Harnessing routinely collected data for the evaluation of early years interventions: insights from a scoping review of evaluation studies

Harnessing routinely collected data for the evaluation of early years interventions: insights from a scoping review of evaluation studies | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Harnessing routinely collected data for the evaluation of early years interventions: insights from a scoping review of evaluation studies View ORCID Profile Kate E. Mooney , Hollie Henderson , Kelly Hollingsworth , View ORCID Profile Sarah L Blower , Jenna Graham , Rina Davidson , Farwa Batool , Dea Nielsen doi: https://doi.org/10.1101/2025.10.29.25338970 Kate E. Mooney 1 Department of Health Sciences, University of York 2 Born in Bradford, Bradford Institute for Health Research Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kate E. Mooney For correspondence: kate.mooney{at}york.ac.uk Hollie Henderson 2 Born in Bradford, Bradford Institute for Health Research Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kelly Hollingsworth 1 Department of Health Sciences, University of York Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah L Blower 1 Department of Health Sciences, University of York Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sarah L Blower Jenna Graham 2 Born in Bradford, Bradford Institute for Health Research Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rina Davidson 2 Born in Bradford, Bradford Institute for Health Research Find this author on Google Scholar Find this author on PubMed Search for this author on this site Farwa Batool 2 Born in Bradford, Bradford Institute for Health Research Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dea Nielsen 3 A Fairer Start, Nesta Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Introduction Routinely collected data (RCD) describes data about individuals that is documented routinely in practice, typically in electronic medical, educational, and service records and registries. Utilisation of RCD for research can overcome limitations with traditional study designs, such as participant burden, attrition and disappointment bias. A systematic understanding of if (and how) RCD is being used to evaluate early years interventions is lacking. Objectives To scope the literature on how RCD is being used to evaluate the effectiveness of early years interventions being delivered in the UK. Methods The study protocol was registered online (osf.io/cug36). Included studies were interventions delivered to expectant parents and parents of children aged 0-5 years. Studies had to use a quantitative measure from RCD as an outcome. The study scope was limited to the UK, from January 2000 to November 2024. Completed studies and study protocols were eligible, encompassing both grey literature and peer reviewed sources. Medline, Psycinfo and Embase databases were searched via Ovid. Results 31 unique studies published 2009-2024 had used or were planning to use RCD as an outcome in an evaluation of an early years intervention. Most studies measured more than one outcome type, and the most common were birth outcomes and child education. Many studies noted limitations of using RCD, particularly being limited by the measures available in RCD, and fewer noted strengths. Conclusion Whilst the use of RCD expands evaluation opportunities, researchers are limited by what measures are available in RCD. We make four specific recommendations to improve the use of RCD in early years evaluations. With careful consideration, RCD can provide powerful insights regarding the impacts of early years interventions. This scoping review reveals an emerging methodology that may have increasing importance for evaluating early years policies and interventions but remains constrained by data availability and quality. Introduction Providing all children with the best start in life is a high priority recommendation highlighted by UK government policies, showing a clear political commitment to early years support and investment ( 1 ). Early intervention delivered to children and their caregivers during pregnancy and in the first five years of their child’s life could prevent the onset of poor parental and child outcomes, and mitigate the potential personal, familial, and societal costs of longer-term negative outcomes ( 2 ). Early interventions can impact a range of caregiver and child outcomes, from physical health (e.g. birth outcomes, breastfeeding), wellbeing (i.e. maternal mental health, child socioemotional development), as well as developmental, cognitive and linguistic skills at the level of the child (i.e. early educational measures). Despite the potential benefits of intervening early being well documented ( 3 ), there remains a lack of robust evidence for the short and long-term effectiveness of interventions for parents of children in the critical early years of life ( 2 , 4 ). This lack of robust evidence for such interventions may be due to the numerous challenges with evaluating them. Where individual services to support parents exist within local authorities, service providers may be reluctant to engage in randomised studies, as they prioritise providing support to those who need it most. Service providers may understandably be reluctant to use a randomised design because it could mean denying a potentially beneficial intervention to a family in need ( 5 – 7 ). Randomised studies that evaluate such interventions may also suffer from limited representativeness and generalisability, as parents who are at the point of ‘readiness to change’ are unlikely to agree to take part in a study in which they