The epidemiology of alopecia areata: a population-based cohort study in UK primary care.

BACKGROUND: There is a lack of population-based information on the disease burden and management of alopecia areata (AA). OBJECTIVES: To describe the epidemiology of AA, focusing on incidence, demographics and patterns of healthcare utilization. METHODS: Population-based cohort study of 4·16 million adults and children, using UK electronic primary care records from the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network database, 2009-2018. The incidence and point prevalence of AA were estimated. Variation in AA incidence by age, sex, deprivation, geographical distribution and ethnicity was examined. Patterns of healthcare utilization were evaluated in people with incident AA. RESULTS: The AA incidence rate was 0·26 per 1000 person-years. AA point prevalence in 2018 was 0·58% in adults. AA onset peaked at age 25-29 years for both sexes, although the peak was broader in females. People of nonwhite ethnicity were more likely to present with AA, especially those of Asian ethnicity [incidence rate ratio (IRR) 3·32 (95% confidence interval 3·11-3·55)]. Higher AA incidence was associated with social deprivation [IRR most vs. least deprived quintile 1·47 (1·37-1·59)] and urban living [IRR 1·23 (1·14-1·32)]. People of higher social deprivation were less likely to be referred for specialist dermatology review. CONCLUSIONS: By providing the first large-scale estimates of the incidence and point prevalence of AA, our study helps to understand the burden of AA on the population. Understanding the variation in AA onset between different population groups may give insight into the pathogenesis of AA and its management.

Investigating the association between COVID-19 vaccination and care home outbreak frequency and duration.

OBJECTIVES: At the end of 2020, many countries commenced a vaccination programme against SARS-CoV-2. Public health authorities aim to prevent and interrupt outbreaks of infectious disease in social care settings. We aimed to investigate the association between the introduction of the vaccination programme and the frequency and duration of COVID-19 outbreaks in Northern Ireland (NI). STUDY DESIGN: We undertook an ecological study using routinely available national data. METHODS: We used Poisson regression to measure the relationship between the number of RT-PCR confirmed COVID-19 outbreaks in care homes, and as a measure of community COVID-19 prevalence, the Office for National Statistics COVID-19 Infection Survey estimated the number of people testing positive for COVID-19 in NI. We estimated the change in this relationship and estimated the expected number of care home outbreaks in the absence of the vaccination programme. A Cox proportional hazards model estimated the hazard ratio of a confirmed COVID-19 care home outbreak closure. RESULTS: Care home outbreaks reduced by two-thirds compared to expected following the introduction of the vaccination programme, from a projected 1625 COVID-19 outbreaks (95% prediction interval 1553-1694) between 7 December 2020 and 28 October 2021 to an observed 501. We estimated an adjusted hazard ratio of 2.53 of the outbreak closure assuming a 21-day lag for immunity. CONCLUSIONS: These findings describe the association of the vaccination with a reduction in outbreak frequency and duration across NI care homes. This indicates probable reduced harm and disruption from COVID-19 in social care settings following vaccination. Future research using individual level data from care home residents will be needed to investigate the effectiveness of the vaccines and the duration of their effects.

Sodium-glucose co-transporter-2 inhibitor cardiovascular outcome trials and generalizability to English primary care.

AIM: To identify people in English primary care with equivalent cardiovascular risk to participants in the sodium-glucose co-transporter-2 inhibitor (SGLT-2i) cardiovascular outcome trials (CVOTs). A secondary objective was to report the usage of SGLT-2is. METHODS: Cross-sectional analysis of people registered with participating practices in the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network on the 31 December 2016. We derived: (1) proportions of the primary care population eligible for inclusion in each SGLT-2i CVOT (CANVAS, DECLARE, EMPA-REG and VERTIS); (2) characteristics of the eligible population compared with trial participants (demographics, disease duration and vascular risk); and (3) differences within the eligible population prescribed SGLT-2is. RESULTS: The proportions of people with type 2 diabetes (N = 84 394) meeting the inclusion criteria for each CVOT were: DECLARE 27% [95% confidence interval (CI) 26.5-27.1]; CANVAS 17% (16.6-17.1); VERTIS 7% (7.1-7.4); and EMPA-REG 7% (6.5-6.8). Primary care populations fulfilling inclusion criteria were 5-8 years older than trial cohorts, and <10% with inclusion criteria of each trial were prescribed an SGLT-2i; a greater proportion were men, and of white ethnicity. CONCLUSIONS: There was variation in proportions of the primary care type 2 diabetes population fulfilling inclusion criteria of SGLT-2i CVOTs. The more stringent the inclusion criteria, the lower the proportion identified in a primary care setting. Prescription rates for SGLT-2is were low in this national database, and there were demographic disparities in prescribing.

