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Prescription Drug Monitoring Programs (PDMPs) are intended to help reduce prescription drug misuse and opioid overdose, yet little is known about the longitudinal patterns of opioid prescribing that may be associated with mortality. This study investigated longitudinal opioid prescribing patterns among patients with opioid use disorder (OUD) and without OUD in relation to mortality using PDMP data. Growth modeling was used to examine opioid prescription data from the California PDMP for a 4-year period before death or a comparable period ending in 2014 for those remaining from a sample of 7728 patients (2576 with OUD, and 5152 matched non-OUD controls) treated in a large healthcare system. Compared to controls, individuals with OUD (alive and deceased) had received significantly more opioid prescriptions, greater number of days' supply, and steeper increases of opioid dosages over time. For morphine equivalents (ME, in grams), the interaction of OUD and mortality was significant at both intercept (β = 10.4, SE = 4.4, P < 0.05) and slope (β = 6.0, SE = 1.1, P < 0.001); deceased OUD patients demonstrated the sharpest increase (ie, an average yearly increment of 7.84 grams over alive patients without OUD) and ended with the highest level of opioids prescribed before they died (ie, 20.2 grams higher). Older age, public health insurance, cancer, and chronic pain were associated with higher number and dose of opioid prescriptions. Besides the amount of prescriptions, clinicians must be alert to patterns of opioid prescription such as escalating dosage as critical warning signals for heightened mortality risks, particularly among patients with OUD.

Rates of Diagnoses Indicating Opioid Dependence After Hospice Live Discharge: A National Study (SCI929)

ObjectiveLong problem lists can be challenging to use. Reorganisation of the problem list by organ system is a strategy for making long problem lists more manageable.MethodsIn a small-town primary care...

Female, Hispanic, and Black patients with opioid use disorder (OUD) are less likely to receive OUD medication treatment than other patients. The PROUD (PRimary care Opioid Use Disorders treatment) trial demonstrated that implementation of primary care (PC) nurse care management increases OUD medication treatment compared with usual care (UC). This study assessed whether the PROUD intervention's effect differed across sex, race and ethnicity. Secondary analyses of cluster-randomized implementation trial. 12 PC clinics (2 per health system) in five states in the USA, randomized to UC or intervention, stratified by health system. PC patients 16-90 years old. Three strategies to implement office-based addiction treatment (OBAT) by nurse care managers: (1) full-time nurse salary; (2) nurse training and technical assistance from expert nurses at Boston Medical Center; (3) ≥3 PC providers willing to prescribe buprenorphine. Nurses were trained in the Massachusetts model of OBAT which includes lowering barriers to OUD treatment, assessing and educating patients, supporting initiation of medications for OUD and providing ongoing medical management, in collaboration with PC providers. The primary outcome was a clinic-level measure of OUD treatment defined as patient-years of OUD treatment per 10 000 PC patients based on orders and procedures for buprenorphine or extended-release naltrexone from electronic health records and insurance claims (hereafter 'OUD treatment'). The mean numbers of patients seen by intervention and UC clinics at baseline were 18 485 and 22 557, respectively. Female patients comprised 60% of the total PC population in intervention clinics and 64% in UC clinics; Asian, Black, Hispanic or smaller racial groups comprised 61% of the PC population in intervention clinics, and 70% in UC clinics. Compared with UC, the intervention increased OUD treatment for male patients [adjusted difference: 13.7 patient-years; 95% confidence interval (CI) = 5.8-21.7], but not female patients (2.9; 95% CI = -4.3 to 10.2); effect modification test, F (1,14) = 4.77; P = 0.046. Exploratory analyses suggest that differences in the intervention's effect on receipt of any OUD treatment in female and male patients, rather than differences in the duration of OUD treatment, may account for findings. There was no significant effect modification by race or ethnic group [effect modification test F (4,44) = 1.50; P = 0.218]. Primary care clinics that implement office-based addiction treatment by nurses increase patient-years of opioid use disorder (OUD) treatment in male but not female patients. Exploratory findings suggest that differences in the proportion of patients treated for OUD, rather than differences in the duration of OUD treatment, account for observed differences across groups.

