Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data:analysis of a prospective cohort study (ALSPAC) in Southwest England

OBJECTIVES: We aimed to examine agreement between common mental disorders (CMDs) from primary care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children) over adolescence and young adulthood, explore factors affecting CMD identification in primary care records, and construct models predicting ALSPAC-derived CMDs using only primary care data. DESIGN AND SETTING: Prospective cohort study (ALSPAC) in Southwest England with linkage to electronic primary care records. PARTICIPANTS: Primary care records were extracted for 11 807 participants (80% of 14 731 eligible). Between 31% (3633; age 15/16) and 11% (1298; age 21/22) of participants had both primary care and ALSPAC CMD data. OUTCOME MEASURES: ALSPAC outcome measures were diagnoses of suspected depression and/or CMDs. Primary care outcome measure were Read codes for diagnosis, symptoms and treatment of depression/CMDs. For each time point, sensitivities and specificities for primary care CMD diagnoses were calculated for predicting ALSPAC-derived measures of CMDs, and the factors associated with identification of primary care-based CMDs in those with suspected ALSPAC-derived CMDs explored. Lasso (least absolute selection and shrinkage operator) models were used at each time point to predict ALSPAC-derived CMDs using only primary care data, with internal validation by randomly splitting data into 60% training and 40% validation samples. RESULTS: Sensitivities for primary care diagnoses were low for CMDs (range: 3.5%–19.1%) and depression (range: 1.6%–34.0%), while specificities were high (nearly all >95%). The strongest predictors of identification in the primary care data for those with ALSPAC-derived CMDs were symptom severity indices. The lasso models had relatively low prediction rates, especially in the validation sample (deviance ratio range: −1.3 to 12.6%), but improved with age. CONCLUSIONS: Primary care data underestimate CMDs compared to population-based studies. Improving general practitioner identification, and using free-text or secondary care data, is needed to improve the accuracy of models using clinical data

Severe and common mental disorders and risk of emergency hospital admissions for ambulatory care sensitive conditions among the UK Biobank cohort

BACKGROUND: People with mental disorders have worse physical health compared with the general population, which could be attributable to receiving poorer quality healthcare. AIMS: To examine the relationship between severe and common mental disorders and risk of emergency hospital admissions for ambulatory care sensitive conditions (ACSCs), and factors associated with increased risk. METHOD: Baseline data for England ( N = 445 814) were taken from UK Biobank, which recruited participants aged 37-73 years during 2006-2010, and linked to hospital admission records up to 31 December 2019. Participants were grouped into those with a history of either schizophrenia, bipolar disorder, depression or anxiety, or no mental disorder. Survival analysis was used to assess the risk of hospital admission for ACSCs among those with mental disorders compared with those without, adjusting for factors in different domains (sociodemographic, socioeconomic, health and biomarkers, health-related behaviours, social isolation and psychological). RESULTS: People with schizophrenia had the highest (unadjusted) risk of hospital admission for ACSCs compared with those with no mental disorder (hazard ratio 4.40, 95% CI 4.04-4.80). People with bipolar disorder (hazard ratio 2.48, 95% CI 2.28-2.69) and depression or anxiety (hazard ratio 1.76, 95% CI 1.73-1.80) also had higher risk. Associations were more conservative when including all admissions, as opposed to first admissions only. The observed associations persisted after adjusting for a range of factors. CONCLUSIONS: People with severe mental disorders have the highest risk of preventable hospital admissions. Ensuring people with mental disorders receive adequate ambulatory care is essential to reduce the large health inequalities they experience

Does administrative data reflect individual experience? Comparing an index of poverty with individually collected data on financial well-being in a multi-ethnic community

The Income Deprivation Affecting Children Index (IDACI) uses administrative data to count children living in households in receipt of both in-work and out-of-work means-tested benefits and provides small area ranking as an indicator of child poverty in neighbourhoods. Benefit take-up rates within an area will affect its reliability. We aimed to examine benefit take-up rates and compare area ranking by the IDACI with ranking using individually reported data across areas of varying ethnic composition. Mothers living in areas with high minority ethnic density were less likely to report claiming a benefit than those in majority White or mixed areas, despite reporting lower incomes. The correlation between self-reported material difficulties and worsening IDACI rank was much lower in areas characterised by minority ethnic populations. Further investigation into the performance of area-based deprivation measures in areas with high minority ethnic density is needed

Glucose, cholesterol and blood pressure in type II diabetes : a longitudinal observational study comparing patients with and without severe mental illness

INTRODUCTION: Patients with both severe mental illness (SMI) and type II diabetes (T2DM) have lower life expectancy than patients with T2DM alone, partly due to poor control of cardiovascular risk factors in comorbid patients. AIM: To compare levels of cholesterol, HbA1c and blood pressure in T2DM patients with and without SMI. METHOD: We analysed longitudinal clinical records of 30,353 people with T2DM (657 with SMI;29,696 controls without SMI) between 2001 and 2013 using the Clinical Practice Research Datalink (CPRD). We used mixed effects regression models to compare cardiovascular risk factors between SMI and controls. RESULTS: Patients with SMI had lower mean systolic blood pressure (SBP) (β -2.49; SE=0.45 P=<0.01) and were more likely to have extreme (high and low) values of HbA1c and SBP (OR 1.38, 95%CI: 1.16,1.64 and 1.76:1.40,2.21 respectively). DISCUSSION: People with T2DM and SMI have similar average values of cardiovascular risk factors to people with T2DM alone but are more likely to have values of HbA1c and SBP indicating increased risk of adverse clinical outcomes. IMPLICATIONS FOR PRACTICE: Improved management of cardiovascular risk factors in general, glycaemic control in particular, is central to addressing the increased risk of adverse outcomes in people with both SMI and T2DM. This article is protected by copyright. All rights reserved