Objective
This study aims to analyse the impact of the pandemic on the amount of use and new medication dispensation for chronic diseases in the Italian population aged 65 years and older (almost 14 million inhabitants).
Methods
The “Pharmaceutical Prescriptions database”, which gathers data on medications, reimbursed by the National Health Service and dispensed by community pharmacies, was employed. Data were analysed as amount of use (defined daily dose—DDD per 1000 inhabitants); variation in DDD between 2020 and 2019 was calculated for the 30 categories with major consumption in 2020. Trends in prevalence and incidence of dispensations between 2020 and 2019 were calculated for four categories: antidiabetics, antihypertensives, antidepressants and drugs for respiratory diseases.
Results
All medications showed a negative variation in DDD/1000 inhabitants between 2020 and 2019 except for anticoagulants (+ 5%). The percentage variation ranged from − 27.7% for antibiotics to − 6.4% for antipsychotics in 85 + year-old persons, but increased for most classes in the youngest (65–69 years). On the other hand, a decrease of the dispensation incidence of antidiabetics, antihypertensives, antidepressants and drugs for pulmonary disease was high, especially in the two extreme age groups, the youngest and the oldest one.
Conclusions and relevance
Great variation in medication use between 2020 and 2019 was observed probably reflecting the low rate of infectious diseases due to the widespread use of protective devices and self-isolation, reduced healthcare access because of the lockdowns and the fear of going to hospital, and the reduction of screening and diagnostics due to health-care system overload.
Synthetic Characters are intelligent agents able to show typical human-like cognitive behavior and an artificially-made perceived personality by means of complex natural language interaction and artificial reasoning and emotional skills. They are mainly spreading on the web as highly interactive digital assistants and tutoring agents on online database systems, e-commerce sites, web-based communities, online psychotherapy, and in several consulting situations where humans need assistance from intelligent software. Until now, synthetic characters, equipped with data, models, and simulation skills, have never been thought as the building blocks for natural language interaction-based intelligent DMSS. This chapter illustrates the first research and development attempt in this sense by an Open Source project in progress centred on the design of a synthetic character-based DMSS.
Computation of complexity in Social Dependence Networks is an interesting research domain to understand evolution processes and group exchange dynamics in natural and artificial intelligent Multi-Agent Systems. We perform an agent-based simulation by NET-PLEX (Conte and Pistolesi, 2000), a new software system able both to build interdependence networks tipically emerging in Multi-Agent System scenarios and to investigate complexity phenomena, i.e., unstability and state-transitions like Hopf bifurcation (Nowak and Lewenstein, 1994), and to describe social self organization phenomena emerging in these artificial social systems by means of complexity measures similar to those introduced by Hubermann and Hogg (1986). By performing analysis of complexity in these kind of artificial societies we observed interesting phenomena in emerging organizations that suggest state-transitions induced by critical configurations of parameters describing the social system similar to those observed in many studies on state-transitions in bifurcation chaos (Schuster, 1988; Ruelle, 1989)
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