Assessing Access to Digital Services in Health Care–Underserved Communities in the United States: A Cross-Sectional Study

Objective To evaluate the spatial association between the access to broadband and social and health care vulnerability in the United States at the county level. Patients and Methods Data from 3108 counties in the contiguous United States was used in this study. Access to broadband was defined as the percentage of population with a high-speed internet subscription. County-level data for access was obtained from the Survey and American Community Survey Geographic Estimates of Internet Use, 1997-2018. Indexes for resource-constrained health system, health care access barriers, and social vulnerability were obtained from the 2021 Surgo COVID-19 Vaccine Uptake Index and the Centers for Diseases and Control. We used spatial bivariate and multivariate analyses to determine the geospatial association between broadband access and the health care and social determinants. After identifying the geospatial clusters, their rates for the health care and social indexes were compared using generalized linear mixed-effects models. Results We found that the United States exhibits a distinct spatial structure with defined vulnerable communities characterized by a high social vulnerability index, a high health access barrier index, and a high resource-constrained health care system index. However, we found a negative geospatial association between these 3 indexes of vulnerability and the access to broadband. We identified a geographical cluster in the southern part of the country with low broadband access and poor social and health indicators. Conclusions Most health care–underserved communities in the United States are located in digital deserts with low high-speed internet access. These digital barriers could prevent the successful expansion of digital health care services and might exacerbate health care disparities in these vulnerable communities.

Impact of healthcare capacity disparities on the COVID-19 vaccination coverage in the United States: A cross-sectional study.

Background: The impact of the COVID-19 vaccination campaign in the US has been hampered by a substantial geographical heterogeneity of the vaccination coverage. Several studies have proposed vaccination hesitancy as a key driver of the vaccination uptake disparities. However, the impact of other important structural determinants such as local disparities in healthcare capacity is virtually unknown. Methods: In this cross-sectional study, we conducted causal inference and geospatial analyses to assess the impact of healthcare capacity on the vaccination coverage disparity in the US. We evaluated the causal relationship between the healthcare system capacity of 2417 US counties and their COVID-19 vaccination rate. We also conducted geospatial analyses using spatial scan statistics to identify areas with low vaccination rates. Findings: We found a causal effect of the constraints in the healthcare capacity of a county and its low-vaccination uptake. Counties with higher constraints in their healthcare capacity were more probable to have COVID-19 vaccination rates ≤50, with 35% higher constraints in low-vaccinated areas (vaccination rates ≤ 50) compared to high-vaccinated areas (vaccination rates > 50). We also found that COVID-19 vaccination in the US exhibits a distinct spatial structure with defined "vaccination coldspots". Interpretation: We found that the healthcare capacity of a county is an important determinant of low vaccine uptake. Our study highlights that even in high-income nations, internal disparities in healthcare capacity play an important role in the health outcomes of the nation. Therefore, strengthening the funding and infrastructure of the healthcare system, particularly in rural underserved areas, should be intensified to help vulnerable communities. Funding: None.

Association Between Vaccination Coverage Disparity and the Dynamics of the COVID-19 Delta and Omicron Waves in the US.

Objective: The US recently suffered the fourth and most severe wave of the COVID-19 pandemic. This wave was driven by the SARS-CoV-2 Omicron, a highly transmissible variant that infected even vaccinated people. Vaccination coverage disparities have played an important role in shaping the epidemic dynamics. Analyzing the epidemiological impact of this uneven vaccination coverage is essential to understand local differences in the spread and outcomes of the Omicron wave. Therefore, the objective of this study was to quantify the impact of vaccination coverage disparity in the US in the dynamics of the COVID-19 pandemic during the third and fourth waves of the pandemic driven by the Delta and Omicron variants. Methods: This cross-sectional study used COVID-19 cases, deaths, and vaccination coverage from 2,417 counties. The main outcomes of the study were new COVID-19 cases (incidence rate per 100,000 people) and new COVID-19 related deaths (mortality rate per 100,000 people) at county level and the main exposure variable was COVID-19 vaccination rate at county level. Geospatial and data visualization analyses were used to estimate the association between vaccination rate and COVID-19 incidence and mortality rates for the Delta and Omicron waves. Results: During the Omicron wave, areas with high vaccination rates (>60%) experienced 1.4 (95% confidence interval [CI] 1.3-1.7) times higher COVID-19 incidence rate compared to areas with low vaccination rates (<40%). However, mortality rate was 1.6 (95% CI 1.5-1.7) higher in these low-vaccinated areas compared to areas with vaccination rates higher than 60%. As a result, areas with low vaccination rate had a 2.2 (95% CI 2.1-2.2) times higher case-fatality ratio. Geospatial clustering analysis showed a more defined spatial structure during the Delta wave with clusters with low vaccination rates and high incidence and mortality located in southern states. Conclusions: Despite the emergence of new virus variants with differential transmission potential, the protective effect of vaccines keeps generating marked differences in the distribution of critical health outcomes, with low vaccinated areas having the largest COVID-19 related mortality during the Delta and Omicron waves in the US. Vulnerable communities residing in low vaccinated areas, which are mostly rural, are suffering the highest burden of the COVID-19 pandemic during the vaccination era.

Implementation of a Vaccination Program Based on Epidemic Geospatial Attributes: COVID-19 Pandemic in Ohio as a Case Study and Proof of Concept.

Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.

Spatiotemporal transmission dynamics of the COVID-19 pandemic and its impact on critical healthcare capacity.

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.