Social determinants of health and COVID-19 infection in North Carolina: a geospatial analysis

Study Objectives: The COVID-19 pandemic has demonstrated that social determinants of health (SDOH) are profoundly linked to the spread and outcomes of COVID-19. However, the relationships between these SDOH and COVID-19 spatial outbreaks have yet to be determined. We conducted spatial analyses with geographic information systems (GIS) mapping of county-level SDOH and regional COVID-19 infection outbreaks to demonstrate the most impactful SDOH and to provide a pragmatic visual guide to prevent future outbreaks.

COVID-19 Infection Experiences and Social Determinants of Health in North Carolina: A Qualitative Analysis

Study Objectives: Social determinants of health (SDOH) influence the health outcomes of COVID-19 patients;yet, little is known about how patients at risk of significant disease burden view this relationship. Our study sought to explore patient perceptions of the influence of SDOH on their COVID-19 infection experience and COVID-19 transmission within their communities. Methods: We conducted a qualitative study of patients in a North Carolina health care system’s registry who tested positive for COVID-19 from March 2020 through February 2021. All patients’ addresses across six counties served were geo-referenced and analyzed by Kernel Density Estimation (KDE) to identify population-dense outbreaks of COVID-19 (hotspots). Spatial autocorrelation analysis was performed to identify census area clusters of white, Black and Hispanic populations, based on the 2019 American Community Survey dataset. Patients were identified by a randomized computer-generated sampling method. After informed consent, patients participated in semi-structured phone interviews in English or Spanish based on patient preference by trained bilingual researchers. Each interview was evaluated using a combination of deductive and inductive content analysis to determine prevalent themes related to COVID-19 knowledge and diagnosis, disease experience, and the impact of SDOH. Results: The 10 patients interviewed from our COVID-19 hotspots were of equal distribution by sex, and predominantly Black (70%), ages 22-70 years (IQR 45-62 years), and presented to the ED for evaluation (70%). The respondents were more frequently publicly insured (50% medicaid/medicare;vs 30% uninsured;vs 20% private). The interviews demonstrated themes surrounding the experience and impact of COVID-19. The perceived risk of contracting COVID-19 and knowledge of how to prevent infection varied greatly among our sample, and could be in part explained by SDOH such as their occupation, living conditions and mode of transportation. The experiences of COVID-19 testing, diagnosis, isolation and medical treatment were most influenced by the timing of infection in relation to the study period. For example, in the early months of the pandemic, the knowledge of isolation requirements and available support systems seemed to have negatively impacted the ability to isolate and follow public health guidance, as well as the support mechanisms provided by employers during this period. Communication of infection status once diagnosed varied greatly, with some voicing feelings of shame, and others advocating for sharing of infection experiences to change community behaviors. Suggestions for how to improve the COVID-19 response included improving communication and enforcing public health guidelines, including raising awareness for vulnerable populations on topics like expected symptoms, financial support, increasing testing, and vaccination delivery. Conclusion: Further exploration of important themes and related SDOH that influenced how the participants experienced the COVID-19 pandemic will be necessary to decrease the negative impacts of SDOH in communities that are high-risk for COVID-19 spread.

Health Insurance and Duration of Symptoms Prior to Emergency Department Visit: An Analysis of 19,850 Patients With Suspected COVID-19

Study Objectives: The COVID-19 pandemic has emphasized disparities in health outcomes across social and economic strata. The mechanisms of this relationship are poorly understood, but the length of time patients exhibit symptoms prior to getting tested for COVID-19 increases the opportunity for community transmission. We hypothesized that there is a relationship between insurance coverage and the duration of COVID-19 symptoms prior to seeking care at the emergency department (ED). Methods: A national, multi-institution (n=45 sites) registry collected information on ED visits in which patients were tested for suspected COVID-19. Demographics and clinical characteristics were summarized for the total cohort. Insurance was categorized into private (private or commercial), public (Medicare, Medicaid, or dual-eligible), worker’s compensation or unknown, or no health insurance. Negative binomial regression was used to analyze both the unadjusted and adjusted relationship between insurance and the time from symptom onset to ED presentation. Adjustments included age, sex, race, ethnicity, medical history, smoking status, drug use, and number of COVID symptoms. Results: Baseline demographic and clinical characteristics of included patients (n=19,850) are displayed in Table 1. The average time from symptom onset to ED presentation among patients with suspected COVID-19 was 5.4 days. In unadjusted analysis, patients with private insurance had significantly longer time of symptom onset prior to ED presentation than patients with public insurance (5.6 vs. 5.3 days, p=0.007). After multivariate adjustment, increased duration of symptoms prior to ED presentation was significantly associated with private insurance [rate ratio (RR) 1.07, 95% confidence interval (CI): 1.03 – 1.10] and no health insurance (RR 1.06, 95% CI: 1.07 – 1.13) compared to public insurance (Figure 1). Patients residing in states with Medicaid expansion were not independently associated with the increased time to ED presentation (RR 1.03, 95% CI: 1.00 – 1.07). Conclusion: Patients with private insurance or no insurance waited significantly longer to present to the ED. The extended duration of symptoms prior to presentation increases the opportunities for community transmission. The results from this study can be used by health systems to target the patients at increased risk for delayed ED presentation. [Formula presented]

