Factors associated with needle sharing among Black male injection drug users in Harris County, Texas

Background. Injection drug users (IDUs) are at increased risk for HIV transmission due to unique risk behaviors, such as sharing needles. In Houston, IDUs account for 18% of all HIV/AIDS cases among Black males. ^ Objectives. This analysis compared demographic, behavioral, and psychosocial characteristics of needle sharing and non-sharing IDUs in a population of Black males in Harris County, Texas. ^ Methods. Data used for this analysis were from the second IDU cycle of the National HIV Behavioral Surveillance System. This dataset included a sample of 288 Black male IDUs. Univariate and multivariate statistical analysis were performed to determine statistically significant associations of needle sharing in this population and to create a functional model to inform local HIV prevention programs. ^ Results. Half of the participants in this analysis shared needles in the past 12 months. Compared to non-sharers, sharers were more likely to be homeless (OR=3.70, p<0.01) or arrested in the past year (OR=2.31, p<0.01), inject cocaine (OR=2.07, p<0.01), report male-to-male sex in the past year (OR=6.97, p<0.01), and to exchange sex for money or drugs. Sharers were less likely than non-sharers to graduate high school (OR=0.36, p<0.01), earn $5,000 or more a year (OR=1.15, p=0.05), get needles from a medical source (OR=0.59, p=0.03), and ever test for HIV (OR=0.17, p<0.01). Sharers were more likely to report depressive symptoms (OR=3.49, p<0.01), lower scores on the family support scale (mean difference 0.41, p=0.01) and decision-making confidence scale (mean difference 0.38, p<0.01), and greater risk-taking (mean difference -0.49, p<0.01) than non-sharers. In a multivariable logistic regression, sharers were less likely to have graduated high school (OR=0.33, p<0.01) and have been tested for HIV (OR=0.12, p<0.01) and were more likely to have been arrested in the past year (OR=2.3, p<0.01), get needles from a street source (OR=3.87, p<0.01), report male-to-male sex (OR=7.01, p<0.01), and have depressive symptoms (OR=2.36, p=0.02) and increased risk-taking (OR=1.78, p=0.01). ^ Conclusions. IDUs that shared needles are different from those that did not, reporting lower socioeconomic status, increased sexual and risk behaviors, increased depressive symptoms and increased risk-taking. These findings suggest that intervention programs that also address these demographic, behavioral, and psychosocial factors may be more successful in decreasing needle sharing among this population.^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Survival of gastrointestinal and stromal tumor patients at UT M.D. Anderson Cancer Center

Objective. Gastrointestinal Stromal Tumors (GISTs) are rare mesenchymal tumors of the gastrointestinal (GI) tract with spindled cell, epithelioid, or occasionally pleomorphic morphology. The primary objective of this paper is to describe the demographic and clinical characteristics and survival among GIST patients registered at the University of Texas M.D. Anderson Cancer Center (MDACC). ^ Methods. This cohort study includes 783 consecutive patients diagnosed with GIST from 1995 to 2007. Demographic, clinical and survival information were obtained from the MDACC cancer registry. ^ Statistical Analysis. Kaplan-Meier survival curves, univariate and multivariate Cox proportional hazards analysis were conducted to estimate survival and identify prognostic clinical factors associated with survival. Results. The age at diagnosis of MDACC GIST cases ranged from 17 to 91 with a mean of 57 years and a male-to-female ratio of 1.3:1. The racial distribution was whites 77%, African-Americans 9.5%, Hispanics 9.3% and other races 4.2%. Fifty per cent of the GISTs arose from stomach, 35% small intestine, 7% retroperitoneal space, 6% colorectal and 2% were omentum and mesentery. About half of the tumors were less than 10 cm in size. Fifty eight per cent of the tumors were localized whereas 36% were metastatic. MDACC GIST patients were generally comparable to SEER patients, but, on the average, were 7 years younger than SEER patients and were predominantly whites. ^ Stratification of 783 GIST cases by year of diagnosis based on the introduction of imatinib treatment in 2000 revealed that 60% of the GIST cases were first diagnosed between 2000 and 2007 whereas, 40% were first diagnosed between 1995 and 1999. There was a significant difference between the two cohorts in the distribution of race, GIST symptom, tumor size, tumor site, and stage of the tumor at diagnosis. The 1- and 5-year survival was 93% and 59% in the 1995–2007 cohort. Multivariate Cox regression analysis identified age at diagnosis (p<0.001), female sex (p=0.047), tumor size (p=0.07), multiple cancers (p=0.002), and GIST diagnosed between 2000 and 2007 (p<0.001) were significantly associated with survival. Approximately, 58% of the cases were treated with imatinib whereas 42% did not receive imatinib in 2000–2005 cohort. There was a significant difference in survival between imatinib and non-imatinib groups and in the distribution of tumor size categories, stage of the tumor at diagnosis and cancers before the diagnosis of GIST. The 1- and 5-year survival for imatinib patients was 99% and 73% and was 91% and 63% for non-imatinib patients. Multivariate Cox regression analysis of the 2000–2007 cohort identified, age at diagnosis and tumor stage as possible prognostic factors associated with survival.^

