Disposition Outcomes Following Prehospital Use of Naloxone in a Large Metropolitan City in the United States.

OBJECTIVES: During a drug overdose, research suggests individuals may not call 9-1-1 out of fear of criminal justice concerns. Of those that call, research is inconclusive about the disposition of the emergency transport. We evaluated transport outcomes for adults with opioid-related overdose in the Emergency Medical Services (EMS) of a large metropolitan city in the United States. METHODS: We reviewed the EMS incident report database from the patient care record system for years 2018 to 2022. We queried all records, searching for relevant terms, and two reviewers cross-checked the database to identify cases that did not result in death at the scene. Study outcome was defined as hospital transportation or no transportation. Multivariable logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) for hospital transport with patient age, sex, race and ethnicity as the independent variables. RESULTS: We identified 5,482 cases of nonfatal opioid-related overdose. Of these, 4,984 (90.9%) were transported to the hospital; 37 (0.7%) were placed in police custody; 304 (5.5%) were not transferred; and 157 (2.9%) had unknown outcomes. Among 5,288 with data on the transport outcome, the majority were male (65%), and the highest proportion were White (39%). Compared to those who were not transported, each 1-year increase in age was related to a 2% increase in the odds of transportation (OR: 1.02, 95% CI: 1.01-1.02). Compared to White patients, Black and Hispanic patients were 43% OR: 1.43, 95% CI: 1.07-1.90) and 44% (OR: 1.44, 95% CI: 1.03-2.00) more likely to be transported. CONCLUSIONS: Individuals with suspected opioid-related overdose who call 9-1-1 are most often transported to the hospital. Current EMS procedures are successful at on-scene treatment and transportation; however, data on the long-term impact of opioid-related overdoses are still needed.

How Financial Beliefs and Behaviors Influence the Financial Health of Individuals Struggling with Opioid Use Disorder.

The surge in opioid use disorder (OUD) over the past decade escalated opioid overdoses to a leading cause of death in the United States. With adverse effects on cognition, risk-taking, and decision-making, OUD may negatively influence financial well-being. This study examined the financial health of individuals diagnosed with OUD by reviewing financial beliefs and financial behaviors. We evaluated quality of life, perceptions of financial condition during active use and recovery, and total debt. We distributed a 20-item survey to 150 individuals in an outpatient treatment program for OUD in a large metropolitan area, yielding a 56% response rate. The results revealed low overall financial health, with a median debt of USD 12,961 and a quality-of-life score of 72.80, 9.4% lower than the U.S. average (82.10). Most participants (65.75%) reported improved financial health during recovery, while a higher majority (79.45%) worsened during active use. Unemployment affected 42% of respondents, and 9.52% were employed only part-time. Regression analysis highlighted a strong association between lack of full-time employment and a lack of financial advising with total debt. High financial anxiety and active use were associated with lower quality of life. Individuals with OUD may benefit from financial interventions, resources, and counseling to improve their financial health.

Demographics and clinical features associated with rates of electronic message utilization in the primary care setting.

INTRODUCTION: Electronic messages are growing as an important form of patient-provider communication, particularly in the primary care setting. However, adoption of healthcare technology has been under-utilized by underserved patient populations. The purpose of this study was to describe how adoption and utilization of electronic messaging occurred within a large primary care urban-based patient population. METHODS: In this retrospective study, the frequency of electronic messages initiated by adult outpatient primary care patients was observed. Patients were classified as either non-portal adopters, non-message utilizers, low message utilizers, and high message utilizers. Logistic regression modeling was used to compare factors associated with message utilization rates to determine disparities in access. RESULTS: Among a sample of 27,453 ethnically diverse adult patients from the Houston, Texas Metropolitan area, 33,497 unique messages were sent (1.22 messages/patient). Message burden was predominantly derived by a small number of high utilizers (individuals who sent 3 or more messages), who comprised 15.7 % of the study population (n = 4302) but accounted for 77 % of the message volume (n = 25,776). These high utilizers were typically older, White, English speaking, from middle to upper income zip codes, had higher number of comorbidities, and a higher number of clinical visits. CONCLUSIONS: Most inbox messages were generated by a small number of patients. While it was reassuring to see older and sicker individuals utilizing electronic messaging, patients from minority and/or lower income background utilized electronic messaging much less. This may propagate systematic bias and decrease the level of care for traditionally underserved patients.

