Comparative Effectiveness of Licensed Influenza Vaccines in Preventing Influenza-related Medical Encounters and Hospitalizations in the 2022-2023 Influenza Season Among Adults ≥65 Years of Age.

BACKGROUND: Influenza causes substantial morbidity, particularly among older individuals. Updated data on the effectiveness of currently licensed vaccines in this population are needed. METHODS: At Kaiser Permanente Southern California, we conducted a retrospective cohort study to evaluate comparative vaccine effectiveness (cVE) of high-dose (HD), adjuvanted, and standard-dose (SD) cell-based influenza vaccines, relative to the SD egg-based vaccine. We included adults aged ≥65 years who received an influenza vaccine between 1 August 2022 and 31 December 2022, with follow-up up to 20 May 2023. Primary outcomes were: (1) influenza-related medical encounters and (2) polymerase chain reaction (PCR)-confirmed influenza-related hospitalization. Adjusted hazard ratios (aHR) were estimated by Cox proportional hazards regression, adjusting for confounders using inverse probability of treatment weighting (IPTW). cVE (%) was calculated as (1-aHR) × 100 when aHR ≤1, and ([1/aHR]-1) × 100 when aHR >1. RESULTS: Our study population (n = 495 119) was 54.9% female, 46.3% non-Hispanic White, with a median age of 73 years (interquartile range [IQR] 69-79). Characteristics of all groups were well balanced after IPTW. Adjusted cVEs against influenza-related medical encounters in the HD, adjuvanted, and SD cell-based vaccine groups were 9.1% (95% confidence interval [CI]: .9, 16.7), 16.9% (95% CI: 1.7, 29.8), and -6.3 (95% CI: -18.3, 6.9), respectively. Adjusted cVEs against PCR-confirmed hospitalization in the HD, adjuvanted, and SD cell-based groups were 25.1% (95% CI: .2, 43.8), 61.6% (95% CI: 18.1, 82.0), and 26.4% (95% CI: -18.3, 55.7), respectively. CONCLUSIONS: Compared to the SD egg-based vaccine, HD and adjuvanted vaccines conferred additional protection against influenza-related outcomes in the 2022-2023 season in adults ≥65 years. Our results provide real-world evidence of the comparative effectiveness of currently licensed vaccines.

The Association of the First Surge of the COVID-19 Pandemic with the High- and Low-Value Outpatient Care Delivered to Adults in the USA.

BACKGROUND: The first surge of the COVID-19 pandemic entirely altered healthcare delivery. Whether this also altered the receipt of high- and low-value care is unknown. OBJECTIVE: To test the association between the April through June 2020 surge of COVID-19 and various high- and low-value care measures to determine how the delivery of care changed. DESIGN: Difference in differences analysis, examining the difference in quality measures between the April through June 2020 surge quarter and the January through March 2020 quarter with the same 2 quarters' difference the year prior. PARTICIPANTS: Adults in the MarketScan® Commercial Database and Medicare Supplemental Database. MAIN MEASURES: Fifteen low-value and 16 high-value quality measures aggregated into 8 clinical quality composites (4 of these low-value). KEY RESULTS: We analyzed 9,352,569 adults. Mean age was 44 years (SD, 15.03), 52% were female, and 75% were employed. Receipt of nearly every type of low-value care decreased during the surge. For example, low-value cancer screening decreased 0.86% (95% CI, -1.03 to -0.69). Use of opioid medications for back and neck pain (DiD +0.94 [95% CI, +0.82 to +1.07]) and use of opioid medications for headache (DiD +0.38 [95% CI, 0.07 to 0.69]) were the only two measures to increase. Nearly all high-value care measures also decreased. For example, high-value diabetes care decreased 9.75% (95% CI, -10.79 to -8.71). CONCLUSIONS: The first COVID-19 surge was associated with receipt of less low-value care and substantially less high-value care for most measures, with the notable exception of increases in low-value opioid use.

Comparison of healthcare resource use and cost between influenza and COVID-19 vaccine coadministration and influenza vaccination only.

