Oral anticoagulant prescribing among patients with cancer and atrial fibrillation in England, 2009-2019.

BACKGROUND: Anticoagulation of patients with atrial fibrillation (AF) and cancer is challenging because of their high risk for stroke and bleeding. Little is known of the variations of oral anticoagulant (OAC) prescribing in patients with AF with and without cancer. METHODS: Patients with first-time AF during 2009-2019 from the Clinical Practice Research Datalink were included. Cancer diagnosis was defined as a history of breast, prostate, colorectal, lung, or hematological cancer. Competing-risk analysis was used to assess the risk of OAC prescribing in patients with AF and cancer adjusted for clinical and sociodemographic factors. RESULTS: Of 177,065 patients with AF, 11.7% had cancer. Compared to patients without cancer, patients with cancer were less likely to receive OAC: prostate cancer (subhazard ratio [SHR], 0.95; 95% CI, 0.91-0.99), breast cancer (SHR, 0.93; 95% CI, 0.89-0.98), colorectal cancer (SHR, 0.93; 95% CI, 0.88-0.99), hematological cancer (SHR, 0.70; 95% CI, 0.65-0.75), and lung cancer (SHR, 0.44; 95% CI, 0.38-0.50). The cumulative incidence function (CIF) of OAC prescribing was lowest for patients with lung cancer and hematological cancer compared with patients without cancer. The difference between the CIF of OAC prescribing in patients with and without cancer becomes narrower in the most deprived areas. Elderly patients (aged ≥85 years) overall had the lowest CIF of OAC prescribing regardless of cancer status. CONCLUSIONS: In patients with AF, underprescribing of OAC is independently associated with certain cancer types. Patients with hematological and lung cancer are the least likely to receive anticoagulation therapy compared with patients without cancer. Underprescribing of OAC in cancer is linked to old age. Further studies of patients with AF and cancer are warranted to assess the net clinical benefit of anticoagulation in certain cancer types.

Incidence of nonvalvular atrial fibrillation and oral anticoagulant prescribing in England, 2009 to 2019: A cohort study.

BACKGROUND: Atrial fibrillation (AF) is an important risk factor for ischaemic stroke, and AF incidence is expected to increase. Guidelines recommend using oral anticoagulants (OACs) to prevent the development of stroke. However, studies have reported the frequent underuse of OACs in AF patients. The objective of this study is to describe nonvalvular atrial fibrillation (NVAF) incidence in England and assess the clinical and socioeconomic factors associated with the underprescribing of OACs. METHODS AND FINDINGS: We conducted a population-based retrospective cohort study using the UK Clinical Practice Research Datalink (CPRD) database to identify patients with NVAF aged ≥18 years and registered in English general practices between 2009 and 2019. Annual incidence rate of NVAF by age, deprivation quintile, and region was estimated. OAC prescribing status was explored for patients at risk for stroke and classified into the following: OAC, aspirin only, or no treatment. We used a multivariable multinomial logistic regression model to estimate relative risk ratios (RRRs) and 95% confidence intervals (CIs) of the factors associated with OAC or aspirin-only prescribing compared to no treatment in patients with NVAF who are recommended to take OAC. The multivariable regression was adjusted for age, sex, comorbidities, socioeconomic status, baseline treatment, frailty, bleeding risk factors, and takes into account clustering by general practice. Between 2009 and 2019, 12,517,191 patients met the criteria for being at risk of developing NVAF. After a median follow-up of 4.6 years, 192,265 patients had an incident NVAF contributing a total of 647,876 person-years (PYR) of follow-up. The overall age-adjusted incidence of NVAF per 10,000 PYR increased from 20.8 (95% CI: 20.4; 21.1) in 2009 to 25.5 (25.1; 25.9) in 2019. Higher incidence rates were observed for older ages and males. Among NVAF patients eligible for anticoagulation, OAC prescribing rose from 59.8% (95% CI: 59.0; 60.6) in 2009 to 83.2% (95% CI: 83.0; 83.4) in 2019. Several conditions were associated with lower risk of OAC prescribing: dementia [RRR 0.52 (0.47; 0.59)], liver disease 0.58 (0.50; 0.67), malignancy 0.74 (0.72; 0.77), and history of falls 0.82 (0.78; 0.85). Compared to white ethnicity, patients from black and other ethnic minorities were less likely to receive OAC; 0.78 (0.65; 0.94) and 0.76 (0.64; 0.91), respectively. Patients living in the most deprived areas were less likely to receive OAC 0.85 (0.79; 0.91) than patients living in the least deprived areas. Practices located in the East of England were associated with higher risk of prescribing aspirin only over no treatment than practices in London (RRR 1.22; 95% CI 1.02 to 1.45). The main limitation of this study is that these findings depends on accurate recording of conditions by health professionals and the inevitable residual confounding due to lack of data on certain factors that could be associated with under-prescribing of OACs. CONCLUSIONS: The incidence of NVAF increased between 2009 and 2015, before plateauing. Underprescribing of OACs in NVAF is associated with a range of comorbidities, ethnicity, and socioeconomic factors, demonstrating the need for initiatives to reduce inequalities in the care for AF patients.

