A mixed-methods study was undertaken to assess the availability and use of surgical services for UI across England and to identify factors that could explain observed variation in practice, including the impact caused by data issues, patients’ experiences and expectations, clinicians’ judgement, and organisational and contextual factors.
Data sources
The Hospital Episode Statistics (HES) database contains information on each episode of admitted patient care (APC) in English NHS hospital trusts. These data are extracted from local patient administration systems as part of the Commissioning Data Set. This data set is submitted to NHS Digital for processing and made available for audit and research as the HES data set.42 Each record contains data on patient demographics (e.g. age, sex, ethnicity and area of residence), the episode of care (e.g. hospital name, date of admission and discharge) and clinical information. Diagnoses are recorded using the International Classification of Diseases, Tenth Revision (ICD-10),43 and procedures using the Office of Population, Censuses and Surveys Classification of Interventions and Procedures, version 4 (OPCS-4).44 Each patient is assigned a unique identifier, making it possible to study longitudinal patterns of care, including tracking any future admission or procedure in the same or a different NHS hospital.
The Clinical Practice Research Datalink (CPRD) (formerly the General Practice Research Database) collates routinely collected anonymised patient data from general practices that have agreed at a practice level to provide data. All patients registered with participating practices are included in the data set, unless they have individually requested to opt out of data sharing at their GP practice. CPRD uses Read codes (used to record symptoms, diagnoses, processes of care) and British National Formulary codes to record information on prescriptions. More than 600 GP practices contribute data to CPRD, covering 9% of the population. CPRD linkage data include patients from 411 practices, covering approximately 75% of contributing CPRD practices in England.45 Compared with the 2011 UK census, CPRD patients are broadly representative of the UK population in terms of age, sex and ethnicity, and comparable for body mass index (BMI) distribution to the Health Survey for England.46 CPRD is deemed ‘up to standard’ (UTS) for research purposes and widely used in epidemiological and health services research. CPRD data have been linked to HES APC and HES Outpatient data by the trusted third party, NHS Digital. Three distinct administrative health-care data sets were used in this project: HES APC, CPRD and CPRD-linked to HES APC and HES Outpatient. We indicate throughout this report which data were used by each WP.
In addition to using the administrative health-care databases HES and CPRD, we conducted primary data collection. This data collection comprised interviews with women who had been referred to secondary care and were considering surgical treatment, and an online survey of gynaecologists using clinical case vignettes (i.e. hypothetical patients). The primary data collection is described in detail in the sections below for WPs 3 and 5.
Methods for work package 1
Work package 1 aimed to assess the consistency, completeness and accuracy of diagnostic and procedure coding for UI in the HES and the CPRD databases, and to develop a coding framework for UI allowing for divergent coding practices among providers.
In the HES database, we first used a ‘forward and backward’ searching strategy. The forward searching step began with a list of ICD-10 and OPCS-4 codes that were compiled iteratively by the clinical and methodological members of the research team. The backwards strategy then explored whether or not records found on the basis of the prespecified ICD-10 codes contain additional relevant OPCS-4 codes that were not prespecified. A similar exercise considers additional ICD-10 codes in records found on the basis of the prespecified OPCS-4 codes. Second, we assessed the frequency with which all of these potentially relevant codes have been used and explore the consistency of diagnosis and procedure codes within the records of individual patients. Third, the variation in consistency of diagnosis and procedure codes among providers was explored to identify providers that have divergent coding practices. We have previously demonstrated that this strategy for using HES data has the potential to produce a coding framework creating groups of patients who are homogeneous with respect to diagnosis, prognosis and treatment.47
In the CPRD database, we started with a previously published code list.48 We tested this existing code list by evaluating the consistency between diagnostic and treatment codes and the temporal consistency of coding within records of the same patient. Next, additional relevant keywords, synonyms and possible codes were identified by searching additional published literature and code list repositories. Finally, the clinicians on the research team manually reviewed the code list (individually and collectively). Recommendations made by each clinician were collated and collectively discussed with the aim of reaching a consensus on the codes that would be regarded as UI diagnosis or treatment codes. The coding work conducted for WP 1 fed directly into the analyses conducted for WPs 2, 4 and 6.
