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Abstract Background Primary Health Care (PHC) plays a crucial role in managing the COVID-19 pandemic, with only 8% of cases requiring hospitalization. However, PHC COVID-19 data often goes unnoticed on European government dashboards and in media discussions. This project aims to examine official information on PHC patient care during the COVID-19 pandemic in Europe, with specific objectives: (1) Describe PHC’s clinical pathways for acute COVID-19 cases, including long-term care facilities, (2) Describe PHC COVID-19 pandemic indicators, (3) Develop COVID-19 PHC activity indicators, (4) Explain PHC’s role in vaccination strategies, and (5) Create a PHC contingency plan for future pandemics. Methods A mixed-method study will employ two online questionnaires to gather retrospective PHC data on COVID-19 management and PHC involvement in vaccination strategies. Validation will occur through focus group discussions with medical and public health (PH) experts. A two-wave Delphi survey will establish a European PHC indicators dashboard for future pandemics. Additionally, a coordinated health system action plan involving PHC, secondary care, and PH will be devised to address future pandemic scenarios. Analysis: Quantitative data will be analysed using STATA v16.0 for descriptive and multivariate analyses. Qualitative data will be collected through peer-reviewed questionnaires and content analysis of focus group discussions. A Delphi survey and multiple focus groups will be employed to achieve consensus on PHC indicators and a common European health system response plan for future pandemics. The Eurodata research group involving researchers from 28 European countries support the development. Discussion While PHC manages most COVID-19 acute cases, data remains limited in many European countries. This study collects data from numerous countries, offering a comprehensive perspective on PHC’s role during the pandemic in Europe. It pioneers the development of a PHC dashboard and health system plan for pandemics in Europe. These results may prove invaluable in future pandemics. However, data may have biases due to key informants’ involvement and may not fully represent all European GP practices. PHC has a significant role in the management of the COVID-19 pandemic, as most of the cases are mild or moderate and only 8% needed hospitalization. However, PHC COVID-19 activity data is invisible on governments’ daily dashboards in Europe, often overlooked in media and public debates.

Background The COVID-19 vaccination campaign in several European countries involved collaboration between public health and Primary Health Care (PHC).Objective To highlight the role of PHC professionals in the COVID-19 vaccination rollout, specifically in terms of vaccine administration, communication and contributing to vaccination population coverage.Methods A descriptive retrospective study of the COVID-19 vaccination campaign across 28 European countries was conducted, covering data from December 2020 to November 2021. Data were collected by key informants recruited from each country, who were health professionals involved in their national vaccination campaigns. Utilising an ad-hoc semi-structured questionnaire, information was gathered on organisation, communication strategies, priority groups, vaccine types, and vaccination pathways in PHC.Results PHC participated in communication strategies in 10 out of 28 countries, and vaccination was voluntary in most of them. The priority groups for vaccination varied across Europe, and the availability of vaccines in PHC differed between countries within the European Union (EU) and non-EU countries. The BioNTech Pfizer vaccine was the most widely available vaccine in most countries, followed by Moderna and AstraZeneca. PHC administered COVID-19 vaccines to the population, being the nurses the most involved, followed by general practitioners. Vaccination appointments were available online in 18/28 or by phone in 15/28, direct appointments at health centres were available in 8/28. In several countries, healthcare professionals who administered vaccines were given extra compensation for their role.Conclusion PHC professionals played a crucial role in the successful distribution and administration of COVID-19 vaccines in European countries.

