Table S1. The RECORD statement. - UCL - London's Global ...
Supplementary AppendixHeng Fan,1 researcher, Ruth Gilbert,1 professor of clinical epidemiology, Finbar O'Callaghan,2 professor of paediatric neuroscience, Leah Li,1 professor of medical statistics and epidemiology1Population, Policy and Practice Programme, Great Ormond Street Institute of Child Health, University College London, London, UK.2Developmental Neurosciences Programme, Great Ormond Street Institute of Child Health, University College London, London, UK. TOC \o "1-3" \h \z \u Table S1. The RECORD statement. PAGEREF _Toc25924645 \h 2Text S1. Development of gestational age in the Clinical Practice Research Datalink (CPRD). PAGEREF _Toc25924646 \h 8Text S2. Outcome identification. PAGEREF _Toc25924647 \h 9Table S2. Codes for outcome identification. PAGEREF _Toc25924648 \h 10Table S3. Most frequent five Read codes for each system-specific malformation. PAGEREF _Toc25924649 \h 11Table S4. Definition of covariates.* PAGEREF _Toc25924650 \h 13Table S5. Unadjusted and propensity-score-adjusted baseline characteristics (N [%]) of children whose mother were prescribed macrolides or penicillins from 14 gestation weeks to delivery (“the second to third trimester”). PAGEREF _Toc25924651 \h 16Table S6. Unadjusted and propensity-score-adjusted baseline characteristics (N [%]) of children whose mother were prescribed macrolides or penicillins from 4 gestation weeks to delivery (“in any trimester”). PAGEREF _Toc25924652 \h 17Table S7. Unadjusted and propensity-score-adjusted baseline characteristics (N [%]) of children whose mother were prescribed macrolides or penicillins 10 to 50 weeks before pregnancy. PAGEREF _Toc25924653 \h 18Table S8. Subgroup analysis according to macrolides subtypes, on the association between adverse child outcomes and macrolides versus penicillins prescribed during pregnancy. PAGEREF _Toc25924654 \h 19Table S9. Subgroup analysis according to duration of treatment (< 7 days or ≥ 7 days), on the association between adverse child outcomes and macrolides versus penicillins prescribed during pregnancy. PAGEREF _Toc25924655 \h 20Table S10. Sensitivity analysis: comparison of the risks (or hazards) between siblings of children prenatally prescribed macrolides and siblings of children prenatally prescribed penicillins in the study cohort, according to timing of prescription. PAGEREF _Toc25924656 \h 22Table S11. Sensitivity analysis on the association between adverse child outcomes and macrolides versus penicillins prescribed during pregnancy: restricting to mothers whose antibiotics were prescribed to respiratory tract infections. PAGEREF _Toc25924657 \h 23Text S3. Probabilistic multiple bias analysis on outcome misclassification and live- birth bias PAGEREF _Toc25924658 \h 24Table S12. Post-hoc analysis on the association between common specific malformation and macrolides versus penicillins prescribed during pregnancy. PAGEREF _Toc25924659 \h 29Table S13. Number of prescriptions matched or not matched with any indication (infection) and number of any major malformation by each indication. PAGEREF _Toc25924660 \h 30Table S14. Previously published studies on the association between maternal exposure of macrolides and major congenital malformations or neurodevelopmental disorders. PAGEREF _Toc25924661 \h 31Reference PAGEREF _Toc25924662 \h 33Table S1. The RECORD statement.Item No.STROBE itemsLocation in manuscript where items are reportedRECORD itemsLocation in manuscript where items are reportedTitle and abstract1(a) Indicate the study’s design with a commonly used term in the title or the abstract (b) Provide in the abstract an informative and balanced summary of what was done and what was found1-2RECORD 1.1: The type of data used should be specified in the title or abstract. When possible, the name of the databases used should be included.RECORD 1.2: If applicable, the geographic region and timeframe within which the study took place should be reported in the title or abstract.RECORD 1.3: If linkage between databases was conducted for the study, this should be clearly stated in the title or abstract.12-IntroductionBackground rationale2Explain the scientific background and rationale for the investigation being reported4Objectives3State specific objectives, including any prespecified hypotheses4MethodsStudy Design4Present key elements of study design early in the paper5Setting5Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection5Participants6(a) Cohort study - Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-upCase-control study - Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controlsCross-sectional study - Give the eligibility criteria, and the sources and methods of selection of participants(b) Cohort study - For matched studies, give matching criteria and number of exposed and unexposedCase-control study - For matched studies, give matching criteria and the number of controls per case5RECORD 6.1: The methods of study population selection (such as codes or algorithms used to identify subjects) should be listed in detail. If this is not possible, an explanation should be provided. RECORD 6.2: Any validation studies of the codes or algorithms used to select the population should be referenced. If validation was conducted for this study and not published elsewhere, detailed methods and results should be provided.RECORD 6.3: If the study involved linkage of databases, consider use of a flow diagram or other graphical display to demonstrate the data linkage process, including the number of individuals with linked data at each stage.55-Variables7Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable.6-7RECORD 7.1: A complete list of codes and algorithms used to classify exposures, outcomes, confounders, and effect modifiers should be provided. If these cannot be reported, an explanation should be provided.Supplementary Text S1-2, table S2,Table S2Data sources/ measurement8For each variable of interest, give sources of data and details of methods of assessment (measurement).Describe comparability of assessment methods if there is more than one group7Bias9Describe any efforts to address potential sources of bias6,8-9Study size10Explain how the study size was arrived at6, Supplementary Text S2Quantitative variables11Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen, and whySupplementaryTable S4Statistical methods12(a) Describe all statistical methods, including those used to control for confounding(b) Describe any methods used to examine subgroups and interactions(c) Explain how missing data were addressed(d) Cohort study - If applicable, explain how loss to follow-up was addressedCase-control study - If applicable, explain how matching of cases and controls was addressedCross-sectional study - If applicable, describe analytical methods taking account of sampling strategy(e) Describe any sensitivity analyses7-8 Data access and cleaning methods..RECORD 12.1: Authors should describe the extent to which the investigators had access to the database population used to create the study population.RECORD 12.2: Authors should provide information on the data cleaning methods used in the study.5Figure 1Linkage..RECORD 12.3: State whether the study included person-level, institutional-level, or other data linkage across two or more databases. The methods of linkage and methods of linkage quality evaluation should be provided.-ResultsParticipants13(a) Report the numbers of individuals at each stage of the study (e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed)(b) Give reasons for non-participation at each stage.(c) Consider use of a flow diagram9-10Figure 1Figure 1RECORD 13.1: Describe in detail the selection of the persons included in the study (i.e., study population selection) including filtering based on data quality, data availability and linkage. The selection of included persons can be described in the text and/or by means of the study flow diagram.Figure 1Descriptive data14(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders(b) Indicate the number of participants with missing data for each variable of interest(c) Cohort study - summarise follow-up time (e.g., average and total amount)Table 1Table 19-10Outcome data15Cohort study - Report numbers of outcome events or summary measures over timeCase-control study - Report numbers in each exposure category, or summary measures of exposureCross-sectional study - Report numbers of outcome events or summary measuresTable 2Main results16(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence interval). Make clear which confounders were adjusted for and why they were included(b) Report category boundaries when continuous variables were categorized(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time periodTable 2, 10-11SupplementaryTable S413Other analyses17Report other analyses done—e.g., analyses of subgroups and interactions, and sensitivity analyses11DiscussionKey results18Summarise key results with reference to study objectives13Limitations19Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias13-14RECORD 19.1: Discuss the implications of using data that were not created or collected to answer the specific research question(s). Include discussion of misclassification bias, unmeasured confounding, missing data, and changing eligibility over time, as they pertain to the study being reported.13-14Interpretation20Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence15-16Generalisability21Discuss the generalisability (external validity) of the study results13Other InformationFunding22Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based17Accessibility of protocol, raw data, and programming code..RECORD 22.1: Authors should provide information on how to access any supplemental information such as the study protocol, raw data, or programming code.Protocol: 6Raw data:18*Reference: Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, S?rensen HT, von Elm E, Langan SM, the RECORD Working Committee. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement. PLoS Medicine 2015; in press.*Checklist is protected under Creative Commons Attribution (CC BY) license.Text S1. Development of gestational age in the Clinical Practice Research Datalink (CPRD).A hierarchy of available pregnancy markers was chosen that reflects their potential accuracy to estimate the start of a pregnancy episode. Pregnancy markers that directly provide gestational age such as gestational age in weeks, prenatal examination, and fertility procedures (IVF) were on the top of the hierarchy. Next hierarchy of markers includes ranges of gestational week indicators (e.g. premature 24-26 weeks) and outcome-specific estimates (e.g. premature labour, imputed as 36 weeks, because around 60% live premature births born at 36 gestational weeks). Gestational weeks imputed from birthweight was on the 3rd hierarchy, based on the intrauterine growth curves published by Irene E. Olsen et al. ADDIN EN.CITE <EndNote><Cite><Author>Olsen</Author><Year>2010</Year><RecNum>49700</RecNum><DisplayText>(1)</DisplayText><record><rec-number>49700</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1540832832">49700</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Olsen, Irene E.</author><author>Groveman, Sue A.</author><author>Lawson, M. Louise</author><author>Clark, Reese H.</author><author>Zemel, Babette S.</author></authors></contributors><titles><title>New Intrauterine Growth Curves Based on United States Data</title><secondary-title>Pediatrics</secondary-title></titles><periodical><full-title>Pediatrics</full-title><abbr-1>Pediatrics</abbr-1></periodical><pages>e214-e224</pages><volume>125</volume><number>2</number><dates><year>2010</year></dates><urls><related-urls><url>;(1) For pregnancies with no information available for the above three hierarchies of markers, full term births were assumed and gestational week 40 were used to calculate pregnancy start dates. Codes used in each hierarchy is referenced from Matcho, A. et al. ADDIN EN.CITE <EndNote><Cite><Author>Matcho</Author><Year>2018</Year><RecNum>49738</RecNum><DisplayText>(2)</DisplayText><record><rec-number>49738</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1548845468">49738</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Matcho, A.</author><author>Ryan, P.</author><author>Fife, D.</author><author>Gifkins, D.</author><author>Knoll, C.</author><author>Friedman, A.</author></authors></contributors><auth-address>Epidemiology Analytics, Janssen Research and Development, LLC, Raritan, New Jersey, United States of America.
Epidemiology, Janssen Research and Development, LLC, Titusville, New Jersey, United States of America.
