The Effect of Income Disparities on Influenza Vaccination Coverage in the United States.

OBJECTIVES: Although influenza vaccinations are widely accessible, many people in the United States do not receive them as recommended by the Centers for Disease Control and Prevention. This study examined the relationship between income and receiving the influenza vaccination among US adults. METHODS: We used 2014-2018 National Health Interview Survey data (N = 138 697). Adults self-reported whether they received a shot or nasal spray vaccine within the previous 12 months and their total family income. We used multivariable logistic regression to obtain odds ratios and 95% CIs. RESULTS: Approximately 43% of adults reported receiving the influenza vaccine in the previous 12 months. After adjustment, adults in lower-income-level categories had decreased odds of influenza vaccine receipt compared with adults with a total family income ≥$100 000. Specifically, adults with a total family income <$35 000 had 21% decreased odds of receiving the influenza vaccine (adjusted odds ratio = 0.79; 95% CI, 0.75-0.83). CONCLUSIONS: In this population of US adults, lower income levels were associated with decreased odds of influenza vaccine receipt. The relationship between income and receipt of the influenza vaccine may have important implications for future influenza vaccination efforts. Increasing influenza vaccination coverage among lower-income adults should be considered a public health priority.

Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities.

PURPOSE: To determine the association of social factors with Covid-19 mortality and identify high-risk clusters. METHODS: Data on Covid-19 deaths across 3,108 contiguous U.S. counties from the Johns Hopkins University and social determinants of health (SDoH) data from the County Health Ranking and the Bureau of Labor Statistics were fitted to Bayesian semi-parametric spatiotemporal Negative Binomial models, and 95% credible intervals (CrI) of incidence rate ratios (IRR) were used to assess the associations. Exceedance probabilities were used for detecting clusters. RESULTS: As of October 31, 2020, the median mortality rate was 40.05 per 100, 000. The monthly urban mortality rates increased with unemployment (IRRadjusted:1.41, 95% CrI: 1.24, 1.60), percent Black population (IRRadjusted:1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRRadjusted:1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRRadjusted: 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRRadjusted:1.07 95% CrI:1.06, 1.08). Higher college education rates were associated with decreased mortality rates in rural and urban counties. The dynamics of exceedance probabilities detected the shifts of high-risk clusters from the Northeast to Southern and Midwestern counties. CONCLUSIONS: Spatiotemporal analyses enabled the inclusion of unobserved latent risk factors and aid in scientifically grounded decision-making at a granular level.

The Association of Social Determinants of Health With COVID-19 Mortality in Rural and Urban Counties.

PURPOSE: To identify the county-level effects of social determinants of health (SDoH) on COVID-19 (corona virus disease 2019) mortality rates by rural-urban residence and estimate county-level exceedance probabilities for detecting clusters. METHODS: The county-level data on COVID-19 death counts as of October 23, 2020, were obtained from the Johns Hopkins University. SDoH data were collected from the County Health Ranking and Roadmaps, the US Department of Agriculture, and the Bureau of Labor Statistics. Semiparametric negative binomial regressions with expected counts based on standardized mortality rates as offset variables were fitted using integrated Laplace approximation. Bayesian significance was assessed by 95% credible intervals (CrI) of risk ratios (RR). County-level mortality hotspots were identified by exceedance probabilities. FINDINGS: The COVID-19 mortality rates per 100,000 were 65.43 for the urban and 50.78 for the rural counties. Percent of Blacks, HIV, and diabetes rates were significantly associated with higher mortality in rural and urban counties, whereas the unemployment rate (adjusted RR = 1.479, CrI = 1.171, 1.867) and residential segregation (adjusted RR = 1.034, CrI = 1.019, 1.050) were associated with increased mortality in urban counties. Counties with a higher percentage of college or associate degrees had lower COVID-19 mortality rates. CONCLUSIONS: SDoH plays an important role in explaining differential COVID-19 mortality rates and should be considered for resource allocations and policy decisions on operational needs for businesses and schools at county levels.

Impact of Comorbidities on SARS-CoV-2 Viral Entry-Related Genes.

Viral entry mechanisms for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are an important aspect of virulence. Proposed mechanisms involve host cell membrane-bound angiotensin-converting enzyme 2 (ACE2), type II transmembrane serine proteases (TTSPs), such as transmembrane serine protease isoform 2 (TMPRSS2), lysosomal endopeptidase Cathepsin L (CTSL), subtilisin-like proprotein peptidase furin (FURIN), and even potentially membrane bound heparan sulfate proteoglycans. The distribution and expression of many of these genes across cell types representing multiple organ systems in healthy individuals has recently been demonstrated. However, comorbidities such as diabetes and cardiovascular disease are highly prevalent in patients with Coronavirus Disease 2019 (COVID-19) and are associated with worse outcomes. Whether these conditions contribute directly to SARS-CoV-2 virulence remains unclear. Here, we show that the expression levels of ACE2, TMPRSS2 and other viral entry-related genes, as well as potential downstream effector genes such as bradykinin receptors, are modulated in the target organs of select disease states. In tissues, such as the heart, which normally express ACE2 but minimal TMPRSS2, we found that TMPRSS2 as well as other TTSPs are elevated in individuals with comorbidities compared to healthy individuals. Additionally, we found the increased expression of viral entry-related genes in the settings of hypertension, cancer, or smoking across target organ systems. Our results demonstrate that common comorbidities may contribute directly to SARS-CoV-2 virulence and we suggest new therapeutic targets to improve outcomes in vulnerable patient populations.

Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates.

PURPOSE: There are growing signs that the COVID-19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county-level change in prevalence rates of COVID-19 by rural-urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates. METHODS: We used crowdsourced data on COVID-19 and linked them to county-level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R-studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis-Ord local statistics. FINDINGS: In the rural counties, the mean prevalence of COVID-19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID-19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID-19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25-49 years had increased COVID-19 prevalence rates. CONCLUSIONS: Our findings show a rapid spread of COVID-19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID-19 prevalence.

Circulating Lactonase Activity but Not Protein Level of PON-1 Predicts Adverse Outcomes in Subjects with Chronic Kidney Disease.

The burden of cardiovascular disease and death in chronic kidney disease (CKD) outpaces that of the other diseases and is not adequately described by traditional risk factors alone. Diminished activity of paraoxonase (PON)-1 is associated with increased oxidant stress, a common feature underlying the pathogenesis of CKD. We aimed to assess the prognostic value of circulating PON-1 protein and PON lactonase activity on adverse clinical outcomes across various stages and etiologies of CKD. Circulating PON-1 protein levels and PON lactonase activity were measured simultaneously in patients with CKD as well as a cohort of apparently healthy non-CKD subjects. Both circulating PON-1 protein levels and PON lactonase activity were significantly lower in CKD patients compared to the non-CKD subjects. Similarly, across all stages of CKD, circulating PON-1 protein and PON lactonase activity were significantly lower in patients with CKD compared to the non-CKD controls. Circulating PON lactonase activity, but not protein levels, predicted future adverse clinical outcomes, even after adjustment for traditional risk factors. The combination of lower circulating protein levels and higher activity within the CKD subjects were associated with the best survival outcomes. These findings demonstrate that diminished circulating PON lactonase activity, but not protein levels, predicts higher risk of future adverse clinical outcomes in patients with CKD.