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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.)

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.)

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.)

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.)

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.)

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., 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. Published by Elsevier B.V.)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Martina Ragettli reports financial support was provided by Swiss Federal Office for the Environment. Martina Ragettli reports financial support was provided by Swiss Federal Office of Public Health. Martina Ragettli reports financial support was provided by Canton of Vaud. Martina Ragettli reports financial support was provided by Republic and Canton Ticino., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., 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., (Published by Elsevier B.V.)

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM 2.5 concentrations. However, How T and PM 2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM 2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM 2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM 2.5 concentrations. In addition, the cumulative lag effects of T/PM 2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM 2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2., 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 © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., 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.)

Dyslipidemia, recognized as a predominant risk factor for atherosclerotic cardiovascular disease (CVD), remains a pressing health concern worldwide, particularly in China with nearly 40 % of the population adversely suffering. Fenofibrate, as one of the most commonly used drugs for dyslipidemia therapy, excreted as the format of fenofibrate-acid, which showed considerable stability in sewage samples and could be detected as WBE-biomarkers to monitor the prevalence of dyslipidemia. In this work, we reported the first research on estimating the prevalence of dyslipidemia by WBE approach. 527 sewage samples from 33 cities in China were extracted by solid phase and analyzed by LC-MS/MS. The detected concentration of fenofibrate acid in sewage was on an average of 120.5 ± 59.9 ng/L, and the reverse-calculated consumption of fenofibrate based on fenofibrate acid was 77.8 ± 25.0 mg/day/1000inh. Detailed analysis unveiled an average prevalence of fenofibrate at 0.056 % ± 0.018 %, and the dyslipidemia prevalence among the population aged over 15 was ultimately estimated to be 37.9 % ± 9.3 % and was in accordance with the China Cardiovascular research result of 40.4 %, which proves that WBE is a substitutable approach of traditional epidemiological investigation methods due to its timeliness and cost-effectiveness. This study demonstrated that estimating dyslipidemia prevalence by WBE with metabolite fenofibrate acid as a biomarker is feasible in most Chinese cities., 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 © 2023. Published by Elsevier B.V.)

Objectives: To analyse spatial-temporal changes and spatial association of homicide rates with violence, sociodemographic, public security and human rights indicators in Brazilian municipalities., Study Design: An ecological study using homicide estimates from the Global Burden of Disease and population from the Brazilian Ministry of Health, 2000 to 2018. The explanatory variables come from the systems of mortality, notifications of violence and security, and the Brazilian Institute of Geography and Statistics., Methods: Moran indices and maps identified clusters of high and low risk for homicides in three trienniums (p < 0.05). Multivariate linear and spatial regressions estimated explanatory factors' contributions for the last triennium., Results: Municipalities with high rates of homicides (>34/100,000) doubled, reaching 21.5 %. Those rates were concentrated in big cities, and increased in smaller municipalities. Increases in critical areas were found in the Northeast and North regions: more than 40 % in the states of Sergipe, Bahia, Ceará, Rio Grande do Norte and Roraima. Decreases occurred in the Southeast and Midwest regions: more than 35 % in São Paulo and Rio de Janeiro states. The spatial model, with an 18.9 % higher R 2 (0.706), showed a positive association for records of violence, Blacks, low-level education, municipalities >50,000 inhabitants and municipalities with homicide and municipal police., Conclusions: An increase in and the interiorisation of homicide risk areas in Brazil was observed, with displacement among regions (from the Southeast to the North/Northeast). The level of violence was the main explanatory factor for homicides. Territorial space proved to be important to understand and prevent lethal crime., (Copyright © 2023 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)

Background: Few large-scale, nationwide studies have assessed cause-specific mortality risks and burdens associated with temperature variability (TV)., Objective: To estimate associations between TV and cause-specific mortality and quantify the mortality burden in China., Methods: Data on daily total and cause-specific mortality in 272 Chinese cities between 2013 and 2015 were recorded. TVs were computed as the standard deviations of daily minimum and maximum temperatures over a duration of 2 to 7 days. The time-series quasi-Poisson regression model with adjustment of the cumulative effects of daily mean temperature over the same duration was applied to evaluate the city-specific associations of TV and mortality. Then, we pooled the effect estimates using a random-effects meta-analysis and calculated the mortality burdens., Results: Overall, TV showed significant and positive associations with total and cause-specific mortality. The TV-mortality associations were generally stronger when using longer durations. A 1 °C increase in TV at 0-7 days (TV 0-7 ) was associated with a 0.79 % [95 % confidence interval (CI): 0.55 %, 0.96 %] increase in total mortality. Mortality fractions attributable to TV 0-7 were 4.37 % for total causes, 4.75 % for overall cardiovascular disease, 4.37 % for coronary heart disease, 5.05 % for stroke, 8.28 % for ischaemic stroke, 1.08 % for haemorrhagic stroke, 6.93 % for respiratory disease, and 6.81 % for COPD, respectively. The mortality risk and burden were generally higher in the temperate monsoon zone, females, and elders., Conclusion: This nationwide study indicated that TV was an independent risk factor of mortality, and could result in significant burden for main cardiorespiratory diseases., 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 © 2023. Published by Elsevier B.V.)

Wastewater-based epidemiology (WBE) is a rapid and cost-effective method that can detect SARS-CoV-2 genomic components in wastewater and can provide an early warning for possible COVID-19 outbreaks up to one or two weeks in advance. However, the quantitative relationship between the intensity of the epidemic and the possible progression of the pandemic is still unclear, necessitating further research. This study investigates the use of WBE to rapidly monitor the SARS-CoV-2 virus from five municipal wastewater treatment plants in Latvia and forecast cumulative COVID-19 cases two weeks in advance. For this purpose, a real-time quantitative PCR approach was used to monitor the SARS-CoV-2 nucleocapsid 1 (N1), nucleocapsid 2 (N2), and E genes in municipal wastewater. The RNA signals in the wastewater were compared to the reported COVID-19 cases, and the strain prevalence data of the SARS-CoV-2 virus were identified by targeted sequencing of receptor binding domain (RBD) and furin cleavage site (FCS) regions employing next-generation sequencing technology. The model methodology for a linear model and a random forest was designed and carried out to ascertain the correlation between the cumulative cases, strain prevalence data, and RNA concentration in the wastewater to predict the COVID-19 outbreak and its scale. Additionally, the factors that impact the model prediction accuracy for COVID-19 were investigated and compared between linear and random forest models. The results of cross-validated model metrics showed that the random forest model is more effective in predicting the cumulative COVID-19 cases two weeks in advance when strain prevalence data are included. The results from this research help inform WBE and public health recommendations by providing valuable insights into the impact of environmental exposures on health outcomes., 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 © 2023 Elsevier B.V. All rights reserved.)