Global Labeling, Janssen Research and Development, LLC, Titusville, New Jersey, United States of America.</auth-address><titles><title>Inferring pregnancy episodes and outcomes within a network of observational databases</title><secondary-title>PLoS One</secondary-title></titles><periodical><full-title>Plos One</full-title></periodical><pages>e0192033</pages><volume>13</volume><number>2</number><edition>2018/02/02</edition><keywords><keyword>Adult</keyword><keyword>Algorithms</keyword><keyword>*Databases, Factual</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Pregnancy</keyword><keyword>*Pregnancy Outcome</keyword><keyword>United Kingdom</keyword><keyword>Young Adult</keyword></keywords><dates><year>2018</year></dates><isbn>1932-6203 (Electronic)
1932-6203 (Linking)</isbn><accession-num>29389968</accession-num><urls><related-urls><url>;(2)For babies in the study population (n=726274), gestational ages were measured from each hierarchy with the following proportion: 27.8% from the first hierarchy (from codes for gestational age), 14.3% from the second hierarchy (from codes for gestational week range), 8.2% from the third hierarchy (imputed based on birthweight), 49.6% imputed as full-term (40 gestational weeks). The distribution of gestational age is consistent with the UK Office of National Statistics, although about 6%-7% full-term births with “true” gestational age of 37-38 weeks might have been estimated to be with 39 or longer gestational weeks (see table below). ADDIN EN.CITE <EndNote><Cite><Author>Office for National Statistics</Author><Year>2014</Year><RecNum>49870</RecNum><DisplayText>(3)</DisplayText><record><rec-number>49870</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1567538332">49870</key></foreign-keys><ref-type name="Dataset">59</ref-type><contributors><authors><author>Office for National Statistics,</author></authors><secondary-authors><author>Office for National Statistics,</author></secondary-authors></contributors><titles><title>Gestation-specific Infant Mortality in England and Wales, 2007-2008</title></titles><dates><year>2014</year></dates><urls><related-urls><url>;(3) This equates to move the measurement window forward about two week earlier (from gestational week 2 instead of 4) for these live births, which would mildly bias the current association for the first trimester towards the null. Similar algorithms (using hierarchical code groups and imputing to estimate pregnancy start dates) were reported and validated in other studies of CPRD, showing close agreement with external data.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NaW5hc3NpYW48L0F1dGhvcj48WWVhcj4yMDE5PC9ZZWFy
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ADDIN EN.CITE.DATA (2, 4) TextS1.Table-1 Distribution of gestational weeks of live births in the target population of this study and according to the UK Office of National Statistics.Gestational age, weeksThis study 1990-2015The UK Office for National Statistics 2007-08 No. of live birthsProportion23-2738480.5%0.5%28-3162710.9%0.8%32-34118351.6%1.9%35-36363865.0%4.1%37-388766612.1%19.3%>=3958026879.9%73.5%Text S2. Outcome identification.The main outcomes of this study were major (any and five system-specific) malformations and four neurodevelopmental disorders. Eligible outcomes for this study include those could potentially result from short-term fetal hypoxia. We therefore included major malformations (any and system-specific malformations) and neurodevelopmental disorders. Malformations with specific known causes such as malformation resulted from maternal infections, fetal alcohol syndrome, Valproate syndrome and chromosomal malformations were not included. Twelve system-specific malformations were defined according to the European Surveillance of Congenital Anomalies (EUROCAT). ADDIN EN.CITE <EndNote><Cite><Author>European Surveillance of Congenital Anomalies</Author><RecNum>49758</RecNum><DisplayText>(5)</DisplayText><record><rec-number>49758</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1554032227">49758</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>European Surveillance of Congenital Anomalies,</author></authors></contributors><titles><title>The EUROCAT Guide</title></titles><number>26 May 2019</number><dates></dates><publisher>European Surveillance of Congenital Anomalies</publisher><urls><related-urls><url> 26th, 2019</custom2></record></Cite></EndNote>(5)We then excluded 1) the musculoskeletal malformation (e.g. club foot, knock-knee and hip dislocation) as a system-specific malformation and as “any major malformation”, because they are not reliably recorded in GP records ADDIN EN.CITE <EndNote><Cite><Author>Sewell</Author><Year>2009</Year><RecNum>49729</RecNum><DisplayText>(6)</DisplayText><record><rec-number>49729</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1548260620">49729</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sewell, M D</author><author>Rosendahl, K</author><author>Eastwood, D M</author></authors></contributors><titles><title>Developmental dysplasia of the hip</title><secondary-title>BMJ</secondary-title></titles><periodical><full-title>Bmj</full-title></periodical><pages>b4454</pages><volume>339</volume><dates><year>2009</year></dates><urls></urls><electronic-resource-num>10.1136/bmj.b4454</electronic-resource-num></record></Cite></EndNote>(6); and 2) system-specific malformations that we had insufficient power to detect a 2-fold relative risk increase at 80% power (5% α level). Five out of the eleven system-specific malformations from the EUROCAT classification fulfilled the power criterion according to its prevalence table and were analysed as system-specific malformations, including nervous system malformation, cardiovascular malformation, gastrointestinal malformation, genital malformation and urinary malformation (details were described in our protocol on [NCT03948620]). ADDIN EN.CITE <EndNote><Cite><Author>European Surveillance of Congenital Anomalies</Author><RecNum>49699</RecNum><DisplayText>(7)</DisplayText><record><rec-number>49699</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1540308863">49699</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>European Surveillance of Congenital Anomalies,</author></authors></contributors><titles><title>EUROCAT Prevalence Data Tables</title></titles><number>26 May 2019</number><dates></dates><publisher>European Surveillance of Congenital Anomalies</publisher><urls><related-urls><url> 26th, 2019</custom2></record></Cite></EndNote>(7) Any of the eleven system-specific malformations (except for musculoskeletal malformations) was evaluated as “any major malformation”, and identified from child GP records by 3 years old using Read codes which were mapped to the tenth edition of the International Classification of Diseases (ICD–10) code lists provided by EUROCAT. ADDIN EN.CITE <EndNote><Cite><Author>European Surveillance of Congenital Anomalies</Author><RecNum>49758</RecNum><DisplayText>(5)</DisplayText><record><rec-number>49758</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1554032227">49758</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>European Surveillance of Congenital Anomalies,</author></authors></contributors><titles><title>The EUROCAT Guide</title></titles><number>26 May 2019</number><dates></dates><publisher>European Surveillance of Congenital Anomalies</publisher><urls><related-urls><url> 26th, 2019</custom2></record></Cite></EndNote>(5) Neurodevelopmental disorders (cerebral palsy, epilepsy, ADHD and ASD) were defined as the time to the first diagnostic or treatment code indicating the outcome by 14 years old. We identified potential cerebral palsy cases based on informative prescription or Read codes using the Random Forest approach, as we have previously described. ADDIN EN.CITE <EndNote><Cite><Author>Fan</Author><Year>2018</Year><RecNum>49710</RecNum><DisplayText>(8)</DisplayText><record><rec-number>49710</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1545761136">49710</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Fan, H</author><author>Li, L</author><author>Gilbert, R</author><author>O'Callaghan, F</author><author>Wijlaars, L</author></authors></contributors><titles><title>A machine learning approach to identify cases of cerebral palsy using the UK primary care database</title><secondary-title>The Lancet</secondary-title></titles><periodical><full-title>The Lancet</full-title></periodical><pages>S33</pages><volume>392</volume><dates><year>2018</year><pub-dates><date>2018/11/01/</date></pub-dates></dates><isbn>0140-6736</isbn><urls><related-urls><url>(18)32077-4</electronic-resource-num></record></Cite></EndNote>(8) The potential cerebral palsy cases were then validated by a paediatric-neurologist (FC) blinded to the prenatal antibiotics exposure. Other neurodevelopmental disorders (epilepsy, ADHD and ASD) were identified using previously validated criteria using diagnostic codes and/or prescriptions (Supplementary Table S2).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ib2xkZW48L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA (9-11) Table S2. Codes for outcome identification.OutcomeCase identificationAny major congenital malformationAny major system specific malformation according to the EUROCAT classification. We use Read code lists mapped to ICD 10 codes Chapter Q. Exclude: 1) minor anomalies post-2005*; 2) malformations caused by known chromosomal abnormalities and teratogens (i.e. Teratogenic syndromes with malformations, Fetal alcohol syndrome, Valproate syndrome, Maternal infections resulting in malformations, Genetic syndromes + microdeletions, Chromosomal malformations); and 3) musculoskeletal malformations.CardiovascularRead codes mapped to ICD 10 (Q20-Q26, exclude Q2111, Q250 if GA <37 weeks, Q2541, Q256 if GA<37 weeks, Q261) GastrointestinalRead codes mapped to ICD 10 (Q38-Q45, Q790, exclude Q381, Q382, Q3850, Q400, Q401, Q4021, Q430, Q4320, Q4381, Q4382) Nervous systemRead codes mapped to ICD 10 (Q00-Q07, exclude Q0461, Q0782)GenitalRead codes mapped to ICD 10 (Q50-Q52, Q54-Q56, exclude Q523, Q525, Q527, Q5520, Q5521)UrinaryRead codes mapped to ICD 10 (Q60-Q64, Q794, exclude Q610, Q627, Q633)Cerebral palsyBesides cases identified by ≥ 1 diagnostic code, we identified cerebral palsy cases from informative prescription or Read codes using the Random Forest approach and were validated by a paediatric-neurologist (FC) blinded to the prenatal antibiotics exposure. ADDIN EN.CITE <EndNote><Cite><Author>Fan</Author><Year>2018</Year><RecNum>49710</RecNum><DisplayText>(8)</DisplayText><record><rec-number>49710</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1545761136">49710</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Fan, H</author><author>Li, L</author><author>Gilbert, R</author><author>O'Callaghan, F</author><author>Wijlaars, L</author></authors></contributors><titles><title>A machine learning approach to identify cases of cerebral palsy using the UK primary care database</title><secondary-title>The