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Available evidence suggests that air pollutants can cause stroke, but little research has investigated the confounding effects of urban-rural differences. Here, we investigated the urban-rural difference in the correlation between particulate matter (PM 2.5 and PM 10 ) exposure and stroke. This cohort study was based on a prospective multi-city community-based cohort (Guizhou Population Health Cohort Study (GPHCS)) in Guizhou Province, China. A total of 7988 eligible individuals (≥18 years) were enrolled with baseline assessments from November 2010 to December 2012, and follow-up was completed by June 2020. Two major particulate matters (PMs, including PM 2.5 and PM 10 ) were assessed monthly from 2000 by using satellite-based spatiotemporal models. The risk of stroke was estimated using a Cox proportional hazard regression model. The association between particulate matters' exposure and stroke in different areas (total, urban, and rural) and the potential modification effect of comorbidities (hypertension, diabetes, and dyslipidemia) and age (≤65/>65 years) were examined using stratified analyses. The risk of stroke increased for every 10 μg/m 3 increase in mean PMs' concentrations during the previous 1 year at the residential address (HR: 1.26, 95%CI: 1.24, 1.29 (PM 2.5 ); HR: 1.13, 95%CI: 1.11, 1.15 (PM 10 )). The presence of diabetes and dyslipidemia increased the risk of PM 10 -induced stroke in whole, urban, and rural areas. Specifically, people living in rural areas were more likely to experience the effects of PMs in causing a stroke. The risk of stroke due to PMs was statistically increased in the young and older populations living in rural areas. In conclusion, long-term exposure to PMs increased the risk of stroke and such association was more pronounced in people living in rural areas with lower income levels. Diabetes and dyslipidemia seemed to strengthen the association between PMs and stroke., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

To control the COVID-19 epidemic, the Japanese government and the local governments have repeatedly implemented non-pharmaceutical interventions (NPIs) throughout 2020-2022. Using Bayesian state-space mediation models, we examined the effect of repeated NPIs on infection spread mitigation, mediated by human mobility changes in each prefecture during three epidemic phases: from April 1, 2020 to February 28, 2021; from March 1, 2021 to December 16, 2021; and from December 17, 2021 to December 31, 2022. In the first phase, controlling downtown populations at nighttime was effective in mitigating the infection spread in almost all prefectures. In the second and third phases, the effect was not clear, especially in metropolitan prefectures. Controlling visitors from the central prefectures of metropolitan areas was effective in mitigating infection spread in the surrounding prefectures during all phases. These results suggest that the local spread of infection can be mitigated by focusing on nighttime human mobility control in downtown areas before the epidemic spreads widely and transmission routes become more diverse, and that the geospatial spread of infection can be prevented by controlling the flows of people from large cities to other areas., (© 2024. The Author(s).)

Background: During the COVID-19 pandemic, changes were seen in city mobility patterns around the world, including in active transportation (walking, cycling, micromobility, and public transit use), creating a unique opportunity for global public health lessons and action. We aimed to analyse a global natural experiment exploring city mobility patterns during the pandemic and how they related to the implementation of COVID-19-related policies., Methods: We obtained data from Apple's Mobility Trends Reports on city mobility indexes for 296 cities from Jan 13, 2020 to Feb 4, 2022. Mobility indexes represented the frequency of Apple Maps queries for driving, walking, and public transit journeys relative to a baseline value of 100 for the pre-pandemic period (defined as Jan 13, 2020). City mobility index trajectories were plotted with stratification by country income level, transportation-related city type, population density, and COVID-19 pandemic severity (SARS-CoV-2 infection rate). We also synthesised global pandemic policies and recovery actions that promoted or restricted city mobility and active transportation (walking, cycling and micromobility, and public transit) using the Shifting Streets dataset. Additionally, a natural experiment on a global scale evaluated the effects of new active transportation policies on walking and public transit use in cities around the world. We used multivariable regression with a difference-in-difference (DID) analysis to explore whether the implementation of walking or public transit promotion policies affected mobility indexes, comparing cities with and without implementation of these policies in the pre-intervention period (Jan 27 to April 12, 2020) and post-intervention period (April 13 to June 28, 2020)., Findings: Based on city mobility index trajectories, we observed an overall decline in mobility indexes for walking, driving, and public transit at the beginning of the pandemic, but these values began to increase in April, 2020. Cities with lower population densities generally had higher driving and walking indexes than cities with higher population density, while cities with higher population densities had higher public transit indexes. Cities with higher pandemic severity generally had higher driving and walking indexes than cities with lower pandemic severity, while cities with lower pandemic severity had higher public transit indexes than other cities. We identified 587 policies in the dataset that had known implementation dates and were relevant to active transportation, which included 305 policies on walking, 321 on cycling and micromobility, and 143 on public transit, across 230 cities within 33 countries (19 high-income, 11 middle-income, and three low-income countries). In the global natural experiment (including 39 cities), implementation of policy interventions promoting walking was significantly associated with a higher absolute value of the walking index (DID coefficient 20·675 [95% CI 8·778-32·572]), whereas no such effect was seen for public transit-promoting policies (0·600 [-13·293 to 14·494])., Interpretation: Our results suggest that the policies implemented to mitigate the COVID-19 pandemic were effective in changing city mobility patterns, especially increasing active transportation. Given the known benefits of active transportation, such policies could be maintained, expanded, and evaluated post pandemic. The discrepancy in the interventions between countries of different incomes highlights that changes to the infrastructure to prioritise safe walking, cycling, and easy access to public transit use could help with the future-proofing of cities in low-income and middle-income countries., Funding: None., Competing Interests: Declaration of interests JT reports grant payments from the National Health and Medical Research Council (NHMRC) and the Australian Research Council (ARC) for projects unrelated to the current manuscript. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objective: Coastal cities, due to their proximity to coastlines and unique climatic conditions, face growing challenges from extreme temperature events associated with climate change. Research on the impact of extreme temperatures on tuberculosis (TB) in these cities is limited, and findings from different regions lack consensus. This study focuses on Shantou, a coastal city in China, to investigate the influence of extreme temperatures on TB within this distinctive geographical context., Methods: Distributed Lag Non-Linear Models (DLNM) were employed to evaluate the effect of extreme temperatures on TB incidence risk in Shantou, a coastal city in China, spanning from 2014 to 2021. Daily TB case data were provided by the Shantou Tuberculosis Prevention and Control Institute. Daily meteorological information was sourced from the Reliable Prognosis website, while daily air pollutant data were obtained from the China Air Quality Online Monitoring and Analysis Platform., Results: The study revealed a significant association between extreme temperatures and TB incidence, with the impact peaking at a lag of 27 days after exposure. Notably, extreme cold temperatures led to a temporary decrease in TB incidence with a lag of 1-2 days. Subgroup analysis indicated that males had a notably higher risk of TB under extreme temperature conditions compared to females. Additionally, individuals aged 65 years and above showed a significant cumulative effect in such conditions., Conclusions: This research enhances our comprehension of the effects of extreme temperatures on TB in coastal cities and carries substantial public health implications for TB prevention in China., Competing Interests: Declarations Competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical responsibility of authors This study did not involve human or animal subjects, and thus, no ethical approval was required., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Autism spectrum disorder (ASD) is a multifactorial, pervasive, neurodevelopmental disorder, of which intestinal symptoms collectively represent one of the most common comorbidities., Methods: In this study, 1,222 children with ASD and 1,206 typically developing (TD) children aged 2-7 years were enrolled from 13 cities in China. Physical measurement and basic information questionnaires were conducted in ASD and TD children. The Childhood Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), and Autism Behavior Checklist (ABC) were used to evaluate the clinical symptoms of children with ASD. The six-item Gastrointestinal Severity Index (6-GSI) was used to evaluate the prevalence of intestinal symptoms in two groups., Results: The detection rates of constipation, stool odor, and total intestinal symptoms in ASD children were significantly higher than those in TD children (40.098% vs. 25.622%, 17.021% vs. 9.287%, and 53.601% vs. 41.294%, respectively). Autistic children presenting with intestinal comorbidity had significantly higher scores on the ABC, SRS, CARS, and multiple subscales than autistic children without intestinal symptoms, suggesting that intestinal comorbidity may exacerbates the core symptoms of ASD children., Conclusion: Intestinal dysfunction was significantly more common in autistic than in TD children. This dysfunction may aggravate the core symptoms of children with ASD., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Temperature-related mortality is the leading cause of weather-related deaths in the United States. Herein, we explore the effect of air masses (AMs) - a relatively novel and holistic measure of environmental conditions - on human mortality across 61 cities in the United States. Geographic and seasonal differences in the effects of each AM on deseasonalized and detrended anomalous lagged mortality are examined using simple descriptive statistics, one-way analyses of variance, relative risks of excess mortality, and regression-based artificial neural network (ANN) models. Results show that AMs are significantly related to anomalous mortality in most US cities, and in most seasons. Of note, two of the three cool AMs (Cool and Dry-Cool) each show a strong, but delayed mortality response in all seasons, with peak mortality 2 to 4 days after they occur, with the Dry-Cool AM having nearly a 15% increased risk of excess mortality. Humid-Warm (HW) air masses are associated with increases in deaths in all seasons 0 to 1 days after they occur. In most seasons, these near-term mortality increases are offset by reduced mortality for 1-2 weeks afterwards; however, in summer, no such reduction is noted. The Warm and Dry-Warm AMs show slightly longer periods of increased mortality, albeit slightly less intensely as compared with HW, but with a similar lag structure by season. Meanwhile, the most seasonally consistent results are with transitional weather, whereby passing cold fronts are associated with a significant decrease in mortality 1 day after they occur, while warm fronts are associated with significant increases in mortality at that same lag time. Finally, ANN modeling reveals that AM-mortality relationships gleaned from a combined meta-analysis can actually lead to more skillful modeling of these relationships than models trained on some individual cities, especially in the cities where such relationships might be masked due to low average daily mortality., (© 2024. The Author(s).)

