Long-term impact of pre-incision antibiotics on children born by caesarean section: a longitudinal study based on UK electronic health records

Article history

The research reported in this issue of the journal was funded by the HTA programme as project number 16/150/01. The contractual start date was in April 2018. The draft report began editorial review in December 2020 and was accepted for publication in June 2021. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HTA editors and publisher have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this report.

Copyright statement

Copyright © 2022 Šumilo et al. This work was produced by Šumilo et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This is an Open Access publication distributed under the terms of the Creative Commons Attribution CC BY 4.0 licence, which permits unrestricted use, distribution, reproduction and adaption in any medium and for any purpose provided that it is properly attributed. See: https://creativecommons.org/licenses/by/4.0/. For attribution the title, original author(s), the publication source – NIHR Journals Library, and the DOI of the publication must be cited.

2022 Šumilo et al.

Background and rationale

In the UK, a caesarean section (CS) is a common surgical procedure, with around one-third of babies being delivered this way. 1–4 The risk of maternal post-partum infections is considerably higher following CS than following vaginal delivery (VD). 5 National Institute for Health and Care Excellence (NICE) Guideline 192 on caesarean birth6 recommends offering women undergoing CS prophylactic antibiotics that work against the organisms causing wound infections, endometritis and urinary tract infections to reduce the risk of post-partum infections.

Before 2011, this NICE clinical guideline recommended giving intravenous prophylactic antibiotics after cord clamping to prevent the baby from being exposed to the maternal antibiotics. In 2011, the recommendation was changed to offering antibiotics to women before skin incision for CS. 7 This change in recommendation was based on evidence that earlier administration reduces the risk of maternal infections, although most of these infections are mild and tend to respond well to treatment. 8 A Cochrane review9 on the timing of intravenous prophylactic antibiotics for preventing post-partum infectious morbidity in women undergoing cs delivery included evidence from 10 randomised controlled trials (including a total of 5041 women). The review showed a significant reduction in the risk of combined post-partum infections [43%, 95% confidence interval (CI) 28% to 55%], wound infection (41%, 95% CI 19% to 56%) and endometritis (46%, 95% CI 21% to 64%) in women receiving antibiotics prior to cord clamping, compared with administering antibiotics after cord clamping. 9

It is known, however, that these high-dose broad-spectrum pre-operative antibiotics cross the placenta, with concentrations detectable within 20–30 minutes, resulting in babies being exposed to the antibiotics at the time of birth. 9,10 Although the Cochrane review found no evidence of neonatal adverse effects,9 intrapartum antibiotics have been shown to significantly disrupt the intestinal microbiota of babies. 11 During birth, the gut is colonised by microbes12 and the mode of delivery is known to significantly affect the composition of intestinal microbiota both in the neonatal period and later in infancy. 13 These microbiota appear to be key for intestinal immune tissue to differentiate resident commensal bacteria from potential pathogens. 14 In addition, there is a growing body of evidence that intestinal microbe composition in infants plays a key role in the development of their immune system, including regulation of response to different antigens and inflammation, and disruptions to intestinal microbiota are associated with susceptibility to asthma, allergies and other immune-related diseases later in childhood. 15–22

A recent systematic review14 explored the association between intestinal microbiota and the development of asthma, eczema and allergic sensitisation to food allergens in newborns and children. The review found evidence that reduced bacterial diversity and a greater or reduced abundance of particular bacteria was associated with the development of these health conditions. There is also an association between reduced intestinal bacterial diversity in the first months of life and development of allergic rhinitis. 23

Some evidence also suggests that gut microbiota may have an influence on the development of autoimmune diseases. For example, gut microbiota have been linked to the development of type 1 diabetes,24 coeliac disease25 and systemic connective tissue disorders. 26,27 The evidence in this area is acknowledged to be limited. It is, however, hypothesised that the mechanisms driving autoimmune disease development are similar to those involved in the development of allergic diseases. 28

There is limited evidence that explores the link between the gut microbiome and infectious and inflammatory disease and other immune system-related conditions. Intestinal microbiota are likely to be involved in the development of necrotising enterocolitis and inflammatory bowel disease. 29 It has also been hypothesised that childhood acute lymphoblastic leukaemia development could have an immune component driven by microbial exposures in early life. 30

One study has shown an increased risk of cerebral palsy in children born to mothers who were given antibiotics in spontaneous pre-term labour. 31 There is also a small body of research evidence that suggests associations between differences in gut microbiota and other neurodevelopmental conditions, but there is a consensus that further longitudinal assessment of the role of the gut microbiome in neurodevelopmental disorders is required. 32,33

In the broader area of child health, there is a small amount of evidence looking at gut microbiota and symptoms, such as colic, and general health measures, such as expected weight gain. Infants affected by colic have been reported to have different levels of intestinal bacterial diversity from unaffected infants. 34 Some types of gut bacteria appear to be associated with different growth patterns in babies. 35

As antibiotic exposure around the time of birth leads to different gut microbiota patterns in newborns and different microbiota patterns are associated with longer-term health conditions, the change in the timing of when mothers receive prophylactic antibiotics presents a natural experiment. Babies born to mothers who were provided antibiotics after cord clamping are exposed to no antibiotics. Those born to mothers given pre-operative antibiotics are exposed to high-dose broad-spectrum antibiotics that are likely to affect their gut microbiota.

