Objectives To estimate the prevalence of oral mucosal diseases and dental caries among HIV-infected children receiving antiretroviral treatment (ART) in West Africa, and to identify factors associated with the prevalence of oral mucosal lesions. Methods Multi-center cross-sectional survey in 5 pediatric HIV clinics in Côte d’Ivoire, Mali and Sénégal. A standardized examination was performed by trained dentists on a random sample of HIV-infected children aged 5 to 15 years receiving ART. The prevalence of oral and dental lesions and mean number of decayed, missing/extracted and filled teeth (DMFdefT) in temporary and permanent dentition were estimated with their 95% confidence interval (95%CI). We used logistic regression to explore the association between children’s characteristics and the prevalence of oral mucosal lesions, expressed as prevalence odds ratio (POR). Results The median age of the 420 children (47% females) enrolled was 10.4 years (interquartile range [IQR]=8.3–12.6). The median duration on ART was 4.6 years (IQR=2.6–6.2); 84 (20.0%) had CD4 count<350 cells/mm3. 35 children (8.3%; 95%CI: [6.1–11.1]) exhibited 42 oral mucosal lesions (24 were candidiasis); 86.0% (95%CI=82.6–89.3) of children had DMFdefT≥1. The presence of oral mucosal lesions was independently associated with CD4 count<350 cells/mm3 (POR=2.96, 95% CI=1.06–4.36) and poor oral hygiene (POR=2.69, 95%CI=1.07–6.76). Conclusions Oral mucosal lesions still occur in HIV-infected African children despite ART, but rarely. However, dental caries were common and severe in this population, reflecting the need to include oral health in the comprehensive care of HIV.

Although preventable, the burden of dental disease was high in children from families affected by HIV in West Africa and was associated with HIV infection and immunosuppression.

This chapter presents recent developments in exponential random graph models (ERGMs), statistical models for social network structure. ERGMs assume that social networks are composed of various network substructures (or network configurations) like reciprocity, brokerage, or transitive closure, which, combined together, explain how the network came into being. The chapter also discusses recent developments for related models—auto-logistic actor attributes models (ALAAMs)—that examine social influence effects. The chapter focuses on three new types of models that have developed in the past few years: directed network models for social influence, multilevel extensions of ERGMs, and multilevel extensions of ALAAMs. The chapter concludes with three empirical applications to demonstrate what new possibilities exist in the application of these new statistical models for social networks to social science questions.

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