Prevalence and Predictors of Post-Traumatic Stress Disorder Symptoms Among Emergency Physicians in the United States During the COVID-19 Pandemic

Study Objectives: Our study aims to identify the prevalence of post-traumatic stress disorder (PTSD) symptoms among emergency physicians in the United States following the COVID-19 pandemic, and explore related factors and predictors of PTSD symptoms. Methods: Study participants included board-certified & board-eligible emergency physicians’ residents, and non-emergency physicians working in an EM setting, who were practicing in the US. Convenience sampling recruitment via multiple national EM listservs was used to complete an anonymous, online self-report survey from September 2020 to April 2021. Research data was stored on Qualtrics, a secure, password-protected multi-user database with access granted to the study team only. Surveys included demographics, a binary PTSD experience question and a PSS-I-5 scale pre-piloted for ease of use and comprehension. Based on Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) criteria, we asked participants to dichotomously self-report trauma. We used the American Psychological Association-approved PTSD Symptom Scale (PSS-I-5), a validated, reliable tool, to assess the severity of the PTSD symptoms in frontline health care workers during the COVID-19 pandemic categorized into minimal 0-8, mild 9-18, moderate 19-30, severe 31-45, and very severe 46-80. Descriptive analyses were reported with percentages, means, and medians using RStudio. Figures were used to visualize variations in reported PSS-I-5 scores through the course of the pandemic. Results: Our sample included 315 total complete surveys of the 362 initiated surveys. PSS-I-5 scores ranged from 0-67 (IQR 4-27, median=13, mean=17.2). The majority of participants are age 35-50 (45.7%), EM board-certified (69.5%), white (86.4%), practice at urban level 1 trauma centers (44.8%), and do not have previous PTSD (91.8%) or other mental health diagnoses (73.3%). More than half (55.9%) of the respondents self-identified as having experienced trauma based on the DSM-5 criteria. PSS-I-5 scores were higher from those completing the survey in March-April 2021 (median=13, mean=17.3) compared to those sent in September-October 2020 (median=11, mean=16.5). Most participants had PTSD symptoms (92.1%);40.7% with minimal (129), 22.1% mild (70), followed by moderate (57, 18.0%), severe (39, 12.3%), and very severe (23, 7.3%). A higher proportion of those reporting severe and very severe PTSD symptoms are female and practice at level 3/4 trauma centers. Of non-emergency physicians who participated in the study, a majority reported severe symptoms (median=31, mean=25.4). Major perceived causes of stress included shift acuity, crowding, fear of self/family/friends getting sick, lack of personal protective equipment, and dissatisfaction with hospital administration. Conclusion: The prevalence of PTSD symptoms among our sample following the COVID-19 pandemic is 92.1%, with higher PSS-I-5 scores generally reported later in the pandemic. Race, age, and practice setting all appear to be associated with more severe PTSD symptoms. More research is needed to describe and reduce the burden of PTSD among those on the COVID front lines in the ED. [Formula presented] Figure 1. Frequency histogram of PSS scores [Formula presented] Figure 2. PSS mean (circle) and median (star) scores per month of survey end date [Formula presented]

COVID-19 Infection Experiences and Social Determinants of Health in North Carolina: A Qualitative Analysis