In this study, we aimed to compare the outcomes for babies born by CS who were and who were not exposed to antibiotics given to the mother at the time of birth. The primary outcomes of interest in this study were asthma and eczema. These are the two most frequent allergy-related, doctor-diagnosed conditions in childhood and are associated with changes in gut microbiota composition in infants. 18,20 They are commonly seen in primary care and are well recorded. 36,37 With a prevalence of 3% in children aged 0–5 years, asthma is a relatively common health condition in childhood, with the largest numbers of emergency hospital admissions for any long-term condition in young children in the UK. 38,39 Although deaths due to asthma in children are rare, they do occur, accounting for 1–2% of all asthma deaths. 38,40

Eczema is a very common condition in childhood, with a prevalence of 18% of ‘ever diagnosed eczema’ in children aged 0–5 years reported in the UK. 41 Eczema usually develops in very early childhood and can affect a child’s sleep, their social and emotional development, and quality of life. It incurs significant societal costs because of lost school days, lost working days for parents and costs to the NHS, including prescription of topical corticosteroids. 42

As the evidence regarding the role of microbiota in the development of diseases is evolving, we also assessed whether or not in-utero exposure to broad-spectrum antibiotics immediately prior to birth increases the risk of a range of other immune system-related health conditions in the first 5 years of life in children born by CS.

Aims and objectives

Study design and setting

The primary objective and first two secondary objectives were addressed by undertaking a controlled interrupted time series study. 43 It comprised a cohort of children born in the UK between 2006 and 2018 and their mothers, whose health-care records were included on a primary care database [i.e. The Health Improvement Network (THIN) or the Clinical Practice Research Datalink (CPRD)] or a secondary care database [Hospital Episode Statistics (HES)].

The interrupted time series component of the study compared rates of diagnosis of asthma, eczema and other immune system-related diseases over the study period in children delivered by CS between time periods when the mother of each child was likely to receive antibiotics before skin incision and periods when it was likely that antibiotics would be administered after cord clamping.

Validity in this comparison of incidence rates is mostly threatened by temporal changes in the diagnosis and recording of health conditions and different exposures that can have an impact on the number of cases identified in routine data, rather than from case mix confounders, as indications for CS and CS incidence have changed little over the study period. As an example, patterns of asthma diagnosis have changed over time, which can, in part, be attributed to the revisions in the national asthma management guidance44 and the potentially conflicting compliance and prevalence issues faced in meeting asthma-specific Quality and Outcomes Framework (QOF) indicators introduced in 2004. 45

Conducting an analysis dependent on adjustment for confounding factors was unlikely to succeed in controlling for these changes, because (1) it is uncertain what all the drivers of all these changes have been, (2) it is uncertain whether or not any covariates exist that accurately describe these changes without substantial missing data and (3) there are challenges in specification of a functional form for the relationship between these covariates and outcome. Instead, to account for such temporal changes, we included children born by VD as a control group, as these children would have been subject to all the same temporal changes as children delivered by CS, but would not have routinely received prophylactic antibiotics. Figure 1 shows that, although the national uptake of pre-incision antibiotic policy has increased over the study period, incidence rates of asthma decreased equally in children born by CS and those born by VD.

FIGURE 1.

Illustration of the need to use VDs as the comparator group because of changing temporal patterns in outcome events unrelated to routine exposure to pre-incision antibiotics, using asthma as an example. (Rates after 2014 are further reduced because of < 5 years of follow-up and much lower incidence of asthma in very young children.)

Our model first computed the ratio of the incidence of outcomes in those receiving CS compared with VD in each year and age group. The incidence of some outcomes, including asthma, is known to differ by delivery mode. 46–48 It then compared these ratios between periods when mothers of children delivered by CS would have received antibiotics before skin incision and periods when mothers of children delivered by CS would have received antibiotics after cord clamping. This ‘ratio-of-ratios’ is an indicator of how the incidence of disease has increased (if > 1) with pre-incision antibiotics, stayed the same (if = 1) or decreased (if < 1), while adjusting for temporal changes through the matching of outcomes in children born vaginally. Subject to the assumption that the model of the difference between CS and VD rates is transferable across the time periods, differences between the observed and counterfactual CS event rates were interpreted as likely to be caused by the change in practice.

In the primary analysis, for primary care data, national policy uptake rates in the year of delivery were used as a basis for estimating a probability that each mother had received pre-incisional antibiotics and, therefore, the analysis actually used this probability as the explanatory variable, rather than a 0–1 variable. For secondary care data, we used each hospital’s response indicating the year of local policy implementation. This was obtained from our national survey of maternity care providers, which provided a 0–1 variable.

To address the final secondary objective, we compared the incidence of post-partum maternal infectious morbidity post intervention with pre-intervention rates to investigate if the effects of reduced morbidity that are shown in randomised controlled trials outside the UK9 that form the basis for the NICE guideline change7 can be replicated in the UK using routine health-care data. This analysis has been carried out for two reasons: (1) to measure the assumed impact using ‘real-world data’ to enable us to compare the benefits of pre-incisional antibiotics on maternal outcomes against any potential harms for the child and (2) to assure us that any lack of effect of the different prophylactic antibiotic policies was not due to the quality of the routine data. We calculated the crude estimates and, because of the observed overall changes in the incidence of different maternal post-partum infections recorded in routine data over time, we also undertook controlled analysis adjusting for delivery mode.

Population and eligibility criteria

To be eligible for inclusion in the study, records of all live-born children needed to meet the following criteria:

• The year of birth was between 2006 and 2018.

• It was possible to link the baby and their mother’s health-care record in primary care (THIN or CPRD) databases or a secondary care (HES) database.

