Enrollment and use of a peer counselor mHealth texting program to support breastfeeding in low-income people: A pilot study in the District of Columbia.

OBJECTIVE: To describe the characteristics of postpartum people who did and did not enroll in a breastfeeding peer-counselor mobile health (mHealth) texting program as well as the issues raised through 2-way texting with peer counselors. DESIGN: Pilot intervention study involving two Special Supplemental Nutrition Programs for Women Infants and Children (WIC) sites in the District of Columbia over 1 year. SAMPLE: WIC recipients. MEASUREMENTS: Descriptive statistics, comparison of recipients who enrolled or not and qualitative content analysis of text messages. INTERVENTION: A breastfeeding peer counselor texting program entitled BfedDC involving routine 1-way programmed messages and 2-way texting capacity for recipients to engage with peer counselors. RESULTS: Among our sample (n = 1642), nearly 90% initiated breastfeeding. A total of 18.5% (n = 304) enrolled in the BfedDC texting program, of whom 19.7% (n = 60) utilized the 2-way texting feature. Message content covered seven content themes and included inquiries about expressing human milk, breastfeeding difficulties, breastfeeding frequency and duration, appointments and more. CONCLUSIONS: Although enrollment was relatively low in BfedDC, benefits included 1-way supportive texts for breastfeeding and the ability to 2-way text with peer counselors. This program aligns with the Surgeon General's Call to Action to Support Breastfeeding and promotes breastfeeding equity in low-income people.

Moving the Needle: A Qualitative Exploration of the School Nurses' Experience with Virginia's Human Papillomavirus Mandate.

In all US localities, students provide proof of compliance with vaccination requirements to attend school. Despite benefits, vaccine legislation remains contentious. The human papillomavirus (HPV) vaccine is recommended for adolescents and prevents cancer, but its inclusion in school immunization requirements is challenged. Virginia was the first state to mandate HPV vaccination. HPV is the only required vaccine in VA that allows caregivers to elect out. School nurses are trusted members of communities and enforce vaccine compliance. This study aims to understand Virginia school nurses' practice in implementing the HPV vaccine mandate through the exploration of their subjective experiences. Semi-structured interviews were conducted. Thematic analysis using the socioecological model guided data analysis. Factors that influence nursing practice were identified at all socioecological model levels The data from this study is intended to provide an understanding of school nursing practice so that interventions to improve HPV vaccination rates can be developed.

Vaccine Attitudes and Uptake Among Latino Residents of a Former COVID-19 Hotspot.

Early in the COVID-19 pandemic, the Centers for Disease Control and Prevention identified Prince William County (PWC), Va. as a hotspot with a high disease rate among Latinos. This study uses spatial, survey, and qualitative data to understand attitudes towards vaccine uptake among PWC Latinos. A quantitative analysis (n=266) estimates the association for vaccine acceptance among Latinos. Next, qualitative interviews with Latinos (n=37) examine vaccine attitudes. Finally, a spatial analysis identifies clusters of social vulnerability and low vaccine uptake in PWC and adjacent counties. Our findings show that a substantial proportion of PWC Latinos had low vaccination rates as of December 2022, two years after the vaccine's release. Side effects and safety and approval concerns were cited in both the quantitative and qualitative studies. Persistent vaccine disparities are concerning given the high hospitalization and mortality rates that prevailed among Latinos early in the pandemic.

The Impact of COVID-19 on Breastfeeding Initiation and Duration in a Low-Income Population, Washington, DC.

