Understanding Spatial Variation in COVID-19 across the United States.
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ABSTRACT:
SUBMITTER: Desmet K
PROVIDER: S-EPMC7948676 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundA 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.MethodsWe 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.ResultsWe 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.ConclusionsOur findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.
| S-EPMC10907669 | biostudies-literature
Project description:The outbreak of the novel coronavirus disease (COVID-19) severely threatens the public health worldwide, but the transmission mechanism and the effectiveness of mitigation measures remain uncertain. Here we assess the role of airborne transmission in spreading the disease and the effectiveness of face covering in preventing inter-human transmission for the top-fifteen infected U.S. states during March 1 and May 18, 2020. For all fifteen states, the curve of total confirmed infections exhibits an initial sub-exponential growth and a subsequent linear growth after implementing social distancing/stay-at-home orders. The linearity extends one to two months for the six states without mandated face covering and to the onset of mandated face covering for the other nine states with this measure, reflecting a dynamic equilibrium between first-order transmission kinetics and intervention. For the states with mandated face covering, significant deviation from this linearity and curve flattening occur after the onset of this measure for seven states, with exceptions for two states. Most states exhibit persistent upward trends in the daily new infections after social distancing/stay-at-home orders, while reversed downward or slowing trends occur for eight states after implementing mandated face covering. The inadequacy of social distancing and stay-at-home measures alone in preventing inter-human transmission is reflected by the continuous linear growth in the total infection curve after implementing these measures, which is mainly driven by airborne transmission. We estimate that the number of the total infections prevented by face covering reaches ~252,000 on May 18 in seven states, which is equivalent to ~17% of the total infections in the nation. We conclude that airborne transmission and face covering play the dominant role in spreading the disease and flattening the total infection curve, respectively. Our findings provide policymakers and the public with compelling evidence that universal face covering, in conjunction with social distancing and hand hygiene, represents the maximal protection against inter-human transmission and the combination of these intervention measures with rapid and extensive testing as well as contact tracing is crucial in containing the COVID-19 pandemic.
| S-EPMC7413050 | biostudies-literature
Project description:During an epidemic, metrics such as R0, doubling time, and case fatality rates are important in understanding and predicting the course of an epidemic. However, if collected over country or regional scales, these metrics hide important smaller-scale, local dynamics. We examine how commonly used epidemiological metrics differ for each individual state within the United States during the initial COVID-19 outbreak. We found that the detected case number and trajectory of early detected cases differ considerably between states. We then test for correlations with testing protocols, interventions and population characteristics. We find that epidemic dynamics were most strongly associated with non-pharmaceutical government actions during the early phase of the epidemic. In particular, early social distancing restrictions, particularly on restaurant operations, was correlated with increased doubling times. Interestingly, we also found that states with little tolerance for deviance from enforced rules saw faster early epidemic growth. Together with other correlates such as population density, our results highlight the different factors involved in the heterogeneity in the early spread of COVID-19 throughout the United States. Although individual states are clearly not independent, they can serve as small, natural experiments in how different demographic patterns and government responses can impact the course of an epidemic.
| S-EPMC7553297 | biostudies-literature
Project description:We describe the use of network modeling to capture the shifting spatiotemporal nature of the COVID-19 pandemic. The most common approach to tracking COVID-19 cases over time and space is to examine a series of maps that provide snapshots of the pandemic. A series of snapshots can convey the spatial nature of cases but often rely on subjective interpretation to assess how the pandemic is shifting in severity through time and space. We present a novel application of network optimization to a standard series of snapshots to better reveal how the spatial centres of the pandemic shifted spatially over time in the mainland United States under a mix of interventions. We find a global spatial shifting pattern with stable pandemic centres and both local and long-range interactions. Metrics derived from the daily nature of spatial shifts are introduced to help evaluate the pandemic situation at regional scales. We also highlight the value of reviewing pandemics through local spatial shifts to uncover dynamic relationships among and within regions, such as spillover and concentration among states. This new way of examining the COVID-19 pandemic in terms of network-based spatial shifts offers new story lines in understanding how the pandemic spread in geography.
| S-EPMC8055907 | biostudies-literature
Project description:The Coronavirus Disease 19 (COVID-19) has quickly spread across the United States (U.S.) since community transmission was first identified in January 2020. While a number of studies have examined individual-level risk factors for COVID-19, few studies have examined geographic hotspots and community drivers associated with spatial patterns in local transmission. The objective of the study is to understand the spatial determinants of the pandemic in counties across the U.S. by comparing socioeconomic variables to case and death data from January 22nd to June 30th 2020. A cluster analysis was performed to examine areas of high-risk, followed by a three-stage regression to examine contextual factors associated with elevated risk patterns for morbidity and mortality. The factors associated with community-level vulnerability included age, disability, language, race, occupation, and urban status. We recommend that cluster detection and spatial analysis be included in population-based surveillance strategies to better inform early case detection and prioritize healthcare resources.
| S-EPMC7498441 | biostudies-literature
Project description:COVID-19 incidence disparities have been documented in the literature, but the different driving factors among age groups have yet to be explicitly explained. This study proposes a community-based COVID-19 spatial disparity model, considering different levels of geographic units (individual and community), various contextual variables, multiple COVID-19 outcomes, and different geographic contextual elements. The model assumes the existence of age nonstationarity effects on health determinants, suggesting that health effects of contextual variables vary among place and age groups. Based on this conceptual model and theory, the study selected 62 county-level variables for 1,748 U.S. counties during the pandemic, and created an Adjustable COVID-19 Potential Exposure Index (ACOVIDPEI) using principal component analysis (PCA). The validation was done with 71,521,009 COVID-19 patients in the U.S. from January 2020 through June 2022, with high incidence rates shifting from the Midwest, South Carolina, North Carolina, Arizona, and Tennessee to the West and East coasts. This study corroborates the age nonstationarity effect of health determinants on COVID-19 exposures. These results empirically identify the geographic disparities of COVID-19 incidence rates among age groups and provide the evidentiary guide for targeting pandemic recovery, mitigation, and preparedness in communities.
