Joseph F Hayes’s research while affiliated with North East London NHS Foundation Trust and other places

Objective To estimate the effect of gabapentinoid treatment on self-harm. Design A population based self-controlled case series study. Setting UK Clinical Practice Research Datalink Aurum database linked to the Hospital Episode Statistics and Office for National Statistics databases. Participants 10 002 adults (aged ≥18 years), with gabapentinoid prescriptions, who had an incident event of self-harm between 1 January 2000 and 31 December 2020. Individual censoring occurred on the date of epilepsy, substance misuse, or cancer diagnosis. Main outcome measures Crude incidence rates of self-harm in different risk periods: 90 days before gabapentinoid treatment, gabapentinoid treatment period, 14 days after treatment periods, and reference periods were calculated. Conditional Poisson regression derived the incidence rate ratio and 95% confidence intervals (CIs) to evaluate the risk of self-harm in different risk periods, compared with reference period for each individual. Results 1 503 597 individuals received gabapentinoid prescriptions and 10 002 individuals were included in the analysis. The incidence rate of self-harm per 100 person years was 16.79 (95% CI 16.65 to 16.92) in the 90 days before treatment period, 9.66 (9.62 to 9.70) in the treatment period, 29.60 (29.09 to 30.11) in the 14 days after treatment period, and 6.75 (6.74 to 6.77) in the reference period. The results yielded an increased risk of self-harm during the 90 day period before treatment, with an adjusted incidence rate ratio of 1.69 (95% CI 1.55 to 1.85). The spline based analysis showed that the risk of self-harm declined gradually around the time of treatment initiation and returned to reference level during the treatment period (adjusted incidence rate ratio 1.06 (0.98 to 1.13)). Adjusted incidence rate ratio for self-harm increased within 14 days after treatment cessation (3.02 (2.53 to 3.60)). The findings remained consistent throughout a series of subgroups and sensitivity analyses. Conclusions The association between gabapentinoids and risk of self-harm seems to be multifaceted: an elevated risk of self-harm is present before initiation of gabapentinoid treatment, which persists during the initial phase of the treatment period, and rises again shortly after treatment discontinuation. These findings do not support a direct effect of gabapentinoid treatment on self-harm but underscore the necessity for close patient monitoring of self-harm throughout the gabapentinoid treatment journey.

Background Metformin is a pharmacological candidate to mitigate second-generation antipsychotic (SGA)-induced weight gain in patients diagnosed with severe mental illnesses (SMI). Objective To determine the incidence, prevalence and demographic patterns of metformin co-prescription among patients diagnosed with SMI initiating SGAs. To estimate the impact of metformin co-prescription on weight over 2 years post-SGA initiation. Methods A cohort study of patients diagnosed with SMI initiating aripiprazole, olanzapine, quetiapine or risperidone in 2005–2019 using primary care data from Clinical Practice Research Datalink. We estimated cumulative incidence and period prevalences of co-prescription and explored prescribing differences by demographic and clinical factors. We compared weight change among patients prescribed an SGA-only versus an SGA plus metformin, accounting for confounders using linear regression. Findings Among 26 537 patients initiating SGAs, 4652 were ever prescribed metformin and 21 885 were not. The two-year incidence of first metformin prescription was 3.3%. The SGA plus metformin group were more ethnically diverse, had greater social deprivation, more comorbidities and higher baseline weight (mean 90.4 vs 76.8 kg). By 2 years post-SGA initiation, mean weight in the SGA-only group had changed by +4.16% (95% CI −1.26 to +9.58) compared with −0.65% (95% CI −4.26 to +2.96) in the SGA plus metformin group. After confounder adjustment, the 2-year mean difference in weight with metformin co-prescription was −1.48 kg (95% CI −4.03 to 1.07) among females and −1.84 kg (95% CI −4.67 to 0.98) among males. Conclusion Metformin is infrequently co-prescribed, despite apparent efficacy and guidelines. Clinical implications Primary and secondary care collaboration should be strengthened and barriers to co-prescribing addressed.