Methods for work package 2
Work package 2 aimed to assess variation in rates of surgery for SUI using HES APC (inpatient) data. We examined variation in rates of SUI surgery between NHS CCGs and other relevant regional units, and the impact of supply-side factors (e.g. primary care characteristics and availability and organisation of secondary care services) on local rates. WP 2 focused on variation in surgery for SUI specifically because SUI symptoms are the most common subtype of UI symptoms; 50% of women with UI indicate solely SUI symptoms and approximately 40% indicate MUI symptoms, where SUI and UUI symptoms coexist.16,17
The cohort comprised women aged ≥ 20 years who had received surgical treatment for SUI between 1 April 2013 and 31 March 2016 and had a SUI diagnosis recorded at the time of the procedure. SUI surgery was defined using UK OPCS-4 codes () based on coding work conducted for WP 1.44 SUI diagnosis was defined using the ICD-10 code N39.3 Stress urinary incontinence.43
The OPCS-4 codes used to define SUI surgery
The outcome measure was rate of surgery for SUI per 100,000 women per year at two geographic levels: 209 CCGs and 44 Sustainability and Transformation Partnership (STP) areas. CCGs are statutory NHS bodies responsible for the planning and commissioning of health care services in a local area (with an average population size of about 104,000 adult females). CCG areas are grouped into 44 STP areas (with an average population size of about 493,000 adult females), which were set up to co-ordinate improvements in the delivery of NHS services.49 Reference denominator populations were derived by aggregating the 2011 census population counts for women aged ≥ 20 years in lower-layer super output areas (LSOAs) that are within the respective boundaries of the CCG and STP areas. There are 32,844 LSOAs (postcode-based geographic units) in England (with an average population of approximately 1700 people).50 Women may have had repeat procedures in the study period, but only the first procedure was counted in calculating the surgery rate.
Sociodemographic factors may explain variations in the rates of surgery for SUI. We adjusted for age, socioeconomic deprivation, ethnicity and limiting long-term illness in our regression models. We handled age as a patient-level characteristic grouped into five categories (i.e. 20–39, 40–49, 50–59, 60–69 and ≥ 70 years). Socioeconomic deprivation, ethnicity and limiting long-term illness were CCG-level characteristics derived from 2011 census data.50 For socioeconomic deprivation, we used the averages of the national ranking of the Index of Multiple Deprivation (IMD) of LSOAs within each CCG, and grouped the CCG averages into national quintiles ranging from 1 (most deprived CCGs) to 5 (least deprived CCGs).51 For ethnicity, we used the percentage of the population reporting a black, Asian and minority ethnic (BAME) background, and for long-term illness we used the percentage of the population who reported that their day-to-day activities were limited because of a health problem or disability that had lasted, or was expected to last, at least 12 months. For each CCG, we took the averages of these percentages for LSOAs and grouped these CCG averages into national quintiles (range: 1 corresponds to CCGs with average percentages in the lowest quintile to 5 the highest quintile).
We calculated the number and the unadjusted and adjusted rates per 100,000 women per year of SUI procedures overall, and according to patient and regional characteristics. Incidence rate ratios (IRRs) were used to represent associations between the procedure rate and regional characteristics. Multilevel Poisson regression models were used to produce empirical Bayes’ estimates of the unadjusted and adjusted incidence rates for each CCG and STP area. In addition, risk-adjusted regression models were used to assess geographic variation in the rates of surgery by year. The empirical Bayes’ estimator produces more precise results by ‘pulling’ estimates for small outlier regions towards the mean.52 For each geographic area level (CCG/STP), we illustrated the amount of variation in adjusted surgery rates using maps and range plots with 99.8% credibility intervals. CCGs and STPs were marked as ‘outliers’ where the national average rate of surgery was not within the 99.8% credibility interval of their rates. All statistical analyses were performed using Stata, version 15 (StataCorp LP, College Station, TX, USA).
Methods for work package 3
Work package 3 aimed to explore the impact of UI on women’s lives and if and when surgery is perceived to be a treatment option, collecting women’s own accounts of expectations of surgical and non-surgical treatments, experiences and outcomes through semistructured interviews. WP 3 used primary data collected from interviews with women. All women considering surgery for their UI could participate in the interviews. We did not restrict the interviews to women with a specific subtype of UI (e.g. SUI or UUI).
