Mesh : Humans Male Female Vision, Low / epidemiology Middle Aged Big Data Aged Netherlands / epidemiology Adult Optometry / statistics & numerical data Insurance Claim Review Adolescent Young Adult Patient Acceptance of Health Care / statistics & numerical data Eye Diseases / therapy epidemiology Health Services Accessibility / statistics & numerical data Child

来  源:   DOI:10.1097/OPX.0000000000002134

Abstract:
CONCLUSIONS: There is a lack of research from high-income countries with various health care and funding systems regarding barriers and facilitators in low vision services (LVS) access. Furthermore, very few studies on LVS provision have used claims data.
OBJECTIVE: This study aimed to investigate which patient characteristics predict receiving multidisciplinary LVS (MLVS) in the Netherlands, a high-income country, based on health care claims data.
METHODS: Data from a Dutch national health insurance claims database (2015 to 2018) of patients with eye diseases causing potentially severe visual impairment were retrieved. Patients received MLVS (n = 8766) and/or ophthalmic treatment in 2018 (reference, n = 565,496). MLVS is provided by professionals from various clinical backgrounds, including nonprofit low vision optometry. Patient characteristics (sociodemographic, clinical, contextual, general health care utilization) were assessed as potential predictors using a multivariable logistic regression model, which was internally validated with bootstrapping.
RESULTS: Predictors for receiving MLVS included prescription of low vision aids (odds ratio [OR], 8.76; 95% confidence interval [CI], 7.99 to 9.61), having multiple ophthalmic diagnoses (OR, 3.49; 95% CI, 3.30 to 3.70), receiving occupational therapy (OR, 2.32; 95% CI, 2.15 to 2.51), mental comorbidity (OR, 1.17; 95% CI, 1.10 to 1.23), comorbid hearing disorder (OR, 1.98; 95% CI, 1.86 to 2.11), and receiving treatment in both a general hospital and a specialized ophthalmic center (OR, 1.23; 95% CI, 1.10 to 1.37), or by a general practitioner (OR, 1.23; 95% CI, 1.18 to 1.29). Characteristics associated with lower odds included older age (OR, 0.30; 95% CI, 0.28 to 0.32), having a low social economic status (OR, 0.91; 95% CI, 0.86 to 0.97), physical comorbidity (OR, 0.87; 95% CI, 0.82 to 0.92), and greater distance to an MLVS (OR, 0.95; 95% CI, 0.92 to 0.98). The area under the curve of the model was 0.75 (95% CI, 0.75 to 0.76; optimism = 0.0008).
CONCLUSIONS: Various sociodemographic, clinical, and contextual patient characteristics, as well as factors related to patients\' general health care utilization, were found to influence MLVS receipt as barriers or facilitators. Eye care practitioners should have attention for socioeconomically disadvantaged older patients when considering MLVS referral.
摘要:
结论:高收入国家缺乏关于低视力服务(LVS)获得障碍和促进者的各种医疗保健和资助系统的研究。此外,很少有关于LVS条款的研究使用索赔数据。
目的:本研究旨在调查在荷兰接受多学科LVS(MLVS)的患者特征,一个高收入国家,基于医疗保健索赔数据。
方法:检索了来自荷兰国家健康保险索赔数据库(2015年至2018年)的导致严重视力损害的眼病患者的数据。2018年患者接受MLVS(n=8766)和/或眼科治疗(参考,n=565,496)。MLVS由来自各种临床背景的专业人士提供,包括非营利性低视力验光。患者特征(社会人口统计学,临床,上下文,一般医疗保健利用率)使用多变量逻辑回归模型评估为潜在预测因子,这是内部验证与引导。
结果:接受MLVS的预测因素包括低视力辅助剂的处方(比值比[OR],8.76;95%置信区间[CI],7.99至9.61),有多个眼科诊断(或,3.49;95%CI,3.30至3.70),接受职业治疗(或,2.32;95%CI,2.15至2.51),精神合并症(或,1.17;95%CI,1.10至1.23),共病听力障碍(或,1.98;95%CI,1.86至2.11),并在综合医院和专业眼科中心接受治疗(OR,1.23;95%CI,1.10至1.37),或由全科医生(或,1.23;95%CI,1.18至1.29)。与较低赔率相关的特征包括年龄较大(OR,0.30;95%CI,0.28至0.32),具有较低的社会经济地位(或者,0.91;95%CI,0.86至0.97),物理合并症(OR,0.87;95%CI,0.82至0.92),到MLVS的距离更大(或,0.95;95%CI,0.92至0.98)。模型曲线下面积为0.75(95%CI,0.75~0.76;乐观=0.0008)。
结论:各种社会人口统计学,临床,和上下文患者特征,以及与患者一般医疗保健利用相关的因素,被发现影响MLVS接收的障碍或促进者。在考虑MLVS转诊时,眼部护理从业人员应注意社会经济上处于不利地位的老年患者。
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