The Korean National Health Insurance Service (KNHIS) created the National Health Information Database (NHID), which includes data on healthcare utilization, health screening, sociodemographic variables, and mortality for over 50 million subjects in Korea (15). The present study evaluated the senior cohort database of the NHID (2002–2013), consisting of 10% of a random sample of the Korean population aged ≥60 years, who qualified for National Health Insurance or the Medical Aid Program at the end of December 2012. Because all persons in Korea are thought to be enrolled in the National Health Insurance or Medical Aid Program, this sample is representative of older Korean adults.
The NSPTA was added to the National Health Screening Program in 2007. The purposes of the NSPTA were to tailor the program according to the age and sex of each subject, and to strengthen post-examination counseling. As part of this program, subjects aged 66 years undergo the TUG and unipedal stance tests to assess mobility. The details of the NSPTA have been described elsewhere (16). In the present study, data were obtained on subjects in the database aged 66 years who participated in the NSPTA in 2007–2008 and underwent TUG tests. Of the 558,147 subjects in the database, 40,774 were further analyzed (Figure 1).
Timed up and go test
The TUG was performed on the day of physical examination during the NSPTA at each subject’s community hospital, as described in the NSPTA manual. Participants were required to sit on a chair, stand and walk a 3 m course at a comfortable speed, walk back to the chair and sit again, while wearing regular footwear and/or using walking aids. The time from standing up to sitting down again was measured, with a time greater than 10 sec categorized as abnormal.
Cardiovascular events, cardiovascular mortality, and all-cause mortality
Data concerning the diagnosis of a CV event, the date of the event, the cause of death, and the date of death were obtained from the senior cohort database of the KNHIS during the period 2007–2012. This analysis assumed that there was no censoring other than death or an event. Because all participants are supposed to be beneficiaries of the National Health Insurance or Medical Aid Program in Korea, drop-out other than death is virtually impossible. Furthermore, because claims for all medical events experienced by subjects should be submitted to the KNHIS by healthcare providers for reimbursement, every CV event should be included in the database. The database was reviewed for International Classification of Diseases 10th Revision (ICD-10) codes for diagnosis and cause of death. CV event or death was defined as a diagnosis of or death caused by ischemic heart diseases (I20-25) or cerebrovascular disease (I60-69). If there was no date of death in the database, the subject was considered alive at the end of 2012. The follow-up time was calculated as the time from the date of the NSPTA to the date of first diagnosis of CV disease for CV events, and to the date of death for CV and all-cause mortality.
Information on cigarette smoking was collected by a self-administered questionnaire at the time of the NSPTA, with subjects classified as current smokers, ex-smokers, or non-smokers. The questionnaire was also used to collect information on alcohol drinking and exercise. At-risk drinking was defined as drinking more than seven drinks per week or three drinks per occasion. Regular exercise was defined as vigorous activity (>20 min/day) more than once per week. Because insurance premiums charged by the KNHIS are determined by participants’ income, insurance premium was regarded as a surrogate marker for income.
Body mass index (BMI) was calculated as weight divided by height squared (kg/m2). BMI was defined according to Asian-specific criteria, with normal BMI defined as between 18.5 and 23 kg/m2 and obesity as ≥ 25 kg/m2.
Hypertension, diabetes mellitus, dyslipidemia, and chronic kidney disease were included in analysis as cardiovascular risk factors. Subjects were regarded as having these conditions if there were records in the database that their physicians had submitted claims based on these diagnoses.
Cognition was measured using the Korean Dementia Screening Questionnaire-Cognition (KDSQ-C), which is included in the NSPTA questionnaire. The KDSQ-C is a self-administered, validated questionnaire (17), consisting of 15 items, each rated on a three-point Likert scale (0, 1, or 2, with a higher score considered worse). Cognitive impairment was defined as a composite score ≥6.
The NSPTA questionnaire included six items about activity of daily living (ADL), which were extracted from the Korean versions of the ADL (K-ADL) and Instrumental ADL (K-IADL) questionnaires (18). The four items extracted from the K-ADL were: “Do you bathe by yourself without help?,” “Do you dress by yourself without help?,” “Do you eat by yourself without help if a meal is prepared?,” and “Do you go to the toilet by yourself without help?” The two items extracted from the K-IADL were “Do you prepare your meal by yourself without help?” and “Do you go outside by yourself to places within walking distance?” ADL was categorized as abnormal if the answer to one or more of these questions was “No.”
Statistical analyses were performed using STATA software (Version 15.1; STATA. Corp, College Station, Tex). Statistical significance was defined as P < 0.05. Continuous variables were reported as mean ± SD and categorical variables as frequencies and percentages. The incidence rates of CV disease, CV mortality, and all-cause mortality per 1,000 person-years were calculated.
Cox proportional hazard models were used to evaluate the association of TUG results with CV events, CV mortality, and all-cause mortality. Three models were built for survival analyses, a crude model and two adjusted models. Model 1 was adjusted for sociodemographic and behavioral factors, such as sex, income, cigarette smoking, at-risk alcohol drinking, and regular exercise. Model 2 included all factors in Model 1, as well as cognitive impairment, ADL, and chronic diseases known to be CV risk factors such as obesity, hypertension, diabetes mellitus, dyslipidemia, and chronic kidney disease. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated for each model.
Participants at risk with abnormal TUG results were identified by subgroup analyses in Model 2, with participants subgrouped by socio-behavioral factors, including sex, cigarette smoking, at-risk alcohol drinking, regular exercise, and chronic diseases such as obesity, hypertension, diabetes mellitus, and dyslipidemia.