Correction: Publicly available data reveals association between asthma hospitalizations and unconventional natural gas development in Pennsylvania.

[This corrects the article DOI: 10.1371/journal.pone.0265513.].

Risk of severe acute respiratory syndrome coronavirus 2 infection among women with polycystic ovary syndrome.

OBJECTIVE: To determine whether women with polycystic ovary syndrome (PCOS) had a higher incidence of testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than those without PCOS and evaluate whether PCOS diagnosis independently increased the risk of moderate or severe disease in those with positive SARS-CoV-2 test results. DESIGN: Retrospective cohort study using the National COVID Cohort Collaborative (N3C). SETTING: National COVID Cohort Collaborative. PATIENT(S): Adult nonpregnant women (age, 18-65 years) enrolled in the N3C with confirmed SARS-CoV-2 testing for any indication. Sensitivity analyses were conducted in women aged 18-49 years and who were obese (body mass index, ≥30 kg/m2). INTERVENTION(S): The exposure was PCOS as identified by the N3C clinical diagnosis codes and concept sets, which are a compilation of terms, laboratory values, and International Classification of Diseases codes for the diagnosis of PCOS. To further capture patients with the symptoms of PCOS, we also included those who had concept sets for both hirsutism and irregular menses. MAIN OUTCOME MEASURE(S): Odds of testing positive for SARS-CoV-2 and odds of moderate or severe coronavirus disease 2019 (COVID-19) in the PCOS cohort compared with those in the non-PCOS cohort. RESULT(S): Of the 2,089,913 women included in our study, 39,459 had PCOS. In the overall cohort, the adjusted odds ratio (aOR) of SARS-CoV-2 positivity was 0.98 (95% confidence interval [CI], 0.97-0.98) in women with PCOS compared to women without PCOS. The aORs of disease severity were as follows: mild disease, 1.02 (95% CI, 1.01-1.03); moderate disease, 0.99 (95% CI, 0.98-1.00); and severe disease, 0.99 (95% CI, 0.99-1.00). There was no difference in COVID-19-related mortality (aOR, 1.00; 95% CI, 0.99-1.00). These findings were similar in the reproductive-age and obese reproductive-age cohorts. CONCLUSION(S): Women with PCOS had a similar likelihood of testing positive for SARS-CoV-2. Among those who tested positive, they were no more likely to have moderate or severe COVID-19 than the non-PCOS cohort. Polycystic ovary syndrome is a chronic condition associated with several comorbidities, including cardiovascular disease and mental health issues. Although these comorbidities are also associated with COVID-19 morbidity, our findings suggest that the comorbidities themselves, rather than PCOS, drive the risk of disease severity.

Medication-Wide Association Study Using Electronic Health Record Data of Prescription Medication Exposure and Multifetal Pregnancies: Retrospective Study.

BACKGROUND: Medication-wide association studies (MWAS) have been applied to assess the risk of individual prescription use and a wide range of health outcomes, including cancer, acute myocardial infarction, acute liver failure, acute renal failure, and upper gastrointestinal ulcers. Current literature on the use of preconception and periconception medication and its association with the risk of multiple gestation pregnancies (eg, monozygotic and dizygotic) is largely based on assisted reproductive technology (ART) cohorts. However, among non-ART pregnancies, it is unknown whether other medications increase the risk of multifetal pregnancies. OBJECTIVE: This study aimed to investigate the risk of multiple gestational births (eg, twins and triplets) following preconception and periconception exposure to prescription medications in patients who delivered at Penn Medicine. METHODS: We used electronic health record data between 2010 and 2017 on patients who delivered babies at Penn Medicine, a health care system in the Greater Philadelphia area. We explored 3 logistic regression models: model 1 (no adjustment); model 2 (adjustment for maternal age); and model 3-our final logistic regression model (adjustment for maternal age, ART use, and infertility diagnosis). In all models, multiple births (MBs) were our outcome of interest (binary outcome), and each medication was assessed separately as a binary variable. To assess our MWAS model performance, we defined ART medications as our gold standard, given that these medications are known to increase the risk of MB. RESULTS: Of the 63,334 distinct deliveries in our cohort, only 1877 pregnancies (2.96%) were prescribed any medication during the preconception and first trimester period. Of the 123 medications prescribed, we found 26 (21.1%) medications associated with MB (using nominal P values) and 10 (8.1%) medications associated with MB (using Bonferroni adjustment) in fully adjusted model 3. We found that our model 3 algorithm had an accuracy of 85% (using nominal P values) and 89% (using Bonferroni-adjusted P values). CONCLUSIONS: Our work demonstrates the opportunities in applying the MWAS approach with electronic health record data to explore associations between preconception and periconception medication exposure and the risk of MB while identifying novel candidate medications for further study. Overall, we found 3 novel medications linked with MB that could be explored in further work; this demonstrates the potential of our method to be used for hypothesis generation.

