Explore the vast range of titles available.

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008–2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients’ residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region’s census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like–township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

Background Up to 20% of Lyme disease cases experience post-treatment Lyme disease syndrome (PTLDS). The biological basis for PTLDS is poorly understood and no evidence-based treatment has been identified. Genetic studies have the potential to elucidate PTLDS pathophysiology and identify treatment targets. Methods We used electronic health record data (EHR) and genetic data from a linked biorepository to conduct a genome-wide association study (GWAS) for PTLDS among patients from a Pennsylvania health system. We evaluated the validity of the GWAS results in two separate conditions that have hypothesized overlapping pathophysiology, fibromyalgia and myalgic encephalomyelitis – chronic fatigue syndrome (ME/CFS). GWAS analyses were performed using logistic regression in SUGEN, assuming an additive genetic model, and adjusting for age, sex, array, and the first 10 principal components calculated from whole genome genotyping to adjust for ancestry, and accounting for relatedness including all 1st degree relationships. The functional mapping and annotation analysis (FUMA) tool was used to explore top findings from our GWAS. Results Among the 161,875 eligible MyCode participants with genotyping, there were 3,585 who met the criteria for treated Lyme disease. A subset of 695 (19.4%) of these patients met the criteria for PTLDS and the remaining 2890 were classified as controls. We identified two PTLDS loci that reached the suggestive significance threshold ( P  < 5 × 10 − 7 ), with lead variants rs77857587, near IRX1 , and rs10833979, near GAS2 . Our top index single nucleotide polymorphism (SNP), rs77857587, is in high linkage disequilibrium with a long-range protein quantitative locus SNP, rs111774530, for the MARC2 (Mitochondrial Amidoxime Reducing Component 2) protein. We identified 5,041 cases of fibromyalgia (150,599 controls) and 2,268 cases of ME/CFS (151,594 controls) among the MyCode participants. Neither of the two suggestively significant loci were associated with fibromyalgia or ME/CFS. Conclusion We identified two PTLDS loci that reached a suggestive significance threshold. Our top index SNP is associated with the MARC2 protein, a protein that has been linked to multiple immune checkpoints. Further study is needed in a larger population to evaluate whether there is genetic evidence of the role of immune response in the occurrence of PTLDS.

Background An understanding of future healthcare needs of patients with chronic rhinosinusitis (CRS) is essential for patients and providers to make informed treatment decisions. Data on the predictors of long-term CRS-related healthcare utilization are limited. While there is evidence of sex differences in CRS, it is unknown whether the factors associated with long-term healthcare needs differ by sex. Objective The objectives of this study were to evaluate associations between CRS symptoms and CRS-related healthcare utilization and how these associations differ by sex. Methods We conducted a prospective study of 7,847 subjects, utilizing questionnaire and electronic health record data to assess CRS-related healthcare utilization. Individuals who met CRS symptom criteria were categorized into one of four symptom profiles in 2014: obstruction and discharge; pain or pressure without smell loss; smell loss without pain or pressure; and pain or pressure and smell loss. Healthcare utilization (2014–2019) was classified into one of six categories based on CRS-related clinical encounters and diagnostic imaging: minimal overall utilization, no CRS-related utilization; one-year of CRS-related utilization; repeated CRS-related utilization; episodic CRS-related utilization; and discontinued utilization. We evaluated associations between symptom profiles and utilization categories using multinomial logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI), assessing effect modification by sex using cross-product terms. Results Of the 7,847 subjects, 62.7% were women and 37.3% were men. Among women, there was no association between CRS symptoms in 2014 and repeated CRS-related healthcare utilization (versus discontinued CRS-related utilization), whereas men with CRS symptoms had nearly twice the odds (OR 1.81 CI: 1.02, 3.20) of repeated utilization. Women who had CRS with pain and pressure, had higher odds of discontinued CRS-related utilization. This was not observed for other symptom profiles. Conclusion Healthcare utilization patterns for CRS varied by symptoms and sex. Women with facial pain/pressure were uniquely at risk for discontinuation of CRS-related care, providing further evidence of sex differences among individuals reporting sinonasal symptoms.

