Dataset Overview

This dataset provides a comprehensive overview of Brazil’s 2022 Census data, focusing on São Paulo’s neighbourhoods. The data combines demographic and socioeconomic information with geospatial shapefiles of São Paulo’s neighbourhoods, enabling users to perform statistical and spatial analyses.

Users can explore patterns, trends, and transformations in São Paulo’s urban landscape by linking census sectors to neighbourhood boundaries.

Key Components

Census 2022 Data

• Source: Brazil's 2022 Census (IBGE)
• Content: Demographic data including age, gender, income, education levels, household size, and population density across census sectors.
• Format: CSV

São Paulo Neighborhood Shapefile

• Source: GIS-based shapefiles for São Paulo neighbourhoods (IBGE Census Sectors and Manually created Neighbourhoods)
• Content: Spatial geometry for São Paulo's neighbourhoods with census sector identifiers.
• Format: Parquet

Use Cases

• Neighborhood Demographics Analysis: Combine census data with shapefiles to generate neighborhood-level demographic reports.
• Urban Development Studies: Study how São Paulo neighbourhoods have grown using historical context and 2022 Census data.
• Spatial Data Visualizations: Create maps showing income distribution, population density, or other demographic factors across neighbourhoods.
• Policy Planning & Research: Support urban planning, resource allocation, and policy development in São Paulo.

Potential Applications

• Analyze the relationship between neighbourhood demographics and urban growth patterns.
• Visualize inequalities in population distribution, income, or education levels.
• Identify trends in housing and population density for urban studies.
• Provide insights into São Paulo’s historical and ongoing transformations.

Why Use This Dataset?

• Comprehensive Coverage: Detailed census data and spatial boundaries allow in-depth analyses.
• Flexible Integration: Easily combine demographic data with shapefiles to enable advanced spatial analyses.

Dataset Details

• File Formats: CSV (Census Data), GeoJSON/Shapefile (Neighborhood Shapefiles)
• Spatial Resolution: Census sector linked to São Paulo’s neighbourhood boundaries

Geographic Scope: São Paulo, Brazil

This dataset is ideal for data scientists, urban planners, and researchers seeking to uncover the dynamics of São Paulo’s neighbourhoods through an intersection of demographic and spatial data.

Contribute to new insights and empower decision-making in understanding Brazil’s largest city!

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1) Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of - 0.2 NDVI to represent an improvement in forest quality. 2) Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3) Topographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

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).

Aedes aegypti is one of the species most favored by changes in the environment caused by urbanization. Its abundance increases rapidly in the face of such changes, increasing the risk of disease transmission. Previous studies have shown that mosquito species that have adapted to anthropogenic environmental changes benefit from urbanization and undergo population expansion. In light of this, we used microsatellite markers to explore how urbanization processes may be modulating Ae. aegypti populations collected from three areas with different levels of urbanization in the city of São Paulo, Brazil. Specimens were collected at eleven sites in three areas with different degrees of urbanization in the city of São Paulo: conserved, intermediate and urbanized. Ten microsatellite loci were used to characterize the populations from these areas genetically. Our findings suggest that as urbanized areas grow and the human population density in these areas increases, Ae. aegypti populations undergo a major population expansion, which can probably be attributed to the species’ adaptability to anthropogenic environmental changes. Our findings reveal a robust association between, on the one hand, urbanization processes and densification of the human population and, on the other, Ae. aegypti population structure patterns and population expansion. This indicates that this species benefits from anthropogenic effects, which are intensified by migration of the human population from rural to urban areas, increasing the risk of epidemics and disease transmission to an ever-increasing number of people.

OBJECTIVE To describe the migration flows of demand for public and private hospital care among the health regions of the state of Sao Paulo, Brazil. METHODS Study based on a database of hospitalizations in the public and private systems of the state of Sao Paulo, Southeastern Brazil, in 2006. We analyzed data from 17 health regions of the state, considering people hospitalized in their own health region and those who migrated outwards (emigration) or came from other regions (immigration). The index of migration effectiveness of patients from both systems was estimated. The coverage (hospitalization coefficient) was analyzed in relation to the number of inpatient beds per population and the indexes of migration effectiveness. RESULTS The index of migration effectiveness applied to the hospital care demand flow allowed characterizing health regions with flow balance, with high emigration of public and private patients, and with high attraction of public and private patients. CONCLUSIONS There are differences in hospital care access and opportunities among health regions in the state of Sao Paulo, Brazil.