Association of part-time employment and sexual risk-taking behaviors among middle school students

Background. Previous research shows inconsistent results as to the association between part-time employment and sexual behavior among younger teens. Studies of older teens cannot be generalized to younger teens because of the wide differences in types of work performed, nature of work environments, and work intensity. Objective. Examine the relationship between part-time employment and sexual behavior in a cross-sectional sample of public middle school students in Houston, Texas. Methods . The study presents a secondary analysis of data from the It’s Your Game…Keep it Real baseline data collection (11/2004–1/2005). It’s Your Game… is an intervention program for middle school students designed to prevent Sexually Transmitted Infections. Statistical analysis. Univariate and multivariate logistic regression analyses were conducted to examine the association between part-time employment and vaginal intercourse: (a) ever had sex; and (b) current sexual activity. Results. Overall, 13.2% of students worked for pay; male students were 1.5 times as likely as females to be working. Of all the students, 11.0% had had sexual intercourse; students who worked were 3 times more likely to be sexually experienced than those who did not. Among students who were sexually experienced, 67.0% were currently sexually active. After adjusting for the other covariates, Hispanic students were almost 3.6 times more likely to report current sexual activity compared to students in other racial/ethnic groups. In univariate analysis, students who worked 1-5 hrs/week were more likely to be sexually experienced than those not currently employed, and the likelihood increased with number of hours worked. There is a similar pattern in the multivariate model, but the odds ratios are too close for the evidence to be more than suggestive. Of sexually experienced students, students working 1-5 hrs/week were 2.7 times more likely to report current sexual intercourse than those not working; those working >5 hrs/week were 4.7 times more likely. The multivariate model showed a similar increase in likelihood, and adjustment for covariates increased these associations: students who worked 1-5 hrs/week were 3.6 times more likely to report current sexual intercourse, and students who worked >5 hrs/week were 4.5 times more likely, than students not currently employed.^

Performance recovery from exercise: A review from a statistical point of view

Background. Research into methods for recovery from fatigue due to exercise is a popular topic among sport medicine, kinesiology and physical therapy. However, both the quantity and quality of studies and a clear solution of recovery are lacking. An analysis of the statistical methods in the existing literature of performance recovery can enhance the quality of research and provide some guidance for future studies. Methods: A literature review was performed using SCOPUS, SPORTDiscus, MEDLINE, CINAHL, Cochrane Library and Science Citation Index Expanded databases to extract the studies related to performance recovery from exercise of human beings. Original studies and their statistical analysis for recovery methods including Active Recovery, Cryotherapy/Contrast Therapy, Massage Therapy, Diet/Ergogenics, and Rehydration were examined. Results: The review produces a Research Design and Statistical Method Analysis Summary. Conclusion: Research design and statistical methods can be improved by using the guideline from the Research Design and Statistical Method Analysis Summary. This summary table lists the potential issues and suggested solutions, such as, sample size calculation, sports specific and research design issues consideration, population and measure markers selection, statistical methods for different analytical requirements, equality of variance and normality of data, post hoc analyses and effect size calculation.^

