{"paper_id":"a6e2886b-c320-4497-99ab-ebc45ffb1ead","body_text":"1 \n \nPreconception indicators and associations with health outcomes reported in UK \nroutine primary care data: a systematic review \n \nDanielle Schoenaker, PhD \nSenior Research Fellow \nSchool of Human Development and Health, Faculty of Medicine, University of Southampton, \nSouthampton, UK  \nMRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK \nNIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital \nSouthampton NHS Foundation Trust, Southampton, UK \n \nElizabeth M Lovegrove, BSc, BMBS, MRCGP  \nGP and NIHR In Practice Fellow \nPrimary Care Research Centre, Faculty of Medicine, University of Southampton, Southampton, UK \n \nEmma H Cassinelli, BSc, MRes \nPhD candidate \nCentre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University \nBelfast, Belfast, UK \n \nJennifer Hall, PhD, FFPH, MBChB \nClinical Associate Professor \nInstitute for Women's Health, University College London, London, UK \n \nMajel McGranahan, MBChB, MPH, MFPH  \nMRC Clinical Research Fellow \nWarwick Medical School, University of Warwick, Coventry, UK \n \nLaura McGowan, PhD \nLecturer in Nutrition and Behaviour Change \nCentre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University \nBelfast, Belfast, UK \n \nHelen Carr, MBBS, MRCGP, MSc \nGP \nNHS Surrey Heartlands, UK \n \nNisreen A Alwan, FFPH, PhD \nProfessor of Public Health  \nSchool of Primary Care, Population Sciences and Medical Education, Faculty of Medicine, University \nof Southampton, Southampton, UK \nNIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital \nSouthampton NHS Foundation Trust, Southampton, UK \nNIHR Applied Research Collaboration Wessex, Southampton, UK \n \nJudith Stephenson, MD, FPH \nProfessor of Sexual and Reproductive Health \nInstitute for Women's Health, University College London, London, UK \n \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n2 \n \nKeith M Godfrey, FMedSci \nProfessor of Epidemiology and Human Development \nSchool of Human Development and Health, Faculty of Medicine, University of Southampton, \nSouthampton, UK  \nMRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK \nNIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital \nSouthampton NHS Foundation Trust, Southampton, UK \n \nCorrespondence to \nDanielle Schoenaker \nUniversity of Southampton \nMRC Lifecourse Epidemiology Centre \nTremona Road, Southampton SO16 6YD, UK \nD.Schoenaker@soton.ac.uk  \nhttps://orcid.org/0000-0002-7652-990X  \n \nWord count: 2,514 words, 4 Tables, 1 Figure, 1 Box.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n3 \n \nAbstract \nBackground: Routine primary care data may be a valuable resource for preconception health \nresearch and informing provision of preconception care. \nAim: To review how primary care data could provide information on the prevalence of \npreconception indicators and examine associations with maternal and offspring health outcomes. \nDesign and Setting: Systematic review of observational studies using UK routine primary care data. \nMethod: Literature searches were conducted in five databases (March 2023) to identify \nobservational studies that used national primary care data from individuals aged 15-49 years. \nPreconception indicators were defined as medical, behavioural and social factors that may impact \nfuture pregnancies. Health outcomes included those that may occur during and after pregnancy. \nScreening, data extraction and quality assessment were conducted by two reviewers. \nResults: From 5,259 records screened, 42 articles were included. The prevalence of 30 \npreconception indicators was described for female patients, ranging from 0.01% for sickle cell \ndisease to >20% for each of advanced maternal age, previous caesarean section (among those with a \nrecorded pregnancy), overweight, obesity, smoking, depression and anxiety (irrespective of \npregnancy). Few studies reported indicators for male patients (n=3) or associations with outcomes \n(n=5). Most studies had low risk of bias, but missing data may limit generalisability.  \nConclusion: Findings demonstrate that routinely collected UK primary care data can be used to \nidentify patients’ preconception care needs. Linking primary care data with health outcomes \ncollected in other datasets is underutilised but could help quantify how optimising preconception \nhealth and care can reduce adverse outcomes for mothers and children. \n \nKeywords: general practice; preconception care; pregnancy outcomes; pre-pregnancy care; primary \ncare. \n  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n4 \n \nHow this fits in: \n• Provision of preconception care is not currently embedded into routine clinical practice but may \nbe informed by routinely collected primary care data.  \n• This systematic review demonstrates that UK primary care data can provide information on the \nprevalence of a range of medical, behavioural and social factors among female patients of \nreproductive age, while limited research has examined male preconception health or \nassociations with maternal and offspring health outcomes. \n• Routinely recorded electronic patient record data can be used by primary healthcare \nprofessionals to search for preconception risk factors and thereby support individualised \npreconception care, while aggregate data can be used by public health agencies to promote \npopulation-level preconception health. \n• Further data quality improvements and linkage of routine health datasets are needed to support \nthe provision of preconception care and future research on its benefits for maternal and \noffspring health outcomes.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n5 \n \nIntroduction \nPreconception care is the provision of biomedical, behavioural and social interventions to people of \nreproductive age (15-49 years) before conception may occur with the aim of improving short- and \nlonger-term parental and child health outcomes.1 Primary care teams have a key role in providing \npreconception care as identified by patients and healthcare professionals.2, 3 Preconception care \ndelivered in primary care improves knowledge and preconception health behaviours in female \npatients, but there is currently less evidence about male patients or the impact on pregnancy and \nlonger-term health outcomes.4, 5 In line with the National Institute for Health and Care Excellence \n(NICE) Clinical Knowledge Summary on preconception advice and management, primary care teams \nare encouraged to consider discussions about preconception health when appropriate, and to \nassess, manage and potentially optimise a range of physical and mental health conditions, health \nbehaviours, and social needs prior to potential pregnancy.6 However, routine provision of \npreconception care is not currently widespread in UK clinical practice.7 \nTo build the case for implementation of strategies and guidelines that optimise the population’s \npreconception health, the UK Preconception Partnership proposed an annual report card to describe \nand monitor preconception health.8 Our scoping review to inform national surveillance identified 65 \npreconception indicators (medical, behavioural and social risk factors that may impact potential \nfuture pregnancies among individuals of reproductive age) that are recorded in existing UK routine \nhealth data.9 A first report card was produced based on 23 indicators recorded in the national \nMaternity Service Data Set (MSDS), demonstrating that nine in 10 women in England enter \npregnancy with at least one potentially modifiable risk factor for adverse pregnancy and birth \noutcomes.10, 11 Similarly, an analysis of primary care data from the Royal College of General \nPractitioners Research and Surveillance Centre found that 91% of women of reproductive age have a \nbehavioural or medical risk factor for adverse pregnancy outcomes.12 These studies have to date \nfocussed on preconception health of women (not men), and have not examined trends and \ntrajectories in medical, behavioural and social indicators during the years leading up to pregnancy. \nDoing so would improve our ability to identify the population’s preconception care needs \nthroughout their reproductive years. Routinely collected primary care data is potentially a unique \nresource to describe and monitor preconception health, and to examine the impact of (changes in) \npreconception indicators on improving outcomes such as gestational diabetes and preterm birth. \nTo inform future research and surveillance, and develop policy and clinical practice \nrecommendations, we aimed to systematically review the literature to explore how UK routine \nprimary care data could provide information on the prevalence of preconception indicators and \nexamine associations with maternal and offspring health outcomes.  \nMethods \nSearch strategy and selection criteria \nThe protocol for this review was registered with PROSPERO,13 and the Preferred Reporting Items for \nSystematic reviews and Meta-Analyses (PRISMA 2020) guideline used to ensure transparent \nreporting.14 A search strategy was developed, and searches conducted on 27 March 2023 (from \ninception date) in five databases: MEDLINE (Ovid), EMBASE (Ovid), Scopus, CINAHL, Web of Science \n(Supplementary Table 1). Supplementary searches using ‘preconception’ and ‘prepregnancy’ terms \nwere conducted using databases from the British Journal of General Practice, and UK primary care \ndatasets.13 Reference lists of included articles were screened for additional studies. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n6 \n \nArticles were selected if they included findings from an observational study among individuals of \nreproductive age (15-49 years), used national patient-level routine primary care data collected in \nEngland, Wales, Scotland and/or Northern Ireland, and reported on the prevalence of at least one \npreconception indicator identified from our previous scoping review (Table 1).9 Articles not including \nnew/original peer-reviewed results, and conference abstracts, were excluded. \nSelection process \nSearch results were collated in EndNote and duplicates removed, before uploading to Covidence \nsoftware. Titles and abstracts, followed by full text articles, were screened independently by two \nreviewers for inclusion. Disagreements or uncertainties were resolved through discussion. \nData extraction and synthesis \nA standardised data extraction form was developed and piloted. Data were extracted by one \nreviewer and checked by a second reviewer. Disagreements were resolved between the two \nreviewers. All extracted data on study characteristics (grouped by primary care database), \nprevalence of preconception indicators, and measures of association between preconception \nindicators and outcomes (grouped by preconception indicator), were presented in tables. Meta-\nanalysis was not conducted due to heterogeneity in preconception indicator definitions and \ninclusion and exclusion criteria of study populations. \nRisk of bias assessment \nRisk of bias was assessed for study findings on the prevalence of preconception indicators using the \n10-item scale developed by Hoy et al rating internal and external validity.15 The Newcastle-Ottawa \nScale (NOS) was used to rate risk of bias of study findings on associations between preconception \nindicators and health outcomes based on seven items related to selection, comparability, and \nexposure/outcome.16 Risk of bias was assessed by one reviewer, and checked by a second reviewer. \nDisagreements were resolved between the two reviewers. Studies were classified as low, moderate \nor high risk of bias (findings on prevalence),15 and good, fair or poor data quality (findings on \nassociations)16 (scoring guides in Supplementary Tables 2, 3A-3B). \nResults \nFrom 9,401 identified records, 4,142 duplicates were removed and after title and abstract screening \n(n=5,259), 117 full-text articles were evaluated for eligibility (Figure 1). 42 articles were included, \nreporting findings from 11 primary care databases such as the Clinical Practice Research Datalink \n(CPRD) and The Health Improvement Network (THIN).  \nMost articles reported findings from primary care databases that included patients from three (n=1) \nor all four UK nations (n=30), or from England (n=6), Scotland (n=3) or Northern Ireland only (n=2) \n(Table 2, Supplementary Table 4). In 11 studies, a primary care dataset was linked with at least one \nother dataset, such as Hospital Episodes Statistics (HES), Office for National Statistics (ONS) mortality \nregister, community prescribing data, or the Avon Longitudinal Study of Parents and Children. All \nstudies included data on female patients; three studies also reported preconception indicators for \nmale patients.  \nPrevalence of preconception indicators \nArticles reported findings on 30 preconception indicators across seven of the 12 domains identified \nin our scoping review.9 Most studies included people of reproductive age irrespective of past/future \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n7 \n \npregnancy (n=26), while other studies included women with a pregnancy or birth recorded during \nthe study period (n=15) or women with a recorded pregnancy and their partners (n=1) \n(Supplementary Table 5).  \nTo obtain population-level estimates of preconception indicators, prevalence data were extracted \nonly if reported (or could be calculated) for the overall study population of females or males of \nreproductive age (i.e. not if reported only in sub-populations such as patients with a specific \ncondition or characteristic) (Table 3). Data on overall prevalence were available for 21 of the 42 \nstudies, with the other 21 studies reporting prevalence estimates only in sub-populations. Additional \npreconception indicators reported in sub-populations included housing, domestic abuse, routine GP \ncheck-up in the past year, paternal age, previous pregnancy loss, history of assisted reproduction, \nalcohol consumption, substance misuse, cervical screening, and cardiovascular disease \n(Supplementary Table 4). \nThe prevalence of preconception indicators reported across studies and primary care databases \nvaried widely, possibly due to differences in preconception indicator definitions, year of data \ncollection (Table 3), and study populations (Supplementary Table 5). The prevalence of \npreconception indicators defined in line with our scoping review (i.e. excluding individual methods of \ncontraception, and prescribed folic acid supplements),9 ranged from 0.01% for sickle cell disease to \n>20% for each of advanced maternal age, previous caesarean section (among those with a recorded \npregnancy), overweight, obesity, smoking and diagnosis of depression and anxiety among female \npatients (irrespective of pregnancy). Only three studies reported preconception indicators for male \npatients, showing for example that the prevalence of depression among fathers (9.2%) was lower \ncompared with mothers (22.2%),17 and the proportion of patients prescribed valproate was \ncomparable among female (0.31%) and male patients (0.37%) in 2004, but much lower among \nfemales (0.16%) than males (0.36%) in 2018.18 \nAssociations of preconception indicators with maternal and offspring outcomes \nFive studies reported associations of preconception indicators (contraception prescription [n=1], \nsexually transmitted disease [n=1] and polycystic ovary syndrome [PCOS] [n=3]) with pregnancy and \nbirth outcomes (Table 4). Outcome data were obtained from primary care data and/or linked HES \ndata. Where two studies reported on comparable indicators and outcomes, consistent findings were \nshown for associations of PCOS with preterm delivery (<37 weeks gestational age) (positive \nassociation), high birthweight (>4kg) (no association) and low birthweight (<2.5kg) (inconclusive \nfindings).19, 20  \nRisk of bias and data quality \nRisk of bias for findings on the prevalence of preconception indicators was generally low (n=18/21 \nstudies), however, none of the studies received a minimal score (no bias) (Supplementary Table 2). \nPotential biases were introduced based on representativeness and sampling frame (e.g. excluding \nwomen with no pregnancy reported or no linked data available), and indicator definition and \nmeasurement (e.g. reporting individual methods of contraception rather than population prescribed \ncontraception, or reliance on medication prescription rather than dispensing data). Moreover, \ndetails of non-response (e.g. impact of missing data) were not reported in approximately half the \nstudies. Data quality for studies examining associations of preconception indicators with health \noutcomes was rated as good for four of the five studies (Supplementary Tables 3A-3B). \nDiscussion \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n8 \n \nSummary \nThis systematic review found that UK routine primary care data can provide valuable information on \npatients’ medical, behavioural and social risk factors before (a potential) pregnancy. Based on 42 \nincluded studies among people of reproductive age or women with a pregnancy recorded during the \nstudy period, the prevalence of 30 preconception indicators was reported. Findings showed that \n>20% of female patients of reproductive age would benefit from support with smoking cessation, \nand management of weight, depression and anxiety. This would optimise their own health, and \nimprove their chance of a successful pregnancy and healthy baby if that is something they want. \nLimited research has used primary care data to examine preconception indicators among male \npatients, or associations of preconception indicators with pregnancy outcomes and longer-term \nmaternal and offspring health outcomes. \nStrengths and limitations \nThis is the first systematic review to demonstrate how national routine primary care databases can \nbe used to describe the population’s preconception health, to inform clinical practice and future \nresearch directions. Comprehensive, prospectively registered review methods were used. Our search \nwas limited to UK primary care data and findings may not be generalisable to other countries. \nPreconception indicators were selected based on our previous scoping review;9 so potentially \nrelevant indicators not included in this review or not reported in the included studies would have \nbeen missed. Moreover, some preconception indicators (such as dietary intake and physical activity) \nare not routinely recorded in general practice.  \nComparison with existing literature \nFindings from our review complement our previous preconception report card based on the MSDS,10 \nshowing that national routine health data are a valuable resource to describe and monitor women’s \npreconception health. Half of the preconception indicators identified in this review were also \nreported in the MSDS, with comparable prevalence estimates for most indicators (e.g. teenage \npregnancy, previous caesarean delivery, overweight, obesity), while other indicators may be \nunderreported in primary care (e.g. over the counter folic acid supplementation) or in the MSDS (e.g. \nmental health conditions).10 Published primary care data reported an additional 15 indicators not \nincluded in the MSDS (e.g. fertility problems, contraception, relevant medical conditions, teratogenic \nmedication use). Linkage of these (and other) national routine health datasets would enhance the \nquality of preconception report cards and surveillance (Box 1). Based on linkage of primary care and \nHES datasets, findings from our review (n=2 studies19, 20) confirm the previously reported association \nof PCOS with increased risk of preterm delivery.21 \nFindings from our review are also in line with previous research reporting primary care data quality \nissues.22-24 Studies included in our review documented substantial missing data (20-60%) for \nethnicity and BMI category, likely varying across sub-populations. Coding quality is related to \nfinancial incentives such as the Quality and Outcomes Framework (QOF), which may improve \naccurate recording of selected indicators but also distort prevalence estimates over time.22 The \nprevalence of some preconception indicators may be underestimated as not all conditions are solely \ndiagnosed and coded in general practice (e.g. sexually transmitted disease),25 or medications and \nsupplements prescribed (e.g. contraception, folic acid supplements).26 Another commonly reported \nlimitation is the representation of selected general practices in research databases,22, 23 often limited \nto practices that use one of four main software platforms to manage electronic patient records \n(EPRs) and further determined by voluntary ‘opt ins’.22, 23 As a result, primary care databases may \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n9 \n \nunderrepresent specific regions and bias national prevalence estimates of preconception indicators \nand associations with health outcomes. \nImplications for research and clinical practice \nOur findings demonstrate that many preconception indicators are routinely recorded in EPRs, \nallowing primary healthcare professionals to search for risk factors and provide individualised \npreconception care. A digital risk screening template has been developed in the Ardens Clinical \nDecision Support System based on the NICE Clinical Knowledge Summary,6 to support primary \nhealthcare professionals to improve their preconception care practice, screening, coding and \nrecording of indicators. Further work is required to co-develop practical guidance and resources to \nsupport integration of preconception care into every day clinical practice (Box 1). \nOur findings identify the need to use standardised definitions when reporting preconception \nindicators (Box 1). Due to heterogeneity in definitions, the prevalence of preconception indicators \nacross UK nations, and changes over time, could not be directly compared across studies. However, \nLee and colleagues applied standardised definitions to CRPD (UK) and SAIL data (Wales), showing \ncomparable prevalence estimates for some indicators (e.g. obesity, depression), but higher (e.g. \nsmoking, underweight, anxiety, asthma) or lower (e.g. advanced maternal age) prevalence for other \nindicators, when comparing pregnant women in Wales with those in the UK overall .27 Moreover, \nstandardised reporting within the same database showed, for example, increases over time in the \nprevalence of type 2 diabetes (1995-2017),29 alongside decreases in poor diabetes control (2004-\n2017).29, 30  \nLastly, the limited reporting of male preconception indicators, and associations of preconception \nhealth with pregnancy, maternal and offspring health outcomes, calls for further research. Many of \nthe preconception indicators reported for female patients are also relevant to male patients (e.g. \nsmoking, obesity), with increasing evidence suggesting better paternal preconception health is \nassociated with reduced risks of infertility and adverse pregnancy and offspring health and \ndevelopmental outcomes.31-33 To enable further research, improvements are needed in the way that \nfamilies (i.e. biological parents and their children) can be identified and data linked.17, 34 Primary care \ndata also provide a unique opportunity to examine trajectories of preconception health during \nreproductive years irrespective of pregnancy, and to quantify the extent to which these reduce \nadverse pregnancy and offspring health outcomes. Future research would be enhanced by linkage of \nprimary care and other routine health datasets beyond the identified existing linkages (e.g. MSDS \nand Community Services Data Set) to determine the short- and longer-term benefits of \npreconception care (Box 1). \nConclusion \nRoutinely collected primary care data in the UK provide a valuable resource for research and \nsurveillance, and can guide to provision of preconception care. Improvements in coding and \nreporting, and linkage of general practice systems and other national routine health datasets, would \ninform evidence-based provision of preconception care in primary care.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n10 \n \nAcknowledgements \nFunding: DS is supported by the National Institute for Health and Care Research (NIHR) through an \nNIHR Advanced Fellowship (NIHR302955) and the NIHR Southampton Biomedical Research Centre \n(NIHR203319). MM is supported by the UK Medical Research Council (MR/W01498X/1). KMG is \nsupported by the UK Medical Research Council (MC_UU_12011/4), the NIHR (NIHR Senior \nInvestigator (NF-SI-0515-10042) and NIHR Southampton Biomedical Research Centre (NIHR203319)) \nand Alzheimer’s Research UK (ARUK-PG2022A-008). For the purpose of Open Access, the author has \napplied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version \narising from this submission. \nCompeting interests: KMG has received reimbursement for speaking at conferences sponsored by \ncompanies selling nutritional products, and is part of an academic consortium that has received \nresearch funding from Abbott Nutrition, Nestec, BenevolentAI Bio Ltd. and Danone, outside the \nsubmitted work. No competing interests declared for other authors. \n  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n11 \n \nReferences \n1. World Health Organization (WHO). 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Available \nfrom: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. [accessed 19/12/2023]. \n17. Davé S, Petersen I, Sherr L, Nazareth I. Incidence of maternal and paternal depression in \nprimary care: a cohort study using a primary care database. Arch Pediatr Adolesc Med. \n2010;164(11):1038-44. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n12 \n \n18. Gaudio M, Konstantara E, Joy M, van Vlymen J, de Lusignan S. Valproate prescription to \nwomen of childbearing age in English primary care: repeated cross-sectional analyses and \nretrospective cohort study. BMC Pregnancy Childbirth. 2022;22(1):73. \n19. Subramanian A, Lee SI, Phillips K, Toulis KA, Kempegowda P, O'Reilly MW, et al. Polycystic \novary syndrome and risk of adverse obstetric outcomes: a retrospective population-based matched \ncohort study in England. BMC Med. 2022;20(1):298. \n20. Rees DA, Jenkins-Jones S, Morgan CL. Contemporary Reproductive Outcomes for Patients \nWith Polycystic Ovary Syndrome: A Retrospective Observational Study. J Clin Endocrinol Metab. \n2016;101(4):1664-72. \n21. Palomba S, de Wilde MA, Falbo A, Koster MP, La Sala GB, Fauser BC. Pregnancy \ncomplications in women with polycystic ovary syndrome. Hum Reprod Update. 2015;21(5):575-92. \n22. Bradley SH, Lawrence NR, Carder P. Using primary care data for health research in England - \nan overview. Future Healthc J. 2018;5(3):207-12. \n23. Jick S, Vasilakis-Scaramozza C, Persson R, Neasham D, Kafatos G, Hagberg KW. Use of the \nCPRD Aurum Database: Insights Gained from New Data Quality Assessments. Clin Epidemiol. \n2023;15:1219-22. \n24. Nicholson BD, Aveyard P, Bankhead CR, Hamilton W, Hobbs FDR, Lay-Flurrie S. Determinants \nand extent of weight recording in UK primary care: an analysis of 5 million adults' electronic health \nrecords from 2000 to 2017. BMC Med. 2019;17(1):222. \n25. den Heijer CDJ, Hoebe C, Driessen JHM, Wolffs P, van den Broek IVF, Hoenderboom BM, et \nal. Chlamydia trachomatis and the Risk of Pelvic Inflammatory Disease, Ectopic Pregnancy, and \nFemale Infertility: A Retrospective Cohort Study Among Primary Care Patients. Clin Infect Dis. \n2019;69(9):1517-25. \n26. French RS, Geary R, Jones K, Glasier A, Mercer CH, Datta J, et al. Where do women and men \nin Britain obtain contraception? Findings from the third National Survey of Sexual Attitudes and \nLifestyles (Natsal-3). BMJ Sex Reprod Health. 2018;44(1):16-26. \n27. Lee SI, Azcoaga-Lorenzo A, Agrawal U, Kennedy JI, Fagbamigbe AF, Hope H, et al. \nEpidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018: a population-\nbased cross-sectional study. BMC Pregnancy Childbirth. 