使用社交媒体量化孕产妇死亡率的社区特征
Quantifying Community Characteristics of Maternal Mortality Using Social Media
摘要：
虽然美国的大多数死亡率都有所下降，但孕产妇死亡率却有所上升，是经合组织国家中最高的。正在进行广泛的公共卫生研究，以更好地了解发病率相对较高或较低的社区的特点。在这项工作中，我们探讨的作用，社交媒体语言可以发挥洞察这样的社区特征。分析美国各县产生的与怀孕相关的推文，我们揭示了一组不同的潜在主题，包括晨吐，名人怀孕和堕胎权利。我们发现，在Twitter上提及这些主题的比率预测孕产妇死亡率的准确性高于标准的社会经济和风险变量，如收入，种族和获得医疗保健，即使在将分析减少到六个主题后，其可解释性和与已知风险因素的联系也是如此。然后，我们调查了社区语言的心理维度，发现使用不太信任，更强调，更消极的情感语言与更高的死亡率显着相关，而信任和负面影响也解释了孕产妇死亡率的种族差异的显着部分。我们讨论了这些见解的潜力，以告知在社区一级的可操作的健康干预措施。
英文摘要：
While most mortality rates have decreased in the US, maternal mortality has increased and is among the highest of any OECD nation. Extensive public health research is ongoing to better understand the characteristics of communities with relatively high or low rates. In this work, we explore the role that social media language can play in providing insights into such community characteristics. Analyzing pregnancy-related tweets generated in US counties, we reveal a diverse set of latent topics including Morning Sickness, Celebrity Pregnancies, and Abortion Rights. We find that rates of mentioning these topics on Twitter predicts maternal mortality rates with higher accuracy than standard socioeconomic and risk variables such as income, race, and access to health-care, holding even after reducing the analysis to six topics chosen for their interpretability and connections to known risk factors. We then investigate psychological dimensions of community language, finding the use of less trustful, more stressed, and more negative affective language is significantly associated with higher mortality rates, while trust and negative affect also explain a significant portion of racial disparities in maternal mortality. We discuss the potential for these insights to inform actionable health interventions at the community-level.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
