Adverse childhood experiences and early initiation of substance use: A survival analysis.

OBJECTIVE: Early adversity, such as adverse childhood experiences (ACEs), is a risk factor for the development of substance use disorder (SUD). ACEs are associated with earlier initiation of substance use. This study examined the relationship between ACEs and age of initiation of substance use using survival analysis. It is hypothesized that individuals with higher ACEs will have an earlier age of initiation. METHOD: Participants were recruited from the University of Kentucky's Laboratory for Human Behavioral Pharmacology. Participants were 18 years or older, English speaking, and actively engaged in substance use. Participants were not in substance abuse treatment nor were they seeking treatment. ACE scores were calculated, and age of substance use initiation was recorded. A Cox proportional hazard model was used to examine the effect of ACE score on age of substance use initiation. RESULTS: A total of 107 participants completed the study. An average number of 2.3 ACEs (SD = 2.2) were endorsed with 24% of participants reporting 4 or more ACEs. Higher ACE scores were associated with cigarette smoking and non-medical prescription opioid use onset ( hazard ratio (HR) = 1.14, 95% CI=1.02-1.28, p = 0.02, and HR=1.19, 95% CI = 1.04-1.37, p = 0.01, respectively. CONCLUSIONS: A significant association was found between higher ACE scores and earlier initiation of cigarette and non-medical prescription opioid use, consistent with prior research. Primary prevention of ACEs, screening for ACEs during childhood, and interventions for ACEs if detected, may help to reduce the risk of substance use/SUD in adulthood.

Comparing Changes in Controlled Substance Prescribing Trends by Provider Type.

BACKGROUND AND OBJECTIVES: Forty-nine out of 50 states have implemented Prescription Drug Monitoring Programs (PDMPs) to monitor controlled substance (CS) prescribing. PDMPs change health care provider behavior, but few studies have examined changes in CS prescription by health care provider type. METHODS: Aggregated yearly data, including number of CS prescriptions, doses, and doses per prescription by health care provider type (physician, advanced practice registered nurse [APRN], and dentist) for each year from 2011 to 2017 was provided by the state PDMP, Kentucky All Schedule Prescription Electronic Reporting System (KASPER). In aggregate, this data set included 64,578,307 total prescriptions and 3,982,130,994 total doses of Schedule II-V medications. RESULTS: Physicians and dentists showed a trend of decreasing prescriptions and doses for Schedule II opioids from 2012 to 2017 (27-32% reduction in 2017 compared to 2011). APRNs showed a substantive increase in the number of doses and prescriptions (121-204% increase in 2017 compared to 2011), with increases remaining when controlling for number of providers. Physicians increased doses and prescriptions of Schedule II stimulants (37% increase for both doses and prescriptions), but by a smaller magnitude than APRN increases in stimulants (334-360% increase). Dentists showed decreases in Schedule II stimulants prescribed (69-80% reduction). Similar trends, but more modest in magnitude, were observed for Schedule III-IV. DISCUSSION AND CONCLUSIONS: Although monitoring and continuing education requirements are similar across all providers in Kentucky, differences in prescription trends for Schedule II opioids and stimulants were noted for physicians, APRNs, and dentists. SCIENTIFIC SIGNIFICANCE: Changes in prescribing following introduction of mandatory use of KASPER markedly differed based on provider type, with increases observed for APRNs compared with physicians and dentists. These findings advance prior research by providing a detailed examination of prescribing trends by provider type subsequent to a PDMPs mandatory use law. (Am J Addict 2019;00:00-00).

A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models.

Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients' data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least one diagnosis of OUD or at least one prescription for buprenorphine or methadone. Controls are prescription opioid users with no OUD diagnoses or buprenorphine or methadone prescriptions. On 100 randomly selected test sets including 47,396 patients, our proposed transformer-based AI model can predict OUD more efficiently (AUC = 0.742 ± 0.021) compared to logistic regression (AUC = 0.651 ± 0.025), random forest (AUC = 0.679 ± 0.026), xgboost (AUC = 0.690 ± 0.027), long short-term memory model (AUC = 0.706 ± 0.026), transformer (AUC = 0.725 ± 0.024), and unweighted ORT model (AUC = 0.559 ± 0.025). Our results show that embedding AI algorithms into clinical care may assist clinicians in risk stratification and management of patients receiving opioid therapy.

Relationships among adverse childhood experiences, delay discounting, impulsivity, and diabetes self-management.

