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      Temporal and Geospatial Trends of Hypertension Management in Korea: a Nationwide Study 2002–2016

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      , MD 1 , 2 , 3 , , MD, PhD 2 , 4 , , MD, PhD, FAHA 1 , 2 , 5 ,
      Korean Circulation Journal
      The Korean Society of Cardiology
      Hypertension, Medication adherence, Spatio-temporal analysis

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          Abstract

          Background and Objectives

          Geographic distribution of hypertension management in Korea has never been reported. We investigated temporal and regional trends of hypertension management in Korea.

          Methods

          For each calendar year from 2002 to 2016, we identified 2,423,245 to 7,549,989 persons aged ≥30 years treated for hypertension (total 80,564,109 cases). We calculated yearly age-sex standardized rates for medication adherence, combination therapy, blood test, and urine test according to geographic regions. We then used multivariate logistic regression to calculate odds ratios for hypertension management adjusted for individual-level sociodemographic factors.

          Results

          Adherence rates have markedly increased from 24.4% (2002) to 71.6% (2016) nationwide. Regional difference was prominent in 2002 (highest, 31.7% in Seoul; lowest, 14.4% in Jeonbuk), but has become less noticeable over 15 years (highest, 73.1% in Daejeon; lowest, 69.0% in Jeonnam, 2016). Combination therapy rates increased from 42.8% (2002) to 61.0% (2011), but are in decreasing trend after 2011. Blood test rates were 58.8% in 2016, whereas urine test rates have been stagnant below 50% across all regions. Geographic variations of combination therapy and complication screening rates were not profound. Results from multivariable logistic regression, adjusted for age and sex, were in agreement with trends observed by standardized rates. The odds ratios remained unchanged when the models were further adjusted for employment status and household income.

          Conclusions

          Regional difference in hypertension management was evident in the past, but has become less apparent over the last 15 years in Korea.

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          Most cited references14

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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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            2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

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              Hypertension and socioeconomic status.

              The impact of socioeconomic status on hypertension is complicated and unclear. In this article, we review the findings of recently published studies pertaining to the association between socioeconomic status and hypertension. Specifically, we focus on several potentially modifiable modes of pathogenesis involved in this association, including education, occupation, and social environment. We also review several mechanisms through which the effects of socioeconomic status on hypertension may be mediated. Several modifiable socioeconomic determinants, such as education and occupation, are associated with hypertension. Additional socioeconomic status markers such as urban or rural dwelling and individual, local or national economic conditions are also associated with hypertension, although these associations are complicated and at times somewhat contradictory. Possible explanations for this impact include awareness of hypertension prevention and control and better accessibility and adherence to medical treatment among higher socioeconomic status groups, as well as low birth weight and higher job strain among lower socioeconomic status groups. Low socioeconomic status is associated with higher blood pressure. There is a need to develop and test culturally appropriate interventions to reduce the prevalence of hypertension among these populations to minimize the resultant cardiovascular morbidity and mortality.
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                Author and article information

                Journal
                Korean Circ J
                Korean Circ J
                KCJ
                Korean Circulation Journal
                The Korean Society of Cardiology
                1738-5520
                1738-5555
                June 2019
                14 February 2019
                : 49
                : 6
                : 514-527
                Affiliations
                [1 ]Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea.
                [2 ]Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
                [3 ]Graduate School, Yonsei University College of Medicine, Seoul, Korea.
                [4 ]Division of Cardiology, Severance Cardiovascular Hospital and Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea.
                [5 ]Big Data Steering Department, National Health Insurance Service, Wonju, Korea.
                Author notes
                Correspondence to Hyeon Chang Kim, MD, PhD, FAHA. Department of Preventive Medicine, Yonsei University College of Medicine, 50-1, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. hckim@ 123456yuhs.ac
                Author information
                https://orcid.org/0000-0002-5034-8422
                https://orcid.org/0000-0001-5362-478X
                https://orcid.org/0000-0001-7867-1240
                Article
                10.4070/kcj.2018.0358
                6554585
                30808085
                0a8cb051-29a0-451b-8135-c343f48d0fb8
                Copyright © 2019. The Korean Society of Cardiology

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 04 October 2018
                : 26 November 2018
                : 04 January 2019
                Funding
                Funded by: Ministry of Health and Welfare, CrossRef https://doi.org/10.13039/501100003625;
                Award ID: HI13C0715
                Funded by: Yonsei University College of Medicine, CrossRef https://doi.org/10.13039/501100008005;
                Categories
                Original Article

                Cardiovascular Medicine
                hypertension,medication adherence,spatio-temporal analysis
                Cardiovascular Medicine
                hypertension, medication adherence, spatio-temporal analysis

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