https://orcid.org/0000-0001-8905-2429
Abstract
How well does a previously developed dynamic prediction model perform in an external, geographical validation in terms of predicting the chances of natural conception at various points in time?
The dynamic prediction model performs well in an external validation on a Scottish cohort.
Prediction models provide information that can aid evidence-based management of unexplained subfertile couples. We developed a dynamic prediction model for natural conception (van Eekelen model) that is able to update predictions of natural conception when couples return to their clinician after a period of unsuccessful expectant management. It is not known how well this model performs in an external population.
A record-linked registry study including the long-term follow-up of all couples who were considered unexplained subfertile following a fertility workup at a Scottish fertility clinic between 1998 and 2011. Couples with anovulation, uni/bilateral tubal occlusion, mild/severe endometriosis or impaired semen quality according to World Health Organization criteria were excluded.
The endpoint was time to natural conception, leading to an ongoing pregnancy (defined as reaching a gestational age of at least 12 weeks). Follow-up was censored at the start of treatment, at the change of partner or at the end of study (31 March 2012). The performance of the van Eekelen model was evaluated in terms of calibration and discrimination at various points in time. Additionally, we assessed the clinical utility of the model in terms of the range of the calculated predictions.
Of a total of 1203 couples with a median follow-up of 1 year and 3 months after the fertility workup, 398 (33%) couples conceived naturally leading to an ongoing pregnancy. Using the dynamic prediction model, the mean probability of natural conception over the course of the first year after the fertility workup was estimated at 25% (observed: 23%). After 0.5, 1 and 1.5 years of expectant management after the completion of the fertility workup, the average probability of conceiving naturally over the next year was estimated at 18% (observed: 15%), 14% (observed: 14%) and 12% (observed: 12%). Calibration plots showed good agreement between predicted chances and the observed fraction of ongoing pregnancy within risk groups. Discrimination was moderate with c statistics similar to those in the internal validation, ranging from 0.60 to 0.64. The range of predicted chances was sufficiently wide to distinguish between couples having a good and poor prognosis with a minimum of zero at all times and a maximum of 55% over the first year after the workup, which decreased to maxima of 43% after 0.5 years, 34% after 1 year and 29% after 1.5 years after the fertility workup.
The model slightly overestimated the chances of conception by ~2–3% points on group level in the first-year post-fertility workup and after 0.5 years of expectant management, respectively. This is likely attributable to the fact that the exact dates of completion of the fertility workup for couples were missing and had to be estimated.
The van Eekelen model is a valid and robust tool that is ready to use in clinical practice to counsel couples with unexplained subfertility on their individualized chances of natural conception at various points in time, notably when couples return to the clinic after a period of unsuccessful expectant management.
This work was supported by a Chief Scientist Office postdoctoral training fellowship in health services research and health of the public research (ref PDF/12/06). There are no conflicts of interest.
Introduction
Approximately 10% of all couples who wish to have a child do not conceive within the first year of trying (Gnoth et al., 2003; Wang et al., 2003). For approximately half of these couples, no clear barrier for conception can be found during the workup and these couples are considered unexplained subfertile (Aboulghar et al., 2009; Brandes et al., 2010). It is unclear whether these couples should start with ART; first, since observational studies report that 18–38% of unexplained subfertile couples will conceive naturally in the year after the fertility workup (Hunault et al., 2004; van der Steeg et al., 2007; van Eekelen et al., 2017a) and second, since there remains uncertainty regarding the effectiveness of ART for unexplained subfertile couples (Pandian et al., 2015; Tjon-Kon-Fat et al., 2016; Veltman-Verhulst et al., 2016; van Eekelen et al., 2017b).
In the absence of clear evidence on the management of unexplained subfertile couples and when to offer ART, an enticing option is to calculate chances of natural conception and to base counselling on this estimated prognosis (van Eekelen et al., 2017b). Fundamental to this approach is to identify couples that are expected to benefit from treatment and those who are not. In clinical practice, this would imply that couples with a good prognosis to conceive naturally are advised to continue to try and become pregnant by sexual intercourse, while couples with an unfavourable prognosis are advised to start ART. Several prediction models for natural conception have been published of which the model by Hunault et al., which calculates a prognosis of conception leading to live birth over the first year after the completion of the fertility workup, has been externally validated and subsequently implemented in the national guidelines and clinical practice in the Netherlands (Hunault et al., 2004; van der Steeg et al., 2007; Leushuis et al., 2009; NVOG, 2010). A practical drawback of the Hunault model is that it cannot give a prediction at later time points when couples who continued expectant management after the fertility workup but did not conceive, return to the clinic. This is because applying the Hunault model at later time points leads to overestimation due to the selection of less fertile couples over time that is not incorporated in the Hunault model (van Eekelen et al., 2017b).
