Dynamics of the COVID-19 epidemic in urban and rural areas in the United States

Prediction models for COVID-19 disease outcomes

SARS-CoV-2 has caused over 6.9 million deaths and continues to produce lasting health consequences. COVID-19 manifests broadly from no symptoms to death. In a retrospective cross-sectional study, we developed personalized risk assessment models that predict clinical outcomes for individuals with COVID-19 and inform targeted interventions. We sequenced viruses from SARS-CoV-2-positive nasopharyngeal swab samples between July 2020 and July 2022 from 4450 individuals in Missouri and retrieved associated disease courses, clinical history, and urban-rural classification. We integrated this data to develop machine learning-based predictive models to predict hospitalization, ICU admission, and long COVID.The mean age was 38.3 years (standard deviation = 21.4) with 55.2% (N = 2453) females and 44.8% (N = 1994) males (not reported, N = 4). Our analyses revealed a comprehensive set of predictors for each outcome, encompassing human, environment, and virus genome-wide genetic markers. Immunosuppression, cardiovascular disease, older age, cardiac, gastrointestinal, and constitutional symptoms, rural residence, and specific amino acid substitutions were associated with hospitalization. ICU admission was associated with acute respiratory distress syndrome, ventilation, bacterial co-infection, rural residence, and non-wild type SARS-CoV-2 variants. Finally, long COVID was associated with hospital admission, ventilation, and female sex.Overall, we developed risk assessment models that offer the capability to identify patients with COVID-19 necessitating enhanced monitoring or early interventions. Of importance, we demonstrate the value of including key elements of virus, host, and environmental factors to predict patient outcomes, serving as a valuable platform in the field of personalized medicine with the potential for adaptation to other infectious diseases.

Association of Federal COVID-19 Funding Distributions With Workforce and Capacity in Health Centers

Using novel national data, we examined the association between 2020 federal COVID-related funding targeted to health centers (i.e., H8 funding) and health center workforce and operational capacity measures that may be important for preserving patient access to care and staff safety. We assigned health centers to quartiles based on federal funding distribution per patient and used adjusted linear probability models to estimate differences in workforce and operational capacity outcomes across quartiles from April 2020 to June 2022. We found a nearly 6-fold difference in 2020 H8 funding per patient when comparing health centers in the lowest versus highest quartiles. Despite this difference, health centers' outcomes improved similarly across quartiles over time, with the lowest-funded health centers having the greatest staffing and service capacity challenges. Our findings suggest that COVID-related health center funding may have contributed to stabilization of health centers' workforce and operations. Amid concerns about staff turnover, sustained investments targeted to supporting workforce retention at health centers can help to ensure ongoing delivery of critical services.

Concurrent disease burden from multiple infectious diseases and the influence of social determinants in the contiguous United States

Social determinants of health are known to underly excessive burden from infectious diseases. However, it is unclear if social determinants are strong enough drivers to cause repeated infectious disease clusters in the same location. When infectious diseases are known to co-occur, such as in the co-occurrence of HIV and TB, it is also unknown how much social determinants of health can shift or intensify the co-occurrence. We collected available data on COVID-19, HIV, influenza, and TB by county in the United States from 2019-2022. We applied the Kulldorff scan statistic to examine the relative risk of each disease by year depending on the data available. Additional analyses using the percent of the county that is below the US poverty level as a covariate were conducted to examine how much clustering is associated with poverty levels. There were three counties identified at the centers of clusters in both the adjusted and unadjusted analysis. In the poverty-adjusted analysis, we found a general shift of infectious disease burden from urban to rural clusters.

COVID-19 mortality: educational inequalities and socio-spatial context in two provinces of Argentina

Motivation for the study. There are very few studies on the educational inequalities in COVID-19 mortality, taking into account social contextual factors. Main findings. We found educational inequalities of COVID-19 mortality during both the 2020 and 2021 waves, regardless of the level of poverty and urbanization in the departments of Mendoza and San Juan provinces (Argentina). Implications. Preventive policies should focus not only in areas with high levels of poverty, but also in areas with adults of low educational level. With the aim of describing the association between sociodemographic characteristics and contextual factors with COVID-19 mortality during 2020-2021 in the provinces of Mendoza and San Juan in Argentina, we conducted an ecological study, which included the sociodemographic factors: age, sex and educational level, and the contextual factors: poverty and urbanization at the departmental level. The analyses were estimated using negative binomial Bayesian hierarchical models. Educational inequalities existed regardless of socioeconomic context and level of urbanization. The exception was the age group 65 years and older during 2021, which, regardless of educational level, showed a higher risk of death by COVID-19 in departments with high levels of structural poverty. In conclusion, educational inequality is an indicator of social inequality that increases vulnerability to COVID-19 mortality.

Tracking the Uneven Outcomes of COVID-19 on Racial and Ethnic Groups: Implications for Health Policy

The socioeconomic shocks of the first COVID-19 pandemic wave disproportionately affected vulnerable groups. But did that trend continue to hold during the Delta and Omicron waves? Leveraging data from the Johns Hopkins Coronavirus Resource Center, this paper examines whether demographic inequalities persisted across the waves of COVID-19 infections. The current study utilizes fixed effects regressions to isolate the marginal relationships between socioeconomic factors with case counts and death counts. Factors include levels of urbanization, age, gender, racial distribution, educational attainment, and household income, along with time- and state-specific COVID-19 restrictions and other time invariant controls captured via fixed effects controls. County-level health outcomes in large metropolitan areas show that despite higher incidence rates in suburban and exurban counties, urban counties still had disproportionately poor outcomes in the latter COVID-19 waves. Policy makers should consider health disparities when developing long-term public health regulatory policies to help shield low-income households from the adverse effects of COVID-19 and future pandemics.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

Racial and Regional Disparities Surrounding In-Hospital Mortality among Patients with 2019 Novel Coronavirus Disease (COVID-19): Evidence from NIS Sample in 2020

OBJECTIVE

This study explores differences in COVID-19 in-hospital mortality rates by patient and geographic factors to identify at-risk populations and analyze how strained health disparities were exacerbated during the pandemic.

METHODS

The latest 2020 United States National Inpatient Sample (NIS) data was used to obtain a population-based estimate for patients with COVID-19. We conducted a cross-sectional retrospective data analysis, and sampling weights were used for all statistical analyses to represent nationwide in-hospital mortality of patients with COVID-19. We used multivariate logistic regression models to identify predictors for how patients with COVID-19 are associated with in-hospital death.

RESULTS

Of 200,531 patients, 88.9% did not have an in-hospital death (n=178,369), and 11.1% had in-hospital death (n=22,162). Patients older than 70 were 10 times more likely to have an in-hospital death than patients younger than 40 (p<0.001). Male patients were 37% more likely to have an in-hospital death than female patients (p<0.001). Hispanic patients were 25% more likely to have in-hospital deaths than White patients (p<0.001). In the sub-analysis, Hispanic patients in the 50-60, 60-70, and 70 age groups were 32%, 34%, and 24%, respectively, more likely to have in-hospital death than White patients (p<0.001). Patients with hypertension and diabetes were 69% and 29%, respectively, more likely to have in-hospital death than patients without hypertension and diabetes.

CONCLUSION

Health disparities in the COVID-19 pandemic occurred across races and regions and must be addressed to prevent future deaths. Age and comorbidities like diabetes have a well-established link to increased disease severity, and we have linked both to higher mortality risk. Low-income patients had a significantly increased risk of in-hospital death starting at over 40 years old.

Spatial Analysis of Health System Factors in Infectious Disease Management: Lessons Learned from the COVID-19 Pandemic in Korea

Infectious disease outbreaks present ongoing and substantial challenges to health systems at local, national, and global levels, testing their preparedness, response capabilities, and resilience. This study aimed to identify and analyze critical health system-level factors that influence infection outbreaks, focusing on the experience of the COVID-19 pandemic in Korea. Conducted as a secondary data analysis, this study utilized national datasets from Korea. Given the inherent spatial dependencies in the spread of infectious diseases, we employed a spatial lag model to analyze data. While city-specific characteristics did not emerge as significant factors, health system variables, particularly the number of community health centers and health budgets, showed significant influence on the course of the COVID-19 outbreak, along with spatial autocorrelation coefficients. Our findings underscore the importance of enhancing public healthcare infrastructure, considering regional specificities, and promoting collaboration among local governments to bolster preparedness for future outbreaks. These insights are crucial for policymakers and healthcare professionals in formulating effective strategies to prevent, manage, and mitigate the impact of infectious disease outbreaks.

