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1.
J Infect ; 2022 Jan 03.
Article in English | MEDLINE | ID: covidwho-1788130

ABSTRACT

Background COVID-19 vaccines approved in the UK are highly effective in general population cohorts, however, data on effectiveness amongst individuals with clinical conditions that place them at increased risk of severe disease are limited. Methods We used GP electronic health record data, sentinel virology swabbing and antibody testing within a cohort of 712 general practices across England to estimate vaccine antibody response and vaccine effectiveness against medically attended COVID-19 amongst individuals in clinical risk groups using cohort and test-negative case control designs. Findings There was no reduction in S-antibody positivity in most clinical risk groups, however reduced S-antibody positivity and response was significant in the immunosuppressed group. Reduced vaccine effectiveness against clinical disease was also noted in the immunosuppressed group; after a second dose, effectiveness was moderate (Pfizer: 59.6%, 95%CI 18.0-80.1%; AstraZeneca 60.0%, 95%CI -63.6-90.2%). Interpretation In most clinical risk groups, immune response to primary vaccination was maintained and high levels of vaccine effectiveness were seen. Reduced antibody response and vaccine effectiveness were seen after 1 dose of vaccine amongst a broad immunosuppressed group, and second dose vaccine effectiveness was moderate. These findings support maximising coverage in immunosuppressed individuals and the policy of prioritisation of this group for third doses.

2.
J Infect ; 2022 Apr 08.
Article in English | MEDLINE | ID: covidwho-1778314

ABSTRACT

OBJECTIVES: To monitor changes in seroprevalence of SARS-CoV-2 antibodies in populations over time and between different demographic groups. METHODS: A subset of practices in the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) sentinel network provided serum samples, collected when volunteer patients had routine blood tests. We tested these samples for SARS-CoV-2 antibodies using Abbott (Chicago, USA), Roche (Basel, Switzerland) and/or Euroimmun (Luebeck, Germany) assays, and linked the results to the patients' primary care computerised medical records. We report seropositivity by region and age group, and additionally examined the effects of gender, ethnicity, deprivation, rurality, shielding recommendation and smoking status. RESULTS: We estimated seropositivity from patients aged 18-100 years old, which ranged from 4.1% (95% CI 3.1-5.3%) to 8.9% (95% CI 7.8-10.2%) across the different assays and time periods. We found higher Euroimmun seropositivity in younger age groups, people of Black and Asian ethnicity (compared to white), major conurbations, and non-smokers. We did not observe any significant effect by region, gender, deprivation, or shielding recommendation. CONCLUSIONS: Our results suggest that prior to the vaccination programme, most of the population remained unexposed to SARS-CoV-2.

3.
Arch Dis Child ; 2022 Mar 31.
Article in English | MEDLINE | ID: covidwho-1769846

ABSTRACT

OBJECTIVES: To describe rates and variation in uptake of pneumococcal and measles, mumps and rubella (MMR) vaccines in children and associated change in vaccine-preventable diseases (VPDs) across the first and second waves of the COVID-19 pandemic. METHODS: Retrospective database study of all children aged <19 registered with a general practice in the Oxford Royal College of General Practitioners Research and Surveillance Centre English national sentinel surveillance network between 2 November 2015 and 18 July 2021. RESULTS: Coverage of booster dose of pneumococcal vaccine decreased from 94.5% (95% CI 94.3% to 94.7%) at its height on International Organization for Standardization (ISO) week 47 (2020) to 93.6% (95% CI 93.4% to 93.8%) by the end of the study. Coverage of second dose of MMR decreased from 85.0% (95% CI 84.7% to 85.3%) at its height on ISO week 37 (2020) to 84.1% (95% CI 83.8% to 84.4%) by the end of the study. The break point in trends for MMR was at ISO week 34 (2020) (95% CI weeks 32-37 (2020)), while for pneumococcal vaccine the break point was later at ISO week 3 (2021) (95% CI week 53 (2020) to week 8 (2021)). Vaccination coverage for children of white ethnicity was less likely to decrease than other ethnicities. Rates of consultation for VPDs fell and remained low since August 2020. CONCLUSION: Childhood vaccination rates started to fall ahead of the onset of the second wave; this fall is accentuating ethnic, socioeconomic and geographical disparities in vaccine uptake and risks widening health disparities. Social distancing and school closures may have contributed to lower rates of associated VPDs, but there may be increased risk as these measures are removed.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324925

