Health Digital Twins in Life Science and Health Care Innovation.

Health digital twins (HDTs) are virtual representations of real individuals that can be used to simulate human physiology, disease, and drug effects. HDTs can be used to improve drug discovery and development by providing a data-driven approach to inform target selection, drug delivery, and design of clinical trials. HDTs also offer new applications into precision therapies and clinical decision making. The deployment of HDTs at scale could bring a precision approach to public health monitoring and intervention. Next steps include challenges such as addressing socioeconomic barriers and ensuring the representativeness of the technology based on the training and validation data sets. Governance and regulation of HDT technology are still in the early stages.

Global distribution of functionally important CYP2C9 alleles and their inferred metabolic consequences.

BACKGROUND: Genetic variability in the cytochrome P450 CYP2C9 constitutes an important predictor for efficacy and safety of various commonly prescribed drugs, including coumarin anticoagulants, phenytoin and multiple non-steroidal anti-inflammatory drugs (NSAIDs). A global map of CYP2C9 variability and its inferred functional consequences has been lacking. RESULTS: Frequencies of eight functionally relevant CYP2C9 alleles (*2, *3, *5, *6, *8, *11, *13 and *14) were analyzed. In total, 108 original articles were identified that included genotype data from a total of 81,662 unrelated individuals across 70 countries and 40 unique ethnic groups. The results revealed that CYP2C9*2 was most abundant in Europe and the Middle East, whereas CYP2C9*3 was the main reason for reduced CYP2C9 activity across South Asia. Our data show extensive variation within superpopulations with up to tenfold differences between geographically adjacent populations in Malaysia, Thailand and Vietnam. Translation of genetic CYP2C9 variability into functional consequences indicates that up to 40% of patients in Southern Europe and the Middle East might benefit from warfarin and phenytoin dose reductions, while 3% of patients in Southern Europe and Israel are recommended to reduce starting doses of NSAIDs. CONCLUSIONS: This study provides a comprehensive map of the genetic and functional variability of CYP2C9 with high ethnogeographic resolution. The presented data can serve as a useful resource for CYP2C9 allele and phenotype frequencies and might guide the optimization of genotyping strategies, particularly for indigenous and founder populations with distinct genetic profiles.

Calibrating variant curation by clinical context based on factors that influence patients' tolerance of uncertainty.

PURPOSE: Shared decision making manages genomic uncertainty by integrating molecular and clinical uncertainties with patient values to craft a person-centered management plan. Laboratories seek genomic report consistency, agnostic to clinical context. Molecular reports often mask laboratory-managed uncertainties from clinical decision making. Better integration of these uncertainty management strategies requires a nuanced understanding of patients' perceptions and reactions to test uncertainties. We explored patients' tolerance to variant uncertainty in 3 parameters: (1) relative causal significance, (2) risk accuracy, and (3) classification validity. METHOD: Deliberative forums were undertaken with 18 patients with predictive testing experience. Uncertainty deliberations were elicited for each parameter. A thematic framework was first developed, and then mapped to whether they justified tolerance to more or less parameter-specific uncertainty. RESULTS: Six identified themes mapped to clinical and personal domains. These domains generated opposing forces when calibrating uncertainty. Personal themes justified tolerance of higher uncertainty and clinical themes lower uncertainty. Decision making in uncertainty focused on reducing management regret. Open communication increased tolerance of classification validity and risk accuracy uncertainty. Using these data, we have developed a nascent clinical algorithm integrating molecular uncertainty with clinical context through a targeted communication framework. CONCLUSION: Maximizing test utility necessitates context-specific recalibration of uncertainty management and communication.

Advancing precision public health for obesity in children.

