We aimed to analyze baseline socio-demographic and clinical factors associated with an increased likelihood of mortality in men and women with coronavirus disease (COVID-19). We conducted a retrospective cohort study (PRECOVID Study) on all 4412 individuals with laboratory-confirmed COVID-19 in Aragon, Spain, and followed them for at least 30 days from cohort entry. We described the socio-demographic and clinical characteristics of all patients of the cohort. Age-adjusted logistic regressions models were performed to analyze the likelihood of mortality based on demographic and clinical variables. All analyses were stratified by sex. Old age, specific diseases such as diabetes, acute myocardial infarction, or congestive heart failure, and dispensation of drugs like vasodilators, antipsychotics, and potassium-sparing agents were associated with an increased likelihood of mortality. Our findings suggest that specific comorbidities, mainly of cardiovascular nature, and medications at the time of infection could explain around one quarter of the mortality in COVID-19 disease, and that women and men probably share similar but not identical risk factors. Nonetheless, the great part of mortality seems to be explained by other patient- and/or health-system-related factors. More research is needed in this field to provide the necessary evidence for the development of early identification strategies for patients at higher risk of adverse outcomes.

Purpose: The purpose was to analyze drug prescription and antibiotic use by age and sex in Italy's Campania Region, and to estimate the distribution of prescription rates in children (≤14 years old), adults (between 15 and 65 years old), and older adults (≥65 years old) at a municipality level. Methods: This was a retrospective analysis of pharmacy records in Campania (Southern Italy), in 2016. Difference in antibiotic prescriptions in different age groups was assessed by prevalence rates. Age-adjusted prevalence rates were categorized into quintiles and mapped by the patient's municipality of residence. Relationship between prevalence rates for the different age groups was estimated using the non-parametric Spearman rank correlation test. Results: There were 2,738,118 were patients with at least one antibiotic prescription. Antibiotics prescription was higher in children aged <5 years and in the older adults aged >70 years. Prevalence rate distribution was different among municipalities in all age groups. A positive correlation between the rank distribution of prevalence rates at municipality level was identified for children and adults (rs=0.56; P<0.01), adults and the older adults (rs=0.79; P<0.01), and children and the older adults (rs=0.46; P<0.01). Among the studied age groups, the most prescribed antibiotic class was penicillin (except the older adults aged ≥85 years) ranging from 45% in children to 27.2% in the older adults. Fluoroquinolones were the least prescribed antibiotic class, ranging from 0.2% in children to 30.2% in the older adults. Conclusion: A considerably high use of antibiotic drugs has been detected in Campania Region, with values exceeding the regional and national average. Prescriptions at municipal level differ from one age group to another. Antibiotic use is often unjustified, and to decrease the number of prescriptions and improve their appropriateness, several measures at territorial level are recommended.

Objectives: Little is known about the specific comorbidities contributing to higher costs in patients with type-2 diabetes mellitus (T2DM), particularly in older cases. We aimed to evaluate the prevalence, type, and cost of comorbidities occurring in older T2DM patients versus older non-T2DM patients, and the factors associated with high cost (HC) T2DM patients.Methods: Retrospective cohort study using information from the Campania Region healthcare database. People aged ≥65 years who received ≥2 prescriptions for antidiabetic drugs were identified as “T2DM patients.” Comorbidities among T2DM and non-T2DM groups were assessed through the RxRiskV Index (modified version). T2DM individuals were classified according to the total cost distribution as HC or “non-high cost.” Two sub-cohorts of HC T2DM patients were assessed: above 90th and 80th percentile of the total cost. Age- and sex-adjusted logistic regression models were created.Results: Among the T2DM cohort, concordant and discordant comorbidities occurred significantly more frequently than in the non-T2DM cohort. Total mean annual cost per T2DM patient due to comorbidities was €7,627 versus €4,401 per non-T2DM patient. Among T2DM patients identified as being above 90th and 80th percentiles of cost distribution, the total annual costs were &gt;€19,577 and &gt;€2,563, respectively. The hospitalization cost was higher for T2DM cases. Strongest predictors of being a HC T2DM patient were having ≥5 comorbidities and renal impairment.Conclusion: HC patients accrued &gt;80% of the total comorbidities cost in older T2DM patients. Integrated care models, with holistic and patient-tailored foci, could achieve more effective T2DM care.

Background The novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the people at high risk of infection. Hence, we aimed (i) to identify clinical predictors of SARS-CoV-2 infection risk, (ii) to develop and validate a score predicting SARS-CoV-2 infection risk, and (iii) to compare it with unspecific scores. Methods Retrospective case-control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each person with SARS-CoV-2 confirmed infection (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS) was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. Discriminant power was used to compare SIS performance with that of other comorbidity scores. Results Subjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS = 4) 1.74 times higher than those unaffected by any SIS contributing conditions (SIS = 1). Conclusion Conditions and diseases making people more vulnerable to SARS-CoV-2 infection were identified by the current study. Our results support decision-makers in identifying high-risk people and adopting of preventive measures to minimize the spread of further epidemic waves.

