Estimating dengue disease and economic burden to inform municipal-level policymakers: Method for a pragmatic city-level observational cohort study.

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.

Epidemiological characteristics and transmission dynamics of dengue fever in China.

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 Reff 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.

Detection of influenza virus in urban wastewater during the season 2022/2023 in Sicily, Italy.

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.

Spatial dynamics of COVID-19 in São Paulo: A cellular automata and GIS approach.

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.

Seroprevalence and risk factors for SARS-CoV-2 infection in middle-sized cities of Burkina Faso: A descriptive cross-sectional study.

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.

Prevalence and genotypes of Chlamydia psittaci in birds and related workers in three cities of China.

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.

COVID-19 IgG seropositivity and its determinants in occupational groups of varying infection risks in two Andean cities of Ecuador before mass vaccination.

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.

Rural-urban difference in the association between particulate matters and stroke incidence: The evidence from a multi-city perspective cohort study.

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 (PM2.5 and PM10) 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 PM2.5 and PM10) 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/m3 increase in mean PMs' concentrations during the previous 1 year at the residential address (HR: 1.26, 95%CI: 1.24, 1.29 (PM2.5); HR: 1.13, 95%CI: 1.11, 1.15 (PM10)). The presence of diabetes and dyslipidemia increased the risk of PM10-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.

Excess mortality during the COVID-19 pandemic and its spatial distribution in the state of Pernambuco, Brazil: an ecological study.

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.

Geography versus sociodemographics as predictors of changes in daily mobility across the USA during the COVID-19 pandemic: a two-stage regression analysis across 26 metropolitan areas.

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.

Can genomics and meteorology predict outbreaks of legionellosis in urban settings?

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.

Prevalence and disease burden of chronic cough in nine cities of China: an observational study.

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.

Trends in HIV/AIDS-Related Mortality and the Impact of Antiretroviral Treatment Strategies in Lu'an City: A Comprehensive Analysis.

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.

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

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

Forecasting and mapping dengue fever epidemics in China: a spatiotemporal analysis.

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.

Impact of Ambient Temperature on Mortality Burden and Spatial Heterogeneity in 16 Prefecture-Level Cities of a Low-Latitude Plateau Area in Yunnan Province: Time-Series Study.

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.

Tegumentary leishmaniasis in Brazil: priority municipalities and spatiotemporal relative risks from 2001 to 2020.

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.

Spatiotemporal analysis of imported and local dengue virus and cases in a metropolis in Southwestern China, 2013-2022.

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.

Urban density, household overcrowding and the spread of COVID-19 in Australian cities.

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.

The impacts of extreme heat events on non-accidental, cardiovascular, and respiratory mortality: An analysis of 12 Canadian cities from 2000 to 2020.

Background: Extreme heat has significant impacts on mortality. In Canada, past research has analyzed the degree to which non-accidental mortality increases during single extreme heat events; however, few studies have considered multiple causes of death and the impacts of extreme heat events on mortality over longer time periods. Data and methods: Daily death counts attributable to non-accidental, cardiovascular, and respiratory causes were retrieved for the 12 most populous cities in Canada from 2000 to 2020. Generalized additive models were applied to quantify daily mortality risks for people aged younger than 65 years and for those aged 65 years and older in each city and for each cause of death. Model results were used to calculate the change in mortality risks and the number of excess deaths attributable to extreme heat during extreme heat events. Results: Elevated mortality risks were observed during extreme heat events in most cities for non-accidental and respiratory causes. The impacts of extreme heat on non-accidental mortality were typically greater for people aged 65 and older than for those aged younger than 65. Significantly higher non-accidental mortality risks were observed during extreme heat events for people aged 65 and older in Montréal, the city of Québec, Surrey, and Toronto. For cardiovascular and respiratory causes, people aged 65 and older had significantly higher mortality risks during extreme heat events in Montréal, and both Montréal and Toronto, respectively. In the 12 cities, approximately 670 excess non-accidental deaths, 115 excess cardiovascular deaths, and 115 excess respiratory deaths were attributable to extreme heat events during the study period. Mortality risks during extreme heat events were generally higher in cities with larger proportions of renter households and fewer extreme heat events. Interpretation: This study estimates the longer-term impacts of extreme heat events on three mortality outcomes in a set of large Canadian cities. As climate change causes more frequent and intense extreme heat events, and as policy makers aim to reduce the health impacts of heat, it is important to understand how and where extreme heat affects health.