Multinomial Logit Modeling of Factors Associated With Multiple Sexual Partners from the Kenya Aids Indicator Survey 2007
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 3, May 2015, Pages: 170-177
Received: Mar. 4, 2015;
Accepted: Mar. 19, 2015;
Published: May 16, 2015
Views 4293 Downloads 187
Beryl Ang’iro, Jomo Kenyatta University of Agriculture and Technology, School of Mathematical Sciences, Nairobi, Kenya
Samuel Mwalili, Jomo Kenyatta University of Agriculture and Technology, School of Mathematical Sciences, Nairobi, Kenya
Josphat Kinyanjui, Karatina University, Department of Mathematics, Statistics & Actuarial sciences, Karatina, Kenya
The number of lifetime sex partners of an individual has an important effect on Human Immunodeficiency Virus (HIV) status of an individual; hence modeling multiple sexual partnerships is an essential component of any analysis of HIV outcome. Multiple sexual partnerships are associated with greater risk of HIV, Sexually Transmitted infections (STIs) and intimate partner violence. This research project presents a general approach for modeling logit of clustered (correlated) ordinal and nominal responses using polytomous data from the Kenya AIDS Indicator Survey 2007 (NASCOP 2010). We review multinomial logit models as generalized linear models. The model is applied to HIV prevalence data among men and women in Kenya, derived from the Kenya AIDS Indicator Survey 2007 (KAIS). We generalize logistic regression to handle multinomial response variables, with separate models for nominal and ordinal cases. When modeling a nominal response variable we are interested in finding if certain predictors have an effect on the probabilities. The baseline category logit model, models the odds of being in one category relative to being in a designated category (last category), for all pairs of categories. It is used for nominal responses. A maximum likelihood estimation (MLE) approach is used for baseline category logit model. To model an ordinal response variable one models the cumulative response probabilities or cumulative odds. The cumulative logit model is used when the response of an individual unit is restricted to one of a finite number of ordinal values. This study shows the practicality of multinomial logit model in analyzing epidemiological data. Other studies have found education to be associated with multiple sexual partners. In this study, we observed that multiple sexual partners is not related to education. Other covariates like Gender, Place of residence, sexually active individuals for the past 12 months and marital status were found to be associated with multiple sexual partners. Individuals that are sexually active for the past 12 months were found to be ten times more likely to have multiple sexual partners compared to those that are not. After controlling for all other factors, the odds of male to female having multiple sexual partners doubled to 7.6 meaning male are almost 8 times likely to have multiple sexual partners compared to female. Partner testing or couples testing is a main strategy of national testing initiatives in Kenya. Respondents are encouraged to learn their test results with their partner.
Multinomial Logit Modeling of Factors Associated With Multiple Sexual Partners from the Kenya Aids Indicator Survey 2007, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 3,
2015, pp. 170-177.
J Adams, Wilson M, Wang W The multidimentional random coefficients multinomial logit model. Applied Psychological Measurements 21: 1-23. 1997
RJ Adams, Wilson MR Formulating thr Rasch model as a mixed coefficients multinomial logit. In: Engelhard G, Wilson MR, eds. Objective measurement: Theory and practice 3:143-66. 1996
MJ Daniels and Gatsonis C. Hierarchical polytomous regression models with application to health services research.
K Johnson and Way A. Risk factors for hiv infection in a national adult population: evidence from the 2003 kenya demographic and health survey. Journal of Acquired Immune De_ciency Syndromes, 42(5):627_636, 2006.
NJ Madise, Ziraba AK, Inungu J, Khamadi SA, and Ezeh A. Are slum dwellers at heightened risk of hiv infection than other urban residents? evidence from population-based hiv prevalence surveys in kenya. Health & Place, 18 (5):1144_1152, 2012.
Aldo Conde Edward C Green. Sexual partner reduction and hiv infection. 2000.
DT Halperin and H Epstein . Concurrent sexual partnerships help to explain africa's high hiv prevalence: implications for prevention. 364(9428):46, 2004.
P McCullagh and Nelder J.A. Generalized Linear models. Chapman and Hall, 1989.
V1 Mishra, Assche SB, Greener R, Vaessen M, Hong R, Ghys PD, Boerma JT, Van Assche A, Khan S, and Rutstein S. Hiv infection does not disproportionately aect the poorer in sub-saharan africa. 2007.
Mohammed O. M. Mohammed. Statistical methods for analysing complex survey data: An application to hiv/aids in ethiopia. 2013.
NASCOP. Kenya aids indicator survey preliminary report. Ministry of Health, Kenya, 2007.
NASCOP. Kenya aids indicator survey preliminary report. Ministry of Health, Kenya, 2012.
Oscar Ngesa, Henry Mwambi, and Thomas Achia. Bayesian spatial semi-parametric modeling of hiv variation in kenya. 2014.
G Rasch. on general laws and the meaning of measurements in psychology. In Neyman J, ed. Proceedings of the 4th Berkeley symposium on mathematical statistics and probability, vol 4. Berkeley, 1961.
Blank Sima and Katherine W. Reeves. Education, occupation, and migration as predictors of multiple sexual partnerships among people tested for hiv in luderitz, namibia. 2011.
DJ Spiegelhalter, Best NG, Carlin BP, and van der Linde A. Bayesian measures of model complexity and t. journal of the royal statistical society series b. Statistical Methodology, 64:583639, 2002.
Low-Beer D RL, Stoneburner. Population-level hiv declines and behavioral risk avoidance in uganda. 304(5671): 7148, 2004.
Nantulya V Potts M Gayle HD Holmes KK. Shelton JD1, Halperin DT. Partner reduction is crucial for balanced "abc" approach to hiv prevention. 328(7444):8913, 2004.
Tutz and Hennevogl W. Random eects in ordinal regression models. Computational Statistics and Data Analysis, 22:53757, 1996.
UNAIDS. Report on the global aids epidemic. Geneva, Switzerland, 2013.