Objective: To assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on breastfeeding initiation (BFI) and duration among women enrolled in the Special Supplemental Nutrition Program for women, infants, and children (WIC) in Washington District of Columbia (DC). Materials and Methods: We used WIC program data from Washington DC to assess the pandemic's impact on BFI and duration among WIC recipients. t-Tests and unadjusted odds ratios compared breastfeeding outcomes before and during the pandemic. Multivariable logistic and linear regression models estimated the pandemic's impact on initiation and duration, respectively, while controlling for social determinants of health and other factors. Results: BFI was similar among women who gave birth before (61.4%) or during the pandemic (60.4%) (p = 0.359). However, the proportion of women who breastfed at 1 month decreased significantly from 56.1% (before pandemic) to 47.6% (during pandemic) (p < 0.0001). This pattern for duration continued at 3 and 6 months: 46.9% to 37.1% (p < 0.0001) at 3 months and 34.8% to 25.7% (p < 0.0001) at 6 months. On average, women who delivered during the pandemic breastfed 33.9 fewer days than those who delivered before (p < 0.0001). Conclusions: BFI among DC WIC recipients was similar for infants born before or during the pandemic, and determinants of initiation remained similar to previous reports (e.g., race/ethnicity, education). However, for women who initiated breastfeeding, average duration was significantly lower for infants born during the pandemic than before. Our findings suggest the importance of leveraging WIC and other breastfeeding supports to promote breastfeeding during pandemics and other emergencies.

Association Between Firearm Purchasing in Response to the COVID-19 Pandemic and Symptoms of Anxiety, Depression, and Stress, August 2021.

Introduction: Firearm sales and firearm-related injuries and deaths increased during the pandemic. Little is known about motivations for firearm purchasing in response to the COVID-19 pandemic and the mental health status of first-time purchasers. The purpose of this study was to estimate the association between firearm purchasing in response to the COVID-19 pandemic and anxiety symptoms, depressive symptoms, and stress. Methods: The authors analyzed data from a survey that approximated a nationally representative sample of American adults (N=3,528) who either did not own firearms (n=2,327) or purchased firearms for the first time in response to the pandemic (n=240). Self-reported stress, depressive symptoms, and anxiety symptoms were determined using standardized self-assessment questionnaires (Perceived Stress Scale, Patient Health Questionnaire, and Depression Anxiety Stress Scale, respectively). Using multivariable logistic regression, the association between firearm purchasing and each mental health measure were assessed after controlling for demographics and other determinants. Results: In each model, first-time firearm owners were more likely to be younger, live in urban areas, believe the government does too much for its citizens, stay home, stock up on items, and keep their children at home. First-time owners had significantly higher odds of anxiety and depressive symptoms than non-owners (AOR=1.05; 95% CI=1.04, 1.07 and AOR=1.15; 95% CI=1.04, 1.26, respectively). Conclusions: First-time firearm purchasers report higher depressive and anxiety symptoms than non-owners, suggesting that there may be a risk of suicide and other related firearm violence.

School Nurses' Experiences and Roles in Promoting and Administering the HPV Vaccine: A Systematic Review Using the Socioecological Framework.

Understanding the school nurse's experience in human papillomavirus (HPV) vaccine promotion can reduce vaccine disparities. HPV vaccination is critical to cancer prevention. Despite the importance of the school nurse in vaccine promotion, there is a lack of understanding. This article aims to examine the knowledge, attitude, experience, and role of school nurses related to HPV vaccination and promotion in school settings. A systematic search for school nurses and their experiences related to HPV vaccination was conducted. A thematic synthesis was undertaken using the socioecological model. This review highlights the complexity of HPV vaccine promotion in schools over time. Multilevel factors impact nursing practice. Nurses have good vaccine knowledge and positive attitudes. Poor workflow processes, competing demands, and vaccine communication challenge school nurses. The themes that were synthesized informed the LEADS model. With the support of school nurses, reaching the goal of eliminating cervical cancer as a public health problem is possible.

Assessing Disparities in Cervical Cancer Screening with Pap Test by Disability Types.

