Study Population and Data Collection
The Ethics Committee of Hallym University (2017-I102) approved the use of these data. The study was exempted from the need for written informed consent by the institutional review board.
This national cohort study relied on data from the Korean NHIS-HEALS . The Korean NHIS selects ~ 10% of random samples (n = ~515,000) directly from the entire population who underwent health evaluations from 2002 through 2013 (n = ~5,150,000). Age- and sex-specific distributions of the cohort population have been described online . The details of the methods used to perform these procedures are provided by the National Health Insurance Sharing Service .
All insured Koreans who are at least 40 years old and their dependents undergo no-cost biannual health evaluations. Each examinee must complete a standard questionnaire for this health evaluation program . Because all Korean citizens are recognized by a 13-digit resident registration number from birth to death, exact population statistics can be determined using this database. It is mandatory for all Koreans to enroll in the NHIS. All Korean hospitals and clinics use the 13-digit resident registration number to register individual patients in the medical insurance system. Therefore, the risk of overlapping medical records is minimal, even if a patient moves from one place to another. Moreover, all medical treatments in Korea can be tracked without exception using the Korean Health Insurance Review & Assessment (HIRA) system. In Korea, providing notice of death to an administrative entity is legally required before a funeral can be held, and the causes and date of death are recorded by medical doctors on a death certificate.
This cohort database included (i) personal information, (ii) health insurance claim codes (procedures and prescriptions), (iii) diagnostic codes using the International Classification of Disease-10 (ICD-10), (iv) death records from the Korean National Statistical Office (using the Korean Standard Classification of disease), (v) socioeconomic data (residence and income), (vi) medical examination data (vii) health check-up data (body mass index [BMI], alcohol consumption, smoking habits, blood pressure, urinalysis, hemoglobin, fasting glucose, lipid parameters, creatinine, and liver enzymes) for each participant over the period from 2002 to 2013 [19, 20].
Out of 514,866 cases with 497,931,549 medical claim codes, we included participants who were diagnosed with sialolithiasis (n = 1,037). The sialolithiasis participants were matched 1:4 with participants (control group) who were never diagnosed with sialolithiasis from 2002 through 2013 among this cohort. The control group was selected from the original population (n = 513,829). Subjects were matched by age group, sex, income group, region of residence, and medical history (e.g., hypertension, diabetes, and dyslipidemia). Participants in the control group were sorted using a random number order and selected from top to bottom to prevent selection bias. It was assumed that the matched control participants were involved at the same time as each matched sialolithiasis participant (index date). Therefore, participants in the control group who died before the index date were excluded. Sialolithiasis participants who had no previous history of health evaluations before the index date were excluded (n = 89). One sialolithiasis participant was excluded due to the lack of a matching participant. Finally, 1:4 matching resulted in the inclusion of 947 sialolithiasis participants and 3,788 control participants. We analyzed the previous health evaluation data in the sialolithiasis and control groups after matching (Fig. 1). In this study, we used the latest health evaluation data before the index date.
Tobacco smoking was categorized based on the current smoking status (nonsmoker or past smoker/current smoker), duration of smoking (nonsmoker, < 20 years, and ≥ 20 years), and current number of cigarettes smoked per day (0 cigarettes per day, < 20 cigarettes per day, and ≥ 20 cigarettes per day, S1 Table). We selected the current smoking status in this study. We used ‘smoking’ to define current or past smokers compared to nonsmokers.
Alcohol consumption was evaluated by the frequency of alcohol consumption (< 1 time per week, and ≥ 1 time per week) and the amount of alcohol consumed at a time (<1 soju bottle, 1 soju bottle, and > 1 soju bottle, S1 Table). Generally, a bottle of Soju contains 17.5% of alcohol with 360ml. A bottle of soju is same about 3.5 bottles of bear. We selected the frequency of alcohol consumption in this study. We used ‘alcohol consumption’ to define alcohol consumption ≥ 1 time per week compared to alcohol consumption < 1 time per week.
Obesity was measured using BMI (kg/m2) and was categorized as < 18.5 (underweight), ≥ 18.5 and < 23 (normal), ≥ 23 and < 25 (overweight), ≥ 25 and < 30 (obese I), and ≥ 30 (obese II) following the WPRO 2000 guidelines .
The age groups were classified using 5-year age intervals: 40-44, 45-49, 50-54, …, and 85+ years old. A total of 10 age groups were designated. The income groups were initially divided into 41 classes according to the premium (one health aid class, 20 self-employment health insurance classes, and 20 employment health insurance classes). These groups were recategorized into 5 classes (class 1 [lowest income]-5 [highest income]). The region of residence was divided into 16 areas according to administrative district. These regions were regrouped into urban (Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan) and rural (Gyeonggi, Gangwon, Chungcheongbuk, Chungcheongnam, Jeollabuk, Jeollanam, Gyeongsangbuk, Gyeongsangnam, and Jeju) areas.
The participants’ prior medical histories were evaluated using ICD-10 codes. To ensure an accurate diagnosis, hypertension (I10 and I15), diabetes (E10-E14), and dyslipidemia (E78) were regarded as present if a participant was treated ≥ 2 times.
Sialolithiasis was diagnosed based on the ICD-10 code K115.
Chi-square tests were used to compare the general characteristics between the sialolithiasis and control groups.
To analyze the ORs of smoking, drinking alcohol, and obesity on sialolithiasis, conditional logistic regression analysis was used. In this analysis, a crude (simple), adjusted model (adjusted model for obesity, smoking status, and frequency of alcohol consumption) was used, and 95% confidence intervals (CIs) were calculated. In these analyses, age group, sex, income group, region of residence, hypertension, diabetes, and dyslipidemia were stratified.
For the subgroup analyses, we divided the participants by age and sex (<60 years old, ≥ 60 years old; men and women). Division of the age groups was determined by the median value of all the participants.
Two-tailed analyses were conducted, and P values less than 0.05 were considered to indicate significance. The results were statistically analyzed using SPSS v. 22.0 (IBM, Armonk, NY, USA).