Preconception indicators and associations with health outcomes reported in UK routine primary care data: a systematic review

Background: Routine primary care data may be a valuable resource for preconception health research and informing provision of preconception care. Aim: To review how primary care data could provide information on the prevalence of preconception indicators and examine associations with maternal and offspring health outcomes. Design and Setting: Systematic review of observational studies using UK routine primary care data.

Literature searches were conducted in five databases (March 2023) to identify observational studies that used national primary care data from individuals aged 15-49 years. Preconception indicators were defined as medical, behavioural and social factors that may impact future pregnancies. Health outcomes included those that may occur during and after pregnancy. Screening, data extraction and quality assessment were conducted by two reviewers.

From 5,259 records screened, 42 articles were included. The prevalence of 30 preconception indicators was described for female patients, ranging from 0.01% for sickle cell disease to >20% for each of advanced maternal age, previous caesarean section (among those with a recorded pregnancy), overweight, obesity, smoking, depression and anxiety (irrespective of pregnancy). Few studies reported indicators for male patients (n=3) or associations with outcomes (n=5). Most studies had low risk of bias, but missing data may limit generalisability.

Findings demonstrate that routinely collected UK primary care data can be used to identify patients’ preconception care needs. Linking primary care data with health outcomes collected in other datasets is underutilised but could help quantify how optimising preconception health and care can reduce adverse outcomes for mothers and children.

general practice; preconception care; pregnancy outcomes; pre-pregnancy care; primary care. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 4 How this fits in: • Provision of preconception care is not currently embedded into routine clinical practice but may be informed by routinely collected primary care data. • This systematic review demonstrates that UK primary care data can provide information on the prevalence of a range of medical, behavioural and social factors among female patients of reproductive age, while limited research has examined male preconception health or associations with maternal and offspring health outcomes. • Routinely recorded electronic patient record data can be used by primary healthcare professionals to search for preconception risk factors and thereby support individualised preconception care, while aggregate data can be used by public health agencies to promote population-level preconception health. • Further data quality improvements and linkage of routine health datasets are needed to support the provision of preconception care and future research on its benefits for maternal and offspring health outcomes. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 5

Preconception care is the provision of biomedical, behavioural and social interventions to people of reproductive age (15-49 years) before conception may occur with the aim of improving short- and longer-term parental and child health outcomes.1 Primary care teams have a key role in providing preconception care as identified by patients and healthcare professionals.2, 3 Preconception care delivered in primary care improves knowledge and preconception health behaviours in female patients, but there is currently less evidence about male patients or the impact on pregnancy and longer-term health outcomes.4, 5 In line with the National Institute for Health and Care Excellence (NICE) Clinical Knowledge Summary on preconception advice and management, primary care teams are encouraged to consider discussions about preconception health when appropriate, and to assess, manage and potentially optimise a range of physical and mental health conditions, health behaviours, and social needs prior to potential pregnancy.6 However, routine provision of preconception care is not currently widespread in UK clinical practice.7 To build the case for implementation of strategies and guidelines that optimise the population’s preconception health, the UK Preconception Partnership proposed an annual report card to describe and monitor preconception health.8 Our scoping review to inform national surveillance identified 65 preconception indicators (medical, behavioural and social risk factors that may impact potential future pregnancies among individuals of reproductive age) that are recorded in existing UK routine health data.9 A first report card was produced based on 23 indicators recorded in the national Maternity Service Data Set (MSDS), demonstrating that nine in 10 women in England enter pregnancy with at least one potentially modifiable risk factor for adverse pregnancy and birth outcomes.10, 11 Similarly, an analysis of primary care data from the Royal College of General Practitioners Research and Surveillance Centre found that 91% of women of reproductive age have a behavioural or medical risk factor for adverse pregnancy outcomes.12 These studies have to date focussed on preconception health of women (not men), and have not examined trends and trajectories in medical, behavioural and social indicators during the years leading up to pregnancy. Doing so would improve our ability to identify the population’s preconception care needs throughout their reproductive years. Routinely collected primary care data is potentially a unique resource to describe and monitor preconception health, and to examine the impact of (changes in) preconception indicators on improving outcomes such as gestational diabetes and preterm birth. To inform future research and surveillance, and develop policy and clinical practice recommendations, we aimed to systematically review the literature to explore how UK routine primary care data could provide information on the prevalence of preconception indicators and examine associations with maternal and offspring health outcomes.

Search strategy and selection criteria The protocol for this review was registered with PROSPERO,13 and the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA 2020) guideline used to ensure transparent reporting.14 A search strategy was developed, and searches conducted on 27 March 2023 (from inception date) in five databases: MEDLINE (Ovid), EMBASE (Ovid), Scopus, CINAHL, Web of Science (Supplementary Table 1). Supplementary searches using ‘preconception’ and ‘prepregnancy’ terms were conducted using databases from the British Journal of General Practice, and UK primary care datasets.13 Reference lists of included articles were screened for additional studies. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 6 Articles were selected if they included findings from an observational study among individuals of reproductive age (15-49 years), used national patient-level routine primary care data collected in England, Wales, Scotland and/or Northern Ireland, and reported on the prevalence of at least one preconception indicator identified from our previous scoping review (Table 1).9 Articles not including new/original peer-reviewed results, and conference abstracts, were excluded. Selection process Search results were collated in EndNote and duplicates removed, before uploading to Covidence software. Titles and abstracts, followed by full text articles, were screened independently by two reviewers for inclusion. Disagreements or uncertainties were resolved through discussion. Data extraction and synthesis A standardised data extraction form was developed and piloted. Data were extracted by one reviewer and checked by a second reviewer. Disagreements were resolved between the two reviewers. All extracted data on study characteristics (grouped by primary care database), prevalence of preconception indicators, and measures of association between preconception indicators and outcomes (grouped by preconception indicator), were presented in tables. Meta- analysis was not conducted due to heterogeneity in preconception indicator definitions and inclusion and exclusion criteria of study populations. Risk of bias assessment Risk of bias was assessed for study findings on the prevalence of preconception indicators using the 10-item scale developed by Hoy et al rating internal and external validity.15 The Newcastle-Ottawa Scale (NOS) was used to rate risk of bias of study findings on associations between preconception indicators and health outcomes based on seven items related to selection, comparability, and exposure/outcome.16 Risk of bias was assessed by one reviewer, and checked by a second reviewer. Disagreements were resolved between the two reviewers. Studies were classified as low, moderate or high risk of bias (findings on prevalence),15 and good, fair or poor data quality (findings on associations)16 (scoring guides in Supplementary Tables 2, 3A-3B).

