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1. Determinants of tap water mistrust among Phoenix, Arizona Latinx adults

   https://doi.org/10.2166/wh.2023.267  

   Colburn, Abigail T; Buman, Matthew P; Wutich, Amber; Vega-López, Sonia; Ohri-Vachaspati, Punam; Kavouras, Stavros A (May 2023, Journal of Water and Health)

   Abstract The purpose of this investigation was to characterize factors that predict tap water mistrust among Phoenix, Arizona Latinx adults. Participants (n = 492, 28 ± 7 years, 37.4% female) completed water security experience-based scales and an Adapted Survey of Water Issues in Arizona. Binary logistic regression determined odds ratios (OR) with 95% confidence intervals (95% CI) for the odds of perceiving tap water to be unsafe. Of all participants, 51.2% perceived their tap water to be unsafe. The odds of mistrusting tap water were significantly greater for each additional favorable perception of bottled compared to tap water (e.g., tastes/smells better; OR = 1.94, 95% CI = 1.50, 2.50), negative home tap water experience (e.g., hard water mineral deposits and rusty color; OR = 1.32, 95% CI = 1.12, 1.56), use of alternatives to home tap water (OR = 1.25, 95% CI = 1.04, 1.51), and with decreased water quality and acceptability (OR = 1.21, 95% CI = 1.01, 1.45; P < 0.05). The odds of mistrusting tap water were significantly lower for those whose primary source of drinking water is the public supply (municipal) (OR = 0.07, 95% CI = 0.01, 0.63) and with decreased water access (OR = 0.56, 95% CI = 0.48, 0.66; P < 0.05). Latinx mistrust of tap water appears to be associated with organoleptic perceptions and reliance on alternatives to the home drinking water system. 

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2. Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

   https://doi.org/10.2196/46347  

   King, Zachary D; Yu, Han; Vaessen, Thomas; Myin-Germeys, Inez; Sano, Akane (January 2024, JMIR mHealth and uHealth)

   BackgroundAs mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants’ responses when implementing new survey delivery methods. ObjectiveThis study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants’ reported emotional state. We examine the factors that affect participants’ receptivity to EMAs in a 10-day wearable and EMA–based emotional state–sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs. MethodsWe collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity. ResultsOur findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions. ConclusionsOur results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points. 

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3. Wildfire Smoke Is Associated With an Increased Risk of Cardiorespiratory Emergency Department Visits in Alaska

   https://doi.org/10.1029/2020GH000349  

   Hahn, M. B.; Kuiper, G.; O'Dell, K.; Fischer, E. V.; Magzamen, S. (May 2021, GeoHealth)

   Abstract Alaskan wildfires have major ecological, social, and economic consequences, but associated health impacts remain unexplored. We estimated cardiorespiratory morbidity associated with wildfire smoke (WFS) fine particulate matter with a diameter less than 2.5 μm (PM_2.5) in three major population centers (Anchorage, Fairbanks, and the Matanuska‐Susitna Valley) during the 2015–2019 wildfire seasons. To estimate WFS PM_2.5, we utilized data from ground‐based monitors and satellite‐based smoke plume estimates. We implemented time‐stratified case‐crossover analyses with single and distributed lag models to estimate the effect of WFS PM_2.5on cardiorespiratory emergency department (ED) visits. On the day of exposure to WFS PM_2.5, there was an increased odds of asthma‐related ED visits among 15–65 year olds (OR = 1.12, 95% CI = 1.08, 1.16), people >65 years (OR = 1.15, 95% CI = 1.01, 1.31), among Alaska Native people (OR = 1.16, 95% CI = 1.09, 1.23), and in Anchorage (OR = 1.10, 95% CI = 1.05, 1.15) and Fairbanks (OR = 1.12, 95% CI = 1.07, 1.17). There was an increased risk of heart failure related ED visits for Alaska Native people (Lag Day 5 OR = 1.13, 95% CI = 1.02, 1.25). We found evidence that rural populations may delay seeking care. As the frequency and magnitude of Alaskan wildfires continue to increase due to climate change, understanding the health impacts will be imperative. A nuanced understanding of the effects of WFS on specific demographic and geographic groups facilitates data‐driven public health interventions and fire management protocols that address these adverse health effects. 