Prior research has found that women with disabilities have often experienced disparities in receipt of cervical cancer screening. However, there is a research gap regarding receipt of cervical cancer screening by types of disability. This study examined the differences in receiving cervical cancer screening through self-reported Pap testing among women by disability type. This cross-sectional study analyzed data from the 2016, 2018, and 2020 Behavioral Risk Factor and Surveillance System (BRFSS). The relative risk of cervical cancer screening through self-reported Pap tests received within the past three years among women aged 21-65 by disability type was compared using modified Poisson regression with robust error variance. A total of 307,142 women from across the USA were sampled. In every disability group, older women were significantly less likely to receive Pap tests than their counterparts without disabilities. Women with multiple disabilities (aRR=0.91; 95% CI, 0.89-0.94) and those with ambulatory disabilities (aRR=0.93; 95% CI, 0.91-0.97) reported being less likely to receive Pap tests than women with no disability. Ambulatory disability and multiple disabilities are associated with a lower likelihood of cervical cancer screening with Pap test, increasing the need to eliminate disability-specific disparities in Pap testing. Future efforts should focus on improving cancer education programs tailored to the needs of women with disabilities, addressing barriers related to mobility and access to healthcare services, and ensuring equitable access to preventive screenings.

A Multilevel Analysis of School-Level Human Papillomavirus Vaccination Coverage in Virginia.

School nurses improve public health through vaccine promotion and mandate compliance. Despite recommendations and support for comprehensive adolescent HPV vaccination from organizations like the National Association of School Nurses as of 2023 only Virginia, Washington DC, Rhode Island, and Hawaii mandate HPV vaccine in schools. Virginia's mandate allows caregivers to opt out of vaccination. It is important to consider how school-level vaccine compliance is associated with school and community factors. A multilevel analysis explored the association between school and county-level factors and HPV vaccination rates. This study shows schools that report higher rates of economically disadvantaged students had higher HPV vaccine coverage. HPV vaccine rates increased from 2019 to 2021 after the implementation of a gender-inclusive mandate. Virginia HPV rates still lag behind states with no mandate. The results suggest that school nursing practice related to HPV vaccine compliance may be impacted by community factors like economic status.

Exploring the influence of behavioral factors on depression and anxiety scores during the COVID-19 pandemic: insights from the Virginia statewide COVIDsmart longitudinal study.

BACKGROUND: Amidst the COVID-19 pandemic, there has been growing concern about the declining mental health and healthy behaviors compared to pre-pandemic levels. Despite this, there is a lack of longitudinal studies that have examined the relationship between health behaviors and mental health during the pandemic. In response, the statewide COVIDsmart longitudinal study was launched. The study's main objective is to better understand the effects of the pandemic on mental health. Findings may provide a foundation for the identification of public health strategies to mitigate future negative impacts of the pandemic. METHODS: Following online recruitment in spring of 2021, adults, ages 18 to 87, filled out social, mental, economic, occupational, and physical health questionnaires on the digital COVIDsmart platform at baseline and through six monthly follow-ups. Changes in the participant's four health behaviors (e.g., tobacco and alcohol consumption, physical activity, and social media use), along with sex, age, loneliness score, and reported social and economic (SE) hardships, were analyzed for within-between group associations with depression and anxiety scores using Mixed Models Repeated Measures. RESULTS: In this study, of the 669 individuals who reported, the within-between group analysis indicated that younger adults (F = 23.81, p < 0.0001), loneliness (F = 234.60, p < 0.0001), SE hardships (F = 31.25, p < 0.0001), increased tobacco use (F = 3.05, p = 0.036), decreased physical activity (F = 6.88, p = 0.0002), and both positive and negative changes in social media use (F = 7.22, p = 0.0001) were significantly associated with worse depression scores. Additionally, females (F = 6.01, p = 0.015), younger adults (F = 32.30, p < 0.0001), loneliness (F = 154.59, p < 0.0001), SE hardships (F = 22.13, p < 0.0001), increased tobacco use (F = 4.87, p = 0.004), and both positive and negative changes in social media use (F = 3.51, p = 0.016) were significantly associated with worse anxiety scores. However, no significant changes were observed in the within-between group measurements of depression and anxiety scores over time (p > 0.05). Physical activity was not associated with anxiety nor was alcohol consumption with both depression and anxiety (p > 0.05). CONCLUSIONS: This study demonstrates the longitudinal changes in behaviors within the context of the COVID-19 pandemic. These findings may facilitate the design of preventative population-based health approaches during the COVID-19 pandemic or future pandemics.