ObjectiveTo characterize the use of standardized vocabularies in real-world electronic laboratory reporting (ELR) messages sent to public health agencies for surveillance.IntroductionThe use of health information systems to electronically deliver clinical data necessary for notifiable disease surveillance is growing. For health information systems to be effective at improving population surveillance functions, semantic interoperability is necessary.Semantic interoperability is “the ability to import utterances from another computer without prior negotiation” (1). Semantic interoperability is achieved through the use of standardized vocabularies which define orthogonal concepts to represent the utterances emitted by information systems. There are standard, mature, and internationally recognized vocabularies for describing tests and results for notifiable disease reporting through ELR (2). Logical Observation Identifiers Names and Codes (LOINC) identify the specific lab test performed. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) identify the diseases and organisms tested for in a lab test.Many commercial laboratory and hospital information systems claim to support LOINC and SNOMED CT on their company websites and in marketing materials, and systems certified for Meaningful Use are required to support LOINC and SNOMED CT. There is little empirical evidence on the use of semantic interoperability standards in practice.MethodsTo characterize the use of standardized vocabularies in electronic laboratory reporting (ELR) messages sent to public health agencies for notifiable disease surveillance, we analyzed ELR messages from two states: Indiana and Wisconsin. We examined the data in the ELR messages where tests and results are reported (3). For each field, the proportion of field values that used either LOINC or SNOMED CT codes were calculated by dividing the number of fields with coded values by the total number of non-null values in fields.ResultsResults are summarized in Table-1. In Indiana, less than 17% of incoming ELR messages contained a standardized code for identifying the test performed by the laboratory, and none of the test result fields contained a standardized vocabulary concept. For Wisconsin, none of the incoming ELR messages contained a standardized code for identifying the test performed, and less than 13% of the test result fields contained a SNOMED CT concept.ConclusionsAlthough Wisconsin and Indiana both have high adoption of advanced health information systems with many hospitals and laboratories using commercial systems which claim to support interoperability, very few ELR messages emanate from real-world systems with interoperable codes to identify tests and clinical results. To effectively use the arriving ELR messages, Indiana and Wisconsin health departments employ software and people workarounds to translate the incoming data into standardized concepts that can be utilized by the states’ surveillance systems. These workarounds present challenges for budget constrained public health departments seeking to leverage Meaningful Use Certified technologies to improve notifiable disease surveillance.

IntroductionMethadone is a schedule II opioid traditionally used to treat opioid use disorder (OUD) and chronic pain. However, following the identification of its contribution to opioid overdose deaths, methadone has become less commonly used for chronic pain indications. In Pennsylvania (PA), prescribers are required to report methadone prescriptions written for pain indications to the prescription drug monitoring program (PDMP), which is an electronic database that enhances the tracking and reporting of prescription data. The primary objective of our study was to describe the geographic methadone prescribing trends recorded by the PA PDMP in order to report methadone’s current use for only pain indications. MethodsState- and county-level methadone prescription data summaries recorded by the PA PDMP for each calendar quarter from August 2016 through March 2020 were collected from the PA Department of Health. The metric reported per quarter consisted of the total number of methadone prescriptions dispensed for pain indications unrelated to OUD.ResultsA total of 341,949 methadone prescriptions were dispensed in PA from the third quarter (Q3) of 2016 to the first quarter (Q1) of 2020 (range = 1106) with an overall 38.7% decrease in methadone prescriptions and a change in the rate of 85.97 per 100,000 population. The counties with the five highest prescription totals were Philadelphia, Allegheny, Bucks, Montgomery, and York (range = 46,969), and the counties with the five highest rates per 100,000 were Montour, Green, Columbia, Northumberland, and Forest (range = 964).ConclusionsMethadone prescribing for pain management unrelated to OUD has decreased in PA from 2016 to 2020 per the PA PDMP. However, it is still prescribed in appreciable amounts for pain management. Further studies are required to understand the prescribing rationale and potential areas for harm reduction interventions.

