Comparative Performance of Three Claims-Based Frailty Measures Among Medicare Beneficiaries.

Frailty is an important predictor of mortality, health care costs and utilization, and health outcomes. Validated measures of frailty are not consistently collected during clinical encounters, making comparisons across populations challenging. However, several claims-based algorithms have been developed to predict frailty and related concepts. This study compares performance of three such algorithms among Medicare beneficiaries. Claims data from 12-month continuous enrollment periods were selected during 2014-2016. Frailty scores, calculated using previously developed algorithms from Faurot, Kim, and RAND, were added to baseline regression models to predict claims-based outcomes measured in the following year. Root mean square error and area under the receiver operating characteristic curve were calculated for each model and outcome combination and tested in subpopulations of interest. Overall, Kim models performed best across most outcomes, metrics, and subpopulations. Kim frailty scores may be used by health systems and researchers for risk adjustment or targeting interventions.

Development and Validation of Algorithms to Predict Activity, Mobility, and Memory Limitations Using Medicare Claims and Post-Acute Care Assessments.

Functional impairment predicts mortality and health care utilization. However, validated measures of functional impairment are not routinely collected during clinical encounters and are impractical to use for large-scale risk-adjustment or targeting interventions. This study's purpose was to develop and validate claims-based algorithms to predict functional impairment using Medicare Fee-for-Service (FFS) 2014-2017 claims data linked with post-acute care (PAC) assessment data and weighted to better represent the overall Medicare FFS population. Using supervised machine learning, predictors were identified that best predicted two functional impairment outcomes measured in PAC data-any memory limitation and a count of 0-6 activity/mobility limitations. The memory limitation algorithm had moderately high sensitivity and specificity. The activity/mobility limitations algorithm performed well in identifying beneficiaries with five or more limitations, but overall accuracy was poor. This dataset shows promise for use in PAC populations, though generalizability to broader older adult populations remains a challenge.

Predictors of Long-Term Opioid Use After Hospitalization for Traumatic Injury in a Racially and Ethnically Diverse Population: A 12-Month Prospective Observational Study.

BACKGROUND: Long-term prescription opioid use is a significant risk factor for opioid morbidity and mortality, and severe traumatic injury is an important initiation point for prescription opioid use. This study examines predictors of long-term prescription opioid use among a racially and ethnically diverse population of patients hospitalized for traumatic injury. METHODS: Study participants (N= 650) from two urban Level I trauma centers were enrolled. Baseline information on demographics, injury characteristics, self-reported pre-injury substance use and mental health, and personality characteristics and attitudes was collected through interviews during the initial hospitalization. Patients were interviewed again at 3 months and 12 months and asked about prescription opioid use in the prior 7 days. Multivariable logistic regressions assessed participants' baseline characteristics associated with opioid use at one or more follow-up interviews. RESULTS: Pre-injury use of prescription painkillers had the strongest association with prescription opioid use at follow-up (adjusted odds ratio: 3.10; 95% confidence interval: 1.86-5.17). Older age, health insurance coverage at baseline, length of hospitalization, higher current pain level, pre-injury post-traumatic stress disorder symptoms, and discharge to a location other than home were also associated with significantly higher odds of prescription opioid use at follow-up. CONCLUSIONS: Providers could consider screening for past use of prescription pain relievers and post-traumatic stress disorder before hospital discharge to identify patients who might benefit from additional resources and support. However, providers should ensure that these patients' pain management needs are still being met and avoid abrupt discontinuation of prescription opioid use among those with a history of long-term use.

A comparison of approaches to identify live births using the medicaid analytic extract.

Medicaid claims are an important, but underutilized source of data for neonatal health services research in the United States. However, identifying live births in Medicaid claims data is challenging due to variation in coding practices by state and year. Methods of identifying live births in Medicaid claims data have not been validated, and it is not known which methods are most appropriate for different research questions. The objective of this study is to describe and validate five approaches to identifying births using Medicaid Analytic eXtract (MAX) from 45 states (2006-2014). We calculated total number of MAX births by state-year using five definitions: (1) any claim within 30 days of birth date listed in personal summary (PS) file, (2) any claim within 7 days of PS birth date, (3) live birth ICD-9 in inpatient or other therapies file, (4) live birth ICD-9 code in inpatient file, (5) live birth ICD-9 in inpatient file with matching PS birth date. We then compared the number of MAX births by state and year to expected counts using outside data sources. Definition 1 identified the most births (14,189,870) and was closest to total expected count (98.3%). Each definition produced over- and underestimates compared to expected counts for given state-years. Findings suggest that the broadest definition of live births (Definition 1) was closest to expected counts, but that the most appropriate definition depends on research question and state-years of interest.

