During the COVID-19 pandemic in 2020, hospitals encountered numerous challenges that compounded their difficulties. Some of these challenges directly impacted patient care, such as the need to expand capacities, adjust services, and use new knowledge to save lives in an ever-evolving situation. In addition, hospitals faced regulatory challenges.

Although many studies have used smartphone apps to examine alcohol consumption, none have clearly delineated long-term (>1 year) consumption among the general population.

Clinical risk prediction models integrated into digitized health care informatics systems hold promise for personalized primary prevention and care, a core goal of precision health. Fairness metrics are important tools for evaluating potential disparities across sensitive features, such as sex and race or ethnicity, in the field of prediction modeling. However, fairness metric usage in clinical risk prediction models remains infrequent, sporadic, and rarely empirically evaluated.

Currently, the methods used to collect dietary intake data in Ireland are inflexible to the needs of certain populations, who are poorly represented in nutrition and health data as a result. As the Irish population is becoming increasingly diverse, there is an urgent need to understand the habitual food intake and diet quality of multiple population subgroups, including different nationalities and ethnic minorities, in Ireland. Foodbook24 is an existing web-based 24-hour dietary recall tool, which has previously been validated for use within the general Irish adult population. Because of its design, Foodbook24 can facilitate the improved inclusion of dietary intake assessment in Ireland.

There is a growing gap between surgeon availability and demand for orthopedic services in the United States. This study analyzes the geographic trends of this gap with a Relative Demand Index to guide precision public health interventions such as resource allocation, residency program expansion, and workforce planning to specific regions.

Perinatal depression remains a significant public health challenge often overlooked or incorrectly diagnosed in numerous developing nations.

Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends despite data lags and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City (NYC), we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity (Black/African American, Hispanic/Latino, and White). However, in real time, it was unclear if estimates were accurate.

Microbial diversity is vast, with bacteria playing a crucial role in human health. However, occurrence records (location, date, observer, relation to host) of bacteria associated with humans remain scarce. This lack of information hinders our understanding of human-microbe relationships and disease prevention. Here, we show that existing solutions, such as France's Système d'Information sur le Patrimoine Naturel (SINP) framework, can be used to efficiently collect and manage occurrence data on human-associated bacteria. This user-friendly system allows medical personnel to easily share and access data on bacterial pathogens. By implementing similar national infrastructures and considering human-associated bacteria as biodiversity data, we can significantly improve public health management and research and our understanding of the One Health concept, which emphasizes the interconnectedness of human, animal, and environmental health.

Smoking is a modifiable risk factor for SARS-CoV-2 infection. Evidence of smoking behavior during the pandemic is ambiguous. Most investigations report an increase in smoking. In this context, Google Trends data monitor real-time public information–seeking behavior and are therefore useful to characterize smoking-related interest over the trajectory of the pandemic.

Increasing HIV rates among young Latino sexual minority men (YLSMM) warrant innovative and rigorous studies to assess prevention and treatment strategies. Ecological momentary assessments (EMA) and electronic pill dispensers (EPD) have been used to measure antiretroviral therapy (ART) adherence repeatedly, in real-time, and in participants’ natural environments, but their psychometric properties among YLSMM are unknown.

Our article provides a viewpoint on population digital health - the use of digital health information sourced from Health IoT and wearable devices for population health modeling - as an emerging research initiative for offering an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. Global healthcare systems are increasingly challenged by rising costs as life expectancy and the average age of people increases. Population digital health looks at how wearables, IoT, and AI can offer an alternative approach for understanding health issues within the population, significantly reducing cost and improving the completeness of information collection by current practices, such as electronic health records - including integration with mhealth personal health records - or survey instruments. This significantly improves our collective understanding of public health priorities, including factors affecting disease prevalence, occurrence and risk factors, ultimately helping to design targeted programmatic interventions apt at reducing the cost of healthcare provision and leading to better life quality, also reducing disparities. Realizing this vision requires overcoming several unique challenges, including data quality, availability, sparsity, and social and technical barriers in the use of health technologies. Our article highlights these challenges and offers solutions and empirical evidence to demonstrate how these challenges can be addressed. As population digital health addresses the impact large-scale sensor data collection and AI can have on improving healthcare delivery and society, we sincerely believe the topic is well within the journal's scope and would be highly interesting to its readership. Our experiments using a combination of real-world health IoT data and electronic health records also highlight the potential cross-disciplinary benefits of population digital health and challenge the research community to address the vision and challenges. Therefore, our article serves the dual purpose of challenging the research community and offering insights into the use of AI and sensor data, and how population digital health can serve as a catalyst for further research by the broader research community.

On average, people in the United States visit a doctor 4 times a year, and many of them have chronic illnesses. Because of the increased use of technology, people frequently rely on the internet to access health information and statistics. People use health care information to make better-educated decisions for themselves and others. Health care dashboards should provide pertinent and easily understood data, such as information on timely cancer screenings, so the public can make better-informed decisions. In order to enhance health outcomes, effective dashboards should provide precise data in an accessible and easily digestible manner.