Factors Predicting Older People's Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study.

BACKGROUND: Mobile health (mHealth) services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. OBJECTIVE: The study aims to identify the predictors of older people's intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. METHODS: A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. RESULTS: Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (ß=.453; P=.003) and social influence (ß=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (ß=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=-2.165). Bootstrapping also confirmed that device trust (ß=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. CONCLUSIONS: This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

Comparison of Newly Proposed LDL-Cholesterol Estimation Equations.

BACKGROUND: Low-density lipoprotein cholesterol is an important marker highly associated with cardiovascular disease. Since the direct measurement of it is inefficient in terms of cost and time, it is common to estimate through the Friedewald equation developed about 50 years ago. However, various limitations exist since the Friedewald equation was not designed for Koreans. This study proposes a new low-density lipoprotein cholesterol estimation equation for South Koreans using nationally approved statistical data. METHODS: This study used data from the Korean National Health and Nutrition Examination Survey from 2009 to 2019. The 18,837 subjects were used to develop the equation for estimating low-density lipoprotein cholesterol. The subjects included individuals with low-density lipoprotein cholesterol levels directly measured among those with high-density lipoprotein cholesterol, triglycerides, and total cholesterol measured. We compared twelve equations developed in the previous studies and the newly proposed equation (model 1) developed in this study with the actual low-density lipoprotein cholesterol value in various ways. RESULTS: The low-density lipoprotein cholesterol value estimated using the estimation formula and the actual low-density lipoprotein cholesterol value were compared using the root mean squared error. When the triglyceride level was less than 400 mg/dL, the root mean squared of the model 1 was 7.96, the lowest compared to other equations, and the model 2 was 7.82. The degree of misclassification was checked according to the NECP ATP III 6 categories. As a result, the misclassification rate of the model 1 was the lowest at 18.9%, and Weighted Kappa was the highest at 0.919 (0.003), which means it significantly reduced the underestimation rate shown in other existing estimation equations. Root mean square error was also compared according to the change in triglycerides level. As the triglycerides level increased, the root mean square error showed an increasing trend in all equations, but it was confirmed that the model 1 was the lowest compared to other equations. CONCLUSION: The newly proposed low-density lipoprotein cholesterol estimation equation showed significantly improved performance compared to the 12 existing estimation equations. The use of representative samples and external verification is required for more sophisticated estimates in the future.

Regional Health Disparities in Hypertension-Related Hospitalization of Hypertensive Patients: A Nationwide Population-Based Nested Case-Control Study.

Objective: This study aims to explore regional health disparities in hypertension-related hospitalizations and confirm this difference according to the states of continuity of care (COC). Methods: We used the National Health Insurance Service National Sample Cohort data from 2002 to 2019. The dependent variable, hypertension-related hospitalization, included hospitalization for hypertensive diseases (I10-I13, I15), ischemic heart disease (I20-I25), and cerebrovascular disease (I60-I69). Nested case-control matching was performed according to age, sex, and income level. We compared hypertension-related hospitalization fractions in urban and rural areas by classifying them according to the state of COC and analyzed them using conditional logistic regression suitable for matched data. Results: The odds of hypertension-related hospitalization of hypertensive patients were higher in the rural areas than in the urban areas; however, as the COC increased, the difference decreased. There was no change in the results according to the COC observation period. Conclusion: To reduce regional health disparities, both the promotion of COC and the improvement of the quality of primary care must be achieved.

Evaluation of an air quality warning system for vulnerable and susceptible individuals in Korea: an interrupted time series analysis.

OBJECTIVES: This study was conducted to elucidate the effects of an air quality warning system (AQWS) implemented in January 2015 in Korea by analyzing changes in the incidence and exacerbation rates of environmental diseases. METHODS: Data from patients with environmental diseases were extracted from the National Health Insurance Service-National Sample Cohort database from 2010 to 2019, and data on environmental risk factors were acquired from the AirKorea database. Patient and meteorological data were linked based on residential area. An interrupted time series analysis with Poisson segmented regression was used to compare the rates before and after AQWS introduction. Adjustment variables included seasonality, air pollutants (carbon monoxide, nitrogen dioxide, sulfur dioxide, particulate matter less than 10 µm in diameter, and ozone), temperature, and humidity. RESULTS: After AQWS implementation, the incidence of asthma gradually decreased by 20.5%. Cardiovascular disease and stroke incidence also significantly decreased (by 34.3 and 43.0%, respectively). However, no immediate or gradual decrease was identified in the exacerbation rate of any environmental disease after AQWS implementation. Sensitivity analyses were performed according to age, disability, and health insurance coverage type. Overall, the AQWS effectively mitigated the occurrence of most environmental diseases in Korea. However, the relationships between alarm system implementation and reduced incidence differed among diseases based on the characteristics of vulnerable and sensitive individuals. CONCLUSIONS: Our results suggest that by tailoring the AQWS to demographic and sociological characteristics and providing enhanced education about the warning system, interventions can become an efficient policy tool to decrease air pollution- related health risks.

