Sociodemographic and Policy Factors Associated with the Transmission of COVID-19: Analyzing Longitudinal Contact Tracing Data from a Northern Chinese City

J Urban Health https://doi.org/10.1007/s11524-022-00639-1 Sociodemographic and Policy Factors Associated with the Transmission of COVID‑19: Analyzing Longitudinal Contact Tracing Data from a Northern Chinese City Han Liu · Zai Liang Lihua Liu · Shiyong Zhang · Accepted: 22 March 2022 © The New York Academy of Medicine 2022 Abstract To examine how sociodemographic characteristics and non-pharmaceutical interventions affect the transmission of COVID-19, we analyze patient profiles and contact tracing data from almost all cases in an outbreak in Shijiazhuang, China, from January to February 2021. Because of universal testing and digital tracing, the data are of high quality. Results from negative binomial models indicate that the counts of close contacts and secondary infections vary with the cases’ age and occupation. Notably, cases under age 18 are causing an increased infection rate among their close contacts and leading to more within-neighborhood secondary infections than adults aged 18–49. Also, county-wide interventions and lockdown are found to be effective at containing the spread of COVID-19. These measures can reduce the number of close contacts that each case has and largely restrict the remaining infections to the case’s neighborhood. These results suggest that transmission risks of COVID-19 are associated with the case’s sociodemographic characteristics and can be reduced with interventions at the county level. Implications on mitigation measures and reopening plans are discussed. Supplementary Information The online version contains supplementary material available at https://doi. org/10.1007/s11524-022-00639-1. Keywords COVID-19 · Social determinants · Non-pharmaceutical interventions · Contact tracing · Shijiazhuang (China) H. Liu (*) · Z. Liang (*) Department of Sociology, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA e-mail: Z. Liang e-mail: Z. Liang Department of Sociology, School of Humanities and Social Science of Xi’an Jiaotong University, Xi’an, Shaanxi Province, China S. Zhang · L. Liu (*) Shijiazhuang Center for Disease Prevention and Control, Shijiazhuang, Hebei Province, China e-mail: Since the COVID-19 pandemic started, researchers have tried to understand the social determinants of its contagion dynamics. Incorporating these factors into empirical research is considered important for both epidemiological models and policy discussions [1]. In the current literature, most empirical studies on COVID-19 transmission are based on comparisons across nations or sub-national units [2]. These ecological analyses have furthered our understanding of area-level factors driving the pandemic and informed policymakers about the efficacy of non-pharmaceutical interventions [3], but their findings may not apply to the individual-level epidemiological dynamics. Vol.: (0123456789) 13 Liu et al. There is also a growing number of studies using individual-level contact tracing data to investigate COVID-19 transmission and evaluate the effectiveness of mitigation policies [4, 5]. These studies have laid the foundation for non-pharmaceutical interventions, but because of limited testing capacities and risks of infringing on privacy, surveillance data used in individual-level research usually have limited representativeness. In this study, we try to fill this gap in the literature by analyzing patient profiles and contact tracing data from a COVID-19 outbreak in a northern Chinese city, Shijiazhuang, in early 2021. During the outbreak, the municipal government conducted universal testing for all residents and used geolocation data from telecommunication providers to assist contact tracing for both symptomatic and asymptomatic/pre-symptomatic cases. These testing and contact tracing efforts provide high-quality data that support accurate analysis of transmission dynamics at the individual level. Specifically, this study assesses sociodemographic factors associated with transmission risks of COVID-19 and evaluates the effectiveness of county-level interventions. In the face of slow vaccination roll-out in many developing countries [6], threats from new variants [7], and the urgent need to safely reopen schools and other social institutions [8, 9], findings from our analyses will not only advance our understanding of social determinants of COVID19 transmission but also help the design of mitigation policies and reopening plans in countries around the globe. Background Transmission Heterogeneities of COVID‑19 The transmission of infectious diseases, like COVID19, is based on person-to-person contagion. Thus, people with more social contacts, once infected by the virus, are also more likely to transmit it to others. Social network research suggests that network size and structure vary with sociodemographic factors, like age [10, 11], gender [12, 13], and socioeconomic status [11, 14]. While the direction and magnitude of these cross-group differences are modified by how Vol:. (1234567890) 13 social networks are conceptualized and measured [15], this literature indicates that certain sociodemographic characteristics may facilitate the spread of germs by exposing the host to more social contacts. Therefore, in this study on COVID-19 transmission, we expect the patients’ age, gender, and socioeconomic status to have an impact on their numbers of close contacts and secondary infections. In addition to network size, the duration and closeness of social interactions may also vary by sociodemographic factors. Children, for example, tend to have prolonged exposure to each other when attending school in person. While in-person schooling plays an essential role in children’s welfare and education, without adequate mitigation policies, it would lead to rapid transmission within schools and their surrounding communities [8, 16, 17]. Similarly, females typically have more contact with relatives than males [11, 18]. Compared to social interactions in the workplace, these kinship-based interactions tend to be closer and consequently pose higher transmission risks [4]. Socioeconomic status can also affect the dynamics of social interactions through conditions of employment and housing. People working or living in overcrowded settings may not be able to comply with social distancing and other public health guidelines [1]. Therefore, patients’ age, gender, and socioeconomic status are also expected to be associated with the risk of causing secondary infections among their social contacts. Fully evaluating transmission heterogeneities by sociodemographic characteristics would help the design of targeted and cost-effective interventions. However, the collection of high-quality contacttracing data is costly and faces concerns over data protection and privacy. During the first wave of the pandemic, the capacities of testing and contact tracing were still limited, leaving public health authorities with no choice but to prio (...truncated)