Association Between Patient Factors and the Effectiveness of Wearable Trackers at Increasing the Number of Steps per Day Among Adults With Cardiometabolic Conditions: Meta-analysis of Individual Patient Data From Randomized Controlled Trials.

BACKGROUND: Current evidence supports the use of wearable trackers by people with cardiometabolic conditions. However, as the health benefits are small and confounded by heterogeneity, there remains uncertainty as to which patient groups are most helped by wearable trackers. OBJECTIVE: This study examined the effects of wearable trackers in patients with cardiometabolic conditions to identify subgroups of patients who most benefited and to understand interventional differences. METHODS: We obtained individual participant data from randomized controlled trials of wearable trackers that were conducted before December 2020 and measured steps per day as the primary outcome in participants with cardiometabolic conditions including diabetes, overweight or obesity, and cardiovascular disease. We used statistical models to account for clustering of participants within trials and heterogeneity across trials to estimate mean differences with the 95% CI. RESULTS: Individual participant data were obtained from 9 of 25 eligible randomized controlled trials, which included 1481 of 3178 (47%) total participants. The wearable trackers revealed that over the median duration of 12 weeks, steps per day increased by 1656 (95% CI 918-2395), a significant change. Greater increases in steps per day from interventions using wearable trackers were observed in men (interaction coefficient -668, 95% CI -1157 to -180), patients in age categories over 50 years (50-59 years: interaction coefficient 1175, 95% CI 377-1973; 60-69 years: interaction coefficient 981, 95% CI 222-1740; 70-90 years: interaction coefficient 1060, 95% CI 200-1920), White patients (interaction coefficient 995, 95% CI 360-1631), and patients with fewer comorbidities (interaction coefficient -517, 95% CI -1188 to -11) compared to women, those aged below 50, non-White patients, and patients with multimorbidity. In terms of interventional differences, only face-to-face delivery of the tracker impacted the effectiveness of the interventions by increasing steps per day. CONCLUSIONS: In patients with cardiometabolic conditions, interventions using wearable trackers to improve steps per day mostly benefited older White men without multimorbidity. TRIAL REGISTRATION: PROSPERO CRD42019143012; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=143012.

Correction to: The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort.

The original article [1] contains an omitted grant acknowledgement and affiliation as relates to the contribution of co-author, Rafael Perera-Salazar. As such, the following two amendments should apply to the original article.

The importance of teamwork climate for preventing burnout in UK general practices.

This short report aims to investigate the association between teamwork and burnout among general practitioners (GPs). A two-stage survey was conducted. In stage one, validated self-report measures of burnout and teamwork were completed by 50 GPs across 12 general practices in Greater Manchester, UK. In stage two, staff members across 3 of the 12 general practices (GPs, nursing staff, managers and admin staff) responded to free text questions about teamwork (n = 20). The results of the stage one survey showed that teamwork in GPs was significantly negatively associated with the emotional exhaustion (r = -0.326, P < 0.05) and depersonalization (r = -0.421, P < 0.01) domains of the burnout measure and significantly positively associated with the personal accomplishment (r = 0.296, P < 0.05) domain. Free text responses in stage two were assigned into three themes: (i) addressing organizational barriers which might threaten teamwork, (ii) promoting the view of teamwork as a shared responsibility among all staff members of the general practice and (iii) implementing improvement strategies which can be embedded in the busy environment of general practices. GPs and other staff members of general practices valued the importance of teamwork for boosting their morale and mitigating burnout. Future research should focus on designing and embedding brief teamwork improvement strategies in general practices.

The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort.

