An increased risk of gastric cancer for pickled vegetable and salted fish intake has been suggested, yet the lack of a dose-response association warrants a quantitative analysis. We conducted a meta-analysis, combining results from our analysis of two large Korean cohort studies and those from previous prospective cohort studies. We investigated the association of pickled vegetable and salted fish intake with gastric cancer in the Korean Genome Epidemiology Study and the Korean Multi-center Cancer Cohort Study using Cox proportional hazard models. We then searched for observational studies published until November 2019 and conducted both dose-response and categorical meta-analyses. The pooled relative risk (RR) of gastric cancer incidence was 1.15 (95% Confidence Interval (CI), 1.07–1.23) for 40 g/day increment in pickled vegetable intake in a dose-response manner (P for nonlinearity = 0.11). As for salted fish intake, the pooled risk of gastric cancer incidence was 1.17 (95% CI, 0.99–1.38) times higher, comparing the highest to the lowest intake. Our findings supported the evidence that high intake of pickled vegetable and salted fish is associated with elevated risk of gastric cancer incidence.

Jung K, et al. J Korean Med Sci. 2019 Feb;34(8):e65. https://doi.org/10.3346/jkms.2019.34.e65

One of the primary goals of epidemiology is to quantify various aspects of a population's health, illness, and death status and the determinants (or risk factors) thereof by calculating health indicators that measure the magnitudes of various conditions. There has been some confusion regarding health indicators, with discrepancies in usage among organizations such as the World Health Organization the, Centers for Disease Control and Prevention (CDC), and the CDC of other countries, and the usage of the relevant terminology may vary across papers. Therefore, in this review, we would like to propose appropriate terminological definitions for health indicators based on the most commonly used meanings and/or the terms used by official agencies, in order to bring clarity to this area of confusion. We have used appropriate examples to make each health indicator easy for the reader to understand. We have included practical exercises for some health indicators to help readers understand the underlying concepts.

We examined the association of coffee drinking with all-cause and cause-specific mortality in a pooled analysis of two Korean prospective cohort studies: The Korea National Health and Nutrition Examination Survey and the Korean Genome and Epidemiology Study. We included 192,222 participants, and a total of 6057 deaths were documented. Cox proportional hazards model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs), and the HRs were combined using a random-effects model. Coffee drinking was associated with a lower risk of all-cause mortality [HR (95% CI) = 0.84 (0.77–0.92), for ≥3 cups/day of coffee drinking versus non-drinkers; p for trend = 0.004]. We observed the potential benefit of coffee drinking for mortality due to cardiovascular disease, respiratory disease, and diabetes, but not for cancer mortality. Overall, we found that moderate coffee drinking was associated with a lower risk of death in population-based cohort analysis of Korean adults.

Objectives: This study aimed to identify the current patterns of cancer incidence and estimate the projected cancer incidence and mortality between 2020 and 2035 in Korea.Methods: Data on cancer incidence cases were extracted from the Korean Statistical Information Service from 2000 to 2017, and data on cancer-related deaths were extracted from the National Cancer Center from 2000 to 2018. Cancer cases and deaths were classified according to the International Classification of Diseases, 10th edition. For the current patterns of cancer incidence, age-standardized incidence rates (ASIRs) and age-standardized mortality rates were investigated using the 2000 mid-year estimated population aged over 20 years and older. A joinpoint regression model was used to determine the 2020 to 2035 trends in cancer.Results: Overall, cancer cases were predicted to increase from 265 299 in 2020 to 474 085 in 2035 (growth rate: 1.8%). The greatest increase in the ASIR was projected for prostate cancer among male (7.84 vs. 189.53 per 100 000 people) and breast cancer among female (34.17 vs. 238.45 per 100 000 people) from 2000 to 2035. Overall cancer deaths were projected to increase from 81 717 in 2020 to 95 845 in 2035 (average annual growth rate: 1.2%). Although most cancer mortality rates were projected to decrease, those of breast, pancreatic, and ovarian cancer among female were projected to increase until 2035.Conclusions: These up-to-date projections of cancer incidence and mortality in the Korean population may be a significant resource for implementing cancer-related regulations or developing cancer treatments.

Objectives: This study aimed to identify the social and policy determinants of coronavirus disease 2019 (COVID-19) infection across 23 countries.Methods: COVID-19 indicators (incidence, mortality, and fatality) for each country were calculated by direct and indirect standardization. Multivariable regression analyses were used to identify the social and policy determinants of COVID-19 infection.Results: A higher number of doctors per population was related to lower incidence, mortality, and fatality rates of COVID-19 in 23 countries (β=-0.672, -0.445, and -0.564, respectively). The number of nurses/midwives per population was associated with lower mortality and fatality rates of COVID-19 in 23 countries (β=-0.215 and -0.372, respectively). Strengthening of policy restriction indicators, such as restrictions of public gatherings, was related to lower COVID-19 incidence (β=-0.423). A national Bacillus Calmette–Guérin vaccination policy conducted among special groups or in the past was associated with a higher incidence of COVID-19 in 23 countries (β=0.341). The proportion of the elderly population (aged over 70 years) was related to higher mortality and fatality rates (β=0.209 and 0.350, respectively), and income support was associated with mortality and fatality rates (β=-0.362 and -0.449, respectively).Conclusions: These findings do not imply causality because this was a country-based correlation study. However, COVID-19 transmission can be influenced by social and policy determinants such as integrated health systems and policy responses to COVID-19. Various social and policy determinants should be considered when planning responses to COVID-19.