The study was covered by general ethical approval (ref: 05/MRE04/87) for studies using Clinical Practice Research Datalink (CPRD) data for observational research approved by its Independent Scientific Advisory Committee (ISAC). This study was approved by ISAC (protocol ref: 19_215R).

The Martin House Research Centre Family Advisory Board [36] was consulted before beginning this work, to understand how transition is experienced by families of children with life-limiting conditions and after completion of initial analyses to discuss the estimation methods used and the findings. This informed the choice of estimation methods used and the recommendation of a preferred estimation method (see Discussion).

Data from the CPRD dataset were used. The CPRD is a research dataset using records from primary care practices in England, chosen to provide a nationally representative sample of the population [37]. The CPRD offers different datasets based on records from different primary care database providers; in this study, GOLD data were requested. Primary care data (2000-2018), Hospital Episode Statistics (HES) [25] Admitted Patient Care (APC, 2000-2018), Outpatient (2000-2018) and Accident and Emergency (A&E, 2007-2018) records were requested from CPRD for individuals aged 12-23 years of age at any point from 1 January 2000 to 31 December 2018. CPRD linked the datasets using NHS number, sex, date of birth and postcode [37].

Read codes (in primary care records) and International Classification of Diseases 10th Revision (ICD-10) (in inpatient and outpatient records) [38] were used to identify chronic and life-limiting conditions using previously developed coding frameworks [39–41] (also available from the corresponding author on request). Chronic conditions were identified using a previously developed coding framework in which chronic conditions were defined as any health problem likely (i.e. in more than 50% of cases) to require follow-up (hospital admissions, outpatient visits, medications) for more than one year [41, 42] (also available from the corresponding author on request). Life-limiting conditions included conditions that shorten life, such as Duchenne Muscular Dystrophy, and conditions that threaten to shorten life, but may be cured, such as cancer [43]. The subdivision was used due to expected differences in care patterns - i.e. those with life-limiting conditions may have more outpatient contact and inpatient admissions as children and would experience a noticeable transition from paediatric to adult care and may transition later; some of those with non-life-limiting chronic conditions may have less outpatient contact and/or fewer inpatient admissions.

A retrospective cohort was constructed including all children and young people who satisfied all of the following criteria:

Presence from age 15 to 20 years was required to make it likely there would be at least one year of records either side of transition, expected to commonly be from 16 to 19 years. Individuals might leave the dataset before age 20 years for a variety of reasons, including moving GP practice or death. A maximum age of 15 years in 2007 was required to make it likely that there would be childhood records classified as paediatric (most paediatric specialty codes were not present before 2007, as detailed below). Diagnoses recorded before age 12 years but never recorded again in ages 12-23 years were considered not relevant (either misdiagnoses, or conditions that had resolved). Individuals with any diagnoses in the life-limiting condition coding frameworks were assigned to a life-limiting condition group; individuals with a diagnosis matching a chronic condition were assigned to the chronic condition group. The condition groups were hierarchical: those with both life-limiting and chronic diagnoses were assigned to the life-limiting group. Individuals entered the cohort at age 12 years or, if later, at first appearance in the CPRD data; they left at age 23 or, if earlier, on leaving the CPRD dataset. (Figure 1).

A subgroup was defined to include individuals with at least one secondary care record (inpatient or outpatient) in each year when aged 15 to 20 years - i.e. a group with frequent inpatient and/or outpatient appointments in the years in which transition was expected to take place. This group should have more data to estimate transition and be likely to feel the impacts of transition more strongly due to having a change in provider of regular hospital care. This groups is hereinafter referred to as the “Frequent Care group”.

Data were managed using Microsoft SQL Server 2019.

Sex and year of birth were provided in CPRD data. Deprivation category (split into five groups from 1 - least deprived to 5 most deprived, using the Index of Multiple Deprivation 2010, based on the last known address of the individual [44]) was provided as linked data. Ethnic group (11 categories: Black African, Black Caribbean, Black Other, Chinese, Bangladeshi, Indian, Pakistani, Other Asian, White, Mixed or Other [45]) was recorded in the linked HES data, based on the census groups [45]. If an individual had more than one ethnic group recorded, it was set by CPRD to the most commonly recorded group, excluding unknown [37].