ABSTRACT

Background: There was excess mortality from the first wave of coronavirus 2019 infection (COVID-19), which mainly affected older people. To mitigate risk, the UK government recommended ‘shielding’ of vulnerable people through self-isolation for 12 weeks. We investigated the impact of primary care-reinforced shielding advice on all-cause mortality.Methods: We conducted a retrospective cohort study using a nationally representative English primary care database. We compare people aged >=40years who were recorded as being advised to shield using a fixed ratio of 1:1, matching (a mixture of exact and propensity score matching) to people with the same diagnoses not advised to shield (n=77,360 per group). Time-to-death was compared using Cox regression, reporting the hazard ratio (HR) of mortality between groups. A sensitivity analysis compared exact matched cohorts (n=24,752 shielded, n=61,566 exact matches). Findings: Over the follow-up period, we found a time-varying HR of mortality between groups. In the first 21 days, the mortality risk in people shielding was half those not (HR=0.50, 95%CI:0.41-0.59. p<0.0001). Over the remaining nine weeks, mortality risk was 54% higher in the shielded group (HR=1.54, 95%CI:1.41-1.70, p<0.0001). Beyond the shielding period, mortality risk was over two-and-a-half times higher in the shielded group (HR=2.61, 95%CI:2.38-2.87, p<0.0001).Interpretation: General practitioner-reinforced advice to shield halved the risk of mortality for 21 days compared to those who were not. Mortality risk became higher across the remainder of the shielding period, rising to two-and-a-half times greater post-shielding. Shielding may be beneficial in the next wave of COVID-19.Funding Statement: NIHR School of Primary Care, Public Health EnglandDeclaration of Interests: SdeL is the director of RCGP RSC. He has unrelated projects funded by GSK, Seqirus and has been a member of Global Advisory Boards for Seqirus and Sanofi. FDRH reports personal fees from Novartis and Boehringer Ingelheim and grants from Pfizer. All other authors declare no competing interests.Ethics Approval Statement: The RCGP RSC’s work concerning SARS-CoV-2 has been approved by Public Health England’s Caldicott Guardian Committee as fitting under Regulation 3 of the Health Service Control Patient Information Regulations 2002. The study was approved by RCGP.

5.
Br J Cancer ; 126(6): 948-956, 2022 04.
Article in English | MEDLINE | ID: covidwho-1585875

ABSTRACT

BACKGROUND: It 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. METHODS: Cohort 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. FINDINGS: Consultations 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. CONCLUSION: Due 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.


Subject(s)
COVID-19 , Neoplasms , COVID-19/epidemiology , Cohort Studies , Communicable Disease Control , Humans , Neoplasms/epidemiology , Neoplasms/therapy , Pandemics , Primary Health Care , Referral and Consultation
6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-293893

ABSTRACT

Background: There are few epidemiological studies of community cases in the current coronavirus-2019 (COVID-19) pandemic. We report on the first 500 COVID-19 cases identified through United Kingdom primary care surveillance and describe risk factors for testing COVID-19 positive. <br><br>Methods: The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), is a nationally representative primary care sentinel network sharing pseudonymised data, including virological test data for COVID-19. We used multivariable logistic regression models with multiple imputation to identify risk factors for positive COVID-19 tests within this surveillance programme. <br><br>Findings: We identified 3,802 COVID-19 results between 28/01/20 and 04/04/2020, 587 were positive. Greater odds of testing COVID-19 positive included: working-age people (40-64years) and older age, (≥75 years) versus 0-17 year olds (adjusted odds ratio [aOR] 5.26, 95%CI:3.26-8.49 and 5.17,95%CI:2.99-8.92, respectively);male gender (aOR 1.56, 95%CI:1.28-1.90);black and mixed ethnicity compared with white (aOR 4.55, 95%CI:2.55-8.10 and 1.84 95%CO:1.1-3.14, respectively));urban compared with rural areas (aOR 4.58, 95%CI:3.57-5.88);people with chronic kidney disease (CKD) (aOR 1.88, 95%CI:1.29-2.75) and increasing body mass index (aOR 1.02, 95%CI:1.00-1.03). People in the least deprived deprivation quintile had lower odds of a positive test (aOR 0.49 95%CI:0.36-0.65) as did current smokers (aOR 0.53, 95%CI:0.38-0.74). <br><br>Interpretation: A positive COVID-19 test result in primary care was associated with similar risk factors for severe outcomes seen in hospital settings, with the exception of smoking. We provide early evidence of potential sociodemographic factors associated with a positive test, including ethnicity, deprivation, population density, and CKD. <br><br>Funding Statement: Public Health England provides the core funding for RCGP RSC, no specific funding was provided for this analysis.<br><br>Declaration of Interests: The authors have no competing interests. SdeL is the Director of the Oxford RCGP RSC, RB, JS, FF, EK and GH are part funded by PHE;and CO and AC by a Wellcome Biomedical resources grant (212763/Z/18/Z). JD is funded by Wellcome Trust (216421/Z/19/Z).<br><br>Ethics Approval Statement: This study was approved by the RCGP RSC study approval committee and was classified as a study of “usual practice”. Therefore, no further ethical approval was required.