Background: Improper antibiotic use is one of the main drivers of bacterial resistance to antibiotics, increasing infectious diseases morbidity and mortality and raising costs of healthcare. The level of antibiotic consumption has been shown to vary according to socioeconomic determinants (SED) such as income and access to education. In many Latin American countries, antibiotics could be easily purchased without a medical prescription in private pharmacies before enforcement of restrictions on over-the-counter (OTC) sales in recent years. Brazil issued a law abolishing OTC sales in October 2010. This study seeks to find SED of antibiotic consumption in the Brazilian state of São Paulo (SSP) and to estimate the impact of the 2010 law. Methods: Data on all oral antibiotic sales having occurred in the private sector in SSP from 2008 to 2012 were pooled into the 645 municipalities of SSP. Linear regression was performed to estimate consumption levels that would have occurred in 2011 and 2012 if no law regulating OTC sales had been issued in 2010. These values were compared to actual observed levels, estimating the effect of this law. Linear regression was performed to find association of antibiotic consumption levels and of a greater effect of the law with municipality level data on SED obtained from a nationwide census. Results: Oral antibiotic consumption in SSP rose from 8.44 defined daily doses per 1,000 inhabitants per day (DID) in 2008 to 9.95 in 2010, and fell to 8.06 DID in 2012. Determinants of a higher consumption were higher human development index, percentage of urban population, density of private health establishments, life expectancy and percentage of females; lower illiteracy levels and lower percentage of population between 5 and 15 years old. A higher percentage of females was associated with a stronger effect of the law. Conclusions: SSP had similar antibiotic consumption levels as the whole country of Brazil, and they were effectively reduced by the policy.

Age distribution by sex for the city of São Paulo, total study population and PHC facilities of individuals 20 years of age and older.

This statistic shows the age structure in Brazil from 2013 to 2023. In 2023 about 19.94 percent of Brazil's total population were aged 0 to 14 years. Population of Brazil Brazil is the fifth largest country in the world by area and population and the largest in both South America and the Latin American region. With a total population of more than 200 million inhabitants in 2013, Brazil also ranks fifth in terms of population numbers. Brazil is a founding member of the United Nations, the G20, CPLP, and a member of the BRIC countries. BRIC is an acronym for Brazil, Russia, India, and China, the four major emerging market countries. The largest cities in Brazil are São Paulo, Rio de Janeiro and Salvador. São Paulo alone reports over 11.1 million inhabitants. Due to a steady increase in the life expectancy in Brazil, the average age of the population has also rapidly increased. From 1950 until 2015, the average age of the population increased by an impressive 12 years; in 2015, the average age of the population in Brazil was reported to be around 31 years. As a result of the increasing average age, the percentage of people aged between 15 and 64 years has also increased: In 2013, about 68.4 percent of the population in Brazil was aged between 15 and 64 years.

In the state of São Paulo, Brazil, the population in 2021 was composed by 63.7 percent of people who identified as white. However, this ethnic group only represented 31 percent of the civilians killed by security agents. Meanwhile, 69 percent of civilian deaths caused by the police were black people, who constituted a little more than a third of the state's population. Moreover, the share of people of black ethnicity killed by the police in the state's capital reached nearly 70 percent of the total that year.

Abstract

Brazil has made significant progress in human development over the last decade, thanks to a series of policy innovations, and equity of access has increased considerably. In health, consolidation of government health financing, the organization of the sector into a country-wide system (Unified Health System, or SUS) and the greater emphasis on primary care have been critical for these improvements.

Increasing the efficiency and effectiveness in the use of health resources to contain rising costs is perhaps the greatest challenge facing the Brazilian health system.