Van Eekelen et al. recently developed a dynamic prediction model that accommodates the need for repeated predictions (van Eekelen et al., 2017a). This model comprises the clinical factors female age, duration of subfertility (both at completion of the fertility workup), percentage of progressively motile sperm, primary or secondary subfertility and being referred to the fertility clinic by a general practitioner or a specialist. In addition to these factors, the model uses as an input the number of menstrual cycles that have passed since the completion of the fertility workup, with zero cycles denoting the prediction is made immediately after the workup. The output is the predicted probability to conceive naturally in the following cycle, leading to ongoing pregnancy, which can be extended to predict over any given number of cycles with a maximum of 2.5 years after the workup (~28–34 cycles). When couples return after a period of expectant management, the number of cycles that have passed since the workup can be changed to update the predicted probability over subsequent cycles.
The model developed by van Eekelen et al. showed promising results in the internal validation, but this in itself is insufficient to advise clinical implementation since models tend to perform better in the cohort they were developed on than in another cohort in which the model may be applied (Steyerberg, 2009).
The aim of this study was to externally validate the van Eekelen model on a large cohort that followed couples for natural conception after registration in the fertility clinic of the Grampian region of Scotland, UK. This is the largest contemporary cohort following couples for natural conception, aside from the Dutch cohort on which the dynamic model was developed.
Materials and Methods
We included couples diagnosed with unexplained subfertility residing in the Grampian region of Scotland who registered with the Aberdeen Fertility Centre (AFC) from 1998 to 2011 (Pandey et al., 2014). Only patients from the Grampian region visiting the AFC were selected because there is no other fertility clinic in the region and it was considered important to have a complete overview of a couple’s trajectory after the fertility workup, which includes treatment information. We combined the AFC registration database with three other data sources using record-linkage to get the complete follow-up for couples from the registration at the AFC until ongoing pregnancy, treatment or end of the study, which was the 31 March 2012.
The AFC database comprises patient characteristics and diagnostic information. Data entry in the AFC database is validated and checked by regular case note audits. First, we record-linked couples registered in the AFC database to the centre’s Assisted Reproduction Unit database which contained dates when treatment was started.
Second, we identified natural conceptions leading to an ongoing pregnancy by record-linkage of the AFC database with the Aberdeen Maternity and Neonatal Databank, which contained gestational age, outcome and delivery date of (early) pregnancies for all women residing in Aberdeen City District. Third, we performed record-linkage with the national Scottish Morbidity Records Maternity database for identifying gestational age, outcome and delivery date of (early) pregnancies for women who delivered elsewhere in Scotland.
The Data Management Team of the University of Aberdeen created a new pseudonomized identifier for all women by using the Community Health Index identifier. This new study-specific identifier cannot be used to trace back to individuals and was then used by author D.J.M. to record-link the databases within the Grampian Data Safe Haven environment. This process was carried out according to the Standard Operating Procedures of the Data Management Team, University of Aberdeen. The resulting linked dataset was thus a combination of these four data sources.
Ethical approval was provided by the North of Scotland Research Ethics Committee (reference: 12/NS/0120). Access to the Aberdeen Fertility Clinic and the Assisted Reproduction Unit databases was approved by the Aberdeen Fertility Databases Steering Committee. Access to the Aberdeen Maternity and Neonatal Databank was approved by the Aberdeen Maternity and Neonatal Database Steering Committee. Access to the Scottish Morbidity Records Maternity database was approved by the Privacy Advisory Committee of Information Services Division Scotland.
We defined unexplained subfertility as couples who tried to conceive for more than 50 weeks before the fertility workup was completed and who had no obvious barriers to conception in terms of uni or bilateral tubal occlusion, anovulation, mild or severe endometriosis according to the revised American Society for Reproductive Medicine (ASRM) score (ASRM, 1997) or impaired semen quality according to World Health Organization (WHO) criteria (WHO, 1999, 2010). We used the gestational age at birth or early pregnancy outcome to derive the date of conception and included only pregnancies in the analysis that occurred after registration of the couple at the clinic and that were ongoing, defined as reaching a gestational age of at least 12 weeks. Time to conception was censored at the date of start of IUI, start of IVF, when the woman returned to the fertility centre with a different male partner or at the end of study.
