Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex.

Social behaviors are crucial to all mammals. Although the prelimbic cortex (PL, part of medial prefrontal cortex) has been implicated in social behavior, it is not clear which neurons are relevant or how they contribute. We found that PL contains anatomically and molecularly distinct subpopulations that target three downstream regions that have been implicated in social behavior: the nucleus accumbens (NAc), amygdala, and ventral tegmental area. Activation of NAc-projecting PL neurons (PL-NAc), but not the other subpopulations, decreased the preference for a social target. To determine what information PL-NAc neurons convey, we selectively recorded from them and found that individual neurons were active during social investigation, but only in specific spatial locations. Spatially specific manipulation of these neurons bidirectionally regulated the formation of a social-spatial association. Thus, the unexpected combination of social and spatial information within the PL-NAc may contribute to social behavior by supporting social-spatial learning.

Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons.

There is increased appreciation that dopamine neurons in the midbrain respond not only to reward1 and reward-predicting cues1,2, but also to other variables such as the distance to reward3, movements4-9 and behavioural choices10,11. An important question is how the responses to these diverse variables are organized across the population of dopamine neurons. Whether individual dopamine neurons multiplex several variables, or whether there are subsets of neurons that are specialized in encoding specific behavioural variables remains unclear. This fundamental question has been difficult to resolve because recordings from large populations of individual dopamine neurons have not been performed in a behavioural task with sufficient complexity to examine these diverse variables simultaneously. Here, to address this gap, we used two-photon calcium imaging through an implanted lens to record the activity of more than 300 dopamine neurons from the ventral tegmental area of the mouse midbrain during a complex decision-making task. As mice navigated in a virtual-reality environment, dopamine neurons encoded an array of sensory, motor and cognitive variables. These responses were functionally clustered, such that subpopulations of neurons transmitted information about a subset of behavioural variables, in addition to encoding reward. These functional clusters were spatially organized, with neighbouring neurons more likely to be part of the same cluster. Together with the topography between dopamine neurons and their projections, this specialization and anatomical organization may aid downstream circuits in correctly interpreting the wide range of signals transmitted by dopamine neurons.

Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model.

BACKGROUND: The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways. METHODS: Population-based cohort study of adults aged 30-85 years at type 2 diabetes diagnosis (2010-2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal-external cross-validation. RESULTS: Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell's C-index 0.802 (95% CI: 0.797-0.817)), the highest clinical utility, and was well calibrated. The model's highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity. DISCUSSION: A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging.

Hormone replacement therapy and cancer mortality in women with 17 site-specific cancers: a cohort study using linked medical records.

BACKGROUND: There is limited evidence on the safety of Hormone Replacement Therapy (HRT) in women with cancer. Therefore, we systematically examined HRT use and cancer-specific mortality in women with 17 site-specific cancers. METHODS: Women newly diagnosed with 17 site-specific cancers from 1998 to 2019, were identified from general practitioner (GP) records, hospital diagnoses or cancer registries in Scotland, Wales and England. Breast cancer patients were excluded because HRT is contraindicated in breast cancer patients. The primary outcome was time to cancer-specific mortality. Time-dependent Cox regression models were used to calculate adjusted hazard ratios (HR) and 95% confidence intervals (95% CIs) for cancer-specific mortality by systemic HRT use. RESULTS: The combined cancer cohorts contained 182,589 women across 17 cancer sites. Overall 7% of patients used systemic HRT after their cancer diagnosis. There was no evidence that HRT users, compared with non-users, had higher cancer-specific mortality at any cancer site. In particular, no increase was observed in common cancers including lung (adjusted HR = 0.98 95% CI 0.90, 1.07), colorectal (adjusted HR = 0.79 95% CI 0.70, 0.90), and melanoma (adjusted HR = 0.77 95% CI 0.58, 1.02). CONCLUSIONS: We observed no evidence of increased cancer-specific mortality in women with a range of cancers (excluding breast) receiving HRT.

Uptake, effectiveness and safety of COVID-19 vaccines in individuals at clinical risk due to immunosuppressive drug therapy or transplantation procedures: a population-based cohort study in England.