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4. Sleep disparities in the first month of college: implications for academic achievement

   https://doi.org/10.1093/sleepadvances/zpac041  

   Bermudez, Vanessa N.; Fearon-Drake, Danielle; Wheelis, Meaghann; Cohenour, Michelle; Suntai, Zainab; Scullin, Michael K. (November 2022, SLEEP Advances)

   Abstract Study ObjectiveWe investigated sleep disparities and academic achievement in college. MethodsParticipants were 6,002 first-year college students attending a midsize private university in the southern United States [62.0% female, 18.8% first-generation, 37.4% Black, Indigenous, or People of Color (BIPOC) students]. During the first 3–5 weeks of college, students reported their typical weekday sleep duration, which we classified as short sleep (<7 hours), normal sleep (7–9 hours), or long sleep (>9 hours). ResultsThe odds for short sleep were significantly greater in BIPOC students (95% CI: 1.34–1.66) and female students (95% CI: 1.09–1.35), and the odds for long sleep were greater in BIPOC students (95% CI: 1.38–3.08) and first-generation students (95% CI: 1.04–2.53). In adjusted models, financial burden, employment, stress, STEM academic major, student athlete status, and younger age explained unique variance in sleep duration, fully mediating disparities for females and first-generation students (but only partially mediating disparities for BIPOC students). Short and long sleep predicted worse GPA across students’ first year in college, even after controlling for high school academic index, demographics, and psychosocial variables. ConclusionsHigher education should address sleep health early in college to help remove barriers to success and reduce disparities. 

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5. Reports of Burnout Among Historically Marginalized and Female Graduating Medical Students During the COVID-19 Pandemic

   https://doi.org/10.1097/ACM.0000000000005854  

   Dyrbye, Liselotte N; Brushaber, Danielle E; West, Colin P (January 2024, Academic Medicine)

   Abstract PurposeTo examine graduating medical student reports of burnout by sex, race and ethnicity, and sexual orientation and explore trends within intersectional demographic groups from 2019–2021 in a national sample. MethodThe authors obtained medical student responses to the 2019–2021 Association of American Medical Colleges (AAMC) Graduation Questionnaires (GQs) linked to data from other AAMC sources. The dataset included year of GQ completion, responses to a modified Oldenburg Burnout Inventory (exhaustion subscale range: 0–24; disengagement subscale range: 0–15), and demographics previously shown to relate to the risk of burnout in medical students, residents, or physicians. Multivariable linear regression analysis was performed to evaluate independent associations between demographics and burnout. ResultsOverall response rate was 80.7%. After controlling for other factors, mean exhaustion scores were higher among Asian (parameter estimate [PE] 0.38, 95% confidence interval [CI] 0.21, 0.54), bisexual (PE 0.97, 95% CI 0.76, 1.17), and gay or lesbian (PE 0.55, 95% CI 0.35, 0.75) students than those who did not identify with each of those respective groups. Mean disengagement scores were lower among female (PE −0.47, 95% CI −0.52, −0.42), Hispanic (PE −0.11, 95% CI −0.22, −0.01), and White (PE −0.10, 95% CI −0.19, 0.00) students and higher among Asian (PE 0.17, 95% CI 0.07, 0.27), Black or African American (PE 0.31, 95% CI 0.18, 0.44), bisexual (PE 0.54, 95% CI 0.41, 0.66), and gay or lesbian (PE 0.23, 95% CI 0.11, 0.35) students than those who did not identify with each of those respective groups. From 2019–2021, mean exhaustion and disengagement scores were relatively stable or improved across nearly all intersectional groups. ConclusionsMale, Asian, Black or African American, and sexual minority students had a higher risk of burnout, while female, Hispanic, White, and heterosexual or straight students had a lower risk of burnout. 

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