Development of an Informatics Algorithm to Link Seasonal Infectious Diseases to Birth-Dependent Diseases Across Species: A Case Study with Osteosarcoma.

Many diseases have been linked with birth seasonality, and these fall into four main categories: mental, cardiovascular, respiratory and women's reproductive health conditions. Informatics methods are needed to uncover seasonally varying infectious diseases that may be responsible for the increased birth month-dependent disease risk observed. We have developed a method to link seasonal infectious disease data from the USA to birth month dependent disease data from humans and canines. We also include seasonal air pollution and climate data to determine the seasonal factors most likely involved in the response. We test our method with osteosarcoma, a rare bone cancer. We found the Lyme disease incidence was the most strongly correlated significant factor in explaining the birth month-osteosarcoma disease pattern (R=0.418, p=2.80X10-23), and this was true across all populations observed: canines, pediatric, and adult populations.

Postpartum complications increased in women with polycystic ovary syndrome.

BACKGROUND: Women with polycystic ovary syndrome are at a higher risk of cardiometabolic and psychiatric comorbidities and preconception and antepartum complications, but the impact of polycystic ovary syndrome during the postpartum period is unknown. OBJECTIVE: This study aimed to investigate the risk of postpartum cardiovascular disease complications and perinatal and postpartum depression. STUDY DESIGN: This was a retrospective cohort study conducted using a United States insurance claims database. Women with and without polycystic ovary syndrome aged 18 to 50 years enrolled continuously in a single health plan during the preconception, antepartum, and postpartum periods between 2000 and 2016 were included. The primary outcome was postpartum cardiovascular disease and depression (perinatal and postpartum). Multivariable logistic regression was used to adjust for covariates including age, geographic location, preterm delivery, assisted reproductive technology use, multiple births, prepregnancy depression, prepregnancy diabetes, prepregnancy hypertension, gestational diabetes, gestational hypertension, obesity, history of hyperlipidemia, smoking, and race. RESULTS: We identified 42,391 unique women with polycystic ovary syndrome and 795,480 women without polycystic ovary syndrome. In multivariable models, women with polycystic ovary syndrome had significantly higher odds of cardiovascular disease complications, including postpartum preeclampsia (adjusted odds ratio, 1.30; 95% confidence interval, 1.17-1.45), eclampsia (adjusted odds ratio, 1.45; 95% confidence interval, 1.14-1.86) cardiomyopathy (adjusted odds ratio, 1.26; 95% confidence interval, 1.03-1.54), hypertensive heart disease (adjusted odds ratio, 1.32: 95% confidence interval, 1.07-1.64), thrombotic disease (adjusted odds ratio, 1.50; 95% confidence interval, 1.20-1.87), congestive heart failure (adjusted odds ratio, 1.35; 95% confidence interval, 1.13-1.61), and cerebrovascular accidents (adjusted odds ratio, 1.21; 95% confidence interval, 1.14-1.29), than those without polycystic ovary syndrome, as well as both perinatal (adjusted odds ratio, 1.27; 95% confidence interval, 1.22-1.33) and postpartum depression (adjusted odds ratio, 1.46; 95% confidence interval, 1.36-1.57). Nonobese women with polycystic ovary syndrome had higher odds of postpartum eclampsia (adjusted odds ratio 1.72; 95% confidence interval, 1.31-2.26), peripartum cardiomyopathy (adjusted odds ratio, 1.43; 95% confidence interval, 1.14-1.79), and cerebrovascular accidents (adjusted odds ratio, 1.28; 95% confidence interval, 1.19-1.38) than nonobese women without polycystic ovary syndrome. In the group of women without prepregnancy depression, the odds of perinatal depression (adjusted odds ratio, 1.32; 95% confidence interval, 1.26-1.39) and postpartum depression (adjusted odds ratio, 1.50; 95% confidence interval, 1.39-1.62) were higher in women with polycystic ovary syndrome than those without polycystic ovary syndrome. CONCLUSION: In a large United States cohort, our study found that women with polycystic ovary syndrome are at increased risk of both cardiovascular and psychiatric complications during the postpartum period. Polycystic ovary syndrome should be recognized as an at-risk condition; our findings underscore the need for routine screening and early interventions for these major comorbidities.

