Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities.

BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-µg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.

OBJECTIVE: Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. METHODS: Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. RESULTS: Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. CONCLUSION: Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

The hidden severity of the COVID-19 pandemic in children and adolescents in Brazil: a territorial analysis of hospital mortality. / A gravidade oculta da pandemia de COVID-19 em crianças e adolescentes no Brasil: uma análise territorial da mortalidade hospitalar.

This article aims to describe the geographical distribution of hospital mortality from COVID-19 in children and adolescents during the 2020-2021 pandemic in Brazil. Ecological, census study (SIVEP GRIPE) with individuals up to 19 years of age, hospitalized with SARS due to COVID-19 or SARS not specified in Brazilian municipalities, stratified in two ways: 1) in the five macro-regions and 2) in three urban agglomerations: capital, municipalities of the metropolitan region and non-capital municipalities. There were 44 hospitalizations/100,000 inhabitants due to COVID-19 and 241/100,000 when including unspecified SARS (estimated underreporting of 81.8%). There were 1,888 deaths by COVID-19 and 4,471 deaths if added to unspecified SARS, estimating 57.8% of unreported deaths. Hospital mortality was 2.3 times higher in the macro-regions when considering only the cases of COVID-19, with the exception of the North and Center-West regions. Higher hospital mortality was also recorded in non-capital municipalities. The urban setting was associated with higher SARS hospital mortality during the COVID-19 pandemic in Brazil. Living in the North and Northeast macro-regions, and far from the capitals offered a higher risk of mortality for children and adolescents who required hospitalization.

O objetivo deste artigo é descrever a distribuição geográfica da mortalidade hospitalar por COVID-19 em crianças e adolescentes durante a pandemia de 2020-2021 no Brasil. Estudo ecológico, censitário (SIVEP GRIPE), de indivíduos até 19 anos, internados com SRAG por COVID-19 ou SRAG não especificada, em municípios brasileiros, estratificados de duas formas: 1) nas cinco macrorregiões e 2) em três aglomerados urbanos: capital, municípios da região metropolitana e do interior. Verificou-se 44 internações/100 mil habitantes por COVID-19 e 241/100 mil ao se incluir a SRAG não especificada (subnotificação estimada de 81,8%). Ocorreram1.888 óbitos por COVID-19 e 4.471 óbitos se somados à SRAG não especificada, estimando-se subnotificação de 57,8% dos óbitos. A mortalidade hospitalar foi 2,3 vezes maior nas macrorregiões quando considerados apenas os casos de COVID-19, com exceção das regiões Norte e Centro-Oeste. Registrou-se também maior mortalidade hospitalar em municípios do interior. O contexto urbano esteve associado à maior mortalidade hospitalar por SRAG durante a pandemia de COVID-19 no Brasil. Residir nas macrorregiões Norte e Nordeste, e distante das capitais, ofereceu maior risco de mortalidade para crianças e adolescentes que necessitaram hospitalização.

The association between humidex and tuberculosis: a two-stage modelling nationwide study in China.

BACKGROUND: Under a changing climate, the joint effects of temperature and relative humidity on tuberculosis (TB) are poorly understood. To address this research gap, we conducted a time-series study to explore the joint effects of temperature and relative humidity on TB incidence in China, considering potential modifiers. METHODS: Weekly data on TB cases and meteorological factors in 22 cities across mainland China between 2011 and 2020 were collected. The proxy indicator for the combined exposure levels of temperature and relative humidity, Humidex, was calculated. First, a quasi-Poisson regression with the distributed lag non-linear model (DLNM) was constructed to examine the city-specific associations between humidex and TB incidence. Second, a multivariate meta-regression model was used to pool the city-specific effect estimates, and to explore the potential effect modifiers. RESULTS: A total of 849,676 TB cases occurred in the 22 cities between 2011 and 2020. Overall, a conspicuous J-shaped relationship between humidex and TB incidence was discerned. Specifically, a decrease in humidex was positively correlated with an increased risk of TB incidence, with a maximum relative risk (RR) of 1.40 (95% CI: 1.11-1.76). The elevated RR of TB incidence associated with low humidex (5th humidex) appeared on week 3 and could persist until week 13, with a peak at approximately week 5 (RR: 1.03, 95% CI: 1.01-1.05). The effects of low humidex on TB incidence vary by Natural Growth Rate (NGR) levels. CONCLUSION: A J-shaped exposure-response association existed between humidex and TB incidence in China. Humidex may act as a better predictor to forecast TB incidence compared to temperature and relative humidity alone, especially in regions with higher NGRs.

Spatial analysis of dengue transmission in an endemic city in Brazil reveals high spatial structuring on local dengue transmission dynamics.

In the last decades, dengue has become one of the most widespread mosquito-borne arboviruses in the world, with an increasing incidence in tropical and temperate regions. The mosquito Aedes aegypti is the dengue primary vector and is more abundant in highly urbanized areas. Traditional vector control methods have showing limited efficacy in sustaining mosquito population at low levels to prevent dengue virus outbreaks. Considering disease transmission is not evenly distributed in the territory, one perspective to enhance vector control efficacy relies on identifying the areas that concentrate arbovirus transmission within an endemic city, i.e., the hotspots. Herein, we used a 13-month timescale during the SARS-Cov-2 pandemic and its forced reduction in human mobility and social isolation to investigate the spatiotemporal association between dengue transmission in children and entomological indexes based on adult Ae. aegypti trapping. Dengue cases and the indexes Trap Positive Index (TPI) and Adult Density Index (ADI) varied seasonally, as expected: more than 51% of cases were notified on the first 2 months of the study, and higher infestation was observed in warmer months. The Moran's Eigenvector Maps (MEM) and Generalized Linear Models (GLM) revealed a strong large-scale spatial structuring in the positive dengue cases, with an unexpected negative correlation between dengue transmission and ADI. Overall, the global model and the purely spatial model presented a better fit to data. Our results show high spatial structure and low correlation between entomological and epidemiological data in Foz do Iguaçu dengue transmission dynamics, suggesting the role of human mobility might be overestimated and that other factors not evaluated herein could be playing a significant role in governing dengue transmission.

The Urban Environment and Cardiometabolic Health.

Urban environments contribute substantially to the rising burden of cardiometabolic diseases worldwide. Cities are complex adaptive systems that continually exchange resources, shaping exposures relevant to human health such as air pollution, noise, and chemical exposures. In addition, urban infrastructure and provisioning systems influence multiple domains of health risk, including behaviors, psychological stress, pollution, and nutrition through various pathways (eg, physical inactivity, air pollution, noise, heat stress, food systems, the availability of green space, and contaminant exposures). Beyond cardiometabolic health, city design may also affect climate change through energy and material consumption that share many of the same drivers with cardiometabolic diseases. Integrated spatial planning focusing on developing sustainable compact cities could simultaneously create heart-healthy and environmentally healthy city designs. This article reviews current evidence on the associations between the urban exposome (totality of exposures a person experiences, including environmental, occupational, lifestyle, social, and psychological factors) and cardiometabolic diseases within a systems science framework, and examines urban planning principles (eg, connectivity, density, diversity of land use, destination accessibility, and distance to transit). We highlight critical knowledge gaps regarding built-environment feature thresholds for optimizing cardiometabolic health outcomes. Last, we discuss emerging models and metrics to align urban development with the dual goals of mitigating cardiometabolic diseases while reducing climate change through cross-sector collaboration, governance, and community engagement. This review demonstrates that cities represent crucial settings for implementing policies and interventions to simultaneously tackle the global epidemics of cardiovascular disease and climate change.

Exploring spatio-temporal impact of COVID-19 on citywide taxi demand: A case study of New York City.

Coronavirus disease 2019 (COVID-19) has brought dramatic changes in our daily life, especially in human mobility since 2020. As the major component of the integrated transport system in most cities, taxi trips represent a large portion of residents' urban mobility. Thus, quantifying the impacts of COVID-19 on city-wide taxi demand can help to better understand the reshaped travel patterns, optimize public-transport operational strategies, and gather emergency experience under the pressure of this pandemic. To achieve the objectives, the Geographically and Temporally Weighted Regression (GTWR) model is used to analyze the impact mechanism of COVID-19 on taxi demand in this study. City-wide taxi trip data from August 1st, 2020 to July 31st, 2021 in New York City was collected as model's dependent variables, and COVID-19 case rate, population density, road density, station density, points of interest (POI) were selected as the independent variables. By comparing GTWR model with traditional ordinary least square (OLS) model, temporally weighted regression model (TWR) and geographically weighted regression (GWR) model, a significantly better goodness of fit on spatial-temporal taxi data was observed for GTWR. Furthermore, temporal analysis, spatial analysis and the epidemic marginal effect were developed on the GTWR model results. The conclusions of this research are shown as follows: (1) The virus and health care become the major restraining and stimulative factors of taxi demand in post epidemic era. (2) The restraining level of COVID-19 on taxi demand is higher in cold weather. (3) The restraining level of COVID-19 on taxi demand is severely influenced by the curfew policy. (4) Although this virus decreases taxi demand in most of time and places, it can still increase taxi demand in some specific time and places. (5) Along with COVID-19, sports facilities and tourism become obstacles on increasing taxi demand in most of places and time in post epidemic era. The findings can provide useful insights for policymakers and stakeholders to improve the taxi operational efficiency during the remainder of the COVID-19 pandemic.

