Search

Background: Health disparities related to socio-economic factors impact access to preventive health interventions. The PRECEDE–PROCEED model, a multidimensional approach to health promotion, has been adapted to optimise cancer screening programs in Lombardy, Italy, addressing these disparities. Methods: This study evaluated the application of systemic audits based on the PRECEDE–PROCEED model across Lombardy cancer screening programs. A systematic region-wide audit was performed in 2019, and follow-up audits were performed in 2022–2023. Data were collected using structured analysis methodologies, including epidemiological, behavioural, and organisational assessments. Results: The 2019 audit showed strengths in participation and quality standards but identified challenges in cervical cancer screening coverage and waiting times for assessments. Improvements plans included the digitisation of processes and stakeholder engagement. The 2022–2023 audits reported increased coverage for breast and colorectal screenings, but a slight decline in participation rates and examination coverage. Organisational improvements were noted, yet gaps in training and equity-targeted actions remained. Conclusion: The PRECEDE–PROCEED model audits led to notable improvements in the quality and equity of cancer screening programs in Lombardy. Sustained focus on digital integration, continuous re-training, and targeted equity interventions is essential for further progress.

Background: Health disparities related to socio-economic factors impact access to preventive health interventions. The PRECEDE–PROCEED model, a multidimensional approach to health promotion, has been adapted to optimise cancer screening programs in Lombardy, Italy, addressing these disparities. Methods: This study evaluated the application of systemic audits based on the PRECEDE–PROCEED model across Lombardy cancer screening programs. A systematic region-wide audit was performed in 2019, and follow-up audits were performed in 2022–2023. Data were collected using structured analysis methodologies, including epidemiological, behavioural, and organisational assessments. Results: The 2019 audit showed strengths in participation and quality standards but identified challenges in cervical cancer screening coverage and waiting times for assessments. Improvements plans included the digitisation of processes and stakeholder engagement. The 2022–2023 audits reported increased coverage for breast and colorectal screenings, but a slight decline in participation rates and examination coverage. Organisational improvements were noted, yet gaps in training and equity-targeted actions remained. Conclusion: The PRECEDE–PROCEED model audits led to notable improvements in the quality and equity of cancer screening programs in Lombardy. Sustained focus on digital integration, continuous re-training, and targeted equity interventions is essential for further progress. [ABSTRACT FROM AUTHOR]

Abstract Background Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. Methods This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. Results Among 199 patients subject to study (median [interquartile range] age 65 [46–78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity. Conclusion Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

The COVID-19 outbreak has led to relevant changes in everyday life worldwide. One of these changes has been a rapid transition to and an increasing implementation of working from home (WH) modality. This study aimed to evaluate the impact of mandatory WH during the COVID-19 pandemic on lifestyle behaviors, Mediterranean diet adherence, body weight, and depression. An online cross-sectional survey was conducted in the early 2022 at the National Research Council of Italy using ad hoc questions and validated scales collecting information on physical activity, sedentary behavior, hobbies/pastimes, dietary habits including adherence to the Mediterranean diet, body weight, and depression during WH compared with before WH. 748 respondents were included in the study. An increased sedentary lifetime was reported by 48% of respondents; however, the subsample of workers who previously performed moderate physical activity intensified this activity. Body weight gain during WH was self-reported in 39.9% of respondents. Mediterranean diet adherence increased (p≪0.001) during WH compared with before WH. The average level of mental health did not record an overall variation; however, the proportion of subjects with mild and moderate depression increased (p = 0.006), while workers who reported values indicative of depression before the transition declared an improvement. These findings highlight health-related impact of WH during the COVID-19 pandemic that may inform future strategies and policies to improve employees' health and well-being. [ABSTRACT FROM AUTHOR]

A healthy and balanced diet is crucial for children's well-being and aids in preventing diet-related illnesses. Furthermore, unhealthy dietary habits indirectly impact children's health, as the food industry stands as one of the primary drivers of climate change. Evidence shows the Mediterranean diet is sustainable for both children's and the planet's health. The aim of this cross-sectional study was to evaluate the eating habits of children aged between 6 months and 3 years, in the province of Modena and Reggio Emilia, in Italy, along with their adherence to the guidelines for a healthy diet, and examine the role of pediatricians in promoting knowledge about nutrition and sustainability. In our sample (218 children), most children exceeded the recommended meat and cheese intake, while consuming insufficient amounts of vegetables, fruit, and legumes. Vegetable and fruit consumption declined with the increase in age category while eating sweets, soft drinks, and processed food increased. Incorporating school meals' data into this analysis, we observed a modification in dietary compliance, characterized by an increase in meat and cheese consumption, alongside improvements in the intake of vegetables, fruits, fish, eggs, and legumes. This study suggests that supporting an integrated approach that combines social and educational initiatives is crucial. Future research should prioritize fostering sustainable eating habits within communities to facilitate dietary habits' transformation and encourage healthier lifestyles. [ABSTRACT FROM AUTHOR]

Purpose: The first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement. Methods: The researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected. Results: In those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research. Conclusion: In conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification. [ABSTRACT FROM AUTHOR]