Study Objectives: Social determinants of health (SDOH) influence the health outcomes of COVID-19 patients;yet, little is known about how patients at risk of significant disease burden view this relationship. Our study sought to explore patient perceptions of the influence of SDOH on their COVID-19 infection experience and COVID-19 transmission within their communities. Methods: We conducted a qualitative study of patients in a North Carolina health care system’s registry who tested positive for COVID-19 from March 2020 through February 2021. All patients’ addresses across six counties served were geo-referenced and analyzed by Kernel Density Estimation (KDE) to identify population-dense outbreaks of COVID-19 (hotspots). Spatial autocorrelation analysis was performed to identify census area clusters of white, Black and Hispanic populations, based on the 2019 American Community Survey dataset. Patients were identified by a randomized computer-generated sampling method. Patients participated in semi-structured phone interviews in English or Spanish based on patient preference by trained bilingual researchers. Each interview was evaluated using a combination of deductive and inductive content analysis to determine prevalent themes related to COVID-19 knowledge and diagnosis, disease experience, and the impact of SDOH. Results: The 10 patients interviewed from our COVID-19 hotspots were of equal distribution by sex, and predominantly Black (70%), ages 22-70 years (IQR 45-62 years), and presented to the ED for evaluation (70%). The respondents were more frequently publicly insured (50% medicaid/medicare;vs 30% uninsured;vs 20% private). The interviews demonstrated themes surrounding the experience and impact of COVID-19. The perceived risk of contracting COVID-19 and knowledge of how to prevent infection varied greatly and could be in part explained by SDOH such as their occupation and living conditions. The experiences of COVID-19 testing, diagnosis, isolation and treatment were most influenced by the timing of infection in relation to the study period. Earlier in the pandemic, the knowledge of isolation requirements and available support systems seemed to have negatively impacted the ability to isolate and follow public health guidance, as well as the support mechanisms provided by employers during this period. Communication of infection status once diagnosed varied greatly, with some voicing feelings of shame, and others advocating for sharing of infection experiences to change community behaviors. Suggestions for how to improve the COVID-19 response included improving communication and enforcing public health guidelines, including raising awareness for vulnerable populations. Conclusion: Further exploration of important themes and related SDOH that influenced how the participants experienced the COVID-19 pandemic will be necessary to decrease the negative impacts of SDOH in communities that are high-risk for COVID-19 spread.

Social Determinants of Health and COVID-19 Infection in North Carolina: A Geospatial Analysis

Study Objectives: The COVID-19 pandemic has demonstrated that social determinants of health (SDOH) are profoundly linked to the spread and outcomes of COVID-19. However, the relationships between these SDOH and COVID-19 spatial outbreaks have yet to be determined. We conducted spatial analyses with geographic information systems (GIS) mapping of county-level SDOH and regional COVID-19 infection outbreaks to demonstrate the most impactful SDOH and to provide a pragmatic visual guide to prevent future outbreaks. Methods: We analyzed the geospatial associations of COVID-19 infections and SDOH to identify areas of overlap. Our sample comprised all patients in a North Carolina health care system’s registry who tested positive for COVID-19 from March 2020-February 2021. Patients’ addresses were geo-referenced and analyzed by Kernel Density Estimation (KDE) to identify population-dense outbreaks of COVID-19 (hotspots). A set of 12 SDOH variables for each county were collected from the American Community Survey (ACS-5) and the Economic Research Service. Principal Component Analysis was applied to SDOH variables in order to reduce dimensions down to 3 geographical SDOH categories: Protective SDOH, High-Risk SDOH and Increased Vulnerability for Infection (Table 1). Using Multivariate Clustering Analysis (MCA), three clusters of census tracts were categorized according to SDOH indicators: decreased social disparities (DSD), equivocal social disparities (ESD) and increased social disparities (ISD) (Image A). Kruskal-Wallis and Dunn's Post-Hoc adjusted with Bonferroni were utilized to verify any difference in the proportion of patients residing in the different clusters (significance p<0.05). Results: A total of 13,733 patients were included in the study. The patients predominantly reside in Durham County (55.4%), are women (56.96%), and between 40 and 69 years old (41.9%). Further, patients are predominantly white (38.7%), non-Hispanic (79.63%), and single (49.6%). The concomitant analysis of KDE and MCA showed an overlap of COVID-19 hotspots with areas of ISD (Image B). The MCA revealed that there are 308 census tracts constituted by six counties, in which 40 form ISD clusters (vs. 109 ESD;vs. 159 DSD). In addition, ISD clusters have the highest rates of infection, with 179.8 patients per 10,000 (vs. 81.7 ESD;vs. 60.1 DSD). The ISD cluster was the most densely populated and was significantly more densely populated from the ESD and DSD clusters (p=0.01). Conclusion: In this sampling of COVID-19 patients, a disproportionate amount of patients come from areas with increased social disparities, suggesting further research and health policy will need to be directed towards addressing negative and vulnerability SDOH to curtail pandemic-related outbreaks. [Formula presented] [Formula presented]