Simulated impact of medicaid expansion on the economic burden of opioid use disorder in North Carolina.

BACKGROUND: Opioid use disorder (OUD) imposes significant costs on state and local governments. Medicaid expansion may lead to a reduction in the cost burden of OUD to the state. METHODS: We estimated the health care, criminal justice and child welfare costs, and tax revenue losses, attributable to OUD and borne by the state of North Carolina in 2022, and then estimated changes in the same domains following Medicaid expansion in North Carolina (adopted in December 2023). Analyses used existing literature on the national and state-level costs attributable to OUD to estimate individual-level health care, criminal justice, and child welfare system costs, and lost tax revenues. We combined Individual-level costs and prevalence estimates to estimate costs borne by the state before Medicaid expansion. Changes in costs after expansion were computed based on a) medication for opioid use disorder (MOUD) access for new enrollees and b) shifting of responsibility for some health care costs from the state to the federal government. Monte Carlo simulation accounted for the impact of parameter uncertainty. Dollar estimates are from the 2022 price year, and costs following the first year were discounted at 3 %. RESULTS: In 2022, North Carolina incurred costs of $749 million (95 % credible interval [CI]: $305 M-$1,526 M) associated with OUD (53 % in health care, 36 % in criminal justice, 7 % in lost tax revenue, and 4 % in child welfare costs). Expanding Medicaid lowered the cost burden of OUD incurred by the state. The state was predicted to save an estimated $72 million per year (95 % CI: $6 M-$241 M) for the first two years and $30 million per year (95 % CI: -$28 M-$176 M) in subsequent years. Over five years, savings totaled $224 million (95 % CI: -$47 M-$949 M). CONCLUSION: Medicaid expansion has the potential to decrease the burden of OUD in North Carolina, and policymakers should expedite its implementation.

Evaluation of respondent-driven sampling in seven studies of people who use drugs from rural populations: findings from the Rural Opioid Initiative.

BACKGROUND: Accurate prevalence estimates of drug use and its harms are important to characterize burden and develop interventions to reduce negative health outcomes and disparities. Lack of a sampling frame for marginalized/stigmatized populations, including persons who use drugs (PWUD) in rural settings, makes this challenging. Respondent-driven sampling (RDS) is frequently used to recruit PWUD. However, the validity of RDS-generated population-level prevalence estimates relies on assumptions that should be evaluated. METHODS: RDS was used to recruit PWUD across seven Rural Opioid Initiative studies between 2018-2020. To evaluate RDS assumptions, we computed recruitment homophily and design effects, generated convergence and bottleneck plots, and tested for recruitment and degree differences. We compared sample proportions with three RDS-adjusted estimators (two variations of RDS-I and RDS-II) for five variables of interest (past 30-day use of heroin, fentanyl, and methamphetamine; past 6-month homelessness; and being positive for hepatitis C virus (HCV) antibody) using linear regression with robust confidence intervals. We compared regression estimates for the associations between HCV positive antibody status and (a) heroin use, (b) fentanyl use, and (c) age using RDS-1 and RDS-II probability weights and no weights using logistic and modified Poisson regression and random-effects meta-analyses. RESULTS: Among 2,842 PWUD, median age was 34 years and 43% were female. Most participants (54%) reported opioids as their drug of choice, however regional differences were present (e.g., methamphetamine range: 4-52%). Many recruitment chains were not long enough to achieve sample equilibrium. Recruitment homophily was present for some variables. Differences with respect to recruitment and degree varied across studies. Prevalence estimates varied only slightly with different RDS weighting approaches, most confidence intervals overlapped. Variations in measures of association varied little based on weighting approach. CONCLUSIONS: RDS was a useful recruitment tool for PWUD in rural settings. However, several violations of key RDS assumptions were observed which slightly impacts estimation of proportion although not associations.

Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties.

BACKGROUND: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. METHODS: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. RESULTS: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. CONCLUSIONS: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

Comparing methods for risk prediction of multicategory outcomes: dichotomized logistic regression vs. multinominal logit regression.