Associations between COVID-19 therapies and outcomes in rural and urban America: A multisite, temporal analysis from the Alpha to Omicron SARS-CoV-2 variants

PURPOSE

To investigate the enduring disparities in adverse COVID-19 events between urban and rural communities in the United States, focusing on the effects of SARS-CoV-2 vaccination and therapeutic advances on patient outcomes.

METHODS

Using National COVID Cohort Collaborative (N3C) data from 2021 to 2023, this retrospective cohort study examined COVID-19 hospitalization, inpatient death, and other adverse events. Populations were categorized into urban, urban-adjacent rural (UAR), and nonurban-adjacent rural (NAR). Adjustments included demographics, variant-dominant waves, comorbidities, region, and SARS-CoV-2 treatment and vaccination. Statistical methods included Kaplan-Meier survival estimates, multivariable logistic, and Cox regression.

FINDINGS

The study included 3,018,646 patients, with rural residents constituting 506,204. These rural dwellers were older, had more comorbidities, and were less vaccinated than their urban counterparts. Adjusted analyses revealed higher hospitalization odds in UAR and NAR (aOR 1.07 [1.05-1.08] and 1.06 [1.03-1.08]), greater inpatient death hazard (aHR 1.30 [1.26-1.35] UAR and 1.37 [1.30-1.45] NAR), and greater risk of other adverse events compared to urban dwellers. Delta increased, while Omicron decreased, inpatient adverse events relative to pre-Delta, with rural disparities persisting throughout. Treatment effectiveness and vaccination were similarly protective across all cohorts, but dexamethasone post-ventilation was effective only in urban areas. Nirmatrelvir/ritonavir and molnupiravir better protected rural residents against hospitalization.

CONCLUSIONS

Despite advancements in treatment and vaccinations, disparities in adverse COVID-19 outcomes persist between urban and rural communities. The effectiveness of some therapeutic agents appears to vary based on rurality, suggesting a nuanced relationship between treatment and geographic location while highlighting the need for targeted rural health care strategies.

Brief Report: Rural-Urban Differences in Disability Prevalence, Healthcare Services Utilization, and Participation in Special Education

Previous research has found that rural children are more likely to be disabled but are less likely to receive care. Both rural and disabled children were significantly impacted by the pandemic, particularly in terms of service utilization. Therefore, this study seeks to identify rural-urban differences in the prevalence of various disability indicators and in the receipt of educational and healthcare services. Data from 12,828 children aged 2-17 who participated in the 2021-2022 National Health Interview Survey (NHIS) was used to examine rural-urban differences in three different disability indicators and in education and health services utilization. Disability indicators included the Washington Group Short Set Composite Disability Indicator, a developmental disability indicator, and a neurodivergence indicator. Bivariate analysis, via Rao-Scott chi-square tests, was used to examine rural-urban disparities. Compared to their urban counterparts, rural children were more likely to have a positive Washington Group Short Set Composite Disability Indicator (14.3% vs. 10.6%) and neurodivergence indicator (17Ð.3% vs. 14.1%). Rural children with disabilities were more likely to have received prescription medication for behavioral, mental, or emotional health or concentration in the past year than urban children (34.2% vs. 25.9%). There was no rural-urban difference in the prevalence of developmental disabilities or other forms of health care use and special education participation. This report highlights the need for further investigation into underlying causes of rural-urban disparities in the prevalence of disabilities, as well as the need for continued support for programs and policies designed to support rural children with disabilities.

Key Determinants of COVID-19 Vaccination Take-Up in Remote Rural Areas: Evidence From Colombia

Objetives

The adoption of vaccines was a crucial factor in overcoming the COVID-19 pandemic. However, vaccination rates between rural and urban areas varied greatly. In this paper, our objective is to understand the individual and institutional factors associated with the uptake of vaccines in remote rural areas in Colombia.

Methods

We interviewed a random sample of 800 households (1,592 individuals) in remote rural areas of Antioquia (Colombia) during February 2022 when vaccinations were available. Then, we use a linear probability model to explain the uptake of the COVID-19 vaccine.

Results

The results indicate that the probability of having at least the first dose of the COVID-19 vaccine is positively associated with access to information, trust in police and army, and the perceived risk of contracting COVID-19. Trust in the church is negatively related to vaccination.

Conclusion

Institutions can play a critical role in the management of pandemics. Timely information on the risks associated with the disease and perceived riskiness are key factors that mobilize the population to take the COVID-19 vaccine.

Attitudes and Misconceptions of Coronavirus Disease and Vaccination Among African Americans in Rural Mississippi

Despite the recommendations for COVID-19 preventive health behaviors, it continues to increase alarmingly. This study examined how media coverage, myths, political leaders, and community leaders influence the attitudes and misconceptions about COVID-19 and COVID-19 vaccination uptake in the Mississippi Delta region. This qualitative study employed focus group discussions (FDGs) with representatives from three rural counties in the Mississippi Delta. A thematic analysis approach was used for data analysis. Participants were aware of how COVID-19 is transmitted, the preventative measures that can be used to mitigate the spread of the virus, and misconceptions and beliefs that lingered in their communities. Participants were uncertain about the need for the COVID-19 vaccine in terms of perceived risks (e.g., side effects, efficacy, and safety) and its novelty. Participants also discussed a wide range of COVID-19 misinformation that resulted in distress and distrust of the vaccine and health behavior recommendations. There are varying misconceptions and beliefs about COVID-19 and COVID-19 vaccine among communities in the rural Mississippi Delta. Thus, multi-sectoral collaborations between agencies that can use risk communication frameworks to deliver accurate health information that can resolve misinformation about COVID-19 in rural communities are needed.

Has the first year of the COVID pandemic impacted the trends in obesity-related CVD mortality between 1999 and 2019 in the United States?

Background

During the covid-19 pandemic there was a marked rise in the number of cardiovascular deaths. Obesity is a well-known modifiable risk factor for cardiovascular disease and has been identified as a factor which leads to poorer covid-19 related outcomes. In this study we aimed to analyse the impact of covid-19 on obesity-related cardiovascular deaths compared to trends seen 20 years prior. We also analysed the influence different demographics had on mortality.

Methods

Multiple Cause of Mortality database was accessed through CDC WONDER to obtain the obesity-related and general cardiovascular crude mortality and age adjusted mortality rates (AMMR) between 1999 and 2020 in the US. The obesity-related sample was stratified by demographics and cardiovascular mortality was subdivided into ischemic heart disease, heart failure, hypertension and cerebrovascular disease. Joinpoint Regression Program (Version 4.9.1.0) was used to calculate the average annual percent change (AAPC) in AAMR, and hence projected AAMR. Excess mortality was calculated by comparing actual AAMR in 2020 to projected values.

Results and discussion

There were an estimated 3058 excess deaths during the early stages of the pandemic impacting all cohorts. The greatest excess mortalities were seen in men, rural populations and in Asian/Pacific Islander and Native Americans. Interestingly the greatest overall mortality was seen in the Black American population. Our study highlights important, both pre and during the pandemic, in obesity related cardiovascular disease mortality which has important implications for ongoing public health measures.

Rural-urban Differences in Long-term Mortality and Readmission Following COVID-19 Hospitalization, 2020 to 2023

Background

We compared long-term mortality and readmission rates after COVID-19 hospitalization based on rural-urban status and assessed the impact of COVID-19 vaccination introduction on clinical outcomes by rurality.

