The Perceived Benefits of an Arts Project for Health and Wellbeing of Older Offenders

Background: The increasing ageing prison population is becoming a pressing issue throughout the criminal justice system. Alongside the rising population, are a host of health and wellbeing issues that contribute to older offenders needs whilst in prison. It has been recommended that meaningful activities can have positive effects on this population and therefore this paper uniquely reviews older offenders accounts of taking part in an arts based project, Good Vibrations, whilst imprisoned. Objective and design: The Good Vibrations project engages individuals in Gamelan music making with an end of project performance. This study used independent in-depth interviews to capture the voices of older offenders who took part in an art based prison project. Analysis and Results: The interview data was analysed using thematic analysis, which highlighted themes that were consistent with other populations who have taken part in a Good Vibrations project, along with specific age relating issues of mobility, motivation, identity and wellbeing.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.