Analgesic prescribing trends in a national sample of older veterans with osteoarthritis: 2012-2017

Few investigations examine patterns of opioid and nonopioid analgesic prescribing and concurrent pain intensity ratings before and after institution of safer prescribing programs such as the October 2013 Veterans Health Administration system-wide Opioid Safety Initiative (OSI) implementation. We conducted a quasi-experimental pre–post observational study of all older U.S. veterans (≥50 years old) with osteoarthritis of the knee or hip. All associated outpatient analgesic prescriptions and outpatient pain intensity ratings from January 1, 2012 to December 31, 2016, were analyzed with segmented regression of interrupted time series. Standardized monthly rates for each analgesic class (total, opioid, nonsteroidal anti-inflammatory drug, acetaminophen, and other study analgesics) were analyzed with segmented negative binomial regression models with overall slope, step, and slope change. Similarly, segmented linear regression was used to analyze pain intensity ratings and percentage of those reporting pain. All models were additionally adjusted for age, sex, and race. Before OSI implementation, total analgesic prescriptions showed a steady rise, abruptly decreasing to a flat trajectory after OSI implementation. This trend was primarily due to a decrease in opioid prescribing after OSI. Total prescribing after OSI implementation was partially compensated by continuing increased prescribing of other study analgesics as well as a significant rise in acetaminophen prescriptions (post-OSI). No changes in nonsteroidal anti-inflammatory drug prescribing were seen. A small rise in the percentage of those reporting pain but not mean pain intensity ratings continued over the study period with no changes associated with OSI. Changes in analgesic prescribing trends were not paralleled by changes in reported pain intensity for older veterans with osteoarthritis

Accounting for the Hierarchical Structure in Veterans Health Administration Data: Differences in Healthcare Utilization between Men and Women Veterans

Women currently constitute 15% of active United States of America military service personnel, and this proportion is expected to double in the next 5 years. Previous research has shown that healthcare utilization and costs differ in women US Veterans Health Administration (VA) patients compared to men. However, none have accounted for the potential effects of clustering on their estimates of healthcare utilization. US Women Veterans are more likely to serve in specific military branches (e.g. Army), components (e.g. National Guard), and ranks (e.g. officer) than men. These factors may confer different risk and protection that can affect subsequent healthcare needs. Our study investigates the effects of accounting for the hierarchical structure of data on estimates of the association between gender and VA healthcare utilization. The sample consisted of data on 406,406 Veterans obtained from VA's Operation Enduring Freedom/ Operation Iraqi Freedom roster provided by Defense Manpower Data Center - Contingency Tracking System Deployment File. We compared three statistical models, ordinary, fixed and random effects hierarchical logistic regression, in order to assess the association of gender with healthcare utilization, controlling for branch of service, component, rank, age, race, and marital status. Gender was associated with utilization in ordinary logistic and, but not in fixed effects hierarchical logistic or random effects hierarchical logistic regression models. This points out that incomplete inference could be drawn by ignoring the military structure that may influence combat exposure and subsequent healthcare needs. Researchers should consider modeling VA data using methods that account for the potential clustering effect of hierarchy

Dual Use of a Patient Portal and Clinical Video Telehealth by Veterans with Mental Health Diagnoses: Retrospective, Cross-Sectional Analysis

BACKGROUND: Access to mental health care is challenging. The Veterans Health Administration (VHA) has been addressing these challenges through technological innovations including the implementation of Clinical Video Telehealth, two-way interactive and synchronous videoconferencing between a provider and a patient, and an electronic patient portal and personal health record, My HealtheVet. OBJECTIVE: This study aimed to describe early adoption and use of My HealtheVet and Clinical Video Telehealth among VHA users with mental health diagnoses. METHODS: We conducted a retrospective, cross-sectional analysis of early My HealtheVet adoption and Clinical Video Telehealth engagement among veterans with one or more mental health diagnoses who were VHA users from 2007 to 2012. We categorized veterans into four electronic health (eHealth) technology use groups: My HealtheVet only, Clinical Video Telehealth only, dual users who used both, and nonusers of either. We examined demographic characteristics and mental health diagnoses by group. We explored My HealtheVet feature use among My HealtheVet adopters. We then explored predictors of My HealtheVet adoption, Clinical Video Telehealth engagement, and dual use using multivariate logistic regression. RESULTS: Among 2.17 million veterans with one or more mental health diagnoses, 1.51% (32,723/2,171,325) were dual users, and 71.72% (1,557,218/2,171,325) were nonusers of both My HealtheVet and Clinical Video Telehealth. African American and Latino patients were significantly less likely to engage in Clinical Video Telehealth or use My HealtheVet compared with white patients. Low-income patients who met the criteria for free care were significantly less likely to be My HealtheVet or dual users than those who did not. The odds of Clinical Video Telehealth engagement and dual use decreased with increasing age. Women were more likely than men to be My HealtheVet or dual users but less likely than men to be Clinical Video Telehealth users. Patients with schizophrenia or schizoaffective disorder were significantly less likely to be My HealtheVet or dual users than those with other mental health diagnoses (odds ratio, OR 0.50, CI 0.47-0.53 and OR 0.75, CI 0.69-0.80, respectively). Dual users were younger (53.08 years, SD 13.7, vs 60.11 years, SD 15.83), more likely to be white, and less likely to be low-income than the overall cohort. Although rural patients had 17% lower odds of My HealtheVet adoption compared with urban patients (OR 0.83, 95% CI 0.80-0.87), they were substantially more likely than their urban counterparts to engage in Clinical Video Telehealth and dual use (OR 2.45, 95% CI 1.95-3.09 for Clinical Video Telehealth and OR 2.11, 95% CI 1.81-2.47 for dual use). CONCLUSIONS: During this study (2007-2012), use of these technologies was low, leaving much potential for growth. There were sociodemographic disparities in access to My HealtheVet and Clinical Video Telehealth and in dual use of these technologies. There was also variation based on types of mental health diagnosis. More research is needed to ensure that these and other patient-facing eHealth technologies are accessible and effectively used by all vulnerable patients

