To our knowledge, this is the first study conducted that examines a multitude of clinical, sociodemographic, and environmental risk factors that can contribute to higher rates of contraction and applies the factors to develop a predictive model that can assess disparities of risk in populations. This retrospective risk of contraction study identified several risk factors also associated with serious illness in prior studies, including older age and greater risk progression with age,3 male gender,16 comorbidities of diabetes,7 and chronic kidney disease,17 higher BMI,18 and immunosuppression.19 However, factors found in previous studies for risk of mortality, including hypertension,3 and other variables associated with groups at higher risk identified by the CDC including those with cardiovascular disease, liver disease, lung disease, or asthma,8 were not significant factors associated with contraction. Being prescribed more than ten medications or having a greater number of chronic conditions was associated with less risk of contraction, suggesting behavioral differences between groups based on perceived risk. Further research is needed to understand differences between risks associated with serious illness and mortality and contraction, as well as factors that may facilitate or impede engagement in physical distancing or other preventative health behaviors, which may vary widely based on barriers, structural inequities, or personal choice.
Healthcare access through a relationship with a primary care provider was associated with a lower risk of contraction; however, this may be a result of higher rates of testing for COVID-19 compared to individuals with no primary care provider. Receiving secure electronic communication through the EMR suggests that access to health advice and education may reduce risk. Further research is needed to identify how healthcare access, utilization, and health communications could reduce risk for vulnerable groups. Serious mental illness and drug use were associated with lower risk; however further study is necessary to understand known mechanisms for risk of contraction. Variability in risk across regional geography necessitates continued study. The findings of this study indicate that risk factors such as socioeconomic status, race, ethnicity, environmental living conditions, and healthcare access are intersecting variables across populations, and may collectively contribute to disparities in the risk of contraction among vulnerable groups.
Older age is associated with both higher risk of contracting COVID-19 and higher mortality20 compared to younger cohorts. Older adults living in senior communities are also at higher risk of contraction, which could be due to dependency on caregivers to complete activities of daily living, which make physical distancing a challenge. Dementia was also associated with risk of contraction, likely due to a higher reliance on daily caregiving.
Higher risk of contraction among black, indigenous, and/or people of color may be associated with other sociodemographic and environmental characteristics found to also be significant in this study. African Americans and Latinos are more likely to live in communities with poor air quality,21 work in jobs that cannot telecommute,22 and lack access to healthcare23 which may increase the risk of contraction and contribute to racial disparities in mortality. Chronic conditions such as obesity, stroke, and diabetes, and premature death also affect racial and ethnic groups disproportionately compared to whites, although differ comparatively between groups.13 More research is needed to identify the risk and protective factors for contraction, including within-group variation and among indigenous communities. Communities of color are also more likely to experience lower socioeconomic status,24 and be employed as essential workers.10 For vulnerable groups, lack of personal transportation is both a barrier to healthcare access25 and increases exposure to others, contributing to disparities in contraction.
Given the known mechanism for community transmission, variables selected as approximations for social and living conditions that might increase the risk of contraction, such as being in a married relationship or having a significant other, being employed, lacking access to a personal vehicle for transportation, and living in overcrowded housing were significant factors for increased risk also evident in disparities across socioeconomic status and race. Religious affiliation was also associated with increased risk, which may be attributed to attendance of large religious services or other behaviors associated with religious identity.
Having limited English proficiency (LEP) can be a barrier to accessing health services and understanding health information, which can be exacerbated when written translations and trained translators are not available.26 Over the course of the pandemic, health information has changed rapidly, which can adversely affect indigenous and immigrant communities. During the Ebola epidemic in West Africa, language barriers were an obstacle to slowing the spread of the disease.27 People with LEP are also more likely to have low health literacy compared to English speakers and are at a higher risk of poor health.28 Anti-immigrant policies also impose barriers to accessing healthcare and discourage care seeking, particularly among undocumented immigrants.29 Culturally and linguistically appropriate interventions are essential, including communication materials of varying formats and reading levels developed through transcreation, where native language speakers work in tandem with English speakers, as well as the use of community health workers that can engage with underserved groups.30
People experiencing housing insecurity may experience challenges with physical distancing, especially when housing is crowded, or may be less able to engage in hand washing when facilities or running water may be limited.31 Both factors could facilitate the spread of the virus. Additional research is needed to understand the impact of housing insecurity, living conditions, and environments on COVID-19 contraction.
The model did not include any patient data outside the Providence Health System. Although the organization serves a diverse patient population across seven states, the generalizability of the study results may be limited to the entire U.S population. Furthermore, inconsistent availability and reliability of the testing could bias the results. With limited testing available and evolving screening guidelines, clinical discernment, and personal bias could impact which individuals received testing and thus, influence rates of testing in certain populations. When developing this model, we intended for the study to include all major covariates; however, since COVID-19 research is changing, it is likely that there are other factors associated with the likelihood of contraction that are not well known yet and, thus not present in the observed data. We were not able to account for people’s behaviors, which could bias the results. Additional research is needed to understand additional factors correlated with higher instances of COVID-19 related to inpatient utilization and risk of mortality.