Missing data
The date of completion of the fertility workup was not reported in the AFC database. The van Eekelen model uses this date as the starting point of follow-up, i.e. the time point from which onwards the model can be used to estimate a prognosis. The date of registration and the diagnosis category were available in the database. Judging from local protocols, we assumed there were 3 months in between registration and completion of the fertility workup for all couples. In a sensitivity analysis, we repeated the validation study assuming 1.5 or 4.5 months between registration and completion of the fertility workup for all couples.
Menstrual cycle length is used to determine the number of elapsed menstrual cycles since the fertility workup when updating predictions using the dynamic prediction model. Cycle length was not recorded in the AFC database and we therefore assumed an average cycle length of 28 days for all women.
Data on outcomes or at least one prognostic factor were missing for ~4% of couples; 0.5% on pregnancy or follow-up, 0.5% on female age, 2.3% on duration of subfertility, 0.5% on primary or secondary subfertility, 1.9% on the percentage of progressive motile sperm and 0.5% on referral status. We had no reason to believe that couples with missing data differed systematically from couples with complete data and we analysed couples for which data was complete.
Analysis
We calculated the predicted probabilities of natural conception over 1 year for all couples in the validation cohort using the formula in the Appendix of the paper by van Eekelen et al. (van Eekelen et al., 2017a). To test the model’s ability to not only predict after the completion of the fertility workup but also when a couple returns after an unsuccessful period of expectant management, we calculated the prognosis at four time points: directly after completion of the workup, after 0.5, 1 and 1.5 years of expectant management. We evaluated model performance in terms of calibration, i.e. the degree of agreement between observed and predicted natural conception rates, and discrimination, i.e. the ability of the dynamic prediction model to distinguish between couples who do conceive and couples who do not conceive.
To assess calibration, we first explored whether the overall prediction of the model was correct by comparing the average predicted probability over a time period with the observed conception rate over that same time period. This is referred to as calibration-in-the-large and assesses whether the model systematically under or overestimates the observed conception rate (Steyerberg, 2009).
Second, we assessed whether the effects of patient characteristics were estimated correctly in three ways: by visuals using calibration plots for risk groups, by calibration within groups with similar patient characteristics and by calculating a calibration slope. For the calibration plots, we ordered the predicted probabilities of couples and divided them in risk groups with similar predictions (n = 135 per risk group). We compared the mean predicted chances within these groups with the corresponding observed fraction of ongoing pregnancy as estimated by the Kaplan–Meier method. We visualized the observed fractions and predicted probabilities per risk group in plots and tabulated the absolute differences. In the plots, the 45° line indicates what would be a perfect agreement between the observed fraction and average predicted probability within a risk group.
We repeated the calibration procedure but instead of grouping based on predicted risks, we grouped couples based on having similar patient characteristics. We again compared the mean predicted chances within these groups with the corresponding observed fraction of ongoing pregnancy as estimated by the Kaplan–Meier method and tabulated the results.
To calculate the calibration slope, we used the prognostic index (i.e. the sum of the multiplication between all patient characteristics and the coefficients from the model) as an explanatory variable in a Cox model for each of the four evaluated time periods (van Houwelingen, 2000). Ideally, the calibration slope is unity, i.e. 1, indicating that the strength of the patient characteristics in the evaluated model perfectly matches the validation data.
Third, we used a recalibration procedure as an alternative way to assess the systematic under or overestimation (calibration-in-the-large) and the strength of the patient characteristics (calibration slope) in the model. We did this by using the same coefficients for the patient characteristics as reported by van Eekelen et al. to calculate a prognostic index but re-estimated the other parameters of the beta-geometric model in the validation dataset (Bongaarts, 1975; Weinberg and Gladen, 1986). The recalibration model re-estimates three parameters, which we compared to those in the van Eekelen model and tested for the difference between the two using independent samples z-tests. Systematic under or overestimation was assessed by comparing the intercept and the variance parameters. The intercept parameter indicates the estimated pregnancy chances in the first cycle after the fertility workup and the variance parameter indicates how fast the estimated chances decrease over consecutive failed natural cycles. Similarity in strength of the patient characteristics was assessed by again calculating a calibration slope parameter, which would ideally be 1.
