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      Socioeconomic inequalities in primary-care and specialist physician visits: a systematic review

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          Abstract

          Background

          Utilization of primary-care and specialist physicians seems to be associated differently with socioeconomic status (SES). This review aims to summarize and compare the evidence on socioeconomic inequalities in consulting primary-care or specialist physicians in the general adult population in high-income countries.

          Methods

          We carried out a systematic search across the most relevant databases (Web of Science, Medline) and included all studies, published since 2004, reporting associations between SES and utilization of primary-care and/or specialist physicians. In total, 57 studies fulfilled the eligibility criteria.

          Results

          Many studies found socioeconomic inequalities in physician utilization, but inequalities were more pronounced in visiting specialists than primary-care physicians. The results of the studies varied strongly according to the operationalization of utilization, namely whether a physician was visited (probability) or how often a physician was visited (frequency). For probabilities of visiting primary-care physicians predominantly no association with SES was found, but frequencies of visits were higher in the most disadvantaged. The most disadvantaged often had lower probabilities of visiting specialists, but in many studies no link was found between the number of visits and SES.

          Conclusion

          This systematic review emphasizes that inequalities to the detriment of the most deprived is primarily a problem in the probability of visiting specialist physicians. Healthcare policy should focus first off on effective access to specialist physicians in order to tackle inequalities in healthcare.

          PROSPERO registration number

          CRD42019123222.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12939-020-01375-1.

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          Most cited references51

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          Testing a tool for assessing the risk of bias for nonrandomized studies showed moderate reliability and promising validity.

          To develop and validate a new risk-of-bias tool for nonrandomized studies (NRSs). We developed the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). A validation process with 39 NRSs examined the reliability (interrater agreement), validity (the degree of correlation between the overall assessments of RoBANS and Methodological Index for Nonrandomized Studies [MINORS], obtained by plotting the overall risk of bias relative to effect size and funding source), face validity with eight experts, and completion time for the RoBANS approach. RoBANS contains six domains: the selection of participants, confounding variables, the measurement of exposure, the blinding of the outcome assessments, incomplete outcome data, and selective outcome reporting. The interrater agreement of the RoBANS tool except the measurement of exposure and selective outcome reporting domains ranged from fair to substantial. There was a moderate correlation between the overall risks of bias determined using RoBANS and MINORS. The observed differences in effect sizes and funding sources among the assessed studies were not correlated with the overall risk of bias in these studies. The mean time required to complete RoBANS was approximately 10 min. The external experts who were interviewed evaluated RoBANS as a "fair" assessment tool. RoBANS shows moderate reliability, promising feasibility, and validity. The further refinement of this tool and larger validation studies are required. Copyright © 2013 Elsevier Inc. All rights reserved.
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            The relationship between self-rated health and objective health status: a population-based study

            Background Self-rated health (SRH), a subjective assessment of health status, is extensively used in the public health field. However, whether SRH can reflect the objective health status is still debatable. We aim to reveal the relationship between SRH and objective health status in the general population. Methods We assessed the relationship between SRH and objective health status by examining the prevalence of diseases, laboratory parameters, and some health-related factors in different SRH groups. Data were collected from 18,000 residents randomly sampled from the general population in five cities of China (3,600 in each city). SRH was assessed by a single-item health measure with five options: “very good,” “good,” “fair,” “bad,” and “very bad.” The differences in prevalence of diseases, laboratory parameters, and health-related factors between the “healthy” (very good plus good), “relatively healthy” (fair), and “unhealthy” (bad plus very bad) groups were examined. The odds ratios (ORs) referenced by the healthy group were calculated using logistic regression analysis. Results The prevalence of all diseases was associated with poorer SRH. The tendency was more prominent in cardio-cerebral vascular diseases, visual impairment, and mental illnesses with larger ORs. Residents with abnormalities in laboratory parameters tended to have poorer SRH, with ORs ranging from 1.62 (for triglyceride) to 3.48 (for hemoglobin among men) in a comparison of the unhealthy and healthy groups. Most of the health-related factors regarded as risks were associated with poorer SRH. Among them, life and work pressure, poor spiritual status, and poor quality of interpersonal relationships were the most significant factors. Conclusions SRH is consistent with objective health status and can serve as a global measure of health status in the general population.
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              Self-reported utilization of health care services: improving measurement and accuracy.

              Self-report is often used to estimate health care utilization. However, the accuracy of such data is of paramount concern. The authors conducted a systematic review of 42 studies that evaluated the accuracy of self-report utilization data, where utilization was defined as a visit to a clinical provider or entity. They also present a broad conceptual model that identifies major issues to consider when collecting, analyzing, and reporting such data. The results show that self-report data are of variable accuracy. Factors that affect accuracy include (1) sample population and cognitive abilities, (2) recall time frame, (3) type of utilization, (4) utilization frequency, (5) questionnaire design, (6) mode of data collection, and (7) memory aids and probes.
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                Author and article information

                Contributors
                sara.lueckmann@uk-halle.de
                Journal
                Int J Equity Health
                Int J Equity Health
                International Journal for Equity in Health
                BioMed Central (London )
                1475-9276
                10 February 2021
                10 February 2021
                2021
                : 20
                : 58
                Affiliations
                [1 ]GRID grid.9018.0, ISNI 0000 0001 0679 2801, Institute of Medical Sociology, Medical Faculty, , Martin Luther University Halle-Wittenberg, ; Magdeburger Str. 8, 06112 Halle (Saale), Germany
                [2 ]GRID grid.461820.9, ISNI 0000 0004 0390 1701, University Hospital Halle (Saale), ; Ernst-Grube-Str. 40, 06120 Halle (Saale), Germany
                [3 ]GRID grid.13652.33, ISNI 0000 0001 0940 3744, Division of Social Determinants of Health, Department of Epidemiology and Health Monitoring, , Robert Koch Institute, ; Berlin, Germany
                [4 ]GRID grid.8842.6, ISNI 0000 0001 2188 0404, Department of Public Health, , Brandenburg University of Technology Cottbus-Senftenberg, ; Senftenberg, Germany
                [5 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, Institute of Medical Sociology, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                Author information
                http://orcid.org/0000-0003-3025-8937
                Article
                1375
                10.1186/s12939-020-01375-1
                7874661
                33568126
                f24e7a35-4b3c-481d-b1c3-33a44f362f23
                © The Author(s) 2021

                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/. 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
                : 1 October 2020
                : 28 December 2020
                Funding
                Funded by: Universitätsklinikum Halle (Saale)
                Categories
                Systematic Review
                Custom metadata
                © The Author(s) 2021

                Health & Social care
                social inequalities,socioeconomic status,primary health care,access to health care

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