Changing Care Settings for Injuries.

Tracking injury rates is important for surveillance purposes but little data exist for injuries outside of emergency department visits. We assess the share and type of injuries reported in urgent care centers (UCCs) compared with other settings. We used FAIR Health claims data from 2016 through the first quarter of 2019 to calculate the percent of claims and most common types of injuries. Of the 197 million injury claims, 62% occurred in office settings and 17% in hospital outpatient departments (HOPDs), 5% in inpatient and in ED settings, and less than 2% in UCCs. Injury claims in UCCs increased 6% from 2016 to 2018, whereas injury claims in EDs declined 24%. Overall, physician offices and HOPDs accounted for the largest share of injury care, but UCCs represented the fastest growing setting to treat injuries.

Claims-based measures of prescription opioid utilization: A practical guide for researchers.

BACKGROUND: Given the increased attention to the opioid epidemic and the role of inappropriate prescribing, there has been a marked increase in the number of studies using claims data to study opioid use and policies designed to curb misuse. Our objective is to review the medical literature for recent studies that use claims data to construct opioid use measures and to develop a guide for researchers using these measures. METHODS: We searched for articles relating to opioid use measured in health insurance claims data using a defined set of search terms for the years 2014-2020. Original research articles based in the United States that used claims-based measures of opioid utilization were included and information on the study population and measures of any opioid use, quantity of opioid use, new opioid use, chronic opioid use, multiple providers, and overlapping prescriptions was abstracted. RESULTS: A total of 164 articles met inclusion criteria. Any opioid use was the most commonly included measure, defined by 85 studies. This was followed by quantity of opioids (68 studies), chronic opioid use (53 studies), overlapping prescriptions (28 studies), and multiple providers (8 studies). Each measure contained multiple, distinct definitions with considerable variation in how each was operationalized. CONCLUSIONS: Claims-based opioid utilization measures are commonly used in research, but definitions vary significantly from study to study. Researchers should carefully consider which opioid utilization measures and definitions are most appropriate for their study and recognize how different definitions may influence study results.

Changes in Opioid Prescribing Following the Implementation of State Policies Limiting Morphine Equivalent Daily Dose in a Commercially Insured Population.

BACKGROUND: Prescription opioid mortality doubled 2002-2016 in the United States. Given the association between high-dose opioid prescribing and opioid mortality, several states have enacted morphine equivalent daily dose (MEDD) policies to limit high-dose prescribing. The study objective is to evaluate the impact of state-level MEDD policies on opioid prescribing among the privately insured. METHODS: Claims data, 2010-2015 from 9 policy states and 2 control states and a comparative interrupted time series design were utilized. Primary outcomes were any monthly opioid use and average monthly MEDD. Stratified analyses evaluated theorized weaker policies (guidelines) and theorized stronger policies (passive alert systems, legislative acts, and rules/regulations) separately. Patient groups explicitly excluded from policies (eg, individuals with cancer diagnoses or receiving hospice care) were also examined separately. Analyses adjusted for covariates, state fixed effects, and time trends. RESULTS: Both guideline and strong policy implementation were both associated with 15% lower odds of any opioid use, relative to control states. However, there was no statistically significant change in the use of high-dose opioids in policy states relative to control states. There was also no difference in direction and significance of the relationship among targeted patient groups. CONCLUSIONS: MEDD policies were associated with decreased use of any opioids relative to control states, but no change in high-dose prescribing was observed. While the overall policy environment in treatment states may have discouraged opioid prescribing, there was no evidence of MEDD policy impact, specifically. Further research is needed to understand the mechanisms through which MEDD policies may influence prescribing behavior.

Barriers and Facilitators to the Implementation of Injury Prevention Programs: A Qualitative Exploration and Model Development.

