[Digital environment and population health]. / Environnement digital et santé des populations.

Health and risk of disease are determined by exposure to the physical, socio-economic, and political environment and to this has been added exposure to the digital environment. Our increasingly digital lives have major implications for people's health and its monitoring, as well as for prevention and care. Digital health, which encompasses the use of health applications, connected devices and artificial intelligence medical tools, is transforming medical and healthcare practices. Used properly, it could facilitate patient-centered, inter-professional and data-driven care. However, its implementation raises major concerns and ethical issues, particularly in relation to privacy, equity, and the therapeutic relationship.

La santé et le risque de maladies sont déterminés par l'exposition aux environnements physiques, socio-économiques et politiques, et à cela s'est ajouté l'exposition à l'environnement digital. Notre vie digitale a des implications majeures, d'une part, sur la santé des populations et son monitoring et, d'autre part, sur la prévention et les soins. Ainsi, la santé digitale (digital health), qui englobe l'utilisation d'applications de santé, d'appareils connectés, ou d'outils médicaux d'intelligence artificielle, modifie les pratiques médico-soignantes. Bien utilisée, elle pourrait faciliter les soins centrés sur le patient, interprofessionnels et guidés par les données. Cependant, sa mise en œuvre soulève d'importants craintes et enjeux éthiques en lien notamment avec la protection des données, l'équité et la relation thérapeutique.

[The analysis of dynamics of indicator of mortality of population of the Siberian Federal Okrug resulted from toxic impact of alcohol in 2011-2020].

The purpose of the study is to investigate dynamics of mortality of population of the Siberian Federal Okrug resulted from toxic impact of alcohol in 2011-2020 and to comparatively analyze mortality of population of the regions of the Siberian Federal Okrug with general Okrug values. The study uses statistical data of the Federal State Statistics Service of the Russian Federation. The analysis was applied to mortality of population of the Russian Federation, the Siberian Federal Okrug and the regions of the Siberian Federal Okrug (the Republic of Altai, the Republic of Tuva, the Republic of Khakassia, the Altai Kray, the Krasnoyarsk Kray, Irkutsk, Kemerovo, Novosibirsk, Omsk and Tomsk Oblast) resulted from toxic impact of alcohol. The comparative analysis of regional and general Okrug indicators of mortality was implemented. The statistical processing of data included common methods of calculating extensive and intensive indicators, indicator average error and evaluation of reliability of differences. It was established that in 2011-2020, mortality of population of the Siberian Federal Okrug resulted from toxic impact of alcohol decreased by 3.3 times. The decrease of mortality of the population was registered in all regions of the Siberian Federal Okrug. The fastest decreasing of mortality of population occurred in the Altai Kray (7.7 times), the Republic of Tuva (7.0 times), the Krasnoyarsk Kray (6.4 times). The investigation of dynamics of mortality of population in the regions of the Siberian Federal Okrug resulted from toxic impact of alcohol permits to identify characteristics of toxicological situation at the regional level and to determine strategic directions of the regional policy of chemical safety and population health protection.

[The personnel maintenance of primary health care physicians in condition of challenges from menaces to population health].