OBJECTIVE: To compare healthcare resource utilization (HCRU) and all-cause medical costs among individuals aged ≥50 years who received influenza and COVID-19 vaccines on the same day and those who received influenza vaccine only. METHODS: We conducted a retrospective cohort study leveraging Optum's de-identified Clinformatics DataMart from 8/31/2021 to 7/31/2023. Individuals aged ≥50 years continuously enrolled in health plans for 1 year prior and until 7/31/2023 were included. Two cohorts were formed based on vaccination status between 8/31/2022 and 1/31/2023: co-administered influenza and COVID-19 vaccines (co-admin cohort) and influenza vaccine only (influenza cohort). Associations between vaccination status and all-cause, influenza-related, COVID-related, pneumonia-related, and cardiorespiratory-related hospitalization, outpatient or emergency room visits and all-cause medical costs were estimated by weighted generalized linear models, adjusting for confounding by stabilized inverse probability of treatment weighting. RESULTS: 613,156 (mean age: 71) and 1,340,011 (mean age: 72) individuals were included in the co-admin and influenza cohorts, respectively. After weighting, the baseline characteristics were balanced between cohorts. The co-admin cohort was at statistically significant lower risk of all-cause (RR: 0.95, 95% CI: 0.93-0.96), COVID-19-related (RR: 0.59, 95% CI: 0.56-0.63), cardiorespiratory-related (RR: 0.94, 95% CI: 0.93-0.96) and pneumonia-related (RR: 0.86, 95% CI: 0.83-0.90) hospitalization but not influenza-related hospitalizations (RR: 0.91, 95% CI: 0.81, 1.04) compared with the influenza cohort. Co-administration was associated with 3% lower all-cause medical cost (cost ratio: 0.974, 95% CI: 0.968, 0.979) during the follow-up period compared to receiving influenza vaccine only. LIMITATIONS: Limitations include the potential residual confounding bias in observational data, measurement errors from claims data, and that the cohort was followed for a single season. CONCLUSION: Receiving co-administered COVID-19 and influenza vaccines versus only receiving influenza vaccination reduced the risk of HCRU, especially COVID-19-related hospitalization and all-cause medical costs. Increasing vaccine coverage, particularly for COVID-19, might have public health and economic benefits.

Seasonal influenza vaccine performance and the potential benefits of mRNA vaccines.

Influenza remains a public health threat, partly due to suboptimal effectiveness of vaccines. One factor impacting vaccine effectiveness is strain mismatch, occurring when vaccines no longer match circulating strains due to antigenic drift or the incorporation of inadvertent (eg, egg-adaptive) mutations during vaccine manufacturing. In this review, we summarize the evidence for antigenic drift of circulating viruses and/or egg-adaptive mutations occurring in vaccine strains during the 2011-2020 influenza seasons. Evidence suggests that antigenic drift led to vaccine mismatch during four seasons and that egg-adaptive mutations caused vaccine mismatch during six seasons. These findings highlight the need for alternative vaccine development platforms. Recently, vaccines based on mRNA technology have demonstrated efficacy against SARS-CoV-2 and respiratory syncytial virus and are under clinical evaluation for seasonal influenza. We discuss the potential for mRNA vaccines to address strain mismatch, as well as new multi-component strategies using the mRNA platform to improve vaccine effectiveness.

Patient Safety Indicators During the Initial COVID-19 Pandemic Surge in the United States.

OBJECTIVE: The COVID-19 pandemic presented a challenge to inpatient safety. It is unknown whether there were spillover effects due to COVID-19 into non-COVID-19 care and safety. We sought to evaluate the changes in inpatient Agency for Healthcare Research and Quality patient safety indicators (PSIs) in the United States before and during the first surge of the pandemic among patients admitted without COVID-19. METHODS: We analyzed trends in PSIs from January 2019 to June 2020 in patients without COVID-19 using data from IBM MarketScan Commercial Database. We included members of employer-sponsored or Medicare supplemental health plans with inpatient, non-COVID-19 admissions. The primary outcomes were risk-adjusted composite and individual PSIs. RESULTS: We analyzed 1,869,430 patients admitted without COVID-19. Among patients without COVID-19, the composite PSI score was not significantly different when comparing the first surge (Q2 2020) to the prepandemic period (e.g., Q2 2020 score of 2.46 [95% confidence interval {CI}, 2.34-2.58] versus Q1 2020 score of 2.37 [95% CI, 2.27-2.46]; P = 0.22). Individual PSIs for these patients during Q2 2020 were also not significantly different, except in-hospital fall with hip fracture (e.g., Q2 2020 was 3.42 [95% CI, 3.34-3.49] versus Q4 2019 was 2.45 [95% CI, 2.40-2.50]; P = 0.01). CONCLUSIONS: The first surge of COVID-19 was not associated with worse inpatient safety for patients without COVID-19, highlighting the ability of the healthcare system to respond to the initial surge of the pandemic.

