Material deprivation and unemployment affect coercive sex among young people in the urban slums of Blantyre, Malawi: A multi-level approach

Abstract

We explore relations among material deprivation (measured by insufficient housing, food insecurity and poor healthcare access), socio-economic status (employment, income and education) and coercive sex. A binary logistic multi-level model is used in the estimation of data from a survey of 1071 young people aged 18–23 years, undertaken between June and July 2013, in the urban slums of Blantyre, Malawi. For young men, unemployment was associated with coercive sex (odds ratio [OR]=1.77, 95% confidence interval [CI]: 1.09–3.21) while material deprivation (OR=1.34, 95% CI: 0.75–2.39) was not. Young women in materially deprived households were more likely to report coercive sex (OR=1.37, 95% CI: 1.07–2.22) than in non-materially deprived households. Analysis of local indicators of deprivation is critical to inform the development of effective strategies to reduce coercive sex in urban slums in Malawi.

Persistence of traditional and emergence of new structural drivers and factors for the HIV epidemic in rural Uganda; a qualitative study

“…The participants in our study mentioned these same factors drive young single mothers into commercial sex work. Although not mentioned in our FGDs, other studies have found young people are also at risk for coercive sex if they are from materially deprived households [28]. Several other studies across Africa [29–31] have also showed a relationship between material deprivation and high risk sexual behavior.…”

Material deprivation and sexual risk nexus among young people in urban disadvantaged settings in Malawi and South Africa

“…Descriptive statistics were used to clarify patterns of HIV risk by sex and country. Logistic regression models were used to determine whether there is a direct association between material deprivation and sexual risk and the results were published elsewhere [11,12]. The current study involved the use of logged coe cients from logistic regression multiplied by their standard deviations [28] to determine measures of material deprivation which explained the most variance in sexual risk.…”

“…Sub study 1 was a secondary analysis of quantitative data (n = 530) extracted from the loveLife survey in South Africa [12]). Sub study 2a included ve focus group discussions, 12 in-depth interviews, undertaken with a total of 60 young people [26], and Sub study 2b was a household survey of young people (n = 1,071) in Malawi [11]. Sub study 1 was used to examine association between material deprivation and sexual risk-taking among 18-23 year old young women and young men in urban disadvantaged settings using secondary data from South Africa and apply the ndings of this analysis to the study design and methods in Malawi.…”

“…Deprivations of material resources such as food, housing and health care are currently receiving increased research attention as critical in uences of sexual risk in disadvantaged urban settings in sub-Saharan Africa [10][11][12][13]. This attention is premised on the understanding that traditional income-related measures of disadvantage (income, wealth and education) do not re ect unique basic needs, particularly for vulnerable groups of young people living in urban disadvantaged settings [3,13,14].…”

Social, behavioural, and cultural factors of HIV in Malawi: a semi-automated systematic review

“…false beliefs) 25,[36][37][38][39] intravaginal practices (women) 32,38,48 commitment to religious congregations 28 ≡ Community stigma [25][26][27][28]36,37 peer pressure/social normsmultiple partners, gender roles, condom use, sexism [28][29][30][31][32][33]40,42,46,49 community organisation (e.g. matrilocality 35 ) and mobilisation 26,27 position of religious leaders [25][26][27] migration of population 44,45 cultural norms 25,36-39 faith 29 ≡ Interpersonal/network relationship power and equity 35 social support and trust (friends and relatives) 35,41 relationship health/intimacy/inter-personal violences 35,46,47 age asymmetry 34 communication level 35,41 social network 28,[30][31][32][33]41…”