Brazil’s federal structure and the decentralized nature of the SUS make the financial flows difficult to track and monitor. Despite continuous upgrading, existing information systems do not permit accurate identification of how resources are allocated within the context of SUS, nor how expenditures are executed and services provided at the health unit level. Information is lacking regarding how much SUS as a whole (including the federal, state and municipal governments) spends on hospital and primary care. The levels of efficiency in health service provision are not systematically documented.

This study assesses how the processes of allocation, transfer and utilization of resources are conducted at the different levels of the system. The study provides valuable information regarding the reality of the executing units of the system and how these relate to the central levels. It also seeks to identify problems related to financial flows, analyze how resources are used at the local level, and estimate their impact on the efficiency and quality of health services in general. In this respect, the study provides a basis for improving the entire cycle of public resource management processes (i.e., planning, budgeting, budget execution, input management, and health service production) in the health sector.

The survey was based on a sample of six states, 17 municipalities in those six states, and 49 hospitals and 20 outpatient units in the sampled municipalities. While the sample is not statistically representative of SUS as a whole because of its small size, an effort was made to capture a variety of situations found in the Brazilian federation so that the findings would exemplify typical conditions found in SUS.

Geographic coverage

States of Amazonas, Ceará, Mato Grosso, Rio de Janeiro, Rio Grande do Sul and São Paulo.

Analysis unit

• Ministry of Health;
• State Health Secretariats;
• Municipal Health Secretariats;
• health units (hospitals and outpatient clinics).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample selected for the study was designed in order to highlight the regional variations between the health units and at the same time to keep logistical costs to a minimum. For these reasons, a non-randomized sampling in three stages was chosen: first, the sample covered states, second, the municipalities located in those states, and third, health units located within the municipalities. This sampling structure was chosen in order to permit tracking of the resource flows within a particular state and the cross-referencing of information at the three levels of the research.

Initially, the sample took into account six states with their respective state health secretariats, 18 municipalities and 76 health units (52 hospitals and 24 outpatient clinics). As a result of data collection being abandoned in one particular municipality as well as in a number of health units, and given the difficulty of accessing certain information, the final sample encompassed 17 municipalities (Municipal Health Secretariats), 49 hospitals (public and philanthropic), and 20 outpatient clinics (state and municipal).

Although the resulting sample reflects the very different circumstances existing within Unified Health System (SUS), it is too small for each stratum of units and consequently does not allow statistical extrapolation of the results.

In the sampling exercise, states were selected to represent each of the six Brazilian major regions (for the southeast region two states were included given the population density and a high concentration of health establishments). One of the main criteria for selection was to reflect the diversity in size and different characteristics of the states, municipalities and health units.

Municipalities were selected on the basis of size. State capitals were included, plus one middle-sized municipality per state (roughly 200,000 inhabitants) and at least one small-sized municipality (of approximately 50,000 inhabitants). The resulting sample of municipalities could be considered reasonably representative of the diverse nature of SUS.

The hospitals selected were required to meet the following requirements: to attend mainly to SUS users, to have a minimum of 50 beds, to possess reasonable information systems and to be broadly representative of SUS as such. Various hospitals were included in the sample that had been included in other recent studies which made it possible to cross-reference and compare information. The proposed distribution focused on public hospitals since the main thrust of the study concerned budget relationships and transfers of resources. This sample was stratified by size (medium-sized/big and small hospitals) and sphere, in order to try and obtain a sufficient number of units of each type to produce representative results. Efforts were also made to include hospitals with different characteristics such as those that undertake teaching and research and public hospitals administered under different kinds of management arrangements.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires were applied in the course of interviews with state health secretaries or someone designated by them (normally a professional charged with a specific area with access to the necessary information); municipal health secretaries (or designates); directors of hospitals; and directors of outpatient departments/clinics. Moreover, concurrent side interviews were undertaken with staff from a number of different technical and administrative divisions with the aim of clarifying and amplifying the research findings. Finally, together with the application of the questionnaire, reports and other supporting documents were requested relating to budgets, plans, management reports, etc.

