JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States

Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries

Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. Leveraging geolocation data from mobile-phone users and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. Users living in low-wealth neighborhoods were less likely to respond by self-isolating, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among users living in low-wealth neighborhoods, those who commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk of experiencing economic stress, facing both the reduction in economic activity in the high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute distances. While confinement policies were predominantly country-wide, these results suggest that, when data to identify vulnerable individuals are not readily available, GPS-based analytics could help design targeted place-based policies to aid the most vulnerable.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-025-00532-2.

Remote instruction adversely impacts parental mental health, less among homeschoolers

This study explores the effects of the COVID-19 pandemic-induced shift to remote instruction on parental mental health, using data from the American Enterprise Institute's Return-to-Learn tracker and the U.S. Census Pulse Survey. We exploit within-state variation in the timing of school closures from August 2020 to June 2021, controlling flexibly for demographic and state-level factors. We find that an increase in the proportion of remote instruction districts correlates with an escalation in parental mental health issues, including heightened anxiety, worry, depression, and a loss of interest in activities. These adverse effects are significantly lessened among parents who choose homeschooling. A one percentage point increase in the share of remote instruction in the state is associated with a 2.2 percentage point rise in homeschooling probability. Our paper contributes to understanding the wider impact of pandemic-induced educational changes, highlighting substantial mental health implications for parents. Taking stock of lessons learned over the Covid-19 pandemic, these findings are pivotal for shaping informed educational policies that consider the well-being of the whole family during crises.

Variation in the Impact of New York on Pause on Traffic Congestion by Racialized Economic Segregation and Environmental Burden

During the 2019 coronavirus pandemic, stay-at-home policies such as New York's (NY) NY on Pause dramatically reduced traffic congestion. Despite high traffic burden in NY's environmental justice communities, this reduction has not been evaluated through an environmental justice lens-our objective in this analysis. We obtained census tract-level traffic congestion data from Google traffic maps hourly for 2018-2020. We defined congestion as the percent of streets in a census tract with heavy traffic (red- or maroon-color). We used the Index of Concentration at the Extremes (ICE) to measure racialized economic segregation and the CDC's Environmental Justice Index (EJI) as a measure of combined environmental, social, and chronic disease burden. We divided census tracts into quintiles of ICE and EJI and used linear mixed models stratified by ICE and EJI quintile in an interrupted time series design. Prior to NY on Pause, less marginalized and burdened census tracts (Q5) tended to have higher levels of traffic congestion; during NY on Pause, this trend reversed. For both ICE and EJI, more marginalized and burdened (Q1-Q2 vs. Q4-Q5) tracts had smaller absolute decreases in percent traffic congestion. For example, percent traffic congestion in ICE Q5 decreased by 7.8% (% change: -36.6%), but in Q1, it decreased by 4.2% (% change: -51.7%). NY on Pause, while protecting residents during COVID-19, may have resulted in inequitable reductions in traffic congestion. It is critical that such inequities are measured and acknowledged so that future policies to reduce traffic congestion and respond to pandemics can enhance equity.

Measuring network dynamics of opioid overdose deaths in the United States

The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index (SCI) as a proxy for real-life social networks across diverse spatial regions that help quantify social connectivity across different spatial units. This is a measure of the relative probability of connections between localities that offers a unique lens to understand the effects of social networks on health outcomes. We use SCI to develop a variable, called "deaths in social proximity", to measure the influence of social networks on opioid overdose deaths (OODs) in US counties. Our results show a statistically significant effect size for deaths in social proximity on OODs in counties in the United States, controlling for spatial proximity, as well as demographic and clinical covariates. The effect size of standardized deaths in social proximity in our cluster-robust linear regression model indicates that a one-standard-deviation increase, equal to 11.70 more deaths per 100,000 population in the social proximity of ego counties in the contiguous United States, is associated with thirteen more deaths per 100, 000 population in ego counties. To further validate our findings, we performed a series of robustness checks using a network autocorrelation model to account for social network effects, a spatial autocorrelation model to capture spatial dependencies, and a two-way fixed-effect model to control for unobserved spatial and time-invariant characteristics. These checks consistently provide statistically robust evidence of positive social influence on OODs in US counties. Our analysis provides a pathway for public health interventions informed by social network structures. The statistical robustness of our primary variable of interest, deaths in social proximity, supports the hypothesis of a social network effect on OODs. Using agent-based modeling (ABM) to simulate social networks can offer an effective method to design interventions that incorporate the dynamics of social networks for maximum impact.

Factors contributing to differences in cervical cancer screening in rural and urban community health centers

INTRODUCTION

Community health centers (CHCs) provide historically marginalized populations with primary care, including cancer screening. Previous studies have reported that women living in rural areas are less likely to be up to date with cervical cancer screening than women living in urban areas. However, little is known about rural-urban differences in cervical cancer screening in CHCs and the contributing factors, and whether such differences changed during the COVID-19 pandemic.

METHODS

Using 8-year pooled Uniform Data System (2014-2021) data and Oaxaca-Blinder decomposition, the extent to which CHC- and catchment area-level characteristics explained rural-urban differences in up-to-date cervical cancer screening was estimated.

RESULTS

Up-to-date cervical cancer screening was lower in rural CHCs than urban CHCs (38.2% vs 43.0% during 2014-2019), and this difference increased during the pandemic (43.5% vs 49.0%). The rural-urban difference in cervical cancer screening in 2014-2019 was mostly explained by differences in CHC-level proportions of patients with limited English proficiency (55.9%) or income below the poverty level (12.3%) and females aged 21 to 64 years (9.8%), and catchment area-level's unemployment (3.4%) and primary care physician density (3.2%). However, Medicaid (-48.5%) or no insurance (-19.6%) counterbalanced the differences between rural-urban CHCs. The contribution of these factors to rural-urban differences in cervical cancer screening generally increased in 2020-2021.

CONCLUSIONS

Rural-urban differences in cervical cancer screening were mostly explained by multiple CHC-level and catchment area-level characteristics. The findings call for tailored interventions, such as providing resources and language services, to improve cancer screening utilization among uninsured, Medicaid, and patients with limited English proficiency in rural CHCs.

Gone with the epidemic? The spatial effects of the Covid-19 on global investment network

The outbreak of Covid-19 epidemic has a prolonged impact on global economic activities. In recent years, many scholars have been motivated to estimate the effects of Covid-19 shock on global foreign direct investment (FDI). However, existing studies have not paid enough attention to the spillover effects caused by the epidemic. Although few academic works have explored the geographic-neighboring spillover effects of epidemic shock on global investment, we further extent the understanding of the spillover effects in an economic network. On the basis of country-month greenfield FDI panels, we construct a spatial Durbin model, and figure out that Covid-19 shock may have positive FDI spillover effects in an economic network via global FDI transfers. Furthermore, we find that such spillovers are greatly conditioned by country-level network position and institutional ties among nations. Our research suggests that global FDI transfers may partly offset economic-adverse effects of the Covid-19 shock. While global countries, especially those in the Global South, should be more closely embedded in the global investment network in such an uncertain environment.

COVID-19 and the city: Did urbanized countries suffer more fatalities?

In this paper we derive a theoretical model of the spread of a viral infection which we use as basis for an estimation strategy to test four interrelated hypotheses on the relationship between country-level COVID-19 mortality rates and the extent of urban development. Using data covering 81 countries we find evidence that countries with a higher population density, a higher share of the urban population living in the largest city, and countries with a higher urbanization rate had on average the same or fewer COVID-19 fatalities compared to less urbanized countries in 2020. Even though COVID-19 spreads faster in cities, fatalities may be lower, conditional on economic development, trust in government, and a well-functioning health care system. Generally, urbanization and city development are associated with economic development: with the resources urbanized countries have, it is easier for them to manage and maintain stricter lockdowns, and to roll out effective pharmaceutical interventions.

JUE Insight: The Geography of Travel Behavior in the Early Phase of the COVID-19 Pandemic

We use U.S. county-level location data derived from smartphones to examine travel behavior and its relationship with COVID-19 cases in the early stages of the outbreak. People traveled less overall and notably avoided areas with relatively larger outbreaks. A doubling of new cases in a county led to a 3 to 4 percent decrease in trips to and from that county. Without this change in travel activity, exposure to out-of-county virus cases could have been twice as high at the end of April 2020. Limiting travel-induced exposure was important because such exposure generated new cases locally. We find a one percent increase in case exposure from travel led to a 0.21 percent increase in new cases added within a county. This suggests the outbreak would have spread faster and to a greater degree had travel activity not dropped accordingly. Our findings imply that the scale and geographic network of travel activity and the travel response of individuals are important for understanding the spread of COVID-19 and for policies that seek to control it.

JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

Internal migration networks and mortality in home communities: Evidence from Italy during the Covid-19 pandemic

Do internal migration networks benefit or harm their home communities in case of a communicable disease? Looking at the spread of Covid in Italy and using pre-determined province-to-province migration, excess mortality and mobile phone tracking data, we document that provinces with a greater share of migrants in outbreak areas show greater compliance with self-isolation measures (information mechanism), but also a greater population inflow from outbreak areas (carrier mechanism). For a subset of localities, the net effect on mortality is negative. However, for the average locality, the effect is positive and large, suggesting that the role of migrants as information providers is trumped by their role as virus carriers. The effect is quantitatively important and could be incorporated in epidemiological models forecasting the spread of communicable diseases.