Huntington's disease in the United States: Variation by demographic and socioeconomic factors.

BACKGROUND: Despite extensive research regarding the etiology of Huntington's disease, relatively little is known about the epidemiology of this rare disorder, particularly in the United States where there are no national-scale estimates of the disease. OBJECTIVES: To provide national-scale estimates of Huntington's disease in a U.S. population and to test whether disease rates are increasing, and whether frequency varies by race, ethnicity, or other factors. METHODS: Using an insurance database of over 67 million enrollees, we retrospectively identified a cohort of 3,707 individuals diagnosed with Huntington's disease between 2003 and 2016. We estimated annual incidence, annual diagnostic frequency, and tested for trends over time and differences in diagnostic frequency by sociodemographic characteristics. RESULTS: During the observation period, the age-adjusted cumulative incidence rate was1.22 per 100,000 persons (95% confidence interval: 1.53, 1.65), and age-adjusted diagnostic frequency was 6.52 per 100,000 persons (95% confidence interval: 5.31, 5.66); both rates remained relatively stable over the 14-year period. We identified several previously unreported differences in Huntington's disease frequency by self-reported sex, income, and race/ethnicity. However, racial/ethnic differences were of lower magnitude than have previously been reported in other country-level studies. CONCLUSIONS: In these large-scale estimates of U.S. Huntington's disease epidemiology, we found stable disease frequency rates that varied by several sociodemographic factors. These findings suggest that disease patterns may be more driven by social or environmental factors than has previously been appreciated. Results further demonstrate the potential utility of administrative Big Data in rare disease epidemiology when other data sources are unavailable. © 2019 International Parkinson and Movement Disorder Society.

Machine Learning for Health Services Researchers.

BACKGROUND: Machine learning is increasingly used to predict healthcare outcomes, including cost, utilization, and quality. OBJECTIVE: We provide a high-level overview of machine learning for healthcare outcomes researchers and decision makers. METHODS: We introduce key concepts for understanding the application of machine learning methods to healthcare outcomes research. We first describe current standards to rigorously learn an estimator, which is an algorithm developed through machine learning to predict a particular outcome. We include steps for data preparation, estimator family selection, parameter learning, regularization, and evaluation. We then compare 3 of the most common machine learning methods: (1) decision tree methods that can be useful for identifying how different subpopulations experience different risks for an outcome; (2) deep learning methods that can identify complex nonlinear patterns or interactions between variables predictive of an outcome; and (3) ensemble methods that can improve predictive performance by combining multiple machine learning methods. RESULTS: We demonstrate the application of common machine methods to a simulated insurance claims dataset. We specifically include statistical code in R and Python for the development and evaluation of estimators for predicting which patients are at heightened risk for hospitalization from ambulatory care-sensitive conditions. CONCLUSIONS: Outcomes researchers should be aware of key standards for rigorously evaluating an estimator developed through machine learning approaches. Although multiple methods use machine learning concepts, different approaches are best suited for different research problems.

Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial.

Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health researchers on the application of machine learning methods to conduct precision medicine research designed to reduce health disparities. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advantages and disadvantages of different learning approaches, describe strategies for interpreting "black box" models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.

Assessment of Risk of Harm Associated With Intensive Blood Pressure Management Among Patients With Hypertension Who Smoke: A Secondary Analysis of the Systolic Blood Pressure Intervention Trial.

