Male migrants' non-spousal sexual partnerships in the place of origin: an in-depth investigation in two rural settings of India

Male migrants in India are at disproportionately high risk for HIV, not only because of their sexual behaviours in destination areas but also due to their risk behaviours in their place of origin. While studies have documented male migrants’ risky behaviours in the home setting, few have attempted to understand the underlying socio-cultural context in which they engage in such behaviours. This paper examines the patterns and context of male migrants’ non-spousal sexual partnerships in two high-out-migration districts of India. Data, drawn from a cross-sectional behavioural mixed-methods study conducted in 2008, included a structured survey with 1272 migrants, followed by in-depth interviews with 33 male migrants. Results suggest that sexual activity was common in the place of origin: around 50% of migrants had sex with a non-spousal female partner and two-fifths had initiated sex in this setting. Migrants’ non-spousal sexual behaviours in the home village were influenced by the prevailing socio-cultural context, including migrants’ enhanced socio-economic status, attitudes to non-spousal sex and accessibility of sexual partners. Male migrants’ non-spousal sexual partnerships in source areas are influenced by socio-cultural factors, which must be considered when designing HIV programmes in India and elsewhere

Modeling approaches to the indirect estimation of migration flows: From entropy to EM

The paper presents probability models to recover information on migration flows from incomplete data. Models are used to predict migration and to combine data from different sources. The parameters of the model are estimated from the data by the maximum likelihood method. If data are incomplete, an extension of the maximum likelihood method, the EM algorithm, may be applied. Two models are considered: the binomial (multinomial) model, which underlies the logit model and the logistic regression, and the Poisson model, which underlies the loglinear model, the log-rate model and the Poisson regression. The binomial model is viewed in relation to the Poisson model. By way of illustration, the probabilistic approach and the EM algorithm are applied to two different missing data problems. The first problem is the prediction of migration flows using spatial interaction models. The probabilistic approach is compared to conventional methods, such as the gravity model and entropy maximization. In fact, spatial interaction models are particular variants of log-linear models. The second problem is one of unobserved heterogeneity. A mixture model is applied to determine the relative sizes of different migrant categories.Migration, Missing data, Probability models, Entropy, Maximum likelihood, EM,