Racial/Ethnic Disparities in the Performance of Prediction Models for Death by Suicide After Mental Health Visits
Racial/Ethnic Disparities in the Performance of Prediction Models for Death by Suicide After Mental Health Visits
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DOI：
10.1001/jamapsychiatry.2021.0493
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发表时间：
2021-04-28
期刊：
影响因子：
25.8
通讯作者：
Shortreed, Susan M.
中科院分区：
文献类型：
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作者：
Coley, R. Yates;Johnson, Eric;Shortreed, Susan M.
问：自杀预测模型的实施是否会加强和恶化护理上的种族/民族差异？在这项诊断/预后研究中，建立了两个90天内自杀预测模型，并在13980570例门诊精神卫生就诊的回顾研究中得到验证。这两个模型都准确地预测了白人、西班牙裔和亚裔患者就诊的自杀风险，但对于黑人和美国印第安人/阿拉斯加原住民患者以及报道的非种族/民族患者的就诊表现很差。这项研究表明，与黑人和美国印第安人/阿拉斯加原住民患者和未记录种族/民族的患者相比，这两种自杀预测模型的实施将不成比例地使白人、西班牙裔和亚洲患者受益。重要根据健康记录数据估计的临床预测模型可能会持续不等价。目的评估种族/民族差异对预测自杀的统计模型的性能的影响。在这项诊断/预后研究中，从2009年1月1日开始进行的这项诊断/预后研究中，设计、设置和参与者截至2017年9月30日，随访至2017年12月31日，对13岁及以上患者对7个大型综合医疗保健系统的所有门诊精神卫生就诊进行评估。在包含50%随机样本(6984184次)患者的所有来访的训练集中，使用套索变量选择和随机森林的Logistic回归来评估预测模型。其余6 996 386人次的服务表现则接受评估，包括白人(4 031 135人次)、西班牙裔(1 664 166人次)、黑人(578 508人次)、亚裔(313 011人次)、美国印第安人/阿拉斯加原住民(48 025人次)及非种族/族裔病人(274 702人次)。对2019年1月1日至2021年2月1日期间的数据进行分析。研究对象包括人口统计学、诊断、处方和使用变量以及患者健康问卷9的回答。主要结果和衡量患者就诊后90天内自杀死亡的指标。结果本研究共纳入1 433 543名患者13 980 570例(女性，平均年龄42[18]岁)。在3143次访问后的90天内，总共观察到768人自杀死亡。自杀率最高的是非种族/族裔患者就诊(n=313次就诊，然后在90天内自杀，比率=每10 000次就诊5.71次)，其次是亚裔就诊(n=187次，然后在90天内自杀，比率=每10 000次就诊2.99次)，白人(n=2134次就诊，然后在90天内自杀，比率=每10 000次就诊2.65次)，美国印第安人/阿拉斯加原住民(n=21次就诊，然后在90天内自杀，比率=每10 000次就诊2.18次)，西班牙裔(n=392次就诊，然后在90天内自杀，黑人(n=65次就诊后在90天内自杀，比率=每10 000次就诊0.56次)。对于白人、西班牙裔和亚裔患者，两种模型的曲线下面积(AUC)和敏感度都很高，而对于黑人、美国印第安人/阿拉斯加原住民和没有种族/民族记录的患者来说，敏感度很低。例如，对于白人患者，Logistic回归模型的AUC值为0.828(95%CI，0.815-0.840)，相比之下，未记录的种族/民族患者的AUC值为0.640(95%CI，0.598-0.681)，美国印第安人/阿拉斯加原住民患者的AUC值为0.599(95%CI，0.513-0.686)。白人患者在第90百分位数的敏感度为62.2%(95%CI，59.2%-65.0%)，而未记录的种族/民族患者为27.5%(95%CI，21.0%-34.7%)，黑人患者为10.0%(95%CI，0%-23.0%)。随机森林模型的结果类似，白人患者的AUC为0.812(95%CI，0.800-0.826)，而未记录的种族/民族患者为0.676(95%CI，0.638-0.714)，美国印第安人/阿拉斯加原住民患者为0.642(95%CI，0.579-0.710)，白人患者的敏感度为52.8%(95%CI，50.0%-55.8%)，白人患者为29.3%(95%CI，22.8%-36.5%)，黑人6.7%(95%CI，0%-16.7%)。结论与白人、西班牙裔和亚裔患者相比，这些自杀预测模型对美国印第安人/阿拉斯加原住民或黑人或未记录种族/民族的患者提供的益处更少，潜在的危害更大。改善弱势人群的预测能力应该优先于改善而不是加剧健康差异。这项关于门诊精神卫生访问的诊断性/预见性研究评估了预测自杀的统计模型性能的种族/民族差异。
Question Could implementation of suicide prediction models reinforce and worsen racial/ethnic disparities in care?Findings In this diagnostic/prognostic study, 2 prediction models for suicide within 90 days were developed and validated in a retrospective study of 13 980 570 outpatient mental health visits. Both models accurately predicted suicide risk for visits by White, Hispanic, and Asian patients, but performance was poor for visits by Black and American Indian/Alaskan Native patients and patients without race/ethnicity reported.Meaning This study suggests that implementation of either suicide prediction model would disproportionately benefit White, Hispanic, and Asian patients compared with Black and American Indian/Alaskan Native patients and patients with unrecorded race/ ethnicity.IMPORTANCE Clinical prediction models estimated with health records data may perpetuate inequities.OBJECTIVE To evaluate racial/ethnic differences in the performance of statistical models that predict suicide.DESIGN, SETTING, AND PARTICIPANTS In this diagnostic/prognostic study, performed from January 1, 2009, to September 30, 2017, with follow-up through December 31, 2017, all outpatient mental health visits