Background: Medical outcomes of interest to clinicians may have multiple categories. Researchers face several options for risk prediction of such outcomes, including dichotomized logistic regression and multinomial logit regression modeling. We aimed to compare these methods and provide practical guidance needed. Methods: We described dichotomized logistic regression and competing risks regression, and an alternative to standard multinomial logit regression, continuation-ratio logit regression for ordinal outcomes. We then applied these methods to develop prediction models of survival and growth outcomes based on the NICHD Extremely Preterm Birth Outcome Tool model. The statistical and practical advantages and flaws of these methods were examined and both discrimination and calibration of the estimated models were assessed. Results: The dichotomized logistic models and multinomial continuation-ratio logit model had similar discrimination and calibration in predicting death and survival without neurodevelopmental impairment. But the continuation-ratio logit model had better discrimination and calibration in predicting probabilities of neurodevelopmental impairment. The sum of predicted probabilities of outcome categories from the logistic models did not equal 100% for about half of the study infants, ranging from 87.7% to 124.0%, and the logistic model of neurodevelopmental impairment greatly overpredicted the risk among low-risk infants and underpredicted among high-risk infants. Conclusions: Estimating multiple logistic regression models of dichotomized outcomes may result in poorly calibrated predictions. For an outcome with multiple ordinal categories, continuation-ratio logit regression is a useful alternative to standard multinomial logit regression. It produces better calibrated predictions and has the advantages of simplicity in model interpretation and flexibility to include outcome category-specific predictors and random-effect terms for patient heterogeneity by hospital.

Racial/Ethnic disparities in exposure to neighborhood violence and lung cancer risk in Chicago.

BACKGROUND: Despite the lower prevalence and frequency of smoking, Black adults are disproportionately affected by lung cancer. Exposure to chronic stress generates heightened immune responses, which creates a cell environment conducive to lung cancer development. Residents in poor and segregated neighborhoods are exposed to increased neighborhood violence, and chronic exposure to violence may have downstream physiological stress responses, which may explain racial disparities in lung cancer in predominantly Black urban communities. METHODS: We utilized retrospective electronic medical records of patients who underwent a screening or diagnostic test for lung cancer at an academic medical center in Chicago to examine the associations between lung cancer diagnosis and individual characteristics (age, gender, race/ethnicity, and smoking status) and neighborhood-level homicide rate. We then used a synthetic population to estimate the neighborhood-level lung cancer risk to understand spatial clusters of increased homicide rates and lung cancer risk. RESULTS: Older age and former/current smoking status were associated with increased odds of lung cancer diagnosis. Hispanic patients were more likely than White patients to be diagnosed with lung cancer, but there was no statistical difference between Black and White patients in lung cancer diagnosis. The odds of being diagnosed with lung cancer were significantly higher for patients living in areas with the third and fourth quartiles of homicide rates compared to the second quartile of homicide rates. Furthermore, significant spatial clusters of increased lung cancer risk and homicide rates were observed on Chicago's South and West sides. CONCLUSIONS: Neighborhood violence was associated with an increased risk of lung cancer. Black residents in Chicago are disproportionately exposed to neighborhood violence, which may partially explain the existing racial disparity in lung cancer. Incorporating neighborhood violence exposure into lung cancer risk models may help identify high-risk individuals who could benefit from lung cancer screening.

Pharmacotherapy for Alcohol Use Disorder: A Systematic Review and Meta-Analysis.