Background: Emerging adults aged 18 to 29 with opioid use disorder (OUD) have the lowest rates of treatment engagement and retention compared to other age groups. Understanding factors that influence retention and completion in this population is critical. Given the unique developmental stage of emerging adulthood, more research is needed on the role of medications for opioid use disorders (MOUD) in improving outcomes. Methods: Using data from the 2022 Treatment Episode Data Set: Discharges (TEDS-D), this study examined factors associated with treatment retention and completion among emerging adults with OUD in outpatient settings (N = 4,126). We also explored how age and sex moderate the association between MOUD and treatment outcomes. Results: Negative binomial regression showed that emerging adults of older age, female sex, White non-Hispanic race/ethnicity, higher educational attainment, and those who received the MOUD plan, without past month opioid use, and referred from institutional sources (compared to self-referral) showed longer treatment retention. Logistic regression indicated that emerging adults of male clients, individuals with higher education, those with no recent opioid use, and those referred from healthcare providers or institutional sources showed a greater likelihood of treatment completion. Moderation analyses revealed that age and sex influenced the relationship between MOUD and treatment completion: Younger emerging adults, compared to older emerging adults, and male emerging adults, compared to female emerging adults, had a lower likelihood of treatment completion when taking MOUD. In conjunction with their shorter treatment duration, this finding implies that younger emerging adults receiving MOUD may be more likely to encounter barriers to treatment completion than older adults. Conclusion: These findings underscore the importance of considering within-group differences among emerging adults and highlight the complex role of MOUD in treatment outcomes. Treatment strategies should be tailored to the developmental stage and demographic characteristics to improve engagement and success in OUD treatment.

BackgroundCOSMIN (Consensus-based Standards for the selection of health Measurement INstruments) provides a framework for selecting and validating patient-reported outcome measurements (PROMs). This study aims to validate the Hungarian version of the GOHAI and, for the first time, to assess its Standard Error of Measurement (SEM), Smallest Detectable Change (SDC), and Measurement Invariance (MI) across general and clinical populations as well as different age groups, following COSMIN guidelines.Materials and methodsThe translation was performed using a forward-backward process. A mixed sample (n = 306) was recruited in Budapest from May 2023 to February 2024, consisting of the general population (45.1%), recruited from health kiosks and a nursing home, and the clinical population (54.9%), sourced from Semmelweis University’s care units. The sample was further divided into two age groups: 18–64 years old (54.9%) and 65 + years old (45.1%). GOHAI was administered twice to 108 stable participants. For both the additive score (ADD-GOHAI) and simple count (SC-GOHAI), structural validity and measurement invariance by subgroups were assessed via Confirmatory Factor Analysis (CFA). Internal consistency was evaluated using Cronbach’s alpha, and test-retest reliability was measured using the intraclass correlation coefficient (ICC). SEM was calculated using the SEM agreement formula, and SDC using: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:SDC=1.96*\\sqrt{2}*SEM$$\\end{document}. Convergent and known-group validity were tested against predefined hypotheses for structural validity.ResultsContrary to a three factor model, a single-factor model showed good fit in all subgroups for both scoring methods, with adequate internal consistency (Cronbach \U0001d6fc: 0.76–0.85). Four of the six hypotheses for convergent validity and all ten hypotheses for known-groups validity supported the predefined criteria. Measurement invariance between clinical and general populations, or by age, was not demonstrated, so GOHAI’s different measurement properties should be considered when comparing subpopulations. Test-retest reliability was adequate (ICC: 0.87–0.96). SDC was ≈5 points using ADD-GOHAI and 2–3 points using SC-GOHAI.ConclusionThe Hungarian version of GOHAI demonstrates satisfactory psychometric properties across both general and clinical populations, as well as among both younger and older age groups. While the measurement properties of SC-GOHAI may be more stable between populations, ADD-GOHAI seems more suitable for individual follow-up. However, observed changes must be considered in relation to the measurement error associated with GOHAI.