A broad narrative review of the patient experience social sciences literature was conducted to map out the issues female UI raises that are in common with other conditions, including those related to shame; embarrassment and stigma; gender, identity and sexuality; social relationships, work and mobility; experiences of care; and medicalisation of the female body. These higher-order topics informed the development of the semistructured interview topic guide and generated the analytical themes that were later used to complement the more descriptive codes derived from the content analysis of the interviews. The topic guide (see Appendix 6) was also informed by discussion with the research team, especially drawing on the input from the public and patient representatives. The topic guide was designed so that interviews would be sufficiently open and flexible to ensure that participants are able to talk at length about issues that most concern them.
Women were recruited from four urogynaecology outpatient clinics in different parts of England: Birmingham, Gillingham (in Kent), Leicester and Southampton. Recruitment was limited to women being treated in England for practical reasons. Between May and December 2017, women who had been referred to these clinics, were aged ≥ 18 years, had not previously had urology surgery (for UI or a related condition) and were now considering surgery for their UI were approached about the project by members of the clinic teams (which included physiotherapists, nurses, surgical consultants and specialist registrars) as part of the patient’s routine appointments. Women who were potentially interested in participating were given an information sheet and form to complete and return directly to the qualitative members of the research team at the London School of Hygiene & Tropical Medicine if they wished to participate. Those women who completed the form were contacted by telephone or e-mail (depending on their preference) and given further information about the study and what the interview would involve. Interviews were then scheduled and conducted either face to face or by telephone (also according to the women’s preferences). Women were given the opportunity to ask further questions about the research before agreeing to participate. Written consent was obtained from all interviewees and women were also informed that they could withdraw their consent at any time, during or after the interviews. Women were reassured that the interviewer was not part of the clinic staff and that their decision to participate or not would have no influence on their care. Ethics approval for the study was granted by the NHS Health Research Authority (Research Ethics Committee reference number 16/IEC08/0044).
Interviewees were initially purposively sampled by region (clinic) and age. The geographical location of those women interviewed did not appear to influence their responses, whereas ageing emerged as a strong theme through the interviews. Interviewees were therefore then sampled by age alone, to ensure that women of a wide range of ages were included. Interviews lasted between 40 and 130 minutes, were audio-recorded and transcribed verbatim. Transcription was undertaken by an external company experienced in dealing with confidential data. Transcripts were imported and coded in NVivo 10 (QSR International, Warrington, UK). Initial coding was undertaken by the interviewer while recruitment and interviews were under way. Following a constant comparative method53,54 emerging themes from the interviews were followed up in more depth in subsequent interviews such that the topic guide became more refined and focused on areas that were most important to the participants themselves.
The codes, drawn both deductively from the interview topic guide and literature review and inductively through early analysis, were augmented and refined through the process of analysis and through discussion with the WP 3 lead. Codes were then ordered into higher-level themes and hierarchies, with codes that were similar collapsed together. These codes were then discussed with the rest of the research team, which included clinicians, leading to further refinement.
Methods for work package 4
Work package 4 aimed to identify determinants of gynaecology outpatient referrals and surgery for women with UI.
Analysis cohorts
Initial non-invasive treatments can be started in primary care without extensive evaluation of the main subtype of UI indicated by a woman’s symptoms. Subtype-specific diagnoses may therefore not be present in primary care (CPRD) data. We therefore defined the cohorts for the analysis of both determinants of referral and determinants of surgery after referral on the basis of a diagnosis of UI, not restricted by UI subtype.
Diagnoses of UI were defined using Read codes and MedCODES identified in WP 1 (). Referral to a specialist was defined using a combination of Read codes and referral specialty codes developed in WP 1 (see Appendix 4, Tables 18 and 19). Surgery for UI was defined using OPCS-4 codes (see Appendix 1, Tables 13–15).
Read codes and MedCODES used to define UI diagnosis for cohort selection in WP 4
Determinants of referrals analysis
The cohort for identifying determinants of referrals was derived from the CPRD data set and comprised women aged ≥ 18 years who had an index diagnosis of UI between 1 April 2004 and 31 March 2014. An index diagnosis of UI was defined among women who had no earlier record of a UI symptom/diagnosis (see Appendix 2, Table 16) or treatment (see Appendix 3, Table 17) within the 12 months prior to the date of first diagnosis in the study period. For the referral analyses, women were followed up until the date of the GP visit that they were referred to a UI specialist, transfer out of practice, death or to 1 April 2014. Women with < 12 months of UTS data prior to index diagnosis were excluded. Women were also excluded if the follow-up period was < 30 days.
