Risk of Autism after Prenatal Topiramate, Valproate, or Lamotrigine Exposure.

BACKGROUND: Maternal use of valproate during pregnancy has been associated with an increased risk of neurodevelopmental disorders in children. Although most studies of other antiseizure medications have not shown increased risks of these disorders, there are limited and conflicting data regarding the risk of autism spectrum disorder associated with maternal topiramate use. METHODS: We identified a population-based cohort of pregnant women and their children within two health care utilization databases in the United States, with data from 2000 through 2020. Exposure to specific antiseizure medications was defined on the basis of prescription fills from gestational week 19 until delivery. Children who had been exposed to topiramate during the second half of pregnancy were compared with those unexposed to any antiseizure medication during pregnancy with respect to the risk of autism spectrum disorder. Valproate was used as a positive control, and lamotrigine was used as a negative control. RESULTS: The estimated cumulative incidence of autism spectrum disorder at 8 years of age was 1.9% for the full population of children who had not been exposed to antiseizure medication (4,199,796 children). With restriction to children born to mothers with epilepsy, the incidence was 4.2% with no exposure to antiseizure medication (8815 children), 6.2% with exposure to topiramate (1030 children), 10.5% with exposure to valproate (800 children), and 4.1% with exposure to lamotrigine (4205 children). Propensity score-adjusted hazard ratios in a comparison with no exposure to antiseizure medication were 0.96 (95% confidence interval [CI], 0.56 to 1.65) for exposure to topiramate, 2.67 (95% CI, 1.69 to 4.20) for exposure to valproate, and 1.00 (95% CI, 0.69 to 1.46) for exposure to lamotrigine. CONCLUSIONS: The incidence of autism spectrum disorder was higher among children prenatally exposed to the studied antiseizure medications than in the general population. However, after adjustment for indication and other confounders, the association was substantially attenuated for topiramate and lamotrigine, whereas an increased risk remained for valproate. (Funded by the National Institute of Mental Health.).

Internal validation of gestational age estimation algorithms in health-care databases using pregnancies conceived through fertility procedures.

Fertility procedures recorded in health-care databases can be used to estimate the start of pregnancy, which can serve as a reference standard to validate gestational age estimates based on International Classification of Diseases codes. In a cohort of 17 398 US MarketScan pregnancies (2011-2020) in which conception was achieved via fertility procedures, we estimated gestational age at the end of pregnancy using algorithms based on (1) time (days) since the fertility procedure (the reference standard); (2) International Classification of Diseases, Ninth Revision (ICD-9)/International Classification of Diseases, Tenth Revision (ICD-10) (before/after October 2015) codes indicating gestational length recorded at the end of pregnancy (method A); and (3) ICD-10 end-of-pregnancy codes enhanced with Z3A codes denoting specific gestation weeks recorded at prenatal visits (method B). We calculated the proportion of pregnancies with an estimated gestational age falling within 14 days ($\pm$14 days) of the reference standard. Method A accuracy was similar for ICD-9 and ICD-10 codes. After 2015, method B was more accurate than method A: For term births, within-14-day agreement was 90.8% for method A and 98.7% for method B. Corresponding estimates were 70.1% and 95.6% for preterm births; 35.3% and 92.6% for stillbirths; 54.3% and 64.2% for spontaneous abortions; and 16.7% and 84.6% for elective terminations. ICD-10-based algorithms that incorporate Z3A codes improve the accuracy of gestational age estimation in health-care databases, especially for preterm births and non-live births.

Hierarchical Clustering Analysis to Inform Classification of Congenital Malformations for Surveillance of Medication Safety in Pregnancy.

