Author: lf5995a

My research question for my large-n research sketch asks: what explains the gendered disproportionality of HIV infections in sub-Saharan Africa. Because this question is so broad, I decided to meet with Dr. Robinson, my mentor, to dissect what I should be looking for. I had to narrow my scope greatly in order to ensure that I would be able to find adequate data for this topic. We agreed that I should likely focus on gender inequity in order to find sources that would positively contribute to my research.

That being said, I found a plethora of data to help me begin my large-n research design. The data I will be discussing is the Global AIDS Monitoring (GAM) from 2017, last updated on September 24, 2018.1 GAM essentially tracks global progress on ending the AIDS epidemic, regarding the United Nations Political Declaration on HIV and AIDS. This database contains country-reported GAM data.2 It includes 4666 geographical areas and 107 variables. The variables include: UNAIDS geographical region, Estimated HIV in new TB cases, Hepatitis B testing, knowledge about HIV prevention in young people, and condom use at last high-risk sex, just to name a few.3 It also separates the data collected by looking at female, male, and both sexes, as well as their age groups (i.e. 15-19, 20-24, 25-49).4 This is good for me, considering my research is particularly focused on young women. I am able to look at very specific data for the key group I am looking to investigate.

This data was collected from countries all over the world. Therefore, I will need to separate data collected regarding sub-Saharan Africa from the rest because that is what I am interested in most. My dependent variable is the number of HIV infections in young women (15-25). This will be broken down by country, where some countries may not exhibit a severely gendered gap in HIV infections. For the purpose of brevity and functionality, I will likely not include data regarding TB, HBV, HCV, or other infectious diseases, as this data set is about half related to those illnesses. 5 However, the remainder of the data is applicable to my research, so I will likely be using it in my research.

References

1 UNAIDS, Global AIDS Monitoring (GAM), distributed by AIDS info, http://aidsinfoonline.org/gam/libraries/aspx/home.aspx.

2 Ibid.

3 Ibid.

4 Ibid.

5 Ibid.

Bibliography

AIDS info. 2017. Global AIDS Monitoring (GAM) (2017 Release). Retrieved from

http://aidsinfoonline.org/gam/libraries/aspx/home.aspx.

I am proposing to research the link between economic disparities in gender and Human Immunodeficiency Virus (HIV) infection rates in women because I want to find out why women are disproportionately more likely to be infected with HIV than any other population in sub Saharan Africa (SSA), in order to help my reader, understand how gender inequity in SSA may correlate to poor health for women.

In recent years, several countries in SSA have experienced an increase in HIV infection rates [1]. SSA accounts for 2% of the global population, but of that 2% 1/3 are infected with HIV. In other words, SSA is one of the few regions of the world where there is an increase in new infections of HIV. Unlike most other communities around the globe, HIV is a generalized epidemic in SSA. This mean as that the disease impacts more than the key populations (men who have sex with men MSM, injection drug users IDU, sex workers SW). However, due to concurrent relationships and complex sexual networks, women in SSA are significantly more likely to contract HIV in their lifetime compared to others in the region[2]. In SSA women are subordinate to men. This means that women have little negotiating power in sexual relationships, leaving them more at risk for HIV.

Economic disparities in gender facilitate the spread of HIV. This is largely due to the fact that risk for HIV is greatly increased by poverty. When a population doesn’t have money for food, clothing, etc. then they most likely will not prioritize sexual health. This may lead many women to become sex workers (SW) or behave in risky sexual behavior in exchange for gifts. Similarly, economic strife can increase intimate partner violence (IPV), which also puts women more at risk for contracting HIV[3]. Therefore, general economic health can influence a country’s HIV risk. However, I’m most interested in investigated how lack of economic opportunities for women increases their risk for contracting HIV. By economic opportunities I mean, access to education, healthcare, and business ventures. For example, because women are less likely to be educated in SSA, they’re also less likely to be formally employed, and therefore more likely to engage in risky behavior (i.e. sex work).