The Impact of COVID-19 on the Financial Performance of Largest Teaching Hospitals.

The COVID-19 pandemic disrupted hospital operations. Anecdotal evidence suggests financial performance likewise suffered, yet little empirical research supports this claim. This study aimed to explore the impact of the pandemic on the financial performance of the most prominent academic hospitals in the United States. Data from the 115 largest major teaching hospitals in the United States were extracted from the American Hospital Directory for three years (2019-2021). We hypothesized that the year and region would moderate the relationship between a hospital's return on assets (financial performance) and specific operational variables. We found evidence through descriptive statistics and multivariate moderated regressions that financial positions rebounded in 2021, mainly through reductions in adjusted full-time employees and liabilities and an increase in non-operating income. Our results also found that the Midwest region significantly outperformed the other three regions, particularly in terms of lower salaries and operational expenses. These findings suggest potential for future initiatives encouraging efficiency and finding alternate sources of income beyond patient income. Hospitals should focus on improving financial reserves, building out non-operational revenue streams, and implementing operational efficiencies to foster better financial resiliency. These suggestions may enable healthcare administrators and facilities to adapt to future pandemics and environmental turbulence.

Impact of COVID-19 lockdown on patient-provider electronic communications.

BACKGROUND: SARS CoV-2 virus (COVID-19) impacted the practice of healthcare in the United States, with technology being used to facilitate access to care and reduce iatrogenic spread. Since then, patient message volume to primary care providers has increased. However, the volume and trend of electronic communications after lockdown remain poorly described in the literature. METHODS: All incoming inbox items (telephone calls, refill requests, and electronic messages) sent to providers from patients amongst four primary care clinics were collected. Inbox item rates were calculated as a ratio of items per patient encountered each week. Trends in inbox rates were assessed during 12 months before and after lockdown (March 1st, 2020). Logistic regression was utilized to examine the effects of the lockdown on inbox item rate post-COVID-19 lockdown as compared to the pre-lockdown period. RESULTS: Before COVID-19 lockdown, 2.07 new inbox items per encounter were received, which increased to 2.83 items after lockdown. However, only patient-initiated electronic messages increased after lockdown and stabilized at a rate higher than the pre-COVID-19 period (aRR 1.27, p-value < 0.001). In contrast, prescription refill requests and telephone calls quickly spiked, then returned to pre-lockdown levels. CONCLUSION: Based on our observations, providers experienced a quick increase in all inbox items. However, only electronic messages had a sustained increase, exacerbating the workload of administrators, staff, and clinical providers. This study directly correlates healthcare technology adoption to a significant disruptive event but also shows additional challenges to the healthcare system that must be considered with these changes.

Does hospital location matter? Association of neighborhood socioeconomic disadvantage with hospital quality in US metropolitan settings.

An aspect of a hospital's location, such as its degree of socioeconomic disadvantage, could potentially affect quality ratings of the hospital; yet, few studies have granularly explored this relationship in United States (US) metropolitan areas characterized by a wide breadth of socioeconomic disparities across neighborhoods. An understanding of the effect of neighborhood socioeconomic disadvantage on hospital quality of care is informative for targeting resources in poor neighborhoods. We assessed the association of neighborhood socioeconomic disadvantage with hospital quality of care across several areas of quality (including mortality, readmission, safety, patient experience, effectiveness of care, summary and overall star rating) in US metropolitan areas. Hospitals in the most disadvantaged neighborhoods, compared to hospitals in the least disadvantaged neighborhoods, had worse mortality scores, readmission scores, safety of care scores, patient experience of care scores, effectiveness of care scores, summary scores and overall star rating. Timeliness of care and efficient use of imaging scores were not strongly associated with neighborhood socioeconomic disadvantage; although, future studies are needed to validate this finding. Policymakers could target innovative strategies for improving neighborhood socioeconomic conditions in more disadvantaged areas, as this may improve hospital quality.

