| Literature DB >> 33250551 |
Kelly F Austin1, Mark D Noble2, Virginia Kuulei Berndt3.
Abstract
HIV/AIDS represents the leading cause of death among women of reproductive age globally, and gender inequalities in the burden of HIV/AIDS are most pronounced in poorer countries. Drawing on ideas from feminist political ecology, we explore linkages between suffering from drought, food insecurity, and women's vulnerability to HIV. Using data from 91 less-developed countries, we construct a structural equation model to analyze the direct and indirect influence of these factors, alongside other socio-economic indicators, on the percentage of the adult population living with HIV that are women. We find that droughts are significant in shaping gender inequalities in the HIV burden indirectly through increased food insecurity. We draw on prior research to argue that due to gendered inequalities, food insecurity increases women's vulnerability to HIV by intensifying biological susceptibilities to the disease, reducing access to social and health resources, and motivating women to engage in risky sexual behaviors, such as transactional sex. Overall, our findings demonstrate that droughts serve as an important underlying factor in promoting HIV transmission among vulnerable women in poor countries, and that food insecurity is a key mechanism in driving this relationship. © Springer Nature B.V. 2020.Entities:
Keywords: Drought; Environment; Food insecurity; Gender; HIV/AIDS
Year: 2020 PMID: 33250551 PMCID: PMC7685297 DOI: 10.1007/s11205-020-02562-x
Source DB: PubMed Journal: Soc Indic Res ISSN: 0303-8300
Fig. 1SEM predicting the percent of the population living with HIV who are women, saturated model
Fig. 2SEM predicting the percent of the population living with HIV who are women. Notes standardized coefficients flagged ***p < 0.001, **p < 0.01, *p < 0.05, (two-tailed tests)
Countries included in the analysis (N = 91)
| Afghanistan | Eswatini | Nepal |
| Algeria | Ethiopia | Niger |
| Angola | Gambia, The | Nigeria |
| Armenia | Georgia | Pakistan |
| Bangladesh | Ghana | Papua New Guinea |
| Belize | Guatemala | Paraguay |
| Benin | Guinea | Peru |
| Bhutan | Guinea-Bissau | Philippines |
| Bolivia | Guyana | Rwanda |
| Bosnia and Herzegovina | Haiti | Senegal |
| Botswana | Honduras | Serbia |
| Burkina Faso | Indonesia | Sierra Leone |
| Burundi | Jamaica | Somalia |
| Cabo Verde | Jordan | South Africa |
| Cambodia | Kenya | South Sudan |
| Cameroon | Kyrgyz Republic | Sri Lanka |
| Central African Republic | Lao PDR | Sudan |
| Chad | Lesotho | Syrian Arab Republic |
| Colombia | Liberia | Tajikistan |
| Comoros | Madagascar | Tanzania |
| Congo, Dem. Rep | Malawi | Togo |
| Congo, Rep | Malaysia | Tunisia |
| Costa Rica | Mali | Uganda |
| Cote d'Ivoire | Mauritania | Ukraine |
| Cuba | Mauritius | Uzbekistan |
| Djibouti | Moldova | Vietnam |
| Dominican Republic | Mongolia | Yemen, Rep |
| Ecuador | Montenegro | Zambia |
| Egypt, Arab Rep | Mozambique | Zimbabwe |
| El Salvador | Myanmar | |
| Eritrea | Namibia |
Correlation matrix and univariate statistics
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (1) | Women's percent of HIV | 1.00 | |||||||||||
| (2) | Droughts | 0.32 | 1.00 | ||||||||||
| (3) | Food insecurity | 0.78 | 0.46 | 1.00 | |||||||||
| (4) | Water | − 0.61 | − 0.27 | − 0.60 | 1.00 | ||||||||
| (5) | Doctors | − 0.59 | − 0.22 | − 0.47 | 0.58 | 1.00 | |||||||
| (6) | Secondary schooling | − 0.52 | − 0.20 | − 0.36 | 0.79 | 0.60 | 1.00 | ||||||