Which indicator should be used? A comparison between the incidence and intensity of catastrophic health expenditure: using difference-in-difference analysis.

BACKGROUND: Catastrophic health expenditure (CHE) represents out-of-pocket payment as a share of household income. Most previous studies have focused on incidence aspects when assessing health policy effects. However, because CHE incidence is a binary variable, the effect of the health policy could not accurately be evaluated. On the contrary, the intensity of CHE is a continuous variable that can yield completely different results from previous studies. This study reassesses the coverage expansion plan for four serious diseases using the intensity of CHE in Korea. METHODS: We used the Korea Health Panel Study from 2013 to 2015 to conduct the analysis. The study population is households with chronic diseases patients. We divided the population into two groups: the policy beneficiary group, i.e., households with a patient of any of the four serious diseases, and the non-beneficiary group. A difference-in-difference model was employed to compare the variation in the intensity and incidence of CHE between the two groups. We defined the incidence of CHE as when the ratio of out-of-pocket medical expenses to household income is more than a threshold of 10%, and the intensity of CHE is the height of the ratio subtracting the threshold 10%. RESULTS: The increased rate of CHE intensity in households with four serious diseases was lower than that in households with other chronic diseases. The interaction term, which represents the effect of the policy, has a significant impact on the intensity but not on the incidence of CHE. CONCLUSIONS: CHE indicators should be applied differently according to the purpose of policy evaluation. The incidence of CHE should be used as the final achievement indicator, and the intensity of CHE should be used as the process indicator. Furthermore, because CHE has an inherent characteristic that is measured by the ratio of household income to medical expenses, to lower this, a differential out-of-pocket maximum policy for each income class is more appropriate than a policy for strengthening the coverage for specific diseases.

Is Avoidable Hospitalization Experienced Prior to Infection Associated With COVID-19-Related Deaths?

Objectives: This study aimed to determine the effect of the presence or absence of avoidable hospitalization before acquiring coronavirus disease (COVID-19) on COVID-19-related deaths. Methods: This study used the total NHIS-COVID-19 dataset comprising domestic COVID-19 patients, provided by the National Health Insurance Service (NHIS) in South Korea. We conducted logistic regression and double robust estimation (DRE) to confirm the effect of avoidable hospitalization on COVID-19-related deaths. Results: Logistic regression analysis confirmed that the odds ratio (OR) of death due to COVID-19 was high in the group that experienced avoidable hospitalization. DRE analysis showed a higher OR of death due to COVID-19 in the group that experienced avoidable hospitalization compared to the group that did not experience avoidable hospitalization, except in the subgroup aged ≤69 years. Conclusion: The effect of avoidable hospitalization on COVID-19-related deaths was confirmed. Therefore, continued health care, preventive medicine, and public health management are essential for reducing avoidable hospitalizations despite the COVID-19 pandemic. Clinicians need to be informed about the importance of continuous disease management.

Chronic Disease Management for People With Hypertension.

Objectives: To assess the effectiveness of continuity of care policies by identifying the impact of a chronic disease management program on the continuity of care in patients with hypertension in South Korea. Methods: The propensity score matching method was used to control selection bias, and the difference-in-differences method was used to compare the impact on the treatment and control groups according to the policy intervention. Results: The continuity of care index of hypertensive patients using the difference-in-differences analysis outcome of the chronic disease management program was higher than that of the non-participating hypertensive patients. Conclusion: Continuous treatment is vital for chronic diseases such as hypertension. However, the proportion of those participating in the intervention was low. Encouraging more hypertensive patients to participate in policy intervention through continuous research and expanding the policy to appropriately reflect the increasing number of chronic diseases is necessary.

Relationship between the living-alone period and depressive symptoms among the elderly.

OBJECTIVE: The study aims to understand the relationship between the living-alone period and depressive symptoms among the elderly in South Korea. METHODS: The study obtained data on the elderly over 65 years old from the 2006 to 2019 Korea Welfare Panel Study. Generalized estimating equation (GEE) analysis was performed to understand changes in depressive symptoms over periods of living alone. Additional subgroup analysis of age and gender was conducted to observe differences. RESULTS: A total 568 (19% of the study samples) elderly transitioned from living with others into a living-alone state. Males and females showed a significant increase in depressive symptoms as they started to live alone, which gradually decreased with prolonged periods of living alone. The result of the GEE analysis demonstrated high odds of depressive symptoms during the first (OR = 1.857; 95% CI: 1.492-2.310), second (OR = 1.414; 95% CI: 1.127-1.774), and third (OR = 1.413; 95% CI: 1.105-1.808) years of living alone compared with living with others. Regarding additional subgroup analysis, the female and old-old groups showed high odds of depressive symptoms in the first 3 years of living alone, whereas the male and young-old groups showed high odds for the first year only. CONCLUSIONS: The study provides valuable insights into the relationship between the living-alone period and depressive symptoms among the elderly. Future research using the living-alone period on various social factors are recommended.