Methods

The study comprised adults hospitalized for COVID-19 at 17 hospitals in 4 US states between March 2020 and July 2022, followed until May 2023. The main analysis included all patients, whereas a sensitivity analysis focused on residents from 4 states containing 17 hospitals. Additional analyses compared the pre- and postvaccination periods.

Results

The main analysis involved 9325 COVID-19 hospitalized patients: 31% were from 187 rural counties in 31 states; 69% from 234 urban counties in 44 states; the mean age was 65 years (rural, 66 years; urban, 64 years); 3894 women (rural, 41%; urban, 42%); 8007 Whites (rural, 87%; urban, 83%); 1738 deaths (rural, 21%; urban, 17%); and 2729 readmissions (rural, 30%; urban, 29%). During a median follow-up of 602 days, rural residence was associated with a 22% higher all-cause mortality (log-rank, P < .001; hazard ratio, 1.22; 95% confidence interval, 1.10-1.34, P < .001), and a trend toward a higher readmission rate (log-rank, P = .038; hazard ratio, 1.06; 95% confidence interval, .98-1.15; P = .130). The results remained consistent in the sensitivity analysis and in both pre- and postvaccination time periods.

Conclusions and Relevance

Patients from rural counties experienced higher mortality and tended to be readmitted more frequently following COVID-19 hospitalization over the long term compared with those from urban counties, a difference that remained even after the introduction of COVID-19 vaccines.

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

Risk perceptions and behaviors concerning rural tourism and economic-political drivers of COVID-19 policy in 2020

When COVID-19 was first introduced to the United States, state and local governments enacted a variety of policies intended to mitigate the virulence of the epidemic. At the time, the most effective measures to prevent the spread of COVID-19 included stay-at-home orders, closing of nonessential businesses, and mask mandates. Although it was well known that regions with high population density and cold climates were at the highest risk for disease spread, rural counties that are economically reliant on tourism were incentivized to enact fewer precautions against COVID-19. The uncertainty of the COVID-19 pandemic, the multiple policies to reduce transmission, and the changes in outdoor recreation behavior had a significant impact on rural tourism destinations and management of protected spaces. We utilize fine-scale incidence and demographic data to study the relationship between local economic and political concerns, COVID-19 mitigation measures, and the subsequent severity of outbreaks throughout the continental United States. We also present results from an online survey that measured travel behavior, health risk perceptions, knowledge and experience with COVID-19, and evaluation of destination attributes by 407 out-of-state visitors who traveled to Maine from 2020 to 2021. We synthesize this research to present a narrative on how perceptions of COVID-19 risk and public perceptions of rural tourism put certain communities at greater risk of illness throughout 2020. This research could inform future rural destination management and public health policies to help reduce negative socioeconomic, health and environmental impacts of pandemic-derived changes in travel and outdoor recreation behavior.

Population mortality before and during the COVID-19 epidemic in two Sudanese settings: a key informant study

BACKGROUND

Population mortality is an important metric that sums information from different public health risk factors into a single indicator of health. However, the impact of COVID-19 on population mortality in low-income and crisis-affected countries like Sudan remains difficult to measure. Using a community-led approach, we estimated excess mortality during the COVID-19 epidemic in two Sudanese communities.

METHODS

Three sets of key informants in two study locations, identified by community-based research teams, were administered a standardised questionnaire to list all known decedents from January 2017 to February 2021. Based on key variables, we linked the records before analysing the data using a capture-recapture statistical technique that models the overlap among lists to estimate the true number of deaths.

RESULTS

We estimated that deaths per day were 5.5 times higher between March 2020 and February 2021 compared to the pre-pandemic period in East Gezira, while in El Obeid City, the rate was 1.6 times higher.

CONCLUSION

This study suggests that using a community-led capture-recapture methodology to measure excess mortality is a feasible approach in Sudan and similar settings. Deploying similar community-led estimation methodologies should be considered wherever crises and weak health infrastructure prevent an accurate and timely real-time understanding of epidemics' mortality impact in real-time.

State variation in neighborhood COVID-19 burden across the United States

BACKGROUND

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

METHODS

We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

RESULTS

We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

CONCLUSIONS

Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

Evolution of HIV Health Care Workforce Needs in the U.S. Mountain West During the COVID-19 Pandemic: A Mixed Method Study

ABSTRACT

The COVID-19 pandemic drastically affected health care delivery for vulnerable populations. Many facilities shifted services to telemedicine, and people with HIV or at risk of acquiring HIV experienced interruptions in care. Simultaneously, traditional training approaches to help providers adapt were disrupted. Using a mixed method approach to examine changes over time, we integrated data on trainee needs collected by the Mountain West AIDS Education and Training Center (AETC): a 10-state needs assessment survey in 2020; feedback from a 2020 community of practice; aggregate training data from 2000 to 2022; and a second survey in 2022. HIV care providers' training needs evolved from wanting support on telemedicine and COVID-19 patient care issues, to a later focus on mental health and substance use, social determinants of health, and care coordination. This integrative analysis demonstrates the vital role that AETCs can play in addressing evolving and emergent public health challenges for the HIV workforce.

Evaluating the dynamic interplay of social distancing policies regarding airborne pathogens through a temporal interaction-driven model that uses real-world and synthetic data

OBJECTIVE

The recent SARS-CoV-2 pandemic has exhibited diverse patterns of spread across countries and communities, emphasizing the need to consider the underlying population dynamics in modeling its progression and the importance of evaluating the effectiveness of non-pharmaceutical intervention strategies in combating viral transmission within human communities. Such an understanding requires accurate modeling of the interplay between the community dynamics and the disease propagation dynamics within the community.

METHODS

We build on an interaction-driven model of an airborne disease over contact networks that we have defined. Using the model, we evaluate the effectiveness of temporal, spatial, and spatiotemporal social distancing policies. Temporal social distancing involves a pure dilation of the timeline while preserving individual activity potential and thus prolonging the period of interaction; spatial distancing corresponds to social distancing pods; and spatiotemporal distancing pertains to the situation in which fixed subgroups of the overall group meet at alternate times. We evaluate these social distancing policies over real-world interactions' data and over history-preserving synthetic temporal random networks. Furthermore, we evaluate the policies for the disease's with different number of initial patients, corresponding to either the phase in the progression of the infection through a community or the number of patients infected together at the initial infection event. We expand our model to consider the exposure to viral load, which we correlate with the meetings' duration.

RESULTS

Our results demonstrate the superiority of decreasing social interactions (i.e., time dilation) within the community over partial isolation strategies, such as the spatial distancing pods and the spatiotemporal distancing strategy. In addition, we found that slow-spreading pathogens (i.e., pathogens that require a longer exposure to infect) spread roughly at the same rate as fast-spreading ones in highly active communities. This result is surprising since the pathogens may follow different paths. However, we demonstrate that the dilation of the timeline considerably slows the spread of the slower pathogens.

CONCLUSIONS

Our results demonstrate that the temporal dynamics of a community have a more significant effect on the spread of the disease than the characteristics of the spreading processes.

Spatial Modeling of Sociodemographic Risk for COVID-19 Mortality

In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine this spatiotemporal variation in COVID-19 deaths with respect to association with socioeconomic, health, demographic, and political factors. We use multivariate regression applied to Health and Human Services (HHS) regions as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three separate time frames which correspond to the spread of distinct viral variants in the US: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022. Multivariate regression results for all regions across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are predictive of a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the value of local features for prediction, such as obesity, which is obscured by coarse-grained analysis. Overall, GWRF results indicate that this more nuanced modeling strategy is useful for determining the spatial variation in the importance of sociodemographic risk factors for predicting COVID-19 mortality.

Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths

In the United States, estimates of excess deaths attributable to the COVID-19 pandemic have consistently surpassed reported COVID-19 death counts. Excess deaths reported to non-COVID-19 natural causes may represent unrecognized COVID-19 deaths, deaths caused by pandemic health care interruptions, and/or deaths from the pandemic's socioeconomic impacts. The geographic and temporal distribution of these deaths may help to evaluate which explanation is most plausible. We developed a Bayesian hierarchical model to produce monthly estimates of excess natural-cause mortality for US counties over the first 30 mo of the pandemic. From March 2020 through August 2022, 1,194,610 excess natural-cause deaths occurred nationally [90% PI (Posterior Interval): 1,046,000 to 1,340,204]. A total of 162,886 of these excess natural-cause deaths (90% PI: 14,276 to 308,480) were not reported to COVID-19. Overall, 15.8 excess deaths were reported to non-COVID-19 natural causes for every 100 reported COVID-19 deaths. This number was greater in nonmetropolitan counties (36.0 deaths), the West (Rocky Mountain states: 31.6 deaths; Pacific states: 25.5 deaths), and the South (East South Central states: 26.0 deaths; South Atlantic states: 25.0 deaths; West South Central states: 24.2 deaths). In contrast, reported COVID-19 death counts surpassed estimates of excess natural-cause deaths in metropolitan counties in the New England and Middle Atlantic states. Increases in reported COVID-19 deaths correlated temporally with increases in excess deaths reported to non-COVID-19 natural causes in the same and/or prior month. This suggests that many excess deaths reported to non-COVID-19 natural causes during the first 30 mo of the pandemic in the United States were unrecognized COVID-19 deaths.

Changes in breakfast and water consumption among adolescents in Canada: examining the impact of COVID-19 in worsening inequity

BACKGROUND

To assess whether changes in breakfast and water consumption during the first full school year after the emergence of the COVID-19 pandemic varied based on sex/gender, race/ethnicity, and socioeconomic status among Canadian adolescents.

METHODS

Prospective annual survey data collected pre- (October 2019-March 2020) and post-COVID-19 onset (November 2020-June 2021) the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behaviour (COMPASS) study. The sample consisted of 8,128 students; mean (SD) age = 14.2 (1.3) years from a convenience sample of 41 Canadian secondary schools. At both timepoints self-reported breakfast and water consumption were dichotomized as daily or not. Multivariable logistic generalized estimating equations with school clustering were used to estimate differences in maintenance/adoption of daily consumption post-COVID-19 based on demographic factors, while controlling for pre-COVID-19 behaviour.

RESULTS

Adjusted odds ratios (AOR) with 95% confidence intervals are reported. Females (AOR = 0.71 [0.63, 0.79]) and lower socioeconomic status individuals (AORLowest:Highest=0.41 [0.16, 1.00]) were less likely to maintain/adopt daily breakfast consumption than male and higher socioeconomic status peers in the 2020-2021 school year. Black identifying individuals were less likely than all other racial/ethnic identities to maintain/adopt plain water consumption every day of the week (AOR = 0.33 [0.15, 0.75], p < 0.001). No significant interaction effects were detected.

CONCLUSIONS

Results support the hypothesis that changes in nutritional behaviours were not equal across demographic groups. Female, lower socioeconomic status, and Black adolescents reported greater declines in healthy nutritional behaviours. Public health interventions to improve adherence to daily breakfast and water consumption should target these segments of the population.

TRIAL REGISTRATION

Not a trial.

Diabetes and COVID-19 Outcomes: An Analysis of Freeman Health System Patients

BACKGROUND

As COVID-19 continues to affect millions of people around the world, it has become vital to understand how comorbidities such as diabetes affect the health outcomes of these patients. While earlier studies have focused on major metropolitan areas, rural settings have been comparatively understudied. The goal of this study is to understand the effect on mortality that these two diseases have in the inpatient setting of a rural population.

METHODS

The electronic medical records of all adult patients admitted to Freeman Health System, Joplin, Missouri, United States, between April 1, 2020, and December 31, 2021, were reviewed for the presence of COVID-19 infection and/or diabetes (type I and type II). Freeman Health is a major health system headquartered in Southwest Missouri. Diagnoses were obtained through the use of standard International Classification of Disease, 10th edition (ICD-10) codes. The initial data set consisted of 19,323 admissions. After excluding duplicate admissions and those who had already been infected with COVID-19, 1,729 patients with COVID-19, 172 patients with type I diabetes, and 3,992 patients with type II diabetes were included in the analysis of inpatient all-cause mortality. We hypothesized that patients with type I and type II diabetes would both show an increased risk of all-cause mortality. Mortality in the context of our study results refers to all-cause mortality.

RESULTS

The all-cause mortality rate was 19.94% (137/687, with a 95% confidence interval (CI) of 16.95%-22.93%) in patients admitted with both diabetes (the combined type I and type II subsets) and COVID-19 (P1). The mortality rate was 16.03% (167/1042, with 95% CI of 13.80%-18.25%) in patients admitted with COVID-19 who did not have diabetes (P2). Patients admitted with a comorbid diagnosis of diabetes but without COVID-19 (P5) had a much lower mortality rate of 5.98% (249/4164, with a 95% CI of 5.26%-6.70%). The combination of both COVID-19 and diabetes together was associated with a higher mortality rate than either of the two separately. The mortality rate was additionally elevated in patients with both type II diabetes and COVID-19 (P4) (134/663, mortality rate of 20.21% with 95% CI of 17.15%-23.27%) versus those with COVID-19 without diabetes (P2) (167/1042, 16.03% with 95% CI of 13.80%-18.25%), an overall difference of 4.18% (95% CI of 0.40%-7.94%). The subset of patients with type I diabetes with COVID-19 (P3) and type I diabetes without COVID-19 (P6) were too small to accurately power individual analysis. The subset of patients with diabetes (type I and type II) and without COVID-19 (P5) had the lowest mortality rate of any subset adequately powered for analysis at 5.98% (249/41464, CI of 5.26%-6.70%).  Conclusions: The results of this study show that type II diabetes is a significant risk factor for mortality in admitted COVID-19 patients. P4 had the highest overall mortality of any subset studied. The study was underpowered to show if type I diabetes patients, with and without COVID-19, had an increased mortality when analyzed separately. COVID-19 significantly increased mortality in all subsets adequately powered for full analysis.

COVID-19-related mortality in Texas border counties vs non-border counties

The state of Texas ranked second in total cases of coronavirus disease 2019 (COVID-19) in the United States during the pandemic. Counties near the US-Mexico border were severely impacted by the pandemic. Mortality and long-term consequences from COVID-19 are associated with comorbidities, illness severity, and patient demographics. However, differences in outcomes between border and non-border counties are unknown. In this retrospective observational study, data were obtained for analysis from the Texas hospital inpatient discharge public use data file from 2020 to 2021 for adult patients with COVID-19 based on the associated international classification of disease 10 codes. Patients were categorized into border or non-border counties. The clinical outcomes included mortality, length of stay, mortality risk, illness severity, and intensive care unit (ICU) or critical care unit (CCU) admissions. Cost differences between border and non-border counties were analyzed. Age, gender, race, ethnicity, admission type, location, and year of diagnosis were covariates. A total of 1,745,312 patients were included in this analysis. 25% of COVID-19 patients admitted in Texas were from border counties. Patient mortality was 5.35% in border counties compared to 3.87% in non-border counties (p = 0.003). In border counties, 36.51% and 32.96% of patients required ICU and CCU admissions compared to 32.96% and 10.72%, respectively in non-border counties. Border counties had significantly higher risk of mortality (relative risk (RR) = 1.26; 95% CI: 1.09-1.46, p = 0.002), ICU admission (RR = 1.15; 95% CI: 1.15; 95% CI: 1.01-1.32, p = 0.038), CCU admission (RR = 2.87; 95% CI: 1.93, 4.27, p < 0.001), and ICU/CCU admission (RR = 1.28; 95% CI: 1.10, 1.48, p < 0.001) which reflects health disparities in the management of COVID-19 in border counties of Texas.

Wave-Based Outcomes Comparison of Hospitalized COVID-19 Patients: A Retrospective Multicenter Cohort Study From Rural Appalachia

BACKGROUND

There has been little to no characterization of the pandemic's effects on rural Central Appalachia, in which health disparities in the pre-COVID-19 era have historically plagued. This is the first study to compare wave-based differences in outcomes of hospitalized patients with COVID-19 in the rural Appalachian region. This study aims to provide a more comprehensive understanding of the effects of the COVID-19 pandemic on large rural communities and Appalachia.