Urban heat islands (UHIs) have become an especially relevant phenomenon as a consequence of global warming and the growing proportion of people living in cities. The health impacts that are sometimes attributed to the rise in temperature generated in an UHI are not always adequately justified. The objective is to analyse what effect UHIs have on maximum (Tmax) and minimum daily temperatures (Tmin) recorded in urban and non-urban observatories, and quantify the impact on morbidity and mortality during heat waves in Spain's five cities. Data were collected on natural-cause daily mortality and unscheduled emergency hospital admissions (ICD-10: A00-R99) registered in these 5 cities across the period 2014-2018. We analysed daily Tmax and Tmin values at urban and non-urban observatories in these cities, and quantified the impact of Tmax and Tmin values during heat waves in each of these cities, using GLM models that included Tmax only, Tmin only, and both. We controlled for air pollution and other meteorological variables, as well as for seasonalities, trend and the autoregressive nature of the series. The UHI effect was observed in Tmin but not in Tmax, and proved to be greater in coastal cities than in inland and more densely populated cities. The UHI value in relation to the mean Tmin in the summer months ranged from 1.2 °C in Murcia to 4.1 °C in Valencia (difference between urban/non-urban observatories). The modelling process showed that, while a statistically significant association (p < 0.05) was observed in inland cities with Tmax for mortality and hospital admissions in heat waves, in coastal cities the association was obtained with Tmin, and the only impact in this case was the UHI effect on morbidity and mortality. No generalisations can be made about the impact of UHI on morbidity and mortality among the exposed population in cities. Studies on a local scale are called for, since it is local factors that determine whether the UHI effect will have a greater or lesser impact on health during heat-wave events., 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 © 2023 Elsevier B.V. All rights reserved.)

Introduction: The association between socioeconomic status and cancer prognosis has been demonstrated in several countries. Despite the existence of indirect evidence of this phenomenon in Brazil, few studies in this regard are available., Objectives: The objective of the present study is to analyse socioeconomic related survival gaps for patients diagnosed with breast, cervical, lung, prostate, and colorectal cancer in the cities of Aracaju (SE) and Curitiba (PR)., Methods: Using population-based data, we estimated net survival by tumour site, year of diagnosis, socioeconomic status and local of residence. Net survival estimation was done with multilevel parametric model allowing flexible spline functions do estimate excess mortality hazards., Results: 28,005 cases were included in survival analysis. Five-year net survival showed positive association with SES. Intermunicipal survival gaps favouring Aracaju where prominent for breast (reaching 16,1% in 5 years) OBJECTIVES: Study the impact of socioeconomic factors on cancer survival in two Brazilian capitals., Methods: Survival analysis using population-based cancer data including patients diagnosed with breast, lung, prostate, cervical and colorectal cancer between 1996 and 2012 in Aracaju and Curitiba. Outcomes were excessive mortality hazard (EMH) and 5- and 8-years net survival (NS). The association of race/skin color and socioeconomic level (SES) with EMH and net survival were analyzed using a multilevel regression model with flexible splines., Results: 28,005 cases were included, 6636 from Aracaju and 21,369 from Curitiba. NS for all diseases studied increased more prominently for Curitiba population. We observed NS gap between the populations of Aracaju and Curitiba that increased or remained stable during the study period, with emphasis on the growth of the difference in NS of lung and colon cancer (among men). Only for cervical cancer and prostate cancer there was a reduction in the intermunicipal gaps. 5-year NS for breast cancer in Aracaju ranged from 55.2% to 73.4% according to SES. In Curitiba this variation was from 66.5% to 83.8%., Conclusion: The results of the present study suggests widening of socioeconomic and regional inequalities in the survival of patients with colorectal, breast, cervical, lung and prostate cancers in Brazil during the 1990 s and 2000 s., Competing Interests: Declaration of Competing Interest The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: There is limited evidence of temporal changes in the association between air temperature and the risk of cause-specific cardiovascular [CVD] and respiratory [RD] mortality., Method: We explored temporal variations in the association between short-term exposures to air temperature and non-accidental and cause-specific CVD and RD mortality in the 15 largest German cities over 24 years (1993-2016) using time-stratified time series analysis. We applied location-specific confounder-adjusted Poisson regression with distributed lag non-linear models with a lag period of 14 days to estimate the temperature-mortality associations. We then pooled the estimates by a multivariate meta-analytical model. We analysed the whole study period and the periods 1993-2004 and 2005-16, separately. We also carried out age- and sex-stratified analysis. Cold and heat effects are reported as relative risk [RR] at the 1st and the 99th temperature percentile, relative to the 25th and the 75th percentile, respectively., Result: We analysed a total of 3,159,292 non-accidental, 1,063,198 CVD and 183,027 RD deaths. Cold-related RR for CVD mortality was seen to rise consistently over time from 1.04 (95% confidence interval [95% CI] 1.02, 1.06) in the period 1993-2004 to 1.10 (95% CI 1.09, 1.11) in the period 2005-16. A similar increase in cold-related RR was also observed for RD mortality with risk increasing from 0.99 (95% CI 0.96, 1.03) to 1.07 (95% CI 1.03, 1.10). Cold-related ischemic, cerebrovascular, and heart failure mortality risk were seen to be increasing over time. Similarly, COPD, the commonly speculated driver of heat-related RD mortality was found to have a constant heat-related risk over time. Males were increasingly vulnerable to cold with time for all causes of death. Females showed increasing sensitivity to cold for CVD mortality. Our results indicated a significant increased cold and heat vulnerability of the youngest age-groups (<64) to non-accidental and RD mortality, respectively. Similarly, the older age group (>65) were found to have significantly increased susceptibility to cold for CVD mortality., Conclusion: We found evidence of rising population susceptibility to both heat- and cold-related CVD and RD mortality risk from 1993 to 2016. Climate change mitigation and targeted adaptation strategies might help to reduce the number of temperature-related deaths in the future., 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 © 2023. Published by Elsevier Inc.)

Background: Non-optimum ambient temperature has recently been acknowledged as an independent risk factor for disease burden, but its impact on atrial fibrillation (AF) episodes was rarely evaluated., Objectives: To examine the associations between ambient non-optimum temperature and symptom onset of AF episodes and calculate the corresponding disease burden., Methods: We conducted an individual-level, time-stratified, case-crossover analysis based on a nationwide registry, which comprises of 94,711 eligible AF patients from 1993 hospitals in 322 Chinese cities from January 2015 to December 2021. Multiple moving 24 h average temperatures prior to the symptom onset of AF episodes were calculated as lag days. The associations were analyzed using conditional logistic regression combined with distributed lag non-linear models with a duration of lag 0-7 days, after controlling for criteria air pollutants. Stratification analyses were performed to explore possible effect modifiers., Results: There was a monotonically increasing relationship of AF onset risk with decreasing temperature. The excess AF risk occurred at lag 1 d and lasted for 5 days. Nationally, the cumulative relative risk of AF episode onset associated with extremely low temperature (-9.3 °C) over lag 0-7 d was 1.25 (95 % confidence interval: 1.08, 1.45), compared with the reference temperature (31.5 °C). The exposure-response curve was steeper in the south than in the north where there was levelling-off at lower temperature. Nationally, 7.59 % of acute AF episodes could be attributable to non-optimum temperatures. The attributable fraction was larger for southern residents, males and patients <65 years., Conclusion: This nationwide study provides novel and robust evidence that declining ambient temperature could increase the risk of AF episode onset. We also provide the first-hand evidence that a considerable proportion of acute AF episodes could be attributable to non-optimum temperatures., 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 © 2023 Elsevier B.V. All rights reserved.)

Objectives: This study aimed to investigate patterns of mortality by road transport injury (RTI) in Brazilian municipalities, focused on deaths of motorcyclists, between 2000 and 2018, and their relation with population size and economic status., Study Design: This was an ecological epidemiological study with a descriptive and analytical nature., Methods: The age-standardized RTI mortality rates were calculated for the Brazilian municipalities, referring to the 3-year periods of 2000/2002 (T1), 2009/2011 (T2), and 2016/2018 (T3). The rates were stratified according to macroregion and population size and were compared in terms of percentage variation from one 3-year period to another. The Moran Global and Local indices were used in the spatial point-pattern analysis of the rates. To verify the association with the gross domestic product (GDP) per capita, the Spearman correlation coefficient was applied., Results: A decline in RTI mortality rates was found between 2000 and 2018, with the most significant declines observed in municipalities from the South and Southeast regions of Brazil. However, increases were observed among motorcyclists. Clusters of municipalities were detected, which presented high mortality rates among the motorcyclists in the Northeast region and in some states of the North and Midwest regions. The mortality rates showed a negative correlation with the GDP per capita of the Brazilian municipalities., Conclusions: Although there were decreases in RTI mortality rates between 1990 and 2018, there was a significant increase in deaths among motorcyclists, especially in the Northeast, North, and Midwest regions of the country. Such differences can be explained by unequal growth in the size of the motorcycle fleet in those regions, by less law enforcement capability, and by the implementation of educational actions., (Copyright © 2023 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)

Background: The COVID-19 pandemic has significant impacts on the US socioeconomic structure. Gun violence is a major public health issue and the effects on this area have not been well-elucidated. The objective of this study was to determine the impacts of the pandemic on mass shootings in six major United States cities with historically high rates of gun violence., Methods: Mass shooting data were extracted from an open-source database, Gun Violence Archive. Mass shooting was defined as four or more people shot at a single event. Data from six cities with the highest incidence of mass shootings were analyzed in 2019 versus 2020 (Baltimore, Chicago, Detroit, New Orleans, Philadelphia, and St. Louis). Geographic data were examined to assess changes in each city's mass shooting geographic distribution over time. Quantitative changes were assessed using the Area Deprivation Index (ADI), and qualitative data were assessed using ArcGIS., Results: In 2020, the overall percentage of mass shootings increased by 46.7% though there was no change in the distribution of these events when assessed quantitatively (no change in average ADI) nor qualitatively (using ArcGIS). In the six cities analyzed, the total proportion of mass shooting events was unchanged during the pandemic (21.8% vs 20.6%, p = 0.64). Chicago, the US city with the highest incidence of mass shootings, did not experience a significant change in 2020 (n = 34/91, 37.3% vs. n = 53/126, 42.1%, p = 0.57). Baltimore had a significant decrease in mass shooting events (n = 18/91, 19.8% vs. 10/126, 7.9%, p = 0.01). The other four cities had no significant change in the number of mass shootings (p>0.05)., Conclusion: This study is the first to use ArcGIS technology to describe the patterns of mass shooting in six major US cities during the COVID-19 pandemic. The number of mass shootings in six US cities remained largely unchanged which suggests that changes in mass shootings is likely occurring in smaller cities. Future studies should focus on the changing patterns of homicides in at-risk communities and other possible social influences., Competing Interests: Declaration of Competing Interest AS is a paid consultant for Aroa Biologics. PG is a paid consultant and speaker for Zimmer Biomet and DePuy Synthes. The other authors declare no conflicts of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (R t ) and the daily per capita efficiency adjusted viral load thresholds of 85 × 10 6 and 200 × 10 6 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 10 6 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 10 6 and 200 × 10 6 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 10 6 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 10 6 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data., 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 © 2023. Published by Elsevier B.V.)

The rapid spread of COVID-19 worldwide led to the implementation of various non-pharmaceutical interventions to limit transmission and hence reduce the number of infections. Using telecom-operator-based mobility data and a spatio-temporal dynamic model, the impact of mobility on the evolution of the pandemic at the level of the 581 Belgian municipalities is investigated. By decomposing incidence, particularly into within- and between-municipality components, we noted that the global epidemic component is relatively more important in larger municipalities (e.g., cities), while the local component is more relevant in smaller (rural) municipalities. Investigation of the effect of mobility on the pandemic spread showed that reduction of mobility has a significant impact in reducing the number of new infections., 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 © 2023 Elsevier Ltd. All rights reserved.)

Most of the existing epidemiological studies have investigated adverse health effects of multiple air pollutants for a limited number of cities, thus the evidence of the health impacts is limited and it is challenging to compare these results because of different modeling approaches and potential publication bias. In this paper, we expand the number of Canadian cities, with the use of the most recent available health data. A multi-pollutant model in a case-crossover design is used to investigate the short-term impacts of air pollution on various health outcomes in 47 Canadian main cities, comparing three age groups (all-age, senior (age 66+), non-senior). The main findings are that a 14 ppb increase of O 3 was associated with a 0.17%-2.78% (0.62%-1.46%) increase in the odds of all-age respiratory mortality (hospitalization). A 12.8 ppb increase of NO 2 was associated with a 0.57%-1.47% (0.68%-1.86%) increase in the odds of all-age (non-senior) respiratory hospitalization. A 7.6 μgm -3 increase of PM 2.5 was associated with a 0.19%-0.69% (0.33%-1.1%) increase in the odds of all-age (non-senior) respiratory hospitalization., 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., (Crown Copyright © 2023. Published by Elsevier Inc. All rights reserved.)

Cardiometabolic multimorbidity (CMM) refers to the presence of multiple cardiovascular and metabolic diseases (CMDs), such as hypertension, diabetes, and cardio-cerebrovascular diseases (CCVD), in the same individual, and has emerge as a significant global health concern due to population aging. Although previous research has demonstrated the association between cardiovascular and metabolic diseases and air pollutants, evidence on the link between CMM and air pollution exposure among Chinese older adults is limited. To address this research gap, we conducted a national representative survey of 222,179 adults aged 60 and older to investigate the epidemiology of CMM and its association with long-term exposure to PM 2.5 and O 3 in China's elderly population. We found that the prevalence of CMM among Chinese older adults was 16.9%, and hypertension and CCVD were the most common CMM cluster (10.8%). After adjusting for confounding variables, we observed a significant positive association between PM 2.5 exposure and the prevalence of hypertension, diabetes, and CCVD, with a respective excess risk increase of 3.2%, 3.6%, and 5.5% for every 10-unit increase. Moreover, every 10-unit increase in PM 2.5 was linked to a higher risk of hypertension and diabetes (2.2%), hypertension and CCVD (5.4%), diabetes and CCVD (5.6%), and hypertension, diabetes, and CCVD combined (7.6%). We also found a U-shaped curve relationship between O 3 exposure and the occurrence of hypertension, diabetes, and CCVD, as well as different subtypes of CMM, with the lowest risk of O 3 exposure was observed near 75-80 μg/m 3 . Furthermore, we identified that female and rural residents are more vulnerable to the health risks of air pollution than male and urban residents. Given the increasing aging of the population and rising prevalence of multimorbidity, policymakers should focus more attention on the female and rural elderly population to prevent and control CMM. This study provides compelling evidence that reducing air pollution levels can be an effective strategy to prevent and manage CMM among older adults., 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 © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Environmental risks accumulate in cities, including polluted air and health disparities, but these risks can be reduced through scientific city planning. The purpose of this study was to investigate the global burden of premature mortality attributable to NO 2 exposure in urban areas and the role of the built environment in this regard., Methods: An approach based on health impact assessment was used to estimate the premature mortality burdens associated with NO 2 exposure in 13,169 urban areas around the world using globally gridded NO 2 and population estimates, baseline mortality, and epidemiologically derived exposure-response functions. We used the most recent WHO recommended value (i.e.,10 μg/m 3 ) as a counterfactual concentration. Finally, the relationship between the characteristics of the built environment at the city level and the burden of NO 2 -related mortality was evaluated., Results: Worldwide, 549,715(95%CI: 276204-815,023) cases of death attributable to NO 2 exposure in urban areas could be prevented if compliance with the latest WHO guideline, accounting for 2.7 % (95%CI:1.4 %-4.0 %) of total mortalities in 2019. Across cities around the world, the age-standardized mortality rate (per 100,000 people) attributable to NO 2 exposure ranged from 51.3 (95%CI:25.8-76.0) in Central Asia to 3.4(95%CI: 1.7-5.1) in Oceania. Although there was a significant decrease in premature mortality attributable to NO 2 exposure globally, considerable regional heterogeneity exists, with cities in Central Asia and Andean Latin America in particular exhibiting an upward trend. Further, we discovered a positive association between population density and street connectivity with mortality attributable to NO 2 . While the increase in green and blue space were significantly associated with a lower NO 2 -associated mortality., Conclusion: The findings of this study provided a comprehensive understanding of the premature mortality burden due to NO 2 in cities throughout the world and the role that urban planning policies can play in reducing the health burden associated with air pollution., 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 © 2023. Published by Elsevier B.V.)

Background and Aim: Short-term associations between air pollution and mortality have been well reported in Japan, but the historical changes in mortality risk remain unknown. We examined temporal changes in the mortality risks associated with short-term exposure to four criteria air pollutants in selected Japanese cities., Methods: We collected daily mortality data for non-accidental causes (n = 5,748,206), cardiovascular (n = 1,938,743) and respiratory diseases (n = 777,266), and air pollutants (sulfur dioxide [SO 2 ], nitrogen dioxide [NO 2 ], suspended particulate matter [SPM], and oxidants [Ox]) in 10 cities from 1977 to 2015. We performed two-stage analysis with 5-year stratification to estimate the relative risk (RR) of mortality per 10-unit increase in the 2-day moving average of air pollutant concentrations. In the first stage, city-specific associations were assessed using a quasi-Poisson generalized linear regression model. In the second stage, city-specific estimates were pooled using a random-effects meta-analysis. Linear trend and ratio of relative risks (RRR) were computed to examine temporal changes., Results: When stratifying the analysis by every 5 years, average concentrations in each sub-period decreased for SO 2 , NO 2 , and SPM (14.2-2.3 ppb, 29.4-17.5 ppb, 52.1-20.6 μg/m 3 , respectively) but increased for Ox (29.1-39.1 ppb) over the study period. We found evidence of a negative linear trend in the risk of cardiovascular mortality associated with SPM across sub-periods. However, the risks of non-accidental and respiratory mortality per 10-unit increase in SPM concentration were significantly higher in the most recent period than in the earliest period. Other gaseous pollutants did not show such temporal risk change. The risks posed by these pollutants were slightly to moderately heterogeneous in the different cities., Conclusions: The mortality risks associated with short-term exposure to SPM changed, with different trends by cause of death, in 10 cities over 39 years whereas the risks for other gaseous pollutants were relatively stable., 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 © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Hypertension is the most common chronic non-infectious disease and a severe problem for public health in China. There were 244.5 million people aged over 18 years in China who had hypertension in 2015, and hypertension-related death accounted for more than 25 % of all causes of death in China every year. To monitor the hypertension prevalence in near real-time, a wastewater-based epidemiology (WBE) approach by using metoprolol acid as a biomarker was conducted in 164 cities in China. LC-MS/MS was utilized to quantify metoprolol acid in sewage, and satisfactory method validation results were achieved. The average concentration of metoprolol acid in sewage was 943.1 ± 671.1 ng/L, and the back-calculated consumption of metoprolol based on metoprolol acid was 932.0 ± 390.5 mg/day/1000inh on average, ranging from 76.7 to 3275.7 mg/day/1000inh. The prevalence of metoprolol was estimated to be 0.83 % ± 0.35 %, and the estimated hypertension prevalence in the population aged over 15 years was ultimately assessed to be 28.56 % ± 10.44 % ranging from 14.28 % to 44.28 % and was consistent with the China Hypertension Survey result of 27.9 %. This research demonstrated that estimating hypertension prevalence by WBE with metoprolol acid as a biomarker is feasible in Chinese cities., 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 © 2022. Published by Elsevier B.V.)

COVID-19 has spread worldwide with a high variability in cases and mortality between populations. This research aims to assess socioeconomic inequities of COVID-19 in the city of Cali, Colombia, during the first and second peaks of the pandemic in this city. An ecological study by neighborhoods was carried out, were COVID-19 cases were analyzed using a Bayesian hierarchical spatial model that includes potential risk factors such as the index of unsatisfied basic needs and socioeconomic variables as well as random effects to account for residual variation. Maps showing the geographic patterns of the estimated relative risks as well as exceedance probabilities were created. The results indicate that in the first wave, the neighborhoods with the greatest unsatisfied basic needs and low socioeconomic strata, were more likely to report positive cases for COVID-19. For the second wave, the disease begins to spread through different neighborhoods of the city and middle socioeconomic strata presents the highest risk followed by the lower strata. These findings indicate the importance of measuring social determinants in the study of the distribution of cases due to COVID-19 for its inclusion in the interventions and measures implemented to contain contagions and reduce impacts on the most vulnerable populations., 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 © 2022 Elsevier Ltd. All rights reserved.)

Few investigations have assessed the impact of short-term ambient temperature change on pneumonia risk. We aimed to study the relation of temperature variability (TV) with daily hospitalizations for pneumonia in China. We conducted a time-series study in 184 major cities by extracting daily hospital data between 2014 and 2017 from a medical insurance claims database of 0.28 billion beneficiaries. TV was calculated as standard deviation of daily minimum and maximum temperatures over exposure days. We estimated associations of pneumonia admissions with TV for each city using over-dispersed generalized linear models controlling for weather conditions and ambient air pollution, and pooled city-specific estimates using random effects meta-analyses. We also investigated exposure-response relationship curve and potential effect modifiers. We identified 4.2 million pneumonia hospitalizations during the study period. TV was positively related to daily pneumonia admissions. At the national-average level, each 1-°C increase in TV at 0-6 days' exposure corresponded to a 0.65 % (95 % CI: 0.34 %-0.96 %) increase in pneumonia admissions. An approximately linear exposure-response curve for the relation of TV with pneumonia admission was noted. The relations were more evident in cities with larger average age (P = 0.038). As the first study in China to assess the impact of temperature change on pneumonia on a national scale, our results indicated that acute TV exposure was related to higher admissions for pneumonia. Our findings should provide new insight into the health impacts associated with climate change., 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 © 2022 Elsevier B.V. All rights reserved.)

Background/aim: Daily air pollution has been linked with mortality from urban studies. Associations in rural areas are still unclear and there is growing interest in testing the role that air pollution has on other causes of death. This study aims to evaluate the association between daily air pollution and cause-specific mortality in all 8092 Italian municipalities., Methods: Natural, cardiovascular, cardiac, ischemic, cerebrovascular, respiratory, metabolic, diabetes, nervous and psychiatric causes of death occurred in Italy were extracted during 2013-2015. Daily ambient PM 10 , PM 2.5 and NO 2 concentrations were estimated through machine learning algorithms. The associations between air pollutants and cause-specific mortality were estimated with a time-series approach using a two-stage analytic protocol where area-specific over-dispersed Poisson regression models where fit in the first stage, followed by a meta-analysis in the second. We tested for effect modification by sex, age class and the degree of urbanisation of the municipality., Results: We estimated a positive association between PM 10 and PM 2.5 and the mortality from natural, cardiovascular, cardiac, respiratory and nervous system causes, but not with metabolic or psychiatric causes of death. In particular, mortality from nervous diseases increased by 4.55% (95% CI: 2.51-6.63) and 9.64% (95% CI: 5.76-13.65) for increments of 10 μg/m 3 in PM 10 and PM 2.5 (lag 0-5 days), respectively. NO 2 was positively associated with respiratory (6.68% (95% CI: 1.04-12.62)) and metabolic (7.30% (95% CI: 1.03-13.95)) mortality for increments of 10 μg/m 3 (lag 0-5). Higher associations with natural mortality were found among the elderly, while there were no differential effects between sex or between rural and urban areas., Conclusions: Short-term exposure to particulate matter was associated with mortality from nervous diseases. Mortality from metabolic diseases was associated with NO 2 exposure. Other associations are confirmed and updated, including the contribution of lowly urbanised areas. Health effects were also found in suburban and rural areas., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Matteo Renzi, Paola Michelozzi, Paolo Giorgi Rossi, Sara Maio, Giovanni Viegi, Massimo Stafoggia reports financial support was provided by National Institute for Insurance against Accidents at Work., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

During the last decades the effects of thermal stress on public health have been a great concern worldwide. Thermal stress is determined by air temperature in combination with other meteorological parameters, such as relative humidity and wind speed. The present study is focused on the Mediterranean city of Thessaloniki, Greece and it aims to explore the association between thermal stress and mortality from cardiovascular diseases, using both air temperature and other thermal indices as indicators. For that, an over-dispersed Poisson regression function was used, in combination with distributed lag non-linear models, in order to capture the delayed and nonlinear effects of temperature. Our results revealed a reverse J-shaped exposure-response curve for the total population and females and a U-shaped association for males. In all cases examined, the minimum mortality temperature was identified around the 80th percentile of each distribution. It is noteworthy that despite the fact that the highest risks of cardiovascular mortality were estimated for exposure to extreme temperatures, moderate temperatures were found to cause the highest burden of mortality. On the whole, our estimations demonstrated that the population in Thessaloniki is more susceptible to cold effects and in regard with gender, females seem to be more vulnerable to ambient thermal conditions., 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 © 2022 Elsevier Inc. All rights reserved.)

Background and Aim: The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally., Methods: We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone., Results: We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m 3 compared to 11.0 g/m 3 ) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries., Conclusion: Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 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 © 2022 Elsevier B.V. All rights reserved.)

Introduction: In France, a heat warning system (HWS) has been implemented almost two decades ago and rely on some official heat wave (HW) definitions. However, no study has compared the burden associated with a large set of alternative HW definitions to the official definitions. Such comparison could be particularly helpful to identify HW conditions for which effective HWS would minimize the health burden across various geographical contexts and possibly update thresholds to trigger HWS. The aim of this study is to identify (and rank) definitions that drive the highest health burden in terms of mortality to inform future HWS across multiple cities in France., Methods: Based on weather data for 16 French cities, we compared the two official definitions used in France to: i) the Excess Heat Factor (EHF) used in Australia, and ii) 18 alternative hypothetical HW definitions based on various combinations of temperature metrics, intensity, and duration. Propensity score matching and Poisson regressions were used to estimate the effect of each HW exposure on non-accidental mortality for the May-September period from 2000 to 2015., Results: The associations between HW and mortality differed greatly depending on the definition. The greatest burden of heat was 1,055 (95% confidence interval "CI": [856; 1,302]) deaths per summer and was obtained with the EHF. The EHF identified HW with 2.46 (95% CI: [1.92; 3.58]) or 8.18 (95% CI: [6.63; 10.61]) times the global burden at the national level obtained with the climatological indicator of the French national weather service and the HW indicator of the French national HWS, respectively and was the most impactful definition pattern for both temperate oceanic and Mediterranean climate types., Conclusion: Identifying the set of extreme heat conditions that drive the highest health burden in a given geographical context is particularly helpful when designing or updating heat early warning systems., 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 © 2022 Elsevier Inc. All rights reserved.)