BACKGROUND: The presence of additional chronic conditions has a significant impact on the treatment and management of type 2 diabetes (T2DM). Little is known about the patterns of comorbidities in this population. The aims of this study are to quantify comorbidity patterns in people with T2DM, to estimate the prevalence of six chronic conditions in 2027 and to identify clusters of similar conditions. METHODS: We used the Clinical Practice Research Datalink (CPRD) linked with the Index of Multiple Deprivation (IMD) data to identify patients diagnosed with T2DM between 2007 and 2017. 102,394 people met the study inclusion criteria. We calculated the crude and age-standardised prevalence of 18 chronic conditions present at and after the T2DM diagnosis. We analysed longitudinally the 6 most common conditions and forecasted their prevalence in 2027 using linear regression. We used agglomerative hierarchical clustering to identify comorbidity clusters. These analyses were repeated on subgroups stratified by gender and deprivation. RESULTS: More people living in the most deprived areas had ≥ 1 comorbidities present at the time of diagnosis (72% of females; 64% of males) compared to the most affluent areas (67% of females; 59% of males). Depression prevalence increased in all strata and was more common in the most deprived areas. Depression was predicted to affect 33% of females and 15% of males diagnosed with T2DM in 2027. Moderate clustering tendencies were observed, with concordant conditions grouped together and some variations between groups of different demographics. CONCLUSIONS: Comorbidities are common in this population, and high between-patient variability in comorbidity patterns emphasises the need for patient-centred healthcare. Mental health is a growing concern, and there is a need for interventions that target both physical and mental health in this population.

Examining trends in type 2 diabetes incidence, prevalence and mortality in the UK between 2004 and 2014.

AIMS: Contemporary data describing type 2 diabetes prevalence, incidence and mortality are limited. We aimed to (1) estimate annual incidence and prevalence rates of type 2 diabetes in the UK between 2004 and 2014, (2) examine relationships between observed rates with age, gender, socio-economic status and geographic region, and (3) assess how temporal changes in incidence and all-cause mortality rates influence changes in prevalence. METHODS: Type 2 diabetes patients aged ≥16 years between January 2004 and December 2014 were identified using the Clinical Practice Research Datalink (CPRD). Up to 5 individuals without diabetes were matched to diabetes patients based on age, gender and the general practice. Annual incidence, prevalence and mortality rates were calculated per 10 000 person-years at risk (95% CI). Survival models compared mortality rates in patients with and without type 2 diabetes. RESULTS: Prevalence rates of type 2 diabetes increased from 3.21% (3.19; 3.22) in 2004 to 5.26% (5.24; 5.29) in 2014. Incidence rates remained stable, overall, throughout the study period. Higher incidence and prevalence rates were related to male gender and deprivation. Individuals with type 2 diabetes were associated with higher risk of mortality (Hazard ratio 1.26 [1.20; 1.32]). Mortality rates declined in patients with and without diabetes throughout the study period. The incidence and prevalence of type 2 diabetes in patients aged 16 to 34 years increased over time. CONCLUSIONS: The rising prevalence of type 2 diabetes in the UK over the last decade is probably explained by patients living longer rather than by increasing incidence of type 2 diabetes.

Temporal trends of in-hospital mortality and its determinants following percutaneous coronary intervention in patients with acute coronary syndrome in England and Wales: A population-based study between 2006 and 2021.

BACKGROUND: There is limited data around drivers of changes in mortality over time. We aimed to examine the temporal changes in mortality and understand its determinants over time. METHODS: 743,149 PCI procedures for patients from the British Cardiovascular Intervention Society (BCIS) database who were aged between 18 and 100 years and underwent Percutaneous Coronary Intervention (PCI) for Acute Coronary Syndrome (ACS) in England and Wales between 2006 and 2021 were included. We decomposed the contributing factors to the difference in the observed mortality proportions between 2006 and 2021 using Fairlie decomposition method. Multiple imputation was used to address missing data. RESULTS: Overall, there was an increase in the mortality proportion over time, from 1.7% (95% CI: 1.5% to 1.9%) in 2006 to 3.1% (95% CI: 3.0% to 3.2%) in 2021. 61.2% of this difference was explained by the variables included in the model. ACS subtypes (percentage contribution: 14.67%; 95% CI: 5.76% to 23.59%) and medical history (percentage contribution: 13.50%; 95% CI: 4.33% to 22.67%) were the strongest contributors to the difference in the observed mortality proportions between 2006 and 2021. Also, there were different drivers to mortality changes between different time periods. Specifically, ACS subtypes and severity of presentation were amongst the strongest contributors between 2006 and 2012 while access site and demographics were the strongest contributors between 2012 and 2021. CONCLUSIONS: Patient factors and the move towards ST-elevated myocardial infarction (STEMI) PCI have driven the short-term mortality changes following PCI for ACS the most.

Examining non-linearity in the association between age and reported opioid use in different socioeconomic strata: cohort study using Health Survey for England waves from 1997 to 2014.