Importance: Alcohol use disorder affects more than 28.3 million people in the United States and is associated with increased rates of morbidity and mortality. Objective: To compare efficacy and comparative efficacy of therapies for alcohol use disorder. Data Sources: PubMed, the Cochrane Library, the Cochrane Central Trials Registry, PsycINFO, CINAHL, and EMBASE were searched from November 2012 to September 9, 2022 Literature was subsequently systematically monitored to identify relevant articles up to August 14, 2023, and the PubMed search was updated on August 14, 2023. Study Selection: For efficacy outcomes, randomized clinical trials of at least 12 weeks' duration were included. For adverse effects, randomized clinical trials and prospective cohort studies that compared drug therapies and reported health outcomes or harms were included. Data Extraction and Synthesis: Two reviewers evaluated each study, assessed risk of bias, and graded strength of evidence. Meta-analyses used random-effects models. Numbers needed to treat were calculated for medications with at least moderate strength of evidence for benefit. Main Outcomes and Measures: The primary outcome was alcohol consumption. Secondary outcomes were motor vehicle crashes, injuries, quality of life, function, mortality, and harms. Results: Data from 118 clinical trials and 20 976 participants were included. The numbers needed to treat to prevent 1 person from returning to any drinking were 11 (95% CI, 1-32) for acamprosate and 18 (95% CI, 4-32) for oral naltrexone at a dose of 50 mg/d. Compared with placebo, oral naltrexone (50 mg/d) was associated with lower rates of return to heavy drinking, with a number needed to treat of 11 (95% CI, 5-41). Injectable naltrexone was associated with fewer drinking days over the 30-day treatment period (weighted mean difference, -4.99 days; 95% CI, -9.49 to -0.49 days) Adverse effects included higher gastrointestinal distress for acamprosate (diarrhea: risk ratio, 1.58; 95% CI, 1.27-1.97) and naltrexone (nausea: risk ratio, 1.73; 95% CI, 1.51-1.98; vomiting: risk ratio, 1.53; 95% CI, 1.23-1.91) compared with placebo. Conclusions and Relevance: In conjunction with psychosocial interventions, these findings support the use of oral naltrexone at 50 mg/d and acamprosate as first-line pharmacotherapies for alcohol use disorder.

Examining buprenorphine diversion through a harm reduction lens: an agent-based modeling study.

BACKGROUND: Recent policies have lessened restrictions around prescribing buprenorphine-naloxone (buprenorphine) for the treatment of opioid use disorder (OUD). The primary concern expressed by critics of these policies is the potential for buprenorphine diversion. However, the population-level effects of increased buprenorphine diversion are unclear. If replacing the use of heroin or fentanyl, use of diverted buprenorphine could be protective. METHODS: Our study aim was to estimate the impact of buprenorphine diversion on opioid overdose using an agent-based model calibrated to North Carolina. We simulated the progression of opioid misuse and opioid-related outcomes over a 5-year period. Our status quo scenario assumed that 50% of those prescribed buprenorphine diverted at least one dose per week to other individuals with OUD and 10% of individuals with OUD used diverted buprenorphine at least once per week. A controlled prescription only scenario assumed that no buprenorphine would be diverted, while an increased diversion scenario assumed that 95% of those prescribed buprenorphine diverted and 50% of individuals with OUD used diverted buprenorphine. We assumed that use of diverted buprenorphine replaced the use of other opioids for that day. Sensitivity analyses increased the risk of overdose when using diverted buprenorphine, increased the frequency of diverted buprenorphine use, and simulated use of diverted buprenorphine by opioid-naïve individuals. Scenarios were compared on opioid overdose-related outcomes over the 5-year period. RESULTS: Our status quo scenario predicted 10,658 (credible interval [CI]: 9699-11,679) fatal opioid overdoses. A scenario simulating controlled prescription only of buprenorphine (i.e., no diversion) resulted in 10,741 (9895-11,650) fatal opioid overdoses versus 10,301 (9439-11,244) within a scenario simulating increased diversion. Compared to the status quo, the controlled prescription only scenario resulted in a similar number of fatal overdoses, while the scenario with increased diversion of buprenorphine resulted in 357 (3.35%) fewer fatal overdoses. Even when increasing overdose risk while using diverted buprenorphine and incorporating use by opioid naïve individuals, increased diversion did not increase overdoses compared to a scenario with no buprenorphine diversion. CONCLUSIONS: A similar number of opioid overdoses occurred under modeling conditions with increased rates of buprenorphine diversion among persons with OUD, with non-statistical trends toward lower opioid overdoses. These results support existing calls for low- to no-barrier access to buprenorphine for persons with OUD.

Trends in Opioid Misuse Among Individuals Aged 12 to 21 Years in the US.

