Creating a postmortem examination decision aid: Suggestions from bereaved parents of a stillborn.

OBJECTIVES: To understand the postmortem decision-making needs and preferences of parents of a stillborn. METHODS: A qualitative content analysis was conducted. Patients who received stillbirth care at the University of Utah in the last 5 years, were 18 years of age or older, and English speakers, were invited to participate via an email and follow-up phone call. Participants were interviewed about their experiences, values, beliefs, decision-making experience regarding the postmortem examinations of their stillborn, and suggestions for how to assist their decision-making needs. RESULTS: Nineteen participants who consented to one or more postmortem examination of their stillborn were interviewed. They expressed needing information, altruism, and/or a belief in science as reasons for consenting. The most common reason for declining was already knowing the stillbirth cause. Recommendations for a decision aid included a description of all stillbirth evaluation options, risks and benefits, and a timeline. CONCLUSION: Participants had a variety of reasons for consenting to or declining postmortem examinations of their stillborn. Recommendations for a decision aid include a full description of each examination, the risks and benefits, and a timeline. PRACTICAL IMPLICATIONS: An example decision aid was created from recommendations, which presents balanced information to help support couple's decision-making.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.

BACKGROUND: Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed. OBJECTIVE: We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction. METHODS: We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes. RESULTS: We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes. DISCUSSION: The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

Understanding the experiences and perspectives of prenatal screening among a diverse cohort.

INTRODUCTION: Rapid advances in prenatal genetic screening technology make it difficult for providers to deliver adequate prenatal counseling. The aim of this study was to understand how prenatal screening educational approaches can meet the needs of patients. METHODS: Qualitative content analysis was conducted on a diverse population who were interviewed to explore their perceived experiences and preferences for prenatal screening educational delivery. RESULTS: Twenty-two women from three US sites were interviewed. Participants were racially/ethnically diverse with 22.7% identifying as Black or African American (n = 5), 40.9% as Hispanic (n = 9), and 4.5% as Pacific Islander (n = 1). Four themes were identified: prenatal screening education, prenatal screening decision-making, return of results, and suggestions for creating a decision aid. Most results were consistent with previous research not targeting a diverse population. DISCUSSION/CONCLUSION: Our results indicate that learning style preferences vary between patients and that current methods are not consistently satisfying patient's desire for understanding, particularly with 'high-risk' results, suggesting that a standardized tool could improve knowledge and decrease decisional conflict. This diverse cohort suggested a list and description of each of the testing options offered, information about each condition being screened for, a timeline for the testing and return of results, costs associated, and non-technical language.

Evaluating visual imagery for participant understanding of research concepts in genomics research.

Informed consent is crucial for participant understanding, engagement, and partnering for research. However, current written informed consents have significant limitations, particularly for complex topics such as genomics and biobanking. Our goal was to identify how participants visually conceptualize terminology used in genomics and biobanking research studies, which might provide a novel approach for informed consent. An online convenience sample was used from May to July 2020 to collect data. Participants were asked to draw 10 randomly chosen words out of 32 possible words commonly used in consent forms for genomics and biobanking research. An electronic application captured drawings that were downloaded into a qualitative software program for analysis. A total of 739 drawings by 269 participants were captured. Participants were mostly female (61.3%), eight different race/ethnicities were represented (15.6% Black, 13.8% Hispanic), and most had some college education (68.8%). Some words had consistent visual themes such as different types of risky activities for risk or consistent specific images such as a double helix for DNA. Several words were frequently misunderstood (e.g., ascend for assent), while others returned few submissions (e.g., phenotype or whole genome sequencing). We found that although some words used in genomics and biobanking research were visually conceptualized in a common fashion, but misunderstood or less well-known words had no, few, or mistaken drawings. Future research can explore the incorporation of visual images to improve participant comprehension during consent processes, and how to utilize visual imagery to address more challenging concepts.

Toward informatics-enabled preparedness for natural hazards to minimize health impacts of climate change.

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.

Social Determinants of Health Factors for Gene-Environment COVID-19 Research: Challenges and Opportunities.