BACKGROUND: Age and socioeconomic status (SES) predict several health-related outcomes, including prescription opioid use. Contrasting findings from previous literature found higher prevalence of opioid use in both people over 65 years old and the working-age population of 35-55 years old. This study aimed to analyse if the association between age and opioid use is non-linear and differs in adults with different SES levels. METHODS: This cohort study used the Health Survey for England waves 1997-2014 data to investigate the shape of the correlation between reported opioid use and income decile, employment status and educational level. A semiparametric Generalised Additive Model was employed, so that linearity of correlation was not assumed. The shape of the relationship was assessed using the effective degrees of freedom (EDF). RESULTS: Positive correlation between age and reported opioid use, more linear in people in the highest income decile (EDF: 1.01, p<0.001) and higher education (EDF: 2.03, p<0.001) was observed. In people on lower income and with lower levels of education, the highes probability of reported opioid use was at around 40-60 years old and slowly decreased after that. Higher income decile and higher levels of education were predictors of a lower probability of reported opioid use (OR: 0.27, 95% CI: 0.21 to 0.36 and OR: 0.48, 95% CI: 0.41 to 0.57, respectively). There was no statistically significant difference in opioid use between employed and unemployed people. CONCLUSION: The relationship between age and the probability of prescribed opioid use varies greatly across different income and educations strata, highlighting different drivers in opioid prescribing across population groups. More research is needed into exploring patterns in opioid use in older people, particularly from disadvantaged socioeconomic backgrounds.

Common Cancer Types and Risk of Stroke and Bleeding in Patients With Nonvalvular Atrial Fibrillation: A Population-Based Study in England.

BACKGROUND: The association between cancer and stroke or bleeding outcomes in atrial fibrillation is unclear. We sought to examine how certain types of cancer influence the balance between stroke and bleeding risk in patients with nonvalvular atrial fibrillation (NVAF). METHODS AND RESULTS: We estimated stroke and bleeding risk among adult patients with NVAF and certain types of cancer (breast, prostate, colorectal, lung, and hematological cancer) from 2009 to 2019 based on data from the UK Clinical Practice Research Datalink GOLD and Aurum databases. The control group included patients with NVAF only. Of 177 065 patients with NVAF, 11379 (6.4%) had cancer (1691 breast, 3955 prostate, 1666 colorectal, 2491 hematological, and 1576 lung). Compared with patients without cancer, stroke risk was higher in patients with breast cancer (adjusted hazard ratio [aHR], 1.20 [95% CI, 1.07-1.35) and with prostate cancer (aHR, 1.11 [95% CI, 1.01-1.12) if diagnosed within 6 months before NVAF. The risk of bleeding was increased in subjects with hematological cancer (aHR, 1.55 [95% CI, 1.40-1.71]), lung cancer (aHR, 1.49 [95% CI, 1.25, 1.77]), prostate cancer (aHR, 1.38 [95% CI, 1.28-1.49]), and colorectal cancer (aHR, 1.36 [95% CI, 1.21-1.53]), but not for subjects with breast cancer. The more recent the cancer diagnosis before NVAF diagnosis (within 6 months), the higher the risk of bleeding. CONCLUSIONS: Breast and prostate cancer are associated with increased stroke risk, whereas in some cancer types, the risk of bleeding seemed to exceed stroke risk. In these patients, prescribing of oral anticoagulant should be carefully evaluated to balance bleeding and stroke risk.

Association of strong opioids and antibiotics prescribing with GP burnout: a retrospective cross-sectional study.

BACKGROUND: Prescribing of strong opioids and antibiotics impacts patient safety, yet little is known about the effects GP wellness has on overprescribing of both medications in primary care. AIM: To examine associations between strong opioid and antibiotic prescribing and practice- weighted GP burnout and wellness. DESIGN AND SETTING: A retrospective cross-sectional study was undertaken using prescription data on strong opioids and antibiotics from the Oxford- Royal College of General Practitioners Research and Surveillance Centre linking to a GP wellbeing survey overlaying the same 4-month period from December 2019 to April 2020. METHOD: Patients prescribed strong opioids and antibiotics were the outcomes of interest. RESULTS: Data for 40 227 patients (13 483 strong opioids and 26 744 antibiotics) were linked to 57 practices and 351 GPs. Greater strong opioid prescribing was associated with increased emotional exhaustion (incidence risk ratio [IRR] 1.19, 95% confidence interval [CI] = 1.10 to 1.24), depersonalisation (IRR 1.10, 95% CI = 1.01 to 1.16), job dissatisfaction (IRR 1.25, 95% CI = 1.19 to 1.32), diagnostic uncertainty (IRR 1.12, 95% CI = 1.08 to 1.19), and turnover intention (IRR 1.32, 95% CI = 1.27 to 1.37) in GPs. Greater antibiotic prescribing was associated with increased emotional exhaustion (IRR 1.19, 95% CI = 1.05 to 1.37), depersonalisation (IRR 1.24, 95% CI = 1.08 to 1.49), job dissatisfaction (IRR 1.11, 95% CI = 1.04 to 1.19), sickness-presenteeism (IRR 1.18, 95% CI = 1.11 to 1.25), and turnover intention (IRR 1.38, 95% CI = 1.31 to 1.45) in GPs. Increased strong opioid and antibiotic prescribing was also found in GPs working longer hours (IRR 3.95, 95% CI = 3.39 to 4.61; IRR 5.02, 95% CI = 4.07 to 6.19, respectively) and in practices in the north of England (1.96, 95% CI = 1.61 to 2.33; 1.56, 95% CI = 1.12 to 3.70, respectively). CONCLUSION: This study found higher rates of prescribing of strong opioids and antibiotics in practices with GPs with more burnout symptoms, greater job dissatisfaction, and turnover intentions; working longer hours; and in practices in the north of England serving more deprived populations.