Background Guidance is lacking on choice of first-line antipsychotic for individuals with incident severe mental illness (SMI). Patients may try several before an effective, well-tolerated drug is identified, delaying symptom improvement. We aimed to develop a personalised selection tool to identify the optimum first-line antipsychotic, based on individual sociodemographic and clinical characteristics. Methods Risk prediction development and validation study using electronic health records (EHRs) from primary care in England (Clinical Practice Research Datalink) linked to Hospital Episode Statistics, including 11,811 individuals with incident SMI prescribed aripiprazole, olanzapine, quetiapine or risperidone as first-line treatment between 2007-2016. The outcome was time to psychiatric hospitalisation or change to different antipsychotic within 3 years of commencing treatment. Prediction algorithms were developed using Cox proportional hazards models in a 70% training sample and validated in a 30% hold-out sample. This baseline model was compared with machine learning survival models of increasing complexity. Potential predictors included demographics, diagnoses, concomitant medications and laboratory findings. Outcomes Among 8,225 individuals in the development cohort, 4,456 (54.2%) experienced the outcome. In model validations, 2,662 (75%) of 3,556 in the validation cohort did not receive the optimal antipsychotic identified by the personalised selection tool. The predicted 3-year outcome risk if all individuals received the medication assigned by the tool was 6.3% lower (95% CI 4.0%-8.5%) than the observed 3-year risk in the validation cohort, and 10.2% lower (95%CI 7.9%-12.5%) than if individuals were randomly assigned an antipsychotic (corresponding numbers need to treat of 16 and 10). Machine learning approaches did not meaningfully improve model performance. Interpretation A personalised tool based on EHR data could improve treatment success rates by optimising first-line antipsychotic selection. Machine learning did not outperform traditional prediction methods. Further research will assess the impact on adverse events and in other populations.

Background The long-term psychiatric and neuropsychiatric sequelae of COVID-19 across diverse populations remain not fully understood. This cohort study aims to investigate the short-, medium-, and long-term risks of psychiatric and neuropsychiatric disorders following COVID-19 infection in five countries. Methods This population-based multinational network study used electronic medical records from France, Italy, Germany, and the UK and claims data from the USA. The initial target and comparator cohorts were identified using an exact matching approach based on age and sex. Individuals diagnosed with COVID-19 or those with a positive SARS-CoV-2 screening test between December 1, 2019, and December 1, 2020, were included as targets. Up to ten comparators without COVID-19 for each target were selected using the propensity score matching approach. All individuals were followed from the index date until the end of continuous enrolment or the last healthcare encounter. Cox proportional hazard regression models were fitted to estimate the risk of incident diagnosis of depression, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, psychoses, personality disorders, self-harm and suicide, sleep disorders, dementia, and neurodevelopmental disorders within the first 6 months (short-term), 6 months to 1 year (medium-term), and 1 to 2 years (long-term) post-infection. Results A total of 303,251 individuals with COVID-19 and 22,108,925 individuals without COVID-19 from five countries were originally included. Within the first 6 months, individuals with COVID-19 had a significantly higher risk of any studied disorders in all databases, with Hazard Ratios (HRs) ranging from 1.14 (95% CI, 1.07–1.22) in Germany to 1.89 (1.64–2.17) in Italy. Increased risks were consistently observed for depression, anxiety disorders, and sleep disorders across almost all countries. During the medium- and long-term periods, higher risks were observed only for depression (medium-term: 1.29, 1.18–1.41; long-term: 1.36, 1.25–1.47), anxiety disorders (medium-term: 1.29, 1.20–1.38; long-term: 1.37, 1.29–1.47), and sleep disorders (medium-term: 1.10, 1.01–1.21; long-term: 1.14, 1.05–1.24) in France, and dementia (medium-term: 1.65, 1.28–2.10) in the UK. Conclusions Our study suggests that increased risks of psychiatric and neuropsychiatric outcomes were consistently observed only within, and not after, the 6-month observation period across all databases, except for certain conditions in specific countries.