In recent years, Kenya’s economy has been struggling due to ongoing drought, weak credit growth, and security concerns[4]. This macro-level struggle leads to micro-level consequences. Meaning, fewer are employed and major industries such as agriculture are struggling, which lessens economic prosperity per capita. Similarly, economic struggle in the agricultural sector leads to food insecurity, which in turn leads to increased risk for HIV. A lack of access to food leaves women worrying about survival, rather than their sexual health. Economic strife leads to lack of economic opportunities which in turn leads to IPV and alternatives sex work. While there is a plethora of sources addressing HIV and IPV, there is a lack of literature addressing lack of economic opportunities, as I defined them, and a correlating increased risk for contracting HIV. Gender inequality in SSA is a serious socio-political issue today, and should be discussed in future research. If this issue is not researched further gender inequity will only increase, leading to a worsening epidemic in the region. Similarly, if economic disparities in gender is not addressed, then the economic prosperity of the region will worsen, possibly leading to political and social conflict as well. Researchers should be interested in what possible social intervention can be instituted in order to combat increasing infection rates in young women? What explains the current drought and struggling agriculture sector?

[1] Daniel T Halperin and Helen Epstein, “The Role of Multiple Concurrent Partnerships and Lack of Male Circumcision : Implications for AIDS Prevention,” The Southern African Journal of HIV Medicine, no. MArCH (2007): 19–25; Catherine MacPhail and Catherine Campbell, “‘I Think Condoms Are Good but, Aai, I Hate Those Things’:Condom Use among Adolescents and Young People in a Southern African Township,” Social Science & Medicine 52, no. 2001 (2001): 1613–1617.

[2] Kaymarlin Govender et al., “HIV Prevention in Adolescents and Young People in Eastern and Southern Africa Region: A Review of Key Challenges Impeding Actions for an Effectiv Response,” The Open AIDS Journal, no. 12 (2018): 53–67.

[3] Suzanne Maman et al., Leveraging Strong Social Ties among Young Men in Dar Es Salaam: A Pilot Intervention of Microfinance and Peer Leadership for HIV and Gender-Based Violence Prevention, n.d.

[4] The World Bank, The World Bank in Kenya, 2018; The National Treasury, Quarterly Economic and Budgetary Review, 2018. Continue reading

I will be discussing two articles that investigate HIV prevalence in adolescents. The pieces are titled, HIV Prevention in Adolescents and Young People in the Eastern and Southern African Region: A Review of Key Challenges Impeding Actions for an Effective Response and HIV, Gender, Race, Sexual Orientation, and Sex Work: A Qualitative Study of Intersectional Stigma Experienced by HIV-Positive Women in Ontario, Canada. The first piece was published in “The Open AIDS Journal” and the second in “PLoS Medicine”. Both articles establish and reiterate that while HIV rates of infection of decreasing internationally, HIV infection rates in adolescents are increasing. However, the authors choice of methodology varies.

I’ll began by discussing the first article. This piece specifically looks at HIV in the Eastern and Southern African Region (ESAR). The author begins by talking about how much HIV has been declining and how far the world has come since HIV was discovered. However, then she contrasts that point by saying that “37% of all new HIV infections in 2017” were attributed to young people (Govender et al. 2018). Then, the author lists her main points of emphasis: epidemiological patterns of HIV, young populations that require targeted HIV preventions, challenges associated with HIV prevention programming and research, and mitigating vulnerability and sustaining the HIV prevention response(Govender et al. 2018). She investigates prevalence of HIV in men and women through graphs and analysis. And lastly, she calls for more effective research to be done in the region in order to meet the Millennium Development Goals framework (reduce number of HIV infections by 75% by 2030) (Govender et al. 2018).

In contrast, the second piece specifically looks at HIV in young adolescent girls in Ontario, Canada. This piece emphasizes how intersectionality plays a major role in HIV infection. The author discussed how minorities and poorer groups in Ontario are disproportionately impacted by the disease (Logie et al. 2011). Racism and stigma also play major roles in the HIV prevalence among these communities. The author furthers their point by creating a diagram to represent the multifaceted influences associated with HIV infection amongst minority women in Ontario. Similar to the previous piece, the author mentions and emphasizes that women are significantly more likely to get HIV in patriarchal societies because they have little negotiating power (negotiating condom use) (Govender et al. 2018; Logie et al. 2011).

