Research Paper Example on Significant Association Between Gender and HIV Infections

Null Hypothesis: H01: There is no statistically significant association between gender and HIV Infections

Alternate Hypothesis: H11: There is a statistically significant association between gender and HIV infections

Statistical Test: Chi-Square tests

Study sample

The study population will consist of 280 patients diagnosed with HIV at Zeta Medical Center between 2010 and 2015.

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, while simple random sampling will be useful in picking the participants from the establish strata. All the participants have to be 18-64 years of age to be enrolled (Adimora et al., 2009). Moreover, there are 147 males and 133 females.

Data Collection Methods

Hospital medical records will be used to determine whether or not HIV infection and self-reported gender from questionnaires will be completed by participants. 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.

Statistical Analyses to be Conducted

The descriptive statistics and chi-square analysis will be conducted to test for the relationship between gender and HIV infection. In addition, SPSS version 22 will be used for analysis. All tests of significance will be computed at a = 0.05.

Final Project: Results

Description of the steps taken to conduct statistical analysis

To get the descriptive statistics for the variables, data on gender and HIV infection were transferred into SPSS version 22. After this, the next step involved by clicking Analyze on the tab bar of the SPSS interface, then descriptive statistics where another interface of descriptive statistics popped up. In this interface, the mean, standard error, standard deviation, and skewness were clicked upon, and the code was pasted directly into a syntax window by clicking the mouse on Paste before running a procedure. Then save the syntax window into a syntax file before ending the SPSS session that resulted in the descriptive statistics in Table 1 below.

For the chi-square tests, the same step was involved by clicking Analyze on the tab bar of the SPSS interface, then descriptive statistics than to Crosstabs where another interface popped up. Here, both DV and IV were transferred using an arrow to Row(s) and Column(s) respectively. After that, statistics box was clicked where chi-square box was made active by clicking it. The next section that was also made active was the Cells where the row, column, and total percentages were also ticked. After these, the code was pasted directly into a syntax window by clicking the mouse on Paste before running a procedure. Then save the syntax window into a syntax file before ending the SPSS session. The results are shown in Table 2 and 3 respectively.

To draw the charts, the next step involved by clicking Analyze on the tab bar of the SPSS interface, then descriptive statistics then to frequencies where all the variables were transferred to the box labeled Variable(s). Again, the box with charts was activated where the histograms were picked, and the code was pasted directly into a syntax window by clicking the mouse on Paste before running a procedure. Then save the syntax window into a syntax file before ending the SPSS session. The results are shown in Figure 1 and 2.

Summary of the statistics

In Table 1, the mean of gender is 0.4750 with a standard error of 0.2990. The standard deviation for the gender is 0.50027. On the other hand, the mean of HIV infections is 0.4036 with a standard error of 0.2937. The standard deviation for the HIV infections is 0.49149. The same findings are shown in Figure 1 and 2 in the Appendix section.

Table 1: Descriptive Statistics

N Mean Std. Deviation Skewness

Statistic Statistic Std. Error Statistic Statistic Std. Error

Gender 280 .4750 .02990 .50027 .101 .146

HIV Infection 280 .4036 .02937 .49149 .395 .146

Valid N (listwise) 280 The results in Table 2 indicate that there were 147 (52.5%) males while females were 133 (47.5%). However, on HIV infections; the number of males affected accounted for 167 (59.6%) while that of females were 113 (40.4%).

Table 2: Gender * HIV Infection Crosstabulation

HIV Infection Total

Male Female Gender Male Count 147 0 147

% within Gender 100.0% 0.0% 100.0%

% within HIV Infection 88.0% 0.0% 52.5%

% of Total 52.5% 0.0% 52.5%

Female Count 20 113 133

% within Gender 15.0% 85.0% 100.0%

% within HIV Infection 12.0% 100.0% 47.5%

% of Total 7.1% 40.4% 47.5%

Total Count 167 113 280

% within Gender 59.6% 40.4% 100.0%

% within HIV Infection 100.0% 100.0% 100.0%

% of Total 59.6% 40.4% 100.0%

To test whether there an association between gender and HIV infection, a chi-square test was conducted. The findings in Table 3 indicate that there is an association between gender and HIV infection since the p-value is less than 0.05. Hence, we reject the null hypothesis and accept the alternate hypothesis that there is a statistically significant association between gender and HIV infections.

