Racial, Ethnic, and Sex Disparities in Nail Psoriasis Clinical Trials: A Systematic Review

<b><i>Introduction:</i></b> Nail psoriasis (NP) disproportionally affects quality of life in females versus males. Demographics of NP research cohorts are not well characterized. In this systematic review, we characterize the representation of racial/ethnic groups and women in NP randomized clinical trials (RCTs). <b><i>Methods:</i></b> A systematic search of MEDLINE was performed; RCTs of NP pharmacologic treatments or cutaneous psoriasis/psoriatic arthritis with the number of NP patients described were included. <b><i>Results:</i></b> Overall, 45 RCTs were analyzed, with 91.1% reporting sex, and 67.9% of participants were men. 7/41 (17%) studies reporting sex included ≥45% female participants. Of 45 RCTs, 35.6% reported race and/or ethnicity. Of the 22 studies with ≥1 US-based site, 13 (59%) reported race/ethnicity; 3 out of 23 (13%) studies with &#x3c;1 US-based site reported these data. Enrollment of nonwhite participants was significantly lower than representation within the US census (13.4% vs. 39.9%; <i>p</i> &#x3c; 0.001). Treatment type, route of administration, location with ≥1 US-based site, funding, and journal type were significantly associated with race/ethnicity reporting (<i>p</i> &#x3c; 0.05 all comparisons). <b><i>Discussion/Conclusion:</i></b> Reporting of racial/ethnic demographics is lacking in NP RCTs. Women and racial/ethnic minorities remain underrepresented in NP research. There is a need for increased reporting and diversification of NP clinical trial participants.

Pandemic-related declines in hospitalization for non-COVID-19-related illness in the United States from January through July 2020

Background The COVID-19 pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially impacted healthcare utilization worldwide. The objective of this retrospective analysis of a large hospital discharge database was to compare all-cause and cause-specific hospitalizations during the first six months of the pandemic in the United States with the same months in the previous four years. Methods Data were collected from all hospitals in the Premier Healthcare Database (PHD) and PHD Special Release reporting hospitalizations from January through July for each year from 2016 through 2020. Hospitalization trends were analyzed stratified by age group, major diagnostic categories (MDCs), and geographic region. Results The analysis included 286 hospitals from all 9 US Census divisions. The number of all-cause hospitalizations per month was relatively stable from 2016 through 2019 and then fell by 21% (57,281 fewer hospitalizations) between March and April 2020, particularly in hospitalizations for non-respiratory illnesses. From April onward there was a rise in the number of monthly hospitalizations per month. Hospitalizations per month, nationally and in each Census division, decreased for 20 of 25 MDCs between March and April 2020. There was also a decrease in hospitalizations per month for all age groups between March and April 2020 with the greatest decreases in hospitalizations observed for patients 50–64 and ≥65 years of age. Conclusions Rates of hospitalization declined substantially during the first months of the COVID-19 pandemic, suggesting delayed routine, elective, and emergency care in the United States. These lapses in care for illnesses not related to COVID-19 may lead to increases in morbidity and mortality for other conditions. Thus, in the current stage of the pandemic, clinicians and public-health officials should work, not only to prevent SARS-CoV-2 transmission, but also to ensure that care for non-COVID-19 conditions is not delayed.

Workforce Diversity

The growing acceptance of marrying across racial and ethnic lines (as reflected in US census statistics) together with the growth of demographic changes across workplaces is fueling fears among some who see their culture being threatened and react by engaging in overt discrimination. One concern regards employers having access to databases containing talented individuals who are ready to work. Their choices hinge on the abilities required to further their enterprise. Paradoxically, a business's culture's greatest strength could be its greatest weakness when not consistent with sound business strategies. Moreover, when such a culture prevents a firm from meeting competitive threats, this can lead to the firm's stagnation and ultimate demise. Diversity has never been thought of as a strategy until now. This chapter explores workforce diversity.

Missed and Delayed Preventive Health Care Visits Among US Children Due to the COVID-19 Pandemic

Objective: The COVID-19 pandemic led to a substantial drop in US children’s preventive care, which had not fully rebounded by the end of 2020. We sought to estimate the overall prevalence of missed, skipped, or delayed preventive checkups among households with children in the last 12 months because of the pandemic. Methods: We used data from the US Census Bureau’s Household Pulse Survey, Phase 3.1 (collected April–May 2021). The analytic sample included 48 824 households with ≥1 child or adolescent aged <18 years. We estimated both national and state-level prevalences, examined associations with sociodemographic and household characteristics, and described reasons for missed or delayed preventive visits. Results: Overall, 26.4% (95% CI, 25.5%-27.2%) of households reported that ≥1 child or adolescent had missed or delayed a preventive visit because of COVID-19; percentages varied by state, from 17.9% in Wyoming to 37.0% in Vermont. The prevalence of missed or delayed preventive visits was significantly higher among respondents who reported material hardships (ie, not caught up on rent/mortgage, difficulty paying usual household expenses, children not eating enough because of lack of affordability) than among respondents who did not report material hardships. The most common reasons for missing or delaying preventive visits were concern about visiting a health care provider, limited appointment availability, and the provider’s location being closed. Conclusions: Programs and policies could reduce gaps in children’s preventive care caused by the pandemic, with a particular focus on addressing social determinants of health.