Background There is limited and conflicting evidence on the comparative cardiometabolic safety and effectiveness of aripiprazole in the management of severe mental illness. We investigated the hypothesis that aripiprazole has a favourable cardiometabolic profile, but similar effectiveness when compared to olanzapine, quetiapine, and risperidone. Methods and findings We conducted an observational emulation of a head-to-head trial of aripiprazole versus olanzapine, quetiapine, and risperidone in UK primary care using data from the Clinical Practice Research Datalink. We included adults diagnosed with severe mental illness (i.e., bipolar disorder, schizophrenia, and other non-organic psychoses) who were prescribed a new antipsychotic between 2005 and 2017, with a 2-year follow-up to 2019. The primary outcome was total cholesterol at 1 year (cardiometabolic safety). The main secondary outcome was psychiatric hospitalisation (effectiveness). Other outcomes included body weight, blood pressure, all-cause discontinuation, and mortality. Analyses adjusted for baseline confounders, including sociodemographics, diagnoses, concomitant medications, and cardiometabolic parameters. We included 26,537 patients (aripiprazole, n = 3,573, olanzapine, n = 8,554, quetiapine, n = 8,289, risperidone, n = 6,121). Median (IQR) age was 53 (42−67) years, 55.4% were female, 82.3% White, and 18.0% were diagnosed with schizophrenia. Patients prescribed aripiprazole had similar total cholesterol levels after 1 year to those prescribed olanzapine (adjusted mean difference [aMD], −0.03, 95% CI, −0.09 to 0.02, p = 0.261), quetiapine (aMD, −0.03, 95% CI, −0.09 to 0.03, p = 0.324), and risperidone (aMD, −0.01, 95% CI, −0.08 to 0.05, p = 0.707). However, there was evidence that patients prescribed aripiprazole had better outcomes on other cardiometabolic parameters, such as body weight and blood pressure, especially compared to olanzapine. After additional adjustment for prior hospitalisation, patients prescribed aripiprazole had similar rates of psychiatric hospitalisation as those prescribed olanzapine (adjusted hazard ratio [aHR], 0.91, 95% CI, 0.82 to 1.01, p = 0.078), quetiapine (aHR, 0.94, 95% CI, 0.85 to 1.04, p = 0.230), or risperidone (aHR, 1.01, 95% CI, 0.91 to 1.12, p = 0.854). Conclusions Data from our large, powered, diverse, real-world target trial emulation sample, followed over 2 years, suggest that adults diagnosed with severe mental illness prescribed aripiprazole have similar total cholesterol 1 year after first prescription compared to those prescribed olanzapine, quetiapine, and risperidone. However, patients prescribed aripiprazole had better outcomes on some other cardiometabolic parameters, and there was little evidence of differences in effectiveness. Our findings inform a common clinical dilemma and contribute to the evidence base for real-world clinical decision-making on antipsychotic choice for patients diagnosed with severe mental illness.

Background Randomized controlled trials (RCTs) of statins as adjunct therapy for severe mental illness (SMI) have produced mixed results. Specific statin-antipsychotic combinations might improve psychiatric symptoms through: 1) blood-brain barrier (BBB) penetrant statins being anti-inflammatory/neuroprotective, and/or 2) statins that inhibit p-glycoprotein enhancing the effects of antipsychotics with high p-glycoprotein affinity. Aim To investigate these mechanisms via three target trial emulation studies. Methods We identified patients with SMI (schizophrenia, bipolar disorder, "other" psychoses) prescribed antipsychotics/mood stabilisers and statins from 2000-2019 in English electronic health records. We defined three hypothetical RCTs and their observational analogues: 1) simvastatin (crosses BBB) vs. atorvastatin/pravastatin/rosuvastatin (non-penetrant); 2A) simvastatin/atorvastatin (p-glycoprotein inhibitors) vs. pravastatin in patients prescribed risperidone/olanzapine/aripiprazole (high p-glycoprotein affinity); 2B) risperidone/olanzapine/aripiprazole vs. quetiapine (low p-glycoprotein affinity) in patients prescribed simvastatin/atorvastatin. Primary outcome: 12-month psychiatric admissions. Secondary outcomes: self-harm, physical health, and accident/injury admissions. Results In 72,096 patients prescribed statins and antipsychotics/mood stabilisers, we found no reduction in psychiatric admissions at 12 months in patients prescribed: 1) BBB-penetrant vs. non-penetrant statins (HR:1.07; 95%CI:0.88-1.31); 2A) antipsychotics with p-glycoprotein affinity and p-glycoprotein inhibiting statins vs. statins without inhibition (HR:0.77; 95%CI:0.28-2.15); 2B) p-glycoprotein inhibiting statins with antipsychotics having p-glycoprotein affinity vs. antipsychotics without affinity (HR:0.93; 95%CI:0.79-1.09). In 2B we observed reduced self-harm (HR:0.60; 95%CI:0.38-0.97) in per-protocol analysis and reduced psychiatric admissions in the "other" psychoses subgroup (HR:0.53; 95%CI:0.34-0.85). Conclusions BBB permeability is unlikely to be the mechanism by which statins improve SMI symptoms. Further research is needed to understand statin-antipsychotic interactions, and whether interaction with p-glycoprotein is a plausible mechanism.