Eligibility of out-of-hospital cardiac arrest patients for extracorporeal cardiopulmonary resuscitation in the United States: A geographic information system model.

BACKGROUND: Recent evidence suggest that extracorporeal cardiopulmonary resuscitation (ECPR) may improve survival rates for nontraumatic out-of-hospital cardiac arrest (OHCA). Eligibility criteria for ECPR are often based on patient age, clinical variables, and facility capabilities. Expanding access to ECPR across the U.S. requires a better understanding of how these factors interact with transport time to ECPR centers. METHODS: We constructed a Geographic Information System (GIS) model to estimate the number of ECPR candidates in the U.S. We utilized a Resuscitation Outcome Consortium (ROC) database to model time-dependent rates of ECPR eligibility and the Cardiac Arrest Registry to Enhance Survival (CARES) registry to determine the total number of OHCA patients who meet pre-specified ECPR criteria within designated transportation times. The combined model was used to estimate the total number of ECPR candidates. RESULTS: There were 588,203 OHCA patients in the CARES registry from 2013 to 2020. After applying clinical eligibility criteria, 22,104 (3.76%) OHCA patients were deemed eligible for ECPR. The rate of ROSC increased with longer resuscitation time, which resulted in fewer ECPR candidates. The proportion of OHCA patients eligible for ECPR increased with older age cutoffs. Only 1.68% (9,889/588,203) of OHCA patients in the U.S. were eligible for ECPR based on a 45-minute transportation time to an ECMO-ready center model. CONCLUSIONS: Less than 2% of OHCA patients are eligible for ECPR in the U.S. GIS models can identify the impact of clinical criteria, transportation time, and hospital capabilities on ECPR eligibility to inform future implementation strategies.

The Impact of Pediatric Opioid-Related Visits on U.S. Emergency Departments.

BACKGROUND: While there is significant research exploring adults' use of opioids, there has been minimal focus on the opioid impact within emergency departments for the pediatric population. METHODS: We examined data from the Agency for Healthcare Research, the National Emergency Department Sample (NEDS), and death data from the Centers for Disease Control and Prevention. Sociodemographic and financial variables were analyzed for encounters during 2014-2017 for patients under age 18, matching diagnoses codes for opioid-related overdose or opioid use disorder. RESULTS: During this period, 59,658 children presented to an ED for any diagnoses involving opioids. The majority (68.5%) of visits were related to overdoses (poisoning), with a mean age of 11.3 years and a majority female (53%). There was a curvilinear relationship between age and encounters, with teens representing the majority of visits, followed by infants. The highest volume was seen in the Southern U.S., with over 58% more opioid visits than the next highest region (Midwest). Charges exceeded USD 157 million, representing 2% of total ED costs, with Medicaid responsible for 54% of the total. CONCLUSIONS: With increases in substance use among children, there is a growing need for pediatric emergency physicians to recognize, refer, and initiate treatments.

Machine Learning for Predicting Risk of Early Dropout in a Recovery Program for Opioid Use Disorder.

BACKGROUND: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program. METHODS: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning approach to predict the individualized risk for dropping out of the program. RESULTS: Our model achieved sensitivity of 0.81 and specificity of 0.65 for dropout at 90 days and improved the performance to sensitivity of 0.86 and specificity of 0.66 for 120 days. Additionally, we identified individual risk factors for dropout, including previous overdose and relapse and improvement in reported quality of life. CONCLUSIONS: Our informatics approach provides insight into an area where programs may allocate additional resources in order to retain high-risk individuals and increase the chances of success in recovery.

Sociodemographic and Clinical Characteristics Associated with Improvements in Quality of Life for Participants with Opioid Use Disorder.

