Blake Alexandre, Hazel Ashley, Jakurama John, Matundu Justy, Bharti Nita
Biology Department, Center for Infectious Disease Dynamics, Penn State University, University Park, Pennsylvania, United States of America.
Francis I. Proctor Foundation, University of California, San Francisco, California, United States of America.
PLOS Digit Health. 2023 Jul 6;2(7):e0000270. doi: 10.1371/journal.pdig.0000270. eCollection 2023 Jul.
Human movement and population connectivity inform infectious disease management. Remote data, particularly mobile phone usage data, are frequently used to track mobility in outbreak response efforts without measuring representation in target populations. Using a detailed interview instrument, we measure population representation in phone ownership, mobility, and access to healthcare in a highly mobile population with low access to health care in Namibia, a middle-income country. We find that 1) phone ownership is both low and biased by gender, 2) phone ownership is correlated with differences in mobility and access to healthcare, and 3) reception is spatially unequal and scarce in non-urban areas. We demonstrate that mobile phone data do not represent the populations and locations that most need public health improvements. Finally, we show that relying on these data to inform public health decisions can be harmful with the potential to magnify health inequities rather than reducing them. To reduce health inequities, it is critical to integrate multiple data streams with measured, non-overlapping biases to ensure data representativeness for vulnerable populations.
人类活动和人口连通性对传染病管理具有重要意义。远程数据，尤其是手机使用数据，在疫情应对工作中经常被用于追踪人员流动情况，但却未对目标人群的代表性进行衡量。我们使用一份详细的访谈工具，对纳米比亚这个中等收入国家中一个流动性高但医疗服务获取机会低的人群在手机拥有情况、流动性以及医疗服务获取方面的人口代表性进行了测量。我们发现：1）手机拥有率低且存在性别偏差；2）手机拥有情况与流动性及医疗服务获取方面的差异相关；3）非城市地区的信号接收在空间上不均衡且信号弱。我们证明，手机数据无法代表那些最需要改善公共卫生状况的人群和地区。最后，我们表明，依靠这些数据来为公共卫生决策提供信息可能是有害的，因为这有可能加剧而非减少健康不平等现象。为了减少健康不平等，至关重要的是整合多个具有经过测量的、不重叠偏差的数据流，以确保数据对弱势群体具有代表性。