might be randomised not to get help, resulting in recruitment bias ( 8 ). Furthermore, such studies are often susceptible to recruitment challenges, particularly relating to a high number of participants being lost to follow up ( 9 ), especially when exploring long term effects of the intervention. For example, even with multiple incentives to support low-income mothers to remain in a parenting intervention, 41% of mothers were lost to follow up ( 10 ). A potential solution to some of these challenges is to use routinely collected data (RCD), which describes data that are collected about individuals when they interact with public services, such as health, education and social care services, as part of their service delivery and practice ( 11 , 12 ). RCD can be used in studies examining the impact of factors such as early interventions, on parent and child outcomes ( 13 , 14 ). Hence, a solution to the issues with evaluating early years interventions is to instead utilise the data which are already, in theory, universally and routinely collected in parent and child records, overcoming limitations with recruitment and generalisability. RCD at a population level can also enhance generalisability and allow better comparison to similar populations ( 7 ). RCD enables both short and long-term outcomes to be obtained, which reduces the participant burden of extra data collection, and potentially reduces attrition and disappointment bias. This can be applied for both randomised and non-randomised designs, which means follow-up periods can be extended for randomised studies, and allows the realisation of observational studies of interventions. The use of non-randomised designs also reduces the ethical concern for the service regarding randomisation; as no participant who desires the intervention will later be randomised to a control group. However, it is also important to be aware of the limitations of non-randomised studies for making causal claims about the impacts of interventions ( 15 ). It is important to note that since RCD is not designed for research purposes, its use for evaluating early years interventions must be considered with limitations in mind. One limitation is that the collection of RCD varies within the nations of the UK. For instance, whilst the Ages and Stages Questionnaire (ASQ) is a routinely used measure of child development as part of the Healthy Child Programme ( 16 ), the Schedule of Growing Skills (SOGS) is used in Wales ( 17 ). These differences limit the scope for evaluations across multiple nations. Previously noted concerns about RCD include the relevance of the RCD to the specific intervention, and the completeness and quality of the data itself ( 18 – 20 ). Regarding the relevance of the RCD, the effects of specific programmes may risk going unobserved if the routinely collected outcome lacks sensitivity to specific changes ( 19 , 20 ). A systematic review of the challenges and strategies for using RCD in research highlighted residual confounding, misdiagnosis, misclassification, and missing data as concerns ( 21 ). New initiatives are facilitating access to routine data at both local ( 22 , 23 ) and national levels ( 12 , 24 , 25 ) for research purposes. For instance, RCD has been used to create Scotland’s first administrative child cohort, linking records for over 198,483 mother-child pairs ( 24 ), and England’s Education and Child Health Insights from Linked Data Mother-Baby (ECHILD-MB) cohort was created by linking 13.6 million baby records to mothers ( 25 ). These datasets can potentially be used to evaluate early years interventions, where information about intervention exposure has been routinely collected, or where population wide policies or interventions can be evaluated via natural experiments ( 15 ). Present study and rationale Evidence for successful early years interventions is lacking, despite a renewed policy interest in providing all children with the best start in life ( 1 , 3 ). Despite its limitations, utilising RCD as an outcome could overcome several evaluation challenges. Hence, a systematic understanding of what components of RCD are being, or have been, used to evaluate early years interventions and/or policies, is crucial for planning of future evaluations. This may enable identification of areas where improvements to existing RCD infrastructures may be required, and identification of RCD that is being underused. Given the geographical variability of what and how data are collected between countries, it is important to consider these questions within the specific area of interest, in this case the United Kingdom. This scoping review therefore aims to gain an overview of both planned and completed studies that have used RCD as an outcome in evaluations of early years interventions (delivered either during pregnancy, or up child age 5-years-old). Our focus is on universal and targeted interventions with a social and behavioural component, rather than interventions or products to treat specific conditions. The scoping review question is: How is routine data being used to evaluate the interventions delivered in the early years in the UK? Methods A preliminary search of PROSPERO, the Cochrane Database of Systematic Reviews and JBI Evidence Synthesis was conducted and no current or underway systematic reviews or scoping reviews on the topic were identified. This scoping review was conducted in accordance with the JBI methodology for scoping reviews ( 26 ), and reported using the PRISMA Extension for Scoping Reviews (PRISMA-ScR) ( 27 ) (Appendix 1). The protocol was registered at https://osf.io/cug36/ . Eligibility criteria