Estimating the reduction in US mortality if cigarettes were largely replaced by e-cigarettes.

BACKGROUND: Recent estimates indicated substantially replacing cigarettes by e-cigarettes would, during 2016-2100, reduce US deaths and life-years lost (millions) by 6.6 and 86.7 (Optimistic Scenario) and 1.6 and 20.8 (Pessimistic). To provide additional insight we use alternative modelling based on a shorter period (1991-2040), four main smoking-associated diseases, deaths aged 30-79 years, and a full product history. We consider variations in: assumed effective dose of e-cigarettes versus cigarettes (F); their relative quitting rate (Q); proportions smoking after 10 years (X); and initiation rate (I) of vaping, relative to smoking. METHODS: We set F = 0.05, X = 5%, Q = 1.0 and I = 1.0 (Main Scenario) and F = 0.4, X = 10%, Q = 0.5 and I = 1.5 (Pessimistic Scenario). Sensitivity Analyses varied Main Scenario parameters singly; F from 0 to 0.4, X 0.01% to 15%, and Q and I 0.5 to 1.5. To allow comparison with prior work, individuals cannot be dual users, re-initiate, or switch except from cigarettes to e-cigarettes. RESULTS: Main Scenario reductions were 2.52 and 26.23 million deaths and life-years lost; Pessimistic Scenario reductions were 0.76 and 8.31 million. These were less than previously, due to the more limited age-range and follow-up, and restriction to four diseases. Reductions in deaths (millions) varied most for X, from 3.22 (X = 0.01%) to 1.31 (X = 15%), and F, 2.74 (F = 0) to 1.35 (F = 0.4). Varying Q or I had little effect. CONCLUSIONS: Substantial reductions in deaths and life-years lost were observed even under pessimistic assumptions. Estimates varied most for X and F. These findings supplement literature indicating e-cigarettes can importantly impact health challenges from smoking.

Independent Validation in a Large Privately Insured Population of the Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose.

OBJECTIVE: To assess the generalizability of the overdose or serious opioid-induced respiratory depression risk index (VHA-RIOSORD), created by Zedler et al., using claims data from a large private insurer. DESIGN: A retrospective nested case-control analysis of health care claims data. SUBJECTS: Commercially insured individuals with a claim for an opioid prescription between October 1, 2014, and September 30, 2016 (N = 1,431,737). METHODS: An overdose or serious opioid-induced respiratory depression (OSORD) occurred in 1,097 patients. Ten controls were selected per case (N = 10,970). Items and the assignment of point values to predictors were consistent with those determined by Zedler et al. Modeling of risk index scores produced predicted probabilities of OSORD; risk classes were defined by the predicted probability distribution. RESULTS: All 15 items of the VHA-RIOSORD were used to determine a member's risk of OSORD. The average predicted probability of experiencing OSORD ranged from 3% in the lowest risk decile to 90% in the highest, with excellent agreement between predicted and observed incidence across risk classes. The model's C-statistic was 0.88. CONCLUSIONS: Consistent with the findings of its developers, the VHA-RIOSORD performed well in identifying members of a large private insurance company who were medical users of prescription opioids at elevated risk of overdose or life-threatening respiratory depression, those most likely to benefit from preventive interventions.

Validation of an administrative claims coding algorithm for serious opioid overdose: A medical chart review.

PURPOSE: A standardized definition for serious opioid overdose has not been clearly established for disease surveillance or assessing the impact of risk mitigation strategies. The purpose of this study was to use medical chart review to clinically validate a claims-based algorithm to identify serious opioid overdose events. METHODS: The algorithm for serious opioid overdose required an opioid poisoning or external cause ICD-9-CM code occurring within 1 day of (a) an adverse effect code for serious central nervous system or respiratory depression or (b) a mechanical ventilation or critical care CPT code. The claims coding algorithm identified a sample of 145 individuals 18 years or older among patients that presented to the emergency department of two large hospitals in metropolitan Atlanta, Georgia from January 2014 to August 2015. Claims-defined cases were evaluated against rigorous clinical definitions for serious opioid overdose using (a) literature-based criteria for typical clinical manifestations of opioid overdose and/or (b) clinical response to the opioid-specific reversal agent naloxone. The positive predictive value (PPV) for a serious opioid overdose was calculated as the percentage of clinically confirmed cases (definite or probable). RESULTS: Among 140 evaluable claims-defined cases, 107 fulfilled clinical criteria for a serious opioid overdose [95 definite and 12 probable; PPV of 76.4% (95% CI 69.4%, 83.5%)]. Among 30 nonconfirmed cases, 20 were polyintoxications involving one or more nonopioid psychoactive agents. CONCLUSIONS: An administrative claims coding algorithm for serious opioid overdose had high clinical predictive performance in a medical chart review.

