ABSTRACT
BACKGROUND: Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030. METHODS: We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030. RESULTS: The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil's municipalities was observed, with a persistent sex differential. CONCLUSION: Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning.
Subject(s)
Bayes Theorem , Cities , Life Expectancy , Mortality , Humans , Brazil/epidemiology , Male , Female , Mortality/trends , Infant , Child, Preschool , Aged , Middle Aged , Adolescent , Adult , Child , Young Adult , Infant, Newborn , Aged, 80 and over , Sex Factors , Age Distribution , Age Factors , Sex Distribution , ForecastingABSTRACT
INTRODUCTION: Given the major impact in terms of morbidity and mortality that episodes of early neonatal sepsis (ENS) have on both newborns and health systems, this study aimed to identify the etiological profile of early neonatal bacterial sepsis by a multiplex quantitative real-time polymerase chain reaction (qPCR). METHODOLOGY: Blood samples from newborns diagnosed with clinical ENS and hospitalized in neonatal intensive care units (NICUs) were collected and analyzed using the multiplex qPCR method to detect Streptococcus agalactiae, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Enterobacter sp., Serratia sp., and Staphylococcus aureus. A universal primer was used in the analysis. RESULTS: A total of 150 neonates with clinical sepsis and 10 newborns as healthy controls were included in the study. The group with clinical sepsis was 100% positive for the presence of bacterial genomic DNA through the universal primer. The control group showed negativity by qPCR. The multiplex qPCR analysis showed that 76% of the samples were positive for Escherichia coli, 34% for Staphylococcus aureus, 13.3% for Streptococcus agalactiae, 7.3% for Pseudomonas aeruginosa, and 0.7% for Enterobacter sp. and Serratia sp. Multiplex qPCR of patients with clinical sepsis matched with 8.1% of the blood samples that tested positive by the microbiological method. CONCLUSIONS: Rapid and sensitive detection of the pathogens causing ENS by this new multi-target approach based on multiplex qPCR could potentially excel compared to microbiological methods, with the simple objective of facilitating the progression to a more rapid and specific antimicrobial therapy, avoiding the abuse of antibiotics in NICUs.