Importance: Although opioid misuse has been decreasing among US youths and adolescents in recent years, it is unclear what has contributed to this trend and how this trend differs by age group and sex over time. Objective: To identify trends in opioid misuse among youths and young adults across and between ages, birth cohorts, and sexes. Design, Setting, and Participants: Cross-sectional National Survey on Drug Use and Health (NSDUH) public-use files were used to produce nationally representative pseudocohorts. The survey population includes the civilian US population in the 50 states and Washington, DC. Individuals without a fixed address and institutionalized individuals were excluded. Respondents to the NSDUH are a population-based sample selected using a stratified cluster design. For the years (January 1, 2002, to December 31, 2019) and ages (12-21 years) analyzed, the sample sizes ranged from 1607 to 3239 respondents. Data were analyzed from January 1, 2022, to April 12, 2023, for the main outcome by age, sex, and pseudocohort. Main Outcomes and Measures: Respondents were asked whether they misused prescription opioids or used heroin in the past year. The analysis hypotheses were formulated and tested after data collection. Results: In a total of 5 pseudocohorts, data from 114 412 respondents aged 12 to 21 years were analyzed; the unweighted distribution of male sex (complement was female) ranged from 47.7% to 52.6% (mean [SD], 50.6% [1.1%]). Response rates ranged from 45.8% to 71.3%. High school-aged youths and young adults had distinctly lower rates of opioid misuse in later pseudocohorts compared with earlier ones. Rates of misuse among individuals aged 16 years were 2.80% (95% CI, 1.06%-4.54%) higher in 2002 vs 2008; among those aged 18 years, rates were 4.36% (95% CI, 1.85%-6.87%) higher in 2002. Similarly, rates of misuse among individuals aged 16 years were 3.93% (95% CI, 2.15%-5.71%) higher in 2008 vs 2014; among those aged 17 years, rates were 3.41% (95% CI, 1.94%-4.88%) higher in 2008. Similar patterns were observed by sex. In earlier cohorts, younger female participants had higher rates of opioid misuse than their male counterparts and older male participants had higher rates than their female counterparts. Sex differences decreased in later cohorts. Conclusions and Results: The findings of this cross-sectional study of US youths and young adults suggest that high school-aged individuals consistently misused fewer opioids in later pseudocohorts overall and by sex. Sex differences in opioid rates also diminished in later pseudocohorts. A decrease in drug availability and general exposure to the harms of opioid use could be contributing to these findings. Future planned research using this pseudocohort approach will examine polysubstance use and evaluate how substance use differs by other sociodemographic characteristics.

Text Analysis of Trends in Health Equity and Disparities From the Internal Revenue Service Tax Documentation Submitted by US Nonprofit Hospitals Between 2010 and 2019: Exploratory Study.

BACKGROUND: Many US hospitals are classified as nonprofits and receive tax-exempt status partially in exchange for providing benefits to the community. Proof of compliance is collected with the Schedule H form submitted as part of the annual Internal Revenue Service Form 990 (F990H), including a free-response text section that is known for being ambiguous and difficult to audit. This research is among the first to use natural language processing approaches to evaluate this text section with a focus on health equity and disparities. OBJECTIVE: This study aims to determine the extent to which the free-response text in F990H reveals how nonprofit hospitals address health equity and disparities, including alignment with public priorities. METHODS: We used free-response text submitted by hospital reporting entities in Part V and VI of the Internal Revenue Service Form 990 Schedule H between 2010 and 2019. We identified 29 main themes connected to health equity and disparities, and 152 related key phrases. We tallied occurrences of these phrases through term frequency analysis, calculated the Moran I statistic to assess geographic variation in 2018, analyzed Google Trends use for the same terms during the same period, and used semantic search with Sentence-BERT in Python to understand contextual use. RESULTS: We found increased use from 2010 to 2019 across all the 29 phrase themes related to health equity and disparities. More than 90% of hospital reporting entities used terms in 2018 and 2019 related to affordability (2018: 2117/2131, 99.34%; 2019: 1620/1627, 99.57%), government organizations (2018: 2053/2131, 96.33%; 2019: 1577/1627, 96.93%), mental health (2018: 1937/2131, 90.9%; 2019: 1517/1627, 93.24%), and data collection (2018: 1947/2131, 91.37%; 2019: 1502/1627, 92.32%). The themes with the largest relative increase were LGBTQ (lesbian, gay, bisexual, transgender, and queer; 1676%; 2010: 12/2328, 0.51%; 2019: 149/1627, 9.16%) and social determinants of health (958%; 2010: 68/2328, 2.92%; 2019: 503/1627, 30.92%). Terms related to homelessness varied geographically from 2010 to 2018, and terms related to equity, health IT, immigration, LGBTQ, oral health, rural, social determinants of health, and substance use showed statistically significant (P<.05) geographic variation in 2018. The largest percentage point increase was for terms related to substance use (2010: 403/2328, 17.31%; 2019: 1149/1627, 70.62%). However, use in themes such as LGBTQ, disability, oral health, and race and ethnicity ranked lower than public interest in these topics, and some increased mentions of themes were to explicitly say that no action was taken. CONCLUSIONS: Hospital reporting entities demonstrate an increasing awareness of health equity and disparities in community benefit tax documentation, but these do not necessarily correspond with general population interests or additional action. We propose further investigation of alignment with community health needs assessments and make suggestions for improvements to F990H reporting requirements.

