Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA.
Department of Health Services Research, Kaiser Permanente Washington Health Research Institute, University of Washington, Seattle, WA.
Med Care. 2018 May;56(5):365-372. doi: 10.1097/MLR.0000000000000893.
New health policies may have intended and unintended consequences. Active surveillance of population-level data may provide initial signals of policy effects for further rigorous evaluation soon after policy implementation.
This study evaluated the utility of sequential analysis for prospectively assessing signals of health policy impacts. As a policy example, we studied the consequences of the widely publicized Food and Drug Administration's warnings cautioning that antidepressant use could increase suicidal risk in youth.
This was a retrospective, longitudinal study, modeling prospective surveillance, using the maximized sequential probability ratio test. We used historical data (2000-2010) from 11 health systems in the US Mental Health Research Network. The study cohort included adolescents (ages 10-17 y) and young adults (ages 18-29 y), who were targeted by the warnings, and adults (ages 30-64 y) as a comparison group. Outcome measures were observed and expected events of 2 possible unintended policy outcomes: psychotropic drug poisonings (as a proxy for suicide attempts) and completed suicides.
We detected statistically significant (P<0.05) signals of excess risk for suicidal behavior in adolescents and young adults within 5-7 quarters of the warnings. The excess risk in psychotropic drug poisonings was consistent with results from a previous, more rigorous interrupted time series analysis but use of the maximized sequential probability ratio test method allows timely detection. While we also detected signals of increased risk of completed suicide in these younger age groups, on its own it should not be taken as conclusive evidence that the policy caused the signal. A statistical signal indicates the need for further scrutiny using rigorous quasi-experimental studies to investigate the possibility of a cause-and-effect relationship.
This was a proof-of-concept study. Prospective, periodic evaluation of administrative health care data using sequential analysis can provide timely population-based signals of effects of health policies. This method may be useful to use as new policies are introduced.
新的卫生政策可能会产生有意和无意的后果。对人群水平数据的积极监测可能会在政策实施后不久提供政策效果的初步信号，以便进行更严格的评估。
本研究评估了序贯分析在前瞻性评估卫生政策影响信号方面的效用。作为一个政策示例，我们研究了广泛宣传的美国食品和药物管理局警告的后果，该警告警告说抗抑郁药的使用可能会增加青少年的自杀风险。
这是一项回顾性、纵向研究，使用最大化序贯概率比检验对前瞻性监测进行建模。我们使用了美国心理健康研究网络中的 11 个卫生系统的历史数据（2000-2010 年）。研究队列包括被警告针对的青少年（10-17 岁）和年轻人（18-29 岁），以及成年人（30-64 岁）作为对照组。观察和预期的结果指标是 2 种可能的意外政策结果：精神药物中毒（作为自杀企图的代理）和完成自杀。
我们在警告后 5-7 个季度内检测到青少年和年轻人自杀行为风险增加的统计学显著（P<0.05）信号。精神药物中毒的超额风险与之前更严格的中断时间序列分析结果一致，但使用最大化序贯概率比检验方法可以及时检测到。虽然我们也在这些年轻群体中检测到自杀完成风险增加的信号，但仅凭这一点，不应被视为政策导致信号的确凿证据。统计信号表明，需要使用严格的准实验研究进一步审查，以调查因果关系的可能性。
这是一项概念验证研究。使用序贯分析对行政医疗保健数据进行前瞻性、定期评估，可以及时提供有关卫生政策影响的基于人群的信号。这种方法可能在引入新政策时有用。