Worldwide, far too many children and adolescents are living with the disease of obesity. Despite decades of public health initiatives, rates are still rising in many countries. This raises the question of whether precision public health may be a more successful approach to preventing obesity in youth. In this review, the objective was to review the literature on precision public health in the context of childhood obesity prevention and to discuss how precision public health may advance the field of childhood obesity prevention. As precision public health is a concept that is still evolving and not fully identifiable in the literature, a lack of published studies precluded a formal review. Therefore, the approach of using a broad interpretation of precision public health was used and recent advances in childhood obesity research in the areas of surveillance and risk factor identification as well as intervention, evaluation and implementation using selected studies were summarized. Encouragingly, big data from a multitude of designed and organic sources are being used in new and innovative ways to provide more granular surveillance and risk factor identification in obesity in children. Challenges were identified in terms of data access, completeness, and integration, ensuring inclusion of all members of society, ethics, and translation to policy. As precision public health advances, it may yield novel insights that can contribute to strong policies acting in concert that ultimately lead to the prevention of obesity in children.

Testing Theory-Based Messages to Encourage Women at Average Risk for Breast Cancer to Consider Biennial Mammography Screening.

BACKGROUND: The US Preventive Services Task Force does not recommend routine annual mammography screening for women aged 40-49 at average risk. Little research has been done to develop theory-based communication interventions to facilitate informed decision-making about reducing potentially low-value mammography screening. PURPOSE: Evaluate the effects of theory-based persuasive messages on women's willingness to consider delaying screening mammography until age 50 or have mammograms biennially. METHODS: We conducted a randomized controlled communication experiment online with a population-based sample of U.S. women aged 40-49 (N = 383) who screened to be at average risk for breast cancer. Women were randomly assigned to the following messaging summaries: annual mammography risks in 40s (Arm 1, n = 124), mammography risks plus family history-based genetic risk (Arm 2, n = 120), and mammography risks, genetic risk, and behavioral alternatives (Arm 3, n = 139). Willingness to delay screening or reduce screening frequency was assessed post-experiment by a set of 5-point Likert scale items. RESULTS: Women in Arm 3 reported significantly greater willingness to delay screening mammography until age 50 (mean = 0.23, SD = 1.26) compared with those in Arm 1 (mean = -0.17, SD = 1.20; p = .04). There were no significant arm differences in willingness to reduce screening frequency. Exposure to the communication messages significantly shifted women's breast cancer-related risk perceptions without increasing unwarranted cancer worry across all three arms. CONCLUSIONS: Providing women with screening information and options may help initiate challenging discussions with providers about potentially low-value screening.

The US Preventive Services Task Force does not recommend routine annual mammography screening for women aged 40­49 at average risk. This study aimed to assess the impact of theory-based persuasive messages on women's willingness to delay mammography screening until age 50 or opt for biennial screenings. In a randomized online experiment, 383 U.S. women aged 40­49 at average risk for breast cancer were assigned to three different message groups. The results showed that women exposed to messaging that included mammography risks, family history-based genetic risk, and behavioral alternatives were significantly more willing to delay screening until age 50. However, there were no significant differences in willingness to reduce screening frequency. The tested communication messages effectively shifted women's breast cancer-related risk perceptions without causing unnecessary worry. Providing women with screening information and options may help initiate challenging discussions with providers about potentially low-value screening.

Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy.

Over the last decade, next-generation sequencing (NGS) methods have become increasingly used in various areas of human genomics. In routine clinical care, their use is already implemented in oncology to profile the mutational landscape of a tumor, as well as in rare disease diagnostics. However, its utilization in pharmacogenomics is largely lacking behind. Recent population-scale genome data has revealed that human pharmacogenes carry a plethora of rare genetic variations that are not interrogated by conventional array-based profiling methods and it is estimated that these variants could explain around 30% of the genetically encoded functional pharmacogenetic variability.To interpret the impact of such variants on drug response a multitude of computational tools have been developed, but, while there have been major advancements, it remains to be shown whether their accuracy is sufficient to improve personalized pharmacogenetic recommendations in robust trials. In addition, conventional short-read sequencing methods face difficulties in the interrogation of complex pharmacogenes and high NGS test costs require stringent evaluations of cost-effectiveness to decide about reimbursement by national healthcare programs. Here, we illustrate current challenges and discuss future directions toward the clinical implementation of NGS to inform genotype-guided decision-making.