INTRODUCTION: Lower Gastrointestinal Bleeding (LGIB) is defined as the bleeding originating from the gastrointestinal tract distal to the ligament of Treitz. LGIB is commonly caused by colonic diverticula, hemorrhoids, colorectal cancer, angiodysplasia, inflammatory bowel disease (IBD), and ischemic colitis. Although the diagnosis and management of LGIB have largely evolved with recent advancements, the incidence and trends of LGIB are unclear. We sought to look at the incidence and trends of LGIB 2002-2017 using a population level database. METHODS: Retrospective analysis of individuals ≥ 18 years who were hospitalized with LGIB was performed using a cloud database (IBM Explorys). Explorys aggregates de-identified data across 300 hospitals in the US. ICD 9 and ICD 10 diagnosis codes are mapped using SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) hierarchy. Using SNOMED CT codes, Individuals who had at least one inpatient encounter with SNOMED CT diagnosis code of LGIB were identified. The rates of hospitalization and annual percentage change (APC) due to various etiologies of LGIB was analyzed between 2002-2017 were calculated using Excel, 2016. RESULTS: Among a cohort of 51,770,740 unique patients, 2.31% hospitalizations in 2017 were due to LGIB. The most common diagnosis associated with LGIB were hemorrhoids (34.56%) followed by diverticular disease (20.03%), IBD (3.23%), colorectal cancer (2.56%), vascular insufficiency including ischemic colitis (1.26%) and angiodysplasia (0.83%). The rates of hospitalization due to LGIB increased from 0.35% in 2002 to 2.31% in 2017 (Figure 1). The APC of LGIB based on etiology is depicted in Figure 3. There was no statistically significant difference when the mean APC of the overall cases of LGIB was compared to the mean APC of individual etiologies. CONCLUSION: The rates of lower gastrointestinal bleeding requiring hospitalization and inpatient management have significantly increased over the last 2 decades. Among LGIB hospitalizations, the proportion of hemorrhoids and CRC have decreased while that of diverticular disease and angiodysplasia have increased over 5-year intervals (Table 1). Patient awareness, better diagnostic techniques, and aging population probably contribute to the increase in the rates of LGIB.

Geographic and demographic variation in worry about extreme heat and COVID-19 risk in summer 2020

Prescription opioid pain medication overuse, misuse and abuse has been a significant contributing factor in the opioid epidemic. The rising death rates from opioid overdose has caused healthcare practitioners and researchers to work on optimizing pain therapy and limiting the prescriptions for pain medications. The state of New York has implemented a prescription drug monitoring program(PDMP), amended public health law to limit the prescription of opioids for acute pain and utilized the resources of the state and county health departments to help in curbing this epidemic. The recent publication of guidelines for prescription opioids from CDC [2] and ASIPP (American Society of Interventional pain practitioners) [4] have independently reviewed literature and found good evidence of limiting opioid prescription for acute and chronic non cancer pain.MethodClinical Decision Support Systems (CDSS) have been developed over the last decade to help in the work flow of healthcare providers since advanced technology is increasing the complexity of electronic health records systems. There are several systematics reviews on the effectivity and utility of CDSSs. The common consensus seems to be that commercially and locally developed CDSS are effective in improving patient measures while actual workload improvement and efficient cost cutting measure are not significantly improved by CDSS. Patient provider involvement in developing CDSS is a determinant of its success and utilization rates. In this light, a plug and play form of CDSS which is independent of the vendors of Electronic Health Records and can be implemented from an external platform through secure channels would be more effective.The Health Level Seven’s (HL7) open licensed interoperability standard called Fast Health Interoperability Resources (FHIR) has a platform, Substitutable Medical Applications and Reusable Technologies (SMART) for CDSS app development by a third party. (Mandl and Kohane) [13] We adopted these open source standard to develop an app for proper implementation of the recently published guidelines for management of pain with opioid pain medications.The goal for this CDSS tool would be to achieve proper monitoring of prescription drugs, patients’ medication list and potential interactive medications, surveillance for abuse/ misuse, patient involvement in alternative therapy, reporting problems and obtaining adequate pain control.