Importance: The randomized Systolic Blood Pressure Intervention Trial (SPRINT) showed that lowering systolic blood pressure targets for adults with hypertension reduces cardiovascular morbidity and mortality in general. However, whether the overall benefit from intensive blood pressure control masks important heterogeneity in risk is unknown. Objective: To test the hypothesis that the overall benefit observed in SPRINT masked important heterogeneity in risk from intensive blood pressure control. Design, Setting, and Participants: In this exploratory, hypothesis-generating, ad hoc, secondary analysis of data obtained from 9361 participants in SPRINT, a random forest-based analysis was used to identify potential heterogeneous treatment effects using half of the trial data. Cox proportional hazards regression models were applied to test potential heterogeneous treatment effects on the remaining data. The original trial was conducted at 102 sites in the United States between November 2010 and March 2013. This analysis was conducted between November 2016 and August 2017. Interventions: Participants were assigned a systolic blood pressure target of less than 120 mm Hg (intervention treatment) or of less than 140 mm Hg (standard treatment). Main Outcomes and Measures: The primary composite cardiovascular outcome was myocardial infarction, other acute coronary syndromes, stroke, heart failure, or death from cardiovascular causes. Results: Of 9361 participants in SPRINT, 466 participants (5.0%) were current smokers with systolic blood pressure greater than 144 mm Hg at baseline, with 230 participants (49.4%) randomized to the training data set and 236 participants (50.6%) randomized to the testing data set; 286 participants (61.4%) were male, and the mean (SD) age was 60.7 (7.2) years. Combinations of 2 covariates (ie, baseline smoking status and systolic blood pressure) distinguished participants who were differentially affected by the intervention. In the testing data, Cox proportional hazards models for the primary outcome revealed a number needed to harm of 43.7 to cause 1 event across 3.3 years among participants who, at baseline, were current smokers with systolic blood pressure greater than 144 mm Hg (10.9% [12 of 110] of primary outcome events for intervention treatment vs 4.8% [6 of 126] for standard treatment; hazard ratio, 10.6; 95% CI, 1.3-86.1; P = .03). This subgroup was also associated with a higher likelihood to experience acute kidney injury under intensive blood pressure control (with a frequency of 10.0% [11 of 110] of acute kidney injury events for intervention treatment vs 3.2% [4 of 126] for standard treatment; hazard ratio, 9.4; 95% CI, 1.2-77.3; P = .04). Conclusions and Relevance: In this secondary analysis of SPRINT data, current smokers with a baseline systolic blood pressure greater than 144 mm Hg had a higher rate of cardiovascular events in the intensive treatment group vs the standard treatment group. Further research is needed to evaluate the potential tradeoffs of intensive blood pressure control in hypertensive smokers.

Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial.

BACKGROUND: The Action for Health in Diabetes (Look AHEAD) trial investigated whether long-term cardiovascular disease morbidity and mortality could be reduced through a weight loss intervention among people with type 2 diabetes. Despite finding no significant reduction in cardiovascular events on average, it is possible that some subpopulations might have derived benefit. In this post-hoc analysis, we test the hypothesis that the overall neutral average treatment effect in the trial masked important heterogeneous treatment effects (HTEs) from intensive weight loss interventions. METHODS: We used causal forest modelling, which identifies HTEs, using a random half of the trial data (the training set). We applied Cox proportional hazards models to test the potential HTEs on the remaining half of the data (the testing set). The analysis was deemed exempt from review by the Columbia University Institutional Review Board, Protocol ID# AAAO3003. FINDINGS: Between Aug 22, 2001, and April 30, 2004, 5145 patients with type 2 diabetes were enrolled in the Look AHEAD randomised controlled trial, of whom 4901 were included in the The National Institute of Diabetes and Digestive and Kidney Diseases Repository and included in our analyses: 2450 for model development and 2451 in the testing dataset. Baseline HbA1c and self-reported general health distinguished participants who differentially benefited from the intervention. Cox models for the primary composite cardiovascular outcome revealed a number needed to treat of 28·9 to prevent 1 event over 9·6 years among participants with HbA1c 6·8% or higher, or both HbA1c less than 6·8% and Short Form Health Survey (SF-36) general health score of 48 or more (2101 [86%] of 2451 participants in the testing dataset; 167 [16%] of 1046 primary outcome events for intervention vs 205 [19%] of 1055 for control, absolute risk reduction of 3·46%, 95% CI 0·21-6·73%, p=0·038) By contrast, participants with HbA1c less than 6·8% and baseline SF-36 general health score of less than 48 (350 [14%] of 2451 participants in the testing data; 27 [16%] of 171 primary outcome events for intervention vs 15 [8%] of 179 primary outcome events for control) had an absolute risk increase of the primary outcome of 7·41% (0·60 to 14·22, p=0·003). INTERPRETATION: Look AHEAD participants with moderately or poorly controlled diabetes (HbA1c 6·8% or higher) and subjects with well controlled diabetes (HbA1c less than 6·8%) and good self-reported health (85% of the overall study population) averted cardiovascular events from a behavioural intervention aimed at weight loss. However, 15% of participants with well controlled diabetes and poor self-reported general health experienced negative effects that rendered the overall study outcome neutral. HbA1c and a short questionnaire on general health might identify people with type 2 diabetes likely to derive benefit from an intensive lifestyle intervention aimed at weight loss. FUNDING: None.

A daily global mesoscale ocean eddy dataset from satellite altimetry.

Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993-2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.

A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science.

Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data.