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SCPHRP Final report form for SCPHRP grants

SCPHRP reference number: 02-09/10

Please complete this form in Verdana 10 point font size Project title: Enhancing information systems to support children’s health and development: exploring options in Glasgow Start date: April 2010

Finish date: January 2013

Investigators: Philip Wilson

Lucy Thompson

Rachael Wood

Matt Forde

Lucy Reynolds

Michele McClung

1.

Summary

Early experiences have a significant long-term impact on health. Despite having relatively sophisticated routine data systems in Scotland, we do not routinely gather information on cognitive, social and emotional development, and it is not clear how child health data in general are collated and utilised. We aimed to explore how existing and novel population based data on early childhood development can be used to support the improvement and evaluation of services for pre-school children. Over three phases we conducted: (1) Interview-based mapping of routine data systems across Scotland; (2) Interview-based feedback from key stakeholders on Phase 1 results; (3) linkage of two research datasets (a - 30 month health visitor (HV) contact pilot; b - Strengths and Difficulties Questionnaire (SDQ) data at school entry) with routinely held child health data. Phase 1 showed a large range of data is routinely gathered about children aged 5 and younger, but developmental measures are usually not recorded. Systems are universally available but do not always achieve universal coverage. More vulnerable children are more likely to be missed. Phase 2 showed that key stakeholders were impressed by the range of data available, but were not always confident in accessing and interpreting the data. Gaps in developmental information were identified. Phase 3 shows good feasibility in linking novel data with routine data using probabilistic methods. Preliminary analyses suggest high-risk SDQ scores at age 5 are associated with mothers being young, smoking in pregnancy and living in a more deprived area, as well as with health-visitor assessed risk status at 6-8 weeks. We gather a lot of data about young children, but not always of the right type or in a way accessible to those making decisions about children’s services. Linkage of research and routine data suggests that high (i.e., poorer) SDQ scores at age 5 are associated with other markers of vulnerability. 2. Original aims •

To explore the current status of data collection systems for pre-school child development throughout Scotland, and planned future improvements.

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To explore ways of systematically collating and presenting cross-sectional data at local population level to support activities such as needs assessment and service evaluation.

To explore what data linkage can add by allowing the analysis of developmental trajectories at individual level.

3. Methodology The work was planned over three distinct phases (aligned with the three original aims). Phase 1: Mapping Survey. We began with a survey exploring the current status of pre-school child development data collection throughout Scotland. Plans for future systems and enhancements to existing systems were also explored. Potential participants were those involved in the commissioning and delivery of services relating to preschool child development in Scotland, e.g., NHS child public health leads, education authority information leads and national level data custodians throughout Scotland. A primary list was drawn up by the study steering group with further participants identified using snowball sampling. Potential participants were contacted via email with the following attachments: a cover letter from the Principal Investigator, a project information sheet, and a consent form. Interviews took place in person or by telephone once a completed consent form had been returned to the research fellow. Interviews were audio recorded and lasted about 30 minutes. The structured interview comprised four sections: socio-demographic information about the respondent, knowledge of current data systems, knowledge of the future development of data systems, and knowledge of other personnel working with data systems for early childhood. Ethical review and approval for Phase 1 was provided by the University of Glasgow Medical Faculty Ethics Committee (ref: FM05809). Phase 2: Utility of data profiles. The central activity of Phase 2 was initially planned as the development of a range of examples of how the data (identified in Phase 1) could be analysed and displayed at local area level within Glasgow City. The intention was that the collated data would be presented to a range of key stakeholders with responsibility for commissioning and delivery of services relating to preschool child development. Formal feedback from these stakeholders would be recorded and presented in a further report. Although a consensus conference to facilitate this was arranged for February 2011, it proved impossible to confirm availability of a critical mass of key stakeholders. Furthermore, it became apparent in Phase 1 that very few preschool child development data were available for demonstration. The steering group therefore agreed an alternative strategy for the management of Aim 2. A selection of respondents in the Glasgow area and some with national remits was selected by the steering group. All were invited to respond to the Phase 1 report via telephone or email. The following areas were addressed: •

Have we missed anything?

