Design
This cross-sectional study was administered in late Spring through mid-Summer of 2020, as part of a larger, longitudinal study of the impact of the COVID-19 pandemic on health and well-being.
Sample and Procedure
This study recruited participants via Rare Patient Voice, LLC and Ipsos Insight, LLC —the former to target patients and caregivers of people with chronic medical conditions; the latter to target a comparison sample of United States (US) adults, nationally representative in terms of age distribution, gender, region, and income. Eligible participants were age 18 or older and able to complete an online questionnaire. Participants with motor, visual, and/or other problems that made it difficult for them to complete the web-based survey instrument enlisted the assistance of someone else to enter the participant’s answers. Individuals with severe cognitive impairment were ineligible. This survey was administered through the secure Alchemer engine (www.alchemer.com), which is compliant with the US Health Insurance Portability and Accountability Act. The protocol was reviewed and approved by the New England Independent Review Board (NEIRB #2021164), and all participants provided informed consent prior to beginning the survey.
Measures
COVID-Specific Questions included selected items compiled by the US National Institutes of Health Office of Behavioral and Social Sciences Research and the NIH Disaster Research program [19]. These items assessed infection status, COVID-specific literacy, self-protective steps, perceived risk-taking behavior, hardship (including financial lack, homelessness, and disruption of healthcare), COVID-specific altruism, social support / isolation, positive and negative coping, substance use, interpersonal conflict, and four items adapted with permission from the Post-Traumatic Growth Inventory [20–22]. (See Supplemental Table 1 for items used.)
Health-Related QOL was assessed with standardized tools appropriate for use across all populations. The PROMIS-10 is a brief measure of general physical and mental health [23]. The NeuroQOL Applied Cognition [24] is a brief measure of perceived difficulties in everyday cognitive abilities (memory, attention, and decision-making) and in applications of mental function (planning, organizing, calculating, working with memory and learning).
Well-Being was assessed using the NeuroQOL Positive Affect and Well-Being short-form [24] and Ryff Psychological Well-Being Scale subscales for Purpose in Life and Environmental Mastery [25, 26]. These subscales have documented reliability and validity [25, 26].
Related to but distinct from the above measures of well-being, the DeltaQuest Wellness Measure© (DQ Wellness) is a recently validated15-item measure tapping attitudes, perspectives, and behaviors relevant to wellness [27]. Thirteen positively-worded items assessed concepts such as joy/zest, self-care/calm, and outward view (i.e., a positive engagement in the world and with others). Two negatively-worded items tapped characteristics antithetical to wellness, namely low energy, and a preoccupation with the negative aspects of one’s life. All items followed an instruction to “indicate how true each of the following statements is for you over the past week” and used rating-scale descriptors ranging from “not at all” (0) to “very much” (4). All items provided an option “do not know/prefer not to answer.” The measure best fit a bifactor model, with one General Wellness score and four specific factors (Outward View, [Lack of] Negativity, Self-Care/Calm, and Joy/Zest). Using the current sample, we validated the measure in terms of cross-sectional reliability, general construct validity, convergent and divergent validity, and known-groups validity [28]. It has also demonstrated negligible differential item function [29] by gender.
Resilience was assessed using the Centers for Disease Control Healthy Days Core Module [30] (see Statistical Analysis below). In this measure, two items ask how many days of the past 30 the respondent’s physical health (Physical Health Problems) or mental health (Mental Health Problems), respectively, was not good. A third item, Activities of Daily Living Impaired (ADL Impaired) asks in how many of the past 30 days these health problems kept them from doing their usual activities, such as self-care, work, or recreation.
Demographic characteristics included year of birth, gender, with whom the person lives, cohabitation/marital status, ethnicity, race, country of parents’ origin, height, weight, difficulty paying bills, employment status, education, occupation, smoking status, year of chronic medical diagnosis (if applicable), comorbidities, disease category, and whether the participant received help to complete the survey. Occupational complexity was assessed using questions querying the job that was closest to the respondent’s current or past reported occupation, which were then scored for complexity using the Occupational Information Network (O*NET) system [31]. Under this comprehensive, in-depth job-classification system, complexity scores range from low [1] to high [5]), with higher scores reflecting more training and skills required to perform that occupation [32].
