DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients\u27 privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide

Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research

Outcome-wide analysis can offer several benefits, including increased power to detect weak signals and the ability to identify exposures with multiple effects on health, which may be good targets for preventive measures. Recently, advanced statistical multivariate techniques for outcome-wide analysis have been developed, but they have been rarely applied to exposome analysis. In this work, we provide an overview of a selection of methods that are well-suited for outcome-wide exposome analysis and are implemented in the R statistical software. Our work brings together six different methods presenting innovative solutions for typical problems arising from outcome-wide approaches in the context of the exposome, including dependencies among outcomes, high dimensionality, mixed-type outcomes, missing data records, and confounding effects. The identified methods can be grouped into four main categories: regularized multivariate regression techniques, multi-task learning approaches, dimensionality reduction approaches, and bayesian extensions of the multivariate regression framework. Here, we compare each technique presenting its main rationale, strengths, and limitations, and provide codes and guidelines for their application to exposome data. Additionally, we apply all selected methods to a real exposome dataset from the Human Early-Life Exposome (HELIX) project, demonstrating their suitability for exposome research. Although the choice of the best method will always depend on the challenges to be faced in each application, for an exposome-like analysis we find dimensionality reduction and bayesian methods such as reduced rank regression (RRR) or multivariate bayesian shrinkage priors (MBSP) particularly useful, given their ability to deal with critical issues such as collinearity, high-dimensionality, missing data or quantification of uncertainty

Reports of COVID-19 vaccine adverse events in predominantly Republican vs Democratic states

IMPORTANCE: Antivaccine sentiment is increasingly associated with conservative political positions. Republican-inclined states exhibit lower COVID-19 vaccination rates, but the association between political inclination and reported vaccine adverse events (AEs) is unexplored. OBJECTIVE: To assess whether there is an association between state political inclination and the reporting rates of COVID-19 vaccine AEs. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used the AE reports after COVID-19 vaccination from the Vaccine Adverse Event Reporting System (VAERS) database from 2020 to 2022, with reports after influenza vaccines from 2019 to 2022 used as a reference. These reports were examined against state-level percentage of Republican votes in the 2020 US presidential election. EXPOSURE: State-level percentage of Republican votes in the 2020 US presidential election. MAIN OUTCOMES AND MEASURES: Rates of any AE among COVID-19 vaccine recipients, rates of any severe AE among vaccine recipients, and the proportion of AEs reported as severe. RESULTS: A total of 620 456 AE reports (mean [SD] age of vaccine recipients, 51.8 [17.6] years; 435 797 reports from women [70.2%]; a vaccine recipient could potentially file more than 1 report, so reports are not necessarily from unique individuals) for COVID-19 vaccination were identified from the VAERS database. Significant associations between state political inclination and state AE reporting were observed for all 3 outcomes: a 10% increase in Republican voting was associated with increased odds of AE reports (odds ratio [OR], 1.05; 95% CI, 1.05-1.05; P \u3c .001), severe AE reports (OR, 1.25; 95% CI, 1.24-1.26; P \u3c .001), and the proportion of AEs reported as severe (OR, 1.21; 95% CI, 1.20-1.22; P \u3c .001). These associations were seen across all age strata in stratified analyses and were more pronounced among older subpopulations. CONCLUSIONS AND RELEVANCE: This cross-sectional study found that the more states were inclined to vote Republican, the more likely their vaccine recipients or their clinicians reported COVID-19 vaccine AEs. These results suggest that either the perception of vaccine AEs or the motivation to report them was associated with political inclination

Studies on Epidemiology and Prediction of Soybean Mosaic Virus

-Some epidemiological factors of SMV were studied in Jinan, Shandong Province from 1984 to 1989. The results indicated that the resistance of soybean cultivars and the amount of primary inoculum sources dominated the dynamic aspects of SMV epidemics. It wOriginating text in Chinese.Citation: Luo, Ruiwu, Shang, Youfen, Yang, Chongliang, Zhao, Jiuhua, Li, Changsong. (1991). Studies on Epidemiology and Prediction of Soybean Mosaic Virus. Journal of Plant Protection / Acta Phytophylacica Sinica, 18(3), 267-271

