HIV incidence estimates and HIV/AIDS concurrent diagnosis in Houston/Harris County

The purpose of this dissertation was to estimate HIV incidence among the individuals who had HIV tests performed at the Houston Department of Health and Human Services (HDHHS) public health laboratory, and to examine the prevalence of HIV and AIDS concurrent diagnoses among HIV cases reported between 2000 and 2007 in Houston/Harris County. ^ The first study in this dissertation estimated the cumulative HIV incidence among the individuals testing at Houston public health laboratory using Serologic Testing Algorithms for Recent HIV Seroconversion (STARHS) during the two year study period (June 1, 2005 to May 31, 2007). The HIV incidence was estimated using two independently developed statistical imputation methods, one developed by the Centers for Disease Control and Prevention (CDC), and the other developed by HDHHS. Among the 54,394 persons who tested for HIV during the study period, 942 tested HIV positive (positivity rate=1.7%). Of these HIV positives, 448 (48%) were newly reported to the Houston HIV/AIDS Reporting System (HARS) and 417 of these 448 blood specimens (93%) were available for STARHS testing. The STARHS results showed 139 (33%) out of the 417 specimens were newly infected with HIV. Using both the CDC and HDHHS methods, the estimated cumulative HIV incidences over the two-year study period were similar: 862 per 100,000 persons (95% CI: 655-1,070) by CDC method, and 925 per 100,000 persons (95% CI: 908-943) by HDHHS method. Consistent with the national finding, this study found African Americans, and men who have sex with men (MSM) accounted for most of the new HIV infections among the individuals testing at Houston public health laboratory. Using CDC statistical method, this study also found the highest cumulative HIV incidence (2,176 per 100,000 persons [95%CI: 1,536-2,798]) was among those who tested in the HIV counseling and testing sites, compared to the sexually transmitted disease clinics (1,242 per 100,000 persons [95%CI: 871-1,608]) and city health clinics (215 per 100,000 persons [95%CI: 80-353]. This finding suggested the HIV counseling and testing sites in Houston were successful in reaching high risk populations and testing them early for HIV. In addition, older age groups had higher cumulative HIV incidence, but accounted for smaller proportions of new HIV infections. The incidence in the 30-39 age group (994 per 100,000 persons [95%CI: 625-1,363]) was 1.5 times the incidence in 13-29 age group (645 per 100,000 persons [95%CI: 447-840]); the incidences in 40-49 age group (1,371 per 100,000 persons [95%CI: 765-1,977]) and 50 or above age groups (1,369 per 100,000 persons [95%CI: 318-2,415]) were 2.1 times compared to the youngest 13-29 age group. The increased HIV incidence in older age groups suggested that persons 40 or above were still at risk to contract HIV infections. HIV prevention programs should encourage more people who are age 40 and above to test for HIV. ^ The second study investigated concurrent diagnoses of HIV and AIDS in Houston. Concurrent HIV/AIDS diagnosis is defined as AIDS diagnosis within three months of HIV diagnosis. This study found about one-third of the HIV cases were diagnosed with HIV and AIDS concurrently (within three months) in Houston/Harris County. Using multivariable logistic regression analysis, this study found being male, Hispanic, older, and diagnosed in the private sector of care were positively associated with concurrent HIV and AIDS diagnoses. By contrast, men who had sex with men and also used injection drugs (MSM/IDU) were 0.64 times (95% CI: 0.44-0.93) less likely to have concurrent HIV and AIDS diagnoses. A sensitivity analysis comparing difference durations of elapsed time for concurrent HIV and AIDS diagnosis definitions (1-month, 3-month, and 12-month cut-offs) affected the effect size of the odds ratios, but not the direction. ^ The results of these two studies, one describing characteristics of the individuals who were newly infected with HIV, and the other study describing persons who were diagnosed with HIV and AIDS concurrently, can be used as a reference for HIV prevention program planning in Houston/Harris County. ^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^