Managing COVID-19 within and across health systems: why we need performance intelligence to coordinate a global response.

BACKGROUND: The COVID-19 pandemic is a complex global public health crisis presenting clinical, organisational and system-wide challenges. Different research perspectives on health are needed in order to manage and monitor this crisis. Performance intelligence is an approach that emphasises the need for different research perspectives in supporting health systems' decision-makers to determine policies based on well-informed choices. In this paper, we present the viewpoint of the Innovative Training Network for Healthcare Performance Intelligence Professionals (HealthPros) on how performance intelligence can be used during and after the COVID-19 pandemic. DISCUSSION: A lack of standardised information, paired with limited discussion and alignment between countries contribute to uncertainty in decision-making in all countries. Consequently, a plethora of different non-data-driven and uncoordinated approaches to address the outbreak are noted worldwide. Comparative health system research is needed to help countries shape their response models in social care, public health, primary care, hospital care and long-term care through the different phases of the pandemic. There is a need in each phase to compare context-specific bundles of measures where the impact on health outcomes can be modelled using targeted data and advanced statistical methods. Performance intelligence can be pursued to compare data, construct indicators and identify optimal strategies. Embracing a system perspective will allow countries to take coordinated strategic decisions while mitigating the risk of system collapse.A framework for the development and implementation of performance intelligence has been outlined by the HealthPros Network and is of pertinence. Health systems need better and more timely data to govern through a pandemic-induced transition period where tensions between care needs, demand and capacity are exceptionally high worldwide. Health systems are challenged to ensure essential levels of healthcare towards all patients, including those who need routine assistance. CONCLUSION: Performance intelligence plays an essential role as part of a broader public health strategy in guiding the decisions of health system actors on the implementation of contextualised measures to tackle COVID-19 or any future epidemic as well as their effect on the health system at large. This should be based on commonly agreed-upon standardised data and fit-for-purpose indicators, making optimal use of existing health information infrastructures. The HealthPros Network can make a meaningful contribution.

Saving bones without risking brain-bisphosphonates and risk of stroke: matched case-control study.

We investigated the association between bisphosphonate treatment and the risk of stroke using a large routine clinical dataset. We found no association between bisphosphonate treatment and risk of stroke, after adjusting for large number of clinical and demographic confounders. INTRODUCTION: There is conflicting evidence on the link between bisphosphonates and stroke with studies variously showing increased, decreased or unchanged risk. We investigated the association between bisphosphonate treatment and the risk of stroke using a large routine clinical dataset. METHODS: We used a matched nested case-control study design analysing routinely collected electronic data from patients registered at primary care practices in England participating in the Royal College of General Practitioners Research and Surveillance Centre. Cases were patients aged 18 years or over, either living or dead, recorded as having had a stroke in the period 1 January 2005 to 31 March 2016. Each case was matched to one control according to age, sex, general practice attended and calendar time. Data were analysed using Stata, version 14.2. and RStudio, version 1.1.463. Conditional logistic regression was used to determine odds ratios for stroke according to bisphosphonate treatment and duration in cases compared with controls. We adjusted for disease risk groups, cardiovascular risk factors, treatments, smoking status, alcohol consumption, ethnicity, bisphosphonate types, fracture and socioeconomic status using IMD (Index of Multiple Deprivation). RESULTS: We included 31,414 cases of stroke with an equal number of matched controls. Overall, 83.2% of cases and controls were aged 65 years or older, and there were similar proportions of females (51.5%) and males (48.5%). Bisphosphonate treatment was not associated with stroke after adjusting for the wide range of confounders considered (OR 0.86, 95% CI 0.62-1.19). CONCLUSIONS: We found no association between bisphosphonate treatment and risk of stroke, after adjusting for other confounders.