• The delivery mode (CS or VD) could be determined based on recording in primary (THIN or CPRD) and/or secondary care (HES).

If children had missing delivery information, they were excluded. In the case of multiple births (such as twins), one of the children was included randomly to ensure independence of observations. 49

The duration of follow-up of children born between 2006 and 2014 was until the age of 5 years. The follow-up of children born in 2015, 2016, 2017 and 2018 was limited to 4, 3, 2 and 1 years, respectively.

For primary care data, the exposure, outcomes and covariates were defined using the Read clinical code classification system. In HES, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) was used for clinical diagnoses and the Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, 4th revision (OPCS-4) for procedures. The code lists are available online at URL: https://github.com/NIHR-Pre-incision-Antibiotics/ClinicalCodes (accessed 8 July 2021).

The process for Read code selection was as follows:

• Stage 1 – a list of key words and synonyms for each variable of interest was compiled.

• Stage 2 – the terms identified in stage 1 were used to search the description column of the Read code dictionary/database to identify the main chapter and appropriate level 3 stem code in the Read code dictionary.

• Stage 3 – stem codes were then used to identify other related Read codes, up to level 7, associated with the main stem codes. These Read codes were then selected and stored.

• Stage 4 – where available, for each outcome of interest, online Read code repositories and supplementary information from published articles that had used a similar set of Read codes were identified. These were then compared with our selected set of Read codes and further useful codes were added at this point.

• Stage 5 – with reference to the inclusion and exclusion criteria, the team identifying and selecting Read codes allocated weights to each Read code as follows: (1) definite (the team were sure that the Read code met the study inclusion criteria), (2) probable (the team felt that these codes could meet the inclusion criteria but needed further discussion), and (3) excluded (the study team felt that these codes definitely did not meet the inclusion criteria).

• Stage 6 – consensus was sought through consultation with study team researchers and public contributors, general practitioners (GPs) or specialty field experts on the final list of codes to be used, which were utilised for electronic data extraction.

Selection of ICD-10 and OPCS-4 codes to obtain admitted patient care data was generally straightforward. The validity of each coding algorithm was checked through triangulation of a number of sources. First, recently published studies based on routinely collected data were screened to check for consensus in the research community as to how to code specific reasons for admission. Second, checks of resources for clinical coders (the NHS coding clinic and the Terminology and Classifications Service in NHS Digital) were carried out. Third, technical appendices of outputs produced by the NHS and the Office for National Statistics were checked to see what coding definitions were used in reporting by public bodies. Last, to check for the specificity of coding (i.e. the extent to which fourth digits were commonly used or, if coding tended to default to ‘other’ or ‘not elsewhere classified’) individual analyses of English admitted patient care data were undertaken.

The delivery mode is recorded well in hospital records. 2 We used OPCS-4 procedure codes, which are available for 98% of births in HES,50 to derive the mode of delivery and ICD-10 codes for the remaining deliveries. Our estimates showed that the delivery mode is also accurately recorded in primary care using Read codes (98% verified against hospital records). The recording, however, was incomplete (the delivery mode was known for 50% and 69% of children in THIN and CPRD data sets, respectively, with the higher proportion in CPRD due to the supplied data set being restricted to the records in the CPRD pregnancy register). For THIN and CPRD records not linked to HES, we used the Read codes to identify the mode of delivery, which increased the number of children in the sample.

Study outcomes

The primary outcomes in this study were the incidence of (1) asthma and (2) eczema. Asthma and eczema outcomes in the THIN–CPRD data set were defined using lists of relevant Read codes, which were created following the steps described above (see Population and eligibility criteria). Furthermore, Read code lists for both asthma and eczema have been previously validated in primary care. 51,52 Therefore, existing validated lists were used as the basis for the outcome definition in this study and further codes were added following consultation with the wider research team, which included a GP and an allergy and immunology consultant. In line with the paper on the validation of eczema coding,51 in addition to a Read code for eczema diagnosis, at least two prescriptions on separate dates for any eczema-related treatment were also required to reflect the chronic nature of the condition. In the HES data set, asthma and eczema were defined if a ICD-10 code for asthma or eczema was used as the main primary diagnosis for the hospital admission.

The main analysis for these outcomes was carried out separately in the primary care data set and the secondary care (HES) data set (the latter analysis including only those hospitals for which the year of antibiotic prescribing policy change was known).

We also explored the relationship between antibiotic exposure and asthma and eczema severity in the primary care data set. Moderate/severe asthma was defined as having a Read code for asthma diagnosis and a prescription code for a corticosteroid inhaler and either a leukotriene receptor antagonist or a systemic steroid. As the severity of asthma is not reliably coded, a prescription for a leukotriene receptor antagonist or a systemic steroid is indicative of asthma that is uncontrolled on inhaled corticosteroids (i.e. moderate or severe asthma). Moderate/severe eczema was defined as having a Read code for eczema diagnosis and at least two prescriptions on separate dates, with at least one prescription for treating moderate or severe eczema (i.e. moderate, potent or very potent steroid, topical calcineurin, systemic therapy or phototherapy), as described in the NICE guideline for eczema. 53

Secondary outcomes are listed in Table 1. We excluded immune deficiencies from the outcome list, as Read and ICD-10 codes did not allow us to differentiate between congenital and acquired immunodeficiencies. We also did not include pelvic abscess as a separate outcome in the analysis of secondary care data, as there was no specific ICD-10 code for this condition.