Depression, Anxiety, Energy, and Fatigue Among Nurses Who Cared for Patients During the COVID-19 Pandemic: A Cross-Sectional Study.

Purpose of Study: Nurses around the world have faced challenges during the coronavirus disease 2019 (COVID-19) pandemic. This study examined the association between depression and anxiety and trait energy and trait fatigue, and baseline health status and work characteristics. Design of Study: A cross-sectional study. Methods: A survey was conducted to collect self-reported data from nurses involved in patient care in Northern Virginia. Depression and anxiety were assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) depression and anxiety scales. To measure trait energy and trait fatigue, the Mental and Physical State and Trait Energy and Fatigue Scale (MPSTEFS) was used. Findings: There was a significant association between depression and energy (b=-0.46, t = -1.78, p < .001) and loneliness (b=1.38, t = 4.00, p < .001) and increased alcohol use (b=2.11, t = 2.04, p = .045). We also found that nurses with depression were significantly more likely to seek mental health counseling (b=-2.91, t = 2.54, p = 0.013), which was also the case for anxiety (b=3.13, t = 2.14, p = .036). Conclusions: Our study highlights the mental health burden among nurses who worked in the early phase of the COVID-19 pandemic and its association with increased alcohol use and loneliness. The findings may help healthcare leaders identify early signals of deterioration in nurses' well-being.

Leptospirosis Risk among Occupational Groups in Brazil, 2010-2015.

Leptospirosis is a zoonotic disease that is primarily transmitted through close contact with contaminated environments or infected animals. Brazil has the highest number of reported cases of leptospirosis in the Americas (approximately 4,000 annual cases). The purpose of this study is to identify the occupational groups with a higher risk of leptospirosis in Brazil from 2010 through 2015 among suspected cases reported to the national surveillance system. Confirmed and unconfirmed cases of leptospirosis with laboratory diagnosis, 20,193 and 59,034 respectively, were classified into 12 occupational groups. Confirmed cases were predominantly male (79.4%), between 25 and 59 years of age (68.3%), white (53.4%), illiterate or with incomplete primary education (51.1%), and participating in agricultural work (19.9%). After controlling for age, sex, race, and area of residency, the multivariate analysis identified that between confirmed and unconfirmed cases of leptospirosis reported to the Brazilian national surveillance system, five occupational groups are at higher risk for leptospirosis: garbage and recycling collectors (odds ratio [OR] = 4.10; 95% CI = 3.36-4.99); agricultural, forestry, and fishery workers (OR = 1.65; 95% CI = 1.49-1.84); prisoners (OR = 1.56; 95% CI = 1.04-2.35); building workers (OR = 1.36; 95% CI = 1.22-1.51); cleaners and mining workers (OR = 1.25; 95% CI = 1.07-1.45). This is the first nationwide study to examine leptospirosis risk by occupational group in Brazil using national surveillance data. Our results suggest that among suspected cases there was an increased risk among occupational groups with low income and low educational levels.

Adaptation and Utilization of a Postmarket Evaluation Model for Digital Contact Tracing Mobile Health Tools in the United States: Observational Cross-sectional Study.