BACKGROUND: In 2006, the American College of Surgeons Committee on Trauma mandated implementation of injury prevention programs as a requirement for Level I and II trauma center designation. Little is known about the factors that facilitate or create barriers to establishing evidence-based injury prevention program implementation. The purpose of this research is to generate hypotheses regarding processes used to implement injury prevention programs at trauma centers, identify the factors that facilitate and serve as a barrier to implementation, and develop a model reflecting these factors and relationships. METHODS: This is a qualitative study of injury prevention programs at trauma centers. Study participants were chosen from 24 sites representing trauma centers of different patient volumes, geographic regions, and settings in the United States. Subjects participated in phone interviews based on guides developed from pilot interviews with prevention coordinators. Transcribed interviews from eight subjects were analyzed using a system of member checking to code; analysis informed the identification of factors that influence the establishment of evidence-based injury prevention programs. RESULTS: Five themes emerged from the data analysis: external factors, internal organizational factors, program capacity, program selection, and program success. Analysis revealed that successful program implementation was related to supportive leaders and collaborative, interdepartmental relationships. Additional themes indicated that while organizations were motivated primarily by verification requirements (external factor), strong institutional leadership (internal factor) was lacking. Employee readiness (program capacity) was hindered by limited training opportunities, and programs were often chosen (selection) based on implementation ease rather than evidence base or local data. CONCLUSIONS: Data analysis reveals five emerging themes of program implementation; using these data, we suggest an initial model of barriers and facilitators for implementing evidence-based injury prevention programs that could serve as the springboard for additional research involving a larger representative sample.

The state of the science in opioid policy research.

OBJECTIVE: Characterize the state of the science in opioid policy research based on a literature review of opioid policy studies. METHODS: We conducted a scoping review of studies evaluating the impact of U.S. state-level and federal-level policies on opioid-related outcomes published in 2005-2018. We characterized: 1) state and federal policies evaluated, 2) opioid-related outcomes examined, and 3) study design and analytic methods (summarized overall and by policy category). RESULTS: In total, 145 studies were reviewed (79 % state-level policies, 21 % federal-level policies) and classified with respect to 8 distinct policy categories and 7 outcome categories. The majority of studies evaluated policies related to prescription opioids (prescription drug monitoring programs (PDMPs), opioid prescribing policies, federal regulation of prescription opioids, pain clinic laws) and considered policy impacts with respect to proximal outcomes (e.g., opioid prescribing behaviors). In total, only 29 (20 % of studies) met each of three key criteria for rigorous design: analysis of longitudinal data with a comparison group design, adjustment for difference between policy-enacting and comparison states, and adjustment for potentially confounding co-occurring policies. These more rigorous studies were predominately published in 2017-2018 and primarily evaluated PDMPs, marijuana laws, treatment-related policies, and overdose prevention policies. CONCLUSIONS: Our results indicated that study design rigor varied notably across policy categories, highlighting the need for broader adoption of rigorous methods in the opioid policy field. More evaluation studies are needed regarding overdose prevention policies and policies related to treatment access. Greater examination of distal outcomes and potential unintended consequences are also warranted.

Variation in the degree of concentration of prescription opioid utilization using different measures.

BACKGROUND: Studies of opioid prescribing patterns have shown that a small percentage of prescribers are responsible for a large portion of total prescriptions. There is some evidence that prescription opioid use may be similarly concentrated, but patterns may differ by population and choice of opioid utilization measure. The objective of this study is to determine what proportion of prescription opioid utilization was attributable to the top utilizers among Medicaid beneficiaries and how this proportion varies by the measure used. METHODS: We analyzed 2008-2009 Medicaid claims data from 14 states and created three claims-based measures of aggregate opioid utilization: total number of annual prescriptions, total annual milligrams morphine equivalent, and total days supply. We tested two versions of the total days supply measure: one assuming consecutive use and the other assuming concurrent use of prescriptions. RESULTS: The top 20 % of prescription opioid users accounted for 66 % of prescriptions, 82-85 % of days supply depending on assumption, and 90 % of morphine milligram equivalents (MME). The degree to which prescription opioid utilization was concentrated among the top 20 % of users varied minimally across states. CONCLUSIONS: A small percentage of prescription opioid users account for a large share of prescription opioid use. Policy efforts should use metrics pertaining to days supply and total MME, not numbers of prescriptions, to more efficiently target heavy utilization. Policies targeting high-dose prescribing should consider the different ways that overlapping prescriptions may be taken.

The Impact of Morphine Equivalent Daily Dose Threshold Guidelines on Prescribed Dose in a Workers' Compensation Population.