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies., (© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)

Background: In 2022, Mato Grosso (MT, Brazil) reported the highest detection rate of new leprosy cases (66.20 per 100,000 inhabitants) among all Brazilian states. Monitoring of leprosy indicators is an important control strategy in hyperendemic areas. We aimed to describe the temporal trends and identify clusters of municipalities according to leprosy surveillance indicators in MT between 2008 and 2022., Methods: Data from the Notifiable Diseases Information System were used to analyze new case detection rate of leprosy (NCDR), new case detection rate of leprosy among children aged <15 years (NCD15), and rate of new cases with grade 2 physical disability (G2DR). Spatial scan statistics with pure spatial analysis and spatial autocorrelation maps were used to analyze the spatial patterns. Joinpoint regression was used to estimate the annual percentage change (APC) in these indicators., Results: The NCDR decreased (APC: -20.2%, 95% confidence interval (CI): -38.7% to -7.4%) between 2019 and 2021. The NCD15 also decreased (APC: -19.2%, 95% CI: -36.4% to -10.3%) between 2018 and 2022. Conversely, the G2DR remained stable throughout the study (APC: 3.2%, 95% CI: -0.1% to 6.7%). Global Moran's index (Moran's I) confirmed the existence of spatial dependence among the municipalities for NCDR (Moran's I=0.348), NCD15 (Moran's I=0.269), and G2DR (Moran's I=0.275). Clusters with high NCDR levels included 13 municipalities in the northern and eastern macroregions, while clusters with high G2DR levels included 12 municipalities in the northwestern, northern, and eastern macroregions., Conclusions: The NCDR and NCD15 decreased, but the G2DR remained stable between 2018 and 2022. The coronavirus disease 2019 (COVID-19) pandemic had a potential negative impact on leprosy case detection, highlighting the need to strengthen leprosy surveillance efforts. The identified clusters of MT municipalities can significantly assist in this task.

Although some studies have explored the role of meteorological factors in the development of tuberculosis (TB), the majority have been confined to single regions, leading to inconsistent findings. Consequently, we conducted a multi-city study not only to determine whether meteorological factors significantly influence the risk of developing TB but also to assess the magnitude of these effects and explore potential modifying factors. Data on daily reported TB cases and meteorological factors were collected from January 1, 2013, to December 31, 2022, across 11 cities in Zhejiang Province. A distributed lag non-linear model using a quasi-Poisson distribution was employed. Multivariate meta-regression was used to obtain overall pooled estimates and assess heterogeneity. From 2013 to 2022, 267,932 TB cases were reported in Zhejiang Province. Notably, a nonlinear relationship was observed between temperature and TB, with the relative risk (RR) peaking at 1.0 °C (RR = 1.882, 95% CI 1.173-3.020). The effect of low temperature was immediate and significant for a 13-day lag period, with the maximum effect at lag0 (RR = 1.014, 95% CI 1.008-1.021). The exposure-response curve between relative humidity (RH) and TB exhibited an M-shape, with the RR peaking at 47.7% (RR = 1.642, 95% CI 1.044-2.582). The lag effect of low RH was significant at lag 25-59, with the highest RR observed at lag 32 (RR = 1.011, 95% CI 1.001-1.022). Gross domestic product (GDP) per person, population density, and latitude demonstrated significant modification effects. Our study showed that low temperature and RH were associated with an increased risk of TB. Additionally, GDP per person, population density, and latitude may play important roles in explaining the association between RH and TB. These findings provide scientific evidence for the development of geographically specific public health policies., (© 2024. The Author(s).)

Large-scale epidemics of arboviruses, such as dengue, have heightened societal awareness regarding the necessity of combating the primary transmission vectors. Equally critical is the identification of environmental conditions and variables that influence vector population dynamics. Aedes aegypti , the primary vector of arboviruses such as dengue and Zika in Brazil, is closely associated with the climatic and geographical conditions of urban environments. This study examines the relationship between precipitation and confirmed dengue cases in Recife (Brazil), employing regression and quantile analyses to evaluate the influence of meteorological conditions on the disease's spread. The findings reveal a direct correlation between monthly averages of precipitation and confirmed cases, although this is apparent only when excluding years of epidemic peaks. The highest number of cases generally aligns with the rainy season, and the lowest with the dry season, with weak, moderate and strong precipitation events being closely linked to increased dengue incidence. However, notable discrepancies were identified: four out of six major outbreaks occurred in drier months, challenging the assumption of a straightforward relationship between rainfall and dengue incidence. These findings underscore the multifaceted nature of dengue dynamics, suggesting that while precipitation plays a significant role, other factors, including serotype circulation and broader climatic phenomena, are equally critical in driving outbreaks. This complexity highlights the need for a more comprehensive understanding of the mechanisms influencing dengue epidemics., Competing Interests: The authors have no relevant financial conflicts of interest. However, it should be noted that Tiago Massoni, Patty Kostkova, Tercio Ambrizzi, and Anwar Musah are members of the Editorial Board of Vector-Borne Diseases -- The Digital One Health Approach. Due to this affiliation, the manuscript has been managed by other editorial members. These author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Borges, Musah, Dutra, Tunali, Lima, Tunali, da Silva, Aldosery, Moreno, dos Santos, Massoni, Yenigün, Kostkova, da Rocha, Campos and Ambrizzi.)

Background: Pregnancy can be a risk factor for the development of more severe COVID-19 with a possible increase in the risk of complications during pregnancy/birth and adverse neonatal outcomes. This study aimed to describe and analyze the clinical and epidemiological aspects of SARS-CoV-2 infection in women in the perinatal period attended in the city of Belém, northern region of Brazil., Methods: This is a clinical, observational, analytical, and cross-sectional study with a quantitative approach, conducted at the Santa Casa de Misericórdia do Pará Foundation (FSCMPA). It included 230 pregnant women hospitalized at FSCMPA with a positive SARS-CoV-2 RT-PCR molecular test between April 2020 and June 2022. Clinical and epidemiological information (origin, gestational age, prenatal care, comorbidities, birth complications, and chest tomography) were obtained from medical records, and correlation was made between the types of cases (mild, moderate, and severe) and maternal outcome. The chi-square test and G test were used to assess the possibility of association between variables., Results: Evidence of association was observed between the severity of COVID-19 and the following parameters: gestational age, specific pregnancy comorbidities, baby and maternal death, birth complications, and prematurity. Dyspnea, headache, anosmia, odynophagia, diarrhea, and chest pain were the symptoms most related to disease aggravation. The maternal mortality rate in the study was 8.7%., Conclusion: Specific pregnancy-related and pre-existing comorbidities associated with SARS-CoV-2 infection directly contribute to the worsening clinical condition, leading to complications such as prematurity, fetal, and maternal death., (© 2024. The Author(s).)