Obstructive sleep apnoea in Type 2 diabetes mellitus: increased risk for overweight as well as obese people included in a national primary care database analysis.

AIMS: To determine obstructive sleep apnoea prevalence in people with Type 2 or Type 1 diabetes in a national primary care setting, stratified by BMI category, and to explore the relationship between patient characteristics and obstructive sleep apnoea. METHODS: Using the Royal College of General Practitioners Research and Surveillance Centre database, a cross-sectional analysis was conducted. Diabetes type was identified using a seven-step algorithm and was grouped by Type 2 diabetes, Type 1 diabetes and no diabetes. The clinical characteristics of these groups were analysed, BMI-stratified obstructive sleep apnoea prevalence rates were calculated, and a multilevel logistic regression analysis was completed on the Type 2 diabetes group. RESULTS: Analysis of 1 275 461 adult records in the Royal College of General Practitioners Research and Surveillance Centre network showed that obstructive sleep apnoea was prevalent in 0.7%. In people with Type 2 diabetes, obstructive sleep apnoea prevalence increased with each increasing BMI category, from 0.5% in those of normal weight to 9.6% in those in the highest obesity class. By comparison, obstructive sleep apnoea prevalence rates for these BMI categories in Type 1 diabetes were 0.3% and 4.3%, and in those without diabetes 1.2% and 3.9%, respectively. Obstructive sleep apnoea was more prevalent in men than women in both diabetes types. When known risk factors were adjusted for, there were increased odds ratios for obstructive sleep apnoea in people with Type 2 diabetes in the overweight and higher BMI categories. CONCLUSIONS: Obstructive sleep apnoea was reported in people with both types of diabetes across the range of overweight categories and not simply in the highest obesity class.

Estimating the burden on general practitioner services in England from increases in respiratory disease associated with seasonal respiratory pathogen activity.

Understanding the burden of respiratory pathogens on health care is key to improving public health emergency response and interventions. In temperate regions, there is a large seasonal rise in influenza and other respiratory pathogens. We have examined the associations between individual pathogens and reported respiratory tract infections to estimate attributable burden. We used multiple linear regression to model the relationship between doctor consultation data and laboratory samples from week 3 2011 until week 37 2015. We fitted separate models for consultation data with in-hours and out-of-hours doctor services, stratified by different age bands. The best fitting all ages models (R2 > 80%) for consultation data resulted in the greatest burden being associated with influenza followed by respiratory syncytial virus (RSV). For models of adult age bands, there were significant associations between consultation data and invasive Streptococcus pneumoniae. There were also smaller numbers of consultations significantly associated with rhinovirus, parainfluenza, and human metapneumovirus. We estimate that a general practice with 10 000 patients would have seen an additional 18 respiratory tract infection consultations per winter week of which six had influenza and four had RSV. Our results are important for the planning of health care services to minimise the impact of winter pressures. •Respiratory pathogen incidence explains over 80% of seasonal variation in respiratory consultation data.•Influenza and RSV are associated with the biggest seasonal rises in respiratory consultation counts.•A third of consultation counts associated with respiratory pathogens were due to influenza.

Significant spike in excess mortality in England in winter 2014/15 - influenza the likely culprit.

Significant increases in excess all-cause mortality, particularly in the elderly, were observed during the winter of 2014/15 in England. With influenza A(H3N2) the dominant circulating influenza A subtype, this paper determines the contribution of influenza to this excess controlling for weather. A standardised multivariable Poisson regression model was employed with weekly all-cause deaths the dependent variable for the period 2008-2015. Adjusting for extreme temperature, a total of 26 542 (95% CI 25 301-27 804) deaths in 65+ and 1942 (95% CI 1834-2052) in 15-64-year-olds were associated with influenza from week 40, 2014 to week 20, 2015. This is compatible with the circulation of influenza A(H3N2). It is the largest estimated number of influenza-related deaths in England since prior to 2008/09. The findings highlight the potential health impact of influenza and the important role of the annual influenza vaccination programme that is required to protect the population including the elderly, who are vulnerable to a severe outcome.

Glucose test provenance recording in UK primary care: was that fasted or random?

