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This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics. [ABSTRACT FROM AUTHOR]

Genetic variants of human papillomavirus types 16 and 18 (HPV16/18) could differ in their cancer risk. We studied the prevalence and association with high-grade cervical lesions of different HPV16/18 variant lineages in a case-control study including 217 cases (cervical intraepithelial neoplasia grade 2 or grade 3 or worse: CIN2 or CIN3+) and 116 controls (no CIN2 or CIN3+ in two-year follow-up). HPV lineages were determined by sequencing the long control region (LCR) and the E6 gene. Phylogenetic analysis of HPV16 confirmed that isolates clustered into previously described lineages: A (260, 87.5%), B (4, 1.3%), C (8, 2.7%), and D (25, 8.4%). Lineage D/lineage A strains were, respectively, detected in 4/82 control patients, 19/126 CIN3+ cases (OR = 3.1, 95%CI: 1.0–12.9, p = 0.04), 6/1 glandular high-grade lesions (OR = 123, 95%CI: 9.7–5713.6, p<0.0001), and 4/5 invasive lesions (OR = 16.4, 95%CI: 2.2–113.7, p = 0.002). HPV18 clustered in lineages A (32, 88.9%) and B (4, 11.1%). Lineage B/lineage A strains were respectively detected in 1/23 control patients and 2/5 CIN3+ cases (OR = 9.2, 95%CI: 0.4–565.4, p = 0.12). In conclusion, lineages A of HPV16/18 were predominant in Spain. Lineage D of HPV16 was associated with increased risk for CIN3+, glandular high-grade lesions, and invasive lesions compared with lineage A. Lineage B of HPV18 may be associated with increased risk for CIN3+ compared with lineage A, but the association was not significant. Large well-designed studies are needed before the application of HPV lineage detection in clinical settings. [ABSTRACT FROM AUTHOR]