Covariates

To assess the validity of the model we used in controlling for temporal confounders, we explored changes in the case mix covariates to identify any differential changes over time in relation to the mode of birth. To identify relevant covariates, a literature review of reviews was conducted on the risk factors for the primary study outcomes (i.e. asthma and eczema) and postnatal maternal infections. MEDLINE and Google Scholar (Google Inc., Mountain View, CA, USA) were searched for articles written in English that reported the effect of any risk factor for the conditions mentioned. Identified covariates included maternal demographic characteristics,54 behaviour-related factors,54–58 pregnancy- and labour-related factors,54,57 antibiotic prescribing during pregnancy,56,59,60 maternal long-term allergy-related conditions,61–64 deprivation,54,55,58,65,66 child demographic and birth characteristics,54,55,58,59,62,64–67 breastfeeding,55,67 child health conditions55,61–65,67 and antibiotic prescribing in childhood55,60,67 (Table 2).

Data sources and mother–baby linkage

Estimated sample size and statistical power

Statistical methods and analyses

The statistical analysis methods for this project can be broken down into the methods used to analyse the primary care data set (THIN–CPRD) and the methods used to analyse the secondary care data set (HES). We describe these methods separately for each data set.

Research Ethics Committee approval

The Ethics Review Committee of the University of Birmingham provided the ethics approval required for this study (reference number ERN_17-1675).

The NHS South-East Multi-Centre Research Ethics Committee approved the use of the THIN database, subject to independent scientific review to help ensure appropriate analysis and interpretation of the data. 84 The Independent Scientific Ethical Advisory Committee – Scientific Review Committee Panel of the data provider IQVIA (Durham, NC, USA) approved the use of the THIN and HES-linked data in this study (reference number 18THIN047).

The CPRD data set has ethics approval from a National Research Ethics Service Committee for observational research using anonymised data. 85 For this study, the use of CPRD has been approved by the Independent Scientific Advisory Committee for MHRA Database Research (reference number 18_181AR2).

The use of the HES database is exempt from NHS Research Ethics Committee approval because it involves the analysis of an existing data set of non-identifiable data. The standard NHS Digital data approval process was followed to obtain approval for the use of HES data. 86 The Health Research Authority confirmed that Health Research Authority approval was not required for this research, as it involved linking anonymised patient data from established databases for this study only.

Changes to the protocol

There were no changes to the protocol.

Patient and public involvement

THIN–HES-linked and THIN primary care data sets descriptions

During the study period, 219,390 deliveries were identified in the THIN–HES-linked data set. We excluded 32,808 delivery records, as they did not have a baby with the same family number as the mother. We then matched a baby for each of the remaining 186,582 deliveries. We excluded a further 72,807 records, either because they were linked to more than one potential mother or because the baby was not registered within the first year from birth. This left us with a total of 113,775 valid deliveries linked to a baby identified in the THIN–HES-linked data set.

In the THIN primary care data set, we looked for incident deliveries (VD or CS) from 1 January 2006 to 31 December 2018 in women aged 13–50 years at the time of delivery who were already registered with the practice. We found a total of 441,540 delivery records in the database, of which 54,939 were excluded, as there was no baby born in the same month and year of delivery with the same family number as the mother. We further excluded 135,318 records of babies who were either linked to more than one potential mother or not registered within a year from birth. This left a total of 251,283 mother–baby pairs identified using Read codes in the THIN primary care database. Finally, we extracted information from records of the babies who had ‘born by vaginal delivery’ or ‘born by caesarean section’ Read codes from the THIN primary care database and found 46,593 records. All of these babies, however, were already present in our linked primary care data set derived from the delivery codes noted in the mother’s medical record and, therefore, did not add any new babies that could be linked to a mother.

Combined, there were a total of 365,058 mother–baby pairs from THIN–HES-linked and THIN primary care data sets. A total of 57,317 of these pairs were identified in both the THIN primary care and THIN–HES-linked databases (i.e. duplicates) and, therefore, we removed those records from the THIN primary care database. This left us with a final total of 307,741 unique mother–baby pairs from the THIN primary care and THIN–HES-linked databases (Figure 2).

FIGURE 2.

Study population selection, including mother–baby data linkage in THIN data sets.

CPRD–HES-linked and CPRD primary care data sets descriptions

The CPRD mother–child-linked database contained 646,967 mother–baby pairs in total. When examining CPRD data, our main goal was to identify linked mothers and babies who had a recorded mode of delivery (VD/CS).

We used the same criteria to define patient start date and patient end date for the CPRD database as we did for THIN. There were 444,823 mother–baby pairs in the CPRD–HES-linked data set. To retain only the valid mother–baby pairs in the CPRD–HES-linked data, we excluded (1) 11,172 records because information on babies was not present in the primary care practice data and (2) 171,269 records because either the mother or baby did not meet the study inclusion criteria (78,229 babies were excluded because they were not registered within 1 year from date of birth, 61,553 mothers were excluded because their delivery date was not in between the patient start date and the patient end date or their age at delivery did not match study inclusion criteria, and 31,487 mother–baby pairs were excluded because both the mother and the baby did not meet the study inclusion criteria). This left us with 262,382 mother–baby pairs that had an OPCS-4 or ICD-10 delivery code identifying the mode of delivery and met our study inclusion criteria (as explained in Chapter 2).

In the CPRD primary care data set, there were 452,353 mother–baby pairs, from which we excluded 121,598 records as they did not meet the study inclusion criteria, leaving us with 330,755 mother–baby pairs with a recorded mode of delivery in the CPRD primary care database.

