Individual, household and community factors associated with HIV test refusal in rural Malawi

Kranzer, Katharina; McGrath, Nuala; Saul, Jacky; Crampin, Amelia C.; Jahn, Andreas; Malema, Simon; Mulawa, Dominic; Fine, Paul E. M.; Żaba, Basia; Glynn, Judith R.

doi:10.1111/j.1365-3156.2008.02148.x

Cited by 31 publications

(39 citation statements)

References 40 publications

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“…In contradiction, we found that being in a male-dominated relationship was associated with higher rates of HIV testing for women. This conflicts with other research that suggests male control over women’s use of HIV testing services prevents women from getting tested (Baiden et al, 2005; Dahl et al, 2008; Kranzer et al, 2009; Perez et al, 2006). It is possible that traditional marital power structures facilitate testing, for example, if men take responsibility for the family’s health and well-being by escorting their wives for testing.…”

Section: Discussioncontrasting

confidence: 66%

“…Through the division of power, male dominance over women may also extend into the realm of health decision making. Indeed, a common reason provided by women who refuse testing in Malawi and elsewhere is the need to discuss the issue with their husband or because the husband refused testing himself (Baiden et al, 2005; Dahl, Mellhammar, Bajunirwe, & Bjorkman, 2008; Kranzer et al, 2009; Perez, Zvandaziva, Engelsmann, & Dabis, 2006). Qualitative research suggests that a double standard exists around HIV testing (i.e., cathexis) such that women need to request permission from their husbands, but men are free to make testing decisions on their own (Maman, Hogan, & Kilonza, 2001).…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Relationship Power Dynamics on HIV Testing in Rural Malawi

Conroy

2014

The Journal of Sex Research

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Using the theory of gender and power (TGP) and data from the Tsogolo la Thanzi (TLT) study, we examined how relationship power shapes young people’s decisions to test for HIV in rural Malawi (N=932), a high-HIV prevalence setting undergoing rapid expansions in testing services. We used generalized estimating equations (GEE) to examine associations among five constructs of relationship power (socioeconomic inequalities, relationship dominance, relationship violence, relationship unity, and mistrust), perceived risk, and receiving an HIV test over a 16-month period. The results indicate that young Malawians are testing for HIV at relatively high rates, repeatedly, and not just during pregnancy. Over the study period, 47.3% of respondents received at least one HIV test outside of TLT (range: 0–4). The GEE analysis revealed that men and women with higher levels of relationship unity were less likely to test for HIV. For men, being a victim of sexual coercion was an additional barrier to testing. Women’s testing decisions were more strongly influenced by perceptions of a partner’s risk for HIV than their own, whereas men relied more on self-assessments. The results highlight that testing decisions are deeply embedded within the relationship context, which should be considered in future HIV testing interventions.

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Section: Discussioncontrasting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

The Influence of Relationship Power Dynamics on HIV Testing in Rural Malawi

Conroy

2014

The Journal of Sex Research

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“…22 23 Individuals who suspect that they are HIV-positive (eg, based on past sexual behaviour) may fear confirmation of their suspicions. 24 The limited available empirical evidence supports the hypothesis that HIV status correlates with participation. A longitudinal study in Malawi showed that among persons aware of a previous HIV test result those who had tested HIV-positive were 4.6 times less likely to consent to a new HIV test than those who had tested HIV-negative.…”

Section: Introductionmentioning

confidence: 96%

National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models

Hogan¹,

Salomon

Canning

et al. 2012

Sex Transm Infect

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ObjectivesPopulation-based HIV testing surveys have become central to deriving estimates of national HIV prevalence in sub-Saharan Africa. However, limited participation in these surveys can lead to selection bias. We control for selection bias in national HIV prevalence estimates using a novel approach, which unlike conventional imputation can account for selection on unobserved factors.MethodsFor 12 Demographic and Health Surveys conducted from 2001 to 2009 (N=138 300), we predict HIV status among those missing a valid HIV test with Heckman-type selection models, which allow for correlation between infection status and participation in survey HIV testing. We compare these estimates with conventional ones and introduce a simulation procedure that incorporates regression model parameter uncertainty into confidence intervals.ResultsSelection model point estimates of national HIV prevalence were greater than unadjusted estimates for 10 of 12 surveys for men and 11 of 12 surveys for women, and were also greater than the majority of estimates obtained from conventional imputation, with significantly higher HIV prevalence estimates for men in Cote d'Ivoire 2005, Mali 2006 and Zambia 2007. Accounting for selective non-participation yielded 95% confidence intervals around HIV prevalence estimates that are wider than those obtained with conventional imputation by an average factor of 4.5.ConclusionsOur analysis indicates that national HIV prevalence estimates for many countries in sub-Saharan African are more uncertain than previously thought, and may be underestimated in several cases, underscoring the need for increasing participation in HIV surveys. Heckman-type selection models should be included in the set of tools used for routine estimation of HIV prevalence.

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“…Also, there may be some groups among the population for whom the stigma associated with being HIV positive is particularly strong, hence inducing more selection bias. The association between the decision to consent to HIV testing and HIV status may vary between these communities as a result (e.g., Kranzer et al, 2008). Therefore, as well as being inefficient, the imposition of a common selection process across all sub-groups could bias sub-national HIV prevalence estimates.…”

Section: Towards a More Flexible Framework For Estimating Hiv Prevalencementioning

confidence: 99%

A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses

Marra

Radice

Bärnighausen

et al. 2017

Journal of the American Statistical Association

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Individual, household and community factors associated with HIV test refusal in rural Malawi

Cited by 31 publications

References 40 publications

The Influence of Relationship Power Dynamics on HIV Testing in Rural Malawi

The Influence of Relationship Power Dynamics on HIV Testing in Rural Malawi

National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models

A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses

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