BACKGROUND: Immunocompromised individuals are at increased risk of severe COVID-19 outcomes, underscoring the importance of COVID-19 vaccination in this population. The lack of comprehensive real-world data on vaccine uptake, effectiveness and safety in these individuals presents a critical knowledge gap, highlighting the urgency to better understand and address the unique challenges faced by immunocompromised individuals in the context of COVID-19 vaccination. METHODS: We analysed data from 12,274,946 people in the UK aged > 12 years from 01/12/2020 to 11/04/2022. Of these, 583,541 (4.8%) were immunocompromised due to immunosuppressive drugs, organ transplants, dialysis or chemotherapy. We undertook a cohort analysis to determine COVID-19 vaccine uptake, nested case-control analyses adjusted for comorbidities and sociodemographic characteristics to determine effectiveness of vaccination against COVID-19 hospitalisation, ICU admission and death, and a self-controlled case series assessing vaccine safety for pre-specified adverse events of interest. RESULTS: Overall, 93.7% of immunocompromised individuals received at least one COVID-19 vaccine dose, with 80.4% having received three or more doses. Uptake reduced with increasing deprivation (hazard ratio [HR] 0.78 [95%CI 0.77-0.79] in the most deprived quintile compared to the least deprived quintile for the first dose). Estimated vaccine effectiveness against COVID-19 hospitalisation 2-6 weeks after the second and third doses compared to unvaccinated was 78% (95%CI 72-83) and 91% (95%CI 88-93) in the immunocompromised population, versus 85% (95%CI 83-86) and 86% (95%CI 85-89), respectively, for the general population. Results showed COVID-19 vaccines were protective against intensive care unit (ICU) admission and death in both populations, with effectiveness of over 92% against COVID-19-related death and up to 95% in reducing ICU admissions for both populations following the third dose. COVID-19 vaccines were generally safe for immunocompromised individuals, though specific doses of ChAdOx1, mRNA-1273 and BNT162b2 raised risks of specific cardiovascular/neurological conditions. CONCLUSIONS: COVID-19 vaccine uptake is high in immunocompromised individuals on immunosuppressive drug therapy or who have undergone transplantation procedures, with documented disparities by deprivation. Findings suggest that COVID-19 vaccines are protective against severe COVID-19 outcomes in this vulnerable population, and show a similar safety profile in immunocompromised individuals and the general population, despite some increased risk of adverse events. These results underscore the importance of ongoing vaccination prioritisation for this clinically at-risk population to maximise protection against severe COVID-19 outcomes.

Striatal circuits for reward learning and decision-making.

The striatum is essential for learning which actions lead to reward and for implementing those actions. Decades of experimental and theoretical work have led to several influential theories and hypotheses about how the striatal circuit mediates these functions. However, owing to technical limitations, testing these hypotheses rigorously has been difficult. In this Review, we briefly describe some of the classic ideas of striatal function. We then review recent studies in rodents that take advantage of optical and genetic methods to test these classic ideas by recording and manipulating identified cell types within the circuit. This new body of work has provided experimental support of some longstanding ideas about the striatal circuit and has uncovered critical aspects of the classic view that are incorrect or incomplete.

Risk of bleeding amongst warfarin and direct oral anticoagulant users prescribed immediate antibiotics for respiratory tract infection: Cohort study.

PURPOSE: Incidence of bleeding amongst warfarin and direct oral anticoagulant (DOAC) users is greater following a respiratory tract infection (RTI). It is unclear whether immediate antibiotics modify this association. We estimated the risk of bleeding amongst warfarin and DOAC users with RTI by antibiotic treatment. METHODS: This retrospective cohort study used data from the Clinical Practice Research Datalink (CPRD) GOLD for adults in England prescribed warfarin or a DOAC, who sought primary care for an RTI between 1st January 2011 and 31st December 2019. Outcomes were major bleeding (hospital admission for intracranial or gastrointestinal bleeding), and non-major bleeding (hospital admission or General Practice consult for epistaxis, haemoptysis, or haematuria). Cox models derived hazard ratios (HRs) and 95% confidence intervals (CIs) for each outcome, adjusting for confounders using inverse probability of treatment weighting. RESULTS: Of 14 817 warfarin and DOAC users consulting for an RTI, 8768 (59%) were prescribed immediate antibiotics and 6049 (41%) were not. Approximately 49% were female, and median age was 76 years. Antibiotics were associated with reduced risk of major bleeding (adjusted HR 0.38, 95% CI 0.25 to 0.58). This was consistent across several sensitivity analyses. Antibiotics were also associated with a reduced risk of non-major bleeding (adjusted HR 0.78, 95% CI 0.61 to 0.99). CONCLUSIONS: Immediate antibiotics were associated with reduced risk of bleeding amongst warfarin and DOAC users with an RTI. Further work is needed to understand mechanisms and confirm whether a lower threshold for antibiotic use for RTI in this population may be beneficial.