The two pieces vary in their approach to the topic. The first investigates how research should be improved in order to learn more about HIV in this population, while the second discusses the political and social implications of HIV in these populations through group participants. I believe both pieces will help me in my own research, as they complement each other well and reiterate my main point as well: that young girls are disproportionately impacted by HIV because of existing patriarchal systems.

Bibliography:

Govender, Kaymarlin et al. 2018. “HIV Prevention in Adolescents and Young People in Eastern and Southern Africa Region: A Review of Key Challenges Impeding Actions for an Effectiv Response.” The Open AIDS Journal (12): 53–67.

Logie, Carmen H., LLana James, Wangari Tharao, and Mona R. Loutfy. 2011. “HIV, Gender, Race, Sexual Orientation, and Sex Work: A Qualitative Study of Intersectional Stigma Experienced by HIV-Positive Women in Ontario, Canada.” PLoS Medicine 8(11).

Ontology is the study of being. More specifically, it examines and dissects the reality we live. In contrast, methodology provides methods and procedures to acquiring knowledge about reality. In Abbott’s book Methods of Discovery: Heuristics for the Social Sciences, he discusses the concepts thoroughly.

As a researcher, there is a clear fundamental debate over whether or not I can be an objective observer of the social world. I believe that is inevitably impossible to be objective when observing the social world because I am a part and co-producer of the social world and therefore that reality. This is because the current reality that you and I existing in is a product of every living thing on earth. Therefore, how could you truly observe objectively if you are thinking, breathing, moving, acting, talking, walking, etc. This is essentially the entire premise behind Constructionism, as discussed in Chapter two of Abbott’s Methods of Discovery: Heuristics for the Social Sciences. According to Abbott, Constructionist theory is grounded in the idea that the social process is made up of people who construct their identities and selves in the process of interaction with one another (Abbott, 2004). Therefore, if the entire “social process” is founded on the principle that we are creating the social process itself, then researchers will never be able to be truly objective. Because in observing, you are contributing to reality, or the social world, and are therefore not objective.

I believe researchers can investigate and make claims about any phenomena or social norm that they have experienced in their own lives. Meaning, researchers can use past experiences as examples of social norms or phenomena. Obviously researchers can make valid claims about things they’ve observed themselves. But also, I think researchers can create valid claims using observations from others. Through surveys and discussions researchers can discover new social phenomena that they may never have experienced first-hand. By utilizing a plethora of sources, they can determine if it was a pattern and therefore important enough to further research.

Andrew Abbott, Methods of Discovery: Heuristics for the Social Sciences, New York: W.W.          Norton & Company, 2004.

When I began to ponder a topic to research this year, I knew I’d want to investigate something that had to do with HIV. Last year, I studied HIV in sub-Saharan Africa in my First Year Seminar, and then worked on my professor’s research paper second semester.  I was intrigued and fascinated by HIV research. So for Olson’s, I decided to continue with this theme, but in a different way. I wondered if there was a correlation between global-warming-caused natural disasters and increased HIV prevalence rates.  I knew that disease lingers in places where there are high rates of poverty. I also knew that natural disasters are followed by severe poverty/destruction. Therefore, I was intrigued by the likelihood of climate change impacting HIV prevalence rates in areas following natural disaster.

Throughout the summer, I searched for articles on this subject matter, but found nothing. Therefore, I decided I needed a different approach. I could investigate geographical locations most impacted by climate change, and look to see if their HIV prevalence rates have increased every 2 years since 2005.

The one puzzle that I’m dealing with right now, is how to collect the data and what type of data should I collect. I think I’m also struggling with which locations would be most impacted by climate change. I believe my meeting with my mentor will help me work through this. I’d like to possibly focus on one or two types of disasters (drought, hurricanes, etc.) and possibly compare them. For example, Should I consider flooding? Should I solely investigate HIV prevalence rates, or should I also consider other infectious diseases like tuberculosis? These are all questions I will be considering in the future.