From 9,401 identified records, 4,142 duplicates were removed and after title and abstract screening (n=5,259), 117 full-text articles were evaluated for eligibility (Figure 1). 42 articles were included, reporting findings from 11 primary care databases such as the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN). Most articles reported findings from primary care databases that included patients from three (n=1) or all four UK nations (n=30), or from England (n=6), Scotland (n=3) or Northern Ireland only (n=2) (Table 2, Supplementary Table 4). In 11 studies, a primary care dataset was linked with at least one other dataset, such as Hospital Episodes Statistics (HES), Office for National Statistics (ONS) mortality register, community prescribing data, or the Avon Longitudinal Study of Parents and Children. All studies included data on female patients; three studies also reported preconception indicators for male patients. Prevalence of preconception indicators Articles reported findings on 30 preconception indicators across seven of the 12 domains identified in our scoping review.9 Most studies included people of reproductive age irrespective of past/future . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 7 pregnancy (n=26), while other studies included women with a pregnancy or birth recorded during the study period (n=15) or women with a recorded pregnancy and their partners (n=1) (Supplementary Table 5). To obtain population-level estimates of preconception indicators, prevalence data were extracted only if reported (or could be calculated) for the overall study population of females or males of reproductive age (i.e. not if reported only in sub-populations such as patients with a specific condition or characteristic) (Table 3). Data on overall prevalence were available for 21 of the 42 studies, with the other 21 studies reporting prevalence estimates only in sub-populations. Additional preconception indicators reported in sub-populations included housing, domestic abuse, routine GP check-up in the past year, paternal age, previous pregnancy loss, history of assisted reproduction, alcohol consumption, substance misuse, cervical screening, and cardiovascular disease (Supplementary Table 4). The prevalence of preconception indicators reported across studies and primary care databases varied widely, possibly due to differences in preconception indicator definitions, year of data collection (Table 3), and study populations (Supplementary Table 5). The prevalence of preconception indicators defined in line with our scoping review (i.e. excluding individual methods of contraception, and prescribed folic acid supplements),9 ranged from 0.01% for sickle cell disease to >20% for each of advanced maternal age, previous caesarean section (among those with a recorded pregnancy), overweight, obesity, smoking and diagnosis of depression and anxiety among female patients (irrespective of pregnancy). Only three studies reported preconception indicators for male patients, showing for example that the prevalence of depression among fathers (9.2%) was lower compared with mothers (22.2%),17 and the proportion of patients prescribed valproate was comparable among female (0.31%) and male patients (0.37%) in 2004, but much lower among females (0.16%) than males (0.36%) in 2018.18 Associations of preconception indicators with maternal and offspring outcomes Five studies reported associations of preconception indicators (contraception prescription [n=1], sexually transmitted disease [n=1] and polycystic ovary syndrome [PCOS] [n=3]) with pregnancy and birth outcomes (Table 4). Outcome data were obtained from primary care data and/or linked HES data. Where two studies reported on comparable indicators and outcomes, consistent findings were shown for associations of PCOS with preterm delivery (4kg) (no association) and low birthweight (<2.5kg) (inconclusive findings).19, 20 Risk of bias and data quality Risk of bias for findings on the prevalence of preconception indicators was generally low (n=18/21 studies), however, none of the studies received a minimal score (no bias) (Supplementary Table 2). Potential biases were introduced based on representativeness and sampling frame (e.g. excluding women with no pregnancy reported or no linked data available), and indicator definition and measurement (e.g. reporting individual methods of contraception rather than population prescribed contraception, or reliance on medication prescription rather than dispensing data). Moreover, details of non-response (e.g. impact of missing data) were not reported in approximately half the studies. Data quality for studies examining associations of preconception indicators with health outcomes was rated as good for four of the five studies (Supplementary Tables 3A-3B).

. CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 8 Summary This systematic review found that UK routine primary care data can provide valuable information on patients’ medical, behavioural and social risk factors before (a potential) pregnancy. Based on 42 included studies among people of reproductive age or women with a pregnancy recorded during the study period, the prevalence of 30 preconception indicators was reported. Findings showed that >20% of female patients of reproductive age would benefit from support with smoking cessation, and management of weight, depression and anxiety. This would optimise their own health, and improve their chance of a successful pregnancy and healthy baby if that is something they want. Limited research has used primary care data to examine preconception indicators among male patients, or associations of preconception indicators with pregnancy outcomes and longer-term maternal and offspring health outcomes. Strengths and limitations This is the first systematic review to demonstrate how national routine primary care databases can be used to describe the population’s preconception health, to inform clinical practice and future research directions. Comprehensive, prospectively registered review methods were used. Our search was limited to UK primary care data and findings may not be generalisable to other countries. Preconception indicators were selected based on our previous scoping review;9 so potentially relevant indicators not included in this review or not reported in the included studies would have been missed. Moreover, some preconception indicators (such as dietary intake and physical activity) are not routinely recorded in general practice. Comparison with existing literature Findings from our review complement our previous preconception report card based on the MSDS,10 showing that national routine health data are a valuable resource to describe and monitor women’s preconception health. Half of the preconception indicators identified in this review were also reported in the MSDS, with comparable prevalence estimates for most indicators (e.g. teenage pregnancy, previous caesarean delivery, overweight, obesity), while other indicators may be underreported in primary care (e.g. over the counter folic acid supplementation) or in the MSDS (e.g. mental health conditions).10 Published primary care data reported an additional 15 indicators not included in the MSDS (e.g. fertility problems, contraception, relevant medical conditions, teratogenic medication use). Linkage of these (and other) national routine health datasets would enhance the quality of preconception report cards and surveillance (Box 1). Based on linkage of primary care and HES datasets, findings from our review (n=2 studies19, 20) confirm the previously reported association of PCOS with increased risk of preterm delivery.21 Findings from our review are also in line with previous research reporting primary care data quality issues.22-24 Studies included in our review documented substantial missing data (20-60%) for ethnicity and BMI category, likely varying across sub-populations. Coding quality is related to financial incentives such as the Quality and Outcomes Framework (QOF), which may improve accurate recording of selected indicators but also distort prevalence estimates over time.22 The prevalence of some preconception indicators may be underestimated as not all conditions are solely diagnosed and coded in general practice (e.g. sexually transmitted disease),25 or medications and supplements prescribed (e.g. contraception, folic acid supplements).26 Another commonly reported