BACKGROUND: Case investigation and contact tracing are core public health activities used to interrupt disease transmission. These activities are traditionally conducted manually. During periods of high COVID-19 incidence, US health departments were unable to scale up case management staff to deliver effective and timely contact-tracing services. In response, digital contact tracing (DCT) apps for mobile phones were introduced to automate these activities. DCT apps detect when other DCT users are close enough to transmit COVID-19 and enable alerts to notify users of potential disease exposure. These apps were deployed quickly during the pandemic without an opportunity to conduct experiments to determine effectiveness. However, it is unclear whether these apps can effectively supplement understaffed manual contact tracers. OBJECTIVE: The aims of this study were to (1) evaluate the effectiveness of COVID-19 DCT apps deployed in the United States during the COVID-19 pandemic and (2) determine if there is sufficient DCT adoption and interest in adoption to meet a minimum population use rate to be effective (56%). To assess uptake, interest and safe use covariates were derived from evaluating DCTs using the American Psychological Association App Evaluation Model (AEM) framework. METHODS: We analyzed data from a nationally representative survey of US adults about their COVID-19-related behaviors and experiences. Survey respondents were divided into three segments: those who adopted a DCT app, those who are interested but did not adopt, and those not interested. Descriptive statistics were used to characterize factors of the three groups. Multivariable logistic regression models were used to analyze the characteristics of segments adopting and interested in DCT apps against AEM framework covariates. RESULTS: An insufficient percentage of the population adopted or was interested in DCTs to achieve our minimum national target effectiveness rate (56%). A total of 17.4% (n=490) of the study population reported adopting a DCT app, 24.7% (n=697) reported interest, and 58.0% (n=1637) were not interested. Younger, high-income, and uninsured individuals were more likely to adopt a DCT app. In contrast, people in fair to poor health were interested in DCT apps but did not adopt them. App adoption was positively associated with visiting friends and family outside the home (odds ratio [OR] 1.63, 95% CI 1.28-2.09), not wearing masks (OR 0.52, 95% CI 0.38-0.71), and adopters thinking they have or had COVID-19 (OR 1.60, 95% CI 1.21-2.12). CONCLUSIONS: Overall, a small percentage of the population adopted DCT apps. These apps may not be effective in protecting adopters' friends and family from their maskless contacts outside the home given low adoption rates. The public health community should account for safe use behavioral factors in future public health contact-tracing app design. The AEM framework was useful in developing a study design to evaluate DCT effectiveness and safety.

A Digital Health Initiative (COVIDsmart) for Remote Data Collection and Study of COVID-19's Impact on the State of Virginia: Prospective Cohort Study.

BACKGROUND: The COVID-19 pandemic has affected people's lives beyond severe and long-term physical health symptoms. Social distancing and quarantine have led to adverse mental health outcomes. COVID-19-induced economic setbacks have also likely exacerbated the psychological distress affecting broader aspects of physical and mental well-being. Remote digital health studies can provide information about the pandemic's socioeconomic, mental, and physical impact. COVIDsmart was a collaborative effort to deploy a complex digital health research study to understand the impact of the pandemic on diverse populations. We describe how digital tools were used to capture the effects of the pandemic on the overall well-being of diverse communities across large geographical areas within the state of Virginia. OBJECTIVE: The aim is to describe the digital recruitment strategies and data collection tools applied in the COVIDsmart study and share the preliminary study results. METHODS: COVIDsmart conducted digital recruitment, e-Consent, and survey collection through a Health Insurance Portability and Accountability Act-compliant digital health platform. This is an alternative to the traditional in-person recruitment and onboarding method used for studies. Participants in Virginia were actively recruited over 3 months using widespread digital marketing strategies. Six months of data were collected remotely on participant demographics, COVID-19 clinical parameters, health perceptions, mental and physical health, resilience, vaccination status, education or work functioning, social or family functioning, and economic impact. Data were collected using validated questionnaires or surveys, completed in a cyclical fashion and reviewed by an expert panel. To retain a high level of engagement throughout the study, participants were incentivized to stay enrolled and complete more surveys to further their chances of receiving a monthly gift card and one of multiple grand prizes. RESULTS: Virtual recruitment demonstrated relatively high rates of interest in Virginia (N=3737), and 782 (21.1%) consented to participate in the study. The most successful recruitment technique was the effective use of newsletters or emails (n=326, 41.7%). The primary reason for contributing as a study participant was advancing research (n=625, 79.9%), followed by the need to give back to their community (n=507, 64.8%). Incentives were only reported as a reason among 21% (n=164) of the consented participants. Overall, the primary reason for contributing as a study participant was attributed to altruism at 88.6% (n=693). CONCLUSIONS: The COVID-19 pandemic has accelerated the need for digital transformation in research. COVIDsmart is a statewide prospective cohort to study the impact of COVID-19 on Virginians' social, physical, and mental health. The study design, project management, and collaborative efforts led to the development of effective digital recruitment, enrollment, and data collection strategies to evaluate the pandemic's effects on a large, diverse population. These findings may inform effective recruitment techniques across diverse communities and participants' interest in remote digital health studies.

