Low Vaccine Coverage and Factors Associated with Incomplete Childhood Immunization in Racial/Ethnic Minorities and Rural Groups, Central Brazil.

Discrimination and limited access to healthcare services in remote areas can affect vaccination coverage. Therefore, this study aimed to estimate vaccination coverage for children living in quilombola communities and rural settlements in the central region of Brazil during their first year of life and to analyze the factors associated with incomplete vaccination. An analytical cross-sectional study was conducted on children born between 2015 and 2017. The percentage of children who received all vaccines recommended by the National Immunization Program in Brazil by 11 months and 29 days was used to calculate immunization coverage. Children who received the following vaccines were considered as having a complete basic vaccination schedule: one dose of BCG; three doses of Hepatitis B, of Diphtheria-Tetanus-Pertussis (DPT), of Haemophilus influenzae type b (Hib), and of Poliovirus (Polio); two doses of Rotavirus, of 10-valent pneumococcal (PCV10), and of Serogroup C meningococcal conjugate (MenC); and one dose of Yellow Fever (YF). Measles-mumps-rubella (MMR) and other doses recommended at or after 12 months were not included. Consolidated logistic regression was used to identify factors associated with incomplete vaccination coverage. Overall vaccination coverage was 52.8% (95% CI: 45.5-59.9%) and ranged from 70.4% for the Yellow Fever vaccine to 78.3% for the Rotavirus vaccine, with no significant differences between the quilombola and settler groups. Notably, the likelihood of incomplete general vaccination coverage was higher among children who did not receive a visit from a healthcare professional. Urgent strategies are required to achieve and ensure health equity for this unique and traditionally distinct group with low vaccination coverage.

Prevalence, Risk Factors and Vaccine Response against Hepatitis B in People Aged 50 Years or Older.

Universal immunization against hepatitis B has contributed to reducing incidence of the disease, but older individuals remain susceptible to acquiring the hepatitis B virus worldwide. Thus, this study aimed to investigate the epidemiology of HBV infection in individuals aged 50 years and over in central Brazil and to evaluate the immunogenicity of the monovalent vaccine against hepatitis B in this age group using two vaccine regimens. METHOD: Initially, a cross-sectional and analytical study was carried out to investigate the epidemiology of hepatitis B. Then, individuals without proof of vaccination for hepatitis B were recruited for a phase IV randomized and controlled clinical trial using two vaccine regimens: Intervention Regimen (IR) (three doses of 40 µg at months 0, 1 and 6) vs. Comparison Regimen (CR) (three doses of 20 µg at months 0, 1 and 6). RESULTS: The overall prevalence of exposure to HBV was 16.6% (95% CI: 14.0%-9.5%). In the clinical trial, statistical differences in protective titers were observed (p = 0.007; IR 96% vs. CR 86%) and the geometric mean of anti-HBs titers was higher in individuals who received the IR (518.2 mIU/mL vs. 260.2 mIU/mL). In addition, the proportion of high responders was higher among those who received the IR (65.3%). CONCLUSION: reinforced doses should be used in individuals aged 50 years or older to overcome the lower efficacy of the vaccine against hepatitis B.

Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine.

Early diagnosis and treatment are the most important strategies to prevent deaths from several diseases. In this regard, data mining and machine learning techniques have been useful tools to help minimize errors and to provide useful information for diagnosis. Our paper aims to present a new feature selection algorithm. In order to validate our study, we used eight benchmark data sets which are commonly used among researchers who developed machine learning methods for medical data classification. The experiment has shown that the performance of our proposed new feature selection method combined with twin-bounded support vector machine (FSTBSVM) is very efficient. The robustness of the FSTBSVM is examined using classification accuracy, analysis of sensitivity, and specificity. The proposed FSTBSVM is a very promising technique for classification, and the results show that the proposed method is capable of producing good results with fewer features than the original data sets. Graphical abstract Model using a new feature selection and grid search with 10-fold CV to optimize model parameters in our FSTBSVM.