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Urban parks have been studied for their effects on health and the environment. Accessing park data from reliable and comparable sources remains challenging, reinforcing the importance of standardized search tools, notably in Latin America. We designed a systematized methodology to identify processes of accessing, collecting, verifying, and harmonizing urban park spatial data in all Brazilian capitals included in the Urban Health in Latin America (SALURBAL) project. We developed a research protocol using official and non-official sources combining the results of Google Maps (GMaps) points and OpenStreetMap (OSM) polygons-GMaps-OSM. Descriptive analyses included the frequency of the distribution of parks before and after harmonization stratified by data source. We used the intraclass correlation coefficient (ICC) to assess agreement in the area between official and GMaps-OSM data. Official data were obtained for 16 cities; for the remaining 11 capitals, we used GMaps-OSM. After verification and harmonization, 302 urban parks were obtained from official data and 128 from GMaps-OSM. In a sub-study of the 16 cities with official data (n = 302 parks), we simulated a collection of non-official data using GMaps-OSM and OSM only. From GMaps-OSM, we obtained 142 parks, and from OSM, 230 parks. Statistical analysis showed a better agreement between official data and OSM. After completing verification and harmonization, the complete dataset (official and GMaps-OSM) included 430 urban parks with a total area of 145.14 km2. The mean number of parks across cities was 16, with a mean size area of 0.33 km2. The median number of parks was nine, with a median area of 0.07 km2. This study highlights the importance of creating mechanisms to access, collect, harmonize, and verify urban park data, which is essential for examining the impact of parks on health. It also stresses the importance of providing reliable urban park spatial data for city officials., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Slovic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Socioeconomic factors have exacerbated the impact of COVID-19 worldwide. Brazil, already marked by significant economic inequalities, is one of the most affected countries, with one of the highest mortality rates. Understanding how inequality and income segregation contribute to excess mortality by COVID-19 in Brazilian cities is essential for designing public health policies to mitigate the impact of the disease. This paper aims to fill in this gap by analyzing the effect of income inequality and income segregation on COVID-19 mortality in large urban centers in Brazil. We compiled weekly COVID-19 mortality rates from March 2020 to February 2021 in a longitudinal ecological design, aggregating data at the city level for 152 Brazilian cities. Mortality rates from COVID-19 were compared across weeks, cities and states using mixed linear models. We estimated the associations between COVID-19 mortality rates with income inequality and income segregation using mixed negative binomial models including city and week-level random intercepts. We measured income inequality using the Gini index and income segregation using the dissimilarity index using data from the 2010 Brazilian demographic census. We found that 88.2% of COVID-19 mortality rates variability was between weeks, 8.5% between cities, and 3.3% between states. Higher-income inequality and higher-income segregation values were associated with higher COVID-19 mortality rates before and after accounting for all adjustment factors. In our main adjusted model, rate ratios (RR) per 1 SD increases in income inequality and income segregation were associated with 17% (95% CI 9% to 26%) and 11% (95% CI 4% to 19%) higher mortality. Income inequality and income segregation are long-standing hallmarks of large Brazilian cities. Risk factors related to the socioeconomic context affected the course of the pandemic in the country and contributed to high mortality rates. Pre-existing social vulnerabilities were critical factors in the aggravation of COVID-19, as supported by the observed associations in this study., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Sousa Filho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Capacity strain negatively impacts patient outcome, and the effects of patient surge are a continuous threat during the COVID-19 pandemic. Evaluating changes in mortality over time enables evidence-based resource planning, thus improving patient outcome. Our aim was to describe baseline risk factors associated with mortality among COVID-19 hospitalized patients and to compare mortality rates over time., Methods: We conducted a retrospective cohort study in the largest referral hospital for COVID-19 patients in Sao Paulo, Brazil. We investigated risk factors associated with mortality during hospitalization. Independent variables included age group, sex, the Charlson Comorbidity Index, admission period according to the stage of the first wave of the epidemic (early, peak, and late), and intubation., Results: We included 2949 consecutive COVID-19 patients. 