| 1. |
- Ares-Blanco, Sara, et al.
(författare)
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Primary care indicators for disease burden, monitoring and surveillance of COVID-19 in 31 European countries: Eurodata Study
- 2024
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Ingår i: EUROPEAN JOURNAL OF PUBLIC HEALTH. - 1101-1262 .- 1464-360X.
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Guisado-Clavero, Marina, et al.
(författare)
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The role of primary health care in long-term care facilities during the COVID-19 pandemic in 30 European countries : A retrospective descriptive study (Eurodata study)
- 2023
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Ingår i: Primary Health Care Research and Development. - 1463-4236. ; 24
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Tidskriftsartikel (refereegranskat)abstract
- Background and aim: Primary health care (PHC) supported long-term care facilities (LTCFs) in attending COVID-19 patients. The aim of this study is to describe the role of PHC in LTCFs in Europe during the early phase of the pandemic. Methods: Retrospective descriptive study from 30 European countries using data from September 2020 collected with an ad hoc semi-structured questionnaire. Related variables are SARS-CoV-2 testing, contact tracing, follow-up, additional testing, and patient care. Results: Twenty-six out of the 30 European countries had PHC involvement in LTCFs during the COVID-19 pandemic. PHC participated in initial medical care in 22 countries, while, in 15, PHC was responsible for SARS-CoV-2 test along with other institutions. Supervision of individuals in isolation was carried out mostly by LTCF staff, but physical examination or symptom's follow-up was performed mainly by PHC. Conclusion: PHC has participated in COVID-19 pandemic assistance in LTCFs in coordination with LTCF staff, public health officers, and hospitals.
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| 3. |
- Marengoni, Alessandra, et al.
(författare)
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Patterns of Multimorbidity in a Population-Based Cohort of Older People : Sociodemographic, Lifestyle, Clinical, and Functional Differences
- 2020
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Ingår i: The journals of gerontology. Series A, Biological sciences and medical sciences. - : Oxford University Press (OUP). - 1079-5006 .- 1758-535X. ; 75:4, s. 798-805
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Roso-Llorach, Albert, et al.
(författare)
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12-year evolution of multimorbidity patterns among older adults based on Hidden Markov Models
- 2022
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Ingår i: Aging. - : Impact Journals, LLC. - 1945-4589. ; 14:24, s. 9805-9817
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Vetrano, Davide L., et al.
(författare)
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Twelve-year clinical trajectories of multimorbidity in a population of older adults
- 2020
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Multimorbidity-the co-occurrence of multiple diseases-is associated to poor prognosis, but the scarce knowledge of its development over time hampers the effectiveness of clinical interventions. Here we identify multimorbidity clusters, trace their evolution in older adults, and detect the clinical trajectories and mortality of single individuals as they move among clusters over 12 years. By means of a fuzzy c-means cluster algorithm, we group 2931 people >= 60 years in five clinically meaningful multimorbidity clusters (52%). The remaining 48% are part of an unspecific cluster (i.e. none of the diseases are overrepresented), which greatly fuels other clusters at follow-ups. Clusters contribute differentially to the longitudinal development of other clusters and to mortality. We report that multimorbidity clusters and their trajectories may help identifying homogeneous groups of people with similar needs and prognosis, and assisting clinicians and health care systems in the personalization of clinical interventions and preventive strategies. 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.
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