Using citation data to improve retrieval from MEDLINE.

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.

Depression, interpersonal violence and lack of social support as barriers to cervical cancer screening among recently immigrated Latinas

Background. Various psychosocial factors have been demonstrated to be barriers for cervical cancer screening among Latinas in the United States, but few studies have researched whether depression and interpersonal violence act as psychosocial barriers to cervical cancer screening. ^ Methods. The proposed study assessed whether depression, interpersonal violence, lack of social support and demographic characteristics such as age, income, education and years in the United States acted as barriers to cervical cancer screening among cantineras in Houston, TX. This secondary data analysis utilized data from a previous cross-sectional study called Project GIRASOL- Community Outreach to Prevent Cervical Cancer among Latinas. The data from the baseline survey (sample size 331) was analyzed using Pearson chi-square and multiple logistic regression. ^ Results. Multiple logistic regression indicates that none and low levels of social support from relatives, depression, and total IPV are significant predictors of non-compliance to cervical cancer screening. ^ Conclusions. Future health interventions or physicians that promote cervical cancer screening among cantineras or recently immigrated Latinas with similar socio-demographic characteristics should try to identify whether Latinas are suffering from depression, interpersonal violence or lack of social support and provide proper referrals to alleviate the problems and positively influence screening behavior. ^

A Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^

REGRESSION ANALYSIS FOR STANDARDIZED MORTALITY RATIOS WITH APPLICATION TO OCCUPATIONAL FOLLOW-UP STUDIES

Traditional comparison of standardized mortality ratios (SMRs) can be misleading if the age-specific mortality ratios are not homogeneous. For this reason, a regression model has been developed which incorporates the mortality ratio as a function of age. This model is then applied to mortality data from an occupational cohort study. The nature of the occupational data necessitates the investigation of mortality ratios which increase with age. These occupational data are used primarily to illustrate and develop the statistical methodology.^ The age-specific mortality ratio (MR) for the covariates of interest can be written as MR(,ij...m) = ((mu)(,ij...m)/(theta)(,ij...m)) = r(.)exp (Z('')(,ij...m)(beta)) where (mu)(,ij...m) and (theta)(,ij...m) denote the force of mortality in the study and chosen standard populations in the ij...m('th) stratum, respectively, r is the intercept, Z(,ij...m) is the vector of covariables associated with the i('th) age interval, and (beta) is a vector of regression coefficients associated with these covariables. A Newton-Raphson iterative procedure has been used for determining the maximum likelihood estimates of the regression coefficients.^ This model provides a statistical method for a logical and easily interpretable explanation of an occupational cohort mortality experience. Since it gives a reasonable fit to the mortality data, it can also be concluded that the model is fairly realistic. The traditional statistical method for the analysis of occupational cohort mortality data is to present a summary index such as the SMR under the assumption of constant (homogeneous) age-specific mortality ratios. Since the mortality ratios for occupational groups usually increase with age, the homogeneity assumption of the age-specific mortality ratios is often untenable. The traditional method of comparing SMRs under the homogeneity assumption is a special case of this model, without age as a covariate.^ This model also provides a statistical technique to evaluate the relative risk between two SMRs or a dose-response relationship among several SMRs. The model presented has application in the medical, demographic and epidemiologic areas. The methods developed in this thesis are suitable for future analyses of mortality or morbidity data when the age-specific mortality/morbidity experience is a function of age or when there is an interaction effect between confounding variables needs to be evaluated. ^

Associations between teacher support and students' fruit and vegetable consumption in low-income elementary schools in central Texas

Background. Similar to parent support in the home environment, teacher support at school may positively influence children's fruit and vegetable (FV) consumption. This study assessed the relationship between teacher support for FV consumption and the FV intake of 4th and 5th grade students in low-income elementary schools in central Texas. Methods. A secondary analysis was performed on baseline data collected from 496 parent-child dyads during the Marathon Kids study carried out by the Michael & Susan Dell Center for Healthy Living at the University of Texas School of Public Health. A hierarchical linear regression analysis adjusting for key demographic variables, parent support, and home FV availability was conducted. In addition, separate linear regression models stratified by quartiles of home FV availability were conducted to assess the relationship between teacher support and FV intake by level of home FV availability. Results. Teacher support was not significantly related to students' FV intake (p = .44). However, the interaction of teacher support and home FV availability was positively associated with students' FV consumption (p < .05). For students in the lowest quartile of home FV availability, teacher support accounted for approximately 6% of the FV intake variance (p = .02). For higher levels of FV availability, teacher support and FV intake were not related. Conclusions. For lower income elementary school-aged children with low FV availability at home, greater teacher support may lead to modest increases in FV consumption.^