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BACKGROUND: Polysubstance use is common among opioid-using women, yet its association with pharmacotherapy for neonatal abstinence syndrome (NAS) remains unclear. We hypothesized that benzodiazepine exposure would increase risk of an infant developing pharmacologically treated NAS. METHODS: We conducted a retrospective cohort study of maternal-infant dyads enrolled in Tennessee Medicaid, using individual-level data linkage of vital records and administrative (ie, outpatient, inpatient, and prescription) data from 2009 to 2011. These data underwent chart review from 2013 to 2016 to obtain clinically relevant exposure data (eg, toxicology testing). The association of antenatal exposures with pharmacologically treated NAS was evaluated by using multivariable logistic regression, controlling for maternal and infant factors and clustered by hospital. RESULTS: Among 112 029 maternal-infant dyads, we confirmed 822 cases of NAS, of which 598 (72.7%) were cases of pharmacologically treated NAS. Infants who developed pharmacologically treated NAS were more likely to have been exposed to antenatal benzodiazepines compared with infants with confirmed NAS not treated pharmacologically (40.9% vs 30.8%; P = .008). In adjusted analyses, benzodiazepine exposure was associated with greater risk of developing pharmacologically treated NAS (odds ratio: 1.51; 95% confidence interval: 1.04–2.21). Alternatively, exposure to tobacco, marijuana, cocaine, gabapentin, and selective serotonin reuptake inhibitors were not associated with increased risk of developing pharmacologically treated NAS. CONCLUSIONS: Among a population of infants with intrauterine polysubstance exposure, benzodiazepine exposure was an independent predictor of an infant developing pharmacologically treated NAS. Obtaining history of antenatal benzodiazepine exposure among opioid-exposed infants may allow for risk stratification and development of personalized care plans.

Objectives To develop and validate clinical risk prediction tools for neonatal abstinence syndrome (NAS). Study design We developed prediction models for NAS based on a set of 30 demographic and antenatal exposure covariates collected during pregnancy. Data (outpatient prescription, vital, and administrative records), were obtained from enrollees in the Tennessee Medicaid Program from 2009 to 2014. Models were created using logistic regression and backward selection based on improvement in the Akaike information criterion, and internally validated using bootstrap cross-validation. Results A total of 218 020 maternal and infant dyads met inclusion criteria, of whom 3208 infants were diagnosed with NAS. The general population model included age, hepatitis C virus infection, days of opioid used by type, number of cigarettes used daily, and the following medications used in the last 30 day of pregnancy: bupropion, antinausea medicines, benzodiazepines, antipsychotics, and gabapentin. Infant characteristics included birthweight, small for gestational age, and infant sex. A high-risk model used a smaller number of predictive variables. Both models discriminated well with an area under the curve of 0.89 and were well-calibrated for low-risk infants. Conclusions We developed 2 predictive models for NAS based on demographics and antenatal exposure during the last 30 days of pregnancy that were able to risk stratify infants at risk of developing the syndrome.

OBJECTIVES: Neonatal abstinence syndrome (NAS) is a postnatal withdrawal syndrome experienced by some infants with opioid exposure. Hospital administrative data are commonly used for research and surveillance but have not been validated for NAS. Our objectives for this study were to validate the diagnostic codes for NAS and to develop an algorithm to optimize identification. METHODS: Tennessee Medicaid claims from 2009 to 2011 (primary sample) and 2016 (secondary sample; post–International Classification of Diseases, 10th Revision, Clinical Modification [ICD-10-CM]) were obtained. Cases of NAS were identified by using International Classification of Diseases, Ninth Revision, Clinical Modification code (2009–2011) 779.5 and ICD-10-CM code (2016) P96.1. Medical record review cases were then conducted by 2 physicians using a standardized algorithm, and positive predictive value (PPV) was calculated. Algorithms were developed for optimizing the identification of NAS in administrative data. RESULTS: In our primary sample of 112 029 mother-infant dyads, 950 potential NAS cases were identified from Medicaid claims data and reviewed. Among reviewed records, 863 were confirmed as having NAS (including 628 [66.1%] cases identified as NAS requiring pharmacotherapy, 224 [23.5%] as NAS not requiring pharmacotherapy, and 11 [1.2%] as iatrogenic NAS), and 87 (9.2%) did not meet clinical criteria for NAS. The PPV of the International Classification of Diseases, Ninth Revision, Clinical Modification code for NAS in clinically confirmed NAS was 91% (95% confidence interval: 88.8%–92.5%). Similarly, the PPV for the ICD-10-CM code in the secondary sample was 98.2% (95% confidence interval: 95.4%–99.2%). Algorithms using elements from the Medicaid claims and from length of stay improved PPV. CONCLUSIONS: In a large population-based cohort of Medicaid participants, hospital administrative data had a high PPV in identifying cases of clinically diagnosed NAS.