2022;22(1):120. \n28. Cea Soriano L, Wallander MA, Andersson S, Filonenko A, García Rodríguez LA. Use of long-\nacting reversible contraceptives in the UK from 2004 to 2010: analysis using The Health \nImprovement Network Database. Eur J Contracept Reprod Health Care. 2014;19(6):439-47. \n29. Gaudio M, Dozio N, Feher M, Scavini M, Caretto A, Joy M, et al. Trends in Factors Affecting \nPregnancy Outcomes Among Women With Type 1 or Type 2 Diabetes of Childbearing Age (2004-\n2017). Front Endocrinol (Lausanne). 2021;11:596633. \n30. Coton SJ, Nazareth I, Petersen I. A cohort study of trends in the prevalence of pregestational \ndiabetes in pregnancy recorded in UK general practice between 1995 and 2012. BMJ Open. \n2016;6(1):e009494. \n31. Fleming TP, Watkins AJ, Velazquez MA, Mathers JC, Prentice AM, Stephenson J, et al. Origins \nof lifetime health around the time of conception: causes and consequences. Lancet. \n2018;391(10132):1842-52. \n32. Caut C, Schoenaker D, McIntyre E, Vilcins D, Gavine A, Steel A. Relationships between \nWomen's and Men's Modifiable Preconception Risks and Health Behaviors and Maternal and \nOffspring Health Outcomes: An Umbrella Review. Semin Reprod Med. 2022;40(3-04):170-83. \n33. Carter T, Schoenaker D, Adams J, Steel A. Paternal preconception modifiable risk factors for \nadverse pregnancy and offspring outcomes: a review of contemporary evidence from observational \nstudies. BMC Public Health. 2023;23(1):509. \n34. Lut I, Harron K, Hardelid P, O’Brien M, Woodman J. ‘What about the dads?’ Linking fathers \nand children in administrative data: A systematic scoping review. Big Data & Society. \n2022;9(1):20539517211069299. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n13 \n \n35. Hope H, Pierce M, Johnstone ED, Myers J, Abel KM. The sexual and reproductive health of \nwomen with mental illness: a primary care registry study. Arch Womens Ment Health. \n2022;25(3):585-93. \n36. Syed S, Gonzalez-Izquierdo A, Allister J, Feder G, Li L, Gilbert R. Identifying adverse childhood \nexperiences with electronic health records of linked mothers and children in England: a multistage \ndevelopment and validation study. Lancet Digit Health. 2022;4(7):e482-e96. \n37. Briggs PE, Praet CA, Humphreys SC, Zhao C. Impact of UK Medical Eligibility Criteria \nimplementation on prescribing of combined hormonal contraceptives. J Fam Plann Reprod Health \nCare. 2013;39(3):190-6. \n38. Rowlands S, Devalia H, Lawrenson R, Logie J, Ineichen B. Repeated use of hormonal \nemergency contraception by younger women in the UK. Br J Fam Plann. 2000;26(3):138-43. \n39. Smith HC, Saxena S, Petersen I. Postnatal checks and primary care consultations in the year \nfollowing childbirth: an observational cohort study of 309 573 women in the UK, 2006-2016. BMJ \nOpen. 2020;10(11):e036835. \n40. Cea-Soriano L, García-Rodríguez LA, Brodovicz KG, Masso-Gonzalez E, Bartels DB, Hernández-\nDíaz S. Real world management of pregestational diabetes not achieving glycemic control for many \npatients in the UK. Pharmacoepidemiol Drug Saf. 2018;27(8):940-8. \n41. Ban L, Tata LJ, Humes DJ, Fiaschi L, Card T. Decreased fertility rates in 9639 women \ndiagnosed with inflammatory bowel disease: a United Kingdom population-based cohort study. \nAliment Pharmacol Ther. 2015 (2);42(7):855-66. \n42. Cea-Soriano L, García Rodríguez LA, Machlitt A, Wallander MA. Use of prescription \ncontraceptive methods in the UK general population: a primary care study. BJOG. 2013;121(1):53-\n60; discussion -1. \n43. Dhalwani NN, Fiaschi L, West J, Tata LJ. Occurrence of fertility problems presenting to \nprimary care: population-level estimates of clinical burden and socioeconomic inequalities across the \nUK. Hum Reprod. 2013;28(4):960-8. \n44. Ban L, Gibson JE, West J, Fiaschi L, Oates MR, Tata LJ. Impact of socioeconomic deprivation \non maternal perinatal mental illnesses presenting to UK general practice. Br J Gen Pract. \n2012;62(603):e671-8. \n45. Given JE, Gray AM, Dolk H. Use of prescribed contraception in Northern Ireland 2010-2016. \nEur J Contracept Reprod Health Care. 2020;25(2):106-13. \n46. Wemakor A, Casson K, Dolk H. Prevalence and sociodemographic patterns of antidepressant \nuse among women of reproductive age: a prescription database study. J Affect Disord. \n2014;167:299-305. \n47. Pasvol TJ, Macgregor EA, Rait G, Horsfall L. Time trends in contraceptive prescribing in UK \nprimary care 2000-2018: a repeated cross-sectional study. BMJ Sex Reprod Health. 2022;48(3):193-\n8. \n48. Parker SE, Jick SS, Werler MM. Intrauterine device use and the risk of pre-eclampsia: a case-\ncontrol study. Bjog. 2016;123(5):788-95. \n49. Berni TR, Morgan CL, Berni ER, Rees DA. Polycystic Ovary Syndrome Is Associated With \nAdverse Mental Health and Neurodevelopmental Outcomes. J Clin Endocrinol Metab. \n2018;103(6):2116-25. \n50. Haase CL, Varbo A, Laursen PN, Schnecke V, Balen AH. Association between body mass \nindex, weight loss and the chance of pregnancy in women with polycystic ovary syndrome and \noverweight or obesity: a retrospective cohort study in the UK. Hum Reprod. 2023;38(3):471-81. \n51. Channon S, Coulman E, Cannings-John R, Henley J, Lau M, Lugg-Widger F, et al. The \nacceptability of asking women to delay removal of a long-acting reversible contraceptive to take part \nin a preconception weight loss programme: a mixed methods study using qualitative and routine \ndata (Plan-it). BMC Pregnancy Childbirth. 2022;22(1):778. \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n14 \n \n52. Ma R, Cecil E, Bottle A, French R, Saxena S. Impact of a pay-for-performance scheme for \nlong-acting reversible contraceptive (LARC) advice on contraceptive uptake and abortion in British \nprimary care: An interrupted time series study. PLoS Med. 2020;17(9):e1003333. \n53. Jackson J, Lewis NV, Feder GS, Whiting P, Jones T, Macleod J, et al. Exposure to domestic \nviolence and abuse and consultations for emergency contraception: nested case-control study in a \nUK primary care dataset. Br J Gen Pract. 2019;69(680):e199-e207. \n54. Richardson E, Bedson J, Chen Y, Lacey R, Dunn KM. Increased risk of reproductive \ndysfunction in women prescribed long-term opioids for musculoskeletal pain: A matched cohort \nstudy in the Clinical Practice Research Datalink. Eur J Pain. 2018;22(9):1701-8. \n55. Nightingale AL, Lawrenson RA, Simpson EL, Williams TJ, MacRae KD, Farmer RD. The effects \nof age, body mass index, smoking and general health on the risk of venous thromboembolism in \nusers of combined oral contraceptives. Eur J Contracept Reprod Health Care. 2000;5(4):265-74. \n56. Khashan AS, Quigley EM, McNamee R, McCarthy FP, Shanahan F, Kenny LC. Increased risk of \nmiscarriage and ectopic pregnancy among women with irritable bowel syndrome. Clin Gastroenterol \nHepatol. 2012;10(8):902-9. \n57. Shawe J, Mulnier H, Nicholls P, Lawrenson R. Use of hormonal contraceptive methods by \nwomen with diabetes. Prim Care Diabetes. 2008;2(4):195-9. \n58. Howard LM, Goss C, Leese M, Appleby L, Thornicroft G. The psychosocial outcome of \npregnancy in women with psychotic disorders. Schizophr Res. 2004;71(1):49-60. \n59. Seaman HE, de Vries CS, Farmer RD. Differences in the use of combined oral contraceptives \namongst women with and without acne. Hum Reprod. 2003;18(3):515-21. \n60. Shorvon SD, Tallis RC, Wallace HK. Antiepileptic drugs: coprescription of proconvulsant drugs \nand oral contraceptives: a national study of antiepileptic drug prescribing practice. J Neurol \nNeurosurg Psychiatry. 2002;72(1):114-5. \n61. Cea-Soriano L, Wallander MA, García Rodríguez LA. Prescribing patterns of combined \nhormonal products containing cyproterone acetate, levonorgestrel and drospirenone in the UK. J \nFam Plann Reprod Health Care. 2016;42(4):247-54. \n62. Ban L, Fleming KM, Doyle P, Smeeth L, Hubbard RB, Fiaschi L, et al. Congenital Anomalies in \nChildren of Mothers Taking Antiepileptic Drugs with and without Periconceptional High Dose Folic \nAcid Use: A Population-Based Cohort Study. PLoS One. 2015 (1);10(7):e0131130. \n63. Krishnamoorthy N, Simpson CD, Townend J, Helms PJ, McLay JS. Adolescent females and \nhormonal contraception: a retrospective study in primary care. J Adolesc Health. 