There is growing interest in the secondary use of healthcare data to evaluate medication safety in pregnancy. Tree-based scan statistics (TBSS) offer an innovative approach to help identify potential safety signals. TBSS utilize hierarchically organized outcomes, generally based on existing clinical coding systems that group outcomes by organ system. When assessing teratogenicity, such groupings often lack a sound embryologic basis given the etiologic heterogeneity of congenital malformations. The study objective was to enhance the grouping of congenital malformations to be used in scanning approaches through implementation of hierarchical clustering analysis (HCA) and to pilot test an HCA-enhanced TBSS approach for medication safety surveillance in pregnancy in two test cases using >4.2 million mother-child dyads from two US-nationwide databases. HCA identified (1) malformation combinations belonging to the same organ system already grouped in existing classifications, (2) known combinations across different organ systems not previously grouped, (3) unknown combinations not previously grouped, and (4) malformations seemingly standing on their own. Testing the approach with valproate and topiramate identified expected signals, and a signal for an HCA-cluster missed by traditional classification. Augmenting existing classifications with clusters identified through large data exploration may be promising when defining phenotypes for surveillance and causal inference studies.

Comparative Safety of In Utero Exposure to Buprenorphine Combined With Naloxone vs Buprenorphine Alone.

Importance: Buprenorphine combined with naloxone is commonly used to treat opioid use disorders outside of pregnancy. In pregnancy, buprenorphine alone is generally recommended because of limited perinatal safety data on the combination product. Objective: To compare perinatal outcomes following prenatal exposure to buprenorphine with naloxone vs buprenorphine alone. Design, Settings, and Participants: Population-based cohort study using health care utilization data from Medicaid-insured beneficiaries in the US from 2000 to 2018. The cohort was restricted to pregnant individuals linked to their liveborn infants, with maternal Medicaid enrollment from 3 months before pregnancy to 1 month after delivery and infant enrollment for the first 3 months after birth, unless they died sooner. Exposure: Use of buprenorphine with naloxone vs buprenorphine alone during the first trimester based on outpatient dispensings. Main Outcomes and Measures: Outcomes included major congenital malformations, low birth weight, neonatal abstinence syndrome, neonatal intensive care unit admission, preterm birth, respiratory symptoms, small for gestational age, cesarean delivery, and maternal morbidity. Confounder-adjusted risk ratios were calculated using propensity score overlap weights. Results: This study identified 3369 pregnant individuals exposed to buprenorphine with naloxone during the first trimester (mean [SD] age, 28.8 [4.6] years) and 5326 exposed to buprenorphine alone or who switched from the combination to buprenorphine alone by the end of the first trimester (mean [SD] age, 28.3 [4.5] years). When comparing buprenorphine combined with naloxone with buprenorphine alone, a lower risk for neonatal abstinence syndrome (absolute risk, 37.4% vs 55.8%; weighted relative risk, 0.77 [95% CI, 0.70-0.84]) and a modestly lower risk for neonatal intensive care unit admission (absolute risk, 30.6% vs 34.9%; weighted relative risk, 0.91 [95% CI, 0.85-0.98]) and small for gestational age (absolute risk, 10.0% vs 12.4%; weighted relative risk, 0.86 [95% CI, 0.75-0.98]) was observed. For maternal morbidity, the comparative rates were 2.6% vs 2.9%, respectively, and the weighted relative risk was 0.90 (95% CI, 0.68-1.19). No differences were observed with respect to major congenital malformations overall, low birth weight, preterm birth, respiratory symptoms, or cesarean delivery. Results were consistent across sensitivity analyses. Conclusions and Relevance: There were similar and, in some instances, more favorable neonatal and maternal outcomes for pregnancies exposed to buprenorphine combined with naloxone compared with buprenorphine alone. For the outcomes assessed, compared with buprenorphine alone, buprenorphine with naloxone during pregnancy appears to be a safe treatment option. This supports the view that both formulations are reasonable options for the treatment of opioid use disorder in pregnancy, affirming flexibility in collaborative treatment decision-making.

Development and Validation of Algorithms to Estimate Live Birth Gestational Age in Medicaid Analytic eXtract Data.

BACKGROUND: While healthcare utilization data are useful for postmarketing surveillance of drug safety in pregnancy, the start of pregnancy and gestational age at birth are often incompletely recorded or missing. Our objective was to develop and validate a claims-based live birth gestational age algorithm. METHODS: Using the Medicaid Analytic eXtract (MAX) linked to birth certificates in three states, we developed four candidate algorithms based on: preterm codes; preterm or postterm codes; timing of prenatal care; and prediction models - using conventional regression and machine-learning approaches with a broad range of prespecified and empirically selected predictors. We assessed algorithm performance based on mean squared error (MSE) and proportion of pregnancies with estimated gestational age within 1 and 2 weeks of the gold standard, defined as the clinical or obstetric estimate of gestation on the birth certificate. We validated the best-performing algorithms against medical records in a nationwide sample. We quantified misclassification of select drug exposure scenarios due to estimated gestational age as positive predictive value (PPV), sensitivity, and specificity. RESULTS: Among 114,117 eligible pregnancies, the random forest model with all predictors emerged as the best performing algorithm: MSE 1.5; 84.8% within 1 week and 96.3% within 2 weeks, with similar performance in the nationwide validation cohort. For all exposure scenarios, PPVs were >93.8%, sensitivities >94.3%, and specificities >99.4%. CONCLUSIONS: We developed a highly accurate algorithm for estimating gestational age among live births in the nationwide MAX data, further supporting the value of these data for drug safety surveillance in pregnancy. See video abstract at, http://links.lww.com/EDE/B989 .

Concordance of neonatal critical condition data between secondary databases: Florida and Texas birth certificate Linkage with medicaid analytic extract.

BACKGROUND: Limited information is available about neonates' critical conditions data quality. The study aim was to measure the agreement regarding presence of neonatal critical conditions between Medicaid Analytic eXtract claims data and Birth Certificate (BC) records. METHODS: Claims data files of neonates born between 1999-2010 and their mothers were linked to birth certificates in the states of Texas and Florida. In claims data, neonatal critical conditions were identified using medical encounter claims records within the first 30 days postpartum, while in birth certificates, the conditions were identified based on predetermined variables. We calculated the prevalence of cases within each data source that were identified by its comparator, in addition to calculating overall agreement and kappa statistics. RESULTS: The sample included 558,224 and 981,120 neonates in Florida and Texas, respectively. Kappa values show poor agreement (< 20%) for all critical conditions except neonatal intensive care unit (NICU) admission, which showed moderate (> 50%) and substantial (> 60%) agreement in Florida and Texas, respectively. claims data resulted in higher prevalences and capture of a larger proportion of cases than the BC, except for assisted ventilation. CONCLUSIONS: Claims data and BC showed low agreement on neonatal critical conditions except for NICU admission. Each data source identified cases most of which the comparator failed to capture, with higher prevalences estimated within claims data except for assisted ventilation.

Validation of claims-based algorithms to identify non-live birth outcomes.

PURPOSE: Perinatal epidemiology studies using healthcare utilization databases are often restricted to live births, largely due to the lack of established algorithms to identify non-live births. The study objective was to develop and validate claims-based algorithms for the ascertainment of non-live births. METHODS: Using the Mass General Brigham Research Patient Data Registry 2000-2014, we assembled a cohort of women enrolled in Medicaid with a non-live birth. Based on ≥1 inpatient or ≥2 outpatient diagnosis/procedure codes, we identified and randomly sampled 100 potential stillbirth, spontaneous abortion, and termination cases each. For the secondary definitions, we excluded cases with codes for other pregnancy outcomes within ±5 days of the outcome of interest and relaxed the definitions for spontaneous abortion and termination by allowing cases with one outpatient diagnosis only. Cases were adjudicated based on medical chart review. We estimated the positive predictive value (PPV) for each outcome. RESULTS: The PPV was 71.0% (95% CI, 61.1-79.6) for stillbirth; 79.0% (69.7-86.5) for spontaneous abortion, and 93.0% (86.1-97.1) for termination. When excluding cases with adjacent codes for other pregnancy outcomes and further relaxing the definition, the PPV increased to 80.6% (69.5-88.9) for stillbirth, 86.6% (80.5-91.3) for spontaneous abortion and 94.9% (91.1-97.4) for termination. The PPV for the composite outcome using the relaxed definition was 94.4% (92.3-96.1). CONCLUSIONS: Our findings suggest non-live birth outcomes can be identified in a valid manner in epidemiological studies based on healthcare utilization databases.

