No abstract

Twin births are an important public health issue due to health complications for both mother and children. While it is known that contemporary factors have drastically changed the epidemiology of twins in certain developed countries, in Brazil, relevant data are still scarce. Thus, we carried out a population-based study of live births in spatial and temporal dimensions using data from Brazil's Live Birth Information System, which covers the entire country. Over 41 million births registered between 2001 and 2014 were classified as singleton, twin or multiple. Twinning rates (TR) averaged 9.41 per 1,000 for the study period and a first-order autoregressive model of time-series analysis revealed a global upward trend over time; however, there were important regional differences. In fact, a Cluster and Outlier Analysis (Anselin Local Moran's I) was performed and identified clusters of high TR in an area stretching from the south of Brazil's Northeast Region to the South Region (Global Moran Index = 0.062, P < 0.001). Spearman's correlation coefficient and a Wilcoxon matched pairs test revealed a positive association between Human Development Index (HDI) and TRs in different scenarios, suggesting that the HDI might be an important indicator of childbearing age and assisted reproduction techniques in Brazil. Furthermore, there was a sharp increase of 26.42% in TR in women aged 45 and over during study period. The upward temporal trend in TRs is in line with recent observations from other countries, while the spatial analysis has revealed two very different realities within the same country. Our approach to TR using HDI as a proxy for underlying socioeconomic changes can be applied to other developing countries with regional inequalities resembling those found in Brazil.

Objectives: To analyze the prevalence at birth and the spatial and temporal distribution of congenital anomalies (CAs) among live births in the state of Maranhão in 2001 to 2016. To describe demographic, gestational and neonatal variables of interest. Methods: Ecological, population-based study, using secondary data from the Live Birth Information System (SINASC). Annual prevalence of total and per-group CAs was calculated. Spatial analyzes were based on the Local Indicators of Spatial Association (LISA) and the Moran I Index, and interactive maps were generated. Demographic, gestational and neonatal variables of interest available from SINASC were described in the group of newborns with CAs. Results: 1,831,830 live births, 6,110 with CAs (33.4/10,000) were included. Higher frequencies occurred in more recent years. Spatial clusters have been observed in specific years. The prevalence of newborns with CAs was different between categories of variables considered as risk factors for this outcome. Conclusion: The prevalence at birth of total CAs was lower than expected for major human defects (3%). The temporal peak of records in 2015/2016 is probably related to the increase in CAs caused by gestational infection by the Zika virus. The spatial clusters were probably due to variations at random due to the small number of births as they are not repeated in other years. Studies like this are the basis for the establishment of CA surveillance programs.

Construction companies usually record customer complaints as unstructured texts, resulting in unsuitable information to understand defect occurrences. Moreover, complaint databases are often manually classified, which is time-consuming and error-prone. However, previous studies have not provided guidance on how to improve customer complaint data collection and analysis. This research aims to devise an information management model for customer complaints in residential projects. Using Design Science Research, a study was undertaken at a Brazilian residential building company. Multiple sources of evidence were used, including interviews, participant observations, and analysis of an existing database. Natural language processing (NLP) was used to build a word menu for customers to lodge a complaint. Moreover, a recommendation system was proposed based on machine learning (ML) and hierarchical defect classification. The system was designed to indicate which defects should be investigated during inspections. The main outcome of this investigation is an information management model that provides an effective classification system for customer complaints, supported by artificial intelligence (AI) applications that improve data collection, and introduce some degree of automation to warranty services. The main theoretical contribution of the study is the use of advanced data management approaches for managing complaints in residential building projects, resulting in the combination of inputs from technical and customer perspectives to support decision-making.

The human leukocyte antigen (HLA) genotype may influence in immune responses during the course of coronavirus disease 2019 (COVID-19). In this ecological study, we collected HLA genotypes from 4,148,713 Brazilian individuals and compared to COVID-19 data. We found a positive significant correlation between the HLA-A*01~B*08~DRB1*03 haplotype and COVID-19 mortality rate.