7
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Changes in older people’s quality of life in the COVID-19 era: a population-based study in Finland

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose

          We investigated how quality of life (QoL) changed between 2018 and 2020, and how its related factors, i.e., communication with friends and family, loneliness, and sleeping difficulties changed amid the early-phase COVID-19 pandemic among Finnish older people.

          Methods

          This study utilizes data from a repeated cross-sectional, population-based FinSote survey in 2018 and 2020. Participants were community-dwelling people aged 75 years or older ( N = 9781 in 2018 and N = 9919 in 2020). QoL was assessed with the EUROHIS-QoL-8 scale. Changes in QoL-related factors were self-evaluated in 2020. Statistical methods included t test, Cohen’s D, and chi-square test. To identify potential risk groups, all analyses were stratified by socio-demographic features including sex, age, economic deprivation, living alone, and difficulties in Instrumental Activities of Daily Living (IADL).

          Results

          QoL improved slightly from 2018 to 2020 (means 3.68 and 3.81, respectively). Only those reporting economic deprivation demonstrated a slight decrease in QoL (3.24 vs. 3.14). Of respondents, 63% reported having less communication with friends and family, 42% having felt lonelier, and 20% having more sleeping difficulties amid the pandemic. Negative changes were more often reported by women, the oldest old, those living alone, reporting economic deprivation, or manifesting IADL difficulties.

          Conclusion

          Finnish older people’s QoL was not affected as much as expected amid the pandemic, although some population groups were, however, more susceptible to the negative effects of the pandemic on QoL-related factors. Results imply that various socio-demographic features may shape the effects of a global pandemic and its control measures on wellbeing.

          Related collections

          Most cited references35

          • Record: found
          • Abstract: found
          • Article: not found

          Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

          Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found

            Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China

            In December 2019, novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, China. The number of cases has increased rapidly but information on the clinical characteristics of affected patients is limited.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

              Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
                Bookmark

                Author and article information

                Contributors
                katja.ilmarinen@thl.fi
                Journal
                Qual Life Res
                Qual Life Res
                Quality of Life Research
                Springer International Publishing (Cham )
                0962-9343
                1573-2649
                4 September 2022
                4 September 2022
                : 1-11
                Affiliations
                GRID grid.14758.3f, ISNI 0000 0001 1013 0499, The Finnish Institute for Health and Welfare, ; PO Box 30, 00271 Helsinki, Finland
                Author information
                http://orcid.org/0000-0003-2054-1341
                Article
                3210
                10.1007/s11136-022-03210-2
                9440997
                36057938
                7094ec91-b3d1-4557-a5de-30c81b53f220
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 July 2022
                Funding
                Funded by: National Institute for Health and Welfare (THL)
                Categories
                Article

                Public health
                wellbeing,lifestyle,aging,coronavirus,resilience,public health
                Public health
                wellbeing, lifestyle, aging, coronavirus, resilience, public health

                Comments

                Comment on this article