BACKGROUND: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant's reported quality of life (QoL) at the beginning of the program and at successive intervals. METHODS: A visual analog scale was used to measure the change in QoL among participants after joining the program. We then identified sociodemographic and clinical characteristics associated with changes in QoL. RESULTS: 71% of the participants (n = 494) experienced an increase in their QoL scores, with an average improvement of 15.8 ± 29 points out of a hundred. We identified 10 factors associated with a significant change in QoL. Participants who relapsed during treatment experienced minor increases in QoL, and participants who attended professional counseling experienced the largest increases in QoL compared with those who did not. CONCLUSIONS: Insight into significant factors associated with increases in QoL may inform programs on areas of focus. The inclusion of counseling and other services that address factors such as psychological distress were found to increase participants' QoL and success in recovery.

County-level factors underlying opioid mortality in the United States.

BACKGROUND: Mortality from overdoses involving opioids in the United States (U.S.) has reached epidemic proportions. More research is needed to examine the underlying factors contributing to opioid-related mortality regionally. This study's objective was to identify and examine the county-level factors most closely associated with opioid-related overdose deaths across all counties in the U.S.Methods: Using a national cross-sectional ecological study design, we analyzed the relationships between 17 county-level characteristics in four categories (i.e. socio-economic, availability of medical care, health-related concerns, and demographics) with opioid mortality. Data were extracted from the Robert Wood Johnson County Health Rankings aggregate database and Centers for Disease Control and Prevention (CDC)'s Wide-ranging Online Data for Epidemiological Research (WONDER) system.Results: There were 1058 counties (33.67% of 3142 nationally) with reported opioid-related fatalities. Median opioid-related mortality was 15.61 per 100,000 persons. Multivariate regression results indicate that counties with the highest opioid-related mortality had increased rates of tobacco use, HIV, Non-Hispanic Caucasians, and females and were rural areas, but lower rates of food insecurity and uninsured adults. The rates of tobacco use and HIV had the strongest association with mortality. Availability of either mental health or primary care providers were not significantly associated with mortality. Severe housing problems, high school graduation rate, obesity, violent crime, and median household income also did not contribute to county-level differences in overdose mortality.Conclusions: Future health policies should fund further investigations and ultimately address the most influential and significant underlying county-level factors associated with opioid-related mortality.

Women and ethnoracial minorities with poor cardiovascular health measures associated with a higher risk of developing mood disorder.

BACKGROUND: Mood disorders (MDS) are a type of mental health illness that effects millions of people in the United States. Early prediction of MDS can give providers greater opportunity to treat these disorders. We hypothesized that longitudinal cardiovascular health (CVH) measurements would be informative for MDS prediction. METHODS: To test this hypothesis, the American Heart Association's Guideline Advantage (TGA) dataset was used, which contained longitudinal EHR from 70 outpatient clinics. The statistical analysis and machine learning models were employed to identify the associations of the MDS and the longitudinal CVH metrics and other confounding factors. RESULTS: Patients diagnosed with MDS consistently had a higher proportion of poor CVH compared to patients without MDS, with the largest difference between groups for Body mass index (BMI) and Smoking. Race and gender were associated with status of CVH metrics. Approximate 46% female patients with MDS had a poor hemoglobin A1C compared to 44% of those without MDS; 62% of those with MDS had poor BMI compared to 47% of those without MDS; 59% of those with MDS had poor blood pressure (BP) compared to 43% of those without MDS; and 43% of those with MDS were current smokers compared to 17% of those without MDS. CONCLUSIONS: Women and ethnoracial minorities with poor cardiovascular health measures were associated with a higher risk of development of MDS, which indicated the high utility for using routine medical records data collected in care to improve detection and treatment for MDS among patients with poor CVH.

Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset.

Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making.

Impact of External Environmental Dimensions on Financial Performance of Major Teaching Hospitals in the U.S.

