INTRODUCTION

Lifestyle factors such as alcohol consumption, smoking, and overweight are known to be important for health. Numerous studies of each component have been conducted, but few studies have sought to identify causal relationships between lifestyle factors and health outcomes [1]. Some research findings, such as the so-called obesity paradox and the J-curve association between alcohol consumption and cardiovascular events, are likely to be attributed to the lack of an established causal relationship or the consequences of confounders [2-4]. If individuals attempt to lose weight, quit smoking, or abstain from alcohol after experiencing a severe disease or serious condition, the possibility of reverse causation between lifestyle behaviors and disease cannot be ruled out in cross-sectional studies [5-7]. In order to clarify the underlying causal relationships, a combination of various theories, instead of a single theory, or more advanced research methods are required to understand behavior change and maintenance [1].

Alcohol consumption, smoking, and weight often change at the same time and are related to each other. Drinking alcohol has been reported to have a positive association with smoking and obesity in various studies [8-12]. In contrast, some studies have reported an inverse relationship between obesity and alcohol consumption [13-15]. Many studies have reported a negative association between smoking and obesity [16-21]. If this relationship is not taken into consideration, it is possible to derive misleading results because of uncontrolled confounding factors. Furthermore, research results can be affected not only by observable confounding variables, but also by unobserved time-invariant confounding variables. To overcome the problem caused by the latter, fixed-effect models are known to be a good alternative for analyses of repeated measurement data [22]. Some studies using fixed-effect models have focused on simultaneous changes in smoking, alcohol consumption, and weight [10,23]. Of note, a previous study found that socioeconomic factors influenced changes in drinking patterns, underscoring the need for interventions [24]. Fixed-effect models can be used to analyze individual behavioral changes that are important for lifestyle modification [25].

The purpose of this study was to identify simultaneous behavioral changes in alcohol consumption, smoking, and weight with a fixed-effect model and to characterize their associations with disease status using the National Health Information Database (NHID) of the National Health Insurance Services (NHIS) in Korea. The database, which covers the entire population (about 50 million) of Korea, includes lifestyle-related variables for about 10 million people every year [26].

METHODS

Data Sources

The National Health Screening Program (NHSP) of Korea includes a questionnaire with items on smoking, alcohol consumption, and biometric information on height and weight. The NHSP includes screening examinations once every 2 years (every year for manual workers) for the entire population aged 40 or over and for employed and self-employed individuals under age 40. The participation rate in the general health screening program was 77.7% in 2016 [27]. The NHID consists of 4 databases: the eligibility database, the national health screening database, the health care utilization database, and the long-term care insurance database. The health care utilization database collects claims data for inpatient and outpatient health care services. Because of the fee-for-service payment system, the NHID includes diagnosis, procedure, and prescription records in the health care utilization database. A retrospective cohort of the NHID (customized NHID) from 2009 to 2016 was used in this study (NHIS-2018-1-145).

Study Participants and Variables

The subjects of this study were those who participated in the NHSP every 2 years from 2009 to 2016 (2009-2010, 2011-2012, 2013-2014, and 2015-2016) and were aged 40 or more. Among the 17 659 972 participants in the NHSP in 2009-2010, a total of 9 763 476 received follow-up every 2 years. After excluding 2 562 947 people under age 40, 7 200 529 were included in this study. We reconstructed the data into an individual-level panel dataset in 4 waves (2009-2010, 2011-2012, 2013-2014, and 2015-2016). Each wave panel dataset included sex, age, insurance type (the employed insured and dependents, and self-employed heads of household and family members), insurance-contribution quartile (a proxy indicator for household income, for which higher quartiles indicate higher income), moderate-risk/high-risk drinking status (based on the questionnaire about alcohol consumption frequency and standard drink amount, regardless of the type of alcohol; alcohol consumption of 40 g/d or more for males and 20 g/d or more for females [28]), overweight (based on the body mass index [BMI] calculated from measured height and weight, using a BMI of 25 kg/m² or more as a threshold), and current smoking status (based on the questionnaire). From the health care utilization database, participants’ medical history of hypertension, diabetes mellitus, ischemic heart disease, stroke, and cancer was included in the analysis. Each disease was classified according to the primary diagnosis codes of the Korean Standard Classification of Diseases based on International Classification of Disease codes (hypertension, I10-I15; diabetes mellitus, E10-E14; ischemic heart disease, I20-I25; stroke, I60-I64; cancer, C00-C97) and medication codes (hypertension and diabetes mellitus). Patients were only classified as having ischemic heart disease and stroke if they received inpatient care, and patients with cancer were defined as those whose medical copayment was reduced due to inclusion in the cancer registry.