Validation of a Screening Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose in a US Commercial Health Plan Claims Database.

Objective: To validate a risk index that estimates the likelihood of overdose or serious opioid-induced respiratory depression (OIRD) among medical users of prescription opioids. Subjects and Methods: A case-control analysis of 18,365,497 patients with an opioid prescription from 2009 to 2013 in the IMS PharMetrics Plus commercially insured health plan claims database (CIP). An OIRD event occurred in 7,234 cases. Four controls were selected per case. Validity of the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (RIOSORD), developed previously using Veterans Health Administration (VHA) patient data, was assessed. Multivariable logistic regression was used within the CIP study population to develop a slightly refined RIOSORD. The composition and performance of the CIP-based RIOSORD was evaluated and compared with VHA-based RIOSORD. Results: VHA-RIOSORD performed well in discriminating OIRD events in CIP (C-statistic = 0.85). Additionally, re-estimation of logistic model coefficients in CIP yielded a 0.90 C-statistic. The resulting comorbidity and pharmacotherapy variables most highly associated with OIRD and retained in the CIP-RIOSORD were largely concordant with VHA-RIOSORD. These variables included neuropsychiatric and cardiopulmonary disorders, impaired drug excretion, opioid characteristics, and concurrent psychoactive medications. The average predicted probability of OIRD ranged from 2% to 83%, with excellent agreement between predicted and observed incidence across risk classes. Conclusions: RIOSORD had excellent predictive accuracy in a large population of US medical users of prescription opioids, similar to its performance in VHA. This practical risk index is designed to support clinical decision-making for safer opioid prescribing, and its clinical utility should be evaluated prospectively.

Risk Factors for Serious Prescription Opioid-Induced Respiratory Depression or Overdose: Comparison of Commercially Insured and Veterans Health Affairs Populations.

Objective: To characterize the risk factors associated with overdose or serious opioid-induced respiratory depression (OIRD) among medical users of prescription opioids in a commercially insured population (CIP) and to compare risk factor profiles between the CIP and Veterans Health Administration (VHA) population. Subjects and Methods: Analysis of data from 18,365,497 patients in the IMS PharMetrics Plus health plan claims database (CIP) who were dispensed a prescription opioid in 2009 to 2013. Baseline factors associated with an event of serious OIRD among 7,234 cases and 28,932 controls were identified using multivariable logistic regression. The CIP risk factor profile was compared with that from a corresponding logistic regression among 817 VHA cases and 8,170 controls in 2010 to 2012. Results: The strongest associations with serious OIRD in CIP were diagnosed substance use disorder (odds ratio [OR] = 10.20, 95% confidence interval [CI] = 9.06-11.40) and depression (OR = 3.12, 95% CI = 2.84-3.42). Other strongly associated factors included other mental health disorders; impaired liver, renal, vascular, and pulmonary function; prescribed fentanyl, methadone, and morphine; higher daily opioid doses; and concurrent psychoactive medications. These risk factors, except depression, vascular disease, and specific opioids, largely aligned with VHA despite CIP being substantially younger, including more females and less chronic disease, and having greater prescribing prevalence of higher daily opioid doses, specific opioids, and most selected nonopioids. Conclusions: Risk factor profiles for serious OIRD among US medical users of prescription opioids with private or public health insurance were largely concordant despite substantial differences between the populations in demographics, clinical conditions, health care delivery systems, and clinical practices.

Comparative genome sequencing of Escherichia coli allows observation of bacterial evolution on a laboratory timescale.

