Dataset on infant mortality rates in Brazil

Abstract

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.

Objective

Providing strategies to support the epidemiological surveillance of infant and fetal mortality is one of the priorities of the Brazilian Ministry of Health (MS), as it aims to reduce the country’s high mortality rates [1].

Reducing infant mortality is a major challenge for any society’s health services. This is a part of the new 2030 global agenda, Sustainable Development Goals (SDG), specifically in SDG 3.2, which aims to end preventable deaths of newborns and children under five years old. Despite progress, there are still obstacles to overcome in achieving this goal [2].

The Infant Mortality Rate (IMR) is an indicator used to measure infant mortality from the following formula:

$$\begin{array}{c}\hfill \frac{\text{Number of deaths of children under 1 year of age}}{\text{Number of live births}}X1.000\end{array}$$

According to the Epidemiological Bulletin of the Secretary of Health Surveillance of the Brazilian Ministry of Health from 1990 to 2019, there was a reduction in IMR in Brazil and in all its Regions. “In 2019, it was estimated that there were 38,619 infant deaths in Brazil, implying an infant death completeness rate of 91.4% and an IMR of 13.3 deaths per thousand live births, returning to the same level as in 2015. The largest reductions were observed in the states of the Northeast Region. In 2019, the lowest and highest IMR were estimated for the Federal District and Amapá, with 8.5 and 22.9 deaths per thousand live births, respectively” [3].

The dataset built by the team of the Platform of Data Science Applied to Health (“Plataforma de Ciência de Dados Aplicada á Saúde” - PCDaS) [4] fulfills its objective, which is to calculate the mortality rates in all Brazilian municipalities, aggregated by epidemiological week. By calculating these mortality rates, it is possible to analyze population and geographic variations in mortality, in addition to contributing to the assessment of health levels and socioeconomic development of the population, in order to support planning processes and political actions aimed at prenatal care, childbirth and the newborn, as explained in [1].

Data description

The mortality rates (fetal, early neonatal, late neonatal, neonatal, and perinatal) calculated in this work are present in the mr-mortality-rates [5] file along with geographic information about the respective Brazilian municipalities, aggregated by epidemiological week.

This dataset and related files are available for download as shown in Table 1

Table 1.

Overview of data files/datasets

Label	Name of data file/dataset	File type (file extension)	Data repository and identifier
Dataset	mr-mortality_rates	Delimited text (.csv)	Synapse: https://doi.org/10.7303/syn47199836 [5]
Data file 1	mr-dict	Delimited text (.csv)	Synapse: https://doi.org/10.7303/syn4719361 [6]
Data file 2	mr-municipalities	Delimited text (.csv)	Synapse: https://doi.org/10.7303/syn47194116 [7]
Data file 3	mr-federation_units	Delimited text (.csv)	Synapse: https://doi.org/10.7303/syn47194124 [8]
Data file 4	mr-extract_transform	Jupyter Notebook (.ipynb)	Synapse: https://doi.org/10.7303/syn47028493 [9]
Data file 5	mr-load	Jupyter Notebook (.ipynb)	Synapse: https://doi.org/10.7303/syn47028485 [10]
Data file 6	mr-utils	Python code (.py)	Synapse: https://doi.org/10.7303/syn47199024 [11]

Data construction - methodology

The development of this dataset is the result of a 3-step construction process: (i) extraction of public health data, (ii) data enrichment, and (iii) data validation and integration of annual bases.

• iThe generated datasets combine data from the Live Birth Information System (SINASC), the Mortality Information System (SIM), and the Mortality Information System Fetal Death Certificate (SIM-DOFET). These datasets are public at DATASUS and represent individual records of live births, deaths, and fetal deaths. The records were aggregated by epidemiological year and week, federative unit, and municipality (mother’s place of residence). The three fundamental datasets were filtered from the epidemiological year 2010. They were also selected in SIM and SIM-DOFET neonatal deaths and then separated into general (0–27 days), early (0–7 days) and late (8–27 days) neonatal deaths, perinatal deaths (22 completed weeks of gestation to seven completed days after birth), and fetal deaths (22 completed weeks of gestation).
• iiThe filtered datasets from the extraction of the different databases were grouped into a single general dataset containing information on fetal, neonatal, early neonatal, late neonatal, and perinatal mortality. Then, the data was enriched with information about the mother’s municipalities, states, and Brazilian regions of residence, thus generating new attributes in addition to the calculation of mortality rates.
• iiiThe database comprises 38 variables (columns) and 2,659,082 records (rows). Each row is equivalent to a municipality in an epidemiological week of a given year. There are 10 attributes referring to the number of deaths and their respective rates (fetal, early neonatal, late neonatal, neonatal and perinatal), 1 attribute referring to the number of births, 4 carrying temporal information, and 23 referring to spatial information. The values are relative to the places of residence and not the places of occurrence.
• ivSince mortality rates were calculated for the lowest possible levels of aggregation, municipalities, and epidemiological week, the researcher can perform aggregations at higher levels in space and time.

