Environmental Determinants Of Health Research Articles

Geospatial Imprecision With Constraints for Precision Public Health: Algorithm Development and Validation.

Location and environmental social determinants of health are increasingly important factors in both an individual's health and the monitoring of community-level public health issues. We aimed to measure the extent to which location obfuscation techniques, designed to protect an individual's privacy, can unintentionally shift geographical coordinates into neighborhoods with significantly different socioeconomic demographics, which limits the precision of findings for public health stakeholders. Point obfuscation techniques intentionally blur geographic coordinates to conceal the original location. The pinwheel obfuscation method is an existing technique in which a point is moved along a pinwheel-like path given a randomly chosen angle and a maximum radius; we evaluate the impact of this technique using 2 data sets by comparing the demographics of the original point and the resulting shifted point by cross-referencing data from the United States Census Bureau. Using poverty measures showed that points from regions of low poverty may be shifted to regions of high poverty; similarly, points in regions with high poverty may be shifted into regions of low poverty. We varied the maximum allowable obfuscation radius; the mean difference in poverty rate before and after obfuscation ranged from 6.5% to 11.7%. Additionally, obfuscation inadvertently caused false hot spots for deaths by suicide in Cook County, Illinois. Privacy concerns require patient locations to be imprecise to protect against risk of identification; precision public health requires accuracy. We propose a modified obfuscation technique that is constrained to generate a new point within a specified census-designated region to preserve both privacy and analytical accuracy by avoiding demographic shifts.

Compliance Considerations in the Geo-enrichment of an EHR Data Warehouse with Social & Environmental Determinants of Health

Compliance Considerations in the Geo-enrichment of an EHR Data Warehouse with Social & Environmental Determinants of Health

Characteristics, enablers, and barriers to the community health and wellbeing assessment component of the health promotion practice cycle: a scoping review protocol.

The proposed review will describe the characteristics, enablers, and barriers to the community health and well-being assessment (CHWA) component of the health promotion practice cycle. CHWA guides health promotion action in communities and populations. A "critical" approach to CHWA can be adopted, which addresses the social, political, cultural, economic, commercial, and environmental determinants of health and well-being to enhance health equity for priority communities and populations. Although tools exist to guide such a critical approach, little is known about the extent to which these tools are being used or the barriers and enablers to applying best practice CHWA. Such evidence is needed to inform future health promotion CHWA and research. This review will consider literature that describes CHWA conducted in health promotion practice, focusing on an organizational, social, or geographical community or population. Literature that focuses on clinical practice or a specific health condition will be excluded. Scopus, PubMed, Web of Science, and CINAHL (EBSCOhost) will be searched to identify peer-reviewed articles. Google Scholar and Google, as well as Public Health, Health & Medical, and Nursing and Allied Health (ProQuest) databases will be searched for gray literature. Articles will be screened and data extracted by 2 or more independent reviewers. The data extraction tool will be developed by the reviewers based on the JBI template and a critical health promotion approach to CHWA. Data will be analyzed and presented as frequency tables and narrative summaries of the characteristics, enablers, and barriers to CHWA. osf.io/jq8th/.

County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study.

We used machine learning methods to explore sociodemographic and environmental determinants of health (SEDH) associated with county-level stroke mortality in the USA. We conducted a cross-sectional analysis of individuals aged ≥15 years who died from all stroke subtypes between 2016 and 2020. We analyzed 54 county-level SEDH possibly associated with age-adjusted stroke mortality rates/100,000 people. Classification and Regression Tree (CART) was used to identify specific county-level clusters associated with stroke mortality. Variable importance was assessed using Random Forest analysis. A total of 501,391 decedents from 2397 counties were included. CART identified 10 clusters, with 77.5% relative increase in stroke mortality rates across the spectrum (28.5 vs 50.7 per 100,000 persons). CART identified 8 SEDH to guide the classification of the county clusters. Including, annual Median Household Income ($), live births with Low Birthweight (%), current adult Smokers (%), adults reporting Severe Housing Problems (%), adequate Access to Exercise (%), adults reporting Physical Inactivity (%), adults with diagnosed Diabetes (%), and adults reporting Excessive Drinking (%). In conclusion, SEDH exposures have a complex relationship with stroke. Machine learning approaches can help deconstruct this relationship and demonstrate associations that allow improved understanding of the socio-environmental drivers of stroke and development of targeted interventions.

