Scientific evidence reports a strong relationship between the adoption of healthy lifestyles and the prevention or delaying of chronic diseases. The present pilot project is aimed at developing a Lifestyle Platform through the collection of lifestyle information based on digital technologies. The most influential health predictors will be addressed, i.e. physical activity, sedentary behaviour, physical fitness, diet, smoke/alcohol/drug habits, sleeping, metabolic and weight status, psychological, neurological and socio-economic-cultural-environmental aspects.
Data will be analysed using a data mining approach to delineate predictive models for the primary prevention of chronic diseases. Innovative digital approaches to assess lifestyle and health indicators in populations aged 6-65 are being defined, such as smart sensors and apps.
Stakeholders’ networks will be established among different institutions with the aim of collaborating and sharing the needed data and information. Therefore, different setting and target populations will be involved, including schools, universities, working places, pharmacies, sport associations, sport centers, fitness centers, pharmacies, prevention departments of local health agencies, outpatients’ clinics, medical offices.
The proposed pilot will allow the designing of patterns that explain the influence of determinants on population health, thus it will contribute to plan targeted, prompt and efficient intervention strategies to tackle chronic diseases and finally improving population health.
The Digi-Vax Pilot, promoted by the University of Palermo, was born out of the need to bridge a gap in the digitalisation of vaccination data in the Sicilian Region and to make such data interoperable with laboratory surveillance data related to vaccine-preventable diseases (VPDs).
In diversi Paesi Europei, quali UK e Spagna, è possibile avere in tempo reale sia lo status vaccinale del soggetto cui viene diagnostica e confermata laboratoristicamente una PPV (quale ad esempio meningite batterica, polmonite batterica, influenza, morbillo, ecc…) sia effettuare una valutazione in “real world” del dato di efficacia vaccinale (per introdurre eventuali modifiche e miglioramenti delle strategie vaccinali in atto).
This is not the case in Italy, except for specific scientific studies conducted locally and on limited populations. Even the coverage rates for vaccines included in the 2023–2025 National Vaccination Prevention Plan are released with significant delays (currently, data is only available as of December 31, 2023).
This "non-virtuous" process hinders both the adaptation of vaccination strategies to the circulation of viruses and bacteria and the implementation of effective measures to improve coverage rates.
The Digi-Vax Project aims to implement a pilot in the Sicilian Region—Italy’s fourth most populous region with approximately 4.8 million residents—to overcome these limitations in data digitalisation and interoperability.
The pilot project aims to develop an AI-driven integrated surveillance platform to investigate risk factors associated with cancer onset, such as HPV infection and precancerous lesions. The platform will be used to predict development risks, perform comprehensive data analyses, and analyse high-dimensional features by enabling interoperability between cancer registries, specialised pathology registries (including molecular data), and general population data (e.g., lifestyle, occupational exposure, etc.), with the goal of implementing innovative, community-based digital primary prevention interventions. The pilot will focus on female cervical cancer, the fourth leading cause of cancer-related death among women worldwide. The implementation of primary and secondary prevention programs using machine learning and deep learning approaches can play a crucial role in analysing large volumes of data and generating insights to predict the early stages of the disease.
The pilot will run for approximately two years and aims to implement highly specialised machine learning-based prediction models for primary prevention, in order to identify complex patterns and relationships between factors that may be responsible for cervical cancer.
Develop digital functionalities:
a) To support an advanced cancer registry, by leveraging artificial intelligence and Lean Six Sigma approaches,allowing to interoperate data from cancer registries with data from primary healthcare system, and
b) to support the operators to shorten the time to data collection and to validate the cancer topography, morphology and grading.
This combined approach will allow to implement innovative digital primary prevention strategies against cancer occurrence.
Prospective design: new cancer cases collected from the CT/ME/EN integrated Cancer Registry and the Palermo Province Cancer Registry (approx. 18,000.00 cases).
To develop a prototypal function to interoperate health, environment, and climate data, to support preparedness and primary prevention strategies on community health, in case of high-intensity exposure to ambient air, water, and ground pollutants.
To study the acute effects on health of the high-intensity exposure to ambient air, water, and ground pollutants on the general population and on communities residing in urban areas or in the proximity to Areas at High Risk of Environmental Crisis (AERCA), and other high-impact polluted sites related to urban waste management.
To develop a prototypal function to interoperate health, environment, and climate data, to support primary prevention interventions on the communities.
To study the health effects in different target populations (newborns, children, pregnant women, adults, elderly) of long-term exposure to ambient air, water, ground pollutants, and physical polluting agents (electromagnetic fields, noise pollution, etc.).
A focus wil be done on communities residing in urban areas or in the proximity to Areas at High Risk of Environmental Crisis (AERCA), and other high-impact polluted sites related to urban waste management..