An early warning precision public health approach for assessing COVID-19 vulnerability in the UK: the Moore-Hill Vulnerability Index (MHVI).

BACKGROUND: Most COVID-19 vulnerability indices rely on measures that are biased by rates of exposure or are retrospective like mortality rates that offer little opportunity for intervention. The Moore-Hill Vulnerability Index (MHVI) is a precision public health early warning alternative to traditional infection fatality rates that presents avenues for mortality prevention. METHODS: We produced an infection-severity vulnerability index by calculating the proportion of all recorded positive cases that were severe and attended by ambulances at small area scale for the East Midlands of the UK between May 2020 and April 2022. We produced maps identifying regions with high and low vulnerability, investigated the accuracy of the index over shorter and longer time periods, and explored the utility of the MHVI compared to other common proxy measures and indices. Analysis included exploring the correlation between our novel index and the Index of Multiple Deprivation (IMD). RESULTS: The MHVI captures geospatial dynamics that single metrics alone often overlook, including the compound health challenges associated with disadvantaged and declining coastal towns inhabited by communities with post-industrial health legacies. A moderate negative correlation between MHVI and IMD reflects spatial analysis which suggests that high vulnerability occurs in affluent rural as well as deprived coastal and urban communities. Further, the MHVI estimates of severity rates are comparable to infection fatality rates for COVID-19. CONCLUSIONS: The MHVI identifies regions with known high rates of poor health outcomes prior to the pandemic that case rates or mortality rates alone fail to identify. Pre-hospital early warning measures could be utilised to prevent mortality during a novel pandemic.

Adaptive Content Tuning of Social Network Digital Health Interventions Using Control Systems Engineering for Precision Public Health: Cluster Randomized Controlled Trial.

BACKGROUND: Social media has emerged as an effective tool to mitigate preventable and costly health issues with social network interventions (SNIs), but a precision public health approach is still lacking to improve health equity and account for population disparities. OBJECTIVE: This study aimed to (1) develop an SNI framework for precision public health using control systems engineering to improve the delivery of digital educational interventions for health behavior change and (2) validate the SNI framework to increase organ donation awareness in California, taking into account underlying population disparities. METHODS: This study developed and tested an SNI framework that uses publicly available data at the ZIP Code Tabulation Area (ZCTA) level to uncover demographic environments using clustering analysis, which is then used to guide digital health interventions using the Meta business platform. The SNI delivered 5 tailored organ donation-related educational contents through Facebook to 4 distinct demographic environments uncovered in California with and without an Adaptive Content Tuning (ACT) mechanism, a novel application of the Proportional Integral Derivative (PID) method, in a cluster randomized trial (CRT) over a 3-month period. The daily number of impressions (ie, exposure to educational content) and clicks (ie, engagement) were measured as a surrogate marker of awareness. A stratified analysis per demographic environment was conducted. RESULTS: Four main clusters with distinctive sociodemographic characteristics were identified for the state of California. The ACT mechanism significantly increased the overall click rate per 1000 impressions (ß=.2187; P<.001), with the highest effect on cluster 1 (ß=.3683; P<.001) and the lowest effect on cluster 4 (ß=.0936; P=.053). Cluster 1 is mainly composed of a population that is more likely to be rural, White, and have a higher rate of Medicare beneficiaries, while cluster 4 is more likely to be urban, Hispanic, and African American, with a high employment rate without high income and a higher proportion of Medicaid beneficiaries. CONCLUSIONS: The proposed SNI framework, with its ACT mechanism, learns and delivers, in real time, for each distinct subpopulation, the most tailored educational content and establishes a new standard for precision public health to design novel health interventions with the use of social media, automation, and machine learning in a form that is efficient and equitable. TRIAL REGISTRATION: ClinicalTrials.gov NTC04850287; https://clinicaltrials.gov/ct2/show/NCT04850287.