To minimize selection bias due to regular health-screening participation, we created a stratified sampling weight variable according to sex, age group (40-49, 50-59, 60-69, 70+), and insurance type, which reflected the entire population of Korea aged 40 or over.

Statistical Analysis

The descriptive analysis of the subjects was presented by frequency according to their characteristics. We used a fixed-effect model for smoking status, heavy alcohol drinking, and overweight (dependent variable for each model). The independent variables were sex, age (as a continuous variable), lifestyle factors (smoking, heavy alcohol drinking, and overweight, except for the dependent variable of each model), insurance contribution quartile, employment status (using insurance type), and disease status (hypertension, diabetes mellitus, ischemic heart disease, stroke, and cancer). Stratification of the fixed-effect model analysis according to sex and age was performed. The sampling weights described in the ‘study participants’ section were applied to the analysis.

Ethics Statement

All components and procedures of this study were approved by the Institutional Review Board (IRB) of the National Health Insurance Service (IRB no. Sa-2018-HR-01-016).

RESULTS

General Characteristics of the Study Population

The general characteristics of the study population are presented in Table 1, including both unweighted and weighted results. The weighted first wave included more females (51.6%) than males (48.4%) out of a total of 20 967 236 participants. By type of insurance, dependents of the employed insured were the most frequent (35.0%). Comparing the prevalence of various behaviors and conditions in the first and fourth waves, that of smoking decreased from 18.3% to 13.8%, that of high-risk drinking decreased from 13.2% to 11.2%, that of overweight increased from 35.3% to 36.9%, that of hypertension increased from 29.2% to 38.7%, that of diabetes mellitus increased from 8.7% to 13.7%, that of ischemic heart disease increased from 1.4% to 2.2%, that of stroke increased from 0.9% to 1.9%, and that of cancer increased from 2.4% to 3.8%.

Fixed-effect Model

The results of the fixed-effect model with the sampling weights applied are presented in Table 2. Non-smokers or ex-smokers who received insurance eligibility of the employed insured type, changed from the fourth to the first quartile of insurance contribution, became high-risk drinkers, or lost weight often became smokers. However, participants who had newly acquired hypertension, diabetes mellitus, ischemic heart disease, stroke, or cancer often became non-smokers (R²=0.814). Non-high-risk drinkers who lost insurance eligibility of the employed insured type, changed from the first to the fourth quartile of insurance contribution, became smokers, or gained weight often became high-risk drinkers. However, participants who had newly acquired hypertension, diabetes mellitus, ischemic heart disease, stroke, or cancer often became non-high-risk drinkers (R²=0.635). Non-overweight participants who received insurance eligibility of the employed insured type, changed from the first to the fourth quartile of insurance contribution, or became non-smokers or high-risk drinkers often became overweight. However, participants who had newly acquired diabetes mellitus, ischemic heart disease, stroke, or cancer often lost weight (R²=0.804).

The results were similar in the stratified analysis (Table 3), and the R2 value was the largest in the 40-49 age group.

DISCUSSION

In this study, we analyzed the interactions of changes in smoking, high-risk drinking, and weight by applying a fixed-effect model, through a novel kind of panel-data analysis. High-risk drinking had a positive association with both smoking and overweight, but smoking had a negative association with overweight. That is, high-risk drinking can increase at the same time if someone starts smoking or resumes smoking after quitting, which also means that there is a high possibility of becoming obese if someone continues high-risk alcohol consumption or quits smoking. In previous studies, smoking, high-risk drinking, and overweight were noted as independent characteristics at specific time points; in contrast, this study has significant implications for changes in healthy lifestyle behavior.

