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Abstract Background Many municipalities in rural areas of Korea are facing population decline due to the aging population phenomenon. This study examined the relationship between residing in municipalities facing population decline and satisfaction with nearby healthcare infrastructure in older aged adults. Methods The 2021 Korea Community Health Survey (KCHS) data were used. Municipalities were classified as those not facing population decline, those at risk, and those facing population decline based on the Population Decline Index. The association between residing in municipalities facing population decline and satisfaction with nearby healthcare infrastructure was examined cross-sectionally using a multi-level logistic regression analysis. Satisfaction with available public transportation was concomitantly examined as it is related to accessing healthcare services. Results Of the 58,568 individuals aged 65 years or above analyzed, 27,471 (46.9%) adults were residing in municipalities without population decline, 4,640 (7.9%) adults in municipalities at risk of population decline, and 26,457 (45.2%) in municipalities with population decline. Individuals living in municipalities with population decline were more likely to be dissatisfied with nearby healthcare infrastructure (OR 1.76, 95% CI 1.41–2.20). Similar tendencies were found for public transportation infrastructure (OR 1.67, 95% CI 1.38–2.03). Conclusions Individuals residing in municipalities with declining populations are more likely to report dissatisfaction with nearby healthcare infrastructure and public transportation. These findings emphasize the importance of providing adequate medical infrastructure to reduce potential health-related disparities.

Abstract Background Investigating the factors associated with unmet medical needs is important since it can reflect access to healthcare. This study examined the relationship between the unmet medical needs of patients with hypertension and their satisfaction with the healthcare services available in their neighborhoods. Methods Data were from the 2021 Korean Community Health Survey. The sample included individuals aged 19 years who were diagnosed with hypertension. The main outcome measure was unmet medical need. The relationship between the outcome measure and independent variables were analyzed using multivariate logistic regressions, along with a subgroup analysis based on whether patients were currently receiving treatment for hypertension. Results Unmet medical needs were found in 4.3% of the study participants. A higher likelihood of unmet medical needs was found in individuals not satisfied with the healthcare services at proximity (adjusted OR = 1.69, 95% CI: 1.49–1.92) compared to those satisfied with services nearby. Similar tendencies were found regardless of whether individuals were currently receiving treatment for hypertension, although larger differences were found between groups in participants who were currently not receiving treatment. Conclusions The findings infer the need to consider patient satisfaction with nearby healthcare services in implementing public health policies that address unmet medical need in patients with hypertension.

Abstract Background Changes in the local population are intricately linked to healthcare infrastructure, which subsequently impacts the healthcare sector. A decreasing local population can result in lagging health infrastructure, potentially leading to adverse health outcomes as patients may be at risk of not receiving optimal care and treatment. While some studies have explored the relationship between chronic diseases and local population decline, evidence regarding cancer is insufficient. In this study, we focused on how deteriorating management of chronic diseases such as dyslipidemia could influence the risk of cancer. We investigated the relationship between changes in the local population and cancer incidence among patients with dyslipidemia. Methods This cohort study was conducted using claims data. Data from adult patients with dyslipidemia from the National Health Insurance Service–National Sample Cohort conducted between 2002 and 2015 were included. Population changes in each region were obtained from the Korean Statistical Information Service and were used to link each individual's regional code. Cancer risk was the dependent variable, and Cox proportional hazards regression was used to estimate the target associations. Results Data from 336,883 patients with dyslipidemia were analyzed. Individuals who resided in areas with a decreasing population had a higher risk of cancer than those living in areas with an increasing population (decrease: hazard ratio (HR) = 1.06, 95% CI = 1.03–1.10; normal: HR = 1.05, 95% CI = 1.02–1.09). Participants living in regions with a low number of hospitals had a higher risk of cancer than those in regions with a higher number of hospitals (HR = 1.20, 95% CI = 1.12–1.29). Conclusion Patients in regions where the population has declined are at a higher risk of cancer, highlighting the importance of managing medical problems caused by regional extinction. This could provide evidence for and useful insights into official policies on population decline and cancer risk.

Summary: Background: Owing to the aging population, the prevalence of dementia is increasing worldwide and has become an important public health problem. In 2018, Korea implemented the National Dementia Care Policy to strengthen the management of dementia and reduce its related burden on medical expenses. This study investigated the effect this policy on total and out-of-pocket costs in elderly patients with dementia. Methods: Data were from the National Health Insurance System. The study population included 10,549,863 individuals aged 65 years or older, recorded between January 1, 2015, and December 31, 2020. The treatment group comprised of dementia patients and the control group those diagnosed with the five most common diseases found in individuals aged 65 years or above. The difference-in-difference was used to explore changes in total and out-of-pocket healthcare costs per diagnosed case between the treatment and control group before and after the intervention period. Findings: Policy implementation was associated with a significant decrease in patient out-of-pocket cost. In the covariate-controlled model, no statistically significant changes were found for total mean healthcare cost. However, patient out-of-pocket cost decreased by 0.05 per diagnosed case. Interpretation: The National Dementia Care Policy led to a reduction in patient out-of-pocket cost in elderly patients with dementia. National policies need to be monitored to reduce the economic burden of patients with dementia while maintaining the financial sustainability of the healthcare system. Funding: This research was financially supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Planning & Evaluation Institute of Industrial Technology (Project No. 20024263).

Abstract The primary objective of this study was to investigate the factors contributing to hyperglycemic adverse events (AEs) associated with the administration of remdesivir in hospitalized patients diagnosed with coronavirus disease 2019 (COVID‐19). Furthermore, the study aimed to develop a risk score model employing various machine learning approaches. A total of 1262 patients were enrolled in this investigation. The relationship between covariates and hyperglycemic AEs was assessed through logistic regression analysis. Diverse machine learning algorithms were employed for the purpose of forecasting hyperglycemia‐related complications. After adjusting for covariates, individuals with a body mass index ≥23 kg/m2, those using proton pump inhibitors, cholinergic medications, or individuals with cardiovascular diseases exhibited approximately 2.41‐, 2.73‐, 2.65‐, and 1.97‐fold higher risks of experiencing hyperglycemic AEs (95% CI 1.271–4.577, 1.223–6.081, 1.168–5.989, and 1.119–3.472, respectively). Multivariate logistic regression, elastic net, and random forest models displayed area under the receiver operating characteristic curve values of 0.65, 0.66, and 0.60, respectively (95% CI 0.572–0.719, 0.640–0.671, and 0.583–0.611, respectively). This study comprehensively explored factors associated with hyperglycemic complications arising from remdesivir administration and, concurrently, leveraged a range of machine learning methodologies to construct a risk scoring model, thereby facilitating the tailoring of individualized remdesivir treatment regimens for patients with COVID‐19.