Leader: Antonio Gasbarrini (UNICATT); Other collaborator(s): Maria Benedetta Donati (NEUROMED), Elisa Gremese (UNICATT); Licia Iacoviello (NEUROMED); Carlo Pappone (UNISR), Antonio Cherubini (INRCA), Francesca Cecchi (UNIFI), Giuseppe Rengo (UNINA)
Task 6.4 activities will focus on the impact of climate change parameters and environmental pollutants exposure on cluster of age-related diseases. Task 6.4 activities will be based on data gathered in task 6.3 and will include the implementation of complex modeling methods
Brief description of the activities and of the intermediate results: The research activities focused on the definition of the study protocol for assessing the impact of climate change and air pollution on clusters of chronic diseases and multimorbidity in selected cohorts of older adults. Specifically, heat waves (defined as the presence of maximum apparent temperature above the monthly threshold for two or more consecutive days) and the concentrations of the main air pollutants recommended by the World Health Organisation (PM2.5, PM10, ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide) will be used as exposure variables. Climatic/atmospheric information will be gathered through the interrogation of databases of meteorological and environmental data and satellite data. Data on patients admitted to the Fondazione Policlinico Gemelli Hospital in Rome will be used to test this approach. The effects of climatic/environmental variables will be assessed for their association with conditions whose incidence increase in the event of heat waves and/or environmental pollution (e.g. acute cardiovascular, neurological, infectious events). Following anonymisation, health information will be collected using codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) retrieved from emergency department (ED) and hospital discharge records.
The extraction of data from emergency department databases and hospital discharge records for the last 10 years has begun and the variables have been coded. Data on heat waves or cold spells (i.e. prolonged periods of extremely high or extremely low temperatures for a particular region) were extracted from the Copernicus database. Clinical and climate databases are being cleaned and analysis activities are being planned.
In the third reporting period of 2024, research activities focused primarily on the definition of the study protocol for assessing the impact of climate change on the incidence of mobility disability and other negative health outcomes in the cohort of older adults with physical frailty and sarcopenia enrolled in the SPRINTT study. Data on heat waves and cold spells have been extracted for all regions where study participants resided during the trial (i.e., 16 cities across 11 European countries). Exploratory phase has been started, which encompassed the exploration and cleaning of clinical and environmental datasets, as well as the identification of key variables for the analysis.
During the last reporting period, the Task 6.4 working group focused its activities on exploring the possible associations between environmental variables (temperature, humidity, pollution) and the incidence of adverse events (functional, cardiovascular, respiratory, muscular, hormonal and neurological) in over 1500 older people aged over 70 years with physical frailty and sarcopenia enrolled in the SPRINTT project (Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies). During the SPRINTT study period (about five years), environmental data for different European locations (i.e. 16 cities in 11 European countries) were collected by Copernicus (the European Union's Earth observation programme). We are now carrying out analyses to assess how extreme events, such as heat or cold waves, or pollutants (including carbon monoxide, nitrogen oxides, black carbon and organic carbon) affect the health of SPRINTT participants. We plan to explore hybrid methods that combine classical statistical techniques with innovative AI solutions to address gaps in clinical data and ensure consistency and reliability of analyses. The next phase will focus on identifying correlations between clinical and environmental data using both classical algorithms and machine learning, with the aim of maximising insights and deepening understanding of the interactions between environmental variables and health outcomes.
Coming soon