Lipidome of mammographic breast density in premenopausal women

BACKGROUND: High mammographic breast density (MBD) is a strong risk factor for breast cancer development, but the biological mechanisms underlying MBD are unclear. Lipids play important roles in cell differentiation, and perturbations in lipid metabolism are implicated in cancer development. Nevertheless, no study has applied untargeted lipidomics to profile the lipidome of MBD. Through this study, our goal is to characterize the lipidome of MBD in premenopausal women. METHODS: Premenopausal women were recruited during their annual screening mammogram at the Washington University School of Medicine in St. Louis, MO. Untargeted lipidomic profiling for 982 lipid species was performed at Metabolon (Durham, NC®), and volumetric measures of MBD (volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)) was assessed using Volpara 1.5 (Volpara Health®). We performed multivariable linear regression models to investigate the associations of lipid species with MBD and calculated the covariate-adjusted least square mean of MBD by quartiles of lipid species. MBD measures were log RESULTS: Of the 705 premenopausal women, 72% were non-Hispanic white, and 23% were non-Hispanic black. Mean age, and BMI were 46 years and 30 kg/m CONCLUSIONS: We report novel lipid species that are associated with MBD in premenopausal women. Studies are needed to validate our results and the translational potential

Suicide Risk Modeling with Uncertain Diagnostic Records

Motivated by the pressing need for suicide prevention through improving behavioral healthcare, we use medical claims data to study the risk of subsequent suicide attempts for patients who were hospitalized due to suicide attempts and later discharged. Understanding the risk behaviors of such patients at elevated suicide risk is an important step towards the goal of "Zero Suicide". An immediate and unconventional challenge is that the identification of suicide attempts from medical claims contains substantial uncertainty: almost 20\% of "suspected" suicide attempts are identified from diagnostic codes indicating external causes of injury and poisoning with undermined intent. It is thus of great interest to learn which of these undetermined events are more likely actual suicide attempts and how to properly utilize them in survival analysis with severe censoring. To tackle these interrelated problems, we develop an integrative Cox cure model with regularization to perform survival regression with uncertain events and a latent cure fraction. We apply the proposed approach to study the risk of subsequent suicide attempt after suicide-related hospitalization for adolescent and young adult population, using medical claims data from Connecticut. The identified risk factors are highly interpretable; more intriguingly, our method distinguishes the risk factors that are most helpful in assessing either susceptibility or timing of subsequent attempt. The predicted statuses of the uncertain attempts are further investigated, leading to several new insights on suicide event identification

ODACH: A One-shot Distributed Algorithm for Cox model with heterogeneous multi-center data

We developed a One-shot Distributed Algorithm for Cox proportional-hazards model to analyze Heterogeneous multi-center time-to-event data (ODACH) circumventing the need for sharing patient-level information across sites. This algorithm implements a surrogate likelihood function to approximate the Cox log-partial likelihood function that is stratified by site using patient-level data from a lead site and aggregated information from other sites, allowing the baseline hazard functions and the distribution of covariates to vary across sites. Simulation studies and application to a real-world opioid use disorder study showed that ODACH provides estimates close to the pooled estimator, which analyzes patient-level data directly from all sites via a stratified Cox model. Compared to the estimator from meta-analysis, the inverse variance-weighted average of the site-specific estimates, ODACH estimator demonstrates less susceptibility to bias, especially when the event is rare. ODACH is thus a valuable privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data

Alcohol consumption among adults with a cancer diagnosis in the All of Us Research Program