We applied whole-genome resequencing of Escherichia coli to monitor the acquisition and fixation of mutations that conveyed a selective growth advantage during adaptation to a glycerol-based growth medium. We identified 13 different de novo mutations in five different E. coli strains and monitored their fixation over a 44-d period of adaptation. We obtained proof that the observed spontaneous mutations were responsible for improved fitness by creating single, double and triple site-directed mutants that had growth rates matching those of the evolved strains. The success of this new genome-scale approach indicates that real-time evolution studies will now be practical in a wide variety of contexts.

RNA polymerase mutants found through adaptive evolution reprogram Escherichia coli for optimal growth in minimal media.

Specific small deletions within the rpoC gene encoding the ß'-subunit of RNA polymerase (RNAP) are found repeatedly after adaptation of Escherichia coli K-12 MG1655 to growth in minimal media. Here we present a multiscale analysis of these mutations. At the physiological level, the mutants grow 60% faster than the parent strain and convert the carbon source 15-35% more efficiently to biomass, but grow about 30% slower than the parent strain in rich medium. At the molecular level, the kinetic parameters of the mutated RNAP were found to be altered, resulting in a 4- to 30-fold decrease in open complex longevity at an rRNA promoter and a ∼10-fold decrease in transcriptional pausing, with consequent increase in transcript elongation rate. At a genome-scale, systems biology level, gene expression changes between the parent strain and adapted RNAP mutants reveal large-scale systematic transcriptional changes that influence specific cellular processes, including strong down-regulation of motility, acid resistance, fimbria, and curlin genes. RNAP genome-binding maps reveal redistribution of RNAP that may facilitate relief of a metabolic bottleneck to growth. These findings suggest that reprogramming the kinetic parameters of RNAP through specific mutations allows regulatory adaptation for optimal growth in new environments.

Functional and metabolic effects of adaptive glycerol kinase (GLPK) mutants in Escherichia coli.

Herein we measure the effect of four adaptive non-synonymous mutations to the glycerol kinase (glpK) gene on catalytic function and regulation, to identify changes that correlate to increased fitness in glycerol media. The mutations significantly reduce affinity for the allosteric inhibitor fructose-1,6-bisphosphate (FBP) and formation of the tetramer, which are structurally related, in a manner that correlates inversely with imparted fitness during growth on glycerol, which strongly suggests that these enzymatic parameters drive growth improvement. Counterintuitively, the glpK mutations also increase glycerol-induced auto-catabolite repression that reduces glpK transcription in a manner that correlates to fitness. This suggests that increased specific GlpK activity is attenuated by negative feedback on glpK expression via catabolite repression, possibly to prevent methylglyoxal toxicity. We additionally report that glpK mutations were fixed in 47 of 50 independent glycerol-adapted lineages. By far the most frequently mutated locus (nucleotide 218) was mutated in 20 lineages, strongly suggesting this position has an elevated mutation rate. This study demonstrates that fitness correlations can be used to interrogate adaptive processes at the protein level and to identify the regulatory constraints underlying selection and improved growth.

A statistical method for excluding non-variable CpG sites in high-throughput DNA methylation profiling.

BACKGROUND: High-throughput DNA methylation arrays are likely to accelerate the pace of methylation biomarker discovery for a wide variety of diseases. A potential problem with a standard set of probes measuring the methylation status of CpG sites across the whole genome is that many sites may not show inter-individual methylation variation among the biosamples for the disease outcome being studied. Inclusion of these so-called "non-variable sites" will increase the risk of false discoveries and reduce statistical power to detect biologically relevant methylation markers. RESULTS: We propose a method to estimate the proportion of non-variable CpG sites and eliminate those sites from further analyses. Our method is illustrated using data obtained by hybridizing DNA extracted from the peripheral blood mononuclear cells of 311 samples to an array assaying 1505 CpG sites. Results showed that a large proportion of the CpG sites did not show inter-individual variation in methylation. CONCLUSIONS: Our method resulted in a substantial improvement in association signals between methylation sites and outcome variables while controlling the false discovery rate at the same level.

Functional states of the genome-scale Escherichia coli transcriptional regulatory system.