Ascertainment, meta-analysis and reporting of adverse reactions in clinical trials of antibiotics

The ascertainment and analysis of adverse reactions to investigational agents presents a significant challenge because of the infrequency of these events, their subjective nature and the low priority of safety evaluations in many clinical trials. A one year review of antibiotic trials published in medical journals demonstrates the lack of standards in identifying and reporting these potentially fatal conditions. This review also illustrates the low probability of observing and detecting rare events in typical clinical trials which include fewer than 300 subjects. Uniform standards for ascertainment and reporting are suggested which include operational definitions of study subjects. Meta-analysis of selected antibiotic trials using multivariate regression analysis indicates that meaningful conclusions may be drawn from data from multiple studies which are pooled in a scientifically rigorous manner. ^

A QUANTITATIVE INVESTIGATION OF SOURCE AND ELEMENTAL COMPOSITION OF INDOOR AND OUTDOOR FINE PARTICULATE AIR POLLUTION IN HOUSTON (TEXAS)

An investigation was undertaken to determine the chemical characterization of inhalable particulate matter in the Houston area, with special emphasis on source identification and apportionment of outdoor and indoor atmospheric aerosols using multivariate statistical analyses.^ Fine (<2.5 (mu)m) particle aerosol samples were collected by means of dichotomous samplers at two fixed site (Clear Lake and Sunnyside) ambient monitoring stations and one mobile monitoring van in the Houston area during June-October 1981 as part of the Houston Asthma Study. The mobile van allowed particulate sampling to take place both inside and outside of twelve homes.^ The samples collected for 12-h sampling on a 7 AM-7 PM and 7 PM-7 AM (CDT) schedule were analyzed for mass, trace elements, and two anions. Mass was determined gravimetrically. An energy-dispersive X-ray fluorescence (XRF) spectrometer was used for determination of elemental composition. Ion chromatography (IC) was used to determine sulfate and nitrate.^ Average chemical compositions of fine aerosol at each site were presented. Sulfate was found to be the largest single component in the fine fraction mass, comprising approximately 30% of the fine mass outdoors and 12% indoors, respectively.^ Principal components analysis (PCA) was applied to identify sources of aerosols and to assess the role of meteorological factors on the variation in particulate samples. The results suggested that meteorological parameters were not associated with sources of aerosol samples collected at these Houston sites.^ Source factor contributions to fine mass were calculated using a combination of PCA and stepwise multivariate regression analysis. It was found that much of the total fine mass was apparently contributed by sulfate-related aerosols. The average contributions to the fine mass coming from the sulfate-related aerosols were 56% of the Houston outdoor ambient fine particulate matter and 26% of the indoor fine particulate matter.^ Characterization of indoor aerosol in residential environments was compared with the results for outdoor aerosols. It was suggested that much of the indoor aerosol may be due to outdoor sources, but there may be important contributions from common indoor sources in the home environment such as smoking and gas cooking. ^

Statistical issues on studies of mixed longitudinal design

Mixed longitudinal designs are important study designs for many areas of medical research. Mixed longitudinal studies have several advantages over cross-sectional or pure longitudinal studies, including shorter study completion time and ability to separate time and age effects, thus are an attractive choice. Statistical methodology used in general longitudinal studies has been rapidly developing within the last few decades. Common approaches for statistical modeling in studies with mixed longitudinal designs have been the linear mixed-effects model incorporating an age or time effect. The general linear mixed-effects model is considered an appropriate choice to analyze repeated measurements data in longitudinal studies. However, common use of linear mixed-effects model on mixed longitudinal studies often incorporates age as the only random-effect but fails to take into consideration the cohort effect in conducting statistical inferences on age-related trajectories of outcome measurements. We believe special attention should be paid to cohort effects when analyzing data in mixed longitudinal designs with multiple overlapping cohorts. Thus, this has become an important statistical issue to address. ^ This research aims to address statistical issues related to mixed longitudinal studies. The proposed study examined the existing statistical analysis methods for the mixed longitudinal designs and developed an alternative analytic method to incorporate effects from multiple overlapping cohorts as well as from different aged subjects. The proposed study used simulation to evaluate the performance of the proposed analytic method by comparing it with the commonly-used model. Finally, the study applied the proposed analytic method to the data collected by an existing study Project HeartBeat!, which had been evaluated using traditional analytic techniques. Project HeartBeat! is a longitudinal study of cardiovascular disease (CVD) risk factors in childhood and adolescence using a mixed longitudinal design. The proposed model was used to evaluate four blood lipids adjusting for age, gender, race/ethnicity, and endocrine hormones. The result of this dissertation suggest the proposed analytic model could be a more flexible and reliable choice than the traditional model in terms of fitting data to provide more accurate estimates in mixed longitudinal studies. Conceptually, the proposed model described in this study has useful features, including consideration of effects from multiple overlapping cohorts, and is an attractive approach for analyzing data in mixed longitudinal design studies.^

