Evaluating a contact tracing course: How universities can develop the public health workforce.

OBJECTIVES: To describe the participants of a university-based COVID-19 contact tracing course and determine whether the course changed knowledge, attitudes, and intention to participate in contact tracing. PARTICIPANTS: Faculty, staff, and students were eligible. METHODS: Surveys evaluated the impact of the course on participant intentions to engage in contact tracing. Logistic regression identified characteristics associated with increased likelihood of participating in contact tracing. RESULTS: Nearly 800 individuals participated, of whom 26.2% identified as Hispanic/Latino and 14.0% as Black. Nearly half (48.8%) planned to conduct contact tracing. While attitudes did not change, knowledge improved (67.9% vs. 93.8% scores on assessments; p < 0.001). Younger participants and Black individuals were more more likely to be confident that they would participate in contact tracing. CONCLUSIONS: Course completion was associated with increased knowledge. Participants were racially and ethnically diverse, highlighting how universities can partner with health departments to develop workforces that reflect local communities.

Examining longitudinal disparities in COVID-19 prevalence in the U.S.: a county level growth rate perspective.

Background: The objectives of the present study are to understand the longitudinal variability in COVID-19 reported cases at the county level and to associate the observed rates of infection with the adoption and lifting of stay-home orders.Materials and Methods: The study uses the trajectory of the pandemic in a county and controls for social and economic risk factors, physical environment, and health behaviors to elucidate the social determinants contributing to the observed rates of infection.Results and conclusion: Results indicated that counties with higher percentages of young individuals, racial and ethnic minorities and, higher population densities experienced greater difficulty suppressing transmission.Except for Education and the Gini Index, all factors were influential on the rate of COVID-19 spread before and after stay-home orders. However, after lifting the orders, six of the factors were not influential on the rate of spread; these included: African-Americans, Population Density, Single Parent Households, Average Daily PM2.5, HIV Prevalence Rate, and Home Ownership. It was concluded that different factors from the ones controlling the initial spread of COVID-19 are at play after stay-home orders are lifted.KEY MESSAGESObserved rates of COVID-19 infection at the County level in the U.S. are not directly associated with adoption and lifting of stay-home orders.Disadvantages in sociodemographic determinants negatively influence the rate of COVID-19 spread.Counties with more young individuals, racial and ethnic minorities, and higher population densities have greater difficulty suppressing transmission.

Transition to telemedicine and its impact on missed appointments in community-based clinics.

BACKGROUND AND OBJECTIVE: The Coronavirus Aid, Relief, and Economic Security Act led to the rapid implementation of telemedicine across health care office settings. Whether this transition to telemedicine has any impact on missed appointments is yet to be determined. This study examined the relationship between telemedicine usage and missed appointments during the COVID-19 pandemic. METHOD: This retrospective study used appointment-level data from 55 Federally Qualified Health Centre clinics in Texas between March and November 2020. To account for the nested data structure of repeated appointments within each patient, a mixed-effects multivariable logistic regression model was used to examine associations between telemedicine use and missed appointments, adjusting for patient sociodemographic characteristics, geographic classification, past medical history, and clinic characteristics. The independent variable was having a telemedicine appointment, defined as an audiovisual consultation started and finalized via a telemedicine platform. The outcome of interest was having a missed appointment (yes/no) after a scheduled and confirmed medical appointment. Results from this initial model were stratified by appointment type (in-person vs. telemedicine). RESULTS: The analytic sample included 278,171 appointments for 85,413 unique patients. The overall missed appointment rate was 18%, and 25% of all appointments were telemedicine appointments. Compared to in-person visits, telemedicine visits were less likely to result in a missed appointment (OR = 0.87, p < .001). Compared to Whites, Asians were less likely to have a missed appointment (OR = 0.82, p < .001) while African Americans, Hispanics, and American Indians were all significantly more likely to have missed appointments (OR = 1.61, p < .001; OR = 1.19, p = .01; OR = 1.22, p < .01, respectively). Those accessing mental health services (OR = 1.57 for in-person and 0.78 for telemedicine) and living in metropolitan areas (OR = 1.15 for in-person and 0.82 for telemedicine) were more likely to miss in-person appointments but less likely to miss telemedicine appointments. Patients with frequent medical visits or those living with chronic diseases were more likely to miss in-person appointments but less likely to miss telemedicine appointments. CONCLUSIONS: Telemedicine is strongly associated with fewer missed appointments. Although our findings suggest a residual lag in minority populations, specific patient populations, including those with frequent prior visits or chronic conditions, those seeking mental health services, and those living in metropolitan areas were less likely to miss telemedicine appointments than in-person visits. These findings highlight how telemedicine can enable effective and accessible care by reducing missed healthcare appointments.KEY MESSAGESTelemedicine was associated with 13% lower odds of missed appointments.Patients with frequent medical visits or those living with chronic diseases were less likely to miss telemedicine appointments but more likely to miss in-person appointments.Patients seeking mental health services were less likely to miss telemedicine appointments but more likely to miss in-person appointments.Similarly, those living in metropolitan areas were less likely to miss telemedicine appointments but more likely to miss in-person appointments.