Background: Many studies have explored the epidemiological characteristics of influenza. However, most previous studies were conducted in a specific region without a national picture which is important to develop targeted strategies and measures on influenza control and prevention., Objectives: To explore the association between ambient temperature and incidence of influenza, to estimate the attributable risk from temperature in 30 Chinese cities with different climatic characteristics for a national picture, and to identify the vulnerable populations for national preventative policy development., Methods: Daily meteorological and influenza incidence data from the 30 Chinese cities over the period 2016-19 were collected. We estimated the city-specific association between daily mean temperature and influenza incidence using a distributed lag non-linear model and evaluated the pooled effects using multivariate meta-analysis. The attributable fractions compared with reference temperature were calculated. Stratified analyses were performed by region, sex and age., Results: Overall, an N-shape relationship between temperature and influenza incidence was found in China. The cumulative relative risk of the peak risk temperature (5.1 °C) was 2.13 (95%CI: 1.41, 3.22). And 60% (95%eCI: 54.3%, 64.3%) of influenza incidence was attributed to ambient temperature during the days with sensitive temperatures (1.6°C-14.4 °C). The ranges of sensitive temperatures and the attributable disease burden due to temperatures varied for different populations and regions. The residents in South China and the children aged ≤5 and 6-17 years had higher fractions attributable to sensitive temperatures., Conclusions: Tailored preventions targeting on most vulnerable populations and regions should be developed to reduce influenza burden from sensitive temperatures., 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 © 2022 Elsevier Inc. All rights reserved.)

Already in early 2000s, concerns have been growing in the EU about increasing use of cocaine and it is estimated that below 1 % of the population administer the drug by smoking crack cocaine. New available data suggests an increase in the use of crack cocaine and an increase in the number of crack cocaine users entering treatment has been reported in several European countries. Robust estimations of crack cocaine use are however not available yet. The use of crack cocaine has long been associated with severe adverse socio-economic conditions as well as mental health problems, such as suicide ideation and depression. The aim of this study was to assess spatial trends in population-normalized mass loads of crack cocaine biomarkers (i.e., anhydroecgonine and anhydroecgonine methyl ester) in 13 European cities in six countries (the Netherlands, Belgium, Ireland, Portugal, Spain and Italy). Furthermore, temporal trends over a five-year period were evaluated through the analysis of historic samples collected in the Netherlands. Finally, the stability of the crack cocaine biomarkers in wastewater was investigated through batch experiments. The samples were analyzed with a new developed and validated hydrophilic interaction liquid chromatography coupled to mass spectrometry method. Targeted crack cocaine biomarkers were found in all cities. Also, crack cocaine biomarker was detected in wastewater from 2017 to 2021 in the Netherlands, but no significance between the years were found. With respect to biomarker in-sample stability, AEME was found to be stable in wastewater. This study assessed crack cocaine use for the first time on a broad scale, both temporal and in cities across Europe, with wastewater-based epidemiology and it shows the importance of wastewater analysis to monitor community loads of crack cocaine use., 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 © 2022 Elsevier B.V. All rights reserved.)

Residential formaldehyde pollution is one of the leading residential harmful pollutants with a large production and consumption globally and remains much uncertainty in Chinese families with huge health burden for children worldwide. A multi-center observation study from 11 cities was conducted to investigate residential formaldehyde pollution levels measured by phenol reagent spectrophotometry. Data on household characteristics and schoolchildren's health were collected by questionnaire. The median concentration of residential formaldehyde was 0.025 (0.002-0.281) mg/m 3 among 11 cities with the total exceeding standard rate of 7.40% according to the reference value of 0.10 mg/m 3 (1-h average). Residential formaldehyde pollution in warm season, bedrooms and northern cities was more serious than that in cold season, living rooms and southern cities, respectively. The potential influencing factors of residential formaldehyde included household characteristics (distance from a traffic road, building history, residence duration, window glass layers, decoration and furniture) and use of air conditioner. The positive regulation effect of temperature on residential formaldehyde was explored with the approximately turning-point temperature of 28.9 °C for peak concentration. Long-term exposure to residential formaldehyde of low concentrations (0.010-0.090 mg/m 3 ) would increase the prevalence of childhood pneumonia and a more stringent criteria value for residential formaldehyde should be discussed cautiously., 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 © 2022 Elsevier Inc. All rights reserved.)

In this paper, we propose a coronavirus disease (COVID-19) epidemiological model called SEIR-FMi (Susceptible-Exposed-Infectious-Recovery with Flow and Medical investments) to study the effects of intra-city population movement, inter-city population movement, and medical resource investment on the spread of the COVID-19 epidemic. We theoretically derived the reproduction number of the SEIR-FMi model by using the next-generation matrix method and empirically simulate the individual impacts of population movement and medical resource investment on epidemic control. We found that intra- and inter-city population movements will increase the risk of epidemic spread, and the effect of inter-city population movement on low-risk areas is higher than that on high-risk areas. Increasing medical resource investment can not only speed up the recover rate of patients but also reduce the growth rate of infected cases and shorten the spread duration of the epidemic. We collected data on intra-city population movement, inter-city population movement, medical resource investment, and confirmed cases in the cities of Wuhan, Jingzhou, and Xiangyang, Hubei Province, China, from January 15 to March 15, 2020. Using the collected data, we validated that the proposed SEIR-FMi model performs well in simulating the spread of COVID-19 in the three cities. Meanwhile, this study confirms that three non-pharmaceutical interventions, namely community isolation, population mobility control, and medical resource aid, applied during the epidemic period are indispensable in controlling the spread of COVID-19 in the three cities., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

