An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data.

Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data. Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting-'the gold standard'. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM. Trends over time from 2004 to 2020 were calculated using Poisson regression modelling. Our algorithm outperformed previously published lists identifying 43-72% of cases in primary care with a specificity ≥ 85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9 and 28% of cases with high specificity (> 96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data. Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time. The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed.

Anxiety and depression among children and young people involved in family justice court proceedings: longitudinal national data linkage study.

BACKGROUND: Little is known about mental health problems of children and young people (CYP) involved with public and private law family court proceedings, and how these CYP fare compared to those not involved in these significant disruptions to family life. AIMS: This study examined records of depression/anxiety in CYP involved in public and private law proceedings using linked population-level data across Wales. METHOD: Retrospective e-cohort study. We calculated the incidence of primary-care-recorded depression/anxiety among CYP involved in these proceedings and in a comparison group, using Poisson regression. Depression/anxiety outcomes following proceedings were evaluated using pairwise Cox regression, with age- and gender-matched controls of CYP who had no involvement with the courts. RESULTS: CYP in the public group had twice the risk of depression (adjusted incidence rate ratio aIRR = 2.2; 95% CI 1.9-2.6) and 20% higher risk of anxiety (aIRR = 1.2; 95% CI 1.0-1.5) relative to the comparison group. The private group had 60% higher risk of depression (aIRR = 1.6; 95% CI 1.4-1.7) and 30% higher risk of anxiety (aIRR = 1.3; 95% CI 1.2-1.4). Following private law proceedings, CYP were more likely to have depression (hazard ratio HR = 1.9; 95% CI 1.7-2.1), and anxiety (HR = 1.4; 95% CI 1.2-1.6) than the control group. Following public proceedings, CYP were more likely to have depression (HR = 2.1; 95% CI 1.7-2.5). Incidence of anxiety or depression following court proceedings was around 4%. CONCLUSIONS: Findings highlight the vulnerability of CYP involved in family court proceedings and increased risk of depression and anxiety. Schools, health professionals, social and family support workers have a role to play in identifying needs and ensuring CYP receive appropriate support before, during and after proceedings.

Premature mortality among people with severe mental illness - New evidence from linked primary care data.

Studies assessing premature mortality in people with severe mental illness (SMI) are usually based in one setting, hospital (secondary care inpatients and/or outpatients) or community (primary care). This may lead to ascertainment bias. This study aimed to estimate standardised mortality ratios (SMRs) for all-cause and cause-specific mortality in people with SMI drawn from linked primary and secondary care populations compared to the general population. SMRs were calculated using the indirect method for a United Kingdom population of almost four million between 2004 and 2013. The all-cause SMR was higher in the cohort identified from secondary care hospital admissions (SMR: 2.9; 95% CI: 2.8-3.0) than from primary care (SMR: 2.2; 95% CI: 2.1-2.3) when compared to the general population. The SMR for the combined cohort was 2.6 (95% CI: 2.5-2.6). Cause specific SMRs in the combined cohort were particularly elevated in those with SMI relative to the general population for ill-defined and unknown causes, suicide, substance abuse, Parkinson's disease, accidents, dementia, infections and respiratory disorders (particularly pneumonia), and Alzheimer's disease. Solely hospital admission based studies, which have dominated the literature hitherto, somewhat over-estimate premature mortality in those with SMI. People with SMI are more likely to die by ill-defined and unknown causes, suicide and other less common and often under-reported causes. Comprehensive characterisation of mortality is important to inform policy and practice and to discriminate settings to allow for proportionate interventions to address this health injustice.

Correction to: Risk of Adverse Outcomes for Older People with Dementia Prescribed Antipsychotic Medication: A Population Based e-Cohort Study.

This article was originally published under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), but has now been made available under a Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The PDF and HTML versions of the paper have been modified accordingly.

Risk of Adverse Outcomes for Older People with Dementia Prescribed Antipsychotic Medication: A Population Based e-Cohort Study.

INTRODUCTION: Over recent years there has been growing evidence of increased risk of mortality associated with antipsychotic use in older people with dementia. Although this concern combined with limited evidence of efficacy has informed guidelines restricting antipsychotic prescription in this population, the use of antipsycotics remains common. Many published studies only report short-term outcomes, are restricted to examining mortality and stroke risk or have other limitations. The aim of this study was to assess adverse outcomes associated with the use of antipsychotics in older people living with dementia in Wales (UK). METHODS: This was a retrospective study of a population-based dementia cohort using the Welsh Secure Anonymised Information Linkage databank. The prior event rate ratio (PERR) was used to estimate the influence of exposure to antipsychotic medication on acute cardiac events, venous thromboembolism, stroke and hip fracture, and adjusted Cox proportional hazard models were used to compare all-cause mortality. RESULTS: A total of 10,339 people aged ≥65 years were identified with newly diagnosed dementia. After excluding those who did not meet the inclusion criteria, 9674 people remained in the main cohort of whom 3735 were exposed to antipsychotic medication. An increased risk of a venous thromboembolic episode [PERR 1.95, 95% confidence interval (CI) 1.83-2.0], stroke (PERR 1.41, 95% CI 1.4-1.46) and hip fracture (PERR 1.62, 95% CI 1.59-1.65) was associated with antipsychotic use. However, there was no long-term increased mortality in people exposed to antipsychotics (adjusted hazard ratio 1.06, 95% CI 0.99-1.13). CONCLUSIONS: The increase in adverse medical events supports guidelines restricting antipsychotic use in this population.