Unmet need for medication for opioid use disorder among persons who inject drugs in 23 U.S. cities.

BACKGROUND: Persons who inject drugs (PWID) are at increased risk of HIV and hepatitis C virus (HCV) infections and premature mortality due to drug overdose. Medication for opioid use disorder (MOUD), such as methadone or buprenorphine, reduces injecting behaviors, HIV and HCV transmission, and mortality from opioid overdose. Using data from National HIV Behavioral Surveillance, we evaluated the unmet need for MOUD among PWID in 23 U.S. cities. METHODS: PWID were recruited by respondent-driven sampling, interviewed, and tested for HIV. This analysis includes PWID who were ≥18 years old and reported injecting drugs and opioid use in the past 12 months. We used Poisson regression to examine factors associated with self-reported unmet need for MOUD and reported adjusted prevalence ratios (aPR) with 95% confidence intervals. RESULTS: Of 10,879 PWID reporting using opioids, 68.8% were male, 48.2% were ≥45 years of age, 38.8% were non-Hispanic White, 49.6% experienced homelessness, and 28.0% reported an unmet need for MOUD in the past 12 months. PWID who were more likely to report unmet need for MOUD experienced homelessness (aPR 1.26; 95% CI: 1.19-1.34), were incarcerated in the past 12 months (aPR 1.15; 95% CI: 1.08-1.23), injected ≥once a day (aPR 1.42; 95% CI: 1.31-1.55), reported overdose (aPR 1.33; 95% CI: 1.24-1.42), and sharing of syringes (aPR 1.14; 95% CI: 1.06-1.23). CONCLUSIONS: The expansion of MOUD provision for PWID is critical. Integrating syringe service programs and MOUD provision and linking PWID who experience overdose, incarceration or homelessness to treatment with MOUD could improve its utilization among PWID.

Effects of primary health care and socioeconomic aspects on the dispersion of COVID-19 in the Brazilian Northeast: Ecological study of the first pandemic wave.

BACKGROUND: The COVID-19 pandemic has had a negative impact on socioeconomic and public health conditions of the population. AIM: To measure the temporal evolution of COVID-19 cases in cities near the countryside outside metropolitan areas of northeastern Brazil and the impact of the primary care organization in its containment. METHODS: This is a time-series study, based on the first three months of COVID-19 incidence in northeastern Brazil. Secondary data were used, the outcome was number of COVID-19 cases. Independent variables were time, coverage and quality score of basic health services, and demographic, socioeconomic and social isolation variables. Generalizable Linear Models with first order autoregression were applied. RESULTS: COVID-19 spreads heterogeneously in cities near the countryside of Northeastern Brazilian cities, showing associations with the city size, socioeconomic and organizational indicators of services. The Family Health Strategy seems to mitigate the speed of progression and burden of the disease, in addition to measures such as social isolation and closure of commercial activities. CONCLUSION: The spread of COVID-19 reveals multiple related factors, which require coordinated intersectoral actions in order to mitigate its problems, especially in biologically and socially vulnerable populations.

Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities.

In the future, novel and highly pathogenic viruses may re-emerge, leading to a surge in healthcare demand. It is essential for urban epidemic control to investigate different cities' spatiotemporal spread characteristics and medical carrying capacity during the early stages of COVID-19. This study employed textual analysis, mathematical statistics, and spatial analysis methods to examine the situation in six highly affected Chinese cities. The findings reveal that these cities experienced three phases during the initial outbreak of COVID-19: "unknown-origin incubation", "Wuhan-related outbreak", and "local exposure outbreak". Cities with a high number of confirmed cases exhibited a multicore pattern, while those with fewer cases displayed a single-core pattern. The cores were distributed hierarchically in the central built-up areas of cities' economic, political, or transportation centers. The radii of these cores shrank as the central built-up area's level decreased, indicating a hierarchical decay and a core-edge structure. It suggests that decentralized built environments (non-clustered economies and populations) are less likely to facilitate large-scale epidemic clusters. Additionally, the deployment of designated hospitals in these cities was consistent with the spatial distribution of the epidemic; however, their carrying capacity requires urgent improvement. Ultimately, the essence of prevention and control is the governance of human activities and the efficient management of limited resources about individuals, places, and materials through leveraging IT and GIS technologies to address supply-demand contradictions.