In total, we had 593,137 mother–baby pairs from both CPRD data sets, of which we excluded 183,230 duplicates (valid mother–baby pairs found independently in both the CPRD primary care and CPRD–HES data sets), removing them from the CPRD primary care data set. This left us with 409,907 valid mother–baby pairs across the two CPRD data sets (Figure 3).

FIGURE 3.

Study population selection in CPRD data sets.

Final THIN–CPRD data set description

Across all data sets, we identified 717,648 valid deliveries. We then excluded 201,703 (28.1%) duplicates (163,408 from THIN data sets and 38,295 from the CPRD data sets). This resulted in a final total of 515,945 mother–baby pairs for inclusion in our analysis [144,333 (28.0%) from THIN data sets combined and 371,612 (72.0%) from CPRD data sets combined] (Figure 4).

FIGURE 4.

Flow diagram of the final THIN–CPRD data set.

Out of the total of 515,945 births, 144,861 (28.1%) were births by CS and the remaining 371,084 (71.9%) were by VD. The number of actual linked mother–baby pairs is smaller than the estimate in our sample size justification. This was mainly because we used THIN data from the year 2010 to derive our estimates and, since 2010, many practices have left the Vision system and in turn the number of practices contributing to THIN and CPRD GOLD has substantially reduced over the years, resulting in decreasing numbers of deliveries in the THIN–CPRD data set in the more recent years (Figure 5). At the time of producing our sample size estimates, we overlooked this phenomenon and did not account for it and, hence, ended up with an overestimate of the number of deliveries in this combined data set.

FIGURE 5.

The number of births by delivery mode and year in the final THIN–CPRD data set.

Overall, 57.2% (295,079/515,945) of the births in the final THIN–CPRD data set were from THIN–HES- or CPRD–HES-linked data sets (Figure 6). The proportion of births from HES-linked data sets decreased over the years, with the majority of births from 2015 coming from primary care databases, and HES-linked data were not available after the beginning of 2018. As the recording of delivery mode is less complete in primary care, the decrease of births from HES-linked data sets explains the larger proportions of CS births in the later years of the study in the final THIN–CPRD data set. Over one-quarter [26.0% (76,819/295,079)] of births identified in HES-linked data sets were delivered by CS.

FIGURE 6.

The number of births by delivery mode and year from the THIN–CPRD data set, with linked HES data used for sensitivity analysis.

HES data set description

The derivation of the HES analysis set covering the whole of England, which was produced from the matching of delivery episodes and baby records, is summarised in Figure 7.

FIGURE 7.

Flow diagram for derivation of the mother–baby-linked HES data set covering the whole of England and linkage to the national survey of the hospital prophylactic antibiotic administration policies.

After randomly selecting one baby in cases of multiple births from 7,222,967 successfully matched mother–baby pairs, the mother-baby-linked HES data set covering the whole of England included 7,147,884 mother–baby pairs, of which 25.4% (1,813,799/7,147,884) were CS births. The data set linked to the responses to the national survey of hospital prophylactic antibiotic administration policies contained records of 3,945,351 mother–baby pairs (with one baby in the data set in cases of multiple births), 25.4% (n = 1,001,598) of which were CS births. Figure 8 shows the absolute numbers of CS and VD births over time in the full HES data set and the HES data set linked to the survey data. The sharp drop in the number in the final year is because it includes births only until the end of March 2017.

FIGURE 8.

The number of births by delivery mode and year in (a) the HES data set covering the whole of England; and (b) the HES data set linked to the data from the national survey of hospital prophylactic antibiotic administration policies used in the main analysis.

Results of the national survey of hospital prophylactic antibiotic administration policies

An overall survey response rate of 75.7% (143/189) was achieved.

The vast majority of hospitals [95.8% (137/143)] reported that their unit’s policy was to provide antibiotic prophylaxis before skin incision and five (or fewer) hospitals had this policy for either pre-labour or in-labour CS only.

Of the hospitals with a pre-incision antibiotic policy, 86.9% (119/137) indicated in which year they had implemented this policy change. The increase in the proportion of hospitals with a pre-incision antibiotics policy was gradual (Figure 9) and there was relatively little variation between different countries in the UK and regions within England (Appendix 1). No hospitals reported that they had implemented a pre-incision antibiotic policy before 2008. By 2013, over half of all hospitals had a policy to administer antibiotic prophylaxis before skin incision.

FIGURE 9.

Cumulative percentage of hospitals over time with policies to administer antibiotic prophylaxis before skin incision for women undergoing CS in the whole of the UK and England separately.

All of the hospitals that responded to the question about the type of prophylactic antibiotics currently administered used broad-spectrum antibiotics. The most common regimens were co-amoxiclav (40.6%, 54/133), cefuroxime (29.3%, 39/133) and cefuroxime together with metronidazole (26.3%, 35/133).

Of the 125 hospitals that had changed the practice to pre-incision antibiotics and responded to whether or not they used the same antibiotics before the policy change, 72.0% (90/125) had not changed their antibiotic regimen. The vast majority (≥ 95%) of those hospitals that had changed antibiotics after implementing pre-incision antibiotic policy had previously used co-amoxiclav.

