Identification of heart failure hospitalization from NHS Digital data: comparison with expert adjudication.

AIMS: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalization for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF. METHODS AND RESULTS: Patients experiencing at least one HHF, as determined by NHS Digital data, and age- and sex-matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases-10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; and NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Five hundred four patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position {I50: 96.2% [95% confidence interval (CI) 94.1-97.7%]; NICOR: 93.3% [CI 90.8-95.4%]; OIS: 95.6% [CI 93.3-97.2%]} but decreased substantially as the number of diagnosis positions expanded. Sensitivity [40.0% (CI 12.2-73.8%)] and positive predictive value (PPV) [highest with I50: 17.4% (CI 8.1-33.6%)] were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly [36.4% (CI 16.6-62.2%)]. CONCLUSIONS: NHS Digital data were not able to accurately identify HHF and should not be used in isolation for this purpose.

Implementation of novel and conventional outbreak control measures in managing COVID-19 outbreaks in a large UK prison.

BACKGROUND: Outbreak control measures during COVID-19 outbreaks in a large UK prison consisted of standard (e.g., self-isolation) and novel measures, including establishment of: (i) reverse cohorting units for accommodating new prison admissions; (ii) protective isolation unit for isolating symptomatic prisoners, and (iii) a shielding unit to protect medically vulnerable prisoners. METHODS: Single-centre prospective longitudinal study (outbreak control study), implementing novel and traditional outbreak control measures to prevent a SARS-COV-2 outbreak. The prison held 977 prisoners and employed 910 staff at that start of the outbreak. RESULTS: 120 probable and 25 confirmed cases among prisoners and staff were recorded between March and June 2020 during the first outbreak. Over 50% of initial cases among prisoners were on the two wings associated with the index case. During the second outbreak, 182 confirmed cases were recorded after probable reintroduction from a staff member. Widespread testing identified 145 asymptomatic prisoners, 16.9% of the total prisoner cases. The cohorting units prevented re-infection from new prison admissions and the shielding unit had no COVID-19 infections linked to either outbreak. CONCLUSIONS: Identifying and isolating infected prisoners, cohorting new admissions and shielding vulnerable individuals helped prevent uncontrollable spread of SARS-COV-2. These novel and cost-effective approaches can be implemented in correctional facilities globally.

Depressive and socially anxious symptoms, psychosocial maturity, and risk perception: Associations with risk-taking behaviour.

Risk-taking behaviour and onset of mental illness peak in adolescence and young adulthood. This study evaluated the interconnectedness of the domains of risk-taking behaviour, mental health (symptoms of depression and social anxiety), psychosocial maturity, risk perception, age, and gender in a sample of 306 adolescents and young adults. Participants between the ages of 16 and 35 completed online self-report measures assessing risk-taking behaviour, depressive symptoms, socially anxious symptoms, psychosocial maturity and risk perception. Socially anxious symptoms, psychosocial maturity, and risk perception were directly associated with risk-taking behaviour. Correlations between depressive symptoms, socially anxious symptoms, and psychosocial maturity were found. Psychosocial maturity proved a better predictor of risk-taking behaviour than age in this cohort. The findings indicate that mental health impacts upon risk-taking behaviour and that consideration should be given to psychosocial maturity in attempts to reduce adolescent and young adult risk-taking behaviour.