The impact of the COVID-19 pandemic on frail older people ageing in place alone in two Italian cities: Functional limitations, care arrangements and available services.

The study aimed to explore and compare effects of lockdown, due to the COVID-19 pandemic in 2020, on frail older people living alone at home in Brescia and Ancona, two urban cities located respectively in Northern and Central Italy. This country was the Western epicenter of the first wave of the pandemic (February-May 2020), which affected the two cities differently as for infections, with a more severe impact on the former. A follow-up study of the IN-AGE research project (2019) was carried out in July-September 2020, by means of telephone interviews, involving 41 respondents. Semi-structured questions focused on the effects of the first wave of the pandemic on their mobility and functional limitations, available care arrangements, and access to health services. The lockdown and social distancing measures overall negatively impacted on frail older people living alone, to a different extent in Ancona and Brescia, with a better resilience of home care services in Brescia, and a greater support from the family in Ancona, where however major problems in accessing health services also emerged. Even though the study was exploratory only, with a small sample that cannot be considered as representative of the population, and despite differences between the two cities, findings overall suggested that enhancing home care services, and supporting older people in accessing health services, could allow ageing in place, especially in emergency times.

Wastewater-based estimation of diabetes mellitus prevalence in 237 cities: A cross-China study.

Diabetes mellitus, a metabolic disease characterized by hyperglycemia, has been witnessed as a rapidly escalating worldwide health crisis. China currently had 140.9 million diabetic population in 2021, which was the largest globally. DM has witnessed a significant surge in the past few decades, leading to an alarming rise in the overall burden caused by this disease. To monitor the near real-time DM prevalence and the consumption of first-line anti-diabetic drugs, a wastewater-based epidemiology (WBE) approach based on the back-calculation of metformin concentration was implemented in 237 cities in China. The quantitative analysis of metformin in wastewater was conducted by LC-MS/MS with satisfactory results of method validation. The average concentration of metformin in wastewater was 14.07 ± 13.16 µg/L, and the per capita consumption was 5.16 ± 2.08 mg/day/inh, ranging from 0.90 to 10.36 ± 4.63 mg/day/inh. The calculated metformin prevalence was found to be 0.52 % ± 0.28 %, and the final estimated DM prevalence was 11.33 % ± 4.99 %, which was nearly consistent with the result of the International Diabetes Federation survey of 9.98 %. The results suggested that metformin might be one of the suitable WBE biomarkers in DM monitoring and WBE strategy could potentially enable the estimation of DM prevalence in most of Chinese cities after reasonable correction of associated parameters.

Artificial intelligence-based assessment of built environment from Google Street View and coronary artery disease prevalence.

BACKGROUND AND AIMS: Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in US cities. METHODS: This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). RESULTS: Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence. CONCLUSIONS: In this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision-enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments.

The association between air pollutions and emergency hospitalizations due to COPD and asthma across 16 Polish cities: population-based study.

OBJECTIVES: In recent years numerous initiatives aimed at reducing air pollution have been undertaken in Poland. The general objective was to examine the correlation between air pollution measured by the level of particulate matter ≤10 µm in diameter (PM10) and emergency hospitalizations due to chronic obstructive pulmonary disease (COPD) and asthma in 16 Polish cities (capitals of the regions). MATERIAL AND METHODS: The authors aimed to diagnose the situation across 16 cities over a 5­year period (2014-2019). Data on the number of hospitalizations was retrieved from the national public insurance system, the National Health Fund. A total number of 22 600 emergency hospitalizations was analyzed (12 000 and 10 600 in 2014 and 2019, respectively). The data on air pollution was accessed via the public register of the Chief Inspectorate for Environmental Protection air quality database. The authors of this article have used the data on PM10 daily exposure in each of the 16 cities in 2014 and 2019. Statistical methods included: non-parametric tests, a 2-stage modelling approach for time-series data, and multivariate meta-analysis of the results. RESULTS: The results indicated that there was a statistically significant decrease in PM10 concentration in 2019 in comparison to 2014 in all cities, mainly in the autumn and winter season. However, the correlation between the improvement in the air quality and a decrease in emergency hospitalizations due to asthma and COPD turned out to not be as strong as expected. The authors observed a strong correlation between PM10 concentrations and hospitalizations due to asthma and COPD, but only when air quality norms were significantly above acceptable levels. CONCLUSIONS: Air pollution measured by PM10 concentration might be used as one of the predictors of the asthma and COPD emergency hospitalization risk, yet other factors like respiratory tract infection, health care organizational aspect, patient self-control, compliance and comorbidities should also be taken into consideration. Int J Occup Med Environ Health. 2024;37(1):110-27.