Are there gaps in what is being collected?

Can the data be better presented?

Can the data be analysed in a different way?

Once feedback was obtained, notes were typed up and emailed to the respondent for verification. Phase 3: Data linkage. Novel child development data (from 30 month HV contacts collected in West Glasgow CHCP in the second half of 2009) and SDQ 1 data collected before school entry and entered on the SEEMIS database in Glasgow City in 2011, were linked to electronic maternity records and existing child health surveillance data from the 1

Goodman (1994). Journal of Child Psychology and Psychiatry.

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universal 10 day and 6-8 week health visitor contacts collected nationally. The full data linkage specification is given in Appendix 1. The first novel dataset (30 month HV contacts) has been described in detail elsewhere 2. It includes the outcome of a brief language development screen 3, the Richman Behaviour Screening Questionnaire 4, and the Parenting Daily Hassles Scale 5. The second novel dataset (SDQ at school entry) includes scores from a brief screen for emotional and behavioural problems which indicates the likelihood of difficulties in five areas: emotional problems, conduct problems, hyperactivity / inattention, peer relationship problems, and prosocial behaviour. As well as five subscale scores, each case has a ‘total difficulties’ score based on the summing of scores on the first four subscales (i.e., all but prosocial behaviour, which is positively scored). These novel datasets will be referred to as ‘Research data’. The routinely collected datasets include a number of potentially useful fields (see Data Specification) and will be referred to as ‘Routine data’. The datasets were labelled as follows: 1a) Research data 30 month health visitor contact pilot 2009 (THM) 1b) Research data SDQ data at school entry 2011 (SDQ) 2a) Routine data maternal birth record (SMR02) 2b) Routine data Child Health Surveillance – Preschool i) First Visit (FV) ii) Six-eight week visit (SIX) iii) Newborn hearing screening (HS) iv) Unscheduled visits (not universal) (Unsch) v) Recall visits (not universal) (Rec) Unique identification numbers were assigned to each case in each of the research datasets (THM and SDQ). Research data (e.g., SDQ scores) were then stripped out of the file, leaving only identifying information and unique identification number. These files were forwarded securely to NHS Information Services Division (ISD) where linkage was made to each of the administrative datasets. ISD then returned six datasets (2a-b above) containing the unique identification numbers and linked data only (identifying information having been stripped out). The researcher was then able to merge the research data back onto the linked files using the unique identification numbers. Prior to data linkage taking place the Data Linkage Specification was reviewed and approved by ISD who agreed to perform the linkage under the existing arrangements with the CSO to support research of this nature. ISD requested to see ethical approval documentation from an NHS Research Ethics Committee and appropriate consents from NHS and Glasgow City Education Services data controllers prior to performing the linkage. Ethical review and approval for this phase of the work was granted by the NHS West of Scotland Research Ethics Committee 1 (Ref: 11/WS/0066). The figure below gives a graphical representation of the different datasets and how they relate to the child’s early life. All of the administrative datasets in bold are universally collected (SMR02, UNHS, HVFV and SIX) with unscheduled and recall visits being universally available but only carried out where need is indicated. The research datasets were universally applicable to those of the appropriate ages in the relevant areas but yielded data from 40% (THM) and 70% (SDQ) of those eligible respectively.

2 3 4 5

Thompson, Wilson & McConnachie (2013). Journal of Nursing Education and Practice. Miniscalco, et al (2006). Developmental Medicine and Child Neurology. Richman & Graham (1971). Journal of Child Psychology and Psychiatry. Crnic & Greenberg (1990). Child Development.

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Figure 1: Graphic representation of datasets in Phase 3

Phase 3: Linkage of administrative datasets with research datasets

= research dataset = administrative dataset = timeline (child’s age)

[4-24 months]

birth

SMR02 UNHS

THM

SDQ 5 years

HVFV SIX Unscheduled visits Recall visits

SMR02 = Scottish Morbidity Record 02 (mother’s obstetric record); UNHS = Universal Newborn Hearing Screening; HVFV = Health Visitor first visit (usually 10 days after birth); SIX = Health Visitor 6 to 8 week visit.