Guidelines for Triage of COVID-19 Patients Presenting With Multisystemic Symptoms.

BACKGROUND AND OBJECTIVES: This article describes how multisystemic symptoms, both respiratory and nonrespiratory, can be used to differentiate coronavirus disease-2019 (COVID-19) from other diseases at the point of patient triage in the community. The article also shows how combinations of symptoms could be used to predict the probability of a patient having COVID-19. METHODS: We first used a scoping literature review to identify symptoms of COVID-19 reported during the first year of the global pandemic. We then surveyed individuals with reported symptoms and recent reverse transcription polymerase chain reaction (RT-PCR) test results to assess the accuracy of diagnosing COVID-19 from reported symptoms. The scoping literature review, which included 81 scientific articles published by February 2021, identified 7 respiratory, 9 neurological, 4 gastrointestinal, 4 inflammatory, and 5 general symptoms associated with COVID-19 diagnosis. The likelihood ratio associated with each symptom was estimated from sensitivity and specificity of symptoms reported in the literature. A total of 483 individuals were then surveyed to validate the accuracy of predicting COVID-19 diagnosis based on patient symptoms using the likelihood ratios calculated from the literature review. Survey results were weighted to reflect age, gender, and race of the US population. The accuracy of predicting COVID-19 diagnosis from patient-reported symptoms was assessed using area under the receiver operating curve (AROC). RESULTS: In the community, cough, sore throat, runny nose, dyspnea, and hypoxia, by themselves, were not good predictors of COVID-19 diagnosis. A combination of cough and fever was also a poor predictor of COVID-19 diagnosis (AROC = 0.56). The accuracy of diagnosing COVID-19 based on symptoms was highest when individuals presented with symptoms from different body systems (AROC of 0.74-0.81); the lowest accuracy was when individuals presented with only respiratory symptoms (AROC = 0.48). CONCLUSIONS: There are no simple rules that clinicians can use to diagnose COVID-19 in the community when diagnostic tests are unavailable or untimely. However, triage of patients to appropriate care and treatment can be improved by reviewing the combinations of certain types of symptoms across body systems.

Combined Symptom Screening and At-Home Tests for COVID-19.

BACKGROUND AND OBJECTIVE: At-home rapid antigen tests provide a convenient and expedited resource to learn about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection status. However, low sensitivity of at-home antigen tests presents a challenge. This study examines the accuracy of at-home tests, when combined with computer-facilitated symptom screening. METHODS: The study used primary data sources with data collected during 2 phases at different periods (phase 1 and phase 2): one during the period in which the alpha variant of SARS-CoV-2 was predominant in the United States and another during the surge of the delta variant. Four hundred sixty-one study participants were included in the analyses from phase 1 and 374 subjects from phase 2. Phase 1 data were used to develop a computerized symptom screening tool, using ordinary logistic regression with interaction terms, which predicted coronavirus disease-2019 (COVID-19) reverse transcription polymerase chain reaction (RT-PCR) test results. Phase 2 data were used to validate the accuracy of predicting COVID-19 diagnosis with (1) computerized symptom screening; (2) at-home rapid antigen testing; (3) the combination of both screening methods; and (4) the combination of symptom screening and vaccination status. The McFadden pseudo-R2 was used as a measure of percentage of variation in RT-PCR test results explained by the various screening methods. RESULTS: The McFadden pseudo-R2 for the first at-home test, the second at-home test, and computerized symptom screening was 0.274, 0.140, and 0.158, respectively. Scores between 0.2 and 0.4 indicated moderate levels of accuracy. The first at-home test had low sensitivity (0.587) and high specificity (0.989). Adding a second at-home test did not improve the sensitivity of the first test. Computerized symptom screening improved the accuracy of the first at-home test (added 0.131 points to sensitivity and 6.9% to pseudo-R2 of the first at-home test). Computerized symptom screening and vaccination status was the most accurate method to screen patients for COVID-19 or an active infection with SARS-CoV-2 in the community (pseudo-R2 = 0.476). CONCLUSION: Computerized symptom screening could either improve, or in some situations, replace at-home antigen tests for those individuals experiencing COVID-19 symptoms.