Clindamycin was the most commonly used prophylactic antibiotic for women with a penicillin allergy (71.4%, 95/133), either administered on its own (53.4%, 71/133) or used along with gentamicin (12.8%, 17/133). Other regimens included gentamicin and teicoplanin and/or metronidazole in 12.8% (17/133) of hospitals. Metronidazole was used on its own or along with other antibiotics (such as cefuroxime or cefaclor) in 7.5% (10/133) of hospitals and teicoplanin alone was administered in 6.0% (8/133) of hospitals. Of the 123 hospitals that had changed their practice to pre-incision antibiotics and responded to whether or not they had used the same antibiotics before the policy change for women with penicillin allergy, 91.1% (112/123) used the same antibiotic regimen.

Eight hospitals with pre-incision antibiotic policies had conducted audits from 2014 onwards regarding the proportion of women given prophylactic antibiotics before skin incision, and reported that 70–100% of women had received pre-incision antibiotics.

Results of covariate analysis

Covariate analysis graphs are included in Appendix 2. There were no marked differential changes over time between the CS and VD groups in terms of maternal, household and child characteristics.

Primary outcome results

Secondary outcome results

Results of subgroup analyses

Implications for practice and research recommendations

The overall findings from this study provide further evidence for the currently prevailing practice in the UK of giving prophylactic antibiotics before skin incision to help reduce the risk of maternal infections in the post-partum period.

Some of the secondary health outcomes we investigated in this study were exploratory because of a lack of or inconclusive evidence regarding an association between newborn microbiota, early exposure to antibiotics and children’s development. Many of the health conditions were also very rare in young children, resulting in large uncertainty around the estimates. We suggest undertaking a further study using routinely collected health-care data in other countries where there was no change in the timing of prophylactic antibiotic administration to assess if there have also been changes in the risk over time of developing these health conditions, particularly ADHD and gastroenteritis, irrespective of the exposure to prophylactic antibiotics around the time of birth. The current study includes health-care records until 2018, but it could also be repeated in the future to capture outcomes of interest in children aged > 5 years.

Other research is ongoing in the UK using the Born in Bradford birth cohort, allowing the ascertainment of antibiotic exposure at an individual level. The original Born in Bradford cohort included 13,776 children, and the majority of these children were born before the recommendation to give antibiotics pre incision. 99 This may limit the ability of this study to assess the risk for most of the outcomes of interest investigated in this research.

Contributions of authors

Dana Šumilo (https://orcid.org/0000-0001-6732-2459) (Consultant and Honorary Senior Research Fellow, Public Health and Epidemiology) contributed to the study design, analysis and interpretation of the results and led the writing of the report.

Krishnarajah Nirantharakumar (https://orcid.org/0000-0002-6816-1279) (Professor, UK Research and Innovation Clinical Fellow and Honorary Consultant, Public Health and Health Informatics) led the preparation of the THIN–CPRD data set and contributed to the analysis, interpretation of the results and writing of the final report.

Brian H Willis (https://orcid.org/0000-0002-0821-8624) (Medical Research Council Clinician Scientist and General Practitioner) contributed to the study design, development of study outcome definitions, interpretation of the results and writing of the final report.

Gavin Rudge (https://orcid.org/0000-0002-9857-6547) (Research Fellow, Health Informatics) was responsible for data linkage and preparation of the HES data set, and contributed to the development of study outcome definitions, analysis, interpretation of the results and writing of the final report.

James Martin (https://orcid.org/0000-0002-6949-4200) (Lecturer, Statistics) was responsible for statistical analysis and contributed to the interpretation of the results and writing of the final report.

Krishna Gokhale (https://orcid.org/0000-0002-2167-9572) (Research Fellow, Computer Science) was responsible for data linkage and preparation of the THIN–CPRD data set and contributed to the analysis, interpretation of the results and writing of the final report.

Rasiah Thayakaran (https://orcid.org/0000-0002-4422-4095) (Research Fellow, Statistics) was responsible for preparation of the THIN–CPRD data set and contributed to the analysis, interpretation of the results and writing of the final report.

Nicola J Adderley (https://orcid.org/0000-0003-0543-3254) (Lecturer, Health Informatics and Epidemiology) contributed to data acquisition, interpretation of the results and writing of the final report.

Joht Singh Chandan (https://orcid.org/0000-0002-9561-5141) (Academic Clinical Lecturer, Public Health) contributed to data acquisition, preparation of the THIN–CPRD data set, development of study outcome definitions and writing of the final report.

Kelvin Okoth (https://orcid.org/0000-0002-2745-4083) (Research Fellow, Epidemiology) contributed to the development of study outcome definitions and preparation of the THIN–CPRD data set.

Isobel M Harris (https://orcid.org/0000-0001-8125-3832) (Research Fellow, Systematic Reviews) contributed to the literature review for this study, and the analysis and writing of the final report.

Ruth Hewston (https://orcid.org/0000-0002-2035-184X) (Patient and Public Contributor) advised on all PPI aspects of the project, including defining study outcomes, wider PPI engagement, interpretation of the results, development of lay messages and writing of the final report.

Magdalena Skrybant (https://orcid.org/0000-0001-7119-2482) (Patient and Public Contributor) advised on all PPI aspects of the project, including defining study outcomes, wider PPI engagement, interpretation of the results, development of lay messages and writing of the final report.

Jonathan J Deeks (https://orcid.org/0000-0002-8850-1971) (Professor, Biostatistics) developed statistical methodology and oversaw the analysis, and contributed to the study design, interpretation of the results and writing of the report.

Peter Brocklehurst (https://orcid.org/0000-0002-9950-6751) (Professor, Women’s Health and Perinatal Epidemiology) contributed to the study design, development of study outcome definitions, interpretation of the results and writing of the report.