7.
JMIR Res Protoc ; 10(10): e30083, 2021 Oct 05.
Article in English | MEDLINE | ID: covidwho-1450770

ABSTRACT

BACKGROUND: Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient's clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient's risk of hospital admission, deterioration, and death. OBJECTIVE: This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization. METHODS: After the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine-learning approaches to impute the missing data for the final analysis. For predictive model development, we will use multiple logistic regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection. RESULTS: As of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform. CONCLUSIONS: The methodology for the development of the RECAP-V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritize COVID-19 patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30083.

8.
JMIR Res Protoc ; 10(10): e30083, 2021 Oct 05.
Article in English | MEDLINE | ID: covidwho-1381352

ABSTRACT

BACKGROUND: Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient's clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient's risk of hospital admission, deterioration, and death. OBJECTIVE: This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization. METHODS: After the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine-learning approaches to impute the missing data for the final analysis. For predictive model development, we will use multiple logistic regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection. RESULTS: As of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform. CONCLUSIONS: The methodology for the development of the RECAP-V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritize COVID-19 patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30083.

9.
BJGP Open ; 5(5)2021 Oct.
Article in English | MEDLINE | ID: covidwho-1328146

ABSTRACT

BACKGROUND: The Platform Randomised trial of INterventions against COVID-19 In older peoPLE (PRINCIPLE) has provided in-pandemic evidence that azithromycin and doxycycline were not beneficial in the early primary care management of coronavirus 2019 disease (COVID-19). AIM: To explore the extent of in-pandemic azithromycin and doxycycline use, and the scope for trial findings impacting on practice. DESIGN & SETTING: Crude rates of prescribing and respiratory tract infections (RTI) in 2020 were compared with 2019, using the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC). METHOD: Negative binomial models were used to compare azithromycin and doxycycline prescribing, lower respiratory tract infections (LRTI), upper respiratory tract infections (URTI), and influenza-like illness (ILI) in 2020 with 2019; reporting incident rate ratios (IRR) between years, and 95% confidence intervals (95% CI). RESULTS: Azithromycin prescriptions increased 7% in 2020 compared with 2019, whereas doxycycline decreased by 7%. Concurrently, LRTI and URTI incidence fell by over half (58.3% and 54.4%, respectively) while ILI rose slightly (6.4%). The overall percentage of RTI-prescribed azithromycin rose from 0.51% in 2019 to 0.72% in 2020 (risk difference 0.214%; 95% CI = 0.211 to 0.217); doxycycline rose from 11.86% in 2019 to 15.79% in 2020 (risk difference 3.93%; 95% CI = 3.73 to 4.14). The adjusted IRR showed azithromycin prescribing was 22% higher in 2020 (IRR = 1.22; 95% CI = 1.19 to 1.26; P<0.0001). For every unit rise in confirmed COVID-19 there was an associated 3% rise in prescription (IRR = 1.03; 95% CI = 1.02 to 1.03; P<0.0001); whereas these measures were static for doxycycline. CONCLUSION: PRINCIPLE demonstrates scope for improved antimicrobial stewardship during a pandemic.