There were 38,352 individuals in the data who met the inclusion criteria (Figure 3); 1,187 with life-limiting conditions and 37,165 without life-limiting conditions but with other chronic conditions. 106 individuals who were eligible on other criteria were excluded due to death (and so leaving the dataset before age 20 years). Of these, 57 had chronic conditions recorded and 49 had life-limiting conditions recorded. 11376 had at least one inpatient or outpatient record in every year aged 15 to 20 years (Frequent Care group). 61% of young people in the cohort with a life-limiting condition were also in the Frequent Care group, compared to only 29% of cohort members with chronic conditions. There were more males than females in the cohort, but fewer males than females in the Frequent Care group. There were many cohort members with unknown ethnic group (27%) mainly due to these individuals lacking hospital records (the source of ethnic group data). In the Frequent Care group, with - by definition - hospital records, under 2% had unknown ethnic group. At least 67% of the whole cohort were known to be White; among those of known ethnic group 92% were White. The least deprived group was largest in the cohort, but the most deprived group was largest in the Frequent Care group.

The classification of cohort member records is illustrated in Figure 4. Few records beyond age 19 years are classified as paediatric (although more in the life-limiting conditions group than in the chronic conditions group). Many records are classified as adult below 16 years of age (8% of all adult classifications were in records aged under 16 years). Inpatient records are more likely than outpatient records to be classified as paediatric below age 16 years, but make up only 14% of the total number of records.

The percentage of individuals for whom transition can be estimated (those with at least one paediatric record and at least one adult record) is illustrated in Figure 5. Transition can be estimated for more of those with life-limiting conditions than with chronic conditions and particularly so in the Frequent Care group - e.g. 93% of those with life-limiting conditions in the Frequent Care group born in 1998 had an estimated transition point compared to 83% of those with life-limiting conditions in the whole cohort; for those with chronic conditions, corresponding figures are 64% (Frequent Care Group) and 37% (whole cohort).

Estimation methods are compared in Figure 6, for the whole cohort and Frequent Care group. Major differences for the whole cohort compared to the Frequent Care group are the higher number of late transitions (age 20 years or higher) when the First Adult approach is used and higher number of early transitions (age 13 years or lower) when the Last Paediatric approach is used. The First Adult approach estimates a large number (>20%) of early transitions for both the whole cohort and the Frequent Care group.

Distributions of differences in transition age estimated by the different methods are illustrated in Figure 7. Agreement between estimation methods is greater for the Frequent Care group than for the whole cohort (narrower distributions around 0 and a higher central peak close to 0), particularly when comparing the Last Paediatric and Fitted approaches. The Last Paediatric and Fitted approaches agree more closely than either do with the Last Adult approach.

The results of the simulation, illustrating the potential impact of using the transition estimation methods set out above compared to a simple age cut-off at 16 or 18 years, are shown in Figure 8. Each panel shows the change in outcome event counts associated with transition for each of the methods with one of the estimation methods set as the ‘true’ transition (i.e. the outcome variable associated with that transition method is used - in Table 1, if the Last Paediatric approach is used as ‘true’ transition then Outcome_LP is used as the dependent variable in the regressions). Using a transition estimation method other than the one set as the ‘true’ transition point (e.g. using, from Table 1 Trans_FA as independent variable with Outcome_LP as dependent variable) results in underestimation of the transition effect. Depending on the estimate used as ‘true’ transition, use of a simple age cut-off underestimates the effect of transition by 70% or more in many cases. The Last Paediatric and Fitted Approaches underestimate by around 50–60% compared to each other. The First Adult Approach shows greatest underestimation compared to the Last Paediatric approach (75% or greater reductions).

There is little evidence of differential bias between methods in sub groups of the cohort - underestimation is broadly similar between groups, at least within the confidence intervals with the studied data. However, the sample size in sub groups may be underpowered to detect any differences.