is the representation of selected general practices in research databases,22, 23 often limited to practices that use one of four main software platforms to manage electronic patient records (EPRs) and further determined by voluntary ‘opt ins’.22, 23 As a result, primary care databases may . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 9 underrepresent specific regions and bias national prevalence estimates of preconception indicators and associations with health outcomes. Implications for research and clinical practice Our findings demonstrate that many preconception indicators are routinely recorded in EPRs, allowing primary healthcare professionals to search for risk factors and provide individualised preconception care. A digital risk screening template has been developed in the Ardens Clinical Decision Support System based on the NICE Clinical Knowledge Summary,6 to support primary healthcare professionals to improve their preconception care practice, screening, coding and recording of indicators. Further work is required to co-develop practical guidance and resources to support integration of preconception care into every day clinical practice (Box 1). Our findings identify the need to use standardised definitions when reporting preconception indicators (Box 1). Due to heterogeneity in definitions, the prevalence of preconception indicators across UK nations, and changes over time, could not be directly compared across studies. However, Lee and colleagues applied standardised definitions to CRPD (UK) and SAIL data (Wales), showing comparable prevalence estimates for some indicators (e.g. obesity, depression), but higher (e.g. smoking, underweight, anxiety, asthma) or lower (e.g. advanced maternal age) prevalence for other indicators, when comparing pregnant women in Wales with those in the UK overall .27 Moreover, standardised reporting within the same database showed, for example, increases over time in the prevalence of type 2 diabetes (1995-2017),29 alongside decreases in poor diabetes control (2004- 2017).29, 30 Lastly, the limited reporting of male preconception indicators, and associations of preconception health with pregnancy, maternal and offspring health outcomes, calls for further research. Many of the preconception indicators reported for female patients are also relevant to male patients (e.g. smoking, obesity), with increasing evidence suggesting better paternal preconception health is associated with reduced risks of infertility and adverse pregnancy and offspring health and developmental outcomes.31-33 To enable further research, improvements are needed in the way that families (i.e. biological parents and their children) can be identified and data linked.17, 34 Primary care data also provide a unique opportunity to examine trajectories of preconception health during reproductive years irrespective of pregnancy, and to quantify the extent to which these reduce adverse pregnancy and offspring health outcomes. Future research would be enhanced by linkage of primary care and other routine health datasets beyond the identified existing linkages (e.g. MSDS and Community Services Data Set) to determine the short- and longer-term benefits of preconception care (Box 1).

Routinely collected primary care data in the UK provide a valuable resource for research and surveillance, and can guide to provision of preconception care. Improvements in coding and reporting, and linkage of general practice systems and other national routine health datasets, would inform evidence-based provision of preconception care in primary care. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 10

Funding: DS is supported by the National Institute for Health and Care Research (NIHR) through an NIHR Advanced Fellowship (NIHR302955) and the NIHR Southampton Biomedical Research Centre (NIHR203319). MM is supported by the UK Medical Research Council (MR/W01498X/1). KMG is supported by the UK Medical Research Council (MC_UU_12011/4), the NIHR (NIHR Senior Investigator (NF-SI-0515-10042) and NIHR Southampton Biomedical Research Centre (NIHR203319)) and Alzheimer’s Research UK (ARUK-PG2022A-008). For the purpose of Open Access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. Competing interests: KMG has received reimbursement for speaking at conferences sponsored by companies selling nutritional products, and is part of an academic consortium that has received research funding from Abbott Nutrition, Nestec, BenevolentAI Bio Ltd. and Danone, outside the submitted work. No competing interests declared for other authors. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 11

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Postnatal checks and primary care consultations in the year following childbirth: an observational cohort study of 309 573 women in the UK, 2006-2016. BMJ Open. 2020;10(11):e036835. 40. Cea-Soriano L, García-Rodríguez LA, Brodovicz KG, Masso-Gonzalez E, Bartels DB, Hernández- Díaz S. Real world management of pregestational diabetes not achieving glycemic control for many patients in the UK. Pharmacoepidemiol Drug Saf. 2018;27(8):940-8. 41. Ban L, Tata LJ, Humes DJ, Fiaschi L, Card T. Decreased fertility rates in 9639 women diagnosed with inflammatory bowel disease: a United Kingdom population-based cohort study. Aliment Pharmacol Ther. 2015 (2);42(7):855-66. 42. Cea-Soriano L, García Rodríguez LA, Machlitt A, Wallander MA. Use of prescription contraceptive methods in the UK general population: a primary care study. BJOG. 2013;121(1):53- 60; discussion -1. 43. Dhalwani NN, Fiaschi L, West J, Tata LJ. Occurrence of fertility problems presenting to primary care: population-level estimates of clinical burden and socioeconomic inequalities across the UK. Hum Reprod. 