A comprehensive integrated dataset on Brazilian health facilities: from 2005 to 2021.

OBJECTIVES: The National Registry of Healthcare Facilities is a system with the registry of every healthcare facility in Brazil with information on the capacity building and healthcare workforce regarding its public or private nature. Despite being publicly available, it can only be accessed in separated disjoint tables, with different primary units of analysis. The objective is to offer an interoperable dataset containing monthly data from 2005 to 2021 with information on healthcare facilities, including their physical and human resources, services and teams, enriched with municipal information. DATA DESCRIPTION: Database with historical data and geographic information for each health facility in Brazil. It is composed by 5 distinct tables, organized according to combinations of time, space, and types of resources, services and teams. This database opens up a range of possibilities for research topics, from case studies in a single health facility and period, analysis of a group of health facilities with characteristics of interest, to a broader study using the entire dataset and aggregated data by municipality. Furthermore, the fact that there is a row for each health facility/month/year facilitates the integration with other datasets from the Brazilian healthcare system. In addition to being a potential object of study in the health area, the dataset is also convenient in data science, especially for studies focused on time series.

Dataset on infant mortality rates in Brazil.

OBJECTIVES: Surveillance of infant and fetal deaths is of paramount importance in thinking about government strategies to reduce these rates, provide greater visibility of these mortality figures in the country, enable the adoption of prevention measures, as well as contribute to a better record of deaths. DATA DESCRIPTION: The dataset comprises fetal, neonatal, early neonatal, late neonatal, and perinatal Mortality Rates of Brazilian municipalities with their respective information, between 2010 to 2020, aggregated by epidemiological week.

Elemental profiles in distant tissues during tumor progression.

BACKGROUND: Essential elements have functions in tumor progression by promoting protumoral cellular processes, such as proliferation, and migration, among others. Obtaining an understanding of how these elements relate to tumor progression processes is of great importance for research. Elemental profile studies in distant tissues, which can be modulated by tumor cells to promote metastasis, have not been sufficiently investigated. The main goal of this study is to evaluate multielemental distribution during tumor progression, focusing on tumor tissue and distant tissues that may be affected. METHODS: Tumor progression in vivo was simulated by inoculating C57BL/6 mice with Lewis Lung Carcinoma (LLC) cells. Samples of the primary tumor and distant tissues were collected during 5 weeks of tumor progression for the control and experimental (tumor-bearing) groups. The biological samples were analyzed using the synchrotron radiation X-Ray fluorescence technique. Data on the concentration of P, S, K, Ca, Mn, Fe, Cu, and Zn in the samples were obtained and statistically analyzed to evaluate the distribution of the elements during tumor progression in the primary tumor as well as distant tissues. RESULTS: It was possible to observe significant changes in the concentrations' distribution of P, S, K, Ca, Mn, Fe, and Cu in distant tissues caused by the presence of tumor cells. It was also possible to detect a greater similarity between tumor tissue (which has the lung as tissue of origin) and a tissue of non-origin, such as the liver, which is an unprecedented result. Moreover, changes in the distributions of concentrations were detected and studied over time for the different tissues analyzed, such as primary tumor, liver and lung, in Control and Tumor groups. CONCLUSIONS: Among other results, this paper could explore the modulation of distant tissues caused by the presence of a primary tumor. This could be achieved by the evaluation of several elements of known biological importance allowing the study of different biological processes involved in cancer. The role of essential elements as modulators of the tumor microenvironment is a relevant aspect of tumor progression and this work is a contribution to the field of tumoral metallomics.

Database on the coverage of the "Bolsa-Família" conditioning cash-transfer program: Brazil, 2005 to 2021.

OBJECTIVES: The "Bolsa-Família" Program (PBF) is a Brazilian conditional cash-transfer program in which families should comply with health, education, and social assistance conditionalities. The program aims to fight poverty and hunger, promoting nutrition and health services for low-income populations. This paper presents a database on the coverage of monitoring and compliance with the PBF health conditionalities in Brazil from January 2005 to July 2021. DATA DESCRIPTION: Database on the PBF conditioning cash-transfer program coverage in Brazil from 2005 to 2021. It comprises information on the number of families benefited, health conditionalities, and the follow-up on vaccination and nutrition of children under seven years old. The cities and semesters are the minimal aggregation units.