BACKGROUND: Prescription opioid overdose has increased markedly and is of great concern among injured workers receiving workers' compensation insurance. Given the association between high daily dose of prescription opioids and negative health outcomes, state workers' compensation boards have disseminated Morphine Equivalent Daily Dose (MEDD) guidelines to discourage high-dose opioid prescribing. OBJECTIVE: To evaluate the impact of MEDD guidelines among workers' compensation claimants on prescribed opioid dose. METHODS: Workers' compensation claims data, 2010-2013 from 2 guideline states and 3 control states were utilized. The study design was an interrupted time series with comparison states and average monthly MEDD was the primary outcome. Policy variables were specified to allow for both instantaneous and gradual effects and additional stratified analyses examined evaluated the policies separately for individuals with and without acute pain, cancer, and high-dose baseline use to determine whether policies were being targeted as intended. RESULTS: After adjusting for covariates, state fixed-effects, and time trends, policy implementation was associated with a 9.26 mg decrease in MEDD (95% confidence interval, -13.96 to -4.56). Decreases in MEDD also became more pronounced over time and were larger in groups targeted by the policies. CONCLUSIONS: Passage of workers' compensation MEDD guidelines was associated with decreases in prescribed opioid dose among injured workers. Disseminating MEDD guidelines to doctors who treat workers' compensation cases may address an important risk factor for opioid-related mortality, while still allowing for autonomy in practice. Further research is needed to determine whether MEDD policies influence prescribing behavior and patient outcomes in other populations.

Reducing High-Dose Opioid Prescribing: State-Level Morphine Equivalent Daily Dose Policies, 2007-2017.

OBJECTIVE: To describe current state-level policies in the United States, January 1, 2007-June 1, 2017, limiting high morphine equivalent daily dose (MEDD) prescribing. METHODS: State-level MEDD threshold policies were reviewed using LexisNexis and Westlaw Next for legislative acts and using Google for nonlegislative state-level policies. The websites of each state's Medicaid agency, health department, prescription drug monitoring program, workers' compensation board, medical board, and pharmacy board were reviewed to identify additional policies. The final policy list was checked against existing policy compilations and academic literature and through contact with state health agency representatives. Policies were independently double-coded on the categories: state, agency/organization, policy type, effective date, threshold level, and policy exceptions. RESULTS: Currently, 22 states have at least one type of MEDD policy, most commonly guidelines (14 states), followed by prior authorizations (four states), rules/regulations (four states), legislative acts (three states), claim denials (two states), and alert systems/automatic patient reports (two states). Thresholds range widely (30-300 mg MEDD), with higher thresholds generally corresponding to more restrictive policies (e.g., claim denial) and lower thresholds corresponding to less restrictive policies (e.g., guidelines). The majority of policies exclude some groups of opioid users, most commonly patients with terminal illnesses or acute pain. CONCLUSIONS: MEDD policies have gained popularity in recent years, but considerable variation in threshold levels and policy structure point to a lack of consensus. This work provides a foundation for future evaluation of MEDD policies and may inform states considering adopting such policies.

Assessing the Impact of Body Mass Index Information on the Performance of Risk Adjustment Models in Predicting Health Care Costs and Utilization.

BACKGROUND: Using electronic health records (EHRs) for population risk stratification has gained attention in recent years. Compared with insurance claims, EHRs offer novel data types (eg, vital signs) that can potentially improve population-based predictive models of cost and utilization. OBJECTIVE: To evaluate whether EHR-extracted body mass index (BMI) improves the performance of diagnosis-based models to predict concurrent and prospective health care costs and utilization. METHODS: We used claims and EHR data over a 2-year period from a cohort of continuously insured patients (aged 20-64 y) within an integrated health system. We examined the addition of BMI to 3 diagnosis-based models of increasing comprehensiveness (ie, demographics, Charlson, and Dx-PM model of the Adjusted Clinical Group system) to predict concurrent and prospective costs and utilization, and compared the performance of models with and without BMI. RESULTS: The study population included 59,849 patients, 57% female, with BMI class I, II, and III comprising 19%, 9%, and 6% of the population. Among demographic models, R improvement from adding BMI ranged from 61% (ie, R increased from 0.56 to 0.90) for prospective pharmacy cost to 29% (1.24-1.60) for concurrent medical cost. Adding BMI to demographic models improved the prediction of all binary service-linked outcomes (ie, hospitalization, emergency department admission, and being in top 5% total costs) with area under the curve increasing from 2% (0.602-0.617) to 7% (0.516-0.554). Adding BMI to Charlson models only improved total and medical cost predictions prospectively (13% and 15%; 4.23-4.79 and 3.30-3.79), and also improved predicting all prospective outcomes with area under the curve increasing from 3% (0.649-0.668) to 4% (0.639-0.665; and, 0.556-0.576). No improvements in prediction were seen in the most comprehensive model (ie, Dx-PM). DISCUSSION: EHR-extracted BMI levels can be used to enhance predictive models of utilization especially if comprehensive diagnostic data are missing.