Background: The COVID-19 pandemic has necessitated changes in European healthcare systems, with a significant proportion of COVID-19 cases being managed on an outpatient basis in primary healthcare (PHC). To alleviate the burden on healthcare facilities, many European countries developed contact-tracing apps and symptom checkers to identify potential cases. As the pandemic evolved, the European Union introduced the Digital COVID-19 Certificate for travel, which relies on vaccination, recent recovery, or negative test results. However, the integration between these apps and PHC has not been thoroughly explored in Europe. Objective: To describe if governmental COVID-19 apps allowed COVID-19 patients to connect with PHC through their apps in Europe and to examine how the Digital COVID-19 Certificate was obtained. Methodology: Design and setting: Retrospective descriptive study in PHC in 30 European countries. An ad hoc, semi-structured questionnaire was developed to collect country-specific data on primary healthcare activity during the COVID-19 pandemic and the use of information technology tools to support medical care from 15 March 2020 to 31 August 2021. Key informants belong to the WONCA Europe network (World Organization of Family Doctors). The data were collected from relevant and reliable official sources, such as governmental websites and guidelines. Main outcome measures: Patient’s first contact with health system, governmental COVID-19 app (name and function), Digital COVID-19 Certification, COVID-19 app connection with PHC. Results: Primary care was the first point of care for suspected COVID-19 patients in 28 countries, and 24 countries developed apps to complement classical medical care. The most frequently developed app was for tracing COVID-19 cases (24 countries), followed by the Digital COVID-19 Certificate app (17 countries). Bulgaria, Italy, Serbia, North Macedonia, and Romania had interoperability between PHC and COVID-19 apps, and Poland and Romania’s apps considered social needs. Conclusions: COVID-19 apps were widely created during the first pandemic year. Contact tracing was the most frequent function found in the registered apps. Connection with PHC was scarcely developed. In future pandemics, connections between health system levels should be guaranteed to develop and implement effective strategies for managing diseases.

AbstractBackground Most COVID-19 patients were treated in primary health care (PHC) in Europe.Objectives To demonstrate the scope of PHC workflow during the COVID-19 pandemic emphasising similarities and differences of patient’s clinical pathways in Europe.Methods Descriptive, cross-sectional study with data acquired through a semi-structured questionnaire in PHC in 30 European countries, created ad hoc and agreed upon among all researchers who participated in the study. GPs from each country answered the approved questionnaire. Main variable: PHC COVID-19 acute clinical pathway. All variables were collected from each country as of September 2020.Results COVID-19 clinics in PHC facilities were organised in 8/30. Case detection and testing were performed in PHC in 27/30 countries. RT-PCR and lateral flow tests were performed in PHC in 23/30, free of charge with a medical prescription. Contact tracing was performed mainly by public health authorities. Mandatory isolation ranged from 5 to 14 days. Sick leave certification was given exclusively by GPs in 21/30 countries. Patient hotels or other resources to isolate patients were available in 12/30. Follow-up to monitor the symptoms and/or new complementary tests was made mainly by phone call (27/30). Chest X-ray and phlebotomy were performed in PHC in 18/30 and 23/30 countries, respectively. Oxygen and low-molecular-weight heparin were available in PHC (21/30).Conclusion In Europe PHC participated in many steps to diagnose, treat and monitor COVID-19 patients. Differences among countries might be addressed at European level for the management of future pandemics.

Abstract Background Possible cases of SARS-CoV-2 infection were diagnosed in primary care in Madrid, some of these cases had pneumonia. Most of the SARS-CoV-2 pneumonia published data came from hospitalised patients. This study set out to describe clinical characteristics of patients with SARS-CoV-2 pneumonia diagnosed in primary care across age groups and type of pneumonia. Methods Observational retrospective study obtaining clinical data from the electronic health records of patients who were followed-up by SARS-CoV-2 possible infection in a primary care practice in Madrid. All the cases were collected by in-person or remote consultation during the 10th March to the 7th of April. Exposure: Diagnosis of SARS-CoV-2 pneumonia by chest X-ray ordered by the GP. Main outcomes and measures: Symptoms of SARS-CoV-2 pneumonia, physical examination and diagnostic tests as a blood test, nasopharyngeal swab results for RT-PCR (Reverse transcriptase-polymerase chain reaction) and chest X-ray results. Results The overall SARS-CoV-2 pneumonias collected were 172 (female 87 [50.6%], mean age 60.5 years standard deviation [SD] 17.0). Comorbidities were body mass index ≥ 25 kg/m2 (90 [52.3%]), hypertension (83 [48.3%]), dyslipidaemia (68 [39.5%]) and diabetes (33 [19.2%]). The sample was stratified by age groups (

Abstract This study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity.

The co-occurrence of chronic diseases in the same person increases the risk of negative health events. Here authors show that grouping people based on their underlying disease patterns helps to identify homogeneous groups of people with similar needs and prognosis, facilitating personalized approaches.

