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      Trends in diabetes-related complications in Singapore, 2013–2020: A registry-based study

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          Abstract

          Background

          Diabetes mellitus (DM) is a growing global health problem. In Singapore, the prevalence of Type 2 DM is rising, but comprehensive information about trends in DM-related complications is lacking.

          Objectives

          We utilized the Singapore Health Services (SingHealth) diabetes registry (SDR) to assess trends in DM micro and macro-vascular complications at the population level, explore factors influencing these trends.

          Methods

          We studied trends for ten DM-related complications: ischemic heart disease (IHD), acute myocardial infarction (AMI), peripheral arterial disease (PAD) and strokes, diabetic eye complications, nephropathy, neuropathy, diabetic foot, major and minor lower extremity amputation (LEA). The complications were determined through clinical coding in hospital (inpatient and outpatient) and primary care settings within the SingHealth cluster. We described event rates for the complications in 4 age-bands. Joinpoint regression was used to identify significant changes in trends.

          Results

          Among 222,705 patients studied between 2013 and 2020. 48.6% were female, 70.7% Chinese, 14.7% Malay and 10.6% Indian with a mean (SD) age varying between 64.6 (12.5) years in 2013 and 65.7 (13.2) years in 2020. We observed an increase in event rates in IHD, PAD, stroke, diabetic eye complications nephropathy, and neuropathy. Joinpoints was observed for IHD and PAD between 2016 to 2018, with subsequent plateauing of event rates. Major and minor LEA event rates decreased through the study period.

          Conclusion

          We found that DM and its complications represent an important challenge for healthcare in Singapore. Improvements in the trends of DM macrovascular complications were observed. However, trends in DM microvascular complications remain a cause for concern.

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          Most cited references42

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          Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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            Permutation tests for joinpoint regression with applications to cancer rates.

            The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates. Copyright 2000 John Wiley & Sons, Ltd.
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              Global trends in diabetes complications: a review of current evidence

              In recent decades, large increases in diabetes prevalence have been demonstrated in virtually all regions of the world. The increase in the number of people with diabetes or with a longer duration of diabetes is likely to alter the disease profile in many populations around the globe, particularly due to a higher incidence of diabetes-specific complications, such as kidney failure and peripheral arterial disease. The epidemiology of other conditions frequently associated with diabetes, including infections and cardiovascular disease, may also change, with direct effects on quality of life, demands on health services and economic costs. The current understanding of the international burden of and variation in diabetes-related complications is poor. The available data suggest that rates of myocardial infarction, stroke and amputation are decreasing among people with diabetes, in parallel with declining mortality. However, these data predominantly come from studies in only a few high-income countries. Trends in other complications of diabetes, such as end-stage renal disease, retinopathy and cancer, are less well explored. In this review, we synthesise data from population-based studies on trends in diabetes complications, with the objectives of: (1) characterising recent and long-term trends in diabetes-related complications; (2) describing regional variation in the excess risk of complications, where possible; and (3) identifying and prioritising gaps for future surveillance and study.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Project administrationRole: Validation
                Role: Formal analysisRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: ValidationRole: Visualization
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 October 2022
                2022
                : 17
                : 10
                : e0275920
                Affiliations
                [1 ] Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
                [2 ] Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
                University of Campania Luigi Vanvitelli: Universita degli Studi della Campania Luigi Vanvitelli, ITALY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ NNMS, GHL and SYC also contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-4518-0477
                https://orcid.org/0000-0002-7591-3141
                Article
                PONE-D-22-22825
                10.1371/journal.pone.0275920
                9553054
                36219616
                b2b617c4-e93a-4fe6-873b-aa25c251768f
                © 2022 Tan et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 August 2022
                : 26 September 2022
                Page count
                Figures: 1, Tables: 3, Pages: 18
                Funding
                Funded by: Agency for Science, Technology and Research (A*STAR)
                Award ID: H19/01/a0/023
                Award Recipient :
                This research is supported by the Agency for Science, Technology and Research (A*STAR), Singapore under its Industry Alignment Pre-Positioning Fund (Grant No. H19/01/a0/023 – Diabetes Clinic of the Future). The funder supported the maintenance of the SingHealth Diabetes Registry for financial year 2022. The funder was not involved in the design of the study; the collection, analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.
                Categories
                Research Article
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Medical Conditions
                Metabolic Disorders
                Diabetes Mellitus
                People and Places
                Geographical Locations
                Asia
                Singapore
                Medicine and Health Sciences
                Nephrology
                Renal Diseases
                Chronic Kidney Disease
                Biology and Life Sciences
                Anatomy
                Head
                Eyes
                Medicine and Health Sciences
                Anatomy
                Head
                Eyes
                Biology and Life Sciences
                Anatomy
                Ocular System
                Eyes
                Medicine and Health Sciences
                Anatomy
                Ocular System
                Eyes
                Medicine and Health Sciences
                Medical Conditions
                Cardiovascular Diseases
                Coronary Heart Disease
                Medicine and Health Sciences
                Cardiology
                Cardiovascular Medicine
                Cardiovascular Diseases
                Coronary Heart Disease
                Medicine and Health Sciences
                Vascular Medicine
                Coronary Heart Disease
                Medicine and Health Sciences
                Health Care
                Patients
                Inpatients
                Medicine and Health Sciences
                Health Care
                Patients
                Outpatients
                Medicine and Health Sciences
                Medical Conditions
                Cerebrovascular Diseases
                Stroke
                Medicine and Health Sciences
                Neurology
                Cerebrovascular Diseases
                Stroke
                Medicine and Health Sciences
                Vascular Medicine
                Stroke
                Custom metadata
                Data cannot be shared publicly because of SingHealth Cluster Data Policy on data sharing restrictions. Data are available from the SingHealth Diabetes Registry Disease Registry Committee for researchers who meet the criteria for access to confidential data. The criteria include Institutional Review Board (IRB) approval, data use approval and a Research Collaboration Agreement. The point of contact for the SingHealth Diabetes Registry Disease Registry Committee is Mr Alvin Chia Yeow Meng (Email: alvin.chia.y.m@ 123456singhealth.com.sg contact number: +65 6705 5503).

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