Ketamine modulates the exploratory dynamics and homebase-related behaviors of adult zebrafish.

Anxiety can be a protective emotion when animals face aversive conditions, but is commonly associated with various neuropsychiatric disorders when pathologically exacerbated. Drug repurposing has emerged as a valuable strategy based on utilizing the existing pharmaceuticals for new therapeutic purposes. Ketamine, traditionally used as an anesthetic, acts as a non-competitive antagonist of the glutamate N-methyl-d-aspartate (NMDA) receptor, and shows potential anxiolytic and antidepressant effects at subanesthetic doses. However, the influence of ketamine on multiple behavioral domains in vertebrates is not completely understood. Here, we evaluated the potential modulatory effect of ketamine on the spatio-temporal exploratory dynamics and homebase-related behaviors in adult zebrafish using the open field test (OFT). Animals were exposed to subanesthetic concentrations of ketamine (0, 2, 20, and 40 mg/L) for 20 min and their locomotion-, exploration- and homebase-related behaviors were assessed in a single 30-min trial. Our data revealed that acute ketamine (20 and 40 mg/L) induced hyperlocomotion, as verified by the increased total distance traveled. All concentrations tested elicited circling behavior, a stereotyped-like response which gradually reduced across the periods of test. We also observed modulatory effects of ketamine on the spatio-temporal exploratory pattern, in which the reduced thigmotaxis and homebase activity, associated with the increased average length of trips, suggest anxiolytic-like effects. Collectively, our findings support the modulatory effects of ketamine on the spatio-temporal exploratory activity, and corroborate the utility of homebase-related measurements to evaluate the behavioral dynamics in zebrafish models.

Bayesian spatio-temporal modeling of the Brazilian fire spots between 2011 and 2022.

Wildfires are among the most common natural disasters in many world regions and actively impact life quality. These events have become frequent due to climate change, other local policies, and human behavior. Fire spots are areas where the temperature is significantly higher than in the surrounding areas and are often used to identify wildfires. This study considers the historical data with the geographical locations of all the "fire spots" detected by the reference satellites covering the Brazilian territory between January 2011 and December 2022, comprising more than 2.2 million fire spots. This data was modeled with a spatio-temporal generalized linear mixed model for areal unit data, whose inferences about its parameters are made in a Bayesian framework and use meteorological variables (precipitation, air temperature, humidity, and wind speed) and a human variable (land-use transition and occupation) as covariates. The meteorological variables humidity and air temperature showed the most significant impact on the number of fire spots for each of the six Brazilian biomes.

Bayesian spatio-temporal modeling to assess the effect of land-use changes on the incidence of Cutaneous Leishmaniasis in the Brazilian Amazon.

Cutaneous Leishmaniasis (CL) is a vector-borne disease caused by a protozoan of the genus Leishmania and is considered one of the most important neglected tropical diseases. The Brazilian Amazon Forest harbors one of the highest diversity of Leishmania parasites and vectors and is one of the main focuses of the disease in the Americas. Previous studies showed that some types of anthropogenic disturbances have affected the abundance and distribution of CL vectors and hosts; however, few studies have thoroughly investigated the influence of different classes of land cover and land-use changes on the disease transmission risk. Here, we quantify the effect of land use and land-cover changes on the incidence of CL in all municipalities within the Brazilian Amazon Forest, from 2001 to 2017. We used a structured spatiotemporal Bayesian model to assess the effect of forest cover, agriculture, livestock, extractivism, and- deforestation on CL incidence, accounting for confounding variables such as population, climate, socioeconomic, and spatiotemporal random effects. We found that the increased risk of CL was associated with deforestation, especially modulated by a positive interaction between forest cover and livestock. Landscapes with ongoing deforestation for extensive cattle ranching are typically found in municipalities within the Amazon Frontier, where a high relative risk for CL was also identified. These findings provide valuable insights into developing effective public health policies and land-use planning to ensure healthier landscapes for people.

Spatiotemporal analysis of a population management intervention for dogs and cats in a municipality in central Mexico.