| S-EPMC10249835 | biostudies-literature
Project description:The emergence of COVID-19 in the United States resulted in a series of federal and state-level lock-downs and COVID-19 related health mandates to manage the spread of the virus. These policies may negatively impact the mental health state of the population. This study focused on the trends in mental health indicators following the COVID-19 pandemic amongst four United States geographical regions, and political party preferences. Indicators of interest included feeling anxious, feeling depressed, and worried about finances. Survey data from the Delphi Group at Carnegie Mellon University were analyzed using clustering algorithms and dynamic connectome obtained from sliding window analysis. Connectome refers to the description of connectivity on a network. United States maps were generated to observe spatial trends and identify communities with similar mental health and COVID-19 trends. Between March 3rd, 2021, and January 10th, 2022, states in the southern geographic region showed similar trends for reported values of feeling anxious and worried about finances. There were no identifiable communities resembling geographical regions or political party preference for the feeling depressed indicator. We observed a high degree of correlation among southern states as well as within Republican states, where the highest correlation values from the dynamic connectome for feeling anxious and feeling depressed variables seemingly overlapped with an increase in COVID-19 related cases, deaths, hospitalizations, and rapid spread of the COVID-19 Delta variant.
| S-EPMC10249855 | biostudies-literature
Project description:This paper presents the first longitudinal study of sex disparities in COVID-19 cases and mortalities across U.S. states, derived from the unique 13-month dataset of the U.S. Gender/Sex COVID-19 Data Tracker. To analyze sex disparities, weekly case and mortality rates by sex and mortality rate ratios were computed for each U.S. state, and a multilevel crossed-effects conditional logistic binomial regression model was fitted to estimate the variation of the sex disparity in mortality over time and across states. Results demonstrate considerable variation in the sex disparity in COVID-19 cases and mortalities over time and between states. These data suggest that the sex disparity, when present, is modest, and likely varies in relation to context-sensitive variables, which may include health behaviors, preexisting health status, occupation, race/ethnicity, and other markers of social experience.
| S-EPMC8743486 | biostudies-literature
Project description:BackgroundDespite the availability of safe and efficacious COVID-19 vaccines, a significant proportion of the American public remains unvaccinated and does not appear immediately interested in receiving the vaccine.MethodsIn this study, we analyzed data from the U.S. Census Bureau's Household Pulse Survey, a biweekly cross-sectional survey of U.S. households. We estimated the prevalence of vaccine hesitancy across states and nationally and assessed the predictors of vaccine hesitancy and vaccine rejection. Additionally, we examined the underlying reasons for vaccine hesitancy, grouped into thematic categories.ResultsA total of 459,235 participants were surveyed from January 6 to March 29, 2021. While vaccine uptake increased from 7.7 to 47 percent, vaccine hesitancy rates remained relatively fixed: overall, 10.2 percent reported that they would probably not get a vaccine, and 8.2 percent would definitely not get a vaccine. Income, education, and state political leaning strongly predicted vaccine hesitancy. However, while both female sex and Black race were factors predicting hesitancy, among those who were hesitant, these same characteristics predicted vaccine reluctance rather than rejection. Those who expressed reluctance invoked mostly "deliberative" reasons while those who rejected the vaccine were also likely to invoke reasons of "dissent" and "distrust".ConclusionVaccine hesitancy comprises a sizable proportion of the population and is large enough to threaten achieving herd immunity. Distinct subgroups of hesitancy have distinctive socio-demographic associations as well as cognitive and affective predilections. Segmented public health solutions are needed to target interventions and optimize vaccine uptake.
| S-EPMC8406882 | biostudies-literature
Project description:BackgroundNewborn care practices that best promote the health and well-being of mother-infant dyads after birth while minimizing transmission of COVID-19 were uncertain at the onset of the COVID-19 pandemic.ObjectiveExamine variation in COVID-19 newborn care practices among U.S. birth hospitals and by hospital characteristics (U.S. census region, highest level of neonatal level of care, and Baby-Friendly hospital status).Study designWe surveyed physicians via American Academy of Pediatrics email listservs and social media between 5/26/2020-6/8/2020. Physicians identified the birth hospital in which they provided newborn care and their hospital's approach to obstetrical and newborn care related to COVID-19. Chi-square tests were used to examine variation in hospital practices by U.S. census region, highest level of neonatal care, and Baby-Friendly hospital status.ResultsFour hundred thirty three physicians responded from 318 hospitals across 46 states. Variation in care of SARS-CoV-2 positive mother-infant dyads was greatest for approaches to location of newborn care (31% separation, 17% rooming-in, and 51% based on shared-decision making), early skin-to-skin care (48% prohibited/discouraged, 11% encouraged, and 40% based on shared-decision making) and direct breastfeeding (37% prohibited/discouraged, 15% encouraged, and 48% based on shared-decision making). Among presumed uninfected dyads, 59% of hospitals discharged at least some mother-infant dyads early. We found variation in practices by U.S. census region.ConclusionApproaches to newborn care and breastfeeding support for mother-infant dyads with positive SARS-CoV-2 testing differed across U.S. birth hospitals during the COVID-19 pandemic. Early discharge of presumed uninfected mother-infant dyads was common.
| S-EPMC8776982 | biostudies-literature