Population-based denominators matter: Bias in U.S. Virgin Islands COVID-19 vaccination coverage under changing population counts.

PURPOSE: The U.S. Virgin Islands (USVI) receives an updated population count once every 10 years and used 2010 decennial census population counts to estimate COVID-19 vaccination coverage during the COVID-19 emergency response. We investigated whether using outdated (2010) or modeled (2020 international database [IDB]) population counts biased vaccination coverage estimates used to inform public health priorities during the 2020-2022 COVID-19 response. METHODS: We estimated percentage of USVI residents with a completed primary COVID-19 vaccination series during December 16, 2020-September 20, 2022. Vaccination coverage was calculated as number of persons who completed the vaccination series divided by 2010 and 2020 decennial census population counts and 2020 IDB intercensal estimate. RESULTS: COVID-19 vaccination coverage using the 2020 population count was 12 % points higher than coverage using 2010 denominator (2010 denominator: 51 %; 2020 denominator: 63 %). Vaccination coverage estimated using 2020 IDB was approximately equal with the 2010 decennial census estimate (52 %). CONCLUSIONS: Using 2010 and modeled population counts underestimated 2020 USVI COVID-19 vaccination coverage given the 18 % population decline during 2010-2020, potentially limiting USVI's ability to assess vaccination progress. Identifying mechanisms for more reliable population enumeration or improved estimate modeling are essential for accurately guiding USVI public health decision-making.

Inflammatory profiles, gut microbiome, and kidney function are impacted after high-fidelity firefighter training.

BACKGROUND: Firefighters are frequently exposed to high temperatures, environmental toxicants, and strenuous physical demands. The health impacts of these occupational exposures on processes including inflammation and kidney function as well as on the gut microbiota are poorly understood. A firefighter training course may provide a controlled environment to assess these health risks. METHODS: Basic health measures, stool, and blood samples were obtained from 24 firefighters participating in a one-week, heat-intensive training course. Indicators of inflammation, gut permeability, kidney health, and stool microbiota composition were measured before and after the training course in 18 participants. Urine specific gravity was measured before and after a heat-intensive training day to evaluate dehydration. RESULTS: The majority of firefighters in this cohort were categorized as hypertensive and experienced multiple heat-related illness symptoms during the training week and dehydration after the heat-intensive training day. While plasma IL-1ß, CXCL8, and NGAL decreased over the training week, other indicators of inflammation and acute kidney injury increased, and estimated kidney function declined. Microbiota composition shifted over the course of the training week, with changes in Peptostreptococcus anaerobius and Streptococcus. CONCLUSIONS: This pilot study conducted in a controlled field setting suggests that the occupational environment of firefighters may increase their risk for systemic inflammation and kidney disease.

Using Twitter for Nursing Research: A Tweet Analysis on Heat Illness and Health.

AIM: To provide an example of a tweet analysis for nurse researchers using Twitter in their research. DESIGN: A content analysis using tweets about "heat illness + health." METHODS: Tweets were pulled from Twitter's application programming interface with premium access using Postman and the key words "heat illness + health." All data cleaning and analysis was performed in R Version 3.5.2, and the tweet set was analyzed for term frequency, sentiment, and topic modeling. Principal R packages included LDAvis, tidytext, tm, and zyuzhet. RESULTS: 6,317 tweets were analyzed with a date range of April 6, 2009, to December 30, 2019. The most common terms in the tweets were heat (n = 4,532), illness (n = 4,085), and health (n = 2,257). Sentiment analysis showed that the majority of tweets (55%) had a negative sentiment. Topic modeling showed that there were three topics within the tweet set: increasing impact, prevention and safety, and symptoms. CONCLUSIONS: Twitter can be a useful tool for nursing researchers, serving as a viable adjunct to current research methodologies. This practical example has facilitated a deeper understanding of the social media representation of heat illness and health that can be applied to other research. CLINICAL RELEVANCE: Twitter serves as a tool for collecting health information for multiple groups, ranging from clinicians and researchers to patients. By utilizing the plethora of data that comes from the platform, we can work towards developing theories and interventions related to numerous health conditions and phenomena.

A modified physiological strain index for workplace-based assessment of heat strain experienced by agricultural workers.

BACKGROUND: As global temperatures rise, increasing numbers of individuals will work in hot environments. Interventions to protect their health are critical, as are reliable methods to measure the physiological strain experienced from heat exposure. The physiological strain index (PSI) is a measure of heat strain that relies on heart rate and core temperature but is challenging to calculate in a real-world occupational setting. METHODS: We modified the PSI for use in field settings where resting temperature and heart rate are not available and used the modified physiological strain index (mPSI) to describe risk factors for high heat strain (mPSI ≥ 7) experienced by agricultural workers in Florida during the summers of 2015 through 2017. mPSI was calculated for 221 workers, yielding 465 days of data. RESULTS: A higher heat index (ß = 0.185; 95% CI: 0.064, 0.307) and higher levels of physical activity at work (0.033; 95% CI: 0.017, 0.050) were associated with a higher maximum mPSI. More years worked in US agriculture (-0.041; 95% CI: -0.061, -0.020) were protective against a higher maximum mPSI. Out of 23 workdays that a participant experienced a maximum mPSI ≥ 7, 22 were also classified as strained by at least one other measure of high heat strain (core temperature [Tc] >38.5°C, sustained heart rate >(180 - age), and mean heart rate > 115 bpm). CONCLUSIONS: This study provides critical information on risk factors for elevated heat strain for agricultural workers and suggests a practical approach for using PSI in field-based settings.

Analyzing the predictors of health care utilization in the agricultural worker population using decision tree analysis: Does language matter?

OBJECTIVES: To analyze the predictors of health care utilization among respondents to the National Agricultural Worker Survey. Specifically, we hypothesized that English proficiency would predict utilization of health care services within the last 2 years. METHODS: Using the 2015-2016 National Agricultural Worker Survey, we performed a secondary data analysis to analyze the predictors of health care utilization within the last 2 years in the United States' agricultural worker population. Data were cleaned and analyzed using decision tree analysis, which produced a classification tree model that was trained on 90% of the data and validated on 10%. RESULTS: Exposure to English was not a predictor of health care utilization in our classification tree. The first major partition that predicted utilization was insurance status. Additional partitions were on age, gender, hypertension diagnosis, and public aid. CONCLUSIONS: By partitioning on insurance status and use of public aid, the decision tree provided evidence that systemic factors are key determinants of health care utilization in the agricultural worker community. This highlights the importance of agencies that connect agricultural workers with resources that provide insurance and improve access to health care. This is especially important given that agricultural workers are one of the highest risk groups for occupational injury or death in the United States.

Antibody Profiles According to Mild or Severe SARS-CoV-2 Infection, Atlanta, Georgia, USA, 2020.

Among patients with coronavirus disease (COVID-19), IgM levels increased early after symptom onset for those with mild and severe disease, but IgG levels increased early only in those with severe disease. A similar pattern was observed in a separate serosurveillance cohort. Mild COVID-19 should be investigated separately from severe COVID-19.