Sex-Based Differences in HIV Infection - Paper Example

Null Hypothesis: H01: There is no statistically significant association between gender and HIV infectionsAlternate Hypothesis: H11:There is a statistically significant association between gender and HIV infections

H1 2: There is a statistically significant association between race/ethnicity and HIV infections

Statistical Test: Cohort profiling, Chi Square tests, logistic regression, Poisson regression and Cox regression.

Study sample

The study population will consist of 280 patients diagnosed with HIV at Zeta Medical Center between 2010 and 2015. The inclusion of patients in the cohort will be done in two phases. The first phase aims at undertaking the events of interest that might have taken place before enrollment of the participants, and in the second phase, will entail observing gender and racial disparities from 2009 to December 2010 among those enrolled into the study. During the first phase will be a retrospective cohort study and will include observation of the events of interest had occurred before the patients were enrolled to including their initial behaviors for instance drug use. The second phase will be mainly prospective that will entail observing of events as they occurred including follow-up their personal behaviors.

The eligible participants will be first identified from the population using random and stratified sampling. Stratified sampling will be useful in grouping individuals in terms of gender and race, while simple random sampling will be useful in picking the participants from the establish strata. All the participants have to be above the age of 18 to be enrolled (Adimora et al., 2009).

Data Collection Methods

The data for the study will be obtained from the collection of observed data from the field by the researcher as the follow-up progresses. The other data will be obtained from the clinical records, by trained information abstractors and converted into standardized forms. The clinical records have to be reviewed at the intervals that would not exceed six months to allow investigation of the events that are recorded during the routine clinical follow-up within each period including clinical histories of the patients. To ensure that the information is consistent, as well as its completeness, it has to be checked at cohort coordination levels, including cross-checking, against the existing information that exists on the clinical records (Addo & Altfeld, 2014).

A standardized self-report questionnaire will also be used to collect information from the participants, which will be administered at the beginning of every follow-up session that is once every 12 months. Some of the major outcomes of interest in the study would include personal behaviors among races and gender, demographic characteristics of each group, health seeking behavior, socio-cultural and religious factors, attitudes towards HIV infection and their knowledge regarding HIV as a disease.

Clinical and epidemiological data will be collected during the entire study period including the socio-demographic data, category of HIV transmission among different races as well as gender, the alcohol and tobacco use, use of illicit drugs, and the family history of the HIV.

Statistical Analyses to be Conducted

According to Griesbeck, Scully and Altfeld (2016), Cohort profiling is a crucial method in cohort studies because it examines the characteristics of the participants including their socio-demographics, the mortality, quality of life of the participants, the number of patients that are lost to follow up, the retention rates, the risks of morbidity and mortality. Profiling will also be useful in determining the clinical manifestation of the disease based on race and gender of the patient in the facility (Xia et al., 2017). It will also help determine the health seeking behaviors and affordability of healthcare across different races as well as gender, which is either HIV-related or those that are not. It also helps to establish general relationships that exist between HIV, gender and racial disparities.

Regression analysis is crucial in establishing the existing relationships between variables and in this HIV, gender and racial disparities. The models compare the different outcomes among participants regarding their racial and gender difference (Griesbeck, Scully & Altfeld, 2016). The influence of such factors would only be determined fully through the employment of the subgroup analyses, and or through adjustment of the regression models. Cox regression and Poisson regression can be used in this study. Cox regression would, for example, analyze a given parameter until an event occurs for instance, death. In our case, Cox regression might be used to examine the subsequent behaviors of a participant until a disease occurs. Logical regression models can also be used to establish whether the observed effects are statistically significant. Measures of confidence including Chi Square would help whether the sampling distribution of the data is chi-squared and that the null hypothesis is true (Griesbeck, Scully & Altfeld, 2016). In addition, the test will be useful in determining the existing frequencies among the groups and also evaluate the likelihood of null hypothesis being true given the presented observations from the field (Griesbeck, Scully & Altfeld, 2016).

Reference

Addo, M. M., & Altfeld, M. (2014). Sex-based differences in HIV type 1 pathogenesis. The Journal of Infectious Diseases, 209(Suppl 3), S86S92.

Adimora, Adaora A., Victor J. Schoenbach, and Michelle A. Floris-Moore (2009). Ending the epidemic of heterosexual hiv transmission among African Americans. American journal of preventive medicine 37(5), 468471.

Griesbeck, M., Scully, E., & Altfeld, M. (2016). Sex and gender differences in HIV-1 infection. Clinical Science, 130(16), 1435-1451.

Xia, Q., Braunstein, S. L., Wiewel, E. W., Hadler, J. L., & Torian, L. V. (2017). Persistent racial disparities in HIV infection in the USA: HIV prevalence matters. Journal of racial and ethnic health disparities, 4(1), 87-93.