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      Multimorbidity and patient experience with general practice: A national cross-sectional survey in Norway

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          Abstract

          Background

          Patient experience is an important indicator of the quality of healthcare. Patients with multimorbidity often face adverse health outcomes and increased healthcare utilisation. General practitioners play a crucial role in managing these patients. The main aim of our study was to perform an in-depth assessment of differences in patient-reported experience with general practice between patients living with chronic conditions and multimorbidity, and those with no chronic conditions.

          Methods

          We performed secondary analyses of a national survey of patient experience with general practice in 2021 (response rate 41.9%, n = 7,912). We described the characteristics of all survey respondents with no, one, two, and three or more self-reported chronic conditions. We assessed patient experience using four scales from the Norwegian patient experience with GP questionnaire (PEQ-GP). These scales were used as dependent variables in bivariate and multivariate analyses and for testing the measurement model, including confirmatory factor analysis and a multigroup CFA to assess measurement invariance. Sentiment and content analysis of free-text comments was also performed.

          Results

          Patients with chronic conditions consistently reported lower scores on the GP and GP practice experience scales, compared to those without chronic conditions. This pattern persisted even after adjustment for patient background variables. The strongest associations were found for the scale of “Enablement”, followed by the scales of “GP” and “Practice”. The subscale “Accessibility” did not correlate statistically significantly with any number of chronic conditions. The analysis of free-text comments echoed the quantitative results. Patients with multimorbidity stressed the importance of time spent on consultations, meeting the same GP, follow-up and relationship more often than patients with no chronic conditions. Our study also confirmed measurement invariance across patients with no chronic conditions and patients with multimorbidity, indicating that the observed differences in patient experience were a result of true differences, rather than artifacts of measurement bias.

          Conclusions

          The findings highlight the need for the healthcare system to provide customised support for patients with chronic conditions and multimorbidity. Addressing the specific needs of patients with multimorbidity is a critical step towards enhancing patient experience and the quality of care in general practice.

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          Most cited references34

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          Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance

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            Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.

            Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. In a cross-sectional study we extracted data on 40 morbidities from a database of 1,751,841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. 42·2% (95% CI 42·1-42·3) of all patients had one or more morbidities, and 23·2% (23·08-23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210,500 vs 194,996). Onset of multimorbidity occurred 10-15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9-11·2% in most deprived area vs 5·9%, 5·8%-6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59-6·90 for five or more disorders vs 1·95, 1·93-1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21-2·32 vs 1·08, 1·05-1·11). Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Scottish Government Chief Scientist Office. Copyright © 2012 Elsevier Ltd. All rights reserved.
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              Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research.

              Measurement invariance assesses the psychometric equivalence of a construct across groups or across time. Measurement noninvariance suggests that a construct has a different structure or meaning to different groups or on different measurement occasions in the same group, and so the construct cannot be meaningfully tested or construed across groups or across time. Hence, prior to testing mean differences across groups or measurement occasions (e.g., boys and girls, pretest and posttest), or differential relations of the construct across groups, it is essential to assess the invariance of the construct. Conventions and reporting on measurement invariance are still in flux, and researchers are often left with limited understanding and inconsistent advice. Measurement invariance is tested and established in different steps. This report surveys the state of measurement invariance testing and reporting, and details the results of a literature review of studies that tested invariance. Most tests of measurement invariance include configural, metric, and scalar steps; a residual invariance step is reported for fewer tests. Alternative fit indices (AFIs) are reported as model fit criteria for the vast majority of tests; χ(2) is reported as the single index in a minority of invariance tests. Reporting AFIs is associated with higher levels of achieved invariance. Partial invariance is reported for about one-third of tests. In general, sample size, number of groups compared, and model size are unrelated to the level of invariance achieved. Implications for the future of measurement invariance testing, reporting, and best practices are discussed.
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                Author and article information

                Contributors
                rebecka.norman@fhi.no
                Journal
                BMC Prim Care
                BMC Prim Care
                BMC Primary Care
                BioMed Central (London )
                2731-4553
                10 July 2024
                10 July 2024
                2024
                : 25
                : 249
                Affiliations
                Norwegian Institute of Public Health, ( https://ror.org/046nvst19) PO Box 222, Skøyen, Oslo, NO-0213 Norway
                Article
                2495
                10.1186/s12875-024-02495-1
                11238367
                38987692
                27eaf486-02b1-4140-8302-7d9290df5ec8
                © The Author(s) 2024

                Open Access This 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 26 October 2023
                : 26 June 2024
                Funding
                Funded by: Norwegian Institute of Public Health (FHI)
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                primary health care,physicians,gp,general practice,patient experiences,survey,psychometrics

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