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Abstract Post COVID-19 Condition (PCC) is a clinical syndrome following COVID-19 disease. PCC symptoms in adults entail significant productivity loss and reduced quality of life. This study aimed at estimating the epidemiological and economic burden of PCC among the working-age population of Italy and the US. This ecological analysis was conducted on data from January 2020 to April 2023, regarding population aged 18–64. PCC incidence for the US was retrieved from publicly reported estimates, while for Italy it was estimated from COVID-19 cases. Prevalence of factors associated with PCC and parameters to calculate temporary productivity losses (TPL) were retrieved. An estimated incidence rate ratio (eIRR) of PCC incidence in Italy and the US was calculated. TPL for reduced earnings and total quality-adjusted life years (QALYs) lost were also estimated. The ecological eIRR Italy/US was 0.842 [95%CI 0.672–1.015], suggesting that, holding COVID-19 cases constant, 15.8% fewer PCC cases have occurred in Italy compared to the US. Overall PCC cases were found to be 12.0 [95%CI 9.9–14.1] million in the US, with 1.9 [95%CI 1.6–2.3] million QALYs lost, and 2.4 [95%CI 1.8–3.0] million in Italy, with 0.4 [95%CI 0.3–0.5] million QALYs lost. Up to April 2023, the TPL was estimated to be Int$7.5 [95%CI 5.8–10.1] billion in Italy and $41.5 [95%CI 34.3–48.7] billion in the US. PCC has had a significant epidemiological and economic impact on the working-age population. The findings from this study may be of use for health planning and policy regarding PCC in working-age adults.

BackgroundPrimary care providers (PCPs) play an essential role in obesity care as they represent the first contact for patients seeking weight loss interventions.ObjectiveThis study explored the knowledge, experiences, and perceptions of PCPs in the Lazio Region of Italy in the management of obesity.Design and subjectsWe conducted an anonymous survey delivered from March to July 2022 via the newsletter of Rome Provincial Order of Physicians and Dentists and at the annual meeting of the regional section of the Italian Obesity Society.ApproachThe survey consisted of 24 closed-ended questions grouped into 5 sections: sociodemographic and work information; assessment of obesity; management of obesity; connections with regional Centres for Obesity Management; attitudes towards obesity.Key resultsA total of 92 PCPs accessed the survey. Of those, 2.2% were excluded because they did not see any patients with obesity. A total of 68 PCPs (75.6%) had complete questionnaires and were included in this analysis. All participants reported asking their patients about their eating habits, lifestyle, and clinical complications at the first assessment. Body weight and blood pressure were measured by 98.5% of participants and 82% calculate body mass index (BMI), while a small proportion of PCPs analysed body composition and fat distribution. Over 80% prescribed laboratory tests and ECG. Approximately 40% of PCPs did not refer patients for nutritional counselling, and most prescribed a low-calorie diet. Sixty-three percent referred patients to an endocrinologist, 48.5% to a psychotherapist, and a minority to specialists for obesity complications. Twenty-three percent prescribed anti-obesity medications and 46.5% referred patients for bariatric surgery only in severe cases. Ninety-one percent stated that obesity is “a complex and multifactorial disease” and 7.4% considered obesity to be secondary to other conditions.ConclusionsDespite most PCPs adopt a correct approach to manage patients with obesity, many aspects could be improved to ensure optimal and multidisciplinary management.

Objectives The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type.Methods A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials.Results Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety.Conclusion This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.