Trajectories of Prescription Opioid Utilization During Pregnancy Among Prepregnancy Chronic Users and Risk of Neonatal Opioid Withdrawal Syndrome.

Little is known about the impact of dose, duration, and timing of prenatal prescription opioid exposure on the risk of neonatal opioid withdrawal syndrome (NOWS). Using a cohort of 18,869 prepregnancy chronic opioid users nested within the 2000-2014 Medicaid Analytic eXtract, we assessed average opioid dosage within biweekly gestational age intervals, created group-based trajectory models, and evaluated the association between trajectory groups and NOWS risk. Women were grouped into 6 distinct opioid use trajectories which, based on observed patterns, were categorized as 1) continuous very low-dose use, 2) continuous low-dose use, 3) initial moderate-dose use with a gradual decrease to very low-dose/no use, 4) initial high-dose use with a gradual decrease to very low-dose use, 5) continuous moderate-dose use, and 6) continuous high-dose use. Absolute risk of NOWS per 1,000 infants was 7.7 for group 1 (reference group), 28.8 for group 2 (relative risk (RR) = 3.7, 95% confidence interval (CI): 2.8, 5.0), 16.5 for group 3 (RR = 2.1, 95% CI: 1.5, 3.1), 64.9 for group 4 (RR = 8.4, 95% CI: 5.6, 12.6), 77.3 for group 5 (RR = 10.0, 95% CI: 7.5, 13.5), and 172.4 for group 6 (RR = 22.4, 95% CI: 16.1, 31.2). Trajectory models-which capture information on dose, duration, and timing of exposure-are useful for gaining insight into clinically relevant groupings to evaluate the risk of prenatal opioid exposure.

Holographic 3D particle reconstruction using a one-stage network.

Volumetric reconstruction of a three-dimensional (3D) particle field with high resolution and low latency is an ambitious and valuable task. As a compact and high-throughput imaging system, digital holography (DH) encodes the 3D information of a particle volume into a two-dimensional (2D) interference pattern. In this work, we propose a one-stage network (OSNet) for 3D particle volumetric reconstruction. Specifically, by a single feed-forward process, OSNet can retrieve the 3D coordinates of the particles directly from the holograms without high-fidelity image reconstruction at each depth slice. Evaluation results from both synthetic and experimental data confirm the feasibility and robustness of our method under different particle concentrations and noise levels in terms of detection rate and position accuracy, with improved processing speed. The additional applications of 3D particle tracking are also investigated, facilitating the analysis of the dynamic displacements and motions for micro-objects or cells. It can be further extended to various types of computational imaging problems sharing similar traits.

Association between SARS-CoV-2 infection during pregnancy and postpartum depressive and anxiety symptoms: finding from the International Registry of Coronavirus Exposure in Pregnancy (IRCEP) study.

While there has been concern over the perinatal mental health implications of the COVID-19 outbreak, evidence on the risk of postpartum depression and anxiety following SARS-CoV-2 infection is limited. We studied this question using the International Registry of Coronavirus Exposure in Pregnancy, which included both a prospective and retrospective cohort. Study participants were required to have been tested for SARS-CoV-2 between the date of last menstrual period and delivery. The exposure of interest was SARS-CoV-2 infection during pregnancy, as well as COVID-19 severity (severe, moderate, mild, and asymptomatic). The outcome was postpartum depression and anxiety symptoms, assessed by the 4-item Patient Health Questionnaire. The final analytic cohort consisted of 3819 participants (COVID-19 positive: 771; COVID-19 negative: 3048). After adjusting for confounding by socio-demographics, prior obstetric and maternal health comorbidities, mothers with severe COVID-19 had an increased risk of depressive (aRR: 1.72; 95%CI: 1.18-2.52) and anxiety (aRR: 1.40; 0.98-2.00) symptoms. The strength of the association was attenuated for women with moderate COVID-19 (aRR = 1.12; 0.86-1.44 for depressive symptoms; aRR = 1.18; 0.96-1.44 for anxiety symptoms). No increased risk was observed for mild or asymptomatic illness. The findings can inform targeted interventions to minimize the risk of adverse COVID-19-related mental health outcomes for pregnant women.

