Scanning the Horizon of Sociogenomics: an Assessment of the Development and Growth of Polygenic Indices for Social and Behavioral Traits

Wildfires are increasing in frequency and intensity, with significant consequences that impact human health. A scoping review was conducted to: (a) understand wildfire-related health effects, (b) identify and describe environmental exposure and health outcome data sources used to research the impacts of wildfire exposures on health, and (c) identify gaps and opportunities to leverage exposure and health data to advance research. A literature search was conducted in PubMed and a sample of 83 articles met inclusion criteria. A majority of studies focused on respiratory and cardiovascular outcomes. Hospital administrative data was the most common health data source, followed by government data sources and health surveys. Wildfire smoke, specifically fine particulate matter (PM2.5), was the most common exposure measure and was predominantly estimated from monitoring networks and satellite data. Health data were not available in real-time, and they lacked spatial and temporal coverage to study health outcomes with longer latency periods. Exposure data were often available in real-time and provided better temporal and spatial coverage but did not capture the complex mixture of hazardous wildfire smoke pollutants nor exposures associated with non-air pathways such as soil, household dust, food, and water. This scoping review of the specific health and exposure data sources used to underpin these studies provides a framework for the research community to understand: (a) the use and value of various environmental and health data sources, and (b) the opportunities for improving data collection, integration, and accessibility to help inform our understanding of wildfires and other environmental exposures.

Behavioral phenotypic traits or “animal personalities” drive critical evolutionary processes such as fitness, disease and information spread. Yet the stability of behavioral traits, essential by definition, has rarely been measured over developmentally significant periods of time, limiting our understanding of how behavioral stability interacts with ontogeny. Based on 32 years of social behavioral data for 179 wild bottlenose dolphins, we show that social traits (associate number, time alone and in large groups) are stable from infancy to late adulthood. Multivariate analysis revealed strong relationships between these stable metrics within individuals, suggesting a complex behavioral syndrome comparable to human extraversion. Maternal effects (particularly vertical social learning) and sex-specific reproductive strategies are likely proximate and ultimate drivers for these patterns. We provide rare empirical evidence to demonstrate the persistence of social behavioral traits over decades in a non-human animal.

ObjectiveWhile there is a growing torrent of data that disease surveillance could leverage, few effective tools exist to help public health professionals make sense of this data or that provide secure work-sharing and communication. Meanwhile, our ever more-connected world provides an increasingly receptive environment for diseases to emerge and spread rapidly making early warning and collaborative decision-making essential to saving lives and reducing the impact of outbreaks. Digital Infuzion's previous work on the Defense Threat Reduction Agency (DTRA)'s Biosurveillance Ecosystem (BSVE) built a cloud-based platform to ingest big data with analytics to provide users a robust surveillance environment. We next enhanced the BSVE data sources and analytics to support an integrated One Health paradigm. The resulting BSVE and Digital Infuzion's HARBINGER platform include: 1) identifying and ingesting data sources that span global human, animal and crop health; 2) inclusion of non-health data such as travel, weather, and infrastructure; 3) the data science tools, analytics and visualizations to make these data useful and 4) a fully-featured Collaboration Center for secure work-sharing and communication across agencies.IntroductionAfter the 2009 H1N1 pandemic, the Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense indicated “biodefense” would include emerging infectious disease. In response, DTRA launched an initiative for an innovative, rapidly emerging capability to enable real-time biosurveillance for early warning and course of action analysis. Through competitive prototyping, DTRA selected Digital Infuzion to develop the platform and next generation analytics. This work was extended to enhance collaboration capabilities and to harness data science and advanced analytics for multi-disciplinary surveillance including climate, crop, and animal as well as human data. New analysis tools ensure the BSVE supports a One Health paradigm to best inform public health action. Digital Infuzion and DTRA first introduced the BSVE to the ISDS community at the 2013 annual conference SWAP Meet. Digital Infuzion is pleased to present the mature platform to this community again as it is now a fully developed capability undergoing FedRAMP certification with the Department of Homeland Security’s National Biosurveillance Integration Center and Is the basis for Digital Infuzion's HARBINGER ecosystem for biosurveillance.MethodsWe integrated over 170 global One Health data sources using cloud-based automated data ingestion workflows that provide unified access with data provenance. We used modular automated workflows to implement data science including Natural Language Processing (NLP), machine learning, anomaly detection, and expert systems for extraction of concepts from unstructured text. A first of its kind ontology for biosurveillance permits linking of data across sources. This ontology allows users to rapidly find all relevant data by looking at semantic relationships within and across data sets having varying quality, types, and usages to understand the best, most complete indicators of impending threats.We applied the following principles to the development of data science tools: 1) mathematics should be fully automated and operate 'under the hood' without need for user intervention; 2) 'At-a-Glance' visualizations should summarize Information, draw attention to key aspects and permit drill down into underlying data; 3) data science analytics and tools need to be validated with real-world data and by disease surveillance experts and 4) secure collaboration capabilities are essential to biosurveillance activities.This was a highly complex effort. We worked closely with surveillance analysts from multiple agencies and organizations to continuously guide the development of capabilities. We drew upon subject matter expertise in public health, machine learning, social media, NLP, semantics, big data integration, computational science, and visualization. A high level of automation, security and immediacy of data was applied to support rapid identification and investigation of potential outbreaks.ResultsThe platform now provisions integrated One Health information. Data sources were harmonized and expanded, along with historical information, to better predict and understand biothreats. These include global social media, human, plant, animal, and weather data. An Analyst Workbench delivers logical, intuitive and interactive visualizations enabling disease surveillance professionals to identify critical, predictive information without extensive manual research. Over 700 approved users currently have access to the prototype.Biosurveillance activities can be performed collaboratively among governmental agencies, public health officials, and the general public using the Collaboration Center and its sharing and messaging systems. Data sharing is HIPAA compliant and distinguishes public from private data using carefully controlled and approved role- and attribute-based access for security.To speed disease surveillance workflows, the workbench generates suggestions to the user on their current work. Anomaly detection to alert to potential developing disease events employs fully automated analytics to conduct over 43 million calculations daily for more than 500 diseases in over 170 data sources, distilling this into a table that ranks the most significant anomalous increases that may indicate an outbreak and warrant investigation.A predictive disease modeling tool based on current and historical data uses fuzzy logic to identify the likeliest outcome, even early in an outbreak when there is much uncertainty about the disease and its characteristics. A complex automated workflow identifies health-related topics that are trending in Twitter and evaluates their severity using novel lexicons and new reactive sentiment analysis. Searches use the ontology to gather all relevant information and are supported by the most advanced NLP with custom surveillance rules to provide succinctly extracted information. This alleviates the need for extensive reading by identifying exactly which data is needed and extracting key concepts from it. Intuitive methods of visual representation, interactive displays, and drill-down capabilities were leveraged in all analytics for rapid understanding of results.Finally, we added a software development kit to enable third party developers to continuously enhance the platform capabilities by adding new data sources and new analytic apps. This allows the platform to be adapted for specific needs and to keep pace with new scientific and technical discoveries and has resulted in over 50 analytic apps.ConclusionsThe addition of One Health data and analytics, and the integration of health data with unconventional data sources and modern approaches to data science and complex workflows, resulted in enhanced situational awareness and decision-making capabilities for users. The expanded Collaboration Center within the workbench, enables users to partner and collaborate with other agencies and biosurveillance professionals both nationally and internationally to maximize the rapidity of responses to serious disease outbreaks.

Health informatics (HI) has become a significant research area due to the massive generation of digital health and medical data by biomedical and health research organizations. The health data sources are available in different forms namely electronic health records (EHRs), biomedical imaging, bio-signals, sensor data, genomic data, medical history, social media data, and so on. The structured health data can be utilized for HI and effective predictive modeling of health data assists in the decision-making process. The recently developed artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques pave a way for effective predictive modeling on health data. Numerous existing works have been presented in the literature depending upon the ML and DL based HI for various applications. With this motivation, this study aims to review the recent state of art ML and DL based predictive models for health sector. This survey primarily identifies the difference between the ML and DL architectures with their significance in health sector. In addition, the existing works are extensively reviewed and compared in terms of different aspects such as objectives, underlying methodology, input source, dataset used, performance validation, metrics, and so on. Finally, the open challenges and future scope of the HI are examined in detail. At the end of the survey, the readers find it useful to identify the present research and possible future scope of the ML and DL based predictive models for HI.