Comorbidity clusters and in-hospital outcomes in patients admitted with acute myocardial infarction in the USA: A national population-based study.

BACKGROUND: The prevalence of multimorbidity in patients with acute myocardial infarction (AMI) is increasing. It is unclear whether comorbidities cluster into distinct phenogroups and whether are associated with clinical trajectories. METHODS: Survey-weighted analysis of the United States Nationwide Inpatient Sample (NIS) for patients admitted with a primary diagnosis of AMI in 2018. In-hospital outcomes included mortality, stroke, bleeding, and coronary revascularisation. Latent class analysis of 21 chronic conditions was used to identify comorbidity classes. Multivariable logistic and linear regressions were fitted for associations between comorbidity classes and outcomes. RESULTS: Among 416,655 AMI admissions included in the analysis, mean (±SD) age was 67 (±13) years, 38% were females, and 76% White ethnicity. Overall, hypertension, coronary heart disease (CHD), dyslipidaemia, and diabetes were common comorbidities, but each of the identified five classes (C) included ≥1 predominant comorbidities defining distinct phenogroups: cancer/coagulopathy/liver disease class (C1); least burdened (C2); CHD/dyslipidaemia (largest/referent group, (C3)); pulmonary/valvular/peripheral vascular disease (C4); diabetes/kidney disease/heart failure class (C5). Odds ratio (95% confidence interval [CI]) for mortality ranged between 2.11 (1.89-2.37) in C2 to 5.57 (4.99-6.21) in C1. For major bleeding, OR for C1 was 4.48 (3.78; 5.31); for acute stroke, ORs ranged between 0.75 (0.60; 0.94) in C2 to 2.76 (2.27; 3.35) in C1; for coronary revascularization, ORs ranged between 0.34 (0.32; 0.36) in C1 to 1.41 (1.30; 1.53) in C4. CONCLUSIONS: We identified distinct comorbidity phenogroups that predicted in-hospital outcomes in patients admitted with AMI. Some conditions overlapped across classes, driven by the high comorbidity burden. Our findings demonstrate the predictive value and potential clinical utility of identifying patients with AMI with specific comorbidity clustering.

Temporal trends of hospitalizations, comorbidity burden and in-hospital outcomes in patients admitted with asthma in the United States: Population-based study.

BACKGROUND: Asthma is a prevalent chronic respiratory condition and remains a common cause for hospitalization. However, contemporary data on asthma hospitalization rates, comorbidity burden, and in-hospital outcomes are lacking. METHODS: Survey-weighted analysis of hospitalization records with a primary diagnosis of asthma using data from the US National (Nationwide) Inpatient Sample between 2004 and 2017. Outcomes were number of hospitalizations per 100,000 population and in-hospital outcomes including receipt of ventilation, length of stay, and hospital costs. Patient and admission characteristics and comorbidity burden were examined over time. Multivariable logistic and linear regression models were fitted for over-time risks of the outcomes. RESULTS: Among 3,098,863 asthma admissions between 2004 and 2017, mean (±SD) age was 29 (±25), 57% females, 36% White, 40% had Medicaid as primary payer. During 2004-2017, asthma hospitalizations declined from 89 to 56 per 100,000 population; length of stay remained overall stable; median (interquartile range IQR) inflation-adjusted hospital costs doubled from $8,446 (9,227) in 2004 to $17,756 (19,434) in 2017. Common comorbidities in patients admitted with asthma were hypertension and diabetes in adults, but gastroesophageal reflux disease, obstructive sleep apnoea, anemia, and obesity in children. Over time, the prevalence of mental illness increased by >50%. Severe asthma (IRR, 2.48; 95%CI: 2.27-2.72) and psychoses (IRR, 1.10; 1.05-1.14) were predictors of prolonged hospitalization. Asian/Pacific Islanders were more likely to receive ventilation (OR: 2.35; 1.73-3.20) than White patients. Hospital costs were significantly higher in females and adults with hypertension (coefficient, 1405.2; 283.1-2527.4) or psychoses (coefficient, 1978.4; 674.9-3282.0). CONCLUSIONS: US asthma hospitalization rates fluctuated in earlier years but declined over time, which may reflect improvements in community care and declining asthma prevalence. Comorbidity burden, including mental illness, increased over time and is associated with in-hospital outcomes. This highlights the changing landscape of asthma admissions which may inform redesigning services to support pre-hospitalization asthma care and help further reduce admissions, particularly among patients with multimorbidity.

