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Background: Olfactory dysfunction is a common COVID-19 symptom, posing treatment challenges. Objectives: We aimed to investigate the efficacy of frequency-controlled ear acupuncture in treating COVID-19-related olfactory dysfunction. Methods: A randomized, participant-blind clinical trial occurred at the Rasoul Akram Hospital (IRCT20210311050671N1). Forty patients were recruited, and 20 patients were randomly assigned to either the experimental or control group. The primary outcome was the improvement in patients' quality of smell. The olfactory dysfunction was confirmed using the Smell Identification Test. The intervention group received two sessions of acupuncture treatment according to auricular frequency treatment, with a one-week interval, while the control group received an equal number of switched-off laser sessions. Both groups were instructed to use nasal betamethasone drops. The patients were asked to rank their ability to smell before and after each intervention on a 10-point visual analog scale. Secondary outcomes were related side effects. Results: Covariance analysis revealed a significant difference in adjusted scores between the groups (F [37, 1] = 37.463; p = 0.000, Eta2 = 0.503). The smell quality improved from 2.80 ± 1.76 to 5.22 ± 3.40 after treatment in the intervention group (p = 0.007), while the control group showed no significant change (p = 0.184). Three patients reported short and transient side effects, such as nausea, headache, and dizziness, in the first hours after the intervention. Conclusion: Frequency-controlled ear acupuncture is an effective option for treating COVID-19-related olfactory dysfunction. The study highlights the potential of alternative therapies in the treatment of this condition, and further research is warranted to investigate its long-term effects. [ABSTRACT FROM AUTHOR]

Objective: A more equal allocation of healthcare funds for patients who must pay high costs of care ensures the welfare of society. This study aimed to estimate the optimal co-insurance for outpatient drug costs for health insurance. Setting: The research population includes outpatient prescription claims made by the Health Insurance Organization that outpatient prescriptions in a timely manner in 2016, 2017, 2018, and 2019 were utilized to calculate the optimal co-insurance. The study population was representative of the research sample. Design: At the secondary level of care, 11 features of outpatient claims were studied cross-sectionally and retrospectively using data mining. Optimal co-insurance was estimated using Westerhut and Folmer's utility model. Participants: One hundred ninety-three thousand five hundred fifty-two individuals were created from 21 776 350 outpatient claims of health insurance. Because of cost-sharing, insured individuals in a low-income subsidy plan and those with refractory diseases were excluded. Results: Insureds were divided into three classes of low, middle, and high risk based on IQR and were separated to three clusters using the silhouette coefficient. For the first, second, and third clusters of the low-risk class, the optimal co-insurance estimates are 0.81, 0.76, and 0.84, respectively. It was equal to one for all middle-class clusters and 0.38, 0.45, and 0.42, respectively, for the high-risk class. The insurer's expenses were altered by $3,130,463, $3,451,194, and $ 1,069,859 profit for the first, second, and third clusters, respectively, when the optimal co-insurance strategy is used for the low-risk class. For middle risks, it was US$29,239,815, US$13,863,810, and US$ 14,573,432 while for high risks, US$4,722,099, US$ 6,339,317, and US$19,627,062, respectively. Conclusions: These findings can improve vulnerable populations' access to costly medications, reduce resource waste, and help insurers distribute funds more efficiently. [ABSTRACT FROM AUTHOR]