Abstract Background The implementation of individual clinical practice guidelines in patients with multimorbidity often results in polypharmacy. Our aim was to analyse medication use according to longitudinal multimorbidity patterns (MP) and determine during a 5-year period (2012–16) which MP are associated with abnormal liver and kidney function in primary care patients over 65 years of age living in Catalonia. Methods Design: Longitudinal study (years 2012 to 2016) based on the electronic health records contained in Information System for Research in Primary Care database of the Catalan Institute of Health (SIDIAP). Variables: age, sex, MP, medication and polypharmacy (drug exposure obtained from the Pharmacy Invoice Registry). Medicines were classified in accordance with the Anatomical Therapeutic Chemical Classification System (ATC). Glomerular filtration rate was used to determine abnormal kidney function, and serum levels of alkaline phosphatase, alanine transaminase and gamma-glutamyl transpeptidase were used to diagnose abnormal liver function. Statistics: For medication use in MP, we calculated annual mean packages of each drug in each MP, and observed/expected ratios were obtained by dividing mean packages in the cluster by mean packages of the same drug in the overall population. Logistic regression models were fitted to estimate the association between MP at baseline and abnormal kidney and liver function tests during follow up. Results Nine hundred sixteen thousand six hundred nineteen patients were included, and 743,827 completed the follow up. We identified one polypharmacy profile per MP, and concluded that the most prescribed drugs in each pattern corresponded to the diseases overrepresented in that specific MP. The median of drugs ranged from 3 (Cluster 1 - Non-Specific) to 8 (Cluster 10 - Multisystem Pattern). Abnormal kidney function was most commonly observed in the Cluster 4 - Cardio-Circulatory and Renal (Odds Ratio [OR] 2.19; Confidence interval [CI] 95% 2.15–2.23) and Cluster 3 - Minority Metabolic Autoimmune-Inflammatory (OR 2.16; CI 95% 2.12–2.20) MP. A higher risk of abnormal liver function was observed in the Cluster 8 - Digestive (OR 3.39; CI 95% 3.30–3.49), and Cluster 4 - Cardio-Circulatory and Renal (OR 1.96; CI 95% 1.91–2.02) MP. Conclusions A higher risk of abnormal kidney and liver function was observed in specific MP. The long-term characterisation of MP and polypharmacy illustrates the burden of chronic multimorbidity and polypharmacy in the elderly population.

Abstract Background Older adults suffer from various chronic conditions which make them particularly vulnerable. The proper management of multiple drug use is therefore crucial. The aim of our study was to describe drug prescription and medication patterns in this population. Methods A cross-sectional study in Barcelona (Spain) using electronic health records from 50 primary healthcare centres. Participants were aged 65 to 94 years, presenting multimorbidity (≥2 chronic diseases), and had been prescribed at least 1 drug for 6 months or longer during 2009. We calculated the prevalence of prescribed drugs and identified medication patterns using multiple correspondence analysis and k-means clustering. Analyses were stratified by sex and age (65–79, 80–94 years). Results We studied 164,513 patients (66.8% women) prescribed a median of 4 drugs (interquartile range [IQR] = 3–7) in the 65–79 age-group and 6 drugs (IQR = 4–8) in the 80–94 age-group. A minimum of 45.9% of patients aged 65–79 years, and 61.8% of those aged 80–94 years, were prescribed 5 or more drugs. We identified 6 medication patterns, a non-specific one and 5 encompassing 8 anatomical groups (alimentary tract and metabolism, blood, cardiovascular, dermatological, musculo-skeletal, neurological, respiratory, and sensory organ). Conclusions Drug prescription is widespread among the elderly. Six medication patterns were identified, 5 of which were related to one or more anatomical group, with associations among drugs from different systems. Overall, guidelines do not accurately reflect the situation of the elderly multimorbid, new strategies for managing multiple drug uses are needed to optimize prescribing in these patients.