To examine the short-term association between gaseous air pollutants (CO, NO 2 , SO 2 , and O 3 ) and all-cause respiratory disease, acute upper respiratory infections (AURIs) as well as acute lower respiratory infections (ALRIs) among children, we conducted the study from 25 major cities in China. Hospitalization records of children aged 0-18 years due to all-cause respiratory diseases (889,926), AURIs (97,858), and ALRIs (642,154) from 2016 to 2019 were extracted. Concentrations of CO, NO 2 , SO 2 , and O 3 were averaged across monitoring stations. Generalized additive models were used to estimate the associations between gaseous air pollutants and daily hospitalizations for all-cause respiratory disease, AURIs, and ALRIs. The meta-analysis was used to combine the city-specific estimates. A 10 mg/m 3 increase in CO at lag01, and a 10 μg/m 3 increase in NO 2 , SO 2 , and O 3 at lag01 were associated with 1.65% (95%CI, 0.41-2.91), 0.54% (95%CI, 0.30-0.79), 0.60% (95%CI, 0.22-0.99), and 0.23% (95%CI, 0.06-0.39) increase of hospitalizations due to all-cause respiratory disease, respectively. For the disease subtype, O 3 only had adverse effects on AURIs, CO and SO 2 mainly on ALRIs, and NO 2 on both AURIs and ALRIs. Children aged 4-6years were more vulnerable to the effects of CO and NO 2 , but those aged <1year were more susceptible to SO 2 and O 3 . Besides, the O 3 effect was stronger in the warm season than in the cold season. The study indicated that short-term exposure to CO, NO 2 , SO 2, and O 3 was associated with increased hospitalization for pediatric respiratory disease, and the association may vary by position of the respiratory tract, age, and season., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RR cum ). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RR cum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

More than half of the world's population is currently living in cities, with more and more people moving to densely populated areas. The experience of growing up and living in crowded environments might influence the way we explore our social environment, mainly how we attend to others. Yet, we know little about how urbanicity affects this vital function of our social life. In two studies, we use mobile eye-tracking to measure participants' social attention, while walking through a shopping mall. Results show that social density of participants' native place impacts how frequently they look at passing strangers. People who experienced more city living from birth to early adolescence, attend more to strangers' faces than their rural counterparts. Our findings demonstrate that the early experience of urban upbringing configures social attention in adulthood. The urbanicity-related bias towards social gazing might reflect a more efficient processing of social information in urban natives., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: High atmospheric temperature has been associated with the occurrence of bacillary dysentery (BD). Recent studies have suggested that hot extremes may influence health outcomes, however, none have examined the association between hot extremes and BD risk, especially at the national level., Objectives: To assess the effect and attributable burden of hot extremes on BD cases and to identify populations at high risk of BD., Methods: Daily incident BD data of 31 provincial capital cities from 2010 to 2018 were collected from the Chinese Center for Disease Control and Prevention, weather data was obtained from the fifth generation of the European Re-Analysis Dataset. Three types of hot extremes, including hot day, hot night, and hot day and night, were defined according to single or sequential occurrence of daytime hot and nighttime hot within 24 h. A two-stage analytical strategy combined with distributed lag non-linear models (DLNM) was used to evaluate city-specific associations and national pooled estimates., Results: Hot extremes were significantly associated with the risk of BD on lagged 1-6 days. The overall cumulative relative risk (RR) was 1.136 [95% confidence interval (CI): 1.022, 1.263] for hot day, 1.181 (95% CI: 1.019, 1.369) for hot night, and 1.154 (95% CI: 1.038, 1.283) for hot day and night. Northern residents, females, and children younger than or equal to 14 years old were vulnerable under hot night, southern residents were vulnerable under hot day, and males were vulnerable under hot day and night. 1.854% (95% CI: 1.294%, 2.205%) of BD cases can be attributable to hot extremes, among which, hot night accounted for a large proportion., Conclusions: Hot extremes may significantly increase the incidence risk and disease burden of BD. Type-specific protective measures should be taken to reduce the risk of BD, especially in those we found to be particularly vulnerable., Competing Interests: Declaration of competing interest None., (Copyright © 2022. Published by Elsevier B.V.)

Numerous studies have been conducted worldwide to investigate if an association exists between meteorological factors and the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection incidence. Although research studies provide conflicting results, which can be partially explained by different methods used, some clear trends emerge on the role of weather conditions and SARS-CoV-2 infection, especially for temperature and humidity. This study sheds more light on the relationship between meteorological factors and SARS-CoV-2 infection incidence in 23 Italian and 52 Spanish cities. For the purposes of this study, daily air temperature, absolute and relative humidity, wind speed, ultraviolet radiation, and rainfall are considered exposure variables. We conducted a two-stage meta-regression. In the first stage, we estimated the exposure-response association through time series regression analysis at the municipal level. In the second stage, we pooled the association parameters using a meta-analytic model. The study demonstrates an association between meteorological factors and SARS-CoV-2 infection incidence. Specifically, low levels of ambient temperatures and absolute humidity were associated with an increased relative risk. On the other hand, low and high levels of relative humidity and ultraviolet radiation were associated with a decreased relative risk. Concerning wind speed and rainfall, higher values contributed to the reduction of the risk of infection. Overall, our results contribute to a better understanding of how the meteorological factors influence the spread of the SARS-CoV-2 and should be considered in a wider context of existing robust literature that highlight the importance of measures such as social distancing, improved hygiene, face masks and vaccination campaign., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objectives: To present the COVID Local Risk Index (CLRI), a measure of city- and neighborhood-level risk for SARS COV-2 infection and poor outcomes, and validate it using sub-city SARS COV-2 outcome data from 47 large U.S. cities., Methods: Cross-sectional validation analysis of CLRI against SARS COV-2 incidence, percent positivity, hospitalization, and mortality. CLRI scores were validated against ZCTA-level SARS COV-2 outcome data gathered in 2020-2021 from public databases or through data use agreements using a negative binomial model., Results: CLRI was associated with each SARS COV-2 outcome in pooled analysis. In city-level models, CLRI was positively associated with positivity in 11/14 cities for which data were available, hospitalization in 6/6 cities, mortality in 13/14 cities, and incidence in 33/47 cities., Conclusions: CLRI is a valid tool for assessing sub-city risk of SARS COV-2 infection and illness severity. Stronger associations with positivity, hospitalization and mortality may reflect differential testing access, greater weight on components associated with poor outcomes than transmission, omitted variable bias, or other reasons. City stakeholders can use the CLRI, publicly available on the City Health Dashboard (www.cityhealthdashboard.com), to guide SARS COV-2 resource allocation., (Copyright © 2022. Published by Elsevier Ltd.)

