For my small-n research approach I ask: What explains why women are more likely to contract HIV than other key populations in Tanzania? More specifically, I am exploring social or economic determinants of health that may potentially contribute to an individual’s likelihood for HIV infection. I chose to focus my research in Tanzania because there are currently 1.5 million people living there with HIV.[1] Most importantly, heterosexual sex accounts for the majority (80%) of all HIV infections in Tanzania, and women are particularly more impacted than men.[2] Therefore, I am most interested in determining why women in Tanzania are most at risk for HIV infection compared to women in other regions of the world. Unlike the Atzili reading, my case will not feature the “most likely case” approach.[3] Instead, my cases will be actual young women, who have been interviewed regarding their social and behavioral habits that possibly contribute to their HIV risk.
My dependent variable is new HIV infections. It is operationalized as a dummy variable (yes, she has HIV, or no, she does not have HIV). For this approach, I would likely have to use a new source that would provide me with detailed interviews with young women. This strategy has been used in almost every source that I’ve read on this topic.[4] For example, in an article by Mantsios, she recruited participants and conducted “27 in-depth interviews”.[5] Researchers asked about the participants age, marital status, how many children they had, education, and HIV status. They found that the majority of young women interviewed (80%) had no formal education past primary school and the majority (73%) were also infected with HIV.[6] In other words, they found a strong correlation to lack of education and increased risk for HIV. I believe that this strategy would be very well suited for my research question.
I could possibly continue on this path. However, the large-n work seems to fit my research question quite well. Whether or not I pursue this path would be dependent on my access to data or my ability to conduct interviews either domestically or internationally.
[1] AVERT, “HIV and AIDS in Tanzania.”
[2] Ibid.
[3] Boaz Atzili, “When Good Fences Make Bad Neighbors: Fixed Borders, State Weakness, and International Conflict,” International Security 31, no. 3 (2006): 139–173.
[4] Andrea Mantsios et al., “‘That’s How We Help Each Other’: Community Savings Groups, Economic Empowerment and HIV Risk among Female Sex Workers in Iringa, Tanzania,” PLoS ONE 13, no. 7 (2018): 1–16; Sarah Palazzolo et al., “Documentation Status as a Contextual Determinant of HIV Risk among Transgender Immigrant Latinas. In Press at LGBT Health.,” LGBT Health Epub ahead, no. 15 December 2015 (2016); Thespina J. Yamanis et al., “Social Venues That Protect against and Promote HIV Risk for Young Men in Dar Es Salaam, Tanzania,” Social Science and Medicine 71, no. 9 (2010): 1601–1609; 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 Suzanne,” HHS Public Access 13, no. 11 (2016): 1–2; Thespina Yamanis et al., “Legal Immigration Status Is Associated with Depressive Symptoms among Latina Transgender Women in Washington, DC,” International Journal of Environmental Research and Public Health 15, no. 6 (2018): 1246, http://www.mdpi.com/1660-4601/15/6/1246.
[5] Mantsios et al., “‘That’s How We Help Each Other’: Community Savings Groups, Economic Empowerment and HIV Risk among Female Sex Workers in Iringa, Tanzania.”
[6] Ibid.
