Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting

INTRODUCTION: Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD). METHODS: We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models. RESULTS: Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users. CONCLUSIONS: Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors - including light and moderate alcohol use - are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data

Does a quality improvement campaign accelerate take-up of new evidence? A ten-state cluster-randomized controlled trial of the Institute for Health Improvement’s Project JOINTS

Background: A decade ago, the Institute for Healthcare Improvement pioneered a quality improvement (QI) campaign, leveraging organizational and personal social networks to disseminate new practices. There have been few rigorous studies of the QI campaign approach. Methods: Project JOINTS (Joining Organizations IN Tackling SSIs) engaged a network of state-based organizations and professionals in a 6-month QI campaign promoting adherence to three new evidence-based practices known to reduce the risk of infection after joint replacement. We conducted a cluster-randomized trial including ten states (five campaign states and five non-campaign states) with 188 hospitals providing joint replacement to Medicare. We measured adherence to the evidence-based practices before and after the campaign using a survey of surgical staff and a difference-in-difference design with multivariable adjustment to compare adherence to each of the relevant practices and an all-or-none composite measure of the three new practices. Results: In the campaign states, there were statistically significant increases in adherence to the three new evidence-based practices promoted by the campaign. Compared to the non-campaign states, the relative increase in adherence to the three new practices in the campaign states ranged between 1.9 and 15.9 percentage points, but only one of these changes (pre-operative nasal screening for Staphylococcus aureus carriage and decolonization prior to surgery) was statistically significant (p < 0.05). On the all-or-none composite measure, adherence to all three evidence-based practices increased from 19.6 to 37.9% in the campaign states, but declined slightly in the comparison states, yielding a relative increase of 23 percentage points (p = 0.004). In the non-campaign states, changes in adherence were not statistically significant. Conclusions: Within 6 months, in a cluster-randomized trial, a multi-state campaign targeting hospitals and professionals involved in surgical care and infection control was associated with an increase in adherence to evidence-based practices that can reduce surgical site infection. Electronic supplementary material The online version of this article (doi:10.1186/s13012-017-0579-7) contains supplementary material, which is available to authorized users

Erratum to: Does a quality improvement campaign accelerate take-up of new evidence? A ten-state cluster-randomized controlled trial of the IHI’s Project JOINTS

Abstract Background A decade ago, the Institute for Healthcare Improvement pioneered a quality improvement (QI) campaign, leveraging organizational and personal social networks to disseminate new practices. There have been few rigorous studies of the QI campaign approach. Methods Project JOINTS (Joining Organizations IN Tackling SSIs) engaged a network of state-based organizations and professionals in a 6-month QI campaign promoting adherence to three new evidence-based practices known to reduce the risk of infection after joint replacement. We conducted a cluster-randomized trial including ten states (five campaign states and five non-campaign states) with 188 hospitals providing joint replacement to Medicare. We measured adherence to the evidence-based practices before and after the campaign using a survey of surgical staff and a difference-in-difference design with multivariable adjustment to compare adherence to each of the relevant practices and an all-or-none composite measure of the three new practices. Results In the campaign states, there were statistically significant increases in adherence to the three new evidence-based practices promoted by the campaign. Compared to the non-campaign states, the relative increase in adherence to the three new practices in the campaign states ranged between 1.9 and 15.9 percentage points, but only one of these changes (pre-operative nasal screening for Staphylococcus aureus carriage and decolonization prior to surgery) was statistically significant (p < 0.05). On the all-or-none composite measure, adherence to all three evidence-based practices increased from 19.6 to 37.9% in the campaign states, but declined slightly in the comparison states, yielding a relative increase of 23 percentage points (p = 0.004). In the non-campaign states, changes in adherence were not statistically significant. Conclusions Within 6 months, in a cluster-randomized trial, a multi-state campaign targeting hospitals and professionals involved in surgical care and infection control was associated with an increase in adherence to evidence-based practices that can reduce surgical site infection

Health warning labels and alcohol selection: a randomised controlled experiment in a naturalistic shopping laboratory

Abstract: Background and aims: Health warning labels (HWLs) on tobacco products reduce smoking. There is an absence of evidence concerning the impact of alcohol HWLs on selection or purchasing in naturalistic settings. Using a commercial‐standard naturalistic shopping laboratory, this study aimed to estimate the impact on selection of alcoholic drinks of HWLs describing adverse health consequences of excessive alcohol consumption. Design: A between‐subjects randomised experiment with three groups was conducted: group 1: image‐and‐text HWL; group 2: text‐only HWL; group 3: no HWL. Setting: A commercial‐standard naturalistic shopping laboratory in the United Kingdom. Participants: Adults (n = 399, 55% female) over the age of 18 years, who purchased beer or wine weekly to drink at home. Interventions: Participants were randomised to one of three groups varying in the HWL displayed on the packaging of the alcoholic drinks: (i) image‐and‐text HWL (n = 135); (ii) text‐only HWL (n = 129); (iii) no HWL (n = 135). Participants completed a shopping task, selecting items from a range of alcoholic and non‐alcoholic drinks, and snacks. Measurement: The primary outcome was the proportion of alcoholic drinks selected. Secondary outcomes included HWL ratings on negative emotional arousal and label acceptability. Findings: There was no clear evidence of a difference in the HWL groups for the percentage of drinks selected that were alcoholic compared to no HWL (44%): image‐and‐text HWL: 46% (odds ratio [OR] = 1.08, 95% confidence interval [CI] = 0.82, 1.42); text‐only HWL: 41% (OR = 0.87, 95% CI = 0.67, 1.14). Concordant with there being no difference between groups, there was extreme evidence in favour of the null hypothesis (Bayes factor [BF] < 0.01). Negative emotional arousal was higher (P < 0.001) and acceptability lower (P < 0.001) in the image‐and‐text HWL group, compared to the text‐only HWL group. Conclusions: In a naturalistic shopping laboratory, there was no evidence that health warning labels describing the adverse health consequences of excessive alcohol consumption changed selection behaviour

Isometric Sliced Inverse Regression for Nonlinear Manifolds Learning

[[abstract]]Sliced inverse regression (SIR) was developed to find effective linear dimension-reduction directions for exploring the intrinsic structure of the high-dimensional data. In this study, we present isometric SIR for nonlinear dimension reduction, which is a hybrid of the SIR method using the geodesic distance approximation. First, the proposed method computes the isometric distance between data points; the resulting distance matrix is then sliced according to K-means clustering results, and the classical SIR algorithm is applied. We show that the isometric SIR (ISOSIR) can reveal the geometric structure of a nonlinear manifold dataset (e.g., the Swiss roll). We report and discuss this novel method in comparison to several existing dimension-reduction techniques for data visualization and classification problems. The results show that ISOSIR is a promising nonlinear feature extractor for classification applications.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

A Minimum Discrepancy Approach to Multivariate Dimension Reduction via K-means Inverse Regression

We proposed a new method to estimate the intra-cluster adjusted central subspace for regressions with multivariate responses. Following Setodji and Cook (2004), we made use of the k-means algorithm to cluster the observed response vectors. Our method was designed to recover the intracluster information and outperformed previous method with respect to estimation accuracies on both the central subspace and its dimension. It also allowed us to test the predictor effects in a model-free approach. Simulation and a real data example were given to illustrate our methodology