Evaluating Biases in Cell-phone-based Healthcare Travel Data: A case study of Texas, USA

Abstract

Healthcare travel behavior is an important determinant of health outcomes and is commonly evaluated by measuring travel time between patients’ residences and healthcare facilities. Recently, anonymized cell-phone mobility data have emerged as a valuable resource for estimating real-world travel behavior at large scales. However, these cell-phone-based mobility datasets are not fully representative of the population, introducing biases in the behavior that is observed¹ . This study characterizes these biases by comparing healthcare-related origin-destination (OD) flows between cell-phone-based mobility data derived from SafeGraph and administrative visitation records from the 2019 Texas Department of State Health Services (TDSHS) Public Use Data Files, which serve as a benchmark independent of smartphone use. OD pairs were defined using patients’ zip codes as origins and Texas hospitals as destinations. We performed comparative analysis of both datasets using descriptive statistics, distance decay modeling, and map visualizations. Hospital-level weighted network distances showed moderate agreement (Pearson r = 0.35) between the two datasets. Spatial analyses revealed similar geographic patterns of hospital use with northeastern and eastern Texas being identified as major hospital service areas. Distance decay patterns were similar between the two datasets with estimated decay coefficients of beta = 0.94 for the cell-phone-based data and beta = 0.96 for the TDSHS data for network distance. Overall, biases were more evident in the estimates of the travel magnitude than in broad spatial patterns because the cell-phone-based mobility data consistently overestimated the travel distance seen in the TDSHS data. These findings support that cell-phone-based mobility data may be more useful for characterizing the geographic organization of healthcare utilization than precise hospital-level estimates.

1. Li, Z., Ning, H., Jing, F., & Lessani, M. N. (2024). Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States.