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Determinants of Alcohol Use, Risky Sexual Behavior and Sexual Health Problems Among Men in Low Income Communities of Mumbai, India

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Abstract

This paper summarizes the main results of the survey component of a mixed methods study of alcohol and sexual risk in a general population of young men 18–29 residing in low income communities in the Greater Mumbai area. The survey included demographic variables, and scales and indices measuring work related stress, social influence, exposure to alcohol in childhood, and currently, hyper masculinity, exposure to media and pornography, risk related leisure time activities and alcohol and alcohol/sex expectancies. Measures of alcohol use included frequency/amount/contextual use of six different types of alcohol, a general estimate of frequency and amount (AUDIT), and an estimate of total ml. alcohol consumed in the past 30 days, based on estimates of alcohol content in all types of alcohol consumed, by unit of consumption (glass, peg, bottle) etc. Sexual outcome measures included types and number of partners ever and in past year with and without alcohol, and a critical event with most recent partner (with or without alcohol) and culturally specific indicators of sexual health related to sexual risk taking. A cluster sampling protocol and the use of a screener produced a sample of 1239 men, 1071 thirty day drinkers and 161 nondrinkers. Logistic regression analysis (binary and multinomial) showed relationships between predictor variables and alcohol consumption and alcohol and sexual risk indicators as well as two of the sexual health indicators associated with extramarital sex. Risk behaviors are associated with higher levels of alcohol consumption in this low risk general population of married and unmarried men. Implications for intervention include: (a) reducing or eliminating home drinking, to reduce early childhood exposure; (b) including alcohol in sexual risk and HIV prevention programs; (c) improving couples (married or unmarried) communication to reduce men’s search for sexual alternatives, and (d) treating garmi as an indicator of sexual risk taking rather than STIs.

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Notes

  1. NIAAA Grant R21 AA014803-01, 2005–2009

  2. The model permits the study of the effect of a unit change in the independent variable on the dependent variable considering the simultaneous effects of several variables. A multinomial logistic regression model is a generalization of binary (or logistic) regression model. In the binary model, a binary outcome (0 or 1) of events is modeled. If p is the probability of outcome being one, then (as explained above) the model specifies ln [p/(1 − p)] = ∑p k x k . In the multinomial model, multinomial outcome 0, 1, 2…j of an event is modeled. If p is the probability of outcome j, then the model specifies, \( \ln \left [{{\frac{{p_{j}}}{{p_{J}}}}} \right] = \Upsigma_{k} b_{jk} x_{k} \) where J = 1, 2… j − 1. Using the condition pj = 1 and setting bjk = 0, the above model determine the coefficients of bjk unequally. Thus the model can be used to estimate the effect of variable xk on the probability of outcome J.

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Acknowledgements

We wish to acknowledge members of the study field team including who were involved in creating the survey sampling frame, managing, collecting and entering study data, and assisting in the analysis: Madhu Battalla, Joshi Chandrashekar, Prabhat Kumar, Barsharani Maharana, Saurabh Singh, Sharad Singh, Mr. Supe, Supriya Verma, Ajeet Vishen, Purva Divedi. Data for this paper were obtained through NIAAA Grant # R21 AA014803-01, 2005–2009.

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Authors and Affiliations

  1. Department of Mathematical Demography and Statistics, International Institute for Population Sciences (IIPS), Govandi Station Road, Deonar, Mumbai, India

    S. K. Singh

  2. Institute for Community Research (ICR), Hartford, CT, USA

    Jean J. Schensul

  3. Department of Migration and Urban Studies, International Institute for Population Sciences (IIPS), Mumbai, India

    Kamla Gupta

  4. International Institute for Population Sciences (IIPS), Mumbai, India

    Barsharani Maharana

  5. University of Connecticut, Storrs, CT, USA

    David Kremelberg

  6. ICR, Hartford, CT, USA

    Marlene Berg

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  1. S. K. Singh
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Correspondence to S. K. Singh.

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Singh, S.K., Schensul, J.J., Gupta, K. et al. Determinants of Alcohol Use, Risky Sexual Behavior and Sexual Health Problems Among Men in Low Income Communities of Mumbai, India. AIDS Behav 14 (Suppl 1), 48–60 (2010). https://doi.org/10.1007/s10461-010-9732-x

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