888 resultados para predictive regression model


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This paper investigates the effects on open-seat races in the United States House of Representatives. This project focuses on the influence that the House leadership exerts on races. Generally, the leadership influences race through spending by party organizations and leadership visits. During each election cycle, national party organizations spend millions of dollars to get their candidates into office. I have developed a multiple regression model that measures different types of spending from the Democratic Congressional Campaign Committee, the National Republican Congressional Committee, and the Republican National Committee and the effects of these spending types on the election results. Also, the study examines the number of visits by each party’s leadership to each race. I introduced control variables that account for the year, the competitiveness of each race, and the individual candidate fundraising. In terms of statistical significance, the results were mixed showing one type of party spending to be highly influential in the outcome of the race. Competitiveness and individual candidate fundraising also achieved statistical significance. The study also includes a qualitative investigation of leadership visits and individual case studies in order to understand better the way in which the data interact in real campaigns.

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This cross-sectional study was undertaken to evaluate the impact in terms of HIV/STD knowledge and sexual behavior that the City of Houston HIV/STD prevention program in HISD high schools has had on students who have participated in it by comparing them with their peers who have not, based on self reports. The study further evaluated the program cost-effectiveness for averting future HIV infections by computing Cost-Utility Ratios based on reported sexual behavior. ^ Mixed results were obtained, indicating a statistically significant difference in knowledge with the intervention group having scored higher (p-value 0.001) but not for any of the behaviors assessed. The knowledge score outcome's overall p-value after adjusting for each stratifying variable (age, grade, gender and ethnicity) was statistically significant. The Odds Ratio of intervention group participants aged 15 years or more scoring 70% or higher was 1.86 times; that of intervention group female participants was 2.29 times; and that of intervention group Black/African American participants was 2.47 times relative to their comparison group counterparts. The knowledge score results remained statistically significant in the logistic regression model, which controlled for age, grade level, gender and ethnicity. The Odds Ratio in this case was 1.74. ^ Three scenarios based on the difference in the risk of HIV infection between the intervention and comparison group were used for computation of Cost-Utility Ratios: Base, worst and best-case scenario. The best-case scenario yielded cost-effective results for male participants and cost-saving results for female participants when using ethnicity-adjusted HIV prevalence. The scenario remained cost-effective for female participants when using the unadjusted HIV prevalence. ^ The challenge to the program is to devise approaches that can enhance benefits for male participants. If it is a threshold problem implying that male participants require more intensive programs for behavioral change, then programs should first be piloted among boys before being implemented across the board. If it is a reflection of gender differences, then we might have to go back to the drawing board and engage boys in focus group discussions that will help formulate more effective programs. Gender-blind approaches currently in vogue do not seem to be working. ^

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Racial/ethnic disparities in diabetes mellitus (DM) and hypertension (HTN) have been observed and explained by socioeconomic status (education level, income level, etc.), screening, early diagnosis, treatment, prognostic factors, and adherence to treatment regimens. To the author's knowledge, there are no studies addressing disparities in hypertension and diabetes mellitus utilizing Hispanics as the reference racial/ethnic group and adjusting for sociodemographics and prognostic factors. This present study examined racial/ethnic disparities in HTN and DM and assessed whether this disparity is explained by sociodemographics. To assess these associations, the study utilized a cross-sectional design and examined the distribution of the covariates for racial/ethnic group differences, using the Pearson Chi Square statistic. The study focused on Non-Hispanic Blacks since this ethnic group is associated with the worst health outcomes. Logistic regression was used to estimate the prevalence odds ratio (POR) and to adjust for the confounding effects of the covariates. Results indicated that except for insurance coverage, there were statistically significant differences between Non-Hispanic Blacks and Non-Hispanic Whites, as well as Hispanics with respect to study covariates. In the unadjusted logistic regression model, there was a statistically significant increased prevalence of hypertension among Non-Hispanic Blacks compared to Hispanics, POR 1.36, 95% CI 1.02-1.80. Low income was statistically significantly associated with increased prevalence of hypertension, POR 0.38, 95% CI 0.32-0.46. Insurance coverage, though not statistically significant, was associated with an increase in the prevalence of hypertension, p>0.05. Concerning DM, Non-Hispanic Blacks were more likely to be diabetic, POR 1.10, 95% CI 0.85-1.47. High income was statistically significantly associated with decreased prevalence of DM, POR 0.47, 95% CI 0.39-0.57. After adjustment for the relevant covariates, the racial disparities between Hispanics and Non-Hispanic Blacks in HTN was removed, adjusted prevalence odds (APOR) 1.21, 95% CI 0.88-1.67. In this sample, there was racial/ethnic disparity in hypertension but not in diabetes mellitus between Hispanics and Non-Hispanic Blacks, with disparities in hypertension associated with socioeconomic status (family income, education, marital status) and also by alcohol, physical activity and age. However, race, education and BMI as class variables were statistically significantly associated with hypertension and diabetes mellitus p<0.0001. ^