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Suarez EA, Nguyen M, Zhang D, Zhao Y, Stojanovic D, Munoz M, Liedtka J, Anderson A, Liu W, Dashevsky I, Cole D, DeLuccia S, Menzin T, Noble J, Maro JC. Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System. Pharmacoepidemiol Drug Saf 2023; 32:126-136. [PMID: 35871766 DOI: 10.1002/pds.5512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/14/2022] [Accepted: 07/21/2022] [Indexed: 01/26/2023]
Abstract
PURPOSE It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeScan™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. METHODS We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score-based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. RESULTS A total of 1791 fluoroquinolone-exposed and 8739 cephalosporin-exposed mother-infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow-up. CONCLUSIONS In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester. TreeScan, with tailored or high-dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy.
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Affiliation(s)
- Elizabeth A Suarez
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Nguyen
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Di Zhang
- Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Yueqin Zhao
- Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Danijela Stojanovic
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Monica Munoz
- Division of Pharmacovigilance, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jane Liedtka
- Division of Pediatric and Maternal Health, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Abby Anderson
- Division of Urology, Obstetrics and Gynecology, Center for Drug and Evaluation Research, US Food and Drug Administration, Beltsville, Maryland, USA
| | - Wei Liu
- Division of Epidemiology, Center for Drug and Evaluation Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Inna Dashevsky
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - David Cole
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Sandra DeLuccia
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Talia Menzin
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer Noble
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
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Kiani B, Raouf Rahmati A, Bergquist R, Hashtarkhani S, Firouraghi N, Bagheri N, Moghaddas E, Mohammadi A. Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018. BMC Public Health 2021; 21:1093. [PMID: 34098917 PMCID: PMC8186231 DOI: 10.1186/s12889-021-11157-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/27/2021] [Indexed: 12/15/2022] Open
Abstract
Background Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-11157-1.
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Affiliation(s)
- Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amene Raouf Rahmati
- Department of Parasitology and Mycology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Robert Bergquist
- Ingerod, Brastad, Lysekil, Sweden.,formerly with the UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Soheil Hashtarkhani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Neda Firouraghi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Nasser Bagheri
- Center for Mental Health Research College of Health and Medicine, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Elham Moghaddas
- Department of Parasitology and Mycology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
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Fralick M, Kulldorff M, Redelmeier D, Wang SV, Vine S, Schneeweiss S, Patorno E. A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes. ENDOCRINOLOGY DIABETES & METABOLISM 2021; 4:e00237. [PMID: 34277962 PMCID: PMC8279599 DOI: 10.1002/edm2.237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/17/2021] [Accepted: 01/23/2021] [Indexed: 12/24/2022]
Abstract
Background Clinical trials are often underpowered to detect serious but rare adverse events of a new medication. We applied a novel data mining tool to detect potential adverse events of canagliflozin, the first sodium glucose co‐transporter 2 (SGLT2 inhibitor) in the United States, using real‐world data from shortly after its market entry and before public awareness of its potential safety concerns. Methods In a U. S. commercial claims dataset (29 March 2013–30 Sept 2015), two pairwise cohorts of patients over 18 years of age with type 2 diabetes (T2D) who were newly dispensed canagliflozin or an active comparator, that is a dipeptidyl peptidase 4 inhibitor (DPP4) or a glucagon‐like peptide 1 receptor agonist (GLP1), were identified and propensity score‐matched. We used variable ratio matching with up to four people receiving a DPP4 or GLP1 for each person receiving canagliflozin. We identified potential safety signals using a hierarchical tree‐based scan statistic data mining method with the hierarchical outcome tree constructed based on international classification of disease coding. We screened for incident adverse events where there were more outcomes observed among canagliflozin vs. comparator initiators than expected by chance, after adjusting for multiple testing. Results We identified two pairwise propensity score variable ratio matched cohorts of 44,733 canagliflozin vs. 99,458 DPP4 initiators, and 55,974 canagliflozin vs. 74,727 GLP1 initiators. When we screened inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe adverse event associated with canagliflozin initiation with p < .05 in both cohorts. When outpatient diagnoses were also considered, signals for female and male genital infections emerged in both cohorts (p < .05). Conclusions and relevance In a large population‐based study, we identified known but no other adverse events associated with canagliflozin, providing reassurance on its safety among adult patients with T2D and suggesting the tree‐based scan statistic method is a useful post‐marketing safety monitoring tool for newly approved medications.