A comparative state policy analysis of the adoption of birth defects surveillance legislation

Birth defects are the leading cause of infant mortality in the United States and are a major cause of lifetime disability. However, efforts to understand their causes have been hampered by a lack of population-specific data. During 1990–2004, 22 state legislatures responded to this need by proposing birth defects surveillance legislation (BDSL). The contrast between these states and those that did not pass BDSL provides an opportunity to better understand conditions associated with US public health policy diffusion. ^ This study identifies key state-specific determinants that predict: (1) the introduction of birth defects surveillance legislation (BDSL) onto states' formal legislative agenda, and (2) the successful adoption of these laws. Secondary aims were to interpret these findings in a theoretically sound framework and to incorporate evidence from three analytical approaches. ^ The study begins with a comparative case study of Texas and Oregon (states with divergent BDSL outcomes), including a review of historical documentation and content analysis of key informant interviews. After selecting and operationalizing explanatory variables suggested by the case study, Qualitative Comparative Analysis (QCA) was applied to publically available data to describe important patterns of variation among 37 states. Results from logistic regression were compared to determine whether the two methods produced consistent findings. ^ Themes emerging from the comparative case study included differing budgetary conditions and the significance of relationships within policy issue networks. However, the QCA and statistical analysis pointed to the importance of political parties and contrasting societal contexts. Notably, state policies that allow greater access to citizen-driven ballot initiatives were consistently associated with lower likelihood of introducing BDSL. ^ Methodologically, these results indicate that a case study approach, while important for eliciting valuable context-specific detail, may fail to detect the influence of overarching, systemic variables, such as party competition. However, QCA and statistical analyses were limited by a lack of existing data to operationalize policy issue networks, and thus may have downplayed the impact of personal interactions. ^ This study contributes to the field of health policy studies in three ways. First, it emphasizes the importance of collegial and consistent relationships among policy issue network members. Second, it calls attention to political party systems in predicting policy outcomes. Finally, a novel approach to interpreting state data in a theoretically significant manner (QCA) has been demonstrated.^

Association between sugar-sweetened beverages and both gestational weight gain and gestational diabetes

This cross-sectional study aimed at evaluating the association between sugar sweetened beverage (SSB) consumption and both excessive gestational weight gain (EGWG) and gestational diabetes mellitus (GDM). The study was conducted in the postpartum units of Memorial Hermann Hospital, Lyndon Baines Johnson General Hospital, the University of Texas Medical Branch at Galveston General Hospital, and the University of Texas at Brownsville Hospital. Between June 2009 and September 2010, women between the ages of 18 and 49 years with singleton pregnancies who delivered an infant born at 37 weeks of gestation or later were approached. Descriptive, univariate and multivariate analysis were employed in our study using the Statistical Analysis System (SAS) software version 9.1 (SAS Institute Inc. Cary, North Carolina). Our investigation did not find statistically significant associations between SSBs and EGWG. Our study reported no evidence of an association between SSBs and GDM except for sports drinks. However, the estimate of this association was deemed very imprecise. In conclusion, our study did not find strong provide strong support for the hypothesis that high consumption of SSBs increases the risk of EGWG or GDM. ^