2013;28(4):960-8. 44. Ban L, Gibson JE, West J, Fiaschi L, Oates MR, Tata LJ. Impact of socioeconomic deprivation on maternal perinatal mental illnesses presenting to UK general practice. Br J Gen Pract. 2012;62(603):e671-8. 45. Given JE, Gray AM, Dolk H. Use of prescribed contraception in Northern Ireland 2010-2016. Eur J Contracept Reprod Health Care. 2020;25(2):106-13. 46. Wemakor A, Casson K, Dolk H. Prevalence and sociodemographic patterns of antidepressant use among women of reproductive age: a prescription database study. J Affect Disord. 2014;167:299-305. 47. Pasvol TJ, Macgregor EA, Rait G, Horsfall L. Time trends in contraceptive prescribing in UK primary care 2000-2018: a repeated cross-sectional study. BMJ Sex Reprod Health. 2022;48(3):193- 8. 48. Parker SE, Jick SS, Werler MM. Intrauterine device use and the risk of pre-eclampsia: a case- control study. Bjog. 2016;123(5):788-95. 49. Berni TR, Morgan CL, Berni ER, Rees DA. Polycystic Ovary Syndrome Is Associated With Adverse Mental Health and Neurodevelopmental Outcomes. J Clin Endocrinol Metab. 2018;103(6):2116-25. 50. Haase CL, Varbo A, Laursen PN, Schnecke V, Balen AH. Association between body mass index, weight loss and the chance of pregnancy in women with polycystic ovary syndrome and overweight or obesity: a retrospective cohort study in the UK. Hum Reprod. 2023;38(3):471-81. 51. Channon S, Coulman E, Cannings-John R, Henley J, Lau M, Lugg-Widger F, et al. The acceptability of asking women to delay removal of a long-acting reversible contraceptive to take part in a preconception weight loss programme: a mixed methods study using qualitative and routine data (Plan-it). BMC Pregnancy Childbirth. 2022;22(1):778. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 14 52. Ma R, Cecil E, Bottle A, French R, Saxena S. Impact of a pay-for-performance scheme for long-acting reversible contraceptive (LARC) advice on contraceptive uptake and abortion in British primary care: An interrupted time series study. PLoS Med. 2020;17(9):e1003333. 53. Jackson J, Lewis NV, Feder GS, Whiting P, Jones T, Macleod J, et al. Exposure to domestic violence and abuse and consultations for emergency contraception: nested case-control study in a UK primary care dataset. Br J Gen Pract. 2019;69(680):e199-e207. 54. Richardson E, Bedson J, Chen Y, Lacey R, Dunn KM. Increased risk of reproductive dysfunction in women prescribed long-term opioids for musculoskeletal pain: A matched cohort study in the Clinical Practice Research Datalink. Eur J Pain. 2018;22(9):1701-8. 55. Nightingale AL, Lawrenson RA, Simpson EL, Williams TJ, MacRae KD, Farmer RD. The effects of age, body mass index, smoking and general health on the risk of venous thromboembolism in users of combined oral contraceptives. Eur J Contracept Reprod Health Care. 2000;5(4):265-74. 56. Khashan AS, Quigley EM, McNamee R, McCarthy FP, Shanahan F, Kenny LC. Increased risk of miscarriage and ectopic pregnancy among women with irritable bowel syndrome. Clin Gastroenterol Hepatol. 2012;10(8):902-9. 57. Shawe J, Mulnier H, Nicholls P, Lawrenson R. Use of hormonal contraceptive methods by women with diabetes. Prim Care Diabetes. 2008;2(4):195-9. 58. Howard LM, Goss C, Leese M, Appleby L, Thornicroft G. The psychosocial outcome of pregnancy in women with psychotic disorders. Schizophr Res. 2004;71(1):49-60. 59. Seaman HE, de Vries CS, Farmer RD. Differences in the use of combined oral contraceptives amongst women with and without acne. Hum Reprod. 2003;18(3):515-21. 60. Shorvon SD, Tallis RC, Wallace HK. Antiepileptic drugs: coprescription of proconvulsant drugs and oral contraceptives: a national study of antiepileptic drug prescribing practice. J Neurol Neurosurg Psychiatry. 2002;72(1):114-5. 61. Cea-Soriano L, Wallander MA, García Rodríguez LA. Prescribing patterns of combined hormonal products containing cyproterone acetate, levonorgestrel and drospirenone in the UK. J Fam Plann Reprod Health Care. 2016;42(4):247-54. 62. Ban L, Fleming KM, Doyle P, Smeeth L, Hubbard RB, Fiaschi L, et al. Congenital Anomalies in Children of Mothers Taking Antiepileptic Drugs with and without Periconceptional High Dose Folic Acid Use: A Population-Based Cohort Study. PLoS One. 2015 (1);10(7):e0131130. 63. Krishnamoorthy N, Simpson CD, Townend J, Helms PJ, McLay JS. Adolescent females and hormonal contraception: a retrospective study in primary care. J Adolesc Health. 2008;42(1):97-101. 64. Krishnamoorthy N, Ekins-Daukes S, Simpson CR, Milne RM, Helms PJ, McLay JS. Adolescent use of the combined oral contraceptive pill: a retrospective observational study. Arch Dis Child. 2005;90(9):903-5. 65. Nwaru BI, Tibble H, Shah SA, Pillinger R, McLean S, Ryan DP, et al. Hormonal contraception and the risk of severe asthma exacerbation: 17-year population-based cohort study. Thorax. 2021;76(2):109-15. 66. Smith D, Willan K, Prady SL, Dickerson J, Santorelli G, Tilling K, et al. Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England. BMJ Open. 2021;11(10):e053624. 67. Reddy A, Watson M, Hannaford P, Lefevre K, Ayansina D. Provision of hormonal and long- acting reversible contraceptive services by general practices in Scotland, UK (2004-2009). J Fam Plann Reprod Health Care. 2014;40(1):23-9. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 15 Figures Figure 1. PRISMA flow diagram for the identification and selection of studies included in the review. Records identified (total n = 9,401): Databases (total n = 7,942) MEDLINE (n = 1,760) EMBASE (n = 2,088) Scopus (n = 2,683) CINAHL (n = 381) Web of Science (n = 1,030) UK primary care database websites (n = 28)

