Mortality in children under 5 years of age with congenital syphilis in Brazil: A nationwide cohort study.

BACKGROUND: Congenital syphilis (CS) is a major and avoidable cause of neonatal death worldwide. In this study, we aimed to estimate excess all-cause mortality in children under 5 years with CS compared to those without CS. METHODS AND FINDINGS: In this population-based cohort study, we used linked, routinely collected data from Brazil from January 2011 to December 2017. Cox survival models were adjusted for maternal region of residence, maternal age, education, material status, self-declared race and newborn sex, and year of birth and stratified according to maternal treatment status, non-treponemal titers and presence of signs and symptoms at birth. Over 7 years, a total of 20 057 013 live-born children followed up (through linkage) to 5 years of age, 93 525 were registered with CS, and 2 476 died. The all-cause mortality rate in the CS group was 7·84/1 000 person-years compared with 2·92/1 000 person-years in children without CS, crude hazard ratio (HR) = 2·41 (95% CI 2·31 to 2·50). In the fully adjusted model, the highest under-five mortality risk was observed among children with CS from untreated mothers HR = 2·82 (95% CI 2·63 to 3·02), infants with non-treponemal titer higher than 1:64 HR = 8·87 (95% CI 7·70 to 10·22), and children with signs and symptoms at birth HR = 7·10 (95% CI 6·60 to 7·63). Among children registered with CS, CS was recorded as the underlying cause of death in 33% (495/1 496) of neonatal, 11% (85/770) of postneonatal, and 2·9% (6/210) of children 1 year of age. The main limitations of this study were the use of a secondary database without additional clinical information and the potential misclassification of exposure status. CONCLUSIONS: This study showed an increased mortality risk among children with CS that goes beyond the first year of life. It also reinforces the importance of maternal treatment that infant non-treponemal titers and the presence of signs and symptoms of CS at birth are strongly associated with subsequent mortality. TRIAL REGISTRATION: Observational study.

A random-censoring Poisson model for underreported data.

A major challenge when monitoring risks in socially deprived areas of under developed countries is that economic, epidemiological, and social data are typically underreported. Thus, statistical models that do not take the data quality into account will produce biased estimates. To deal with this problem, counts in suspected regions are usually approached as censored information. The censored Poisson model can be considered, but all censored regions must be precisely known a priori, which is not a reasonable assumption in most practical situations. We introduce the random-censoring Poisson model (RCPM) which accounts for the uncertainty about both the count and the data reporting processes. Consequently, for each region, we will be able to estimate the relative risk for the event of interest as well as the censoring probability. To facilitate the posterior sampling process, we propose a Markov chain Monte Carlo scheme based on the data augmentation technique. We run a simulation study comparing the proposed RCPM with 2 competitive models. Different scenarios are considered. RCPM and censored Poisson model are applied to account for potential underreporting of early neonatal mortality counts in regions of Minas Gerais State, Brazil, where data quality is known to be poor.