Table 3: Chi-Square Tests

Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)

Pearson Chi-Square 209.404a 1 .000 Continuity Correctionb 205.889 1 .000 Likelihood Ratio 265.069 1 .000 Fisher's Exact Test .000 .000

Linear-by-Linear Association 208.656 1 .000 N of Valid Cases 280 a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 53.68.

b. Computed only for a 2x2 table

Discussion

This study sought to determine whether there is an association between gender and HIV Infections among patients diagnosed with HIV at Zeta Medical Center between 2010 and 2015. The specific research question was to: Is there an association between gender and HIV infection? The corresponding hypotheses were: HO; There is no statistically significant association between gender and HIV Infections and H1, There is a statistically significant association between gender and HIV infections.

There are gender inequalities in the prevalence of HIV infections. In the current study, descriptive data analysis showed that the proportion of male HIV infections (59.6%) exceeded that of the females (40.4%). Previous studies indicate that women have higher rates of HIV infections than men. For instance, Hertog (2008) reported a higher prevalence of HIV infection among women. Specifically, Hertog (2008) found out that the rate of HIV infection in women is twice the rate of HIV infection in men. Gender disparities in HIV infections were predicted by the number of sexual partners an individual had. That is, increased number of sexual partners increased the risk of HIV infection. Specifically, men and women who reported one intimate partner in their lifetime showed no statistically significant gender differences in the odds of prevalent HIV infection. However, gender disparity in HIV infection increased with the increase in the number of sexual partnership. Among women and men who reported two lifetime sexual partners, odds of females HIV infection was 2.57 times that of their male counterparts Hertog (2008).

The findings of the current study are also inconsistent with the Griesbeck, Scully, and Altfeld (2016) findings. Griesbeck et al. (2016) explained that higher susceptibility of women to HIV infection compared to men may be linked to their lower viral loads during acute infection. Additionally, higher prevalence of HIV infection in women is as a result of their stronger antiviral responses and their higher immune activation than men. Similarly, Landman et al. (2008) found out that monogamous females have a higher risk of HIV infection (19%) than their male counterparts (4%). In the same study, increased number of sexual partners was reported to lead to increased risk of HIV infection. Increased HIV seropositivity among female participants was associated with lower education and age.

Findings of chi-square analysis also showed a statistically significant association between gender and HIV infections (X2 (1)> = 209.404, p = .000). This shows that there is an association between gender and HIV infection. The observed patterns in HIV infection by gender should be interpreted with caution, given that chi-square test cannot be used to establish causality. In the present study, men had higher rates of HIV infection than women. These findings are inconsistent with those reported in previous studies. For instance, Magadi (2011) reported higher HIV infection in women than men. Higher incidences of HIV infection in women was linked to lower awareness of modes of transmission of HIV as well as lower awareness of ways of avoiding HIV infection. The author further noted that gender disparity in HIV infection is manifested through early infection in women than men. It is also worth noting that gender disparity is higher among younger age groups (15 to 34 years). Additionally, higher rates of HIV infection of women than men are affected by marital status. More specifically, unmarried women have the highest vulnerability to HIV infection. The never-married women have also been reported to have more than two times (26%) the rate of HIV infection than he unmarried men (10%). Gender disparity in HIV infection can be attributed to socio-economic and demographic factors. Some of the factors that are statistically significantly correlated with the high prevalence of HIV infections in women include younger ages, primary level of education, polygamous and female-headed households.

In conclusion, the uncharacteristically high HIV infection rates in men than women, social change implications for the population that the study was sampled from. The observed higher rates of HIV infection in the sample calls for efforts to curb the trend. The society where the sample is picked from should be given more access to HIV-related healthcare facilities, such as HIV testing and counseling centers. Additionally, healthcare professionals should educate the public on the causes of HIV as well as on the ways to reduce the risk of infection.

The current study has some limitations. First, because the data was cross-sectional in nature and was analyzed using chi-square test, it is impossible to establish a causal relationship between the variables of interest. Therefore, reverse causality cannot be ruled out in this study. Future studies should examine if other socio-demographic variables mediate the association between gender and HIV infection. Some of these socio-demographic variables include ethnicity, level of education, socio-economic status, marital status, residence (urban or rural), and age. Additionally, future studies research should explore whether there is a relationship among other STIs, gender, and HIV infection. Lastly, there is a need to investigate the determinants of high prevalence of HIV infections among men than women observed in the current study.

Reference

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.

Hertog, S. (2008). Explaining gender differences in the risk of prevalent HIV infection: analyses of the Tanzania and Cote dIvoire AIS. New York: United Nations.

Landman, K. Z., Ostermann, J., Crump, J. A., Mgonja, A., Mayhood, M. K., Itemba, D....