Determinants of surgery analysis
The cohort for identifying determinants of surgery after referral comprised women aged ≥ 18 years who had an index UI diagnosis (defined as described above) and a referral to a urinary incontinence specialist in secondary care between 1 April 2004 and 31 March 2014. The cohort of women for surgery after referral was derived from the CPRD linked to HES APC data set and was therefore restricted to women registered in primary care practices that had linked CPRD–HES data. Women were followed up from the date of referral until the date of surgery, transfer out of practice, death or to 1 April 2014.
Outcome measures
Determinants of referrals analysis
For the determinants of referrals analysis, the outcome measure was referral to a UI specialist within 30 days of diagnosis.
Determinants of surgery analysis
For the determinants of surgery analysis, the primary outcome measure was risk of any UI surgery. (The codes for SUI surgery are shown in and the codes for UUI surgery are shown in Appendix 1, Table 14.)
Potential determinants of referral and surgery
Potential determinants of both referral and surgery (defined a priori with the project team clinicians) were age at index diagnosis (analysed as 18–39, 40–49, 50–59, 60–69, 70–79, ≥ 80 years), BMI (< 20 kg/m2 = underweight, 20–24 kg/m2 = normal, 25–29 kg/m2 = overweight, 30–39 kg/m2 = obese, ≥ 40 kg/m2 = severely obese), smoking (non-, current or former smoker) and ethnic background (white, Asian/Asian-British, black/black-British, mixed or other ethnic group, or missing) and comorbidities. The comorbidities included were pelvic organ prolapse (POP), UTI, type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD) (as defined as any cardiovascular or ischaemic heart disease, heart failure or hypertension), renal disease, respiratory disease [asthma or chronic obstructive pulmonary disease (COPD)], anxiety or depression and cancer. Comorbidities were defined using Read codes (from the clinical codes repository www.clinicalcodes.org; accessed 6 April 2020) in the 12 months before the start of follow-up (‘index date’), apart from UTI (defined as in the 30 days before). The ‘index date’ was the date of UI diagnosis for the referrals analysis and the referral date for the surgery analysis. A clinical code repository list was not available for POP, so this code list was developed for this project by the research team, including the clinicians (see Appendix 5, Table 20). BMI and smoking status were defined using the value recorded closest to the index date. To reduce the number of missing data, and given the more fixed nature of ethnic background, no time-restrictions were placed on ethnicity codes. Practice-level characteristics were IMD (quintiles: 1 = most deprived, 5 = least deprived), practice country for the referrals analysis (England, Northern Ireland, Scotland and Wales) and English region [for the surgery analysis, 10 strategic health authorities (SHAs): North East, North West, Yorkshire and The Humber, East Midlands, West Midlands, East of England, South West, South Central, London and South East Coast].
Statistical analyses
For the determinants of both referrals and surgery analyses, patient and practice characteristics were summarised using descriptive statistics. In both analyses, multiple imputation was used to impute missing values for BMI (missing for 5% of women) and smoking status (missing for 0.1% of women), with statistical coefficients obtained from imputed data sets, pooled using Rubin’s rules.55 In the determinants of referral analysis, ethnicity data were available from the CPRD database only and were missing for 55% of women. A separate ‘missing’ category was therefore included in the models. In the determinants of surgery analysis, ethnicity data were available from both the CPRD and the HES databases and were missing for just 5% of women, allowing multiple imputation to be used to impute missing values. All statistical calculations were performed using Stata.
Determinants of referrals analysis
For the determinants of referrals analysis we used multivariate logistic regression, with cluster standard error estimands to account for clustering within general practices, to identify factors associated with referral within 30 days.
