Abstract：
OBJECTIVE: To determine the geographic variability of Medicaid acceptance among allergists in the US.
METHODS: Geospatial analysis predicted Medicaid acceptance across space, and a multivariable regression identified area-level population demographic variables associated with acceptance.
METHODS: We used the National Plan & Provider Enumeration System database to identify allergists. Medicaid acceptance was determined from lists or search engines from state Medicaid offices and calls to provider offices. Spatial analysis was performed using the empirical Bayesian kriging tool. Multivariate logistic regression was used to identify county-level characteristics associated with provider Medicaid acceptance.
RESULTS: Of 5694 allergists, 55.5% accepted Medicaid. Acceptance in each state ranged from 13% to 90%. Washington, Arizona, and the Northeast had lowest predicted proportion of both Medicaid acceptance and Medicaid acceptance per 10,000 enrollees. Overall, county-level characteristics were not associated with the likelihood of accepting Medicaid in multivariate analyses. Only the percentage of individuals living in poverty was associated with a higher likelihood of providers accepting Medicaid (OR, 1.245; 95% CI, 1.156-1.340; P < .001).
CONCLUSIONS: A barrier to accessing allergy-related health care is finding a provider who accepts a patient\'s insurance, which is largely variable by state. Lack of access to allergy care likely affects health outcomes for children with prevalent atopic conditions such as food allergy.
METHODS: Geospatial analysis predicted Medicaid acceptance across space, and a multivariable regression identified area-level population demographic variables associated with acceptance.
METHODS: We used the National Plan & Provider Enumeration System database to identify allergists. Medicaid acceptance was determined from lists or search engines from state Medicaid offices and calls to provider offices. Spatial analysis was performed using the empirical Bayesian kriging tool. Multivariate logistic regression was used to identify county-level characteristics associated with provider Medicaid acceptance.
RESULTS: Of 5694 allergists, 55.5% accepted Medicaid. Acceptance in each state ranged from 13% to 90%. Washington, Arizona, and the Northeast had lowest predicted proportion of both Medicaid acceptance and Medicaid acceptance per 10,000 enrollees. Overall, county-level characteristics were not associated with the likelihood of accepting Medicaid in multivariate analyses. Only the percentage of individuals living in poverty was associated with a higher likelihood of providers accepting Medicaid (OR, 1.245; 95% CI, 1.156-1.340; P < .001).
CONCLUSIONS: A barrier to accessing allergy-related health care is finding a provider who accepts a patient\'s insurance, which is largely variable by state. Lack of access to allergy care likely affects health outcomes for children with prevalent atopic conditions such as food allergy.
摘要：
目的：确定美国变态反应学家接受医疗补助的地域差异。
方法：地理空间分析预测了跨空间的医疗补助接受度，多变量回归确定了与接受度相关的地区级人口统计学变量。
方法：我们使用国家计划和提供者枚举系统数据库来识别过敏原。医疗补助的接受程度是根据州医疗补助办公室的名单或搜索引擎以及对提供者办公室的电话确定的。使用经验贝叶斯克里金工具进行空间分析。多变量逻辑回归用于确定与提供者医疗补助接受度相关的县级特征。
结果：在5694名过敏症患者中，55.5％接受了医疗补助。每个州的接受度从13%到90%不等。华盛顿,亚利桑那,东北地区的医疗补助接受率和医疗补助接受率的预测比例最低。总的来说,在多变量分析中,县级特征与接受医疗补助的可能性无关.只有生活在贫困中的个人百分比与提供者接受医疗补助的可能性更高(OR，1.245；95%CI，1.156-1.340；P<.001）。
结论：获得过敏相关医疗保健的障碍是寻找接受患者保险的提供者，这在很大程度上取决于状态。缺乏过敏护理可能会影响患有食物过敏等普遍特应性疾病的儿童的健康结果。
方法：地理空间分析预测了跨空间的医疗补助接受度，多变量回归确定了与接受度相关的地区级人口统计学变量。
方法：我们使用国家计划和提供者枚举系统数据库来识别过敏原。医疗补助的接受程度是根据州医疗补助办公室的名单或搜索引擎以及对提供者办公室的电话确定的。使用经验贝叶斯克里金工具进行空间分析。多变量逻辑回归用于确定与提供者医疗补助接受度相关的县级特征。
结果：在5694名过敏症患者中，55.5％接受了医疗补助。每个州的接受度从13%到90%不等。华盛顿,亚利桑那,东北地区的医疗补助接受率和医疗补助接受率的预测比例最低。总的来说,在多变量分析中,县级特征与接受医疗补助的可能性无关.只有生活在贫困中的个人百分比与提供者接受医疗补助的可能性更高(OR，1.245；95%CI，1.156-1.340；P<.001）。
结论：获得过敏相关医疗保健的障碍是寻找接受患者保险的提供者，这在很大程度上取决于状态。缺乏过敏护理可能会影响患有食物过敏等普遍特应性疾病的儿童的健康结果。
