Project description:Background/objectivesLimited knowledge exists regarding sex differences in prescribing potentially inappropriate medications (PIMs) for various multimorbidity patterns. This study sought to determine sex differences in PIM prescribing in older adults with cardiovascular-metabolic patterns.DesignRetrospective cohort study.SettingHealth and Retirement Study (HRS) 2004-2014 interview data, linked to HRS-Medicare claims data annualized for 2005-2014.Study sampleSix thousand three-hundred and forty-one HRS participants aged 65 and older with two and more chronic conditions.MeasurementsPIM events were calculated using 2015 American Geriatrics Society Beers Criteria. Multimorbidity patterns included: "cardiovascular-metabolic only," "cardiovascular-metabolic plus other physical conditions," "cardiovascular-metabolic plus mental conditions," and "no cardiovascular-metabolic disease" patterns. Logistic regression models were used to determine the association between PIM and sex, including interaction between sex and multimorbidity categories in the model, for PIM overall and for each PIM drug class.ResultsWomen were prescribed PIMs more often than men (39.4% vs 32.8%). Overall, women had increased odds of PIM (Adj. odds ratio [OR] = 1.30, 95% confidence interval [CI]: 1.16-1.46). Women had higher odds of PIM than men with cardiovascular-metabolic plus physical patterns (Adj. OR = 1.25, 95% CI: 1.07-1.45) and cardiovascular-metabolic plus mental patterns (Adj. OR = 1.25, 95% CI: 1.06-1.48), and there were no sex differences in adults with a cardiovascular-metabolic only patterns (Adj. OR = 1.13, 95% CI: 0.79-1.62). Women had greater odds of being prescribed the following PIMs: anticholinergics, antidepressants, antispasmodics, benzodiazepines, skeletal muscle relaxants, and had lower odds of being prescribed pain drugs and sulfonylureas compared with men.ConclusionThis study evaluated sex differences in PIM prescribing among adults with complex cardiovascular-metabolic multimorbidity patterns. The effect of sex varied across multimorbidity patterns and by different PIM drug classes. This study identified important opportunities for future interventions to improve medication prescribing among older adults at risk for PIM.
Project description:Hypothesis testing with multiple outcomes requires adjustments to control Type I error inflation, which reduces power to detect significant differences. Maintaining the prechosen Type I error level is challenging when outcomes are correlated. This problem concerns many research areas, including neuropsychological research in which multiple, interrelated assessment measures are common. Standard p value adjustment methods include Bonferroni-, Sidak-, and resampling-class methods. In this report, the authors aimed to develop a multiple hypothesis testing strategy to maximize power while controlling Type I error. The authors conducted a sensitivity analysis, using a neuropsychological dataset, to offer a relative comparison of the methods and a simulation study to compare the robustness of the methods with respect to varying patterns and magnitudes of correlation between outcomes. The results lead them to recommend the Hochberg and Hommel methods (step-up modifications of the Bonferroni method) for mildly correlated outcomes and the step-down minP method (a resampling-based method) for highly correlated outcomes. The authors note caveats regarding the implementation of these methods using available software.
Project description:Objectivesto measure sex differences in the risk of receiving potentially inappropriate prescription drugs and to examine what are the factors that contribute to these differences.Designa retrospective cohort study.Settingcommunity setting of British Columbia, Canada.Participantsresidents of British Columbia aged 65 and older (n = 660,679).Measurementswe measured 2013 period prevalence of prescription dispensations satisfying the American Geriatrics Society's 2012 version of the Beers Criteria for potentially inappropriate medication use in older adults. We used logistic regressions to test for associations between this outcome and a number of clinical and socioeconomic factors.Resultsa larger share of women (31%) than of men (26%) filled one or more potentially inappropriate prescription in the community. The odds of receiving potentially inappropriate prescriptions are associated with several clinical and socioeconomic factors. After controlling for those factors, community-dwelling women were at 16% higher odds of receiving a potentially inappropriate prescription than men (adjusted odds ratio = 1.16, 95% confidence interval = 1.12-1.21). Much of this sex difference stemmed from women's increased odds of receiving potentially inappropriate prescriptions for benzodiazepines and other hypnotics, for tertiary tricyclic antidepressants and for non-selective NSAIDs.Conclusionthere are significant sex differences in older adults' risk of receiving a potentially inappropriate prescription as a result of complex intersections between gender and other social constructs. Appropriate responses will therefore require changes in the information, norms and expectations of both prescribers and patients.
Project description:BackgroundVisual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. Still, such exploration is challenging as there is a need for visualizations that are tailored for the purpose.ResultsThe OmicLoupe software was developed to facilitate visual data exploration and provides more than 15 interactive cross-dataset visualizations for omics data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three different studies, where it allowed for detection of both technical data limitations and biological trends across different omic layers. An example is an analysis of SARS-CoV-2 infection based on two previously published studies, where OmicLoupe facilitated the identification of gene products with consistent expression changes across datasets at both the transcript and protein levels.ConclusionsOmicLoupe provides fast exploration of omics data with tailored visualizations for comparisons within and across data layers. The interactive visualizations are highly informative and are expected to be useful in various analyses of both newly generated and previously published data. OmicLoupe is available at quantitativeproteomics.org/omicloupe.
Project description:Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).
Project description:There is no published evidence on the possible differences in multimorbidity, inappropriate prescribing, and adverse outcomes of care, simultaneously, from a sex perspective in older patients. We aimed to identify those possible differences in patients hospitalized because of a chronic disease exacerbation. A multicenter, prospective cohort study of 740 older hospitalized patients (≥65 years) was designed, registering sociodemographic variables, frailty, Barthel index, chronic conditions (CCs), geriatric syndromes (GSs), polypharmacy, potentially inappropriate prescribing (PIP) according to STOPP/START criteria, and adverse drug reactions (ADRs). Outcomes were length of stay (LOS), discharge to nursing home, in-hospital mortality, cause of mortality, and existence of any ADR and its worst consequence. Bivariate analyses between sex and all variables were performed, and a network graph was created for each sex using CC and GS. A total of 740 patients were included (53.2% females, 53.5% ≥85 years old). Women presented higher prevalence of frailty, and more were living in a nursing home or alone, and had a higher percentage of PIP related to anxiolytics or pain management drugs. Moreover, they presented significant pairwise associations between CC, such as asthma, vertigo, thyroid diseases, osteoarticular diseases, and sleep disorders, and with GS, such as chronic pain, constipation, and anxiety/depression. No significant differences in immediate adverse outcomes of care were observed between men and women in the exacerbation episode.
Project description:BackgroundAs high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations.ResultsWe performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS(A): DE genes with non-zero effect sizes in all studies, (2) HS(B): DE genes with non-zero effect sizes in one or more studies and (3) HS(r): DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively.ConclusionsThe aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS(A), HS(B), and HS(r)). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author's publication website.