Spatial analyzes of HLA data in Rio Grande do Sul, south Brazil: genetic structure and possible correlation with autoimmune diseases

HLA haplotypes and differential regional mortality caused by COVID-19 in Brazil: an ecological study based on a large bone marrow donor bank dataset

The coronavirus disease 2019 (COVID-19) mortality rates varied among the states of Brazil during the course of the pandemics. The human leukocyte antigen (HLA) is a critical component of the antigen presentation pathway. Individuals with different HLA genotypes may trigger different immune responses against pathogens, which could culminate in different COVID-19 responses. HLA genotypes are variable, especially in the highly admixed Brazilian population. In this ecological study, we aimed to investigate the correlation between HLA haplotypes and the different regional distribution of COVID-19 mortality in Brazil. HLA data was obtained from 4,148,713 individuals registered in The Brazilian Voluntary Bone Marrow Donors Registry. COVID-19 data was retrieved from epidemiological bulletins issued by State Health Secretariats via Brazil's Ministry of Health from February/2020 to July/2022. We found a positive significant correlation between the HLA-A*01~B*08~DRB1*03 haplotype and COVID-19 mortality rates when we analyzed data from 26 states and the Federal District. This result indicates that the HLA-A*01~B*08~DRB1*03 haplotype may represent an additional risk factor for dying due to COVID-19. This haplotype should be further studied in other populations for a better understanding of the variation in COVID-19 outcomes across the world.

Geographic Information Systems in Spatial Epidemiology: Unveiling New Horizons in Dental Public Health

Objectives:

Research on the role of environment and place in various aspects of dental public health using geographic information systems (GIS) is escalating rapidly. Yet, the understanding of GIS and the analytical tools that it offers are still vaguely understood. This narrative review therefore draws from the utilization of GIS in the dental public health research.

Materials and Methods:

Electronic databases such as Google Scholar, PUBMED, and Scopus were searched using terms “spatial epidemiology,” “GIS,” “geographic information systems,” “health geography,” “environment public health tracking,” “spatial distribution,” “disease mapping,” “geographic correlation studies,” “cartography,” “big data,” and “disease clustering” through December 2019.

Results:

This review builds upon the prospects of GIS application in various aspects of dental public health. Studies were classified as: (1) GIS for mapping of disease, population at risk, and risk factors; (2) GIS in geographic correlation studies; (3) GIS for gauging healthcare accessibility and spatial distribution of healthcare providers. We also identified the commonly used GIS analytical techniques in oral epidemiology.

Conclusions:

We anticipate that this review will spur advancement in the utilization of spatial analytical techniques and GIS in the dental public health research.

Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining

The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman's rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.

Behavioral mapping of children's physical activities and social behaviors in an indoor preschool facility: methodological challenges in revealing the influence of space in play

BACKGROUND

GIS (Geographic Information Systems) based behavior maps are useful for visualizing and analyzing how children utilize their play spaces. However, a GIS needs accurate locational information to ensure that observations are correctly represented on the layout maps of play spaces. The most commonly used tools for observing and coding free play among children in indoor play spaces require that locational data be collected alongside other play variables. There is a need for a practical, cost-effective approach for extending most tools for analyzing free play by adding geospatial locational information to children's behavior data collected in indoor play environments.

RESULTS

We provide a non-intrusive approach to adding locational information to behavior data acquired from video recordings of preschool children in their indoor play spaces. The gridding technique showed to be a cost-effective method of gathering locational information about children from video recordings of their indoor physical activities and social behaviors. Visualizing the proportions of categories and observed intervals was done using bubble pie charts which allowed for the merging of multiple categorical information on one map. The addition of locational information to other play activity and social behavior data presented the opportunity to assess what types of equipment or play areas may encourage different physical activities and social behaviors among preschool children.

CONCLUSIONS

Gridding is an effective method for providing locational data when analyzing physical activities and social behaviors of preschool children in indoor spaces. It is also reproducible for most GIS behavior mapping focusing on indoor environments. This bypasses the need to have positioning devices attached to children during observations, which can raise ethical considerations regarding children's privacy and methodological implications with children playing less naturally. It also supports visualizations on behavior maps making them easier to interpret.

HLA diversity in Brazil

Brazil is the fifth largest country in the world in area and the fifth most populous. The Brazilian voluntary Bone Marrow Donor Registry is the third largest in terms of number of donors in the world, being a valuable source of HLA genetics to characterize the donor population of Brazil as well. The genetic background of the Brazilian population is quite heterogeneous, resulting from 5 centuries of admixture among Native Americans, Europeans and Africans, making the Brazilian population unique in terms of genetic ancestry. The unique characteristics of populations in different Brazilian regions make them an exciting focus for genetic diversity studies. Studies on HLA genetic diversity of Brazilian populations have been conducted since the late 1980s and, in this review, we highlight the main findings from studies carried out in Brazil based on classical HLA. In addition, we calculated the genetic distance from the molecular data of the studies included in this review in order to have a broader view of the HLA diversity in Brazilian populations. We emphasize that characterization of HLA diversity is not only important for transplantation programs, but can shed a light on ancestry, history and other demographic patterns with or without association with autoimmune disease.