Health Equity and Colorectal Cancer Awareness: a Community Health Educator Initiative.

Disparities in colorectal cancer (CRC) incidence and mortality persist in rural and underserved communities. Our Community Outreach and Engagement (COE) activities are grounded in a bi-directional Community-to-Bench model in which the National Outreach Network Community Health Educator (NON CHE) Screen to Save (S2S) initiative was implemented. In this study, we assessed the impact of the NON CHE S2S in rural and underserved communities. Descriptive and comparative analyses were used to examine the role of the NON CHE S2S on CRC knowledge and CRC screening intent. Data included demographics, current CRC knowledge, awareness, and future CRC health plans. A multivariate linear regression was fit to survey scores for CRC knowledge. The NON CHE S2S engaged 441 participants with 170 surveys completed. The difference in participants' CRC knowledge before and after the NON CHE S2S intervention had an overall mean of 0.92 with a standard deviation of 2.56. At baseline, White participants had significantly higher CRC knowledge scores, correctly answering 1.94 (p = 0.007) more questions on average than Black participants. After the NON CHE S2S intervention, this difference was not statistically significant. Greater than 95% of participants agreed that the NON CHE S2S sessions impacted their intent to get screened for CRC. Equity of access to health information and the health care system can be achieved with precision public health strategies. The COE bi-directional Community-to-Bench model facilitated community connections through the NON CHE and increased awareness of CRC risk reduction, screening, treatment, and research. The NON CHE combined with S2S is a powerful tool to engage communities with the greatest health care needs and positively impact an individual's intent to "get screened" for CRC.

Heterogeneity in how women value risk-stratified breast screening.

PURPOSE: Risk-stratified screening has potential to improve the cost effectiveness of national breast cancer screening programs. This study aimed to inform a socially acceptable and equitable implementation framework by determining what influences a woman's decision to accept a personalized breast cancer risk assessment and what the relative impact of these key determinants is. METHODS: Multicriteria decision analysis was used to elicit the relative weights for 8 criteria that women reported influenced their decision. Preference heterogeneity was explored through cluster analysis. RESULTS: The 2 criteria valued most by the 347 participants related to program access, "Mode of invitation" and "Testing process". Both criteria significantly influenced participation (P < .001). A total of 73% preferred communication by letter/online. Almost all women preferred a multidisease risk assessment with potential for a familial high-risk result. Four preference-based subgroups were identified. Membership to the largest subgroup was predicted by lower educational attainment, and women in this subgroup were concerned with program access. Higher relative perceived breast cancer risk predicted membership to the smallest subgroup that was focused on test parameters, namely "Scope of test" and "Test specificity". CONCLUSION: Overall, Australian women would accept a personalized multidisease risk assessment, but when aligning with their preferences, it will necessitate a focus on program access and the development of online communication frameworks.

COVID-19 GPH: tracking the contribution of genomics and precision health to the COVID-19 pandemic response.

The scientific response to the COVID-19 pandemic has produced an abundance of publications, including peer-reviewed articles and preprints, across a wide array of disciplines, from microbiology to medicine and social sciences. Genomics and precision health (GPH) technologies have had a particularly prominent role in medical and public health investigations and response; however, these domains are not simply defined and it is difficult to search for relevant information using traditional strategies. To quantify and track the ongoing contributions of GPH to the COVID-19 response, the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention created the COVID-19 Genomics and Precision Health database (COVID-19 GPH), an open access knowledge management system and publications database that is continuously updated through machine learning and manual curation. As of February 11, 2022, COVID-GPH contained 31,597 articles, mostly on pathogen and human genomics (72%). The database also includes articles describing applications of machine learning and artificial intelligence to the investigation and control of COVID-19 (28%). COVID-GPH represents about 10% (22983/221241) of the literature on COVID-19 on PubMed. This unique knowledge management database makes it easier to explore, describe, and track how the pandemic response is accelerating the applications of genomics and precision health technologies. COVID-19 GPH can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action .

