Ash Arlene S, Mick Eric O, Ellis Randall P, Kiefe Catarina I, Allison Jeroan J, Clark Melissa A
Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester.
Department of Economics, Boston University, Boston, Massachusetts.
JAMA Intern Med. 2017 Oct 1;177(10):1424-1430. doi: 10.1001/jamainternmed.2017.3317.
Managed care payment formulas commonly allocate more money for medically complex populations, but ignore most social determinants of health (SDH).
To add SDH variables to a diagnosis-based payment formula that allocates funds to managed care plans and accountable care organizations.
DESIGN, SETTING, AND PARTICIPANTS: Using data from MassHealth, the Massachusetts Medicaid and Children's Health Insurance Program, we estimated regression models predicting Medicaid spending using a diagnosis-based and SDH-expanded model, and compared the accuracy of their cost predictions overall and for vulnerable populations. MassHealth members enrolled for at least 6 months in 2013 in fee-for-service (FFS) programs (n = 357 660) or managed care organizations (MCOs) (n = 524 607).
We built cost prediction models from a fee-for-service program. Predictors in the diagnosis-based model are age, sex, and diagnoses from claims. The SDH model adds predictors describing housing instability, behavioral health issues, disability, and neighborhood-level stressors.
Overall model explanatory power and overpayments and underpayments for subgroups of interest for all Medicaid-reimbursable expenditures excepting long-term support services (mean annual cost = $5590 per member).
We studied 357 660 people who were FFS participants and 524 607 enrolled in MCOs with a combined 806 889 person-years of experience. The FFS program experience included more men (49.6% vs 43.6%), older patients (mean age of 26.1 years vs 21.6 years), and sicker patients (mean morbidity score of 1.16 vs 0.89) than MCOs. Overall, the SDH model performed well, but only slightly better than the diagnosis-based model, explaining most of the spending variation in the managed care population (validated R2 = 62.4) and reducing underpayments for several vulnerable populations. For example, raw costs for the quintile of people living in the most stressed neighborhoods were 9.6% ($537 per member per year) higher than average. Since greater medical morbidity accounts for much of this difference, the diagnosis-based model underpredicts costs for the most stressed quintile by about 2.1% ($130 per member per year). The expanded model eliminates the neighborhood-based underpayment, as well as underpayments of 72% for clients of the Department of Mental Health (observed costs of about $30 000 per year) and of 7% for those with serious mental illness (observed costs of about $16 000 per year).
Since October 2016, MassHealth has used an expanded model to allocate payments from a prespecified total budget to managed care organizations according to their enrollees' social and medical risk. Extra payments for socially vulnerable individuals could fund activities, such as housing assistance, that could improve health equity.
管理式医疗支付公式通常会为医疗复杂人群分配更多资金,但却忽略了大多数健康的社会决定因素(SDH)。
将SDH变量添加到基于诊断的支付公式中,该公式用于向管理式医疗计划和责任医疗组织分配资金。
设计、背景和参与者:利用马萨诸塞州医疗补助计划(MassHealth)、马萨诸塞州医疗补助和儿童健康保险计划的数据,我们使用基于诊断和扩展了SDH的模型估计了预测医疗补助支出的回归模型,并比较了它们在总体以及弱势群体成本预测方面的准确性。2013年在按服务收费(FFS)计划(n = 357660)或管理式医疗组织(MCO)(n = 524607)中注册至少6个月的MassHealth成员。
我们从一个按服务收费计划构建了成本预测模型。基于诊断的模型中的预测因素是年龄、性别和索赔中的诊断。SDH模型增加了描述住房不稳定、行为健康问题、残疾和社区层面压力源的预测因素。
总体模型解释力以及除长期支持服务外所有医疗补助可报销支出(每位成员平均年度成本 = 5590美元)的感兴趣亚组的多付和少付情况。
我们研究了357660名FFS参与者和524607名注册MCO的人,总计806889人年的经验。FFS计划的参与者中男性更多(49.6%对43.6%)、患者年龄更大(平均年龄26.1岁对21.6岁)且病情更重(平均发病评分1.16对0.89)。总体而言,SDH模型表现良好,但仅略优于基于诊断的模型,解释了管理式医疗人群中的大部分支出变化(验证后的R2 = 62.4),并减少了几个弱势群体的少付情况。例如,生活在压力最大社区的人群五分之一的原始成本比平均水平高9.6%(每年每位成员537美元)。由于更高的医疗发病率占了这种差异的很大一部分,基于诊断的模型对压力最大的五分之一人群的成本预测低了约2.1%(每年每位成员130美元)。扩展后的模型消除了基于社区的少付情况,以及对心理健康部客户少付72%(每年观察到的成本约为30000美元)和对严重精神疾病患者少付7%(每年观察到的成本约为16000美元)的情况。
自2016年10月以来,MassHealth使用扩展模型根据参保人的社会和医疗风险将预先设定的总预算中的支付分配给管理式医疗组织。为社会弱势群体提供的额外支付可以为诸如住房援助等活动提供资金,从而改善健康公平性。