Order of Occurrence of COVID-19 Symptoms.

BACKGROUND AND OBJECTIVES: COVID-19 symptoms change after onset-some show early, others later. This article examines whether the order of occurrence of symptoms can improve diagnosis of COVID-19 before test results are available. METHODS: In total, 483 individuals who completed a COVID-19 test were recruited through Listservs. Participants then completed an online survey regarding their symptoms and test results. The order of symptoms was set according to (a) whether the participant had a "history of the symptom" due to a prior condition; and (b) whether the symptom "occurred first," or prior to, other symptoms of COVID-19. Two LASSO (Least Absolute Shrinkage and Selection Operator) regression models were developed. The first model, referred to as "time-invariant," used demographics and symptoms but not the order of symptom occurrence. The second model, referred to as "time-sensitive," used the same data set but included the order of symptom occurrence. RESULTS: The average cross-validated area under the receiver operating characteristic (AROC) curve for the time-invariant model was 0.784. The time-sensitive model had an AROC curve of 0.799. The difference between the 2 accuracy levels was statistically significant (α < .05). CONCLUSION: The order of symptom occurrence made a statistically significant, but small, improvement in the accuracy of the diagnosis of COVID-19.

The Role of Symptom Clusters in Triage of COVID-19 Patients.

BACKGROUND AND OBJECTIVE: COVID-19 manifests with a broad range of symptoms. This study investigates whether clusters of respiratory, gastrointestinal, or neurological symptoms can be used to diagnose COVID-19. METHODS: We surveyed symptoms of 483 subjects who had completed COVID-19 laboratory tests in the last 30 days. The survey collected data on demographic characteristics, self-reported symptoms for different types of infections within 14 days of onset of illness, and self-reported COVID-19 test results. Robust LASSO regression was used to create 3 nested models. In all 3 models, the response variable was the COVID-19 test result. In the first model, referred to as the "main effect model," the independent variables were demographic characteristics, history of chronic symptoms, and current symptoms. The second model, referred to as the "hierarchical clustering model," added clusters of variables to the list of independent variables. These clusters were established through hierarchical clustering. The third model, referred to as the "interaction-terms model," also added clusters of variables to the list of independent variables; this time clusters were established through pairwise and triple-way interaction terms. Models were constructed on a randomly selected 80% of the data and accuracy was cross-validated on the remaining 20% of the data. The process was bootstrapped 30 times. Accuracy of the 3 models was measured using the average of the cross-validated area under the receiver operating characteristic curves (AUROCs). RESULTS: In 30 bootstrap samples, the main effect model had an AUROC of 0.78. The hierarchical clustering model had an AUROC of 0.80. The interaction-terms model had an AUROC of 0.81. Both the hierarchical cluster model and the interaction model were significantly different from the main effect model (α = .04). Patients with different races/ethnicities, genders, and ages presented with different symptom clusters. CONCLUSIONS: Using clusters of symptoms, it is possible to more accurately diagnose COVID-19 among symptomatic patients.

Disparities in Breastfeeding Initiation Among African American and Black Immigrant WIC Recipients in the District of Columbia, 2007-2019.

Objectives. To estimate differences in breastfeeding initiation (BFI) rates between African Americans and Black immigrants enrolled in the District of Columbia Special Supplemental Nutrition Program for Women, Infants and Children (WIC) between 2007 and 2019. Methods. We used data collected as part of routine WIC program activities for first-time mothers (n = 38 142). Using multivariable logistic regression models, we identified determinants of BFI for African Americans, Black immigrants, non-Hispanic Whites, and Hispanics. To assess the trend in BFI over time, we calculated the average of the annual percentage changes. Results. Compared with African Americans, Black immigrants had a 2.7-fold higher prevalence and Hispanics had a 5.8-fold higher prevalence of BFI. The average of the annual percentage changes was 0.85 for Hispanics, 3.44 for Black immigrants, 4.40 for Non-Hispanic Whites, and 4.40 for African Americans. African Americans had the only statistically significant change (P < .05). Disparities in BFI persisted over the study period, with African Americans demonstrating the lowest rates each year. Conclusions. Significant differences exist in BFI between Black immigrants and African Americans. Combining African Americans and Black immigrants masks important differences, overestimates rates among African Americans, and may lead to missed opportunities for targeting interventions and policies to improve breastfeeding. (Am J Public Health. 2022;112(4):671-674. https://doi.org/10.2105/AJPH.2021.306652).