A gender-specific drug utilization study was performed in the Campania region, Southern Italy. Data were based on outpatient drug prescriptions collected from administrative databases. The study population included all patients with at least one drug prescription in 2018. Prevalence was used as a measure to estimate the degree of exposure to drugs. A total of 3,899,360 patients were treated with at least one drug (54.2% females). The number of prescriptions was higher in females than males (55.6% vs. 44.4%). Females recorded higher prevalence for the majority of therapeutic groups (ATC II—anatomical therapeutic chemical), as well as for anti-inflammatory and antirheumatic products drugs (M01) (25.6% vs. 18.7%, risk ratio (RR): 0.73), beta blocking agents (C07) (14.5% vs. 11.6%, RR: 0.80), psychoanaleptics (N06) (7.1% vs. 3.7%, RR: 0.52), and antianemic preparations (B03) (2.8% vs. 6.7%, RR: 0.4). Higher prevalence was identified for males only for drugs used in diabetes (A10) (6.8% vs. 6.2%, RR: 1.1), particularly for biguanides (A10BA). Conversely, treatment duration was longer among males, explaining the higher mean cost per treated patient. This real-world study showed substantial gender differences in terms of medication use and duration of treatment and costs. These results are relevant to promoting and supporting the emerging role of precision and personalized medicine.

The World Health Organization considers the non-adherence to medication a significant issue with global impact, especially in chronic conditions such as type 2 diabetes. We aim to study antidiabetic treatment initiation, add-on, treatment switching, and medication persistence. We conducted an observational study on 4247 individuals initiating antidiabetic treatment between 2013 and 2014 in the EpiChron Cohort (Spain). We used Cox regression models to estimate the likelihood of non-persistence after a one-year follow-up, expressed as hazard ratios (HRs). Metformin was the most frequently used first-line antidiabetic (80% of cases); combination treatment was the second most common treatment in adults aged 40–79 years, while dipeptidyl peptidase-4 inhibitors were the second most common in individuals in their 80s and over, and in patients with renal disease. Individuals initiated on metformin were less likely to present addition and switching events compared with any other antidiabetic. Almost 70% of individuals initiated on monotherapy were persistent. Subjects aged 40 and over (HR 0.53–0.63), living in rural (HR 0.79) or more deprived areas (HR 0.77–0.82), or receiving polypharmacy (HR 0.84), were less likely to show discontinuation. Our findings could help identify the population at risk of discontinuation, and offer them closer monitoring for proper integrated management to improve prognosis and health outcomes.

This study aims to identify baseline medications that, as a proxy for the diseases they are dispensed for, are associated with increased risk of mortality in COVID-19 patients from two regions in Spain and Italy using real-world data. We conducted a cross-country, retrospective, observational study including 8570 individuals from both regions with confirmed SARS-CoV-2 infection between 4 March and 17 April 2020, and followed them for a minimum of 30 days to allow sufficient time for the studied event, in this case death, to occur. Baseline demographic variables and all drugs dispensed in community pharmacies three months prior to infection were extracted from the PRECOVID Study cohort (Aragon, Spain) and the Campania Region Database (Campania, Italy) and analyzed using logistic regression models. Results show that the presence at baseline of potassium-sparing agents, antipsychotics, vasodilators, high-ceiling diuretics, antithrombotic agents, vitamin B12, folic acid, and antiepileptics were systematically associated with mortality in COVID-19 patients from both countries. Treatments for chronic cardiovascular and metabolic diseases, systemic inflammation, and processes with increased risk of thrombosis as proxies for the conditions they are intended for can serve as timely indicators of an increased likelihood of mortality after the infection, and the assessment of pharmacological profiles can be an additional approach to the identification of at-risk individuals in clinical practice.

Coronavirus disease 2019 (COVID-19) has substantially challenged healthcare systems worldwide. By investigating population characteristics and prescribing profiles, it is possible to generate hypotheses about the associations between specific drug-utilisation profiles and susceptibility to COVID-19 infection. A retrospective drug-utilisation study was carried out using routinely collected information from a healthcare database in Campania (Southern Italy). We aimed to discover the prevalence of drug utilisation (monotherapy and polytherapy) in COVID-19 versus non-COVID-19 patients in Campania (~ 6 million inhabitants). The study cohort comprised 1532 individuals who tested positive for COVID-19. Drugs were grouped according to the Anatomical Therapeutic Chemical (ATC) classification system. We noted higher prevalence rates of the use of drugs in the ATC categories C01, B01 and M04, which was probably linked to related comorbidities (i.e., cardiovascular and metabolic). Nevertheless, the prevalence of the use of drugs acting on the renin-angiotensin system, such as antihypertensive drugs, was not higher in COVID-19 patients than in non-COVID-19 patients after adjustments for age and sex. These results highlight the need for further case–control studies to define the effects of medications and comorbidities on susceptibility to and associated mortality from COVID-19.