Temporality of body mass index, blood tests, comorbidities and medication use as early markers for pancreatic ductal adenocarcinoma (PDAC): a nested case-control study.

OBJECTIVE: Prior studies identified clinical factors associated with increased risk of pancreatic ductal adenocarcinoma (PDAC). However, little is known regarding their time-varying nature, which could inform earlier diagnosis. This study assessed temporality of body mass index (BMI), blood-based markers, comorbidities and medication use with PDAC risk . DESIGN: We performed a population-based nested case-control study of 28 137 PDAC cases and 261 219 matched-controls in England. We described the associations of biomarkers with risk of PDAC using fractional polynomials and 5-year time trends using joinpoint regression. Associations with comorbidities and medication use were evaluated using conditional logistic regression. RESULTS: Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while following a U-shaped relationship for BMI and haemoglobin. Five-year trends showed biphasic BMI decrease and HbA1c increase prior to PDAC; early-gradual changes 2-3 years prior, followed by late-rapid changes 1-2 years prior. Liver markers and blood counts (white blood cell, platelets) showed monophasic rapid-increase approximately 1 year prior. Recent diagnosis of pancreatic cyst, pancreatitis, type 2 diabetes and initiation of certain glucose-lowering and acid-regulating therapies were associated with highest risk of PDAC. CONCLUSION: Risk of PDAC increased with raised HbA1c, liver markers, white blood cell and platelets, while followed a U-shaped relationship for BMI and haemoglobin. BMI and HbA1c derange biphasically approximately 3 years prior while liver markers and blood counts (white blood cell, platelets) derange monophasically approximately 1 year prior to PDAC. Profiling these in combination with their temporality could inform earlier PDAC diagnosis.

Risk of Myocarditis After Sequential Doses of COVID-19 Vaccine and SARS-CoV-2 Infection by Age and Sex.

BACKGROUND: Myocarditis is more common after severe acute respiratory syndrome coronavirus 2 infection than after COVID-19 vaccination, but the risks in younger people and after sequential vaccine doses are less certain. METHODS: A self-controlled case series study of people ages 13 years or older vaccinated for COVID-19 in England between December 1, 2020, and December 15, 2021, evaluated the association between vaccination and myocarditis, stratified by age and sex. The incidence rate ratio and excess number of hospital admissions or deaths from myocarditis per million people were estimated for the 1 to 28 days after sequential doses of adenovirus (ChAdOx1) or mRNA-based (BNT162b2, mRNA-1273) vaccines, or after a positive SARS-CoV-2 test. RESULTS: In 42 842 345 people receiving at least 1 dose of vaccine, 21 242 629 received 3 doses, and 5 934 153 had SARS-CoV-2 infection before or after vaccination. Myocarditis occurred in 2861 (0.007%) people, with 617 events 1 to 28 days after vaccination. Risk of myocarditis was increased in the 1 to 28 days after a first dose of ChAdOx1 (incidence rate ratio, 1.33 [95% CI, 1.09-1.62]) and a first, second, and booster dose of BNT162b2 (1.52 [95% CI, 1.24-1.85]; 1.57 [95% CI, 1.28-1.92], and 1.72 [95% CI, 1.33-2.22], respectively) but was lower than the risks after a positive SARS-CoV-2 test before or after vaccination (11.14 [95% CI, 8.64-14.36] and 5.97 [95% CI, 4.54-7.87], respectively). The risk of myocarditis was higher 1 to 28 days after a second dose of mRNA-1273 (11.76 [95% CI, 7.25-19.08]) and persisted after a booster dose (2.64 [95% CI, 1.25-5.58]). Associations were stronger in men younger than 40 years for all vaccines. In men younger than 40 years old, the number of excess myocarditis events per million people was higher after a second dose of mRNA-1273 than after a positive SARS-CoV-2 test (97 [95% CI, 91-99] versus 16 [95% CI, 12-18]). In women younger than 40 years, the number of excess events per million was similar after a second dose of mRNA-1273 and a positive test (7 [95% CI, 1-9] versus 8 [95% CI, 6-8]). CONCLUSIONS: Overall, the risk of myocarditis is greater after SARS-CoV-2 infection than after COVID-19 vaccination and remains modest after sequential doses including a booster dose of BNT162b2 mRNA vaccine. However, the risk of myocarditis after vaccination is higher in younger men, particularly after a second dose of the mRNA-1273 vaccine.

