Department of Psychiatry and Behavioral Sciences, University of Minnesota, MN, USA; Institute for Health Informatics, University of Minnesota, MN, USA.
Institute for Health Informatics, University of Minnesota, MN, USA.
Vaccine. 2024 Aug 30;42(21):126198. doi: 10.1016/j.vaccine.2024.126198. Epub 2024 Aug 5.
Major barriers to addressing SARS-CoV-2 vaccine hesitancy include limited knowledge of what causes delay/refusal of SARS-CoV-2 vaccination and limited ability to predict who will remain unvaccinated over significant time periods despite vaccine availability. The present study begins to address these barriers by developing a machine learning model that prospectively predicts who will persist in not vaccinating against SARS-CoV-2.
Unvaccinated individuals (n = 325) who completed a baseline survey were followed over the six-month period when vaccines against SARS-CoV-2 were first widely available (April-October 2021). A random forest model was used to predict who would remain unvaccinated against SARS-CoV-2 from their baseline measures, including demographic information (e.g., age), medical history (e.g., of influenza vaccination), Health-Belief Model constructs (e.g., perceived vaccine dangerousness), conspiracist ideation, and task-based metrics of vulnerability to conspiracist ideation (e.g., tendency toward illusory pattern perception).
The resulting model significantly predicted vaccination status (AUC-PR = 0.77, 95%CI [0.56 0.90]). At the optimal probability threshold determined by the Generalized Threshold Shifting Protocol, the model was moderately precise (0.83) when identifying individuals who remained unvaccinated (n = 80), and had a very low rate (0.04) of false-positives (incorrectly suggesting that individuals remained unvaccinated). Permutational importance tests suggested that baseline SARS-CoV-2 vaccine intentions conveyed the most information about future SARS-CoV-2 vaccination status. Conspiracist ideation was the second most informative predictor, suggesting that misinformation influences vaccination behavior. Other important predictors included perceived vaccine dangerousness, as expected under the Health Belief Model, and influenza vaccination history.
The model we developed can accurately and prospectively identify individuals who remain unvaccinated against SARS-CoV-2. It could therefore facilitate empirically-informed allocation of interventions that encourage vaccine uptake. The predictive value of conspiracist ideation, perceived vaccine dangerousness, and vaccine intentions in our model is consistent with potential causal relations between these variables and SARS-CoV-2 vaccine uptake.
解决对 SARS-CoV-2 疫苗犹豫不决的主要障碍包括对导致 SARS-CoV-2 疫苗接种延迟/拒绝的原因知之甚少，以及尽管疫苗供应充足，但在相当长的时间内预测谁将继续未接种疫苗的能力有限。本研究通过开发一种前瞻性预测谁将继续不接种 SARS-CoV-2 疫苗的机器学习模型来解决这些障碍。
未接种疫苗的个体（n=325）完成基线调查后，在 SARS-CoV-2 疫苗首次广泛可用的六个月期间（2021 年 4 月至 10 月）进行了随访。使用随机森林模型根据他们的基线测量值（包括人口统计学信息，例如年龄），医疗史（例如流感疫苗接种），健康信念模型结构（例如，对疫苗的危险感知），阴谋论观念，以及对阴谋论观念的脆弱性的基于任务的指标（例如，对虚幻模式感知的倾向），预测谁将继续不接种 SARS-CoV-2 疫苗。
所得到的模型显着预测了疫苗接种状态（AUC-PR=0.77，95%CI [0.56 0.90]）。在广义阈值转移协议确定的最佳概率阈值下，当识别未接种疫苗的个体（n=80）时，该模型具有中等精度（0.83），并且具有非常低的假阳性率（错误地表明个体未接种疫苗）（0.04）。置换重要性测试表明，SARS-CoV-2 疫苗接种意向在很大程度上传递了有关未来 SARS-CoV-2 疫苗接种状态的信息。如健康信念模型所预期的那样，阴谋论观念是第二大重要预测因素，表明错误信息会影响疫苗接种行为。其他重要的预测因素包括预期的疫苗危险感知以及流感疫苗接种史。
我们开发的模型可以准确，前瞻性地识别出继续未接种 SARS-CoV-2 疫苗的个体。因此，它可以促进针对鼓励疫苗接种的干预措施进行经验性分配。我们的模型中阴谋论观念，对疫苗的危险感知以及疫苗接种意向的预测价值与这些变量与 SARS-CoV-2 疫苗接种率之间的潜在因果关系一致。