AIMS: To describe the proportion of glucose tests with unrecorded provenance in routine primary care data and identify the impact on clinical practice. METHODS: A cross-sectional analysis was conducted of blood glucose measurements from the Royal College of General Practitioner Research and Surveillance Centre database, which includes primary care records from >100 practices across England and Wales. All blood glucose results recorded during 2013 were identified. Tests were grouped by provenance (fasting, oral glucose tolerance test, random, none specified and other). A clinical audit in a single primary care practice was also performed to identify the impact of failing to record glucose provenance on diabetes diagnosis. RESULTS: A total of 2 137 098 people were included in the cross-sectional analysis. Of 203 350 recorded glucose measurements the majority (117 893; 58%) did not have any provenance information. The most commonly reported provenance was fasting glucose (75 044; 37%). The distribution of glucose values where provenance was not recorded was most similar to that of fasting samples. The glucose measurements of 256 people with diabetes in the audit practice (size 11 514 people) were analysed. The initial glucose measurement had no provenance information in 164 cases (64.1%). A clinician questioned the provenance of a result in 41 cases (16.0%); of these, 14 (34.1%) required repeating. Lack of provenance led to delays in the diagnosis of diabetes [median (range) 30 (3-614) days]. CONCLUSIONS: The recording of glucose provenance in UK primary care could be improved. Failure to record provenance causes unnecessary repeated testing, delayed diagnosis and wasted clinician time.

Association between glycaemic control and common infections in people with Type 2 diabetes: a cohort study.

AIM: To investigate the impact of glycaemic control on infection incidence in people with Type 2 diabetes. METHODS: We compared infection rates during 2014 in people with Type 2 diabetes and people without diabetes in a large primary care cohort in the UK (the Royal College of General Practitioners Research and Surveillance Centre database). We performed multilevel logistic regression to investigate the impact of Type 2 diabetes on presentation with infection, and the effect of glycaemic control on presentation with upper respiratory tract infections, bronchitis, influenza-like illness, pneumonia, intestinal infectious diseases, herpes simplex, skin and soft tissue infections, urinary tract infections, and genital and perineal infections. People with Type 2 diabetes were stratified by good [HbA1c < 53 mmol/mol (< 7%)], moderate [HbA1c 53-69 mmol/mol (7-8.5%)] and poor [HbA1c > 69 mmol/mol (> 8.5%)] glycaemic control using their most recent HbA1c concentration. Infection incidence was adjusted for important sociodemographic factors and patient comorbidities. RESULTS: We identified 34 278 people with Type 2 diabetes and 613 052 people without diabetes for comparison. The incidence of infections was higher in people with Type 2 diabetes for all infections except herpes simplex. Worsening glycaemic control was associated with increased incidence of bronchitis, pneumonia, skin and soft tissue infections, urinary tract infections, and genital and perineal infections, but not with upper respiratory tract infections, influenza-like illness, intestinal infectious diseases or herpes simplex. CONCLUSIONS: Almost all infections analysed were more common in people with Type 2 diabetes. Infections that are most commonly of bacterial, fungal or yeast origin were more frequent in people with worse glycaemic control.

The emergence of enterovirus D68 in England in autumn 2014 and the necessity for reinforcing enterovirus respiratory screening.

In autumn 2014, enterovirus D68 (EV-D68) cases presenting with severe respiratory or neurological disease were described in countries worldwide. To describe the epidemiology and virological characteristics of EV-D68 in England, we collected clinical information on laboratory-confirmed EV-D68 cases detected in secondary care (hospitals), between September 2014 and January 2015. In primary care (general practitioners), respiratory swabs collected (September 2013-January 2015) from patients presenting with influenza-like illness were tested for EV-D68. In secondary care 55 EV-D68 cases were detected. Among those, 45 cases had clinical information available and 89% (40/45) presented with severe respiratory symptoms. Detection of EV-D68 among patients in primary care increased from 0.4% (4/1074; 95% CI 0.1-1.0) (September 2013-January 2014) to 0.8% (11/1359; 95% CI 0.4-1.5) (September 2014-January 2015). Characterization of EV-D68 strains circulating in England since 2012 and up to winter 2014/2015 indicated that those strains were genetically similar to those detected in 2014 in USA. We recommend reinforcing enterovirus surveillance through screening respiratory samples of suspected cases.

Uptake and impact of a new live attenuated influenza vaccine programme in England: early results of a pilot in primary school-age children, 2013/14 influenza season.

