Course of Untreated High Blood Pressure in the Emergency Department

<p>Introduction: No clear understanding exists about the course of a patient’s blood pressure (BP) during an emergency department (ED) visit. Prior investigations have demonstrated that BP can be reduced by removing patients from treatment areas or by placing patients supine and observing them for several hours. However, modern EDs are chaotic and noisy places where patients and their families wait for long periods in an unfamiliar environment. We sought to determine the stability of repeated BP measurements in the ED environment.</p> <p>Methods: A prospective study was performed at an urban ED. Research assistants trained and certified in BP measurement obtained sequential manual BPs and heart rates on a convenience sample of 76 patients, beginning with the patient arrival in the ED. Patients were observed through their stay for up to 2 hours, and BP was measured at 10-minute intervals. Data analysis with SAS PROC MIXED (SAS Institute, Cary, North Carolina) for regression models with correlated data determined the shape of the curve as BP changed over time. Patients were grouped on the basis of their presenting BP as normal (less than 140/90), elevated (140–160/90–100), or severely elevated (greater than 160/100) for the regression analysis.</p> <p>Results: A statistically significant downward trend in systolic and diastolic BP was observed only for those patients presenting with severely elevated BPs (ie, greater than 160/100).</p> <p>Conclusion: We demonstrate a statistically significant decline in systolic and diastolic BP over time spent in the ED only for patients with severely elevated presenting BPs. [West J Emerg Med. 2011;12(4):421–425.]</p

Using multilevel regression mixture models to identify level-1 heterogeneity in level-2 effects

This article proposes a novel exploratory approach for assessing how the effects of Level-2 predictors differ across Level-1 units. Multilevel regression mixture models are used to identify latent classes at Level 1 that differ in the effect of 1 or more Level-2 predictors. Monte Carlo simulations are used to demonstrate the approach with different sample sizes and to demonstrate the consequences of constraining 1 of the random effects to 0. An application of the method to evaluate heterogeneity in the effects of classroom practices on students is used to show the types of research questions that can be answered with this method and the issues faced when estimating multilevel regression mixtures

Impact of an equality constraint on the class-specific residual variances in regression mixtures:a Monte Carlo simulation study

Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made

The analysis of bridging constructs with hierarchical clustering methods: An application to identity

When analyzing psychometric surveys, some design and sample size limitations challenge existing approaches. Hierarchical clustering, with its graphics (heat maps, dendrograms, means plots), provides a nonparametric method for analyzing factorially-designed survey data, and small samples data. In the present study, we demonstrated the advantages of using hierarchical clustering (HC) for the analysis of non-higher-order measures, comparing the results of HC against those of exploratory factor analysis. As a factorially-designed survey, we used the Identity Labels and Life Contexts Questionnaire (ILLCQ), a novel measure to assess identity as a bridging construct for the intersection of identity domains and life contexts. Results suggest that, when used to validate factorially-designed measures, HC and its graphics are more stable and consistent compared to EFA

Evaluating differential effects using regression interactions and regression mixture models

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The article aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design

Heroin Use and Sex: Some Patterns in Miami-Dade County, Florida

Much of the literature on heroin and opioid addiction holds that regular, long-term users of heroin and other opioids lose interest in sex as their drug using careers lengthen. Analysis of self-reports collected from IDUs in two cross- sectional surveys on patterns of risk behavior in Miami-Dade County, Florida, reveals that large proportions of IDUs report using heroin before or during sex across a wide range of self-injection experience, from as little as twelve months to over 40 years. One half or more of respondents to both surveys reported using heroin in their recent sexual experiences, with similar proportions reported by both males and females. The same IDUs, however, tend not to report using prescription painkillers before or during sex. This finding indicates that co-occurring risk behavior related to both sexual behavior and heroin use may be more prevalent among long-term IDUs than previous literature has suggested

Racial discrimination in medical care settings and opioid pain reliever misuse in a U.S. cohort: 1992 to 2015

BACKGROUND: In the United States whites are more likely to misuse opioid pain relievers (OPRs) than blacks, and blacks are less likely to be prescribed OPRs than whites. Our objective is to determine whether racial discrimination in medical settings is protective for blacks against OPR misuse, thus mediating the black-white disparities in OPR misuse. METHODS: We used data from 3528 black and white adults in the Coronary Artery Risk Development in Young Adults (CARDIA) study, an ongoing multi-site cohort. We employ causal mediation methods, with race (black vs white) as the exposure, lifetime discrimination in medical settings prior to year 2000 as the mediator, and OPR misuse after 2000 as the outcome. RESULTS: We found black participants were more likely to report discrimination in a medical setting (20.3% vs 0.9%) and less likely to report OPR misuse (5.8% vs 8.0%, OR = 0.71, 95% CI = 0.55, 0.93, adjusted for covariates). Our mediation models suggest that when everyone is not discriminated against, the disparity is wider with black persons having even lower odds of reporting OPR misuse (OR = 0.63, 95% CI = 0.45, 0.89) compared to their white counterparts, suggesting racial discrimination in medical settings is a risk factor for OPR misuse rather than protective. CONCLUSIONS: These results suggest that racial discrimination in a medical setting is a risk factor for OPR misuse rather than being protective, and thus could not explain the seen black-white disparity in OPR misuse

The effects of sample size on the estimation of regression mixture models

Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture’s ability to produce “stable” results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that may occur with small samples in real data sets. The results suggest that (a) when class separation is low, very large sample sizes may be needed to obtain stable results; (b) it may often be necessary to consider a preponderance of evidence in latent class enumeration; (c) regression mixtures with ordinal outcomes result in even more instability; and (d) with small samples, it is possible to obtain spurious results without any clear indication of there being a problem

Effects of Antiretroviral Therapy and Depressive Symptoms on All-Cause Mortality Among HIV-Infected Women

Abstract Depression affects up to 30% of human immunodeficiency virus (HIV)-infected individuals. We estimated joint effects of antiretroviral therapy (ART) initiation and depressive symptoms on time to death using a joint marginal structural model and data from a cohort of HIV-infected women from the Women's Interagency HIV Study (conducted in the United States) from 1998–2011. Among 848 women contributing 6,721 years of follow-up, 194 participants died during follow-up, resulting in a crude mortality rate of 2.9 per 100 women-years. Cumulative mortality curves indicated greatest mortality for women who reported depressive symptoms and had not initiated ART. The hazard ratio for depressive symptoms was 3.38 (95% confidence interval (CI): 2.15, 5.33) and for ART was 0.47 (95% CI: 0.31, 0.70). Using a reference category of women without depressive symptoms who had initiated ART, the hazard ratio for women with depressive symptoms who had initiated ART was 3.60 (95% CI: 2.02, 6.43). For women without depressive symptoms who had not started ART, the hazard ratio was 2.36 (95% CI: 1.16, 4.81). Among women reporting depressive symptoms who had not started ART, the hazard ratio was 7.47 (95% CI: 3.91, 14.3). We found a protective effect of ART initiation on mortality, as well as a harmful effect of depressive symptoms, in a cohort of HIV-infected women