Internal validation of gestational age estimation algorithms in healthcare databases using pregnancies conceived with fertility procedures.

Fertility procedures recorded in healthcare 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 (ICD) codes. In a cohort of 17,398 pregnancies conceived by fertility procedures in MarketScan (2011-2020), we estimated gestational age at the end of pregnancy using algorithms based on (1) days since fertility procedure (the reference); (2) ICD-9/ICD-10 (before/after October 2015) codes indicating gestational length recorded at the end of pregnancy (method A); and (3) ICD-10 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 within 14 days of the reference. 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-agreements were 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 healthcare databases, especially for preterm and non-live births.

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.).

First Trimester Use of Buprenorphine or Methadone and the Risk of Congenital Malformations.

Importance: Use of buprenorphine or methadone to treat opioid use disorder is recommended in pregnancy; however, their teratogenic potential is largely unknown. Objective: To compare the risk of congenital malformations following in utero exposure to buprenorphine vs methadone. Design, Setting, and Participants: This population-based cohort study used health care utilization data from publicly insured Medicaid beneficiaries in the US from 2000 to 2018. A total of 13 360 pregnancies with enrollment from 90 days prior to pregnancy start through 1 month after delivery and first trimester use of buprenorphine or methadone were included and linked to infants. Data were analyzed from July to December 2022. Exposure: A pharmacy dispensing of buprenorphine or a code for administration of methadone in the first trimester. Main Outcomes and Measures: Primary outcomes included major malformations overall and malformations previously associated with opioids (any cardiac malformations, ventricular septal defect, secundum atrial septal defect/nonprematurity-related patent foramen ovale, neural tube defects, clubfoot, and oral clefts). Secondary outcomes included other organ system-specific malformations. Risk differences and risk ratios (RRs) were estimated comparing buprenorphine with methadone, adjusting for confounders with propensity score overlap weights. Results: The cohort included 9514 pregnancies with first-trimester buprenorphine exposure (mean [SD] maternal age, 28.4 [4.6] years) and 3846 with methadone exposure (mean [SD] maternal age, 28.8 [4.7] years). The risk of malformations overall was 50.9 (95% CI, 46.5-55.3) per 1000 pregnancies for buprenorphine and 60.6 (95% CI, 53.0-68.1) per 1000 pregnancies for methadone. After confounding adjustment, buprenorphine was associated with a lower risk of malformations compared with methadone (RR, 0.82; 95% CI, 0.69-0.97). Risk was lower with buprenorphine for cardiac malformations (RR, 0.63; 95% CI, 0.47-0.85), including both ventricular septal defect (RR, 0.62; 95% CI, 0.39-0.98) and secundum atrial septal defect/nonprematurity-related patent foramen ovale (RR, 0.54; 95% CI, 0.30-0.97), oral clefts (RR, 0.65; 95% CI, 0.35-1.19), and clubfoot (RR, 0.55; 95% CI, 0.32-0.94). Results for neural tube defects were uncertain given low event counts. In secondary analyses, buprenorphine was associated with a decreased risk of central nervous system, urinary, and limb malformations but a greater risk of gastrointestinal malformations compared with methadone. These findings were consistent in sensitivity and bias analyses. Conclusions and Relevance: In this cohort study, the risk of most malformations previously associated with opioid exposure was lower in buprenorphine-exposed infants compared with methadone-exposed infants, independent of measured confounders. Malformation risk is one factor that informs the individualized patient decision regarding medications for opioid use disorder in pregnancy.

Prescription Stimulant Use During Pregnancy and Risk of Neurodevelopmental Disorders in Children.