lists (n = 1,431) Duplicate records removed (n = 4,142) Records screened (n = 5,259) Records excluded based on title and

screening (n = 5,142) Reports sought for retrieval (n = 117) Reports not retrieved (n = 0) Articles assessed for eligibility (n = 117) Articles excluded (n = 75): No new/original results (n = 2) Not national primary care data (n = 15) Not peer-reviewed or full-length (n = 23) No data on preconception indicator (n = 24) No data on reproductive-aged individuals (n = 11) Articles included (n = 42) Primary care databases included (n = 11) Identification Screening Included . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 16 Box 1. Recommendations to improve the use of UK routine primary care data for clinical practice, research and surveillance of preconception health and care. • Routine use of a standardised digital risk screening template (i.e. existing template in Ardens Clinical Decision Support System) to support implementation of the NICE Clinical Knowledge Summary on preconception advice and management.6 • Development of coding practice standards with appropriate incentives to improve data quality. • Standardisation of reporting of preconception indicators and pregnancy and offspring health outcomes, for example through the development of core outcome sets. • Improvements in the coding and identification of family and household members to enable linkage of data from biological parents and their children. • Nationwide linkage of general practice systems, and linkage of primary care datasets with other routine health datasets (such as Hospital Episode Statistics, Maternity Services Data Set and Community Services Data Set). . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 17 Tables Table 1. PICOS statement Population Individuals of reproductive age who may or may not be(come) pregnant/conceive a pregnancy (any gender, aged 15-49 years). Intervention/ exposure Preconception indicators as identified in Schoenaker et al.9 Preconception indicators are defined as medical, behavioural and social risk factors or exposures as well as wider determinants of health that may impact potential future pregnancies among all individuals of reproductive age. Studies do not have to identify relevant factors or exposures as ‘preconception indicators’. Comparator/ control Not applicable. Outcome Maternal health outcomes: any outcome that may occur during pregnancy (e.g. gestational diabetes), delivery (e.g. caesarean section), postpartum (e.g. mortality), or beyond (no age limit) (e.g. type 2 diabetes). Offspring health and developmental outcomes (including social/educational outcomes): any outcome that may occur during pregnancy (e.g. stillbirth), delivery (e.g. preterm birth), infancy (e.g. neonatal intensive care unit admission), or beyond (no age limit) (e.g. learning difficulty). Study design Observational studies (including cohort, cross-sectional and case-control studies). . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 18 Table 2. Characteristics of included studies reporting on the prevalence of preconception indicators in the overall population of people of reproductive age First author, year (reference) Dataset Country Study design Data collection period Total or maximum sample size Population characteristi cs: sex, age Preconception indicators reported Maternal and offspring outcomes reported Clinical Practice Research Datalink (CPRD) Lee, 2022 (27) CPRD GOLD UK Cross- sectional study 2018 37,641 Female, 15- 49 years Maternal age, ethnicity, deprivation, weight, smoking, depression, anxiety, severe mental health condition, asthma, PCOS, infertility, thyroid disease, eating disorder, endometriosis, hypertension, thromboembolism, sickle-cell disease, epilepsy, diabetes None Hope, 2022 (35) CPRD GOLD UK Cohort study 1990-2017 2,680,149 Female, 14- 45 years Ethnicity, mental health condition None Subramanian, 2022 (19) CPRD GOLD linked with HES England Cohort and case-control study 1997-2020 299,866 Female, 15- 49 years Ethnicity, PCOS Preterm delivery, mode of delivery, high or low birthweight, stillbirth, small and large for gestational age Syed, 2022 (36) CPRD GOLD linked with HES and Office for National Statistics (ONS) mortality register England Cohort study 2004- 2018 211,393 Female, 16- 55 years Deprivation, ethnicity, maternal age None den Heijer, 2019 (25) CPRD GOLD linked with IMD England Cohort 2000-2013 857,324 Female, 12- 25 years Smoking, deprivation, folic acid supplementation, sexually transmitted disease, PCOS, endometriosis, thyroid disease Ectopic pregnancy General Practice Research Database (GPRD) . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 19 First author, year (reference) Dataset Country Study design Data collection period Total or maximum sample size Population characteristi cs: sex, age Preconception indicators reported Maternal and offspring outcomes reported Briggs, 2013 (37) N/A UK Repeated cross- sectional study 2004-2010 1,103,669 Female, 15- 49 years Contraception None Rowlands, 2000 (38) N/A UK Cohort study 1994-1997 95,007 Female, 14- 29 years Contraception None The Health Improvement Network (THIN) Smith, 2020 (39) N/A UK Cohort study 2006-2016 241,662 Female, 15- 49 years Maternal age, deprivation, previous caesarean delivery, smoking None Cea Soriano, 2018 (40) N/A UK Cohort study and case- control study 1995-2012 251,581 Female, 15- 45 years Diabetes mellitus None Coton, 2016 (30) N/A UK Cohort study 1995-2012 301,794 Female, 16 and over Diabetes mellitus None Ban, 2015 (2) (41) N/A UK Cohort study 1990-2010 2,141,503 Female, 15- 44 years Inflammatory bowel disease None Cea Soriano, 2014 (28) N/A UK Cohort study 2004-2010 N/R Female, 18- 44 years Contraception None Cea Soriano, 2013 (42) N/A UK Cohort study 2008 574,185 Female, 12- 49 years Contraception None Dhalwani, 2013 (43) N/A UK Cohort study 1990-2010 1,776,746 Female, 15- 49 years Fertility problems None Ban, 2012 (44) N/A UK Cohort study 1994-2009 116,457 Female, 15- 45 years Depression, anxiety, serious mental illness, deprivation None Dave, 2010 (17) Use of family identification number to link mothers, fathers and children living in the same household UK Cohort study 1993-2007 86,957 Female and male, 15-≥35 years Depression None Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) network . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 20 First author, year (reference) Dataset Country Study design Data collection period Total or maximum sample size Population characteristi cs: sex, age Preconception indicators reported Maternal and offspring outcomes reported Gaudio, 2022 (18) N/A England Repeated cross- sectional study 2004-2018 729,662 Female and male, 12-46 years Valproate prescription None Gaudio, 2021 (29) N/A England Repeated cross- sectional study 2004-2017 465,898 Female, 16- 45 years Diabetes mellitus None Enhanced Prescribing Database (EPD) Given, 2020 (45) EPD linked with GP Patient Registrations Index Northern Ireland Cohort study 2010-2016 560,074 Female, 12- 49 years Contraception None Wemakor, 2014 (46) EPD linked with the 2010 Northern Ireland Multiple Deprivation Measure (NIMDM) Northern Ireland Cross- sectional study 2009 268,917 Female, 15- 45 years Deprivation, anti-depressant use None Other datasets Lee, 2022 (27) Secure Anonymised Information Linkage (SAIL) Databank Wales Cross- sectional study 2018 27,782 Female, 15- 49 years Maternal age, ethnicity, deprivation, weight, smoking, mental health problem, severe mental health condition, asthma, PCOS, infertility, thyroid disease, eating disorder, endometriosis, hypertension, thromboembolism, sickle-cell disease, epilepsy, diabetes None Pasvol, 2022 (47) IQVIA Medical Research Data (IMRD) database UK Repeated cross- sectional study 2000-2018 2,705,638 Female, 15- 49 years Contraception None ALSPAC, Avon Longitudinal Study of Parents and Children; CPRD, Clinical Practice Research Datalink; EPD, Enhanced Prescribing Database; HES, Hospital Episodes Statistics; IMD, index of multiple deprivation; PCOS, polycystic ovary syndrome; SD, standard deviation. . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 21 Table 3. Prevalence of (and trends in) preconception indicators reported for people of reproductive age in UK routine primary care data First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Domain: Wider determinants of health Indicator: Deprivation Ban, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] living in the most socio- economically deprived area (based on Townsend Index of Deprivation quintiles) 116,457 14.2 den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women living in the most socio-economically deprived area (based on IMD quintiles) 857,324 13.6 Syed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] living in the most socio-economically deprived area (based on IMD quintiles) 211,393 18.5 Smith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] living in the most socio-economically deprived area (based on Townsend Index of Deprivation quintiles) 277,114 16.0 Wemakor, 2014 (46) 2009 EPD Percentage of women living in the most socio-economically deprived area (based on NIMDM quintiles) 264,798 22.6 Lee, 2022 (27) 2018 CPRD (England) Percentage of women [with a conception date during the study period] living in the most socio-economically deprived area (based on IMD quintiles) 9,800 19.5 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] living in the most socio-economically deprived area (based on IMD quintiles) 24,538 15.6 Indicator: Ethnicity Hope, 2022 (35) 1990-2017 CPRD Percentage of women [with a pregnancy during the study period] from an ethnic minority

2,680,149 15.1 Coton, 2016 (30) 1995-2012 THIN Percentage of women [with a pregnancy during the study period] from an ethnic minority