Lancet</secondary-title></titles><periodical><full-title>The Lancet</full-title></periodical><pages>S33</pages><volume>392</volume><dates><year>2018</year><pub-dates><date>2018/11/01/</date></pub-dates></dates><isbn>0140-6736</isbn><urls><related-urls><url>(18)32077-4</electronic-resource-num></record></Cite></EndNote>(8)EpilepsyTwo prescriptions of antiepileptic drug (AED, identified based on British National Formula Chapter 4.8) within four months or ≥ 1 diagnosis ADDIN EN.CITE <EndNote><Cite><Author>Meeraus</Author><Year>2015</Year><RecNum>49543</RecNum><DisplayText>(11)</DisplayText><record><rec-number>49543</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1515709449">49543</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Wilhelmine Hadler Meeraus</author></authors></contributors><titles><title>Adverse Paediatric Outcomes of Antibiotic Treatment in Pregnancy</title></titles><volume>PhD</volume><dates><year>2015</year></dates><publisher>University College London</publisher><urls></urls></record></Cite></EndNote>(11)Attention deficit hyperactivity disorder (ADHD)≥ 2 occurrence of prescriptions for ADHD (identified based on British National Formula Chapter 4.4) or diagnoses (attention deficit hyperactivity disorder, hyperkinetic disorders, hyperkinetic syndrome, hyperkinetic reaction of childhood or adolescence, overactive child syndrome and disturbance of activity and attention) within 4 month ADDIN EN.CITE <EndNote><Cite><Author>Holden</Author><Year>2013</Year><RecNum>49556</RecNum><DisplayText>(9)</DisplayText><record><rec-number>49556</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1516217579">49556</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Holden, SE</author><author>Jenkins-Jones, S</author><author>Poole, CD</author><author>Morgan, CL</author><author>Coghill, D</author><author>Currie, CJ</author></authors></contributors><titles><title>The prevalence and incidence, resource use and financial costs of treating people with attention deficit/hyperactivity disorder (ADHD) in the United Kingdom (1998 to 2010)</title><secondary-title>Child Adolesc Psychiatry Ment Health</secondary-title></titles><periodical><full-title>Child Adolesc Psychiatry Ment Health</full-title></periodical><pages>34</pages><volume>7</volume><number>1</number><dates><year>2013</year></dates><label>ADHD</label><urls></urls></record></Cite></EndNote>(9) Autism spectrum disorder (ASD)At least one diagnostic code ((infantile or childhood) autism, Asperger’s syndrome, Rett’s syndrome, Heller’s syndrome, Autistic spectrum disorder, disintegrative disorder, and other pervasive developmental disorders) ADDIN EN.CITE <EndNote><Cite><Author>Hagberg</Author><Year>2017</Year><RecNum>49759</RecNum><DisplayText>(10)</DisplayText><record><rec-number>49759</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1554034201">49759</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hagberg, K. W.</author><author>Jick, S. S.</author></authors></contributors><auth-address>Boston Collaborative Drug Surveillance Program, Boston University School of Public Health, Lexington, MA, USA.</auth-address><titles><title>Validation of autism spectrum disorder diagnoses recorded in the Clinical Practice Research Datalink, 1990-2014</title><secondary-title>Clin Epidemiol</secondary-title><alt-title>Clinical epidemiology</alt-title></titles><periodical><full-title>Clin Epidemiol</full-title></periodical><alt-periodical><full-title>Clinical Epidemiology</full-title></alt-periodical><pages>475-482</pages><volume>9</volume><edition>2017/10/06</edition><keywords><keyword>Asd</keyword><keyword>Cprd</keyword><keyword>autism spectrum disorder</keyword><keyword>validation</keyword></keywords><dates><year>2017</year></dates><isbn>1179-1349 (Print)
1179-1349</isbn><accession-num>28979165</accession-num><urls></urls><custom2>PMC5608225</custom2><electronic-resource-num>10.2147/clep.s139107</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(10)*The mapping from ICD 10 code to Read code was performed using R package “CALIBERcodelists”. EUROCAT revised its list of minor anomalies at 2005, and we applied the updated “Excluded minor anomalies post-2005” list in this study. GA: gestational age.Table S3. Most frequent five Read codes for each system-specific malformation.TypeDescriptionRead codeICD10 CodeFrequencyCardiovascularVentricular septal defectP54..00Q210382Patent ductus arteriosusP70..00Q250189Atrial septal defect NOSP550.00Q211116Ostium secundum atrial septal defectP71..00Q21135Coarctation of aortaP55..00Q25133GenitalHypospadiasPC60.00Q54293Hypospadias, glandularPC60312Q54020Hypospadias, penilePC60000Q54114Hypospadias, glanularPC60311Q54010Hooded penisPCyy000Q5410NeurologicalMicrocephalusP21..00Q0244Spina bifidaP1...00Q0526Congenital hydrocephalusP23..00Q0312Septo-optic dysplasiaP246.00Q0446MicrencephalyP211.00Q026EyeCongenital ptosisP360.00Q10046Congenital cataract, unspecifiedP330.00Q12014Coloboma of irisP344200Q13014Congenital cataract and lens anomaliesP33..00Q1212Congenital lacrimal passage anomaliesP364.00Q10610Orofacial cleftCleft palateP90..00Q3538Cleft palate with cleft lipP92..00Q3737Repair of cleft palate7525.12Q3534Repair of cleft lip operations7502.11Q3634Primary repair of cleft palate, unspecified7525000Q3527UrinaryCongenital hydronephrosisPD23.00Q62030Multicystic kidneyPD13.11Q611-Q61419Congenital absence of kidneyPD02.00Q600-Q60215Dysplasia of kidneyPD04.00Q61414Horseshoe kidneyPD38.00Q63110GastrointestinalHirschsprung's diseasePB30.00Q43127Imperforate anusPB26.00Q42317Atresia of oesophagusPA30.00Q3911Other anomalies of lipPA2A.00Q3809Atresia of duodenumPB10100Q4106RespiratoryChoanal atresiaP80..00Q3007Other lung anomaliesP86..00Q3386Congenital cystic lungP84..00Q3305Congenital bronchomalaciaP83yB00Q322<5Congenital bronchogenic cystP843.12Q330<5Ear & faceEar anomalies with hearing impairmentP40..00Q1698Eustachian tube anomaliesP423.00Q164<5Other specified face and neck anomaliesP4y..00Q188<5Absence of ear NOSP401011Q160<5Deafness due to congenital anomaly NECP40z.11Q169<5Abdominal wall defectsGastroschisisPG71.00Q79319ExomphalosPG70.00Q792<5Abdominal wall anomaliesPG7..00Q795<5OtherCraniosynostosisPG03.00Q75029Urticaria pigmentosaPH32100Q82219Ichthyosis congenitaPH1..00Q8014Imperfect fusion of skullPG06.00Q75012ScaphocephalyPG03.11Q7508*The frequencies were calculated for Read codes (not diagnosis) without de-duplication. In accordance with the confidentiality preserving policy of CPRD, we suppressed the information where the frequency cell contains <5 events (noted as “<5”).Table S4. Definition of covariates.*CovariatesTime for measurementValueDescriptionAge at delivery-Grouped into categories of 5 calendar years (roughly): 14-19; 20-24; 25-29; 30-34; 35-50.Defined as the calendar year of delivery minus mothers’ year of birth.Calendar year of delivery-Grouped into categories of 5 calendar years: 1990-1994; 1995-1999; 2000-2004; 2005-2009; 2010-2016.-Parity -Categorised as “0”, and “≥ 1”Number of times that the women has given live-birth which were captured in the CPRD Mother Baby Link before the current pregnancy. Multiple births-“Singleton”, and “(One of the) Twin, triplets, or quadruplets captured in the database”.Alcohol misuseMost recent measurement from 10 years before pregnancy to the end of pregnancy.“Yes” and “No”Alcohol misuse was defined as ≥ 14 units of alcohol per week, including moderate or severe drinker. Self-reported alcohol consumption was collected prospectively and coded by general practitioners or practice nurses on the consultation date in CPRD. The most recent alcohol consumption record was used to classify participants drinking behaviour, and “ex-drinker” was categorised as not alcohol misuser if there was no evidence of alcohol withdraw before pregnancy start. Alcohol misuse was defined using: One of the Read codes indicating alcohol consumption; or,A prescription for disulfarim or acamprosate; or,Self-reported average weekly alcohol intakes >= 14 units in the “Additional Clinical Details”.We applied the code list of alcohol consumption developed by Bell at al. ADDIN EN.CITE <EndNote><Cite><Author>Bell</Author><Year>2017</Year><RecNum>49717</RecNum><DisplayText>(12)</DisplayText><record><rec-number>49717</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1545912979">49717</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bell, Steven</author><author>Daskalopoulou, Marina</author><author>Rapsomaniki, Eleni</author><author>George, Julie</author><author>Britton, Annie</author><author>Bobak, Martin</author><author>Casas, Juan P.</author><author>Dale, Caroline E.</author><author>Denaxas, Spiros</author><author>Shah, Anoop D.</author><author>Hemingway, Harry</author></authors></contributors><titles><title>Association between clinically recorded alcohol consumption and initial presentation of 12 cardiovascular diseases: population based cohort study using linked health records</title><secondary-title>BMJ</secondary-title></titles><periodical><full-title>Bmj</full-title></periodical><pages>j909</pages><volume>356</volume><dates><year>2017</year></dates><urls><related-urls><url>;(12) Illicit drug useMost recent measurement from 10 years before pregnancy to the end of pregnancy.“Yes” and “No”Illicit drug use was defined using:One of the Read codes indicating drug use, addiction, and overdose; orA prescription for methadone treatment.We assume that although a mother may stop using illicit drugs, the underlying behaviour was unlikely to vary significantly over time.ObesityMost recent measurement from 3 years before pregnancy till the end of the first trimester.“Yes” and “No”Mothers who were obese prior to the 2nd trimester of pregnancy were identified from the Read codes for obesity (or a BMI of ≥30 kg/m2 - either directly entered or calculated from the most recent height measurement and median pre-pregnancy weight after excluding outliers. I.e. height outside the range 1-2m and weight outside the range 35-300kg, were removed). It was assumed that once a mother reached clinical obesity, the chance of her returning to a normal BMI in three years was minimal.Tobacco useMost recent measurement from 3 years before pregnancy to the end of pregnancy.