10.
Stud Health Technol Inform ; 281: 759-763, 2021 May 27.
Article in English | MEDLINE | ID: covidwho-1247806

ABSTRACT

The effect of the 2020 pandemic, and of the national measures introduced to control it, is not yet fully understood. The aim of this study was to investigate how different types of primary care data can help quantify the effect of the coronavirus disease (COVID-19) crisis on mental health. A retrospective cohort study investigated changes in weekly counts of mental health consultations and prescriptions. The data were extracted from one the UK's largest primary care databases between January 1st 2015 and October 31st 2020 (end of follow-up). The 2020 trends were compared to the 2015-19 average with 95% confidence intervals using longitudinal plots and analysis of covariance (ANCOVA). A total number of 504 practices (7,057,447 patients) contributed data. During the period of national restrictions, on average, there were 31% (3957 ± 269, p < 0.001) fewer events and 6% (4878 ± 1108, p < 0.001) more prescriptions per week as compared to the 2015-19 average. The number of events was recovering, increasing by 75 (± 29, p = 0.012) per week. Prescriptions returned to the 2015-19 levels by the end of the study (p = 0.854). The significant reduction in the number of consultations represents part of the crisis. Future service planning and quality improvements are needed to reduce the negative effect on health and healthcare.


Subject(s)
COVID-19 , Mental Health , Humans , Prescriptions , Primary Health Care , Referral and Consultation , Retrospective Studies , SARS-CoV-2
11.
J Infect ; 83(2): 228-236, 2021 08.
Article in English | MEDLINE | ID: covidwho-1230619

ABSTRACT

OBJECTIVES: To mitigate risk of mortality from coronavirus 2019 infection (COVID-19), the UK government recommended 'shielding' of vulnerable people through self-isolation for 12 weeks. METHODS: A retrospective cohort study using a nationally representative English primary care database comparing people aged >= 40 years who were recorded as being advised to shield using a fixed ratio of 1:1, matching to people with the same diagnoses not advised to shield (n = 77,360 per group). Time-to-death was compared using Cox regression, reporting the hazard ratio (HR) of mortality between groups. A sensitivity analysis compared exact matched cohorts (n = 24,752 shielded, n = 61,566 exact matches). RESULTS: We found a time-varying HR of mortality between groups. In the first 21 days, the mortality risk in people shielding was half those not (HR = 0.50, 95%CI:0.41-0.59. p < 0.0001). Over the remaining nine weeks, mortality risk was 54% higher in the shielded group (HR=1.54, 95%CI:1.41-1.70, p < 0.0001). Beyond the shielding period, mortality risk was over two-and-a-half times higher in the shielded group (HR=2.61, 95%CI:2.38-2.87, p < 0.0001). CONCLUSIONS: Shielding halved the risk of mortality for 21 days. Mortality risk became higher across the remainder of the shielding period, rising to two-and-a-half times greater post-shielding. Shielding may be beneficial in the next wave of COVID-19.


Subject(s)
COVID-19 , Cohort Studies , Humans , Primary Health Care , Retrospective Studies , SARS-CoV-2
12.
JMIR Res Protoc ; 10(5): e29072, 2021 May 25.
Article in English | MEDLINE | ID: covidwho-1211771