Clinical code usage in UK general practice: a cohort study exploring 18 conditions over 14 years.

OBJECTIVE: To assess the diagnostic Read code usage for 18 conditions by examining their frequency and diversity in UK primary care between 2000 and 2013. DESIGN: Population-based cohort study SETTING: 684 UK general practices contributing data to the Clinical Practice Research Datalink (CPRD) GOLD. PARTICIPANTS: Patients with clinical codes for at least one of asthma, chronic obstructive pulmonary disease, diabetes, hypertension (HT), coronary heart disease, atrial fibrillation (AF), heart failure, stroke, hypothyroidism, chronic kidney disease, learning disability (LD), depression, dementia, epilepsy, severe mental illness (SMI), osteoarthritis, osteoporosis and cancer. PRIMARY AND SECONDARY OUTCOME MEASURES: For the frequency ranking of clinical codes, canonical correlation analysis was applied to correlations of clinical code usage of 1, 3 and 5 years. Three measures of diversity (Shannon entropy index of diversity, richness and evenness) were used to quantify changes in incident and total clinical codes. RESULTS: Overall, all examined conditions, except LD, showed positive monotonic correlation. HT, hypothyroidism, osteoarthritis and SMI codes' usage had high 5-year correlation. The codes' usage diversity remained stable overall throughout the study period. Cancer, diabetes and SMI had the highest richness (code lists need time to define) unlike AF, hypothyroidism and LD. SMI (high richness) and hypothyroidism (low richness) can last for 5 years, whereas cancer and diabetes (high richness) and LD (low richness) only last for 2 years. CONCLUSIONS: This is an under-reported research area and the findings suggest the codes' usage diversity for most conditions remained overall stable throughout the study period. Generated mental health code lists can last for a long time unlike cardiometabolic conditions and cancer. Adopting more consistent and less diverse coding would help improve data quality in primary care. Future research is needed following the transfer to the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) coding.

Derivation and External Validation of a Clinical Model to Predict Heart Failure Onset in Patients With Incident Diabetes.

OBJECTIVE: Heart failure (HF) often develops in patients with diabetes and is recognized for its role in increased cardiovascular morbidity and mortality in this population. Most existing models predict risk in patients with prevalent rather than incident diabetes and fail to account for sex differences in HF risk factors. We derived sex-specific models in Ontario, Canada to predict HF at diabetes onset and externally validated these models in the U.K. RESEARCH DESIGN AND METHODS: Retrospective cohort study using international population-based data. Our derivation cohort comprised all Ontario residents aged ≥18 years who were diagnosed with diabetes between 2009 and 2018. Our validation cohort comprised U.K. patients aged ≥35 years who were diagnosed with diabetes between 2007 and 2017. Primary outcome was incident HF. Sex-stratified multivariable Fine and Gray subdistribution hazard models were constructed, with death as a competing event. RESULTS: A total of 348,027 Ontarians (45% women) and 54,483 U.K. residents (45% women) were included. At 1, 5, and 9 years, respectively, in the external validation cohort, the C-statistics were 0.81 (95% CI 0.79-0.84), 0.79 (0.77-0.80), and 0.78 (0.76-0.79) for the female-specific model; and 0.78 (0.75-0.80), 0.77 (0.76-0.79), and 0.77 (0.75-0.79) for the male-specific model. The models were well-calibrated. Age, rurality, hypertension duration, hemoglobin, HbA1c, and cardiovascular diseases were common predictors in both sexes. Additionally, mood disorder and alcoholism (heavy drinker) were female-specific predictors, while income and liver disease were male-specific predictors. CONCLUSIONS: Our findings highlight the importance of developing sex-specific models and represent an important step toward personalized lifestyle and pharmacologic prevention of future HF development.