Publications

Šumilo D, Nirantharakumar K, Willis BH, Rudge G, Martin J, Gokhale K, et al. Long term impact of giving antibiotics before skin incision versus after cord clamping on children born by caesarean section: protocol for a longitudinal study based on UK electronic health records. BMJ Open 2019;9:e033013.

Šumilo D, Willis BH, Rudge GM, Martin J, Gokhale K, Thayakaran R, et al. Long term impact of prophylactic antibiotic use before incision versus after cord clamping on children born by caesarean section: longitudinal study of UK electronic health records. BMJ 2022;377:e069704.

Data-sharing statement

All data queries and requests should be submitted to the corresponding author for consideration in the first instance. Please note that data for this study were derived from THIN and CPRD GOLD primary care and linked data obtained under licence from IQVIA World Publications Ltd and UK MHRA. A secondary care database was derived from HES data obtained under licence from the Health and Social Care Information Centre (NHS Digital). Data for similar cohorts can be requested from IQVIA World Publications Ltd, the UK MHRA and NHS Digital, subject to protocol approval and licence agreements.

Patient data

This work uses data provided by patients and collected by the NHS as part of their care and support. Using patient data is vital to improve health and care for everyone. There is huge potential to make better use of information from people’s patient records, to understand more about disease, develop new treatments, monitor safety, and plan NHS services. Patient data should be kept safe and secure, to protect everyone’s privacy, and it’s important that there are safeguards to make sure that it is stored and used responsibly. Everyone should be able to find out about how patient data are used. #datasaveslives You can find out more about the background to this citation here: https://understandingpatientdata.org.uk/data-citation.

Disclaimers

This report presents independent research funded by the National Institute for Health and Care Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, the HTA programme or the Department of Health and Social Care. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, the HTA programme or the Department of Health and Social Care.

Maternal and household characteristics

FIGURE 12.

Proportion of delivery mode in the THIN–CPRD data set over time and across different maternal age groups at childbirth: (a) < 20 years; (b) 20–29 years; (c) 30–39 years; and (d) ≥ 40 years.

FIGURE 13.

Proportion of delivery mode in the HES data set over time and by maternal age groups at childbirth: (a) < 20 years; (b) 20–29 years; (c) 30–39 years; and (d) ≥ 40 years.

FIGURE 14.

Proportion of delivery mode in the THIN–CPRD data set over time and by maternal ethnicity: (a) black; (b) missing; (c) mixed; (d) other; (e) South Asian; and (f) white.

FIGURE 15.

Proportion of delivery mode in the HES data set over time and by maternal ethnicity: (a) black; (b) missing; (c) mixed; (d) other; (e) South Asian; and (f) white.

FIGURE 16.

Proportion of delivery mode in the THIN–CPRD data set over time and by maternal smoking status before delivery: (a) discontinued; (b) missing; (c) no; and (d) yes.

FIGURE 17.

Proportion of delivery mode in the THIN–CPRD data set over time and by different maternal body mass index groups (latest measurement was 9 months before delivery): (a) 0–18.5 kg/m²; (b) 18.5–25 kg/m²; (c) 25–30 kg/m²; (d) 30–34 kg/m²; (e) ≥ 35 kg/m²; and (f) missing.

FIGURE 18.

Proportion of delivery mode in the HES data set over time and by parity: (a) 0; (b) 1; (c) ≥ 2; and (d) missing.

FIGURE 19.

Proportion of delivery mode in the HES data set with premature rupture of membranes over time.

FIGURE 20.

Proportion of delivery mode in the HES data set with post-partum haemorrhage over time.

FIGURE 21.

Proportion of delivery mode in the HES data set with manual placental removal/retained products of conception over time.

FIGURE 22.

Proportion of women by delivery mode in the THIN–CPRD data set who were prescribed antibiotics during pregnancy over time.

FIGURE 23.

Proportion of women by delivery mode in the THIN–CPRD data set over time and with a record of (a) asthma; (b) eczema; and (c) allergic rhinitis and conjunctivitis.

FIGURE 24.

Proportion of delivery mode in the THIN–CPRD data set over time and living in different areas of deprivation (as measured by Townsend score, with 1 being most affluent and 5 being most deprived): (a) Townsend score of 1; (b) Townsend score of 2; (c) Townsend score of 3; (d) Townsend score of 4; (e) Townsend score of 5; and (f) missing.

FIGURE 25.

Proportion of delivery mode in the HES data set over time and living in different areas of deprivation (as measured by Index of Multiple Deprivation 2015 score, with the lowest score being most affluent and the highest score being most deprived): (a) 0–0.2; (b) 0.2–0.4; (c) 0.4–0.6; (d) 0.6–0.8; (e) 0.8–1; and (f) missing.

Child characteristics

FIGURE 26.

Proportion of boys in CS and VD births over time in (a) the THIN–CPRD data set and (b) the HES data set.

FIGURE 27.

Proportion of delivery mode in the THIN–CPRD data set over time and by child’s ethnicity: (a) black; (b) missing; (c) mixed; (d) other; (e) South Asian; and (f) white.

FIGURE 28.

Proportion of delivery mode in the HES data set over time and by child’s ethnicity: (a) black; (b) missing; (c) mixed; (d) other; (e) South Asian; and (f) white.

FIGURE 29.

Proportion of delivery mode in the HES data set over time and by gestational age: (a) < 32 weeks; (b) 32–36 weeks; (c) ≥ 37 weeks; and (d) missing.

FIGURE 30.