Teaching hospitals have a unique mission to not only deliver graduate medical education but to also provide both inpatient and ambulatory care and to conduct clinical medical research; therefore, they are under constant financial pressure, and it is important to explore what types of external environmental components affect their financial performance. This study examined if there is an association between the short-term and long-term financial performance of major teaching hospitals in the United States and the external environmental dimensions, as measured by the Resource Dependence Theory. Data for 226 major teaching hospitals spanning 46 states were analyzed. The dependent variable for short-term financial performance was days cash on hand, and dependent variable for long-term financial performance was return on assets, both an average of most recently available 4-year data (2014-2017). Utilizing linear regression model, results showed significance between outpatient revenue and days cash on hand as well as significant relationship between population of the metropolitan statistical area, unemployment rate of the metropolitan statistical area, and teaching hospital's return on assets. Additionally, system membership, type of ownership/control, and teaching intensity also showed significant association with return on assets. By comprehensively examining all major teaching hospitals in the U.S. and analyzing the association between their short-term and long-term financial performance and external environmental dimensions, based upon Resource Dependence Theory, we found that by offering diverse outpatient services and novel delivery options, administrators of teaching hospitals may be able to increase organizational liquidity.

Feasibility and outcomes from an integrated bridge treatment program for opioid use disorder.

OBJECTIVE: With a significant proportion of individuals with opioid use disorder not currently receiving treatment, it is critical to find novel ways to engage and retain patients in treatment. Our objective is to describe the feasibility and preliminary outcomes of a program that used emergency physicians to initiate a bridge treatment, followed by peer support services, behavioral counseling, and ongoing treatment and follow-up. METHODS: We developed a program called the Houston Emergency Opioid Engagement System (HEROES) that provides rapid access to board-certified emergency physicians for initiation of buprenorphine, plus at least 1 behavioral counseling session and 4 weekly peer support sessions over the course of 30 days. Follow-ups were conducted by phone and in person to obtain patient-reported outcomes. Primary outcomes included percentage of patients who completed the 30-day program and the percentage for successful linkage to more permanent ongoing treatment after the initial program. RESULTS: There were 324 participants who initiated treatment on buprenorphine from April 2018 to July 2019, with an average age of 36 (±9.6 years) and 52% of participants were males. At 30 days, 293/324 (90.43%) completed the program, and 203 of these (63%) were successfully connected to a subsequent community addiction medicine physician. There was a significant improvement (36%) in health-related quality of life. CONCLUSION: Lack of insurance is a predictor for treatment failure. Implementation of a multipronged treatment program is feasible and was associated with positive patient-reported outcomes. This approach holds promise as a strategy for engaging and retaining patients in treatment.

Patterns and risk factors of opioid-suspected EMS overdose in Houston metropolitan area, 2015-2019: A Bayesian spatiotemporal analysis.

BACKGROUND: Opioid-related overdose deaths are the top accidental cause of death in the United States, and development of regional strategies to address this epidemic should begin with a better understanding of where and when overdoses are occurring. METHODS AND FINDINGS: In this study, we relied on emergency medical services data to investigate the geographical and temporal patterns in opioid-suspected overdose incidents in one of the largest and most ethnically diverse metropolitan areas (Houston Texas). Using a cross sectional design and Bayesian spatiotemporal models, we identified zip code areas with excessive opioid-suspected incidents, and assessed how the incidence risks were associated with zip code level socioeconomic characteristics. Our analysis suggested that opioid-suspected overdose incidents were particularly high in multiple zip codes, primarily south and central within the city. Zip codes with high percentage of renters had higher overdose relative risk (RR = 1.03; 95% CI: [1.01, 1.04]), while crowded housing and larger proportion of white citizens had lower relative risks (RR = 0.9; 95% CI: [0.84, 0.96], RR = 0.97, 95% CI: [0.95, 0.99], respectively). CONCLUSIONS: Our analysis illustrated the utility of Bayesian spatiotemporal models in assisting the development of targeted community strategies for local prevention and harm reduction efforts.

Prevalence and charges of opioid-related visits to U.S. emergency departments.