Importance: Use of medications for attention-deficit/hyperactivity disorder (ADHD) during pregnancy is increasing in the US. Whether exposure to these medications in utero impacts the risk of neurodevelopmental disorders in children is uncertain. Objective: To evaluate the association of childhood neurodevelopmental disorders with in utero exposure to stimulant medications for ADHD. Design, Setting, and Participants: This cohort study included health care utilization data from publicly insured (Medicaid data from 2000 to 2018) and commercially insured (MarketScan Commercial Claims Database data from 2003 to 2020) pregnant individuals aged 12 to 55 years in the US with enrollment from 3 months prior to pregnancy through 1 month after delivery, linked to children. Children were monitored from birth until outcome diagnosis, disenrollment, death, or end of the study (December 2018 for Medicaid and December 2020 for MarketScan). Exposures: Dispensing of amphetamine/dextroamphetamine or methylphenidate in the second half of pregnancy. Main Outcomes and Measures: Autism spectrum disorder, ADHD, and a composite of any neurodevelopmental disorder were defined using validated algorithms. Hazard ratios were estimated comparing amphetamine/dextroamphetamine and methylphenidate to no exposure. Results: The publicly insured cohort included 2 496 771 stimulant-unexposed, 4693 amphetamine/dextroamphetamine-exposed, and 786 methylphenidate-exposed pregnancies with a mean (SD) age of 25.2 (6.0) years. The commercially insured cohort included 1 773 501 stimulant-unexposed, 2372 amphetamine/dextroamphetamine-exposed, and 337 methylphenidate-exposed pregnancies with a mean (SD) age of 31.6 (4.6) years. In unadjusted analyses, amphetamine/dextroamphetamine and methylphenidate exposure were associated with a 2- to 3-fold increased risk of the neurodevelopmental outcomes considered. After adjustment for measured confounders, amphetamine/dextroamphetamine exposure was not associated with any outcome (autism spectrum disorder: hazard ratio [HR], 0.80; 95% CI, 0.56-1.14]; ADHD: HR, 1.07; 95% CI, 0.89-1.28; any neurodevelopmental disorder: HR, 0.91; 95% CI, 0.81-1.28). Methylphenidate exposure was associated with an increased risk of ADHD (HR, 1.43; 95% CI, 1.12-1.82]) but not other outcomes after adjustment (autism spectrum disorder: HR, 1.06; 95% CI, 0.62-1.81; any neurodevelopmental disorder: HR, 1.15; 95% CI, 0.97-1.36). The association between methylphenidate and ADHD did not persist in sensitivity analyses with stricter control for confounding by maternal ADHD. Conclusions and Relevance: The findings in this study suggest that amphetamine/dextroamphetamine and methylphenidate exposure in utero are not likely to meaningfully increase the risk of childhood neurodevelopmental disorders.

High-throughput microplastic assessment using polarization holographic imaging.

Microplastic (MP) pollution has emerged as a global environmental concern due to its ubiquity and harmful impacts on ecosystems and human health. MP assessment has therefore become increasingly necessary and common in environmental and experimental samples. Microscopy and spectroscopy are widely employed for the physical and chemical characterization of MPs. However, these analytical methods often require time-consuming pretreatments of samples or expensive instrumentation. In this work, we develop a portable and cost-effective polarization holographic imaging system that prominently incorporates deep learning techniques, enabling efficient, high-throughput detection and dynamic analysis of MPs in aqueous environments. The integration enhances the identification and classification of MPs, eliminating the need for extensive sample preparation. The system simultaneously captures holographic interference patterns and polarization states, allowing for multimodal information acquisition to facilitate rapid MP detection. The characteristics of light waves are registered, and birefringence features are leveraged to classify the material composition and structures of MPs. Furthermore, the system automates real-time counting and morphological measurements of various materials, including MP sheets and additional natural substances. This innovative approach significantly improves the dynamic monitoring of MPs and provides valuable information for their effective filtration and management.

Enhancing length of stay prediction by learning similarity-aware representations for hospitalized patients.