Mortality from angiotensin-converting enzyme-inhibitors and angiotensin receptor blockers in people infected with COVID-19: a cohort study of 3.7 million people.

BACKGROUND: Concerns have been raised that angiotensin-converting enzyme-inhibitors (ACE-I) and angiotensin receptor blockers (ARBs) might facilitate transmission of severe acute respiratory syndrome coronavirus 2 leading to more severe coronavirus disease (COVID-19) disease and an increased risk of mortality. We aimed to investigate the association between ACE-I/ARB treatment and risk of death amongst people with COVID-19 in the first 6 months of the pandemic. METHODS: We identified a cohort of adults diagnosed with either confirmed or probable COVID-19 (from 1 January to 21 June 2020) using computerized medical records from the Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care database. This comprised 465 general practices in England, United Kingdom with a nationally representative population of 3.7 million people. We constructed mixed-effects logistic regression models to quantify the association between ACE-I/ARBs and all-cause mortality among people with COVID-19, adjusted for sociodemographic factors, comorbidities, concurrent medication, smoking status, practice clustering, and household number. RESULTS: There were 9,586 COVID-19 cases in the sample and 1,463 (15.3%) died during the study period between 1 January 2020 and 21 June 2020. In adjusted analysis ACE-I and ARBs were not associated with all-cause mortality (adjusted odds ratio [OR] 1.02, 95% confidence interval [CI] 0.85-1.21 and OR 0.84, 95% CI 0.67-1.07, respectively). CONCLUSION: Use of ACE-I/ARB, which are commonly used drugs, did not alter the odds of all-cause mortality amongst people diagnosed with COVID-19. Our findings should inform patient and prescriber decisions concerning continued use of these medications during the pandemic.

Ethnic disparities in COVID-19 outcomes: a multinational cohort study of 20 million individuals from England and Canada.

BACKGROUND: Heterogeneous studies have demonstrated ethnic inequalities in the risk of SARS-CoV-2 infection and adverse COVID-19 outcomes. This study evaluates the association between ethnicity and COVID-19 outcomes in two large population-based cohorts from England and Canada and investigates potential explanatory factors for ethnic patterning of severe outcomes. METHODS: We identified adults aged 18 to 99 years in the QResearch primary care (England) and Ontario (Canada) healthcare administrative population-based datasets (start of follow-up: 24th and 25th Jan 2020 in England and Canada, respectively; end of follow-up: 31st Oct and 30th Sept 2020, respectively). We harmonised the definitions and the design of two cohorts to investigate associations between ethnicity and COVID-19-related death, hospitalisation, and intensive care (ICU) admission, adjusted for confounders, and combined the estimates obtained from survival analyses. We calculated the 'percentage of excess risk mediated' by these risk factors in the QResearch cohort. RESULTS: There were 9.83 million adults in the QResearch cohort (11,597 deaths; 21,917 hospitalisations; 2932 ICU admissions) and 10.27 million adults in the Ontario cohort (951 deaths; 5132 hospitalisations; 1191 ICU admissions). Compared to the general population, pooled random-effects estimates showed that South Asian ethnicity was associated with an increased risk of COVID-19 death (hazard ratio: 1.63, 95% CI: 1.09-2.44), hospitalisation (1.53; 1.32-1.76), and ICU admission (1.67; 1.23-2.28). Associations with ethnic groups were consistent across levels of deprivation. In QResearch, sociodemographic, lifestyle, and clinical factors accounted for 42.9% (South Asian) and 39.4% (Black) of the excess risk of COVID-19 death. CONCLUSION: International population-level analyses demonstrate clear ethnic inequalities in COVID-19 risks. Policymakers should be cognisant of the increased risks in some ethnic populations and design equitable health policy as the pandemic continues.