Background: The COVID-19 pandemic has spread through pre-existing fault lines in societies, deepening structural barriers faced by precarious workers, low-income populations, and racialized communities in lower income sub-city units. Many studies have quantified the magnitude of inequalities in COVID-19 distribution within cities, but few have taken an international comparative approach to draw inferences on the ways urban epidemics are shaped by social determinants of health., Methods: Guided by critical epidemiology, this study quantifies sub-city unit-level COVID-19 inequalities across eight of the largest metropolitan areas of Latin America and Canada. Leveraging new open-data sources, we use concentration indices to quantify income- and vulnerability-related inequalities in incidence, test positivity, and deaths over the first 125 weeks of the pandemic between January 2020 and May 2022., Results: Our findings demonstrate that incidence, deaths, and test positivity are all less concentrated in low-income sub-city units than would be expected, with incidence ranging concentration in lower income neighbourhoods in Toronto (CI = -0.07) to concentration in higher income neighbourhoods in Mexico City (CI = 0.33). Drawing on relevant studies and evaluations of data reliability, we conclude that the best available public surveillance data for the largest cities in Latin America are likely not reliable measures of the true COVID-19 disease burden. We also identify recurring trends in the evolution of inequalities across most cities, concluding that higher income sub-city units were frequent early epicentres of COVID-19 transmission across the Latin America and Canada., Conclusions: Just as critical epidemiology points to individuals biologically embodying the material and social conditions in which we live, it may be just as useful to think of cities reifying their material and social inequities in the form of sub-city unit-level infectious disease inequities. By shifting away from a typical vulnerability-based social determinants of health frame, policymakers could act to redress and reduce externalities stemming from sub-city unit-level income inequality through redistributive and equity-promoting policies to shift the centre of gravity of urban health inequalities before the next infectious disease epidemic occurs., (© 2024. The Author(s).)

Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon's diversity index, number of bus stops, floor-area ratio, and distance from the city center played important roles at different scales. These non-linear results provide a more refined reference for pandemic responses at different scales from an urban planning perspective and offer useful recommendations for a sustainable COVID-19 post-pandemic response., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gao 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.)

The increasing abundance of animal species thriving in urban environments is a source of conflicts with managers and users of public spaces. Although opportunistic urban species often use resources originating from human food leftovers, the potential impact of a reduction in these resources on their demography is hard to quantify. The COVID-19 epidemic, which led many countries to set up lockdowns, gave us the opportunity to estimate the impact of a drastic reduction in such food resources and human activities on the demography of an urban bird population. Based on 7 years (2015-2021) of capture-mark-recapture of carrion crows (Corvus corone) in the city of Paris, France, we used multi-state models to examine the intra-annual (3-month time steps) apparent survival and movement patterns of crows during and outside COVID-19 lockdowns. We showed that the apparent survival of juvenile carrion crows decreased down during lockdown, while adult movements increased during this period, with more adult crows moving out of the urban district. Lockdown modified the demography of this urban crow population, suggesting that the reduction in food resources was sufficient to affect fitness and reduce carrying capacity., (© 2024. The Author(s).)

Objectives: The control chart is a classic statistical technique in epidemiology for identifying trends, patterns, or alerts. One meaningful use is monitoring and tracking Infant Mortality Rates, which is a priority both domestically and for the World Health Organization, as it reflects the effectiveness of public policies and the progress of nations. This study aims to evaluate the applicability and performance of this technique in Brazilian cities with different population sizes using infant mortality data., Results: In this article, we evaluate the effectiveness of the statistical process control chart in the context of Brazilian cities. We present three categories of city groups, divided based on population size and classified according to the quality of the analyses when subjected to the control method: consistent, interpretable, and inconsistent. In cities with a large population, the data in these contexts show a lower noise level and reliable results. However, in intermediate and small-sized cities, the technique becomes limited in detecting deviations from expected behaviors, resulting in reduced reliability of the generated patterns and alerts., (© 2024. The Author(s).)

Members of the genus Bocaparvovirus have a significant impact on human health and can infect a wide range of hosts, increasing the likelihood of crossing species barriers. Among the various mammalian hosts, rodents are widely recognized as important reservoirs for emerging and zoonotic viruses. However, despite recent reports of bocavirus infections in rodents, our current understanding of rat bocavirus (RBoV) genetic diversity and evolution is limited. In this study, rodent samples were collected from the urban areas of Guangzhou city, Southern China, to investigate the presence and genetic diversity of RBoV. Through PCR-based screening of 296 rodent spleens, 54 samples were determined to be positive for RBoV infection, and 12 nearly complete genome sequences of RBoV were recovered. Phylogenetic analysis revealed distinct lineages and sub-lineages of RBoV, and six recombination events with strong statistical support were identified, with five of these events involving sequences obtained from this study. These results highlight the genetic diversity of RBoV circulating in rodents in Guangzhou city and emphasize the importance of extensive surveillance to gain a better understanding of RBoV epidemiology, evolutionary characteristics, and potential for cross-species transmission., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Urban sprawl threatens biodiversity and is responsible for significant changes in the species that live in these environments. Given the high cost of comprehensive surveillance, monitoring disease indirectly, such as detecting skin lesions in birds, may help us better understand the prevalence of diseases affecting wild populations. We assessed the frequency of leg skin lesions, as a proxy of disease presence, in 1,565 individuals of 25 species, along the urban matrix of a large Neotropical city, Brasília, Federal District, Brazil. We tested the hypothesis that there is an increase in the frequency of skin lesions in birds due to urban intensification. We observed an increasing trend in some bird species between the frequency of occurrence of lesions and the intensity of urbanization. Species with a higher number of captures had an increase in the percentage of lesions, indicating that the occurrence of lesions may be linked to higher population density or that detection of the effect occurs only when sample sizes are high and controlled among urbanization categories. Our study highlights how the intensity of urbanization may increase the risk of disease transmission for these species. Unfortunately, studies on this topic are scarce in Neotropical regions, despite the region's high biodiversity and urban expansion., (© Wildlife Disease Association 2024.)

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

Background: The effectiveness of dengue control interventions depends on an effective integrated surveillance system that involves analysis of multiple variables associated with the natural history and transmission dynamics of this arbovirus. Entomological indicators associated with other biotic and abiotic parameters can assertively characterize the spatiotemporal trends related to dengue transmission risk. However, the unpredictability of the non-linear nature of the data, as well as the uncertainty and subjectivity inherent in biological data are often neglected in conventional models., Methods: As an alternative for analyzing dengue-related data, we devised a fuzzy-logic approach to test ensembles of these indicators across categories, which align with the concept of degrees of truth to characterize the success of dengue transmission by Aedes aegypti mosquitoes in an endemic city in Brazil. We used locally gathered entomological, demographic, environmental and epidemiological data as input sources using freely available data on digital platforms. The outcome variable, risk of transmission, was aggregated into three categories: low, medium, and high. Spatial data was georeferenced and the defuzzified values were interpolated to create a map, translating our findings to local public health managers and decision-makers to direct further vector control interventions., Results: The classification of low, medium, and high transmission risk areas followed a seasonal trend expected for dengue occurrence in the region. The fuzzy approach captured the 2020 outbreak, when only 14.06% of the areas were classified as low risk. The classification of transmission risk based on the fuzzy system revealed effective in predicting an increase in dengue transmission, since more than 75% of high-risk areas had an increase in dengue incidence within the following 15 days., Conclusions: Our study demonstrated the ability of fuzzy logic to characterize the city's spatiotemporal heterogeneity in relation to areas at high risk of dengue transmission, suggesting it can be considered as part of an integrated surveillance system to support timely decision-making., (© 2024. The Author(s).)