ABSTRACT

BACKGROUND: During the pandemic, remote consultations have become the norm for assessing patients with signs and symptoms of COVID-19 to decrease the risk of transmission. This has intensified the clinical uncertainty already experienced by primary care clinicians when assessing patients with suspected COVID-19 and has prompted the use of risk prediction scores, such as the National Early Warning Score (NEWS2), to assess severity and guide treatment. However, the risk prediction tools available have not been validated in a community setting and are not designed to capture the idiosyncrasies of COVID-19 infection. OBJECTIVE: The objective of this study is to produce a multivariate risk prediction tool, RECAP-V1 (Remote COVID-19 Assessment in Primary Care), to support primary care clinicians in the identification of those patients with COVID-19 that are at higher risk of deterioration and facilitate the early escalation of their treatment with the aim of improving patient outcomes. METHODS: The study follows a prospective cohort observational design, whereby patients presenting in primary care with signs and symptoms suggestive of COVID-19 will be followed and their data linked to hospital outcomes (hospital admission and death). Data collection will be carried out by primary care clinicians in four arms: North West London Clinical Commissioning Groups (NWL CCGs), Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), Covid Clinical Assessment Service (CCAS), and South East London CCGs (Doctaly platform). The study involves the use of an electronic template that incorporates a list of items (known as RECAP-V0) thought to be associated with disease outcome according to previous qualitative work. Data collected will be linked to patient outcomes in highly secure environments. We will then use multivariate logistic regression analyses for model development and validation. RESULTS: Recruitment of participants started in October 2020. Initially, only the NWL CCGs and RCGP RSC arms were active. As of March 24, 2021, we have recruited a combined sample of 3827 participants in these two arms. CCAS and Doctaly joined the study in February 2021, with CCAS starting the recruitment process on March 15, 2021. The first part of the analysis (RECAP-V1 model development) is planned to start in April 2021 using the first half of the NWL CCGs and RCGP RSC combined data set. Posteriorly, the model will be validated with the rest of the NWL CCGs and RCGP RSC data as well as the CCAS and Doctaly data sets. The study was approved by the Research Ethics Committee on May 27, 2020 (Integrated Research Application System number: 283024, Research Ethics Committee reference number: 20/NW/0266) and badged as National Institute of Health Research Urgent Public Health Study on October 14, 2020. CONCLUSIONS: We believe the validated RECAP-V1 early warning score will be a valuable tool for the assessment of severity in patients with suspected COVID-19 in the community, either in face-to-face or remote consultations, and will facilitate the timely escalation of treatment with the potential to improve patient outcomes. TRIAL REGISTRATION: ISRCTN registry ISRCTN13953727; https://www.isrctn.com/ISRCTN13953727. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29072.

13.
PLoS One ; 16(3): e0248123, 2021.
Article in English | MEDLINE | ID: covidwho-1133687

ABSTRACT

INTRODUCTION: Rapid Point of Care Testing (POCT) for influenza could be used to provide information on influenza vaccine effectiveness (IVE) as well as influencing clinical decision-making in primary care. METHODS: We undertook a test negative case control study to estimate the overall and age-specific (6 months-17 years, 18-64 years, ≥65 years old) IVE against medically attended POCT-confirmed influenza. The study took place over the winter of 2019-2020 and was nested within twelve general practices that are part of the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), the English sentinel surveillance network. RESULTS: 648 POCT were conducted. 193 (29.7%) of those who were swabbed had received the seasonal influenza vaccine. The crude unadjusted overall IVE was 46.1% (95% CI: 13.9-66.3). After adjusting for confounders the overall IVE was 26.0% (95% CI: 0-65.5). In total 211 patients were prescribed an antimicrobial after swab testing. Given a positive influenza POCT result, the odds ratio (OR) of receiving an antiviral was 21.1 (95%CI: 2.4-182.2, p = <0.01) and the OR of being prescribed an antibiotic was 0.6 (95%CI: 0.4-0.9, p = <0.01). DISCUSSION: Using influenza POCT in a primary care sentinel surveillance network to estimate IVE is feasible and provides comparable results to published IVE estimates. A further advantage is that near patient testing of influenza is associated with improvements in appropriate antiviral and antibiotic use. Larger, randomised studies are needed in primary care to see if these trends are still present and to explore their impact on outcomes.


Subject(s)
Influenza Vaccines/therapeutic use , Influenza, Human/diagnosis , Point-of-Care Testing , Sentinel Surveillance , Adolescent , Adult , Age Factors , Aged , Child , Child, Preschool , England , Female , Humans , Infant , Influenza, Human/prevention & control , Male , Middle Aged , Primary Health Care/methods , Primary Health Care/statistics & numerical data , Seasons , Treatment Outcome , Young Adult
14.
Hypertension ; 77(3): 846-855, 2021 03 03.
Article in English | MEDLINE | ID: covidwho-1083929