The internal structure of the questionnaires was common to all types of units researched (SES and SMS, hospitals and outpatient clinics), although obviously the content of each section is specific to each type of unit.

The basic format of the questionnaire was organized around planning and budget allocation and implementation processes and the main inputs used in health service delivery (i.e., materials and medical drugs, human resources and equipment/installations). The component sections of the questionnaire were the following: • Section A - Information from the secretariats or health units. This section gives the identity details of the units researched, the name of the person responsible for the unit and details about the profile and type of unit (in the case of hospitals and outpatient clinics, the number of beds and services on offer are included). • Section B - Budgetary planning and processes. This section examines the budget and planning process at its different stages, the degree of autonomy in the preparation and implementation stages of the budget, the delays in releasing and applying funds, the differences between the values requested, approved and executed, including the use of the ‘up-front’ payment/petty cash system. • Section C - Purchases, materials and drugs management. This section deals with information regarding the purchasing and storage systems, including pharmacy. Surveys were done basically to elucidate the physical condition of stocks, delays in bidding processes and the impact of these elements on service delivery. • Section D - Equipment and installations. This section examined the equipment estate, covering inter alia the frequency rate of breakdowns/breakages in addition to examining the physical conditions of installations. • Section E - Human resources. Information was sought in the section regarding the staff, its distribution, qualifications, absenteeism and any failure to comply with working hours. • Section F - Hospital and outpatient clinic expenditure. In this section data was sought on the expenditure by type and receipts by source, together with an analysis of the service providers and the impact of receipts from SUS on overall expenditure. • Section G - Hospital and outpatient clinic productivity. Data was collected regarding the productivity of the units and, wherever possible, performance and quality indicators were calculated.

Supplementary documentation requested included: • Municipal/State Health Agenda (2002-2003); • Municipal/State Health Plan (2002-2003); • Current Multi-Year Plan (referring to health); • Budget Guidelines Law (2002-2003); • Municipal/State Health Budget (2002-2003); • Documentary evidence of present budget execution (2002 and first half of 2003); • Municipal/State Balance Sheets, Annex 2, 6 (Health section),10 and 11, for 2002; • Management Reports (2002). • Personnel Allocation Chart • Organization chart of Institution

The Southeast Region in Brazil, was the region with the highest density of surgeons in the country in 2022, with **** surgeons per 100,000 people. The most populated cities in Brazil, like Rio de Janeiro and São Paulo, are located in this region. That year, São Paulo was the city with the highest number of doctors in the country.

Aim: As a step towards providing support for an ecological approach to strengthening marine protected areas (MPAs) and meeting international commitments, this study combines cumulative impact assessment and conservation planning approach to undertake a large-scale spatial prioritisation.

Location: Exclusive Economic Zone (EEZ) of Brazil, Southwest Atlantic Ocean

Methods: We developed a prioritisation approach to protecting different habitat types, threatened species ranges, and ecological connectivity, while also mitigating the impacts of multiple threats on biodiversity. When identifying priorities for conservation, we accounted for the co-occurrence of 24 human threats and the distribution of 161 marine habitats and 143 threatened species, as well as their associated vulnerabilities. Additionally, we compared our conservation priorities with MPAs proposed by local stakeholders.

Results: We show that impacts to habitats and species are widespread and identify hotspots of cumulative impacts on inshore and offshore areas. Industrial fisheries, climate change, and land-based activities were the most severe threats to biodiversity. The highest priorities were mostly found towards the coast due to the high cumulative impacts found in nearshore areas. As expected, our systematic approach showed a better performance on selecting priority sites when compared to the MPAs proposed by local stakeholders without a typical conservation planning exercise, increasing the existing coverage of MPAs by only 7.9%. However, we found that proposed MPAs still provide some opportunities to protect areas facing high levels of threats.

Main conclusions: The study presents a blueprint of how to embrace a comprehensive ecological approach when identifying strategic priorities for conservation. We advocate protecting these crucial areas from degradation in emerging conservation efforts is key to maintain their biodiversity value.