Determinants of surgery analysis
For the determinants of surgery analysis we used the cumulative incidence function to estimate surgery risk as a function of time from the initial referral to first surgical procedure. Death was considered as a competing event and patients reaching the end of the follow-up period were censored.56 We used a multivariable Fine–Gray model to estimate subdistribution hazard ratios (sdHRs) to assess the association between patient and provider characteristics and the risk of surgery, with robust standard errors to account for within-hospital homogeneity.57
Methods for work package 5
Work package 5 used primary data collected from an online survey. The survey used hypothetical simulated patients described by clinical case vignettes to measure variation in clinicians’ approaches to recommending surgical treatment for female SUI. This method has been used to measure variation in clinicians’ approaches to the diagnosis and treatment of patients with a range of similar health problems and is considered to be a cost-effective way of studying how clinicians respond to specific characteristics of their patients when making decisions rather than using medical records or standardised patients.58–62
Survey
We conducted an online survey of all members of the British Society of Urogynaecology (BSUG) and members of the Royal College of Obstetricians and Gynaecologists (RCOG) who indicated that they had specialist interest in urogynaecology at the time of their RCOG registration (n = 1139). Data collection was carried out using the online survey platform, SurveyMonkey® (Palo Alto, CA, USA). In June 2017, a link to the online survey was e-mailed to the participants, with information about the survey and how to complete it. Three reminder e-mails were sent in the 1-month period following the initial e-mail. The survey included an information screen providing a brief description of the project, questions on the clinicians’ demographic characteristics, a page providing additional information and a number of clinical assumptions we asked the clinicians to make when responding to the survey (see Box 1). This was followed by the 18 case vignettes and response options.
Example of a clinical case vignette
Case vignette development
We used a three-stage approach to select the patient characteristics and their levels to be included in the clinical case vignettes. The first stage involved ‘item identification’. A targeted non-systematic literature search of English-language studies including female patients only was carried out to identify patient characteristics associated with surgical treatment for UI. We also drew on national guidelines and four senior clinical experts from the project team.
The second stage was ‘item reduction’. Each of the clinical experts reviewed the patient characteristics and selected those patients that they considered most likely to influence clinicians’ decisions about surgery for UI to be included in the vignettes. The recommendations made were collated and collectively discussed by the clinicians, reaching consensus on the most important characteristics for inclusion in the case vignette profiles. Seven potential characteristics were selected: age, BMI, type of SUI (pure SUI, stress predominant or MUI), previous SUI surgery, leakage, bother and physical status. Further discussions were held to decide on the relevant levels for these characteristics. Here the aim was to create maximum difference between the levels for each characteristic while ensuring that the clinical profiles captured in the vignettes were relevant and realistic. The seven patient characteristics and their levels (six characteristics with three levels and one with two) that were used in the case vignettes are presented in . Physical status was described according to categories recognised by the classification of the American Society of Anesthesiologists (ASA) at levels 2 (i.e. ‘a patient with mild systemic disease’) and 3 (i.e. ‘a patient with severe systemic disease’).
Patient characteristics included in the case vignette profiles
The third stage involved creating the ‘experimental design’. The total number of possible different cases using these seven patient characteristics is 1458 ( = 36 × 21) in a full factorial design. We used an orthogonal fractional factorial design [using SPSS software version 25 (SPSS Inc., Chicago, IL, USA)], which reduced the number of clinical case vignettes to 18.63 This design reduces the number of vignettes while maximising the amount of information collected and retaining the absence of correlation between patient characteristics.64 In addition to the 18 case profiles generated, we included two extra case profiles (‘holdout profiles’) to test our model specification.
The case vignettes described the clinical profile of women referred to secondary care for further assessment and management of their SUI. Each case profile comprised a short patient description according to the seven characteristics followed by one question: ‘Would you recommend that this patient has surgical treatment now?’. The clinicians were asked to score their recommendation on a five-point Likert scale ranging from ‘certainly yes’ to ‘certainly not’. A pilot study was undertaken among eight clinicians to optimise the clarity of the vignettes. An example of one of the 18 clinical case vignettes is presented in Box 1.
Statistical analyses
We used descriptive statistics to summarise the characteristics of the responding clinicians. We calculated the means of the response scores and the 25th and 75th percentiles to describe the recommendations of the clinicians for each of the 18 case vignettes.
To assess the relative influence of the patients’ characteristics (i.e. ‘weight’) on the clinicians’ recommendations, we calculated the means of the recommendation score by level of each of the characteristics. The weight of a patient characteristic was defined as the difference between the lowest and the highest mean recommendation score for that characteristic, divided by the sum of these differences for all seven clinical characteristics.65 In other words, the weights express as a percentage the influence of each characteristic on the clinicians’ recommendations relative to the total overall weight of all patient characteristics. We used a mixed-effects analysis of variance model to test the statistical significance of differences in the clinicians’ recommendation scores according to level of the patient characteristics. This mixed-effects model recognised that the recommendation scores for the 18 case vignettes were nested within clinicians. The same mixed-effects model was used to analyse the impact of the clinicians’ own characteristics (subspecialty, gender and age group) on their recommendations. This was carried out by modelling the interaction between the patient characteristics and clinicians’ characteristics with a likelihood ratio test for composite models.