Background Little is known about risk factors for early (e.g., erythema migrans) and disseminated Lyme disease manifestations, such as arthritis, neurological complications, and carditis. No study has used both diagnoses and free text to classify Lyme disease by disease stage and manifestation. Methods We identified Lyme disease cases in 2012–2016 in the electronic health record (EHR) of a large, integrated health system in Pennsylvania. We developed a rule-based text-matching algorithm using regular expressions to extract clinical data from free text. Lyme disease cases were then classified by stage and manifestation using data from both diagnoses and free text. Among cases classified by stage, we evaluated individual, community, and health care variables as predictors of disseminated stage (vs. early) disease using Poisson regression models with robust errors. Final models adjusted for sociodemographic factors, receipt of Medical Assistance (i.e., Medicaid, a proxy for low socioeconomic status), primary care contact, setting of diagnosis, season of diagnosis, and urban/rural status. Results Among 7310 cases of Lyme disease, we classified 62% by stage. Overall, 23% were classified using both diagnoses and text, 26% were classified using diagnoses only, and 13% were classified using text only. Among the staged diagnoses (n = 4530), 30% were disseminated stage (762 arthritis, 426 neurological manifestations, 76 carditis, 95 secondary erythema migrans, and 76 other manifestations). In adjusted models, we found that persons on Medical Assistance at least 50% of time under observation, compared to never users, had a higher risk (risk ratio [95% confidence interval]) of disseminated Lyme disease (1.20 [1.05, 1.37]). Primary care contact (0.59 [0.54, 0.64]) and diagnosis in the urgent care (0.22 [0.17, 0.29]), compared to the outpatient setting, were associated with lower risk of disseminated Lyme disease. Conclusions The associations between insurance payor, primary care status, and diagnostic setting with disseminated Lyme disease suggest that lower socioeconomic status and less health care access could be linked with disseminated stage Lyme disease. Intervening on these factors could reduce the individual and health care burden of disseminated Lyme disease. Our findings demonstrate the value of both diagnostic and narrative text data to identify Lyme disease manifestations in the EHR.

Chronic rhinosinusitis (CRS) is a prevalent condition. Clinical diagnosis requires subjective evidence (i.e. symptoms) and objective evidence of inflammation (e.g. sinus computed tomography [CT]). Few studies have assessed differences in common CT scoring approaches for CRS, the Lund-Mackay (LM) system and its modified version (mLM); none in a general population sample. The aims of this study were to answer the following: (1) Is mLM superior to LM? (2) Should nasal cavity opacification be included in scoring? (3) How should location-specific scores be utilized? (4) If location-specific scores are summed, what should be the cutoff? (5) Are associations of opacification with symptoms observed when using different measurement approaches? We scored sinus CTs using LM and mLM from 526 subjects selected from a larger CRS study. Exploratory factor analysis (EFA) assessed similarity of mLM and LM. Latent class analysis (LCA) identified subgroups of sinus opacification patterns. Factors associated with group membership and relations with nasal and sinus symptoms (NSS) guided clinical relevance. EFA suggested no differences between LM and mLM, or after addition of nasal cavity opacification. LCA identified three opacification groups: no/mild, localized, and diffuse. Males were 2.7x more likely to have diffuse opacification than females, as were those with asthma or hay fever. A LM cutoff of 3 had similar performance to the currently used 4. Diffuse opacification was associated with nasal blockage and smell loss. Differing patterns of opacification may be clinically relevant, improving measurement of objective evidence in studies of CRS and sinus diseases.

ObjectivesTo evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions.DesignNested case–control study within the open dynamic cohort of health system patients.SettingLarge, integrated health system in 37 counties in central and northeastern Pennsylvania, USA.Participants and analysisWe used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types.ResultsBorough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds.ConclusionsUrban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.

Background: Longer time between symptom onset and treatment of Lyme disease has been associated with poor outcomes. Reducing time-to-treatment requires knowledge of risks for treatment delays. We conducted a population-based study to evaluate factors associated with delayed treatment of Lyme disease and the relation between delayed treatment and post-treatment Lyme disease syndrome (PTLDS). Methods: We mailed questionnaires to 5,314 individuals with a Lyme disease diagnosis or blood test followed by an antibiotic order in the medical record of a Pennsylvania health system from 2015 to 2017. Analyses were confined to 778 respondents who reported that they were treated for Lyme disease within the past 5 years and reported a rash and/or a positive blood test for Lyme disease. Time-to-treatment was calculated as the sum of two windows before and after seeking care for Lyme disease symptoms: time to first medical contact and time under care. We used logistic regression to evaluate factors associated with delayed time-to-treatment in each time window (>14 days vs. ≤14 days) and the association between total time-to-treatment (>30 days vs. ≤30 days) and PTLDS. We used inverse probability weighting to calculate estimates for the study's source population (5,314 individuals sent questionnaires). Results: In the source population, 25% had time to first contact >14 days, 21% had time under care >14 days, and 31% had a total time-to-treatment >30 days. Being uninsured and attributing initial symptoms to something other than Lyme disease were positively associated with delayed time to first medical contact, while seeking care at an urgent care or emergency setting (vs. primary care) was negatively associated. Diagnoses between November and April, and the absence of rash were positively associated with delays. Individuals whose treatment was delayed, defined as time-to treatment >30 days had 2.26 (95% confidence interval: 1.25, 4.05) times the odds of PTLDS as those who were treated within 30 days of symptom onset. Conclusions: In a population-based study in Pennsylvania, one-third of Lyme disease patients reported delayed treatment, which was associated with PTLDS. To improve Lyme disease outcomes, prevention efforts should aim to reduce the time before and after seeking care.