There are difficulties in classification of records using treatment specialty or consultant main specialty, with many records at ages likely to be pre-transition (i.e. under 14 years) classified as adult or unclassified. This is less of a problem for children with life-limiting conditions and for inpatient records. However, inpatient records make up only a small share of the available records and using these alone would prevent or limit transition estimation for young people with no or few inpatient records.

It appears that many children receive treatment from all-age rather than specialist paediatric services. Provision of paediatric services is known to vary [49], but treatment centre information was not provided in the data, so could not be explored. Secondary care provision in the NHS in England may take place in specialist or teaching hospitals serving large communities (often large cities) and have paediatric and adult departments for many specialties or may take place in smaller District General Hospitals, which may not have as many separate adult and paediatric departments [49, 50]. It may be appropriate for a child to be treated in an all-age department in the local hospital rather than travelling further for a specialist paediatric service, depending on care needs, although there are concerns about training [50]. This has implications not only for estimating transition, but also, potentially, for care quality and outcomes [51–56] and may raise many of the same issues as transition itself [17, 18].

Transition can be estimated for the majority of young people with life-limiting conditions, particularly for those with at least one inpatient or outpatient record each year. This is important as this group is most likely to experience any impacts of transition, being in receipt of frequent health care - often from a number of providers, many of whom change at transition [17, 18]. Estimation is possible for fewer of those with chronic (but non-life-limiting) conditions, mainly due to a lack of records, but also due to a lack of classifiable records. Even among those with a record in each year from 15 to 20 years of age only a maximum of 63% (for those born in 1998) of those with a non-life-limiting chronic condition could have a transition point estimated. This may be due to treatment in all-age services, as discussed above. Chronic conditions are a broad group and some conditions will have transition estimated much more readily than others due to differences in hospital use [57, 58]. Asthma, for example, was included, but for many young people this can be managed in primary care without hospital visits [59]. There is however an upward trend in the share of those with chronic conditions for whom transition can be estimated; estimation may be possible for more of those born after 1998 as more data become available.

The transition methods cannot be compared against a gold standard (as none exists for these data [20]) but only against each other and with reference to the issues outlined above.

The First Adult approach is limited by the large number of records classified as adult at ages unlikely to represent adult care (e.g. under 14 years). One strategy to compensate for this might be to add an age cut-off below which a record cannot be classified as adult. The difficulty here is in where to apply that cut-off [20]. Also, for the First Adult approach, the large number of records in childhood that are classified as adult records would mean that the age cut-off would, for many individuals, simply become the estimated age of transition and make it similar to the simpler approaches of using a universal age cut-off. The limitations in record classification mean that the First Adult approach should not be used for these data.

The Fitted Approach will also be influenced by the presence of early adult records, but to a lesser extent, due to also taking into account later paediatric records.

The Last Paediatric approach is much less affected by early adult records - they have no direct relevance, although incorrect classification of a paediatric record as adult could move the last paediatric record to a younger age.

The estimation approaches should also be considered in relation to experience of care. While it is easy to imagine a child pre-transition being occasionally treated in adult services as discussed above, it seems much less likely that an adult, formally transitioned to adult care, would receive further treatment in a paediatric setting (although this is not completely unknown [27, 28]). There is an argument for favouring the Last Paediatric approach over the Fitted approach as it provides a transition point that is clearly defined by an experienced event (last paediatric appointment). It is at this point that access to familiar paediatric experts that the family and young person may have developed a relationship with over many years is withdrawn, so may have the most relevance to assessment of changes in healthcare use related to the changes at transition. The other approaches identify transition points at which the family and young person may still have access to the familiar paediatric services in addition to adult services. Discussions with the Martin House Research Centre Family Advisory Board suggest that this may reflect experience better than the Fitted Approach which places transition in the middle of that process. This - and its lower sensitivity to the presence young-age records classified as adult - suggests that the Last Paediatric method should be the favoured approach for these data.