This paper focuses on predicting the length of stay for patients on the first day of admission and propose a predictive model named DGLoS. In order to capture the influence of various complex factors on the length of stay as well as the dependencies among various factors, DGLoS uses a deep neural network to model both the patient information and diagnostic information. Targeting at different attribution types, we utilize different coding methods to convert raw data to the input features. Besides, we find that similar patients have closer lengths of stay. Therefore, we further design a module based on graph representation learning to generate patients' similarity-aware representations, capturing the similarity between patients and therefore enhancing predictions. These similarity-aware representations are incorporated into the output of the deep neural network to jointly perform the prediction. We have conducted comprehensive experiments on a real-world hospitalization dataset. The performance comparison shows that our proposed DGLoS model improves predictive performance and the significance test demonstrates the improvement is significant. The ablation study verifies the effectiveness of each of the proposed components and the hyper-parameter investigation shows the robustness of the proposed model.

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.

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 .

Vaspin Attenuates Atrial Abnormalities by Promoting ULK1/FUNDC1-Mediated Mitophagy.

The worldwide incidence and prevalence of atrial fibrillation (AF) are increasing, making it a life-threatening condition due to the higher numbers of people suffering from obesity. Vaspin, an adipokine derived from epicardial adipose tissue, has been reported to reduce inflammation, inhibit apoptosis, and induce autophagy; however, its role in the pathogenesis of AF is not known. In this study, we investigated the role of vaspin in patients with AF and explored the molecular mechanisms using atrial myocytes in vitro. Our data showed that vaspin levels were significantly reduced in the plasma of patients with AF. Lower plasma levels of vaspin were also associated with a higher risk of AF in patients with obesity. Vaspin treatment in vitro alleviated cardiomyocyte injury, atrial fibrosis, atrial myocyte apoptosis, and mitochondrial injury in atrial myocytes following Ang-II stress. Moreover, our results demonstrated that vaspin protected against Ang-II-induced atrial myocyte dysfunction by inducing mitophagy. We also observed that vaspin treatment enhanced the phosphorylation of Fun14 domain-containing protein 1 (FUNDC1) at Ser17 by unc-51 like autophagy activating kinase 1 (ULK1), resulting in the induction of mitophagy. These positive effects of vaspin were reversed by ULK1 silencing in Ang-II-stimulated HL-1 cells. Our study is the first to propose that vaspin plays a vital role in AF pathogenesis via ULK1/FUNDC1-regulated mitophagy and could be a novel therapeutic target for AF.

Prenatal diagnosis and genetic counseling of an inherited Xq24q25 deletion associated with normal phenotype.

BACKGROUND: Copy number variants (CNVs) are an important source of normal and pathogenic genome variations. CNVs identified in prenatal cases need careful considerations and correct interpretation if those are harmless or harmful variants from the norm. CASE PRESENTATION: A 28-year-old, gravida 1, para 0, woman underwent amniocentesis at 17 weeks of gestation because the noninvasive prenatal testing (NIPT) results revealed a 9.8 Mb deletion from Xq24 to Xq25. GTG-banding karyotype analysis was performed on cultured amniocytes. Chromosomal microarray analysis (CMA) on uncultured amniocytes was performed. RESULTS: Chromosomal GTG-banding of the cultured amniocytes revealed a karyotype of 46,XX. CMA detected a 9.5-Mb chromosomal deletion in the region of Xq24q25 (arr[GRCh37] Xq24q25(118,975,436_128,444,692) × 1). CONCLUSION: The present report highlights that an integration of prenatal ultrasound, NIPT, karyotype analysis, CMA and genetic counseling is helpful for the prenatal diagnosis of chromosomal deletions/duplications.

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.

Association of Antidepressant Use During Pregnancy With Risk of Neurodevelopmental Disorders in Children.