To develop prototypal digital functions to support an advanced cancer registry, allowing to interoperate data from cancer registries with data from specialised pathology/clinical registries focusing on the six major digestive tract cancers, and other data flows (lifestyles, other social and health determinants, occupational exposures, etc.).
These digital functions will allow to:
Sample size: linkage of the databases from the Sicilian cancer registries, accounting for 12000 new cancer cases of liver cancer, and the SINTESI databases, including data of over 50,000 individuals with metabolic, autoimmune and virological diseases of the liver.
To develop prototypal digital functions to support an advanced cancer registry, allowing to interoperate data from cancer registries with data from environment monitoring systems, which detect carcinogenic pollutants in ambient air and indoor, in water and in ground.
To study the long-terms effects in terms of cancer occurrence of exposure to environment pollutants on the general population and, above all, on communities residing in large urban areas (Catania, Messina e Palermo) and in the proximity to Areas at High Risk of Environmental Crisis (AERCA), to prevent cancer incidence and to improve cancer prevention strategies.
Retrospective design: the historic cohort of new cancer cases collected by the sicilian network of cancer registries since 2003 to the last available year of incidence.
Prospective design: a cohort of 20000 new cancer cases accessed through the epidemiological surveillance covering the entire sicilian poluation.
To develop an interoperable web-based platform with advanced digital functions in support of community trial studies. Beyond of interconnecting research centers with local prevention and health services, general practitioners, pediatricians, community pharmacies, and other territorial primary health care services, when appropriate, this platform will allow to interoperate different digital environments and to interplay with the recruited individuals using digital tools.
Based on the prevalence among general population of the single communicable or non-communicable disease and on a 95% desired level of significance, the sample sizes will be defined.
To implement digitalised primary prevention pathways against chronic illnesses and physical-cognitive declines in solid organ transplanted (SOT) individuals with sustained remission of end-organ dysfunction, ensuring their long-term ability to live independently and maintain a desirable quality of life through digital health, following a community-based approach.
Use of the World Health Organization Integrated care tool to predict disease occurrence in elder healthy people and to develop innovative primary preventive approaches and to promote healthy aging. To this end, the information derived from the ICOPE application will be integrated with health and non-health information sources.
Healthy adults over 60 years of age.
Subjects with a range of clinical characteristics can be generated. Subjects can also be stratified to select the most suitable strategy.
Scientific evidence reports a strong relationship between the adoption of healthy lifestyles and the prevention or delaying of chronic diseases. The present pilot project is aimed at developing a Lifestyle Platform through the collection of lifestyle information based on digital technologies. The most influential health predictors will be addressed, i.e. physical activity, sedentary behaviour, physical fitness, diet, smoke/alcohol/drug habits, sleeping, metabolic and weight status, psychological, neurological and socio-economic-cultural-environmental aspects.
Data will be analysed using a data mining approach to delineate predictive models for the primary prevention of chronic diseases. Innovative digital approaches to assess lifestyle and health indicators in populations aged 6-65 are being defined, such as smart sensors and apps.
Stakeholders’ networks will be established among different institutions with the aim of collaborating and sharing the needed data and information. Therefore, different setting and target populations will be involved, including schools, universities, working places, pharmacies, sport associations, sport centers, fitness centers, pharmacies, prevention departments of local health agencies, outpatients’ clinics, medical offices.
The proposed pilot will allow the designing of patterns that explain the influence of determinants on population health, thus it will contribute to plan targeted, prompt and efficient intervention strategies to tackle chronic diseases and finally improving population health.
The Digi-Vax Pilot, promoted by the University of Palermo, was born out of the need to bridge a gap in the digitalisation of vaccination data in the Sicilian Region and to make such data interoperable with laboratory surveillance data related to vaccine-preventable diseases (VPDs).
In diversi Paesi Europei, quali UK e Spagna, è possibile avere in tempo reale sia lo status vaccinale del soggetto cui viene diagnostica e confermata laboratoristicamente una PPV (quale ad esempio meningite batterica, polmonite batterica, influenza, morbillo, ecc…) sia effettuare una valutazione in “real world” del dato di efficacia vaccinale (per introdurre eventuali modifiche e miglioramenti delle strategie vaccinali in atto).
This is not the case in Italy, except for specific scientific studies conducted locally and on limited populations. Even the coverage rates for vaccines included in the 2023–2025 National Vaccination Prevention Plan are released with significant delays (currently, data is only available as of December 31, 2023).
This "non-virtuous" process hinders both the adaptation of vaccination strategies to the circulation of viruses and bacteria and the implementation of effective measures to improve coverage rates.
The Digi-Vax Project aims to implement a pilot in the Sicilian Region—Italy’s fourth most populous region with approximately 4.8 million residents—to overcome these limitations in data digitalisation and interoperability.