“Yes” and “No”Tobacco use was defined as daily cigarette consumption of 1-100 cigarettes per day or other tobacco use. The most recent tobacco consumption record was used to classify participants drinking behaviour, and “ex-smoker” was categorised as non-recent smoker. Tobacco use was defined using: One of the Read codes indicating tobacco consumption; or,A prescription for smoking cessation aid; or,Self-reported daily cigarette consumption of 1-100 cigarettes per day in the “Additional Clinical Details”.Hypertension50 weeks prior to delivery“Yes” and “No”Mothers with hypertension during pregnancy were identified based on Systolic and diastolic blood pressure was above 140mmHg and 90mmHg, respectively, or,One of the Read code for hypertension and associated diagnoses (including pre-eclampsia, eclampsia and HELLP syndrome), or,One prescription for hypertension drugs from sections 2.2 and 2.5 of the BNF. This variable identified mothers with both treated and untreated hypertension in pregnancy. Diabetes 50 weeks prior to delivery“Yes” and “No”Mothers with diabetes during pregnancy were identified based on:One of the Read codes for type I, type II, or gestational diabetes; orTwo or more prescriptions for anti-diabetic medication; orOne of laboratory tests indicating diabetes (defined as ≥2 abnormal glucose tests, fasting glucose >7.0 millimoles per litre [mmol/L] or >126 milligrams per decilitre [mg/dL], plasma glucose after glucose tolerance test >11.1 mmol/L or 200mg/dL, glycated haemoglobin ≥ 6.5%, or within diabetes annual review) recorded in the “Additional Clinical Details”.Epilepsy50 weeks prior to delivery“Yes” and “No”≥ 2 prescriptions of antiepileptic drugs (AEDs) within 4 months or ≥ 1 diagnosisDepression50 weeks prior to delivery“Yes” and “No”≥ 2 occurrences of diagnostic code, treatment code or symptom Anxiety50 weeks prior to delivery“Yes” and “No”≥ 2 occurrences of diagnostic code, treatment code or symptomTreatment of chronic medical conditions during pregnancyDuring pregnancy“Yes” and “No”Existence of chronic medical conditions are defined as conditions that are sufficiently severe to require on-going treatment during pregnancy. Mothers were considered to have a chronic medical condition if they were issued ≥ 2 prescriptions (on separate days during pregnancy and not more than four months apart) for drugs from the same BNF section or paragraph. Drugs used to treat common conditions in pregnancy, including reflux (BNF section 1.2), nausea and vomiting (BNF section 4.6), and constipation (BNF section 1.3), were not included.Genitourinary tract infectionDuring pregnancy“Yes” and “No”Common terms categorised as “Genitourinary tract infection” include urinary tract infection, cystitis, vaginitis and the prescription of Nitrofurantoin.Sexually Transmitted InfectionDuring pregnancy“Yes” and “No”Common terms categorised as “Sexually Transmitted Infection” include chlamydia infection, trachoma, “TORCH” (Toxoplasmosis, Other agents such as HIV, Rubella, Cytomegalovirus and Herpes simplex) and other sexually transmitted infections (STIs).*When the key codes indicating a binary condition were not identified in the medical history of a subject, we classified the subject as absence of the condition. There were no missing for multi-categorical covariates in this study (“Age at delivery” and “Calendar year of delivery”).Table S5. Unadjusted and propensity-score-adjusted baseline characteristics (N [%]) of children whose mother were prescribed macrolides or penicillins from 14 gestation weeks to delivery (“the second to third trimester”).?CharacteristicUnadjustedPropensity-score-adjusted*MacrolidesPenicillinsSt.diffMacrolidesPenicillinsSt.diffNumber of children646273429646273400Maternal baseline characteristicAge at delivery0.080.00413-19232 (3.6)2889 (3.9)232 (3.6)2631.5 (3.6)20-24825 (12.8)10560 (14.4)825 (12.8)9273.3 (12.6)25-291562 (24.2)19105 (26.0)1562 (24.2)17779.2 (24.2)30-342165 (33.5)23514 (32.0)2165 (33.5)24589.0 (33.5)35-501678 (26.0)17361 (23.6)1678 (26.0)19127.0 (26.1)Calendar year of delivery0.0540.0031990-1994606 (9.4)7594 (10.3)606 (9.4)6907.4 (9.4)1995-19991067 (16.5)13023 (17.7)1067 (16.5)12111.0 (16.5)2000-20041344 (20.8)15025 (20.5)1344 (20.8)15234.9 (20.8)2005-20091688 (26.1)18005 (24.5)1688 (26.1)19248.1 (26.2)2010-20161757 (27.2)19782 (26.9)1757 (27.2)19898.6 (27.1)Alcohol misuse308 (4.8)3526 (4.8)0.002308 (4.8)3494.4 (4.8)<0.001Illicit drug use81 (1.3)739 (1.0)0.02381 (1.3)911.0 (1.2)0.001Tobacco use2136 (33.1)23351 (31.8)0.0272136 (33.1)24190.8 (33.0)0.002Obesity795 (12.3)8046 (11.0)0.042795 (12.3)8979.4 (12.2)0.002Hypertension507 (7.8)5355 (7.3)0.021507 (7.8)5765.8 (7.9)<0.001Diabetes254 (3.9)2359 (3.2)0.039254 (3.9)2868.7 (3.9)0.001Anxiety187 (2.9)1820 (2.5)0.026187 (2.9)2118.0 (2.9)<0.001Depression714 (11.0)6891 (9.4)0.055714 (11.0)8033.0 (10.9)0.003Epilepsy35 (0.5)474 (0.6)0.01435 (0.5)429.1 (0.6)0.006Pregnancy related characteristicParity ≥12367 (36.6)26444 (36.0)0.0132367 (36.6)26964.5 (36.7)0.002Multiple births182 (2.8)2018 (2.7)0.004182 (2.8)2071.6 (2.8)<0.001Genitourinary tract infection271 (4.2)8725 (11.9)0.286271 (4.2)3112.9 (4.2)0.002Sexually Transmitted Infection179 (2.8)936 (1.3)0.106179 (2.8)2079.6 (2.8)0.004Treatment of chronic medical conditions1328 (20.6)12718 (17.3)0.0831328 (20.6)14976.0 (20.4)0.004*Exposure propensity scores were measured as the predicted probability of receiving macrolides versus penicillins, conditional on the maternal and pregnancy related characteristics included in this table. 50 Strata were created based on the distribution of the propensity score of macrolides group. Weights for the penicillins group were calculated according to the distribution of the macrolides group among the strata and were used to estimate adjusted baseline characteristics. A meaningful between-group imbalance was assessed by an absolute standardised difference (Std.diff, the difference in means in units of standard deviation) of more than 0.1. Numbers in adjusted penicillins group were non-integer, because they were weighted based on the distribution of propensity score of macrolides group. Table S6. Unadjusted and propensity-score-adjusted baseline characteristics (N [%]) of children whose mother were prescribed macrolides or penicillins from 4 gestation weeks to delivery (“in any trimester”).?CharacteristicUnadjustedPropensity-score-adjusted*MacrolidesPenicillinsSt.diffMacrolidesPenicillinsSt.diffNumber of children863295973863295971Maternal baseline characteristicAge at delivery0.0630.00313-19362 (4.2)3875 (4.0)362 (4.2)3992.9 (4.2)20-241202 (13.9)14070 (14.7)1202 (13.9)13291.0 (13.8)25-292086 (24.2)25328 (26.4)2086 (24.2)23169.9 (24.1)30-342829 (32.8)30406 (31.7)2829 (32.8)31559.9 (32.9)35-502153 (24.9)22294 (23.2)2153 (24.9)23957.2 (25.0)Calendar year of delivery0.0660.0031990-1994776 (9.0)9819 (10.2)776 (9.0)8662.3 (9.0)1995-19991385 (16.0)16769 (17.5)1385 (16.0)15461.5 (16.1)2000-20041840 (21.3)19429 (20.2)1840 (21.3)20448.7 (21.3)2005-20092256 (26.1)23599 (24.6)2256 (26.1)25073.7 (26.1)2010-20162375 (27.5)26357 (27.5)2375 (27.5)26324.9 (27.4)Alcohol misuse437 (5.1)4573 (4.8)0.014437 (5.1)4823.4 (5.0)0.002Illicit drug use112 (1.3)982 (1.0)0.026112 (1.3)1193.9 (1.2)0.005Tobacco use2926 (33.9)30763 (32.1)0.0392926 (33.9)32235.1 (33.6)0.007Obesity1057 (12.2)10624 (11.1)0.0371057 (12.2)11688.6 (12.2)0.002Hypertension668 (7.7)6978 (7.3)0.018668 (7.7)7379.0 (7.7)0.002Diabetes322 (3.7)3141 (3.3)0.025322 (3.7)3551.3 (3.7)0.002Anxiety261 (3.0)2376 (2.5)0.034261 (3.0)2841.3 (3.0)0.004Depression941 (10.9)9179 (9.6)0.044941 (10.9)10393.5 (10.8)0.002Epilepsy60 (0.7)629 (0.7)0.00560 (0.7)666.4 (0.7)<0.001Pregnancy related characteristicParity >=13149 (36.5)34524 (36.0)0.0113149 (36.5)35080.6 (36.6)0.002Multiple births234 (2.7)2553 (2.7)0.003234 (2.7)2594.6 (2.7)<0.001Genitourinary tract infection361 (4.2)11521 (12.0)0.29361 (4.2)3964.7 (4.1)0.003Sexually Transmitted Infection281 (3.3)1237 (1.3)0.132281 (3.3)3075.3 (3.2)0.003Treatment of chronic medical conditions1750 (20.3)16784 (17.5)0.0711750 (20.3)19480.0 (20.3)0.001*Exposure propensity scores were measured as the predicted probability of receiving macrolides versus penicillins, conditional on the maternal and pregnancy related characteristics included in this table. 50 Strata were created based on the distribution of the propensity score of macrolides group. Weights for the penicillins group were calculated according to the distribution of the macrolides group among the strata and were used to estimate adjusted baseline characteristics. A meaningful between-group imbalance was assessed by an absolute standardised difference (St.diff, the difference in means in units of standard deviation) of more than 0.1. Numbers in adjusted penicillins group were non-integer, because they were weighted based on the distribution of propensity score of macrolides group. Table S7. Unadjusted and propensity-score-adjusted baseline characteristics (N [%]) of children whose mother were prescribed macrolides or penicillins 10 to 50 weeks before pregnancy. ?CharacteristicUnadjustedPropensity-score-adjusted*MacrolidesPenicillinsSt.diffMacrolidesPenicillinsSt.diffNumber of children11874704401187470425.1Maternal baseline characteristicAge at delivery0.0280.00313-19499 (4.2)3150 (4.5)499 (4.2)2975.8 (4.2)20-241706 (14.4)10482 (14.9)1706 (14.4)10091.3 (14.3)25-293099 (26.1)18495 (26.3)3099 (26.1)18437.4 (26.2)30-343760 (31.7)22346 (31.7)3760 (31.7)22240.1 (31.6)35-502810 (23.7)15967 (22.7)2810 (23.7)16680.6 (23.7)Calendar year of delivery0.0380.0031990-19941034 (8.7)6060 (8.6)1034 (8.7)6109.9 (8.7)1995-19991986 (16.7)12376 (17.6)1986 (16.7)11827.1 (16.8)2000-20042451 (20.6)14977 (21.3)2451 (20.6)14593.5 (20.7)2005-20093030 (25.5)18099 (25.7)3030 (25.5)17960.3 (25.5)2010-20163373 (28.4)18928 (26.9)3373 (28.4)19934.4 (28.3)Alcohol misuse607 (5.1)3248 (4.6)0.023607 (5.1)3584.8 (5.1)0.001Illicit drug use144 (1.2)695 (1.0)0.022144 (1.2)852.0 (1.2)<0.001Tobacco use3991 (33.6)22730 (32.3)0.0293991 (33.6)23798.6 (33.8)0.004Obesity1448 (12.2)8015 (11.4)0.0251448 (12.2)8605.3 (12.2)0.001Hypertension899 (7.6)5107 (7.3)0.012899 (7.6)5328.1 (7.6)<0.001Diabetes395 (3.3)2339 (3.3)<0.001395 (3.3)2355.4 (3.3)0.001Anxiety311 (2.6)1681 (2.4)0.015311 (2.6)1846.0 (2.6)<0.001Depression1180 (9.9)6601 (9.4)0.0191180 (9.9)7001.3 (9.9)<0.001Epilepsy73 (0.6)432 (0.6)<0.00173 (0.6)437.7 (0.6)0.001Pregnancy related characteristicParity ≥14197 (35.3)26876 (38.2)0.0584197 (35.3)24940.7 (35.4)0.001Multiple births378 (3.2)1822 (2.6)0.036378 (3.2)2240.8 (3.2)<0.001Genitourinary tract infection1270 (10.7)6146 (8.7)0.0671270 (10.7)7510.5 (10.7)0.001Sexually Transmitted Infection188 (1.6)913 (1.3)0.024188 (1.6)1123.6 (1.6)0.001Treatment of chronic medical conditions2175 (18.3)12110 (17.2)0.0292175 (18.3)12935.9 (18.4)0.001*Exposure propensity scores were measured as the predicted probability of receiving macrolides versus penicillins, conditional on the maternal and pregnancy related characteristics included in this table. 