Methods

Habitat Data We We conceptualised a GIS-based habitat map (also often labelled ‘ecosystems’) at a national scale by assembling data from peer-reviewed literature, publicly-available and unpublished datasets, governmental and nongovernmental reports, and performed complementary GIS analysis. We developed a hierarchical classification scheme that accomplished a fine delineation of benthic habitats throughout Exclusive Economic Zone (EEZ) of Brazil and resulted in 161 distinct and non-overlapping classes of habitat types. Recognising the spatial structure of the marine environment, we developed separate pelagic (N=11) and benthic habitats (N=150) to account for different types and resolution of available data. The benthic habitats were delineated following a nested hierarchical classification scheme as a result of specific combinations of ecoregions (sensu Spalding et al. 2007), depth zones, seascape units (delineation of the seabed into hard, soft, or mixed substrate types), and within-habitat specificities (in places where more detailed data were available). Whenever possible, we obtained digital maps containing the extension of marine habitats (e.g., nearshore banks of coral reefs); however, some spatial data were manually digitised and inserted in the GIS (e.g., seagrass meadows). We assigned unique code identifiers, names, and descriptions to the marine habitats (Table S1 of the paper). We used expert opinion within the authors to revise the draft habitats produced by the analytic steps described in Supporting Information of the paper. The level-1 habitats comprised eight ecoregions (1. Amazon; 2. Northeastern; 3. Eastern; 4. Fernando de Noronha and Atoll das Rocas; 5. São Pedro and São Paulo Islands; 6. Trindade and Martin Vaz Islands; 7. Southeastern Brazil; and 8. Rio Grande), from costal to abyssal environments. The level-2 habitats provided a sub-delineation of the level-1 habitats following a depth-related differentiation in habitat distribution defined by geophysical constraints (Last et al., 2010), which resulted in six depth zones: coastal shelf (< 25 m), mid shelf (25-75 m), outer shelf (75-200 m), upper slope (200-700 m), lower slope (700-2,000 m), and abyss (>2,000 m). The level-3 habitats provided a further partition of the previous habitats and were based on mappable structures, which are of conservation interest, defined by habitat forming species or geomorphological structures, and assumed to be surrogates for distinctive biological assemblages. The seascape units were delimited by convenient boundaries and included: (i) beaches/sand dunes; (ii) Bryozoa reefs; (iii) canyons; (iv) cold-water reefs (based on the occurrence of deep-sea reef-constructing corals Lophelia pertusa, Solenosmilia variabilis, Enallopsammia rostrate, and Madrepora oculata); (v) estuaries; (vi) Halimeda reefs; (vii) kelps; (viii) mesophotic reefs; (ix) rhodolith beds; (x) seagrass meadows; (xi) shallow-water coral reefs; (xii) shallow-water rocky reefs; (xiii) mangroves; (xiv) seamounts; (xv) Bryozoan reefs ; and (xvi) submarine fan deltas. The pelagic habitats were classified based on a cluster analysis of ecological data that serve as surrogates for assemblages of pelagic species. For pelagic habitats, we acquired environmental parameters from open-access sources (Bio-ORACLE v2.0; (Assis et al., 2018) for the top surface layer of the ocean (2000-2014), including salinity (mean), sea surface temperature (mean, min, max), dissolved molecular oxygen (mean), chlorophyll a (mean), and nutrients (nitrate, phosphate, and silicate; mean). We downloaded the images into ArcGIS 10.1 (ESRI, 2011) and all raster datasets were projected to an Albers equal-area projection with metre measurement units. With every pixel in the study area characterized by all parameters, we used a cluster analysis to understand the distinctive habitats with within the study area. We used the hierarchical Iso Cluster Unsupervised Classification tool in ArcGIS to define pelagic habitats. We used default settings for all parameters.Descriptions of each physical environmental characteristics underpinning each pelagic habitat are summarized in Table S2.