A Population-Based Digital Approach to Cervical Cancer, Aimed at Promoting Interoperability Between Cancer Registries, Specialised Clinico-Pathological Networks, and Data Flows Through the Use of Advanced Machine Learning Models Tracking the Pathway From HPV Infection to Precancerous Lesions.
The pilot project aims to develop an AI-driven integrated surveillance platform to investigate risk factors associated with cancer onset, such as HPV infection and precancerous lesions. The platform will be used to predict development risks, perform comprehensive data analyses, and analyse high-dimensional features by enabling interoperability between cancer registries, specialised pathology registries (including molecular data), and general population data (e.g., lifestyle, occupational exposure, etc.), with the goal of implementing innovative, community-based digital primary prevention interventions. The pilot will focus on female cervical cancer, the fourth leading cause of cancer-related death among women worldwide. The implementation of primary and secondary prevention programs using machine learning and deep learning approaches can play a crucial role in analysing large volumes of data and generating insights to predict the early stages of the disease.
The pilot will run for approximately two years and aims to implement highly specialised machine learning-based prediction models for primary prevention, in order to identify complex patterns and relationships between factors that may be responsible for cervical cancer.
Develop digital functionalities:
a) To support an advanced cancer registry, by leveraging artificial intelligence and Lean Six Sigma approaches,allowing to interoperate data from cancer registries with data from primary healthcare system, and
b) to support the operators to shorten the time to data collection and to validate the cancer topography, morphology and grading.
This combined approach will allow to implement innovative digital primary prevention strategies against cancer occurrence.
Prospective design: new cancer cases collected from the CT/ME/EN integrated Cancer Registry and the Palermo Province Cancer Registry (approx. 18,000.00 cases).
To develop a prototypal function to interoperate health, environment, and climate data, to support preparedness and primary prevention strategies on community health, in case of high-intensity exposure to ambient air, water, and ground pollutants.
To study the acute effects on health of the high-intensity exposure to ambient air, water, and ground pollutants on the general population and on communities residing in urban areas or in the proximity to Areas at High Risk of Environmental Crisis (AERCA), and other high-impact polluted sites related to urban waste management.
To develop a prototypal function to interoperate health, environment, and climate data, to support primary prevention interventions on the communities.
To study the health effects in different target populations (newborns, children, pregnant women, adults, elderly) of long-term exposure to ambient air, water, ground pollutants, and physical polluting agents (electromagnetic fields, noise pollution, etc.).
A focus wil be done on communities residing in urban areas or in the proximity to Areas at High Risk of Environmental Crisis (AERCA), and other high-impact polluted sites related to urban waste management..
To develop prototypal digital functions to support an advanced cancer registry, allowing to interoperate data from cancer registries with data from specialised pathology/clinical registries focusing on the six major digestive tract cancers, and other data flows (lifestyles, other social and health determinants, occupational exposures, etc.).
These digital functions will allow to:
Sample size: linkage of the databases from the Sicilian cancer registries, accounting for 12000 new cancer cases of liver cancer, and the SINTESI databases, including data of over 50,000 individuals with metabolic, autoimmune and virological diseases of the liver.
To develop prototypal digital functions to support an advanced cancer registry, allowing to interoperate data from cancer registries with data from environment monitoring systems, which detect carcinogenic pollutants in ambient air and indoor, in water and in ground.
To study the long-terms effects in terms of cancer occurrence of exposure to environment pollutants on the general population and, above all, on communities residing in large urban areas (Catania, Messina e Palermo) and in the proximity to Areas at High Risk of Environmental Crisis (AERCA), to prevent cancer incidence and to improve cancer prevention strategies.
Retrospective design: the historic cohort of new cancer cases collected by the sicilian network of cancer registries since 2003 to the last available year of incidence.
Prospective design: a cohort of 20000 new cancer cases accessed through the epidemiological surveillance covering the entire sicilian poluation.
To develop an interoperable web-based platform with advanced digital functions in support of community trial studies. Beyond of interconnecting research centers with local prevention and health services, general practitioners, pediatricians, community pharmacies, and other territorial primary health care services, when appropriate, this platform will allow to interoperate different digital environments and to interplay with the recruited individuals using digital tools.
Based on the prevalence among general population of the single communicable or non-communicable disease and on a 95% desired level of significance, the sample sizes will be defined.
To implement digitalised primary prevention pathways against chronic illnesses and physical-cognitive declines in solid organ transplanted (SOT) individuals with sustained remission of end-organ dysfunction, ensuring their long-term ability to live independently and maintain a desirable quality of life through digital health, following a community-based approach.
Use of the World Health Organization Integrated care tool to predict disease occurrence in elder healthy people and to develop innovative primary preventive approaches and to promote healthy aging. To this end, the information derived from the ICOPE application will be integrated with health and non-health information sources.
Healthy adults over 60 years of age.
Subjects with a range of clinical characteristics can be generated. Subjects can also be stratified to select the most suitable strategy.