301,794 14.9 Subramanian, 2022 (19) 1997-2020 CPRD Percentage of women [with a record of delivery during the study period] from an ethnic minority background 299,866 20.0 Syed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] from an ethnic minority background 211,393 13.7 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] from an ethnic minority background 37,641 12.8 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] from an ethnic minority background 27,782 15.1 . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 22 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Domain: Reproductive health and family planning Indicator: Teenage pregnancy Syed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] aged ≤19 at time of childbirth 211,393 3.3 Smith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] aged 15-19 at time of most recent pregnancy 309,573 3.1 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] aged 15-19 at time of index pregnancy 37,641 6.7 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] aged 15-19 at time of index pregnancy 27,782 5.5 Indicator: Advanced maternal age Syed, 2022 (36) 2004-2018 CPRD Percentage of women [with a live birth recorded during the study period] aged ≥40 at time of childbirth 211,393 27.0 Smith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] aged 35-49 at time of most recent pregnancy 309,573 26.1 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] aged 35-49 at time of most recent pregnancy 37,641 20.1 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] aged 35-49 at time of most recent pregnancy 27,782 15.1 Indicator: Previous caesarean delivery Smith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] with a previous caesarean delivery (based on most recent pregnancy) 98,932 23.7 Indicator: Fertility problems Dhalwani, 2013 (43) 1990-2010 THIN Percentage of women with a history of fertility problems (at least one record for a fertility problem based on Read codes) 1,776,746 3.3 Lee, 2022 (27) 2018 CRPD Percentage of women [with a conception date during the study period] with a history of fertility problems (based on Read codes for (possible) female infertility) 37,641 3.81 (3.62-4.01) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with a history of fertility problems (based on Read codes for (possible) female infertility) 27,782 1.18 Indicator: Contraception Rowlands, 2000 (38) 1994-1997 GPRD Percentage of women who use contraception (assessed based on prescription codes for all regular methods, excluding emergency contraception) 95,007 70.1 . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 23 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women who use contraception (oral contraception) 857,324 5.7 Pasvol, 2022 (47) 2000 IMRD Percentage of women who use contraception (COCP, POP and LARC) 2,705,638 32.9 (32.7-33.0) 2018 IMRD As above 2,705,638 29.2 (29.1-29.3) Cea Soriano, 2014 (28) 2004 THIN Percentage of women who use contraception (progesterone-only implant assessed using Read and MULTILEX codes) N/R 0.5 2010 THIN As above N/R 3.4 2004 THIN Percentage of women who use contraception (levonorgestrel releasing-intrauterine system (LNG-IUS) assessed using Read and MULTILEX codes) N/R 3.1 2010 THIN As above N/R 5.2 2004 THIN Percentage of women who use contraception (copper intrauterine devices assessed using Read and MULTILEX codes) N/R 5.4 2010 THIN As above N/R 4.8 2004 THIN Percentage of women who use contraception (progestogen-only injections assessed using Read and MULTILEX codes) N/R 3.6 2010 THIN As above N/R 3.2 Briggs, 2013 (37) 2004 GPRD Percentage of women who use contraception (assessed based on combined hormonal contraception prescription data) 1,018,835 19.6 2005 GPRD As above 1,040,629 19.6 2006 GPRD As above 1,053,353 19.1 2007 GPRD As above 1,067,462 18.7 2008 GPRD As above 1,085,149 18.2 2009 GPRD As above 1,100,002 17.4 2010 GPRD As above 1,103,669 16.3 Cea Soriano, 2013 (42) 2008 THIN Percentage of women who use contraception (combined oral contraceptive assessed using Read and MULTILEX codes) 574,185 16.2 (16.1-16.3) 2008 THIN Percentage of women who use contraception (progesterone-only pill assessed using Read and MULTILEX codes) 574,185 5.6 (5.5-5.6) 2008 THIN Percentage of women who use contraception (copper intrauterine device assessed using Read and MULTILEX codes) 574,185 4.5 (4.4-4.5) 2008 THIN Percentage of women who use contraception (levonorgestrel-releasing intrauterine system assessed using Read and MULTILEX codes) 574,185 4.2 (4.1-4.2) . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 24 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) 2008 THIN Percentage of women who use contraception (progesterone-only implant assessed using Read and MULTILEX codes) 574,185 1.5 (1.5-1.6) 2008 THIN Percentage of women who use contraception (progestogen-only injection assessed using Read and MULTILEX codes) 574,185 2.4 (2.3-2.4) 2008 THIN Percentage of women who use contraception (contraceptive patch assessed using Read and MULTILEX codes) 574,185 0.1 (0.1-0.2) Given, 2020 (45) 2010 EPD Percentage of women who use contraception (based on dispensed prescriptions of COCP, POP, emergency contraceptive, injection, implant, intrauterine device, transdermal patch, vaginal ring and gel) 465,912 25.7 2016 EPD As above 479,908 26.1 Domain: Health behaviours and weight Indicator: Folic acid supplementation den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women prescribed folic acid supplementation 857,324 0.3 Indicator: Underweight Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] in the underweight BMI category (<18.5 kg/m2) 30,910 3.9 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] in the underweight BMI category (<18.5 kg/m2) 21,802 5.9 Indicator: Overweight Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] in the overweight BMI category (25.0-29.9 kg/m2) 30,910 26.1 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] in the overweight BMI category (25.0-29.9 kg/m2) 21,802 26.0 Indicator: Obesity Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] in the obesity BMI category (≥30.0 kg/m2) 30,910 23.2 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] in the obesity BMI category (≥30.0 kg/m2) 21,802 24.6 Indicator: Smoking den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women who currently smoke 857,324 25.8 . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 25 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Smith, 2020 (39) 2006-2016 THIN Percentage of women [who gave birth to a single live infant during the study period] who currently smoke 263,575 13.1 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] who currently smoke 36,339 22.7 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] who currently smoke 24,785 26.7 Domain: Immunisation and infections Indicator: Sexually transmitted disease den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women diagnosed with gonorrhoea 857,324 0.01 Domain: Mental health conditions Indicator: Mental health condition Hope, 2022 (35) 1990-2017 CPRD Percentage of women [with a pregnancy during the study period] with a mental illness (assessed based on prescription, diagnosis and symptom data for depression, anxiety, psychosis, substance or alcohol misuse disorder, eating or personality disorder) 2680149 19.8 Indicator: Depression Dave, 2010 (17) 1993-2007 THIN Percentage of mothers with depression (assessed based on Read codes for unipolar depression and/or a prescription for an antidepressant for treatment of depression) 86,957 22.2 Dave, 2010 (17) 1993-2007 THIN Percentage of fathers with depression (assessed based on Read codes for unipolar depression and/or a prescription for an antidepressant for treatment of depression) 86,957 9.2 Ban, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] with depression during 9 months before pregnancy (assessed based on record of depression diagnosis and/or antidepressant prescription) 116,457 9.3 (9.1-9.4) Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with depression (based on read codes for diagnosis of depression and history of depression) 37,641 23.43 (23.01- 23.87) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with depression (based on read codes for diagnosis of depression and history of depression) 27,782 24.07 (23.57- 24.58) Indicator: Anxiety Ban, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] with anxiety during 9 months before pregnancy (assessed based on record of anxiety diagnosis and/or anxiolytic prescription & record of anxiety diagnosis and antidepressant prescription but no depression diagnosis) 116,457 4.1 (4.0-4.3) Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with anxiety (based on read codes for diagnosis of anxiety disorders and history of anxiety disorders) 37,641 18.98 (18.58- 19.38) . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 26 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with anxiety (based on read codes for diagnosis of anxiety disorders and history of anxiety disor ders) 27,782 23.05 (22.56- 23.55) Indicator: Serious mental illness Ban, 2012 (44) 1994-2009 THIN Percentage of women [with a pregnancy during the study period] with serious mental illness during 9 months before pregnancy (assessed based on record of bipolar disorder, schizophrenia or other psychotic disorders and/or prescription of lithium or mood stabilisers) 116,457 0.12 (0.11-0.14) Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with severe mental illness (based on read codes for diagnosis or history of bipolar disorder / affective psychosis and schizophrenia / non-affective psychosis) 37,641 2.42 (2.26-2.58) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with severe mental illness (based on read codes for diagnosis or history of bipolar disorder / affective psychosis and schizophrenia / non-affective psychosis) 27,782 2.07 (1.91-2.24) Domain: Physical health conditions Indicator: Epilepsy Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with epilepsy (based on read codes for epilepsy diagnosis, treatment and advice) 37,641 1.44 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with epilepsy (based on read codes for epilepsy diagnosis, treatment and advice) 27,782 1.30 Indicator: Diabetes mellitus Cea Soriano, 2018 (40) 1995-2012 THIN Percentage of women [with a pregnancy during the study period] with type 1 diabetes 251,581 0.36 Cea Soriano, 2018 (40) 1995-2012 THIN Percentage of women [with a pregnancy during the study period] with type 2 diabetes 251,581 0.24 Coton, 2016 (30) 1995 THIN Percentage of women [who gave birth during the study period] with type 1 diabetes (based on diagnostic Read codes and prescriptions for antidiabetics from medical records) N/R 0.16 Cea Soriano, 2018 (40) 1995-2012 THIN Percentage of women [with a pregnancy during the study period] with diabetes (any type) (based on Read codes suggestive of diabetes and for insulin prescriptions) 251,581 0.60 Coton, 2016 (30) 1995 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based on diagnostic Read codes and prescriptions for antidiabetics from medical records) N/R 0.23 Gaudio, 2021 (29) 2004 RCGP RCS Percentage of women with diabetes (any type) 316,461 1.0 Gaudio, 2021 (29) 2004 RCGP RCS Percentage of women with type 2 diabetes 316,461 0.6 . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 27 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Gaudio, 2021 (29) 2004 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 1 diabetes patients) 788 50.0 Gaudio, 2021 (29) 2004 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 2 diabetes patients) 1,098 33.1 Coton, 2016 (30) 2008 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based on diagnostic Read codes and prescriptions for antidiabetics from medical records) N/R 0.51 Coton, 2016 (30) 2009 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based on diagnostic Read codes and prescriptions for antidiabetics from medical records) N/R 0.67 Coton, 2016 (30) 2012 THIN Percentage of women [who gave birth during the study period] with type 2 diabetes (based on diagnostic Read codes and prescriptions for antidiabetics from medical records) N/R 1.06 Coton, 2016 (30) 2012 THIN Percentage of women [who gave birth during the study period] with type 1 diabetes (based on diagnostic Read codes and prescriptions for antidiabetics from medical records) N/R 0.41 Gaudio, 2021 (29) 2017 RCGP RCS Percentage of women with diabetes (any type) 465,898 1.4 Gaudio, 2021 (29) 2017 RCGP RCS Percentage of women with type 2 diabetes 465,898 0.9 Gaudio, 2021 (29) 2017 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 1 diabetes patients) 1,579 40.8 Gaudio, 2021 (29) 2017 RCGP RCS Percentage of women with poor diabetes control (HbA1c ≥8.5%) (type 2 diabetes patients) 3,041 24.7 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with type 1 diabetes 37,641 0.56 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with type 1 diabetes 27,782 0.49 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with type 2 diabetes 37,641 0.71 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with type 2 diabetes 27,782 0.68 Indicator: Polycystic ovary syndrome (PCOS) Subramanian, 2022 (19) 1997-2020 CPRD Percentage of women [with a record of delivery during the study period] with PCOS (based on Read codes for PCOS, polycystic ovaries or symptoms) 299,866 6.5 den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women with PCOS 857,324 0.2 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with PCOS (based on read codes for PCOS, isosexual virilisation, polycystic ovaries, Stein - Leventhal syndrome, multicystic ovaries and endoscopic drilling of ovary) 37,641 4.66 (4.45-4.88) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with PCOS (based on read codes for PCOS, isosexual virilisation, polycystic ovaries, Stein - Leventhal syndrome, multicystic ovaries and endoscopic drilling of ovary) 27,782 3.96 (3.73-4.20) Indicator: Endometriosis den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women with endometriosis 857,324 0.06 . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 28 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with endometriosis (based on read codes for diagnosis of endometriosis and procedures of endometriosis) 37,641 1.68 (1.55-1.82) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with endometriosis (based on read codes for diagnosis of endometriosis and procedures of endometriosis) 27,782 1.31 (1.18-1.45) Indicator: Eating disorder Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with an eating disorder (based on read codes for eating disorders, including anorexia, binge eating, bulimia, compulsive eating disorder, referral to eating disorder clinic) 37,641 1.88 (1.74-2.02) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with an eating disorder (based on read codes for eating disorders, including anorexia, binge eati ng, bulimia, compulsive eating disorder, referral to eating disorder clinic) 27,782 1.80 (1.65-1.97) Indicator: Thyroid disease den Heijer, 2019 (25) 2000-2013 CPRD Percentage of women with thyroid disease 857,324 0.07 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with thyroid disease (based on read codes for hyperthyroidism and hypothyroidism) 37,641 3.34 (3.16-3.52) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with thyroid disease (based on read codes for hyperthyroidism and hypothyroidism) 27,782 2.45 (2.28-2.64) Indicator: Hypertension Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with hypertension (based on read codes related to hypertension) 37,641 0.87 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with hypertension (based on read codes related to hypertension) 27,782 0.67 Indicator: Thromboembolism Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with thromboembolism (based on read codes for history, diagnosis or procedure for pulmonary embolism) 37,641 0.65 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with thromboembolism (based on read codes for history, diagnosis or procedure for pulmonary embolism) 27,782 0.60 Indicator: Asthma . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 29 First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with asthma (based on read codes on asthma, including diagnosis, treatment, education and review) 37,641 14.63 (14.27- 14.99) Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with asthma (based on read codes on asthma, including diagnosis, treatment, education and review) 27,782 17.17 (16.73- 17.62) Indicator: Inflammatory bowel disease (IBD) Ban, 2015 (2) (41) 1990-2010 THIN Percentage of women [with a singleton live birth recorded during the study] with IBD (based on Read codes) 2,141,503 0.45 Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with IBD (Crohn’s disease, ulcerative colitis) 37,641 0.60 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with IBD (Crohn’s disease, ulcerative colitis) 27,782 0.58 Indicator: Sickle cell disease Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with sickle cell disease (based on read codes for diagnosis of sickle-cell anaemia or history of sickle-cell anaemia) 37,641 0.01 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with sickle cell disease (based on read codes for diagnosis of sickle-cell anaemia or history of sickle-cell anaemia) 27,782 <0.02 Indicator: Cancer Lee, 2022 (27) 2018 CPRD Percentage of women [with a conception date during the study period] with previous cancer diagnosis (breast, lung, bowel, cervical, ovarian, uterine, thyroid, skin, l ymphoma, leukaemia, metastatic) 37,641 0.51 Lee, 2022 (27) 2018 SAIL Percentage of women [with a conception date during the study period] with previous cancer diagnosis (breast, lung, bowel, cervical, ovarian, uterine, thyroid, skin, lymphoma, leukaemia, metastatic) 27,782 0.60 Domain: Medication Indicator: Medication not recommended when planning pregnancy Gaudio, 2022 (18) 2004 RCGP RCS Percentage of women prescribed valproate 533,627 0.31 (0.18-0.44) 2018 RCGP RCS As above 729,662 0.16 (0.07-0.24) 2004 RCGP RCS Percentage of men prescribed valproate N/R 0.37 (0.35-0.38) 2018 RCGP RCS As above N/R 0.36 (0.34-0.37) . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 30 CI, confidence interval; COCP, combined oral contraceptive pill; CPRD, Clinical Practice Research Datalink; EPD, Enhanced Prescribing Database; IMD, index of multiple deprivation; LARC, long-acting reversible contraception; NIMDM, Northern Ireland Multiple Deprivation Measure; N/R, not reported; PCOS, polycystic ovary syndrome; POP, progestogen-only pill; SAIL, Secure Anonymised Information Linkage. First author, year (reference) Year for datapoint Dataset Preconception indicator measure Sample size Prevalence of preconception indicator (% with 95% CI) Wemakor, 2014 (46) 2014 EPD Percentage of women prescribed anti-depressant medication 43,770 16.3 (16.1-16.4) . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 31 Table 4. Associations of preconception indicators with outcomes in women and offspring First author, year (reference)

