The Evolution of Media Coverage of Historical Sports Controversies

This thesis will cover sports controversies throughout the 20th Century in the context of the media’s newspaper coverage of the events. The 1919 Black Sox Scandal, the debate over American participation in the 1936 Olympics, and Muhammad Ali’s conversion to the Nation of Islam, standing as a notorious public figure, and conscientious objection to the Vietnam War will represent the three sports controversies. The media’s adherence to cultural norms is clear in all three cases. The consistent devotion to the cultural and racial atmosphere of their respective eras was constant and helped to perpetuate accepted, mainstream cultural attitudes. Cultural and racial norms were followed in the coverage of the three discussed controversies. The anti-Semitism and racially intolerant sentiments in America during great waves of immigration in the early 1900s allowed for journalists to freely vilify Jews as corrupters of baseball and the ballplayers who were rumored to have thrown the 1919 World Series. The white ballplayers were supported in the press, who protected their own and blamed outsiders. Jim Crow and the Americanization movement forced African American and Jewish newspapers to limit their journalistic bias on both sides of the debate over American participation in the 1936 Berlin Olympics. The white, mainstream press was void of bias as the spirit of isolationism in America triumphed over journalist’s leanings in the Olympic debate. The racial tension created by the Civil Rights movement of the 1960s created an atmosphere that allowed mainstream journalists to heap endless criticism on Muhammad Ali as he gained fame. By portraying him as a villain of society as both a religious radical and traitor to America, journalists created a common enemy in the minds of white America. In all three cases, a pattern of journalists expressing the state of cultural and racial norms of the era is present and significant.

Health insurance coverage of Hispanic adults: An assessment of subgroup difference and the impact of immigration

This research explores the health insurance coverage of various Hispanic subgroups in comparison to non-Hispanic whites and blacks. The impact of immigration status is also considered as we hypothesize that nativity, duration, and naturalization tap a possible process of structural acculturation that increases access to insurance coverage for Hispanic groups. We find that the immigration variables impact the type of insurance reported. However, race/ethnic disparities continue to exist, with the various Hispanic subgroups more likely to report miscellaneous government health insurance or no health insurance coverage as compared to non-Hispanic whites.

QTc interval and resting heart rate as long-term predictors of mortality in type 1 and type 2 diabetes mellitus: a 23-year follow-up

We evaluated the association of QT interval corrected for heart rate (QT(c)) and resting heart rate (rHR) with mortality (all-causes, cardiovascular, cardiac, and ischaemic heart disease) in subjects with type 1 and type 2 diabetes.

Opportunistic and systematic screening for chlamydia: a study of consultations by young adults in general practice

BACKGROUND: Opportunistic screening for genital chlamydia infection is being introduced in England, but evidence for the effectiveness of this approach is lacking. There are insufficient data about young peoples' use of primary care services to determine the potential coverage of opportunistic screening in comparison with a systematic population-based approach. AIM: To estimate use of primary care services by young men and women; to compare potential coverage of opportunistic chlamydia screening with a systematic postal approach. DESIGN OF STUDY: Population based cross-sectional study. SETTING: Twenty-seven general practices around Bristol and Birmingham. METHOD: A random sample of patients aged 16-24 years were posted a chlamydia screening pack. We collected details of face-to-face consultations from general practice records. Survival and person-time methods were used to estimate the cumulative probability of attending general practice in 1 year and the coverage achieved by opportunistic and systematic postal chlamydia screening. RESULTS: Of 12 973 eligible patients, an estimated 60.4% (95% confidence interval [CI] = 58.3 to 62.5%) of men and 75.3% (73.7 to 76.9%) of women aged 16-24 years attended their practice at least once in a 1-year period. During this period, an estimated 21.3% of patients would not attend their general practice but would be reached by postal screening, 9.2% would not receive a postal invitation but would attend their practice, and 11.8% would be missed by both methods. CONCLUSIONS: Opportunistic and population-based approaches to chlamydia screening would both fail to contact a substantial minority of the target group, if used alone. A pragmatic approach combining both strategies might achieve higher coverage.

Estimation with Interval Censored Data in Longitudinal Studies

In biostatistical applications interest often focuses on the estimation of the distribution of a time-until-event variable T. If one observes whether or not T exceeds an observed monitoring time at a random number of monitoring times, then the data structure is called interval censored data. We extend this data structure by allowing the presence of a possibly time-dependent covariate process that is observed until end of follow up. If one only assumes that the censoring mechanism satisfies coarsening at random, then, by the curve of dimensionality, typically no regular estimators will exist. To fight the curse of dimensionality we follow the approach of Robins and Rotnitzky (1992) by modeling parameters of the censoring mechanism. We model the right-censoring mechanism by modeling the hazard of the follow up time, conditional on T and the covariate process. For the monitoring mechanism we avoid modeling the joint distribution of the monitoring times by only modeling a univariate hazard of the pooled monitoring times, conditional on the follow up time, T, and the covariates process, which can be estimated by treating the pooled sample of monitoring times as i.i.d. In particular, it is assumed that the monitoring times and the right-censoring times only depend on T through the observed covariate process. We introduce inverse probability of censoring weighted (IPCW) estimator of the distribution of T and of smooth functionals thereof which are guaranteed to be consistent and asymptotically normal if we have available correctly specified semiparametric models for the two hazards of the censoring process. Furthermore, given such correctly specified models for these hazards of the censoring process, we propose a one-step estimator which will improve on the IPCW estimator if we correctly specify a lower-dimensional working model for the conditional distribution of T, given the covariate process, that remains consistent and asymptotically normal if this latter working model is misspecified. It is shown that the one-step estimator is efficient if each subject is at most monitored once and the working model contains the truth. In general, it is shown that the one-step estimator optimally uses the surrogate information if the working model contains the truth. It is not optimal in using the interval information provided by the current status indicators at the monitoring times, but simulations in Peterson, van der Laan (1997) show that the efficiency loss is small.

Smooth Hazard Function Estimation for Interval Censored Data with Time Varying Covariates

In this paper we propose methods for smooth hazard estimation of a time variable where that variable is interval censored. These methods allow one to model the transformed hazard in terms of either smooth (smoothing splines) or linear functions of time and other relevant time varying predictor variables. We illustrate the use of this method on a dataset of hemophiliacs where the outcome, time to seroconversion for HIV, is interval censored and left-truncated.

The Two-Interval Line-Segment Problem

In this paper, the NPMLE in the one-dimensional line segment problem is defined and studied, where line segments on the real line through two non-overlapping intervals are observed. The self-consistency equations for the NPMLE are defined and a quick algorithm for solving them is provided. Supnorm weak convergence to a Gaussian process and efficiency of the NPMLE is proved. The problem has a strong geological application in the study of the lifespan of species.

On the Accelerated Failure Time Model for Current Status and Interval Censored Data

This paper introduces a novel approach to making inference about the regression parameters in the accelerated failure time (AFT) model for current status and interval censored data. The estimator is constructed by inverting a Wald type test for testing a null proportional hazards model. A numerically efficient Markov chain Monte Carlo (MCMC) based resampling method is proposed to simultaneously obtain the point estimator and a consistent estimator of its variance-covariance matrix. We illustrate our approach with interval censored data sets from two clinical studies. Extensive numerical studies are conducted to evaluate the finite sample performance of the new estimators.

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory.