In 2024, approximately 11.9 million people lived in São Paulo, making it the largest municipality in Brazil and one of the most populous cities in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. Brazil's cities Brazil is home to two large metropolises: São Paulo with close to 11.9 million inhabitants, and Rio de Janeiro with around 6.7 million inhabitants. It also contains a number of smaller but well-known cities, such as Brasília, Salvador, Belo Horizonte, and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. While smaller than some of the other cities, Brasília was chosen to be the capital because of its relatively central location. The city is also well-known for its modernist architecture and utopian city plan, which is quite controversial - criticized by many and praised by others. Sports venues capitals A number of Brazil’s medium-sized and large cities were chosen as venues for the 2014 World Cup, and the 2015 Summer Olympics also took place in Rio de Janeiro. Both of these events required large sums of money to support infrastructure and enhance mobility within a number of different cities across the country. Billions of dollars were spent on the 2014 World Cup, which went primarily to stadium construction and renovation but also to a number of different mobility projects. Other short-term spending on infrastructure for the World Cup and the Rio Olympic Games was estimated at 50 billion U.S. dollars. While these events have poured a lot of money into urban infrastructure, a number of social and economic problems within the country remain unsolved.

In 2025, approximately 23 million people lived in the São Paulo metropolitan area, making it the biggest in Latin America and the Caribbean and the sixth most populated in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. The second place for the region was Mexico City with 22.75 million inhabitants. Brazil's cities Brazil is home to two large metropolises, only counting the population within the city limits, São Paulo had approximately 11.45 million inhabitants, and Rio de Janeiro around 6.21 million inhabitants. It also contains a number of smaller, but well known cities such as Brasília, Salvador, Belo Horizonte and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. Mexico City Mexico City's metropolitan area ranks sevenths in the ranking of most populated cities in the world. Founded over the Aztec city of Tenochtitlan in 1521 after the Spanish conquest as the capital of the Viceroyalty of New Spain, the city still stands as one of the most important in Latin America. Nevertheless, the preeminent economic, political, and cultural position of Mexico City has not prevented the metropolis from suffering the problems affecting the rest of the country, namely, inequality and violence. Only in 2023, the city registered a crime incidence of 52,723 reported cases for every 100,000 inhabitants and around 24 percent of the population lived under the poverty line.

In Brazil, 70.6 percent of consumers earned at least the equivalent of the highest 40 percent of global income earners as of 2022 in purchasing power parity (PPP) terms. Those who earned at least the equivalent of the top 10 percent of global income earners stood at 7.4 percent.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

In 2024, six of the eight Brazilian cities with the highest homicide rates were in the Northeast. Feira da Santana led the ranking of the most violent city in Brazil, with a murder rate of ***** per 100,000 inhabitants. It was followed followed by Recife, with a homicide rate of more than ** per 100,000 inhabitants. In Latin America and the Caribbean, Feira da Santana was the **** most deadly city.

ABSTRACT This research aimed to analyze opinions and attitudes of professionals of the network of care to women in violence situation in 10 Brazilian cities. It is a quantitative cross-sectional study, carried out through semi-structured interviews with the participants of the workshops in these cities, totaling 438 individuals. A descriptive analysis was performed with frequency distributions, bivariate analysis and correspondence analysis. The number of professionals working on the suspected cases is higher than the number of those working on the confirmed cases of violence against women. Less than half of the professionals who attended the suspected cases has taken action on the matter of the fact. The adoption of some attitudes by the professionals was more common - even also being less than half of the majority of actions - in the face of the confirmed cases. Underreporting occurs in suspected cases and confirmed cases. Most of the interviewed people consider to be responsibility of the public health sector to develop preventing actions toward violence against women, with a high rate of unanswered cases. It is concluded that there is a long way for care to women in violence situation to be properly offered; professionals routinely refer more than address the cases, they poorly report them, they do not feel qualified, and sometimes do not even see themselves as the responsible for this care.

This dataset provides information on 52 in State of Rio de Janeiro, Brazil as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Acrelandia, located in the northern state of Acre, was the most polluted city in Brazil in 2022, based on fine air particulate matter concentration (PM2.5). Throughout the year, the city had an average particulate matter concentration of 23.3 micrograms per cubic meter. The World Health Organization's air quality standards recommend a maximum annual average concentration of 10 μg/m³. Four of the top five most polluted cities in Brazil that year were located in the state of Acre.

This dataset provides information on 37 in State of Rio de Janeiro, Brazil as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Description Supplementary Material from the original research article entitled: “Temperature and tree number drive of the tree crown-dwelling arthropod diversity in Brazilian semi-arid cities”. Authors: Arleu Barbosa Viana-Junior, Luiz Filipe Santos Silva, Edíson Cardoso Pinheiro-Júnior, Edna Karolyne do Nascimento Santos, Matheus Carvalho Araújo, Ítalo Emmanuel Costa Alves, Bruno da Silva Martins, Joselice da Silva Pereira, Rafaella Santana Santos, Bráulio Almeida Santos, Maria Avany Bezerra-Gusmão. ARTICLE INFORMATION: This study investigates the variations in community structure and taxonomic composition of the tree-dwelling arthropods in 10 Brazilian cities of semi-arid climate located in the dry forest region (Caatinga), taking into account temperature gradients and number of street trees along of the cities. We collected a total of 22,911 arthropod specimens belonging to two classes (Insecta and Arachnida) and 24 orders. Insecta accounted for 95% of the specimens and was dominated by the orders Coleoptera, Hemiptera, Hymenoptera, Thysanoptera, and Psocoptera. In the class Arachnida, Araneae was the most abundant order. As expected, temperature (min: 21.7°C, max: 26.8°C) proved to be a significant predictor of arthropod diversity in semi-arid cities. Cities with higher temperatures reduce taxonomic unit richness (0D) by 33% and diversity (1D and 2D) in up to 75% and affect composition of arthropod orders composition. On the other hand, the effect of tree number showed distinct responses among the sampled orders, positively contributing to the abundance of Psocoptera, while exerting a negative effect on the abundance of Thysanoptera.

SARS-CoV-2 spread rapidly in the Brazilian Amazon. Mortality was elevated, despite the young population, with the health services and cemeteries overwhelmed. The attack rate in this region is an estimate of the final epidemic size in an unmitigated epidemic. Here we show that by June, one month after the epidemic peak in Manaus, capital of the Amazonas state, 44% of the population had detectable IgG antibodies. This equates to a cumulative incidence of 52% after correcting for the false-negative rate of the test. Further correcting for the effect of antibody waning we estimate that the final attack rate was 66%. This is higher than seen in other settings, but lower than the predicted final size for an unmitigated epidemic in a homogeneously mixed population. This discrepancy may be accounted for by population structure as well as some limited physical distancing and non-pharmaceutical measures adopted in the city.

Methods Selection of blood samples for serology testing

Both the FPS and HEMOAM blood centers routinely store residual blood samples for six months after donation. In order to cover a period starting from the introduction of SARSCoV-2 in both cities, we retrieved stored samples covering the months of February to May in São Paulo, and February to June in Manaus, at which point testing capacity became available. In subsequent months blood samples were prospectively selected for testing. The monthly target was to test 1,000 samples at each study site. However, due to problems with purchasing the kits, supply chain issues, and the period of test validity, some months were under and others over the target (to avoid wasting kits soon to expire). We aimed to include donations starting from the second week of each month. Part of the remit of the wider project is to develop a system to prospectively select blood donation samples, based on the donor’s residential address, so as to capture a spatially representative sample of each participating city. For example, FPS receives blood donations from people living across the whole greater metropolitan region of São Paulo. The spatial distribution of donors does not follow the population density, with some areas over- and others under-represented. We used residential zip codes (recorded routinely at FPS) to select only individuals living within the city of São Paulo. We then further divided the city into 32 regions (subprefeituras) and used their projected population sizes for 2020 to define sampling weights, such that the number of donors selected in any given subprefeitura was proportional to the population size. We piloted this approach in São Paulo and have developed an information system to operationalize this process at the participating center. However, at the time of data collection the system was not implemented in HEMOAM and therefore it was not possible to use this sampling strategy. As such, we simply tested consecutive blood donations, beginning from the second week of each month until the target was reached.

Quantifying antibody waning and rate of seroreversion

We sought to quantify the rate of decline of the anti-nucleocapsid IgG antibody that is detected by the Abbott CMIA. We tested paired serum samples from our cohort of convalescent plasma donors (described above). We calculated the rate of signal decay as the difference in log2 S/C between the first and second time points divided by the number of days between the two visits. We used simple linear regression to determine the mean slope and 95% CI.

Analysis of seroprevalence data

Using the manufacturer's threshold of 1.4 S/C to define a positive result we first calculated the monthly crude prevalence of anti-SARS-CoV-2 antibodies as the number of positive samples/total samples tested. The 95% confidence intervals (CI) were calculated by the exact binomial method. We then re-weighted the estimates for age and sex to account for the different demographic make-up of blood donors compared to the underlying populations of São Paulo and Manaus (Fig. S4). Because only people aged between 16 and 70 years are eligible to donate blood, the re-weighting was based on the projected populations in the two cities in this age range only. The population projections for 2020 are available from (https://demografiaufrn.net/laboratorios/lepp/). We further adjusted these estimates for the sensitivity and specificity of the assay using the Rogan and Gladen method As a sensitivity analysis, we took two approaches to account for the effect of seroreversion through time. Firstly, the manufacturer's threshold of 1.4 optimizes specificity but misses many true-cases in which the S/C level is in the range of 0.4 – 1.4 (see ref and main text). In addition, individuals with waning antibody levels would be expected to fall initially into this range. Therefore, we present the results using an alternative threshold of 0.4 to define a positive result and adjust for the resultant loss in specificity. Secondly, we corrected the prevalence with a model-based method assuming that the probability of seroreversion for a given patient decays exponentially with time. In the model-based method for correcting the prevalence, only the months between March and August were considered. The measured prevalence used as input for this method was obtained using the manufacturer’s threshold of 1.4, and the correction based on the test specificity (99.9%) and sensitivity (84%) was applied, as well as the normalization by age and sex. Confidence intervals were calculated through bootstrapping, assuming a beta distribution for the input measured prevalence. It is worth noting that even though this model is limited by the exponential decay assumption, assuming distributions with more degrees of freedom may lead to overfitting due to the small number of samples of 9[7]. Finally, the obtained values for - and " must be interpreted as parameters for this model, and not estimates for the actual decay rate and seroreversion probability as they may absorb the effect of variables that are not taken into account by this model.

Infection fatality ratio

We calculated the global infection fatality ratio in Manaus and São Paulo. The total number of infections was estimated as the product of the population size in each city and the antibody prevalence in June (re-weighted and adjusted for sensitivity and specificity). The number of deaths were taken from the SIVEP-Gripe system, and we used both confirmed COVID-19 deaths, and deaths due to severe acute respiratory syndrome of unknown cause. The latter category likely represents COVID-19 cases in which access to diagnostic testing was limited , and more closely approximate the excess mortality. We calculated age-specific infection fatality ratios by assuming equal prevalence across all age groups.

Effective reproduction number

We calculated the effective reproduction number for São Paulo and Manaus using the renewal method9, with the serial interval as estimated by Ferguson (2020)10. Calculations were made using daily severe acute respiratory syndrome cases with PCR-confirmed COVID-19 in the SIVEP-Gripe system. Region-specific delays between the PCR result release and the date of symptom onset were accounted for using the technique proposed by Lawless (1994).

This dataset provides information on 1,707 in State of São Paulo, Brazil as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 78 in State of Minas Gerais, Brazil as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 370 in State of Minas Gerais, Brazil as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 20 in State of Rondônia, Brazil as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Abstract This article presents a critical reading of the text "Best in Travel 2015: Top 10 cities" and its translation to Brazilian Portuguese, both published online in 2014 by one of the world's largest tourism publishing houses, Lonely Planet. The study aims at revising some of the characteristics of the ongoing tourism discourse through an analysis of the network of people and practices involved in these publications, their textual features and images. The theoretical/analytical framework used includes Critical Discourse Analysis and a corpus-based tool used to interpret different aspects of this tourism discourse. The places advertised as "Top 10" are presented to an exclusive audience that must have digital literacy, economic power and the will to consume fetish-like, or "gourmetized" products.

Abstract

This study is an experiment designed to compare the performance of three methodologies for sampling households with migrants:

• a stratified sample using the census to sample census tracts randomly, in which each household is then listed and screened to determine whether or not it has a migrant, with the full length questionnaire then being applied in a second phase only to the households of interest;
• a snowball survey in which households are asked to provide referrals to other households with migrant members;
• an intercept point survey (or time-and-space sampling survey), in which individuals are sampled during set time periods at a prespecified set of locations where households in the target group are likely to congregate.

Researchers from the World Bank applied these methods in the context of a survey of Brazilians of Japanese descent (Nikkei), requested by the World Bank. There are approximately 1.2-1.9 million Nikkei among Brazil’s 170 million population.

The survey was designed to provide detail on the characteristics of households with and without migrants, to estimate the proportion of households receiving remittances and with migrants in Japan, and to examine the consequences of migration and remittances on the sending households.

The same questionnaire was used for the stratified random sample and snowball surveys, and a shorter version of the questionnaire was used for the intercept surveys. Researchers can directly compare answers to the same questions across survey methodologies and determine the extent to which the intercept and snowball surveys can give similar results to the more expensive census-based survey, and test for the presence of biases.

Geographic coverage

Sao Paulo and Parana states

Analysis unit

Japanese-Brazilian (Nikkei) households and individuals

The 2000 Brazilian Census was used to classify households as Nikkei or non-Nikkei. The Brazilian Census does not ask ethnicity but instead asks questions on race, country of birth and whether an individual has lived elsewhere in the last 10 years. On the basis of these questions, a household is classified as (potentially) Nikkei if it has any of the following: 1) a member born in Japan; 2) a member who is of yellow race and who has lived in Japan in the last 10 years; 3) a member who is of yellow race, who was not born in a country other than Japan (predominantly Korea, Taiwan or China) and who did not live in a foreign country other than Japan in the last 10 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

1) Stratified random sample survey

Two states with the largest Nikkei population - Sao Paulo and Parana - were chosen for the study.

The sampling process consisted of three stages. First, a stratified random sample of 75 census tracts was selected based on 2000 Brazilian census. Second, interviewers carried out a door-to-door listing within each census tract to determine which households had a Nikkei member. Third, the survey questionnaire was then administered to households that were identified as Nikkei. A door-to-door listing exercise of the 75 census tracts was then carried out between October 13th, 2006, and October 29th, 2006. The fieldwork began on November 19, 2006, and all dwellings were visited at least once by December 22, 2006. The second wave of surveying took place from January 18th, 2007, to February 2nd, 2007, which was intended to increase the number of households responding.

2) Intercept survey

The intercept survey was designed to carry out interviews at a range of locations that were frequented by the Nikkei population. It was originally designed to be done in Sao Paulo city only, but a second intercept point survey was later carried out in Curitiba, Parana. Intercept survey took place between December 9th, 2006, and December 20th, 2006, whereas the Curitiba intercept survey took place between March 3rd and March 12th, 2007.

Consultations with Nikkei community organizations, local researchers and officers of the bank Sudameris, which provides remittance services to this community, were used to select a broad range of locations. Interviewers were assigned to visit each location during prespecified blocks of time. Two fieldworkers were assigned to each location. One fieldworker carried out the interviews, while the other carried out a count of the number of people with Nikkei appearance who appeared to be 18 years old or older who passed by each location. For the fixed places, this count was made throughout the prespecified time block. For example, between 2.30 p.m. and 3.30 p.m. at the sports club, the interviewer counted 57 adult Nikkeis. Refusal rates were carefully recorded, along with the sex and approximate age of the person refusing.

In all, 516 intercept interviews were collected.

3) Snowball sampling survey

The questionnaire that was used was the same as used for the stratified random sample. The plan was to begin with a seed list of 75 households, and to aim to reach a total sample of 300 households through referrals from the initial seed households. Each household surveyed was asked to supply the names of three contacts: (a) a Nikkei household with a member currently in Japan; (b) a Nikkei household with a member who has returned from Japan; (c) a Nikkei household without members in Japan and where individuals had not returned from Japan.

The snowball survey took place from December 5th to 20th, 2006. The second phase of the snowballing survey ran from January 22nd, 2007, to March 23rd, 2007. More associations were contacted to provide additional seed names (69 more names were obtained) and, as with the stratified sample, an adaptation of the intercept survey was used when individuals refused to answer the longer questionnaire. A decision was made to continue the snowball process until a target sample size of 100 had been achieved.

The final sample consists of 60 households who came as seed households from Japanese associations, and 40 households who were chain referrals. The longest chain achieved was three links.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Stratified sampling and snowball survey questionnaire

This questionnaire has 36 pages with over 1,000 variables, taking over an hour to complete.

If subjects refused to answer the questionnaire, interviewers would leave a much shorter version of the questionnaire to be completed by the household by themselves, and later picked up. This shorter questionnaire was the same as used in the intercept point survey, taking seven minutes on average. The intention with the shorter survey was to provide some data on households that would not answer the full survey because of time constraints, or because respondents were reluctant to have an interviewer in their house.

2) Intercept questionnaire

The questionnaire is four pages in length, consisting of 62 questions and taking a mean time of seven minutes to answer. Respondents had to be 18 years old or older to be interviewed.

Response rate

1) Stratified random sampling 403 out of the 710 Nikkei households were surveyed, an interview rate of 57%. The refusal rate was 25%, whereas the remaining households were either absent on three attempts or were not surveyed because building managers refused permission to enter the apartment buildings. Refusal rates were higher in Sao Paulo than in Parana, reflecting greater concerns about crime and a busier urban environment.

2) Intercept Interviews 516 intercept interviews were collected, along with 325 refusals. The average refusal rate is 39%, with location-specific refusal rates ranging from only 3% at the food festival to almost 66% at one of the two grocery stores.

This dataset contains data samples of particulate matter concentration collected during four seasons between 2018 and 2019, in five Brazilian cities: three located in the South of the country (Camboriú, Florianópolis and Novo Hamburgo), one in the Southeast (Limeira), and one in the Midwest (Catalão). All samplings were made with High Volume Samplers (HiVol). For PM10 and PM2.5, the HiVols (Energetica®) had a constant flow of 1.13 m³/min, while for TSP the flow varied between 1.1 and 1.7 m³/min. PM10 was sampled in all the cities, while TSP was sampled in Catalão and Limeira and PM2.5 was collected only in Catalão. The meteorological data presented in this dataset are the average temperature (°C), pressure (mmHg), relative humidity (%), (wind speed (km;h), and rainfall (mm). The manual stations are located at these coordinates: (i) Federal Institute Catarinense (Camboriú, 27°0'51.372”S; 48°39'53.924”W); (ii) Federal University of Catalão (Catalão, 18°09'17.3"S; 47°55'38.7"W); (iii) Federal University of Santa Catarina (Florianópolis, 27°36'0.0”S; 48°31'12.0”W); (iv) School of Technology of the University of Campinas (Limeira, 22°33’43’’S; 47°25’23’’W); and (v) Feevale University (Novo Hamburgo, 29°40'12.4"S; 51°07'15.7"W).

This statistic shows the ten largest cities in Italy in 2025. In 2025, around 2.75 million people lived in Rome, making it the largest city in Italy. Population of Italy Italy has high population figures and a high population density in comparison to other European countries. A vast majority of Italians lives in urban areas and in the metropolises (as can be seen in this statistic), while other areas, such as the island Sardinia, are rather sparsely inhabited. After an increase a few years ago, Italy’s fertility rate, i.e. the average amount of children born to a woman of childbearing age, is now on a slow decline; however, it is still high enough to offset any significant effect the decrease might have on the country’s number of inhabitants. The median age of Italy’s population has been increasing rapidly over the past 50 years – which mirrors a lower mortality rate – and Italy is now among the countries with the highest life expectancy worldwide, only surpassed by two Asian countries, namely Japan and Hong Kong. Currently, the average life expectancy at birth in Italy is at about 83 years. Most of Italy’s population is of Roman Catholic faith. The country actually boasts one of the largest numbers of Catholics worldwide; other such countries include Brazil, Mexico and the United States. The central government of the Roman Catholic Church, the Holy See, is located in Vatican City in the heart of Italy’s capital and ruled by the Bishop of Rome, the Pope. Officially, Vatican City does not belong to Italy, but is a sovereign state with its own legislation and jurisdiction. It has about 600 inhabitants, who are almost exclusively members of the clergy or government officials.

BackgroundAn interferon-γ release assay, QuantiFERON-TB (QFT) test, has been introduced an alternative test for the diagnosis of latent Mycobacterium tuberculosis infection (LTBI). Here, we compared the performance of QFT with tuberculin skin test (TST) measured at two different cut-off points among primary health care work (HCW) in Brazil.MethodsA cross-sectional study was carried out among HCWs in four Brazilian cities with a known history of high incidence of TB. Results of the QFT were compared to TST results based on both ≥5 mm and ≥10 mm as cut-off points.ResultsWe enrolled 632 HCWs. When the cut-off value of ≥10 mm was used, agreement between QFT and TST was 69% (k = 0.31), and when the cut-off of ≥5 mm was chosen, the agreement was 57% (k = 0.22). We investigated possible factors of discordance of TST vs QFT. Compared to the TST−/QFT− group, risk factors for discordance in the TST+/QFT− group with TST cut-off of ≥5 mm included age between 41–45 years [OR = 2.70; CI 95%: 1.32–5.51] and 46–64 years [OR = 2.04; CI 95%: 1.05–3.93], BCG scar [OR = 2.72; CI 95%: 1.40–5.25], and having worked only in primary health care [OR = 2.30; CI 95%: 1.09–4.86]. On the other hand, for the cut-off of ≥10 mm, BCG scar [OR = 2.26; CI 95%: 1.03–4.91], being a household contact of a TB patient [OR = 1.72; CI 95%: 1.01–2.92] and having had a previous TST [OR = 1.66; CI 95%: 1.05–2.62], were significantly associated with the TST+/QFT− group. No statistically significant associations were found among the TST−/QFT+ discordant group with either TST cut-off value.ConclusionsAlthough we identified BCG vaccination to contribute to the discordance at both TST cut-off measures, the current Brazilian recommendation for the initiation of LTBI treatment, based on information gathered from medical history, TST, chest radiograph and physical examination, should not be changed.