Longitudinal Analysis of Latvian Child Growth: Anthropometric Parameters Dynamics from Birth to Adolescence.

This study provides a comprehensive analysis of the physical development patterns from birth to adolescence, utilizing a longitudinal dataset of 70 children monitored from birth until 17 years of age. The research focuses on the variability of growth trajectories, emphasizing the role of genetic and environmental factors in influencing these patterns. Key findings indicate that most children undergo one or two periods of accelerated growth, with significant variability in the timing and magnitude of these growth spurts. The study also highlights the adaptive nature of growth changes over generations, influenced by ecological, nutritional, and socio-economic conditions. The longitudinal approach reveals critical insights into the timing of peak growth velocities, demonstrating that girls reach their growth peak approximately one year earlier than boys. The analysis of intergenerational growth patterns suggests a significant increase in average height over the century, attributed to genetic diversity and changes in lifestyle and nutrition. This study's findings emphasize the importance of updating physical development standards regularly to reflect the changing genetic and environmental landscape. The variability in growth patterns and their correlation with health outcomes in later life highlights the need for targeted public health strategies that address the underlying socio-economic and environmental determinants of health. This research contributes to the understanding of physical development trajectories and provides a foundation for future studies aimed at optimizing health outcomes from early childhood through adolescence. The primary objective of this article is to meticulously analyze the dynamics of height growth and accurately identify the periods of accelerated bodily development within the context of longitudinal research.

211 A Machine Learning Approach to Reduce Disparities in Compliance with Public Health Interventions

OBJECTIVES/GOALS: To establish the root causes of vaccine hesitancy in populations who have less equitable access to health and healthcare services, and experience healthcare inequities, related to the environmental and social determinants of health, through community engagement and conversations, collaboration, circulation, and communication. METHODS/STUDY POPULATION: Existing data from a cross-sectional survey, vaccine hesitancy (VH) parent study, entitled 'Western New York (WNY) COVID-19 Collaborative to Promote Vaccine Acceptance,' conducted July to November 2022, after IRB approval, will be qualitatively analyzed. In the parent study, surveys were administered in WNY community congregations and community centers to individuals that historically have less equitable access to healthcare resources and may encounter health and healthcare disparities. Minorities, in urban and rural areas, age eighteen and older were identified through the NYS Department of Health’s Immunization Information System for daily vaccination rates. A qualitative analysis, promoting fact base HL, and building an inferential statical machine learning tool are the next steps. RESULTS/ANTICIPATED RESULTS: We anticipate the results to show an interplay of multiple factors, including personal, cultural, historical, social, and political, and varies depending on circumstances of time, place, and the type of vaccine being offered. Additionally, a lack of awareness or understanding of vulnerabilities and seriousness of vaccine-preventable diseases, lack of trust in health care providers, social norms, distrust of the healthcare system, biomedical research, and government policy, limited knowledge and understanding of vaccine safety and efficacy, and fear/uncomfortable with needles, as well as the less addressed environmental and social determinants of health associated with racial/ethnic minorities in communities with limited resources may also contribute to VH and less favorable health outcomes. DISCUSSION/SIGNIFICANCE: Identifying people who historically have less equitable access to healthcare resources and may be more likely to resist healthcare services, due to distrust in the system is important. Creating and evaluating an innovative tool to predict refusal of public health interventions is essential to avoid spreading preventable diseases.

