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Prior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods. We pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide prevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection. Among 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV <50.0%. The individual symptoms most greatly associated with SARS-CoV-2 positivity were fever (OR = 5.34, p<0.001), anosmia (OR = 4.08, p<0.001), ageusia (OR = 2.38, p = 0.006), and cough (OR = 2.86, p<0.001). Results from EFA identified two primary symptom clusters most associated with SARS-CoV-2 infection: (1) ageusia, anosmia, and fever; and (2) shortness of breath, cough, and chest pain. Moreover, being non-white (13.6% vs. 2.3%, p<0.001), Hispanic (27.9% vs. 2.5%, p<0.001), or living in an Urban area (5.4% vs. 3.8%, p<0.001) was associated with infection. Symptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials.

Early studies on COVID-19 identified unequal patterns in hospitalization and mortality in urban environments for racial and ethnic minorities. These studies were primarily single center observational studies conducted within the first few weeks or months of the pandemic. We sought to examine trends in COVID-19 morbidity, hospitalization, and mortality over time for minority and rural populations, especially during the U.S. fall surge. Data were extracted from a statewide cohort of all adult residents in Indiana tested for SARS-CoV-2 infection between March 1 and December 31, 2020, linked to electronic health records. Primary measures were per capita rates of infection, hospitalization, and death. Age adjusted rates were calculated for multiple time periods corresponding to public health mitigation efforts. Comparisons across time within groups were compared using ANOVA. Morbidity and mortality increased over time with notable differences among sub-populations. Initially, hospitalization rates among racial minorities were 3-4 times higher than whites, and mortality rates among urban residents were twice those of rural residents. By fall 2020, hospitalization and mortality rates in rural areas surpassed those of urban areas, and gaps between black/brown and white populations narrowed. Changes across time among demographic groups was significant for morbidity and hospitalization. Cumulative morbidity and mortality were highest among minority groups and in rural communities. The synchronicity of disparities in COVID-19 by race and geography suggests that health officials should explicitly measure disparities and adjust mitigation as well as vaccination strategies to protect those sub-populations with greater disease burden.

Background Public health surveillance requires timely access to actionable data at every level. Current approaches for accessing chronic disease surveillance data are not sufficient, and health departments are increasingly looking to augment surveillance efforts using electronic health records (EHRs). While proven effective for acute syndromic surveillance, the utilization of EHR systems and health data networks for monitoring chronic conditions remains sparse. This study tested the generalizability of a previously validated hypertension computable phenotype. Methods A previously developed phenotype was used to estimate prevalence of hypertension in a geographically and clinically distinct region from its development. To test validity, the results were compared to available, statewide Behavioral Risk Factor Surveillance System (BRFSS) data using the two one-sided t-test (TOST) of equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race/ethnicity. Results Compared to statewide hypertension prevalence of 34.5% in the BRFSS, an EHR-based phenotype estimated an overall prevalence of 24.1%. Estimates were not equivalent overall or across most subpopulations. Like BRFSS, we observed higher prevalence among Black men and women as well as increasing prevalence with age. Conclusion With caveats, this study demonstrates that EHR-derived prevalence estimates may serve as a complement for population-based survey estimates. Utilizing available EHR data should increase timeliness of surveillance as well as enhance the ability of states and local health agencies to more readily address the burden of chronic disease in their respective jurisdictions.

Background: Despite the popularity of maternal and infant health mobile apps, ongoing consumer engagement and sustained app use remain barriers. Few studies have examined user experiences or perceived benefits of maternal and infant health app use from consumer perspectives. Objective: This study aims to assess users’ self-reported experiences with maternal and infant health apps, perceived benefits, and general feedback by analyzing publicly available user reviews on two popular app stores—Apple App Store and Google Play Store. Methods: We conducted a qualitative assessment of publicly available user reviews (N=2422) sampled from 75 maternal and infant health apps designed to provide health education or decision-making support to pregnant women or parents and caregivers of infants. The reviews were coded and analyzed using a general inductive qualitative content analysis approach. Results: The three major themes included the following: app functionality, where users discussed app features and functions; technical aspects, where users talked about technology-based aspects of an app; and app content, where users specifically focused on the app content and the information it provides. The six minor themes included the following: patterns of use, where users highlighted the frequency and type of use; social support, where users talked about receiving social support from friends, family and community of other users; app cost, where users talked about the cost of an app within the context of being cost-effective or a potential waste of money; app comparisons, where users compared one app with others available in app stores; assistance in health care, where users specifically highlighted the role of an app in offering clinical assistance; and customer care support, where users specifically talked about their interaction with the app customer care support team. Conclusions: Users generally tend to value apps that are of low cost and preferably free, with high-quality content, superior features, enhanced technical aspects, and user-friendly interfaces. Users also find app developer responsiveness to be integral, as it offers them an opportunity to engage in the app development and delivery process. These findings may be beneficial for app developers in designing better apps, as no best practice guidelines currently exist for the app environment.