ABSTRACT

Hypertension has been identified as a risk factor for coronavirus disease 2019 (COVID-19) and associated adverse outcomes. This study examined the association between preinfection blood pressure (BP) control and COVID-19 outcomes using data from 460 general practices in England. Eligible patients were adults with hypertension who were tested or diagnosed with COVID-19. BP control was defined by the most recent BP reading within 24 months of the index date (January 1, 2020). BP was defined as controlled (<130/80 mm Hg), raised (130/80-139/89 mm Hg), stage 1 uncontrolled (140/90-159/99 mm Hg), or stage 2 uncontrolled (≥160/100 mm Hg). The primary outcome was death within 28 days of COVID-19 diagnosis. Secondary outcomes were COVID-19 diagnosis and COVID-19-related hospital admission. Multivariable logistic regression was used to examine the association between BP control and outcomes. Of the 45 418 patients (mean age, 67 years; 44.7% male) included, 11 950 (26.3%) had controlled BP. These patients were older, had more comorbidities, and had been diagnosed with hypertension for longer. A total of 4277 patients (9.4%) were diagnosed with COVID-19 and 877 died within 28 days. Individuals with stage 1 uncontrolled BP had lower odds of COVID-19 death (odds ratio, 0.76 [95% CI, 0.62-0.92]) compared with patients with well-controlled BP. There was no association between BP control and COVID-19 diagnosis or hospitalization. These findings suggest BP control may be associated with worse COVID-19 outcomes, possibly due to these patients having more advanced atherosclerosis and target organ damage. Such patients may need to consider adhering to stricter social distancing, to limit the impact of COVID-19 as future waves of the pandemic occur.


Subject(s)
Blood Pressure/drug effects , COVID-19/epidemiology , Hypertension/epidemiology , Pandemics , SARS-CoV-2 , Adult , Aged , Aged, 80 and over , Antihypertensive Agents/therapeutic use , Atherosclerosis/epidemiology , COVID-19/prevention & control , Comorbidity , England/epidemiology , Female , Follow-Up Studies , Hospitalization/statistics & numerical data , Humans , Hypertension/drug therapy , Logistic Models , Male , Middle Aged , Odds Ratio , Primary Health Care/statistics & numerical data , Retrospective Studies , Risk Factors , Severity of Illness Index , Survival Analysis , Treatment Outcome
15.
Diagn Progn Res ; 5(1): 4, 2021 Feb 08.
Article in English | MEDLINE | ID: covidwho-1069608

ABSTRACT

BACKGROUND: The aim of RApid community Point-of-care Testing fOR COVID-19 (RAPTOR-C19) is to assess the diagnostic accuracy of multiple current and emerging point-of-care tests (POCTs) for active and past SARS-CoV2 infection in the community setting. RAPTOR-C19 will provide the community testbed to the COVID-19 National DiagnOstic Research and Evaluation Platform (CONDOR). METHODS: RAPTOR-C19 incorporates a series of prospective observational parallel diagnostic accuracy studies of SARS-CoV2 POCTs against laboratory and composite reference standards in patients with suspected current or past SARS-CoV2 infection attending community settings. Adults and children with suspected current SARS-CoV2 infection who are having an oropharyngeal/nasopharyngeal (OP/NP) swab for laboratory SARS-CoV2 reverse transcriptase Digital/Real-Time Polymerase Chain Reaction (d/rRT-PCR) as part of clinical care or community-based testing will be invited to participate. Adults (≥ 16 years) with suspected past symptomatic infection will also be recruited. Asymptomatic individuals will not be eligible. At the baseline visit, all participants will be asked to submit samples for at least one candidate point-of-care test (POCT) being evaluated (index test/s) as well as an OP/NP swab for laboratory SARS-CoV2 RT-PCR performed by Public Health England (PHE) (reference standard for current infection). Adults will also be asked for a blood sample for laboratory SARS-CoV-2 antibody testing by PHE (reference standard for past infection), where feasible adults will be invited to attend a second visit at 28 days for repeat antibody testing. Additional study data (e.g. demographics, symptoms, observations, household contacts) will be captured electronically. Sensitivity, specificity, positive, and negative predictive values for each POCT will be calculated with exact 95% confidence intervals when compared to the reference standard. POCTs will also be compared to composite reference standards constructed using paired antibody test results, patient reported outcomes, linked electronic health records for outcomes related to COVID-19 such as hospitalisation or death, and other test results. DISCUSSION: High-performing POCTs for community use could be transformational. Real-time results could lead to personal and public health impacts such as reducing onward household transmission of SARS-CoV2 infection, improving surveillance of health and social care staff, contributing to accurate prevalence estimates, and understanding of SARS-CoV2 transmission dynamics in the population. In contrast, poorly performing POCTs could have negative effects, so it is necessary to undertake community-based diagnostic accuracy evaluations before rolling these out. TRIAL REGISTRATION: ISRCTN, ISRCTN14226970.

