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.

No abstract

The correct management of patients with multimorbidity remains one of the main challenges for healthcare systems worldwide. In this study, we analyze the existence of multimorbidity patterns in the general population based on gender and age. We conducted a cross-sectional study of individuals of all ages from the EpiChron Cohort, Spain (1,253,292 subjects), and analyzed the presence of systematic associations among chronic disease diagnoses using exploratory factor analysis. We identified and clinically described a total of 14 different multimorbidity patterns (12 in women and 12 in men), with some relevant differences in the functions of age and gender. The number and complexity of the patterns was shown to increase with age in both genders. We identified associations of circulatory diseases with respiratory disorders, chronic musculoskeletal diseases with depression and anxiety, and a very consistent pattern of conditions whose co-occurrence is known as metabolic syndrome (hypertension, diabetes, obesity, and dyslipidaemia), among others. Our results demonstrate the potential of using real-world data to conduct large-scale epidemiological studies to assess the complex interactions among chronic conditions. This could be useful in designing clinical interventions for patients with multimorbidity, as well as recommendations for healthcare professionals on how to handle these types of patients in clinical practice.

ObjectivesThe objective was to identify the systematic associations among chronic diseases and drugs in the form of patterns and to describe and clinically interpret the constituted patterns with a focus on exploring the existence of potential drug-drug and drug-disease interactions and prescribing cascades.MethodsThis observational, cross-sectional study used the demographic and clinical information from electronic medical databases and the pharmacy billing records of all users of the public health system of the Spanish region of Aragon in 2015. An exploratory factor analysis was conducted based on the tetra-choric correlations among the diagnoses of chronic diseases and the dispensed drugs in 887,572 patients aged ≤65 years. The analysis was stratified by age and sex. To name the constituted patterns, assess their clinical nature, and identify potential interactions among diseases and drugs, the associations found in each pattern were independently reviewed by two pharmacists and two doctors and tested against the literature and the information reported in the technical medicinal forms.ResultsSix multimorbidity-polypharmacy patterns were found in this large-scale population study, named as respiratory, mental health, cardiometabolic, endocrinological, osteometabolic, and mechanical-pain. The nature of the patterns in terms of diseases and drugs differed by sex and age and became more complex as age advanced.ConclusionsThe six clinically sound multimorbidity-polypharmacy patterns described in this non-elderly population confirmed the existence of systematic associations among chronic diseases and medications, and revealed some unexpected associations suggesting the prescribing cascade phenomenon as a potential underlying factor. These findings may help to broaden the focus and orient the early identification of potential interactions when caring for multimorbid patients at high risk of adverse health outcomes due to polypharmacy.

PurposeThe purpose of this study was to evaluate and compare medication adherence to chronic therapies in older populations across different regions in Europe.MethodsThis explorative study applied a harmonized method of data extraction and analysis from pharmacy claims databases of three European countries to compare medication adherence at a cross-country level. Data were obtained for the period between January 1, 2010, and December 31, 2011. Patients (aged ≥65 years) who newly initiated to oral antidiabetics, antihyperlipidemics, or antiosteoporotics were identified and followed for over a 12-month period. Main outcome measures were medication adherence (medication possession ratio, [MPR]; implementation) and persistence on index treatment. All country-specific data sets were prepared by employing a common data input model. Outcome measures were calculated for each country and pooled using random effect models.ResultsIn total, 39,186 new users were analyzed. In pooled data from the three countries, suboptimal implementation (MPR <80%) was 52.45% (95% CI: 33.43–70.79) for antihy-perlipidemics, 61.35% (95% CI: 52.83–69.22) for antiosteoporotics, and 30.33% (95% CI: 25.53–35.60) for oral antidiabetics. Similarly, rates of non-persistence (discontinuation) were 55.63% (95% CI: 35.24–74.29) for antihyperlipidemics, 60.24% (95% CI: 45.35–73.46) for antiosteoporotics, and 46.80% (95% CI: 36.40–57.4) for oral antidiabetics.ConclusionMedication adherence was suboptimal with >50% of older people non-adherent to antihyperlipidemics and antiosteoporotics in the three European cohorts. However, the degree of variability in adherence rates among the three countries was high. A harmonized method of data extraction and analysis across health-related database in Europe is useful to compare medication-taking behavior at a cross-country level.

