How health care delay and avoidance decisions are affected by finances and health insurance

Objective. To identify how an individual's finances and health insurance coverage affects their decision whether to avoid or delay medical care. Methods. Secondary data analysis of The Effects of Financial and Insurance Considerations on Health Care Utilization 2007 telephone survey data. Study inclusion criteria. 18 years old, Harris County resident, and had a need for medical care within the past year. Post weighing was done to correct for non-response bias. Results. Survey decision makers were predominately minorities (60%), Female (70%), and insured (71%). Ninety-two percent of participants sought care when needed, however, of this population 39% delayed medical care. Fifty-six percent of participants who delayed medical care sought care in the Doctor's office. For those who replied "Yes" to considering health insurance and finances in deciding to avoid medical care, 61% stated that they were confused about their insurance coverage as the explanation why. Fifty-five percent of Respondents indicated that delaying medical care was due to not knowing whether medical care was necessary. Conclusion. Additional research needs to be conducted to examine the relationship between onset of medical symptoms and final medical diagnosis to identify whether survey participants who delayed or avoided medical care actions were appropriate responses to their initial medical symptoms and final diagnosis. ^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^

Predictors of diagnostic delay among tuberculosis patients in a U.S.-Mexico border community

Delays in diagnosis of pulmonary tuberculosis have detrimental effects on the health of the ailing patient as well as the people around him or her. These effects are magnified in highly-travelled parts of the world. Identifying factors predictive of diagnostic delay is challenging, as these vary widely by culture and geography. Predictors of delay for tuberculosis patients living in the Northeastern Mexican city of Matamoros, a binationally-transited area, have yet to be described. Using secondary analysis of a retrospective survey, this study sought to identify predictors of diagnostic delay in a sample of culture-positive tuberculosis patients in Matamoros. Sociodemographic, behavioral, and health-related factors were measured and compared. Using bivariate and step-wise regression analyses at an alpha level of 0.05, the author found the following to be statically significant predictors for this sample (R 2=0.171): prior treatment of diabetes, recurrence of tuberculosis, and having ever used cocaine. A question assessing knowledge of immunocompromised subgroups was also identified as a predictor, although its implications are unclear. Notably, the instrument did not distinguish between patient and health system delay. In summary, more research should be conducted in the Matamoros area in order to fully understand the dynamics of delayed diagnosis and its application to public health practice.^