Safety of Tenofovir Disoproxil Fumarate for Pregnant Women Facing the Coronavirus Disease 2019 Pandemic.

We assessed the teratogenicity of tenofovir, a human immunodeficiency virus (HIV) drug similar to remdesivir that is currently being evaluated for the treatment of coronavirus disease 2019 (COVID-19). Using US Medicaid Analytic eXtract (MAX) claims data (2000-2014), we identified a population-based pregnancy cohort of women with HIV who filled at least 1 prescription for antiretroviral therapies (ART) during the first trimester. Women on tenofovir disoproxil fumarate (TDF) were compared with women receiving ART without TDF. Major malformations were identified by International Classification of Diseases, Ninth Revision, codes using validated algorithms. Relative risks and 95% confidence intervals were estimated using propensity score stratification to control for potential confounders. We incorporated the results into prior knowledge by conducting a systematic literature review and a meta-analysis. Major congenital malformations were diagnosed in 37 out of 866 (4.27%) infants exposed to TDF and 38 out of 1,020 (3.73%) infants exposed to ART other than TDF; the adjusted relative risk was 1.21 (95% confidence interval: 0.77, 1.90). Estimates for specific malformations were imprecise. The pooled relative risk from the meta-analysis with 6 prior studies was 0.88 (95% confidence interval: 0.75, 1.03). Based on evidence accumulated in patients with HIV, first-trimester TDF use does not increase the risk of major congenital malformations overall in the newborn compared with other ART.

Active Surveillance of the Safety of Medications Used During Pregnancy.

The scientific community relies on postmarketing approaches to define the risk of using medications in pregnancy because information available at the time of drug approval is limited. Most studies carried out in pregnancy focus on a single outcome or selected outcomes. However, women must balance the benefit of treatment against all possible adverse effects. We aimed to apply and evaluate a tree-based scan statistic data-mining method (TreeScan; Martin Kulldorff, Harvard Medical School, Boston, Massachusetts) as a safety surveillance approach that allows for simultaneous evaluation of a comprehensive range of adverse pregnancy outcomes, while preserving the overall rate of false-positive alerts. We evaluated TreeScan with a cohort design and adjustment via propensity score techniques, using 2 test cases: 1) opioids and neonatal opioid withdrawal syndrome and 2) valproate and congenital malformations, implemented in pregnancy cohorts nested within the Medicaid Analytic eXtract (January 1, 2000-December 31, 2014) and the IBM MarketScan Research Database (IBM, Armonk, New York) (January 1, 2003-September 30, 2015). In both cases, we identified known safety concerns, with only 1 previously unreported alert at the preset statistical alerting threshold. This evaluation shows the promise of TreeScan-based approaches for systematic drug safety monitoring in pregnancy. A targeted screening approach followed by deeper investigation to refine understanding of potential signals will ensure that pregnant women and their physicians have access to the best available evidence to inform treatment decisions.

Validation of a Claims-based Algorithm to Identify Pregestational Diabetes Among Pregnant Women in the United States.