A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this "pseudo-stoichiometric" matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.

Gene expression profiling of peripheral blood leukocytes identifies potential novel biomarkers of chronic obstructive pulmonary disease in current and former smokers.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an inflammatory lung disease with associated systemic effects. OBJECTIVE: To use gene expression microarrays in peripheral blood leukocytes of current and former cigarette smokers to identify differences associated with COPD. MATERIALS AND METHODS: Random forest modelling and a split-sample case-control approach were used to identify candidate predictors. RESULTS: We identified 1013 genes and one smoking exposure variable that differentiated current and former smokers with or without COPD. This predictor set was reduced to a nine-gene classifier (IL6R, CCR2, PPP2CB, RASSF2, WTAP, DNTTIP2, GDAP1, LIPE and RPL14). CONCLUSION: These gene expression profiles represent potential biomarkers for COPD and may help increase mechanistic understanding of the disease.

A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.

An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

Predicting gene essentiality using genome-scale in silico models.

Genome-scale metabolic models of organisms can be reconstructed using annotated genome sequence information, well-curated databases, and primary research literature. The metabolic reaction stoichiometry and other physicochemical factors are incorporated into the model, thus imposing constraints that represent restrictions on phenotypic behavior. Based on this premise, the theoretical capabilities of the metabolic network can be assessed by using a mathematical technique known as flux balance analysis (FBA). This modeling framework, also known as the constraint-based reconstruction and analysis approach, differs from other modeling strategies because it does not attempt to predict exact network behavior. Instead, this approach uses known constraints to separate the states that a system can achieve from those that it cannot. In recent years, this strategy has been employed to probe the metabolic capabilities of a number of organisms, to generate and test experimental hypotheses, and to predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the constraint-based modeling approach and focuses on its application to computationally predicting gene essentiality.

A comparison of approaches to identify possible cases of local anesthetic systemic toxicity in the FDA Adverse Event Reporting System (FAERS) database.

BACKGROUND: Local anesthetic systemic toxicity (LAST) is a rare but potentially serious adverse event . METHODS: Data from the US Food and Drug Administration Adverse Event Reporting System were examined for liposomal bupivacaine (LB), bupivacaine, or other injectable local anesthetics. Possible LAST cases were identified based on MedDRA system organ classes (Approach 1), a recent publication (Approach 2), and a novel approach based on LAST literature (Approach 3). Disproportionality analyses compared possible LAST cases for LB and bupivacaine with other injectable local anesthetics. RESULTS: Approaches 1, 2, and 3 identified 75, 42, and 29 possible LAST cases associated with LB, respectively, compared with 9,595, 3,422, and 549 for other injectable local anesthetics. The proportional reporting ratios (95% CI) for LB versus other injectable local anesthetics for the 3 approaches were 1.9 (1.6-2.3), 2.9 (2.2-3.9), and 1.6 (1.1-2.2), respectively. Based on sales data, the estimated incidence of possible LAST with LB was 0.1 per 10,000 uses; literature estimates for LAST with other injectable local anesthetics were 0 to 18 per 10,000 uses. CONCLUSIONS: Our findings suggest the estimated incidence of possible LAST cases with LB is similar to, or less than, the reported incidence with other injectable local anesthetics.

Long-range periodic patterns in microbial genomes indicate significant multi-scale chromosomal organization.

Genome organization can be studied through analysis of chromosome position-dependent patterns in sequence-derived parameters. A comprehensive analysis of such patterns in prokaryotic sequences and genome-scale functional data has yet to be performed. We detected spatial patterns in sequence-derived parameters for 163 chromosomes occurring in 135 bacterial and 16 archaeal organisms using wavelet analysis. Pattern strength was found to correlate with organism-specific features such as genome size, overall GC content, and the occurrence of known motility and chromosomal binding proteins. Given additional functional data for Escherichia coli, we found significant correlations among chromosome position dependent patterns in numerous properties, some of which are consistent with previously experimentally identified chromosome macrodomains. These results demonstrate that the large-scale organization of most sequenced genomes is significantly nonrandom, and, moreover, that this organization is likely linked to genome size, nucleotide composition, and information transfer processes. Constraints on genome evolution and design are thus not solely dependent upon information content, but also upon an intricate multi-parameter, multi-length-scale organization of the chromosome.

