Remote Care Adoption in Underserved Congenital Heart Disease Patients During the COVID-19 Era.

The COVID-19 pandemic restricted in-person appointments and prompted an increase in remote healthcare delivery. Our goal was to assess access to remote care for complex pediatric cardiology patients. We performed a retrospective chart review of Texas Children's Hospital (TCH) pediatric cardiology outpatient appointments from March 2020 to December 2020 for established congenital heart disease (CHD) patients 1 to 17 yo. Primary outcome variables were remote care use of telemedicine and patient portal activation. Primary predictor variables were age, sex, insurance, race/ethnicity, language, and location. Descriptive statistics were used to analyze patient demographics. Multivariate logistic regression determined associations with remote care use (p < 0.05). We identified 5,410 established patients with clinic appointments during the identified timeframe. Adopters of telemedicine included 13% of patients (n = 691). Of the prior non patient portal users, 4.5% activated their accounts. On multivariate analysis, older age (10-17 yo) was associated with increased telemedicine (OR 2.04, 95%CI 1.71, 2.43) and patient portal use (OR 1.70, 95%CI 1.33, 2.17). Public insurance (OR 1.66, 95%CI 1.25, 2.20) and Spanish speaking were associated with increased patient portal adoption. Race/ethnicity was not significantly associated with telemedicine use or patient portal adoption. Telehealth adoption among older children may be indicative of their ability to aid in the use of these technologies. Higher participation in patient portal activation among publicly insured and Spanish speaking patients is encouraging and demonstrates ability to navigate some degree of remote patient care. Adoption of remote patient care may assist in reducing access to care disparities.

COVID-19 Vaccine Uptake among US Adults According to Standard Occupational Groups.

This cross-sectional ecological study examined the relationship between neighborhood-level standard occupational groups in the USA and COVID-19 vaccine uptake using 774 census tract data, each consisting of approximately 1600 housing units. The neighborhood-level COVID-19 vaccination uptake data were retrieved from Harris County Public Health, Harris County, Texas. The standard occupational group data were from the US Census Bureau. We calculated the incidence rate ratios (IRRs) for vaccine uptake using bivariate and multivariable Poisson regression models. In the adjusted models, we found that the healthcare practitioner/technician (IRR: 1.008; 95% CI: 1.003-1.014; p = 0.001), business/management/legal (IRR: 1.011; 95% CI: 1.008-1.013; p < 0.001), computer/engineering/life/physical/social science (IRR: 1.018; 95% CI: 1.013-1.023; p < 0.001), and arts/design/entertainment/sports/media (IRR: 1.031; 95% CI: 1.018-1.044; p < 0.001) occupational groups were more likely to have received the full regimen of a COVID-19 vaccine. On the contrary, the building/installation/maintenance/repair (IRR: 0.991; 95% CI: 0.987-0.995; p < 0.001), construction/extraction/production (IRR: 0.991; 95% CI: 0.988-0.995; p < 0.001), transportation/material moving (IRR: 0.992; 95% CI: 0.987-0.997; p = 0.002), food preparation/serving related (IRR: 0.995; 95% CI: 0.990-0.999; p = 0.023), and personal care/services (IRR: 0.991; 95% CI: 0.985-0.998; p = 0.017) groups were less likely to have received the complete dose of a COVID-19 vaccine. White-collar workers were more likely to be vaccinated than blue-collar workers. We adjusted for age, sex, and race/ethnicity in the multivariable analysis. The low vaccine uptake among certain occupational groups remains a barrier to pandemic control. Engaging labor-centered stakeholders in the development of vaccination interventions may increase uptake.

COVID-19 Community Incidence and Associated Neighborhood-Level Characteristics in Houston, Texas, USA.

Central to developing effective control measures for the COVID-19 pandemic is understanding the epidemiology of transmission in the community. Geospatial analysis of neighborhood-level data could provide insight into drivers of infection. In the current analysis of Harris County, Texas, we used custom interpolation tools in GIS to disaggregate COVID-19 incidence estimates from the zip code to census tract estimates-a better representation of neighborhood-level estimates. We assessed the associations between 29 neighborhood-level characteristics and COVID-19 incidence using a series of aspatial and spatial models. The variables that maintained significant and positive associations with COVID-19 incidence in our final aspatial model and later represented in a geographically weighted regression model were the percentage of the Black/African American population, percentage of the foreign-born population, area derivation index (ADI), percentage of households with no vehicle, and percentage of people over 65 years old inside each census tract. Conversely, we observed negative and significant association with the percentage employed in education. Notably, the spatial models indicated that the impact of ADI was homogeneous across the study area, but other risk factors varied by neighborhood. The current findings could enhance decision making by local public health officials in responding to the COVID-19 pandemic. By understanding factors that drive community transmission, we can better target disease control measures.