Implementation and Evaluation of a Digitally Enabled Precision Public Health Intervention to Reduce Inappropriate Gabapentinoid Prescription: Cluster Randomized Controlled Trial.

BACKGROUND: Digital technologies can enable rapid targeted delivery of audit and feedback interventions at scale. Few studies have evaluated how mode of delivery affects clinical professional behavior change and none have assessed the feasibility of such an initiative at a national scale. OBJECTIVE: The aim of this study was to develop and evaluate the effect of audit and feedback by digital versus postal (letter) mode of delivery on primary care physician behavior. METHODS: This study was developed as part of the Veterans' Medicines Advice and Therapeutics Education Services (MATES) program, an intervention funded by the Australian Government Department of Veterans' Affairs that provides targeted education and patient-specific audit with feedback to Australian general practitioners, as well as educational material to veterans and other health professionals. We performed a cluster randomized controlled trial of a multifaceted intervention to reduce inappropriate gabapentinoid prescription, comparing digital and postal mode of delivery. All veteran patients targeted also received an educational intervention (postal delivery). Efficacy was measured using a linear mixed-effects model as the average number of gabapentinoid prescriptions standardized by defined daily dose (individual level), and number of veterans visiting a psychologist in the 6 and 12 months following the intervention. RESULTS: The trial involved 2552 general practitioners in Australia and took place in March 2020. Both intervention groups had a significant reduction in total gabapentinoid prescription by the end of the study period (digital: mean reduction of 11.2%, P=.004; postal: mean reduction of 11.2%, P=.001). We found no difference between digital and postal mode of delivery in reduction of gabapentinoid prescriptions at 12 months (digital: -0.058, postal: -0.058, P=.98). Digital delivery increased initiations to psychologists at 12 months (digital: 3.8%, postal: 2.0%, P=.02). CONCLUSIONS: Our digitally delivered professional behavior change intervention was feasible, had comparable effectiveness to the postal intervention with regard to changes in medicine use, and had increased effectiveness with regard to referrals to a psychologist. Given the logistical benefits of digital delivery in nationwide programs, the results encourage exploration of this mode in future interventions.

The Development, Preliminary Validation, and Clinical Application of the Quick Parenting Assessment.

To prevent diseases, efforts are needed to determine how to address Adverse Childhood Experiences (ACEs), including parenting behaviors. The objective of this study, conducted in Nashville TN in 2017, was to initiate testing the psychometric properties of two new Adverse Childhood Experiences (ACEs) screening tools, the Quick Parenting Assessment (QPA) and Other Childhood Stressors (OCS). In a clinic serving low-income families, caregivers of children ages 2-10 completed assessments of parenting (QPA), other stressors (OCS), child behavior problems ((Strength and Difficulties Questionnaire (SDQ)), and Attitudes Toward Spanking (ATS). The QPA takes 1 min to complete and assesses for healthy and unhealthy parenting behaviors. Seventy-five percent of eligible participants completed the survey (N=558). A reduced 10-item QPA yielded a Cronbach's alpha of 0.79 and, in 4-10-year-olds, was associated with high SDQ conduct, hyperactivity, and total difficulties scores (r=0.44, 0.48, and 0.47; all p< 0.001). Children with QPAs of >4 were nine times more likely than those children with scores of ≤2 to have behavior problems (OR=8.93, 95% CI = 3.74-21.32). Elevated QPAs were associated with the ATS (r=0.47, p < .001). The OCS was also associated with high SDQ total difficulties scores (r=0.28, p< 0.001). Two pediatric ACEs screening tools, the QPA and the OCS, have promising psychometric properties. The findings suggest that parenting behaviors may play an outsized role in the pathogenesis of outcomes associated with ACEs. We discuss the clinical application of QPA at our institution and the theoretical potential for this instrument to reduce the rates of short- and long-term health problems.

Transcriptomics and RNA-Based Therapeutics as Potential Approaches to Manage SARS-CoV-2 Infection.

