Background and aim: Addressing global environmental threats requires research studies based on large epidemiological assessments covering multiple regions. Multi-centre studies offer an excellent framework for this purpose but present various methodological and logistical issues. This overview contribution illustrates the experience of the Multi-Country Multi-City (MCC) Collaborative Research Network, an international collaboration working on a global research program on the associations between environment, climate, and health.
Methods: The MCC Network has established an international collaboration based on mutual contribution and data sharing. This partnership has led to the collection of one of the largest databases for environmental health studies, with daily time series of health outcomes and environmental indices from 1,044 locations within 50 countries in the period 1969-2020. A research protocol formalizes a flexible mode of collaboration that offers scientific independence in addressing a variety of research topics, while ensuring collective participation and control on the use of data. The research is supported by intense methodological developments that have provided state-of-the-art study design and statistical techniques.
Results: The MCC Network has contributed key evidence on environmental health risks, with peer-reviewed publications in leading environmental, epidemiological and medical journals. The research scope of the collaboration spans from studies of the health risks of known risk factors such as air pollution and non-optimal temperature, to analyses of more specific environmental stressors such as wildfires and extreme weather events, to projections of health impacts under climate change scenarios.
Conclusions: The research of the MCC Network has already provided an exceptional contribution to our understanding of environment-health associations and the impacts of climate change. The collaborative framework can be replicated to address other research questions in this area and beyond.
Keywords: time series; pollution; temperature; climate change.

Multi-location analyses have proven to be useful settings for assessing the health impact of environmental hazards at regional and global scales. This study design is being widely applied in climate epidemiology since it allows the quantification of health impacts across highly heterogeneous populations and the identification of vulnerability patterns using a common method. Since 2014, the MCC consortium has developed and applied advanced methodologies to provide robust estimations of mortality impacts of non-optimal temperatures at different spatiotemporal scales in the present time and future climate change scenarios. This was possible thanks to the wide geographical coverage and high statistical power of the data collected. In this work, we aimed to review and summarize findings from published global studies on temperature-related mortality impacts using the MCC dataset.
We collected published articles until March 2022 by the MCC Network on health impact assessments of non-optimal temperature and mortality. We included global historical assessments and health impact projections in which heat, cold or both were considered and then combined the estimates by topic and geographical region.
Findings from the 20 publications showed good agreement in terms of both spatial and temporal patterns. We found that heat and cold-related risks have decreased over time, and higher vulnerabilities are mostly found in Southeastern regions of Europe and Asia, and in large and highly urbanized cities. Results from the projection studies suggest that heat-related mortality would steeply increase in all regions, and in most of them, this increase would counteract the decrease in cold-related risks leading to a net increase in temperature-mortality under the most pessimistic scenarios.
The MCC collaborative research network has greatly contributed to advancing knowledge on the impacts of climate change on health by providing valuable and unique epidemiological evidence on the mortality impacts of non-optimal temperatures.
Keywords: climate change, mortality, heat, cold

Background and Aim: The relationship between air pollution and daily mortality remains to be determined at a global scale. Moreover, few studies reported the interaction between air pollution and heat on mortality.

Methods: We collected daily air pollution, temperature, and mortality data from the Multi-country Multi-city (MCC) network. The air pollutants included particulate matter (PM) with aerodynamic diameter <= 10 μm (PM10) and <= 2.5 μm (PM2.5), ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2). We applied a two-stage time-series analysis, with over-dispersed generalized linear models and multilevel meta-analysis. The joint effect between air temperature and pollutants was analyzed in the warm season, with product terms between non-linear air temperature and linear air pollutants.

Results: We observed significant associations of air pollution with daily mortality. For instance, 10 μg/m3 increase in the 2-day moving average of PM2.5 and O3 concentrations were associated with increases of 0.44% (95% confidence interval [CI], 0.39 to 0.50) and 0.18% (95%CI, 0.12 to 0.24) in daily all-cause mortality, respectively. We also found evidence of interaction between heat and air pollution. For example, an increase in mean temperature from 75th to 99th percentile was associated with 9.0% (95%CI: 7.1%, 11.0%), 10.4% (95%CI: 8.4%, 12.4%), and 13.6% (95%CI: 10.6%, 16.6%) increases in mortality when PM10 was equal to 5th, 50th and 95th percentiles. Similarly, 10 μg/m3 increment in PM10 was associated with a 0.24% (95%CI: 0.02%, 0.46%), 0.46% (95%CI: 0.34%, 0.59%), and 0.76% (95%CI: 0.42%, 1.11%) increases in mortality when air temperature was at 5th, 50th and 95th percentiles, respectively.