Assessing the Association Between Network-based Provider Communities and Patient Mortality in the Medicare Population with Multiple Chronic Conditions.

Understanding the complexity of care delivery and care coordination for patients with multiple chronic conditions is challenging. Network analysis can model the relationship between providers and patients to find factors associated with patient mortality. We constructed a network by connecting the providers through shared patients, which was then partitioned into tightly connected communities using a community detection algorithm. After adjusting for patient characteristics, the odds ratio of death for one standard deviation increase in degree centrality ratio between primary care providers (PCPs) and non-PCPs was 0.95 (0.92-0.98). Our result suggest that the centrality of PCPs may be a modifiable factor for improving care delivery. We demonstrated that network analysis can be used to find higher order features associated with health outcomes in addition to patient-level features.

Discovering Associations between Social Determinants and Health Outcomes: Merging Knowledge Graphs from Literature and Electronic Health Data.

Social Determinants of Health (SDoH) are an increasingly important part of the broader research and public health efforts in understanding individuals' physical and mental well-being. Despite this, non-clinical factors affecting health are poorly recorded in electronic health databases and techniques to study how SDoH might relate to population outcomes are lacking. This paper proposes an approach to systematically identify and quantify associations between SDoH and health-related outcomes in a specific cohort of people by (1) leveraging published evidence from literature to build a knowledge graph of health and social factor associations and (2) analysing a large dataset of claims and medical records where those associations may be found. This work demonstrates how the proposed approach could be used to generate hypotheses and inform further research on SDoH in a data-driven manner.

Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression.

Importance: The lack of standards in methods to reduce bias for clinical algorithms presents various challenges in providing reliable predictions and in addressing health disparities. Objective: To evaluate approaches for reducing bias in machine learning models using a real-world clinical scenario. Design, Setting, and Participants: Health data for this cohort study were obtained from the IBM MarketScan Medicaid Database. Eligibility criteria were as follows: (1) Female individuals aged 12 to 55 years with a live birth record identified by delivery-related codes from January 1, 2014, through December 31, 2018; (2) greater than 80% enrollment through pregnancy to 60 days post partum; and (3) evidence of coverage for depression screening and mental health services. Statistical analysis was performed in 2020. Exposures: Binarized race (Black individuals and White individuals). Main Outcomes and Measures: Machine learning models (logistic regression [LR], random forest, and extreme gradient boosting) were trained for 2 binary outcomes: postpartum depression (PPD) and postpartum mental health service utilization. Risk-adjusted generalized linear models were used for each outcome to assess potential disparity in the cohort associated with binarized race (Black or White). Methods for reducing bias, including reweighing, Prejudice Remover, and removing race from the models, were examined by analyzing changes in fairness metrics compared with the base models. Baseline characteristics of female individuals at the top-predicted risk decile were compared for systematic differences. Fairness metrics of disparate impact (DI, 1 indicates fairness) and equal opportunity difference (EOD, 0 indicates fairness). Results: Among 573 634 female individuals initially examined for this study, 314 903 were White (54.9%), 217 899 were Black (38.0%), and the mean (SD) age was 26.1 (5.5) years. The risk-adjusted odds ratio comparing White participants with Black participants was 2.06 (95% CI, 2.02-2.10) for clinically recognized PPD and 1.37 (95% CI, 1.33-1.40) for postpartum mental health service utilization. Taking the LR model for PPD prediction as an example, reweighing reduced bias as measured by improved DI and EOD metrics from 0.31 and -0.19 to 0.79 and 0.02, respectively. Removing race from the models had inferior performance for reducing bias compared with the other methods (PPD: DI = 0.61; EOD = -0.05; mental health service utilization: DI = 0.63; EOD = -0.04). Conclusions and Relevance: Clinical prediction models trained on potentially biased data may produce unfair outcomes on the basis of the chosen metrics. This study's results suggest that the performance varied depending on the model, outcome label, and method for reducing bias. This approach toward evaluating algorithmic bias can be used as an example for the growing number of researchers who wish to examine and address bias in their data and models.

Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge.

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches.

Evaluating artificial intelligence in medicine: phases of clinical research.

Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical trial phases for drugs and medical devices, and we present study design and methodological guidance for each stage.

Hierarchical patient-centric caregiver network method for clinical outcomes study.

In clinical outcome studies, analysis has traditionally been performed using patient-level factors, with minor attention given to provider-level features. However, the nature of care coordination and collaboration between caregivers (providers) may also be important in determining patient outcomes. Using data from patients admitted to intensive care units at a large tertiary care hospital, we modeled the caregivers that provided medical service to a specific patient as patient-centric subnetwork embedded within larger caregiver networks of the institute. The caregiver networks were composed of caregivers who treated either a cohort of patients with particular disease or any patient regardless of disease. Our model can generate patient-specific caregiver network features at multiple levels, and we demonstrate that these multilevel network features, in addition to patient-level features, are significant predictors of length of hospital stay and in-hospital mortality.

Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning.

Using electronic health data to predict adverse drug reaction (ADR) incurs practical challenges, such as lack of adequate data from any single site for rare ADR detection, resource constraints on integrating data from multiple sources, and privacy concerns with creating a centralized database from person-specific, sensitive data. We introduce a federated learning framework that can learn a global ADR prediction model from distributed health data held locally at different sites. We propose two novel methods of local model aggregation to improve the predictive capability of the global model. Through comprehensive experimental evaluation using real-world health data from 1 million patients, we demonstrate the effectiveness of our proposed approach in achieving comparable performance to centralized learning and outperforming localized learning models for two types of ADRs. We also demonstrate that, for varying data distributions, our aggregation methods outperform state-of-the-art techniques, in terms of precision, recall, and accuracy.

Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here.

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms "diabetes" and "artificial intelligence." The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers.

Prescription fill rates for acute and chronic medications in claims-EMR linked data.

Nonadherence to prescribed medications poses a significant public health problem. Prescription data in electronic medical records (EMRs) linked with pharmacy claims data provides an opportunity to examine the prescription fill rates and factors associated with it.Using a claims-EMR linked data, patients who had a prescription for either an antibiotic, antihypertensive, or antidiabetic in EMR were identified (index prescription). Prescription fill was defined as a pharmacy claim found within the 90 days following the EMR prescription. For each medication group, patient characteristics and fill rates were examined using descriptive statistics. Multivariate logistic regression was used to evaluate the association between fill rates and factors such as age, race, brand vs generic, and prior treatment during 365 days before the index date.Among 77,996 patients with index antibiotic prescription, 78,462 with index antihypertensive prescription, and 24,013 with index antidiabetic prescription, the prescription fill rate was 73%, 74%, and 76%, respectively. Overall, African American race was negatively associated with fill rates (odds ratio [OR] 0.8 for all 3 groups). Prior treatment history was positively associated with antihypertensives (OR 5.6, 95% confidence interval [CI] 5.4-5.7) or antidiabetics (OR 4.1, CI 3.8-4.4) but negatively with antibiotics (OR 0.6, CI 0.6-0.6). Older age was an additional factor that was negatively associated with first time fill rate among patients without prior treatment.Significant proportions of patients, especially patients with no prior treatment history, did not fill prescriptions for antibiotics, antihypertensives, or antidiabetics. The association between patient factors and medication fill rates varied across different medication groups.

Use of haloperidol versus atypical antipsychotics and risk of in-hospital death in patients with acute myocardial infarction: cohort study.

