ABSTRACT
BACKGROUND: There are no psychometrically validated measures of the willingness to engage in public health screening and prevention efforts, particularly mobile health (mHealth)-based tracking, that can be adapted to future crises post-COVID-19. OBJECTIVE: The psychometric properties of a novel measure of the willingness to participate in pandemic-related screening and tracking, including the willingness to use pandemic-related mHealth tools, were tested. METHODS: Data were from a cross-sectional, national probability survey deployed in 3 cross-sectional stages several weeks apart to adult residents of the United States (N=6475; stage 1 n=2190, 33.82%; stage 2 n=2238, 34.56%; and stage 3 n=2047, 31.62%) from the AmeriSpeak probability-based research panel covering approximately 97% of the US household population. Five items asked about the willingness to use mHealth tools for COVID-19-related screening and tracking and provide biological specimens for COVID-19 testing. RESULTS: In the first, exploratory sample, 3 of 5 items loaded onto 1 underlying factor, the willingness to use pandemic-related mHealth tools, based on exploratory factor analysis (EFA). A 2-factor solution, including the 3-item factor, fit the data (root mean square error of approximation [RMSEA]=0.038, comparative fit index [CFI]=1.000, standardized root mean square residual [SRMR]=0.005), and the factor loadings for the 3 items ranged from 0.849 to 0.893. In the second, validation sample, the reliability of the 3-item measure was high (Cronbach α=.90), and 1 underlying factor for the 3 items was confirmed using confirmatory factor analysis (CFA): RMSEA=0, CFI=1.000, SRMR=0 (a saturated model); factor loadings ranged from 1.000 to 0.962. The factor was independently associated with COVID-19-preventive behaviors (eg, "worn a face mask": r=0.313, SE=0.041, P<.001; "kept a 6-foot distance from those outside my household": r=0.282, SE=0.050, P<.001) and the willingness to provide biological specimens for COVID-19 testing (ie, swab to cheek or nose: r=0.709, SE=0.017, P<.001; small blood draw: r=0.684, SE=0.019, P<.001). In the third, multiple-group sample, the measure was invariant, or measured the same thing in the same way (ie, difference in CFI [ΔCFI]<0.010 across all grouping categories), across age groups, gender, racial/ethnic groups, education levels, US geographic region, and population density (ie, rural, suburban, urban). When repeated across different samples, factor-analytic findings were essentially the same. Additionally, there were mean differences (ΔM) in the willingness to use mHealth tools across samples, mainly based on race or ethnicity and population density. For example, in SD units, suburban (ΔM=-0.30, SE=0.13, P=.001) and urban (ΔM=-0.42, SE=0.12, P<.001) adults showed less willingness to use mHealth tools than rural adults in the third sample collected on May 30-June 8, 2020, but no differences were detected in the first sample collected on April 20-26, 2020. CONCLUSIONS: Findings showed that the screener is psychometrically valid. It can also be adapted to future public health crises. Racial and ethnic minority adults showed a greater willingness to use mHealth tools than White adults. Rural adults showed more mHealth willingness than suburban and urban adults. Findings have implications for public health screening and tracking and understanding digital health inequities, including lack of uptake.
ABSTRACT
The COVID-19 pandemic, which began in China in late 2019, and subsequently spread across the world during the first several months of 2020, has had a dramatic impact on all facets of life. At the same time, it has not manifested in the same way in every nation. Some countries experienced a large initial spike in cases and deaths, followed by a rapid decline, whereas others had relatively low rates of both outcomes throughout the first half of 2020. The United States experienced a unique pattern of the virus, with a large initial spike, followed by a moderate decline in cases, followed by second and then third spikes. In addition, research has shown that in the United States the severity of the pandemic has been associated with poverty and access to health care services. This study was designed to examine whether the course of the pandemic has been uniform across America, and if not how it differed, particularly with respect to poverty. Results of a random intercept multilevel mixture model revealed that the pandemic followed four distinct paths in the country. The least ethnically diverse (85.1% white population) and most rural (82.8% rural residents) counties had the lowest death rates (0.06/1000) and the weakest link between deaths due to COVID-19 and poverty (b = 0.03). In contrast, counties with the highest proportion of urban residents (100%), greatest ethnic diversity (48.2% nonwhite), and highest population density (751.4 people per square mile) had the highest COVID-19 death rates (0.33/1000), and strongest relationship between the COVID-19 death rate and poverty (b = 46.21). Given these findings, American policy makers need to consider developing responses to future pandemics that account for local characteristics. These responses must take special account of pandemic responses among people of color, who suffered the highest death rates in the nation.
ABSTRACT
BACKGROUND: The coronavirus disease 2019 outbreak is a significant challenge for health-care systems around the world. OBJECTIVE: The objective of the study was to assess the impact of comorbidities on the case fatality rate (CFR) and the development of adverse events in patients positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the Mexican population. MATERIALS AND METHODS: We analyzed the data from 13,842 laboratory-confirmed SARS-CoV-2 patients in Mexico between January 1, 2020, and April 25, 2020. We investigated the risk of death and the development of adverse events (hospitalization, pneumonia, orotracheal intubation, and intensive care unit [ICU] admission), comparing the number of comorbidities of each patient. RESULTS: The patient mean age was 46.6 ± 15.6 years, 42.3% (n = 5853) of the cases were women, 38.8% of patients were hospitalized, 4.4% were intubated, 29.6% developed pneumonia, and 4.4% had critical illness. The CFR was 9.4%. The risk of hospitalization (odds ratio [OR] = 3.1, 95% confidence interval [CI]: 2.7-3.7), pneumonia (OR = 3.02, 95% CI: 2.6-3.5), ICU admission (OR = 2, 95% CI: 1.5-2.7), and CFR (hazard ratio = 3.5, 95% CI: 2.9-4.2) was higher in patients with three or more comorbidities than in patients with 1, 2, or with no comorbidities. CONCLUSIONS: The number of comorbidities may be a determining factor in the clinical course and its outcomes in SARS-CoV-2-positive patients.