Statistical analyses Phase 3 was designed to answer two broad questions: 1. Is it technically possible to link data reliably from four sources (birth records, Child Health Surveillance records, health visitor contacts at 30 months and nursery teacher SDQ questionnaires in the preschool year)? 2. Does linking data from the four sources allow us more easily to understand key

factors in children's social and emotional development, and therefore their readiness to learn?

Data were merged to a single dataset using unique identification numbers. Following extensive data cleaning, recoding and computation of composite variables, frequency analyses were used to describe the proportion of the administrative data which was successfully matched to research cases. Univariate analyses (chi-squared test, one-way Analysis of Variance (ANOVA), independent samples t-test and bivariate correlations) were conducted to examine the association between Routine data and Research data. Binary logistic regression and linear multiple regression were then applied to assess the unique contribution of sets of predictor variables to the outcome variables in the research datasets. 4. Results Phase 1 Thirty-one participants were recruited from six NHS Scotland regions, three Local Authorities, NHS ISD and the Scottish Government. A range of professions was represented, including medical consultants, service managers, academic staff, IT managers, system managers and administrators.

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The types of developmental data collected in Scotland, and the current uses of these data, are summarised in Tables 1 and 2 of the full Phase 1 report (Appendix 2). The survey findings demonstrate that: •

There is a range of universal data systems used in health and education sectors where varying levels of developmental data could potentially be recorded. However: o

Most do not routinely record data about developmental measures for children aged 5 and younger.

o

Although systems are universally available, these do not always achieve universal coverage.

Many of the data collected from health and education services are recorded in an electronic format which could potentially allow greater use among service planners and practitioners. However, many of those interviewed reported lack of access as a barrier to using the system in their practice.

Primary care data (held by GPs) is currently under-utilised and novel systems and governance arrangements would be necessary to allow regional or national level analysis.

The full Phase 1 report can be viewed in Appendix 2. Phase 2 Nineteen stakeholders were approached to give feedback: contact was made successfully with 12 (multiple attempts were made to contact the remaining seven) and detailed feedback was given by seven senior managers. Each question generated detailed discussion of the positive and negative aspects of respondents’ interaction with routinely collected data. Key themes are noted in Table 1 below. Table 1: Summary of responses to Phase 2 Question

Types of response

Have we missed anything? Are there any data that you or your colleagues know are being gathered for all children but aren’t detailed here?

-

Would be nice to see some specialist systems covered in more detail, e.g., Carefirst

-

Could we benefit from data gathered within projectbased datasets? E.g., Child Smile or Family Nurse Partnership

-

There is a wealth of electronic data system development happening across Scotland – this should be reflected

-

Report was comprehensive and informative

-

It’s useful to be able to know what actually happens to children

-

Lack of accurate data on children affected by alcohol and drug misuse

-

Useful to have more info on problems / diagnoses children have on a universal level, including a ‘no problems’ category

-

Recognition of the benefits possible with current developments, but acknowledgement that it will be years before systems are reliable

-

A lack of coherence and capacity to overlap information, which is necessary for multi-agency decision making

Are there gaps in what is being collected? Are there any data that would be helpful in decision making in your role, but aren’t currently gathered or available to you or your colleagues?

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Question

Can the data be better presented? Are there ways of presenting the data, e.g., graphs, tables, reports, that would be more useful to you in your role?

Can the data be analysed in a different way? Are the levels of analysis available right for you? What would you like to see?

Other comments

Types of response -

Data are driven by organisational needs which can be conflicting and make it difficult to generate a coherent picture

-

We don’t collect enough on social and emotional development and there is too great an age gap in routine data collection – we need to be better at getting the right information at the right time.