Impacts of timing, length, and intensity of behavioral interventions to COVID-19 dynamics: North Carolina county-level examples.

We sought to examine how the impact of revocable behavioral interventions, e.g., shelter-in-place, varies throughout an epidemic, as well as the role that the proportion of susceptible individuals had on an intervention's impact. We estimated the theoretical impacts of start day, length, and intensity of interventions on disease transmission and illustrated them on COVID-19 dynamics in Wake County, North Carolina, to inform how interventions can be most effective. We used a Susceptible, Exposed, Infectious, and Recovered (SEIR) model to estimate epidemic curves with modifications to the disease transmission parameter (ß). We designed modifications to simulate events likely to increase transmission (e.g., long weekends, holiday seasons) or behavioral interventions likely to decrease it (e.g., shelter-in-place, masking). We compared the resultant curves' shape, timing, and cumulative case count to baseline and across other modified curves. Interventions led to changes in COVID-19 dynamics, including moving the peak's location, height, and width. The proportion susceptible, at the start day, strongly influenced their impact. Early interventions shifted the curve, while interventions near the peak modified shape and case count. For some scenarios, in which the transmission parameter was decreased, the final cumulative count increased over baseline. We showed that the timing of revocable interventions has a strong impact on their effect. The same intervention applied at different time points, corresponding to different proportions of susceptibility, resulted in qualitatively differential effects. Accurate estimation of the proportion susceptible is critical for understanding an intervention's impact. The findings presented here provide evidence of the importance of estimating the proportion of the population that is susceptible when predicting the impact of behavioral infection control interventions. Greater emphasis should be placed on the estimation of this epidemic component in intervention design and decision-making. Our results are generic and are applicable to other infectious disease epidemics, as well as to future waves of the current COVID-19 epidemic. Developed into a publicly available tool that allows users to modify the parameters to estimate impacts of different interventions, these models could aid in evaluating behavioral intervention options prior to their use and in predicting case increases from specific events.

Modeling HIV transmission among persons who inject drugs (PWID) at the "End of the HIV Epidemic" and during the COVID-19 pandemic.

BACKGROUND: We explore injecting risk and HIV incidence among PWID in New York City (NYC), from 2012 to 2019, when incidence was extremely low, <0.1/100 person-years at risk, and during disruption of prevention services due to the COVID-19 pandemic. METHODS: We developed an Agent-Based model (ABM) to simulate sharing injecting equipment and measure HIV incidence in NYC. The model was adapted from a previous ABM model developed to compare HIV transmission with "high" versus "low" dead space syringes. Data for applying the model to NYC during the period of very low HIV incidence was taken from the "Risk Factors" study, a long-running study of participants entering substance use treatment in NYC. Injecting risk behavior had not been eliminated in this population, with approximately 15 % reported recent syringe sharing. Data for possible transmission during COVID-19 disruption was taken from previous HIV outbreaks and early studies of the pandemic in NYC. RESULTS: The modeled incidence rates fell within the 95 % confidence bounds of all of the empirically observed incidence rates, without any additional calibration of the model. Potential COVID-19 disruptions increased the probability of an outbreak from 0.03 to 0.25. CONCLUSIONS: The primary factors in the very low HIV incidence were the extremely small numbers of PWID likely to transmit HIV and that most sharing occurs within small, relatively stable, mostly seroconcordant groups. Containing an HIV outbreak among PWID during a continuing pandemic would be quite difficult. Pre-pandemic levels of HIV prevention services should be restored as quickly as feasible.

National polydrug use patterns among people who misuse prescription opioids and people who use heroin. Results from the National Household Survey on Drug Use and Health.

