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      Effect of medication adherence on long-term all-cause-mortality and hospitalization for cardiovascular disease in 65,067 newly diagnosed type 2 diabetes patients

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          Abstract

          This study determined the effects of anti-diabetic medication adherence on the long-term all-cause mortality and hospitalization for cerebrovascular disease and myocardial infarction among newly diagnosed patients. The study used retrospective cohort from the National Health Insurance Service. Study participants were 65,076 newly diagnosed type 2 diabetes mellitus patients aged ≥40 years. The medication adherence was evaluated from the proportion of days covered (PDC) between 2006 and 2007. Outcome variables were mortality, newly diagnosed cerebrovascular disease (CVD) and myocardial infarction (MI) in 2008–2017. Cox-proportional hazard regression analysis was performed. After adjusting for sex, age, monthly contribution, insurance type, medical institution type, Charlson comorbidity index score, disability, hypertension, and active ingredients of oral hypoglycemic agents, the adjusted hazard ratio (aHR) for all-cause-mortality of the lowest PDC group (<0.20) was 1.45 (95% confidence interval [CI] = 1.36–1.54) as compared to the highest PDC (≥0.8). The aHR for all-cause-mortality associated with PDC levels of 0.60–0.79, 0.40–0.59, and 0.20–0.39 were 1.19, 1.26, and 1.34, respectively ( P trend < 0.001). Compared to the highest PDC group, diabetic patients with the lowest PDC had elevated risk for CVD (aHR = 1.41; 95% CI = 1.30–1.52; P trend < 0.001). Improving anti-diabetic medication adherence among newly diagnosed type 2 diabetes mellitus patients is essential to the reduce risk for cardiovascular disease and long-term all-cause mortality.

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          Medication adherence: its importance in cardiovascular outcomes.

          Medication adherence usually refers to whether patients take their medications as prescribed (eg, twice daily), as well as whether they continue to take a prescribed medication. Medication nonadherence is a growing concern to clinicians, healthcare systems, and other stakeholders (eg, payers) because of mounting evidence that it is prevalent and associated with adverse outcomes and higher costs of care. To date, measurement of patient medication adherence and use of interventions to improve adherence are rare in routine clinical practice. The goals of the present report are to address (1) different methods of measuring adherence, (2) the prevalence of medication nonadherence, (3) the association between nonadherence and outcomes, (4) the reasons for nonadherence, and finally, (5) interventions to improve medication adherence.
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            Impact of medication adherence on hospitalization risk and healthcare cost.

            The objective of this study was to evaluate the impact of medication adherence on healthcare utilization and cost for 4 chronic conditions that are major drivers of drug spending: diabetes, hypertension, hypercholesterolemia, and congestive heart failure. The authors conducted a retrospective cohort observation of patients who were continuously enrolled in medical and prescription benefit plans from June 1997 through May 1999. Patients were identified for disease-specific analysis based on claims for outpatient, emergency room, or inpatient services during the first 12 months of the study. Using an integrated analysis of administrative claims data, medical and drug utilization were measured during the 12-month period after patient identification. Medication adherence was defined by days' supply of maintenance medications for each condition. The study consisted of a population-based sample of 137,277 patients under age 65. Disease-related and all-cause medical costs, drug costs, and hospitalization risk were measured. Using regression analysis, these measures were modeled at varying levels of medication adherence. For diabetes and hypercholesterolemia, a high level of medication adherence was associated with lower disease-related medical costs. For these conditions, higher medication costs were more than offset by medical cost reductions, producing a net reduction in overall healthcare costs. For diabetes, hypercholesterolemia, and hypertension, cost offsets were observed for all-cause medical costs at high levels of medication adherence. For all 4 conditions, hospitalization rates were significantly lower for patients with high medication adherence. For some chronic conditions, increased drug utilization can provide a net economic return when it is driven by improved adherence with guidelines-based therapy.
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              Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea

              Data resource basics The National Health Information Database (NHID) is a public database on health care utilization, health screening, socio-demographic variables, and mortality for the whole population of South Korea, formed by the National Health Insurance Service. The population included in the data is over 50 million, and the participation rate in the health screening programs was 74.8% in 2014. The NHID covers data between 2002 and 2014. Those insured by NHI pay insurance contributions and receive medical services from their health care providers. The NHIS, as the single insurer, pays costs based on the billing records of health care providers (Figure 1). To govern and carry out these processes in the NHI, the NHIS built a data warehouse to collect the required information on insurance eligibility, insurance contributions, medical history, and medical institutions. In 2012, the NHIS formed the NHID using information from medical treatment and health screening records and eligibility data from an existing database system. Figure 1. The governance of the National Health Insurance of South Korea. Data collected The eligibility database includes information about income-based insurance contributions, demographic variables, and date of death. The national health screening database includes information on health behaviors and bio-clinical variables. The health care utilization database includes information on records on inpatient and outpatient usage (diagnosis, length of stay, treatment costs, services received) and prescription records (drug code, days prescribed, daily dosage). The long-term care insurance database includes information about activities of daily living and service grades. The health care provider database includes data about the types of institutions, human resources, and equipment. In the NHID, de-identified join keys replacing the personal identifiers are used to interlink these databases. Data resource use Papers published covered various diseases or health conditions like infectious diseases, cancer, cardiovascular diseases, hypertension, diabetes mellitus, and injuries and risk factors such as smoking, alcohol consumption, and obesity. The impacts of health care and public health policies on health care utilization have been also explored since the data include all the necessary information reflecting patterns of health care utilization. Reasons to be cautious First, information on diagnosis and disease may not be optimal for identifying disease occurrence and prevalence since the data have been collected for medical service claims and reimbursement. However, the NHID also collects prescription data with secondary diagnosis, so the accuracy of the disease information can be improved. Second, the data linkage with other secondary national data is not widely available due to privacy issues in Korea. Governmental discussions on the statutory reform of data linkage using the NHID are under way. Collaboration and data access Access to the NHID can be obtained through the Health Insurance Data Service home page (http://nhiss.nhis.or.kr). An ethics approval from the researchers’ institutional review board is required with submission of a study proposal, which is reviewed by the NHIS review committee before providing data. Further inquiries on data use can be obtained by contacting the corresponding author. Funding and competing interests This work was supported by the NHIS in South Korea. The authors declare no competing interests.
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                Author and article information

                Contributors
                smpark.snuh@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 August 2018
                15 August 2018
                2018
                : 8
                Affiliations
                [1 ]GRID grid.454124.2, Big Data Steering Department, , National Health Insurance Service, ; Wonju, Korea
                [2 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Department of Health Policy and Management, , Seoul National University College of Medicine, ; Seoul, Korea
                [3 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Institute of Health Policy and Management, , Seoul National University Research Center, ; Seoul, Korea
                [4 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Department of Family Medicine, , Seoul National University College of Medicine, ; Seoul, Korea
                [5 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Department of Biomedical Sciences, , Seoul National University College of Medicine, ; Seoul, Korea
                Article
                30740
                10.1038/s41598-018-30740-y
                6093904
                30111867
                © The Author(s) 2018

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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