Proportion of delivery mode in the HES data set over time and by the child’s birthweight: (a) < 2500 g; (b) 2500–3999 g; (c) ≥ 4000 g; and (d) missing.

FIGURE 31.

Proportion of women feeding their babies with breast milk only in the few days after birth by delivery mode (data source: Care Quality Commission’s Maternity Surveys in 2007, 2010, 2013 and 2017). 79

FIGURE 32.

Proportion of women feeding their babies with breast milk only at 3 months of age by delivery mode (data source: National Perinatal Epidemiology Unit maternity surveys in 2006, 2010 and 201478).

Outcomes in the THIN–CPRD data set

FIGURE 33.

Incidence rate of asthma per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 34.

Incidence rate of eczema per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 35.

Incidence rate of allergic rhinitis and conjunctivitis per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 36.

Incidence rate of food allergy and intolerance per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 37.

Incidence rate of having two or more allergy-related conditions (e.g. asthma, eczema, food allergy/intolerance, allergic rhinitis and conjunctivitis) per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 38.

Incidence rate of penicillin allergy per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 39.

Incidence rate of anaphylaxis per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 40.

Incidence rate of high risk of anaphylactic reaction (prescribing of automatic injection devices containing adrenaline) per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 41.

Incidence rate of type 1 diabetes per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 42.

Incidence rate of coeliac disease per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 43.

Incidence rate of childhood vitiligo per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 44.

Incidence rate of wheeze per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 45.

Incidence rate of upper respiratory tract infections per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 46.

Incidence rate of lower respiratory tract infections per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 47.

Incidence rate of bronchiolitis per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 48.

Incidence rate of gastroenteritis per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 49.

Incidence rate of urinary tract infection per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 50.

Incidence rate of first-time antibiotic prescription per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 51.

Incidence rate of cerebral palsy per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 52.

Incidence rate of autism spectrum disorder per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 53.

Incidence rate of ADHD per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 54.

Incidence rate of colic per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 55.

Incidence rate of failure to thrive per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set. Age (a) 0 years; (b) 1 year; (c) 2 years; (d) 3 years; and (e) 4 years.

FIGURE 56.

Incidence rate of primary care consultations in the first 12 months of life per 1000 person-years in children born by CS and VD by age and year of birth in the THIN–CPRD data set.

FIGURE 57.

Incidence of maternal composite infectious morbidity (e.g. wound infection, endometritis/endomyometritis, pelvic abscess and maternal sepsis) per 1000 births by CS and VD by year of delivery in the THIN–CPRD data set.

FIGURE 58.

Incidence of wound infection per 1000 births by CS and VD by year of delivery in the THIN–CPRD data set.

FIGURE 59.

Incidence of endometritis/endomyometritis per 1000 births by CS and VD by year of delivery in the THIN–CPRD data set.

FIGURE 60.

Incidence rate of maternal sepsis per 1000 births by CS and VD by year of delivery in the THIN–CPRD data set.

FIGURE 61.

Incidence rate of maternal urinary tract infection per 1000 births by CS and VD by year of delivery in the THIN–CPRD data set.

FIGURE 62.

Incidence of antibiotic prescribing in the post partum period per 1000 births by CS and VD by year of delivery in the THIN–CPRD data set.

Outcomes in the HES data set

As HES captures all hospital admissions in England, all children were assumed to be followed up until age 5 years if they were born before 2015. For the birth years of 2015, 2016 and 2017, children were followed up to age 4, 3 and 2 years, respectively, and, therefore, for these years the incidence rate reflects the incidence of the outcome of interest in these age groups.

FIGURE 63.

Incidence rate of asthma per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 64.

Incidence rate of eczema per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 65.

Incidence rate of anaphylaxis per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 66.

Incidence rate of type 1 diabetes per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 67.

Incidence rate of juvenile idiopathic arthritis per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 68.

Incidence rate of autoimmune (idiopathic) thrombocytopenic purpura per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 69.

Incidence of late-onset neonatal sepsis per 1000 births in children born by CS and VD by year of birth in the HES data set.

FIGURE 70.

Incidence of early-onset neonatal sepsis per 1000 births in children born by CS and VD by year of birth in the HES data set.

FIGURE 71.

Incidence rate of other sepsis in children (developed after the neonatal period of 28 days after birth) per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 72.

Incidence rate of lower respiratory tract infections per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 73.

Incidence rate of bronchiolitis per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 74.

Incidence rate of gastroenteritis per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 75.

Incidence rate of urinary tract infections per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 76.

Incidence of necrotising enterocolitis per 1000 births in children born by CS and VD by year of birth in the HES data set.

FIGURE 77.

Incidence rate of leukaemia per 1000 person-years in children born by CS and VD by year of birth in the HES data set.

FIGURE 78.

Incidence rate of first-time hospital admission per 1000 person-years in children born by CS and VD by year of birth in HES data set.

FIGURE 79.

Incidence of maternal composite infectious morbidity (e.g. wound infection, endometritis/endomyometritis and maternal sepsis) per 1000 births by CS and VD by year of delivery in HES data set.

FIGURE 80.

Incidence of wound infection per 1000 births by CS and VD by year of delivery in HES data set.

FIGURE 81.

Incidence of maternal sepsis per 1000 births by CS and VD by year of delivery in HES data set.

FIGURE 82.

Incidence of maternal urinary tract infection per 1000 births by CS and VD by year of delivery in HES data set.

Primary outcomes

Secondary outcomes

Subgroup analyses