Primary health care coverage in Brazil: a dataset from 1998 to 2020.

OBJECTIVES: Primary health care builds the backbone of an effective healthcare system and can improve population health, reduce cost growth, and lessen inequality. We offer a machine-readable and open-access dataset on primary health care coverage in Brazil from 1998 to 2020. This dataset is interoperable with epidemiological data from two major studies and reusable by the research community worldwide for other purposes, such as monitoring progress toward universal health coverage and studying the association between primary health care and health outcomes. DATA DESCRIPTION: The dataset gathers official and public information from the "e-Gestor AB" platform of the Ministry of Health of Brazil and restricted data obtained by the Brazilian Access to Information Law. It includes 1,509,870 observations and 35 attributes aggregated by months/years and policy-relevant geographic units (country, macroregions, states, municipalities, and capitals) on primary health care team count and their absolute and relative population coverage estimates, information on the More Doctors Program implementation and physician counts, and spatial, demographic, and socioeconomic characteristics. We automated all data processing and curation in the free and open software R. The codes can be audited, replicated, and reused to produce alternative analyses.

Brazilian spatial, demographic, and socioeconomic data from 1996 to 2020.

OBJECTIVES: We present a database on Brazilian spatial, demographic, and socioeconomic characteristics from 1996 to 2020. This database aims for integration and harmonization with epidemiological data from two major studies. It can also be a valuable database for designing and conducting various types of epidemiologic research, such as health inequality studies, ecological studies (mapping and time-trends), and multi-level analysis. DATA DESCRIPTION: The database gathers official information obtained via open sources from the Brazilian Institute of Geography and Statistics, the Institute for Applied Economic Research, and the Ministry of Health. It includes 139,153 observations and 26 attributes aggregated by years and policy-relevant geographic units on geocoding of municipality centroids, total population size, child population by age-group, birth and mortality measures, Brazilian Municipal Human Development Index, Gini coefficient, Gross Domestic Product, and sanitation. We automated all data processing and curation in the free and open software R.

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.