BACKGROUND: Identifying pregestational diabetes in pregnant women using administrative claims databases is important for studies of the safety of antidiabetic treatment in pregnancy, but limited data are available on the validity of case-identifying algorithms. The purpose of this study was to evaluate the validity of an administrative claims-based algorithm to identify pregestational diabetes. METHODS: Using a cohort of pregnant women nested within the Medicaid Analytic Extract (MAX) database, we developed an algorithm to identify pregestational type 1 and type 2 diabetes, distinct from gestational diabetes. Within a single large healthcare system in the Boston area, we identified women who delivered an infant between 2000 and 2010 and were covered by Medicaid, and linked their electronic health records to their Medicaid claims within MAX. Medical records were reviewed by two physicians blinded to the algorithm classification to confirm or rule out pregestational diabetes, with disagreements resolved by discussion. We calculated positive predictive values with 95% confidence intervals using the medical record as the reference standard. RESULTS: We identified 49 pregnancies classified by the claims-based algorithm as pregestational diabetes that were linked to the electronic health records and had records available for review. The PPV for any pregestational diabetes was 92% [95% confidence interval (CI) 82%, 97%], type 2 diabetes 87% (68%, 95%), and type 1 diabetes 57% (37%, 75%). CONCLUSIONS: The claims-based algorithm for pregestational diabetes and type 2 diabetes performed well; however, the PPV was low for type 1 diabetes.

Deep learning for digital holography: a review.

Recent years have witnessed the unprecedented progress of deep learning applications in digital holography (DH). Nevertheless, there remain huge potentials in how deep learning can further improve performance and enable new functionalities for DH. Here, we survey recent developments in various DH applications powered by deep learning algorithms. This article starts with a brief introduction to digital holographic imaging, then summarizes the most relevant deep learning techniques for DH, with discussions on their benefits and challenges. We then present case studies covering a wide range of problems and applications in order to highlight research achievements to date. We provide an outlook of several promising directions to widen the use of deep learning in various DH applications.

Hydroxychloroquine early in pregnancy and risk of birth defects.

BACKGROUND: Hydroxychloroquine is generally considered safe in pregnancy for the treatment of rheumatic conditions, but studies have been too small to evaluate teratogenicity. Quantifying the risk of congenital malformations associated with early pregnancy exposure to hydroxychloroquine is important in both the context of its ongoing use for rheumatological disorders and its potential future use for coronavirus disease 2019 prophylaxis, for which a number of clinical trials are ongoing despite initial trials for coronavirus disease 2019 treatment having been negative. OBJECTIVE: The study objective was to evaluate the risk of major congenital malformations associated with exposure to hydroxychloroquine during the first trimester of pregnancy, the period of organogenesis. STUDY DESIGN: We performed a population-based cohort study nested in the Medicaid Analytic eXtract (MAX, 2000-2014) and IBM MarketScan Research Database (MarketScan, 2003-2015). The source cohort included 2045 hydroxychloroquine-exposed pregnancies and 3,198,589 pregnancies not exposed to hydroxychloroquine continuously enrolled in their respective insurance program for 3 months before the last menstrual period through at least 1 month after delivery; infants were enrolled for at least 3 months after birth. We compared the risk of congenital malformations in women using hydroxychloroquine during the first trimester of pregnancy with that of those not using hydroxychloroquine, restricting the cohort to women with rheumatic disorders and using propensity score matching to control for indication, demographics, medical comorbidities, and concomitant medications (1867 hydroxychloroquine-exposed pregnancies and 19,080 pregnancies not exposed to hydroxychloroquine). The outcomes considered included major congenital malformations diagnosed during the first 90 days after delivery and specific malformation types for which there were at least 5 exposed events: oral cleft, cardiac, respiratory, gastrointestinal, genital, urinary, musculoskeletal, and limb defects. RESULTS: Overall, 54.8 per 1000 infants exposed to hydroxychloroquine were born with a major congenital malformation versus 35.3 per 1000 unexposed infants, corresponding to an unadjusted relative risk of 1.51 (95% confidence interval, 1.27-1.81). Patient characteristics were balanced in the restricted, propensity score-matched cohort. The adjusted relative risk was 1.26 (95% confidence interval, 1.04-1.54); it was 1.33 (95% confidence interval, 1.08-1.65) for a daily dose of ≥400 mg and 0.95 (95% confidence interval, 0.60-1.50) for a daily dose of <400 mg. Among the different malformation groups considered, more substantial increases in the risk of oral clefts, respiratory anomalies, and urinary defects were observed, although estimates were imprecise. No pattern of malformation was identified. CONCLUSION: Our findings suggest a small increase in the risk of malformations associated with first-trimester hydroxychloroquine use. For most patients with autoimmune rheumatic disorders, the benefits of treatment during pregnancy will likely outweigh this risk. If hydroxychloroquine were shown to be effective for coronavirus disease 2019 prophylaxis in ongoing trials, the risk of malformations would need to be balanced against such benefits.