-

The ability to link data, to match to population level and to track effectively over time – this would allow us to design and adapt services more effectively and in response to local need

-

Not in the habit of directly accessing data – too complex and time-consuming

-

Lack of confidence as to where to go and which data are most reliable

-

The form of presentation is not child-focused, but driven by organisation priorities

-

It would be useful if data were accessible through a single, quality-assured, source

-

It might be useful to combine the work in the report with the child health profiles prepared by ScotPHO 6 to create an ongoing resource

-

Any future organisation of indicators and presentation of data should be in line with GIRFEC 7 terminology – there will need to be national agreement about alignment of specific indicators with SHANARRI 8 categories

-

There are good examples outside of Scotland: o

ChiMat (see www.chimat.org.uk/)

o

NHS Atlas (www.rightcare.nhs.uk/atlas)

-

Accessibility is a primary issue for people in roles like ours – reliant on what ‘lands on our doorstep’

-

Not always right for the specific question we have – broad indicators have to be used as proxy measures, which is not ideal

-

The feedback loop to those providing data is really important - we will not attain good data without engaging properly with practitioners

-

It would helpful to link the children's data to the census and to use GIS mapping to present. Anything which can be used to manipulate the data on a geographic basis is helpful and to look at trend data so that we can see change over time.

-

Has the GIRFEC agenda been incorporated into existing systems? Have we ‘future-proofed’ historical systems for the long-term GIRFEC

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http://www.scotpho.org.uk/comparative-health/profiles/2010-chp-profiles Getting It Right For Every Child. http://www.scotland.gov.uk/Topics/People/Young-People/gettingitright/introduction 8 In order to be Successful Learners, Confident Individuals, Effective Contributors and Responsible Citizens, GIRFEC states that children need to be: Safe, Healthy, Achieving, Nurtured, Active, Respected, Responsible, Included. Collectively referred to as the "SHANARRI" wellbeing indicators. 7

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Question

Types of response context? -

This work should be used to shape the work of the Early Years Collaborative

-

We need a Scotland-wide agreed system to manage all these different databases

Phase 3 Linking the datasets In the THM dataset the CHI numbers were either complete or could be ascertained simply. There were six duplicate patients removed and one set of twins who shared the same CHI was separated. Therefore the THM dataset holds good information for 469 of the 475 records submitted. The SDQ dataset started with 4346 records submitted to ISD. Of these 177 were ‘incomplete’, meaning that more than one of four variables (date of birth, name, sex & postcode) were missing. Of the remaining 4169 records it was possible to find 4060 linked CHI numbers (97.4%). At this stage 28 duplicate patients were removed leaving good information for 4032 of the 4346 records submitted. From there all information available relating to the linked patients from the specified child health data sets and SMR02 was added. The table below quantifies the proportion of cases in the linked dataset that contained data from each of the original datasets (1a-2b). For example, the second row shows that there were 2857 cases with data in each of the Routine datasets and the SDQ dataset. There were so few cases with Unscheduled (n=8) or Recall (n=23) visits that these data were not used in analyses. Table 3: Numbers in subsets of Phase 3 linked dataset SMR02*   

  

FV*                  

SIX*                  

HS*      

SDQ

THM

THM P

THM R

THM L

     

     

        

N 3342 2857 183 3720 3167 211 4115 3517 228 121 109 231 137 129 270 148 140 291

*a tick under SMR02, FV, SIX or HS simply indicates a record exists (not that it is complete)

SDQ = Total Difficulties score present; THM = thirty month complete data on all three measures; THM P = thirty month complete data on Parenting Daily Hassles Scale; THM R = thirty month complete data on Richman Behaviour Checklist; THM L = thirty month complete data on Language Screen; SMR02 = maternal birth record; FV = health visitor First Visit; SIX = health visitor 6-8 wk visit; HS = new born hearing screening