OBJECTIVE: An overwhelming responsibility for responding to the opioid epidemic falls on hospital emergency departments (ED). We sought to examine the overall prevalence rate and associated charges of opioid-related diagnoses and overdoses. Although charge data do not necessarily represent cost, they are proxy indicators of resource utilization and burden. METHODS: We conducted a retrospective study of the National Emergency Department Sample (NEDS) dataset, the largest all-payer ED database in the United States. We queried using specific relevant ICD-10 codes to estimate the number of adult ED visits for both opioid poisonings and other opioid-related diagnoses during 2016 and 2017, which was the most recent publicly available data. Prevalence rates and financial charges were calculated by year and odds ratios were used to examine differences. RESULTS: Of approximately 234 million adult visits to EDs across 2016 and 2017, 2.88 million (1.23%) were related to opioids, with overdoses comprising nearly 27.5% and visits for other opioid-related diagnoses totaling 72.5%. As the primary diagnosis, opioids were responsible for 37% of all ED visits across both years. Total opioid-related visits for the two years accounted for $9.57 billion in ED charges, or $4.78 billion annually, with Medicaid and Medicare responsible for 66% of all charges. CONCLUSION AND RELEVANCE: Approximately one of every 80 visits to the ED were opioid-related, leading to financial charges approaching $5 billion per year. Since both prevalence and the economic burden of opioid-related visits are high, targeted interventions to address this epidemic's impact on healthcare systems should be a national priority.

Predicting cardiovascular health trajectories in time-series electronic health records with LSTM models.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in the United States (US). Better cardiovascular health (CVH) is associated with CVD prevention. Predicting future CVH levels may help providers better manage patients' CVH. We hypothesized that CVH measures can be predicted based on previous measurements from longitudinal electronic health record (EHR) data. METHODS: The Guideline Advantage (TGA) dataset was used and contained EHR data from 70 outpatient clinics across the United States (US). We studied predictions of 5 CVH submetrics: smoking status (SMK), body mass index (BMI), blood pressure (BP), hemoglobin A1c (A1C), and low-density lipoprotein (LDL). We applied embedding techniques and long short-term memory (LSTM) networks - to predict future CVH category levels from all the previous CVH measurements of 216,445 unique patients for each CVH submetric. RESULTS: The LSTM model performance was evaluated by the area under the receiver operator curve (AUROC): the micro-average AUROC was 0.99 for SMK prediction; 0.97 for BMI; 0.84 for BP; 0.91 for A1C; and 0.93 for LDL prediction. Model performance was not improved by using all 5 submetric measures compared with using single submetric measures. CONCLUSIONS: We suggest that future CVH levels can be predicted using previous CVH measurements for each submetric, which has implications for population cardiovascular health management. Predicting patients' future CVH levels might directly increase patient CVH health and thus quality of life, while also indirectly decreasing the burden and cost for clinical health system caused by CVD and cancers.

Opioid prescribing patterns and overdose deaths in Texas.

BACKGROUND: Opioid use disorder has recently been declared a public health emergency, yet it is unknown whether opioid prescribing patterns have changed over time. Our objective is to examine opioid prescribing behavior and overdose fatalities in one large state prior to state-mandated usage of a prescription drug monitoring program (PDMP). Methods: We relied on de-identified longitudinal data from state and national databases for opioid prescriptions and overdose deaths in Texas between 2013 and 2017. Descriptive statistics and trend analyses were used to assess proportional differences and changes over time. Results: Prescriptions for opioids represented over 45% of the total controlled medications dispensed across the entire period. This equates to roughly 17.7 million opioid prescriptions dispensed per year, or 63.7 opioid prescriptions per 100 persons, slightly less than the reported national average. Hydrocodone was the most widely prescribed opioid (32.9%), followed by tramadol (26.9%) and codeine (21.5%). The overall controlled substance prescribing rate appears to be decreasing in the latest year, and the composition of opioids has shifted. We found a reduction in schedule II medications (such as hydrocodone and fentanyl) and increase in schedule IV medications such as tramadol. At the same time, total overdose fatalities increased 42% during this time, and population-adjusted rates increased 34% to 5.87 deaths per 100,000 persons. Conclusions: While prescribing rates have decreased in Texas, overdose deaths from both legal and illicit opioids are rising, suggesting that changing physician prescribing behavior alone may not be sufficient to curb the epidemic. Policies and community interventions should be considered to address increases in both prescription and illicit opioid deaths.