WellExplorer: an integrative resource linking hydraulic fracturing chemicals with hormonal pathways and geographic location.

Exposure to hydraulic fracturing fluid in drinking water increases the risk of many adverse health outcomes. Unfortunately, most individuals and researchers are unaware of the health risks posed by a particular well due to the diversity of chemical ingredients used across sites. We constructed WellExplorer (http://WellExplorer.org), an interactive tool for researchers and community members to use for retrieving information regarding the hormonal, testosterone and estrogen modulators located at each well. We found that wells in Alabama use a disproportionately high number of ingredients targeting estrogen pathways, while Illinois, Ohio and Pennsylvania use a disproportionately high number of ingredients targeting testosterone pathways. Researchers can utilize WellExplorer to study health outcomes related to exposure to fracturing chemicals in their population-based cohorts. Community members can use this resource to search their home or work locations (e.g. town or zip code) to determine proximity between where they live or work and specific hormonal exposures.

Applied Veterinary Informatics: Development of a Semantic and Domain-Specific Method to Construct a Canine Data Repository.

Animals are used to study the pathogenesis of various human diseases, but typically as animal models with induced disease. However, companion animals develop disease spontaneously in a way that mirrors disease development in humans. The purpose of this study is to develop a semantic and domain-specific method to enable construction of a data repository from a veterinary hospital that would be useful for future studies. We developed a two-phase method that combines semantic and domain-specific approaches to construct a canine data repository of clinical data collected during routine care at the Matthew J Ryan Veterinary Hospital of the University of Pennsylvania (PennVet). Our framework consists of two phases: (1) a semantic data-cleaning phase and (2) a domain-specific data-cleaning phase. We validated our data repository using a gold standard of known breed predispositions for certain diseases (i.e., mitral valve disease, atrial fibrillation and osteosarcoma). Our two-phase method allowed us to maximize data retention (99.8% of data retained), while ensuring the quality of our result. Our final population contained 84,405 dogs treated between 2000 and 2017 from 194 distinct dog breeds. We observed the expected breed associations with mitral valve disease, atrial fibrillation, and osteosarcoma (P < 0.05) after adjusting for multiple comparisons. Precision ranged from 60.0 to 83.3 for the three diseases (avg. 74.2) and recall ranged from 31.6 to 83.3 (avg. 53.3). Our study describes a two-phase method to construct a clinical data repository using canine data obtained during routine clinical care at a veterinary hospital.

Disease associations depend on visit type: results from a visit-wide association study.