OBJECTIVE: To compare the risk of in-hospital mortality associated with haloperidol compared with atypical antipsychotics in patients admitted to hospital with acute myocardial infarction. DESIGN: Cohort study using a healthcare database. SETTING: Nationwide sample of patient data from more than 700 hospitals across the United States. PARTICIPANTS: 6578 medical patients aged more than 18 years who initiated oral haloperidol or oral atypical antipsychotics (olanzapine, quetiapine, risperidone) during a hospital admission with a primary diagnosis of acute myocardial infarction between 2003 and 2014. MAIN OUTCOME MEASURE: In-hospital mortality during seven days of follow-up from treatment initiation. RESULTS: Among 6578 patients (mean age 75.2 years) treated with an oral antipsychotic drug, 1668 (25.4%) initiated haloperidol and 4910 (74.6%) initiated atypical antipsychotics. The mean time from admission to start of treatment (5.3 v 5.6 days) and length of stay (12.5 v 13.6 days) were similar, but the mean treatment duration was shorter in patients using haloperidol compared with those using atypical antipsychotics (2.4 v 3.9 days). 1:1 propensity score matching was used to adjust for confounding. In intention to treat analyses with the matched cohort, the absolute rate of death per 100 person days was 1.7 for haloperidol (129 deaths) and 1.1 for atypical antipsychotics (92 deaths) during seven days of follow-up from treatment initiation. The survival probability was 0.93 in patients using haloperidol and 0.94 in those using atypical antipsychotics at day 7, accounting for the loss of follow-up due to hospital discharge. The unadjusted and adjusted hazard ratios of death were 1.51 (95% confidence interval 1.22 to 1.85) and 1.50 (1.14 to 1.96), respectively. The association was strongest during the first four days of follow-up and decreased over time. By day 5, the increased risk was no longer evident (1.12, 0.79 to 1.59). In the as-treated analyses, the unadjusted and adjusted hazard ratios were 1.90 (1.43 to 2.53) and 1.93 (1.34 to 2.76), respectively. CONCLUSION: The results suggest a small increased risk of death within seven days of initiating haloperidol compared with initiating an atypical antipsychotic in patients with acute myocardial infarction. Although residual confounding cannot be excluded, this finding deserves consideration when haloperidol is used for patients admitted to hospital with cardiac morbidity.

Continuation of Atypical Antipsychotic Medication During Early Pregnancy and the Risk of Gestational Diabetes.

OBJECTIVE: Some atypical antipsychotics are associated with metabolic side effects, which are risk factors for gestational diabetes. The authors examined the risk of developing gestational diabetes associated with the continuation of treatment with aripiprazole, ziprasidone, quetiapine, risperidone, and olanzapine during pregnancy compared with discontinuation of these antipsychotic drugs. METHOD: Nondiabetic pregnant women who were linked to a live-born infant and enrolled in Medicaid (2000-2010) and who received one or more prescriptions dispensed for an antipsychotic drug during the 3 months before pregnancy were included in the analyses. Among 1,543,334 pregnancies, some expectant mothers at baseline were receiving treatment with aripiprazole (N=1,924), ziprasidone (N=673), quetiapine (N=4,533), risperidone (N=1,824), or olanzapine (N=1,425). For each antipsychotic drug, women with two or more dispensings ("continuers") were compared with women with no dispensings ("discontinuers") during the first half of pregnancy. A generalized linear model and propensity-score stratification were used to obtain absolute and relative risks of developing gestational diabetes, with adjustment for confounders. RESULTS: Women who continued antipsychotic treatment during pregnancy generally had higher comorbidity and longer baseline antipsychotic use. The crude risk of developing gestational diabetes among continuers compared with discontinuers, respectively, was 4.8% and 4.5% for aripiprazole, 4.2% and 3.8% for ziprasidone, 7.1% and 4.1% for quetiapine, 6.4% and 4.1% for risperidone, and 12.0% and 4.7% for olanzapine. The adjusted relative risks were 0.82 (95% CI=0.50-1.33) for aripiprazole, 0.76 (95% CI=0.29-2.00) for ziprasidone, 1.28 (95% CI=1.01-1.62) for quetiapine, 1.09 (95% CI=0.70-1.70) for risperidone, and 1.61 (95% CI=1.13-2.29) for olanzapine. CONCLUSIONS: Compared with women who discontinued use of an atypical antipsychotic medication before the start of pregnancy, women who continued treatment with olanzapine or quetiapine had an increased risk of gestational diabetes that may be explained by the metabolic effects associated with these two drugs.

