Wang Tim T, Lee Cameron C, Gross Andrew J, Hajibandeh Jeffrey T, Peacock Zachary S
Resident, Division of Oral and Maxillofacial Surgery, Massachusetts General Hospital and Clinical Fellow, Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, MA.
Head and Neck Oncology Fellow, University of Maryland Medical Center, Baltimore, MD; Clinical Research Fellow, Division of Oral and Maxillofacial Surgery, Massachusetts General Hospital, Boston, MA.
J Oral Maxillofac Surg. 2024 May;82(5):554-562. doi: 10.1016/j.joms.2024.02.002. Epub 2024 Feb 10.
There is a lack of consensus on the optimal triage pathway for emergency department (ED) patients with mandibular fractures. It remains unclear if patient insurance payers predict hospital admission given potentially competing logistical and health system incentives.
To generate nationally representative estimates of the frequency of hospital admission and its association with primary insurance payers for ED patients with mandible fractures.
This retrospective cohort study used the 2018 Nationwide Emergency Department Sample, the largest all-payer database in the United States, to identify patients with mandible fractures. The database includes a stratified sample with discharge weights to generate nationally representative estimates. Patients with other facial fractures and/or concomitant injuries that independently warranted admission were excluded.
The primary predictor variable was primary payer (public, private, self-pay, and other/no charge).
The primary outcome variable was hospital admission (yes/no).
Covariates included patient-, medical/injury-, and hospital-related variables.
Descriptive statistics, along with bivariate and multivariate logistic regression with Bonferroni correction, were used to produce national estimates and identify predictors of admission. P < .01 was considered significant.
The cohort included 27,238 weighted encounters involving isolated mandible fractures, of which 5,345(20%) were admitted. The payers for admitted patients were 46% public, 25% private, 22% self-pay, and 7% no charge/other. In bivariate analyses, public insurance was associated with a higher likelihood of admission than private insurance (RR 1.24, 95% CI 1.06 to 1.45), though there was no association in the multivariate model (OR 1.03, 95% CI 0.83 to 1.28). In multivariate analysis, higher Charlson Comorbidity Index (OR 1.32, 95% CI 1.18 to 1.48), alcohol-related disorder (OR 3.47, 95% CI 2.74 to 4.39), substance-related disorder (OR 1.43, 95% CI 1.20 to 1.71), and more mandible fractures (OR 3.08, 95% CI 2.65 to 3.59) were associated with admission. Compared to body fractures, subcondylar (OR 3.83, 95% CI 2.39 to 6.14), angle (OR 3.53, 95% CI 2.84 to 6.09), and symphysis (OR 4.14, 95% CI 2.84 to 6.09) fractures had higher odds of admission. Finally, level I (OR 4.11, 95% CI 2.41 to 6.98) and level II (OR 3.16, 95% CI 1.85 to 5.39) trauma centers had higher odds of admission.
In 2018, 20% of ED patients with isolated mandible fractures were admitted. Several patient and hospital characteristics were predictors of admission. Insurance status was not associated with admission.
对于急诊科(ED)下颌骨骨折患者的最佳分诊途径,目前尚无共识。鉴于可能存在相互竞争的后勤和卫生系统激励因素,患者保险支付方是否能预测住院情况仍不明确。
对下颌骨骨折的急诊科患者的住院频率及其与主要保险支付方的关联进行全国代表性估计。
这项回顾性队列研究使用了2018年全国急诊科样本(美国最大的全支付方数据库)来识别下颌骨骨折患者。该数据库包括一个带有出院权重的分层样本,以生成全国代表性估计值。排除了因其他面部骨折和/或伴随损伤而独立需要住院治疗的患者。
主要预测变量是主要支付方(公共、私人、自费和其他/免费)。
主要结果变量是住院(是/否)。
协变量包括患者、医疗/损伤和医院相关变量。
使用描述性统计以及经邦费罗尼校正的双变量和多变量逻辑回归,以得出全国估计值并确定住院的预测因素。P <.01被认为具有统计学意义。
该队列包括27238例涉及孤立下颌骨骨折的加权病例,其中5345例(20%)住院。住院患者的支付方中,46%为公共支付,25%为私人支付,22%为自费,7%为免费/其他。在双变量分析中,公共保险与住院可能性高于私人保险相关(相对风险1.24,95%置信区间1.06至1.45),但在多变量模型中无关联(比值比1.03,95%置信区间0.83至1.28)。在多变量分析中,较高的查尔森合并症指数(比值比1.32,95%置信区间1.18至1.48)、酒精相关障碍(比值比3.47,95%置信区间2.74至4.39)、物质相关障碍(比值比1.43,95%置信区间1.20至1.71)以及更多的下颌骨骨折(比值比3.08,95%置信区间2.65至3.59)与住院相关。与身体其他部位骨折相比,髁突(比值比3.83,95%置信区间2.39至6.14)、角部(比值比3.53,95%置信区间2.84至6.09)和正中联合部(比值比4.14,95%置信区间2.84至6.09)骨折的住院几率更高。最后,I级(比值比4.11,95%置信区间2.41至6.98)和II级(比值比3.16,95%置信区间1.85至5.39)创伤中心的住院几率更高。
2018年,20%的孤立下颌骨骨折急诊科患者住院。几个患者和医院特征是住院的预测因素。保险状况与住院无关。