Asthma is one of the most prevalent chronic conditions in the United States and is particularly sensitive to environmental changes in urban areas. While it is known that traffic congestion contributes to increased vehicle emissions and poorer air quality, its direct association with asthma incidence has not been thoroughly explored. This study aimed to address this void by analyzing 148 city-level observations from 2016 to 2020 in Texas, using data from the Texas A&M Transportation Institute and Definitive Healthcare. We investigated the association between traffic congestion, measured by the travel time index, and annual city-level asthma hospital discharges, while adjusting for refinery productivity, minority groups, and education levels through multivariate regression. Our findings revealed a significant positive correlation between the travel time index and asthma visits, indicating that higher traffic congestion is associated with increased hospital visits for asthma. This finding remains consistent across different models, regardless of whether control variables are included. For the control variables, we found that higher refinery productivity was linked to elevated risks of asthma-related hospitalizations, aligning with previous research findings. Although correlations with Black or African American and Hispanic or Latino populations, as well as those with less than a high school education, were not statistically significant, a positive trend was observed. These results emphasize the impact of traffic congestion on asthma prevalence and the necessity for targeted public health interventions and urban planning strategies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yang, Wang. 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.)

Using publicly available data, this paper investigates the diffusion of COVID-19 across neighborhoods in two major cities, London and New York. We link neighborhood demographics to incidence, and we investigate patterns of change over time in conjunction with changing policy responses to the pandemic. By comparing and contrasting these two cities, we are able to exploit surveillance and policy differences, demonstrating how each contributes information to the other. We conclude that better coordination can be translated into improved health policy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 de Coulon, Scott. 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.)

Objective: This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome (HFRS) in Qingdao City, China., Methods: Information was collected on HFRS cases in Qingdao City from 2010 to 2022. Descriptive epidemiologic, seasonal decomposition, spatial autocorrelation, and spatio-temporal cluster analyses were performed., Results: A total of 2,220 patients with HFRS were reported over the study period, with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%. The male:female ratio was 2.8:1. 75.3% of patients were aged between 16 and 60 years old, 75.3% of patients were farmers, and 11.6% had both "three red" and "three pain" symptoms. The HFRS epidemic showed two-peak seasonality: the primary fall-winter peak and the minor spring peak. The HFRS epidemic presented highly spatially heterogeneous, street/township-level hot spots that were mostly distributed in Huangdao, Pingdu, and Jiaozhou. The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak., Conclusion: The distribution of HFRS in Qingdao exhibited periodic, seasonal, and regional characteristics, with high spatial clustering heterogeneity. The typical symptoms of "three red" and "three pain" in patients with HFRS were not obvious., (Copyright © 2024 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.)

The aims of this study were to analyze patient survival, identify the prognostic factors for patients with COVID-19 deaths considering the length of hospital stay, and evaluate the spatial distribution of these deaths in the city of Jundiaí, São Paulo, Brazil. We examined prognostic variables and survival rates of COVID-19 patients hospitalized at a reference hospital in Jundiaí, Brazil. A retrospective cohort of hospitalized cases from April to July of 2020 was included. Descriptive analysis, Kaplan-Meier curves, univariate and multivariate Cox regression, and binary logistic regression models were used. Among the 902 reported and confirmed cases, there were 311 deaths (34.5%). The median survival was 27 days, and the mean for those discharged was 46 days. Regardless of the length of hospital stay, desaturation, immunosuppression, age over 60, kidney disease, hypertension, lung disease, and hypertension were found to be independent predictors of death in both Cox and logistic regression models.

China has experienced successive waves of dengue epidemics over the past decade. Nationwide data on 95,339 dengue cases, 89 surveillance sites for mosquito density and population mobility between 337 cities during 2013-20 were extracted. Weekly dengue time series including time trends and harmonic terms were fitted using seasonal regression models, and the amplitude and peak timing of the annual and semiannual cycles were estimated. A data-driven model-inference approach was used to simulate the epidemic at city-scale and estimate time-evolving epidemiological parameters. We found that the geographical distribution of dengue cases was expanding, and the main imported areas as well as external sources of imported cases changed. Dengue cases were predominantly concentrated in southern China and it exhibited an annual peak of activity, typically peaking in September. The annual amplitude of dengue epidemic varied with latitude (F = 19.62, P = 0.0001), mainly characterizing by large in southern cities and small in northern cities. The effective reproduction number R eff across cities is commonly greater than 1 in several specific months from July to November, further confirming the seasonal fluctuations and spatial heterogeneity of dengue epidemics. The results of this national study help to better informing interventions for future dengue epidemics in China., (© 2024. The Author(s).)

This study used the time series data of Ganzhou city to explore the individual and interaction effects of temperature and humidity on COPD death, and identify vulnerable subgroups of the population. We collected daily COPD mortality and meteorological data in Ganzhou from 2016 to 2019. The nonlinear distribution lag model was used to examine the associations and interaction between daily mean temperature and humidity and COPD mortality. For the total population, male and 65 years old or above, the relative risk (RR) for COPD mortality could be significant at extremely low temperature (3.3 ℃), reaching 1.799 (95% confidence interval [CI]: 1.216, 2.662), 1.894 (95% CI: 1.164, 3.084) and 1.779 (95% CI:1.185, 2.670). Also, at extremely low humidity (47.8%), the risk reached 1.888 (95% CI: 1.217, 2.930), 1.837 (95% CI: 1.066, 3.165) and 2.166 (95% CI: 1.375, 3.414). The cumulative COPD death risk for females was 3.524 (95% CI: 1.340, 9.267) at high temperature (30.7 ℃), 1.953(95% CI: 1.036, 3.683) at low humidity (47.8%) and 1.726 (95% CI: 1.048, 2.845) at high humidity (96.7%). For the total COPD deaths and subgroups, the interaction effects between daily temperature and humidity were not significant (p > 0.05). Both extremely low temperature and low humidity increased the risk of COPD death in Ganzhou city, especially for males and people over 65 years old. Females were more sensitive to extremely high temperature and humidity. Patients with COPD should pay attention to self-protection under extreme temperature and humidity weather conditions., (© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)