Background and Aim: The overpopulation of dogs and cats has generated socioeconomic, political, and animal welfare problems, in addition to an important public health problem, due to the risk of zoonotic diseases. This study aimed to analyze the spatiotemporal coverage of canine and feline sterilization services provided by a governmental agency in the rural and urban areas of the municipality of Tequisquiapan, Querétaro. Materials and Methods: This cross-sectional study was conducted in Tequisquiapan's municipality, Querétaro, Mexico, from July 2019 to September 2022. The total number of sterilized dogs and cats was obtained from the monthly records of the Tequisquiapan Animal Health and Welfare Services Center (CESSBA, by its Spanish acronym). The collected information was related to the sterilized animals (species and sex) and their responsible guardians (sex and address). Access to dog and cat sterilization services was assessed using a geographic information system. Kernel density and directional ellipse tools were used to analyze the CESSBA coverage of care. Indicators were estimated to compare magnitudes and changes at the census tract level. Results: A total of 4,489 animals were sterilized, with n = 2,611 (58%) dogs, of which 1,939 were female and 672 were male. The remaining n = 1,878 animals were cats, representing 42% of the total, with 1,257 females and 621 males. Up to 73% of the sterilized animals were owned by women. The population management of dogs and cats allowed us to increase the territorial coverage from 71.8% in 2019 to 92.3% in 2022. According to the temporal analysis (2019-2022), there was an annual upward trend in the number of sterilizations performed by CESSBA, with a rate of between 55.6 and 94.3 registered sterilizations per 100 inhabited dwellings and between 166.4 and 302.8 registered sterilizations per 1000 inhabitants. Conclusion: The analysis of the dog and cat sterilization service coverage revealed an upward trend, consisting of an increase in accessibility and participation of responsible caregivers who resided in both urban and rural areas of Tequisquiapan. Although it was not possible to evaluate the impact of the program, the use of georeferenced data and geospatial analysis showed that it can support the control of animal overpopulation.

Spatiotemporal Modeling of Aedes aegypti Risk: Enhancing Dengue Virus Control through Meteorological and Remote Sensing Data in French Guiana.

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities.

Bayesian Modeling and Estimation of Spatial Risk for Hospitalization and Mortality from Ischemic Heart Disease in Paraná, Brazil.

Objective: Despite significant advancements in understanding risk factors and treatment strategies, ischemic heart disease (IHD) remains the leading cause of mortality worldwide, particularly within specific regions in Brazil, where the disease is a burden. Therefore, the aim of this study was to estimate the risk of hospitalization and mortality from IHD in the state of Paraná (Brazil), using spatial analysis to identify areas with higher risk based on socioeconomic, demographic and health variables. Methods: This is an ecological study based on secondary and retrospective IHD hospitalization and mortality data obtained from the Brazilian Hospitalization and Mortality Information Systems during the 2010-2021 period. Data were analyzed for 399 municipalities and 22 health regions in the state of Paraná. To assess the spatial patterns of the disease and identify relative risk (RR) areas, we constructed a risk model by Bayesian inference using the R-INLA and SpatialEpi packages in R software. Results: A total of 333,229 hospitalizations and 73,221 deaths occurred in the analyzed period, and elevated RR of hospitalization (RR = 27.412, CI 21.801; 34.466) and mortality (RR = 15.673, CI 2.148; 114.319) from IHD occurred in small-sized municipalities. In addition, medium-sized municipalities also presented elevated RR of hospitalization (RR = 6.533, CI 1.748; 2.006) and mortality (RR = 6.092, CI 1.451; 2.163) from IHD. Hospitalization and mortality rates were higher in white men aged 40-59 years. A negative association was found between Municipal Performance Index (IPDM) and IHD hospitalization and mortality. Conclusion: Areas with increased risk of hospitalization and mortality from IHD were found in small and medium-sized municipalities in the state of Paraná, Brazil. These results suggest a deficit in health care attention for IHD cases in these areas, potentially due to a low distribution of health care resources.

Hospital case fatality and mortality related to Chagas disease in Brazil over two decades.