As part of the introduction and roll-out of a universal childhood live-attenuated influenza vaccination programme, 4­11 year-olds were vaccinated in seven pilot areas in England in the 2013/14 influenza season. This paper presents the uptake and impact of the programme for a range of disease indicators. End-of-season uptake was defined as the number of children in the target population who received at least one dose of influenza vaccine. Between week 40 2013 and week 15 2014, cumulative disease incidence per 100,000 population (general practitioner consultations for influenza-like illness and laboratory-confirmed influenza hospitalisations), cumulative influenza swab positivity in primary and secondary care and cumulative proportion of emergency department respiratory attendances were calculated. Indicators were compared overall and by age group between pilot and non-pilot areas. Direct impact was defined as reduction in cumulative incidence based on residence in pilot relative to non-pilot areas in 4­11 year-olds. Indirect impact was reduction between pilot and non-pilot areas in <4 year-olds and >11 year-olds. Overall vaccine uptake of 52.5% (104,792/199,475) was achieved. Although influenza activity was low, a consistent, though not statistically significant, decrease in cumulative disease incidence and influenza positivity across different indicators was seen in pilot relative to non-pilot areas in both targeted and non-targeted age groups, except in older age groups, where no difference was observed for secondary care indicators.

Automated identification of miscoded and misclassified cases of diabetes from computer records.

AIMS: To develop a computer processable algorithm, capable of running automated searches of routine data that flag miscoded and misclassified cases of diabetes for subsequent clinical review. METHOD: Anonymized computer data from the Quality Improvement in Chronic Kidney Disease (QICKD) trial (n = 942,031) were analysed using a binary method to assess the accuracy of data on diabetes diagnosis. Diagnostic codes were processed and stratified into: definite, probable and possible diagnosis of Type 1 or Type 2 diabetes. Diagnostic accuracy was improved by using prescription compatibility and temporally sequenced anthropomorphic and biochemical data. Bayesian false detection rate analysis was used to compare findings with those of an entirely independent and more complex manual sort of the first round QICKD study data (n = 760,588). RESULTS: The prevalence of definite diagnosis of Type 1 diabetes and Type 2 diabetes were 0.32% and 3.27% respectively when using the binary search method. Up to 35% of Type 1 diabetes and 0.1% of Type 2 diabetes were miscoded or misclassified on the basis of age/BMI and coding. False detection rate analysis demonstrated a close correlation between the new method and the published hand-crafted sort. Both methods had the highest false detection rate values when coding, therapeutic, anthropomorphic and biochemical filters were used (up to 90% for the new and 75% for the hand-crafted search method). CONCLUSIONS: A simple computerized algorithm achieves very similar results to more complex search strategies to identify miscoded and misclassified cases of both Type 1 diabetes and Type 2 diabetes. It has the potential to be used as an automated audit instrument to improve quality of diabetes diagnosis.

Miscoding, misclassification and misdiagnosis of diabetes in primary care.

AIMS: To determine the effectiveness of self-audit tools designed to detect miscoding, misclassification and misdiagnosis of diabetes in primary care. METHODS: We developed six searches to identify people with diabetes with potential classification errors. The search results were automatically ranked from most to least likely to have an underlying problem. Eight practices with a combined population of 72,000 and diabetes prevalence 2.9% (n = 2340) completed audit forms to verify whether additional information within the patients' medical record confirmed or refuted the problems identified. RESULTS: The searches identified 347 records, mean 42 per practice. Pre-audit 20% (n = 69) had Type 1 diabetes, 70% (n = 241) had Type 2 diabetes, 9% (n = 30) had vague codes that were hard to classify, 2% (n = 6) were not coded and one person was labelled as having gestational diabetes. Of records, 39.2% (n = 136) had important errors: 10% (n = 35) had coding errors; 12.1% (42) were misclassified; and 17.0% (59) misdiagnosed as having diabetes. Thirty-two per cent (n = 22) of people with Type 2 diabetes (n = 69) were misclassified as having Type 1 diabetes; 20% (n = 48) of people with Type 2 diabetes (n = 241) did not have diabetes; of the 30 patients with vague diagnostic terms, 50% had Type 2 diabetes, 20% had Type 1 diabetes and 20% did not have diabetes. Examples of misdiagnosis were found in all practices, misclassification in seven and miscoding in six. CONCLUSIONS: Volunteer practices successfully used these self-audit tools. Approximately 40% of patients identified by computer searches (5.8% of people with diabetes) had errors; misdiagnosis is commonest, misclassification may affect treatment options and miscoding in omission from disease registers and the potential for reduced quality of care.