The UN-Habitat World Cities Report 2020 highlighted that overcrowded housing, not urban density, is the major contributing factor to the spread of COVID-19. The relatively successful ability of densely populated cities such as Seoul, Singapore, Tokyo and New York City to manage virus spread supports this. We hypothesise that, given the complexity of the interaction between people and place, the relative contribution of density and crowding to the spread of infectious diseases may be contingent on local factors. To directly compare the role of urban density and household overcrowding, we examine each in relation to COVID-19 incidence in the three largest cities in Australia, Sydney, Melbourne and Brisbane, as the pandemic unfolded from July 2021 to January 2022. Using ecological models adjusted for spatial autocorrelation and area-level measures of age and socio-economic factors, we assess the association between population density, overcrowding in homes, and COVID-19 infections in local neighbourhoods. Challenging prevailing assumptions, we find evidence for an effect of both density and overcrowding on COVID-19 infections depending on the city and area within cities; that is, depending on the local context. For example, in the southwestern suburbs of Sydney, the case rate decreases by between 0.4 and 6.4 with every one-unit increase in gross density however the case rate increases by between 0.01 and 9.6 with every one-unit increase in total overcrowding. These findings have important implications for developing pandemic response strategies: public health measures that target either density (e.g., lockdowns and restricted range of travel) or overcrowding (e.g., restricting number of people relative to dwelling, mask-wearing indoors, vaccination prioritisation) must be cognisant of the geographically local contexts in which they are implemented., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Dengue fever is a viral illness, mainly transmitted by Aedes aegypti and Aedes albopictus. With climate change and urbanisation, more urbanised areas are becoming suitable for the survival and reproduction of dengue vector, consequently are becoming suitable for dengue transmission in China. Chongqing, a metropolis in southwestern China, has recently been hit by imported and local dengue fever, experiencing its first local outbreak in 2019. However, the genetic evolution dynamics of dengue viruses and the spatiotemporal patterns of imported and local dengue cases have not yet been elucidated. Hence, this study implemented phylogenetic analyses using genomic data of dengue viruses in 2019 and 2023 and a spatiotemporal analysis of dengue cases collected from 2013 to 2022. We sequenced a total of 15 nucleotide sequences of E genes. The dengue viruses formed separate clusters and were genetically related to those from Guangdong Province, China, and countries in Southeast Asia, including Laos, Thailand, Myanmar and Cambodia. Chongqing experienced a dengue outbreak in 2019 when 168 imported and 1,243 local cases were reported, mainly in September and October. Few cases were reported in 2013-2018, and only six were imported from 2020 to 2022 due to the COVID-19 lockdowns. Our findings suggest that dengue prevention in Chongqing should focus on domestic and overseas population mobility, especially in the Yubei and Wanzhou districts, where airports and railway stations are located, and the period between August and October when dengue outbreaks occur in endemic regions. Moreover, continuous vector monitoring should be implemented, especially during August-October, which would be useful for controlling the Aedes mosquitoes. This study is significant for defining Chongqing's appropriate dengue prevention and control strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: The effect of heat waves on mortality is well known, but current evidence on morbidity is limited. Establishing the consequences of these events in terms of morbidity is important to ensure communities and health systems can adapt to them., Methods: We thus collected data on total daily emergency hospital admissions, admissions to critical care units, emergency department admissions, and emergency admissions for specific diagnoses to Hospital Universitario de Son Espases from 1 January 2005 to 31 December 2021. A heat wave was defined as a period of ≥ 2 days with a maximum temperature ≥ 35 °C, including a 7 day lag effect (inclusive). We used a quasi-Poisson generalized linear model to estimate relative risks (RRs; 95%CI) for heat wave-related hospital admissions., Results: Results showed statistically significant increases in total emergency admissions (RR 1.06; 95%CI 1 - 1.12), emergency department admissions (RR 1.12; 95%CI 1.07 - 1.18), and admissions for ischemic stroke (RR 1.26; 95%CI 1.02 - 1.54), acute kidney injury (RR 1.67; 95%CI 1.16 - 2.35), and heat stroke (RR 18.73, 95%CI 6.48 - 45.83) during heat waves., Conclusion: Heat waves increase hospitalization risk, primarily for thromboembolic and renal diseases and heat strokes., (© 2024. The Author(s).)

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., (Copyright © 2024 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)

Hepatitis E virus (HEV) is an important cause of acute hepatitis, however, is highly neglected and largely underreported. This study aimed to describe the detailed epidemiology of hepatitis E (HE) through a 10-year surveillance. A community-based active hepatitis surveillance was conducted between November 2007 and October 2017 in 11 townships of Dongtai City in China, involving 355,673 residents. Serum samples were obtained from patients presenting with hepatitis symptoms for more than 3 days. Serum alanine aminotransferase (ALT) levels greater than 2.5 times the upper limit of normal (ULN) were considered acute hepatitis. Samples were subsequently tested for IgG and IgM anti-HEV antibodies, HEV RNA, and hepatitis B surface antigen (HBsAg). The data indicated the incidence of HE fluctuated downward from 2007 to 2017, with an average annual age-standardized incidence of 17.50 per 100,000, exceeding the 10.26 per 100,000 in the National Notifiable Disease Report System (NNDRS). The incidence was notably higher among males (20.95 per 100,000) and individuals aged 50-69 years (37.47 per 100,000). Genotype 4 (HEV-4) was the predominantly circulating genotype during the study period. Furthermore, the study revealed the incidence of hepatitis with HEV and hepatitis B virus (HBV) co-infection was 4.99 per 100,000. The active surveillance system identified a higher incidence of HE compared to NNDRS, with a decreased prevalence over a 10-year period. While efforts are still needed to prevent HE in high-risk populations, including individuals with hepatitis B and the elderly.

Background: The COVID-19 pandemic has caused over 68.7 million infections and 1.35 million deaths in South America. There are limited data on SARS-CoV-2 seropositivity and its determinants from Andean countries prior to mass vaccinations against COVID-19., Objective: To estimate SARS-CoV-2 seropositivity and its determinants before vaccination in occupational groups of adults presumed to have different levels of exposure and associations with potential symptomatology., Methods: We measured seropositivity of anti-SARS-CoV-2 IgG antibodies in a cross-sectional study of vaccine-naïve adults aged 18 years and older, recruited within three occupational risk groups (defined as low [LR], moderate [MR], and high [HR]) between January and September 2021 in two Andean cities in Ecuador. Associations with risk factors were estimated using logistic regression., Results: In a sample of 882 adults, IgG seropositivity for the three different occupational risk groups was 39.9% (CI 95% 35.3-44.6), 74.6% (CI 95% 66.4-81.4), and 39.0% (CI 95% 34.0-44.4) for the HR, MR, and LR groups, respectively. History of an illness with loss of taste and/or smell was significantly associated with seropositivity in all occupational groups, with adjusted ORs of 14.31 (95%CI, 5.83-35.12; p<0.001), 14.34 (95%CI 3.01-68.42; p<0.001), and 8.79 (95%CI 2.69-28.72; p<0.001), for the HR, MR, and LR groups, respectively; while fever was significant for the LR group with an adjusted OR of 1.24 (95%CI, 1.11-4.57; p = 0.025) and myalgia for the HR group with an adjusted OR of 2.07 (95%CI, 1.13-3.81; p = 0.019)., Conclusion: Notable proportions of seropositivity were seen in all occupational groups between January and September 2021 prior to mass vaccination. Loss of taste and/or smell was strongly associated with presence of anti-SARS-CoV-2 IgG antibodies irrespective of presumed occupational exposure risk., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Leon-Rojas 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.)

Legionella pneumophila is ubiquitous and sporadically infects humans causing Legionnaire's disease (LD). Globally, reported cases of LD have risen fourfold from 2000 to 2014. In 2016, Sydney, Australia was the epicenter of an outbreak caused by L. pneumophila serogroup 1 (Lpsg1). Whole-genome sequencing was instrumental in identifying the causal clone which was found in multiple locations across the city. This study examined the epidemiology of Lpsg1 in an urban environment, assessed typing schemes to classify resident clones, and investigated the association between local climate variables and LD outbreaks. Of 223 local Lpsg1 isolates, we identified dominant clones with one clone isolated from patients in high frequency during outbreak investigations. The core genome multi-locus sequence typing scheme was the most reliable in identifying this Lpsg1 clone. While an increase in humidity and rainfall was found to coincide with a rise in LD cases, the incidence of the major L. pneumophila outbreak clone did not link to weather phenomena. These findings demonstrated the role of high-resolution typing and weather context assessment in determining source attribution for LD outbreaks in urban settings, particularly when clinical isolates remain scarce.IMPORTANCEWe investigated the genomic and meteorological influences of infections caused by Legionella pneumophila in Sydney, Australia. Our study contributes to a knowledge gap of factors that drive outbreaks of legionellosis compared to sporadic infections in urban settings. In such cases, clinical isolates can be rare, and thus, other data are needed to inform decision-making around control measures. The study revealed that core genome multi-locus sequence typing is a reliable and adaptable technique when investigating Lpsg1 outbreaks. In Sydney, the genomic profile of Lpsg1 was dominated by a single clone, which was linked to numerous community cases over a period of 40 years. Interestingly, the peak in legionellosis cases during Autumn was not associated with this prevalent outbreak clone. Incorporating meteorological data with Lpsg1 genomics can support risk assessment strategies for legionellosis in urban environments, and this approach may be relevant for other densely populated regions globally., Competing Interests: The authors declare no conflict of interest.