Statistical Analysis
Descriptive statistics summarized the sample demographic characteristics and scores on person-reported outcomes. We used wave analysis [33] to assess selection bias by correlating 23 key variables with date of survey submission. COVID-specific variables were grouped by content and summarized via means of related item sets. For COVID infection status, an Ambiguity score was devised on the basis of degree of agreement across three sources of information: (a) COVID test; (b) healthcare provider’s assessment; and (c) participant’s self-assessment. This Ambiguity score could take values of 0, 0.5, 1, or 2; 0 indicated that all sources agreed on whether the person had been infected, and 2 showed direct contradiction between different sources. As an attempt to assess the role of infection status most sensitively, this Ambiguity variable was included as a main effect and in an interaction term with COVID status in linear regression models described below.
Multivariable linear regression was used to investigate the associations between COVID-specific variables and QOL outcomes, after adjusting for the following demographic characteristics: age, gender, body mass index, occupational complexity, and number of comorbidities.
To operationalize Resilience in the mediation structural equation models (SEM), we built on a precedent for using residual modeling to infer Resilience based on the behavior of other variables in the model [34–36]. This approach has been used in multiple studies of chronically-ill people and their caregivers [37–39]. The method involves regressing the CDC Healthy Days ADL Impaired on Physical Health Problems, Mental Health Problems, and their interaction. The residuals from the regression model were saved and multiplied by negative one (-1). Thus, a high Resilience score reflects “over-performance,” or more days than expected that the respondent was able to function despite physical or mental health problems or their synergistic effect [18]. Similarly, a low Resilience score reflects “under-performance.” Further, in a separate use of residual modeling, our mediation analysis to predict Resilience employed a version of it that had first been regressed on (adjusted for) eight demographic and health-related variables: education level, age, gender, race, Hispanic ethnicity, body mass index, number of comorbidities, and number of comorbidities squared.
Structural Equation Modeling (SEM) was then implemented to investigate whether the links between Resilience (dependent variable) and COVID-specific variables (independent variables) were mediated by attitudes, perspectives, and behaviors relevant to wellness (as captured by the DQ Wellness Measure). We tested General Wellness as the mediator for several reasons. First, it assesses individual-difference variables that are likely amenable to modification and intervention. Thus, if results affirm its meaningfulness in promoting resilience, they might motivate psychosocial interventions to help people be more resilient in the face of the pandemic. Second, using the DQ General Wellness score rather than the four specific factor scores enhanced model parsimony and interpretability. The General Wellness score (hereafter simply Wellness) has high marginal reliability [40] of 0.89, and summarizes the inter-item relationships well (explains 58% of the variance in the 15 items) [27]. Finally, this score reflects complex content related to wellness, rather than more circumscribed content as would be the case with the Ryff Purpose in Life and Environmental Mastery subscales.
We began building the SEM investigating whether we could group the positively- vs. negatively-oriented variables together as part of latent constructs of “support” and “stress,” respectively. Our results indicated that variables within each set were not highly correlated with each other (see Supplemental Table 2), leading to poor reliability. Fit statistics were also unsatisfactory. Therefore, all positively-oriented and negatively-oriented variables were entered into the model as separate indicators in subsequent models. We did not include COVID-specific Growth in the SEM because it would be too closely related to the concept assessed by Wellness.
In order to hone the SEM predictors further, a hierarchical series of models tested simple mediation effects (i.e., one independent variable, Wellness as mediator, resilience as outcome). To test the effect of COVID status, we compared model-fit statistics using COVID status as a covariate versus separate mediation models for those infected and not infected.
IBM SPSS version 27 [41] and Mplus version 8.4 [42] were used for all analyses.