Background People with severe mental illness (SMI) are at increased risk of cardiovascular disease (CVD), and initiatives for CVD risk factor screening in the UK have not reduced disparities. Objectives To describe the annual screening prevalence for CVD risk factors in people with SMI from April 2000 to March 2018, and to identify factors associated with receiving no screening and regular screening. Methods We identified adults with a diagnosis of SMI (schizophrenia, bipolar disorder or ‘other psychosis’) from UK primary care records in Clinical Practice Research Datalink. We calculated the annual prevalence of screening for blood pressure, cholesterol, glucose, body mass index, alcohol consumption and smoking status using multinomial logistic regression to identify factors associated with receiving no screening and complete screening. Results Of 216 136 patients with SMI, 55% received screening for all six CVD risk factors at least once during follow-up and 35% received all six within a 1-month period. Our findings suggest that patient characteristics and financial incentivisation influence screening prevalence of individual CVD risk factors, the likelihood of receiving screening for all six CVD risk factors annually and risk of receiving no screening. Conclusions The low proportion of people with SMI receiving regular comprehensive CVD risk factor screening is concerning. Screening needs to be embedded as part of broad physical health checks to ensure the health needs of people with SMI are being met. If we are to improve cardiovascular health, interventions are needed where risk of receiving no screening or not receiving regular screening is highest.

Background Contemporary data relating to antipsychotic prescribing in UK primary care for patients diagnosed with severe mental illness (SMI) are lacking. Aims To describe contemporary patterns of antipsychotic prescribing in UK primary care for patients diagnosed with SMI. Method Cohort study of patients with an SMI diagnosis (i.e. schizophrenia, bipolar disorder, other non-organic psychoses) first recorded in primary care between 2000 and 2017 derived from Clinical Practice Research Datalink. Patients were considered exposed to antipsychotics if prescribed at least one antipsychotic in primary care between 2000 and 2019. We compared characteristics of patients prescribed and not prescribed antipsychotics; calculated annual prevalence rates for antipsychotic prescribing; and computed average daily antipsychotic doses stratified by patient characteristics. Results Of 309 378 patients first diagnosed with an SMI in primary care between 2000 and 2017, 212,618 (68.7%) were prescribed an antipsychotic between 2000 and 2019. Antipsychotic prescribing prevalence was 426 (95% CI, 420–433) per 1000 patients in the year 2000, reaching a peak of 550 (547–553) in 2016, decreasing to 470 (468–473) in 2019. The proportion prescribed antipsychotics was higher among patients diagnosed with schizophrenia (81.0%) than with bipolar disorder (64.6%) and other non-organic psychoses (65.7%). Olanzapine, quetiapine, risperidone and aripiprazole accounted for 78.8% of all antipsychotic prescriptions. Higher mean olanzapine equivalent total daily doses were prescribed to patients with the following characteristics: schizophrenia diagnosis, ethnic minority status, male gender, younger age and greater relative deprivation. Conclusions Antipsychotic prescribing is dominated by olanzapine, quetiapine, risperidone and aripiprazole. We identified potential disparities in both the receipt and prescribed doses of antipsychotics across subgroups. To inform efforts to optimise prescribing and ensure equity of care, further research is needed to understand why certain groups are prescribed higher doses and are more likely to be treated with long-acting injectable antipsychotics compared with others.

Objectives To validate codelists for defining a range of mental health (MH) conditions with primary care data, using a mixed qualitative and quantitative approach and without requiring external data. Methods We validated Read codelists, selecting and classifying them in three steps. The qualitative step included an in‐depth revision of the codes by six doctors. Simultaneously, the quantitative step performed on UK primary care data included an exploratory factor analysis to cluster Read codes in MH conditions to obtain an independent classification. The statistical results informed the qualitative conclusions, generating a final selection and classification. Results From a preselected list of 2007 Read codes, a total of 1638 were selected by all doctors. Later, they agreed on classifying these codes into 12 categories of MH disorders. From the same preselected list, a total of 1364 were quantitatively selected. Using data from 497,649 persons who used these Read codes at least once, we performed the exploratory factor analysis, retaining five factors (five categories). Both classifications showed good correspondence, while discrepancies informed decisions on reclassification. Conclusions We produced a comprehensive set of medical codes lists for 12 MH conditions validated by a combination of clinical consensus panel and quantitative cluster analysis with cross‐validation.