Polypharmacy during pregnancy and associated risk factors: a retrospective analysis of 577 medication exposures among 1.5 million pregnancies in the UK, 2000-2019.

BACKGROUND: The number of medications prescribed during pregnancy has increased over the past few decades. Few studies have described the prevalence of multiple medication use among pregnant women. This study aims to describe the overall prevalence over the last two decades among all pregnant women and those with multimorbidity and to identify risk factors for polypharmacy in pregnancy. METHODS: A retrospective cohort study was conducted between 2000 and 2019 using the Clinical Practice Research Datalink (CPRD) pregnancy register. Prescription records for 577 medication categories were obtained. Prevalence estimates for polypharmacy (ranging from 2+ to 11+ medications) were presented along with the medications commonly prescribed individually and in pairs during the first trimester and the entire pregnancy period. Logistic regression models were performed to identify risk factors for polypharmacy. RESULTS: During the first trimester (812,354 pregnancies), the prevalence of polypharmacy ranged from 24.6% (2+ medications) to 0.1% (11+ medications). During the entire pregnancy period (774,247 pregnancies), the prevalence ranged from 58.7 to 1.4%. Broad-spectrum penicillin (6.6%), compound analgesics (4.5%) and treatment of candidiasis (4.3%) were commonly prescribed. Pairs of medication prescribed to manage different long-term conditions commonly included selective beta 2 agonists or selective serotonin re-uptake inhibitors (SSRIs). Risk factors for being prescribed 2+ medications during the first trimester of pregnancy include being overweight or obese [aOR: 1.16 (1.14-1.18) and 1.55 (1.53-1.57)], belonging to an ethnic minority group [aOR: 2.40 (2.33-2.47), 1.71 (1.65-1.76), 1.41 (1.35-1.47) and 1.39 (1.30-1.49) among women from South Asian, Black, other and mixed ethnicities compared to white women] and smoking or previously smoking [aOR: 1.19 (1.18-1.20) and 1.05 (1.03-1.06)]. Higher and lower age, higher gravidity, increasing number of comorbidities and increasing level of deprivation were also associated with increased odds of polypharmacy. CONCLUSIONS: The prevalence of polypharmacy during pregnancy has increased over the past two decades and is particularly high in younger and older women; women with high BMI, smokers and ex-smokers; and women with multimorbidity, higher gravidity and higher levels of deprivation. Well-conducted pharmaco-epidemiological research is needed to understand the effects of multiple medication use on the developing foetus.

Epidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018: a population-based cross-sectional study.

BACKGROUND: Although maternal death is rare in the United Kingdom, 90% of these women had multiple health/social problems. This study aims to estimate the prevalence of pre-existing multimorbidity (two or more long-term physical or mental health conditions) in pregnant women in the United Kingdom (England, Northern Ireland, Wales and Scotland). STUDY DESIGN: Pregnant women aged 15-49 years with a conception date 1/1/2018 to 31/12/2018 were included in this population-based cross-sectional study, using routine healthcare datasets from primary care: Clinical Practice Research Datalink (CPRD, United Kingdom, n = 37,641) and Secure Anonymized Information Linkage databank (SAIL, Wales, n = 27,782), and secondary care: Scottish Morbidity Records with linked community prescribing data (SMR, Tayside and Fife, n = 6099). Pre-existing multimorbidity preconception was defined from 79 long-term health conditions prioritised through a workshop with patient representatives and clinicians. RESULTS: The prevalence of multimorbidity was 44.2% (95% CI 43.7-44.7%), 46.2% (45.6-46.8%) and 19.8% (18.8-20.8%) in CPRD, SAIL and SMR respectively. When limited to health conditions that were active in the year before pregnancy, the prevalence of multimorbidity was still high (24.2% [23.8-24.6%], 23.5% [23.0-24.0%] and 17.0% [16.0 to 17.9%] in the respective datasets). Mental health conditions were highly prevalent and involved 70% of multimorbidity CPRD: multimorbidity with ≥one mental health condition/s 31.3% [30.8-31.8%]). After adjusting for age, ethnicity, gravidity, index of multiple deprivation, body mass index and smoking, logistic regression showed that pregnant women with multimorbidity were more likely to be older (CPRD England, adjusted OR 1.81 [95% CI 1.04-3.17] 45-49 years vs 15-19 years), multigravid (1.68 [1.50-1.89] gravidity ≥ five vs one), have raised body mass index (1.59 [1.44-1.76], body mass index 30+ vs body mass index 18.5-24.9) and smoked preconception (1.61 [1.46-1.77) vs non-smoker). CONCLUSION: Multimorbidity is prevalent in pregnant women in the United Kingdom, they are more likely to be older, multigravid, have raised body mass index and smoked preconception. Secondary care and community prescribing dataset may only capture the severe spectrum of health conditions. Research is needed urgently to quantify the consequences of maternal multimorbidity for both mothers and children.