17.
JMIR Public Health Surveill ; 6(4): e21434, 2020 11 17.
Article in English | MEDLINE | ID: covidwho-976102

ABSTRACT

BACKGROUND: Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. OBJECTIVE: This study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities. METHODS: We described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system-independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks. RESULTS: Our use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225). CONCLUSIONS: The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.


Subject(s)
Biological Ontologies , COVID-19/epidemiology , Primary Health Care/methods , Sentinel Surveillance , Humans , Pandemics
18.
Br J Gen Pract ; 70(701): e890-e898, 2020 12.
Article in English | MEDLINE | ID: covidwho-881363

ABSTRACT

BACKGROUND: The SARS-CoV-2 pandemic has passed its first peak in Europe. AIM: To describe the mortality in England and its association with SARS-CoV-2 status and other demographic and risk factors. DESIGN AND SETTING: Cross-sectional analyses of people with known SARS-CoV-2 status in the Oxford RCGP Research and Surveillance Centre (RSC) sentinel network. METHOD: Pseudonymised, coded clinical data were uploaded from volunteer general practice members of this nationally representative network (n = 4 413 734). All-cause mortality was compared with national rates for 2019, using a relative survival model, reporting relative hazard ratios (RHR), and 95% confidence intervals (CI). A multivariable adjusted odds ratios (OR) analysis was conducted for those with known SARS-CoV-2 status (n = 56 628, 1.3%) including multiple imputation and inverse probability analysis, and a complete cases sensitivity analysis. RESULTS: Mortality peaked in week 16. People living in households of ≥9 had a fivefold increase in relative mortality (RHR = 5.1, 95% CI = 4.87 to 5.31, P<0.0001). The ORs of mortality were 8.9 (95% CI = 6.7 to 11.8, P<0.0001) and 9.7 (95% CI = 7.1 to 13.2, P<0.0001) for virologically and clinically diagnosed cases respectively, using people with negative tests as reference. The adjusted mortality for the virologically confirmed group was 18.1% (95% CI = 17.6 to 18.7). Male sex, population density, black ethnicity (compared to white), and people with long-term conditions, including learning disability (OR = 1.96, 95% CI = 1.22 to 3.18, P = 0.0056) had higher odds of mortality. CONCLUSION: The first SARS-CoV-2 peak in England has been associated with excess mortality. Planning for subsequent peaks needs to better manage risk in males, those of black ethnicity, older people, people with learning disabilities, and people who live in multi-occupancy dwellings.


Subject(s)
COVID-19 , Noncommunicable Diseases/epidemiology , SARS-CoV-2/isolation & purification , Age Factors , COVID-19/diagnosis , COVID-19/epidemiology , Electronic Health Records/statistics & numerical data , England/epidemiology , Family Characteristics , Female , Humans , Male , Middle Aged , Mortality , Risk Assessment/methods , Risk Factors , Sentinel Surveillance , Sex Factors
19.
JMIR Public Health Surveill ; 6(3): e19773, 2020 07 02.
Article in English | MEDLINE | ID: covidwho-791866