PURPOSE: To assess the safety of psychotropic medication use in children and adolescents, it is critical to be able to identify suicidal behaviors from medical claims data and distinguish them from other injuries. The purpose of this study was to develop an algorithm using administrative claims data to identify medically treated suicidal behavior in a cohort of children and adolescents. METHODS: The cohort included 80 183 youth (6-18 years) enrolled in Tennessee's Medicaid program from 1995-2006 who were prescribed antidepressants. Potential episodes of suicidal behavior were identified using external cause-of-injury codes (E-codes) and ICD-9-CM codes corresponding to the potential mechanisms of or injuries resulting from suicidal behavior. For each identified episode, medical records were reviewed to determine if the injury was self-inflicted and if intent to die was explicitly stated or could be inferred. RESULTS: Medical records were reviewed for 2676 episodes of potential self-harm identified through claims data. Among 1162 episodes that were classified as suicidal behavior, 1117 (96%) had a claim for suicide and self-inflicted injury, poisoning by drugs, or both. The positive predictive value of code groups to predict suicidal behavior ranged from 0-88% and improved when there was a concomitant hospitalization but with the limitation of excluding some episodes of confirmed suicidal behavior. CONCLUSIONS: Nearly all episodes of confirmed suicidal behavior in this cohort of youth included an ICD-9-CM code for suicide or poisoning by drugs. An algorithm combining these ICD-9-CM codes and hospital stay greatly improved the positive predictive value for identifying medically treated suicidal behavior. Copyright © 2013 John Wiley & Sons, Ltd. Language: en

Purpose Studies of non-steroidal anti-inflammatory drugs (NSAIDs) and cardiovascular events using administrative data require identification of incident acute myocardial infarctions (AMIs) and information on whether confounders differ by NSAID status. Methods We identified patients with a first AMI hospitalization from Tennessee Medicaid files as those with primary ICD-9 discharge diagnosis 410.x and hospitalization stay of > 2 calendar days. Eligible persons were non-institutionalized, aged 50–84 years between 1999–2004, had continuous enrollment and no AMI, stroke, or non-cardiovascular serious medical illness in the prior year. Of 5524 patients with a potential first AMI, a systematic sample (n = 350) was selected for review. Using defined criteria, we classified events using chest pain history, EKG, and cardiac enzymes, and calculated the positive predictive value (PPV) for definite or probable AMI. Results 337 of 350 (96.3%) charts were abstracted and 307 (91.1%), 6 (1.8%), and 24 (7.1%) events were categorized as definite, probable, and no AMI, respectively. PPV for any definite or probable AMI was 92.8% (95% CI 89.6–95.2); for an AMI without an event in the past year 91.7% (95% CI 88.3–94.2), and for an incident AMI was 72.7% (95% CI 67.7–77.2). Age-adjusted prevalence of current smoking (46.4% vs. 39.1%, p = 0.35) and aspirin use (36.9% vs. 35.9%, p = 0.90) was similar among NSAID users and non-users Conclusions ICD-9 code 410.x had high predictive value for identifying AMI. Among those with AMI, smoking and aspirin use was similar in NSAID exposure groups, suggesting these factors will not confound the relationship between NSAIDs and cardiovascular outcomes. Copyright © 2009 John Wiley & Sons, Ltd.

Objective Guidelines and quality of care measures for the evaluation of adolescent suicidal behavior recommend prompt mental health evaluation, hospitalization of high-risk youth, and specific follow-up plans—all of which may be influenced by sociodemographic factors. The aim of this study was to identify sociodemographic characteristics associated with variations in the evaluation of youth with suicidal behavior. Methods We conducted a large cohort study of youth, aged 7 to 18, enrolled in Tennessee Medicaid from 1995 to 2006, who filled prescriptions for antidepressants and who presented for evaluation of injuries that were determined to be suicidal on the basis of external cause-of-injury codes (E codes) and ICD-9-CM codes and review of individual medical records. Chi-square tests and logistic regression were performed to assess the relationship between sociodemographic characteristics and documentation of mental health evaluation, hospitalization, and discharge instructions. Results Of 929 episodes of suicidal behavior evaluated in an acute setting, rural-residing youth were less likely to be admitted to a psychiatric hospital (adjusted odds ratio [AOR] 0.72; 95% confidence interval [CI] 0.55–0.95) and more likely to be medically hospitalized only (AOR 1.92; 95% CI 1.39–2.65). Female subjects were less likely to be admitted to a psychiatric hospital (AOR 0.55; 95% CI 0.41–0.74) and more likely to be discharged home (AOR 1.44; 95% CI 1.01–2.04). Only 40% of those discharged to home had documentation of discharge instructions with both follow-up provider and date. Conclusions In this statewide cohort of youth with suicidal behavior, there were significant differences in disposition associated with sociodemographic characteristics.

The authors investigated the time scales of the learning of a mirror-tracing task to reexamine G. S. Snoddy's (1926) original claim and the received theoretical view (A. Newell & P. S. Rosenbloom, 1981) that motor learning follows a power law. Adult participants (N = 16) learned the tracing task in either a normal or a reversed visual-image condition over 5 consecutive days of practice and then performed 1 day of practice 1 week later and again 1 month later. The reversed-image group's performance was poorer than that of the normal-image group throughout the practice. An exponential was the best fitting function on individual data, but the power-law function was the best fit on the group-averaged data. The findings provided preliminary evidence that 2 characteristic time scales, (a) fast, dominated by warm-up, and (b) slow, dominated by persistent change, capture individuals' performance in the learning of the mirror-tracing task.