Background: In an ageing society, retirement impacts on behavioural risk factors and health outcomes should be carefully assessed. Scant evidence exists from longitudinal studies on the short- and long-term consequences of the transition to retirement on smoking habit. Methods: We conducted a longitudinal study based on the Survey of Health, Ageing and Retirement in Europe (SHARE) data from 27 European countries plus Israel collected in 2004–2020. To estimate relative risks (RR) and corresponding 95% confidence intervals (CI) for smoking status and intensity at seven time periods before and after retirement, we fitted adjusted generalised estimating equation (GEE) models for repeated measures. Results: We selected a cohort of 8998 individuals employed at baseline and retired at follow-up (median follow-up time: 9 years; maximum: 16 years). As compared to the year of retirement, the RR of smoking was 1.59 (95% CI 1.44–1.76) at 10 years or more before retirement, 1.35 (95% CI 1.25–1.46) from 5 to 9 years before retirement, and 1.18 (95% CI 1.10–1.27) from 1 to 4 years before retirement. Smoking steadily decreased after retirement, being 0.94 (95% CI 0.87–1.01) from 1 to 4 years after retirement, 0.76 (95% CI 0.69–0.84) from 5 to 9 years, and 0.58 (95% CI 0.46–0.74) 10 years or more after retirement. In smokers, the estimated number of cigarettes smoked/day decreased from about 27 cigarettes/day at 10 years or more before retirement to 9 cigarettes/day at 10 years or more after retirement (p trend < 0.001). Conclusion: Longitudinal data suggest that lifestyles might favourably change with retirement. Further studies are needed to direct healthy ageing promotion policies better. [ABSTRACT FROM AUTHOR]

Aims. Depression is among the main contributors to older adults' mental health burden. Retirement, one of the major life transitions, has been claimed to influence mental health substantially. Following up on a previous meta-analysis, the study aims to assess from a longitudinal perspective short- and long-term impacts of transitioning to retirement on depression risk and suicidality in older adults across Europe. Methods. We conducted a longitudinal study using data from the Survey of Health, Ageing and Retirement in Europe (SHARE), collected between 2004 and 2020 in 27 European countries plus Israel. To estimate relative risks (RR) and 95% confidence intervals (95% CIs) for depression and suicidality at seven time intervals before and after retirement, we fitted adjusted generalized estimating equation models for repeated measures. Results. We included 8,998 individuals employed at baseline and retired at follow-up (median follow-up time: 9 years; maximum: 16 years). Compared to the year of retirement, the risk of depression was 11% lower in the following year (RR 0.89; 95% CI 0.81-0.99), 9% lower after 2 years (RR 0.91; 95% CI 0.82-1.00) and after 3 years (RR 0.91; 95% CI 0.81-1.01). Significant estimates remained among females, married individuals, those with an intermediate or higher level of education, former manual workers and those who retired at or before their country's median retirement age. A significant increase in depressive symptoms emerged from the tenth year after retirement among former non-manual workers (RR 1.21; 95% CI 1.05-1.40) and late retirees (RR 1.37; 95% CI 1.16-1.63). No heterogeneity emerged among strata. As for suicidality, we reported an increase in risk only 5 years or more after retirement, namely +30% 5-9 years after retirement (RR 1.30; 95% CI 1.04-1.64) and +47% 10 or more years after retirement (RR 1.47; 95% CI 1.09-1.98). Sensitivity analyses excluding subjects who reported a diagnosis of depression over the study period and those retirees who declared to receive a disability pension confirmed the results obtained in the overall analysis. Conclusions. Longitudinal adjusted data suggest an independent effect of retiring associated with a reduction in depression and suicidality risk in the short run, with its effect decreasing in the long run. Such trends are particularly evident among selected subgroups of elderly populations. If greater flexibility in pensionable age may help prevent depression late in life, the transition to retirement is to be accompanied by targeted health promotion interventions. In an ageing society, welfare policies should be evaluated, considering their long-term impact on mental health. [ABSTRACT FROM AUTHOR]

COVID-19 has been in the headlines for the past two years. Diagnosing this infection with minimal false rates is still an issue even with the advent of multiple rapid antigen tests. Enormous data are being collected every day that could provide insight into reducing the false diagnosis. Machine learning (ML) and deep learning (DL) could be the way forward to process these data and reduce the false diagnosis rates. In this study, ML and DL approaches have been applied to the data set collected using an ultra-fast COVID-19 diagnostic sensor (UFC-19). The ability of ML and DL to specifically detect SARS-CoV-2 signals against SARS-CoV, MERS-CoV, Human CoV, and Influenza was investigated. UFC-19 is an electrochemical sensor that was used to test these virus samples and the obtained current response dataset was used to diagnose SARS-CoV-2 using different algorithms. Our results indicate that the convolution neural networks algorithm could diagnose SARS-CoV-2 samples with a sensitivity of 96.15%, specificity of 98.17%, and accuracy of 97.20%. Combining this DL model with the existing UFC-19 could selectively identify SARS-CoV-2 presence within two minutes. [ABSTRACT FROM AUTHOR]