The COVID-19 pandemic, caused by the highly transmissible SARS-CoV-2 virus, has overloaded health systems in many contexts Conant and Wolfe (2008). Brazil has experienced more than 345,000 deaths, as of April/2021 Conant and Wolfe (2008), with dire consequences for the country's public and private health systems. This paper aims to estimate the synchronization graph between the cities' contagion waves from public COVID-19 data records. For this purpose, the Motif-Synchronization method Magwire et al. (2011) was applied to publicly available COVID-19 data records to determine the sequential relationship of occurrence of the waves among Bahia's cities. We find synchronization between waves of infection between cities, suggesting diffusion of the disease in Bahia and a potential role for inter-city transportation Saba et al. (2018), Saba et al. (2014), Araújo et al. (2018) in the dynamics of this phenomenon McKee and Stuckler (2020), Chinazzi et al. (2020), Tizzoni et al. (2014). Our main contribution lies in the use of the Motif-Synchronization method applied to COVID-19 data records, with the results revealing a pattern of disease spread that extends beyond city boundaries., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

The COVID-19 pandemic has stimulated wastewater-based surveillance, allowing public health to track the epidemic by monitoring the concentration of the genetic fingerprints of SARS-CoV-2 shed in wastewater by infected individuals. Wastewater-based surveillance for COVID-19 is still in its infancy. In particular, the quantitative link between clinical cases observed through traditional surveillance and the signals from viral concentrations in wastewater is still developing and hampers interpretation of the data and actionable public-health decisions. We present a modelling framework that includes both SARS-CoV-2 transmission at the population level and the fate of SARS-CoV-2 RNA particles in the sewage system after faecal shedding by infected persons in the population. Using our mechanistic representation of the combined clinical/wastewater system, we perform exploratory simulations to quantify the effect of surveillance effectiveness, public-health interventions and vaccination on the discordance between clinical and wastewater signals. We also apply our model to surveillance data from three Canadian cities to provide wastewater-informed estimates for the actual prevalence, the effective reproduction number and incidence forecasts. We find that wastewater-based surveillance, paired with this model, can complement clinical surveillance by supporting the estimation of key epidemiological metrics and hence better triangulate the state of an epidemic using this alternative data source., (Crown Copyright © 2022. Published by Elsevier B.V. All rights reserved.)

To prevent the Corona Virus Disease 2019 (COVID-19) spreading, Chinese government takes a series of corresponding measures to restrict human mobility, including transportation lock-down and industries suspension, which significantly influenced the ambient air quality and provided vary rare time windows to assess the impacts of anthropological activities on air pollution. In this work, we divided the studied timeframe (2019/12/24-2020/2/24) into four periods and selected 88 cities from 31 representative urban agglomerations. The indicators of PM 2.5 /PM 10 and NO 2 /SO 2 were applied, for the first time, to analyze the changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China. The results indicated that the ratios of NO 2 /SO 2 presented a responding decline, especially in YRD (-5.01), YH (-3.87), and MYR (-3.84), with the sharp reduction of traffic in post-COVID-19 periods (P3-P4: 2.34 ± 0.94 m/m) comparing with pre-COVID-19 periods (P1-P2: 4.49 ± 2.03 m/m). Whereas the ratios of PM 2.5 /PM 10 increased in P1-P3, then decreased in P4 with relatively higher levels (>0.5) in almost all urban agglomerations. Furthermore, NO 2 presented a stronger association with PM 2.5 /PM 10 variation than CO; and PM 2.5 with NO 2 /SO 2 variation than PM 10 . In summary, the economic structure, lockdown measures and meteorological conditions could explain the noteworthy variations in different urban agglomerations. These results would be in great help for improving air quality in the post-epidemic periods., (Copyright © 2022. Published by Elsevier Inc.)

Background: Women are vulnerable to depression during postpartum period. While several studies have shown associations between ambient air pollution exposure and depression in general population, there was few studies focused on the effect of various air pollutants on postpartum depression (PPD)., Objective: This study is designed to explore the association between prenatal exposure to air pollutants and PPD, and to reveal the potential vulnerable exposure time point., Methods: The study enrolled 10,209 pregnant women who delivered between October 2019 and February 2021 in 5 participating hospitals from 3 cities in China. Edinburgh Postnatal Depression Scale (EPDS) was administered at 6 weeks postpartum to identify PPD symptoms. Associations between PPD symptoms and exposure levels in PM 2.5 , PM 10, SO 2 , CO, NO 2 , and O 3 averaged over the whole pregnancy and each trimester were estimated using logistic regression models after adjusting for potential confounding factors. Distributed lag models (DLMs) were used to determine the relevant associations in each gestational week., Results: The risk for developing PPD symptoms was significant following a 10 μg/m 3 increase in PM 10 (aOR = 1.47, 95%CI:1.36-1.59), NO 2 (aOR = 1.63, 95%CI:1.44-1.85), and 0.1 mg/m 3 increase in CO (aOR = 2.31, 95%CI: 1.99-2.69) during the whole pregnancy. Similar results were also found in exposure during each trimester of pregnancy. Besides, SO 2 exposure during the second trimester was a major risk factor for developing PPD symptoms (aOR = 1.10, 95%CI:1.03-1.18). Consistent effects were also observed in DLMs, except for PM 2.5 and O 3 , which showed no significant sensitive windows throughout pregnancy period., Conclusion: Exposure to PM 10 , CO, NO 2 , and SO 2 in pregnancy is associated with increased risks of developing depression at 6 weeks postpartum. Our findings reveal the importance of air pollution control for preventing maternal mental health disorders among the public., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Accumulating studies have suggested an important role of environmental factors (e.g. air pollutants) on the occurrence and development of coronavirus disease 2019 (COVID-19). Evidence concerning the relationship of greenness on COVID-19 is still limited. This study aimed to assess the association between greenness and COVID-19 incidence in 266 Chinese cities. A total of 12,377 confirmed COVID-19 cases were identified through February 29th, 2020. We used the average normalized difference vegetation index (NDVI) during January and February 2020 from MOD13A2 product, to represent the city-level greenness exposure. A generalized linear mixed-effects model was used to estimate the association between NDVI exposure and COVID-19 incidence using COVID-19 cases as the outcome. We evaluated whether the association was modified by population density, GDP per capita, and urbanization rate, and was mediated by air pollutants. We also performed a series of sensitivity analyses to discuss the robustness of our results. Per 0.1 unit increment in NDVI was negatively associated with COVID-19 incidence (IRR: 0.921, 95% CI: 0.898, 0.944) after adjustment for confounders. Associations with COVID-19 incidence were stronger in cities with lower population density, lower GDP per capita, and lower urbanization rate. We failed to detect any mediation effect of air pollutants on the association between NDVI and COVID-19 incidence. Sensitivity analyses also indicated consistent estimates. In conclusion, our study suggested a beneficial association between city-level greenness and COVID-19 incidence. We could not establish which mechanisms may explain this relationship., (Copyright © 2022. Published by Elsevier Inc.)

This work is focused on the multilevel control of the population confinement in the city of Buenos Aires and its surroundings due to the pandemic generated by the COVID-19 outbreak. The model used here is known as SEIRD and two objectives are sought: a time-varying identification of the infection rate and the inclusion of a controller. A control differential equation has been added to regulate the transitions between confinement and normal life, according to five different levels. The plasma treatment from recovered patients has also been considered in the control algorithm. Using the proposed strategy the ICU occupancy is reduced, and as a consequence, the number of deaths is also decreased., 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 © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)