Geospatial Correlation of Amyopathic Dermatomyositis With Fixed Sources of Airborne Pollution: A Retrospective Cohort Study

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Autoimmune pre-disease

Approximately 5% of the world-wide population is affected by autoimmune diseases. Overall, autoimmune diseases are still difficult to treat, impose a high burden on patients, and have a significant economic impact. Like other complex diseases, e.g., cancer, autoimmune diseases develop over several years. Decisive steps in the development of autoimmune diseases are (i) the development of autoantigen-specific lymphocytes and (often) autoantibodies and (ii) potentially clinical disease manifestation at a later stage. However, not all healthy individuals with autoantibodies develop disease manifestations. Identifying autoantibody-positive healthy individuals and monitoring and inhibiting their switch to inflammatory autoimmune disease conditions are currently in their infancy. The switch from harmless to inflammatory autoantigen-specific T and B-cell and autoantibody responses seems to be the hallmark for the decisive factor in inflammatory autoimmune disease conditions. Accordingly, biomarkers allowing us to predict this progression would have a significant impact. Several factors, such as genetics and the environment, especially diet, smoking, exposure to pollutants, infections, stress, and shift work, might influence the progression from harmless to inflammatory autoimmune conditions. To inspire research directed at defining and ultimately targeting autoimmune predisease, here, we review published evidence underlying the progression from health to autoimmune predisease and ultimately to clinically manifest inflammatory autoimmune disease, addressing the following 3 questions: (i) what is the current status, (ii) what is missing, (iii) and what are the future perspectives for defining and modulating autoimmune predisease.

The Geospatial Distribution of Myositis and Its Phenotypes in the United States and Associations With Roadways: Findings From a National Myositis Patient Registry

Background

Little is known about the spatial distribution of idiopathic inflammatory myopathies (IIM) in the United States (U.S.), or their geospatial associations.

Methods

We studied a national myositis patient registry, with cases diagnosed in the contiguous U.S. from 1985–2011 and comprised of dermatomyositis (DM, n = 484), polymyositis (PM, n = 358), and inclusion body myositis (IBM, n = 318) patients. To assess the association of myositis prevalence with distance from roads, we employed log-Gaussian Cox process models, offset with population density.

Results

The U.S. IIM case distribution demonstrated a higher concentration in the Northest. DM, IBM, and cases with lung disease were more common in the East, whereas PM cases were more common in the Southeast. One area in the West and one area in the South had a significant excess in cases of DM relative to PM and of cases with lung disease relative to those without lung disease, respectively. IIM cases tended to cluster, with between-points interactions more intense in the Northeast and less in the South. There was a trend of a higher prevalence of IIM and its major phenotypes among people living within 50 m of a roadway relative to living beyond 200 m. Demographic characteristics, rural-urban commuting area, and female percentage were significantly associated with the prevalence of IIM and with major phenotypes.

Conclusions

Using a large U.S. database to evaluate the spatial distribution of IIM and its phenotypes, this study suggests clustering in some regions of the U.S. and a possible association of proximity to roadways.

Environmental triggers of dermatomyositis: a narrative review

Dermatomyositis (DM) is an autoimmune disease that affects the skin, lungs, and muscle. Although the pathogenesis of DM is not completely understood, several environmental triggers have been linked to DM onset or flare. This article specifically examines the effects of herbal supplements, drugs, infections, ultraviolet (UV) radiation, and environmental pollutants on the onset or exacerbation of DM. Herbal supplements such as Spirulina platensis, Aphanizomenon flos-aquae, Chlorella, Echinacea, and Alfalfa have been implicated and are frequently used in health foods. Medications such as hydroxyurea, TNF-α inhibitors, immune checkpoint inhibitors (ICI), and penicillamine, as well as certain viral infections, such as parvovirus B19, coxsackie virus, polyomavirus, Epstein-Barr virus (EBV), hepatitis, influenza, and human immunodeficiency viruses (HIV) have been associated with DM onset. Bacterial infections and vaccinations have also been linked to the development of DM. Additional environmental factors, including UV radiation and air pollutants, such as silica, biological/mineral dust, and particulate air matter from vehicle and industrial emissions, may also play a role in DM pathogenesis. Overall, there is general agreement that an autoimmune attack of the skin, muscle, and lungs in DM can be triggered by various environmental factors and warrants further investigation.