IntroductionGender-related stereotypes of pain may account for some assessment and treatment disparities among patients. Among health care providers, demographic factors including gender and profession may influence the use of gender cues in pain management decision-making. The Gender Role Expectations of Pain Questionnaire was developed to assess gender-related stereotypic attributions of pain regarding sensitivity, endurance, and willingness to report pain, and has not yet been used in a sample of health care providers. The purpose of this study was to examine the presence of gender role expectation of pain among health care providers. It was hypothesized that health care providers of both genders would endorse gender stereotypic views of pain and physicians would be more likely than dentists to endorse these views.MethodsOne-hundred and sixty-nine providers (89 dentists, 80 physicians; 40% women) were recruited as part of a larger study examining providers’ use of demographic cues in making pain management decisions. Participants completed the Gender Role Expectations of Pain Questionnaire to assess the participant’s views of gender differences in pain sensitivity, pain endurance, and willingness to report pain.ResultsResults of repeated measures analysis of variance revealed that health care providers of both genders endorsed stereotypic views of pain regarding willingness to report pain (F(1,165)=34.241, P<0.001; d=0.479). Furthermore, female dentists rated men as having less endurance than women (F(1,165)=4.654, P=0.032; d=0.333).ConclusionThese findings affirm the presence of some gender-related stereotypic views among health care providers and suggest the presence of a view among health care providers that men are underreporting their pain in comparison to women. Future work can refine the effects of social learning history and other psychosocial factors that contribute to gender and provider differences in pain management decisions.

Objectives To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (1/1/2018-4/7/2022). HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within one week of precision for 475,433 (58.2%) episodes. 62,540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data. Lay Summary The National COVID Cohort Collaborative (N3C) provides researchers a unique opportunity to use electronic health record data from more than 12 million individuals from over seventy healthcare systems across the U.S. to study the impact of COVID-19 on pregnancy and women’s health. However, doing research with electronic health record data from different sources can be challenging as data can often be reported in many ways and formats. To address this challenge, we developed an approach known as Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS) that can 1) find the start and end of a pregnancy, 2) infer whether the pregnancy resulted in a live birth or pregnancy loss, and 3) determine the gestational age at the end of pregnancy. We observed from a subset of data that our approach had high agreement with how clinicians would collect this information from electronic health records. When applying our approach on all the data in N3C, we identified 816K pregnancies from 628K individuals. Of these individuals, 62K had COVID-19 during pregnancy. Our research demonstrates that our HIPPS approach can enable COVID-19-related research in pregnancy with electronic health record data.

Background Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. Methods A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease, and hospitalization were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. Results Of the 2,453,799 adults diagnosed with COVID-19, 191,520 (7.81%) had a pre-existing AID diagnosis and 278,095 (11.33%) had a pre-existing IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a pre-existing AID (OR = 1.13, 95% CI 1.09 - 1.17; P&lt; 0.001), IS (OR= 1.27, 95% CI 1.24 - 1.30; P&lt; 0.001), or both (OR = 1.35, 95% CI 1.29 - 1.40; P&lt; 0.001) were more likely to have a life-threatening COVID-19 disease. These results were consistent when evaluating hospitalization. A sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66- 0.96; P=0.017) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; P&lt; 0.001). Conclusions Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.

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