In condition of challenges from menaces to health due to COVID-19 the role of primary health care physicians increases that requires development of management decisions at the regional level. The purpose of the study. On the basis of analysis of medical demographic indicators and data concerning provision of primary health care personnel in the Russian Federation, proposals were made related to reforming of primary health care system in conditions of implementation of general medical practice with purpose to counteract menaces to population health. The analytical and statistical methods were applied. The official statistics data from the Minzdrav of the Russian Federation and Rosstat, results of original researches with content analysis and interpretation were used. The COVID-19 pandemic negatively impacted population health. The indicator of newly detected morbidity in the Russian Federation in 2020 decreased as compared to 2019 from 78024.3 to 75989.7‰oo (by 2.6%). This is the result of decreasing of dispensary and preventive activities among population. In next COVID-19 year (2021), as compared to 2020, indicator of primary morbidity increased to 85531.6‰oo that 12.6% higher than in 2020. The increase occurred in all classes of diseases that caused necessity of rehabilitation of these patients. The COVID-19 morbidity increased up to 2.4 times i.e. from 3391.1 in 2020 to 8085.7‰oo in 2021. The provision of physicians in the Russian Federation was 37.7‰o in 2021. Across the Federal Okrugs differences in indicators made up to 1.3 times and in subjects of the Russian Federation - up to 2.7 times. In conditions of COVID-19 significance of menaces to population health worsened. The situation requires both enhancement of primary health care and implementation of physicians of new formation - general practitioners responsible for patient health. The general practice (GP) widely developed in Russia in the 1990s during last ten years loses its significance in most subjects of the Russian Federation. In 2021, provision of general practitioners in the Russian Federation made up to 0.67‰o. In the Federal Okrugs, difference between indicators made up to 5.6 times. In the subjects of the Russian Federation the difference is enormous - 141.5 times. The article presents and scientifically substantiates prospective functional organizational models of general practice. The established situation with COVID-19 infection requires development of management decisions and measures at the regional level concerning improvement of organization of primary health care and implementation of general practice in conditions of counteracting risks of menaces to population health and health preservation.

Measuring social determinants of health in the All of Us Research Program.

To accelerate medical breakthroughs, the All of Us Research Program aims to collect data from over one million participants. This report outlines processes used to construct the All of Us Social Determinants of Health (SDOH) survey and presents the psychometric characteristics of SDOH survey measures in All of Us. A consensus process was used to select SDOH measures, prioritizing concepts validated in diverse populations and other national cohort surveys. Survey item non-response was calculated, and Cronbach's alpha was used to analyze psychometric properties of scales. Multivariable logistic regression models were used to examine associations between demographic categories and item non-response. Twenty-nine percent (N = 117,783) of eligible All of Us participants submitted SDOH survey data for these analyses. Most scales had less than 5% incalculable scores due to item non-response. Patterns of item non-response were seen by racial identity, educational attainment, income level, survey language, and age. Internal consistency reliability was greater than 0.80 for almost all scales and most demographic groups. The SDOH survey demonstrated good to excellent reliability across several measures and within multiple populations underrepresented in biomedical research. Bias due to survey non-response and item non-response will be monitored and addressed as the survey is fielded more completely.

An ensemble penalized regression method for multi-ancestry polygenic risk prediction.

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of L 1 (lasso) and L 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

Urban environment and health: a cross-sectional multiregional project based on population health surveys in Spain (DAS-EP project) - study protocol.

INTRODUCTION: The European Environment Agency estimates that 75% of the European population lives in cities. Despite the many advantages of city life, the risks and challenges to health arising from urbanisation need to be addressed in order to tackle the growing burden of disease and health inequalities in cities. This study, Urban environment and health: a cross-sectional multiregional project based on population health surveys in Spain (DAS-EP project), aims to investigate the complex association between the urban environmental exposures (UrbEEs) and health. METHODS AND ANALYSIS: DAS-EP is a Spanish multiregional cross-sectional project that combines population health surveys (PHS) and geographical information systems (GIS) allowing to collect rich individual-level data from 17 000 adult citizens participating in the PHS conducted in the autonomous regions of the Basque Country, Andalusia, and the Valencian Community, and the city of Barcelona in the years 2021-2023. This study focuses on the population living in cities or metropolitan areas with more than 100 000 inhabitants. UrbEEs are described by objective estimates at participants' home addresses by GIS, and subjective indicators present in PHS. The health outcomes included in the PHS and selected for this study are self-perceived health (general and mental), prevalence of chronic mental disorders, health-related quality of life, consumption of medication for common mental disorders and sleep quality. We aim to further understand the direct and indirect effects between UrbEEs and health, as well as to estimate the impact at the population level, taking respondents' sociodemographic and socioeconomic characteristics, and lifestyle into consideration. ETHICS AND DISSEMINATION: The study was approved by the regional Research Ethics Committee of the Basque Country (Ethics Committee for Research Involving Medicinal Products in the Basque Country; PI2022138), Andalusia (Biomedical Research Ethics Committee of the Province of Granada; 2078-N-22), Barcelona (CEIC-PSMar; 2022/10667) and the Valencian Community (Ethics Committee for Clinical Research of the Directorate General of Public Health and Center for Advanced Research in Public Health; 20221125/04). The results will be communicated to the general population, health professionals, and institutions through conferences, reports and scientific articles.