INTRODUCTION: Widespread adoption of Electronic Health Records (EHR) increased the number of reported disease association studies, or Phenome-Wide Association Studies (PheWAS). Traditional PheWAS studies ignore visit type (i.e., department/service conducting the visit). In this study, we investigate the role of visit type on disease association results in the first Visit-Wide Association Study or 'VisitWAS'. RESULTS: We studied this visit type effect on association results using EHR data from the University of Pennsylvania. Penn EHR data comes from 1,048 different departments and clinics. We analyzed differences between cancer and obstetrics/gynecologist (Ob/Gyn) visits. Some findings were expected (i.e., increase of neoplasm diagnoses among cancer visits), but others were surprising, including an increase in infectious disease conditions among those visiting the Ob/Gyn. CONCLUSION: We conclude that assessing visit type is important for EHR studies because different medical centers have different visit type distributions. To increase reproducibility among EHR data mining algorithms, we recommend that researchers report visit type in studies.

A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors.

Mutations in transcription factor (TF) genes are frequently observed in tumors, often leading to aberrant transcriptional activity. Unfortunately, TFs are often considered undruggable due to the absence of targetable enzymatic activity. To address this problem, we developed CRAFTT, a computational drug-repositioning approach for targeting TF activity. CRAFTT combines ChIP-seq with drug-induced expression profiling to identify small molecules that can specifically perturb TF activity. Application to ENCODE ChIP-seq datasets revealed known drug-TF interactions, and a global drug-protein network analysis supported these predictions. Application of CRAFTT to ERG, a pro-invasive, frequently overexpressed oncogenic TF, predicted that dexamethasone would inhibit ERG activity. Dexamethasone significantly decreased cell invasion and migration in an ERG-dependent manner. Furthermore, analysis of electronic medical record data indicates a protective role for dexamethasone against prostate cancer. Altogether, our method provides a broadly applicable strategy for identifying drugs that specifically modulate TF activity.

A method for probing disease relatedness using common clinical eligibility criteria.

Clinical trial eligibility criteria define fine-grained characteristics of research volunteers for various disease trials and hence are a promising data source for disease profiling. This paper explores the feasibility of using disease-specific common eligibility features (CEFs) for representing diseases and understanding their relatedness. We extracted disease-specific CEFs from eligibility criteria on ClinicalTrials.gov for three illustrative categories - cancers, mental disorders and chronic diseases - each including seven diseases. We then constructed disease-specific CEF networks to assess the degree of overlap among the diseases. Using these automatically derived networks, we observed several findings that were confirmed in medicine. For example, we highlighted connections among schizophrenia, epilepsy and depression. We also identified a link between Crohn's disease and arthritis. These observations confirm the value of using clinical trial eligibility criteria for identifying disease relatedness. We further discuss the implications of CEFs for standardizing clinical trial eligibility criteria through reuse.

Discovering medical conditions associated with periodontitis using linked electronic health records.

AIM: To use linked electronic medical and dental records to discover associations between periodontitis and medical conditions independent of a priori hypotheses. MATERIALS AND METHODS: This case-control study included 2475 patients who underwent dental treatment at the College of Dental Medicine at Columbia University and medical treatment at NewYork-Presbyterian Hospital. Our cases are patients who received periodontal treatment and our controls are patients who received dental maintenance but no periodontal treatment. Chi-square analysis was performed for medical treatment codes and logistic regression was used to adjust for confounders. RESULTS: Our method replicated several important periodontitis associations in a largely Hispanic population, including diabetes mellitus type I (OR = 1.6, 95% CI 1.30-1.99, p < 0.001) and type II (OR = 1.4, 95% CI 1.22-1.67, p < 0.001), hypertension (OR = 1.2, 95% CI 1.10-1.37, p < 0.001), hypercholesterolaemia (OR = 1.2, 95% CI 1.07-1.38, p = 0.004), hyperlipidaemia (OR = 1.2, 95% CI 1.06-1.43, p = 0.008) and conditions pertaining to pregnancy and childbirth (OR = 2.9, 95% CI: 1.32-7.21, p = 0.014). We also found a previously unreported association with benign prostatic hyperplasia (OR = 1.5, 95% CI 1.05-2.10, p = 0.026) after adjusting for age, gender, ethnicity, hypertension, diabetes, obesity, lipid and circulatory system conditions, alcohol and tobacco abuse. CONCLUSIONS: This study contributes a high-throughput method for associating periodontitis with systemic diseases using linked electronic records.