Associations between state-level healthcare access and COVID-19 case trajectories in the United States (preprint)

Introduction: We conducted an ecological study to determine if state-level healthcare access is associated with trajectories of daily reported COVID-19 cases in the United States. Our focus is on trajectories of daily reported COVID-19 cases, rather than cumulative cases, as trajectories help us identify trends in how the pandemic naturally develops over time, and study the shapes of the curve in different states. Methods: We analyzed data on daily reported confirmed and probable COVID-19 cases from January 21 to June 16, 2020 in 50 states, adjusted for the population size of each state. Cluster analysis for time-series data was used to split the states into clusters that have distinct trajectories of daily cases. Differences in socio-demographic characteristics and healthcare access between clusters were tested. Adjusted models were used to determine if healthcare access is associated with reporting a high trajectory of COVID-19 cases. Results: Two clusters of states were identified. One cluster had a high trajectory of population-adjusted COVID-19 cases, and comprised of 19 states, including New York and New Jersey. The other cluster of states (n=31) had a low trajectory of population-adjusted COVID-19 cases. There were significantly more Black residents (p=0.027) and more nursing facility residents (p=0.001) in states reporting high trajectory of COVID-19 cases. States reporting a high trajectory of COVID-19 cases also had fewer uninsured persons (p=0.005), fewer persons who reported having to forgo medical care due to cost (p=0.016), more registered physicians (p=0.002) and more nurses (p=0.03), higher health spending per capita (p=0.01), fewer residents in Health Professional Shortage Areas per 100,000 population (p=0.027), and higher adoption of Medicaid Expansion (p=0.05). In adjusted models, a higher proportion of uninsured persons (OR: 0.51 [0.25-0.85]; p=0.032), higher proportion of patients who had to forgo medical care due to cost (OR: 0.55 [0.28-0.95]; p=0.048), and no adoption of Medicaid expansion (OR: 0.05 [0-0.59]; p=0.04), were associated with reporting a low trajectory of COVID-19 cases. Conclusion: Our findings from adjusted models suggest that healthcare access can partially explain variations in COVID-19 case trajectories by state.

Social media and smartphone app use predicts maintenance of physical activity during Covid-19 enforced isolation in psychiatric outpatients (preprint)

Public health professionals have raised concerns that the social and physical distancing measures implemented in response to the Covid-19 pandemic may negatively impact health in other areas, via both decreased physical activity and increased social isolation. Here, we investigated whether increased engagement with digital social tools may help mitigate effects of enforced isolation on physical activity and mood, in a naturalistic study of at-risk individuals. Passively sensed smartphone app use and actigraphy data, collected from a sample of psychiatric outpatients both before and during imposition of strict lockdown conditions (N=163), were analysed using Gaussian graphical models: a form of network analysis which gives insight into the predictive relationships between measures across timepoints. Within-individuals, we found evidence of a positive predictive path between digital social engagement, general smartphone use, and physical activity - selectively under lockdown conditions. Further, we observed a positive relationship between social media use and total daily steps across individuals during (but not prior to) lockdown. We interpret these findings in terms of individuals using these digital tools to harness online social support structures, which may help guard against negative effects of in-person social deprivation and other pandemic-related stress. Monitoring of these measures is low burden and unintrusive and therefore, given appropriate consent, could potentially help identify individuals who are failing to engage this mechanism, providing a route to early intervention in this and other vulnerable populations.

Time courses of COVID-19 infection and local variation in socioeconomic and health disparities in England (preprint)

Objective: To identify factors associated with local variation in the time course of COVID-19 case burden in England. Methods: We analyzed laboratory-confirmed COVID-19 case data for 150 upper tier local authorities, from the period from January 30 to May 6, 2020, as reported by Public Health England. Using methods suitable for time-series data, we identified clusters of local authorities with distinct trajectories of daily cases, after adjusting for population size. We then tested for differences in sociodemographic, economic, and health disparity factors between these clusters. Results: Two clusters of local authorities were identified: a higher case trajectory that rose faster over time to reach higher peak infection levels, and a lower case trajectory cluster that emerged more slowly, and had a lower peak. The higher case trajectory cluster (79 local authorities) had higher population density (p<0.001), higher proportion of Black and Asian residents (p=0.03; p=0.02), higher multiple deprivation scores (p<0.001), a lower proportions of older adults (p=0.005), and higher preventable mortality rates (p=0.03). Local authorities with higher proportions of Black residents were more likely to belong to the high case trajectory cluster, even after adjusting for population density, deprivation, proportion of older adults and preventable mortality (p=0.04). Conclusion: Areas belonging to the trajectory with significantly higher COVID-19 case burden were more deprived, and had higher proportions of ethnic minority residents. A higher proportion of Black residents in regions belonging to the high trajectory cluster was not fully explained by differences in population density, deprivation, and other overall health disparities between the clusters.