| (7) | Female secondary schooling | − 0.67 | − 0.38 | − 0.78 | 0.61 | 0.47 | 0.68 | 1.00 | |||||
| (8) | fertility rate | 0.68 | 0.28 | 0.72 | − 0.62 | − 0.41 | − 0.59 | − 0.85 | 1.00 | ||||
| (9) | Births attended | − 0.50 | − 0.21 | − 0.51 | 0.53 | 0.42 | 0.58 | 0.72 | − 0.66 | 1.00 | |||
| (10) | Contraceptives | − 0.55 | 0.15 | − 0.36 | 0.66 | 0.51 | 0.81 | 0.71 | − 0.69 | 0.59 | 1.00 | ||
| (11) | GDP per capita | − 0.46 | − 0.18 | − 0.47 | 0.70 | 0.58 | 0.76 | 0.58 | − 0.49 | 0.45 | 0.59 | 1.00 | |
| (12) | Percent Muslim | .01 | − 0.06 | 0.02 | − 0.11 | − 0.09 | − 0.25 | − 0.32 | 0.23 | − 0.21 | − 0.70 | − 0.21 | 1.00 |
| Mean | 45.5 | 1.4 | 45.0 | 73.6 | .9 | 64.4 | 64.2 | 3.4 | 78.8 | 42.9 | 4918.1 | 31.5 | |
| S.D | 16.8 | 2.7 | 23.1 | 20.4 | 1.3 | 26.3 | 29.0 | 1.4 | 21.1 | 21.4 | 3561.6 | 38.8 |
Regression estimates for SEM equations predicting the percent of the population living with HIV who are women
| Regression path | B | SE(B) | ||
|---|---|---|---|---|
| 1 | Health infrastructure → Water | 0.874*** | 0.035 | 1.000 |
| 2 | Health infrastructure → Doctors | 0.701*** | 0.066 | .053 |
| 3 | Health infrastructure → Secondary schooling | 0.912*** | 0.033 | 1.348 |
| 4 | Women’s socio-health status → Births attended | 0.746*** | 0.058 | 1.000 |
| 5 | Women’s socio-health status → Female Sec schooling | 0.968*** | 0.022 | 1.757 |
| 6 | Women’s socio-health status → Fertility rate | − 0.883*** | 0.031 | − 0.077 |
| 7 | Health infrastructure | 0.327** | 0.111 | 0.288 |
| 8 | GDP per capita | 0.814*** | 0.045 | 0.397 |
| 9 | Women’s socio-health status → Contraceptive use | 0.333* | 0.157 | 0.459 |
| 10 | Health infrastructure → Contraceptive use | 0.310* | 0.169 | 0.377 |
| 11 | Health infrastructure | − 0.300** | 0.103 | − 0.045 |
| 12 | Food insecurity → Women’s socio-health status | − 0.594*** | 0.095 | − 0.407 |
| 13 | Percent Muslim → Contraceptive Use | − 0.486*** | 0.097 | − 0.272 |
| 14 | Droughts → Food insecurity | 0.280** | 0.097 | 2.430 |
| 15 | Food insecurity | 0.527*** | 0.101 | 0.382 |
| 16 | Percent Muslim → Women's percent of HIV | − 0.326* | 0.129 | − 0.140 |
| 17 | Contraceptives → Women's percent of HIV | − 0.523** | 0.153 | − 0.401 |
| 18 | Health infrastructure → Food insecurity | − 0.578*** | 0.093 | − 0.743 |
| 19 | Percent Muslim → Women’s socio-health status | − 0.190** | 0.072 | − 0.077 |
Standardized coefficients flagged ***p < 0.001, **p < 0.01, *p < 0.05, (two-tailed tests)
Direct, indirect, and total effects of the predictors of the percent of the population living with HIV who are women
| Predictor | Women’s percent of HIV | ||
|---|---|---|---|
| Direct | Indirect | Total | |
| Droughts | – | 0.176** | 0.176** |
| – | (0.063) | (0.063) | |
| – | [1.110] | [1.110] | |
| Food insecurity | 0.527*** | 0.103+ | 0.630*** |
| (0.101) | (.056) | (.090) | |
| [.382] | [.075] | [.457] | |
| GDP per capita | – | − 0.517*** | − 0.517*** |
| – | (0.064) | (0.064) | |
| – | [− 0.235] | [− 0.235] | |
| Percent Muslim | − 0.326* | 0.287** | − 0.039 |
| (0.129) | (0.105) | (0.075) | |
| [− 0.140] | [0.124] | [− 0.017] | |
| Health infrastructure | – | − 0.636*** | − 0.636*** |
| – | (0.066) | (0.066) | |
| – | [− 0.593] | [− 0.593] | |
| Women’s socio-health status | – | − 0.174* | − 0.174* |
| – | (0.091) | (0.091) | |
| – | [− 0.184] | [− 0.184] | |
| Contraceptive use | − 0.523** | – | − 0.523** |
| (0.153) | – | (0.153) | |
| [− 0.401] | – | [− 0.401] | |
Standardized coefficients flagged ***p < 0.001, **p < 0.01, *p < 0.05, (two-tailed tests); standard errors in parentheses; unstandardized coefficients in brackets