Matrix formalism to describe functional states of transcriptional regulatory systems.

Complex regulatory networks control the transcription state of a genome. These transcriptional regulatory networks (TRNs) have been mathematically described using a Boolean formalism, in which the state of a gene is represented as either transcribed or not transcribed in response to regulatory signals. The Boolean formalism results in a series of regulatory rules for the individual genes of a TRN that in turn can be used to link environmental cues to the transcription state of a genome, thereby forming a complete transcriptional regulatory system (TRS). Herein, we develop a formalism that represents such a set of regulatory rules in a matrix form. Matrix formalism allows for the systemic characterization of the properties of a TRS and facilitates the computation of the transcriptional state of the genome under any given set of environmental conditions. Additionally, it provides a means to incorporate mechanistic detail of a TRS as it becomes available. In this study, the regulatory network matrix, R, for a prototypic TRS is characterized and the fundamental subspaces of this matrix are described. We illustrate how the matrix representation of a TRS coupled with its environment (R*) allows for a sampling of all possible expression states of a given network, and furthermore, how the fundamental subspaces of the matrix provide a way to study key TRS features and may assist in experimental design.

Toward whole cell modeling and simulation: comprehensive functional genomics through the constraint-based approach.

The increasing availability of various system-level, or so-called 'omics', datasets, in concert with existing data from the primary research literature, is facilitating the development of genome-scale metabolic models for many organisms. By incorporating the metabolic reaction stoichiometry as well as other physicochemical properties into systemic network reconstructions, these models account for the constraints that restrict an organism's phenotypic behavior. Accordingly, unlike many contemporary modeling strategies, this constraint-based modeling approach does not attempt to predict network behavior exactly; rather, it seeks to clearly distinguish those network states that a system can achieve from those that it cannot. A variety of analytical tools have been designed and developed to probe these models, thus enabling studies that investigate the metabolic capabilities of a number of organisms, that generate and test experimental hypotheses, and that predict accurately metabolic phenotypes and evolutionary outcomes. This chapter introduces the concepts that underlie the constraint-based modeling approach, and describes several of its applications with an emphasis on those potentially relevant to the drug development field. In addition, while this chapter focuses on the primary application of the constraint-based approach to date, namely in modeling metabolic networks, the latter sections of the chapter discuss its relatively recent application to modeling other cellular systems. Finally, the chapter concludes with an assessment of future directions focusing on the efforts that will be required to utilize the constraint-based approach in generating a holistic model of a viable organism.

The prevalence of nasal obstruction as a consideration in the treatment of opioid overdose.

OBJECTIVES: The intranasal route of administration for naloxone delivery is one treatment for opioid overdose, but treatment failures with this modality have been documented. This study determines the incidence of obstructive nasal pathology in patients who experienced serious opioid-induced respiratory depression (OIRD). DESIGN: Retrospective analysis of the IMS LifeLink: Health Plan Claims Database to detect patients with at least one opioid pharmacy claim from 2009 to 2013 and who experienced serious OIRD. Four controls were randomly assigned to each case. MAIN OUTCOME MEASURES: A multivariable analysis determined the adjusted odds ratio of OIRD for patients with obstructive nasal pathology. RESULTS: A total of 7,234 patients experienced a serious OIRD event; 840 (11.6 percent) had obstructive nasal pathology: 20 (2.4 percent) had deviated nasal septum (International Classification of Disease, 9th revision [ICD-9] 470), 246 (29.3 percent) had polyp of the nasal cavity (ICD-9 470.1), 130 (15.5 percent) had hypertrophy of nasal turbinates (ICD-9 478.0), and 659 (78.5 percent) had other disease of the nasal cavity (ICD-9 478.19). The adjusted odds ratio for patients who experienced serious OIRD having concurrent obstructive nasal pathology was 1.28 (95% confidence interval 1.13-1.46). CONCLUSIONS: Obstructive nasal pathology is relatively common in patients who experience serious OIRD, and in itself is associated with a higher risk of having OIRD.

