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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.

IntroductionThrough the INTernational ConsoRtium of Primary Care BIg Data Researchers (INTRePID), we compared the pandemic impact on the volume of primary care visits and uptake of virtual care in Australia, Canada, China, Norway, Singapore, South Korea, Sweden, the UK and the USA.MethodsVisit definitions were agreed on centrally, implemented locally across the various settings in INTRePID countries, and weekly visit counts were shared centrally for analysis. We evaluated the weekly rate of primary care physician visits during 2019 and 2020. Rate ratios (RRs) of total weekly visit volume and the proportion of weekly visits that were virtual in the pandemic period in 2020 compared with the same prepandemic period in 2019 were calculated.ResultsIn 2019 and 2020, there were 80 889 386 primary care physician visits across INTRePID. During the pandemic, average weekly visit volume dropped in China, Singapore, South Korea, and the USA but was stable overall in Australia (RR 0.98 (95% CI 0.92 to 1.05, p=0.59)), Canada (RR 0.96 (95% CI 0.89 to 1.03, p=0.24)), Norway (RR 1.01 (95% CI 0.88 to 1.17, p=0.85)), Sweden (RR 0.91 (95% CI 0.79 to 1.06, p=0.22)) and the UK (RR 0.86 (95% CI 0.72 to 1.03, p=0.11)). In countries that had negligible virtual care prepandemic, the proportion of visits that were virtual were highest in Canada (77.0%) and Australia (41.8%). In Norway (RR 8.23 (95% CI 5.30 to 12.78, p<0.001), the UK (RR 2.36 (95% CI 2.24 to 2.50, p<0.001)) and Sweden (RR 1.33 (95% CI 1.17 to 1.50, p<0.001)) where virtual visits existed prepandemic, it increased significantly during the pandemic.ConclusionsThe drop in primary care in-person visits during the pandemic was a global phenomenon across INTRePID countries. In several countries, primary care shifted to virtual visits mitigating the drop in in-person visits.

BackgroundIt remains unclear to what extent reductions in urgent referrals for suspected cancer during the COVID-19 pandemic were the result of fewer patients attending primary care compared to GPs referring fewer patients.MethodsCohort study including electronic health records data from 8,192,069 patients from 663 English practices. Weekly consultation rates, cumulative consultations and referrals were calculated for 28 clinical features from the NICE suspected cancer guidelines. Clinical feature consultation rate ratios (CRR) and urgent referral rate ratios (RRR) compared time periods in 2020 with 2019.FindingsConsultations for cancer clinical features decreased by 24.19% (95% CI: 24.04–24.34%) between 2019 and 2020, particularly in the 6–12 weeks following the first national lockdown. Urgent referrals for clinical features decreased by 10.47% (95% CI: 9.82–11.12%) between 2019 and 2020. Overall, once patients consulted with primary care, GPs urgently referred a similar or greater proportion of patients compared to previous years.ConclusionDue to the significant fall in patients consulting with clinical features of cancer there was a lower than expected number of urgent referrals in 2020. Sustained efforts should be made throughout the pandemic to encourage the public to consult their GP with cancer clinical features.

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

Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service. Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65's, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables. A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65's population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings. This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources.

Chickenpox is a common childhood disease caused by varicella-zoster virus (VZV). VZV vaccination is not part of the UK childhood immunisation programme, but its potential inclusion is regularly assessed. It is therefore important to understand the ongoing burden of VZV in the community to inform vaccine policy decisions. General practitioner (GP) chickenpox consultations were studied from 1 September 2016 to 9 December 2022. Over the study period, the mean weekly chickenpox consultation rate per 100,000 population in England was 3.4, with a regular peak occurring between weeks 13 and 15. Overall, rates decreased over time, from a mean weekly rate of 5.5 in 2017 to 4.2 in 2019. The highest mean weekly rates were among children aged 1–4 years. There was no typical epidemic peak during the COVID-19 pandemic, but in 2022, rates were proportionally higher among children aged < 1 year old compared to pre-pandemic years. Chickenpox GP consultation rates decreased in England, continuing a longer-term decline in the community. The COVID-19 pandemic impacted rates, likely caused by the introduction of non-pharmaceutical interventions to prevent SARS-CoV-2 transmission. The lasting impact of the interruption of typical disease transmission remains to be seen, but it is important to monitor the chickenpox burden to inform decisions on vaccine programmes.

Point-of-care lateral flow device antigen testing has been used extensively to identify individuals with active SARS-CoV-2 infection in the community. This study aimed to evaluate the diagnostic accuracy of two point-of-care tests (POCTs) for SARS-CoV-2 in routine community care. Adults and children with symptoms consistent with suspected current COVID-19 infection were prospectively recruited from 19 UK general practices and two COVID-19 testing centres between October 2020 and October 2021. Participants were tested by trained healthcare workers using at least one of two index POCTs (Roche-branded SD Biosensor Standard™ Q SARS-CoV-2 Rapid Antigen Test and/or BD Veritor™ System for Rapid Detection of SARS-CoV-2). The reference standard was laboratory triplex reverse transcription quantitative PCR (RT-PCR) using a combined nasal/oropharyngeal swab. Diagnostic accuracy parameters were estimated, with 95% confidence intervals (CIs), overall, in relation to RT-PCR cycle threshold and in pre-specified subgroups. Of 663 participants included in the primary analysis, 39.2% (260/663, 95% CI 35.5% to 43.0%) had a positive RT-PCR result. The SD Biosensor POCT had sensitivity 84.0% (178/212, 78.3% to 88.6%) and specificity 98.5% (328/333, 96.5% to 99.5%), and the BD Veritor POCT had sensitivity 76.5% (127/166, 69.3% to 82.7%) and specificity 98.8% (249/252, 96.6% to 99.8%) compared with RT-PCR. Sensitivity of both devices dropped substantially at cycle thresholds ≥30 and in participants more than 7 days after onset of symptoms. Both POCTs assessed exceed the Medicines and Healthcare products Regulatory Agency target product profile's minimum acceptable specificity of 95%. Confidence intervals for both tests include the minimum acceptable sensitivity of 80%. In symptomatic patients, negative results on these two POCTs do not preclude the possibility of infection. Tests should not be expected to reliably detect disease more than a week after symptom onset, when viral load may be reduced. ISRCTN142269.

