Context

Brazilian summarized census data for each county and the calculated Human Development Index calculated by United Nations Development Programme (PNUD Brasil).

Content

Census data by municipality for the years 1191, 200, adn 2010. PNUD Brasil also calculates the Human Development Index (IDH in Portuguese).

Acknowledgements

Data was cleaned, summarized and published by the United Nations Development Programme

Inspiration

How to improve Brasil?

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.

Abstract Aim: This study aimed to examine the association between physical activity (PA) indicators and TV viewing as a function of the Human Development Index (HDI). Method: This cross-sectional study was based on data from the National School Health Survey, which was composed of 102,072 students (14.28±1.03; 51.3% girls). Total PA, active commuting to school (ACS) and TV viewing were assessed by questionnaires and classified through a gradual scale ranging from “F” (low) to “A+” (high). The correlation between total PA, ACS, TV viewing and HDI was verified by Spearman’s Correlation and presented in rs. Results: HDI was positively associated with total PA [girls: rs = 0.572 (p < 0.001); boys: rs = 0.843 (p < 0.001)] and ACS [girls: rs = 0.433 (p < 0.001); boys: rs = 0.554 (p < 0.001)]; while a negative correlation was found between HDI and TV viewing [girls: rs = -0.330 (p < 0.001); boys: rs = -0.348 (p < 0.001)]. Conclusions: Brazilian adolescents from states with higher HDI were more active and spent more time watching TV than their counterparts from states with lower HDI.

In addition to the individual values for 2001 and 2014, the average value, percentage variation, standard deviation and the parameters estimated in the autoregressive (AR) models are also shown.

Brazil Insurance Premium: Automotive: by Company: Hdi Seguros S.A. data was reported at 372,753,616.000 BRL in Sep 2025. This records a decrease from the previous number of 406,204,814.000 BRL for Aug 2025. Brazil Insurance Premium: Automotive: by Company: Hdi Seguros S.A. data is updated monthly, averaging 162,673,875.000 BRL from Sep 2000 (Median) to Sep 2025, with 301 observations. The data reached an all-time high of 427,527,407.000 BRL in Jul 2024 and a record low of 7,407,002.000 BRL in Sep 2000. Brazil Insurance Premium: Automotive: by Company: Hdi Seguros S.A. data remains active status in CEIC and is reported by Superintendence of Private Insurance. The data is categorized under Brazil Premium Database’s Insurance Sector – Table BR.RGB: Insurance Premium: Automotive: by Company.

0.76 (score) in 2020. A composite index measuring average achievement in three basic dimensions of human development—a long and healthy life, knowledge and a decent standard of living. 1=the most developed.

Introduction

This dataset contains information related to Brazilian states, like names, abbreviations, population size, latitude, longitude, capitals, area, GDP, HDI and much more. This data was compiled extracting several datasets from IBGE.

Association between physical activity time during physical education classes and geographic region of residence according to sex (n = 8,251).

CKDMR according to age groups and years for Brazil and its region time trend regression model for HDI.

Abstract: This study has as objective to verify the convergence betwixt Infant Mortality Rate decreasement and Human Development Index Municipal increasement in Big Regions and Federation Units in Brazil between 2000 to 2010. The type of study adopted is ecological. It is drawn to a conclusion that the Infant Mortality Rate's reduction and the Human Development Index Municipal's increase, happened in a non-homogeneous form in the areas of study.

Abstract Background Chronic noncommunicable diseases (CNCDs) caused more than 734,000 deaths (55% of all deaths) in Brazil in 2019, with an important socioeconomic impact. Objectives To analyze the mortality rates from CNCDs in Brazil from 1980 to 2019 and their association with socioeconomic indicators. Method This was a descriptive, time-series study of deaths from CNCDs in Brazil from 1980 to 2019. Data on the annual frequencies of deaths and on population were obtained from the Department of Informatics of the Brazilian Unified Health System. Crude and standardized mortality rates per 100,000 inhabitants were estimated using the direct method (Brazilian population in 2000). The quartiles of each CNCD were calculated, where a quartile change, due to an increase in mortality rate, was represented by a chromatic gradient. The Municipal Human Development Index (MHDI) of each Brazilian federative unit was extracted from the Atlas Brasil website and correlated with the rates of CNCD mortality. Results There was a reduction in mortality rates due to diseases of the circulatory system during the period, except in the Northeast Region. There was also an increase in mortality from neoplasia and diabetes, while the rates of chronic respiratory diseases showed little variation. There was an inverse correlation between the federative units with greater reduction in CNCD mortality rates and the MHDI. Conclusions The observed decrease in mortality due to diseases of the circulatory system may reflect an improvement in socioeconomic indicators in Brazil during the period. The increase in mortality rates due to neoplasms is probably related to the aging of the population. The higher mortality rates of diabetes seem to be associated with an increase in the prevalence of obesity in Brazilian women.