The current status of risk-stratified breast screening.

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Smoking and COVID-19 outcomes: an observational and Mendelian randomisation study using the UK Biobank cohort.

BACKGROUND: Conflicting evidence has emerged regarding the relevance of smoking on risk of COVID-19 and its severity. METHODS: We undertook large-scale observational and Mendelian randomisation (MR) analyses using UK Biobank. Most recent smoking status was determined from primary care records (70.8%) and UK Biobank questionnaire data (29.2%). COVID-19 outcomes were derived from Public Health England SARS-CoV-2 testing data, hospital admissions data, and death certificates (until 18 August 2020). Logistic regression was used to estimate associations between smoking status and confirmed SARS-CoV-2 infection, COVID-19-related hospitalisation, and COVID-19-related death. Inverse variance-weighted MR analyses using established genetic instruments for smoking initiation and smoking heaviness were undertaken (reported per SD increase). RESULTS: There were 421 469 eligible participants, 1649 confirmed infections, 968 COVID-19-related hospitalisations and 444 COVID-19-related deaths. Compared with never-smokers, current smokers had higher risks of hospitalisation (OR 1.80, 95% CI 1.26 to 2.29) and mortality (smoking 1-9/day: OR 2.14, 95% CI 0.87 to 5.24; 10-19/day: OR 5.91, 95% CI 3.66 to 9.54; 20+/day: OR 6.11, 95% CI 3.59 to 10.42). In MR analyses of 281 105 White British participants, genetically predicted propensity to initiate smoking was associated with higher risks of infection (OR 1.45, 95% CI 1.10 to 1.91) and hospitalisation (OR 1.60, 95% CI 1.13 to 2.27). Genetically predicted higher number of cigarettes smoked per day was associated with higher risks of all outcomes (infection OR 2.51, 95% CI 1.20 to 5.24; hospitalisation OR 5.08, 95% CI 2.04 to 12.66; and death OR 10.02, 95% CI 2.53 to 39.72). INTERPRETATION: Congruent results from two analytical approaches support a causal effect of smoking on risk of severe COVID-19.

External validation of the QCovid risk prediction algorithm for risk of COVID-19 hospitalisation and mortality in adults: national validation cohort study in Scotland.

BACKGROUND: The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland. METHODS: We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020. RESULTS: Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell's C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell's C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively. CONCLUSIONS: Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively.

The risk of all-cause and cause-specific mortality in people prescribed mirtazapine: an active comparator cohort study using electronic health records.

BACKGROUND: Studies have reported an increased risk of mortality among people prescribed mirtazapine compared to other antidepressants. The study aimed to compare all-cause and cause-specific mortality between adults prescribed mirtazapine or other second-line antidepressants. METHODS: This cohort study used English primary care electronic medical records, hospital admission records, and mortality data from the Clinical Practice Research Datalink (CPRD), for the period 01 January 2005 to 30 November 2018. It included people aged 18-99 years with depression first prescribed a selective serotonin reuptake inhibitor (SSRI) and then prescribed mirtazapine (5081), a different SSRI (15,032), amitriptyline (3905), or venlafaxine (1580). Follow-up was from starting to stopping the second antidepressant, with a 6-month wash-out window, censoring at the end of CPRD follow-up or 30 November 2018. Age-sex standardised rates of all-cause mortality and death due to circulatory system disease, cancer, or respiratory system disease were calculated. Survival analyses were performed, accounting for baseline characteristics using inverse probability of treatment weighting. RESULTS: The cohort contained 25,598 people (median age 41 years). The mirtazapine group had the highest standardised mortality rate, with an additional 7.8 (95% confidence interval (CI) 5.9-9.7) deaths/1000 person-years compared to the SSRI group. Within 2 years of follow-up, the risk of all-cause mortality was statistically significantly higher in the mirtazapine group than in the SSRI group (weighted hazard ratio (HR) 1.62, 95% CI 1.28-2.06). No significant difference was found between the mirtazapine group and the amitriptyline (HR 1.18, 95% CI 0.85-1.63) or venlafaxine (HR 1.11, 95% CI 0.60-2.05) groups. After 2 years, the risk was significantly higher in the mirtazapine group compared to the SSRI (HR 1.51, 95% CI 1.04-2.19), amitriptyline (HR 2.59, 95% CI 1.38-4.86), and venlafaxine (HR 2.35, 95% CI 1.02-5.44) groups. The risks of death due to cancer (HR 1.74, 95% CI 1.06-2.85) and respiratory system disease (HR 1.72, 95% CI 1.07-2.77) were significantly higher in the mirtazapine than in the SSRI group. CONCLUSIONS: Mortality was higher in people prescribed mirtazapine than people prescribed a second SSRI, possibly reflecting residual differences in other risk factors between the groups. Identifying these potential health risks when prescribing mirtazapine may help reduce the risk of mortality.