BackgroundIn longitudinal health research, combining the richness of cohort data to the extensiveness of routine data opens up new possibilities, providing information not available from one data source alone. In this study, we set out to extend information from a longitudinal birth cohort study by linking to the cohort child’s routine primary and secondary health care data. The resulting linked datasets will be used to examine health outcomes and patterns of health service utilisation for a set of common childhood health problems. We describe the experiences and challenges of acquiring and linking electronic health records for participants in a national longitudinal study, the UK Millennium Cohort Study (MCS).MethodWritten parental consent to link routine health data to survey responses of the MCS cohort member, mother and her partner was obtained for 90.7% of respondents when interviews took place at age seven years in the MCS. Probabilistic and deterministic linkage was used to link MCS cohort members to multiple routinely-collected health data sources in Wales and Scotland.ResultsOverall linkage rates for the consented population using country-specific health service data sources were 97.6% for Scotland and 99.9% for Wales. Linkage rates between different health data sources ranged from 65.3% to 99.6%. Issues relating to acquisition and linkage of data sources are discussed.ConclusionsLinking longitudinal cohort participants with routine data sources is becoming increasingly popular in population data research. Our results suggest that this is a valid method to enhance information held in both sources of data.

ObjectivesAdministrative health data is commonly used for epidemiological research, however it is not well understood how disease phenotypes replicate across different data sources. ApproachUK Biobank is a prospective cohort study of 500,000 adults, with ascertainment of health outcomes using administrative health data. Prevalence at recruitment for 33 diseases were calculated in each health data source: self-report, primary-care, and hospital episode statistics (HES). Consistency of disease identification between sources, and median days between first diagnosis across data sources was determined. Linear regression was used to investigate determinant of differences in the average time between first diagnosis in primary-care and HES data. ResultsHypertension was the most commonly identified disease in both self-report and HES (26.5% and 12.1% respectively), and anxiety in primary-care (12.7%). Diseases could be grouped into: 1) those identified largely by self-report alone (e.g. migraine, constipation), with inconsistency in the date first diagnosed; 2) those identified largely by primary care alone (e.g. anxiety, depression), also with inconsistency in the date first diagnosed; and 3) those that appeared mostly in all three sources, with highly consistent date of first report (many were emergency hospital admissions [e.g. stroke]). A number of variables were associated with time between primary-care and HES diagnosis. For example, heavier smokers had a significantly shorter period between first primary-care and first HES record for asthma, diabetes and hypertension. Conclusions and ImplicationsThese results indicate that there are inherent biases in diseases ascertained from linked health data that must be taken into account for epidemiological studies.

The ability of an individual to cope with stressors in their environment is going to be paramount in the face of climate change. Rangeland livestock experience harsh conditions including heat and/or cold stress, water and feed restrictions and risk of predation. In extensive management systems, the ability to capture the capacity to cope with stressors of an individual in its environment is encumbered by remote locations with difficult terrain and no access to power or the internet. GPS collars can provide insights into individual land-use behavior, e.g., their impact on the environment or ability to cope with stressors, and social interactions, e.g., mothering ability. GPS collars (n = 112) developed at the University of Idaho were deployed on 57 ewes and 55 lambs (45 ewe-lamb pairs) in an extensive rangeland environment at the Great Basin Research and Extension Center in Eureka, NV. The collars recorded location data every 10 minutes from July 27th to August 18, 2022. Coordinate fixes were successfully recorded 67.11 ± 27.12% of the time and resulted in a kept record occurring every 60 ± 202 minutes. Of the ewe-lamb pair devices, 43 pairs contributed 801 ± 703 paired records (i.e., records that occurred within 5 minutes of each other). GPS coordinate locations, and their respective capture times, allow for the analysis of distance traveled, water usage, dispersion, and ewe-lamb distance. Distance traveled was analyzed on a daily basis with a weighted median distance imputed for all travel times that lasted more than 900 seconds. Dispersion was defined as the distance of an individual from the centroid of the flock in meters, and was transformed to a Z-score based on the position of individuals with GPS coordinates captured within 10 minutes of the measurement of the individual. Ewe-lamb pair distances were cube root transformed. Twin status, dam line, age, and day were fitted as fixed classes to estimate the repeatability of records for mature ewes. Repeatability estimates of daily distance traveled, daily water usage, and daily dispersion were 0.22 ± 0.05, 0.30 ± 0.06 and 0.10 ± 0.03, respectively. Daily ewe-lamb distances had a repeatability estimate of 0.45 ± 0.08. Day of recording was significant for daily distance traveled (P-value = 2.72×10-4) and water usage (P-value = 1.06×10-3). No other effects were significant in any of the models. In conclusion, PLF tools are an enabling technology that allow for passive data capture in remote locations. Our findings suggest that GPS collars can elucidate a variety of land use and social behavior traits that could serve as meaningful criteria for selection in extensive livestock production settings. Selection on these indicators of resilience may increase livestock productivity and welfare by increasing heat stress tolerance via the analysis of novel behavioral traits seen on extensive rangelands.