SARS-CoV-2 is a coronavirus family member that appeared in China in December 2019 and caused the disease called COVID-19, which was declared a pandemic in 2020 by the World Health Organization. In recent months, great efforts have been made in the field of basic and clinical research to understand the biology and infection processes of SARS-CoV-2. In particular, transcriptome analysis has contributed to generating new knowledge of the viral sequences and intracellular signaling pathways that regulate the infection and pathogenesis of SARS-CoV-2, generating new information about its biology. Furthermore, transcriptomics approaches including spatial transcriptomics, single-cell transcriptomics and direct RNA sequencing have been used for clinical applications in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models for SARS-CoV-2. Consequently, RNA-based therapeutics and their relationship with SARS-CoV-2 have emerged as promising strategies to battle the SARS-CoV-2 pandemic with the assistance of novel approaches such as CRISPR-CAS, ASOs, and siRNA systems. Lastly, we discuss the importance of precision public health in the management of patients infected with SARS-CoV-2 and establish that the fusion of transcriptomics, RNA-based therapeutics, and precision public health will allow a linkage for developing health systems that facilitate the acquisition of relevant clinical strategies for rapid decision making to assist in the management and treatment of the SARS-CoV-2-infected population to combat this global public health problem.

Gene-environment interactions related to maternal exposure to environmental and lifestyle-related chemicals during pregnancy and the resulting adverse fetal growth: a review.

BACKGROUND: There are only limited numbers of reviews on the association of maternal-child genetic polymorphisms and environmental and lifestyle-related chemical exposure during pregnancy with adverse fetal growth. Thus, this article aims to review: (1) the effect of associations between the above highlighted factors on adverse fetal growth and (2) recent birth cohort studies regarding environmental health risks. METHODS: Based on a search of the PubMed database through August 2021, 68 epidemiological studies on gene-environment interactions, focusing on the association between environmental and lifestyle-related chemical exposure and adverse fetal growth was identified. Moreover, we also reviewed recent worldwide birth cohort studies regarding environmental health risks. RESULTS: Thirty studies examined gene-smoking associations with adverse fetal growth. Sixteen maternal genes significantly modified the association between maternal smoking and adverse fetal growth. Two genes significantly related with this association were detected in infants. Moreover, the maternal genes that significantly interacted with maternal smoking during pregnancy were cytochrome P450 1A1 (CYP1A1), X-ray repair cross-complementing protein 3 (XRCC3), interleukin 6 (IL6), interleukin 1 beta (IL1B), human leukocyte antigen (HLA) DQ alpha 1 (HLA-DQA1), HLA DQ beta 1 (HLA-DQB1), and nicotinic acetylcholine receptor. Fetal genes that had significant interactions with maternal smoking during pregnancy were glutathione S-transferase theta 1 (GSTT1) and fat mass and obesity-associated protein (FTO). Thirty-eight studies examined the association between chemical exposures and adverse fetal growth. In 62 of the 68 epidemiological studies (91.2%), a significant association was found with adverse fetal growth. Across the studies, there was a wide variation in the analytical methods used, especially with respect to the genetic polymorphisms of interest, environmental and lifestyle-related chemicals examined, and the study design used to estimate the gene-environment interactions. It was also found that a consistently increasing number of European and worldwide large-scale birth cohort studies on environmental health risks have been conducted since approximately 1996. CONCLUSION: There is some evidence to suggest the importance of gene-environment interactions on adverse fetal growth. The current knowledge on gene-environment interactions will help guide future studies on the combined effects of maternal-child genetic polymorphisms and exposure to environmental and lifestyle-related chemicals during pregnancy.

Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.

BACKGROUND: The COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance. OBJECTIVE: We sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis. METHODS: Our modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020. RESULTS: The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%. CONCLUSIONS: Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.

Population structure and pharmacogenomic risk stratification in the United States.