Risk models for recurrence and survival after kidney cancer: a systematic review.

OBJECTIVE: To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent. MATERIALS AND METHODS: We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models. RESULTS: Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival. CONCLUSION: Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates.

Patterns of Child Mental Health Service Utilization Within a Multiple EBP System of Care.

The current study (1) characterizes patterns of mental health service utilization over 8 years among youth who received psychotherapy in the context of a community implementation of multiple evidence-based practices (EBPs), and (2) examined youth-, provider- and service-level predictors of service use patterns. Latent profile analyses were performed on 5,663,930 administrative claims data furnished by the county department of mental health. Multinomial logistic regression with Vermunt's method was used to examine predictors of care patterns. Based on frequency, course, cost, and type of services, three distinct patterns of care were identified: (1) Standard EBP Care (86.3%), (2) Less EBP Care (8.5%), and (3) Repeated/Chronic Care (5.2%). Youth age, ethnicity, primary language, primary diagnosis and secondary diagnosis, provider language and provider type, and caregiver involvement and service setting were significant predictors of utilization patterns. Although the majority of youth received care aligned with common child EBP protocols, a significant portion of youth (13.7%) received no evidence-based care or repeated, costly episodes of care. Findings highlight opportunities to improve and optimize services, particularly for youth who are adolescents or transition-aged, Asian-American/Pacific Islander, Spanish-speaking, or presenting with comorbidities.

Hormone replacement therapy in women with cancer and risk of cancer-specific mortality and cardiovascular disease: a protocol for a cohort study from Scotland and Wales.

BACKGROUND: Hormone replacement therapy (HRT) is widely used and has proven benefits for women with menopausal symptoms. An increasing number of women with cancer experience menopausal symptoms but the safety of HRT use in women with cancer is unclear. There are particular concerns that HRT could accelerate cancer progression in women with cancer, and also that HRT could increase the risk of cardiovascular disease in such women. Therefore, our primary aim is to determine whether HRT use alters the risk of cancer-specific mortality in women with a range of common cancers. Our secondary objectives are to investigate whether HRT alters the risk of second cancers, cardiovascular disease, venous thromboembolism and all-cause mortality. METHODS: The study will utilise independent population-based data from Wales using the SAIL databank and Scotland based upon the national Prescribing Information System. The study will include women newly diagnosed with common cancers from 2000 to 2016, identified from cancer registries. Women with breast cancers will be excluded. HRT will be ascertained using electronic prescribing in Wales or dispensing records in Scotland. The primary outcome will be time to cancer-specific mortality from national mortality records. Time-dependent cox regression models will be used to calculate hazard ratios (HR) and 95% confidence intervals (95% CIs) for cancer specific death in HRT users compared with non-users after cancer diagnosis after adjusting for relevant confounders, stratified by cancer site. Analysis will be repeated investigating the impact of HRT use immediately before cancer diagnosis. Secondary analyses will be conducted on the risk of second cancers, cardiovascular disease, venous thromboembolism and all-cause mortality. Analyses will be conducted within each cohort and pooled across cohorts. DISCUSSION: Our study will provide evidence to inform guidance given to women diagnosed with cancer on the safety of HRT use and/or guide modifications to clinical practice.

The Use of Primary Care Big Data in Understanding the Pharmacoepidemiology of COVID-19: A Consensus Statement From the COVID-19 Primary Care Database Consortium.