315 Social and Environmental Determinants of Health and Incident Chronic Kidney Disease in Michigan

315 Social and Environmental Determinants of Health and Incident Chronic Kidney Disease in Michigan

123 Utilizing Project ECHO to mitigate environmental impacts on health through collaborative provider education

OBJECTIVES/GOALS: Launch a case-based learning collaborative on best practices that meet social, emotional and physical health needs of underserved communities as they relate to environmental toxins—specifically those related to the train derailment in OH. Topics discussed could also include disasters and spills, air quality, extreme heat, and water. METHODS/STUDY POPULATION: In response to a call for action delivered by PA’s Acting Secretary of Health, we established a partnership between Penn State CTSI, Project ECHO at Penn State, and Primary Health Network (PHN). PHN is the largest Federally Qualified Health Center in PA, making it uniquely qualified to reach rural providers diagnosing and treating patients impacted by environmental events. Utilizing the ECHO model, we are hosting monthly, 1-hour sessions on environmental determinants of health starting October 2023. Experts in pulmonology, toxicology, atmospheric science, and rural medicine (whom many participants would have limited access to outside of the ECHO platform) and participants have the opportunity to share and learn from their varied experiences exemplifying a culture of ‘all teach, all learn’. RESULTS/ANTICIPATED RESULTS: Project ECHO is an ideal model for upscaling workforce quickly, allowing participants to be responsive in the care of their community, regardless of location and access to specialty clinics. 74 participants across 26 PA counties registered for the series, ranging from PCPs, medical directors, and state officials. Upon registration, nearly half of our direct patient-care participants do not routinely conduct an environmental exposure history and almost 70% report receiving questions from their patients related to how the environment might impact their health. More than half of those providers reported feeling unprepared to answer patients’ questions related to the environment’s impact on their health. Evaluation data will be collected at enrollment, after each session, and post-series. DISCUSSION/SIGNIFICANCE: This series could result in: * Reduction of health disparities caused by environmental events (no cost, virtual learning) * Increased preparedness to quickly address health questions/symptoms related to environmental exposures * Increased awareness of the environmental impacts on health. * Improved testing/treatment for patients

Trends in non-profit hospitals’ investments in community environmental improvements in New Jersey, 2010-2018

Healthcare organizations play a vital role in addressing the health needs of their service areas, including those of environmental degradation. In 2010, environmental improvements were added as an expenditures category for “community building” on the non-profit hospital tax form. In this report, we identified and analyzed 36 non-profit hospital organizations in New Jersey with complete data from fiscal years 2010 to 2018 using the Community Benefits Insight Hospital Data Set. Between 5.3-15.9% of hospital entities reported any environmental improvement expenses averaging $45,395 and accounting for 0.00021% to 0.0017% of hospital operating expenses. The average statewide annual environmental improvements expenses were $204,959. Few hospitals reported environmental improvement expenses and there was no increase in expenditure over time. Given the increasing awareness for environmental determinants of health, tax and other incentives may be a way to stimulate investment in mitigation of environmental hazards to the community.

Optimisation of socio-economic, environmental and public health determinants of national security for post-pandemic recovery

The COVID-19 pandemic became a significant test for social and economic stability worldwide. Its destructive effect forces authorities to overcome negative impacts and ensure socio-economic recovery. The research distinguishes socio-economic, environmental and public health determinants of national security before and after the pandemic. The post-pandemic recovery strategy covers various perspectives: urban, tourism, employment, healthcare, economic recovery; sustainable development; innovations and investments; environment protection; social and psychological support. We develop the National Security Index. It comprises 16 indicators of economic, environmental and social proxies of national security. The Index concerns 34 European countries in 2000-2022. Several methods were applied. Firstly, the natural and Savage normalisation (to bring indicators to a comparable form). Secondly, the principal component analysis, the Fishburn formula and ranking (to define the significance of indicators). Thirdly, the additive-multiplicative convolution (to integrate individual parameters). The National Security Index showed a delayed COVID-19 destructive impact (in 2020 the Index decreased in 2 out of 34 countries). Negative trends of the pandemic revealed actively in 2021 (the Index decreased in 14 out of 34 countries) and became dramatical in 2022 (the Index decreased in 28 out of 34 countries). Factor analysis showed that national security improvement in 2019, 2020 and 2022 mostly depended on 3-4 determinants while 6 determinants became especially crucial for the national security in 2021. Summarising the results, we can single out a range of important steps to achieve the post-pandemic recovery in national security. Economic measure might be focused on overcoming inflation, government expenditures, optimisation and maintenance of the socio-economic resilience by increase of total reserves in months of imports. Environmental measures consist in strengthening energy independence (reduction of energy production and distribution losses, fall of greenhouse gas emissions). Social measures are simulating employment. The regression analysis findings confirm no statistically significant impact of public health determinants on national security in 2019-2022.