2008;42(1):97-101. \n64. Krishnamoorthy N, Ekins-Daukes S, Simpson CR, Milne RM, Helms PJ, McLay JS. Adolescent \nuse of the combined oral contraceptive pill: a retrospective observational study. Arch Dis Child. \n2005;90(9):903-5. \n65. Nwaru BI, Tibble H, Shah SA, Pillinger R, McLean S, Ryan DP, et al. Hormonal contraception \nand the risk of severe asthma exacerbation: 17-year population-based cohort study. Thorax. \n2021;76(2):109-15. \n66. Smith D, Willan K, Prady SL, Dickerson J, Santorelli G, Tilling K, et al. Assessing and predicting \nadolescent and early adulthood common mental disorders using electronic primary care data: \nanalysis of a prospective cohort study (ALSPAC) in Southwest England. BMJ Open. \n2021;11(10):e053624. \n67. Reddy A, Watson M, Hannaford P, Lefevre K, Ayansina D. Provision of hormonal and long-\nacting reversible contraceptive services by general practices in Scotland, UK (2004-2009). J Fam \nPlann Reprod Health Care. 2014;40(1):23-9. \n  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n15 \n \nFigures \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFigure 1. PRISMA flow diagram for the identification and selection of studies included in the review. \n  \nRecords identified (total n = 9,401): \nDatabases (total n = 7,942) \n MEDLINE (n = 1,760) \nEMBASE (n = 2,088) \n Scopus (n = 2,683) \n CINAHL (n = 381) \n Web of Science (n = 1,030) \nUK primary care database websites (n = 28) \nReference lists (n = 1,431) \n \nDuplicate records \nremoved  (n = 4,142) \nRecords screened \n(n = 5,259) \nRecords excluded based on title and \nabstract screening  \n(n = 5,142) \nReports sought for retrieval \n(n = 117) Reports not retrieved (n = 0) \nArticles assessed for eligibility \n(n = 117) \nArticles excluded (n = 75): \nNo new/original results (n = 2) \nNot national primary care data (n = 15) \nNot peer-reviewed or full-length (n = 23) \nNo data on preconception indicator          \n(n = 24) \nNo data on reproductive-aged \nindividuals (n = 11) \nArticles included (n = 42) \nPrimary care databases \nincluded (n = 11) \nIdentification \nScreening \n \nIncluded \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n16 \n \nBox 1. Recommendations to improve the use of UK routine primary care data for clinical practice, \nresearch and surveillance of preconception health and care.  \n• Routine use of a standardised digital risk screening template (i.e. existing template in \nArdens Clinical Decision Support System) to support implementation of the NICE Clinical \nKnowledge Summary on preconception advice and management.6 \n• Development of coding practice standards with appropriate incentives to improve data \nquality. \n• Standardisation of reporting of preconception indicators and pregnancy and offspring \nhealth outcomes, for example through the development of core outcome sets. \n• Improvements in the coding and identification of family and household members to enable \nlinkage of data from biological parents and their children. \n• Nationwide linkage of general practice systems, and linkage of primary care datasets with \nother routine health datasets (such as Hospital Episode Statistics, Maternity Services Data \nSet and Community Services Data Set). \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n17 \n \nTables \nTable 1. PICOS statement \nPopulation Individuals of reproductive age who may or may not be(come) \npregnant/conceive a pregnancy (any gender, aged 15-49 years). \nIntervention/ \nexposure \nPreconception indicators as identified in Schoenaker et al.9  \nPreconception indicators are defined as medical, behavioural and social risk \nfactors or exposures as well as wider determinants of health that may impact \npotential future pregnancies among all individuals of reproductive age. \nStudies do not have to identify relevant factors or exposures as ‘preconception \nindicators’. \nComparator/ \ncontrol \nNot applicable. \nOutcome Maternal health outcomes: any outcome that may occur during pregnancy (e.g. \ngestational diabetes), delivery (e.g. caesarean section), postpartum (e.g. \nmortality), or beyond (no age limit) (e.g. type 2 diabetes). \nOffspring health and developmental outcomes (including social/educational \noutcomes): any outcome that may occur during pregnancy (e.g. stillbirth), \ndelivery (e.g. preterm birth), infancy (e.g. neonatal intensive care unit admission), \nor beyond (no age limit) (e.g. learning difficulty). \nStudy design Observational studies (including cohort, cross-sectional and case-control studies). \n \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n18 \n \nTable 2. Characteristics of included studies reporting on the prevalence of preconception indicators in the overall population of people of reproductive age \nFirst author, \nyear \n(reference) \nDataset  Country Study design Data \ncollection \nperiod \nTotal or \nmaximum \nsample \nsize \nPopulation \ncharacteristi\ncs: sex, age \nPreconception indicators reported Maternal and \noffspring \noutcomes \nreported \nClinical Practice Research Datalink (CPRD) \nLee, 2022 (27) CPRD GOLD \n \nUK \n \nCross-\nsectional \nstudy \n2018 37,641 \n \n \nFemale, 15-\n49 years \nMaternal age, ethnicity, \ndeprivation, weight, smoking, \ndepression, anxiety, severe mental \nhealth condition, asthma, PCOS, \ninfertility, thyroid disease, eating \ndisorder, endometriosis, \nhypertension, thromboembolism, \nsickle-cell disease, epilepsy, \ndiabetes \nNone \nHope, 2022 \n(35) \nCPRD GOLD  UK Cohort study 1990-2017 2,680,149 Female, 14-\n45 years \nEthnicity, mental health condition  None \nSubramanian, \n2022 (19) \nCPRD GOLD linked with \nHES \nEngland Cohort and \ncase-control \nstudy \n1997-2020 299,866  Female, 15-\n49 years \nEthnicity, PCOS Preterm delivery, \nmode of delivery, \nhigh or low \nbirthweight, \nstillbirth, small \nand large for \ngestational age \nSyed, 2022 (36) CPRD GOLD linked with \nHES and Office for \nNational Statistics \n(ONS) mortality \nregister \nEngland Cohort study 2004- 2018 211,393 Female, 16-\n55 years \nDeprivation, ethnicity, maternal \nage \nNone \nden Heijer, \n2019 (25) \nCPRD GOLD linked with \nIMD \nEngland Cohort  2000-2013 857,324 Female, 12-\n25 years \nSmoking, deprivation, folic acid \nsupplementation, sexually \ntransmitted disease, PCOS, \nendometriosis, thyroid disease \nEctopic \npregnancy \nGeneral Practice Research Database (GPRD) \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n19 \n \nFirst author, \nyear \n(reference) \nDataset  Country Study design Data \ncollection \nperiod \nTotal or \nmaximum \nsample \nsize \nPopulation \ncharacteristi\ncs: sex, age \nPreconception indicators reported Maternal and \noffspring \noutcomes \nreported \nBriggs, 2013 \n(37) \nN/A UK Repeated \ncross-\nsectional \nstudy \n2004-2010 1,103,669 Female, 15-\n49 years \nContraception None \nRowlands, \n2000 (38) \nN/A UK Cohort study 1994-1997 95,007 Female, 14-\n29 years \nContraception None \nThe Health Improvement Network (THIN) \nSmith, 2020 \n(39) \nN/A UK Cohort study 2006-2016 241,662 Female, 15-\n49 years \nMaternal age, deprivation, \nprevious caesarean delivery, \nsmoking \nNone \nCea Soriano, \n2018 (40) \nN/A UK Cohort study \nand case-\ncontrol study \n1995-2012 251,581 Female, 15-\n45 years \nDiabetes mellitus  None \nCoton, 2016 \n(30) \nN/A UK Cohort study 1995-2012 301,794 Female, 16 \nand over \nDiabetes mellitus None \nBan, 2015 (2) \n(41) \nN/A UK Cohort study 1990-2010 2,141,503 Female, 15-\n44 years \nInflammatory bowel disease None \nCea Soriano, \n2014 (28) \nN/A UK Cohort study 2004-2010 N/R Female, 18-\n44 years \nContraception None \nCea Soriano, \n2013 (42) \nN/A UK Cohort study 2008 574,185 Female, 12-\n49 years \nContraception None \nDhalwani, \n2013 (43) \nN/A UK Cohort study 1990-2010 1,776,746 Female, 15-\n49 years \nFertility problems None \nBan, 2012 (44) N/A UK Cohort study 1994-2009 116,457 Female, 15-\n45 years \nDepression, anxiety, serious \nmental illness, deprivation \nNone \nDave, 2010 \n(17) \nUse of family \nidentification number \nto link mothers, fathers \nand children living in \nthe same household \nUK Cohort study 1993-2007 86,957 Female and \nmale, 15-≥35 \nyears \nDepression None \nRoyal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n20 \n \nFirst author, \nyear \n(reference) \nDataset  Country Study design Data \ncollection \nperiod \nTotal or \nmaximum \nsample \nsize \nPopulation \ncharacteristi\ncs: sex, age \nPreconception indicators reported Maternal and \noffspring \noutcomes \nreported \nGaudio, 2022 \n(18) \nN/A England Repeated \ncross-\nsectional \nstudy \n2004-2018 729,662 Female and \nmale, 12-46 \nyears \nValproate prescription None \nGaudio, 2021 \n(29) \nN/A England Repeated \ncross-\nsectional \nstudy \n2004-2017 465,898 Female, 16-\n45 years \nDiabetes mellitus None \nEnhanced Prescribing Database (EPD)  \nGiven, 2020 \n(45) \nEPD linked with GP \nPatient Registrations \nIndex \nNorthern \nIreland \nCohort study 2010-2016 560,074 Female, 12-\n49 years \nContraception None    \nWemakor, \n2014 (46) \nEPD linked with the \n2010 Northern Ireland \nMultiple Deprivation \nMeasure (NIMDM) \nNorthern \nIreland \nCross-\nsectional \nstudy \n2009 268,917 Female, 15-\n45 years \nDeprivation, anti-depressant use None \nOther datasets \nLee, 2022 (27) Secure Anonymised \nInformation Linkage \n(SAIL) Databank \nWales Cross-\nsectional \nstudy \n2018 27,782 Female, 15-\n49 years \nMaternal age, ethnicity, \ndeprivation, weight, smoking, \nmental health problem, severe \nmental health condition, asthma, \nPCOS, infertility, thyroid disease, \neating disorder, endometriosis, \nhypertension, thromboembolism, \nsickle-cell disease, epilepsy, \ndiabetes \nNone \nPasvol, 2022 \n(47) \nIQVIA Medical \nResearch Data (IMRD) \ndatabase \nUK Repeated \ncross-\nsectional \nstudy \n2000-2018 2,705,638 Female, 15-\n49 years \nContraception None \nALSPAC, Avon Longitudinal Study of Parents and Children; CPRD, Clinical Practice Research Datalink; EPD, Enhanced Prescribing Database; HES, Hospital Episodes Statistics; \nIMD, index of multiple deprivation; PCOS, polycystic ovary syndrome; SD, standard deviation.  \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n21 \n \nTable 3. Prevalence of (and trends in) preconception indicators reported for people of reproductive age in UK routine primary care data \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nDomain: Wider determinants of health \nIndicator: Deprivation \nBan, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] living in the most socio-\neconomically deprived area (based on Townsend Index of Deprivation quintiles) \n116,457 14.2 \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women living in the most socio-economically deprived area (based on IMD \nquintiles) \n857,324 13.6 \nSyed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] living in the most \nsocio-economically deprived area (based on IMD quintiles) \n211,393 18.5 \nSmith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] living in \nthe most socio-economically deprived area (based on Townsend Index of Deprivation \nquintiles) \n277,114 16.0 \nWemakor, 2014 \n(46) \n2009 EPD Percentage of women living in the most socio-economically deprived area (based on NIMDM \nquintiles) \n264,798 22.6 \nLee, 2022 (27) 2018 CPRD \n(England) \nPercentage of women [with a conception date during the study period] living in the most \nsocio-economically deprived area (based on IMD quintiles) \n9,800 19.5 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] living in the most \nsocio-economically deprived area (based on IMD quintiles) \n24,538 15.6 \nIndicator: Ethnicity \nHope, 2022 (35) 1990-2017 CPRD Percentage of women [with a pregnancy during the study period] from an ethnic minority \nbackground  \n2,680,149 15.1 \nCoton, 2016 (30) 1995-2012 THIN Percentage of women [with a pregnancy during the study period] from an ethnic minority \nbackground  \n301,794 14.9 \nSubramanian, \n2022 (19) \n1997-2020 CPRD Percentage of women [with a record of delivery during the study period] from an ethnic \nminority background  \n299,866 20.0 \nSyed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] from an ethnic \nminority background  \n211,393 13.7 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] from an ethnic \nminority background  \n37,641 \n \n12.8 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] from an ethnic \nminority background  \n27,782 15.1 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n22 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nDomain: Reproductive health and family planning \nIndicator: Teenage pregnancy \nSyed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] aged ≤19 at time \nof childbirth \n211,393 3.3 \nSmith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] aged \n15-19 at time of most recent pregnancy \n309,573 3.1 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] aged 15-19 at time \nof index pregnancy \n37,641 6.7 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] aged 15-19 at time \nof index pregnancy \n27,782 5.5 \nIndicator: Advanced maternal age \nSyed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] aged ≥40 at time \nof childbirth \n211,393 27.0 \nSmith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] aged \n35-49 at time of most recent pregnancy \n309,573 26.1 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] aged 35-49 at time \nof most recent pregnancy \n37,641 20.1 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] aged 35-49 at time \nof most recent pregnancy \n27,782 15.1 \nIndicator: Previous caesarean delivery \nSmith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] with a \nprevious caesarean delivery (based on most recent pregnancy) \n98,932 23.7 \nIndicator: Fertility problems \nDhalwani, 2013 (43) 1990-2010 THIN Percentage of women with a history of fertility problems (at least one record for a fertility \nproblem based on Read codes) \n1,776,746 3.3 \nLee, 2022 (27) 2018 CRPD Percentage of women [with a conception date during the study period] with a history of \nfertility problems (based on Read codes for (possible) female infertility) \n37,641 3.81 (3.62-4.01) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with a history of \nfertility problems (based on Read codes for (possible) female infertility) \n27,782 1.18 \nIndicator: Contraception \nRowlands, 2000 \n(38) \n1994-1997 GPRD Percentage of women who use contraception (assessed based on prescription codes for all \nregular methods, excluding emergency contraception) \n95,007 70.1 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n23 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women who use contraception (oral contraception) 857,324 5.7 \nPasvol, 2022 (47) 2000 IMRD Percentage of women who use contraception (COCP, POP and LARC) 2,705,638 32.9 (32.7-33.0) \n 2018 IMRD As above 2,705,638 29.2 (29.1-29.3) \nCea Soriano, 2014 \n(28) \n2004 THIN Percentage of women who use contraception (progesterone-only implant assessed using \nRead and MULTILEX codes) \nN/R 0.5 \n 2010 THIN As above N/R 3.4 \n 2004 THIN Percentage of women who use contraception (levonorgestrel releasing-intrauterine system \n(LNG-IUS) assessed using Read and MULTILEX codes) \nN/R 3.1 \n 2010 THIN As above N/R 5.2 \n 2004 THIN Percentage of women who use contraception (copper intrauterine devices assessed using \nRead and MULTILEX codes) \nN/R 5.4 \n 2010 THIN As above N/R 4.8 \n 2004 THIN Percentage of women who use contraception (progestogen-only injections assessed using \nRead and MULTILEX codes) \nN/R 3.6 \n 2010 THIN As above N/R 3.2 \nBriggs, 2013 (37) 2004 GPRD Percentage of women who use contraception (assessed based on combined hormonal \ncontraception prescription data) \n1,018,835 19.6 \n 2005 GPRD As above 1,040,629 19.6 \n 2006 GPRD As above 1,053,353 19.1 \n 2007 GPRD As above 1,067,462 18.7 \n 2008 GPRD As above 1,085,149 18.2 \n 2009 GPRD As above 1,100,002 17.4 \n 2010 GPRD As above 1,103,669 16.3 \nCea Soriano, 2013 \n(42) \n2008 THIN Percentage of women who use contraception (combined oral contraceptive assessed using \nRead and MULTILEX codes) \n574,185 16.2 (16.1-16.3) \n 2008 THIN Percentage of women who use contraception (progesterone-only pill assessed using Read \nand MULTILEX codes) \n574,185 5.6 (5.5-5.6) \n 2008 THIN Percentage of women who use contraception (copper intrauterine device assessed using \nRead and MULTILEX codes) \n574,185 4.5 (4.4-4.5) \n 2008 THIN Percentage of women who use contraception (levonorgestrel-releasing intrauterine system \nassessed using Read and MULTILEX codes) \n574,185 4.2 (4.1-4.2) \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n24 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \n 2008 THIN Percentage of women who use contraception (progesterone-only implant assessed using \nRead and MULTILEX codes) \n574,185 1.5 (1.5-1.6) \n 2008 THIN Percentage of women who use contraception (progestogen-only injection assessed using \nRead and MULTILEX codes) \n574,185 2.4 (2.3-2.4) \n 2008 THIN Percentage of women who use contraception (contraceptive patch assessed using Read and \nMULTILEX codes) \n574,185 0.1 (0.1-0.2) \nGiven, 2020 (45) 2010 EPD Percentage of women who use contraception (based on dispensed prescriptions of COCP, \nPOP, emergency contraceptive, injection, implant, intrauterine device, transdermal patch, \nvaginal ring and gel) \n465,912 25.7 \n 2016 EPD As above 479,908 26.1 \nDomain: Health behaviours and weight \nIndicator: Folic acid supplementation \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women prescribed folic acid supplementation  857,324 0.3 \nIndicator: Underweight   \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] in the underweight \nBMI category (<18.5 kg/m2) \n30,910 3.9 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] in the underweight \nBMI category (<18.5 kg/m2) \n21,802 5.9 \nIndicator: Overweight   \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] in the overweight \nBMI category (25.0-29.9 kg/m2) \n30,910 26.1 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] in the overweight \nBMI category (25.0-29.9 kg/m2) \n21,802 26.0 \nIndicator: Obesity   \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] in the obesity BMI \ncategory (≥30.0 kg/m2) \n30,910 23.2 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] in the obesity BMI \ncategory (≥30.0 kg/m2) \n21,802 24.6 \nIndicator: Smoking \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women who currently smoke 857,324 25.8 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n25 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nSmith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] who \ncurrently smoke \n263,575 13.1 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] who currently smoke 36,339 22.7 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] who currently smoke 24,785 26.7 \nDomain: Immunisation and infections \nIndicator: Sexually transmitted disease \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women diagnosed with gonorrhoea  857,324 0.01 \nDomain: Mental health conditions \nIndicator: Mental health condition \nHope, 2022 (35) 1990-2017 CPRD Percentage of women [with a pregnancy during the study period] with a mental illness \n(assessed based on prescription, diagnosis and symptom data for depression, anxiety, \npsychosis, substance or alcohol misuse disorder, eating or personality disorder)  \n2680149 19.8 \nIndicator: Depression \nDave, 2010 (17) 1993-2007 THIN Percentage of mothers with depression (assessed based on Read codes for unipolar \ndepression and/or a prescription for an antidepressant for treatment of depression)  \n86,957 22.2 \nDave, 2010 (17) 1993-2007 THIN Percentage of fathers with depression (assessed based on Read codes for