Standard CGIF interoperability in Amine

The adoption of standard CGIF by CG tools will enable interoperability between them to be achieved, and in turn lead to the interoperability between CG tools and other tools. The integration of ISO Common Logic’s standard CGIF notation in the Amine platform is presented. It also describes the first steps towards full interoperability between the Amine CG tool (through its Synergy component) and CharGer, a representative CG tool that supports similar interoperability and for process (or ‘active’) knowledge as well as declarative knowledge. N-adic relations are addressed as well as semantic compatibility across the two tools. The work remarks on the successes achieved, highlighting certain issues along the way, and offering a real impetus to achieve interoperability.</p

The transaction pattern through automating TrAM

Transaction Agent Modelling (TrAM) has demonstrated how the early requirements of complex enterprise systems can be captured and described in a lucid yet rigorous way. Using Geerts and McCarthy’s REA (Resource-Events-Agents) model as its basis, the TrAM process manages to capture the ‘qualitative’ dimensions of business transactions and business processes. A key part of the process is automated model-checking, which CG has revealed to be beneficial in this regard. It enables models to retain the high-level business concepts yet providing a formal structure at that high-level that is lacking in Use Cases. Using a conceptual catalogue informed by transactions, we illustrate the automation of a transaction pattern from which further specialisations impart a tested specification for system implementation, which we envisage as a multi-agent system in order to reflect the dynamic world of business activity. It would furthermore be able to interoperate across business domains as they would share the generalised TM as a pattern.</p

Investigating physical health and related secondary care use in people with severe mental illness using electronic health records

Background: People diagnosed with severe mental illness (SMI) have poorer physical health and higher mortality than those without SMI. In this thesis I investigate the physical health and planned and emergency physical health hospital use of people with SMI compared to people without SMI. Methods: I conducted a meta-analysis to quantify excess hospital use for non mental health causes in people with SMI and five physical long-term conditions (LTCs). I used primary care records to investigate the prevalence of 24 LTCs in those with and without SMI, to identify patterns of multimorbidity, and to investigate cancer incidence and mortality. I then linked primary and secondary care records to investigate planned and emergency physical health hospital admissions in people with SMI. Results: Patients with SMI had higher prevalence of 19 of 24 physical LTCs, and of multimorbidity (odds ratio:1.84, 95%CI:1.80-1.88) than those without SMI, with 13 LTCs elevated at or before SMI diagnosis. However, clusters of LTCs were similar between those with and without SMI. Patients with schizophrenia had a lower risk of cancer diagnosis (hazard ratio[HR]:0.83, 95%CI:0.78-0.89) but were at greater risk of cancer mortality (HR:1.93, 95%CI:1.54-2.41) and had fewer planned physical health admissions (incidence rate ratio [IRR]:0.80, 95%CI:0.72- 0.90) than those without SMI. Patients with SMI had more emergency physical health admissions, particularly avoidable admissions (IRR:2.88, 95%CI:2.60- 3.19). Conclusions: Interventions to improve physical health should focus on similar groups of conditions as for the general population, but at a younger age, and early in the course of SMI. The low incidence of cancer and of planned physical health admissions in people with schizophrenia suggest a need for interventions to improve access to preventative and planned services. The high rate of avoidable admissions in those with SMI suggests interventions are required to improve the management of existing physical LTCs

Cancer rates and mortality in people with severe mental illness: Further evidence of lack of parity

Background: Severe mental illness (SMI) is associated with poorer physical health, however the relationship between SMI and cancer is complex and previous study findings are inconsistent. Low incidence of cancer in those with SMI has been attributed to premature mortality, though evidence for this is lacking. We aimed to investigate the relationship between SMI and cancer incidence and mortality, and to assess the effect of premature mortality on cancer incidence rates. / Methods: In this UK-wide matched cohort study using primary care records we calculated incidence and mortality rates of all-cancer, and bowel, lung, breast or prostate cancer, in patients with SMI, compared to matched patients without SMI. We used competing risks regression to account for mortality from other causes. / Findings: 69,632 patients had an SMI diagnosis. The rate of all-cancer diagnoses was reduced in those with SMI (Hazard ratio (HR):0·95; 95%CI 0·93–0·98) compared to those without SMI, and particularly in those with schizophrenia (HR:0·82; 95%CI 0·77–0·88) compared to those without SMI. When accounting for the competing risk of premature mortality, incidence remained lower only in patients with schizophrenia. All-cause mortality after cancer was increased in the SMI group, and cancer-specific mortality was increased in those with schizophrenia (hazard ratio: 1.96; 95%CI 1.57–2.44). / Interpretation: Patients with schizophrenia have lower rates of cancer diagnosis but higher all-cause and cancer-specific mortality rates following diagnosis compared to those without SMI. Premature mortality does not explain these differences, suggesting the findings reflect barriers to cancer diagnosis and treatment, which need to be identified and addressed