BackgroundPatients with COVID-19 are follow-up in primary care and long COVID is scarcely defined. The study aim was to describe SARS-CoV-2 pneumonia and cut-offs for defining long COVID in primary care follow-up patients.MethodsA retrospective observational study in primary care in Madrid, Spain, was conducted. Data was collected during 6 months (April to September) in 2020, during COVID-19 first wave, from patients ≥ 18 years with SARS-CoV-2 pneumonia diagnosed. Variables: sociodemographic, comorbidities, COVID-19 symptoms and complications, laboratory test and chest X-ray. Descriptive statistics were used, mean (standard deviation (SD)) and medians (interquartile range (IQR)) respectively. Differences were detected applying X2 test, Student's T-test, ANOVA, Wilcoxon-Mann-Whitney or Kruskal-Wallis depending on variable characteristics.Results155 patients presented pneumonia in day 7.8 from the onset (79.4% were hospitalized, median length of 7.0 days (IQR: 3.0, 13.0)). After discharge, the follow-up lasted 54.0 median days (IQR 42.0, 88.0) and 12.2 mean (SD 6.4) phone calls were registered per patient. The main symptoms and their duration were: cough (41.9%, 12 days), dyspnoea (31.0%, 15 days), asthenia (26.5%, 21 days). Different cut-off points were applied for long COVID and week 4 was considered the best milestone (28.3% of the sample still had symptoms after week 4) versus week 12 (8.3%). Patients who still had symptoms >4 weeks follow-up took place over 81.0 days (IQR: 50.5, 103.0), their symptoms were more prevalent and lasted longer than those ≤ 4 weeks: cough (63.6% 30 days), dyspnoea (54.6%, 46 days), and asthenia (56.8%, 29 days). Embolism was more frequent in patients who still had symptoms >4 weeks than those with symptoms ≤4 weeks (9.1% vs 1.8%, p value 0.034).ConclusionMost patients with SARS-CoV-2 pneumonia recovered during the first 4 weeks from the beginning of the infection. The cut-off point to define long COVID, as persisting symptoms, should be between 4 to 12 weeks from the onset of the symptoms.

An amendment to this paper has been published and can be accessed via the original article.

Abstract Background The purpose of this study was to ascertain multimorbidity patterns using a non-hierarchical cluster analysis in adult primary patients with multimorbidity attended in primary care centers in Catalonia. Methods Cross-sectional study using electronic health records from 523,656 patients, aged 45–64 years in 274 primary health care teams in 2010 in Catalonia, Spain. Data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), a population database. Diagnoses were extracted using 241 blocks of diseases (International Classification of Diseases, version 10). Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex. Results The 408,994 patients who met multimorbidity criteria were included in the analysis (mean age, 54.2 years [Standard deviation, SD: 5.8], 53.3% women). Six multimorbidity patterns were obtained for each sex; the three most prevalent included 68% of the women and 66% of the men, respectively. The top cluster included coincident diseases in both men and women: Metabolic disorders, Hypertensive diseases, Mental and behavioural disorders due to psychoactive substance use, Other dorsopathies, and Other soft tissue disorders. Conclusion Non-hierarchical cluster analysis identified multimorbidity patterns consistent with clinical practice, identifying phenotypic subgroups of patients.

Abstract Background Multimorbidity is the coexistence of more than two chronic diseases in the same individual; however, there is no consensus about the best definition. In addition, few studies have described the variability of multimorbidity patterns over time. The aim of this study was to identify multimorbidity patterns and their variability over a 6-year period in patients older than 65 years attended in primary health care. Methods A cohort study with yearly cross-sectional analysis of electronic health records from 50 primary health care centres in Barcelona. Selected patients had multimorbidity and were 65 years of age or older in 2009. Diagnoses (International Classification of Primary Care, second edition) were extracted using O’Halloran criteria for chronic diseases. Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex and age group (65–79 and ≥80 years) at the beginning of the study period. Results Analysis of 2009 electronic health records from 190,108 patients with multimorbidity (59.8% women) found a mean age of 71.8 for the 65–79 age group and 84.16 years for those over 80 (Standard Deviation [SD] 4.35 and 3.46, respectively); the median number of chronic diseases was seven (Interquartil range [IQR] 5–10). We obtained 6 clusters of multimorbidity patterns (1 nonspecific and 5 specifics) in each group, being the specific ones: Musculoskeletal, Endocrine-metabolic, Digestive/Digestive-respiratory, Neurological, and Cardiovascular patterns. A minimum of 42.5% of the sample remained in the same pattern at the end of the study, reflecting the stability of these patterns. Conclusions This study identified six multimorbidity patterns per each group, one nonnspecific pattern and five of them with a specific pattern related to an organic system. The multimorbidity patterns obtained had similar characteristics throughout the study period. These data are useful to improve clinical management of each specific subgroup of patients showing a particular multimorbidity pattern.

Objectives The aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.Design A cross-sectional study was conducted based on data from electronic health records.Setting 284 primary healthcare centres in Catalonia, Spain (2012).Participants 916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measures We extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.Results Multimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.Conclusions Multimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.