METHODS

This is a retrospective cohort study of hospitalized patients with COVID-19 between April 2020 and June 2022, which includes 13 Appalachian Regional Healthcare (ARH) hospitals. The primary outcome of the study was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) stay, need for mechanical ventilation, length of hospital stay, 1-30-day re-admittance, 30-60-day re-admittance, and thromboembolism incidence risk.

RESULTS

The second wave of infections during the pandemic demonstrated the highest mortality with higher odds of affecting younger patients. The third wave demonstrated similar mortality to the first wave. Elderly patients and patients with chronic morbidities demonstrated the highest mortality and morbidity and the highest requirement for mechanical ventilation across the three waves. Vaccination lowered the odds of mechanical ventilation and ICU stay.

CONCLUSIONS

This study comprehensively characterizes the impact of the COVID-19 pandemic in rural regions of Appalachian Kentucky and West Virginia. Future studies comparing differences between rural and urban geographies may be able to distinguish whether the disparities in these regions played a role in the impact on residents.

Rural populations facilitated early SARS-CoV-2 evolution and transmission in Missouri, USA

In the United States, rural populations comprise 60 million individuals and suffered from high COVID-19 disease burdens. Despite this, surveillance efforts are biased toward urban centers. Consequently, how rurally circulating SARS-CoV-2 viruses contribute toward emerging variants remains poorly understood. In this study, we aim to investigate the role of rural communities in the evolution and transmission of SARS-CoV-2 during the early pandemic. We collected 544 urban and 435 rural COVID-19-positive respiratory specimens from an overall vaccine-naïve population in Southwest Missouri between July and December 2020. Genomic analyses revealed 53 SARS-CoV-2 Pango lineages in our study samples, with 14 of these lineages identified only in rural samples. Phylodynamic analyses showed that frequent bi-directional diffusions occurred between rural and urban communities in Southwest Missouri, and that four out of seven Missouri rural-origin lineages spread globally. Further analyses revealed that the nucleocapsid protein (N):R203K/G204R paired substitutions, which were detected disproportionately across multiple Pango lineages, were more associated with urban than rural sequences. Positive selection was detected at N:204 among rural samples but was not evident in urban samples, suggesting that viruses may encounter distinct selection pressures in rural versus urban communities. This study demonstrates that rural communities may be a crucial source of SARS-CoV-2 evolution and transmission, highlighting the need to expand surveillance and resources to rural populations for COVID-19 mitigation.

Addressing an increase in surgical site infections during the COVID-19 pandemic-Identifying opportunities during a chaotic time

BACKGROUND

Health care systems saw increases in device-associated infections and decreases in surgical site infections (SSI) during the COVID-19 pandemic. However, following an increase in SSIs, an acute care hospital assessed the risk and preventative factors of SSIs among patients.

METHODS

A retrospective cohort study on surgeries performed between January 2020 and September 2021 analyzed associations of SSI with risk and preventive factors utilizing χ2, t-tests, and odds ratios. A secondary analysis was utilized to determine the association of case urgency and prevention practice performance.

RESULTS

There was a significant difference in the administration of correct perioperative antibiotic selection between SSI (78.1%) and non-SSI (86.2%) along with 2.9 greater odds of developing an SSI with incorrect perioperative antibiotics. Patients who had urgent cases were significantly less likely than elective to receive preoperative chlorhexidine gluconate wipes (81.6%, 61.5%, respectively), correct antibiotic selection and timing (93.2%, 70.8%, respectively) and chlorhexidine and alcohol skin preparation (81.6%, 67.5%, respectively).

DISCUSSION

Disruption of perioperative workflow during the COVID-19 pandemic likely resulted in an increase in SSI. Numerous opportunities were identified for focused prevention efforts.

CONCLUSIONS

The next steps include implementing strategies to improve SSI prevention and establish a culture that can withstand workflow disruptions to maintain a safe environment during significant changes.

Trends and Outcomes of Type 2 Myocardial Infarction During the COVID-19 Pandemic in the United States

BACKGROUND AND OBJECTIVES

There is limited data on the impact of type 2 myocardial infarction (T2MI) during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

The National Inpatient Sample (NIS) database from January 2019 to December 2020 was queried to identify T2MI hospitalizations based on the appropriate International Classification of Disease, Tenth Revision-Clinical Modification codes. Monthly trends of COVID-19 and T2MI hospitalizations were evaluated using Joinpoint regression analysis. In addition, the multivariate logistic and linear regression analysis was used to compare in-hospital mortality, coronary angiography use, and resource utilization between 2019 and 2020.

RESULTS

A total of 743,535 patients hospitalized with a diagnosis of T2MI were identified in the years 2019 (n=331,180) and 2020 (n=412,355). There was an increasing trend in T2MI hospitalizations throughout the study period corresponding to the increase in COVID-19 hospitalizations in 2020. The adjusted odds of in-hospital mortality associated with T2MI hospitalizations were significantly higher in 2020 compared with 2019 (11.1% vs. 8.1%: adjusted odds ratio, 1.19 [1.13-1.26]; p<0.01). In addition, T2MI hospitalizations were associated with lower odds of coronary angiography and higher total hospitalization charges, with no difference in the length of stay in 2020 compared with 2019.

CONCLUSIONS

We found a significant increase in T2MI hospitalizations with higher in-hospital mortality, total hospitalization costs, and lower coronary angiography use during the early COVID-19 pandemic corresponding to the trends in the rise of COVID-19 hospitalizations. Further research into the factors associated with increased mortality can increase our preparedness for future pandemics.

Nationwide Genomic Surveillance and Response to COVID-19: The VA SeqFORCE and SeqCURE Consortiums

Background

The US Department of Veterans Affairs (VA) has dedicated significant resources toward countering the COVID-19 pandemic. Sequencing for Research Clinical and Epidemiology (SeqFORCE) and Sequencing Collaborations United for Research and Epidemiology (SeqCURE) were developed as clinical and research consortiums, respectively, focused on the genetic COVID-19 surveillance.

Observations

Through genetic sequencing, VA SeqFORCE and SeqCURE collaborations contributed to the COVID-19 pandemic response and scientific understanding. Future directions for each program include the assessment of the unique impact of COVID-19 on the veteran population, as well as the adaptation of these programs to future infectious disease threats. We foresee the use of these established platforms beyond infectious diseases.

Conclusions

VA SeqFORCE and SeqCURE were established as clinical and research programs dedicated to sequencing COVID-19 as part of ongoing clinical and surveillance efforts. In the future, we anticipate that having these programs embedded within the largest integrated health care system in the US will enable the study of pathogens and pandemics beyond COVID-19 and at an unprecedented scale. The investment in these programs will form an integral part of our nation's response to emerging infectious diseases, with future applications to precision medicine and beyond.

A Retrospective Analysis of Hyperlipidemia and COVID-19 Outcomes Investigated in a Rural Midwestern Population

Background COVID-19 is a respiratory disease caused by SARS-CoV-2, a coronavirus discovered in 2019. Its impact on the world continues to be studied due to the significant death toll of the disease. As the COVID-19 pandemic remains ongoing, examining the association of COVID-19 with comorbidities and resulting mortality is necessary. This study focuses on population health outcomes with COVID-19 infection and hyperlipidemia (total cholesterol greater than or equal to 200 mg/dL) as a comorbidity, including potential associations with age and sex. Methods As a retrospective analytical study, patients were divided into three populations based on COVID-19 and/or hyperlipidemia based on the International Classification of Diseases, Tenth Edition (ICD-10) codes reported in the electronic medical record system at Freeman Health System (FHS) in Southwest Missouri from April 1, 2020, to December 31, 2021. Wald's methods and two sample proportion summary hypotheses with confidence intervals (CIs) were used for comparison. The populations were subdivided and analyzed for age and sex differences. Results Patients with both COVID-19 and hyperlipidemia had a higher mortality rate than patients with COVID-19 and without hyperlipidemia and patients with hyperlipidemia and without COVID-19; patients with COVID-19 and without hyperlipidemia had a higher mortality rate than patients with hyperlipidemia and without COVID-19. All comparisons across these populations were statistically significant (p-value < 0.05). While increased age was associated with increased mortality in all groups, sex was not predictive in this regard. Conclusion Our study provides insights into variables affecting COVID-19 outcomes in a rural Midwestern population by showing how the comorbidity hyperlipidemia contributes to increased mortality.