Investigating the links between diagnostic uncertainty, emotional exhaustion, and turnover intention in General Practitioners working in the United Kingdom.

Background: General Practitioners (GPs) report high levels of burnout, job dissatisfaction, and turnover intention. The complexity of presenting problems to general practice makes diagnostic uncertainty a common occurrence that has been linked to burnout. The interrelationship between diagnostic uncertainty with other factors such as burnout, job satisfaction and turnover intention have not been previously examined. Objectives: To examine associations between diagnostic uncertainty, emotional exhaustion (EE), depersonalization (DP), job satisfaction, and turnover intention in GPs. Methods: Seventy general practices in England were randomly selected through the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC). A total of 348 GPs within 67 these practices completed a 10-item online questionnaire which included questions on GP characteristics, work-life balance, job satisfaction, sickness presenteeism, diagnostic uncertainty, turnover intention as well as EE and DP. Associations between diagnostic uncertainty and each of EE, DP, job satisfaction, and turnover intention were evaluated in multivariate mixed-effect ordinal logistic regressions whilst adjusting for covariates, to account for the correlation in the three outcomes of interest. Results: Almost one-third of GPs (n = 101; 29%) reported experiencing >10% of diagnostic uncertainty in their day-to-day practice over the past year. GPs reporting greater diagnostic uncertainty had higher levels of EE [OR = 3.90; 95% CI = (2.54, 5.99)], job dissatisfaction [OR = 2.01; 95% CI = (1.30, 3.13)] and turnover intention [OR = 4.51; 95% CI = (2.86, 7.11)]. GPs with no sickness presenteeism had lower levels of EE [OR = 0.53; 95% CI = (0.35, 0.82)], job dissatisfaction [OR = 0.56; 95% CI = (0.35, 0.88)], and turnover intention [OR = 0.61; 95% CI = (0.41, 0.91)]. Conclusion: Diagnostic uncertainty may not only negatively impact on the wellbeing of GPs, but could also have adverse implications on workforce retention in primary care.

Association of socioeconomic deprivation with opioid prescribing in primary care in England: a spatial analysis.

BACKGROUND: The increasing trends in opioid prescribing and opioid-related deaths in England are concerning. A greater understanding of the association of deprivation with opioid prescribing is needed to guide policy responses and interventions. METHODS: The 2018/2019 English national primary care prescribing data were analysed spatially. Prescribing of opioids in general practice was quantified by defined daily doses (DDD) and attributed to 32 844 lower layer super output areas (LSOAs), the geographical units representing ~1500 people. Linear regression was used to model the effect of socioeconomic deprivation (quintiles) on opioid prescribing while accounting for population demographics and the prevalence of specific health conditions. Adjusted DDD estimates were compared at each deprivation level within higher organisational areas (Clinical Commissioning Groups, CCGs). RESULTS: In total, 624 411 164 DDDs of opioids were prescribed. LSOA-level prescribing varied between 1.7 and 121.04 DDD/1000 population/day. Prescribing in the most deprived areas in the North of England was 1.2 times higher than the national average for areas with similar deprivation levels and 3.3 times higher than the most deprived areas in London. Prescribing in the most deprived areas was on average 9.70 DDD/1000 people/day (95% CI 9.41 to 10.00) higher than the least deprived areas. Deprivation-driven disparities varied between individual CCGs. In the most unequal CCG, prescribing in the most deprived areas was twice that in the least deprived areas. CONCLUSION: Opioid prescribing varied substantially across England and deprivation was strongly associated with prescribing. This paper provides evidence for guiding policy interventions and allocation of resources to areas with the highest levels of opioid prescribing.

Assessing the severity of cardiovascular disease in 213 088 patients with coronary heart disease: a retrospective cohort study.

OBJECTIVE: Most current cardiovascular disease (CVD) risk stratification tools are for people without CVD, but very few are for prevalent CVD. In this study, we developed and validated a CVD severity score in people with coronary heart disease (CHD) and evaluated the association between severity and adverse outcomes. METHODS: Primary and secondary care data for 213 088 people with CHD in 398 practices in England between 2007 and 2017 were used. The cohort was randomly divided into training and validation datasets (80%/20%) for the severity model. Using 20 clinical severity indicators (each assigned a weight=1), baseline and longitudinal CVD severity scores were calculated as the sum of indicators. Adjusted Cox and competing-risk regression models were used to estimate risks for all-cause and cause-specific hospitalisation and mortality. RESULTS: Mean age was 64.5±12.7 years, 46% women, 16% from deprived areas, baseline severity score 1.5±1.2, with higher scores indicating a higher burden of disease. In the training dataset, 138 510 (81%) patients were hospitalised at least once, and 39 944 (23%) patients died. Each 1-unit increase in baseline severity was associated with 41% (95% CI 37% to 45%, area under the receiver operating characteristics (AUROC) curve=0.79) risk for 1 year for all-cause mortality; 59% (95% CI 52% to 67%, AUROC=0.80) for cardiovascular (CV)/diabetes mortality; 27% (95% CI 26% to 28%) for any-cause hospitalisation and 37% (95% CI 36% to 38%) for CV/diabetes hospitalisation. Findings were consistent in the validation dataset. CONCLUSIONS: Higher CVD severity score is associated with higher risks for any-cause and cause-specific hospital admissions and mortality in people with CHD. Our reproducible score based on routinely collected data can help practitioners better prioritise management of people with CHD in primary care.