Multi-site and multi-organizational teams are increasingly common in epidemiologic research; however, there is a lack of standards or best practices for achieving success in collaborative research networks in epidemiology. We summarize our experiences and lessons learned from the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a collaborative agreement between the Centers for Disease Control and Prevention and research teams at Drexel University, New York University, Johns Hopkins University and Geisinger, and the University of Alabama at Birmingham. We present a roadmap for success in collaborative epidemiologic research, with recommendations focused on the following areas to maximize efficiency and success in collaborative research agreements: 1) operational and administrative considerations; 2) data access and sharing of sensitive data; 3) aligning network research aims; 4) harmonization of methods and measures; and 5) dissemination of findings. Future collaborations can be informed by our experiences and ultimately dedicate more resources to achieving scientific aims and efficiently disseminating scientific work products.

Variation in the land use environment (LUE) impacts the continuum of walkability to car dependency, which has been shown to have effects on health outcomes. Existing objective measures of the LUE do not consider whether the measurement of the construct varies across different types of communities along the rural/urban spectrum. To help meet the goals of the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, we developed a national, census tract‐level LUE measure which evaluates the road network and land development. We tested for measurement invariance by LEAD community type (higher density urban, lower density urban, suburban/small town, and rural) using multiple group confirmatory factor analysis. We determined that metric invariance does not exist; thus, measurement of the LUE does vary across community type with average block length, average block size, and percent developed land driving most shared variability in rural tracts and with intersection density, street connectivity, household density, and commercial establishment density driving most shared variability in higher density urban tracts. As a result, epidemiologic studies need to consider community type when assessing the LUE to minimize place‐based confounding. Plain Language Summary A community's land use environment (LUE) describes its citizens ability to walk from place to place versus their reliance on vehicles for transportation. Some factors that might influence LUE include the street network, size of road blocks, density of walkable establishments (food stores, restaurants, schools, etc.), and the mixture of residential and commercial establishments. Existing objective measures of the LUE do not consider urbanicity, which can lead to differences due only to places being more or less rural. The Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network created a national, census tract‐level LUE measure and assessed whether the LUE construct differed across LEAD community type groups (higher density urban, lower density urban, suburban/small town, and rural). We found that the LUE construct does vary across LEAD community type. Future epidemiologic studies examining the LUE need to consider urbanicity to account for these differences. Key Points Variation in the land use environment (LUE) impacts the continuum of walkability to car dependency, which has effects on health outcomes We assessed measurement invariance of a land use construct across levels of urbanicity using multiple group confirmatory factor analysis We determined that measurement of the LUE does vary across urbanicity which can lead to place‐based confounding

Background Genetics explains a small proportion of variance in body mass index at the population level. Epigenetics, commonly measured by gene methylation, holds promise for understanding obesity risk factors and mechanisms. Methods Participants were 431 adolescents aged 10–15 years. BMI z-score, waist circumference z-score, and percent body fat were measured. Saliva samples were collected and methylation of promoter regions of four candidate genes or sequences ( LEP , ICAM-1 , CRH , and LINE-1) were measured in 3–4 CpG sites each. Linear regression was used to identify associations of methylation with obesity-related outcomes. Results After adjusting for age, in sex-stratified analysis, the three obesity-related outcomes were negatively associated with LEP methylation in obese boys only. There were no associations of methylation of the other genes or sequences and the obesity-related outcomes. Conclusions Our results are consistent with prior studies that reported sex differences in associations of obesity-related outcomes with LEP methylation, and also as would be expected in adipose tissue, the source of circulating leptin. The findings suggest that saliva might be an acceptable tissue for epigenetics studies in adolescents.