Factors associated with missed assessments in a 2-year longitudinal study of acute respiratory distress syndrome survivors.

BACKGROUND: To evaluate participant-related variables associated with missing assessment(s) at follow-up visits during a longitudinal research study. METHODS: This is a prospective, longitudinal, multi-site study of 196 acute respiratory distress syndrome (ARDS) survivors. More than 30 relevant sociodemographic, physical status, and mental health variables (representing participant characteristics prior to ARDS, at hospital discharge, and at the immediately preceding follow-up visit) were evaluated for association with missed assessments at 3, 6, 12, and 24-month follow-up visits (89-95% retention rates), using binomial logistic regression. RESULTS: Most participants were male (56%), white (58%), and ≤ high school education (64%). Sociodemographic characteristics were not associated with missed assessments at the initial 3-month visit or subsequent visits. The number of dependencies in Activities of Daily Living (ADLs) at hospital discharge was associated with higher odds of missed assessments at the initial visit (OR: 1.26, 95% CI: 1.12, 1.43). At subsequent 6-, 12-, and 24 months visits, post-hospital discharge physical and psychological status were not associated with subsequent missed assessments. Instead, the following were associated with lower odds of missed assessments: indicators of poorer health prior to hospital admission (inability to walk 5 min (OR: 0.46; 0.23, 0.91), unemployment due to health (OR: 0.47; 0.23, 0.96), and alcohol abuse (OR: 0.53; 0.28, 0.97)) and having the preceding visit at the research clinic rather than at home/facility, or by phone/mail (OR: 0.54; 0.31, 0.96). Inversely, variables associated with higher odds of missed assessments at subsequent visits include: functional dependency prior to hospital admission (i.e. dependency with > = 2 Instrumental Activities of Daily Living (IADLs) (OR: 1.96; 1.08, 3.52), and missing assessments at preceding visit (OR: 2.26; 1.35, 3.79). CONCLUSIONS: During the recovery process after hospital discharge, dependencies in physical functioning (e.g. ADLs, IADLs) prior to hospitalization and at hospital discharge were associated with higher odds of missed assessments. Conversely, other indicators of poorer health at baseline were associated with lower odds of missed assessments after the initial post-discharge visit. To reduce missing assessments, longitudinal clinical research studies may benefit from focusing additional resources on participants with dependencies in physical functioning prior to hospitalization and at hospital discharge.

High-Risk Prescribing to Medicaid Enrollees Receiving Opioid Analgesics: Individual- and County-Level Factors.

BACKGROUND: Prescription opioid overdoses have increased dramatically in recent years, with the highest rates among Medicaid enrollees. High-risk prescribing includes practices associated with overdoses and a range of additional opioid-related problems. OBJECTIVES: To identify individual- and county-level factors associated with high-risk prescribing among Medicaid enrollees receiving opioids. METHODS: In a four-states, cross-sectional claims data study, Medicaid enrollees 18-64 years old with a new opioid analgesic treatment episode 2007-2009 were identified. Multivariate regression analyses were conducted to identify factors associated with high-risk prescribing, defined as high-dose opioid prescribing (morphine equivalent daily dose ≥100 mg for >6 days), opioid overlap, opioid-benzodiazepine overlap. RESULTS: High-risk prescribing occurred in 39.4% of episodes. Older age, rural county of residence, white race, and major depression diagnosis were associated with higher rates of all types of high-risk prescribing. Individuals with prior opioid, alcohol, and hypnotic/sedative use disorder diagnoses had lower odds of high-dose opioid prescribing but higher odds of opioid overlap and opioid-benzodiazepine overlap than individuals without such disorders. High-dose opioid prescribing in Massachusetts was less common than in California, Illinois, and New York, whereas the rate of benzodiazepine overlap in Massachusetts was more common than in other states. Conclusions/Importance: High-risk prescribing was common and associated with several important demographic, clinical, and community factors. Findings can be used to inform targeted interventions designed to reduce such prescribing, and given state variation observed, further research is needed to better understand the effects of state policies on high-risk prescribing.