50 Strata were created based on the distribution of the propensity score of macrolides group. Weights for the penicillins group were calculated according to the distribution of the macrolides group among the strata and were used to estimate adjusted baseline characteristics. A meaningful between-group imbalance was assessed by an absolute standardised difference (St.diff, the difference in means in units of standard deviation) of more than 0.1. Numbers in adjusted penicillins group were non-integer, because they were weighted based on the distribution of propensity score of macrolides group. Table S8. Subgroup analysis according to macrolides subtypes, on the association between adverse child outcomes and macrolides versus penicillins prescribed during pregnancy.Adverse OutcomesNo. of eventsRisk per 1,000 live births or Rate per 1,000 person-year?Adj. RR/HR(95% CI)P value?MacrolidesPenicillinsMacrolidesPenicillinsErythromycinAny major malformation??1st trimester5339827.3917.651.50 (1.13-1.99)0.0052nd -3rd trimester112126818.5117.271.07 (0.88-1.29)0.507Nervous system malformation1st trimester6273.101.202.47 (1.03-5.96)0.0442nd -3rd trimester5700.830.950.84 (0.34-2.08)0.706Cardiovascular malformation1st trimester191499.826.611.48 (0.92-2.37)0.1082nd -3rd trimester414776.776.501.02 (0.74-1.41)0.889Gastrointestinal malformation1st trimester<520-0.800.55 (0.07-4.09)0.562nd -3rd trimester10671.650.911.75 (0.90-3.39)0.099Genital malformation1st trimester10685.173.021.62 (0.84-3.14)0.1512nd -3rd trimester262274.303.091.45 (0.96-2.17)0.075Urinary malformation1st trimester<541-1.820.54 (0.13-2.22)0.3922nd -3rd trimester81051.321.430.96 (0.47-1.97)0.906Cerebral palsy1st trimester<555-0.350.21 (0.03-1.56)0.1282nd -3rd trimester151180.350.221.49 (0.87-2.57)0.147Epilepsy1st trimester121600.891.020.88 (0.49-1.58)0.6632nd -3rd trimester355250.810.990.84 (0.59-1.18)0.312ADHD1st trimester121230.880.781.12 (0.61-2.04)0.7142nd -3rd trimester313920.720.740.97 (0.67-1.40)0.868ASD1st trimester191811.401.151.15 (0.71-1.84)0.5752nd -3rd trimester485951.111.120.99 (0.74-1.33)0.937ClarithromycinAny major malformation??1st trimester639836.8117.651.83 (0.83-4.04)0.1332nd -3rd trimester11126833.2317.272.07 (1.15-3.71)0.015*The macrolides group included 7987 (clarithromycin), 494 (clarithromycin) and 151 (azithromycin) children. In accordance with the confidentiality preserving policy of CPRD, we suppressed the information where the frequency cell contains <5 events (noted as “<5”) and where necessary to avoid deduction. For clarithromycin, we only analysed any major malformation due to the limited number of events of other adverse child outcomes (there were six events of the four neurodevelopmental disorders in total in children prenatally prescribed clarithromycin). 151 azithromycin were prescribed during the whole pregnancy with <5 events of malformation, which precluded the analyses. ADHD: attention-deficit/hyperactivity disorder; ASD: autism spectrum disorder; CI: confidence interval; RR: risk ratio; HR: hazard ratio. Table S9. Subgroup analysis according to duration of treatment (< 7 days or ≥ 7 days), on the association between adverse child outcomes and macrolides versus penicillins prescribed during pregnancy.Adverse OutcomesNo. of eventsRisk per 1,000 live births or Rate per 1,000 person-year?Adj. RR/HR(95% CI)P value?MacrolidesPenicillinsMacrolidesPenicillinsAny major malformation??<7 days, 1st trimester1714437.2816.582.11 (1.27-3.51)0.004≥7 days, 1st trimester3323123.9818.341.34 (0.94-1.92)0.104<7 days, 2nd -3rd trimester2546615.3016.460.88 (0.59-1.33)0.553≥7 days, 2nd -3rd trimester8472420.7017.811.18 (0.94-1.47)0.154Nervous system malformation<7 days, 1st trimester<511-1.270 ( 0-Inf)0.991≥7 days, 1st trimester<514-1.111.96 (0.56-6.82)0.289<7 days, 2nd -3rd trimester<526-0.921.18 (0.28-4.94)0.820≥7 days, 2nd -3rd trimester<539-0.960.54 (0.13-2.24)0.398Cardiovascular malformation<7 days, 1st trimester75915.356.792.39 (1.10-5.23)0.028≥7 days, 1st trimester13829.456.511.45 (0.81-2.60)0.207<7 days, 2nd -3rd trimester91675.515.90.87 (0.44-1.69)0.675≥7 days, 2nd -3rd trimester302717.396.671.10 (0.75-1.60)0.624Gastrointestinal malformation<7 days, 1st trimester<59-1.040 ( 0-Inf)0.990≥7 days, 1st trimester<510-0.790.86 (0.11-6.69)0.888<7 days, 2nd -3rd trimester<525-0.882.36 (0.71-7.87)0.162≥7 days, 2nd -3rd trimester8371.970.912.02 (0.95-4.32)0.069Genital malformation<7 days, 1st trimester<525-2.881.22 (0.29-5.07)0.787≥7 days, 1st trimester6404.363.180.86 (0.11-6.69)0.888<7 days, 2nd -3rd trimester6933.673.282.36 (0.71-7.87)0.162≥7 days, 2nd -3rd trimester201274.933.122.02 (0.95-4.32)0.069Urinary malformation<7 days, 1st trimester<513-1.501.55 ( 0.20-11.85)0.674≥7 days, 1st trimester<526-2.060.76 (0.18-3.20)0.704<7 days, 2nd -3rd trimester<544-1.550.39 (0.05-2.79)0.345≥7 days, 2nd -3rd trimester8561.971.381.45 (0.69-3.05)0.322Cerebral palsy<7 days, 1st trimester<520-0.280 (0-0)<0.001≥7 days, 1st trimester<531-0.390.60 (0.14-2.53)0.487<7 days, 2nd -3rd trimester<556-0.230.31 (0.04-2.25)0.246≥7 days, 2nd -3rd trimester13570.500.211.98 (1.05-3.73)0.034Epilepsy<7 days, 1st trimester<571-1.010.71 (0.22-2.26)0.559≥7 days, 1st trimester5810.571.020.51 (0.21-1.28)0.154<7 days, 2nd -3rd trimester122320.820.980.91 (0.51-1.64)0.762≥7 days, 2nd -3rd trimester222620.850.990.87 (0.56-1.35)0.543ADHD<7 days, 1st trimester5551.280.781.53 (0.61-3.87)0.367≥7 days, 1st trimester8610.910.771.24 (0.58-2.64)0.573<7 days, 2nd -3rd trimester141910.960.801.25 (0.73-2.15)0.418≥7 days, 2nd -3rd trimester121880.460.710.63 (0.35-1.13)0.122ASD<7 days, 1st trimester5741.281.061.43 (0.62-3.30)0.406≥7 days, 1st trimester11961.251.210.74 (0.38-1.45)0.385<7 days, 2nd -3rd trimester142570.961.080.93 (0.54-1.59)0.786≥7 days, 2nd -3rd trimester322991.241.131.07 (0.74-1.54)0.715*97772 (93.5%) children in the study cohort were with non-missing duration of treatment. The macrolides group included 456 (<7 days, 1st trimester), 1376 (≥7 days, 1st trimester), 1634 (<7 days, 2nd -3rd trimester) and 4058 (≥7 days, 2nd -3rd trimester) children. The penicillins group included 8683 (<7 days, 1st trimester), 12592 (≥7 days, 1st trimester), 28314 (<7 days, 2nd -3rd trimester) and 40659 (≥7 days, 2nd -3rd trimester) children. In accordance with the confidentiality preserving policy of CPRD, we suppressed the information where the frequency cell contains <5 events (noted as “<5”) and where necessary to avoid deduction. Within macrolides prescription during the 1st trimester, 95% prescriptions less than 7 days were of 5-6 days, and 93% prescriptions >= 7 days were of 7 days. Overall, 94.7% macrolides or penicillins prescriptions were of 5 to 7 days. ADHD: attention-deficit/hyperactivity disorder; ASD: autism spectrum disorder; CI: confidence interval; RR: risk ratio; HR: hazard ratio. Table S10. Sensitivity analysis: comparison of the risks (or hazards) between siblings of children prenatally prescribed macrolides and siblings of children prenatally prescribed penicillins in the study cohort, according to timing of prescription.Adverse outcomesNo. of events in siblings of children prescribedRisk per 1,000 live births or Rate per 1,000 person-year in siblings of children prescribed?Adj. RR/HR in siblings (95% CI)P value?MacrolidesPenicillinsMacrolidesPenicillinsAny major malformation??1st trimester2521021.2218.061.18 (0.78-1.78)0.4292nd -3rd trimester6566519.5017.691.10 (0.85-1.41)0.479Nervous system malformation1st trimester<59-1.200 (0-inf)0.9902nd -3rd trimester6401.801.061.73 (0.73-4.07)0.213Cardiovascular malformation1st trimester7815.946.960.87 (0.40-1.88)0.7272nd -3rd trimester232306.906.121.12 (0.73-1.72)0.598Gastrointestinal malformation1st trimester<520-1.720.44 (0.06-3.26)0.4222nd -3rd trimester6331.800.881.88 (0.79-4.46)0.152Genital malformation1st trimester6425.093.611.44 (0.61-3.37)0.4072nd -3rd trimester131393.903.701.06 (0.60-1.87)0.844Urinary malformation1st trimester<510-0.864.08 (1.27-13.07)0.0182nd -3rd trimester5511.501.361.10 (0.44-2.75)0.843Cerebral palsy1st trimester<520-0.210.46 (0.06-3.43)0.4482nd -3rd trimester6660.220.210.99 (0.42-2.29)0.973Epilepsy1st trimester<5810.320.840.35 (0.11-1.11)0.0752nd -3rd trimester232760.840.870.96 (0.62-1.47)0.841ADHD1st trimester7760.740.790.91 (0.42-1.98)0.8072nd -3rd trimester222640.800.830.99 (0.63-1.56)0.973ASD1st trimester13931.380.961.36 (0.76-2.42)0.2972nd -3rd trimester523691.901.161.59 (1.16-2.17)0.004*1178 (macrolides, 1st trimester), 11631 (penicillins, 1st trimester), 3334 (macrolides, 2nd-3rd trimester), and 37592 (penicillins, 2nd-3rd trimester) children were included in the analyses. In accordance with the confidentiality preserving policy of CPRD, we suppressed the information where the frequency cell contains <5 events (noted as “<5”) and where necessary to avoid deduction. Higher risks for genital malformation were observed for the both groups in the sibling cohort for unknown reason. ADHD: attention-deficit/hyperactivity disorder; ASD: autism spectrum disorder; CI: confidence interval; RR: risk ratio; HR: hazard ratio.Table S11. Sensitivity analysis on the association between adverse child outcomes and macrolides versus penicillins prescribed during pregnancy: restricting to mothers whose antibiotics were prescribed to respiratory tract infections.Adverse OutcomesNo. of eventsRisk per 1,000 live births or Rate per 1,000 person-year?Adj. RR/HR(95% CI)P value?MacrolidesPenicillinsMacrolidesPenicillinsAny major malformation??1st trimester3015935.4218.711.81 (1.24-2.66)0.0022nd -3rd trimester4346216.0016.520.99 (0.73-1.35)0.944Nervous system malformation1st trimester<57-0.821.46 (0.18-11.88)0.7232nd -3rd trimester<525-0.890.88 (0.21-3.73)0.862Cardiovascular malformation1st trimester116112.997.181.79 (0.94-3.38)0.0752nd -3rd trimester161875.956.690.91 (0.55-1.52)0.723Gastrointestinal malformation1st trimester<57-0.821.27 (0.16-10.14)0.8232nd -3rd trimester5251.860.892.19 (0.84-5.75)0.110Genital malformation1st trimester92610.633.063.30 (1.56-6.99)0.0022nd -3rd trimester10723.722.571.49 (0.77-2.89)0.235Urinary malformation1st trimester<516-1.881.24 (0.29-5.37)0.7752nd -3rd trimester5511.121.500.75 (0.23-2.41)0.626Cerebral palsy1st trimester<525-0.400.46 (0.06-3.38)0.4442nd -3rd trimester7410.380.201.82 (0.81-4.08)0.146Epilepsy1st trimester<553-0.850.62 (0.19-1.98)0.4182nd -3rd trimester152150.811.050.77 (0.45-1.30)0.332ADHD1st trimester<547-0.760.70 (0.22-2.24)0.5432nd -3rd trimester151510.810.731.17 (0.69-1.98)0.565ASD1st trimester6801.041.290.75 (0.33-1.71)0.4912nd -3rd trimester141950.760.950.78 (0.45-1.34)0.368*In accordance with the confidentiality preserving policy of CPRD, we suppressed the information where the frequency cell contains <5 events (noted as “<5”) and where necessary to avoid deduction. ADHD: attention-deficit/hyperactivity disorder; ASD: autism spectrum disorder; CI: confidence interval; RR: risk ratio; HR: hazard ratio. Text S3. Probabilistic multiple bias analysis on outcome misclassification and live- birth biasThe Clinical Practice Research Datalink (CPRD) has been used increasingly widely in pharmacoepidemiology studies within academic, regulatory, and pharmaceutical organisations to inform treatment guidelines and clinical practice guidance. ADDIN EN.CITE <EndNote><Cite><Author>Ghosh</Author><Year>2019</Year><RecNum>49872</RecNum><DisplayText>(13)</DisplayText><record><rec-number>49872</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1567677264">49872</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ghosh, R. E.</author><author>Crellin, E.</author><author>Beatty, S.</author><author>Donegan, K.</author><author>Myles, P.</author><author>Williams, R.</author></authors></contributors><auth-address>Clinical Practice Research Datalink, Medicines and Healthcare Products Regulatory Agency, London, UK.
