Primary care indicators for disease burden, monitoring and surveillance of COVID-19 in 31 European countries: Eurodata Study.

BACKGROUND: During the COVID-19 pandemic, the majority of patients received ambulatory treatment, highlighting the importance of primary health care (PHC). However, there is limited knowledge regarding PHC workload in Europe during this period. The utilization of COVID-19 PHC indicators could facilitate the efficient monitoring and coordination of the pandemic response. The objective of this study is to describe PHC indicators for disease surveillance and monitoring of COVID-19's impact in Europe. METHODS: Descriptive, cross-sectional study employing data obtained through a semi-structured ad hoc questionnaire, which was collectively agreed upon by all participants. The study encompasses PHC settings in 31 European countries from March 2020 to August 2021. Key-informants from each country answered the questionnaire. Main outcome: the identification of any indicator used to describe PHC COVID-19 activity. RESULTS: Out of the 31 countries surveyed, data on PHC information were obtained from 14. The principal indicators were: total number of cases within PHC (Belarus, Cyprus, Italy, Romania and Spain), number of follow-up cases (Croatia, Cyprus, Finland, Spain and Turkey), GP's COVID-19 tests referrals (Poland), proportion of COVID-19 cases among respiratory illnesses consultations (Norway and France), sick leaves issued by GPs (Romania and Spain) and examination and complementary tests (Cyprus). All COVID-19 cases were attended in PHC in Belarus and Italy. CONCLUSIONS: The COVID-19 pandemic exposes a crucial deficiency in preparedness for infectious diseases in European health systems highlighting the inconsistent recording of indicators within PHC organizations. PHC standardized indicators and public data accessibility are urgently needed, conforming the foundation for an effective European-level health services response framework against future pandemics.

Open data for monitoring COVID-19 in Spain: Descriptive study.

BACKGROUND: The indicators of the pandemic have been based on the total number of diagnosed cases of COVID-19, the number of people hospitalized or in intensive care units, and deaths from the infection. The aim of this study is to describe the available data on diagnostic tests, health service used for the diagnosis of COVID-19, case detection and monitoring. METHOD: Descriptive study with review of official data available on the websites of the Spanish health councils corresponding to 17 Autonomous Communities, 2 Autonomous cities and the Ministry of Health. The variables collected refer to contact tracing, technics for diagnosis, use of health services and follow-up. RESULTS: All regions of Spain show data on diagnosed cases of COVID-19 and deaths. Hospitalized cases and intensive care admissions are shown in all regions except the Balearic Islands. Diagnostic tests for COVID-19 have been registered in all regions except Madrid region and Extremadura, with scarcely information on what type of test has been performed (present in 7 CCAA), requesting service and study of contacts. CONCLUSIONS: The information available on the official websites of the Health Departments of the different regions of Spain are heterogeneous. Data from the use of health service or workload in Primary Care, Emergency department or Out of hours services are almost non-existent.

12-year evolution of multimorbidity patterns among older adults based on Hidden Markov Models.

BACKGROUND: The evolution of multimorbidity patterns during aging is still an under-researched area. We lack evidence concerning the time spent by older adults within one same multimorbidity pattern, and their transitional probability across different patterns when further chronic diseases arise. The aim of this study is to fill this gap by exploring multimorbidity patterns across decades of age in older adults, and longitudinal dynamics among these patterns. METHODS: Longitudinal study based on the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) on adults ≥60 years (N=3,363). Hidden Markov Models were applied to model the temporal evolution of both multimorbidity patterns and individuals' transitions over a 12-year follow-up. FINDINGS: Within the study population (mean age 76.1 years, 66.6% female), 87.2% had ≥2 chronic conditions at baseline. Four longitudinal multimorbidity patterns were identified for each decade. Individuals in all decades showed the shortest permanence time in an Unspecific pattern lacking any overrepresented diseases (range: 4.6-10.9 years), but the pattern with the longest permanence time varied by age. Sexagenarians remained longest in the Psychiatric-endocrine and sensorial pattern (15.4 years); septuagenarians in the Neuro-vascular and skin-sensorial pattern (11.0 years); and octogenarians and beyond in the Neuro-sensorial pattern (8.9 years). Transition probabilities varied across decades, sexagenarians showing the highest levels of stability. INTERPRETATION: Our findings highlight the dynamism and heterogeneity underlying multimorbidity by quantifying the varying permanence times and transition probabilities across patterns in different decades. With increasing age, older adults experience decreasing stability and progressively shorter permanence time within one same multimorbidity pattern.

Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models.

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.

Patterns of Multimorbidity in a Population-Based Cohort of Older People: Sociodemographic, Lifestyle, Clinical, and Functional Differences.

BACKGROUND: The aim of this study is to identify clusters of older persons based on their multimorbidity patterns and to analyze differences among clusters according to sociodemographic, lifestyle, clinical, and functional characteristics. METHODS: We analyzed data from the Swedish National Study on Aging and Care in Kungsholmen on 2,931 participants aged 60 years and older who had at least two chronic diseases. Participants were clustered by the fuzzy c-means cluster algorithm. A disease was considered to be associated with a given cluster when the observed/expected ratio was ≥2 or the exclusivity was ≥25%. RESULTS: Around half of the participants could be classified into five clinically meaningful clusters: respiratory and musculoskeletal diseases (RESP-MSK) 15.7%, eye diseases and cancer (EYE-CANCER) 10.7%, cognitive and sensory impairment (CNS-IMP) 10.6%, heart diseases (HEART) 9.3%, and psychiatric and respiratory diseases (PSY-RESP) 5.4%. Individuals in the CNS-IMP cluster were the oldest, with the worst function and more likely to live in a nursing home; those in the HEART cluster had the highest number of co-occurring diseases and drugs, and they exhibited the highest mean values of serum creatinine and C-reactive protein. The PSY-RESP cluster was associated with higher levels of alcoholism and neuroticism. The other half of the cohort was grouped in an unspecific cluster, which was characterized by gathering the youngest individuals, with the lowest number of co-occurring diseases, and the best functional and cognitive status. CONCLUSIONS: The identified multimorbidity patterns provide insight for setting targets for secondary and tertiary preventative interventions and for designing care pathways for multimorbid older people.