Background: Recent trials have confirmed the effectiveness of promising dengue control technologies - two vaccines and Wolbachia . These would generally be applied at the municipal level. To help local officials decide which, if any, control strategy to implement, they need affordable, timely, and accurate data on dengue burden. Building on our previous work in Mexico, Indonesia, and Thailand, we developed a streamlined prospective method to estimate dengue burden at the municipal level quickly, accurately, and efficiently., Methods: The method entails enrolling and repeatedly interviewing 100 patients with laboratory-confirmed dengue. They will be selected after screening and testing about 1,000 patients with clinical dengue. The method will capture both acute and chronic effects relating to disease, economic burden, and psychological impacts (presenteeism). The total time requirements are 1.5 years, comprised of 0.25 years for planning and approvals, 1 year for data collection (a full dengue cycle), and 0 .25 years for data cleaning and analysis. A collaboration with municipal and academic colleagues in the city of Semarang, Central Java, Indonesia shows how the method could be readily applied in Indonesia's eighth largest city (population 1.8 million)., Conclusions: Many surveillance studies gather only information on numbers of cases. This proposed method will provide a comprehensive picture of the dengue burden to the health system, payers, and households at the local level., Competing Interests: Competing interests: Within the last 3 years, DSS has received grant funding from Sanofi, and DSS and PA-H from Takeda Vaccines, Inc., all unrelated to the present study. NNW acknowledges consultation and travel support from Takeda Vaccines, Inc, also unrelated to the present study., (Copyright: © 2024 Wilastonegoro NN et al.)

Chlamydia psittaci-a zoonotic pathogen in birds-may be transmitted to humans, causing severe respiratory disease. Individuals working in or living near poultry farms are highly susceptible to C. psittaci infection. In this study, we assessed the prevalence and genotypes of C. psittaci in poultries and humans in three cities of China by collecting fecal samples from different poultry species and throat swab samples and serum samples from workers in poultry farms and zoos. These samples were screened by real-time fluorescence quantitative PCR (qPCR) targeting C. psittaci ompA. The positive samples were subjected to PCR amplification and sequencing of ompA. The strains detected in the samples were genotyped on the basis of the phylogenetic analysis of ompA sequences. In total, 3.13% (40/1278) poultry fecal samples were positive in the qPCR assay, whereas 3.82% (6/157) of throat swab samples and 42.59% (46/108) of serum samples from the workers were positive in the qPCR and indirect fluorescent antibody assays, respectively. The strains detected in the 32 poultry samples and 6 human samples were genotyped as type A, indicating that the workers were infected with C. psittaci that originated in poultry birds in farms. Additionally, eight peacocks showed strains with the genotype CPX0308, which was identified in China for the first time. Elucidating the distribution of C. psittaci in animals and poultry-related workers may provide valuable insights for reducing the risk of C. psittaci infection within a population., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Hou 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: Since March 2020, COVID-19 has evolved from a localized outbreak to a global pandemic. We assessed the seroprevalence of COVID-19 in three towns in the Centre Sud region of Burkina Faso., Methods: A population-based cross-sectional survey was conducted in three middle-sized cities in Burkina Faso's Centre Sud region, from June to July 2021. Subjects aged 16 or over at the time of the survey were considered for this seroprevalence study. The Biosynex COVID-19 BSS rapid test was used to detect immunoglobulin G (IgG) and immunoglobulin M (IgM) against SARS-CoV-2. A standardized questionnaire was also administered to collect additional information., Results: A total of 2449 eligible participants (age ≥ 16 years) were identified. Serological tests for COVID-19 were performed in 2155 individuals, of which 2143 valid tests were retained and analyzed. Out of the entire sample, 246 positive tests were observed, corresponding to a prevalence of 11.48%. Prevalence was 9.35% (58 cases) in Kombissiri, 12.86% (80 cases) in Manga and 11.99% (108 cases) in Pô. By gender, 13.37% of women (164 cases) tested positive, and 8.95% of men (82 cases). Women accounted for 66.67% of all positive test subjects. The results from the multivariate analysis show a significantly higher seroprevalence in women (p = 0.007), people over 55 years old (p = 0.004), overweight people (p = 0.026) and those with drinking water sources at home (p = 0.013)., Conclusions: The results of this study show that the COVID-19 virus also circulates in the population of middle-sized cities in Burkina Faso, far more than officially reported by the information service of the government of Burkina Faso, given the lack of systematic testing in the general population in the country. The study also highlighted the greater vulnerability of women, older and overweight individuals to the epidemic. The preventive measures put in place to fight the pandemic must take these different factors into account., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sana 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.)

Objective: To investigate the prevalence of canine Leishmania infections in villages endemic for visceral leishmaniasis in Xin'an County, Luoyang City, so as to provide insights into visceral leishmaniasis prevention and control., Methods: All dogs were captured from Huzhanggou Village, Xin'an County, Luoyang City in August 2020, where local cases with visceral leishmaniasis lived. The basic characteristics of dogs were collected, and venous blood was collected via the ear or neck veins of dogs. The serum anti- Leishmania antibody was detected using the rk39 immunochromatographic test and Leishmania nucleic acid was detected using PCR assay, and the prevalence of Leishmania infection was estimated in dogs., Results: A total of 133 domestic dogs were captured from Huzhanggou Village, with a median age of 18.0 (28.5) months. The sero-prevalence of anti- Leishmania antibody was 24.81% (33/133) and the prevalence of a positive PCR assay was 14.29% (19/133) in dogs. The median ages of Leishmania -infected and uninfected dogs were 24.0 (36.0) months and 12.0 (18.0) months, respectively ( U = 872.000, P = 0.000), and the prevalence of Leishmania infection was 55.56% (5/9) in "mangy dogs" and 24.19% (30/124) in asymptomatic dogs (χ 2 = 2.793, P = 0.095)., Conclusions: There are a large number of asymptomatic dogs with Leishmania infections in Xin'an County, Luoyang City, with a high transmission risk of visceral leishmaniasis. Timely prevention and control measures are required to control the spread of visceral leishmaniasis.

Background: Long-standing health inequalities in Australian society that were exposed by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were described as "fault lines" in a recent call to action by a consortium of philanthropic organizations. With asthma a major contributor to childhood disease burden, studies of its spatial epidemiology can provide valuable insights into the emergence of health inequalities early in life., Objective: The aims of this study were to characterize the spatial variation of asthma prevalence among children living within Australia's 4 largest cities and quantify the relative contributions of climatic and environmental factors, outdoor air pollution, and socioeconomic status in determining this variation., Methods: A Bayesian model with spatial smoothing was developed to regress ecologic health status data from the 2021 Australian Census against groups of explanatory covariates intended to represent mechanistic pathways., Results: The prevalence of asthma in children aged 5 to 14 years averages 7.9%, 8.2%, 8.5%, and 7.6% in Sydney, Melbourne, Brisbane, and Perth, respectively. This small inter-city variation contrasts against marked intracity variation at the small-area level, which ranges from 6% to 12% between the least and most affected locations in each. Statistical variance decomposition on a subsample of Australian-born, nonindigenous children attributes 66% of the intracity spatial variation to the assembled covariates. Of these covariates, climatic and environmental factors contribute 30%, outdoor air pollution contributes 19%, and areal socioeconomic status contributes the remaining 51%., Conclusion: Geographic health inequalities in the prevalence of childhood asthma within Australia's largest cities reflect a complex interplay of factors, among which socioeconomic status is a principal determinant., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

