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HIV Outcome Relationships Among Gend...
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McGlonn, Kassandra L.
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HIV Outcome Relationships Among Gender Inequality, Poverty, and Population Growth in Africa.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
HIV Outcome Relationships Among Gender Inequality, Poverty, and Population Growth in Africa./
作者:
McGlonn, Kassandra L.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:
239 p.
附註:
Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:
Dissertations Abstracts International85-07B.
標題:
Epidemiology. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30696123
ISBN:
9798381404838
HIV Outcome Relationships Among Gender Inequality, Poverty, and Population Growth in Africa.
McGlonn, Kassandra L.
HIV Outcome Relationships Among Gender Inequality, Poverty, and Population Growth in Africa.
- Ann Arbor : ProQuest Dissertations & Theses, 2023 - 239 p.
Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Thesis (D.P.H.)--Florida Agricultural and Mechanical University, 2023.
Countries with the highest HIV incidence, HIV prevalence, and AIDS mortality are located in Africa, making it important to identify which factors are significantly contributing to these outcomes. This study focused on the relationship HIV incidence, HIV prevalence, and AIDS mortality have with gender inequality, poverty, and population growth. An ecological study was performed using country-level aggregate data. SAS OnDemand {phono}{mlrhring} was used for all tests ran with a = .05. Each dependent variable (HIV incidence, HIV prevalence, AIDS mortality) was tested for correlation with each primary independent variable (poverty, population growth, gender inequality) individually and jointly with secondary independent variables (urbanization, ART coverage, tuberculosis incidence, malaria incidence) included as controls. The most conservative regression models were used. A regional analysis was performed for the five regions in Africa. Gender inequality was the sole primary independent variable that had no relationship with HIV outcomes in Africa. Poverty and population growth influence HIV incidence and HIV prevalence in paradoxical ways that require more investigation. Poverty had a consistently negative relationship with HIV incidence (parameter estimates: simple linear regression = -3.0164; MLR = -2.5418). Population growth also had a negative relationship with HIV incidence{A0}(parameter estimate: simple linear regression = -0.6222; MLR = -0.6342). Poverty had a negative relationship with HIV prevalence, having a relatively large influence (parameter estimates: simple linear regression = -12.4550; MLR = -11.9021). Population growth's negative influence on HIV prevalence was relatively moderate (parameter estimate: simple linear regression = -2.1422; MLR = -2.7456). No relationships were found among AIDS mortality and poverty or population growth variables. No HIV outcomes were found to have statistically significant relationships with gender inequality, poverty, or population growth at regional levels in Africa. It is recommended future population level studies on HIV outcomes for Africa are performed using aggregate data stratified by gender and age. Intra-regional and individual level studies that include tribal and local data are also recommended. Furthermore, many efforts can benefit from innovative approaches in targeting marginalized and high-risk groups. Recommended approaches include the incorporation of telemedicine, uniform combined HIV and TB testing, and biomedical products in Africa.
ISBN: 9798381404838Subjects--Topical Terms:
568544
Epidemiology.
Subjects--Index Terms:
Gender inequality
HIV Outcome Relationships Among Gender Inequality, Poverty, and Population Growth in Africa.
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Countries with the highest HIV incidence, HIV prevalence, and AIDS mortality are located in Africa, making it important to identify which factors are significantly contributing to these outcomes. This study focused on the relationship HIV incidence, HIV prevalence, and AIDS mortality have with gender inequality, poverty, and population growth. An ecological study was performed using country-level aggregate data. SAS OnDemand {phono}{mlrhring} was used for all tests ran with a = .05. Each dependent variable (HIV incidence, HIV prevalence, AIDS mortality) was tested for correlation with each primary independent variable (poverty, population growth, gender inequality) individually and jointly with secondary independent variables (urbanization, ART coverage, tuberculosis incidence, malaria incidence) included as controls. The most conservative regression models were used. A regional analysis was performed for the five regions in Africa. Gender inequality was the sole primary independent variable that had no relationship with HIV outcomes in Africa. Poverty and population growth influence HIV incidence and HIV prevalence in paradoxical ways that require more investigation. Poverty had a consistently negative relationship with HIV incidence (parameter estimates: simple linear regression = -3.0164; MLR = -2.5418). Population growth also had a negative relationship with HIV incidence{A0}(parameter estimate: simple linear regression = -0.6222; MLR = -0.6342). Poverty had a negative relationship with HIV prevalence, having a relatively large influence (parameter estimates: simple linear regression = -12.4550; MLR = -11.9021). Population growth's negative influence on HIV prevalence was relatively moderate (parameter estimate: simple linear regression = -2.1422; MLR = -2.7456). No relationships were found among AIDS mortality and poverty or population growth variables. No HIV outcomes were found to have statistically significant relationships with gender inequality, poverty, or population growth at regional levels in Africa. It is recommended future population level studies on HIV outcomes for Africa are performed using aggregate data stratified by gender and age. Intra-regional and individual level studies that include tribal and local data are also recommended. Furthermore, many efforts can benefit from innovative approaches in targeting marginalized and high-risk groups. Recommended approaches include the incorporation of telemedicine, uniform combined HIV and TB testing, and biomedical products in Africa.
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