Importance: Due to the evolving variants of coronavirus disease 2019 (COVID-19), it is important to understand the relationship between the disease condition and socioeconomic, demographic, and health indicators across regions. Background: Studies examining the relationships between infectious disease and socioeconomic variables are not yet well established. Design: A total of 3042 counties in the United States are included as the observation unit in the study. Two outcome variables employed in the study are the control of disease spread and infection prevalence rates in each county. Method: Data are submitted to quantile regression, hierarchical regression, and random forest analyses to understand the extent to which health outcomes are affected by demographics, socioeconomics, and health indicators. Results: Counties with better control of the disease spread tend to have lower infection rates, and vice versa. When measuring different outcome variables, the common risk factors for COVID-19 with a 5% level of statistical significance include employment ratio, female labor ratio, young population ratio, and residents’ average health risk factors, while protective factors include land size, housing value, travel time to work, female population ratio, and ratio of residents who identify themselves as mixed race. Conclusions: The implications of the findings are that the ability to maintain social distancing and personal hygiene habits are crucial in deterring disease transmission and lowering incidence rates, especially in the early stage of disease formation. Relevant authorities should identify preventive factors and take early actions to fight infectious diseases in the future.

Background: Existing research highlights the necessity of tailoring cost-containment policies to specific treatments due to the varying benefits across different diseases. This study contributes additional insights by examining the impact of such policies on a non-acute condition—cataracts. Methods: Leveraging 16 years of national health insurance claim data, this research assesses the influence of three prevalent cost-containment payment schemes on healthcare service utilization. Outcome variables for analysis include the decision to adopt intraocular lens (IOL) insertion, outpatient visit volume, and healthcare expenditures. The robustness of the findings is enhanced through the use of statistical methods, such as logit, Poisson, negative binomial, and panel fixed-effect models. Results: Global budgeting reduces the likelihood of procedure adoption and negatively impacts the volume of outpatient consultation services. Cost sharing does not affect procedure adoption but significantly impacts outpatient service volume. The prospective payment scheme for cataract IOL treatment shows no long-term effects on service utilization, with treatment rates stabilizing after a few years of policy implementation. Despite reimbursement points remaining unchanged for over two decades, there is no evidence of the under-provision of treatment. Conclusions: This study underscores the significant responsiveness of both patients and providers to policy reforms in the non-acute disease category. Manipulating payment schemes can lead to cost savings, particularly when treatment plans and procedures exhibit increased elasticity in their provision.

Objectives: Extant research on cost-sharing finds no impact on health care utilization when the amount is insubstantial. This research investigates the effects on nonacute outpatient services for schoolchildren with refractive errors in Taiwan and discusses the potential harm caused by cost sharing and relevant cost containment policies. Methods: Longitudinal claims data from the National Health Insurance database are employed. District demographic information is also used for aggregate-level analyses. Interventional modeling is conducted on pooled individual-level data with a Poisson model and negative binomial models. Generalized least square modeling is performed on aggregate district-level data to elucidate the impacts of cost sharing and the reimbursement rate with controls for patient and institutional characteristics, district socioeconomic factors, and competitiveness among institutions. Results: The findings of this study show that cost sharing does not significantly affect children’s utilization of outpatient services in the patient-level analyses. However, it significantly decreases the service volume based on the results of district aggregate analyses. There are potentially marginal patients in society, and they are more likely to be girls in poorer families, whose chances of seeking medical care significantly decrease when cost sharing increases. Conclusions: The gap in health inequity can be widened when stringent cost-containment policies are implemented. The offset effect caused by delayed care may also result in higher health care expenditures later. Cost sharing for children should be separately and prudently designed to better protect them from deprivations caused by changes in health policies.