unipolar \ndepression and/or a prescription for an antidepressant for treatment of depression)  \n86,957 9.2 \nBan, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] with depression during 9 \nmonths before pregnancy (assessed based on record of depression diagnosis and/or \nantidepressant prescription) \n116,457 9.3 (9.1-9.4) \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with depression \n(based on read codes for diagnosis of depression and history of depression)  \n37,641 23.43 (23.01-\n23.87) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with depression \n(based on read codes for diagnosis of depression and history of depression)  \n27,782 24.07 (23.57-\n24.58) \nIndicator: Anxiety \nBan, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] with anxiety during 9 \nmonths before pregnancy (assessed based on record of anxiety diagnosis and/or anxiolytic \nprescription & record of anxiety diagnosis and antidepressant prescription but no depression \ndiagnosis) \n116,457 4.1 (4.0-4.3) \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with anxiety (based \non read codes for diagnosis of anxiety disorders and history of anxiety disorders)  \n37,641 18.98 (18.58-\n19.38) \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n26 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with anxiety (based \non read codes for diagnosis of anxiety disorders and history of anxiety disor ders) \n27,782 23.05 (22.56-\n23.55) \nIndicator: Serious mental illness \nBan, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] with serious mental illness \nduring 9 months before pregnancy (assessed based on record of bipolar disorder, \nschizophrenia or other psychotic disorders and/or prescription of lithium or mood \nstabilisers) \n116,457 0.12 (0.11-0.14) \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with severe mental \nillness (based on read codes for diagnosis or history of bipolar disorder / affective psychosis \nand schizophrenia / non-affective psychosis) \n37,641 2.42 (2.26-2.58) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with severe mental \nillness (based on read codes for diagnosis or history of bipolar disorder / affective psychosis \nand schizophrenia / non-affective psychosis) \n27,782 2.07 (1.91-2.24) \nDomain: Physical health conditions \nIndicator: Epilepsy \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with epilepsy (based \non read codes for epilepsy diagnosis, treatment and advice) \n37,641 1.44 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with epilepsy (based \non read codes for epilepsy diagnosis, treatment and advice) \n27,782 1.30 \nIndicator: Diabetes mellitus \nCea Soriano, 2018 \n(40) \n1995-2012 THIN Percentage of women [with a pregnancy during the study period] with type 1 diabetes  251,581 0.36 \nCea Soriano, 2018 \n(40) \n1995-2012 THIN Percentage of women [with a pregnancy during the study period] with type 2 diabetes  251,581 0.24 \nCoton, 2016 (30) 1995 THIN Percentage of women [who gave birth during the study period] with type 1 diabetes (based \non diagnostic Read codes and prescriptions for antidiabetics from medical records) \nN/R 0.16 \nCea Soriano, 2018 \n(40) \n1995-2012 THIN Percentage of women [with a pregnancy during the study period] with diabetes (any type) \n(based on Read codes suggestive of diabetes and for insulin prescriptions)  \n251,581 0.60 \nCoton, 2016 (30) 1995 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based \non diagnostic Read codes and prescriptions for antidiabetics from medical records) \nN/R 0.23 \nGaudio, 2021 (29) 2004 RCGP RCS Percentage of women with diabetes (any type) 316,461 1.0 \nGaudio, 2021 (29) 2004 RCGP RCS Percentage of women with type 2 diabetes  316,461 0.6 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n27 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nGaudio, 2021 (29) 2004 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 1 diabetes patients) 788 50.0 \nGaudio, 2021 (29) 2004 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 2 diabetes patients) 1,098 33.1 \nCoton, 2016 (30) 2008 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based \non diagnostic Read codes and prescriptions for antidiabetics from medical records) \nN/R 0.51 \nCoton, 2016 (30) 2009 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based \non diagnostic Read codes and prescriptions for antidiabetics from medical records)  \nN/R 0.67 \nCoton, 2016 (30) 2012 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based \non diagnostic Read codes and prescriptions for antidiabetics from medical records) \nN/R 1.06 \nCoton, 2016 (30) 2012 THIN Percentage of women [who gave birth during the study period] with type 1 diabetes (based \non diagnostic Read codes and prescriptions for antidiabetics from medical records)  \nN/R 0.41 \nGaudio, 2021 (29) 2017 RCGP RCS Percentage of women with diabetes (any type) 465,898 1.4 \nGaudio, 2021 (29) 2017 RCGP RCS Percentage of women with type 2 diabetes  465,898 0.9 \nGaudio, 2021 (29) 2017 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 1 diabetes patients) 1,579 40.8 \nGaudio, 2021 (29) 2017 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 2 diabetes patients) 3,041 24.7 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with type 1 diabetes  37,641 0.56 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with type 1 diabetes  27,782 0.49 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with type 2 diabetes  37,641 0.71 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with type 2 diabetes  27,782 0.68 \nIndicator: Polycystic ovary syndrome (PCOS) \nSubramanian, \n2022 (19) \n1997-2020 CPRD Percentage of women [with a record of delivery during the study period] with PCOS (based \non Read codes for PCOS, polycystic ovaries or symptoms) \n299,866 6.5 \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women with PCOS  857,324 0.2 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with PCOS (based on \nread codes for PCOS, isosexual virilisation, polycystic ovaries, Stein - Leventhal syndrome, \nmulticystic ovaries and endoscopic drilling of ovary) \n37,641 4.66 (4.45-4.88) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with PCOS (based on \nread codes for PCOS, isosexual virilisation, polycystic ovaries, Stein - Leventhal syndrome, \nmulticystic ovaries and endoscopic drilling of ovary) \n27,782 3.96 (3.73-4.20) \nIndicator: Endometriosis \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women with endometriosis  857,324 0.06 \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n28 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with endometriosis \n(based on read codes for diagnosis of endometriosis and procedures of endometriosis)  \n37,641 1.68 (1.55-1.82) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with endometriosis \n(based on read codes for diagnosis of endometriosis and procedures of endometriosis) \n27,782 1.31 (1.18-1.45) \nIndicator: Eating disorder \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with an eating \ndisorder (based on read codes for eating disorders, including anorexia, binge eating, bulimia, \ncompulsive eating disorder, referral to eating disorder clinic) \n37,641 1.88 (1.74-2.02) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with an eating \ndisorder (based on read codes for eating disorders, including anorexia, binge eati ng, bulimia, \ncompulsive eating disorder, referral to eating disorder clinic) \n27,782 1.80 (1.65-1.97) \nIndicator: Thyroid disease \nden Heijer, 2019 \n(25) \n2000-2013 CPRD Percentage of women with thyroid disease  857,324 0.07 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with thyroid disease \n(based on read codes for hyperthyroidism and hypothyroidism) \n37,641 3.34 (3.16-3.52) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with thyroid disease \n(based on read codes for hyperthyroidism and hypothyroidism) \n27,782 2.45 (2.28-2.64) \nIndicator: Hypertension \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with hypertension \n(based on read codes related to hypertension) \n37,641 0.87 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with hypertension \n(based on read codes related to hypertension) \n27,782 0.67 \nIndicator: Thromboembolism \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with \nthromboembolism (based on read codes for history, diagnosis or procedure for pulmonary \nembolism) \n37,641 0.65 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with \nthromboembolism (based on read codes for history, diagnosis or procedure for pulmonary \nembolism) \n27,782 0.60 \nIndicator: Asthma \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n29 \n \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with asthma (based \non read codes on asthma, including diagnosis, treatment, education and review)  \n37,641 14.63 (14.27-\n14.99) \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with asthma (based \non read codes on asthma, including diagnosis, treatment, education and review)  \n27,782 17.17 (16.73-\n17.62) \nIndicator: Inflammatory bowel disease (IBD) \nBan, 2015 (2) (41) 1990-2010 THIN Percentage of women [with a singleton live birth recorded during the study] with IBD (based \non Read codes) \n2,141,503 0.45 \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with IBD (Crohn’s \ndisease, ulcerative colitis) \n37,641 0.60 