1895 of them were admitted to the ICU, and 1473 required mechanical ventilation. Median length of stay in the ICU was 10 (IQR 5-17) days. Overall mortality rate was 35%, and the adjusted odds ratios for mortality increased with age, male sex, higher Charlson Comorbidity index, need for mechanical ventilation, and being admitted to the hospital during the wave peak of the epidemic. Being admitted to the hospital during the wave peak was associated with a 33% higher risk of mortality., Conclusions: In-hospital mortality was independently affected by the epidemic period. The recognition of modifiable operational variables associated with patient outcome highlights the importance of a preparedness plan and institutional protocols that include evidence-based practices and allocation of resources., Competing Interests: Dr Ferreira received speaker fees from Medtronic, outside of the submitted work. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Postoperative nausea and vomiting (PONV) is a common postsurgical complication. Palonosetron is effective for PONV prevention at the usual dose of 75 μg, but the ideal dose for obese patients has not yet been investigated. The aim of this study was to compare body weight-adjusted and fixed doses of palonosetron for preventing PONV in obese female patients., Materials and Methods: We performed a prospective, randomized, double-blind trial involving 80 female patients, aged 18-80 years with an American Society of Anesthesiologists physical status of 2 and 3 and a body mass index (BMI) ≥ 30 kg m-2 who were scheduled to undergo elective breast surgery. Patients received an intravenous body weight-adjusted dose of palonosetron (1 μg kg -1, GI = 40 patients) or a fixed dose of palonosetron (75 μg, GII = 40 patients). All patients received dexamethasone (4 mg). The incidence of PONV, complete response rate (CR), severity of nausea and need for rescue antiemetics and analgesics were assessed at: 0-1 h, 1-6 h, 6-24 h and 24-48 h postoperatively., Results: The mean (± SD) BMI was 35.0 (±5.2) kg m-2 for GI and 35.7 (±3.6) kg m-2 for GII. There was no significant difference between groups in PONV incidence, CR, severity of nausea, and need for rescue antiemetics or analgesics. The incidence of PONV for GI and GII was 15% and 27.5%, respectively, during the first 48 h (P = 0.17)., Conclusions: A body weight-adjusted dose of palonosetron was as effective as 75 μg for preventing PONV for 48 h in obese female patients who underwent breast surgery. Hence, the fixed dose may be preferable to the body weight-adjusted dose., Competing Interests: The authors have declared that no competing interests exist.

Objectives: We therefore sought to identify the exposures associated with lung surface in long-term residents of São Paulo, Brazil., Methods: Lung surface carbon were analyzed in 72 autopsy specimens by image analysis. Smoking history, measured PM10 nearest to the home, distance to main road, and distance-weighted traffic density were used as exposure variables. Data are summarized as median (IQR), and compared by Mann Whitney Test, with correlations done by Spearman's correlation., Results: There was no association between lung surface and age or gender. There was no statistically significant association in lung surface between smokers and non-smokers 6.74 cm2 (3.47 to 10.02) versus 5.20cm2 (2.29 to 7.54), and there was no significant association between lung surface carbon and exposure to environmental PM and markers of traffic exposure., Conclusion: We did not find a statistically significant association between lung surface and smokers and non-smokers, and no statistically significant association between lung surface carbon and environmental exposure variables. These results suggest that lung surface carbon in long-term residents of São Paulo may predominately be from environmental PM, but the most appropriate environmental exposure marker remains unclear.

Background: Atmospheric pollution is a major public health concern. It can affect placental function and restricts fetal growth. However, scientific knowledge remains too limited to make inferences regarding causal associations between maternal exposure to air pollution and adverse effects on pregnancy. This study evaluated the association between low birth weight (LBW) and maternal exposure during pregnancy to traffic related air pollutants (TRAP) in São Paulo, Brazil., Methods and Findings: Analysis included 5,772 cases of term-LBW (<2,500 g) and 5,814 controls matched by sex and month of birth selected from the birth registration system. Mothers' addresses were geocoded to estimate exposure according to 3 indicators: distance from home to heavy traffic roads, distance-weighted traffic density (DWTD) and levels of particulate matter ≤10 µg/m3 estimated through land use regression (LUR-PM10). Final models were evaluated using multiple logistic regression adjusting for birth, maternal and pregnancy characteristics. We found decreased odds in the risk of LBW associated with DWTD and LUR-PM10 in the highest quartiles of exposure with a significant linear trend of decrease in risk. The analysis with distance from heavy traffic roads was less consistent. It was also observed that mothers with higher education and neighborhood-level income were potentially more exposed to TRAP., Conclusions: This study found an unexpected decreased risk of LBW associated with traffic related air pollution. Mothers with advantaged socioeconomic position (SEP) although residing in areas of higher vehicular traffic might not in fact be more expose to air pollution. It can also be that the protection against LBW arising from a better SEP is stronger than the effect of exposure to air pollution, and this exposure may not be sufficient to increase the risk of LBW for these mothers.