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It is clear that the linkage was successful and has generated a significant proportion of linked cases for analyses. Lack of complete data in some fields, however, especially in the THM research dataset, will limit the potential for meaningful analysis of this pilot dataset. Using the linked data to examine developmental trajectories Univariate analyses showed some variables in Routine datasets to be consistently related to Research dataset outcomes. Mother’s age at child’s birth, SIMD at child’s birth, smoking in pregnancy, mother’s smoking at HV first visit and HPI at the 6-8 week HV visit and were consistently related to SDQ, RBSQ and PDHS outcomes. Language delay rarely showed a significant relationship with other variables (most likely due to very low N for this binary outcome). Birthweight, gestation at birth, parity, and mode of feeding on discharge (from the maternity ward) and mode of feeding at HV first visit all showed specific relationships of interest. Full univariate analysis outcomes are tabulated in Appendix 3. Predicting outcomes at thirty months Linear regressions show that low SIMD (i.e., mother living in a more deprived area) at birth, mother smoking at HV first visit and breastfeeding at HV first visit were all significantly predictive of higher PDHS-I scores at thirty months, whereas young maternal age at the time of baby’s birth was the only significant predictor of higher RBSQ scores. Children with low birthweight were more likely to show language delay (having a vocabulary of less than 50 words), as were those whose mothers smoked during pregnancy. Paradoxically, this pattern appears to have reversed for smoking at HV first visit. Crosstabulations of smoking status during pregnancy and at the HV first visit show that the outcomes from the regression analyses are most likely to be due to very low numbers in the THM dataset who showed language delay and had data for mother’s smoking status either in the SMR02 or HVFV datasets. Table 4: Regression analyses on the thirty month visit (THM) dataset Linear forward stepwise models PDHS-I

RBSQ

SIMD (1=Most deprived, 5=Least) Mothers age

-0.26**

Smoking in pregnancy(0=No, 1=Yes) Smoking at FV (0=No, 1=Yes) Infant feeding (1= Breast, 2=mixed, 3=Bottle) HPI (1=Core, 2=Add, 3=Intensive) Gestation Birthweight Parity R squared. (unadjusted) *p<0.05 **p<0.01 NS= Not significant

NS 0.20** -0.17* NS NS NS NS 0.11

NS 0.25** NS NS NS NS NS NS NS 0.06

NS

Logistic forced entry models 2 word 50 word utterances vocabulary NS NS NS

NS

NS NS NS NS NS NS NS N/A

1.86* -2.05* NS NS NS -1.97* NS 0.17

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Table 5: Crosstabulation of Smoking status during pregnancy by language delay (50 words) at thirty months

Did you smoke during your pregnancy (SMR02)

Can your child say 50 words? (THM) Yes No %(n) % (n) Total No 72.1 (142) 60.0 (12) 71 (154) Yes 27.9 (55) 40.0 (8) 29 (63) Total 197 20 217

Table 6: Crosstabulation of Smoking status at HV first visit by language delay (50 words) at thirty months

Is mother a smoker? (HVFV)

Can your child say 50 words? (THM) Yes No %(n) % (n) Total No 77.0 (188) 78.6 (22) 77.2 (210) Yes 23.0 (56) 21.4 (6) 22.8 (62) Total 244 28 272

Predicting outcomes at 5 years (school entry) Higher Total Difficulties scores on the SDQ were predicted by low SIMD at birth, young motherâ&#x20AC;&#x2122;s age at birth, smoking in pregnancy, being assigned to an Intensive HPI at 6-8 weeks, and being the motherâ&#x20AC;&#x2122;s first child.

SIMD (1=Most deprived, 5=Least deprived) Mothers age Smoking in pregnancy (0=No, 1=Yes) Smoking at FV (0=No, 1=Yes) Infant feeding (1= Breast, 2=mixed, 3=Bottle) HPI (1=Core, 2=Add, 3=Intensive) Gestation Birthweight Parity R squared (unadjusted)

*p<0.05 **p<0.01 NS= Not significant

-0.71** -0.10** 0.10** NS NS 0.07** NS NS 0.72** 0.46

NS NS NS 0.07** 0.09** NS NS NS NS 0.02

NS -0.08** 0.12** NS NS NS NS NS NS 0.02

-0.06* -0.07** 0.09** NS NS 0.72** -0.07** NS NS 0.04

-0.07** -0.08** NS NS NS NS NS NS 0.10** 0.02

ProSocial behaviour

Peer Problems

Hyperactivity / inattention

Conduct problems

Emotional difficulties

Total Difficulties

Table 7: Regression analyses on the SDQ at school entry (SDQ) dataset

0.08** NS -0.06** NS NS -0.08** NS NS NS 0.02

In summary, Phase 3 has shown that routinely gathered child development data can be successfully linked to and augmented by data gathered for research / evaluation purposes. The findings detailed here offer an indication of the level of analysis possible on a whole population level.