COVID-19 epidemiology and rural healthcare: a survey in a Spanish village

We used primary care data to retrospectively describe the entry, spread, and impact of COVID-19 in a remote rural community and the associated risk factors and challenges faced by the healthcare team. Generalized linear models were fitted to assess the relationship between age, sex, period, risk group status, symptom duration, post-COVID illness, and disease severity. Social network and cluster analyses were also used. The first six cases, including travel events and a social event in town, contributed to early infection spread. About 351 positive cases were recorded and 6% of patients experienced two COVID-19 episodes in the 2.5-year study period. Five space-time case clusters were identified. One case, linked with the social event, was particularly central in its contact network. The duration of disease symptoms was driven by gender, age, and risk factors. The probability of suffering severe disease increased with symptom duration and decreased over time. About 27% and 23% of individuals presented with residual symptoms and post-COVID illness, respectively. The probability of developing a post-COVID illness increased with age and the duration of COVID-associated symptoms. Carefully registered primary care data may help optimize infection prevention and control efforts and upscale local healthcare capacities in vulnerable rural communities.

Care coordination between rural primary care and telemedicine to expand medication treatment for opioid use disorder: Results from a single-arm, multisite feasibility study

PURPOSE

The use of telemedicine (TM) has accelerated in recent years, yet research on the implementation and effectiveness of TM-delivered medication treatment for opioid use disorder (MOUD) has been limited. This study investigated the feasibility of implementing a care coordination model involving MOUD delivered via an external TM provider for the purpose of expanding access to MOUD for patients in rural settings.

METHODS

The study tested a care coordination model in 6 rural primary care sites by establishing referral and coordination between the clinic and a TM company for MOUD. The intervention spanned approximately 6 months from July/August 2020 to January 2021, coinciding with the peak of the COVID-19 pandemic. Each clinic tracked patients with OUD in a registry during the intervention period. A pre-/post-intervention design (N = 6) was used to assess the clinic-level outcome as patient-days on MOUD based on patient electronic health records.

FINDINGS

All clinics implemented critical components of the intervention, with an overall TM referral rate of 11.7% among patients in the registry. Five of the 6 sites showed an increase in patient-days on MOUD during the intervention period compared to the 6-month period before the intervention (mean increase per 1,000 patients: 132 days, P = .08, Cohen's d = 0.55). The largest increases occurred in clinics that lacked MOUD capacity or had a greater number of patients initiating MOUD during the intervention period.

CONCLUSIONS

To expand access to MOUD in rural settings, the care coordination model is most effective when implemented in clinics that have negligible or limited MOUD capacity.

Associations between rurality and regional differences in sociodemographic factors and the 1918-20 influenza and 2020-21 COVID-19 pandemics in Missouri counties: An ecological study

This study compares pandemic experiences of Missouri's 115 counties based on rurality and sociodemographic characteristics during the 1918-20 influenza and 2020-21 COVID-19 pandemics. The state's counties and overall population distribution have remained relatively stable over the last century, which enables identification of long-lasting pandemic attributes. Sociodemographic data available at the county level for both time periods were taken from U.S. census data and used to create clusters of similar counties. Counties were also grouped by rural status (RSU), including fully (100%) rural, semirural (1-49% living in urban areas), and urban (>50% of the population living in urban areas). Deaths from 1918 through 1920 were collated from the Missouri Digital Heritage database and COVID-19 cases and deaths were downloaded from the Missouri COVID-19 dashboard. Results from sociodemographic analyses indicate that, during both time periods, average farm value, proportion White, and literacy were the most important determinants of sociodemographic clusters. Furthermore, the Urban/Central and Southeastern regions experienced higher mortality during both pandemics than did the North and South. Analyses comparing county groups by rurality indicated that throughout the 1918-20 influenza pandemic, urban counties had the highest and rural had the lowest mortality rates. Early in the 2020-21 COVID-19 pandemic, urban counties saw the most extensive epidemic spread and highest mortality, but as the epidemic progressed, cumulative mortality became highest in semirural counties. Additional results highlight the greater effects both pandemics had on county groups with lower rates of education and a lower proportion of Whites in the population. This was especially true for the far southeastern counties of Missouri ("the Bootheel") during the COVID-19 pandemic. These results indicate that rural-urban and socioeconomic differences in health outcomes are long-standing problems that continue to be of significant importance, even though the overall quality of health care is substantially better in the 21st century.

Socio-Economic Factors Associated with Ethnic Disparities in SARS-CoV-2 Infection and Hospitalization

BACKGROUND

There is evidence that non-Italians presented higher incidence of infection and worse health outcomes if compared to native populations in the COVID-19 pandemic. The aim of the study was to compare Italian- and non-Italian-born health outcomes, accounting for socio-economic levels.

METHODS

We analyzed data relative to 906,463 people in Umbria (Italy) from 21 February 2020 to 31 May 2021. We considered the National Deprivation Index, the Urban-Rural Municipalities Index and the Human Development Index (HDI) of the country of birth. We used a multilevel logistic regression model to explore the influence of these factors on SARS-CoV-2 infection and hospitalization rates. Diagnosis in the 48 h preceding admission was an indicator of late diagnosis among hospitalized cases.

RESULTS

Overall, 54,448 persons tested positive (6%), and 9.7% of them were hospitalized. The risk of hospital admission was higher among non-Italians and was inversely related to the HDI of the country of birth. A diagnosis within 48 h before hospitalization was more frequent among non-Italians and correlated to the HDI level.

CONCLUSIONS

COVID-19 had unequal health outcomes among the population in Umbria. Reduced access to primary care services in the non-Italian group could explain our findings. Policies on immigrants' access to primary healthcare need to be improved.

Reversed urban-rural gradient in COVID-19 seroprevalence and related factors in a nationally representative survey, Poland, 29 March to 14 May 2021

BackgroundWe anticipated that people in rural areas and small towns with lower population density, lower connectivity and jobs less dependent on social interaction will be less exposed to COVID-19. Still, other variables correlated with socioeconomic inequalities may have a greater impact on transmission.AimWe investigated how COVID-19 affected rural and urban communities in Poland, focussing on the most exposed groups and disparities in SARS-CoV-2 transmission.MethodsA random digit dial sample of Polish adults stratified by region and age was drawn from 29 March to 14 May 2021. Serum samples were tested for anti-S1 and anti-N IgG antibodies, and positive results in both assays were considered indicative of past infection. Seroprevalence estimates were weighted to account for non-response. Adjusted odds ratios (AORs) were calculated using multivariable logistic regression.ResultsThere was serological evidence of infection in 32.2% (95% CI: 30.2-34.4) of adults in rural areas/small towns (< 50,000 population) and 26.6% (95% CI: 24.9-28.3) in larger cities. Regional SARS-CoV-2 seroprevalence ranged from 23.4% (95% CI: 18.3-29.5) to 41.0% (95% CI: 33.5-49.0) and was moderately positively correlated (R = 0.588; p = 0.017; n = 16) with the proportion of respondents living in rural areas or small cities. Upon multivariable adjustment, both men (AOR = 1.60; 95% CI: 1.09-2.35) and women (AOR = 2.26; 95% CI: 1.58-3.21) from these areas were more likely to be seropositive than residents of larger cities.ConclusionsWe found an inverse urban-rural gradient of SARS-CoV-2 infections during early stages of the COVID-19 pandemic in Poland and suggest that vulnerabilities of populations living in rural areas need to be addressed.

Sociodemographic Differences in COVID-19 Pandemic Experiences Among Families in the United States

Importance

Few population-based studies in the US collected individual-level data from families during the COVID-19 pandemic.

