A history of MST is associated with chronic pain diagnoses. Weaknesses of this study are those applicable to analyses of any retrospective database study. Specifically, the data are limited by the accuracy of physician coding and reporting. The strength of this study is that it represents a comprehensive, retrospective evaluation of potential sources for chronic pain within the female veteran population. In summary, we found that female veteran survivors of MST face an increased burden of chronic pain, including a broad range of pain conditions independent of the psychological effects of MST.

BackgroundThe goal of these analyses was to determine whether there were systematic differences in Emergency Severity Index (ESI) scores, which are intended to determine priority of treatment and anticipate resource needs, across categories of race and ethnicity, after accounting for patient-presenting vital signs and examiner characteristics, and whether these differences varied among male and female Veterans Affairs (VA) ED patients.Methods and FindingsWe used a large national database of electronic medical records of ED patients from twenty-two U.S. Department of Veterans Affairs ED stations to determine whether ESI assignments differ systematically by race or ethnicity. Multi-level, random effects linear modeling was used to control for demographic characteristics and patient’s vital signs (heart rate, respiratory rate, and pain level), as well as age, gender, and experience of triage nurses. The dataset included 129,991 VA patients presenting for emergency care between 2008 and 2012 (91% males; 61% non-Hispanic White, 28% Black, 7% Hispanic, 2% Asian, <1% American Indian/Alaska Native, 1% mixed ethnicity) and 774 nurses for a total of 359,642 patient/examiner encounters. Approximately 13% of the variance in ESI scores was due to patient characteristics and 21% was due to the nurse characteristics. After controlling for characteristics of nurses and patients, Black patients were assigned less urgent ESI scores than White patients, and this effect was more prominent for Black males compared with Black females. A similar interaction was found for Hispanic males. It remains unclear how these results may generalize to EDs and patient populations outside of the U.S. VA Health Care system.ConclusionsThe findings suggest the possibility that subgroups of VA patients receive different ESI ratings in triage, which may have cascading, downstream consequences for patient treatment quality, satisfaction with care, and trust in the health equity of emergency care.

The distinct pattern of prescribing shown in this cohort may mean COT is prescribed for CPP and this prescribing pattern contributes to the adverse events associated with COT. As COT is not recommended for CPP, physicians need more education on the therapies available to help CPP patients.

Many mathematical models have been proposed to predict death following COVID-19; all started with comorbidity subsets for this still-little understood disease. Thus, we derived a novel predicted probability of death model (PDeathDx) upon all diagnostic codes documented in the Department of Veterans Affairs. We present the conceptual underpinnings and analytic approach in estimating the independent contribution of pre-existing conditions. This is the largest study to-date following patients with COVID-19 to predict mortality. Cases were identified with at least one positive nucleic acid amplification test (NAAT). Starting in 1997, we use diagnoses from the first time a patient sought care until 14 days before a positive NAAT. We demonstrate the clear advantage of using an unrestricted set of pre-existing conditions to model COVID-19 mortality, as models using conventional comorbidity indices often assign little weight or usually do not include some of the highest-risk conditions; the same is true of conditions associated with COVID-19 severity. Our findings suggest that it is risky to pick comorbidities for analysis without a systematic review of all those experienced by the cohort. Unlike conventional approaches, our comprehensive methodology provides the flexibility that has been advocated for comorbidity indices since 1993; such an approach can be readily adapted for other diseases and outcomes. With our comorbidity risk adjustment approach outperforming conventional indices for predicting COVID-19 mortality, it shows promise for predicting outcomes for other conditions of interest.

Objective The objective of this study was to compare women with a known diagnosis of interstitial cystitis (IC) to a population that might be at risk for the diagnosis of IC, women with diagnoses of both chronic pelvic pain (CPP) and overactive bladder (OAB). Methods We conducted a retrospective study of data from the Veterans Affairs Corporate Data Warehouse. The cohort included all female veterans who had established care with a primary care provider from 1997 to present. International Classification of Diseases, Ninth Revision codes were used to identify women with a diagnosis of IC, CPP, and OAB. Demographic data and comorbidities were compared between groups. Results A total of 596,815 women were identified. Two thousand three hundred one women (0.4%) were diagnosed with IC; 4459 women (0.7%) were diagnosed with CPP and OAB. At baseline, women with OAB and CPP were more likely to identify as minority (P < 0.001). Anxiety (57.3% vs 49.5%), depression (39.0% vs 46.0%), and posttraumatic stress disorder (29.7 vs 26.4%) were all more common in the CPP and OAB group than in the IC group. In the multivariable model, women with CPP and OAB were more likely to identify as a minority, use tobacco, and carry a diagnosis of anxiety. Conclusions There were more patients diagnosed with CPP and OAB compared with patients diagnosed with IC in this population of female veterans. Given the high rate of comorbid anxiety and depression in both groups, further study is warranted to determine whether these women are misdiagnosed.