BACKGROUND: Polysubstance use among people who misuse opioids (PWMO) is highly prevalent, but understudied. We defined, estimated, and analyzed national polysubstance use patterns among PWMO using National Household Survey on Drug Use and Health data (2017-2019). METHODS: We obtained estimates of past-month patterns of polydrug use using cluster analysis and latent class/profile analysis. We considered misuse of prescription opioids and use of heroin, cocaine (including crack), marijuana, alcohol, and "other" substances. RESULTS: We identified a five-cluster solution for binary indicators of past-month use and a six-cluster solution for frequency of use. The largest binary cluster (37%) included misuse of prescription opioids and use of alcohol. The second-largest cluster (15%) included misuse of prescription opioids, alcohol, marijuana, and "other" substances. Among those who used heroin, 36% used methamphetamine. In terms of frequency of use, the largest cluster among people who misuse opioid who used multiple substances (almost 40%) misused prescription pain relievers, alcohol, and marijuana infrequently. The second-largest cluster (23%) used marijuana almost daily and misused prescription pain relievers an average of 6.6 days. PWMO in a cluster of almost daily heroin use indicated use of methamphetamine, marijuana, and prescription opioids. Those who used methamphetamine, were using it more than 15 days a month. CONCLUSIONS: We have developed reference measures of polydrug patterns among US household population and estimated their demographic characteristics. We identified clusters of high-risk polydrug use. These findings have implications for the development of prevention and treatment solutions in the United States.

Using Named Entity Recognition to Identify Substances Used in the Self-medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data.

BACKGROUND: The cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances that people use or their effects. OBJECTIVE: The aim of this study is to validate a methodology for identifying the substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit. METHODS: We developed a named entity recognition model to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. To identify effects that are symptoms of opioid withdrawal and substances that are potential remedies for these symptoms, we deduplicated substances and effects by using clustering and manual review, then built a network of substance and effect co-occurrence. For each of the 16 effects identified as symptoms of opioid withdrawal in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, we identified the 10 most strongly associated substances. We classified these pairs as follows: substance is a Food and Drug Administration-approved or commonly used treatment for the symptom, substance is not often used to treat the symptom but could be potentially useful given its pharmacological profile, substance is a home or natural remedy for the symptom, substance can cause the symptom, or other or unclear. We developed the Withdrawal Remedy Explorer application to facilitate the further exploration of the data. RESULTS: Our named entity recognition model achieved F1 scores of 92.1 (substances) and 81.7 (effects) on hold-out data. We identified 458 unique substances and 235 unique effects. Of the 130 potential remedies strongly associated with withdrawal symptoms, 54 (41.5%) were Food and Drug Administration-approved or commonly used treatments for the symptom, 17 (13.1%) were not often used to treat the symptom but could be potentially useful given their pharmacological profile, 13 (10%) were natural or home remedies, 7 (5.4%) were causes of the symptom, and 39 (30%) were other or unclear. We identified both potentially promising remedies (eg, gabapentin for body aches) and potentially common but harmful remedies (eg, antihistamines for restless leg syndrome). CONCLUSIONS: Many of the withdrawal remedies discussed by Reddit users are either clinically proven or potentially useful. These results suggest that this methodology is a valid way to study the self-treatment behavior of a web-based community of people who use opioids. Our Withdrawal Remedy Explorer application provides a platform for using these data for pharmacovigilance, the identification of new treatments, and the better understanding of the needs of people undergoing opioid withdrawal. Furthermore, this approach could be applied to many other disease states for which people self-manage their symptoms and discuss their experiences on the web.

Enhancing the prediction of hospitalization from a COVID-19 agent-based model: A Bayesian method for model parameter estimation.

Agent-based models (ABMs) have become a common tool for estimating demand for hospital beds during the COVID-19 pandemic. A key parameter in these ABMs is the probability of hospitalization for agents with COVID-19. Many published COVID-19 ABMs use either single point or age-specific estimates of the probability of hospitalization for agents with COVID-19, omitting key factors: comorbidities and testing status (i.e., received vs. did not receive COVID-19 test). These omissions can inhibit interpretability, particularly by stakeholders seeking to use an ABM for transparent decision-making. We introduce a straightforward yet novel application of Bayes' theorem with inputs from aggregated hospital data to better incorporate these factors in an ABM. We update input parameters for a North Carolina COVID-19 ABM using this approach, demonstrate sensitivity to input data selections, and highlight the enhanced interpretability and accuracy of the method and the predictions. We propose that even in tumultuous scenarios with limited information like the early months of the COVID-19 pandemic, straightforward approaches like this one with discrete, attainable inputs can improve ABMs to better support stakeholders.

Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic.

Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents' COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May-December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0-1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.

Exploration of the Impact of Combining Risk Phenotypes on the Likelihood of Alcohol Problems in Young Adults.

AIMS: We tested the hypothesis that high novelty seeking (NS-an externalizing trait), sweet-liking (SL-a phenotype that may reflect processing of hedonic stimuli) and initial insensitivity to the impairing effects of alcohol (SRE-A) act independently and synergistically to increase the likelihood of having alcohol-related problems in young adults. METHODS: A sample of 145 young adults, ages 18-26, balanced for gender and alcohol use disorders identification test (AUDIT) scores <8 or ≥8 were selected from a prior sample. NS, SL and SRE-A were assessed along with AUDIT score and family history of alcoholism (FH). The effect of phenotypes and their interaction on the likelihood of alcohol problems was assessed. RESULTS: All three phenotypes contribute to the total AUDIT score. The best-fitting model explaining 35.8% of AUDIT variance includes all three phenotypes and an interaction between NS and SL/sweet-disliking (SDL) status. The addition of FH to the model explains an additional 4% of variance in both models. Classification and regression tree analysis showed that the main phenotype influencing AUDIT score is NS. The SL/SDL phenotype is a strong modifying factor for high NS. SRE-A was shown to be a weak modifier for individuals with low NS. CONCLUSION: The evidence supports the hypothesis that the presence of multiple alcohol use disorders (AUD) risk phenotypes with different underlying neurobiological mechanisms within an individual (SL, NS and SRE-A) represents a higher likelihood for developing alcohol-related problems and may allow for a graded assessment of risk for AUD and offer the possibility for early intervention strategies.

Predictive model of multiple emergency department visits among adults: analysis of the data from the National Survey of Drug Use and Health (NSDUH).

BACKGROUND: In this methodological paper, we use a novel, predictive approach to examine how demographics, substance use, mental and other health indicators predict multiple visits (≥3) to emergency departments (ED) within a year. METHODS: State-of-the-art predictive methods were used to evaluate predictive ability and factors predicting multiple visits to ED within a year and to identify factors that influenced the strength of the prediction. The analysis used public-use datasets from the 2015-2018 National Surveys on Drug Use and Health (NSDUH), which used the same questionnaire on the variables of interest. Analysis focused on adults aged ≥18 years. Several predictive models (regressions, trees, and random forests) were validated and compared on independent datasets. RESULTS: Predictive ability on a test set for multiple ED visits (≥3 times within a year) measured as the area under the receiver operating characteristic (ROC) reached 0.8, which is good for a national survey. Models revealed consistency in predictive factors across the 4 survey years. The most influential variables for predicting ≥3 ED visits per year were fair/poor self-rated health, being nervous or restless/fidgety, having a lower income, asthma, heart condition/disease, having chronic obstructive pulmonary disease (COPD), nicotine dependence, African-American race, female sex, having diabetes, and being of younger age (18-20). CONCLUSIONS: The findings reveal the need to address behavioral and mental health contributors to ED visits and reinforce the importance of developing integrated care models in primary care settings to improve mental health for medically vulnerable patients. The presented modeling approach can be broadly applied to national and other large surveys.

Modeling inpatient and outpatient antibiotic stewardship interventions to reduce the burden of Clostridioides difficile infection in a regional healthcare network.