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Affiliation(s)
- Michael Fralick
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA.,Sinai Health System and the Department of Medicine University of Toronto Toronto ON Canada
| | - Martin Kulldorff
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Donald Redelmeier
- Sunnybrook Research Institute Sunnybrook Health Sciences Centre Toronto ON Canada.,ICES Sunnybrook Health Sciences Centre Toronto ON Canada
| | - Shirley V Wang
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Seanna Vine
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
| | - Elisabetta Patorno
- Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA
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Schachterle SE, Hurley S, Liu Q, Petronis KR, Bate A. An Implementation and Visualization of the Tree-Based Scan Statistic for Safety Event Monitoring in Longitudinal Electronic Health Data. Drug Saf 2020; 42:727-741. [PMID: 30617498 DOI: 10.1007/s40264-018-00784-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Longitudinal electronic healthcare data hold great potential for drug safety surveillance. The tree-based scan statistic (TBSS), as implemented by the TreeScan® software, allows for hypothesis-free signal detection in longitudinal data by grouping safety events according to branching, hierarchical data coding systems, and then identifying signals of disproportionate recording (SDRs) among the singular events or event groups. OBJECTIVE The objective of this analysis was to identify and visualize SDRs with the TBSS in historical data from patients using two antifungal drugs, itraconazole or terbinafine. By examining patients who used either itraconazole or terbinafine, we provide a conceptual replication of a previous TBSS analyses by varying methodological choices and using a data source that had not been previously used with the TBSS, i.e., the Optum Clinformatics™ claims database. With this analysis, we aimed to test a parsimonious design that could be the basis of a broadly applicable method for multiple drug and safety event pairs. METHODS The TBSS analysis was used to examine incident events and any itraconazole or terbinafine use among US-based patients from 2002 through 2007. Event frequencies before and after the first day of drug exposure were compared over 14- and 56-day periods of observation in a Bernoulli model with a self-controlled design. Safety events were classified into a hierarchical tree structure using the Clinical Classifications Software (CCS) which mapped International Classification of Diseases, 9th Revision (ICD-9) codes to 879 diagnostic groups. Using the TBSS, the log likelihood ratio of observed versus expected events in all groups along the CCS hierarchy were compared, and groups of events that occurred at disproportionally high frequencies were identified as potential SDRs; p-values for the potential SDRs were estimated with Monte-Carlo permutation based methods. Output from TreeScan® was visualized and plotted as a network which followed the CCS tree structure. RESULTS Terbinafine use (n = 223,968) was associated with SDRs for diseases of the circulatory system (14- and 56-day p = 0.001) and heart (14-day p = 0.026 and 56-day p = 0.001) as well as coronary atherosclerosis and other heart disease (14-day p = 0.003 and 56-day p = 0.004). For itraconazole use (n = 36,025), the TBSS identified SDRs for coronary atherosclerosis and other heart disease (p = 0.002) and complications of an implanted or grafted device (14-day p = 0.001 and 56-day p < 0.05). Use of both drugs was associated with SDRs for diseases of the digestive system at 14 days (p < 0.05) and this SDR had been observed among terbinafine users in a previous TBSS analysis with a different data source. The TreeScan® visualization facilitated the identification of the atherosclerosis and other heart disease SDRs as well as highlighting the consistency of the SDR for diseases of the digestive system across drugs and data sources. CONCLUSION With the TBSS, we identified potential SDRs related to the circulatory system that may reflect the cardiac risk that was described in the itraconazole product label. SDRs for diseases of the digestive system among terbinafine users were also reported in a previous signal detection analysis, although other SDRs from the previous publications were not replicated. The TBSS visualizations aided in the understanding and interpretation of the TBSS output, including the comparisons to the previous publications. In this conceptual replication, differences in the results observed in our analysis and the previous analyses could be attributable to variation in modeling and design choices as well as factors that were intrinsic to the underlying data sources. The broad consistency, but far from perfect concordance, of our results with the known safety profile of these antifungals including the risks from the itraconazole product label supports the rationale for continued investigations of signal detection methods across differing data sources and populations.
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Affiliation(s)
- Stephen E Schachterle
- Worldwide Safety and Regulatory, Pfizer Inc., 219 E. 42nd St, New York, NY, 10017, USA.
- City University of New York Graduate School of Public Health and Health Policy, 55 W 125th Street, New York, NY, 10027, USA.
| | - Sharon Hurley
- Worldwide Safety and Regulatory, Pfizer Inc., 219 E. 42nd St, New York, NY, 10017, USA
| | - Qing Liu
- Worldwide Safety and Regulatory, Pfizer Inc., 219 E. 42nd St, New York, NY, 10017, USA
| | - Kenneth R Petronis
- Worldwide Safety and Regulatory, Pfizer Inc., 219 E. 42nd St, New York, NY, 10017, USA
| | - Andrew Bate
- Worldwide Safety and Regulatory, Pfizer Inc., 219 E. 42nd St, New York, NY, 10017, USA
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