We assessed discrimination by calculating Harrel’s c statistic at the four time points, which we compared to those found at internal validation (Harrell et al., 1996).
Finally, we explored the range of predicted probabilities at the four time points to see if they facilitate meaningful prognostic stratification of couples (Coppus et al., 2009).
All analyses were conducted in R version 3.4.3 and RStudio (R Core Team, 2013). A P-value below 0.05 was considered statistically significant.
Results
Data of 1203 couples were included (Fig. 1). The baseline characteristics of the couples are shown in Table I.
Flow chart of couples with unexplained subfertility who were considered for inclusion in the external validation.
Baseline characteristics at completion of the fertility workup.
| n = 1203 | Mean or n | 5th–95th Percentile or % |
|---|---|---|
| Female age, in years | 33.3 | 25–41 |
| Duration of subfertility, in years | 2.7 | 1.3–5.6 |
| Primary female subfertility | 697 | 58% |
| Percentage of progressive motile sperm | 51 | 24–76 |
| Referral by secondary care | 84 | 7% |
| n = 1203 | Mean or n | 5th–95th Percentile or % |
|---|---|---|
| Female age, in years | 33.3 | 25–41 |
| Duration of subfertility, in years | 2.7 | 1.3–5.6 |
| Primary female subfertility | 697 | 58% |
| Percentage of progressive motile sperm | 51 | 24–76 |
| Referral by secondary care | 84 | 7% |
Baseline characteristics at completion of the fertility workup.
| n = 1203 | Mean or n | 5th–95th Percentile or % |
|---|---|---|
| Female age, in years | 33.3 | 25–41 |
| Duration of subfertility, in years | 2.7 | 1.3–5.6 |
| Primary female subfertility | 697 | 58% |
| Percentage of progressive motile sperm | 51 | 24–76 |
| Referral by secondary care | 84 | 7% |
| n = 1203 | Mean or n | 5th–95th Percentile or % |
|---|---|---|
| Female age, in years | 33.3 | 25–41 |
| Duration of subfertility, in years | 2.7 | 1.3–5.6 |
| Primary female subfertility | 697 | 58% |
| Percentage of progressive motile sperm | 51 | 24–76 |
| Referral by secondary care | 84 | 7% |
Cumulative chances of natural conception leading to ongoing pregnancy. Cumulative chances after the completion of fertility workup (upper panel) and updated chances of natural conception over the course of 1 year at the completion of the fertility workup or 0.5, 1 and 1.5 years thereafter (lower panel) in the validation cohort. Percentages are Kaplan–Meier estimates of the observed fraction of natural conception leading to ongoing pregnancy.
The calibration plots for the four time periods are presented in Fig. 3. The dynamic prediction model was well calibrated based on the upward trends observed in the four plots, indicating that higher predicted probabilities correspond to higher observed rates, and the CIs from the observed rates which all but one cover the ideal 45° line. The second calibration plot starting at 0.5 year after the fertility workup showed a slight overestimation since all points are below the 45° line. The absolute differences between observed fractions and predicted probabilities of natural conception within risk groups are shown in Table II. This was on average 2.8% points and 9.6 at the highest.
Calibration of the predictions of the dynamic prediction model: predicted vs observed 1-year natural conception rates at four fixed time points.
Calibration of the dynamic prediction model by risk groups.
| Mean difference | Max difference | Number of risk groups | |
|---|---|---|---|
| After completion of workup | 3.2 | 9.6 | 9 |
| After 0.5-year EM | 3.0 | 4.7 | 7 |
| After 1-year EM | 2.1 | 3.5 | 5 |
| After 1.5-year EM | 2.7 | 4.5 | 4 |
| Total | 2.8 | 9.6 | 25 |
| Mean difference | Max difference | Number of risk groups | |
|---|---|---|---|
| After completion of workup | 3.2 | 9.6 | 9 |
| After 0.5-year EM | 3.0 | 4.7 | 7 |
| After 1-year EM | 2.1 | 3.5 | 5 |
| After 1.5-year EM | 2.7 | 4.5 | 4 |
| Total | 2.8 | 9.6 | 25 |
Data are the mean and maximum of the absolute differences (in percentage points) between predicted and observed 1-year natural conception rates per risk group of n = 135, stratified by the elapsed period of expectant management (EM).