Importance: Antidepressant use during pregnancy has been associated with neurodevelopmental disorders in children in some studies. However, results may be explained by uncontrolled confounding by parental mental health status, genetics, and environmental factors. Objective: To evaluate the association between antidepressant use in pregnancy and neurodevelopmental outcomes in children. Design, Setting, and Participants: This cohort study of health care utilization data was separated into cohorts of publicly and privately insured pregnant individuals and their children nested in the Medicaid Analytic eXtract (MAX; 2000-2014) and the IBM MarketScan Research Database (MarketScan; 2003-2015). A total of 1.93 million pregnancies in MAX and 1.25 million pregnancies in MarketScan were recorded. Children were followed from birth until outcome diagnosis, disenrollment, death, or end of study (maximum 14 years). Analyses were conducted between August 2020 and July 2021. Exposures: Dispensing of antidepressant medication from gestational week 19 until delivery, the period of synaptogenesis. Main Outcomes and Measures: Neurodevelopmental disorders in children defined using validated algorithms. Early pregnancy exposure was considered in sensitivity analyses, and approaches to confounding adjustment included propensity score fine stratification, discontinuers comparison, and sibling analyses. Results: Among the individuals included in the analysis, there were 145 702 antidepressant-exposed and 3 032 745 unexposed pregnancies; the mean (SD) age among the antidepressant exposed and unexposed was 26.2 (5.7) and 24.3 (5.8) years in MAX and 32.7 (4.6) and 31.9 (4.6) years in MarketScan, respectively; and in MAX, which collected information on race and ethnicity, 72.4% of the antidepressant-exposed and 37.1% of the unexposed individuals were White. Crude results suggested up to a doubling in risk of neurodevelopmental outcomes associated with antidepressant exposure; however, no association was observed in the most fully adjusted analyses. When comparing antidepressant-exposed and unexposed siblings, hazard ratios were 0.97 (95% CI, 0.88-1.06) for any neurodevelopmental disorder, 0.86 (95% CI, 0.60-1.23) for autism spectrum disorder, 0.94 (95% CI, 0.81-1.08) for attention-deficit/hyperactivity disorder, 0.77 (95% CI, 0.42-1.39) for specific learning disorders, 1.01 (95% CI, 0.88-1.16) for developmental speech/language disorder, 0.79 (95% CI, 0.54-1.17) for developmental coordination disorder, 1.00 (95% CI, 0.45-2.22) for intellectual disability, and 0.95 (95% CI, 0.80-1.12) for behavioral disorders. Results were generally consistent for antidepressant classes and drugs and across exposure windows. Conclusions and Relevance: The results of this cohort study suggest that antidepressant use in pregnancy itself does not increase the risk of neurodevelopmental disorders in children. However, given strong crude associations, antidepressant exposure in pregnancy may be an important marker for the need of early screening and intervention.

Astragalus polysaccharides combined with Codonopsis pilosula polysaccharides modulates the physiological characteristics of trophoblasts via miR-92a-1-5p/CCR7 axis.

BACKGROUND: When the abnormality occurs in proliferation, differentiation and apoptosis of trophoblasts, the invasion ability of placental trophoblast is weakened, which is prone to trigger the occurrence of various pregnancy diseases such as repeated spontaneous abortion (RSA). Clinically, Astragalus and Codonopsis pilosula polysaccharides (APS and CPPS) are used for the treatment of unexplained recurrent spontaneous abortion (URSA). Therefore, we aimed to probe into the roles of APS and CPPS in biological behaviors of placental trophoblasts. METHODS: The trophoblasts were treated with APS and CPPS, and transfected with miR-92a-1-5p mimic and CCR7 plasmid to explore the roles of APS and CPPS. Cell viability and apoptosis were determined by CCK-8 and flow cytometry, respectively. The levels of miRNA/mRNA and protein were measured by qRT-PCR and western blot, respectively. The interaction between miR-92a-1-5p and CCR7 was analyzed by TargetScan and dual-luciferase reporter assay. Invasion and migration rates were assessed by Transwell and wound healing assays, respectively. RESULTS: APS combined with CPPS enhanced viability, Bcl-2 expression, and migration and invasion capabilities, while suppressing apoptosis, and expressions of Bax, Bim and miR-92a-1-5p in trophoblasts. Nevertheless, miR-92a-1-5p mimic produced the inverse modulations in trophoblasts, and partially reversed the effects of APS and CPPS. Furthermore, overexpression of CCR7, the target of miR-92a-1-5p, partially offset the effect of miR-92a-1-5p mimic in trophoblasts. CONCLUSION: Astragalus combined with Codonopsis pilosula polysaccharides modulates the biological behaviors of trophoblasts via miR-92a-1-5p/CCR7 axis. The regulatory axis we studied will be helpful for the treatment of URSA in the future.