Early Impacts of the COVID-19 Pandemic on Telehealth Patterns in Primary Care, Mental Health, and Specialty Care Facilities in Texas.

OBJECTIVES: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, many US clinics have shifted some or all of their practice from in-person to virtual visits. In this study, we assessed the use of telehealth among primary care and specialty clinics, by targeting healthcare administrators via multiple channels. METHODS: Using an online survey, we assessed the use of, barriers to, and reimbursement for telehealth. Respondents included clinic administrators (chief executive officers, vice presidents, directors, and senior-level managers). RESULTS: A total of 85 complete responses were recorded, 79% of which represented solo or group practices and 63% reported a daily patient census >50. The proportion of clinics that delivered ≥50% of their consults using telehealth increased from 16% in March to 42% in April, 35% in May, and 30% in June. Clinics identified problems with telehealth reimbursement; although 63% of clinics reported that ≥75% of their telehealth consults were reimbursed, only 51% indicated that ≥75% of their telehealth visits were reimbursed at par with in-person office visits. Sixty-five percent of clinics reported having basic or foundational telehealth services, whereas only 9% of clinics reported advanced telehealth maturity. Value-based care participating clinics were more likely to report advanced telehealth services (27%), compared with non-value-based care clinics (3%). CONCLUSIONS: These findings highlight the adaptability of clinics to quickly transition and adopt telehealth. Uncertainty about reimbursement and policy changes may make the shift temporal, however.

COVID-19 and Telehealth Operations in Texas Primary Care Clinics: Disparities in Medically Underserved Area Clinics.

The COVID-19 pandemic has dramatically altered the landscape of health care delivery, prompting a rapid, widespread adoption of telehealth in primary care practices. Using a pooled sample of 1,344 primary care clinics in Texas, we examined the adoption of telehealth in Texas during the initial months of the COVID-19 pandemic, by comparing medically underserved area (MUA) clinics and non-medically underserved area (non-MUA) clinics. Our analysis suggests that compared with MUA clinics, clinics in non-MUAs were more likely to conduct a majority of their visits via telehealth before May 1st, 2020. However, later surveys indicated that differences in telehealth use between MUA and non-MUA clinics lessened, suggesting that some of the barriers that MUA clinics initially faced might have resolved over time. This research provides an additional perspective in discussions about telehealth adoption on a widespread, permanent basis in Texas and the U.S.

Uniting Public Health and Primary Care for Healthy Communities in the COVID-19 Era and Beyond.

The Coronavirus disease 2019 (COVID-19) pandemic has laid bare the dis-integrated health care system in the United States. Decades of inattention and dwindling support for public health, coupled with declining access to primary care medical services have left many vulnerable communities without adequate COVID-19 response and recovery capacity. "Health is a Community Affair" is a 1966 effort to build and deploy local communities of solution that align public health, primary care, and community organizations to identify health care problem sheds, and activate local asset sheds. After decades of independent effort, the COVID-19 pandemic offers an opportunity to reunite and align the shared goals of public health and primary care. Imagine how different things might look if we had widely implemented the recommendations from the 1966 report? The ideas and concepts laid out in "Health is a Community Affair" still offer a COVID-19 response and recovery approach. By bringing public health and primary care together in community now, a future that includes a shared vision and combined effort may emerge.

Assessing COVID-19 risk, vulnerability and infection prevalence in communities.

BACKGROUND: The spread of coronavirus in the United States with nearly five and half million confirmed cases and over 170,000 deaths has strained public health and health care systems. While many have focused on clinical outcomes, less attention has been paid to vulnerability and risk of infection. In this study, we developed a planning tool that examines factors that affect vulnerability to COVID-19. METHODS: Across 46 variables, we defined five broad categories: 1) access to medical services, 2) underlying health conditions, 3) environmental exposures, 4) vulnerability to natural disasters, and 5) sociodemographic, behavioral, and lifestyle factors. The developed tool was validated by comparing the estimated overall vulnerability with the real-time reported normalized confirmed cases of COVID-19. ANALYSIS: A principal component analysis was undertaken to reduce the dimensions. In order to identify vulnerable census tracts, we conducted rank-based exceedance and K-means cluster analyses. RESULTS: All of the 5 vulnerability categories, as well as the overall vulnerability, showed significant (P-values <<0.05) and relatively strong correlations (0.203<ρ<0.57) with the normalized confirmed cases of COVID-19 at the census tract level. Our study showed a total of 722,357 (~17% of the County population) people, including 171,403 between the ages of 45-65 (~4% of County's population), and 76,719 seniors (~2% of County population), are at a higher risk based on the aforementioned categories. The exceedance and K-means cluster analysis demonstrated that census tracts in the northeastern, eastern, southeastern and northwestern regions of the County are at highest risk. CONCLUSION: Policymakers can use this planning tool to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions.