Five year prognosis in patients with angina identified in primary care : incident cohort study

Peer reviewedPublisher PD

A retrospective cohort study assessing patient characteristics and the incidence of cardiovascular disease using linked routine primary and secondary care data

This is the final version. Available from the publisher via the DOI in this record.Objectives: Data linkage combines information from several clinical data sets. The authors examined whether coding inconsistencies for cardiovascular disease between components of linked data sets result in differences in apparent population characteristics. Design: Retrospective cohort study. Setting: Routine primary care data from 40 Scottish general practitioner (GP) surgeries linked to national hospital records. Participants: 240 846 patients, aged 20 years or older, registered at a GP surgery. Outcomes: Cases of myocardial infarction, ischaemic heart disease and stroke (cerebrovascular disease) were identified from GP and hospital records. Patient characteristics and incidence rates were assessed for all three clinical outcomes, based on GP, hospital, paired GP/hospital (similar diagnoses recorded simultaneously in both data sets) or pooled GP/hospital records (diagnosis recorded in either or both data sets). Results: For all three outcomes, the authors found evidence (p<0.05) of different characteristics when using different methods of case identification. Prescribing of cardiovascular medicines for ischaemic heart disease was greatest for cases identified using paired records (p≤0.013). For all conditions, 30-day case fatality rates were higher for cases identified using hospital compared with GP or paired data, most noticeably for myocardial infarction (hospital 20%, GP 4%, p=0.001). Incidence rates were highest using pooled GP/hospital data and lowest using paired data. Conclusions: Differences exist in patient characteristics and disease incidence for cardiovascular conditions, depending on the data source. This has implications for studies using routine clinical data

Integrating telehealth care-generated data with the family practice electronic medical record:qualitative exploration of the views of primary care staff

BACKGROUND: Telehealth care is increasingly being employed in the management of long-term illness. Current systems are largely managed via “stand-alone” websites, which require additional log-ons for clinicians to view their patients’ symptom records and physiological measurements leading to frustrating delays and sometimes failure to engage with the record. However, there are challenges to the full integration of patient-acquired data into family physicians’ electronic medical records (EMR) in terms of reliability, how such data can best be summarized and presented to avoid overload to the clinicians, and how clarity of responsibility is managed when multiple agencies are involved. OBJECTIVE: We aimed to explore the views of primary care clinicians on the acceptability, clinical utility, and, in particular, the benefits and risks of integrating patient-generated telehealth care data into the family practice EMR and to explore how these data should be summarized and presented in order to facilitate use in routine care. METHODS: In our qualitative study, we carried out semi-structured interviews with clinicians with experience of and naïve to telehealth care following demonstration of pilot software, which illustrated various methods by which data could be incorporated into the EMR. RESULTS: We interviewed 20 clinicians and found 2 overarching themes of “workload” and “safety”. Although clinicians were largely positive about integrating telehealth care data into the EMR, they were concerned about the potential increased workload and safety issues, particularly in respect to error due to data overload. They suggested these issues could be mitigated by good system design that summarized and presented data such that they facilitated seamless integration with clinicians’ current routine processes for managing data flows, and ensured clear lines of communication and responsibility between multiple professionals involved in patients’ care. CONCLUSIONS: Family physicians and their teams are likely to be receptive to and see the benefits of integrating telehealth-generated data into the EMR. Our study identified some of the key challenges that must be overcome to facilitate integration of telehealth care data. This work particularly underlines the importance of actively engaging with clinicians to ensure that systems are designed that align well with existing practice data-flow management systems and facilitate safe multiprofessional patient care

Adequate Levels of Adherence with Controller Medication Is Associated with Increased Use of Rescue Medication in Asthmatic Children

Peer reviewedPublisher PD

Risk of prostate cancer associated with benign prostate disease:a primary care case-control study

BACKGROUND: Benign diseases of the prostate are common in the general male population, and prostate cancer is the most common cancer in men. Uncertaintyastothe nature of the association between benign and malignant disease is a source of concern for patients and clinicians. AIM: To determine the likelihood of men with benign prostate disease developing prostate cancer compared with men without disease. DESIGN: Incident matched case-control study METHOD: All incident cases of prostate cancer (n = 984) were identified in a nationally representative community-based population, and each was matched by age with two controls with no prostate cancer (n = 1968). Participants' records of the previous 5 years were searched for diagnoses of benign prostate disease. Analyses investigated an a priori hypothesis that clinicians may record disease as benign until proven to be malignant, causing misleading significant associations between benign and malignant diagnoses. RESULTS: There was a significant association between a diagnosis of prostate cancer and a benign diagnosis at any time in the previous 5 years: odds ratio (OR) 1.57 (95% confidence interval [CI] = 1.32 to 1.88). However, there was no significant association when benign diagnoses within 6 months and within 12 months of cancer diagnoses were excluded: OR 1.19 (95% CI = 0.97 to 1.46) and OR 1.00 (95% CI = 0.79 to 1.27) respectively. CONCLUSION: Findings from this study suggest that unless prostate cancer is detected within 6 months, men diagnosed for the first time with benign disease are at no greater risk of prostate cancer than those with no recorded prostate disease