Background Motivated by the Triple Aim, US health care policy is expanding its focus from individual patient care to include population health management. Health Information Exchanges are positioned to play an important role in that expansion. Objective The objective is to describe the evolution of the Indiana Network for Patient Care (INPC) and discuss examples of its innovations that support both population health and clinical applications. Methods A descriptive analytical approach was used to gather information on the INPC. This included a literature review of recent systematic and scoping reviews, collection of research that used INPC data as a resource, and data abstracted by Regenstrief Data Services to understand the breadth of uses for the INPC as a data resource. Results Although INPC data are primarily gathered from and used in healthcare settings, their use for population health management and research has increased. By December 2023, the INPC contained nearly 25 million patients, a significant growth from 3.5 million in 2004. This growth was a result of the use of INPC data for population health surveillance, clinical applications for data, disease registries, Patient-Centered Data Homes, non-clinical population health advancements, and accountable care organization connections with Health Information Exchanges. Conclusion By structuring services on the fundamental building blocks, expanding the focus to population health, and ensuring value in the services provided to the stakeholders, Health Information Exchanges are uniquely positioned to support both population health and clinical applications.

Background Similar to other nations, hypertension control is a challenge for public health in South Africa. To control hypertension, the condition must be screened and diagnosed in the public health system. This study examines how the prevalence of undiagnosed hypertension is influenced by various risk factors including demographic, socioeconomic, environmental, and behavioural factors, with primary focus on self-perceived health well-being and time since the last healthcare consultation. Methods The study utilized data from the National Income Dynamics Study (NiDS), a panel survey conducted approximately every two years between 2008 and 2017. Participants aged 15 years and older with at least two blood pressure measurements were included. The sample included 15,712 unique individuals (60.0% women) and 31,340 observations from the five survey waves (5,357 in 2008; 5,206 in 2010/11; 6,385 in 2012; 7,060 in 2014/15; and 7,332 in 2017). Both descriptive and analytical methods were used to study undiagnosed hypertension. Multilevel logistic regression was used to explore the relationship between undiagnosed hypertension with various risk factors including perceived health well-being and time since last health consultation. Results The study showed that in 2008 (wave 1) and in 2017 (wave 5), approximately 53.5% and 42.6% respectively, of individuals aged 15 and above were unaware they could be hypertensive. Those who perceived their health as very good or excellent were respectively 2.15, 95% CI [1.89–2.44] and 3.29, 95% CI [2.85–3.80] times more likely to have undiagnosed hypertension, compared to those who perceived their health as fair or poor. Additionally, the risk of having undiagnosed hypertension increased gradually with time since last consultation on one’s health. Those whose last consultation was more than two years ago were 12.10, 95% CI [10.50–13.90] times more likely to have undiagnosed hypertension compared to those who had consulted within the last 30 days. Conclusion Given that hypertension is often asymptomatic, self-perceived health can be misleading. This underscores the need for health programs to emphasize the importance of regular blood pressure checkups. Regular consultations with healthcare providers are crucial for early detection and management of hypertension.

Recognizing patient symptoms is fundamental to medicine, research, and public health. However, symptoms are often underreported in coded formats even though they are routinely documented in physician notes. Large language models (LLMs), noted for their generalizability, could help bridge this gap by mimicking the role of human expert chart reviewers for symptom identification. The primary objective of this multisite study was to measure the accurate identification of infectious respiratory disease symptoms using LLMs instructed to follow chart review guidelines. The secondary objective was to evaluate LLM generalizability in multisite settings without the need for site-specific training, fine-tuning, or customization. Four LLMs were evaluated: GPT-4, GPT-3.5, Llama2 70B, and Mixtral 8×7B. LLM prompts were instructed to take on the role of chart reviewers and follow symptom annotation guidelines when assessing physician notes. Ground truth labels for each note were annotated by subject matter experts. Optimal LLM prompting strategies were selected using a development corpus of 103 notes from the emergency department at Boston Children's Hospital. The performance of each LLM was measured using a test corpus with 202 notes from Boston Children's Hospital. The performance of an International Classification of Diseases, Tenth Revision (ICD-10)-based method was also measured as a baseline. Generalizability of the most performant LLM was then measured in a validation corpus of 308 notes from 21 emergency departments in the Indiana Health Information Exchange. Symptom identification accuracy was superior for every LLM tested for each infectious disease symptom compared to an ICD-10-based method (F1-score=45.1%). GPT-4 was the highest scoring (F1-score=91.4%; P<.001) and was significantly better than the ICD-10-based method, followed by GPT-3.5 (F1-score=90.0%; P<.001), Llama2 (F1-score=81.7%; P<.001), and Mixtral (F1-score=83.5%; P<.001). For the validation corpus, performance of the ICD-10-based method decreased (F1-score=26.9%), while GPT-4 increased (F1-score=94.0%), demonstrating better generalizability using GPT-4 (P<.001). LLMs significantly outperformed an ICD-10-based method for respiratory symptom identification in emergency department electronic health records. GPT-4 demonstrated the highest accuracy and generalizability, suggesting that LLMs may augment or replace traditional approaches. LLMs can be instructed to mimic human chart reviewers with high accuracy. Future work should assess broader symptom types and health care settings.