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5.

Discussion

We aimed to explore how existing and novel population based data on early childhood development can be used to support the improvement and evaluation of services for preschool children. Mapping the available data gathered routinely on early child development across Scotland (Phase 1) and obtaining feedback on the findings from key stakeholders (Phase 2) confirmed that we have good routine data systems, but that they do not always include the right type or level of information that will be useful to those designing, commissioning and delivering child public services. For example, at the CHSPPS 6-8 week visit a range of developmental measures is recorded, including social awareness / communication. These outcome measures are not monitored until the child reaches primary school. Delays in language development are a key predictor of longterm development 9 and merit detailed evaluation. Since the Phase 1 survey took place there are encouraging developments: a new universal 27 month health visitor contact incorporating measures of social and emotional wellbeing and language development, and the establishment of the Scottish Early Years Collaborative. The Phase 1 and 2 work focused on universally collected data, although there was some debate about what is really meant by â&#x20AC;&#x2DC;universalâ&#x20AC;&#x2122; in this context. Although systems such as CHSP-PS are universally available, these do not always achieve universal coverage. There is reasonable evidence to suggest that it is the more vulnerable children who are being missed from these datasets as they are less likely to come into contact with services 10. This must be taken into account in interpreting data and subsequent service and policy design. There was some debate amongst the steering group regarding the inclusion of those systems which are not universally applied but are universally available, such as social work data systems. This was also raised by respondents in Phase 2. It was felt that although it was clear that the remit of this project was to map universally applied data systems, it would also be useful to have a profile of all universally available systems. Phase 2 respondents considered that data systems should not only be more comprehensive but also better integrated across sectors. This would be greatly aided by the use of common electronic systems. Many of the data collected from health and education services are now recorded in an electronic format which should allow greater use among service planners and practitioners, but many of those interviewed in Phase 1 reported lack of access as a barrier to using the system in their practice. Improvement in accessible electronic systems, similar to the changes taking place in NHSGGC CHSP-PS systems (see Appendix 2), has become an increasing priority in Scotland during the life of this project. Respondents in Phase 2 expressed a desire to have a single reliable source where child development data from across the public sector (i.e., health, education, social work, etc) could be easily accessed. There are examples in the form of ScotPHO profiles or the CHIMAT project which could be usefully developed based on the findings of the ChILD project. One resource for early child development data which remains untapped for integrated patient care, service design / evaluation and population based research is that held in primary care systems. GPs have regular contact with children and good access to information systems. There is however no expectation placed upon GPs to routinely record developmental outcomes for children and they often do not have access to developmental data collected by others. Nor is there currently the capacity to mine primary care data in a practical way on a regional or national level. Optimising the utility of primary care data systems must be a priority within any attempts to develop more streamlined and integrated child development data resources. Phase 3 has shown that routinely gathered child development data can be successfully linked to and augmented by data gathered for research / evaluation purposes. We were 9

Law (2009). In The Encyclopedia of Language and Literacy Development. http://literacyencyclopedia.ca/index.php?fa=items.show&topicId=263. 10 Wood et al (2012). BMJ Open.

