 130/80 mm Hg. The proportion of patients with anemia significantly rose as renal function declined. We suggest that stage 3–5 CKD is easily detected in existing computerized records. The associated comorbidity and management is readily available enabling intervention and targeting of specialist resources.

Objective. The purpose of this study was to examine whether Internet-based surveys of health professionals can provide a valid alternative to traditional survey methods. Methods. (i) Systematic review of published Internet-based surveys of health professionals focusing on criteria of external validity, specifically sample representativeness and response bias. (ii) Internet-based survey of GPs, exploring attitudes about using an Internet-based decision support system for the management of familial cancer. Results. The systematic review identified 17 Internet-based surveys of health professionals. Whilst most studies sampled from professional e-directories, some studies drew on unknown denominator populations by placing survey questionnaires on open web sites or electronic discussion groups. Twelve studies reported response rates, which ranged from nine to 94%. Sending follow-up reminders resulted in a substantial increase in response rates. In our own survey of GPs, a total of 268 GPs participated (adjusted response rate = 52.4%) after five e-mail reminders. A further 72 GPs responded to a brief telephone survey of non-respondents. Respondents to the Internet survey were more likely to be male and had significantly greater intentions to use Internet-based decision support than non-respondents. Conclusions. Internet-based surveys provide an attractive alternative to postal and telephone surveys of health professionals, but they raise important technical and methodological issues which should be carefully considered before widespread implementation. The major obstacle is external validity, and specifically how to obtain a representative sample and adequate response rate. Controlled access to a national list of NHSnet e-mail addresses of health professionals could provide a solution.

Objectives: To introduce a psychological model of behaviour; the COM-B model and describe how this has been used in combination with the behaviour change wheel (BCW) in developing an intervention which aims to promote regular, long-term use of hearing aids by adults with acquired hearing loss. Design: Qualitative structured interview study using the COM-B model to identify the determinants of behavioural planning on the part of audiologists; a potentially important factor in encouraging long-term hearing-aid use. Study sample: Ten audiologists drawn from a random sample of five English audiology departments. Results: The analysis suggests that behavioural planning might be more likely to occur if audiologists’ psychological capability, physical and social opportunity, and reflective and automatic motivation were addressed. This analysis forms the basis of an intervention design, using the BCW, to encourage behavioural planning by audiologists and subsequent hearing-aid use by people with hearing loss. Conclusions: The COM-B model and BCW can be applied successfully in the context of audiology to analyse the behaviour of both people with hearing loss and professionals working with them, supplying information that is being used in intervention design. The effectiveness of the intervention will be tested in a clinical trial.