Chronic prescription opioid use in pregnancy in the United States.

PURPOSE: To evaluate chronic opioid utilization patterns during pregnancy using nationwide data from publicly and commercially insured women. METHODS: Pregnancy cohorts were identified using data from the Medicaid Analytic eXtract 2008-2014 and the IBM Health MarketScan Research Database 2008-2015. Opioid dispensing was evaluated using claims from filled prescriptions. Two different definitions of chronic opioid use were employed: ≥90 days' supply and ≥180 days' supply of prescription opioids during pregnancy. Patient characteristics were assessed and variations in the prevalence of chronic opioid therapy were described by geographic region and over time. RESULTS: 1.50% of 975 169 Medicaid-insured and 0.32% of 1 037 599 commercially insured beneficiaries filled opioid prescriptions for ≥90 days' supply; 0.78% (Medicaid) and 0.17% (commercially insured) filled prescriptions for ≥180 days' supply. Prevalence approximately doubled in Medicaid beneficiaries during the study period, while it remained relatively stable for commercial insurance beneficiaries. The most commonly prescribed opioid for chronic therapy was hydrocodone, followed by oxycodone and tramadol. Indications commonly associated with chronic use were back/neck pain, abdominal/pelvic pain, musculoskeletal pain and migraine/headache. Substantial regional variation was observed, with several states reporting a frequency of ≥90 days' supply in excess of 3% in Medicaid-insured patients. CONCLUSIONS: Despite growing awareness of the risks associated with chronic opioid use and emphasis on improving opioid prescription patterns, prevalence of chronic use in pregnancy among publicly insured women nearly doubled from 2008-2014 and was 5-fold more common when compared to commercially insured women. Findings call for the development of guidelines on chronic pain management during pregnancy.

Validity of claims-based algorithms to identify neurodevelopmental disorders in children.

PURPOSE: To validate healthcare claim-based algorithms for neurodevelopmental disorders (NDD) in children using medical records as the reference. METHODS: Using a clinical data warehouse of patients receiving outpatient or inpatient care at two hospitals in Boston, we identified children (≤14 years between 2010 and 2014) with at least one of the following NDDs according to claims-based algorithms: autism spectrum disorder/pervasive developmental disorder (ASD), attention deficit disorder/other hyperkinetic syndromes of childhood (ADHD), learning disability, speech/language disorder, developmental coordination disorder (DCD), intellectual disability, and behavioral disorder. Fifty cases per outcome were randomly sampled and their medical records were independently reviewed by two physicians to adjudicate the outcome presence. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated. RESULTS: PPVs were 94% (95% CI, 83%-99%) for ASD, 88% (76%-95%) for ADHD, 98% (89%-100%) for learning disability, 98% (89%-100%) for speech/language disorder, 82% (69%-91%) for intellectual disability, and 92% (81%-98%) for behavioral disorder. A total of 19 of the 50 algorithm-based cases of DCD were confirmed as severe coordination disorders with functional impairment, with a PPV of 38% (25%-53%). Among the 31 false-positive cases of DCD were 7 children with coordination deficits that did not persist throughout childhood, 7 with visual-motor integration deficits, 12 with coordination issues due to an underlying medical condition and 5 with ADHD and at least one other severe NDD. CONCLUSIONS: PPVs were generally high (range: 82%-98%), suggesting that claims-based algorithms can be used to study NDDs. For DCD, additional criteria are needed to improve the classification of true cases.