The ‘Population, Concept, Context’ framework was used to describe the eligible studies. Broadly, we focused on studies that included interventions delivered to caregivers with a 0-5-year-old child, and/or interventions delivered directly to children aged 0-5 years. The concept of the interventions was broad and could be any kind of intervention and/or policy that was delivered when parents were pregnant, or when the child was aged 0-5-years-old. The context was UK only, as the purpose was to inform the use of routine data use in intervention evaluations within UK contexts. Studies published since 1st January 2000 were included, to ensure that any results are relevant to modern data systems. Table 1 describes the framework and eligibility criteria. A more detailed table with exclusion criteria for guiding the screening process was published with the study protocol (see osf.io/cug36/). At abstract screening, we included any studies that: (a) were interventions and used a quantitative design, (b) were about parents/children (aged 0-5-years-old) and (b) were in the UK, or it was not clear where the study took place. View this table: View inline View popup Table 1. Participant, Concept, Context (PCC) Framework and eligibility criteria View this table: View inline View popup Table 2. Included studies characteristics Types of Sources Peer reviewed studies and grey literature (including reports, websites, and conference abstracts) were both eligible for inclusion. We included studies at any stage from study protocols, interim findings, to study reports of outcomes. Published literature search strategy The full search is provided in Appendix 2. The words associated with the population, concept and context of the review were used to guide the search terms. The National Institute for Health and Care Excellence (NICE) filters were used to identify studies conducted within the United Kingdom ( 28 ). Medline, Psycinfo and Embase were searched via Ovid for peer reviewed literature published between January 2000 and 13th November 2024. Grey literature search strategy Grey literature searches took place in November 2024. We targeted specific websites relevant to our topics, namely National Institute for Health Research (NIHR), Education Endowment Foundation (EEF), Early Intervention Foundation, National Foundation for Education Research (NFER), ISRCTN ( https://www.isrctn.com/ ), Clinicaltrials.gov , GOV.UK ( https://www.gov.uk/search/all ),and GOV.wales ( https://www.gov.wales/ ). Records of websites searched, number of potentially relevant items, and number scanned were kept in Microsoft Excel ( 29 ). Evidence selection Titles and abstracts were uploaded to Covidence for screening. A piloting process took place with 100 studies, and agreement between all reviewers was reviewed. After piloting, amendments were made to the inclusion criteria to improve the clarity of the included studies. All titles and abstracts were screened by two reviewers according to the criteria described above for abstract screening. Cohen’s Kappa scores were reviewed regularly as a team, since lower than desired agreement (<.60) occurred between some pairs of reviewers. Regular meetings and reviews took place with the whole team to resolve disagreements, and Cohen’s Kappas improved. After abstract screening, the full texts were screened and agreed by two reviewers (DN or KEM). Data extraction Data was extracted from eligible papers by three reviewers (RD, JG, KH). A proportion of 20% of selected articles was second checked by KEM to ensure accuracy. Data included specific details about the participants, concept, context, study methods and key detail regarding the outcome measurement and source of RCD. Discussions of studies were examined to extract a list of specific strengths and limitations associated with using RCD. Data analysis and presentation A full list of studies and characteristics is provided in Appendix 3. The included studies were summarised in terms of their study types, designs, intervention types, and outcomes used including the type of outcome, timing, and whether the outcome was reported at the parent or child level. Data were charted by these study characteristics, and bar graphs created where they provided additional insights. Results Figure 1 presents the flow of included studies. There were two studies using the same data source, but they contained different periods of follow up routine data, hence, we retained these two studies in the review. The studies are ( 1 ) a protocol of a randomised study to evaluate the Family Nurse Partnership ( 30 ) and the results from the routine data follow up from that study ( 31 ). Download figure Open in new tab Figure 1. PRISMA flow diagram for scoping review process Search results Download figure Open in new tab Figure 2. Number of studies published per year Study characteristics Table 3 summarises key characteristics of the included studies, and the extracted data for all studies is provided in Appendix 3. This information is used in the narrative synthesis below. Narrative synthesis Description of studies Out of 32 studies, 20 were published studies with results (62%), and 12 were study protocols (38%). Out of 31 unique studies, most (n=22, 71%) were observational (ie. non-randomised interventional studies). The remainder were randomised control trials (n=9, 29%) ( 30 , 32 – 40 ). The first eligible study was published in 2009. Generally, the number of published studies eligible for our review has increased with time, with the most being five studies in 2023, which included two protocols and three published studies. Intervention type Most (n=9, 