AIDSVu Cities' Progress Toward HIV Care Continuum Goals: Cross-Sectional Study.

BACKGROUND: Public health surveillance data are critical to understanding the current state of the HIV and AIDS epidemics. Surveillance data provide significant insight into patterns within and progress toward achieving targets for each of the steps in the HIV care continuum. Such targets include those outlined in the National HIV/AIDS Strategy (NHAS) goals. If these data are disseminated, they can be used to prioritize certain steps in the continuum, geographic locations, and groups of people. OBJECTIVE: We sought to develop and report indicators of progress toward the NHAS goals for US cities and to characterize progress toward those goals with categorical metrics. METHODS: Health departments used standardized SAS code to calculate care continuum indicators from their HIV surveillance data to ensure comparability across jurisdictions. We report 2018 descriptive statistics for continuum steps (timely diagnosis, linkage to medical care, receipt of medical care, and HIV viral load suppression) for 36 US cities and their progress toward 2020 NHAS goals as of 2018. Indicators are reported categorically as met or surpassed the goal, within 25% of attaining the goal, or further than 25% from achieving the goal. RESULTS: Cities were closest to meeting NHAS goals for timely diagnosis compared to the goals for linkage to care, receipt of care, and viral load suppression, with all cities (n=36, 100%) within 25% of meeting the goal for timely diagnosis. Only 8% (n=3) of cities were >25% from achieving the goal for receipt of care, but 69% (n=25) of cities were >25% from achieving the goal for viral suppression. CONCLUSIONS: Display of progress with graphical indicators enables communication of progress to stakeholders. AIDSVu analyses of HIV surveillance data facilitate cities' ability to benchmark their progress against that of other cities with similar characteristics. By identifying peer cities (eg, cities with analogous populations or similar NHAS goal concerns), the public display of indicators can promote dialogue between cities with comparable challenges and opportunities.

Impacts of heatwaves on type 2 diabetes mortality in China: a comparative analysis between coastal and inland cities.

The impacts of extreme temperatures on diabetes have been explored in previous studies. However, it is unknown whether the impacts of heatwaves appear variations between inland and coastal regions. This study aims to quantify the associations between heat exposure and type 2 diabetes mellitus (T2DM) deaths in two cities with different climate features in Shandong Province, China. We used a case-crossover design by quasi-Poisson generalized additive regression with a distributed lag model with lag 2 weeks, controlling for relative humidity, the concentration of air pollution particles with a diameter of 2.5 µm or less (PM2.5), and seasonality. The wet- bulb temperature (Tw) was used to measure the heat stress of the heatwaves. A significant association between heatwaves and T2DM deaths was only found in the coastal city (Qingdao) at the lag of 2 weeks at the lowest Tw = 14℃ (relative risk (RR) = 1.49, 95% confidence interval (CI): 1.11-2.02; women: RR = 1.51, 95% CI: 1.02-2.24; elderly: RR = 1.50, 95% CI: 1.08-2.09). The lag-specific effects were significant associated with Tw at lag of 1 week at the lowest Tw = 14℃ (RR = 1.14, 95% CI: 1.03-1.26; women: RR = 1.15, 95% CI: 1.01-1.31; elderly: RR = 1.15, 95% CI: 1.03-1.28). However, no significant association was found in Jian city. The research suggested that Tw was significantly associated with T2DM mortality in the coastal city during heatwaves on T2DM mortality. Future strategies should be implemented with considering socio-environmental contexts in regions.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan.

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical information measurements such as conditional entropy and mutual information, we identify major factors of order-1 and order-2 that influence the peak value and curvature at the peak of the curves, crucial features characterizing the infection rates. Additionally, we examine the impact of geographic and socioeconomic factors on the curves by encoding each of the 79 districts with two binary characteristics: North-vs-South and Urban-vs-Suburban. Furthermore, leveraging this data-driven understanding at the district level, we explore the fine-scale behavioral effects on disease spread by examining the similarity among 96 age-group-specific curves within urban districts of Taipei and suburban districts of New Taipei City, which collectively represent a substantial portion of the nation's population. Our findings highlight the implicit influence of human behaviors related to living, traveling, and working on the dynamics of Covid-19 transmission in Taiwan.

