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.

Economic evaluation is a type of comparative analysis between interventions in terms of both their resource use and health outcomes. Due to the good prognosis of thyroid cancer (TC), the socioeconomic burden of TC patients post-diagnosis is increasing. Therefore, economic evaluation studies focusing on TC are recommended. This study aimed to describe the concept and methods of economic evaluation and reviewed previous TC studies. Several previous studies compared the costs of interventions or evaluated recurrence, complications, or quality of life as measures of their effectiveness. Regarding costs, most studies focused on direct costs and applied hypothetical models. Cost-minimization analysis should be distinguished from simple cost analysis. Furthermore, due to the universality of the term “cost-effectiveness analysis” (CEA), several studies have not distinguished CEA from cost-utility analysis; this point needs to be considered in future research. Cost-benefit analyses have not been conducted in previous TC research. Since TC has a high survival rate and good prognosis, the need for economic evaluations has recently been pointed out. Therefore, correct concepts and methods are needed to obtain clear economic evaluation results. On this basis, it will be possible to provide appropriate guidelines for TC treatment and management in the future.

Economic evaluation is a type of comparative analysis between interventions in terms of both their resource use and health outcomes. Due to the good prognosis of thyroid cancer (TC), the socioeconomic burden of TC patients post-diagnosis is increasing. Therefore, economic evaluation studies focusing on TC are recommended. This study aimed to describe the concept and methods of economic evaluation and reviewed previous TC studies. Several previous studies compared the costs of interventions or evaluated recurrence, complications, or quality of life as measures of their effectiveness. Regarding costs, most studies focused on direct costs and applied hypothetical models. Cost-minimization analysis should be distinguished from simple cost analysis. Furthermore, due to the universality of the term “cost-effectiveness analysis” (CEA), several studies have not distinguished CEA from cost-utility analysis; this point needs to be considered in future research. Cost-benefit analyses have not been conducted in previous TC research. Since TC has a high survival rate and good prognosis, the need for economic evaluations has recently been pointed out. Therefore, correct concepts and methods are needed to obtain clear economic evaluation results. On this basis, it will be possible to provide appropriate guidelines for TC treatment and management in the future.

Background: This study aimed to assess the effects of iodine intake, thyroid function, and their combined effect on the risk of papillary thyroid cancer (PTC) and papillary thyroid microcarcinoma (PTMC). Methods: A case-control study was conducted including 500 community-based controls who had undergone a health check-up, and 446 overall PTC cases (209 PTC and 237 PTMC) from the Thyroid Cancer Longitudinal Study. Urinary iodine concentration (UIC), was used as an indicator of iodine intake, and serum for thyroid function. The risk of PTC and PTMC was estimated using unconditional logistic regression. Results: Excessive iodine intake (UIC ≥220 μg/gCr) was associated with both PTC (odds ratio [OR], 18.13 95% confidence interval [CI], 8.87 to 37.04) and PTMC (OR, 8.02; 95% CI, 4.64 to 13.87), compared to adequate iodine intake (UIC, 85 to 219 μg/gCr). Free thyroxine (T4) levels ≥1.25 ng/dL were associated with PTC (OR, 1.97; 95% CI, 1.36 to 2.87) and PTMC (OR, 2.98; 95% CI, 2.01 to 4.41), compared to free T4 levels of 0.7 to 1.24 ng/dL. Individuals with excessive iodine intake and high free T4 levels had a greatly increased OR of PTC (OR, 43.48; 95% CI, 12.63 to 149.62), and PTMC (OR, 26.96; 95% CI, 10.26 to 70.89), compared to individuals with adequate iodine intake and low free T4 levels. Conclusion Excessive iodine intake using creatinine-adjusted UIC and high free T4 levels may have a synergistic effect on PTC and PTMC. Considering both iodine intake and thyroid function is important to assess PTC and PTMC risk.