Antibiotic exposure can lead to unintended outcomes, including drug-drug interactions, adverse drug events, and healthcare-associated infections like Clostridioides difficile infection (CDI). Improving antibiotic use is critical to reduce an individual's CDI risk. Antibiotic stewardship initiatives can reduce inappropriate antibiotic prescribing (e.g., unnecessary antibiotic prescribing, inappropriate antibiotic selection), impacting both hospital (healthcare)-onset (HO)-CDI and community-associated (CA)-CDI. Previous computational and mathematical modeling studies have demonstrated a reduction in CDI incidence associated with antibiotic stewardship initiatives in hospital settings. Although the impact of antibiotic stewardship initiatives in long-term care facilities (LTCFs), including nursing homes, and in outpatient settings have been documented, the effects of specific interventions on CDI incidence are not well understood. We examined the relative effectiveness of antibiotic stewardship interventions on CDI incidence using a geospatially explicit agent-based model of a regional healthcare network in North Carolina. We simulated reductions in unnecessary antibiotic prescribing and inappropriate antibiotic selection with intervention scenarios at individual and network healthcare facilities, including short-term acute care hospitals (STACHs), nursing homes, and outpatient locations. Modeled antibiotic prescription rates were calculated using patient-level data on antibiotic length of therapy for the 10 modeled network STACHs. By simulating a 30% reduction in antibiotics prescribed across all inpatient and outpatient locations, we found the greatest reductions on network CDI incidence among tested scenarios, namely a 17% decrease in HO-CDI incidence and 7% decrease in CA-CDI. Among intervention scenarios of reducing inappropriate antibiotic selection, we found a greater impact on network CDI incidence when modeling this reduction in nursing homes alone compared to the same intervention in STACHs alone. These results support the potential importance of LTCF and outpatient antibiotic stewardship efforts on network CDI burden and add to the evidence that a coordinated approach to antibiotic stewardship across multiple facilities, including inpatient and outpatient settings, within a regional healthcare network could be an effective strategy to reduce network CDI burden.

Heroin type, injecting behavior, and HIV transmission. A simulation model of HIV incidence and prevalence.

BACKGROUND AND AIMS: Using mathematical modeling to illustrate and predict how different heroin source-forms: "black tar" (BTH) and powder heroin (PH) can affect HIV transmission in the context of contrasting injecting practices. By quantifying HIV risk by these two heroin source-types we show how each affects the incidence and prevalence of HIV over time. From 1997 to 2010 PH reaching the United States was manufactured overwhelmingly by Colombian suppliers and distributed in the eastern states of the United States. Recently Mexican cartels that supply the western U.S. states have started to produce PH too, replacing Colombian distribution to the east. This raises the possibility that BTH in the western U.S. may be replaced by PH in the future. DESIGN: We used an agent-based model to evaluate the impact of use of different heroin formulations in high- and low-risk populations of persons who inject drugs (PWID) who use different types of syringes (high vs. low dead space) and injecting practices. We obtained model parameters from peer-reviewed publications and ethnographic research. RESULTS: Heating of BTH, additional syringe rinsing, and subcutaneous injection can substantially decrease the risk of HIV transmission. Simulation analysis shows that HIV transmission risk may be strongly affected by the type of heroin used. We reproduced historic differences in HIV prevalence and incidence. The protective effect of BTH is much stronger in high-risk compared with low-risk populations. Simulation of future outbreaks show that when PH replaces BTH we expect a long-term overall increase in HIV prevalence. In a population of PWID with mixed low- and high-risk clusters we find that local HIV outbreaks can occur even when the overall prevalence and incidence are low. The results are dependent on evidence-supported assumptions. CONCLUSIONS: The results support harm-reduction measures focused on a reduction in syringe sharing and promoting protective measures of syringe rinsing and drug solution heating.

Analyzing influenza outbreaks in Russia using an age-structured dynamic transmission model.

In this study, we addressed the ability of a minimalistic SEIR model to satisfactorily describe influenza outbreak dynamics in Russian settings. For that purpose, we calibrated an age-specific influenza dynamics model to Russian acute respiratory infection (ARI) incidence data over 2009-2016 and assessed the variability of proportion of non-immune individuals in the population depending on the regarded city, the non-epidemic indicence baseline, the contact structure considered and the used calibration method. The experiments demonstrated the importance of distinguishing characteristics of different age groups, such as contact intensities and background immunity levels. It was also found that the current model calibration process, which relies mostly on ARI incidence, demonstrates notable variation of output parameter values. Employing additional sources of data, such as strain-specific influenza incidence and external assessments on underreporting levels in different age groups, might enhance the plausibility of parameter values obtained by model calibration, along with reducing the assessment variation.