Threatened species

We obtained species distribution ranges and assessment of identified threats available for 143 animal species (invertebrates, fishes, mammals, turtles, and seabirds) listed under national legislation with a status of Critically Endangered, Endangered, or Vulnerable. Range maps for all species were obtained from shapefiles downloaded from the National Red List of Threatened Species spatial data repository (i.e., the national agency for biodiversity conservation, ICMBio), the literature, and the Aquamaps dataset (Kesner-Reyes, K. Kaschner, Kullander, Garilao, Barile, & Froese, 2016). The spatial data were processed by constraining them to the geographic distribution within the appropriate depth ranges for each species according to the text information in each species assessment. Following established practice, for wide-ranging (i.e., > 20,000 km2) mammals, seabirds, and turtles, distribution maps corresponded to key areas for species conservation (breeding, foraging, calving or nursery areas) rather than encompassing large portions of habitats discontinuities (e.g., whale migration routes). However, spatial distribution data of breeding, foraging, or nursery grounds for fishes and invertebrates with large range extents were not available at the national scale; thus, the geographic distribution of the species within these groups was delimited using occurrence records and reported depth ranges. We checked for the quality of all distribution data for each species when more than one data provider was identified. We are confident this represents the best available database on the distribution of threatened marine species in Brazil.

Spatial data on threats

Industrial fishing information consisted of vessel monitoring systems (VMS) data from 10 fisheries and associated gear types that are monitored within Brazilian waters by government agencies (Programa nacional de rastreamento das embarcações pesqueiras por satélite - PREPS). We estimated the density of points (fishing operations) for each gear type separately over a period of three years (2015-2017). For this analysis, we obtained a total of 4,205,607 data points representing signals produced from 905 vessels in fishing operations. We created a layer for each gear type by dividing the number of signals in each raster cell of 1 km2 by the total number of signals emitted by all vessels operating that specific gear type. This produced our map of relative threats for each gear type. For global warming, we used the 4-km Advanced Very High Resolution Radiometer (AVHRR) Version 5.0 sea surface temperature (SST) data produced by NOAA’s National Oceanographic Data Center spanning the time period (1985-2009) to calculate rate of warming using non-linear mixed effect models (package nlme in R) as described by (Magris, Heron, & Pressey, 2015). SST data were composited to monthly resolution (from weekly) for calculation of trends. Information on ultraviolet (UV) radiation, ocean acidification, shipping lanes, and invasive species were obtained from global, publicly available datasets (Halpern et al., 2012, 2008). The coastal development index was calculated as described by (Aubrecht et al., 2008), whose index was measured by distance from emission of night-time lights, a proxy for human settlement and urbanisation. Night-time satellite imagery was obtained by NOAA's National Geophysical Data Center (NGDC). We chose a 25 km radius as a reasonable distance from a source of high night light at which many key human impacts from coastal development (e.g. domestic housing and presence of engineering structures) might affect the marine environment.

Information on mining (i.e. geographic areas of active or exploratory mines [N=564]) and oil/gas fields (i.e. geographic areas either licensed for exploration

In 2024, there were over ******* specialized doctors registered in Brazil. The medical practice with the highest number of physicians was internal medicine, with over ****** medical professionals. Pediatrics followed, with around ****** specialists, while general surgeons amounted to approximately ****** professionals. Physician workforce in Brazil In Brazil, the total number of registered physicians reached over ******* licensed professionals in 2024, with most doctors being specialists. Broken down by gender, the South American country had more men actively practicing medicine than women, with around **** female doctors for every male physician. That year, São Paulo was the state with the most registered doctors in Brazil, followed by Rio de Janeiro and Minas Gerais. Telemedicine services in Brazil Telemedicine, or telehealth, often refers to the provision of healthcare services by technological means. Through telemedicine, health practitioners can provide a wide variety of services, including remote diagnosis, treatment, and monitoring of health conditions. In Brazil, revenue stemming from online doctor consultations is expected to increase to approximately *********** U.S. dollars by 2027 along with the number of users of online doctor consultations, which are forecast to reach over ************ people by the same year.