29%) were perinatal interventions, meaning that they were broad perinatal support interventions that started antenatally, with some continuing postnatally. This included programmes such as the Family Nurse Partnership ( 30 , 38 , 41 , 42 ), “Pregnancy Circles” ( 35 , 36 ), and “Baby Steps” ( 43 ). The second most common category were service and/or policy evaluations (n=7, 23%). This included a broad remit of intervention types ranging from, for example, the availability of Community Perinatal Mental Health teams in local areas ( 44 ), to an evaluation of the impact of a Growth Assessment Protocol implemented in maternity ( 40 ). The other categories were smoking cessation in pregnancy (n=3, 10%), breastfeeding support programmes (n=3, 10%), parenting programmes (n=3, 10%), voucher schemes (n=2, 6%), weight programmes (n=2, 6%), and the effect of COVID-19 policies (n=2, 6%). Routinely collected outcomes Note: studies may have measured >1 outcome, so double counting of studies occurs . There was an even spread of studies which focused on only parent outcomes (n=10, 32%), only child outcomes (n=11, 35%), and both parent and child outcomes (n=10, 32%). Figure 3 shows that we categorised outcomes into 14 broad categories. More than half of the studies measured more than one outcome type (n=19, 61%), with the most being six different outcome types in one study ( 41 ). The most common category was birth outcomes (n=12, 39%), which included a broad range such as gestational age, birth type, and stillbirths. These studies mentioned use of maternity electronic patient records ( 45 ), Hospital Episode Statistics (HES) ( 46 ), and routinely collected NHS data ( 47 ). The next most common was child education and/or development outcomes (n=7, 23%), with all but one of these studies mentioning use of the Early Years Foundation Stage Profile (EYFSP) linked via the National Pupil Database ( 31 , 39 , 41 , 42 , 48 , 49 ). The one study which did not use the EYFSP was a protocol for evaluating the impact of the universal health visiting pathway in Scotland on child developmental concerns at age 27-30 months, though does not state the exact measure used ( 50 ). Download figure Open in new tab Figure 3. Outcome types measured in all studies, separated by protocol and publishe studies The next most common was child maltreatment (n=5, 16%), with one study linking this via child protection status in administrative data ( 51 ), two using social care data ( 41 , 50 ), one using local authority child protection register ( 52 ), and one ascertaining this via Child in Need (CIN) status via the National Pupil Database ( 42 ). The next most common outcome was service implementation (n=4, 12%), which related to the proportion of women attending antenatal bookings ( 35 , 36 ), child health reviews conducted by GPs ( 53 ), and service use of a community perinatal mental health team ( 44 ). The next most common was smoking status (n=4, 14%), recorded in maternity records during pregnancy ( 50 , 54 – 56 ). We categorised studies into the timing of their most recently routinely collected outcome. The most common period was up to 3 months post birth (n=10, 32%). This included length of stay in hospital ( 40 , 46 ) and readmission ( 46 ), breastfeeding outcomes ( 32 – 34 ), survival for the parent and/or baby ( 40 , 57 ). The next most common follow up period was at birth (n=7, 23%). Very few studies examined outcomes beyond 5 years (n=3, 10%). Limitations and strengths noted in studies Appendix 3 presents the list of strengths and limitations per study, and we summarise the findings here. Most of the studies (n=20, 62% of 32 studies total) mentioned one or more specific limitations of using RCD. A common limitation noted by nine studies was that the choice of outcome measurement was limited by what was available in routine data ( 31 , 43 , 48 – 51 , 53 , 56 , 58 ). Another limitation was the high level of missing data ( 44 , 52 , 54 , 59 ). Other common limitations noted were the length of time to gain permissions to access and receive data ( 41 , 48 ), and concerns in the accuracy of the data ( 32 , 34 , 44 , 60 ). Fewer studies mentioned strengths of using RCD (n=9, 28% of 33 studies total). Several studies noted the increased sample size achieved through using RCD, noting whole population coverage( 32 , 35 , 45 , 57 ), low missing data, low loss to follow up ( 33 ), and improved power and/or greater precision in estimates ( 44 , 54 ). Two studies noted that using RCD is cost-effective, highlighting the low burden for participants ( 42 , 47 ). Two studies noted the relevance of the outcomes to clinical practice and/or policy making decisions ( 45 , 49 ). One study noted the minimisation of bias in self-reporting, and the potential to follow up participants over time ( 42 ). Discussion Summary of studies We identified a total of 31 unique studies published between 2000-2024 that had used or were planning to use RCD as an outcome to evaluate an early years intervention. The majority were publications with results (64%), with the remainder being study protocols (38%). The studies were predominantly based in England (65%), followed by Scotland (23%), Wales (6%), or multiple nations (6%). The few studies using data from >1 nation may suggest a challenge in harmonising RCD across the UK’s devolved nations, since these nations operate separate early years services and hence separate data collection systems ( 16 , 17 ). The first eligible study was published in 2009. The number of eligible studies has increased over time, with the highest number being five in 2023. This could reflect both a developing field