Patients with multimorbidity (defined as the co-occurrence of multiple chronic diseases) frequently experience fragmented care, which increases the risk of negative outcomes. A recently proposed Integrated Multimorbidity Care Model aims to overcome many issues related to fragmented care. In the context of Joint Action CHRODIS-PLUS, an implementation methodology was developed for the care model, which is being piloted in five sites. We aim to (1) explain the methodology used to implement the care model and (2) describe how the pilot sites have adapted and applied the proposed methodology. The model is being implemented in Spain (Andalusia and Aragon), Lithuania (Vilnius and Kaunas), and Italy (Rome). Local implementation working groups at each site adapted the model to local needs, goals, and resources using the same methodological steps: (1) Scope analysis; (2) situation analysis—“strengths, weaknesses, opportunities, threats” (SWOT) analysis; (3) development and improvement of implementation methodology; and (4) final development of an action plan. This common implementation strategy shows how care models can be adapted according to local and regional specificities. Analysis of the common key outcome indicators at the post-implementation phase will help to demonstrate the clinical effectiveness, as well as highlight any difficulties in adapting a common Integrated Multimorbidity Care Model in different countries and clinical settings.

Background: The steady increase in type 2 diabetes prevalence and the availability of new antidiabetic drugs (AD) have risen the use of these drugs with a change in the patterns of specific drug utilization. The complexity of this treatment is due to successive treatment initiation, switching and addition in order to maintain glycaemic control. The aim of this study was to describe the utilization patterns of ADs at initiation, treatment addition, and switching profiles and to measure factors influencing persistence to therapy. Methods: Retrospective observational study. Data were retrieved from the Campania Regional Database for Medication Consumption. Population consisted of patients receiving at least one prescription of ADs between January 1 and December 31, 2016. We calculated time to treatment switching or add-on as median number of days and interquartile range (IQR). Persistence rates were estimated using the Kaplan–Meier method. We used Cox regression models to estimate the likelihood of non-persistence over 1 year of follow-up. Hazard ratios and 95% confidence intervals were calculated. Results: Of 14,679 patients, 86.9% started with monotherapy and 13.1% with combination therapy. Most common initial treatment was metformin in both monotherapy and combination therapy. First-line prescription of sulfonylurea was observed in 6.9% of patients aged 60–79 years and in 10.8% of patients aged ≥80 years. Patients starting with metformin showed fewer treatment modifications (10.4%) compared to patients initiating with sulfonylureas (35.2%). Newer ADs were utilized during treatment progression. Patients who initiated with sulfonylurea were approximately 70% more likely to discontinue treatment compared to those initiated on metformin. Oldest age group (≥80 years) was more likely to be non-persistent, and likelihood of non-persistence was highest in polymedicated patients. Patients changing therapy were more likely to be persistent. Conclusions: Our results show that treatment of T2D in Italy is consistent with clinical guidelines. Even if newer ADs were utilized during disease progression, they seem not to be preferred in patients with a higher comorbidity score, although these patients could benefit from this kind of treatment. Our study highlights patients’ characteristics that might help identify those who would benefit from counselling from their health-care practitioner on better AD usage.

Background Clinical outcomes among COVID-19 patients vary greatly with age and underlying comorbidities. We aimed to determine the demographic and clinical factors, particularly baseline chronic conditions, associated with an increased risk of severity in COVID-19 patients from a population-based perspective and using data from electronic health records (EHR). Methods Retrospective, observational study in an open cohort analyzing all 68,913 individuals (mean age 44.4 years, 53.2% women) with SARS-CoV-2 infection between 15 June and 19 December 2020 using exhaustive electronic health registries. Patients were followed for 30 days from inclusion or until the date of death within that period. We performed multivariate logistic regression to analyze the association between each chronic disease and severe infection, based on hospitalization and all-cause mortality. Results 5885 (8.5%) individuals showed severe infection and old age was the most influencing factor. Congestive heart failure (odds ratio -OR- men: 1.28, OR women: 1.39), diabetes (1.37, 1.24), chronic renal failure (1.31, 1.22) and obesity (1.21, 1.26) increased the likelihood of severe infection in both sexes. Chronic skin ulcers (1.32), acute cerebrovascular disease (1.34), chronic obstructive pulmonary disease (1.21), urinary incontinence (1.17) and neoplasms (1.26) in men, and infertility (1.87), obstructive sleep apnea (1.43), hepatic steatosis (1.43), rheumatoid arthritis (1.39) and menstrual disorders (1.18) in women were also associated with more severe outcomes. Conclusions Age and specific cardiovascular and metabolic diseases increased the risk of severe SARS-CoV-2 infections in men and women, whereas the effects of certain comorbidities are sex specific. Future studies in different settings are encouraged to analyze which profiles of chronic patients are at higher risk of poor prognosis and should therefore be the targets of prevention and shielding strategies.