The use of big data containing millions of primary care medical records provides an opportunity for rapid research to help inform patient care and policy decisions during the first and subsequent waves of the coronavirus disease 2019 (COVID-19) pandemic. Routinely collected primary care data have previously been used for national pandemic surveillance, quantifying associations between exposures and outcomes, identifying high risk populations, and examining the effects of interventions at scale, but there is no consensus on how to effectively conduct or report these data for COVID-19 research. A COVID-19 primary care database consortium was established in April 2020 and its researchers have ongoing COVID-19 projects in overlapping data sets with over 40 million primary care records in the United Kingdom that are variously linked to public health, secondary care, and vital status records. This consensus agreement is aimed at facilitating transparency and rigor in methodological approaches, and consistency in defining and reporting cases, exposures, confounders, stratification variables, and outcomes in relation to the pharmacoepidemiology of COVID-19. This will facilitate comparison, validation, and meta-analyses of research during and after the pandemic.

Incidence and prevalence of primary care antidepressant prescribing in children and young people in England, 1998-2017: A population-based cohort study.

BACKGROUND: The use of antidepressants in children and adolescents remains controversial. We examined trends over time and variation in antidepressant prescribing in children and young people in England and whether the drugs prescribed reflected UK licensing and guidelines. METHODS AND FINDINGS: QResearch is a primary care database containing anonymised healthcare records of over 32 million patients from more than 1,500 general practices across the UK. All eligible children and young people aged 5-17 years in 1998-2017 from QResearch were included. Incidence and prevalence rates of antidepressant prescriptions in each year were calculated overall, for 4 antidepressant classes (selective serotonin reuptake inhibitors [SSRIs], tricyclic and related antidepressants [TCAs], serotonin and norepinephrine reuptake inhibitors [SNRIs], and other antidepressants), and for individual drugs. Adjusted trends over time and differences by social deprivation, region, and ethnicity were examined using Poisson regression, taking clustering within general practitioner (GP) practices into account using multilevel modelling. Of the 4.3 million children and young people in the cohort, 49,434 (1.1%) were prescribed antidepressants for the first time during 20 million years of follow-up. Males made up 52.0% of the cohorts, but only 34.1% of those who were first prescribed an antidepressant in the study period. The largest proportion of the cohort was from London (24.4%), and whilst ethnicity information was missing for 39.5% of the cohort, of those with known ethnicity, 75.3% were White. Overall, SSRIs (62.6%) were the most commonly prescribed first antidepressant, followed by TCAs (35.7%). Incident antidepressant prescribing decreased in 5- to 11-year-olds from a peak of 0.9 in females and 1.6 in males in 1999 to less than 0.2 per 1,000 for both sexes in 2017, but incidence rates more than doubled in 12- to 17-year-olds between 2005 and 2017 to 9.7 (females) and 4.2 (males) per 1,000 person-years. The lowest prescription incidence rates were in London, and the highest were in the South East of England (excluding London) for all sex and age groups. Those living in more deprived areas were more likely to be prescribed antidepressants after adjusting for region. The strongest trend was seen in 12- to 17-year-old females (adjusted incidence rate ratio [aIRR] 1.12, 95% confidence interval [95% CI] 1.11-1.13, p < 0.001, per deprivation quintile increase). Prescribing rates were highest in White and lowest in Black adolescents (aIRR 0.32, 95% CI 0.29-0.36, p < 0.001 [females]; aIRR 0.32, 95% CI 0.27-0.38, p < 0.001 [males]). The 5 most commonly prescribed antidepressants were either licensed in the UK for use in children and young people (CYP) or included in national guidelines. Limitations of the study are that, because we did not have access to secondary care prescribing information, we may be underestimating the prevalence and misidentifying the first antidepressant prescription. We could not assess whether antidepressants were dispensed or taken. CONCLUSIONS: Our analysis provides evidence of a continuing rise of antidepressant prescribing in adolescents aged 12-17 years since 2005, driven by SSRI prescriptions, but a decrease in children aged 5-11 years. The variation in prescribing by deprivation, region, and ethnicity could represent inequities. Future research should examine whether prescribing trends and variation are due to true differences in need and risk factors, access to diagnosis or treatment, prescribing behaviour, or young people's help-seeking behaviour.