Use of a simple age cut-off appears likely to underestimate the association between transition status and an outcome, but the simulations do not provide evidence that it does this more for one group than another. It should be noted that some of the groups are small and this may mask differences, particularly for ethnic group where small numbers meant that only White and non-White groups were compared. There may also be systematic differences in transition age between particular conditions or by region, but there were insufficient data available in this study to explore this. Small groups with wider confidence intervals will, of course, be more likely to have confidence intervals including no effect if underestimation of effect size occurs. It is therefore possible that use of a simple age cut-off might result not only in underestimation of an association, but also in the conclusion that there is no statistically significant association at all.

There are also large differences in the simulation between the estimation methods, particularly between the Last Paediatric and First Adult approaches. The Fitted and Last Paediatric approaches do however give results more similar to each other than either compared to a simple age cut-off.

The transition estimates used as ‘true’ transition in the simulations will include their own errors (they will not be free of misclassification bias) and so the simulations are likely to provide an overestimate of the benefits of estimating transition. They do however highlight the importance of correctly assigning transition status.

This study used a nationally representative sample of primary care and hospital data. Although developed using data from England, the methods are directly applicable to the other nations in the United Kingdom, with similar health services and healthcare records. The methods could be adapted to healthcare data from any country in there is a transition from paediatric to adult healthcare and in which records may be classified as paediatric or adult. Different healthcare systems, with different record information, may require different classification schemes, but the methods of estimating transition from classified records should be transferable. Different conclusions may be drawn about the most effective estimation approach in data for other countries, particularly if transition policy differs.

The Martin House Research Centre Family Advisory Board was consulted before and after analyses and helped to put the results in context and understand the real-world experience of transition and applicability of the possible methods. The estimation methods explored here arise from suggestions in the literature and discussions of healthcare transition with the Martin House Research Centre Family Advisory Board and were chosen to be meaningful with respect to young people’s and families’ experiences of transition.

There are also limitations, particularly in relation to the data. Splitting of main treatment specialty into paediatric and adult specialties only became widespread in England in 2007. This limits the ability to estimate transition for anyone reaching transition ages before this point as data may be incomplete. This was mitigated by excluding individuals older than 15 years in 2007, but at the expense of reducing sample size. Requirements on years present (15 to 20 years of age) also mean those who died before age 20 were excluded. Those that die before transition are not relevant to analysis of transition (as transition does not take place) but there is a significant number of young people with life-limiting conditions dying between ages 16 and 20 years, who may transition before death [60]. These (49, 4% of those with life-limiting conditions eligible for inclusion under other criteria) were excluded in this study, due to a pragmatic decision to construct a cohort for whom transition was likely to have taken place within years of available data, but there is no reason why the methods set out here could not also be applied to these individuals.

The data used also have potential issues with individuals entering and leaving the cohort due to changing GP practice from an included practice to an excluded practice. Individuals who moved practice between 15 and 20 years of age would be excluded from this study and may have different characteristics to those who remained at the same practice. Young people leaving home for work or higher education at 18 years may move GP practice and be lost from the data (possibly less of an issue for the group with life-limiting conditions for whom it may be more common to remain in the family home post-18).

Any comparison of post- and pre-transition care requires transition point to be defined, but there may not be a single well defined point of healthcare transition for all individuals [27]. As noted by the Centre’s Family Advisory Board, there are a number of other disruptive transitions beyond healthcare, such as transitions in social care, education and availability of health related benefits and support [61]. This study, using healthcare data, was unable to explore any of these issues, but they should be kept in mind when studying effects of healthcare transition as alternative or additional potential causes of observed changes.

This study demonstrates the feasibility of using routinely collected healthcare data to estimate the transition point from paediatric to adult care, with potential to improve sensitivity when assessing changes in care events associated with the transition. These methods should be applied in future research evaluating the impacts of transition, enabling use of large, nationally representative datasets with reduced risk of misclassification bias. Comparisons should also be made with use of a simple age cut-off to assess impact across a range of real-world healthcare event outcomes. Beyond the UK, the approaches outlined here should be adapted and evaluated for data from other countries that include indications of paediatric of adult care in healthcare records.

The estimation methods could also be applied to other transitions, in healthcare and beyond, for any data that include records that can be classified into two or more states. They could be used to explore transitions between health states and stages of condition, using - for example - presence or absence of particular medications.