Distinctive injury deaths: the role of environment, policy and measurement across states.

BACKGROUND: Maps identifying the most distinctive feature of each state have become popular on social media, but may also have important public health applications. A map identifying the most distinctive injury death in each state could be a useful tool for policymakers, enabling them to identify potential gaps in prevention efforts. OBJECTIVE: To identify the most distinctive cause of injury death in each state and explore potential reasons for the geographical variation. METHODS: The Centers for Disease Control Web-based Injury Statistics Query and Reporting System was used to identify the injury death for each state with a rate which was the largest multiple of the national rate. Analyses were conducted with and without inclusion of 'indefinite' codes, which include injury causes of death of undetermined intent, unspecified person killed in a motor vehicle crash (MVC; vehicle occupant, cyclist, pedestrian, etc) or unspecified injury. RESULTS: Noteworthy patterns included seven states in Appalachia and the Southeast with high relative rates of unintentional firearm deaths (2.14-4.06 times the national average) and five states on the West Coast with high relative rates of legal intervention deaths (1.76-3.49 times the national average). Sensitivity analyses indicated that use of 'undetermined intent' classifications and the level of detail in coding MVCs vary substantially by state. CONCLUSIONS: These analyses highlight potential areas for prevention, such as promotion of safe storage laws in states with relatively high rates of unintentional firearm deaths and areas where standardisation of cause of death codes could be improved.

Evaluating Clinical Practice Guidelines Based on Their Association with Return to Work in Administrative Claims Data.

OBJECTIVE: To examine the association between non-adherence to clinical practice guidelines (CPGs) and time to return to work (RTW) for patients with workplace injuries. DATA SOURCES/STUDY SETTING: Secondary analysis of medical billing and disability data for 148,199 for shoulder and back injuries from a workers' compensation insurer. STUDY DESIGN: Cox proportional hazard regression is used to estimate the association between time to RTW and receipt of guideline-discordant care. We test the robustness of our findings to an omitted confounding variable. DATA COLLECTION: Collected by the insurer from the time an injury was reported, through recovery or last follow-up. PRINCIPAL FINDINGS: Receiving guideline-discordant care was associated with slower RTW for only some guidelines. Early receipt of care, and getting less than the recommended amount of care, were correlated with faster RTW. Excessive physical therapy, bracing, and injections were associated with slower RTW. CONCLUSIONS: There is not a consistent relationship between performance on CPGs and RTW. The association between performance on CPG and RTW is difficult to measure in observational data, because analysts cannot control for omitted variables that affect a patient's treatment and outcomes. CPGs supported by observational studies or randomized trials may have a more certain relationship to health outcomes.

Clinical Significance of Pain at Hospital Discharge Following Traumatic Orthopedic Injury: General Health, Depression, and PTSD Outcomes at 1 Year.

OBJECTIVES: The purpose of this study was to determine whether pain at hospital discharge is associated with general health and depression and posttraumatic stress disorder (PTSD) at 1 year following traumatic orthopedic injury. MATERIALS AND METHODS: This study prospectively enrolled 213 patients, 19 to 86 years of age, admitted to an academic level 1 trauma center for surgical treatment of a traumatic lower-extremity or upper-extremity orthopedic injury. Pain at hospital discharge was measured with the Brief Pain Inventory. At 1-year follow-up, physical and mental health was assessed with the SF-12 and depressive and PTSD symptoms with the 9-item Patient Health Questionnaire (PHQ-9) and PTSD Checklist-Civilian Version (PCL-C), respectively. Cut-off scores of 10 on the PHQ-9 and 44 on the PCL-C classified patients as having depression or PTSD. RESULTS: A total of 133 patients (62%) completed follow-up at 1 year. Responders and nonresponders did not differ significantly on baseline characteristics. Multivariable regression found that increased pain at discharge was significantly associated with depression (odds ratio=3.3; P<0.001) and PTSD (odds ratio=1.4; P=0.03) at 1 year, after controlling for age, education, injury severity score, and either depressive or PTSD symptoms at hospital discharge. Early postoperative pain was not a significant risk factor for long-term physical and mental health. DISCUSSION: Findings highlight the importance of early screening for uncontrolled postoperative pain to identify patients at high risk for poor psychological outcomes and who could benefit from more aggressive pain management. Results suggest early interventions are needed to address pain severity in patients with orthopedic trauma.