Data Management Policies and Issues with Life Science Data

Data management and sharing are relatively new concepts in the health and life sciences fields. This presentation will cover some basic policies as well as the impediments to data sharing unique to health and life sciences data.

A survey of the implementation status of environmental management systems in U.S. colleges and universities, and, An environmental management system implementation model for U.S. colleges and universities

A census of 925 U.S. colleges and universities offering masters and doctorate degrees was conducted in order to study the number of elements of an environmental management system as defined by ISO 14001 possessed by small, medium and large institutions. A 30% response rate was received with 273 responses included in the final data analysis. Overall, the number of ISO 14001 elements implemented among the 273 institutions ranged from 0 to 16, with a median of 12. There was no significant association between the number of elements implemented among institutions and the size of the institution (p = 0.18; Kruskal-Wallis test) or among USEPA regions (p = 0.12; Kruskal-Wallis test). The proportion of U.S. colleges and universities that reported having implemented a structured, comprehensive environmental management system, defined by answering yes to all 16 elements, was 10% (95% C.I. 6.6%–14.1%); however 38% (95% C.I. 32.0%–43.8%) reported that they had implemented a structured, comprehensive environmental management system, while 30.0% (95% C.I. 24.7%–35.9%) are planning to implement a comprehensive environmental management system within the next five years. Stratified analyses were performed by institution size, Carnegie Classification and job title. ^ The Osnabruck model, and another under development by the South Carolina Sustainable Universities Initiative, are the only two environmental management system models that have been proposed specifically for colleges and universities, although several guides are now available. The Environmental Management System Implementation Model for U.S. Colleges and Universities developed is an adaptation of the ISO 14001 standard and USEPA recommendations and has been tailored to U.S. colleges and universities for use in streamlining the implementation process. In using this implementation model created for the U.S. research and academic setting, it is hoped that these highly specialized institutions will be provided with a clearer and more cost-effective path towards the implementation of an EMS and greater compliance with local, state and federal environmental legislation. ^

A comparison of healthcare access, healthcare utilization and asthma self-management for Hispanic children in border and non-border regions of Texas

The purpose of this culminating experience was to investigate the relationships between healthcare utilization, insurance coverage, and socioeconomic characteristics of children with asthma along the Texas-Mexico Border. A secondary data analysis was conducted on cross-sectional data from the Texas Child Asthma Call-back Survey, a follow-up survey to the random digit dialed Behavior Risk Factor Surveillance Study (BRFSS) conducted between 2006-2009 ( n = 556 adults living in households with a child with asthma).^ The proportion of Hispanic children with asthma in Border areas of Texas was more than twice that of non-Border areas (84.8% vs. 28.8%). Parents in Border areas were less likely to have their own health insurance (OR = 0.251, 95% C.I. = 0.117-0.540) and less likely to complete the survey in English than Spanish (OR = 0.251 95% C.I. = 0.117-0.540) than parents in non-Border areas. No significant socio-economic or health care utilization differences were noted between Hispanic children living in Border areas compared to Hispanic children living in non-Border areas. Children with asthma along the Texas-Mexico Border, regardless of ethnicity and language, have insurance coverage rates, reported cost barriers to care, symptom management, and medication usage patterns similar to those in non-Border areas. When compared to English-speakers, Spanish-speaking parents in Texas as a whole are far less likely to be taught what to do during an asthma attack (50.2% vs. 78.6%).^ Language preference, rather than ethnicity or geographical residence, played a larger role on childhood asthma-related health disparities for children in Texas. Spanish-speaking parents in are less likely to receive adequate asthma self-management education. Investigating the effects of Hispanic acculturation rates and incongruent parent-child health insurance coverage may provide better insight into the health disparities of children along the Texas-Mexico Border.^

Overview of Data Management Planning Tools

These Data Management Plans are more comprehensive and complex than in the past. Libraries around the nation are trying to put together tools to help researchers write plans that conform to the new requirements. This session will look at some of these tools.

Developing and Integrating the Management of Elder Abuse in Primary Practice: A Case Study Using A Web-Based CME Course Format

Introduction: As the population in the United States continues to age, more attention in primary practice settings is now devoted toward managing the care of the elderly. The occurrence of elder abuse is a growing problem. It is a condition many professionals in primary care may be ill prepared with the knowledge or resources to identify and manage. [See PDF for complete abstract]

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. ^

Bayesian joint modeling of longitudinal and survival data

The joint modeling of longitudinal and survival data is a new approach to many applications such as HIV, cancer vaccine trials and quality of life studies. There are recent developments of the methodologies with respect to each of the components of the joint model as well as statistical processes that link them together. Among these, second order polynomial random effect models and linear mixed effects models are the most commonly used for the longitudinal trajectory function. In this study, we first relax the parametric constraints for polynomial random effect models by using Dirichlet process priors, then three longitudinal markers rather than only one marker are considered in one joint model. Second, we use a linear mixed effect model for the longitudinal process in a joint model analyzing the three markers. In this research these methods were applied to the Primary Biliary Cirrhosis sequential data, which were collected from a clinical trial of primary biliary cirrhosis (PBC) of the liver. This trial was conducted between 1974 and 1984 at the Mayo Clinic. The effects of three longitudinal markers (1) Total Serum Bilirubin, (2) Serum Albumin and (3) Serum Glutamic-Oxaloacetic transaminase (SGOT) on patients' survival were investigated. Proportion of treatment effect will also be studied using the proposed joint modeling approaches. ^ Based on the results, we conclude that the proposed modeling approaches yield better fit to the data and give less biased parameter estimates for these trajectory functions than previous methods. Model fit is also improved after considering three longitudinal markers instead of one marker only. The results from analysis of proportion of treatment effects from these joint models indicate same conclusion as that from the final model of Fleming and Harrington (1991), which is Bilirubin and Albumin together has stronger impact in predicting patients' survival and as a surrogate endpoints for treatment. ^

Identification of maltreatment type in children with disabilities using the National Child Abuse and Neglect Data System (NCANDS)

This study was a retrospective design and used secondary data from the National Child Abuse and Neglect Data System (NCANDS), provided by the National Data Archive on Child Abuse and Neglect Family Life Development Center administered by Cornell University. The dataset contained information for the year 2005 on children from birth to 18 years of age. Child abuse and neglect for disabled children, was evaluated in-depth in the present study. Descriptive and statistical analysis was performed using the children with and without disabilities. It was found that children with disabilities have a lower rate of substantiation that likely indicates the interference of reporting due to their handicap. The results of this research demonstrate the important need to teach professionals and laypersons alike on how to recognize and substantiate abuse among disabled children.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^