Suicide, Human Development Index (IDHM), and Discouragement Datasets and Analysis from Southern Brazil

Context

The dataset extracted from the website of the Brazilian Institute of Geography and Statistics (IBGE) contains all demographic, economic, geographic and human development information on Brazilian cities.

Content

There was no complete dataset to download all this information. So, I did a webscrapping that entered all the pages of each Brazilian cities and got all the information available. After that, I consolidated everything into a single file and now share with you to serve as research and studies of Brazil's performance on development, economics, and other topics.

This file contains 14 columns and 5571 rows (with headers):

• IBGECode - ID of the cities
• LocalCidade - Name of the cities
• LocalUF - State initials of the cities
• LocalEstado - Full name of city states
• RegiaoBrasil - IBGE's Region for each city
• Latitude - city ​​latitude
• Longitude - city ​​longitude
• Gentilico - name given to people born in the city
• PopEstimada_2018 - Population estimated in 2018
• PopCenso 2010 - Population in 2010
• IDHM - HDI of each city
• ReceitasRealizadas_2014 - Revenues realized by cities
• DespesasEmpenhadas_2014 - Expenditure incurred by cities
• Pib_2014 - GDP by cities

Acknowledgements

I thank my co-workers who helped me develop web scrapping and distribute the consolidated information to all of you.

Inspiration

Questions to be answered about this dataset:

1. What is the forecast of the GDP of each city in Brazil?
2. Can we group the cities by some specific division related to HDI or GDP?
3. What will be the total population of the Brazilian in the South of the Country in 2020?

And so on.

Context

It seeks to identify the influence of the human development index and the fatality rate of COVID-19.

Content

This dataframe contains the countries, number of cases and deaths until April 14, 2020. And the HDI - 2014 of the countries.

tx is rate of fatality (death/cases)

Acknowledgements

https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200413-sitrep-84-covid-19.pdf?sfvrsn=44f511ab_2 https://www.br.undp.org/content/brazil/pt/home/idh0/rankings/idh-global.html

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Brazil Insurance: Direct Premium: Automotive: by Company: Hdi Global Seguros S.A. data was reported at 2,641.000 BRL in Jun 2023. This records an increase from the previous number of 381.000 BRL for Dec 2022. Brazil Insurance: Direct Premium: Automotive: by Company: Hdi Global Seguros S.A. data is updated monthly, averaging 13,411.000 BRL from Nov 2019 (Median) to Jun 2023, with 37 observations. The data reached an all-time high of 170,031.000 BRL in Nov 2019 and a record low of -11,973.000 BRL in Aug 2020. Brazil Insurance: Direct Premium: Automotive: by Company: Hdi Global Seguros S.A. data remains active status in CEIC and is reported by Superintendence of Private Insurance. The data is categorized under Brazil Premium Database’s Insurance Sector – Table BR.RGB002: Direct Premium: Automotive: by Company.

Rotated component matrix for the variables included in the factor analysis for census sector.

Fixed and random effects parameters in the multilevel mixed-effect Poisson regression analysis for the null model according to outcome.

Brazil Insurance Premium Earned: Automotive: by Company: Hdi Global Seguros S.A. data was reported at 160.000 BRL in Nov 2024. This records a decrease from the previous number of 299.000 BRL for Oct 2024. Brazil Insurance Premium Earned: Automotive: by Company: Hdi Global Seguros S.A. data is updated monthly, averaging 17,891.000 BRL from Nov 2019 (Median) to Nov 2024, with 61 observations. The data reached an all-time high of 157,759.000 BRL in Sep 2022 and a record low of -31,759.000 BRL in Jul 2023. Brazil Insurance Premium Earned: Automotive: by Company: Hdi Global Seguros S.A. data remains active status in CEIC and is reported by Superintendence of Private Insurance. The data is categorized under Brazil Premium Database’s Insurance Sector – Table BR.RGB006: Premium Earned: Automotive: by Company.

Brazil Insurance: Loss Ratio: Automotive: by Company: Hdi Seguros de Automóveis e Bens S.A. data was reported at 0.840 % in Mar 2006. This records an increase from the previous number of 0.740 % for Feb 2006. Brazil Insurance: Loss Ratio: Automotive: by Company: Hdi Seguros de Automóveis e Bens S.A. data is updated monthly, averaging 0.790 % from Nov 2005 (Median) to Mar 2006, with 5 observations. The data reached an all-time high of 0.840 % in Mar 2006 and a record low of 0.700 % in Dec 2005. Brazil Insurance: Loss Ratio: Automotive: by Company: Hdi Seguros de Automóveis e Bens S.A. data remains active status in CEIC and is reported by Superintendence of Private Insurance. The data is categorized under Brazil Premium Database’s Insurance Sector – Table BR.RGD002: Insurance Loss Ratio: Automotive: by Company.