School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA.
Dakota County Public Health, Apple Valley, Minnesota, USA.
J Am Med Inform Assoc. 2023 Oct 19;30(11):1818-1825. doi: 10.1093/jamia/ocad148.
Theory-based research of social and behavioral determinants of health (SBDH) found SBDH-related patterns in interventions and outcomes for pregnant/birthing people. The objectives of this study were to replicate the theory-based SBDH study with a new sample, and to compare these findings to a data-driven SBDH study.
Using deidentified public health nurse-generated Omaha System data, 2 SBDH indices were computed separately to create groups based on SBDH (0-5+ signs/symptoms). The data-driven SBDH index used multiple linear regression with backward elimination to identify SBDH factors. Changes in Knowledge, Behavior, and Status (KBS) outcomes, numbers of interventions, and adjusted R-squared statistics were computed for both models.
There were 4109 clients ages 13-40 years. Outcome patterns aligned with the original research: KBS increased from admission to discharge with Knowledge improving the most; discharge KBS decreased as SBDH increased; and interventions increased as SBDH increased. Slopes of the data-driven model were steeper, showing clearer KBS trends for data-driven SBDH groups. The theory-based model adjusted R-squared was 0.54 (SE = 0.38) versus 0.61 (SE = 0.35) for the data-driven model with an entirely different set of SBDH factors.
The theory-based approach provided a framework to identity patterns and relationships and may be applied consistently across studies and populations. In contrast, the data-driven approach can provide insights based on novel patterns for a given dataset and reveal insights and relationships not predicted by existing theories. Data-driven methods may be an advantage if there is sufficiently comprehensive SBDH data upon which to create the data-driven models.
基于理论的健康社会和行为决定因素(SBDH)研究发现,干预措施和孕妇/分娩人群的结果存在 SBDH 相关模式。本研究的目的是用新样本复制基于理论的 SBDH 研究,并将这些发现与基于数据的 SBDH 研究进行比较。
使用匿名公共卫生护士生成的奥马哈系统数据,分别计算了 2 个 SBDH 指数,根据 SBDH(0-5+体征/症状)创建组。数据驱动的 SBDH 指数使用多元线性回归和向后消元来确定 SBDH 因素。计算了两种模型的知识、行为和状态(KBS)结果变化、干预次数和调整后的 R 平方统计量。
共有 4109 名 13-40 岁的客户。结果模式与原始研究一致:从入院到出院,KBS 增加,知识提高最多;随着 SBDH 的增加,出院 KBS 下降;随着 SBDH 的增加,干预次数增加。数据驱动模型的斜率更陡,显示出数据驱动 SBDH 组更清晰的 KBS 趋势。基于理论的模型调整后的 R 平方为 0.54(SE=0.38),而数据驱动模型为 0.61(SE=0.35),后者具有完全不同的 SBDH 因素集。
基于理论的方法提供了一种识别模式和关系的框架,可以在研究和人群中一致应用。相比之下,数据驱动的方法可以根据特定数据集提供新颖模式的见解,并揭示现有理论未预测的见解和关系。如果有足够全面的 SBDH 数据来创建数据驱动模型,则数据驱动方法可能是一个优势。