Vigilance and Risk Management of Medicines, Medicines and Healthcare Products Regulatory Agency, London, UK.</auth-address><titles><title>How Clinical Practice Research Datalink data are used to support pharmacovigilance</title><secondary-title>Ther Adv Drug Saf</secondary-title></titles><periodical><full-title>Ther Adv Drug Saf</full-title></periodical><pages>2042098619854010</pages><volume>10</volume><edition>2019/06/19</edition><keywords><keyword>big data</keyword><keyword>electronic health records</keyword><keyword>medical record linkage</keyword><keyword>pharmacovigilance</keyword><keyword>primary health care</keyword><keyword>Executive Agency of the Department of Health, but have no conflicts of interest</keyword><keyword>that are directly relevant to the contents of this study. The MHRA has statutory</keyword><keyword>responsibility for the pharmacovigilance of medicinal products on the UK market.</keyword></keywords><dates><year>2019</year></dates><isbn>2042-0986 (Print)
2042-0986</isbn><accession-num>31210923</accession-num><urls></urls><custom2>PMC6545638</custom2><electronic-resource-num>10.1177/2042098619854010</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(13) However, outcome measurements derived from administrative databases such as CPRD are not perfect and misclassification bias may exist. As CPRD data were prospectively collected as part of routine healthcare, it is reasonable to assume that measurement errors of outcomes were non-differential between macrolides and penicillins groups. This non-differential outcome misclassification is likely to bias the relative risk (RR) estimates towards the null. ADDIN EN.CITE <EndNote><Cite><Author>Greenland</Author><Year>1996</Year><RecNum>49715</RecNum><DisplayText>(14)</DisplayText><record><rec-number>49715</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1545853994">49715</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Greenland, S.</author></authors></contributors><auth-address>Department of Epidemiology, UCLA School of Public Health 90095-1772, USA.</auth-address><titles><title>Basic methods for sensitivity analysis of biases</title><secondary-title>Int J Epidemiol</secondary-title></titles><periodical><full-title>Int J Epidemiol</full-title></periodical><pages>1107-16</pages><volume>25</volume><number>6</number><edition>1996/12/01</edition><keywords><keyword>*Bias</keyword><keyword>Case-Control Studies</keyword><keyword>*Epidemiologic Methods</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/etiology/*mortality</keyword><keyword>Occupational Diseases/etiology/*mortality</keyword><keyword>Occupational Exposure/*adverse effects</keyword><keyword>Odds Ratio</keyword><keyword>Reproducibility of Results</keyword><keyword>Resins, Plant/*adverse effects</keyword><keyword>Risk Factors</keyword><keyword>Sensitivity and Specificity</keyword></keywords><dates><year>1996</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0300-5771 (Print)
0300-5771 (Linking)</isbn><accession-num>9027513</accession-num><urls><related-urls><url>;(14)Besides, we included only pregnancies that resulted in live-born children, thus some severe adverse outcomes (e.g. nervous system, cardiovascular and gastrointestinal malformations) that result in fetal deaths were missed. This depletion of affected fetuses may occur more often among women exposed to macrolides (versus penicillins), as shown in our systematic review ADDIN EN.CITE <EndNote><Cite><Author>Fan</Author><Year>2018</Year><RecNum>49737</RecNum><DisplayText>(15)</DisplayText><record><rec-number>49737</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1548763410">49737</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Heng Fan</author><author>Ruth Gilbert</author><author>Leah Li</author><author>Linda Wijlaars </author></authors></contributors><titles><title>Associations between use of macrolide antibiotics during pregnancy and adverse child outcomes: a systematic review and meta-analysis </title><secondary-title>Lancet</secondary-title></titles><periodical><full-title>Lancet</full-title></periodical><edition>22/11/2018</edition><dates><year>2018</year><pub-dates><date>22/11/2018</date></pub-dates></dates><work-type>Meeting abstract</work-type><urls></urls></record></Cite></EndNote>(15). Therefore, risk ratio of these outcomes measured only in live births would be subject to selection (live-birth) bias with unknown direction.We thus conducted probabilistic multiple bias analyses to quantify the bias due to outcome misclassification as well as jointly with live-birth bias to facilitate interpretation. Specifically, we estimated adjusted RR (95% CI) for each adverse child outcome for first-trimester macrolides (versus penicillins) prescribing using bias parameters stemming from both previous studies and educated guess. Multiple bias analyses (which provided bias-adjusted RR estimates using standard 2x2 tables) were described in detail elsewhere ADDIN EN.CITE <EndNote><Cite><Author>Lash</Author><Year>2009</Year><RecNum>49960</RecNum><DisplayText>(16)</DisplayText><record><rec-number>49960</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1573603459">49960</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lash, Timothy</author><author>Fox, Matthew</author><author>Fink, Aliza</author></authors></contributors><titles><title>Applying Quantitative Bias Analysis to Epidemiologic Data</title></titles><dates><year>2009</year><pub-dates><date>01/01</date></pub-dates></dates><urls></urls><electronic-resource-num>10.1007/978-0-387-87959-8_8</electronic-resource-num></record></Cite></EndNote>(16). Briefly, frequencies in the tables were adjusted by a set of bias parameters, i.e. sensitivity and specificity for outcome misclassification, and probability of live birth for selection bias. These parameters were randomly sampled from given probability distributions (e.g. 5,000 iterations from triangular distributions in this study). In each iteration, we adjusted for misclassification bias and selection bias by sampling and adjusting the frequencies sequentially, incorporated with a random error to obtain the adjusted estimates with 95% limits. The analyses were performed using RStudio version 3.5.1 and R package “episensr”. ADDIN EN.CITE <EndNote><Cite><Author>Haine</Author><Year>2019</Year><RecNum>49889</RecNum><DisplayText>(17)</DisplayText><record><rec-number>49889</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1568627183">49889</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>Denis Haine</author></authors></contributors><titles><title>episensr: Basic Sensitivity Analysis of Epidemiological Results</title></titles><number>30 Sep 2019</number><dates><year>2019</year></dates><publisher>Denis Haine</publisher><urls><related-urls><url> ;(17) The bias parameters used and bias-adjusted results were presented in Text S3. Table-1 and Text S3. Table-2, respectively. Results show that given the assumptions described above, adjustment for the outcome misclassification and live-birth bias resulted in elevated RRs for malformations. The RR increased from 1.62 to 1.78 for cardiovascular malformations, and slightly from 1.55 to 1.58 for any major malformation. RRs for the nervous system and genital malformations increased and became statistically significant with wide 95% limits. The adjustment for outcome misclassification did not alter our findings for neurodevelopmental disorders. Text S3. Table-1. Summary of Prior Distributions of the Bias Parameters for the Probabilistic Multiple Bias Analyses.ParametersEvidence on bias parametersDistributions of bias parametersOutcome misclassificationSensitivityMajor malformations: The CPRD primary care database was considered a more complete source to investigate major malformation compared with national malformation registry, because primary care follow up records for registered patients. 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ADDIN EN.CITE.DATA (18-21) Based on our data, the prevalence of major malformation and major cardiovascular malformation were 17.0 and 6.3 per 1000 by the age of 3, respectively. These prevalence rates were slightly higher than those reported by the European Surveillance of Congenital Anomalies (EUROCAT) UK estimates (15.3 and 4.3 per 1000). The prevalence of major cardiovascular malformation in our data was also consistent with other reports using CPRD, of 5.1 to 8.3 per 1000 from ages 1 to age 6 in CPRD. ADDIN EN.CITE <EndNote><Cite><Author>Wurst</Author><Year>2007</Year><RecNum>49583</RecNum><DisplayText>(19)</DisplayText><record><rec-number>49583</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1516619174">49583</key><key app="ENWeb" db-id="">0</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Wurst, K. E.</author><author>Ephross, S. A.</author><author>Loehr, J.</author><author>Clark, D. W.</author><author>Guess, H. A.</author></authors></contributors><auth-address>University of North Carolina, Chapel Hill, North Carolina 27709, USA. keele.e.wurst@</auth-address><titles><title>Evaluation of the General Practice Research Database congenital heart defects prevalence: comparison to United Kingdom national systems</title><secondary-title>Birth Defects Res A Clin Mol Teratol</secondary-title></titles><periodical><full-title>Birth Defects Res A Clin Mol Teratol</full-title><abbr-1>Birth defects research. Part A, Clinical and molecular teratology</abbr-1></periodical><pages>309-16</pages><volume>79</volume><number>4</number><keywords><keyword>Child</keyword><keyword>Child, Preschool</keyword><keyword>Databases, Factual/*standards</keyword><keyword>Europe/epidemiology</keyword><keyword>*Family Practice</keyword><keyword>Heart Defects, Congenital/*epidemiology</keyword><keyword>Humans</keyword><keyword>Infant</keyword><keyword>Infant, Newborn</keyword><keyword>Prevalence</keyword><keyword>Registries/*standards</keyword><keyword>United Kingdom/epidemiology</keyword></keywords><dates><year>2007</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1542-0752 (Print)