Objective

To examine differences in COVID-19 pandemic-related experiences in a large sociodemographically diverse sample of children and caregivers.

Design, Setting, and Participants

The Environmental influences on Child Health Outcomes (ECHO) multi-cohort consortium is an ongoing study that brings together 64 individual cohorts with participants (24 757 children and 31 700 caregivers in this study) in all 50 US states and Puerto Rico. Participants who completed the ECHO COVID-19 survey between April 2020 and March 2022 were included in this cross-sectional analysis. Data were analyzed from July 2021 to September 2022.

Main Outcomes and Measures

Exposures of interest were caregiver education level, child life stage (infant, preschool, middle childhood, and adolescent), and urban or rural (population <50 000) residence. Dependent variables included COVID-19 infection status and testing; disruptions to school, child care, and health care; financial hardships; and remote work. Outcomes were examined separately in logistic regression models mutually adjusted for exposures of interest and race, ethnicity, US Census division, sex, and survey administration date.

Results

Analyses included 14 646 children (mean [SD] age, 7.1 [4.4] years; 7120 [49%] female) and 13 644 caregivers (mean [SD] age, 37.6 [7.2] years; 13 381 [98%] female). Caregivers were racially (3% Asian; 16% Black; 12% multiple race; 63% White) and ethnically (19% Hispanic) diverse and comparable with the US population. Less than high school education (vs master's degree or more) was associated with more challenges accessing COVID-19 tests (adjusted odds ratio [aOR], 1.88; 95% CI, 1.06-1.58), lower odds of working remotely (aOR, 0.04; 95% CI, 0.03-0.07), and more food access concerns (aOR, 4.14; 95% CI, 3.20-5.36). Compared with other age groups, young children (age 1 to 5 years) were least likely to receive support from schools during school closures, and their caregivers were most likely to have challenges arranging childcare and concerns about work impacts. Rural caregivers were less likely to rank health concerns (aOR, 0.77; 95% CI, 0.69-0.86) and social distancing (aOR, 0.82; 95% CI, 0.73-0.91) as top stressors compared with urban caregivers.

Conclusions

Findings in this cohort study of US families highlighted pandemic-related burdens faced by families with lower socioeconomic status and young children. Populations more vulnerable to public health crises should be prioritized in recovery efforts and future planning.

Using wastewater to overcome health disparities among rural residents

The SARS-CoV-2 pandemic highlighted the need for novel tools to promote health equity. There has been a historical legacy around the location and allocation of public facilities (such as health care) focused on efficiency, which is not attainable in rural, low-density, United States areas. Differences in the spread of the disease and outcomes of infections have been observed between urban and rural populations throughout the COVID-19 pandemic. The purpose of this article was to review rural health disparities related to the SARS-CoV-2 pandemic while using evidence to support wastewater surveillance as a potentially innovative tool to address these disparities more widely. The successful implementation of wastewater surveillance in resource-limited settings in South Africa demonstrates the ability to monitor disease in underserved areas. A better surveillance model of disease detection among rural residents will overcome issues around the interactions of a disease and social determinants of health. Wastewater surveillance can be used to promote health equity, particularly in rural and resource-limited areas, and has the potential to identify future global outbreaks of endemic and pandemic viruses.

Predictors of student mask mandate policies in United States school districts during the COVID-19 pandemic

Introduction

Although factors such as urbanicity, population demographics, and political affiliation have been linked with COVID-19 masking behavior and policy in community settings, little work has investigated factors associated with school mask policies. We sought to characterize United States state and school district student COVID-19 masking policies during the 2021-22 school year and determine predictors of these mandates at four time points, including before and after federal guidance relaxed school mask recommendations in February 2022.

Methods

Student mask policies for US states and the District of Columbia, as well as a sample of 56 districts were categorized as prohibited, recommended, or required in September 2021, November 2021, January 2022, and March 2022 based on the Johns Hopkins eSchool+ Initiative School Reopening Tracker. Changes in policies over time were characterized. Generalized estimating equations and logistic regression were used to evaluate whether political affiliation of governor, urbanicity, economic disadvantage, and race/ethnic composition of district students, and county-level COVID-19 incidence predicted the presence of a district mask mandate at any time point and at all four time points.

Results

State and district policies changed over time. Districts that implemented student mandates at any point were more likely to be in states with Democratic governors (AOR: 5.52; 95% CI: 2.23, 13.64) or in non-rural areas (AOR: 8.20; 95% CI: 2.63, 25.51). Districts that retained mask mandates at all four time points were more likely to have Democratic governors (AOR: 5.39; 95% CI: 2.69, 10.82) and serve a smaller proportion of economically disadvantaged students (AOR: 0.97; 95% CI: 0.95, 0.99). Districts serving a larger proportion of students from minoritized racial/ethnic groups were more likely to have mask mandates at any or all timepoints. Notably, county-level COVID-19 prevalence was not related to the presence of a mask mandate at any or all time points. By March 2022, no factors were significantly associated with district mask policy.

Discussion

Political, geographic, and demographic characteristics predicted the likelihood of student mask mandates in the 2021-22 school year. Public health promotion messages and policy must account for variation in these factors, potentially through centralized and consistent messaging and unbiased, trustworthy communication.

The Impact of the COVID-19 Pandemic on Outcomes in Acute Pancreatitis: A Propensity Score Matched Study Comparing before and during the Pandemic

We aimed to evaluate the outcomes and survival of patients with acute pancreatitis who shared the same clinical form, age, and sex before the pandemic, during the pandemic, and among those with confirmed COVID-19 infection upon hospital admission. This consideration used the sparse data in the existing literature on the influence of the pandemic and COVID-19 infection on patients with acute pancreatitis. To accomplish this, we conducted a multicentric, retrospective case-control study using propensity score matching with a 2:1 match of 28 patients with SARS-CoV-2 infection and acute pancreatitis, with 56 patients with acute pancreatitis pre-pandemic, and 56 patients with acute pancreatitis during the pandemic. The study outcome demonstrated a six-fold relative risk of death in patients with acute pancreatitis and SARS-CoV-2 infection compared to those with acute pancreatitis before the pandemic. Furthermore, restrictive measures implemented during the pandemic period led to a partial delay in the care of patients with acute pancreatitis, which likely resulted in an impairment of their immune state. This, in certain circumstances, resulted in a restriction of surgical treatment indications, leading to a three-fold relative risk of death in patients with acute pancreatitis during the pandemic compared to those with acute pancreatitis before the pandemic.

A collaborative approach to improving representation in viral genomic surveillance

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building 'next generation' genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness.

Preparedness for a pandemic: Independent community pharmacists' experiences delivering COVID-19 vaccines

OBJECTIVE

To identify the experiences and preparedness for independent community pharmacies to deliver COVID-19 vaccines.

DESIGN

A mixed-methods study collected quantitative and qualitative data.

SETTING AND PARTICIPANTS

Every independent community pharmacy providing COVID-19 vaccines in North Dakota.

OUTCOME MEASURES

During state-required site visits, quantitative data determined by a Centers for Disease Control and Prevention (CDC)-developed reviewer guide were collected on each pharmacy's preparedness on vaccine provision and quality assurance to provide COVID-19 vaccines. Qualitative data to describe the lived experiences of pharmacists were collected through site visit documentation and semistructured interviews with participating pharmacists.

RESULTS

Fifty-two pharmacies received site visits. All visited pharmacies met full compliance with CDC's reviewer guide for billing and documentation of vaccine, vaccine procedures, recipient communication, and handling of ancillary supplies. Pharmacies varied in their compliance of vaccine storage and handling requirements. Forty-three interviews yielded 4 main themes about pharmacies' role in vaccination during the pandemic: (1) professional role, (2) accessibility, (3) patient relations, and (4) community role.

CONCLUSION

Site visits demonstrated that independent community pharmacies were prepared to provide COVID-19 vaccines in response to public need, despite varying degrees of experience with and provision of routine immunizations. Interviews revealed that pharmacists recognized their important contribution to COVID-19 vaccine provision efforts.