Risk of serious infections associated with use of immunosuppressive agents in pregnant women with autoimmune inflammatory conditions: cohort study.

Objective To compare the risk of serious infections associated with use of systemic steroids, non-biologic agents, or tumor necrosis factor α (TNF) inhibitors in pregnancy.Design Observational cohort study.Setting Public (Medicaid, 2001-10) or private (Optum Clinformatics, 2004-15) health insurance programs in the US.Participants 4961 pregnant women treated with immunosuppressive drugs for rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, psoriatic arthritis, or inflammatory bowel disease.Exposure for observational studies Exposure was classified into steroid, non-biologic, or TNF inhibitors on first filled prescription during pregnancy. Because TNF inhibitors are not used to treat systemic lupus erythematosus, patients with this condition were excluded from comparisons involving TNF inhibitors.Main outcome measure The main outcome was occurrence of serious infections during pregnancy, defined by hospital admission for bacterial or opportunistic infections. Hazard ratios were derived using Cox proportional hazard regression models after adjustment for confounding with propensity score fine stratification. A logistic regression model was used to conduct a dose-response analysis among women filling at least one steroid prescription.Results 71 out of 4961 pregnant women (0.2%) treated with immunosuppressive agents experienced serious infections. The crude incidence rates of serious infections per 100 person years among 2598 steroid users, 1587 non-biologic users, and 776 TNF inhibitors users included in this study were 3.4 (95% confidence interval 2.5 to 4.7), 2.3 (1.5 to 3.5), and 1.5 (0.7 to 3.0), respectively. No statistically significant differences in the risk of serious infections during pregnancy were observed among users of the three immunosuppressive drug classes: non-biologics v steroids, hazard ratio 0.81 (95% confidence interval 0.48 to 1.37), TNF inhibitors v steroids 0.91 (0.36 to 2.26), and TNF inhibitors v non-biologics 1.36 (0.47 to 3.93). In the dose-response analysis, higher steroid dose was associated with an increased risk of serious infections during pregnancy (coefficient for each unit increase in average prednisone equivalent mg daily dose=0.019, P=0.02).Conclusions Risk of serious infections is similar among pregnant women with systemic inflammatory conditions using steroids, non-biologics, and TNF inhibitors. However, high dose steroid use is an independent risk factor of serious infections in pregnancy.

Antipsychotic Medication Use Among Publicly Insured Pregnant Women in the United States.

OBJECTIVE: Given the increasing use and broadening of indications for use of antipsychotic medications in the general population, as well as the paucity of information on the safety of this drug class during pregnancy, the study documented patterns of antipsychotic medication use among pregnant women. METHODS: Medicaid Analytic eXtract data (2001-2010) from pregnant women who delivered live-born infants were used. Antipsychotic use at both the class and the individual drug level was defined based on dispensed outpatient prescriptions. Users' characteristics, including mental disorder diagnoses, were described. Temporal trends in use, as well as discontinuation patterns and psychotropic polytherapy, during pregnancy were evaluated. RESULTS: Among 1,522,247 pregnancies, the prevalence of use of second-generation antipsychotics at any time during pregnancy increased threefold, from .4% to 1.3%, over the ten-year period, while the use of first-generation antipsychotics remained stable at around .1%. The increased use of second-generation antipsychotics was largely driven by more frequent use among patients with bipolar disorder. Quetiapine and aripiprazole were the most frequently dispensed drugs, and polytherapy with antipsychotics and antidepressants (65.2%), benzodiazepines (24.9%), and other mood stabilizers (22.0%) was common. More than 50% of women receiving an antipsychotic in the three months prior to pregnancy discontinued the drug during pregnancy. CONCLUSIONS: A growing number of pregnant women in Medicaid are exposed to second-generation antipsychotics, frequently in combination with other psychotropic medications. This study highlights the importance of documenting the use and safety of these drugs during pregnancy to inform therapeutic decision making for pregnant women with psychiatric disorders.