The Transition to an Energy Sufficient Economy

Nigeria is an energy-rich nation with a huge energy resource base. The country is the largest reserves holder and largest producer of oil and gas in the African continent. Despite this, only about 40% of its 158 million people have access to modern energy services. Around 80% of its rural population depend on traditional biomass. This paper presents an overview of ongoing research to examine energy policies in Nigeria. The aims are: 1) to identify and quantify the barriers to sustainable energy development and 2) to provide an integrated tool to aid energy policy evaluation and planning. System dynamics modelling is shown to be a useful tool to map the interrelations between critical energy variables with other key sectors of the economy, and for understanding the energy use dynamics (impact on society and the environment). It is found that the critical factors are burgeoning population, lack of capacity utilisation, and inadequate energy investments. Others are lack of suitably trained manpower, weak institutional frameworks, and inconsistencies in energy policies. These remain the key barriers hampering Nigeria\u27s smooth transition from energy poverty to an energy sufficient economy

Critical success index or F measure to validate the accuracy of administrative healthcare data identifying epilepsy in deceased adults in Scotland

Background: Methods to undertake diagnostic accuracy studies of administrative epilepsy data are challenged bylack of a way to reliably rank case-ascertainment algorithms in order of their accuracy. This is because it isdifficult to know how to prioritise positive predictive value (PPV) and sensitivity (Sens). Large numbers of truenegative (TN) instances frequently found in epilepsy studies make it difficult to discriminate algorithm accuracyon the basis of negative predictive value (NPV) and specificity (Spec) as these become inflated (usually &gt;90%).This study demonstrates the complementary value of using weather forecasting or machine learning metricscritical success index (CSI) or F measure, respectively, as unitary metrics combining PPV and sensitivity. Wereanalyse data published in a diagnostic accuracy study of administrative epilepsy mortality data in Scotland.Method: CSI was calculated as 1/[(1/PPV) + (1/Sens) – 1]. F measure was calculated as 2.PPV.Sens/(PPV +Sens). CSI and F values range from 0 to 1, interpreted as 0 = inaccurate prediction and 1 = perfect accuracy. Thepublished algorithms were reanalysed using these and their accuracy re-ranked according to CSI in order to allowcomparison to the original rankings.Results: CSI scores were conservative (range 0.02–0.826), always less than or equal to the lower of the correspondingPPV (range 39–100%) and sensitivity (range 2–93%). F values were less conservative (range0.039–0.905), sometimes higher than either PPV or sensitivity, but were always higher than CSI. Low CSI and Fvalues occurred when there was a large difference between PPV and sensitivity, e.g. CSI was 0.02 and F was0.039 in an instance when PPV was 100% and sensitivity was 2%. Algorithms with both high PPV and sensitivityperformed best in terms of CSI and F measure, e.g. CSI was 0.826 and F was 0.905 in an instance when PPV was90% and sensitivity was 91%.Conclusion: CSI or F measure can combine PPV and sensitivity values into a convenient single metric that is easierto interpret and rank in terms of diagnostic accuracy than trying to rank diagnostic accuracy according to the twomeasures themselves. CSI or F prioritise instances where both PPV and sensitivity are high over instances wherethere are large differences between PPV and sensitivity (even if one of these is very high), allowing diagnosticaccuracy thresholds based on combined PPV and sensitivity to be determined. Therefore, CSI or F measures maybe helpful complementary metrics to report alongside PPV and sensitivity in diagnostic accuracy studies ofadministrative epilepsy data

Case-control study developing Scottish Epilepsy Deaths Study score to predict epilepsy-related death

This study aims to develop a risk prediction model for epilepsy-related death in adults. In this age- and sex-matched case-control study, we compared adults (aged ≥16 years) who had epilepsy-related death between 2009-2016 to living adults with epilepsy in Scotland. Cases were identified from validated administrative national datasets linked to mortality records. ICD-10 cause-of-death coding was used to define epilepsy-related death. Controls were recruited from a research database and epilepsy clinics. Clinical data from medical records were abstracted and used to undertake univariable and multivariable conditional logistic regression to develop a risk prediction model consisting of four variables chosen a priori. A weighted sum of the factors present was taken to create a risk index - the Scottish Epilepsy Deaths Study Score (SEDS Score). Odds ratios (OR) were estimated with 95% confidence intervals (CIs). 224 deceased cases (mean age 48 years, 114 male) and 224 matched living controls were compared. In univariable analysis, predictors of epilepsy-related death were recent epilepsy-related accident and emergency (A&E) attendance (OR 5.1, 95% CI 3.2-8.3), living in deprived areas (OR 2.5, 95% CI 1.6-4.0), developmental epilepsy (OR 3.1, 95% CI 1.7-5.7), raised Charlson Comorbidity Index (CCI) score (OR 2.5, 95% CI 1.2-5.2), alcohol abuse (OR 4.4, 95% CI 2.2-9.2), absent recent neurology review (OR 3.8, 95% CI 2.4-6.1), and generalised epilepsy (OR 1.9, 95% CI 1.2-3.0). SEDS Score model variables were derived from the first four listed above, with CCI ≥2 given 1 point, living in the two most deprived areas given 2 points, having an inherited or congenital aetiology or risk factor for developing epilepsy given 2 points, and recent epilepsy-related A&E attendance given 3 points. Compared to having a SEDS Score of 0, those with a SEDS Score of 1 remained low risk, with OR 1.6 (95% CI 0.5-4.8). Those with a SEDS Score of 2-3 had moderate risk, with OR 2.8 (95% CI 1.3-6.2). Those with a SEDS Score of 4-5 and 6-8 were high risk, with OR 14.4 (95% CI 5.9-35.2) and 24.0 (95% CI 8.1-71.2), respectively. The SEDS Score may be a helpful tool for identifying adults at high risk of epilepsy-related death and requires external validation