OBJECTIVE: To analyse hospital case fatality and mortality related to Chagas disease (CD) in Brazil, 2000-2019. METHOD: This is a mixed ecological study with spatial and temporal trends, based on national population data from the Brazilian Ministry of Health - hospital admissions (HA) and death certificates (DC). Records with CD as a primary or secondary cause of death in HA and/or as an underlying or associated cause of death in DC were evaluated. Temporal trends were analysed by Joinpoint regression and the spatial distribution of age- and gender-adjusted rates, spatial moving averages, and standardized morbidity ratios. RESULTS: There were a total of 4,376 HA due to CD resulting in death in Brazil, with a hospital case fatality rate of 0.11/100,000 inhabitants. The Southeast region had the highest rate (63.9%, n = 2,796; 0.17/100,000 inhabitants). The general trend for this indicator in Brazil is upwards (average annual percentage change [AAPC] 7.5; 95% confidence interval [CI] 5.3 to 9.9), with increases in the North, Northeast and Southeast regions. During the same period 122,275 deaths from CD were registered in DC, with a mortality rate of 3.14/100,000 inhabitants. The highest risk of CD-related death was found among men (relative risk [RR] 1.27) and Afro-Brazilians (RR 1.63). There was a downward trend in CD mortality in the country (AAPC - 0.7%, 95%CI -0.9 to -0.5), with an increase in the Northeast region (AAPC 1.1%, 95%CI 0.6 to 1.6). Municipalities with a very high Brazilian Deprivation Index tended to show an increase in mortality (AAPC 2.1%, 95%CI 1.6 to 2.7), while the others showed a decrease. CONCLUSION: Hospital case fatality and mortality due to CD are a relevant public health problem in Brazil. Differences related to gender, ethnicity, and social vulnerability reinforce the need for comprehensive care, and to ensure equity in access to health in the country. Municipalities, states, and regions with indicators that reveal higher morbidity and mortality need to be prioritized.

Trophic plasticity of a tropical seabird revealed through DNA metabarcoding and stable isotope analyses.

DNA metabarcoding and stable isotope analysis have significantly advanced our understanding of marine trophic ecology, aiding systematic research on foraging habits and species conservation. In this study, we employed these methods to analyse faecal and blood samples, respectively, to compare the trophic ecology of two Red-billed Tropicbird (Phaethonaethereus; Linnaeus, 1758) colonies on Mexican islands in the Pacific. Trophic patterns among different breeding stages were also examined at both colonies. Dietary analysis reveals a preference for epipelagic fish, cephalopods, and small crustaceans, with variations between colonies and breeding stages. Isotopic values (δ15N and δ13C) align with DNA metabarcoding results, with wider niches during incubation stages. Differences in diet are linked to environmental conditions and trophic plasticity among breeding stages, influenced by changing physiological requirements and prey availability. Variations in dietary profiles reflect contrasting environmental conditions affecting local prey availability.

Spatiotemporal variability of pesticides concentration in honeybees (Apis mellifera) and their honey from western Mexico. Risk assessment for honey consumption.

The study conducted in the state of Colima, western Mexico, aimed to assess the 1) occurrence, 2) temporal variability, 3) spatial variability, and 4) potential risk for honeybees and human consumption of pesticide-contaminated honey. For that purpose, 48 pesticides were determined in bees and their honey during both dry and wet seasons. The research considered two variables: land use categorization (irrigated agriculture, rainfed agriculture, grassland, and forest area) and location (coastal, valley, and mountain). Bee and honey samples were collected, pre-treated using solid-phase extraction (SPE), and analyzed using LC-MS/MS and GC-MS techniques. Occurrence: of the total number of pesticides, 17 were detected in the bee samples and 12 in the honey samples. The pesticides with the highest concentrations in the bee samples were glufosinate ammonium, picloram, and permethrin, while in the honey samples, picloram, permethrin, and atrazine were the most prevalent. Temporal variability: analyses revealed significant differences between dry and wet seasons for glufosinate ammonium and DEET in bee samples and only for glufosinate ammonium in honey samples. Spatial variability: analyses showed a trend in the number of detected pesticides, with irrigated agriculture areas having the highest detection and grassland areas having the least. The human potential risk assessment of contaminated honey consumption indicated no risk. The bee's potential risk for consumption of pesticides contaminated honey revealed chronic effects due to permethrin in a general scenario, and carbofuran, diazinon and permethrin in the worst scenario, and potential risk of acute effects by permethrin. The findings of this study contribute to understanding the contamination levels of pesticides in bees and their honey, emphasizing the importance of monitoring and mitigating the adverse effects of pesticide exposure on bee populations and environmental health.

Spatial heterogeneity in climate change effects across Brazilian biomes.