and comparison group Indicator data source Maternal or offspring outcome Outcome data source Sample size Statistical

definition

Adjustment for confounders Contraception Parker, 2016 (48) Intrauterine device (IUD) use prior to start of pregnancy vs. no IUD use prior to start of pregnancy (determined using procedure codes, drug codes and record review) CPRD Pre-eclampsia CPRD 13,900 Adjusted OR (95% CI) 0.76 (0.58-0.98) Age, general practice, year of delivery, BMI, smoking, parity, induced abortion, fertility problems, pre- existing diabetes Sexually transmitted disease den Heijer, 2019 (25) Positive chlamydia test vs. negative chlamydia test CPRD Ectopic pregnancy CPRD GOLD 2,484 Adjusted HR (95% CI) 1.87 (1.38-2.54) Age, smoking status, history of gonorrhea Polycystic ovary syndrome (PCOS) Subramanian, 2022 (19) PCOS vs. no PCOS (assessed based on Read codes for PCOS, polycystic ovaries or symptoms) CRPD Preterm delivery (< 37 weeks of gestational age at delivery) HES 137,930 Adjusted OR (95% CI) 1.11 (1.06-1.17) Age, ethnicity, deprivation, dysglycaemia, hypertension, thyroid disorders, numbers of babies born at the delivery, pre-gravid body mass index Rees, 2016 (20) PCOS vs. no PCOS CPRD Preterm birth HES and CRPD 19,502 Adjusted HR (95% CI) 1.24 (1.09-1.41) Age, BMI, primary care practice, multiple gestation, number of previous births, smoking history . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 32 First author, year (reference)

and comparison group Indicator data source Maternal or offspring outcome Outcome data source Sample size Statistical

definition

Adjustment for confounders Subramanian, 2022 (19) PCOS vs. no PCOS (assessed based on Read codes for PCOS, polycystic ovaries or symptoms) CPRD High birthweight (>4kg) for at least one of the babies HES 137,930 Adjusted OR (95% CI) 0.97 (0.92-1.01) Age, ethnicity, deprivation, dysglycaemia, hypertension, thyroid disorders, numbers of babies born at the delivery, pre-gravid body mass index, gestational age Rees, 2016 (20) PCOS vs. no PCOS CPRD High birth weight HES 12,363 Adjusted OR (95% CI) 0.97 (0.67-1.41) Age, BMI, primary care practice, multiple gestation, number of previous births, smoking history Subramanian, 2022 (19) PCOS vs. no PCOS (assessed based on Read codes for PCOS, polycystic ovaries or symptoms) CPRD Low birthweight (<2.5 kg) for at least one of the babies HES 137,930 Adjusted OR (95% CI) 1.03 (0.95-1.13) Age, ethnicity, deprivation, dysglycaemia, hypertension, thyroid disorders, numbers of babies born at the delivery, pre-gravid body mass index, gestational age Rees, 2016 (20) PCOS vs. no PCOS CPRD Low birth weight HES 12,363 Adjusted OR (95% CI) 1.19 (1.00-1.42) Age, BMI, primary care practice, multiple gestation, number of previous births, smoking history Subramanian, 2022 (19) PCOS vs. no PCOS (assessed based on Read codes for PCOS, polycystic CPRD Emergency caesarean section HES 137,930 Adjusted OR (95% CI) 1.10 (1.05-1.15) Age, ethnicity, deprivation, dysglycaemia, hypertension, thyroid disorders, numbers of . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 33 First author, year (reference)

and comparison group Indicator data source Maternal or offspring outcome Outcome data source Sample size Statistical

definition

Adjustment for confounders ovaries or symptoms) babies born at the delivery, pre-gravid body mass index, gestational age Subramanian, 2022 (19) As above CPRD Elective/ other/unspecified caesarean section delivery As above As above Adjusted OR (95% CI) 1.07 (1.03-1.12) As above Subramanian, 2022 (19) As above CPRD Instrumental vaginal delivery As above As above Adjusted OR (95% CI) 1.04 (1.00-1.09) As above Subramanian, 2022 (19) As above CPRD Stillbirth As above As above Adjusted OR (95% CI) 0.99 (0.81-1.21) Age, ethnicity, deprivation, dysglycaemia, hypertension, thyroid disorders, numbers of babies born at the delivery, pre-gravid body mass index Subramanian, 2022 (19) As above CPRD Very preterm (<32 weeks of gestational age at delivery) As above As above Adjusted OR (95% CI) 1.07 (0.97-1.18) As above Subramanian, 2022 (19) As above CPRD Extremely preterm (90th percentile) for at least one of the babies As above As above Adjusted OR (95% CI) 1.00 (0.97-1.04) As above Subramanian, 2022 (19) As above CPRD Small for gestational age (<10th percentile) for at least one of the babies As above As above Adjusted OR (95% CI) 1.03 (0.96-1.11) As above . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint 34 First author, year (reference)

and comparison group Indicator data source Maternal or offspring outcome Outcome data source Sample size Statistical

definition

Adjustment for confounders Berni, 2018 (49) PCOS vs. no PCOS (assessed based on Read codes C164.00, C164.12, C165.00) CPRD Offspring ADHD HES 17,668 Adjusted OR (95% CI) 1.34 (0.96-1.89) Age, body mass index category, primary care practice, history of prior mental health disorder As above CPRD Offspring autism spectrum disorder As above As above Adjusted OR (95% CI) 1.75 (1.27-2.46) As above Rees, 2016 (20) PCOS vs. no PCOS CPRD Miscarriage resulting in hospital admission HES 22075 Adjusted HR (95% CI) 1.70 (1.56-1.84) Age, BMI, primary care practice, number of previous births, smoking history Rees, 2016 (20) As above CPRD Pre-eclampsia HES and CRPD 19,502 Adjusted HR (95% CI) 1.31 (1.16-1.49) Age, BMI, primary care practice, multiple gestation, number of previous births, smoking history Rees, 2016 (20) As above CPRD Gestational diabetes HES and CRPD 19,502 Adjusted HR (95% CI) 1.42 (1.21-1.67) As above Rees, 2016 (20) As above CPRD Jaundice HES 12,363 Adjusted OR (95% CI) 1.20 (1.03-1.39) As above Rees, 2016 (20) As above CPRD Respiratory complications (offspring) HES 12,363 Adjusted OR (95% CI) 1.20 (1.06-1.37) As above Rees, 2016 (20) As above CPRD Hypoglycaemia (offspring) HES 12,363 Adjusted OR (95% CI) 1.31 (0.99-1.74) As above Rees, 2016 (20) As above CPRD Feeding issues HES 12,363 Adjusted OR (95% CI) 1.21 (0.96-1.52) As above CI, confidence intervals; CPRD, Clinical Practice Research Datalink; HES, hospital episodes statistics; HR, Hazard Ratio; OR, odds ratio; PCOS, polycystic ovary syndrome . CC-BY 4.0 International licenseIt is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint The copyright holder for thisthis version posted February 6, 2024. ; https://doi.org/10.1101/2024.02.05.24302342doi: medRxiv preprint