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with IBD (Crohn’s \ndisease, ulcerative colitis) \n27,782 0.58 \nIndicator: Sickle cell disease \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with sickle cell \ndisease (based on read codes for diagnosis of sickle-cell anaemia or history of sickle-cell \nanaemia) \n37,641 0.01 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with sickle cell \ndisease (based on read codes for diagnosis of sickle-cell anaemia or history of sickle-cell \nanaemia) \n27,782 <0.02 \nIndicator: Cancer \nLee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with previous cancer \ndiagnosis (breast, lung, bowel, cervical, ovarian, uterine, thyroid, skin, l ymphoma, \nleukaemia, metastatic) \n37,641 0.51 \nLee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with previous cancer \ndiagnosis (breast, lung, bowel, cervical, ovarian, uterine, thyroid, skin, lymphoma, \nleukaemia, metastatic) \n27,782 0.60 \nDomain: Medication \nIndicator: Medication not recommended when planning pregnancy  \nGaudio, 2022 (18) 2004 RCGP RCS Percentage of women prescribed valproate  533,627 0.31 (0.18-0.44) \n 2018 RCGP RCS As above 729,662 0.16 (0.07-0.24) \n 2004 RCGP RCS Percentage of men prescribed valproate N/R 0.37 (0.35-0.38) \n 2018 RCGP RCS As above N/R 0.36 (0.34-0.37) \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n30 \n \nCI, confidence interval; COCP, combined oral contraceptive pill; CPRD, Clinical Practice Research Datalink; EPD, Enhanced Prescribing Database; IMD, index of multiple \ndeprivation; LARC, long-acting reversible contraception; NIMDM, Northern Ireland Multiple Deprivation Measure; N/R, not reported; PCOS, polycystic ovary syndrome; \nPOP, progestogen-only pill; SAIL, Secure Anonymised Information Linkage.  \nFirst author, year \n(reference) \nYear for \ndatapoint \nDataset Preconception indicator measure Sample \nsize \nPrevalence of \npreconception \nindicator  \n(% with 95% CI) \nWemakor, 2014 \n(46) \n2014 EPD Percentage of women prescribed anti-depressant medication 43,770 16.3 (16.1-16.4) \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n31 \n \nTable 4. Associations of preconception indicators with outcomes in women and offspring \nFirst author, \nyear \n(reference) \nReference and \ncomparison group \nIndicator \ndata source \nMaternal or offspring \noutcome \nOutcome data \nsource \nSample \nsize \nStatistical \nresults \ndefinition \nResult Adjustment for \nconfounders \nContraception \nParker, 2016 \n(48) \nIntrauterine device \n(IUD) use prior to \nstart of pregnancy \nvs. no IUD use \nprior to start of \npregnancy \n(determined using \nprocedure codes, \ndrug codes and \nrecord review) \nCPRD Pre-eclampsia CPRD 13,900 Adjusted OR \n(95% CI) \n0.76 (0.58-0.98) Age, general practice, \nyear of delivery, BMI, \nsmoking, parity, \ninduced abortion, \nfertility problems, pre-\nexisting diabetes \nSexually transmitted disease \nden Heijer, \n2019 (25) \nPositive chlamydia \ntest vs. negative \nchlamydia test \nCPRD  Ectopic pregnancy CPRD GOLD 2,484 Adjusted HR \n(95% CI) \n1.87 (1.38-2.54) Age, smoking status, \nhistory of gonorrhea \nPolycystic ovary syndrome (PCOS) \nSubramanian, \n2022 (19) \nPCOS vs. no PCOS \n(assessed based on \nRead codes for \nPCOS, polycystic \novaries or \nsymptoms) \nCRPD Preterm delivery (< 37 \nweeks of gestational \nage at delivery) \nHES 137,930 Adjusted OR \n(95% CI) \n1.11 (1.06-1.17) Age, ethnicity, \ndeprivation, \ndysglycaemia, \nhypertension, thyroid \ndisorders, numbers of \nbabies born at the \ndelivery, pre-gravid \nbody mass index \nRees, 2016 (20) PCOS vs. no PCOS CPRD Preterm birth HES and CRPD 19,502 Adjusted HR \n(95% CI) \n1.24 (1.09-1.41) Age, BMI, primary care \npractice, multiple \ngestation, number of \nprevious births, \nsmoking history \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n32 \n \nFirst author, \nyear \n(reference) \nReference and \ncomparison group \nIndicator \ndata source \nMaternal or offspring \noutcome \nOutcome data \nsource \nSample \nsize \nStatistical \nresults \ndefinition \nResult Adjustment for \nconfounders \nSubramanian, \n2022 (19) \nPCOS vs. no PCOS \n(assessed based on \nRead codes for \nPCOS, polycystic \novaries or \nsymptoms) \nCPRD High birthweight \n(>4kg) for at least one \nof the babies \nHES 137,930 Adjusted OR \n(95% CI) \n0.97 (0.92-1.01) Age, ethnicity, \ndeprivation, \ndysglycaemia, \nhypertension, thyroid \ndisorders, numbers of \nbabies born at the \ndelivery, pre-gravid \nbody mass index, \ngestational age \nRees, 2016 (20) PCOS vs. no PCOS CPRD High birth weight HES 12,363 Adjusted OR \n(95% CI) \n0.97 (0.67-1.41) Age, BMI, primary care \npractice, multiple \ngestation, number of \nprevious births, \nsmoking history \nSubramanian, \n2022 (19) \nPCOS vs. no PCOS \n(assessed based on \nRead codes for \nPCOS, polycystic \novaries or \nsymptoms) \nCPRD Low birthweight (<2.5 \nkg) for at least one of \nthe babies \nHES 137,930 Adjusted OR \n(95% CI) \n1.03 (0.95-1.13) Age, ethnicity, \ndeprivation, \ndysglycaemia, \nhypertension, thyroid \ndisorders, numbers of \nbabies born at the \ndelivery, pre-gravid \nbody mass index, \ngestational age \nRees, 2016 (20) PCOS vs. no PCOS CPRD Low birth weight HES 12,363 Adjusted OR \n(95% CI) \n1.19 (1.00-1.42) Age, BMI, primary care \npractice, multiple \ngestation, number of \nprevious births, \nsmoking history \nSubramanian, \n2022 (19) \nPCOS vs. no PCOS \n(assessed based on \nRead codes for \nPCOS, polycystic \nCPRD Emergency caesarean \nsection \nHES 137,930 Adjusted OR \n(95% CI) \n1.10 (1.05-1.15) Age, ethnicity, \ndeprivation, \ndysglycaemia, \nhypertension, thyroid \ndisorders, numbers of \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n33 \n \nFirst author, \nyear \n(reference) \nReference and \ncomparison group \nIndicator \ndata source \nMaternal or offspring \noutcome \nOutcome data \nsource \nSample \nsize \nStatistical \nresults \ndefinition \nResult Adjustment for \nconfounders \novaries or \nsymptoms) \nbabies born at the \ndelivery, pre-gravid \nbody mass index, \ngestational age \nSubramanian, \n2022 (19) \nAs above CPRD Elective/ \nother/unspecified \ncaesarean section \ndelivery \nAs above As above Adjusted OR \n(95% CI) \n1.07 (1.03-1.12) As above \nSubramanian, \n2022 (19) \nAs above CPRD Instrumental vaginal \ndelivery \nAs above As above Adjusted OR \n(95% CI) \n1.04 (1.00-1.09) As above \nSubramanian, \n2022 (19) \nAs above CPRD Stillbirth As above As above Adjusted OR \n(95% CI) \n0.99 (0.81-1.21) Age, ethnicity, \ndeprivation, \ndysglycaemia, \nhypertension, thyroid \ndisorders, numbers of \nbabies born at the \ndelivery, pre-gravid \nbody mass index \nSubramanian, \n2022 (19) \nAs above CPRD Very preterm (<32 \nweeks of gestational \nage at delivery) \nAs above As above Adjusted OR \n(95% CI) \n1.07 (0.97-1.18) As above \nSubramanian, \n2022 (19) \nAs above CPRD Extremely preterm \n(<28 weeks of \ngestational age at \ndelivery) \nAs above As above Adjusted OR \n(95% CI) \n1.13 (0.98-1.29) As above \nSubramanian, \n2022 (19) \nAs above CPRD Large for gestational \nage (>90th percentile) \nfor at least one of the \nbabies \nAs above As above Adjusted OR \n(95% CI) \n1.00 (0.97-1.04) As above \nSubramanian, \n2022 (19) \nAs above CPRD Small for gestational \nage (<10th percentile) \nfor at least one of the \nbabies \nAs above As above Adjusted OR \n(95% CI) \n1.03 (0.96-1.11) As above \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint \n\n34 \n \nFirst author, \nyear \n(reference) \nReference and \ncomparison group \nIndicator \ndata source \nMaternal or offspring \noutcome \nOutcome data \nsource \nSample \nsize \nStatistical \nresults \ndefinition \nResult Adjustment for \nconfounders \nBerni, 2018 (49) PCOS vs. no PCOS \n(assessed based on \nRead codes \nC164.00, C164.12, \nC165.00) \nCPRD Offspring ADHD HES 17,668 Adjusted OR \n(95% CI) \n1.34 (0.96-1.89) Age, body mass index \ncategory, primary care \npractice, history of \nprior mental health \ndisorder \n As above CPRD Offspring autism \nspectrum disorder \nAs above As above Adjusted OR \n(95% CI) \n1.75 (1.27-2.46) As above \nRees, 2016 (20) PCOS vs. no PCOS CPRD Miscarriage resulting in \nhospital admission \nHES 22075 Adjusted HR \n(95% CI) \n1.70 (1.56-1.84) Age, BMI, primary care \npractice, number of \nprevious births, \nsmoking history \nRees, 2016 (20) As above CPRD Pre-eclampsia  HES and CRPD 19,502 Adjusted HR \n(95% CI) \n1.31 (1.16-1.49) Age, BMI, primary care \npractice, multiple \ngestation, number of \nprevious births, \nsmoking history \nRees, 2016 (20) As above CPRD Gestational diabetes HES and CRPD 19,502 Adjusted HR \n(95% CI) \n1.42 (1.21-1.67) As above \nRees, 2016 (20) As above CPRD Jaundice HES 12,363 Adjusted OR \n(95% CI) \n1.20 (1.03-1.39) As above \nRees, 2016 (20) As above CPRD Respiratory \ncomplications \n(offspring) \nHES 12,363 Adjusted OR \n(95% CI) \n1.20 (1.06-1.37) As above \nRees, 2016 (20) As above CPRD Hypoglycaemia \n(offspring) \nHES 12,363 Adjusted OR \n(95% CI) \n1.31 (0.99-1.74) As above \nRees, 2016 (20) As above CPRD Feeding issues HES 12,363 Adjusted OR \n(95% CI) \n1.21 (0.96-1.52) As above \nCI, confidence intervals; CPRD, Clinical Practice Research Datalink; HES, hospital episodes statistics; HR, Hazard Ratio; OR, odds ratio; PCOS, polycystic ovary syndrome \n . CC-BY 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}