The most common medications dispensed to lactating persons: An electronic health record-based approach

被引:2
作者
Palmsten, Kristin [1 ]
Vazquez-Benitez, Gabriela [1 ]
JaKa, Meghan M. [2 ]
Bandoli, Gretchen [3 ,4 ]
Ahrens, Katherine A. [5 ]
Kharbanda, Elyse O. [1 ]
机构
[1] HealthPartners Inst, Pregnancy & Child Hlth Res Ctr, Minneapolis, MN 55440 USA
[2] HealthPartners Inst, Ctr Evaluat & Survey Res, Minneapolis, MN USA
[3] Univ Calif San Diego, Dept Pediat, San Diego, CA USA
[4] Univ Calif San Diego, Dept Family Med, San Diego, CA USA
[5] Univ Southern Maine, Muskie Sch Publ Serv, Portland, ME USA
基金
美国国家卫生研究院;
关键词
breastfeeding; electronic health records; epidemiology; lactation; pharmacoepidemiology; prevalence; PRESCRIPTION MEDICATIONS; FETAL;
D O I
10.1002/pds.5643
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Purpose: Using a novel, electronic health record (EHR)-based approach, to estimate the prevalence of prescription medication use at 2, 4, and 6 months postpartum among lactating individuals. Methods: We utilized automated EHR data from a US health system that records infant feeding information at well-child visits. We linked mothers who received prenatal care to their infants born May 2018-June 2019, and we required infants to have >= 1 well-child visit between 31 and 90 days of life (i.e., 2-month well-child visit with a +/- 1 month window). Mothers were classified as lactating at the 2-month well-child visit if their infant received breast milk at the 2-month well-child visit. For subsequent well-child visits at 4 and 6 months, mothers were considered lactating if their infant was still receiving breast milk. Results: We identified 6013 mothers meeting inclusion criteria, and 4158 (69.2%) were classified as lactating at the 2-month well-child visit. Among those classified as lactating, the most common medication classes dispensed around the 2-month well-child visit were oral progestin contraceptives (19.1%), selective serotonin reuptake inhibitors (8.8%), first generation cephalosporins (4.3%), thyroid hormones (3.5%), nonsteroidal anti-inflammatory agents (3.4%), penicillinase-resistant penicillins (3.1%), topical corticosteroids (2.9%), and oral imidazole-related antifungals (2.0%). The most common medication classes were similar around the 4 and 6-month well-child visits although prevalence estimates were often lower. Conclusions: Progestin-only contraceptives, antidepressants, and antibiotics were the most dispensed medications among lactating mothers. With routine collection of breastfeeding information, mother-infant linked EHR data may overcome limitations in previous studies of medication utilization during lactation. These data should be considered for studies of medication safety during lactation given the need for human safety data.
引用
收藏
页码:1113 / 1120
页数:8
相关论文
共 50 条
  • [31] Evaluating Population Coverage in a Regional Distributed Data Network: Implications for Electronic Health Record-Based Public Health Surveillance
    Scott, Kenneth A.
    Bacon, Emily
    Kraus, Emily McCormick
    Steiner, John F.
    Budney, Gregory
    Bondy, Jessica
    McEwen, L. Dean
    Davidson, Arthur J.
    [J]. PUBLIC HEALTH REPORTS, 2020, 135 (05) : 621 - 630
  • [32] Risk Factors for Suboptimal Medication Adherence in Persons With Multiple Sclerosis: Development of an Electronic Health Record-Based Explanatory Model for Disease-Modifying Therapy Use
    Gromisch, Elizabeth S.
    Turner, Aaron P.
    Leipertz, Steven L.
    Beauvais, John
    Haselkorn, Jodie K.
    [J]. ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION, 2020, 101 (05): : 807 - 814
  • [33] Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison
    McDonough, Caitrin W.
    Smith, Steven M.
    Cooper-DeHoff, Rhonda M.
    Hogan, William R.
    [J]. JMIR MEDICAL INFORMATICS, 2020, 8 (02)
  • [34] ED syphilis and gonorrhea/chlamydia cotesting practices before and after the implementation of an electronic health record-based alert
    Ford, James S.
    Chechi, Tasleem
    Otmar, Michella
    Baker, Melissa
    Waldman, Sarah
    Morgan, Brittany
    Tan, David
    Tran, Nam K.
    May, Larissa
    [J]. EMERGENCY MEDICINE JOURNAL, 2022, 39 (10) : 753 - 759
  • [35] Electronic health record-based facilitation of familial hypercholesterolaemia detection sensitivity of different algorithms in genetically confirmed patients
    Mohammadnia, Niekbachsh
    Akyea, Ralph K.
    Qureshi, Nadeem
    Bax, Willem A.
    Cornel, Jan H.
    [J]. EUROPEAN HEART JOURNAL - DIGITAL HEALTH, 2022, 3 (04): : 578 - 586
  • [36] Approach to Estimating Adherence to Heart Failure Medications Using Linked Electronic Health Record and Pharmacy Data
    Blecker, Saul
    Zhao, Yunan
    Li, Xiyue
    Kronish, Ian M.
    Mukhopadhyay, Amrita
    Stokes, Tyrel
    Adhikari, Samrachana
    [J]. JOURNAL OF GENERAL INTERNAL MEDICINE, 2025, 40 (04) : 811 - 817
  • [37] Evaluation of the Effect of Diabetes on Rheumatoid Arthritis-related Outcomes in an Electronic Health Record-based Rheumatology Registry
    Yun, Huifeng
    Xie, Fenglong
    Chen, Lang
    Yang, Shuo
    Ferri, Leticia
    Alemao, Evo
    Curtis, Jeffrey R.
    [J]. JOURNAL OF RHEUMATOLOGY, 2021, 48 (07) : 992 - 1001
  • [38] Addressing Bias in Electronic Health Record-based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise
    Julie K. Bower
    Sejal Patel
    Joyce E. Rudy
    Ashley S. Felix
    [J]. Current Epidemiology Reports, 2017, 4 (4) : 346 - 352
  • [39] Translating electronic health record-based patient safety algorithms from research to clinical practice at multiple sites
    Zimolzak, Andrew J.
    Singh, Hardeep
    Murphy, Daniel R.
    Wei, Li
    Memon, Sahar A.
    Upadhyay, Divvy K.
    Korukonda, Saritha
    Zubkoff, Lisa
    Sittig, Dean F.
    [J]. BMJ HEALTH & CARE INFORMATICS, 2022, 29 (01)
  • [40] J-CKD-DB: a nationwide multicentre electronic health record-based chronic kidney disease database in Japan
    Nakagawa, Naoki
    Sofue, Tadashi
    Kanda, Eiichiro
    Nagasu, Hajime
    Matsushita, Kunihiro
    Nangaku, Masaomi
    Maruyama, Shoichi
    Wada, Takashi
    Terada, Yoshio
    Yamagata, Kunihiro
    Narita, Ichiei
    Yanagita, Motoko
    Sugiyama, Hitoshi
    Shigematsu, Takashi
    Ito, Takafumi
    Tamura, Kouichi
    Isaka, Yoshitaka
    Okada, Hirokazu
    Tsuruya, Kazuhiko
    Yokoyama, Hitoshi
    Nakashima, Naoki
    Kataoka, Hiromi
    Ohe, Kazuhiko
    Okada, Mihoko
    Kashihara, Naoki
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)