Study population and data collection
This national cohort study relied on data from the Korean NHIS-HEALS, previously used in several peer reviewed publications [20,21,22,23]. The ethics committee of Hallym University (2017-I102) approved the use of these data. The NHIS provides a representative sample database with a substantial volume of representative information that does not require privacy regulation for public health research and policy development. Therefore, the data used in our research is allowed to be made publicly available. In this reason, written informed consent was exempted by the Institutional Review Board.
The Korean NHIS selects random samples of ~ 10% (n = ~ 515,000) directly from the entire population that 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 and the duplication of records can be identified. It is mandatory for all Koreans to enroll in the NHIS. Moreover, all medical treatments in Korea can be tracked without exception using the Korean Health Insurance Review & Assessment (HIRA) system. In Korea, it is legally required to provide notice of death to an administrative entity before the funeral 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 based on 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, and (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 for the period from 2002 to 2013 [25, 26]. Alcohol consumption and smoking habits were surveyed, and BMI was calculated by measuring height and weight at health check-ups.
Out of 514,866 cases with 497,931,549 medical claim codes, we included participants who were diagnosed with sialolithiasis (n = 1037). The sialolithiasis participants were matched 1:4 with participants among this cohort who were never diagnosed with sialolithiasis from 2002 through 2013 (control group). 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 participant with sialolithiasis (index date). Therefore, participants in the control group who died before the index date were excluded. Participants with sialolithiasis who had no history of health evaluations before the index date were excluded (n = 89). One participant with sialolithiasis was excluded due to the lack of a matching participant. Finally, 1:4 matching resulted in the inclusion of 947 participants with sialolithiasis and 3788 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 most recent 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. Current and past smokers were defined as smokers and were compared to nonsmokers.
Alcohol consumption was evaluated by frequency (< 1 time per week, and ≥ 1 time per week) and by 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 per 360 ml. A bottle of soju is equivalent to approximately 3.5 bottles of beer. 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. Income was initially divided into 41 classes according to health care premium (one health assistance class, 20 self-employment health insurance classes, and 20 employment health insurance classes). These groups were recategorized into 5 classes (classes 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 of the sialolithiasis and control groups.
To analyze the ORs of smoking, drinking alcohol, and obesity in sialolithiasis patients, conditional logistic regression analysis was used. In this analysis, a crude (simple) adjusted model (adjusted 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, ≥ 60 years; male and female). The division of the age groups was determined by the median age 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).