Understanding the interplay of occupational, public health, and climate-related risks for informal workers: A new framework with findings from Zimbabwe and India

Globally, there are 2 billion ‘informal’ workers, who lack access to social protection while facing profound health risks and socioeconomic exclusions. The informal economy has generated most jobs in Low and Middle-Income Countries (LMICs), but few studies have explored informal workers' complex health vulnerabilities, including in the face of climate change.This paper will discuss recent action-research in Indore (India), Harare, and Masvingo (Zimbabwe) with informal workers like vendors, waste-pickers, and urban farmers. We conducted qualitative interviews (N = 110 in India), focus group discussions (N = 207 in Zimbabwe), and a quantitative survey (N = 418 in Zimbabwe). Many informal workers live in informal settlements (‘slums’), and we highlight the interrelated health risks at their homes and workplaces. We explore how climate-related threats—including heatwaves, drought, and floods—negatively affect informal workers' health and livelihoods. These challenges often have gender-inequitable impacts. We also analyse workers' individual and collective responses.We propose a comprehensive framework to reveal the drivers of health in the informal economy, and we complement this holistic approach with a new research agenda. Our framework highlights the socioeconomic, environmental, and political determinants of informal workers' health. We argue that informal workers may face difficult trade-offs, due to competing priorities in the face of climate change and other risks. Future interventions will need to recognise informal workers’ array of risks and co-develop multifaceted solutions, thereby helping to avoid such impossible choices. We recommend holistic initiatives to foster health and climate resilience, as well as participatory action-research partnerships and qualitative, intersectional data-collection with informal workers.

Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.

Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drivers of psoriasis risk. However, it is increasingly recognized that health behavior arises in the context of larger social, cultural, economic and environmental determinants of health. We aimed to identify the top risk factors that significantly impact the incidence of psoriasis at the neighborhood level using populational data from the province of Quebec (Canada) and advanced tree-based machine learning (ML) techniques. Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1year prior to psoriasis onset were obtained from the Canadian Urban Environment Health Consortium (CANUE) and Statistics Canada (StatCan) and were input as predictors into the gradient boosting ML. Model performance was evaluated using the area under the curve (AUC). Parsimonious models and partial dependence plots were determined to assess directionality of the relationship. The incidence of psoriasis varied geographically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious model (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amongst top predictors, ultraviolet (UV) radiation, maximum daily temperature, proportion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggested a higher psoriasis incidence in the middle socioeconomic class neighborhoods. This is the first study to highlight highly variable psoriasis incidence rates on a jurisdictional level and suggests that living environment, notably climate, vegetation, urbanization and neighborhood socioeconomic characteristics may have an association with psoriasis incidence.