Limitations

• Due to the aggregation by epidemiological weeks and municipalities, multiple records do not have births or deaths and, consequently, do not have their respective values on infant mortality rates. When the record has births but no deaths, the rate is blank. Approximately 0.3% of the records have the births field empty. Around 92% of the records have no fetal mortality rate, 96% have no early neonatal mortality rate, and 98% have no late neonatal rate. Meanwhile, neonatal and perinatal mortality rates are missing in about 95% and 99% of records, respectively. Roughly 88% of the lines have neither rate. The southern region is the most affected, and this occurs in about 92% of its records. The least affected is the northern region, with 84%. The years up to 2015 also do not have a rate in approximately 88% of its records, which rises to about 89% in the following years.

• The infant mortality rate is calculated per thousand births. However, only 1,147 records (approximately 0.04% of the database) have a number of births greater than or equal to one thousand. This occurs relatively uniformly across all regions, states, and years. The Federal District (midwest region) has no record of a number of births greater than one thousand.

• There are 438 records (approximately 0.01% of the dataset) in which the number of deaths exceeds the number of births. The north is the least affected region (only about 0.006% of its records), and the southeast is the most affected one (about 0.02%). The year 2014 is the most affected (approximately 0.022%), and 2018 the least (0.011%). Numerous deaths combined with an excessive number of births lead to high rates.

• The dataset requires correction of the under-enumeration of deaths and live births (the latter on a smaller scale) for directly calculating the rate from data from continuous recording systems, especially in the North and Northeast regions. These circumstances dictate the use of indirect calculations, based on age-proportional mortality, concerning the infant mortality rate estimated by specific demographic methods.

Abbreviations

MS

Brazilian Ministry of Health, “Ministério da Saúde” in Portuguese.

Fiocruz

Oswaldo Cruz Foundation, “Fundação Oswaldo Cruz” in Portuguese.

PCDaS

Platform of Data Science Applied to Health, “Plataforma de Ciência de Dados aplicada á Saúde” in Portuguese.

SINASC

Live Birth Information System, “Sistema de Informações de Nascidos Vivos” in Portuguese.

SIM

Mortality Information System, “Sistema de Informações sobre Mortalidade” in Portuguese.

SIM-DOFET

Mortality Information System Fetal Death Certificate, “Sistema de Informações sobre Mortalidade - Declaração de óbitos Fetais” in Portuguese.y

Author contributions

All authors contributed equally to the study. MM and GS conceptualized the study design; GS, MM, VR, RG, VK, BP, CC and LC acquired the data; GS and MM conducted the data Extraction, Transformation, Loading (ETL) process; GS and MM documented the ETL process and wrote the manuscript. Furthermore SC; CL; RF; LZ; GA; AA; CB; NN; RS; JL; MP revised it critically for intellectual content. All authors read and approved the final manuscript.

Funding

The Bill & Melinda Gates Foundation [Grant ID INV-027,961] sponsored and supported the presented work, performing a crucial role in the design, data collection, and data interpretation. Under the grant conditions of the Bill & Melinda Gates Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.

Availability of data and materials

The data described in this Data Note are freely and openly available on the Synapse repository (https://doi.org/10.7303/syn26343262). The individual data files are available at: mr-mortality_rates [5]; mr-dict [6]; mr-municipalities [7]; mr-federation_units [8]; mr-extract_transform [9]; mr-load [10]; mr-utils [11].

Declarations

Ethics approval and consent to participate

We used data from open sources. The Brazilian Institute of Geography and Statistics, the Institute for Applied Economic Research, and the Ministry of Health of Brazil are committed to respecting the ethical precepts and ensuring data privacy and security. The Brazilian legislation exempts the use of public and anonymized secondary data from ethical approval.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.