We present a methodology designed to study the spatial heterogeneity of climate change. Our approach involves decomposing the observed changes in temperature patterns into multiple trend, cycle, and seasonal components within a spatio-temporal model. We apply this method to test the hypothesis of a global long-term temperature trend against multiple trends in distinct biomes. Applying this methodology, we delve into the examination of heterogeneity of climate change in Brazil-a country characterized by a spectrum of climate zones. The findings challenge the notion of a global trend, revealing the presence of distinct trends in warming effects, and more accelerated trends for the Amazon and Cerrado biomes, indicating a composition between global warming and deforestation in determining changes in permanent temperature patterns.

Zoonotic sporotrichosis in humans and domestic cats: Profile of notifications and spatio-temporal distribution in Southeastern Brazil between 2017 and 2023.

The research addresses zoonotic sporotrichosis in Brazil, particularly caused by Sporothrix brasiliensis, highlighting its epidemiological severity. Transmission occurs through contact with sick animals, especially felines, and diagnosis in humans is challenging due to the low fungal load in the lesions. The study analyzed data from Information System for Notifiable Diseases (SINAN) and Zoonosis Surveillance Unit (UVZ) from January 2017 to March 2023, carried out in Contagem, Minas Gerais. Geospatial tools and statistical analysis revealed a significant increase in cases, peaking in 2021 for felines and 2022 for humans. The geospatial analysis highlighted areas of higher incidence, suggesting a correlation between human and feline populations. The research contributes to the understanding of sporotrichosis in Contagem, emphasizing the importance of integrated approaches for surveillance and control strategies, aiming to mitigate impacts on the local community.

Space-time distribution of intestinal infectious diseases and their association with socioeconomic variables in Ecuador.

Background: Intestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables. Methods: In Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran's index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined. Results: During the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services. Conclusion: This study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies.

Mortality due to visceral leishmaniasis in Brazil by municipalities, 2001-2018: a spatial-temporal analysis of estimates from the Global Burden of Disease study.

OBJECTIVES: In the American regions, Brazil accounts for 97% of visceral leishmaniasis (VL) cases, with a case fatality rate of approximately 10%. This study aimed to investigate the VL mortality distribution in Brazil and identify high-priority and high-risk areas for intervention strategies. STUDY DESIGN: This was an ecological study that analysed the spatial-temporal patterns of VL mortality in Brazilian municipalities. METHODS: Age-standardised VL mortality rates from the Global Burden of Disease study from 2001 to 2018 were used. The distribution of mortality in the municipalities was assessed, and subsequently the Local Index of Spatial Autocorrelation (LISA) analysis was conducted to identify contiguous areas with high mortality rates. Scan analysis identified clusters of high spatial-temporal risks. RESULTS: The highest mortality rates and clusters were in municipalities located in the Northeast region and in the states of Tocantins and Roraima (North region), Mato Grosso do Sul (Central-West region), and Minas Gerais (Southeast region). According to LISA, there was an increase in the number of municipalities classified as high priority from the first 3-year period (n = 434) to the last 3-year period (n = 644). The spatio-temporal analysis identified 21 high-risk clusters for VL mortality. CONCLUSION: Areas with a high risk of VL mortality should prioritise preventing transmission, invest in early diagnosis and treatment, and promote the training of healthcare professionals.

Geographical coverage of SARS-CoV-2 screening and care centers in Haiti: what do national surveillance data tell us?

BACKGROUND: In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years. METHODS: We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection. RESULTS: Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection. CONCLUSIONS: This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.

Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia.

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.

Spatiotemporal Bayesian modeling of the risk of congenital syphilis in São Paulo, SP, Brazil.

The aim of this study is to analyze the spatiotemporal risk of congenital syphilis (CS) in high-prevalence areas in the city of São Paulo, SP, Brazil, and to evaluate its relationship with socioeconomic, demographic, and environmental variables. An ecological study was conducted based on secondary CS data with spatiotemporal components collected from 310 areas between 2010 and 2016. The data were modeled in a Bayesian context using the integrated nested Laplace approximation (INLA) method. Risk maps showed an increasing CS trend over time and highlighted the areas that presented the highest and lowest risk in each year. The model showed that the factors positively associated with a higher risk of CS were the Gini index and the proportion of women aged 18-24 years without education or with incomplete primary education, while the factors negatively associated were the proportion of women of childbearing age and the mean per capita income.

Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru.

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.

Characterizing Complex Spatiotemporal Patterns from Entropy Measures.

In addition to their importance in statistical thermodynamics, probabilistic entropy measurements are crucial for understanding and analyzing complex systems, with diverse applications in time series and one-dimensional profiles. However, extending these methods to two- and three-dimensional data still requires further development. In this study, we present a new method for classifying spatiotemporal processes based on entropy measurements. To test and validate the method, we selected five classes of similar processes related to the evolution of random patterns: (i) white noise; (ii) red noise; (iii) weak turbulence from reaction to diffusion; (iv) hydrodynamic fully developed turbulence; and (v) plasma turbulence from MHD. Considering seven possible ways to measure entropy from a matrix, we present the method as a parameter space composed of the two best separating measures of the five selected classes. The results highlight better combined performance of Shannon permutation entropy (SHp) and a new approach based on Tsallis Spectral Permutation Entropy (Sqs). Notably, our observations reveal the segregation of reaction terms in this SHp×Sqs space, a result that identifies specific sectors for each class of dynamic process, and it can be used to train machine learning models for the automatic classification of complex spatiotemporal patterns.

Spatiotemporal Gait Analysis of Patients with Spinocerebellar Ataxia Types 3 and 10 Using Inertial Measurement Units: A Comparative Study.

Given the high morbidity related to the progression of gait deficits in spinocerebellar ataxias (SCA), there is a growing interest in identifying biomarkers that can guide early diagnosis and rehabilitation. Spatiotemporal parameter (STP) gait analysis using inertial measurement units (IMUs) has been increasingly studied in this context. This study evaluated STP profiles in SCA types 3 and 10, compared them to controls, and correlated them with clinical scales. IMU portable sensors were used to measure STPs under four gait conditions: self-selected pace (SSP), fast pace (FP), fast pace checking-boxes (FPCB), and fast pace with serial seven subtractions (FPS7). Compared to healthy subjects, both SCA groups had higher values for step time, variability, and swing time, with lower values for gait speed, cadence, and step length. We also found a reduction in speed gain capacity in both SCA groups compared to controls and an increase in speed dual-task cost in the SCA10 group. However, there were no significant differences between the SCA groups. Swing time, mean speed, and step length were correlated with disease severity, risk of falling and functionality in both clinical groups. In the SCA3 group, fear of falling was correlated with cadence. In the SCA10 group, results of the Montreal cognitive assessment test were correlated with step time, mean speed, and step length. These results show that individuals with SCA3 and SCA10 present a highly variable, short-stepped, slow gait pattern compared to healthy subjects, and their gait quality worsened with a fast pace and dual-task involvement.

Geographical disparities in access to surgical treatment and mortality rates from abdominal aortic aneurysms in Brazil: A retrospective longitudinal study.

INTRODUCTION: Abdominal aortic aneurysm (AAA) is a growing public health problem, and not all patients have access to surgery when needed. This study aimed to analyze spatiotemporal variations in AAA mortality and surgical procedures in Brazilian intermediate geographic regions and explore the impact of different surgical techniques on operative mortality. METHODS: A retrospective longitudinal study was conducted to evaluate AAA mortality from 2008 to 2020 using space-time cube (STC) analysis and the emerging hot spot analysis tool through the Getis-Ord Gi* method. RESULTS: There were 34,255 deaths due to AAA, 13,075 surgeries to repair AAA, and a surgical mortality of 14.92%. STC analysis revealed an increase in AAA mortality rates (trend statistic = +1.7693, p = 0.0769) and a significant reduction in AAA surgery rates (trend statistic = -3.8436, p = 0.0001). Analysis of emerging hotspots revealed high AAA mortality rates in the South, Southeast, and Central-West, with a reduction in procedures in São Paulo and Minas Gerais States (Southeast). In the Northeast, there were extensive areas of increasing mortality rates and decreasing procedure rates (cold spots). CONCLUSION: AAA mortality increased in several regions of the country while surgery rates decreased, demonstrating the need for implementing public health policies to increase the availability of surgical procedures, particularly in less developed regions with limited access to services.