Associations between environmental perception and self-rated health in the city hierarchy of China: Findings from a national cross-sectional survey

Identifying environmental determinants of health and clarifying their variations is crucial for health promotion in different cities by providing tailored intervention strategies. Although the association between perceived urban environment and health (e.g., self-rated health) has been repeatedly explored, most studies have focused on cities of a specific size, and it is still unknown whether either significant environment variables or the magnitude of the association would vary across different-sized cities. This study investigated how perceived urban environment variables significantly associated with individuals’ self-rated health varied from small cities to mega cities in China, based on a national survey including 5963 valid respondents. The results showed that the relationship between self-rated health and city size was U-shaped, with respondents in medium and large cities reporting a low-level self-rated health. Perceived greenness, public facilities, housing supply, and medical services were positively and significantly associated with self-rated health, with the odds ratio (OR) of 1.37 (95%CI: 1.29–1.46), 1.27 (95%CI: 1.19–1.35), 1.14 (95%CI: 1.09–1.20), and 1.17 (95%CI: 1.10–1.24), respectively. Furthermore, the magnitude of the association was significantly larger in mega cities. These findings provide useful evidence for promoting public health in cities of different sizes for achieving health equity and indicate that smaller cities and their health-supportive environment need further attention.

Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.

Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS). Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan's socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests. Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES. Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.

“I think they should give primary health care a little more priority”. The primary health care in Caribbean SIDS: what can be said about adaptation to the changing climate? The case of Dominica— a qualitative study

BackgroundAdaptation to climate change (CC) is a priority for Small Island Developing States (SIDS) in the Caribbean, as these countries and territories are particularly vulnerable to climate-related events. Primary health care (PHC) is an important contributor to CC adaptation. However, knowledge on how PHC is prepared for CC in Caribbean SIDS is very limited. The aim of this paper is to discuss health system adaptation to climate change, with a focus on PHC.MethodsWe explored the perspectives of PHC professionals in Dominica on PHC adaptation to climate change. Focus group discussions (FGDs) were conducted in each of the seven health districts in Dominica, a Caribbean SIDS, between November 2021 and January 2022. The semi-structured interview guide was based on the Essential Public Health Functions: assessment, access to health care services, policy development and resource allocation. Data coding was organized accordingly.ResultsFindings suggest that health care providers perceive climate change as contributing to an increase in NCDs and mental health problems. Climate-related events create barriers to care and exacerbate the chronic deficiencies within the health system, especially in the absence of high-level policy support. Healthcare providers need to take a holistic view of health and act accordingly in terms of disease prevention and health promotion, epidemiological surveillance, and ensuring the widest possible access to healthcare, with a particular focus on the environmental and social determinants of vulnerability.ConclusionThe primary health care system is a key stakeholder in the design and operationalization of adaptation and transformative resilience. The Essential Public Health Functions should integrate social and climate and other environmental determinants of health to guide primary care activities to protect the health of communities. This study highlights the need for improved research on the linkages between climate events and health outcomes, surveillance, and development of plans informed by contextual knowledge in the SIDS.

County-level socio-environmental factors and obesity prevalence in the United States.

To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques. We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest. Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%). There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

Noise and mental health: evidence, mechanisms, and consequences.

The recognition of noise exposure as a prominent environmental determinant of public health has grown substantially. While recent years have yielded a wealth of evidence linking environmental noise exposure primarily to cardiovascular ailments, our understanding of the detrimental effects of noise on the brain and mental health outcomes remains limited. Despite being a nascent research area, an increasing body of compelling research and conclusive findings confirms that exposure to noise, particularly from sources such as traffic, can potentially impact the central nervous system. These harms of noise increase the susceptibility to mental health conditions such as depression, anxiety, suicide, and behavioral problems in children and adolescents. From a mechanistic perspective, several investigations propose direct adverse phenotypic changes in brain tissue by noise (e.g. neuroinflammation, cerebral oxidative stress), in addition to feedback signaling by remote organ damage, dysregulated immune cells, and impaired circadian rhythms, which may collectively contribute to noise-dependent impairment of mental health. This concise review linking noise exposure to mental health outcomes seeks to fill research gaps by assessing current findings from studies involving both humans and animals.