Abstract: Crabs of the genus Persephona are intensely captured in shrimp fisheries as bycatch in the Cananéia region off the coast of the state of São Paulo, Brazil. The analysis of the spatial and temporal distribution of Persephona punctata and Persephona mediterranea could provide information about variation in the abundance of these species, as well as the environmental variables affecting their distribution and the existence of a possible habitat partitioning. Using a shrimp fishery boat equipped with double-rig nets, crabs were monthly captured from July 2012 to June 2014 in seven sites: four in the coastal area adjacent to the Cananéia region and three in the Mar Pequeno estuarine area. The abundances of both species were compared according to spatial (among sites) and temporal (years and seasons) scale distribution. A total of 396 individuals of P. punctata and 64 of P. mediterranea were captured. The abundance of both species was higher in the second sampling year (July 2013-June 2014) and in coastal areas; only one individual of each species was captured in the estuarine area due to the low salinity at this location (approximately 27.7‰). The temperature was the environmental variable that most affected the distribution of both species, which was more abundant in warmer periods. The temporal variation in abundance was modulated by temperature, while salinity modulated the spatial distribution of P. punctata and P. mediterranea. The spatial-temporal distribution of both species differered in Cananéia, pointing to a similar use of the environment's resources.

In this eight-year retrospective study, we evaluated the associations between climatic variations and the biological rhythms in plasma lipids and lipoproteins in a large population of Campinas, São Paulo state, Brazil, as well as temporal changes of outcomes of cardiovascular hospitalizations. Climatic variables were obtained at the Center for Meteorological and Climatic Research Applied to Agriculture (University of Campinas - Unicamp, Brazil). The plasma lipid databases surveyed were from 27,543 individuals who had their lipid profiles assessed at the state university referral hospital in Campinas (Unicamp). The frequency of hospitalizations was obtained from the Brazilian Public Health database (DATASUS). Temporal statistical analyses were performed using the methods Cosinor or Friedman (ARIMA) and the temporal series were compared by cross-correlation functions. In normolipidemic cases (n=11,892), significantly different rhythmicity was observed in low-density lipoprotein (LDL)- and high-density lipoprotein (HDL)-cholesterol (C) both higher in winter and lower in summer. Dyslipidemia (n=15,651) increased the number and amplitude of lipid rhythms: LDL-C and HDL-C were higher in winter and lower in summer, and the opposite occurred with triglycerides. The number of hospitalizations showed maximum and minimum frequencies in winter and in summer, respectively. A coincident rhythmicity was observed of lower temperature and humidity rates with higher plasma LDL-C, and their temporal series were inversely cross-correlated. This study shows for the first time that variations of temperature, humidity, and daylight length were strongly associated with LDL-C and HDL-C seasonality, but moderately to lowly associated with rhythmicity of atherosclerotic outcomes. It also indicates unfavorable cardiovascular-related changes during wintertime.

Abstract In this study we investigated the distribution of Macrobrachium olfersii (Wiegmann, 1836) along ~150 km of the Ribeira de Iguape river, São Paulo, Brazil. We compared the abundance and spatio-temporal distribution, and checked for differences in size and proportion of each sex in the collections, using two sampling methods. Shrimps were collected monthly at four sites (Eldorado, Sete Barros, Registro, and Iguape), from January to December 2007, using traps and sieves. We obtained a total of 23,818 individuals. The abundance was significantly higher at the Iguape-site, which was the closest to the estuary. There was a positive cross-correlation between abundance and rainfall, indicating an increase in abundance with a decrease in rainfall. The body size increased significantly upstream, suggesting a juvenile upstream migration, controlled by the rainfall regime and the amphidromous behavior of M. olfersii. More than 95% of the individuals were captured by sieving through the marginal vegetation of the river. The average size and sex ratio of each sample varied depending on the capture method: traps captured more and larger males than the sieve. Therefore, we recommend the combined use of these methods to obtain a better coverage of the population biology of freshwater shrimps.

IntroductionThis study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment.MethodsFocusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities.ResultsResults of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city.DiscussionIntegrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.