BACKGROUND: Pharmacogenomic (PGx) variants mediate how individuals respond to medication, and response differences among racial/ethnic groups have been attributed to patterns of PGx diversity. We hypothesized that genetic ancestry (GA) would provide higher resolution for stratifying PGx risk, since it serves as a more reliable surrogate for genetic diversity than self-identified race/ethnicity (SIRE), which includes a substantial social component. We analyzed a cohort of 8628 individuals from the United States (US), for whom we had both SIRE information and whole genome genotypes, with a focus on the three largest SIRE groups in the US: White, Black (African-American), and Hispanic (Latino). Our approach to the question of PGx risk stratification entailed the integration of two distinct methodologies: population genetics and evidence-based medicine. This integrated approach allowed us to consider the clinical implications for the observed patterns of PGx variation found within and between population groups. RESULTS: Whole genome genotypes were used to characterize individuals' continental ancestry fractions-European, African, and Native American-and individuals were grouped according to their GA profiles. SIRE and GA groups were found to be highly concordant. Continental ancestry predicts individuals' SIRE with > 96% accuracy, and accordingly, GA provides only a marginal increase in resolution for PGx risk stratification. In light of the concordance between SIRE and GA, taken together with the fact that information on SIRE is readily available to clinicians, we evaluated PGx variation between SIRE groups to explore the potential clinical utility of race and ethnicity. PGx variants are highly diverged compared to the genomic background; 82 variants show significant frequency differences among SIRE groups, and genome-wide patterns of PGx variation are almost entirely concordant with SIRE. The vast majority of PGx variation is found within rather than between groups, a well-established fact for almost all genetic variants, which is often taken to argue against the clinical utility of population stratification. Nevertheless, analysis of highly differentiated PGx variants illustrates how SIRE partitions PGx variation based on groups' characteristic ancestry patterns. These cases underscore the extent to which SIRE carries clinically valuable information for stratifying PGx risk among populations, albeit with less utility for predicting individual-level PGx alleles (genotypes), supporting the concept of population pharmacogenomics. CONCLUSIONS: Perhaps most interestingly, we show that individuals who identify as Black or Hispanic stand to gain far more from the consideration of race/ethnicity in treatment decisions than individuals from the majority White population.

Containing COVID-19 Among 627,386 Persons in Contact With the Diamond Princess Cruise Ship Passengers Who Disembarked in Taiwan: Big Data Analytics.

BACKGROUND: Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation. OBJECTIVE: We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020. METHODS: The smart contact tracing-based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2. RESULTS: As of February 29, a total of 67 contacts who were tested by reverse transcription-polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020. CONCLUSIONS: Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing.

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.

BACKGROUND: The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events. OBJECTIVE: We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time. METHODS: Our method uses the following as inputs: (a) official health reports, (b) COVID-19-related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks. RESULTS: Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces. CONCLUSIONS: Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.

Understanding the potential of state-based public health genomics programs to mitigate disparities in access to clinical genetic services.

PURPOSE: State health agencies (SHAs) have developed public health genomics (PHG) programs that play an instrumental role in advancing precision public health, but there is limited research on their approaches. This study examines how PHG programs attempt to mitigate or forestall health disparities and inequities in the utilization of genomic medicine. METHODS: We compared PHG programs in three states: Connecticut, Michigan, and Utah. We analyzed 85 in-depth interviews with SHA internal and external collaborators and program documents. We employed a qualitative coding process to capture themes relating to health disparities and inequities. RESULTS: Each SHA implemented population-level approaches to identify individuals who carry genetic variants that increase risk of hereditary cancers. However, each SHA developed a unique strategy-which we label public health action repertoires-to reach specific subgroups who faced barriers in accessing genetic services. These strategies varied across states given demographics of the state population, state-level partnerships, and availability of healthcare services. CONCLUSION: Our findings illustrate the imperative of tailoring PHG programs to local demographic characteristics and existing community resources. Furthermore, our study highlights how integrating genomics into precision public health will require multilevel, multisector collaboration to optimize efficacy and equity.