Background: This study aimed to assess the effects of iodine intake, thyroid function, and their combined effect on the risk of papillary thyroid cancer (PTC) and papillary thyroid microcarcinoma (PTMC). Methods: A case-control study was conducted including 500 community-based controls who had undergone a health check-up, and 446 overall PTC cases (209 PTC and 237 PTMC) from the Thyroid Cancer Longitudinal Study. Urinary iodine concentration (UIC), was used as an indicator of iodine intake, and serum for thyroid function. The risk of PTC and PTMC was estimated using unconditional logistic regression. Results: Excessive iodine intake (UIC ≥220 μg/gCr) was associated with both PTC (odds ratio [OR], 18.13 95% confidence interval [CI], 8.87 to 37.04) and PTMC (OR, 8.02; 95% CI, 4.64 to 13.87), compared to adequate iodine intake (UIC, 85 to 219 μg/gCr). Free thyroxine (T4) levels ≥1.25 ng/dL were associated with PTC (OR, 1.97; 95% CI, 1.36 to 2.87) and PTMC (OR, 2.98; 95% CI, 2.01 to 4.41), compared to free T4 levels of 0.7 to 1.24 ng/dL. Individuals with excessive iodine intake and high free T4 levels had a greatly increased OR of PTC (OR, 43.48; 95% CI, 12.63 to 149.62), and PTMC (OR, 26.96; 95% CI, 10.26 to 70.89), compared to individuals with adequate iodine intake and low free T4 levels. Conclusion Excessive iodine intake using creatinine-adjusted UIC and high free T4 levels may have a synergistic effect on PTC and PTMC. Considering both iodine intake and thyroid function is important to assess PTC and PTMC risk.

OBJECTIVES: The aim of this study was to clarify the clinical trait of familial diabetes mellitus (DM) by analyzing participants’ risk of DM according to the age of DM onset in parents and siblings, and to evaluate individuals’ risk of DM-associated cardiometabolic diseases. METHODS: Altogether, 211,173 participants aged ≥40 years from the Korean Genome and Epidemiology Study were included in this study. The participants were divided into groups based on the number (1 or 2 relatives) and age of onset (no DM and early, common, or late onset) of familial DM. Participants’ risk of DM was assessed using a Cox regression model with hazard ratios and 95% confidence intervals (CIs). A logistic regression model with odds ratios was used to evaluate associations among the participants’ likelihood of acquiring cardiometabolic diseases such as hypertension, chronic kidney disease (CKD), and cardiovascular disease. RESULTS: The risk of developing DM was 2.02-fold (95% CI, 1.88 to 2.18) and 2.88-fold (95% CI, 2.50 to 3.33) higher, respectively, in participants with 1 and 2 family members diagnosed with familial DM. It was 2.72-fold (95% CI, 2.03 to 3.66) higher in those with early-onset familial DM. In the early-onset group, the respective risks of hypertension and CKD were 1.87-fold (95% CI, 1.37 to 2.55) and 4.31-fold (95% CI, 2.55 to 7.27) higher than in the control group. CONCLUSIONS The risk of DM and related cardiometabolic diseases was positively associated with the number of family members diagnosed with DM and an early diagnosis in family members with DM.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.

Objectives: This study aimed to compare the Delta, Greenland, and Monte Carlo methods for estimating 95% confidence intervals (CIs) of the population-attributable fraction (PAF). The objectives were to identify the optimal method and to determine the influence of primary parameters on PAF calculations. Methods: A dataset was simulated using hypothetical values for primary parameters (population, relative risk [RR], prevalence, and variance of the beta estimator ) involved in PAF calculations. Three methods (Delta, Greenland, and Monte Carlo) were used to estimate the 95% CIs of the PAFs. Perturbation analysis was performed to assess the sensitivity of the PAF to changes in these parameters. An R Shiny application, the “GDM-PAF CI Explorer,” was developed to facilitate the analysis and visualization of these computations. Results: No significant differences were observed among the 3 methods when both the RR and p-value were low. The Delta method performed well under conditions of low prevalence or minimal RR, while Greenland’s method was effective in scenarios with high prevalence. Meanwhile, the Monte Carlo method calculated 95% CIs of PAFs that were stable overall, though it required intensive computational resources. In a novel approach that utilized perturbation for sensitivity analysis, was identified as the most influential parameter in the estimation of CIs. Conclusions This study emphasizes the necessity of a careful approach for comparing 95% CI estimation methods for PAFs and selecting the method that best suits the context. It provides practical guidelines to researchers to increase the reliability and accuracy of epidemiological studies.