ABSTRACT

BACKGROUND: Routinely recorded primary care data have been used for many years by sentinel networks for surveillance. More recently, real world data have been used for a wider range of research projects to support rapid, inexpensive clinical trials. Because the partial national lockdown in the United Kingdom due to the coronavirus disease (COVID-19) pandemic has resulted in decreasing community disease incidence, much larger numbers of general practices are needed to deliver effective COVID-19 surveillance and contribute to in-pandemic clinical trials. OBJECTIVE: The aim of this protocol is to describe the rapid design and development of the Oxford Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID) and its first two platforms. The Surveillance Platform will provide extended primary care surveillance, while the Trials Platform is a streamlined clinical trials platform that will be integrated into routine primary care practice. METHODS: We will apply the FAIR (Findable, Accessible, Interoperable, and Reusable) metadata principles to a new, integrated digital health hub that will extract routinely collected general practice electronic health data for use in clinical trials and provide enhanced communicable disease surveillance. The hub will be findable through membership in Health Data Research UK and European metadata repositories. Accessibility through an online application system will provide access to study-ready data sets or developed custom data sets. Interoperability will be facilitated by fixed linkage to other key sources such as Hospital Episodes Statistics and the Office of National Statistics using pseudonymized data. All semantic descriptors (ie, ontologies) and code used for analysis will be made available to accelerate analyses. We will also make data available using common data models, starting with the US Food and Drug Administration Sentinel and Observational Medical Outcomes Partnership approaches, to facilitate international studies. The Surveillance Platform will provide access to data for health protection and promotion work as authorized through agreements between Oxford, the Royal College of General Practitioners, and Public Health England. All studies using the Trials Platform will go through appropriate ethical and other regulatory approval processes. RESULTS: The hub will be a bottom-up, professionally led network that will provide benefits for member practices, our health service, and the population served. Data will only be used for SQUIRE (surveillance, quality improvement, research, and education) purposes. We have already received positive responses from practices, and the number of practices in the network has doubled to over 1150 since February 2020. COVID-19 surveillance has resulted in tripling of the number of virology sites to 293 (target 300), which has aided the collection of the largest ever weekly total of surveillance swabs in the United Kingdom as well as over 3000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology samples. Practices are recruiting to the PRINCIPLE (Platform Randomised trial of INterventions against COVID-19 In older PeopLE) trial, and these participants will be followed up through ORCHID. These initial outputs demonstrate the feasibility of ORCHID to provide an extended national digital health hub. CONCLUSIONS: ORCHID will provide equitable and innovative use of big data through a professionally led national primary care network and the application of FAIR principles. The secure data hub will host routinely collected general practice data linked to other key health care repositories for clinical trials and support enhanced in situ surveillance without always requiring large volume data extracts. ORCHID will support rapid data extraction, analysis, and dissemination with the aim of improving future research and development in general practice to positively impact patient care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19773.


Subject(s)
Clinical Trials as Topic , Coronavirus Infections/epidemiology , General Practice/organization & administration , Medical Records Systems, Computerized , Pneumonia, Viral/epidemiology , Public Health Surveillance , COVID-19 , Humans , Pandemics , Primary Health Care/organization & administration , Societies, Medical , United Kingdom/epidemiology
20.
BMJ Open ; 10(8): e038562, 2020 08 24.
Article in English | MEDLINE | ID: covidwho-781178

ABSTRACT

INTRODUCTION: Safety-netting in primary care is the best practice in cancer diagnosis, ensuring that patients are followed up until symptoms are explained or have resolved. Currently, clinicians use haphazard manual solutions. The ubiquitous use of electronic health records provides an opportunity to standardise safety-netting practices.A new electronic safety-netting toolkit has been introduced to provide systematic ways to track and follow up patients. We will evaluate the effectiveness of this toolkit, which is embedded in a major primary care clinical system in England:Egerton Medical Information System(EMIS)-Web. METHODS AND ANALYSIS: We will conduct a stepped-wedge cluster RCT in 60 general practices within the RCGP Research and Surveillance Centre (RSC) network. Groups of 10 practices will be randomised into the active phase at 2-monthly intervals over 12 months. All practices will be activated for at least 2 months. The primary outcome is the primary care interval measured as days between the first recorded symptom of cancer (within the year prior to diagnosis) and the subsequent referral to secondary care. Other outcomes include referrals rates and rates of direct access cancer investigation.Analysis of the clustered stepped-wedge design will model associations using a fixed effect for intervention condition of the cluster at each time step, a fixed effect for time and other covariates, and then include a random effect for practice and for patient to account for correlation between observations from the same centre and from the same participant. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the North West-Greater Manchester West National Health Service Research Ethics Committee (REC Reference 19/NW/0692). Results will be disseminated in peer-reviewed journals and conferences, and sent to participating practices. They will be published on the University of Oxford Nuffield Department of Primary Care and RCGP RSC websites. TRIAL REGISTRATION NUMBER: ISRCTN15913081; Pre-results.


Subject(s)
Electronic Health Records , Neoplasms , Electronics , England , Humans , Neoplasms/diagnosis , Neoplasms/therapy , Primary Health Care , Randomized Controlled Trials as Topic , State Medicine
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