The temporal relationship between severe mental illness diagnosis and chronic physical comorbidity: a UK primary care cohort study of disease burden over 10 years

BACKGROUND: Despite increased rates of physical health problems in people with schizophrenia, bipolar disorder, and other psychotic illnesses, the temporal relationship between physical disease acquisition and diagnosis of a severe mental illness remains unclear. We aimed to determine the cumulative prevalence of 24 chronic physical conditions in people with severe mental illness from 5 years before to 5 years after their diagnosis. METHODS: In this cohort study, we used the UK Clinical Practice Research Datalink (CPRD) to identify patients aged 18-100 years who were diagnosed with severe mental illness between Jan 1, 2000, and Dec 31, 2018. Each patient with severe mental illness was matched with up to four individuals in the CPRD without severe mental illness by sex, 5-year age band, primary care practice, and year of primary care practice registration. Individuals in the matched cohort were assigned an index date equal to the date of severe mental illness diagnosis in the patient with severe mental illness to whom they were matched. Our primary outcome was the cumulative prevalence of 24 physical health conditions, based on the Charlson and Elixhauser comorbidity indices, at 5 years, 3 years, and 1 year before and after severe mental illness diagnosis and at the time of diagnosis. We used logistic regression to compare people with severe mental illness with the matched cohort, adjusting for key variables such as age, sex, and ethnicity. FINDINGS: We identified 68 789 patients diagnosed with a severe mental illness between Jan 1, 2000, and Dec 31, 2018, and we matched them to 274 827 patients without a severe mental illness diagnosis. In both cohorts taken together, the median age was 40·90 years (IQR 29·46-56·00), 175 138 (50·97%) people were male, and 168 478 (49·03%) were female. The majority of patients were of White ethnicity (59 867 [87·03%] patients with a severe mental illness and 244 566 [88·99%] people in the matched cohort). The most prevalent conditions at the time of diagnosis in people with severe mental illness were asthma (10 581 [15·38%] of 68 789 patients), hypertension (8696 [12·64%]), diabetes (4897 [7·12%]), neurological disease (3484 [5·06%]), and hypothyroidism (2871 [4·17%]). At diagnosis, people with schizophrenia had increased odds of five of 24 chronic physical conditions compared with matched controls, and nine of 24 conditions were diagnosed less frequently than in matched controls. Individuals with bipolar disorder and other psychoses had increased odds of 15 conditions at diagnosis. At 5 years after severe mental illness diagnosis, these numbers had increased to 13 conditions for schizophrenia, 19 for bipolar disorder, and 16 for other psychoses. INTERPRETATION: Elevated odds of multiple conditions at the point of severe mental illness diagnosis suggest that early intervention on physical health parameters is necessary to reduce morbidity and premature mortality. Some physical conditions might be under-recorded in patients with schizophrenia relative to patients with other severe mental illness subtypes. FUNDING: UK Office For Health Improvement and Disparities

Clustering of physical health multimorbidity in people with severe mental illness: An accumulated prevalence analysis of United Kingdom primary care data