Vocational Rehabilitation and Labor Market Outcomes: Evidence from World War I Veterans

This article uses a linked sample of World War I Army veterans from the state of Missouri to study the impact of vocational rehabilitation on labor market outcomes for men wounded and disabled during the war. Veterans’ military service abstracts are linked to the 1940 US Census and a subset are linked to rehabilitation records. This creates a new dataset that contains information on military service, rehabilitation, and labor market outcomes. I find that 70 percent of veterans that were both wounded in action and disabled when discharged from the army participated in the rehabilitation program. These same veterans had significantly better labor market outcomes, which can be attributed to the rehabilitation program under certain assumptions.

Data-Driven Real-Time Strategic Placement of Mobile Vaccine Distribution Sites

The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations. Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings.

Reducing Loneliness Among Aging Adults: The Roles of Personal Voice Assistants and Anthropomorphic Interactions

The perception of feeling lonely is an influential factor in determining quality of life among aging adults. As the US Census Bureau projects that the number of Americans ages 65 and older will double by 2060, reducing loneliness is imperative. Personal voice assistants (PVAs) such as Amazon's Echo offer the ease-of-use of voice control with a friendly, helpful artificial intelligence. This study aimed to understand the influence of a PVA on loneliness reduction among adults of advanced ages, i.e., 75+, and explore anthropomorphism as a potential underlying mechanism. Participants (N = 16) ages 75 or older used an Amazon Echo PVA for 8 weeks in an independent living facility in the Midwest. Surveys were used to collect information about perceived loneliness, and PVA interaction data was recorded and analyzed. Participants consistently exceeded the required daily interactions. As hypothesized, after the first 4 weeks of the intervention, aging adults reported significantly lower loneliness (baseline mean = 2.22, SD = 0.42; week 4 mean = 1.99, SD = 0.45, Z = −2.45, and p = 0.01). Four dominant anthropomorphic themes emerged after thematic analysis of the entire 8 weeks' PVA interaction data (Cohen's Kappa = 0.92): (1) greetings (user-initiated, friendly phrases); (2) comments/questions (user-initiated, second-person pronoun), (3) polite interactions (user-initiated, direct-name friendly requests), (4) reaction (user response to Alexa). Relational greetings predicted loneliness reductions in the first 4 weeks and baseline loneliness predicted relational greetings with the PVA during the entire 8 weeks, suggesting that anthropomorphization of PVAs may play a role in mitigating loneliness in aging adults.

Frequent mask-wearing during the COVID-19 pandemic and acne: an infodemiology study from Google research trends (Preprint)

BACKGROUND Researchers have been increasingly using the internet as a major source of health-related information and infodemiological methods have provided new approaches for studying the impact of coronavirus disease (COVID-19). OBJECTIVE To verify whether frequent mask-wearing during the COVID-19 pandemic was associated with an increase in acne search popularity. METHODS Data for mask-wearing were obtained from a NYT survey, with 250,000 responses between July 2 and 14, 2020, and from Google COVID-19 symptoms dataset for weekly acne and anxiety search popularity. All data in the study were presented in relation to US county levels. Each county was classified in the frequent mask-wearing group if the proportion of frequent users was above the third quartile. To make search trends comparable from one week to another and from one county to another, search trends were normalized on a relative 100-point scale, with the maximum value corresponding to the highest search popularity for a particular term in a specific week and a specific county. Other sources of data included the US census bureau datasets. Acne search popularity outcome was analyzed using a logistic regression, with COVID-19 incidence, metropolitan status of the county and anxiety search popularity as covariates, and mask-wearing status as the exposure variable. 2019 data, no mask-wearing, was used as a calibration control for acne search weight. RESULTS The final dataset consisted of 2893 counties with complete cases. Frequent mask-wearing was associated with an important increase in acne search popularity (OR=1.69; 95% CI (1.30-2.21); P<.001). A high relative incidence of COVID-19 was associated with an even greater acne search popularity (OR=8.42; 95% CI (6.48-10.96); P<.001). CONCLUSIONS Despite various biases, the use of infodemiology will keep increasing. Observational statistical methods need to be adapted to manage the large amounts of bias concerning web-based information more efficiently.

Emergency activations for chest pain and ventricular arrhythmias related to regional COVID-19 across the US

Evidence that patients may avoid healthcare facilities for fear of COVID-19 infection has heightened the concern that true rates of myocardial infarctions have been under-ascertained and left untreated. We analyzed data from the National Emergency Medical Services Information System (NEMSIS) and incident COVID-19 infections across the United States (US) between January 1, 2020 and April 30, 2020. Grouping events by US Census Division, multivariable adjusted negative binomial regression models were utilized to estimate the relationship between COVID-19 and EMS cardiovascular activations. After multivariable adjustment, increasing COVID-19 rates were associated with less activations for chest pain and non-ST-elevation myocardial infarctions. Simultaneously, increasing COVID-19 rates were associated with more activations for cardiac arrests, ventricular fibrillation, and ventricular tachycardia. Although direct effects of COVID-19 infections may explain these discordant observations, these findings may also arise from patients delaying or avoiding care for myocardial infarction, leading to potentially lethal consequences.

Creating Unbiased Machine Learning Models by Design

Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents a modeling procedure that carefully builds models around protected class information in order to make sure that the final machine learning model is independent of protected class status, even in a nonlinear sense. This procedure works for any machine learning method. The procedure was tested on subprime credit card data combined with demographic data by zip code from the US Census. The census data serves as an imperfect proxy for borrower demographics but serves to illustrate the procedure.