OBJECTIVE: Adverse childhood experiences (ACEs) have been linked to risky health behaviors, as well as the development of chronic health conditions such as both type 1 and type 2 diabetes mellitus. A connection between ACEs and diabetes self-management has not yet been established. The current study aims to investigate the relationships among ACEs, delay discounting, impulsivity, and diabetes self-management. METHOD: A total of 227 adults aged 18 to 77 with type 1 diabetes, type 2 diabetes, and prediabetes were recruited to complete an online survey via Amazon's mechanical Turk. Participants completed validated measures of diabetes self-care, delay discounting, and impulsivity, as well as questions regarding diabetes history and financial strain. RESULTS: In the overall sample and controlling for financial strain, increased number of ACEs was significantly associated with poorer diabetes management (r = -.15, p < .05). Higher delay discounting was associated with fewer ACEs (r = -.31, p < .05) and better diabetes care (r = .42, p < .01), as well as increased number of diabetes-related complications (r = .33, p < .01), controlling for financial strain. Participants who use insulin to manage their diabetes had significantly better diabetes self-care scores (t(225) = 8.19, p < .01), higher levels of delay discounting (t(101) = 3.15, p < .01), and fewer reported ACEs (t(224) = -2.19, p < .05). CONCLUSIONS: ACEs are associated with poorer diabetes self-management later in life. This may be an important consideration for clinicians treating patients with diabetes and prediabetes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Sexual Violence Perpetration as a Risk Factor for Current Depression or Posttraumatic Symptoms in Adolescents.

Sexual violence perpetration (SVP), including coerced, physically forced, and alcohol- or drug-facilitated unwanted sex, occurs frequently in adolescence and may represent a risk factor for future perpetration. Sexual violence victimization (SVV) has been found to be a risk factor for increased rates of depression and posttraumatic stress disorder (PTSD); however, the associations of SVP with depression or posttraumatic stress symptoms (PTSS) have been less well described. This study examined associations between symptoms of depression and PTSS with SVP in the prior 12 months among high school students. In this cross-sectional analysis, a representative sample of public high school students (ninth-12th grades) completed self-reported surveys on peer SVP and SVV within the past year. Among 16,784 students completing surveys, 7.2% disclosed SVP against another high school student in the past 12 months; 64.4% of students disclosing SVP also experienced SVV. Both SVP and SVV, alone or in combination, were associated with a greater likelihood of symptoms of depression or PTSS. These associations were similar by sex and sexual minority status (e.g., lesbian, gay, bisexual, transgender, and queer [LGBTQ+]). These findings highlight the need for continued primary prevention efforts. Additional screening to recognize adolescent SVP can allow both early treatment of depression and PTSD and address the individual risks of SVP to reduce subsequent repeated sexual assaults.

Assessing adolescent tobacco use in a substance use treatment program with self-report and biological measures.

Tobacco use in adolescents can alter their lifetime health outcomes. Despite the importance of early identification and treatment, adolescent tobacco use, including that of electronic vapor products (e.g., e-cigarettes), is often missed. In a state-funded substance use treatment program, we added biological measures, including urinary cotinine and exhaled carbon monoxide to self-report measures to assess recent and lifetime tobacco use. We conducted a retrospective review of the de-identified charts to examine the feasibility of screening for self-report and biological measures of tobacco use. Self-report, urinary cotinine, and exhaled carbon monoxide samples were obtained at every visit, including intake and follow-up. There were 52 adolescents with a total of 400 clinic visits to the program. Of those 400 visits, 258 included self-reported tobacco use and 142 included a denial of using any form of tobacco. However, of those 142 visits with a negative self-report of tobacco, 31 tested positive for cotinine and 6 had positive exhaled carbon monoxide. Although 111 of the 142 had negative cotinine, 5 had positive carbon monoxide, but all of those self-reported recent cannabis use. Despite using a sensitive measure of self-report of tobacco use, almost 22% of visits had a discordant self-report with a biological measure that indicated tobacco use. Considering the lifelong impact of adolescent tobacco use, clinicians should consider augmenting self-report with biological measures of tobacco use. Identification of tobacco use in adolescents with substance use can assist clinicians in providing education about tobacco use, such as electronic vapor products, and individualizing treatments.

Neonatal Abstinence Syndrome and Childhood Mental Health Conditions, 2009-2015: Commercial Versus Medicaid Populations.

OBJECTIVE: This study aimed to examine mental health conditions of children diagnosed with neonatal abstinence syndrome (NAS) in a commercially insured population and compare them with a multistate Medicaid-insured population identified in prior research. METHODS: Data from the IBM MarketScan Commercial Database from January 1, 2009, to September 30, 2015, were used to identify mental health conditions among children ages 1-5 both with and without NAS. Frequency analyses were conducted to ascertain intrapopulation differences and differences between the commercially insured and Medicaid populations. RESULTS: The NAS rate in the Medicaid population was 28.7 times higher than in the commercially insured population. Although the sample of children with NAS was small, and the results must be interpreted with caution, elevated rates of childhood mental health conditions observed in the commercially insured population were comparable to the Medicaid population. CONCLUSIONS: This analysis emphasizes the difference in rates of NAS between commercially insured and Medicaid populations.

Integrating data science into the translational science research spectrum: A substance use disorder case study.

The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.