BACKGROUND: People with severe mental illness (SMI) have higher rates of a range of physical health conditions, yet little is known regarding the clustering of physical health conditions in this population. We aimed to investigate the prevalence and clustering of chronic physical health conditions in people with SMI, compared to people without SMI. METHODS AND FINDINGS: We performed a cohort-nested accumulated prevalence study, using primary care data from the Clinical Practice Research Datalink (CPRD), which holds details of 39 million patients in the United Kingdom. We identified 68,783 adults with a primary care diagnosis of SMI (schizophrenia, bipolar disorder, or other psychoses) from 2000 to 2018, matched up to 1:4 to 274,684 patients without an SMI diagnosis, on age, sex, primary care practice, and year of registration at the practice. Patients had a median of 28.85 (IQR: 19.10 to 41.37) years of primary care observations. Patients with SMI had higher prevalence of smoking (27.65% versus 46.08%), obesity (24.91% versus 38.09%), alcohol misuse (3.66% versus 13.47%), and drug misuse (2.08% versus 12.84%) than comparators. We defined 24 physical health conditions derived from the Elixhauser and Charlson comorbidity indices and used logistic regression to investigate individual conditions and multimorbidity. We controlled for age, sex, region, and ethnicity and then additionally for health risk factors: smoking status, alcohol misuse, drug misuse, and body mass index (BMI). We defined multimorbidity clusters using multiple correspondence analysis (MCA) and K-means cluster analysis and described them based on the observed/expected ratio. Patients with SMI had higher odds of 19 of 24 conditions and a higher prevalence of multimorbidity (odds ratio (OR): 1.84; 95% confidence interval [CI]: 1.80 to 1.88, p < 0.001) compared to those without SMI, particularly in younger age groups (males aged 30 to 39: OR: 2.49; 95% CI: 2.27 to 2.73; p < 0.001; females aged 18 to 30: OR: 2.69; 95% CI: 2.36 to 3.07; p < 0.001). Adjusting for health risk factors reduced the OR of all conditions. We identified 7 multimorbidity clusters in those with SMI and 7 in those without SMI. A total of 4 clusters were common to those with and without SMI; 1, heart disease, appeared as one cluster in those with SMI and 3 distinct clusters in comparators; and 2 small clusters were unique to the SMI cohort. Limitations to this study include missing data, which may have led to residual confounding, and an inability to investigate the temporal associations between SMI and physical health conditions. CONCLUSIONS: In this study, we observed that physical health conditions cluster similarly in people with and without SMI, although patients with SMI had higher burden of multimorbidity, particularly in younger age groups. While interventions aimed at the general population may also be appropriate for those with SMI, there is a need for interventions aimed at better management of younger-age multimorbidity, and preventative measures focusing on diseases of younger age, and reduction of health risk factors

Whatcom County Adult Correction Facilities Environmental Impact Statement

Whatcom County is concerned with the current poor conditions of its adult correctional facilities and has proposed to construct a new facility. The new facility is proposed to hold up to 2,450 beds by 2050. For operational purposes, a horizontally designed facility is preferred

The impact of comorbid severe mental illness and common chronic physical health conditions on hospitalisation: A systematic review and meta-analysis

BACKGROUND: People with severe mental illness (SMI) are at higher risk of physical health conditions compared to the general population, however, the impact of specific underlying health conditions on the use of secondary care by people with SMI is unknown. We investigated hospital use in people managed in the community with SMI and five common physical long-term conditions: cardiovascular diseases, COPD, cancers, diabetes and liver disease. METHODS: We performed a systematic review and meta-analysis (Prospero: CRD42020176251) using terms for SMI, physical health conditions and hospitalisation. We included observational studies in adults under the age of 75 with a diagnosis of SMI who were managed in the community and had one of the physical conditions of interest. The primary outcomes were hospital use for all causes, physical health causes and related to the physical condition under study. We performed random-effects meta-analyses, stratified by physical condition. RESULTS: We identified 5,129 studies, of which 50 were included: focusing on diabetes (n = 21), cardiovascular disease (n = 19), COPD (n = 4), cancer (n = 3), liver disease (n = 1), and multiple physical health conditions (n = 2). The pooled odds ratio (pOR) of any hospital use in patients with diabetes and SMI was 1.28 (95%CI:1.15-1.44) compared to patients with diabetes alone and pooled hazard ratio was 1.19 (95%CI:1.08-1.31). The risk of 30-day readmissions was raised in patients with SMI and diabetes (pOR: 1.18, 95%CI:1.08-1.29), SMI and cardiovascular disease (pOR: 1.27, 95%CI:1.06-1.53) and SMI and COPD (pOR:1.18, 95%CI: 1.14-1.22) compared to patients with those conditions but no SMI. CONCLUSION: People with SMI and five physical conditions are at higher risk of hospitalisation compared to people with that physical condition alone. Further research is warranted into the combined effects of SMI and physical conditions on longer-term hospital use to better target interventions aimed at reducing inappropriate hospital use and improving disease management and outcomes

The incidence rate of planned and emergency physical health hospital admissions in people diagnosed with severe mental illness: a cohort study