This study examines household pets as potential epidemiological links between environments contaminated with pathogenic leptospires and humans in Santa Fe, Argentina. The aims of our study were: (a) to characterize the habits and exposure to environmental sources of leptospirosis in the population of dogs and cats attending to municipal spay and neutering campaigns in Santa Fe, Argentina, (b) to assess the seroprevalence of anti-Leptospira antibodies in asymptomatic dogs and cats, (c) to evaluate factors that could increase seropositivity, and (d) to identify spatial clusters of seropositive dogs and cats in the capital city of Santa Fe. From May to November 2022, a cross-sectional serosurvey was conducted during municipal spaying/neutering campaigns. Eligible household dogs and cats were over 6 months old, apparently healthy, and not vaccinated against leptospirosis in the past 6 months. We used microagglutination test (MAT) to assess anti-Leptospira antibodies using a panel of 10 reference strains. We used generalized linear mixed effects models (GLMM) to examine individual and census tract-level risk factors for seropositivity, and local Moran's I statistic for spatial clusters. Results showed higher leptospiral antibody prevalence in dogs (18.2 %) than cats (3.6 %, p = 0.002). Dogs with street access had higher likelihood of being seropositive (OR: 3.8, 95 % CI: 1.2; 11.9), and areas with chronic poverty showed an elevated risk of presenting seropositive animals (RR: 4.0, 95 % CI: 1.1; 14.4). Spatial analysis didn't reveal significant seropositivity clusters among census tracts. These findings shed light on widespread Leptospira seropositivity in pets in this endemic region. Understanding seroprevalence and risk factors can guide public and veterinary health strategies, emphasizing increased leptospirosis vaccination for dogs in vulnerable areas and promoting responsible pet care., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study examines the spread of COVID-19 in São Paulo, Brazil, using a combination of cellular automata and geographic information systems to model the epidemic's spatial dynamics. By integrating epidemiological models with georeferenced data and social indicators, we analyse how the virus propagates in a complex urban setting, characterized by significant social and economic disparities. The research highlights the role of various factors, including mobility patterns, neighbourhood configurations, and local inequalities, in the spatial spreading of COVID-19 throughout São Paulo. We simulate disease transmission across the city's 96 districts, offering insights into the impact of network topology and district-specific variables on the spread of infections. The study seeks to fine-tune the model to extract epidemiological parameters for further use in a statistical analysis of social variables. Our findings underline the critical importance of spatial analysis in public health strategies and emphasize the necessity for targeted interventions in vulnerable communities. Additionally, the study explores the potential of mathematical modelling in understanding and mitigating the effects of pandemics in urban environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: To investigate excess mortality during the COVID-19 pandemic and its spatial distribution in Pernambuco, Brazil., Methods: This was an ecological, descriptive and analytical study of deaths, by municipality, recorded on the Mortality Information System, in 2020 and 2021. Excess mortality was measured by comparing observed and expected deaths, the latter estimated by calculating standardized mortality ratio (SMR). SMR and respective confidence intervals (95%CI) were calculated. Spatial analysis was performed by calculating the Global and Local Moran Index., Results: Excess mortality was 20.6% and 27.5%, respectively, in 2020 and 2021, with positive spatial correlation (p-value < 0.05). More populous municipalities (2020: SMR = 1.26; 95%CI 1.24;1.27 and 2021: SMR = 1.34; 95%CI 1.32;1.34), more developed municipalities (2020: SMR = 1.43; 95%CI 1.41;1.44 and 2021: SMR = 1.51;95%CI 1.50;1.53) and municipalities in the Sertão region (2020:SMR = 1.31;95%CI 1.30;1.33 and 2021: SMR = 1.44; 95%CI 1.42;1.46) showed greater excess deaths., Conclusion: Excess mortality coincided with peak periods of COVID-19 transmission.

BACKGROUND There are many factors that affect human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS)-related deaths, and different antiretroviral therapy (ART) strategies may affect HIV/AIDS-related fatality rates. However, studies on this area are very limited. This study aimed to evaluate the factors associated with HIV/AIDS-related mortality and the impact of different ART strategies in Lu'an City, Anhui Province, China, 1999-2023. MATERIAL AND METHODS Data of HIV/AIDS cases were downloaded from the China HIV/AIDS Comprehensive Response Information Management System, and were assessed to evaluate the impact of different ART strategies on the related fatality rate using interrupted time series (ITS). RESULTS We found that age at diagnosis of 15 years, 25 years, 40 years, and 60 years, as well as receiving ART, were protective factors against death (with P below 0.05), while lower CD4 count at the last CD4 count and the year of diagnosis before 2007 and between 2007 and 2016 were risk factors (with P below 0.05). ITS analysis revealed that in the year of the introduction of free ART in 2006, the fatality rate decreased by 38.60% (P=0.015). The fatality rate trend from 2006 to 2015 was -1.1%, which was not statistically significant (P=0.434). The fatality rate trend from 2016 to 2023 was -0.33%, indicating a decreasing trend (P=0.000). CONCLUSIONS Children under 15 years old and elderly patients had a higher risk of death. The main reasons for the decrease in HIV/AIDS-related fatality rate were ART, especially the "early treatment" strategy.

Introduction: Seasonal influenza generally represents an underestimated public health problem with significant socioeconomic implications. Monitoring and detecting influenza epidemics are important tasks that require integrated strategies. Wastewater-based epidemiology (WBE) is an emerging field that uses wastewater data to monitor the spread of disease and assess the health of a community. It can represent an integrative surveillance tool for better understanding the epidemiology of influenza and prevention strategies in public health., Methods: We conducted a study that detected the presence of Influenza virus RNA using a wastewater-based approach. Samples were collected from five wastewater treatment plants in five different municipalities, serving a cumulative population of 555,673 Sicilian inhabitants in Italy. We used the RT-qPCR test to compare the combined weekly average of Influenza A and B viral RNA in wastewater samples with the average weekly incidence of Influenza-like illness (ILI) obtained from the Italian national Influenza surveillance system. We also compared the number of positive Influenza swabs with the viral RNA loads detected from wastewater. Our study investigated 189 wastewater samples., Results: Cumulative ILI cases substantially overlapped with the Influenza RNA load from wastewater samples. Influenza viral RNA trends in wastewater samples were similar to the rise of ILI cases in the population. Therefore, wastewater surveillance confirmed the co-circulation of Influenza A and B viruses during the season 2022/2023, with a similar trend to that reported for the weekly clinically confirmed cases., Conclusion: Wastewater-based epidemiology does not replace traditional epidemiological surveillance methods, such as laboratory testing of samples from infected individuals. However, it can be a valuable complement to obtaining additional information on the incidence of influenza in the population and preventing its spread., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Maida, Mazzucco, Priano, Palermo, Graziano, Costantino, Russo, Andolina, Restivo, Giangreco, Iaia, Santino, Li Muli, Guzzetta, Vitale and Tramuto.)

Background: The relation between climate change and human health has become one of the major worldwide public health issues. However, the evidence for low-latitude plateau regions is limited, where the climate is unique and diverse with a complex geography and topography., Objectives: This study aimed to evaluate the effect of ambient temperature on the mortality burden of nonaccidental deaths in Yunnan Province and to further explore its spatial heterogeneity among different regions., Methods: We collected mortality and meteorological data from all 129 counties in Yunnan Province from 2014 to 2020, and 16 prefecture-level cities were analyzed as units. A distributed lagged nonlinear model was used to estimate the effect of temperature exposure on years of life lost (YLL) for nonaccidental deaths in each prefecture-level city. The attributable fraction of YLL due to ambient temperature was calculated. A multivariate meta-analysis was used to obtain an overall aggregated estimate of effects, and spatial heterogeneity among 16 prefecture-level cities was evaluated by adjusting the city-specific geographical characteristics, demographic characteristics, economic factors, and health resources factors., Results: The temperature-YLL association was nonlinear and followed slide-shaped curves in all regions. The cumulative cold and heat effect estimates along lag 0-21 days on YLL for nonaccidental deaths were 403.16 (95% empirical confidence interval [eCI] 148.14-615.18) and 247.83 (95% eCI 45.73-418.85), respectively. The attributable fraction for nonaccidental mortality due to daily mean temperature was 7.45% (95% eCI 3.73%-10.38%). Cold temperature was responsible for most of the mortality burden (4.61%, 95% eCI 1.70-7.04), whereas the burden due to heat was 2.84% (95% eCI 0.58-4.83). The vulnerable subpopulations include male individuals, people aged <75 years, people with education below junior college level, farmers, nonmarried individuals, and ethnic minorities. In the cause-specific subgroup analysis, the total attributable fraction (%) for mean temperature was 13.97% (95% eCI 6.70-14.02) for heart disease, 11.12% (95% eCI 2.52-16.82) for respiratory disease, 10.85% (95% eCI 6.70-14.02) for cardiovascular disease, and 10.13% (95% eCI 6.03-13.18) for stroke. The attributable risk of cold effect for cardiovascular disease was higher than that for respiratory disease cause of death (9.71% vs 4.54%). Furthermore, we found 48.2% heterogeneity in the effect of mean temperature on YLL after considering the inherent characteristics of the 16 prefecture-level cities, with urbanization rate accounting for the highest proportion of heterogeneity (15.7%) among urban characteristics., Conclusions: This study suggests that the cold effect dominated the total effect of temperature on mortality burden in Yunnan Province, and its effect was heterogeneous among different regions, which provides a basis for spatial planning and health policy formulation for disease prevention., (© Yang Chen, Lidan Zhou, Yuanyi Zha, Yujin Wang, Kai Wang, Lvliang Lu, Pi Guo, Qingying Zhang. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