IMPORTANCE: Alcohol consumption is associated with adverse oncologic and treatment outcomes among individuals with a diagnosis of cancer. As a key modifiable behavioral factor, alcohol consumption patterns among cancer survivors, especially during treatment, remain underexplored in the United States. OBJECTIVE: To comprehensively characterize alcohol consumption patterns among US cancer survivors. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data from May 6, 2018, to January 1, 2022, from the National Institutes of Health All of Us Research Program, a diverse US cohort with electronic health record (EHR) linkage, and included 15 199 participants who reported a cancer diagnosis and 1839 patients among a subset with EHR data who underwent treatment within the past year of the baseline survey. Data analysis was performed from October 1, 2022, to January 31, 2023. MAIN OUTCOMES AND MEASURES: Prevalence of current drinking and of risky drinking behaviors, including exceeding moderate drinking (\u3e2 drinks on a typical drinking day), binge drinking (≥6 drinks on 1 occasion), and hazardous drinking (Alcohol Use Disorders Identification Test-Consumption [AUDIT-C] score ≥3 for women or ≥4 for men). RESULTS: This study included 15 199 adults (mean [SD] age at baseline, 63.1 [13.0] years; 9508 women [62.6%]) with a cancer diagnosis. Overall, 11 815 cancer survivors (77.7%) were current drinkers. Among current drinkers, 1541 (13.0%) exceeded moderate drinking, 2812 (23.8%) reported binge drinking, and 4527 (38.3%) engaged in hazardous drinking. After multivariable adjustment, survivors who were younger than 65 years, men, or of Hispanic ethnicity or who received a diagnosis before 18 years of age or ever smoked were more likely to exceed moderate drinking (aged \u3c50 years: odds ratio [OR], 2.90 [95% CI, 2.41-3.48]; aged 50-64 years: OR, 1.84 [95% CI, 1.58-2.15]; men: OR, 2.38 [95% CI, 2.09-2.72]; Hispanic ethnicity: OR, 1.31 [95% CI, 1.04-1.64]; aged \u3c18 years at diagnosis: OR, 1.52 [95% CI, 1.04-2.24]; former smokers: OR, 2.46 [95% CI, 2.16-2.79]; current smokers: OR, 4.14 [95% CI, 3.40-5.04]) or binge drink (aged \u3c50 years: OR, 4.46 [95% CI, 3.85-5.15]; aged 50-64 years: OR, 2.15 [95% CI, 1.90-2.43]; men: OR, 2.10 [95% CI, 1.89-2.34]; Hispanic ethnicity: OR, 1.31 [95% CI, 1.09-1.58]; aged \u3c18 years at diagnosis: OR, 1.71 [95% CI, 1.24-2.35]; former smokers: OR, 1.69 [95% CI, 1.53-1.87]; current smokers: OR, 2.27 [95% CI, 1.91-2.71]). Survivors with cancer diagnosed before 18 years of age or who ever smoked were more likely to be hazardous drinkers (aged \u3c18 years at diagnosis: OR, 1.52 [95% CI, 1.11-2.08]; former smokers: OR, 1.83 [95% CI, 1.68-1.99]; current smokers: OR, 2.13 [95% CI, 1.79-2.53]). Of 1839 survivors receiving treatment as captured in the EHR, 1405 (76.4%) were current drinkers, and among these, 170 (12.1%) exceeded moderate drinking, 329 (23.4%) reported binge drinking, and 540 (38.4%) engaged in hazardous drinking, with similar prevalence across different types of cancer treatment. CONCLUSIONS AND RELEVANCE: This cross-sectional study of a diverse US cohort suggests that alcohol consumption and risky drinking behaviors were common among cancer survivors, even among individuals receiving treatment. Given the adverse treatment and oncologic outcomes associated with alcohol consumption, additional research and implementation studies are critical in addressing this emerging concern among cancer survivors

The association of prescription opioid use with suicide attempts: An analysis of statewide medical claims data

BACKGROUND: Suicides and opioid overdose deaths are among the most pressing public health concerns in the US. However direct evidence for the association between opioid use and suicidal behavior is limited. The objective of this article is to examine the association between frequency and dose of prescription opioid use and subsequent suicide attempts. METHODS AND FINDINGS: This retrospective cohort study analyzed 4 years of statewide medical claims data from the Connecticut All-Payer Claims Database. Commercially insured adult patients in Connecticut (n = 842,773) who had any medical claims beginning in January 2012 were followed through December 2015. The primary outcome was suicide attempt identified using International Classification of Diseases (ICD 9) diagnosis codes. Primary predictor variables included frequency of opioid use, which was defined as the number of months with claims for prescription opioids per year, and strength of opioid dose, which was standardized using morphine milligram equivalent (MME) units. We also controlled for psychiatric and medical comorbidities using ICD 9 codes. We used Cox proportional hazards regression to examine the association between frequency, dose, and suicide attempts, adjusting for medical and psychiatric comorbid conditions. Interactions among measures of opioid use and comorbid conditions were analyzed. In this cohort study with follow-up time up to 4 years (range = 2-48 months, median = 46 months), the hazard ratios (HR) from the time-to-event analysis indicated that patients prescribed opioid medications for at least 6 months during the past year and at 20-50 MME levels or higher had 4.44 (95% CI: [3.71, 5.32]) to 7.23 (95% CI: [6.22, 8.41]) times the risk of attempted suicide compared to those not prescribed opioids. Risk of suicide attempt was sharply elevated among patients with psychiatric conditions other than anxiety who were prescribed more frequent and higher opioid doses. In contrast, more frequent and higher doses of prescription opioids were associated with lower risk of suicide attempts among patients with medical conditions necessitating pain management. This study is limited by its exclusive focus on commercially insured patients and does not include patients covered by public insurance. It is also limited to patients\u27 receipt of prescription opioids and does not take into account opioids obtained through other means, nor does it include measures of actual patient opioid use. CONCLUSIONS: This analysis provides evidence of a complex relationship among prescription opioids, mental health, pain and other medical comorbidities, and suicide risk. Findings indicate the need for proactive suicide surveillance among individuals diagnosed with affective or psychotic disorders who are receiving frequent and high doses of opioids. However, appropriate opioid treatment may have significant value in reducing suicide risk for those without psychiatric comorbidities