Association of Antipsychotic Drug Exposure in Pregnancy With Risk of Neurodevelopmental Disorders: A National Birth Cohort Study.

Importance: Although antipsychotic drugs cross the placenta and animal data suggest potential neurotoxic effects, information regarding human neurodevelopmental teratogenicity is limited. Objective: To evaluate whether children prenatally exposed to antipsychotic medication are at an increased risk of neurodevelopmental disorders (NDD). Design, Settings, and Participants: This birth cohort study used data from the Medicaid Analytic eXtract (MAX, 2000-2014) and the IBM Health MarketScan Research Database (MarketScan, 2003-2015) for a nationwide sample of publicly (MAX) and privately (MarketScan) insured mother-child dyads with up to 14 years of follow-up. The MAX cohort consisted of 2 034 883 children who were not prenatally exposed and 9551 who were prenatally exposed to antipsychotic medications; the MarketScan consisted of 1 306 408 and 1221 children, respectively. Hazard ratios were estimated through Cox proportional hazards regression, using propensity score overlap weights for confounding control. Estimates from both cohorts were combined through meta-analysis. Exposures: At least 1 dispensing of a medication during the second half of pregnancy (period of synaptogenesis), assessed for any antipsychotic drug, at the class level (atypical and typical), and for the most commonly used drugs (aripiprazole, olanzapine, quetiapine, risperidone, and haloperidol). Main Outcomes and Measures: Autism spectrum disorder, attention-deficit/hyperactivity disorder, learning disability, speech or language disorder, developmental coordination disorder, intellectual disability, and behavioral disorder, identified using validated algorithms, and the composite outcome of any NDD. Data were analyzed from April 2020 to January 2022. Results: The MAX cohort consisted of 2 034 883 unexposed pregnancies and 9551 pregnancies with 1 or more antipsychotic drug dispensings among women with a mean (SD) age of 26.8 (6.1) years, 204 (2.1%) of whom identified as Asian/Pacific Islander, 2720 (28.5%) as Black, 500 (5.2%) as Hispanic/Latino, and 5356 (56.1%) as White. The MarketScan cohort consisted of 1 306 408 unexposed and 1221 exposed pregnancies among women with a mean (SD) age of 33.1 (5.0) years; race and ethnicity data were not available. Although the unadjusted results were consistent with an approximate 2-fold increased risk for most exposure-outcome contrasts, risks were no longer meaningfully increased after adjustment (eg, pooled unadjusted vs adjusted hazard ratios [95% CI] for any NDD after any antipsychotic exposure: 1.91 [1.79-2.03] vs 1.08 [1.01-1.17]), with the possible exception of aripiprazole (1.36 [1.14-1.63]). Results were consistent across sensitivity analyses. Conclusions and Relevance: The findings of this birth cohort study suggest that the increased risk of NDD seen in children born to women who took antipsychotic drugs late in pregnancy seems to be explained by maternal characteristics and is not causally related with prenatal antipsychotic exposure. This finding highlights the importance of closely monitoring the neurodevelopment of the offspring of women with mental illness to ensure early intervention and support. The potential signal for aripiprazole requires replication in other data before causality can be assumed.

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.

Neurodevelopmental Disorders Among Publicly or Privately Insured Children in the United States.