COVID-19 mortality among veterans with serious mental illness in the veterans health administration

OBJECTIVE

Persons with serious mental illness (SMI: schizophrenia-spectrum disorders, bipolar disorder) experience increased risk of mortality after contracting COVID-19 based on the results of several international evaluations. However, information about COVID-19 mortality risk among patients with SMI in the Veterans Health Administration (VHA) has been limited, precluding identification of protective factors. The current evaluation was conducted to assess COVID-19 mortality risk among VHA patients with SMI and to evaluate potential protective factors in mitigating mortality risk following a positive COVID-19 diagnosis.

METHODS

National VHA administrative data was used to identify all patients (N = 52,916) who received a positive COVID-19 test result between March 1, 2020, and September 30, 2020. Mortality risk was assessed by SMI status via bivariate comparisons and multivariate regression analyses.

RESULTS

In unadjusted analyses, VHA patients with SMI overall and patients with bipolar disorder in particular did not experience increased mortality risk in the 30 days following a positive COVID test, although patients with schizophrenia had increased risk. Within adjusted analyses, patients with schizophrenia remained at increased mortality risk (OR = 1.38), but at reduced levels relative to previous evaluations in other healthcare settings.

CONCLUSIONS

Within VHA, patients with schizophrenia, but not those with bipolar disorder, experience increased mortality risk in the 30 days following a positive COVID-19 test. Large integrated healthcare settings such as VHA may offer services which may protect against COVID-19 mortality for vulnerable groups such as persons with SMI. Additional work is needed to identify practices which may reduce the risk of COVID-19 mortality among persons with SMI.

Excess Mortality at Veterans Health Administration Facilities During the COVID-19 Pandemic

IMPORTANCE

The COVID-19 pandemic resulted in excess mortality among the general US population and at Veterans Health Administration (VHA) facilities. It is critical to understand the characteristics of facilities that experienced the highest and lowest pandemic-related mortality to inform future mitigation efforts.

OBJECTIVE

To identify facility-level excess mortality during the pandemic and to correlate these estimates with facility characteristics and community-wide rates of COVID-19 burden.

DESIGN

We used pre-pandemic data to estimate mortality risk prediction models using 5-fold cross-validation and Poisson quasi-likelihood regression. We then estimated excess mortality and observed versus expected (O/E) mortality ratios by the VHA facility from March to December 2020. We examined facility-level characteristics by excess mortality quartile.

PARTICIPANTS

Overall, there were 11.4 million VHA enrollees during 2016 and 2020.

MAIN MEASURES

Facility-level O/E mortality ratios and excess all-cause mortality.

RESULT

VHA-enrolled veterans experienced 52,038 excess deaths from March to December 2020, equating to 16.8% excess mortality. Facility-specific rates ranged from -5.5% to +63.7%. Facilities in the lowest quartile for excess mortality experienced fewer COVID-19 deaths (0.7-1.51, P <0.001) and cases (52.0-63.0, P =0.002) per 1,000 population compared with the highest quartile. The highest quartile facilities had more hospital beds (276.7-187.6, P =0.024) and a higher percent change in the share of visits conducted via telehealth from 2019 to 2020 (183%-133%, P <0.008).

CONCLUSIONS

There was a large variation in mortality across VHA facilities during the pandemic, which was only partially explained by the local COVID-19 burden. Our work provides a framework for large health care systems to identify changes in facility-level mortality during a public health emergency.

Effects of dynamic zero COVID-19 policy on anxiety status and lifestyle changes of pregnant women in rural South China: a survey-based analysis by propensity score matching method

Introduction

The coronavirus disease 2019 (COVID-19) pandemic triggered a global public health crisis and has brought an unprecedented impact on pregnant women. The problems faced by pregnant women in the rural areas of China during the epidemic are different from those in urban areas. Although the epidemic situation in China has gradually improved, studying the impact of the previous dynamic zero COVID-19 policy on the anxiety status and lifestyle of pregnant women in rural areas of China, is still necessary.

Methods

A cross-sectional survey of pregnant women in rural South China was conducted from September 2021 to June 2022.Using questionnaires, sociodemographic characteristics, anxiety status, physical activity, sleep quality, and dietary status of the population were collected. Using the propensity score matching method, the effect of the dynamic zero COVID-19 strategy on the anxiety status and lifestyle of pregnant women was analyzed.

Results

Among the pregnant women in the policy group (n = 136) and the control group (n = 680), 25.7 and 22.4% had anxiety disorders, 83.1 and 84.7% had low or medium levels of physical activity, and 28.7 and 29.1% had sleep disorders, respectively. However, no significant difference (p > 0.05) was observed between the two groups. Compared with control group, the intake of fruit in the policy group increased significantly (p = 0.019), whereas that of aquatic products and eggs decreased significantly (p = 0.027). Both groups exhibited an unreasonable dietary structure and poor compliance with the Chinese dietary guidelines for pregnant women (p > 0.05). The proportion of pregnant women in the policy group, whose intake of stable food (p = 0.002), soybean, and nuts (p = 0.004) was less than the recommended amount, was significantly higher than that in the control group.

Discussion

The dynamic zero COVID-19 strategy had little impact on the anxiety status, physical activity, and sleep disorders of pregnant women in the rural areas of South China. However, it affected their intake of certain food groups. Improving corresponding food supply and organized nutritional support should be addressed as a strategic approach to improve the health of pregnant women in rural South China during the pandemic.

Rural/urban differences in access to paid sick leave among full-time workers

PURPOSE

Access to paid sick leave is critically important to promoting good health, caregiving, and stopping the spread of disease. In this study, we estimate whether access to paid sick leave among US full-time workers differs between rural and urban residents.

METHODS

We used data from the 2020 National Health Interview Survey and included adult respondents between the ages of 18 and 64 who were employed full-time (n = 12,086). We estimated bivariate differences in access to paid sick leave by rural/urban residence, and then calculated the predicted probability of access to paid sick leave, adjusting for sociodemographic and health characteristics, across different education levels.

FINDINGS

We find a nearly 10-percentage point difference in access to paid sick leave between rural and urban adults (68.1% vs 77.1%, P<.001). The difference in access to paid sick leave between rural and urban residents remained significant even after adjusting for sociodemographic and health characteristics. The fully adjusted predicted probability of paid sick leave for rural full-time workers was 69.8%, compared with 76.4% for urban full-time workers (P<.001). We also identified lower levels of paid leave for rural (vs urban) workers within each educational category.

CONCLUSIONS

Full-time workers in rural areas have less access to paid sick leave than full-time workers in urban areas. Without access to paid sick leave, rural and urban residents may go to work while contagious or forego necessary health care. Left to individual employers or localities, rural inequities in access to paid sick leave will likely persist.

Disproportionate impacts of COVID-19 in a large US city

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

Importance of Balanced Attention Toward Coronavirus Disease 2019 and Neglected Tropical Diseases

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has been spreading since 2019, causing a worldwide pandemic. Amid the COVID-19 pandemic, tuberculosis, AIDS, and malaria have adversely affected the quality of life of patients and killed millions of people. In addition, COVID-19 continues to impede the delivery of health services, including those for the control of neglected tropical diseases (NTDs). Furthermore, NTDs have been reported as possible co-pathogens among patients infected with COVID-19. However, studies regarding parasitic co-infection among these patients have been limited. This review aimed to explore and describe the cases and reports of parasitic infections in the backdrop of COVID-19 to provide comprehensive knowledge regarding this aspect. We reviewed seven cases of patients who had parasitic co-infection and tested positive for COVID-19, and summarized the literature on the importance of controlling parasitic diseases. In addition, we identified recommendations for the control of parasitic diseases under possible difficulties, such as declining funding for parasitic diseases in 2020. This review highlights the growing burden of NTDs under COVID-19 that may be caused by the deficiency of healthcare infrastructure and human resources as the main reasons. Clinicians should remain vigilant for possible co-infections with parasites in COVID-19 patients, while policymakers are urged to reinforce a balanced and long-term health strategy that addresses both NTDs and COVID-19.