A national population-based e-cohort of people with psychosis (PsyCymru) linking prospectively ascertained phenotypically rich and genetic data to routinely collected records: overview, recruitment and linkage.

PsyCymru was initially established as a proof of concept to investigate the feasibility of linking a prospectively ascertained, well-characterised (linked clinical cohort) of people with psychosis in Wales, UK with large amounts of anonymised routinely collected health record data. We are now additionally linking genetic data. PsyCymru aims to create a research platform and infrastructure for psychosis research in Wales by the establishment of two cohorts. The first is a well characterised clinically-assessed cohort of 490 individuals aged 16 and over, including genetic data. Consented individuals underwent a structured interview using a series of well-validated questionnaires and gave blood for the purpose of DNA extraction for sequencing and candidate gene identification. This data was linked to routinely collected health and social datasets with identity encryption used to protect privacy. The second is a much larger (12,097 individuals) but less well characterised population-based e-cohort of prevalent psychosis cases created using a previously validated algorithm applied to anonymised routine data. Both cohorts can be tracked prospectively and retrospectively using anonymised routinely collected electronic health and administrative data in the Secure Anonymised Information Linkage (SAIL) databank. This unique platform pools data together from multiple sources; linking clinical, psychological, biological, genetic and health care factors to address a wide variety of research questions. This resource will continue to expand over the coming years in size, breadth and depth of data, with continued recruitment and additional measures planned.

Using anonymized, routinely collected health data in Wales to estimate the incidence of depression after burn injury.

Burn injuries are associated with depression. Patients show variable incidence of postburn depression. The purpose of this study was to use anonymized, routinely collected health-related data in Wales (United Kingdom) to estimate the incidence of depression postburns. The incidence of postburn depression was estimated using routinely collected health data of complete years (1999-2007) from all general practitioner surgeries in Swansea and all National Health Service hospitals in Wales. This had been collected, double encrypted, and stored at the Secure Anonymised Information Linkage databank of the Health Information Research Unit for Wales at College of Medicine, Swansea University. The incidence of depression within 5 years after the burn injury was 5.9% in patients registered with general practitioner surgeries in Swansea. The incidence was 7.4% in female patients and 4.3% in male patients. The incidence of depression within 5 years after the burn was 3.2% in patients admitted to National Health Service hospitals in Wales. The incidence was 4.5% in female patients and 2.6% in male patients. The advantages of using the anonymized, routinely collected data were avoiding bias, protecting patients' confidentiality, including all patients thus minimizing attrition and greatly reduced costs. It is concluded that anonymized, routinely collected, health-related data may have value in monitoring postburn depression in Wales.

The health informatics cohort enhancement project (HICE): using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder.

BACKGROUND: We have previously demonstrated that routinely collected primary care data can be used to identify potential participants for trials in depression [1]. Here we demonstrate how patients with psychotic disorders can be identified from primary care records for potential inclusion in a cohort study. We discuss the strengths and limitations of this approach; assess its potential value and report challenges encountered. METHODS: We designed an algorithm with which we searched for patients with a lifetime diagnosis of psychotic disorders within the Secure Anonymised Information Linkage (SAIL) database of routinely collected health data. The algorithm was validated against the "gold standard" of a well established operational criteria checklist for psychotic and affective illness (OPCRIT). Case notes of 100 patients from a community mental health team (CMHT) in Swansea were studied of whom 80 had matched GP records. RESULTS: The algorithm had favourable test characteristics, with a very good ability to detect patients with psychotic disorders (sensitivity > 0.7) and an excellent ability not to falsely identify patients with psychotic disorders (specificity > 0.9). CONCLUSIONS: With certain limitations our algorithm can be used to search the general practice data and reliably identify patients with psychotic disorders. This may be useful in identifying candidates for potential inclusion in cohort studies.

The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial.

BACKGROUND: Recruitment to clinical trials can be challenging. We identified anonymous potential participants to an existing pragmatic randomised controlled depression trial to assess the feasibility of using routinely collected data to identify potential trial participants. We discuss the strengths and limitations of this approach, assess its potential value, report challenges and ethical issues encountered. METHODS: Swansea University's Health Information Research Unit's Secure Anonymised Information Linkage (SAIL) database of routinely collected health records was interrogated, using Structured Query Language (SQL). Read codes were used to create an algorithm of inclusion/exclusion criteria with which to identify suitable anonymous participants. Two independent clinicians rated the eligibility of the potential participants' identified. Inter-rater reliability was assessed using the kappa statistic and inter-class correlation. RESULTS: The study population (N = 37263) comprised all adults registered at five general practices in Swansea UK. Using the algorithm 867 anonymous potential participants were identified. The sensitivity and specificity results > 0.9 suggested a high degree of accuracy from the algorithm. The inter-rater reliability results indicated strong agreement between the confirming raters. The Intra Class Correlation Coefficient (Cronbach's Alpha) > 0.9, suggested excellent agreement and Kappa coefficient > 0.8; almost perfect agreement. CONCLUSIONS: This proof of concept study showed that routinely collected primary care data can be used to identify potential participants for a pragmatic randomised controlled trial of folate augmentation of antidepressant therapy for the treatment of depression. Further work will be needed to assess generalisability to other conditions and settings and the inclusion of this approach to support Electronic Enhanced Recruitment (EER).