Evaluating tools to support a new practical classification of diabetes: excellent control may represent misdiagnosis and omission from disease registers is associated with worse control.

AIMS: To conduct a service evaluation of usability and utility on-line clinical audit tools developed as part of a UK Classification of Diabetes project to improve the categorisation and ultimately management of diabetes. METHOD: We conducted the evaluation in eight volunteer computerised practices all achieving maximum pay-for-performance (P4P) indicators for diabetes; two allowed direct observation and videotaping of the process of running the on-line audit. We also reported the utility of the searches and the national levels of uptake. RESULTS: Once launched 4235 unique visitors accessed the download pages in the first 3 months. We had feedback about problems from 10 practices, 7 were human error. Clinical audit naive staff ran the audits satisfactorily. However, they would prefer more explanation and more user-familiar tools built into their practice computerised medical record system. They wanted the people misdiagnosed and misclassified flagged and to be convinced miscoding mattered. People with T2DM misclassified as T1DM tended to be older (mean 62 vs. 47 years old). People misdiagnosed as having T2DM have apparently 'excellent' glycaemic control mean HbA1c 5.3% (34 mmol/mol) vs. 7.2% (55 mmol/mol) (p<0.001). People with vague codes not included in the P4P register (miscoded) have worse glycaemic control [HbA1c 8.1% (65 mmol/mol) SEM=0.42 vs.7.0% (53mmol/mol) SEM=0.11, p=0.006]. CONCLUSIONS: There was scope to improve diabetes management in practice achieving quality targets. Apparently 'excellent' glycaemic control may imply misdiagnosis, while miscoding is associated with worse control. On-line clinical audit toolkits provide a rapid method of dissemination and should be added to the armamentarium of quality improvement interventions.

Trends and transient change in end-digit preference in blood pressure recording: studies of sequential and longitudinal collected primary care data.

BACKGROUND: End-digit preference (EDP) is a known cause of inaccurate BP recording. Distortion has been reported around pay-for-performance (P4P) indicators. METHODS: We studied sequential datasets (n = 148,000 to n = 900,000) and performed a longitudinal analysis of CONDUIT data (n = 250,000) over a 10-year period. We examined general trends in EDP and investigated the impact of diabetes and chronic kidney disease (CKD) P4P targets. RESULTS: EDP reduces over time in both datasets; the percentage of patients with a zero EDP declined from 70% to 27% and 68% to 26% for SBP and DBP respectively. There is more zero EDP at the extremes of BP, but in people with chronic disease, the use of zero EDP was mainly seen at higher BP levels. P4P targets are associated with increased preference for the even end-digit just below target: in diabetes odds ratio (OR) is 1.47 (p = 0.003) for SBP, 1.19 (p = 0.09) for DBP and in CKD OR 1.65 (p < 0.001) for SBP and 1.48 (p = 0.0001) for DBP. Trends observed in pilot data were validated with a longitudinal set. CONCLUSIONS: The decline in EDP is levelling off and P4P targets are associated with sub-target-EDP. Primary care should automate BP measurement and recording.

First-dose ChAdOx1 and BNT162b2 COVID-19 vaccines and thrombocytopenic, thromboembolic and hemorrhagic events in Scotland.

