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Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Objective: Thresholds defining medication adherence are rarely evidence-based. A threshold of 0.8 is typically presumed to achieve improved outcomes. We aimed to assess the optimal threshold of adherence to lipid-lowering drugs (LLD) in predicting cardiovascular-related (CV) outcomes in patients with hypertension. Design: Cohort study of new users of LLDs. Setting: Comprehensive healthcare administrative databases of the province of Alberta (Canada) from 2008 to 2016. Participants: Patients with hypertension, who were new users of LLDs. Patients who had the outcomes prior to the initiation of LLD were excluded. Main outcomes measures: Hospitalization for acute coronary syndrome (ACS)/stroke, CV-related mortality and all-cause mortality. Statistical analysis: Adherence to LLDs was assessed as the proportion of days covered (PDC) by any LLD, from drug initiation to censoring, outcome, or study end. Three methods were used to assess the threshold: Contal and O'Quigley method, minimum distance method, and Youden's J index. Cox regressions were used to assess the risk associated with each method-specific threshold and Akaike information criteria were used to retain the optimal threshold after adjustment. Results: 52229 patients were included; 4.0% were hospitalized for ACS/stroke, 3.4% died, and 1.3% died from CV-related cause. In predicting ACS/stroke, CV-related and all-cause mortality, the optimal adherence threshold was 0.52 (range: 0.51–0.54), 0.79 (0.45–0.87), and 0.84 (0.79–0.89), respectively. These results were consistent among patients aged ≥ 65 years (n = 19804). However, the results varied among those aged < 65 years, where the incidence rates of outcomes were low. Conclusion: In new-users of LLDs with hypertension, approximately 50% days covered by LLDs may be enough to prevent long-term occurrence of ACS, or stroke. However, a threshold near 0.80 may be needed to prevent or reduce the risk of all-cause or CV-related mortality. [ABSTRACT FROM AUTHOR]

Introduction: Compliance with the recommendation that all tuberculosis (TB) patients be tested for human immunodeficiency virus (HIV) has not yet been achieved in Canada or globally. Methods: The experience of “opt-out” HIV testing of TB patients in the Province of Alberta, Canada is described over a 10-year period, 2003–2012. Testing rates are reported before and after the introduction of the “opt-out” approach. Risk factors for HIV seropositivity are described and demographic, clinical and laboratory characteristics of TB patients who were newly diagnosed versus previously diagnosed with HIV are compared. Genotypic clusters, defined as groups of two or more cases whose isolates of Mycobacterium tuberculosis had identical DNA fingerprints over the 10-year period or within 2 years of one another, were analyzed for their ability to predict HIV co-infection. Results: HIV testing rates were 26% before and 90% after the introduction of “opt-out” testing. During the “opt-out” testing years those <15 or >64 years of age at diagnosis were less likely to have been tested. In those tested the prevalence of HIV was 5.6%. In the age group 15–64 years, risk factors for HIV were: age (35–64 years), Canadian-born Aboriginal or foreign-born sub-Saharan African origin, and combined respiratory and non-respiratory disease. Compared to TB patients previously known to be HIV positive, TB patients newly discovered to be HIV positive had more advanced HIV disease (lower CD4 counts; higher viral loads) at diagnosis. Large cluster size was associated with Aboriginal ancestry. Cluster size predicted HIV co-infection in Aboriginal peoples when clusters included all cases reported over 10 years but not when clusters included cases reported within 2 years of one another. Conclusion: “Opt-out” HIV testing of TB patients is effective and well received. Universal HIV testing of TB patients (>80% of patients tested) has immediate (patients) and longer-term (TB/HIV program planning) benefits. [ABSTRACT FROM AUTHOR]