All of Us Program Illustrates What Less-Diverse Genomics Studies Might Be Missing.

This Medical News story discusses new findings from genomics studies involving diverse cohorts.

The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring.

Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.

Low-Income, Poor Physical Health, Poor Mental Health, and Other Social Risk Factors Are Associated With Decreased Access to Care in Patients With Carpal Tunnel Syndrome.

BACKGROUND: Carpal Tunnel Syndrome (CTS) is associated with a significant personal and societal burden. Evaluating access to care can identify barriers, limitations, and disparities in the delivery of healthcare services in this population. The purpose of this study was to evaluate access to overall healthcare and healthcare utilization among patients with CTS. METHODS: This is a retrospective cohort study conducted with the All of Us database. Patients diagnosed with CTS that completed the access to care survey were included and matched to a control group. The primary outcomes were access to care across 4 domains: (1) delayed care, (2) could not afford care, (3) skipped medications, and (4) over 1 year since seeing provider. Secondary analysis was then performed to identify patient-specific factors associated with reduced access to care. RESULTS: In total, 7649 patients with CTS were included and control matched to 7649 patients without CTS. In the CTS group, 33.7% (n = 2577) had delayed care, 30.4% (n = 2323) could not afford care, 15.4% (n = 1180) skipped medications, and 1.6% (n = 123) had not seen a provider in more than 1 year. Within the CTS cohort, low-income, worse physical health, and worse mental health were associated with poor access to care. CONCLUSION: Patients experience notable challenges with delayed care, affordability of care, and medication adherence regardless of having a diagnosis of CTS. Targeted interventions on modifiable risk factors such as low income, poor mental health, and poor physical health are important opportunities to improve access to care in this population.

Inclusion of Polypills for Prevention of Cardiovascular Disease in the 23rd World Health Organization Model List of Essential Medicines: A Significant Step Towards Reducing Global Cardiovascular Morbidity and Mortality.

This commentary describes the potential impact of inclusion of polypills for prevention of cardiovascular disease in the 23rd WHO Model List of Essential Medicines, and provides a roadmap for adoption, implementation, sustainment, and scale-up. The World Health Organization's endorsement of polypills is essential for improving global access, particularly in low- and middle-income countries. The greatest health gains are expected in a primary prevention population which has a significantly higher burden of fatal and non-fatal cardiovascular disease compared with the population of individuals with prevalent cardiovascular disease. A focus on adoption, implementation, sustainment, and scale-up of polypills for prevention of cardiovascular disease is needed including increasing supply of available polypills and incorporating polypills into the World Health Organization HEARTS technical package for integration into primary care systems to realize these benefits for population health. Widespread implementation of polypills for prevention of cardiovascular disease has the potential to equitably reduce the impact of cardiovascular disease globally by simplifying treatment options and expanding accessibility across economic levels, both across and within countries.

Chatbots and COVID-19: Taking Stock of the Lessons Learned.

While digital innovation in health was already rapidly evolving, the COVID-19 pandemic has accelerated the generation of digital technology tools, such as chatbots, to help increase access to crucial health information and services to those who were cut off or had limited contact with health services. This theme issue titled "Chatbots and COVID-19" presents articles from researchers and practitioners across the globe, describing the development, implementation, and evaluation of chatbots designed to address a wide range of health concerns and services. In this editorial, we present some of the key challenges and lessons learned arising from the content of this theme issue. Most notably, we note that a stronger evidence base is needed to ensure that chatbots and other digital tools are developed to best serve the needs of population health.

Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity.

Importance: Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity. Objective: To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity. Design, Setting, and Participants: In this US population-based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis. Exposure: Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared. Main Outcome and Measures: Incident obesity (BMI ≥30). Results: A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively (P = 1.0 × 10-20). The BMI PRS demonstrated an 81% increase in obesity risk (P = 3.57 × 10-20) while mean step count demonstrated a 43% reduction (P = 5.30 × 10-12) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d. Conclusions and Relevance: In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.

Building for Value: A Foundational Structure to Support Population Health.

The journey to value relies heavily on a strong foundation in population health and on supporting systems of care. However, as the Centers for Medicare & Medicaid Services and commercial insurers rethink reimbursements to achieve cost savings, both patients and payments to health care organizations are at risk. The case for value-based care is ever stronger yet health systems will have to mature their culture, population health infrastructure, technologies and analytics capabilities, and leadership and management systems. In this article, the authors describe the functional organizational structure of the clinical transformation team responsible for population health in the University Hospitals Accountable Care Organizations (ACO). Based on their experiences building and evolving population health for the University Hospitals ACO, the authors layout the 3 pillars supporting their structure, including operations, clinical design, and data and analytics, and key areas of focus for each pillar.

Post stroke pain: Is there under-diagnosis in Black versus White patients?

Stroke incidence is higher and stroke outcomes are poorer in Black patients compared to White patients. Poststroke pain, however, is not a well understood stroke outcome. Using the National Institutes of Health All of Us Research Program database, we hypothesized that the dataset would demonstrate proportionately higher relative risk of poststroke pain in the Black poststroke patient population compared to the White poststroke patient population. However, our analysis showed that Black stroke patients were diagnosed with poststroke pain at a similar rate as White stroke patients. As our results are not consistent with other poststroke outcomes in the literature, this study identifies a potentially underdiagnosed patient population, highlighting the need for further research.

Relative predictive value of sociodemographic factors for chronic diseases among All of Us participants: a descriptive analysis.

BACKGROUND: Although sociodemographic characteristics are associated with health disparities, the relative predictive value of different social and demographic factors remains largely unknown. This study aimed to describe the sociodemographic characteristics of All of Us participants and evaluate the predictive value of each factor for chronic diseases associated with high morbidity and mortality. METHODS: We performed a cross-sectional analysis using de-identified survey data from the All of Us Research Program, which has collected social, demographic, and health information from adults living in the United States since May 2018. Sociodemographic data included self-reported age, sex, gender, sexual orientation, race/ethnicity, income, education, health insurance, primary care provider (PCP) status, and health literacy scores. We analyzed the self-reported prevalence of hypertension, coronary artery disease, any cancer, skin cancer, lung disease, diabetes, obesity, and chronic kidney disease. Finally, we assessed the relative importance of each sociodemographic factor for predicting each chronic disease using the adequacy index for each predictor from logistic regression. RESULTS: Among the 372,050 participants in this analysis, the median age was 53 years, 59.8% reported female sex, and the most common racial/ethnic categories were White (54.0%), Black (19.9%), and Hispanic/Latino (16.7%). Participants who identified as Asian, Middle Eastern/North African, and White were the most likely to report annual incomes greater than $200,000, advanced degrees, and employer or union insurance, while participants who identified as Black, Hispanic, and Native Hawaiian/Pacific Islander were the most likely to report annual incomes less than $10,000, less than a high school education, and Medicaid insurance. We found that age was most predictive of hypertension, coronary artery disease, any cancer, skin cancer, diabetes, obesity, and chronic kidney disease. Insurance type was most predictive of lung disease. Notably, no two health conditions had the same order of importance for sociodemographic factors. CONCLUSIONS: Age was the best predictor for the assessed chronic diseases, but the relative predictive value of income, education, health insurance, PCP status, race/ethnicity, and sexual orientation was highly variable across health conditions. Identifying the sociodemographic groups with the largest disparities in a specific disease can guide future interventions to promote health equity.