Associations of prenatal exposure to a mixture of persistent organic pollutants with social traits and cognitive and adaptive function in early childhood: Findings from the EARLI study

ObjectiveA case study on the visual-material components of story map journals as visual, new media interactive health reporting used in population health surveillance. The story map journal is demonstrated an effective tool that visually reports, maps and tracks global support networks and health resources for post-polio (PPS) survivors.IntroductionHow are interactive story map journals situated within the genre of interactive, health science reporting? How can reporting information to public audiences be theorized through traditional and contemporary understandings of new media genres in technical, health and science communication (1-7). Although the polio vaccine has eradicated the disease in the United States, and 99% worldwide (8), PPS has emerged as a present-day condition that continues to affect many polio survivors years after the initial onset and recovery. Since the symptoms of PPS are oftentimes mis-identified as other illnesses, the diagnosis and management of disease is especially challenging for PPS survivors due to the limited knowledge of and access to PPS resources and support networks (9-11).In 2011, Esri created the ArcGIS story map initiative to meet a need for public audiences who sought how to critically think, better understand, communicate, and interact with world news events. ArcGIS is a geospatially-driven, new media platform that enables audiences to engage with interactive storytelling of news events. Public health and news reporting agencies are now turning to Esri and similar interactive, geospatially driven new media platforms for health and disease surveillance (12-14). Esri’s ArcGIS mobile and web technology platform visually reports, maps and tracks population health data information. With the emergence of such new media applications, it is therefore important to recognize multimodal, visualization strategies that investigate how interaction design choices within the story map journal influence and engage public health audiences. In the field of technical and professional communication (15), applied concept of visual-material rhetorics is a useful mode of inquiry in the study of interactive story map journals. Propen’s concept presents a new understanding of how researchers in disease and public health surveillance can analyze the effectiveness of text and new media technology in relationship to space, place, and geospatial mapping. More specifically, Propen’s concept situates the visual-material as the applied use of text with visual, interactive multimodal components inclusive of images, video, and GPS/GIS mapping technologies.MethodsThis presentation includes a discussion of genre analysis as applied to visual-material components used to study the genre of new media-driven story map journals for the reporting of public and population health resources. Post-Polio Syndrome (PPS) is presented as a case study of how story map journals in population health can be used to create information about global support networks and resources for PPS survivors.ResultsThe story map journal is an effective genre of new media, interactive reporting in health and disease surveillance. The analysis alongside Propen’s mode of inquiry demonstrates the effectiveness of visual-material components of story maps, and how PPS survivors and medical clinicians can use the story map journal to easily access, visualize, and interact with information about diagnosis and disease management, as well as find connections to local and global support networks.ConclusionsStory map journals as visual, interactive reporting should be considered when analyzing the accessibility and surveillance of health data for public audiences. The case study of PPS global networks and resources, provides one example of how story map journals can assist public audiences who experience difficulties finding support networks and public health resources

Introduction:Racial health inequities are explained by inequities in access to medical advice and treatment, and the physiological effects of inequities in material conditions and everyday life; however, Canadian evidence on racial health inequities is limited. This review describes promising practices in population survey methods and approaches that can strengthen sampling, measurement and monitoring of racial health inequities and determinants of health for population subgroups within Canada—particularly Black Canadians. Methods:We employed three steps to identify promising practices in Canada’s peer countries and their applicability to the Canadian context. First, we conducted a scan of websites based on prior knowledge of population-level health surveys and datasets. Second, we conducted a review of publications from 2010 to 2020 to identify any missed surveys and datasets. Third, we conducted a targeted review of Canadian population-level health surveys and data sources to identify challenges to and opportunities for implementing these promising practices.Results:We identified 20 relevant surveys and data sources from the US, the UK, Australia and New Zealand. In several of Canada’s peer countries, information on area-level racial or ethnic concentration of residents is used to conduct targeted sampling strategies, increasing the non-White sample. Our search of the available Canadian datasets found that Canadian health surveys and administrative sources do not routinely incorporate these strategies. Conclusion:Canada could improve the measurement and monitoring of racial health inequities by applying enhanced sampling practices to collect racial data in surveys and improving procedures for administrative and other routinely collected data sources. There are also novel predictive methods being used to improve sampling of non-White groups, though further investigation of these methods is required.

BackgroundEfficient effective child product safety (PS) responses require data on hazards, injury severity and injury probability. PS responses in Australia largely rely on reports from manufacturers/retailers, other jurisdictions/regulators, or consumers....