Governments and health care providers are keen to find innovative ways to deliver care more efficiently. Interest in electronic consultation (e-consultation) has grown, but the evidence of benefit is uncertain. This study aimed to assess the evidence of delivering e-consultation using secure email and messaging or video links in primary care. A systematic review was conducted on the use and application of e-consultations in primary care. We searched 7 international databases (MEDLINE, EMBASE, CINAHL, Cochrane Library, PsycINFO, EconLit, and Web of Science; 1999-2017), identifying 52 relevant studies. Papers were screened against a detailed inclusion and exclusion criteria. Independent dual data extraction was conducted and assessed for quality. The resulting evidence was synthesized using thematic analysis. This review included 57 studies from a range of countries, mainly the United States (n=30) and the United Kingdom (n=13). There were disparities in uptake and utilization toward more use by younger, employed adults. Patient responses to e-consultation were mixed. Patients reported satisfaction with services and improved self-care, communication, and engagement with clinicians. Evidence for the acceptability and ease of use was strong, especially for those with long-term conditions and patients located in remote regions. However, patients were concerned about the privacy and security of their data. For primary health care staff, e-consultation delivers challenges around time management, having the correct technological infrastructure, whether it offers a comparable standard of clinical quality, and whether it improves health outcomes. E-consultations may improve aspects of care delivery, but the small scale of many of the studies and low adoption rates leave unanswered questions about usage, quality, cost, and sustainability. We need to improve e-consultation implementation, demonstrate how e-consultations will not increase disparities in access, provide better reassurance to patients about privacy, and incorporate e-consultation as part of a manageable clinical workflow.

Physician associates, known internationally as physician assistants, are a mid-level practitioner, well established in the United States of America but new to the United Kingdom. A small number work in primary care under the supervision of general practitioners, where they most commonly see patients requesting same day appointments for new problems. As an adjunct to larger study, we investigated the quality of the patient consultation of physician associates in comparison to that of general practitioners. We conducted a comparative observational study using video recordings of consultations by volunteer physician associates and general practitioners with consenting patients in single surgery sessions. Recordings were assessed by experienced general practitioners, blinded to the type of the consulting practitioner, using the Leicester Assessment Package. Assessors were asked to comment on the safety of the recorded consultations and to attempt to identify the type of practitioner. Ratings were compared across practitioner type, alongside the number of presenting complaints discussed in each consultation and the number of these which were acute, minor, or regarding a chronic condition. We assessed 62 consultations (41 general practitioner and 21 physician associates) from five general practitioners and four physician associates. All consultations were assessed as safe; but general practitioners were rated higher than PAs in all elements of consultation. The general practitioners were more likely than physician associates to see people with multiple presenting complaints (p<0.0001) and with chronic disease related complaints (p = 0.008). Assessors correctly identified general practitioner consultations but not physician associates. The Leicester Assessment Package had limited inter-rater and intra-rater reliability. The physician associate consultations were with a less complex patient group. They were judged as competent and safe, although general practitioner consultations, unsurprisingly, were rated as more competent. Physician associates offer a complementary addition to the medical workforce in general practice.

Globally, there are marked inconsistencies in how immunosuppression is characterized and subdivided into clinical risk groups. This is detrimental to the precision and comparability of disease surveillance efforts-which has negative implications for the care of those who are immunosuppressed and their health outcomes. This was particularly apparent during the COVID-19 pandemic; despite collective motivation to protect these patients, conflicting clinical definitions created international rifts in how those who were immunosuppressed were monitored and managed during this period. We propose that international clinical consensus be built around the conditions that lead to immunosuppression and their gradations of severity concerning COVID-19. Such information can then be formalized into a digital phenotype to enhance disease surveillance and provide much-needed intelligence on risk-prioritizing these patients. We aim to demonstrate how electronic Delphi objectives, methodology, and statistical approaches will help address this lack of consensus internationally and deliver a COVID-19 risk-stratified phenotype for \"adult immunosuppression.\" Leveraging existing evidence for heterogeneous COVID-19 outcomes in adults who are immunosuppressed, this work will recruit over 50 world-leading clinical, research, or policy experts in the area of immunology or clinical risk prioritization. After 2 rounds of clinical consensus building and 1 round of concluding debate, these panelists will confirm the medical conditions that should be classed as immunosuppressed and their differential vulnerability to COVID-19. Consensus statements on the time and dose dependencies of these risks will also be presented. This work will be conducted iteratively, with opportunities for panelists to ask clarifying questions between rounds and provide ongoing feedback to improve questionnaire items. Statistical analysis will focus on levels of agreement between responses. This protocol outlines a robust method for improving consensus on the definition and meaningful subdivision of adult immunosuppression concerning COVID-19. Panelist recruitment took place between April and May of 2024; the target set for over 50 panelists was achieved. The study launched at the end of May and data collection is projected to end in July 2024. This protocol, if fully implemented, will deliver a universally acceptable, clinically relevant, and electronic health record-compatible phenotype for adult immunosuppression. As well as having immediate value for COVID-19 resource prioritization, this exercise and its output hold prospective value for clinical decision-making across all diseases that disproportionately affect those who are immunosuppressed. PRR1-10.2196/56271.