Calibration of the dynamic prediction model by risk groups.
| Mean difference | Max difference | Number of risk groups | |
|---|---|---|---|
| After completion of workup | 3.2 | 9.6 | 9 |
| After 0.5-year EM | 3.0 | 4.7 | 7 |
| After 1-year EM | 2.1 | 3.5 | 5 |
| After 1.5-year EM | 2.7 | 4.5 | 4 |
| Total | 2.8 | 9.6 | 25 |
| Mean difference | Max difference | Number of risk groups | |
|---|---|---|---|
| After completion of workup | 3.2 | 9.6 | 9 |
| After 0.5-year EM | 3.0 | 4.7 | 7 |
| After 1-year EM | 2.1 | 3.5 | 5 |
| After 1.5-year EM | 2.7 | 4.5 | 4 |
| Total | 2.8 | 9.6 | 25 |
Data are the mean and maximum of the absolute differences (in percentage points) between predicted and observed 1-year natural conception rates per risk group of n = 135, stratified by the elapsed period of expectant management (EM).
The calibration slopes using Cox models were 0.86, 1.01, 1.01 and 0.62 for the four time periods, respectively. None of the corresponding P-values were below 0.05, indicating no statistical evidence for under or overfitting.
In the recalibration model, the intercept and variance parameters were similar to those reported by van Eekelen et al. (P = 0.69 and P = 0.29 for the difference, respectively), indicating similar underlying chances of pregnancy in the first cycle after the workup and a similar decrease in chances as time progresses. The slope was 0.90 (P = 0.37), indicating a similar strength of patient characteristics in the validation cohort and no significant difference from 1.
The discriminative ability of the model in the validation cohort was moderate and similar to that in the Dutch development cohort, ranging over time from a c statistic of 0.61 (95%CI 0.57–0.64) in the first year, 0.62 (95% CI 0.58–0.67) from 0.5 year, 0.63 (95% CI 0.57–0.69) from 1 year, to 0.60 (95% CI 0.52–0.67) for 1.5 years after the completion of the fertility workup, all for conceiving in the following year. The c statistics were around 0.61 for all four time periods and seemed stable over time.
The range of predictions varied between 0% and 55% over the course of the first year after the fertility workup. After 0.5, 1 and 1.5 years of expectant management the ranges narrowed to 0–43%, 0–34% and 0–29% respectively, all over the course of the following year, facilitating a distinction between couples with a good or poor prognosis.
Sensitivity analyses
Results from the two sensitivity analyses are reported online as Supplementary data. The analysis where we assumed 1.5 months between registration and completion of the fertility workup showed a very good performance of the dynamic prediction model (Supplementary Table SI, Supplementary Figs S1 and S2). The analysis assuming 4.5 months between registration and completion of the fertility workup showed similar results to the primary analysis but with slightly more overestimation of chances by the model (Supplementary Table SII, Supplementary Figs S3 and S4).
Discussion
We conducted an external, geographical validation of the van Eekelen model that can be used for repeated predictions of natural conception when couples return to the clinic after unsuccessful expectant management. The model performed well in a Scottish cohort of couples with unexplained subfertility that visited a fertility clinic and the model is expected to be generalizable to other fertility centres and countries where the procedure of managing unexplained subfertile couples is comparable to the Netherlands and the UK. In addition, the predicted probabilities varied sufficiently to aid in distinguishing between couples with a good and poor prognosis in terms of natural conception.
The data from the AFC was of high quality, registering every unexplained subfertile couple in the Grampian region. All natural conceptions leading to ongoing pregnancy, including after miscarriages and other early pregnancy outcomes, were found using data linkage with maternity records. Indications for the fertility workup and definitions of censoring and prognostic characteristics in the Scottish cohort were very similar to the Dutch cohort, aiding comparability (van Eekelen et al., 2017a).
The model was well calibrated, which we consider of higher importance than discrimination since the c statistics can be expected to be moderate due to the limited range of predicted chances in fertility (Mol et al., 2005; Cook, 2007). This restricts the maximum possible c statistics, even if a model was to produce perfect predictions. Recalibration, in which one or more parameters of the prediction model are updated to accommodate better predictions in a different country or clinical setting, was not necessary since the recalibration model showed similar values for all parameters as observed in the development cohort.