1542-0752 (Linking)</isbn><accession-num>17286297</accession-num><label>CDM</label><urls><related-urls><url>;(19) Considering there would be a small portion of malformations diagnosed after age 3 years, ADDIN EN.CITE <EndNote><Cite><Author>Sokal</Author><Year>2013</Year><RecNum>49882</RecNum><DisplayText>(21)</DisplayText><record><rec-number>49882</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1568107492">49882</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sokal, R.</author><author>Fleming, K. M.</author><author>Tata, L. J.</author></authors></contributors><auth-address>Division of Epidemiology and Public Health, University of Nottingham, Nottingham, United Kingdom. mcxrs7@nottingham.ac.uk</auth-address><titles><title>Potential of general practice data for congenital anomaly research: Comparison with registry data in the United Kingdom</title><secondary-title>Birth Defects Res A Clin Mol Teratol</secondary-title></titles><periodical><full-title>Birth Defects Res A Clin Mol Teratol</full-title><abbr-1>Birth defects research. Part A, Clinical and molecular teratology</abbr-1></periodical><pages>546-53</pages><volume>97</volume><number>8</number><edition>2013/08/06</edition><keywords><keyword>Congenital Abnormalities/*epidemiology/etiology</keyword><keyword>Fetal Death/epidemiology</keyword><keyword>General Practice</keyword><keyword>Humans</keyword><keyword>Infant</keyword><keyword>Infant, Newborn</keyword><keyword>Population Surveillance</keyword><keyword>*Registries</keyword><keyword>United Kingdom/epidemiology</keyword><keyword>Eurocat</keyword><keyword>congenital anomalies</keyword><keyword>epidemiology</keyword><keyword>prevalence</keyword><keyword>primary care data</keyword></keywords><dates><year>2013</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1542-0752</isbn><accession-num>23913362</accession-num><urls></urls><electronic-resource-num>10.1002/bdra.23150</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(21) we hence assume a not perfect but high sensitivity of malformation in our study, e.g. 0.95, with the range from 0.90 to 1.Triangular (0.90, 0.95, 1)*Cerebral palsy: The prevalence is from 2 to 2.5 per 1000 for the whole population in the UK. ADDIN EN.CITE <EndNote><Cite><Author>National Institute for Health and Care Excellence</Author><Year>2017</Year><RecNum>49886</RecNum><DisplayText>(22)</DisplayText><record><rec-number>49886</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1568484793">49886</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>National Institute for Health and Care Excellence,</author></authors><secondary-authors><author>National Institute for Health and Care Excellence,</author></secondary-authors></contributors><titles><title>Cerebral palsy in under 25s: assessment and management</title></titles><dates><year>2017</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>(22) We observed a prevalence of 1.8 per 1000 live births till age 14 in this study, and thus assumed a sensitivity from 0.70 to 0.90, with a mode of 0.80. Triangular (0.70, 0.80, 0.90)Epilepsy: The prevalence is 7 to 8 per 1000 for the whole population in the UK. ADDIN EN.CITE <EndNote><Cite><Author>National Institute for Health and Care Excellence</Author><Year>2012</Year><RecNum>49887</RecNum><DisplayText>(23)</DisplayText><record><rec-number>49887</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1568490376">49887</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>National Institute for Health and Care Excellence,</author></authors><secondary-authors><author>National Institute for Health and Care Excellence,</author></secondary-authors></contributors><titles><title>The Epilepsies: The diagnosis and management of the epilepsies in adults and children in primary and secondary care</title></titles><dates><year>2012</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>(23) We observed a prevalence of 6.2 per 1000 live births till age 14 in this study, and thus assumed a sensitivity from 0.78 to 0.89, with a mode of 0.84. Triangular (0.78, 0.84, 0.89)ADHD: The prevalence estimates vary widely across studies. While the prevalence in screening studies using the Development and Well-Being Assessment (DAWBA) was 36 per 1000 boys and 9 per 1000 girls, studies based on CPRD reported much lower prevalence rates of ADHD ranging from 4.4 to 8.7 per 1000 boys, and 0.5 to 1.2 per 1000 girls. PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ib2xkZW48L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA (26, 27) We assumed a sensitivity from 0.50 to 0.90, with a mode of 0.70. Triangular (0.50, 0.70, 0.90)ASD: The prevalence is about 10 per 1000 for the whole population in the UK. ADDIN EN.CITE <EndNote><Cite><Author>National Institute for Health and Clinical Excellence</Author><Year>2011</Year><RecNum>49885</RecNum><DisplayText>(28)</DisplayText><record><rec-number>49885</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1568478955">49885</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>National Institute for Health and Clinical Excellence,</author></authors><secondary-authors><author>National Institute for Health and Clinical Excellence,</author></secondary-authors></contributors><titles><title>Autism: recognition, referral and diagnosis of children and young people on the autism spectrum</title></titles><dates><year>2011</year></dates><pub-location>London</pub-location><urls></urls></record></Cite></EndNote>(28) We observed a prevalence of 7.7 per 1,000 live births till age 14, and thus assumed a sensitivity from 0.77 to 1, with a mode of 0.89. Triangular (0.77, 0.89, 1)SpecificitySpecificity is not commonly measured for rarer outcomes in CPRD. However, a high specificity for all outcomes was expected in this study, due to both the low prevalence and the high positive predictive value (PPV). The high PPV of diagnosis in CPRD has been addressed by a number of studies. The PPV for major malformations, including cardiovascular malformations and hypospadias, has been reported to be 93% to 96% PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XdXJzdDwvQXV0aG9yPjxZZWFyPjIwMDc8L1llYXI+PFJl
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ADDIN EN.CITE.DATA (9, 10, 30) We thus assume a PPV of 95% for all outcomes in general population.Based on the definition of specificity,Specificity=1-False positiveTrue negative=1-NObserved positive×1-PPVNall×1-prevalence=1-NObserved positiveNall×0.05>0.95=1-(<0.05)×(0.05>.95)>0.997We then assume a specificity for all outcomes from 0.997 to 1, with a mode of 0.999. Triangular (0.997, 0.999, 1)Live-birth bias for the association between first trimester macrolides prescribing and severe malformations (i.e. nervous system malformation, cardiovascular malformation and gastrointestinal malformation)Probability of live-birth (selection)P (live-birth|(non-malformed, penicillin)): 0.83. Around 17% pregnancies were terminated with non-clinical indication. ADDIN EN.CITE <EndNote><Cite><Author>Mortensen</Author><Year>2016</Year><RecNum>49760</RecNum><DisplayText>(31)</DisplayText><record><rec-number>49760</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1554132702">49760</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mortensen, Laust H.</author><author>Catalano, Ralph A.</author><author>Bruckner, Tim A.</author></authors></contributors><titles><title>Spontaneous Pregnancy Loss in Denmark Following Economic Downturns</title><secondary-title>American Journal of Epidemiology</secondary-title></titles><periodical><full-title>American Journal of Epidemiology</full-title></periodical><pages>701-708</pages><volume>183</volume><number>8</number><dates><year>2016</year></dates><isbn>0002-9262</isbn><urls><related-urls><url> %J American Journal of Epidemiology</electronic-resource-num><access-date>4/1/2019</access-date></record></Cite></EndNote>(31) We thus assumed that the probability of live birth in penicillins group without malformation was with a mode of 0.83, and a range of 10%.Triangular (0.78, 0.83, 0.88)P (live-birth|(malformed, penicillin)): 0.63, 0.73 and 0.78 for nervous system malformation, cardiovascular malformation and gastrointestinal malformation respectively. Based on estimated risk of termination, stillbirth, and first day neonatal death among cases with specific malformations, we assume 20%, 10% and 5% of cases with nervous system malformation, cardiovascular malformation and gastrointestinal malformation were dead before registration with the general practice. ADDIN EN.CITE <EndNote><Cite><Author>Heinke</Author><Year>2018</Year><RecNum>49888</RecNum><DisplayText>(32)</DisplayText><record><rec-number>49888</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1568546265">49888</key></foreign-keys><ref-type name="Thesis">32</ref-type><contributors><authors><author>Dominique Heinke</author></authors></contributors><titles><title>An Evaluation of Competing Risks in Studies of Perinatal Mortality and Birth Defects</title><secondary-title>T.H. Chan School of Public Health</secondary-title></titles><volume>Degree of Doctor of Science</volume><dates><year>2018</year></dates><pub-location>Boston</pub-location><publisher>Harvard University</publisher><urls></urls></record></Cite></EndNote>(32) Therefore, the probability of live birth is estimated to be 1-17%-(20%, 10% or 5%)=63%, 73% or 78% for cases with these three malformations, respectively. We estimated a range of 10%.Nervous system malformation: Triangular (0.58, 0.63, 0.68)Cardiovascular malformation: Triangular (0.68, 0.73, 0.78)Gastrointestinal malformation: Triangular (0.73, 0.78, 0.83)P (live-birth|(non-malformed, macrolides)) = P (live birth|(non-malformed, penicillin))-10%=0.73. Based on our previous system review, where the pooled odds ratio for miscarriage between macrolides and penicillins was 1.82, we assumed that first trimester macrolides exposure would decrease the probability of live birth by up to 10% (based on a probability of miscarriage of 12% in penicillin group*82%), compared