Objective: We investigated whether a zip code's location or demographics are most predictive of changes in daily mobility throughout the course of the COVID-19 pandemic., Design: We used a population-level study to examine the predictability of daily mobility during the COVID-19 pandemic using a two-stage regression approach, where generalised additive models (GAM) predicted mobility trends over time at a large spatial level, then the residuals were used to determine which factors (location, zip code-level features or number of non-pharmaceutical interventions (NPIs) in place) best predict the difference between a zip code's measured mobility and the average trend on a given date., Setting: We analyse zip code-level mobile phone records from 26 metropolitan areas in the USA on 15 March-31 September 2020, relative to October 2020., Results: While relative mobility had a general trend, a zip code's city-level location significantly helped to predict its daily mobility patterns. This effect was time-dependent, with a city's deviation from general mobility trends differing in both direction and magnitude throughout the course of 2020. The characteristics of a zip code further increased predictive power, with the densest zip codes closest to a city centre tended to have the largest decrease in mobility. However, the effect on mobility change varied by city and became less important over the course of the pandemic., Conclusions: The location and characteristics of a zip code are important for determining changes in daily mobility patterns throughout the course of the COVID-19 pandemic. These results can determine the efficacy of NPI implementation on multiple spatial scales and inform policy makers on whether certain NPIs should be implemented or lifted during the ongoing COVID-19 pandemic and when preparing for future public health emergencies., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Chronic cough (CC) is common in the general population of China, creating a difficult-to-ignore public health burden. However, there is a lack of research on the nationwide prevalence and disease burden of CC in the Chinese population. We aim to use an insurance claims database to assess the prevalence and the corresponding economic burden owing to CC in China., Methods: This was a retrospective observational study based on an administrative medical insurance database in 2015, 2016 and 2017, from nine cities in North, South, East, South-West, and North-West regions of China. The study population was Chinese adults (≥ 18 years old) who had been identified as CC patients. Descriptive data analyses were used in statistical analysis., Results: A total of 44,472, 55,565, and 56,439 patients with mean ages of 53.2 (16.3) years were identified as patients with CC in 2015, 2016, and 2017, respectively. Of these, 55.24% were women. In addition, 8.90%, 9.46%, and 8.37% of all patients in 2015, 2016, and 2017, who had applied for medical insurance, had CC, respectively, with a three-year average probability of 8.88%. The median number of outpatient visits within a calendar year was 27 per year due to any reason during the period of 2015-2017. The median medical cost of each patient per year increased from 935.30 USD to 1191.47 USD from 2015 to 2017., Conclusion: CC is common among medical insurance users, with a substantial utilization of medical resources, highlighting the huge burden of CC in China., (© 2024. Merck & Co., Inc., Rahway, NJ, USA and its affiliates & Fang Yi, Kefang Lai 2024.)

Background: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China., Methods: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk., Results: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E-H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China., Conclusions: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease., (© 2024. The Author(s).)

Understanding the distribution of tegumentary leishmaniasis (TL) in different periods enables the adequate conduction of actions at the public health level. The present study analyzes the spatiotemporal evolution of TL incidence rates in the municipalities of Brazil and identifies priority areas from 2001 to 2020. Notifications of new cases were analyzed employing space-time scan statistics and Local Indicators of Spatial Association. As TL incidence rates presented a downward trend in most Brazilian municipalities, spatiotemporal clusters of high relative risks (RR) were more frequent in the first decade of the series. There was a concentration of those clusters in the North and Northeast regions, mainly in the Legal Amazon area. More recent high-RR areas were identified in municipalities of different regions. The number of priority municipalities showed a stable trend in Brazil. There was a great concentration of such municipalities in the states of Acre, Mato Grosso, Rondônia, Pará, and Amapá, as well as large areas in Roraima, Amazonas, Maranhão, and Tocantins, and smaller areas in the states of Goiás, Ceará, Bahia, Minas Gerais, São Paulo, and Paraná. The present study contributes to the understanding of the historical evolution of TL in Brazil and subsidizes actions to combat the disease.

Background: Temperature affects influenza transmission; however, currently, limited evidence exists about its effect in China at the national and city levels as well as how temperature can be integrated into influenza interventions., Methods: Meteorological, pollutant, and influenza data from 201 cities in mainland China between 2013 and 2018 were analyzed at both the city and national levels to investigate the relationship between temperature and influenza prevalence. We examined the impact of temperature on the time-varying reproduction number (R t ) using generalized additive quasi-Poisson regression models combined with the distributed lag nonlinear model. Threshold temperatures were determined for seven regions based on the early warning threshold of serious influenza outbreaks, set at R t  = 1.2. A multivariate random-effects meta-analysis was employed to assess region-specific associations. The excess risk (ER) index was defined to investigate the correlation between R t and temperature, modified based on seasonal and regional characteristics., Results: At the national level and in the central, northern, northwestern, and southern regions, temperature was found to be negatively correlated with relative risk, whereas the shapes of the data curves for the eastern, southwestern, and northeastern regions were not well defined. Low temperatures had an observable effect on influenza prevalence; however, the effects of high temperatures were not obvious. At an R t of 1.2, the threshold temperatures for reaching a warning for serious influenza outbreaks were - 24.3 °C in the northeastern region, 16.6 °C in the northwestern region, and between 1℃ and 10 °C in other regions., Conclusion: The study findings revealed that temperature had a varying effect on influenza transmission trends (R t ) across different regions in China. By identifying region-specific temperature thresholds at R t  = 1.2, more effective early warning systems for influenza outbreaks could be tailored. These findings emphasize the significance of the region-specific adaptation of influenza prevention and control measures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Women's mortality at a reproductive age has been a global concern, and its decrease has been incorporated as a target of the UN Sustainable Development Goals. The aim of this study was to describe the spatial-temporal evolution of mortality rates among women of reproductive age in Brazilian municipalities by groups of causes and socioeconomic indicators from 2000 to 2018., Study Design: Ecological analysis., Methods: This work was an ecological, descriptive study that analyzed estimates of mortality rates among women of reproductive age (15-49 years) by main groups of causes of death from the Global Burden of Disease (GBD) study in three consecutive trienniums, T1 (2000-2002), T2 (2009-2011), and T3 (2016-2018). To quantify the temporal evolution in mortality rates, the present study calculated the percentage change for each triennium. The spatial analysis of mortality rates was carried out using Moran's index. The Pearson coefficient was used to analyze the correlation between the data., Results: A significant decline in mortality rates was found for all groups of causes in all regions of the country. Despite the downward trend, the percentage change from 2009 to 2011 to 2016 to 2018 showed a decrease in the group of Noncommunicable Diseases (NCDs) and external causes. The decline in mortality rates of women due to external causes showed only a minimal change in the North and Northeast regions from T2 to T3, whereas a cluster of neighboring municipalities with high mortality rates persisted in the municipalities of the South region and in the state of Roraima. The ranking of the main causes of death in Brazilian municipalities showed an increase in neoplasms in detriment to cardiovascular diseases (CVDs)., Conclusions: The main causes of death in women of reproductive age at a more local level could be used to recognize inequalities and to develop interventions aimed at tackling premature and preventable deaths., (Copyright © 2024 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)