The main limitation to our study was missing data in terms of dates of completion of the fertility workup and menstrual cycle lengths. Menstrual cycle length was not considered very influential since the estimations of the number of cycles per individual are reasonable approximations due to the narrow range of possible cycle lengths in our selection of unexplained subfertile couples, but we did have to make strong assumptions about the date of completion of the fertility workup. We assumed 3 months between registration and completion of the fertility workup, which resulted in ongoing pregnancies before 3 months after registration being excluded. The ‘starting’ moment of follow-up thus differed from the Dutch development cohort since in the latter, the date of last tubal test was used as the end of the workup. Some Dutch clinics did not conduct a visual test of tubal patency, i.e. laparoscopy or hysterosalpingography after a negative result for the chlamydia antibody test. In those Dutch clinics, the workup was thus considered as complete earlier after registration compared to the AFC where visual tests of tubal patency are a part of the standard protocol. This may have led to the observed slight overestimation in the first year after the fertility workup and after 0.5 year of expectant management but, despite these differences, the dynamic model was still able to estimate a prognosis that was reasonably accurate on cohort and risk group level. The results from the sensitivity analysis assuming 1.5 months between registration and completion of the fertility workup were very good because the resulting population more closely resembled that of the Dutch development cohort in which the same average duration was observed between registration and the workup completion. Accordingly, in the analysis assuming 4.5 months between registration and completion of the fertility workup, the performance of the dynamic model was poorer because the populations differed more due to additional selection that occurred.
The dynamic model is able to reassess the chance of natural conception after any given period of expectant management from the completion of the fertility workup onwards. For example, a couple with 1-year secondary subfertility is referred by a general practitioner to the fertility clinic of which the woman is 33 years old at the completion of the fertility workup and the man has 40% progressive motile sperm. Applying our model gives a predicted 38% chance of natural conception over the first year after the workup and they might be advised expectant management. When the couple returns to the clinic after 10 unsuccessful months/cycles, reapplying the model yields 25% chance over the following year, which is a realistic decrease given they have tried for an additional 10 months. This could be a reason to consider starting treatment.
Both the Hunault model and the dynamic model performed well in external validations, indicating that the added value of the dynamic model lies in the ability to update predictions at later time points (van Eekelen et al., 2017a). This provides clinicians and patients with information regarding their prognosis of natural conception not only right after the completion of the fertility workup but also when the couple returns after an additional, unsuccessful period of expectant management, thus aiding in making clinical decisions at multiple time points throughout a couple’s trajectory. The ability to update predictions also aids in studies which include the prognosis of natural conception as an in- or exclusion criterion, since the prognosis of couples who return after unsuccessful expectant management can be updated accurately, leading to the desired homogeneity of the study sample (van den Boogaard et al., 2014). The dynamic model is flexible and can be used to predict over any desired number of menstrual cycles, for instance when the couple is interested in time periods shorter or longer than 1 year. In short, the dynamic model has a wider clinical applicability than the Hunault model and should be the model of choice.
Conclusion
The van Eekelen model is a valid and robust tool that is ready to use in clinical practice to counsel couples with unexplained subfertility on their individualized chances of natural conception at various points in time, notably when couples return to the clinic after a period of unsuccessful expectant management.
Acknowledgements
The authors would like to thank Prof. Egbert te Velde for all of his efforts regarding development of the dynamic prediction model and the current validation study. The authors acknowledge the data management support of the Grampian Data Safe Haven (DaSH) and the associated financial support of NHS Research Scotland, through NHS Grampian investment in the Grampian DaSH. For more information, visit the DaSH website http://www.abdn.ac.uk/iahs/facilities/grampian-data-safe-haven.php. The authors would like to thank all the staff at Aberdeen Fertility Clinic for their help with database queries.
Authors’ roles
NvG, MDJ, BS, FvdV, MvW and MJE conceived the study. MDJ performed the data linkage, storage in the Safe Haven and cleaned the data. RvE, NvG and MJE designed the statistical analysis plan. RvE, MDJ and NvG analysed the data. RvE drafted the manuscript. All authors contributed critical revision to the paper and approved the final manuscript.
Funding
Chief Scientist Office postdoctoral training fellowship in health services research and health of the public research (ref PDF/12/06). The views expressed here are those of the authors and not necessarily those of the Chief Scientist Office. The funder did not have any role in the study design; the collection, analysis and interpretation of data; the writing of the report nor the decision to submit the paper for publication.
Conflict of interest
None declared.