IMPORTANCE: Neurodevelopmental disorders are associated with poor health and social outcomes. Population-based data on incidence, age at diagnosis, and demographic variations are essential to identify modifiable risk factors and inform the planning of services and interventions. OBJECTIVES: To assess the incidence and timing of diagnosis of neurodevelopmental disorders during childhood in the US and to evaluate differences by population characteristics. DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study used nationwide data on birth cohorts nested in the 2000-2014 Medicaid Analytic eXtract and the 2003-2015 IBM MarketScan Research Database on 2 070 541 publicly and 1 309 900 privately insured children enrolled at birth. Data were analyzed between May 1, 2020, and June 30, 2021. MAIN OUTCOMES AND MEASURES: Neurodevelopmental disorders, autism spectrum disorders, attention-deficit/hyperactivity disorder, learning disabilities, speech or language disorders, developmental coordination disorders, intellectual disabilities, and behavioral disorders were identified based on validated algorithms. Kaplan-Meier analyses were used to estimate the incidence and timing of diagnosis, stratified by child's sex, birth year, maternal age at delivery, and race and ethnicity. RESULTS: The cohorts comprised 2 070 541 publicly insured children (1 045 426 boys [50.5%]) and 1 309 900 privately insured children (667 607 boys [51.0%]) enrolled at birth. By 8 years of age, 23.9% of publicly insured children and 11.0% of privately insured children received a diagnosis of 1 or more neurodevelopmental disorders (autism spectrum disorder, 1.6% and 1.3%; attention-deficit/hyperactivity disorder, 14.5% and 5.8%; learning disability, 1.2% and 0.6%; speech or language disorder, 8.4% and 4.5%; developmental coordination disorder, 0.9% and 0.7%; intellectual disability, 0.7% and 0.1%; and behavioral disorder, 8.4% and 1.5%). Risks were substantially higher among boys (incidence of ≥1 neurodevelopmental disorder by age 8 years for boys vs girls: 30.7% vs 16.7% among publicly insured children and 15.0% vs 6.7% among privately insured children) and White children (30.2% vs 9.1% among Asian children, 23.0% among Black children, 15.4% among Hispanic children, and 22.7% among children of unknown race or ethnicity; information on race and ethnicity was available only for publicly insured children). The association of maternal age and birth year with incidence of neurodevelopmental disorders varied by outcome. Except for attention-deficit/hyperactivity disorder, the diagnosis tended to be established somewhat earlier for privately insured children. The association of race and ethnicity with age at diagnosis varied by outcome. Co-occurring neurodevelopmental disorders were common, especially among children with autism spectrum disorder and intellectual disability (>70% had ≥1 other disorder). CONCLUSIONS AND RELEVANCE: In this population-based cohort study, a relatively high incidence of and co-occurrence of neurodevelopmental disorders as well as the disparity in incidence and timing of diagnosis by insurance type and race and ethnicity were found. These findings represent important public health concerns and underscore the need for timely and accessible developmental assessments and educational services to help reduce the burden of these disorders.

Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction.

The prediction of crowd flows is an important urban computing issue whose purpose is to predict the future number of incoming and outgoing people in regions. Measuring the complicated spatial-temporal dependencies with external factors, such as weather conditions and surrounding point-of-interest (POI) distribution is the most difficult aspect of predicting crowd flows movement. To overcome the above issue, this paper advises a unified dynamic deep spatio-temporal neural network model based on convolutional neural networks and long short-term memory, termed as (DHSTNet) to simultaneously predict crowd flows in every region of a city. The DHSTNet model is made up of four separate components: a recent, daily, weekly, and an external branch component. Our proposed approach simultaneously assigns various weights to different branches and integrates the four properties' outputs to generate final predictions. Moreover, to verify the generalization and scalability of the proposed model, we apply a Graph Convolutional Network (GCN) based on Long Short Term Memory (LSTM) with the previously published model, termed as GCN-DHSTNet; to capture the spatial patterns and short-term temporal features; and to illustrate its exceptional accomplishment in predicting the traffic crowd flows. The GCN-DHSTNet model not only depicts the spatio-temporal dependencies but also reveals the influence of different time granularity, which are recent, daily, weekly periodicity and external properties, respectively. Finally, a fully connected neural network is utilized to fuse the spatio-temporal features and external properties together. Using two different real-world traffic datasets, our evaluation suggests that the proposed GCN-DHSTNet method is approximately 7.9%-27.2% and 11.2%-11.9% better than the AAtt-DHSTNet method in terms of RMSE and MAPE metrics, respectively. Furthermore, AAtt-DHSTNet outperforms other state-of-the-art methods.