A conceptual analysis of the public health-architecture nexus within rapidly developing informal urban contexts

Background: The interactions between people and places can have a significant impact on the wellbeing of individuals, particularly in warm, arid countries such as South Africa. Coupled to this are the various risks that climate change poses to the development of communities, particularly in informal settlements. Several of these risks include the increasing prevalence of contaminants in water and land, as well as the challenges of managing the effects of climate change.Objectives: This conceptual analysis aims to highlight the need for a transdisciplinary approach to investigating the interaction between informal urban living spaces, environmental and social determinants of health and further propose a framework that incorporates tools and strategies to improve health and wellbeing.Discussion: Despite the various advantages of living in an urban environment, many people in informal settlements still lack access to adequate sanitation and water services. This is why it is important that we develop a comprehensive understanding of how these changes can be accommodated in the future. This can be done through the collection of data from the people who live in these communities. A comprehensive understanding of the environmental and social determinants of health, coupled with innovative monitoring systems, would provide a more holistic approach to suggesting a practical strategy for promoting health and wellbeing in communities while working towards developing safe and sustainable health-promoting living spaces.Conclusion: Currently there is a paucity of information regarding access to water, sanitation and health services, coupled with environmental pollution and poor living conditions causing diseases affecting informal communities, furthermore practical and viable changes to address these concerns are similarly lacking. To ensure that healthier built environments are provided for children and adults, transdisciplinary approaches between researchers and collaboration with, communities within informal settlements and with policymakers to promote health advocacy is essential.

The relationship between perceptions of electronic health record usability and clinical importance of social and environmental determinants of health on provider documentation.

Social and environmental determinants of health (SEDH) data in the electronic health record (EHR) can be inaccurate and incomplete. Providers are in a unique position to impact this issue as they both obtain and enter this data, however, the variability in screening and documentation practices currently limits the ability to mobilize SEDH data for secondary uses. This study explores whether providers' perceptions of clinical importance of SEDH or EHR usability influenced data entry by analyzing two relationships: (1) provider charting behavior and clinical consideration of SEDH and (2) provider charting behavior and ease of EHR use in charting. We performed a cross-sectional study using an 11-question electronic survey to assess self-reported practices related to clinical consideration of SEDH elements, EHR usability and SEDH documentation of all staff physicians, identified using administrative listserves, at Penn State Health Hershey Medical Center during September to October 2021. A total of 201 physicians responded to and completed the survey out of a possible 2,478 identified staff physicians (8.1% response rate). A five-point Likert scale from "never" to "always" assessed charting behavior and clinical consideration. Responses were dichotomized as consistent/inconsistent and vital/not vital respectively. EHR usability was assessed as "yes" or "no" responses. Fisher's exact tests assessed the relationship between charting behavior and clinical consideration and to compare charting practices between different SEDHs. Cumulative measures were constructed for consistent charting and ease of charting. A generalized linear mixed model (GLMM) compared SDH and EDH with respect to each cumulative measure and was quantified using odds ratios (OR) and 95% confidence intervals (CI). Our results show that provider documentation frequency of an SEDH is associated with perceived clinical utility as well as ease of charting and that providers were more likely to consistently chart on SDH versus EDH. Nuances in these relationships did exist with one notable example comparing the results of smoking (SDH) to infectious disease outbreaks (EDH). Despite similar percentages of physicians reporting that both smoking and infectious disease outbreaks are vital to care, differences in charting consistency and ease of charting between these two were seen. Taken as a whole, our results suggest that SEDH quality optimization efforts cannot consider physician perceptions and EHR usability as siloed entities and that EHR design should not be the only target for intervention. The associations found in this study provide a starting point to understand the complexity in how clinical utility and EHR usability influence charting consistency of each SEDH element, however, further research is needed to understand how these relationships intersect at various levels in the SEDH data optimization process.

Cohort profile: The Scottish SHARE Mental Health (SHARE-MH) cohort – linkable survey, genetic and routinely collected data for mental health research

PurposeThe SHARE Mental Health (SHARE-MH) cohort was established to address the paucity of clinical and genetic data available for mental health research. The cohort brings together detailed mental health questionnaire...