to 7 large integrated health care systems by patients 13 years or older were evaluated. Prediction models were estimated using logistic regression with LASSO variable selection and random forest in a training set that contained all visits from a 50% random sample of patients (6 984 184 visits). Performance was evaluated in the remaining 6 996 386 visits, including visits from White (4 031 135 visits), Hispanic (1 664 166 visits), Black (578 508 visits), Asian (313 011 visits), and American Indian/Alaskan Native (48 025 visits) patients and patients without race/ethnicity recorded (274 702 visits). Data analysis was performed from January 1, 2019, to February 1, 2021.EXPOSURES Demographic, diagnosis, prescription, and utilization variables and Patient Health Questionnaire 9 responses.MAIN OUTCOMES AND MEASURES Suicide death in the 90 days after a visit.RESULTS This study included 13 980 570 visits by 1 433 543 patients (64% female; mean [SD] age, 42 [18] years. A total of 768 suicide deaths were observed within 90 days after 3143 visits. Suicide rates were highest for visits by patients with no race/ethnicity recorded (n = 313 visits followed by suicide within 90 days, rate = 5.71 per 10 000 visits), followed by visits by Asian (n = 187 visits followed by suicide within 90 days, rate = 2.99 per 10 000 visits), White (n = 2134 visits followed by suicide within 90 days, rate = 2.65 per 10 000 visits), American Indian/Alaskan Native (n = 21 visits followed by suicide within 90 days, rate = 2.18 per 10 000 visits), Hispanic (n = 392 visits followed by suicide within 90 days, rate = 1.18 per 10 000 visits), and Black (n = 65 visits followed by suicide within 90 days, rate = 0.56 per 10 000 visits) patients. The area under the curve (AUC) and sensitivity of both models were high for White, Hispanic, and Asian patients and poor for Black and American Indian/Alaskan Native patients and patients without race/ethnicity recorded. For example, the AUC for the logistic regression model was 0.828 (95% CI, 0.815-0.840) for White patients compared with 0.640 (95% CI, 0.598-0.681) for patients with unrecorded race/ethnicity and 0.599 (95% CI, 0.513-0.686) for American Indian/Alaskan Native patients. Sensitivity at the 90th percentile was 62.2% (95% CI, 59.2%-65.0%) for White patients compared with 27.5% (95% CI, 21.0%-34.7%) for patients with unrecorded race/ethnicity and 10.0% (95% CI, 0%-23.0%) for Black patients. Results were similar for random forest models, with an AUC of 0.812 (95% CI, 0.800-0.826) for White patients compared with 0.676 (95% CI, 0.638-0.714) for patients with unrecorded race/ethnicity and 0.642 (95% CI, 0.579-0.710) for American Indian/Alaskan Native patients and sensitivities at the 90th percentile of 52.8% (95% CI, 50.0%-55.8%) for White patients, 29.3% (95% CI, 22.8%-36.5%) for patients with unrecorded race/ethnicity, and 6.7% (95% CI, 0%-16.7%) for Black patients.CONCLUSIONS AND RELEVANCE These suicide prediction models may provide fewer benefits and more potential harms to American Indian/Alaskan Native or Black patients or those with undrecorded race/ethnicity compared with White, Hispanic, and Asian patients. Improving predictive performance in disadvantaged populations should be prioritized to improve, rather than exacerbate, health disparities.This diagnostic/prognostic study of outpatient mental health visits evaluates racial/ethnic differences in the performance of statistical models that predict suicide.