Epidemiology of visceral leishmaniasis in municipalities of Mato Grosso and the performance of surveillance activities: an updated investigation.

Visceral leishmaniasis (VL) is considered a globally neglected disease. To address the problem of VL endemic to Brazil, the Visceral Leishmaniasis Control Program (VLCP) was created, which recommends the development of health surveillance actions such as the identification of human and canine cases, vector control and prevention of disease. We aimed to investigate the epidemiological situation of VL in municipalities of the State of Mato Grosso (MT) and assess the execution of VLCP activities. Data on human cases were obtained from the Information System for Notifiable Diseases (SINAN), and data from entomological and canine inquiry were provided by the State's Health Department. Analyzes from the period 2019 - 2021 recorded 30 cases of human VL, distributed among 16 municipalities. Vectors were identified in 50% of the municipalities where entomological investigations were carried out, and the predominant specie was Lutzomyia longipalpis. A total of 15,585 dogs were subjected to serological examination, of which 18.91% tested seropositive for Leishmania infantum. However, it must be emphasized that only three municipalities conducted consecutive inquiries involving canine VL. Although VL is distributed widely throughout the State, only a few municipalities have undertaken the actions of the VLCP, thus highlighting the neglected status of the disease.

Socio-environmental modifiers of heat-related mortality in eight Swiss cities: A case time series analysis.

In the light of growing urbanization and projected temperature increases due to climate change, heat-related mortality in urban areas is a pressing public health concern. Heat exposure and vulnerability to heat may vary within cities depending on structural features and socioeconomic factors. This study examined the effect modification of the temperature-mortality association of three socio-environmental factors in eight Swiss cities and population subgroups (<75 and ≥ 75 years, males, females): urban heat islands (UHI) based on within-city temperature contrasts, residential greenness measured as normalized difference vegetation index (NDVI) and neighborhood socioeconomic position (SEP). We used individual death records from the Swiss National Cohort occurring during the warm season (May to September) in the years 2003-2016. We performed a case time series analysis using conditional quasi-Poisson and distributed lag non-linear models with a lag of 0-3 days. As exposure variables, we used daily maximum temperatures (Tmax) and a binary indicator for warm nights (Tmin ≥20 °C). In total, 53,593 deaths occurred during the study period. Overall across the eight cities, the mortality risk increased by 31% (1.31 relative risk (95% confidence interval: 1.20-1.42)) between 22.5 °C (the minimum mortality temperature) and 35 °C (the 99th percentile) for warm-season Tmax. Stratified analysis suggested that the heat-related risk at 35 °C is 26% (95%CI: -4%, 67%) higher in UHI compared to non-UHI areas. Indications of smaller risk differences were observed between the low vs. high greenness strata (Relative risk difference = 13% (95%CI: -11%; 44%)). Living in low SEP neighborhoods was associated with an increased heat related risk in the non-elderly population (<75 years). Our results indicate that UHI are associated with increased heat-related mortality risk within Swiss cities, and that features beyond greenness are responsible for such spatial risk differences.

Haze weather and mortality in China from 2014 to 2020: Definitions, vulnerability, and effect modification by haze characteristics.

Haze weather, characterized by low visibility due to severe air pollution, has aroused great public concern. However, haze definitions are inconclusive, and multicentre studies on the health impacts of haze are scarce. We collected data on the daily number of deaths and environmental factors in 190 Chinese cities from 2014 to 2020. The city-specific association was estimated using quasi-Poisson regression and then pooled using meta-analysis. We found a negative association between daily visibility and non-accidental deaths, and mortality risk sharply increased when visibility was < 10 km. Haze weather, defined as a daily average visibility of < 10 km without a limit for humidity, produced the best model fitness and greatest effect on mortality. A haze day was associated with an increase of 2.53% (95% confidence interval [CI]:1.96, 3.10), 2.84 (95% CI: 2.13, 3.56), and 2.99% (95% CI: 1.94, 4.04) in all non-accident, cardiovascular and respiratory mortality, respectively. Haze had the greatest effect on lung cancer mortality. The haze-associated risk of mortality increased with age. Severe haze (visibility <2 km) and damp haze (haze with relative humidity >90%) had greater health impacts. Our findings can help in the development of early warning systems and effective public health interventions for haze.