to penicillins in fetuses with or without malformation. ADDIN EN.CITE <EndNote><Cite><Author>Fan</Author><Year>2018</Year><RecNum>49737</RecNum><DisplayText>(15)</DisplayText><record><rec-number>49737</rec-number><foreign-keys><key app="EN" db-id="0wraexv91frtdie0rpbppxeezvttvateprtz" timestamp="1548763410">49737</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Heng Fan</author><author>Ruth Gilbert</author><author>Leah Li</author><author>Linda Wijlaars </author></authors></contributors><titles><title>Associations between use of macrolide antibiotics during pregnancy and adverse child outcomes: a systematic review and meta-analysis </title><secondary-title>Lancet</secondary-title></titles><periodical><full-title>Lancet</full-title></periodical><edition>22/11/2018</edition><dates><year>2018</year><pub-dates><date>22/11/2018</date></pub-dates></dates><work-type>Meeting abstract</work-type><urls></urls></record></Cite></EndNote>(15)Triangular (0.68, 0.73, 0.78)P (live-birth|(malformed, macrolides)) = P (live birth|(malformed, penicillin))-10%=0.53, 0.63 and 0.68 for nervous system malformation, cardiovascular malformation and gastrointestinal malformation respectively.Nervous system malformation: Triangular (0.48, 0.53, 0.58)Cardiovascular malformation: Triangular (0.58, 0.63, 0.68)Gastrointestinal malformation: Triangular (0.63, 0.68, 0.73)*Triangular (min, mode, max): Triangular distribution with minimum value, mode and maximum value. ADHD: attention-deficit/hyperactivity disorder; ASD: autism spectrum disorder.Text S3. Table-2. Risk ratios adjusted by propensity score, and adjusted by bias due to outcome misclassification and conditioning on live-birth with random error for first trimester macrolides (versus penicillins) prescribing.Child adverse outcomesNo. of eventsAdjusted risk ratio (95% CI)b+ Adjust bias due to outcome misclassification?with random error(95% limits)+ Adjust bias due to live-birth bias?with random error(95% limits)MacrolidesPenicillinsa?Any major malformation60400.71.55 (1.21, 2.03)1.58 (1.22, 2.08)Nervous system malformation627.12.30 (0.95, 5.55)5.17 (1.53, 31.24)5.64 (1.62, 104.15)Cardiovascular malformation23146.91.62 (1.05, 2.51)1.74 (1.11, 2.74)1.78 (1.12, 2.80)Gastrointestinal malformation<5c-1.00 (0.23, 4.28)1.04 (0.24, 4.31)1.00 (0.23, 4.14)Genital malformation1168.11.68 (0.89, 3.16)2.04 (1.03, 3.94)Urinary malformation<5c-0.65 (0.20, 2.08)0.49 (0.14, 1.62)Cerebral palsy<5c-0.39 (0.10, 1.61)0.27 (0.06, 1.15)Epilepsy12160.40.78 (0.43, 1.39)0.74 (0.41, 1.30)ADHD14122.11.19 (0.69, 2.06)1.24 (0.71, 2.16)ASD19198.90.99 (0.62, 1.58)0.99 (0.60, 1.56)a: The numbers of event in penicillins group were weighted based on the distribution of propensity score of macrolides group, which were used to calculate the adjusted risk/hazard ratio in the main analyses. b: Because the risk ratios for cerebral palsy, epilepsy, ADHD and ASD were comparable with the reported hazard ratios, we measured their risk ratios for simplicity. c: In accordance with the confidentiality preserving policy of CPRD, we suppressed the information where the frequency cell contains <5 events (noted as “<5”) and where necessary to avoid deduction. CI: confidence interval; RR: risk ratio; HR: hazard ratio. ADHD: attention-deficit/hyperactivity disorder; ASD: autism spectrum disorderTable S12. Post-hoc analyses on the association between common specific malformation and macrolides versus penicillins prescribed during pregnancy.Adverse OutcomesNo. of eventsRisk per 1,000 live births or Rate per 1,000 person-year?Adj. RR/HR(95% CI)P value?MacrolidesPenicillinsMacrolidesPenicillinsVentricular septal defect1st trimester13855.993.771.66 (0.93-2.98)0.0882nd -3rd trimester252523.873.431.11 (0.73-1.67)0.626Hypospadias*1st trimester10618.865.261.45?(0.75-2.81)0.2682nd -3rd trimester262068.165.471.56?(1.04-2.35)0.032Atrial septal defect1st trimester5262.31.152.01 (0.77-5.22)0.1542nd -3rd trimester5890.771.210.59 (0.24-1.44)0.244Patent ductus arteriosus1st trimester<540-1.770.84 (0.26-2.74)0.7782nd -3rd trimester121271.861.731.02 (0.57-1.84)0.946Cleft palate/lip1st trimester<529-1.290.75 (0.18-3.14)0.6922nd -3rd trimester11941.71.281.29 (0.69-2.40)0.425Craniosynostosis1st trimester<55-0.224.16 (0.81-21.45)0.0882nd -3rd trimester5140.770.193.87 (1.40-10.67)0.009*Calculated in male babies. In accordance with the confidentiality preserving policy of CPRD, we only analyses outcomes where there were at least 5 cases in 1st trimester or 2nd to 3rd trimester, macrolides group. We suppressed the information where the frequency cell contains <5 events (noted as “<5”) and where necessary to avoid deduction. CI: confidence interval; RR: risk ratio; HR: hazard ratio. Table S13. Number of prescriptions matched or not matched with any indication (infection) and number of any major malformation by each indication.No. of prescriptions matched or not matched with indication (infection)MacrolidesPenicillinsNo. of any major malformationTotalErythromycinClarithromycinAzithromycinMacrolidesPenicillinsAntibiotics matched with any indication4726 (55%)4366 (55%)287 (58%)73 (48%)52293 (54%)94 (1.99%)915 (1.75%)Respiratory tract infection3534 (75%)3298 (76%)224 (78%)12 (16%)36462 (70%)73 (2.07%)621 (1.70%)Skin infection377 (8%)363 (8%)-<53019 (6%)9 (2.39%)65 (2.15%)IndicationHead & Neck infection306 (6%)274 (6%)-<52979 (6%)6 (1.96%)51 (1.71%)Genitourinary infection197 (4%)191 (4%)<5<59515 (18%)<5173 (1.82%)Sexual transmitted infection163 (3%)107 (2%)<5-63 (0%)<5<5Gastrointestinal infection135 (3%)121 (3%)-<5171 (0%)<55 (2.92%)Other infections14 (0%)12 (0%)<5<584 (0%)<5<5Antibiotics unmatched with any indication3906 (45%)3621 (45%)207 (42%)78 (52%)43680 (46%)92 (2.36%)751 (1.72%)Total86327987494151959731861666*An indication was defined as an infection episode recorded within 6 days before a macrolide or penicillin prescription. In accordance with the confidentiality preserving policy of CPRD, we suppressed the information where the frequency cell contains <5 events (noted as “<5”) and where necessary to avoid deduction.Table S14. Previously published studies on the association between maternal exposure of macrolides and major congenital malformations or neurodevelopmental disorders.StudiesStudy typeExposureReference groupOutcomeNo. of cases/Total in exposure groupRR/OR (95% Confidence interval)CommentsEinarson, 1990Prospective cohortClarithromycin,4-14 weeksNon-teratogenic antibioticsMajor CM3/1571.60 (0.26-9.69)Czeizel,1999Paired case-control, HungarianErythromycin, 2-3 month and whole pregnancyNon-exposure to erythromycin Isolated CMs23 cases 1.50 (0.80-2.60) for Cardiovascular CAKallen, 2005Swedish Medical Birth RegisterErythromycin, 1st trimesterGeneral population and indirectly penicillin VCardiovascular CM31/18441.84 (1.29-2.62)Penicillins Versus general population: 0.99 (0.80-1.23)Sakar, 2006Prospective cohort, CanadaAzithromycin, whole pregnancyAntibiotics, Non-teratogensMajor CM3/123Not reportedUnder powerKenyon, 2008Randomised Clinical TrialErythromycin + co-amoxiclav or erythromycin only, 3rd trimesterco-amoxiclav or placeboCerebral palsy18/783 in pPROM, 35/795 in SPL0.91 (0.48-1.71) in pPROM. 2.28 (1.24-4.21) in SPLKenyon, 2008Randomised Clinical TrialErythromycin + co-amoxiclav or erythromycin only, 3rd trimesterco-amoxiclav or placeboEpilepsy18/783 in pPROM, 35/795 in SPL0.89 (0.59-1.32) in pPROM, 1.18 (0.84-1.66) in SPLCooper, 2009Tennessee MedicaidErythromycin, azithromycin, first 4 lunar monthsNo antibioticsMajor and system CM 23 major CM/903 in erythromycin group; 23 major CM/559 in azithromycin group0.86 (0.55-1.34) for erythromycin, major CM; 1.37 (0.85-2.22) for azithromycin, major CMCrider, 2009case-control, HungarianErythromycin, whole pregnancyNo erythromycinSelected Birth Defects>300 CM case in totalAnencephaly 2.4 (1.1-5.3) and transverse limb deficiency 2.1(1.0-4.2) Associations with other outcomes were not significant. Any heart defect 1.0 (0.7-1.3). Bar-Oz, 2012Prospective cohort,CzechMacrolides (Clarithromycin, azithromycin and roxithromycin), 1st trimesterNon-teratogenic exposuresMajor and cardiovascular CM15/441 (Major CM); 7/441 (cardiovascular CM)1.42 (0.70, 2.88) for macrolides and major CM;1.91 (0.63, 5.62) for macrolides and cardiovascular CMRomoren, 2012Medical Birth Registry of NorwayMacrolides, 1ST trimesterPenicillin V Major and cardiovascular CM69/2549 (Major CM); 25/2549 (cardiovascular CM)0.96 (0.76,1.22) for major CM;0.96 (0.65,1.43) for cardiovascular CMGestational week 5-8: 1.36 (0.75, 2.47) for cardiovascular CM.Andersen, 2013Danish Fertility DatabaseClarithromycin,1ST trimesterNo clarithromycinMajor CM9/2531.03 (0.53–2.00)Bahat, 2013Retrospective cohort, IsraelMacrolides, 1st and 3rd trimesterNo macrolidesMajor and cardiovascular CMNumber of cases unreported, 1033 macrolides in total.1.07 (0.84–1.38) for major CM;0.95 (0.65–1.40) for cardiovascular CMLin, 2013Case-control, Slone Epidemiology Center Birth Defects StudyMacrolides and Erythromycin, 1-3 trimesterNo erythromycinCardiovascular malformation140 Cardiovascular CM cases in total0.9 (0.6-1.3) for cardiovascular CM exposed to macrolides during 1st trimesterBerard, 2015Prospective cohort, Quebec Pregnancy CohortErythromycin, azithromycin, and clarithromycin, 1st trimesterUnexposedMajor and cardiovascular CM66/734 erythromycin, 120/914 azithromycin, and 79/686 clarithromycin.0.96 (0.74–1.24) erythromycin, 1.19 (0.98–1.44) azithromycin and 1.12 (0.99–1.42) clarithromycinMeeraus, 2015Retrospective cohort, UKMacrolides, whole pregnancyPenicillinsCerebral palsy or epilepsy28/27491.78 (1.18-2.69)Muanda, 2017Prospective cohort, Quebec Pregnancy CohortMacrolides, 1st trimesterPenicillinsMajor and system CM265/2332 major CM, 35/2332 gastrointestinal CM, 18/2332 genital tract CM1.13 (0.98–1.31) for major CM, 1.48 (0.99–2.20) for gastrointestinal CM, and 0.93 (0.55–1.56) for genital tract CM Associations with other outcomes were not significant. 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