BACKGROUND: People with severe mental illness (SMI) have more physical health conditions than the general population, resulting in higher rates of hospitalisations and mortality. In this study, we aimed to determine the rate of emergency and planned physical health hospitalisations in those with SMI, compared to matched comparators, and to investigate how these rates differ by SMI diagnosis. METHODS: We used Clinical Practice Research DataLink Gold and Aurum databases to identify 20,668 patients in England diagnosed with SMI between January 2000 and March 2016, with linked hospital records in Hospital Episode Statistics. Patients were matched with up to four patients without SMI. Primary outcomes were emergency and planned physical health admissions. Avoidable (ambulatory care sensitive) admissions and emergency admissions for accidents, injuries and substance misuse were secondary outcomes. We performed negative binomial regression, adjusted for clinical and demographic variables, stratified by SMI diagnosis. RESULTS: Emergency physical health (aIRR:2.33; 95% CI 2.22-2.46) and avoidable (aIRR:2.88; 95% CI 2.60-3.19) admissions were higher in patients with SMI than comparators. Emergency admission rates did not differ by SMI diagnosis. Planned physical health admissions were lower in schizophrenia (aIRR:0.80; 95% CI 0.72-0.90) and higher in bipolar disorder (aIRR:1.33; 95% CI 1.24-1.43). Accident, injury and substance misuse emergency admissions were particularly high in the year after SMI diagnosis (aIRR: 6.18; 95% CI 5.46-6.98). CONCLUSION: We found twice the incidence of emergency physical health admissions in patients with SMI compared to those without SMI. Avoidable admissions were particularly elevated, suggesting interventions in community settings could reduce hospitalisations. Importantly, we found underutilisation of planned inpatient care in patients with schizophrenia. Interventions are required to ensure appropriate healthcare use, and optimal diagnosis and treatment of physical health conditions in people with SMI, to reduce the mortality gap due to physical illness

Management Of Community-Acquired Pneumonia:An Observational Study In UK Primary Care

CCW is an employee and Director of Oxford PharmaGenesis Ltd and a shareholder and Director of Oxford PharmaGenesis Holdings Ltd. DR is a Consultant Strategic Medical Director of Optimum Patient Care. DBP has board membership with Amgen, AstraZeneca, Boehringer Ingelheim, Chiesi, Circassia, Mylan, Mundipharma, Napp, Novartis, Regeneron Pharmaceuticals, Sanofi Genzyme, Teva Pharmaceuticals; consultancy agreements with Amgen, AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline, Mylan, Mundipharma, Napp, Novartis, Pfizer, Teva Pharmaceuticals, Theravance; grants and unrestricted funding for investigator-initiated studies (conducted through Observational and Pragmatic Research Institute Pte Ltd) from AKL Research and Development Ltd, AstraZeneca, Boehringer Ingelheim, British Lung Foundation, Chiesi, Circassia, Mylan, Mundipharma, Napp, Novartis, Pfizer, Regeneron Pharmaceuticals, Respiratory Effectiveness Group, Sanofi Genzyme, Teva Pharmaceuticals, Theravance, UK National Health Service, Zentiva (Sanofi Generics); payment for lectures/speaking engagements from AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, GlaxoSmithKline, Kyorin, Mylan, Merck, Mundipharma, Novartis, Pfizer, Regeneron Pharmaceuticals, Sanofi Genzyme, Teva Pharmaceuticals; payment for manuscript preparation from Mundipharma, Teva Pharmaceuticals; payment for the development of educational materials from Mundipharma, Novartis; payment for travel/accommodation/meeting expenses from AstraZeneca, Boehringer Ingelheim, Circassia, Mundipharma, Napp, Novartis, Teva Pharmaceuticals; funding for patient enrolment or completion of research from Chiesi, Novartis, Teva Pharmaceuticals, Zentiva (Sanofi Generics); stock/stock options from AKL Research and Development Ltd which produces phytopharmaceuticals; owns 74% of the social enterprise Optimum Patient Care Ltd (Australia and UK) and 74% of Observational and Pragmatic Research Institute Pte Ltd (Singapore); and is peer reviewer for grant committees of the Efficacy and Mechanism Evaluation programme, and Health Technology Assessment. The authors report no other conflicts of interest in this work.Peer reviewedPublisher PD