Anguzu, GodwinFlynn, AndrewMusaazi, JosephKasirye, RonnieAtuhaire, Leonard K.Kiragga, Agnes N.Kabagenyi, AllenMujugira, Andrew2022-10-312022-10-312019Anguzu, G., Flynn, A., Musaazi, J., Kasirye, R., Atuhaire, L. K., Kiragga, A. N., ... & Mujugira, A. (2019). Relationship between socioeconomic status and risk of sexually transmitted infections in Uganda: multilevel analysis of a nationally representative survey. International journal of STD & AIDS, 30(3), 284-291. DOI: 10.1177/095646241880411510.1177/0956462418804115https://nru.uncst.go.ug/handle/123456789/5080Socioeconomic status (SES) appears to have positive and negative associations with sexually transmitted infection (STI) risk in resource-limited settings, but few studies have evaluated nationally representative data.We assessed multiple SES measures and their effect on STI risk. We conducted a secondary analysis of data from the Uganda Demographic and Health Survey (UDHS 2011). The primary outcome (STI risk) was self-reported STIs and/or symptoms in the prior 12 months. We examined associations between multiple SES measures and STI risk using a mixed-effects Poisson regression model. The results showed that of the 9256 sexually active individuals, 7428 women and 1828 men were included in the analysis. At an individual level, middle wealth quintile and disposable income were associated with STI risk, whereas being in the richest wealth quintile was protective. Residence in wealthier regions (adjusted incidence rate ratio [aIRR] 3.92, 3.62, and 2.75, for Central, Western, and Eastern regions; p<0.01) was associated with increased STI risk. Regional level analysis revealed stochastic variability of STI risk across geographical region (variance 0.03; p¼0.01). The bilateral association between SES and STI risk underscores the need for multi-sectoral interventions to address the upstream effects of poverty on STI risk and downstream effects of STIs on health and economic productivity.enUganda Demographic and Health SurveySocioeconomic statusSexually transmitted infectionsMixed-effects regression modelsArticle