Increasing the capacity to provide quality, timely and reliable data is one of the targets of the SDGs, especially in small island states like Timor-Leste. This research aims to analyze Faculty of Public Health students' access to public health data and their understanding of key health indicators. This research used a quantitative method with a cross-sectional survey approach with a structured questionnaire that was self-completed by the respondents. The sample consisted of 152 purposively selected students from the first to fourth semester. Data were analyzed using descriptive statistics. The results showed that most students had accessed various sources of health data, such as the Timor-Leste Ministry of Health (91%), WHO (78%), and UNICEF (63%). However, students' understanding of important health indicators was low; 86% did not understand the underweight indicator, 80% did not understand life expectancy, and 90% did not understand infant mortality. This research implies the need for curriculum development that focuses on data literacy and statistics to improve students' ability to access, analyze, and understand public health data. This intervention is important to prepare graduates who are able to face future data-driven public health challenges.

Social and behavioural traits in children with primary nocturnal enuresis were compared with children who had outgrown their enuresis and children who had never bed-wetted after three years of age. The study group included 14 children with primary nocturnal enuresis, 15 children who had had primary nocturnal enuresis and 15 age- and sex-matched controls. The mothers of all children were interviewed using a 32-item questionnaire. If primary nocturnal enuresis were a neurotic disease, we would have expected a higher frequency of emotional dysfunction in children with enuresis and an increase in the symptoms or symptom substitution when bed-wetting was resolved. No significant differences in emotional or behavioural traits among the three groups were found. We conclude that children with primary nocturnal enuresis were well adjusted individuals and display similar social and behavioural traits as their peers. This study lends further support to the theory that primary nocturnal enuresis is not a psychological disorder.

This article is part of a Focus Theme of Methods of Information in Medicine on Health Record Banking. Healthcare is often ineffective and costs are steadily rising. This is in a large part due to the inaccessibility of medical and health data stored in multiple silos. Furthermore, in most cases molecular differences between individuals that result in different susceptibilities to drugs and diseases as well as targeted interventions cannot be taken into account. Technological advances in genome sequencing and the interaction of 'omics' data with environmental data on one hand and mobile health on the other, promise to generate the longitudinal health data that will form the basis for a more personalized, precision medicine. For this new medicine to become a reality, however, millions of personal health data sets have to be aggregated. The value of such aggregated personal data has been recognized as a new asset class and many commercial entities are competing for this new asset (e.g. Google, Facebook, 23andMe, PatientsLikeMe). The primary source and beneficiary of personal health data is the individual. As a collective, society should be the beneficiary of both the economic and health value of these aggregated data and (health) information. We posit that empowering citizens by providing them with a platform to safely store, manage and share their health-related data will be a necessary element in the transformation towards a more effective and efficient precision medicine. Such health data platforms should be organized as cooperatives that are solely owned and controlled by their members and not by shareholders. Members determine which data they want to share for example with doctors or to contribute to research for the benefit of their health and that of society. Members will also decide how the revenues generated by granting third parties access to the anonymized data that they agreed to share, should be invested in research, information or education. Currently no functional Health Data Cooperatives exist yet. The relative success of health data repositories such as 23andme and PatientsLikeMe indicates that citizens are willing to participate in research even if - and in contrast to the cooperative model - the commercial value of these data does not go back to the collective of users. In the Health Data Cooperative model, the citizens with their data would take the center stage in the healthcare system and society would benefit from the health-related and financial benefits that aggregation of these data brings.

Background: Digital medicine and smartphone-enabled health technologies provide a novel source of human health and human biology data. However, in part due to its intricacies, few methods have been established to analyze and interpret data in this domain. We previously conducted a six-month interventional trial examining the efficacy of a comprehensive smartphone-based health monitoring program for individuals with chronic disease. This included 38 individuals with hypertension who recorded 6,290 blood pressure readings over the trial.Methods: In the present study, we provide a hypothesis testing framework for unstructured time series data, typical of patient-generated mobile device data. We used a mixed model approach for unequally spaced repeated measures using autoregressive and generalized autoregressive models, and applied this to the blood pressure data generated in this trial.Results: We were able to detect, roughly, a 2 mmHg decrease in both systolic and diastolic blood pressure over the course of the trial despite considerable intra- and inter-individual variation. Furthermore, by supplementing this finding by using a sequential analysis approach, we observed this result over three months prior to the official study end—highlighting the effectiveness of leveraging the digital nature of this data source to form timely conclusions.Conclusions: Health data generated through the use of smartphones and other mobile devices allow individuals the opportunity to make informed health decisions, and provide researchers the opportunity to address innovative health and biology questions. The hypothesis testing framework we present can be applied in future studies utilizing digital medicine technology or implemented in the technology itself to support the quantified self.