Background Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or nicotine consumption is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. Methods Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n  = 8214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in either April or June of 2020. Before testing, participants provided demographic information and self-reported health status and nicotine and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco or nicotine with SARS-CoV-2 positivity on either PCR or antibody tests. Results Compared to people with self-identified “excellent” or very good health status, those reporting “good” or “fair” health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. Conclusions In a statewide, community-based population drawn for SARS-CoV-2 testing, we find that overall health status was associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

Background Data are lacking regarding the risk of viral SARS-CoV-2 transmission during a large indoor sporting event involving fans utilizing a controlled environment. We sought to describe case characteristics, mitigation protocols used, variants detected, and secondary infections detected during the 2021 National Collegiate Athletic Association (NCAA) Men’s Basketball Tournament involving collegiate athletes from across the U.S. Methods This retrospective cohort study used data collected from March 16 to April 3, 2021, as part of a closed environment which required daily reverse transcription-polymerase chain reaction (RT-PCR) testing, social distancing, universal masking, and limited contact between tiers of participants. Nearly 3000 players, staff, and vendors participated in indoor, unmasked activities that involved direct exposure between cases and noninfected individuals. The main outcome of interest was transmission of SARS-CoV-2 virus, as measured by the number of new infections and variant(s) detected among positive cases. Secondary infections were identified through contact tracing by public health officials. Results Out of 2660 participants, 15 individuals (0.56%) screened positive for SARS-CoV-2. Four cases involved players or officials, and all cases were detected before any individual played in or officiated a game. Secondary transmissions all occurred outside the controlled environment. Among those disqualified from the tournament (4 cases; 26.7%), all individuals tested positive for the Iota variant (B.1.526). All other cases involved the Alpha variant (B.1.1.7). Nearly all teams ( N  = 58; 85.3%) reported that some individuals had received at least one dose of a vaccine. Overall, 17.9% of participants either had at least one dose of the vaccine or possessed documented infection within 90 days of the tournament. Conclusion In this retrospective cohort study of the 2021 NCAA Men’s Basketball Tournament closed environment, only a few cases were detected, and they were discovered in advance of potential exposure. These findings support the U.S. Centers for Disease Control and Prevention (CDC) guidelines for large indoor sporting events during the COVID-19 pandemic.

Timely surveillance of diabetes mellitus remains a challenge for public health agencies. In this study, researchers compared type 2 diabetes (T2D) prevalence estimates using electronic health record (EHR) data and computable phenotypes (CPs) as defined and applied by 2 independent networks. One network, Diabetes in Children, Adolescents, and Young Adults, was a research consortium, and the other, the Multi-State EHR-Based Network for Disease Surveillance, is a practice-based public health surveillance network. This study sought to determine the equivalence of T2D prevalence estimates generated by 2 distinct, yet conceptually related, CPs using EHR data. Each network used diagnostic, laboratory, and medication data for young adults (aged 18-44 years) extracted from the Indiana Network for Patient Care (INPC) to independently calculate prevalence of T2D using distinct CPs for the year 2022. The INPC is a statewide health information exchange that receives EHR data from multiple health care systems and supports public health use cases such as surveillance. The two one-sided tests method for independence with a predefined margin of -2.5 to +2.5 percentage points was used to compare the estimated prevalence as previously derived from the Multi-State EHR-Based Network for Disease Surveillance and Diabetes in Children, Adolescents, and Young Adults networks. The two one-sided tests for equivalence show that any observed difference between 2 estimates is small and practically insignificant. Results at the overall level, and stratified by sex, age, and race or ethnicity, were examined. Overall prevalence estimates for 2022 were 4.1% for CP 1 and 2.4% for CP 2. Although prevalence estimates for CP 1 were consistently higher than those for CP 2, absolute differences were generally less than 2.5 percentage points, which did not result in a statistically significant (P<.001) difference between estimates. The only exception was for Hispanic individuals, where prevalence was significantly different (P=0.2) for CP 1 (5.4%) versus CP 2 (3.0%), yielding a margin of 2.4 (95% CI 2.2-2.6) percentage points. Other groups that had relatively higher but statistically nonsignificant prevalence included male individuals (4.6% for CP 1 vs 2.3% for CP 2), individuals aged 35-44 years (6.9% for CP 1 vs 4.9% for CP 2), and African American individuals (5.5% for CP 1 vs 3.7% for CP 2). Therefore, we concluded that the 2 CPs largely produced equivalent estimates of T2D prevalence. The 2 independent CPs demonstrated equivalent T2D prevalence estimates, except in Hispanic individuals. Although the CPs can be considered statistically equivalent, the data driving each CP may impact accuracy and completeness. CP 1 was broader, incorporating clinical diagnoses, laboratory data, and medication, whereas CP 2 used clinical diagnostic codes alone. These results have implications for improving harmonization of CPs for public health surveillance.