Identifying a Sample of HIV-Positive Beneficiaries From Medicaid Claims Data and Estimating Their Treatment Costs
Identifying a Sample of HIV-Positive Beneficiaries From Medicaid Claims Data and Estimating Their Treatment Costs
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DOI：
10.2105/ajph.2014.302263
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发表时间：
2015-03-01
影响因子：
12.7
通讯作者：
Desmond, Katherine
中科院分区：
文献类型：
--
作者：
Leibowitz, Arleen A.;Desmond, Katherine
目标。我们试图从医疗保险和医疗补助索赔数据中确定艾滋病毒/艾滋病患者，以估计加州治疗艾滋病毒/艾滋病的医疗补助费用。我们还研究了确定相关样本的替代方法如何影响人均成本的估计。我们分析了2007年医疗保险或医疗补助索赔数据中登记的患有艾滋病毒/艾滋病的加州人的数据。我们通过检查抗逆转录病毒药物的使用、艾滋病毒特异性监测测试和医疗费用来比较不同的选择标准。我们将最终样本和平均成本与2007年加州医疗补助覆盖的艾滋病毒/艾滋病人口规模及其平均治疗成本的其他估计进行了比较。87%（18290）潜在可识别的艾滋病毒阳性个体满足至少一项确认标准。近80%的已证实的观察结果声称进行了艾滋病毒特异性检测，而在排除的病例中，这一比例仅为3%。女性医疗补助接受者尤其有可能被错误地编码为艾滋病毒携带者。2007年，加州艾滋病患者的医疗补助治疗费用平均为33720美元。该算法具有良好的内部有效性和外部有效性。在索赔数据中准确识别艾滋病毒病例对于避免得出有偏见的结论非常重要，并且对于设定适当的艾滋病毒管理护理人均费率是必要的。
Objectives. We sought to identify people living with HIV/AIDS from Medicare and Medicaid claims data to estimate Medicaid costs for treating HIV/AIDS in California. We also examined how alternate methods of identifying the relevant sample affect estimates of per capita costs.Methods. We analyzed data on Californians enrolled in Medicaid with an HIV/AIDS diagnosis reported in 2007 Medicare or Medicaid claims data. We compared alternative selection criteria by examining use of antiretroviral drugs, HIV-specific monitoring tests, and medical costs. We compared the final sample and average costs with other estimates of the size of California's HIV/AIDS population covered by Medicaid in 2007 and their average treatment costs.Results. Eighty-seven percent (18 290) of potentially identifiable HIV-positive individuals satisfied at least 1 confirmation criterion. Nearly 80% of confirmed observations had claims for HIV-specific tests, compared with only 3% of excluded cases. Female Medicaid recipients were particularly likely to be miscoded as having HIV. Medicaid treatment spending for Californians with HIV averaged $ 33 720 in 2007.Conclusions. The proposed algorithm displays good internal and external validity. Accurately identifying HIV cases in claims data is important to avoid drawing biased conclusions and is necessary in setting appropriate HIV managed-care capitation rates.