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      The impact of patient registration on utilisation and quality of care: a propensity score matching and staggered difference-in-differences analysis of a cohort of 16,775 people with type 2 diabetes

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          Abstract

          Background

          In 2012, Luxembourg introduced a Referring Doctor (RD) policy, whereby patients voluntarily register with a primary care practitioner, who coordinates patients’ health care and ensures optimal follow-up. We contribute to the limited evidence base on patient registration by evaluating the effects of the RD policy.

          Methods

          We used data on 16,775 people with type 2 diabetes on oral medication (PWT2D), enrolled with the Luxembourg National Fund from 2010 to 2018. We examined the utilisation of primary and specialist outpatient care, quality of care process indicators, and reimbursed prescribed medicines over the short- (until 2015) and medium-term (until 2018). We used propensity score matching to identify comparable groups of patients with and without an RD. We applied difference-in-differences methods that accounted for patients’ registration with an RD in different years.

          Results

          There was low enrolment of PWT2D in the RD programme. The differences-in-differences parallel trends assumption was not met for: general practitioner (GP) consultations, GP home visits (medium-term), HbA1c test (short-term), complete cholesterol test (short-term), kidney function (urine) test (short-term), and the number of repeat prescribed cardiovascular system medicines (short-term). There was a statistically significant increase in the number of: HbA1c tests (medium-term: 0.09 (95% CI: 0.01 to 0.18)); kidney function (blood) tests in the short- (0.10 (95% CI: 0.01 to 0.19)) and medium-term (0.11 (95% CI: 0.03 to 0.20)); kidney function (urine) tests (medium-term: 0.06 (95% CI: 0.02 to 0.10)); repeat prescribed medicines in the short- (0.19 (95% CI: 0.03 to 0.36)) and medium-term (0.18 (95% CI: 0.02 to 0.34)); and repeat prescribed cardiovascular system medicines (medium-term: 0.08 (95% CI: 0.01 to 0.15)). Sensitivity analyses also revealed increases in kidney function (urine) tests (short-term: 0.07 (95% CI: 0.03 to 0.11)) and dental consultations (short-term: 0.06, 95% CI: 0.00 to 0.11), and decreases in specialist consultations (short-term: -0.28, 95% CI: -0.51 to -0.04; medium-term: -0.26, 95% CI: -0.49 to -0.03).

          Conclusions

          The RD programme had a limited effect on care quality indicators and reimbursed prescribed medicines for PWT2D. Future research should extend the analysis beyond this cohort and explore data linkage to include clinical outcomes and socio-economic characteristics.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12875-024-02505-2.

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          Most cited references29

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          Matching methods for causal inference: A review and a look forward.

          When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
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            Type 2 diabetes mellitus

            Type 2 diabetes mellitus (T2DM) is an expanding global health problem, closely linked to the epidemic of obesity. Individuals with T2DM are at high risk for both microvascular complications (including retinopathy, nephropathy and neuropathy) and macrovascular complications (such as cardiovascular comorbidities), owing to hyperglycaemia and individual components of the insulin resistance (metabolic) syndrome. Environmental factors (for example, obesity, an unhealthy diet and physical inactivity) and genetic factors contribute to the multiple pathophysiological disturbances that are responsible for impaired glucose homeostasis in T2DM. Insulin resistance and impaired insulin secretion remain the core defects in T2DM, but at least six other pathophysiological abnormalities contribute to the dysregulation of glucose metabolism. The multiple pathogenetic disturbances present in T2DM dictate that multiple antidiabetic agents, used in combination, will be required to maintain normoglycaemia. The treatment must not only be effective and safe but also improve the quality of life. Several novel medications are in development, but the greatest need is for agents that enhance insulin sensitivity, halt the progressive pancreatic β-cell failure that is characteristic of T2DM and prevent or reverse the microvascular complications. For an illustrated summary of this Primer, visit: http://go.nature.com/V2eGfN.
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              Difference-in-Differences with multiple time periods

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                Author and article information

                Contributors
                valerie.moran@lih.lu , valerie.moran@liser.lu
                Journal
                BMC Prim Care
                BMC Prim Care
                BMC Primary Care
                BioMed Central (London )
                2731-4553
                12 July 2024
                12 July 2024
                2024
                : 25
                : 254
                Affiliations
                [1 ]Socio-Economic and Environmental Health and Health Services Research Group, Department of Precision Health, Luxembourg Institute of Health, ( https://ror.org/012m8gv78) Strassen, Luxembourg
                [2 ]Socio-Economic and Environmental Health and Health Services Research Group, Living Conditions Department, Luxembourg Institute of Socio-Economic Research, ( https://ror.org/040jf9322) Belval, Luxembourg
                [3 ]Labour Market Department, Luxembourg Institute of Socio-Economic Research, ( https://ror.org/040jf9322) Belval, Luxembourg
                [4 ]Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, ( https://ror.org/00a0jsq62) London, UK
                [5 ]Nomenclature, Conventions, Analysis and Forecasting Department, National Health Fund, Luxembourg, Luxembourg
                [6 ]Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, ( https://ror.org/012m8gv78) Strassen, Luxembourg
                Article
                2505
                10.1186/s12875-024-02505-2
                11245844
                38997673
                550ae251-c9b3-4290-a87f-8c0049c9ad7a
                © 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
                : 4 January 2024
                : 1 July 2024
                Funding
                Funded by: Luxembourg National Research Fund
                Award ID: C19/BM/13723812
                Award ID: C19/BM/13723812
                Award ID: C19/BM/13723812
                Award ID: C19/BM/13723812
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                primary care reform,patient registration,type 2 diabetes,health insurance claims data,propensity score matching,staggered difference-in-differences

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