目标:效用值可以在各种健康状况之间进行相对比较,为医疗资源的有效分配提供信息。这项研究旨在(1)量化日本成年人口腔健康相关生活质量(OHRQoL)的效用值,以及(2)开发将14项口腔健康影响概况(OHIP-14)评分转换为EuroQoL5维5级(EQ-5D-5L)的效用值的模型。
方法:这是一项横断面研究。分析了2022年对日本成年人进行的大规模互联网调查的数据(n=28405;平均年龄48.2岁)。拟合多元线性回归模型以研究OHIP-14评分与基于EQ-5D-5L的效用值之间的关联,适应混杂因素。转换模型是使用随机的一半参与者开发的,并比较另一半的观察和预测效用值,以评估模型性能。
结果:在参与者中,在OHIP-14上,55.2%的得分为0,20.9%的得分为1-5,23.9%的得分为6-56,对应的平均效用值分别为0.93、0.90和0.84。OHIP-14评分增加1分与较低的效用值相关(系数:-0.0053;95%置信区间:健康相关生活质量-0.0056,-0.0051)。OHIP-14的估计效用值为-23.3/1000个人,大于其他流行的慢性病,包括高血压和糖尿病(每1000个人-2.9和-7.1,分别)。转换模型结合了OHIP-14总分,年龄,性和自我评价的健康,平均预测效用分数,并根据牙齿脱落的数量捕获差异。然而,在较低效用值组中,预测和观察到的效用值之间存在差异。
结论:OHRQoL大大影响了人口水平的效用值。OHIP-14具有作为根据口腔健康状况预测平均效用值的有价值工具的潜力;然而,对于健康相关生活质量较低的个体,预测表现相对较低.
OBJECTIVE: Utility values enable relative comparisons across various health conditions, providing information for efficient allocation of healthcare resources. This study aimed to (1) quantify the utility values attributable to oral health-related quality of life (OHRQoL) in Japanese adults and (2) develop models for converting the 14-item Oral Health Impact Profile (OHIP-14) scores into EuroQoL 5-dimension 5-level (EQ-5D-5L)-based utility values.
METHODS: This was a cross-sectional study. Data from a large-scale Internet survey of Japanese adults conducted in 2022 (n = 28 405; mean age 48.2 years) were analysed. Multiple linear regression models were fitted to investigate the association between OHIP-14 scores and EQ-5D-5L-based utility values, adjusting for confounders. Conversion models were developed using a random half of the participants, and the observed and predicted utility values in the other half were compared to evaluate the model performance.
RESULTS: Among the participants, 55.2% scored 0, 20.9% scored 1-5 and 23.9% scored 6-56 on the OHIP-14, corresponding mean utility values of 0.93, 0.90 and 0.84, respectively. A one-point increase in the OHIP-14 score was associated with a lower utility value (coefficient: -0.0053; 95% confidence interval:health-related quality of life -0.0056, -0.0051). The estimated utility value attributable to OHIP-14 was -23.3 per 1000 individuals, greater than that for other prevalent chronic conditions, including hypertension and diabetes (-2.9 and -7.1 per 1000 individuals, respectively). The conversion model incorporated the OHIP-14 total score, age, sex and self-rated health, predicted utility scores on average and captured differences according to the number of teeth lost. However, there was a discrepancy between predicted and observed utility values in the lower utility value groups.
CONCLUSIONS: OHRQoL substantially impacted utility values at the population level. The OHIP-14 holds the potential as a valuable tool for predicting average utility values based on oral health conditions; however, the prediction performance was relatively low for individuals with a lower health-related quality of life.