Estimation of COVID-19 Under-Reporting in the Brazilian States Through SARI.

Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant misinformation. Therefore, this work aims to estimate the under-reporting of cases and deaths of COVID-19 in Brazilian states using data from the InfoGripe. InfoGripe targets notifications of Severe Acute Respiratory Infection (SARI). The methodology is based on the combination of data analytics (event detection methods) and time series modeling (inertia and novelty concepts) over hospitalized SARI cases. The estimate of real cases of the disease, called novelty, is calculated by comparing the difference in SARI cases in 2020 (after COVID-19) with the total expected cases in recent years (2016-2019). The expected cases are derived from a seasonal exponential moving average. The results show that under-reporting rates vary significantly between states and that there are no general patterns for states in the same region in Brazil. The states of Minas Gerais and Mato Grosso have the highest rates of under-reporting of cases. The rate of under-reporting of deaths is high in the Rio Grande do Sul and the Minas Gerais. This work can be highlighted for the combination of data analytics and time series modeling. Our calculation of under-reporting rates based on SARI is conservative and better characterized by deaths than for cases.

Neonatal mortality rates in Brazilian municipalities: from 1996 to 2017.

OBJECTIVES: Neonatal mortality is a global public health problem, and the efforts to reduce child mortality is one of the goals of the 2030 Agenda for Sustainable Development, launched in 2015 by the United Nations. The availability of historical neonatal mortality rates (NMR) data in Brazilian municipalities is crucial to evaluate trends at local, regional and national level, identifying gaps and vulnerable territories. Therefore, the objective of this article is to offer an integrated dataset containing monthly data in a historical series from 1996 to 2017 with information on all births, neonatal deaths, and NMR (total, early and late components) enriched with information related to the municipality. DATA DESCRIPTION: It is a dataset of historical data with information on the number of births, the number of neonatal deaths, the neonatal mortality rate (including early and late), and geographic information for each month (between January 1996 and December 2017) and Brazilian municipality.

An analysis of malaria in the Brazilian Legal Amazon using divergent association rules.

In data analysis, the mining of frequent patterns plays an important role in the discovery of associations and correlations between data. During this process, it is common to produce thousands of association rules (ARs), making the study of each one arduous. This problem weakens the process of finding useful information. There is a scientific effort to develop approaches capable of filtering interesting patterns, balancing the number of ARs produced with the goal of not being trivial and known by specialists. However, even when such approaches are adopted, the number of produced ARs can still be high. This work contributes by presenting Divergent Association Rules Approach (DARA), a novel approach for obtaining ARs that presents themselves in divergence with the data distribution. DARA is applied right after traditional approaches to filtering interesting patterns. To validate our approach, we studied the dataset related to the occurrence of malaria in the Brazilian Legal Amazon. The discovered patterns highlight that ARs brought relevant insights from the data. This article contributes both in the medical and computer science fields since this novel computational approach enabled new findings regarding malaria in Brazil.

An integrated dataset of malaria notifications in the Legal Amazon.

OBJECTIVES: Malaria is an infectious disease that annually presents around 200,000 cases in Brazil. The availability of data on malaria is crucial for enabling and supporting studies that can promote actions to prevent it. Therefore, the goal of this paper is to contribute to such studies by offering an integrated dataset containing data on reported and suspected cases of malaria in the Brazilian Legal Amazon comprising the period from the years 2009 to 2019. DATA DESCRIPTION: This paper presents a dataset with all medical records of patients who were tested for malaria in the Brazilian Legal Amazon from 2009 to 2019. The dataset has 40 attributes and 22,923,977 records of suspected cases of malaria. Around 12% of the data correspond to confirmed cases of malaria. The attributes include data regarding the notifications, examinations, as well as personal patient information, which are organized into health regions.