Digital holographic imaging and classification of microplastics using deep transfer learning.

We devise an inline digital holographic imaging system equipped with a lightweight deep learning network, termed CompNet, and develop the transfer learning for classification and analysis. It has a compression block consisting of a concatenated rectified linear unit (CReLU) activation to reduce the channels, and a class-balanced cross-entropy loss for training. The method is particularly suitable for small and imbalanced datasets, and we apply it to the detection and classification of microplastics. Our results show good improvements both in feature extraction, and generalization and classification accuracy, effectively overcoming the problem of overfitting. This method could be attractive for future in situ microplastic particle detection and classification applications.

Validation of algorithms to estimate gestational age at birth in the Medicaid Analytic eXtract-Quantifying the misclassification of maternal drug exposure during pregnancy.

PURPOSE: Accurate ascertainment of gestational age (GA) has been a challenge in perinatal epidemiologic research. To date, no study has validated GA algorithms in Medicaid Analytic eXtract (MAX). METHODS: We linked livebirths of mothers enrolled in Medicaid ≥30 days after delivery in 1999-2010 MAX to state birth certificates. We used clinical/obstetric estimate of gestation on the birth certificates as gold standard to validate claims-based GA algorithms. We calculated the proportions of deliveries with algorithm-estimated GA within 1-/2-weeks of the gold standard, the sensitivity, specificity, and positive/negative predictive value (PPV/NPV) of exposure to select medications during specific gestation windows, and quantified the impact of exposure misclassification on hypothetical relative risk (RR) estimates. RESULTS: We linked 1 336 495 eligible deliveries. Within 1-week agreement was 77%-80% overall and 47%-56% for preterm deliveries. The trimester-specific drug exposure status had high sensitivities and PPVs (88.5%-98.5%), and specificities and NPVs (>99.0%). Assuming a hypothetical RR of 2.0, bias associated with exposure misclassification during first trimester ranged from 10% to 40% under non-differential/differential misclassification assumptions. CONCLUSIONS: Claims-based GA algorithms had good agreement with the gold standard overall, but lower agreement among preterm deliveries, potentially resulting in biased risk estimated for pregnancy exposure evaluations.

Usability of encounter data for Medicaid comprehensive managed care vs traditional Medicaid fee-for-service claims among pregnant women.

BACKGROUND: The completeness of medical encounters capture among Medicaid enrollees in comprehensive managed care (CMC) has been shown to vary across states and years. CMC penetration has grown, and CMC encounter capture specific to pregnancy care is understudied. OBJECTIVES: To compare the completeness of encounter data for pregnant beneficiaries in CMC versus traditional fee-for-service (FFS) in Texas and Florida between 2007 and 2010. METHODS: Using Medicaid Analytic eXtract (MAX) data linked to Florida and Texas birth certificate records, for each state and study year, we compared proportions using seven themes: (a) delivery; (b) prenatal visits; (c) dispensed prescriptions during pregnancy; (d) gestational diabetes and blood glucose testing; (e) antidiabetics and diagnosis of diabetes mellitus; (f) antibiotics for urinary tract infection and outpatient encounter; and (g) bacterial vaginosis and dispensing for metronidazole or clindamycin. We considered CMC data to be acceptable if proportions were no less than 10% below the corresponding (2007 to 2010) FFS control values. RESULTS: Pregnancy-related characteristics of FFS vs CMC denominators were comparable. Proportions for the seven measures among FFS controls ranged from 26% to 98%. In Texas, CMC encounter data met the thresholds for all measures between 2007 and 2010. Florida had usable CMC encounter data starting from 2009 with incomplete medical and pharmacy records in 2007 and 2008. CONCLUSIONS: The quality of CMC encounter data in MAX files for pregnant women varied in Florida and Texas and improved over time. Use of pregnancy-specific measures can aid researchers in selecting states and years with acceptable encounter data quality.