Am J Epidemiol. 2023 May 5;192(5):757-759. doi: 10.1093/aje/kwad010.
Ensuring that patients with opioid use disorder (OUD) have access to optimal medication therapies is a critical challenge in substance use epidemiology. Rudolph et al. (Am J Epidemiol. 2023;XXX(X):XXXX-XXXX) demonstrated that sophisticated data-adaptive statistical techniques can be used to learn optimal, individualized treatment rules that can aid providers in choosing a medication treatment modality for a particular patient with OUD. This important work also highlights the effects of the mathematization of epidemiologic research. Here, we define mathematization and demonstrate how it operates in the context of effectiveness research on medications for OUD using the paper by Rudolph et al. as a springboard. In particular, we address the normative dimension of mathematization and how it tends to resolve a fundamental tension in epidemiologic practice between technical sophistication and public health considerations in favor of more technical solutions. The process of mathematization is a fundamental part of epidemiology; we argue not for eliminating it but for balancing mathematization and technical demands equally with practical and community-centric public health needs.
确保患有阿片类药物使用障碍（OUD）的患者能够获得最佳药物治疗是物质使用流行病学中的一个关键挑战。Rudolph 等人（Am J Epidemiol. 2023;XXX(X):XXXX-XXXX）表明，可以使用复杂的数据自适应统计技术来学习最佳的个体化治疗规则，从而帮助提供者为特定的 OUD 患者选择药物治疗方式。这项重要工作还强调了流行病学研究数学化的影响。在这里，我们定义了数学化，并展示了它如何在使用 Rudolph 等人的论文作为跳板对 OUD 药物的有效性研究中发挥作用。特别是，我们解决了数学化的规范性维度，以及它如何倾向于在流行病学实践中解决技术复杂性和公共卫生考虑之间的基本紧张关系，有利于更技术性的解决方案。数学化过程是流行病学的一个基本部分；我们不是主张消除它，而是主张在技术需求和以实践和社区为中心的公共卫生需求之间平等地平衡数学化。
