Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review.

There is currently no consensus on approaches to defining asthma or assessing asthma outcomes using electronic health record-derived data. We explored these approaches in the recent literature and examined the clarity of reporting.We systematically searched for asthma-related articles published between January 1, 2014 and December 31, 2015, extracted the algorithms used to identify asthma patients and assess severity, control and exacerbations, and examined how the validity of these outcomes was justified.From 113 eligible articles, we found significant heterogeneity in the algorithms used to define asthma (n=66 different algorithms), severity (n=18), control (n=9) and exacerbations (n=24). For the majority of algorithms (n=106), validity was not justified. In the remaining cases, approaches ranged from using algorithms validated in the same databases to using nonvalidated algorithms that were based on clinical judgement or clinical guidelines. The implementation of these algorithms was suboptimally described overall.Although electronic health record-derived data are now widely used to study asthma, the approaches being used are significantly varied and are often underdescribed, rendering it difficult to assess the validity of studies and compare their findings. Given the substantial growth in this body of literature, it is crucial that scientific consensus is reached on the underlying definitions and algorithms.

Evidence-based medicine: a persisting desire under fire.

Healthcare infrastructure and medical schools in Syria have been greatly compromised by military conflict and humanitarian disaster. Medical students and healthcare professionals reached out for remote learning opportunities. Surprisingly, they desired a curriculum in evidence-based medicine. We report on a curriculum that was delivered to 126 learners using an online remote delivery platform. This experience demonstrates the feasibility of this approach in disaster-stricken areas and underscores the importance of evidence-based medicine even under such conditions.

Impact of air pollution on educational attainment for respiratory health treated students: A cross sectional data linkage study.

INTRODUCTION: There is some evidence that exam results are worse when students are acutely exposed to air pollution. Studies investigating the association between air pollution and academic attainment have been constrained by small sample sizes. METHODS: Cross sectional educational attainment data (2009-2015) from students aged 15-16 years in Cardiff, Wales were linked to primary health care data, modelled air pollution and measured pollen data, and analysed using multilevel linear regression models. Annual cohort, school and individual level confounders were adjusted for in single and multi-pollutant/pollen models. We stratified by treatment of asthma and/or Seasonal Allergic Rhinitis (SAR). RESULTS: A unit (10µg/m3) increase of short-term exposure to NO2 was associated with 0.044 (95% CI: -0.079, -0.008) reduction of standardised Capped Point Score (CPS) after adjusting for individual and household risk factors for 18,241 students. This association remained statistically significant after controlling for other pollutants and pollen. There was no association of PM2.5, O3, or Pollen with standardised CPS remaining after adjustment. We found no evidence that treatment for asthma or SAR modified the observed NO2 effect on educational attainment. CONCLUSION: Our study showed that short-term exposure to traffic-related air pollution, specifically NO2, was associated with detrimental educational attainment for students aged 15-16. Longitudinal investigations in different settings are required to confirm this possible impact and further work may uncover the long-term economic implications, and degree to which impacts are cumulative and permanent.

Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol.

Asthma and chronic obstructive pulmonary disease (COPD) are two common different clinical diagnoses with overlapping clinical features. Both conditions have been increasingly studied using electronic health records (EHR). Asthma-COPD overlap syndrome (ACOS) is an emerging concept where clinical features from both conditions co-exist, and for which, however, there is no consensus definition. Nonetheless, we expect EHR data of people with ACOS to be systematically different from those with "asthma only" or "COPD only". We aim to develop a latent class model to understand the overlap between asthma and COPD in EHR data. From the Secure Anonymised Information Linkage (SAIL) databank, we will use routinely collected primary care data recorded in or before 2014 in Wales for people who aged 40 years or more on 1st Jan 2014. Based on this latent class model, we will train a classification algorithm and compare its performance with commonly used objective and self-reported case definitions for asthma and COPD. The resulting classification algorithm is intended to be used to identify people with ACOS, 'asthma only', and 'COPD only' in primary care datasets.