due to an increased policy demand for effective early years interventions ( 1 , 61 ), and an increase in data linkage research and capabilities ( 13 , 25 ). Most studies employed observational designs (n=22, 71%). Observational studies were likely most frequent due to the nature of RCD, which is that it is primarily collected for purposes other than research ( 62 , 63 ). RCTs typically represent funded studies which do not usually need to rely upon RCD. The most common type of intervention were perinatal programmes (29%). This may reflect the availability of RCD in maternity records, meaning these programmes are readily evaluable using RCD on birth related outcomes. Outcomes in routinely collected data The common use of both parent and child outcomes demonstrates the benefits of using RCD, as it can assess the dual impact often targeted by complex interventions delivered in the early years. Evaluations that measure both categories are best positioned to capture the full scope of an intervention’s potential effectiveness. Most studies measured more than one outcome type via RCD. This highlights a strength of using RCD, as it has the capacity for comprehensive and multi-purpose evaluation with no additional data collection burden for participants or researchers. This aggregation may allow evaluations to move beyond a single “primary” outcome, providing a more comprehensive understanding of an intervention’s effects across various domains. As noted by many of these studies, use of RCD is highly cost-effective and low burden compared to collecting multiple different outcomes through bespoke surveys. The most common outcomes were birth outcomes and child education/development. The commonality of birth outcomes may, again, reflect the availability of RCD in maternity records to be used in such evaluations. These birth outcome data indicators may be recorded with higher completeness than other RCD, because they are embedded into midwifery practice, and essential for clinical decision-making. These indicators therefore may serve as reliable, early indicators of intervention success, particularly for antenatal programs. The frequent use of the EYFSP to measure child development is likely due to it being a mandated assessment in England, meaning it has high coverage for this population at school entry ( 64 ). Use of the EYFSP may allow researchers to assess the long-term developmental impact of perinatal interventions, a key advantage over short-term studies. This would also reflect a key policy interest, with the UK Government target for 75% of children to have a Good Level of Development on the EYFSP by 2028 ( 1 ). The routinely collected ASQ by health visitors at the 2-2 ½ year review completed in England’s Healthy Child Programme ( 16 , 65 ) was not indicated to be used by any studies, except potentially by one ( 50 ). The ASQ is a broad measure of a child’s early development and could be a targeted outcome for many interventions, and this suggests that this potential measure may be being underutilised. However, whilst the ASQ should be universally completed as a mandated assessment in England ( 16 , 65 ), recent studies have indicated variation in the completeness of the ASQ ( 66 ), which may explain its underuse as an outcome in evaluations. Whilst many studies targeted the same outcomes, they differed in their descriptions of their data source. For instance, several studies utilised routine maternity data, however, it was challenging to determine if they had used the same data sources e.g. “maternity electronic patient records,” and “routinely collected NHS data”. These terms may describe the same underlying data source, or distinct datasets, and the variation in language makes this difficult to ascertain. The lack of standardisation in describing routine data sources presents a barrier for researchers seeking to use maternity data to evaluate early years interventions. The timing of the outcome varied, with the most common being up to 3 months post birth (32%), at birth (23%), and at child age 5 years (13%). This indicates that most studies used RCD in the very earliest years of children’s lives, and fewer studies successfully leveraged RCD for medium-to-long-term follow-up (with only 3 studies examining outcomes post 5 years-old). A main benefit of RCD is the possibility for long term follow-up with minimal participant burden. It is possible that as access to RCD improves, more studies linking long-term RCD to evaluations will emerge. Strengths and limitations noted in studies Most studies noted specific limitations in using RCD in their studies, with the most frequent being the limited choice of outcome measurement based on what was available in the RCD. This limitation highlights that crucial, but more nuanced, proximal outcomes (e.g., parental confidence, quality of parent-child interaction) that might typically be collected may be missed in a study relying upon RCD ( 19 , 20 ). Further, the measures collected in RCD may not be the most valid and reliable measures of specific outcomes. This could lead to an incomplete picture of an intervention’s effect, particularly if the mechanism of change is not directly measurable in RCD. Political interest in using population-based data for evidence-based decisions is crucial for developing new databases and data collection systems ( 7 ), hence the current Government Best Start policy ( 1 ) highlights the timeliness of this review. Authors also noted concerns regarding accuracy of the data, and this should be investigated by researchers, for instance via studies which examine the implementation of RCD in local practice ( 67 ). These limitations reflect that RCD are not designed for research purposes, and its use should always be considered with its limitations in mind ( 18 , 21 ). Fewer studies noted strengths (30% of 33 studies), which most frequently included an increased sample size and whole population coverage, and low missing data or low loss to follow up. RCD may address previously noted challenges with evaluating early years interventions, including that such interventions are subject to a high attrition rate ( 8 , 10 ). Altogether, the limitations and strengths highlighted a critical trade-off when using RCD to evaluate interventions: whilst the use of RCD addresses some challenges with evaluating early years interventions, researchers are also limited by what was available in RCD. Recommendations Policymakers to integrate further outcomes into RCD . RCD currently misses key outcomes for early years interventions. Policymakers should mandate the integration of standardised, brief self-report measures (e.g., validated scales for parental mental health or confidence) into routine collection points (e.g., universal health visitor checks). This integrated data could be designed for both clinical and evaluation use. Researchers to communicate the use of RCD to services . Many studies noted difficulties with accessing high quality and well completed RCD. Researchers must communicate the impact of RCD findings back to service providers. Demonstrating how this data informs intervention evaluation could improve service delivery, and boost motivation for services to ensure data completeness, accuracy, and accessibility. Researchers, services, and policymakers to develop a standardised terminology for describing RCD sources and access . Descriptions of RCD sources and measures differed between studies. Clear, consistent descriptions of datasets would enable researchers to better understand the characteristics of different data sources, including their coverage, completeness, and the specific variables captured. A co-produced, standardised terminology could enable researchers to better navigate the process of obtaining data, and support more efficient use of RCD. Researchers and services to improve data linkage and utility of underrepresented outcomes . Some key outcomes were underutilised, potentially indicating poor data quality and/or accessibility. Future work should be dedicated to understanding reasons for their underuse, and developing robust linkage mechanisms for these currently underused datasets. Researchers to continue to utilise RCD and bespoke collected outcomes to understand the effects of interventions . RCD is not sufficient to be used on its own to evaluate interventions and policies. Hence, evaluations with bespoke data collection are still required. Data from these studies can be combined with outcomes nested in RCD where possible, to build understanding of the effects of early life interventions. Limitations We did not formally assess the quality or risk of bias of the included studies. Consequently, we could not evaluate the quality of the RCD sources used or the potential impact of data quality on intervention evaluations. Since the use of RCD for research purposes is a rapidly evolving field, these review findings may require updating within a few years. Finally, although we made efforts to include grey literature, sources such as trial registrations and conference abstracts often lacked the detailed methodological information needed to confirm eligibility. This means the current review likely underestimates the volume of relevant studies that exist and may become more widely available in the near future. Conclusions Altogether, this review has highlighted a critical trade-off when using RCD to evaluate interventions: whilst RCD opens up evaluation opportunities, researchers are also limited by what measures are available in RCD. The recommendations call for ( 1 ) policymakers to integrate relevant measures into routine data (RCD), ( 2 ) researchers to actively communicate findings to services to improve data quality; ( 3 ) stakeholders to standardise RCD source terminology and ( 4 ) researchers and services to improve data linkage for underused RCD. With careful considerations, RCD can provide powerful insights into the effectiveness of early years interventions, and researchers should continue to use it in combination with other available data. This scoping review reveals an emerging methodology that may have increasing importance for evaluating early years policies and interventions, but remains constrained by data availability and quality. Data Availability All data produced in the present study are available upon reasonable request to the authors. Funding This study has received funding from the National Lottery Community Fund (previously the Big Lottery Fund) as part of the A Better Start programme (Ref 10094849). The funder was not involved in the design of the study nor in writing the manuscript. Conflicts of interest There is no conflict of interest in this project. Data availability This scoping review did not include any primary data collection. All data produced in the present study are available upon reasonable request to the authors. Ethics statement This study did not involve any primary data collection and hence did not require ethical approval. All data is extracted from publicly available studies. Acknowledgements The authors of this study are grateful for the contributions from the Better Start Bradford Innovation Hub who made meaningful recommendations to the development of the protocol. 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