Modeling the Probability of COVID-19 Based on Symptom Screening and Prevalence of Influenza and Influenza-Like Illnesses.

BACKGROUND: The importance of various patient-reported signs and symptoms to the diagnosis of coronavirus disease 2019 (COVID-19) changes during, and outside, of the flu season. None of the current published studies, which focus on diagnosis of COVID-19, have taken this seasonality into account. OBJECTIVE: To develop predictive algorithm, which estimates the probability of having COVID-19 based on symptoms, and which incorporates the seasonality and prevalence of influenza and influenza-like illness data. METHODS: Differential diagnosis of COVID-19 and influenza relies on demographic characteristics (age, race, and gender), and respiratory (eg, fever, cough, and runny nose), gastrointestinal (eg, diarrhea, nausea, and loss of appetite), and neurological (eg, anosmia and headache) signs and symptoms. The analysis was based on the symptoms reported by COVID-19 patients, 774 patients in China and 273 patients in the United States. The analysis also included 2885 influenza and 884 influenza-like illnesses in US patients. Accuracy of the predictions was calculated using the average area under the receiver operating characteristic (AROC) curves. RESULTS: The likelihood ratio for symptoms, such as cough, depended on the flu season-sometimes indicating COVID-19 and other times indicating the reverse. In 30-fold cross-validated data, the symptoms accurately predicted COVID-19 (AROC of 0.79), showing that symptoms can be used to screen patients in the community and prior to testing. CONCLUSION: Community-based health care providers should follow different signs and symptoms for diagnosing COVID-19 during, and outside of, influenza season.

Differential diagnosis of COVID-19 and influenza.

This study uses two existing data sources to examine how patients' symptoms can be used to differentiate COVID-19 from other respiratory diseases. One dataset consisted of 839,288 laboratory-confirmed, symptomatic, COVID-19 positive cases reported to the Centers for Disease Control and Prevention (CDC) from March 1, 2019, to September 30, 2020. The second dataset provided the controls and included 1,814 laboratory-confirmed influenza positive, symptomatic cases, and 812 cases with symptomatic influenza-like-illnesses. The controls were reported to the Influenza Research Database of the National Institute of Allergy and Infectious Diseases (NIAID) between January 1, 2000, and December 30, 2018. Data were analyzed using case-control study design. The comparisons were done using 45 scenarios, with each scenario making different assumptions regarding prevalence of COVID-19 (2%, 4%, and 6%), influenza (0.01%, 3%, 6%, 9%, 12%) and influenza-like-illnesses (1%, 3.5% and 7%). For each scenario, a logistic regression model was used to predict COVID-19 from 2 demographic variables (age, gender) and 10 symptoms (cough, fever, chills, diarrhea, nausea and vomiting, shortness of breath, runny nose, sore throat, myalgia, and headache). The 5-fold cross-validated Area under the Receiver Operating Curves (AROC) was used to report the accuracy of these regression models. The value of various symptoms in differentiating COVID-19 from influenza depended on a variety of factors, including (1) prevalence of pathogens that cause COVID-19, influenza, and influenza-like-illness; (2) age of the patient, and (3) presence of other symptoms. The model that relied on 5-way combination of symptoms and demographic variables, age and gender, had a cross-validated AROC of 90%, suggesting that it could accurately differentiate influenza from COVID-19. This model, however, is too complex to be used in clinical practice without relying on computer-based decision aid. Study results encourage development of web-based, stand-alone, artificial Intelligence model that can interview patients and help clinicians make quarantine and triage decisions.