A Swedish Population-based Study of Adverse Birth Outcomes among Pregnant Women Treated with Buprenorphine or Methadone: Preliminary Findings.

BACKGROUND: Untreated opioid dependence in pregnant women is associated with adverse birth outcomes. Buprenorphine and methadone are options for opioid agonist medication-assisted treatment during pregnancy. OBJECTIVE: The aim of this study was to describe adverse birth outcomes observed with buprenorphine or methadone treatment compared to the general population in Sweden. METHODS: Pregnant women and their corresponding births during 2005-2011 were identified in the Swedish Medical Birth Register. Data on stillbirth, neonatal/infant death, mode of delivery, gestational age at birth, Apgar score, growth outcomes, neonatal abstinence syndrome, and congenital malformations were examined. Frequencies were compared using two-sided Fisher's exact tests. Unadjusted estimates of birth outcomes for women treated with buprenorphine or methadone were compared to the registered general population. RESULTS: A total of 746,257 pregnancies among 538,178 unique women resulted in 746,485 live births. Among the 194 women treated with buprenorphine (N = 176) or methadone (N = 52), no stillbirths or neonatal/infant deaths occurred. Neonatal abstinence syndrome developed in 23.3% and 38.5% of infants born to mothers treated with buprenorphine and methadone, respectively. The frequency of the selected adverse birth outcomes assessed in women treated with buprenorphine as compared to the general population was not significantly different. However, a significantly higher frequency of preterm birth and congenital malformations was observed in women treated with methadone as compared to the general population. Compared with the general population, methadone-treated women were significantly older than buprenorphine-treated women, and both treatment groups began prenatal care later, were more likely to smoke cigarettes, and did not cohabitate with the baby's father. CONCLUSIONS: An increased frequency of the selected adverse birth outcomes was not observed with buprenorphine treatment during pregnancy. Twofold increased frequency of preterm birth [2.21 (1.11, 4,41)] and congenital malformations [2.05 (1.08, 3.87)] was observed in the methadone group, which may be partly explained by older average maternal age and differences in other measured and unmeasured confounders.

Buprenorphine compared with methadone to treat pregnant women with opioid use disorder: a systematic review and meta-analysis of safety in the mother, fetus and child.

AIMS: To assess the safety of buprenorphine compared with methadone to treat pregnant women with opioid use disorder. METHODS: We searched PubMed, Embase and the Cochrane Library from inception to February 2015 for randomized controlled trials (RCT) and observational cohort studies (OBS) that compared buprenorphine with methadone for treating opioid-dependent pregnant women. Two reviewers assessed independently the titles and abstracts of all search results and full texts of potentially eligible studies reporting original data for maternal/fetal/infant death, preterm birth, fetal growth outcomes, fetal/congenital anomalies, fetal/child neurodevelopment and/or maternal adverse events. We ascertained each study's risk of bias using validated instruments and assessed the strength of evidence for each outcome using established methods. We computed effect sizes using random-effects models for each outcome with two or more studies. RESULTS: Three RCTs (n = 223) and 15 cohort OBSs (n = 1923) met inclusion criteria. In meta-analyses using unadjusted data and methadone as comparator, buprenorphine was associated with lower risk of preterm birth [RCT risk ratio (RR) = 0.40, 95% confidence interval (CI) = 0.18, 0.91; OBS RR = 0.67, 95% CI = 0.50, 0.90], greater birth weight [RCT weighted mean difference (WMD) = 277 g, 95% CI = 104, 450; OBS WMD = 265 g, 95% CI = 196, 335] and larger head circumference [RCT WMD = 0.90 cm, 95% CI = 0.14, 1.66; OBS WMD = 0.68 cm, 95% CI = 0.41, 0.94]. No treatment differences were observed for spontaneous fetal death, fetal/congenital anomalies and other fetal growth measures, although the power to detect such differences may be inadequate due to small sample sizes. CONCLUSIONS: Moderately strong evidence indicates lower risk of preterm birth, greater birth weight and larger head circumference with buprenorphine treatment of maternal opioid use disorder during pregnancy compared with methadone treatment, and no greater harms.