Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.

BACKGROUND: Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drivers of psoriasis risk. However, it is increasingly recognized that health behavior arises in the context of larger social, cultural, economic and environmental determinants of health. We aimed to identify the top risk factors that significantly impact the incidence of psoriasis at the neighborhood level using populational data from the province of Quebec (Canada) and advanced tree-based machine learning (ML) techniques. METHODS: Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1 year prior to psoriasis onset were obtained from the Canadian Urban Environment Health Consortium (CANUE) and Statistics Canada (StatCan) and were input as predictors into the gradient boosting ML. Model performance was evaluated using the area under the curve (AUC). Parsimonious models and partial dependence plots were determined to assess directionality of the relationship. RESULTS: The incidence of psoriasis varied geographically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious model (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amongst top predictors, ultraviolet (UV) radiation, maximum daily temperature, proportion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggested a higher psoriasis incidence in the middle socioeconomic class neighborhoods. CONCLUSION: This is the first study to highlight highly variable psoriasis incidence rates on a jurisdictional level and suggests that living environment, notably climate, vegetation, urbanization and neighborhood socioeconomic characteristics may have an association with psoriasis incidence.

Immunosuppression and cancer risk in kidney transplant recipients: A retrospective cohort study.

We assessed whether contemporary immunosuppression agents were associated with cancer among kidney transplant recipients (KTR), and if this association varied by age and sex. We studied a retrospective province-wide cohort of primary KTR (1997-2016). Employing multivariable Cox models, we estimated associations of cumulative doses of prednisone, mycophenolate and tacrolimus administered over the past 10 years, lagged by 2 years, with the incidence of primary malignant neoplasms (PMN). We assessed interactions with age and sex. To assess the impact of exposure recency, we used weighted cumulative exposure (WCE) modeling. Among 1064 KTR, 108 (10.2%) developed PMN over median follow-up of 73 months (interquartile range: 32-120). Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) of 0.96 (0.64-1.43), 1.34 (0.96-1.86), and 1.06 (0.88-1.29) were estimated for cumulative daily doses of prednisone (5 mg), mycophenolate (1000 mg), and tacrolimus (2 mg) administered continuously over the past 10 years, respectively. PMN risk associated with cumulative tacrolimus exposure was modified by age (interaction p = .035) and was more pronounced in 15-year and 30-year-old KTR (aHRs of 1.57 [1.08-2.28] and 1.31 [1.03-1.66], respectively) in comparison to older KTR. PMN risk increase associated with higher cumulative mycophenolate dose was more pronounced in females (aHR = 1.86 [1.15-3.00]) than in males (aHR = 1.16 [0.74-1.81]; interaction p = .131). WCE analyses suggested increased PMN risk the higher the mycophenolate doses taken 5-10 years ago. A trend toward increased PMN risk with long-term mycophenolate exposure, particularly in females, and more pronounced risk with long-term tacrolimus exposure in younger KTR, identify opportunities for tailored immunosuppression to mitigate cancer risk.

Evaluating the incremental benefits of raising high-density lipoprotein cholesterol levels during lipid therapy after adjustment for the reductions in other blood lipid levels.

BACKGROUND: The role of high-density lipoprotein cholesterol (HDL-C) as a therapeutic target to prevent cardiovascular (CV) events remains unclear. We examined data from the Framingham Offspring Study from 1975 through 2003 to determine whether increases in HDL-C levels after lipid therapy was started were independently associated with a reduction in CV events. METHODS: Using Cox proportional-hazards regression, we evaluated the risk of a CV event associated with changes in blood lipid levels among individuals who started lipid therapy. The independent effect of HDL-C levels on future CV risk (average follow-up, 8 years) was estimated after adjustment for changes in low-density lipoprotein cholesterol, plasma triglycerides, and pretreatment blood lipid levels. Potential confounders (eg, smoking status, weight, and the use of beta-blockers) were then added to the model. Interactions between blood lipid levels were also explored. RESULTS: The change in HDL-C level was a strong independent risk factor for CV events (hazard ratio, 0.79 per 5-mg/dL increase; 95% confidence interval, 0.67-0.93) after adjustment for the other lipid changes associated with treatment. This relationship remained stable across a wide range of patient subgroups and did not appear to be associated with a specific drug class. An important interaction was observed: the lower the pretreatment low-density lipoprotein cholesterol level, the greater the impact of raising the HDL-C. CONCLUSIONS: Raising HDL-C levels with commonly used lipid medications appears to be an important determinant of the benefits associated with lipid therapy. These results support the further evaluation of therapies to raise HDL-C levels to prevent CV events.