Latent class analysis was used to determine if mutually exclusive groups of clinicians (‘latent classes’) could be identified whose recommendations suggested a similar practice style.66 Clinicians classed within the same group are expected to be more homogeneous with respect to their recommendation scores than gynaecologists classed in different groups. We used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) to determine the optimal number of latent classes. For both the AIC and BIC, a smaller value represents a better balance between the number of classes and the fit of the statistical model. The predicted posterior probabilities of latent class membership were used to assign each clinician to a group. Where a clinician did not have a posterior probability > 50% for one particular group, group membership was considered to be unknown. Statistical analyses were performed using Stata.
Methods for additional work package: work package 6
With growing concerns about the long-term outcomes of MUT procedures (of a subset of SUI procedures) changing the context of surgery for SUI, we also conducted additional research. The objective of this additional WP (WP 6) was to estimate the long-term rates of mesh tape removal and reoperation in women who had a MUT inserted for SUI.
Analysis cohort
The cohort for the additional work on mesh tape removal and reoperation comprised women aged ≥ 18 years who underwent a MUT insertion procedure for SUI for the first time between 1 April 2006 and 31 December 2015 in the HES APC data set. SUI was defined by the ICD-10 code N39.3. MUT insertions were defined with the OPCS-4 codes M53.3 (introduction of tension-free vaginal tape) and M53.6 (introduction of transobturator tape). The procedure was considered to be the ‘initial’ MUT insertion procedure in which there was no record of a MUT insertion in the preceding 3 years. Follow-up was from the date of the initial procedure to the date of a MUT removal, the date of reoperation or 31 March 2016, whichever was earliest. The minimum follow-up period was therefore 3 months and the maximum was 10 years.
Outcome measures
The outcomes were risk of MUT removal, reoperation for SUI and any reoperation (MUT removal and/or reoperation for SUI) (the codes are in Appendix 1, Table 13).
Potential determinants of mesh tape removal and reoperation
Potential determinants were age at initial procedure (analysed as 18–39, 40–49, 50–59, 60–69, ≥ 70 years); patient-level IMD, an area-based measure of economic deprivation (quintiles of the national distribution: 1 = most deprived, 5 = least deprived); ethnic background (white, Asian/Asian-British, black/black-British or other); number of comorbidities [as defined using the Royal College of Surgeons (RCS)’s Charlson Comorbidity Index, grouped as 0 or 1 or more]; route of MUT insertion (retropubic or transobturator); previous non-mesh SUI procedures in the 3 years prior to the initial MUT insertion; and concurrent prolapse repair procedures in the same episode of care as the initial MUT insertion. In a retropubic insertion, the mesh tape supporting the urethra is passed through two small incisions just above the pubic area. In a transobturator insertion it is passed through two small incisions on the inside of both thighs. The potential determinants at the organisational level that were related to the hospital where the initial procedure was performed were number of MUT insertions performed in the same year as the initial operation and the hospitals’ status as a specialist urogyneacology unit (according to accreditation by the BSUG unit) at any point in the study period.
Statistical analyses
The cumulative incidence function was used to estimate the risk of MUT removal and of reoperation as a function of time from the initial MUT insertion procedure. Death was considered as a competing event and patients reaching the end of the follow-up period were censored.56 We used a multivariable Fine–Gray model to estimate sdHRs to assess the association between patient and provider characteristics and the risk of surgery, with robust standard errors to account for within-hospital homogeneity.57 Multiple imputation was used to impute missing values for ethnic background (missing for 8.3% of women), with statistical coefficients obtained from 10 imputed data sets, pooled using Rubin’s rules.55
Patient and public involvement
All WPs benefited from the input from two patient and public involvement (PPI) representatives who were members of the Project Advisory Group. They shared their experience of the NHS as patients, which guided the project’s overall design as well as the specific design of the qualitative and quantitative WPs. They also commented on all results and contributed to their interpretation.
Further PPI input was provided by the co-vice chairperson of the RCOG’s Women’s Network who was a member of the Project Steering Committee. In this capacity, she was able to comment on the results of the project from the perspective of the wider and diverse group of women represented by this network.