Effectiveness of collaborative care in reducing suicidal ideation: An individual participant data meta-analysis.

To assess whether CC is more effective at reducing suicidal ideation in people with depression compared with usual care, and whether study and patient factors moderate treatment effects. METHOD: We searched Medline, Embase, PubMed, PsycINFO, CINAHL, CENTRAL from inception to March 2020 for Randomised Controlled Trials (RCTs) that compared the effectiveness of CC with usual care in depressed adults, and reported changes in suicidal ideation at 4 to 6 months post-randomisation. Mixed-effects models accounted for clustering of participants within trials and heterogeneity across trials. This study is registered with PROSPERO, CRD42020201747. RESULTS: We extracted data from 28 RCTs (11,165 patients) of 83 eligible studies. We observed a small significant clinical improvement of CC on suicidal ideation, compared with usual care (SMD, -0.11 [95%CI, -0.15 to -0.08]; I2, 0·47% [95%CI 0.04% to 4.90%]). CC interventions with a recognised psychological treatment were associated with small reductions in suicidal ideation (SMD, -0.15 [95%CI -0.19 to -0.11]). CC was more effective for reducing suicidal ideation among patients aged over 65 years (SMD, - 0.18 [95%CI -0.25 to -0.11]). CONCLUSION: Primary care based CC with an embedded psychological intervention is the most effective CC framework for reducing suicidal ideation and older patients may benefit the most.

Eleven-year multimorbidity burden among 637 255 people with and without type 2 diabetes: a population-based study using primary care and linked hospitalisation data.

OBJECTIVES: To compare the patterns of 18 physical and mental health comorbidities between people with recently diagnosed type 2 diabetes (T2D) and people without diabetes and how these change by age, gender and deprivation over time between 2004 and 2014. Also, to develop a metric to identify most prevalent comorbidities in people with T2D. DESIGN: Population-based cohort study. SETTING: Primary and secondary care, England, UK. PARTICIPANTS: 108 588 people with T2D and 528 667 comparators registered in 391 English general practices. Each patient with T2D aged ≥16 years between January 2004 and December 2014 registered in Clinical Practice Research Datalink GOLD practices was matched to up to five comparators without diabetes on age, gender and general practice. PRIMARY AND SECONDARY OUTCOME MEASURES: Prevalence of 18 physical and mental health comorbidities in people with T2D and comparators categorised by age, gender and deprivation. Odds for association between T2D diagnosis and comorbidities versus comparators. A metric for comorbidities with prevalence of ≥5% and/or odds ≥2 in patients with T2D. RESULTS: Overall, 77% of patients with T2D had ≥1 comorbidity and all comorbidities were more prevalent in patients with T2D than in comparators. Across both groups, prevalence rates were higher in older people, women and those most socially deprived. Conditional logistic regression models fitted to estimate (OR, 95% CI) for association between T2D diagnosis and comorbidities showed that T2D diagnosis was significantly associated with higher odds for all conditions including myocardial infarction (OR 2.13, 95% CI 1.85 to 2.46); heart failure (OR 2.12, 1.84 to 2.43); depression (OR 1.75, 1.62 to 1.89), but non-significant for cancer (OR 1.12, 0.98 to 1.28). In addition to cardiovascular disease, the metric identified osteoarthritis, hypothyroidism, anxiety, schizophrenia and respiratory conditions as highly prevalent comorbidities in people with T2D. CONCLUSIONS: T2D diagnosis is associated with higher likelihood of experiencing other physical and mental illnesses. People with T2D are twice as likely to have cardiovascular disease as the general population. The findings highlight highly prevalent and under-reported comorbidities in people with T2D. These findings can inform future research and clinical guidelines and can have important implications on healthcare resource allocation and highlight the need for more holistic clinical care for people with recently diagnosed T2D.