Reports of ChAdOx1 vaccine-associated thrombocytopenia and vascular adverse events have led to some countries restricting its use. Using a national prospective cohort, we estimated associations between exposure to first-dose ChAdOx1 or BNT162b2 vaccination and hematological and vascular adverse events using a nested incident-matched case-control study and a confirmatory self-controlled case series (SCCS) analysis. An association was found between ChAdOx1 vaccination and idiopathic thrombocytopenic purpura (ITP) (0-27 d after vaccination; adjusted rate ratio (aRR) = 5.77, 95% confidence interval (CI), 2.41-13.83), with an estimated incidence of 1.13 (0.62-1.63) cases per 100,000 doses. An SCCS analysis confirmed that this was unlikely due to bias (RR = 1.98 (1.29-3.02)). There was also an increased risk for arterial thromboembolic events (aRR = 1.22, 1.12-1.34) 0-27 d after vaccination, with an SCCS RR of 0.97 (0.93-1.02). For hemorrhagic events 0-27 d after vaccination, the aRR was 1.48 (1.12-1.96), with an SCCS RR of 0.95 (0.82-1.11). A first dose of ChAdOx1 was found to be associated with small increased risks of ITP, with suggestive evidence of an increased risk of arterial thromboembolic and hemorrhagic events. The attenuation of effect found in the SCCS analysis means that there is the potential for overestimation of the reported results, which might indicate the presence of some residual confounding or confounding by indication. Public health authorities should inform their jurisdictions of these relatively small increased risks associated with ChAdOx1. No positive associations were seen between BNT162b2 and thrombocytopenic, thromboembolic and hemorrhagic events.

Incorrect and incomplete coding and classification of diabetes: a systematic review.

AIMS: To conduct a systematic review to identify types and implications of incorrect or incomplete coding or classification within diabetes or between diabetes and other conditions; also to determine the availability of evidence regarding frequency of occurrence. METHODS: Medical Subject Headings (MeSH) and free-text terms were used to search relevant electronic databases for papers published to the end of August 2008. Two researchers independently reviewed titles and abstracts and, subsequently, the full text of potential papers. Reference lists of selected papers were also reviewed and authors consulted. Three reviewers independently extracted data. RESULTS: Seventeen eligible studies were identified, including five concerned with distinguishing between Type 1 and Type 2 diabetes. Evidence was also identified regarding: the distinction between diabetes and no-diabetes, failure to specify type of diabetes, and diagnostic errors or difficulties involving maturity-onset diabetes of the young, latent autoimmune diabetes in adults, pancreatic diabetes, persistence of foetal haemoglobin and acquired immune deficiency syndrome (AIDS). The sample was too heterogeneous to derive accurate information about frequency, but our findings suggested that misclassification occurs most commonly in young people. Implications relating to treatment options and risk management were highlighted, in addition to psychological and financial implications and the potential impact on the validity of quality of care evaluations and research. CONCLUSIONS: This review draws attention to the occurrence and implications of incorrect or incomplete coding or classification of diabetes, particularly in young people. A pragmatic and clinically relevant approach to classification is needed to assist those involved in making decisions about types of diabetes.

A method of identifying and correcting miscoding, misclassification and misdiagnosis in diabetes: a pilot and validation study of routinely collected data.

AIMS: Incorrect classification, diagnosis and coding of the type of diabetes may have implications for patient management and limit our ability to measure quality. The aim of the study was to measure the accuracy of diabetes diagnostic data and explore the scope for identifying errors. METHOD: We used two sets of anonymized routinely collected computer data: the pilot used Cutting out Needless Deaths Using Information Technology (CONDUIT) study data (n = 221 958), which we then validated using 100 practices from the Quality Improvement in Chronic Kidney Disease (QICKD) study (n = 760,588). We searched for contradictory diagnostic codes and also compatibility with prescription, demographic and laboratory test data. We classified errors as: misclassified-incorrect type of diabetes; misdiagnosed-where there was no evidence of diabetes; or miscoded-cases where it was difficult to infer the type of diabetes. RESULTS: The standardized prevalence of diabetes was 5.0 and 4.0% in the CONDUIT and the QICKD data, respectively: 13.1% (n = 930) of CONDUIT and 14.8% (n = 4363) QICKD are incorrectly coded; 10.3% (n = 96) in CONDUIT and 26.2% (n = 1143) in QICKD are misclassified; nearly all of these cases are people classified with Type 1 diabetes who should be classified as Type 2. Approximately 5% of T2DM in both samples have no objective evidence to support a diagnosis of diabetes. Miscoding was present in approximately 7.8% of the CONDUIT and 6.1% of QICKD diabetes records. CONCLUSIONS: The prevalence of miscoding, misclassification and misdiagnosis of diabetes is high and there is substantial scope for further improvement in diagnosis and data quality. Algorithms which identify likely misdiagnosis, misclassification and miscoding could be used to flag cases for review.