Conclusions: We found robust evidence on the short-term effects of air pollution on daily mortality. We also found evidence of effect modification between air temperature and air pollutants on mortality during the warm period.

Keywords: Mortality, Short-term Exposure, Temperature, Air pollution, Effect modification

BACKGROUND AND AIM
Current knowledge is limited on global burden of mortality associated with short-term exposure to environmental factors, because global observed data of mortality and environmental factors are not available. This presentation aims to illustrate extensions of the analytical framework established within the MCC collaboration to provide global maps of health impacts associated with exposure to environmental factors (e.g., non-optimal temperatures, temperature variability, and tropical cyclones).

METHODS
We used time-series data on mortality and environmental data from 750 locations in 43 countries and meta-predictors at a grid size of 0.5° × 0.5° across the globe. We developed a three-stage analysis strategy. First, a time-series regression was used to estimate the association between environmental factors and mortality for each location by. Second, a multivariate meta-regression model was used to model the association between location-specific estimates and meta-predictors. Finally, the grid-specific exposure–mortality association was predicted by the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to exposure to environmental factors were then calculated for each grid across the world.

RESULTS
Globally, 5,083,173 deaths (95% empirical CI [eCI] 4,087,967–5,965,520) were associated with non-optimal temperatures per year, accounting for 9.43% (95% eCI 7.58–11.07) of all deaths (8.52% [6.19–10·47] were cold-related and 0.91% [0.56–1.36] were heat-related). Totally 1,753,392 deaths (95% eCI 1,159,901–2,357,718) were associated with temperature variability per year, accounting for 3.4% (2.2–4.6) of all deaths. An overall estimate of 199,740 deaths (95% eCI 185,988–267,278) were due to exposure to tropic cyclones globally from 1980 to 2019. Those estimates show geographical and temporal heterogeneity across region and nation.

CONCLUSIONS
Estimating global, regional and national burden of mortality associated with short-term exposure to environmental factors is essential for developing preparedness and prevention strategies to reduce their impacts.

KEYWORDS
Global mortality burden, temperature, temperature variability, tropical cyclone

Background and Aim. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread across the globe, traversing diverse climatic and environmental conditions. From the early phase of the pandemic, there has been speculation that weather conditions could modulate SARS-CoV-2 transmission patterns.
Methods. In this talk, I will present the current evidence and methodological challenges in evaluating potential drivers of COVID-19 seasonality. I will also present the results of two global studies based on an extensive city-level dataset, collected by the Multi-Country Multi-City MCC Collaborative Research Network, where we studied the relationship between weather conditions and COVID-19 spread using ecological and time-series designs. Finally, I will discuss planned future studies based on data collected until March 2022.
Results. We collected data from 455 cities across 20 countries around the globe over an observation period ranging from 3 February to 31 October 2020. For each location, daily COVID-19 cases were linked with meteorological (mean temperature, relative and absolute humidity and UV radiation) variables derived from the Copernicus ERA5 dataset, and with the Oxford Governmental Stringency Index to account for the effect of lockdown measures. These data were analysed using ecological designs (spatial and time-series approaches) giving evidence of effects of mean temperature and absolute humidity on COVID-19 transmission. However, these studies underline regional heterogeneity of weather-related effects on COVID-19 transmission and the dominant role of non-pharmaceutical interventions. We are extending the data collection until March 2022 to investigate the interdependent role of seasonality, meteorological factors, environmental stressors (air pollution), non-pharmaceutical interventions and vaccination.
Conclusion.
This research activity is an example of the flexibility of the MCC Network in adapting and using its collaborative platform and data collection to address urgent and challenging research questions.
Key words: Temperature, Humidity, UV Radiation, COVID-19, DLNM, Global Analysis, Meta-Regression