A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

BACKGROUND: Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. OBJECTIVE: The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people. METHODS: NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions. RESULTS: Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid\u27s user interface and a number of definitions, and added 4502 more definitions in CoDeMed. CONCLUSIONS: Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients

Risk factors associated with healthcare utilization for spine pain

OBJECTIVE: This study examined potential risk factors associated with healthcare utilization among patients with spine (i.e., neck and back) pain. METHODS: A two-stage sampling approach examined spine pain episodes of care among veterans with a yearly outpatient visit for six consecutive years. Descriptive and bivariate statistics, followed by logistic regression analyses, examined baseline characteristics of veterans with new episodes of care who either continued or discontinued spine pain care. A multivariable logistic regression model examined correlates associated with seeking continued spine pain care. RESULTS: Among 331,908 veterans without spine pain episodes of care during the two-year baseline observation period, 16.5% (n = 54,852) had a new episode of care during the following two-year observation period. Of those 54,852 veterans, 37,025 had an outpatient visit data during the final two-year follow-up period, with 53.7% (n = 19,865) evidencing continued spine pain care. Those with continued care were more likely to be overweight or obese, non-smokers, Army veterans, have higher education, and had higher rates of diagnoses of all medical and mental health conditions examined at baseline. Among several important findings, women had 13% lower odds of continued care during the final two-year observation period, OR 0.87 (0.81, 0.95). CONCLUSIONS: A number of important demographics and clinical correlates were associated with increased likelihood of seeking new and continued episodes of care for spine pain; however, further examination of risk factors associated with healthcare utilization for spine pain is indicated

Racial and Ethnic Differences in Total Knee Arthroplasty in the Veterans Affairs Health Care System, 2001-2013

OBJECTIVE: To examine black-white and Hispanic-white differences in total knee arthroplasty from 2001 to 2013 in a large cohort of patients diagnosed with osteoarthritis (OA) in the Veterans Affairs (VA) health care system. METHODS: Data were from the VA Musculoskeletal Disorders cohort, which includes data from electronic health records of more than 5.4 million veterans with musculoskeletal disorders diagnoses. We included white (non-Hispanic), black (non-Hispanic), and Hispanic (any race) veterans, age ≥50 years, with an OA diagnosis from 2001-2011 (n = 539,841). Veterans were followed from their first OA diagnosis until September 30, 2013. As a proxy for increased clinical severity, analyses were also conducted for a subsample restricted to those who saw an orthopedic or rheumatology specialist (n = 148,844). We used Cox proportional hazards regression to examine racial and ethnic differences in total knee arthroplasty by year of OA diagnosis, adjusting for age, sex, body mass index, physical and mental diagnoses, and pain intensity scores. RESULTS: We identified 12,087 total knee arthroplasty procedures in a sample of 473,170 white, 50,172 black, and 16,499 Hispanic veterans. In adjusted models examining black-white and Hispanic-white differences by year of OA diagnosis, total knee arthroplasty rates were lower for black than for white veterans diagnosed in all but 2 years. There were no Hispanic-white differences regardless of when diagnosis occurred. These patterns held in the specialty clinic subsample. CONCLUSION: Black-white differences in total knee arthroplasty appear to be persistent in the VA, even after controlling for potential clinical confounders

Experimental transmission of Stony Coral Tissue Loss Disease results in differential microbial responses within coral mucus and tissue

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Huntley, N., Brandt, M., Becker, C., Miller, C., Meiling, S., Correa, A., Holstein, D., Muller, E., Mydlarz, L., Smith, T., & Apprill, A. Experimental transmission of Stony Coral Tissue Loss Disease results in differential microbial responses within coral mucus and tissue. ISME Communications, 2(1), (2022): 46, https://doi.org/10.1038/s43705-022-00126-3.Stony coral tissue loss disease (SCTLD) is a widespread and deadly disease that affects nearly half of Caribbean coral species. To understand the microbial community response to this disease, we performed a disease transmission experiment on US Virgin Island (USVI) corals, exposing six species of coral with varying susceptibility to SCTLD. The microbial community of the surface mucus and tissue layers were examined separately using a small subunit ribosomal RNA gene-based sequencing approach, and data were analyzed to identify microbial community shifts following disease acquisition, potential causative pathogens, as well as compare microbiota composition to field-based corals from the USVI and Florida outbreaks. While all species displayed similar microbiome composition with disease acquisition, microbiome similarity patterns differed by both species and mucus or tissue microhabitat. Further, disease exposed but not lesioned corals harbored a mucus microbial community similar to those showing disease signs, suggesting that mucus may serve as an early warning detection for the onset of SCTLD. Like other SCTLD studies in Florida, Rhodobacteraceae, Arcobacteraceae, Desulfovibrionaceae, Peptostreptococcaceae, Fusibacter, Marinifilaceae, and Vibrionaceae dominated diseased corals. This study demonstrates the differential response of the mucus and tissue microorganisms to SCTLD and suggests that mucus microorganisms may be diagnostic for early disease exposure.This work was funded by an International Coral Reef Society student grant to N.H., National Science Foundation (NSF) VI EPSCoR 0814417 and 1946412 and NSF (Biological Oceanography) award numbers 1928753 to MEB and TBS, 1928609 to AMSC, 1928817 to EMM, 19228771 to LDM, 1927277 to DMH as well as 1928761 and 1938112 to AA, NSF EEID award number 2109622 to MEB, AA, LDM, and AMSC, and a NOAA OAR Cooperative Institutes award to AA (#NA19OAR4320074). Samples were collected under permit #DFW19057U authorized by the Department of Planning and Natural Resources Coastal Zone Management

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care

Introduction: A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. Method: This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. Results: The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Discussion: Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset

Impact of Cigarette Smoking Status on Pain Intensity Among Veterans With and Without Hepatitis C

Objective: Chronic pain is a significant problem in patients living with hepatitis C virus (HCV). Tobacco smoking is an independent risk factor for high pain intensity among veterans. This study aims to examine the independent associations with smoking and HCV on pain intensity, as well as the interaction of smoking and HCV on the association with pain intensity. Design/Particpants: Cross-sectional analysis of a cohort study of veterans of Operations Enduring Freedom/Iraqi Freedom/New Dawn (OEF/OIF/OND) who had at least one visit to a Veterans Health Administration (VHA) primary care clinic between 2001 and 2014. Methods: HCV was identified using ICD-9 codes from electronic medical records (EMRs). Pain intensity, reported on a 0-10 numeric rating scale, was categorized as none/mild (0-3) and moderate/severe (4-10). Results: Among 654,841 OEF/OIF/OND veterans (median age [interquartile range] = 26 [23-36] years), 2,942 (0.4%) were diagnosed with HCV. Overall, moderate/severe pain intensity was reported in 36% of veterans, and 37% were current smokers. The adjusted odds of reporting moderate/severe pain intensity were 1.23 times higher (95% confidence interval [CI] = 1.14-1.33) for those with HCV and 1.26 times higher (95% CI = 1.25-1.28) for current smokers. In the interaction model, there was a significant Smoking Status x HCV interaction (P = 0.03). Among veterans with HCV, smoking had a significantly larger association with moderate/severe pain (adjusted odds ratio [OR] = 1.50, P \u3c 0.001) than among veterans without HCV (adjusted OR = 1.26, P \u3c 0.001). Conclusions: We found that current smoking is more strongly linked to pain intensity among veterans with HCV. Further investigations are needed to explore the impact of smoking status on pain and to promote smoking cessation and pain management in veterans with HCV

Gender Differences in Demographic and Clinical Correlates among Veterans with Musculoskeletal Disorders

Background Studies suggest that women may be at greater risk for developing chronic pain and pain-related disability. Methods Because musculoskeletal disorders (MSD) are the most frequently endorsed painful conditions among veterans, we sought to characterize gender differences in sociodemographic and clinical correlates among veterans upon entry into Veterans Health Administration's Musculoskeletal Disorders Cohort (n = 4,128,008). Results Women were more likely to be younger, Black, unmarried, and veterans of recent conflicts. In analyses adjusted for gender differences in sociodemographics, women were more likely to have diagnoses of fibromyalgia, temporomandibular disorders, and neck pain. Almost one in five women (19.4%) had more than one MSD diagnosis, compared with 15.7% of men; this higher risk of MSD multimorbidity remained in adjusted analyses. Adjusting for sociodemographics, women with MSD were more likely to have migraine headache and depressive, anxiety, and bipolar disorders. Women had lower odds of cardiovascular diseases, substance use disorders, and several MSDs, including back pain conditions. Men were more likely to report “no pain” on the pain intensity Numeric Rating Scale, whereas more women (41%) than men (34%) reported moderate to severe pain (Numeric Rating Scale 4+). Conclusions Because women veterans are more likely to have conditions such as fibromyalgia and mental health conditions, along with greater pain intensity in the setting of MSD, women-specific pain services may be needed