Development of an algorithm for determining smoking status and behaviour over the life course from UK electronic primary care records.

BACKGROUND: Patients' smoking status is routinely collected by General Practitioners (GP) in UK primary health care. There is an abundance of Read codes pertaining to smoking, including those relating to smoking cessation therapy, prescription, and administration codes, in addition to the more regularly employed smoking status codes. Large databases of primary care data are increasingly used for epidemiological analysis; smoking status is an important covariate in many such analyses. However, the variable definition is rarely documented in the literature. METHODS: The Secure Anonymised Information Linkage (SAIL) databank is a repository for a national collection of person-based anonymised health and socio-economic administrative data in Wales, UK. An exploration of GP smoking status data from the SAIL databank was carried out to explore the range of codes available and how they could be used in the identification of different categories of smokers, ex-smokers and never smokers. An algorithm was developed which addresses inconsistencies and changes in smoking status recording across the life course and compared with recorded smoking status as recorded in the Welsh Health Survey (WHS), 2013 and 2014 at individual level. However, the WHS could not be regarded as a "gold standard" for validation. RESULTS: There were 6836 individuals in the linked dataset. Missing data were more common in GP records (6%) than in WHS (1.1%). Our algorithm assigns ex-smoker status to 34% of never-smokers, and detects 30% more smokers than are declared in the WHS data. When distinguishing between current smokers and non-smokers, the similarity between the WHS and GP data using the nearest date of comparison was κ = 0.78. When temporal conflicts had been accounted for, the similarity was κ = 0.64, showing the importance of addressing conflicts. CONCLUSIONS: We present an algorithm for the identification of a patient's smoking status using GP self-reported data. We have included sufficient details to allow others to replicate this work, thus increasing the standards of documentation within this research area and assessment of smoking status in routine data.

Mining Primary Care Electronic Health Records for Automatic Disease Phenotyping: A Transparent Machine Learning Framework.

(1) Background: We aimed to develop a transparent machine-learning (ML) framework to automatically identify patients with a condition from electronic health records (EHRs) via a parsimonious set of features. (2) Methods: We linked multiple sources of EHRs, including 917,496,869 primary care records and 40,656,805 secondary care records and 694,954 records from specialist surgeries between 2002 and 2012, to generate a unique dataset. Then, we treated patient identification as a problem of text classification and proposed a transparent disease-phenotyping framework. This framework comprises a generation of patient representation, feature selection, and optimal phenotyping algorithm development to tackle the imbalanced nature of the data. This framework was extensively evaluated by identifying rheumatoid arthritis (RA) and ankylosing spondylitis (AS). (3) Results: Being applied to the linked dataset of 9657 patients with 1484 cases of rheumatoid arthritis (RA) and 204 cases of ankylosing spondylitis (AS), this framework achieved accuracy and positive predictive values of 86.19% and 88.46%, respectively, for RA and 99.23% and 97.75% for AS, comparable with expert knowledge-driven methods. (4) Conclusions: This framework could potentially be used as an efficient tool for identifying patients with a condition of interest from EHRs, helping clinicians in clinical decision-support process.