Measurement invariance of the Dark Triad Dirty Dozen across nonclinical and clinical populations, genders, and age groups

Age-related Impact of the CDC's 2016 Guideline for Prescribing Opioids for Chronic Pain on Opioid Prescribing Levels and Health Outcomes among the Patients with Sickle Cell Disease

Background: The overprescribing of opioids is growing and prescribing rates are highest among pain medicine and surgical specialties. Objective: To evaluate the opioid prescription practice behavior among medical specialty physicians (MSP) and surgical specialty physicians (SSP). Methods: A survey was sent to all physicians at two major institutions. The primary outcome measure was total opioid prescription rate and duration per specialty. The secondary outcome measure was pre-evaluation, counseling, multimodal pain management, and Prescription Drug Monitoring Program (PDMP) query rate. Result: 84.0% (n = 217) of MSP and 93.0% (n = 129) of SSP prescribed opioids during the past year. Prescribing opioids for >2 weeks was significantly more common in MSP vs. SSP (34% vs.14%, p < .001). More MSP than SSP expressed concerns about patients developing opioid dependence (90% vs. 77%, p = .004). MSPs were more likely than SSPs to evaluate the patient’s history of substance abuse and to provide counseling for proper opioid use and disposal (p < .05). Only 58% (N = 176) of the MSP and 50% (N = 63) of SSP queried the PDMP every time before prescribing opioids (p = .092). Conclusion: >2- week opioid prescriptions were significantly more common in MSP, but they were also much more likely to thoroughly discuss opioid use and abuse with patients compared to SSP. A more compliant use of PDMP is encouraged.

FROM 1999 TO 2010 THE NUMBER OF PEOPLE IN the United States dying annually from opioid analgesic–related overdoses quadrupled, from 4030 to 16 651. Patients’ predisposition to overdose could not have changed substantially in that time; what has changed substantially is their exposure to opioids. During this same time, the amount of opioids prescribed also quadrupled. The increase in prescribing occurred in the context of a greater emphasis on treating pain following efforts by the American Pain Society, the Veterans Health Administration, The Joint Commission, and others to increase recognition and management of pain, as well as advocacy by pain societies urging physicians to use opioids more readily for patients with chronic noncancer pain. Even though it is well known that prescription opioid use can lead to addiction or overdose, some opioid manufacturers and pain specialists suggest that few patients are susceptible to these risks. To distinguish low-risk from highrisk patients, use of screening tools, including the Screener and Opioid Assessment for Patients with Pain, has been advocated. Medication guides include statements such as “the chance [of abuse or addiction] is higher if you are, or have been, addicted to or abused other medicines, street drugs, or alcohol, or if you have a history of mental problems.” While there is likely to be a gradient of risk across patients, this statement may reassure clinicians that people with opioid addiction are different from most patients for whom they provide care. However, opioid dependence is much more common than previously believed and has been estimated to affect more than one-third of patients with chronic pain. No screening tool has sufficiently high sensitivity to rule out problems with opioids. Reported sensitivities of these tests for observed “aberrant drug-related behavior” (eg, dose escalation outside the treatment plan or forging prescriptions) among patients with chronic pain are generally within a range between 70% and 90%, which means that they miss 10% to 30% of patients at high risk of misuse or addiction. In addition, some industry-sponsored educational brochures suggest that physicians should ignore signs of opioid dependence in low-risk patients. For instance, some patients might not be considered at high risk of misuse even though they may use more opioids than prescribed (one definition of misuse). Some authors have stated that behaviors such as taking more opioids than prescribed may represent pseudoaddiction, a concept introduced in a case report in 1989 as “abnormal behavior developing as a direct consequence of inadequate pain management.” However, this concept remains untested, without scientific studies validating diagnostic criteria or describing long-term clinical outcomes. Nonetheless, some pain societies have promoted this concept and suggest that some patients demonstrating behaviors typical of opioid addiction may actually require higher doses. Rather than representing iatrogenic undertreatment of pain, however, behaviors described as pseudoaddiction may represent predictable responses to opioid exposure. Longterm opioid use typically results in tolerance. A standard clinical solution is to increase opioid dose. However, contrary to the view that there is no maximum safe dose if opioids are increased gradually over time, death from opioid overdose becomes more likely at higher doses. The most important risk factor for opioid analgesic– associated dependence or overdose is not a feature of any individual patient but instead simply involves receiving a prescription for opioids. For example, newly prescribed opioids after short-stay surgery are associated with a 44% increase in risk of becoming a long-term opioid user within 1 year. Another potential complication of screening for risk of opioid abuse is that identifying patients who should not receive opioids can stigmatize them, leading to consequences that do not help them. Patients who are questioned about substance use and then excluded from an expected treatment may feel embarrassed or abandoned. The decision to address a patient’s pain should not depend on