The observed association between drinking alcohol and smoking supports the results of previous studies [8,12]. Regarding the relationship between drinking alcohol and obesity, controversy exists in the literature [9-11,13-15]; however, a positive relationship was found in this study. Regarding the relationship between smoking and obesity, our results support the findings of previous studies showing a negative correlation [16-21]. In the study of Arif and Rohrer [13], obesity was lower in light alcohol drinkers and higher in heavy alcohol drinkers than in non-drinkers. In our study, only high-risk drinking was analyzed, meaning that our results support those of Arif and Rohrer [13]’s study. Tolstrup et al. [14] reported an inverse relationship between the frequency of drinking alcohol and waist-circumference changes in a population-based study in Denmark. In the study of Tolstrup et al. [14], various factors, such as physical activity, caloric intake, smoking, and education level, were adjusted; however, the large difference between alcohol drinkers and non-drinkers constituted a limitation in the baseline characteristics. Liu et al. [15] reported that, after adjusting for age, race, height, education, health status, smoking, diet, physical activity, and total non-alcoholic caloric intake, those who consumed 1.0-6.9 drinks/wk had a lower probability of weight loss or gain. However, for those who consumed ≥2.0 drinks/d, the difference was not statistically significant. In addition, the focus of their research was purely on the effects of alcohol consumption, as calories were adjusted for in the model; this is a limitation because total caloric intake is the most important factor contributing to obesity. A previous study [29] reported that alcohol consumption does not reduce the intake of other calories; therefore, it is also plausible that obesity increases because of the calories contained in alcoholic beverages.

French et al. [10] reported that smoking cessation was negatively correlated with alcohol consumption and positively correlated with weight gain in a fixed-effect model. Although similar results were obtained in our study, the study of French et al. [10] has a limitation in that the sample size was about 2000 and the age range was limited to those aged 50 and over. In our study, we derived results based on data from roughly 7.2 million people, and investigated the associations of lifestyle changes with major diseases.

Hypertension was positively correlated with overweight, as is already well known [30]. However, smoking and high-risk drinking were negatively associated with hypertension, diabetes mellitus, ischemic heart disease, stroke, and cancer. Overweight was negatively associated only with hypertension. As described in the Introduction section of the present study, we considered the possibility of confounders in the obesity paradox and the J-curve of alcohol and disease complications. The observation of a negative association supports the hypothesis that these relationships between lifestyle factors and disease status are confounded by lifestyle changes. Our study did not investigate long-term health effects, because it analyzed simultaneous changes through a fixed-effect model. However, it is expected that further epidemiological studies will be conducted based on the results of this study.

For the stratified results, the explanatory power of the model (R²) was higher in males and younger participants. In previous studies of attitudes towards smoking cessation, males and younger individuals were more likely to quit smoking [31-33]. The results of this study therefore support those of previous research, although further studies are needed to generalize these results.

There are some limitations to this study. First, given the limitations of the available variables, smoking and alcohol use data were obtained from a self-reported questionnaire, and the type of alcohol was not considered. Second, there may be have been inaccuracies in the disease codes, such as those for hypertension and diabetes mellitus, because of the characteristics of insurance claims data. Third, although this study applied sampling weights for the whole population, the possibility of selection bias still remains. Fourth, simultaneous changes were the primary focus of the analysis, so there may be limitations in the interpretation of causal relationships.

The results of this study documented interactions among lifestyle factors in a large-scale population-based dataset. These results will provide guidance on how lifestyle factors should be handled in future epidemiological studies, and suggest that policies targeting smoking, high-risk drinking, and obesity should be carried out complementarily, rather than independently.

Notes

CONFLICT OF INTEREST

The authors have no conflicts of interest associated with the material presented in this paper.

ACKNOWLEDGEMENTS

Notes

AUTHOR CONTRIBUTIONS

Conceptualization: YYK, JHP. Data curation: YYK, SH, HJK. Formal analysis: YYK. Funding acquisition: None. Methodology: YYK. Project administration: None. Visualization: None. Writing - original draft: YYK. Writing - review & editing: YYK, HJK, SH, JHP.

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Figure & Data

References

Citations

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