The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID-19. In practice, gene-environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene-environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.

K-means cluster analysis of cooperative effects of CO, NO2, O3, PM2.5, PM10, and SO2 on incidence of type 2 diabetes mellitus in the US.

Air pollution (AP) has been shown to increase the risk of type 2 diabetes mellitus, as well as other cardiometabolic diseases. AP is characterized by a complex mixture of components for which the composition depends on sources and metrological factors. The US Environmental Protection Agency (EPA) monitors and regulates certain components of air pollution known to have negative consequences for human health. Research assessing the health effects of these components of AP often uses traditional regression models, which might not capture more complex and interdependent relationships. Machine learning has the capability to simultaneously assess multiple components and find complex, non-linear patterns that may not be apparent and could not be modeled by other techniques. Here we use k-means clustering to assess the patterns associating PM2.5, PM10, CO, NO2, O3, and SO2 measurements and changes in annual diabetes incidence at a US county level. The average age adjusted annual decrease in diabetes incidence for the entire US populations is -0.25 per 1000 but the change shows a significant geographic variation (range: -17.2 to 5.30 per 1000). In this paper these variations were compared with the local daily AP concentrations of the pollutants listed above from 2005 to 2015, which were matched to the annual change in diabetes incidence for the following year. A total of 134,925 daily air quality observations were included in the cluster analysis, representing 125 US counties and the District of Columbia. K-means successfully clustered AP components and indicated an association between exposure to certain AP mixtures with lower decreases on T2D incidence.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses.

BACKGROUND: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS: Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION: Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.

The limited use of US residual newborn screening dried bloodspots for health disparity research.

PURPOSE: State-run newborn screening programs screen nearly all babies born in the United States at the time of delivery. After newborn screening has been completed, some states store the residual dried bloodspots. It is unknown how they have been used to address health disparities-related research. METHODS: In 2017-2018, a scoping review was conducted to evaluate the extent, type, and nature of how residual dried bloodspots. The review included 654 eligible publications, worldwide, published before May 2017. A post hoc analysis of the US-based studies using residual dried bloodspots (n = 192) were analyzed. RESULTS: There were 32 (16.7%) articles identified that studied a condition of a known health disparity or focused on a key population: 25 studies assessed a disease or condition, 6 expressly enrolled a key population, and 1 study included both (i.e., heart disease and African American/Black). CONCLUSION: Excluding 12 studies that researched leukemia or a brain tumor, only 20 studies addressed a known health disparity, with 6 stating a specific aim to address a health disparity. This resource could be used to gain further knowledge about health disparities, but is currently underutilized.

Standardized ultrasound protocol, trained sonographers and digital system for carotid atherosclerosis screening.

BACKGROUND AND PURPOSE: Carotid intima-media thickness (IMT), a valid measure of atherosclerotic disease, has been proposed to be included in the algorithms for cardiovascular risk stratification. However, assessment of carotid IMT is still not easily performed in an office setting. In the present study, we evaluated the reproducibility of a standardized protocol for carotid artery atherosclerosis screening. METHODS: Carotid arteries of 30 patients were scanned twice (interval 1-10 days) by six trained sonographers, using portable ultrasound systems. A screening protocol was adapted from methods used in clinical trials in which carotid IMT was the primary outcome measure. To test the reproducibility of the method, variability between the two scans was analyzed. RESULTS: A high level of agreement was found between the scans for measurement of mean common carotid IMT [mean difference S0.002, 95% confidence interval (CI) S0.011 to 0.006, PU0.435], maximum region common carotid IMT (mean difference S0.002, 95% CIS0.017 to 0.014, PU0.779) and mean maximum IMT including the common, bifurcation and internal carotid arteries (mean differences 0.021, 95% CI S0.006 to 0.047, PU0.166). No significant differences were found between scans with regard to the average number of carotid segments visualized, the number of atherosclerotic plaques or plaque burden. CONCLUSION: Reliable IMT measurements can be obtained using a standardized protocol performed by trained sonographers using a digital portable ultrasound system in an office setting.