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able to match a large proportion of the Research cases to their corresponding Routine data through deterministic (using CHI numbers in the THM dataset) and probabilistic methods (using date of birth, name, gender and postcode in the SDQ dataset). The CHI number was central to this successful linkage and there is scope for using this across other sectors if appropriately secure systems can be developed. For example, in NHSGGC the SDQ is being used in the new 27 month contact and Education Services use it as part of the transition assessment at age 5 years. Appropriate data sharing would improve planning for individual children’s learning needs and for allow services to evaluate change over time. We found that socioeconomic deprivation at birth and mother’s smoking behaviour during pregnancy and in the first few days of a child’s life are important predictors of parent and child-based outcomes at thirty months and five years. Birthweight and mode of feeding also appear to predict later outcomes. Establishing more comprehensive and integrated data systems would allow regular analysis of these sorts of relationships on a whole population scale without the need for laborious data linkage exercises and thus could enable better understanding of Scottish children’s developmental trajectories. We have provided insight into what could be achievable with more comprehensive and integrated routine data systems for early child development. There are naturally methodological considerations which limit the findings. The Phase 1 survey and feedback obtained in Phase 2 were constrained in only receiving information from those willing and able to take part. Whilst every effort was made to attain a wide and representative group of respondents, we must acknowledge the likelihood that those who did not take part would have had valuable and unique contributions to add. Phase 3 data were not comprehensive: the two Research datasets included only 70% (SDQ) and 40% (THM) of their target populations and not all were able to be matched to Routine data. Although the number of cases in the SDQ dataset was high enough to allow reliable multivariate analyses, those in the THM dataset were low and it is difficult to reach conclusions based on the analyses of language delay data, possibly one of the most important indicators of development. 6. Conclusions The Childhood Information for Learning and Development (ChILD) project has demonstrated the value of Scottish routine systems for recording early child development data. In order to maximise on the potential of these systems to inform important decisions regarding children’s health, education and social welfare, there is a need to move to a more comprehensive and integrated model which ensures we are able to capture data on ALL children and present it to a range of end users in an accessible and meaningful way. 7. Importance to policy & practice and possible implementation There are a number of policy and practice implications arising from the ChILD project. Developmental data should be routinely collected throughout the preschool years. When this project was conceived Child Health Surveillance had been significantly curtailed in Scotland following the recommendations of Health for All Children 4 11. There have been positive steps towards reinstating routine contacts (at 27 months) with a developmental focus. It will be important to monitor and evaluate the implementation, outcomes and impact of the 27 month contact across Scotland. Developmental data should be accessible to a range of potential users, including clinicians, policy makers, planners, commissioners. This would ideally be in the form of a single website with data presented or accessible at a level (e.g., regional, age-based) relevant to service configuration and meaningful for children’s developmental stages. 11

Hall & Elliman (2003).

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Primary care data should be better utilised. Whilst health visitors are the key practitioner for children under the age of five, GPs also have unique access to valuable information on children’s development. It is encouraging to see that other child health data systems are being developed along the lines of the systems GPs have successfully used for a number of years (e.g., EMISweb in CHSP-PS) – this presents an important early step in the potential for integrating data. Datasets should be better integrated across sectors. The best routine data systems are based on individuals receiving a unique identification number at birth (or upon immigration into the country). We do this in Scotland in the form of the Community Health Index (CHI) number, but this is restricted to the health sector (unlike Scandinavian systems). Using the CHI number as a unique identifier in all public services as we move to greater use of electronic data systems will enable deterministic linkages and a much more coherent understanding of children’s needs on an individual, group and area level. Data systems / core datasets need to be developed or adapted to coincide with big policy initiatives such as GIRFEC. In order for GIRFEC to become truly embedded in making individual and service level decisions for children, it will be important to ensure we can measure the SHANARRI indicators robustly and in a universally agreed manner. This principle applies to any large-scale policy initiative – data indicators must be a consideration in development and data systems must be flexible. The new 27 month child health surveillance contact – making use of data strategically as well as in the interests of individual children. At present child health surveillance data are collected and used by individual practitioners in the interests of implementing care pathways for children and their families. These data are also useful at a strategic level. Currently only practitioners have access to full and rich information about children – local child health surveillance and ISD systems tend to only collect minimal indicators (often just a tick box for the presence or absence of a developmental issue) whereas the continuous indicators (e.g., SDQ scores) are more informative and data systems need to be capable of managing such data. Are we learning from all the right people? We found it difficult to obtain feedback from all the key stakeholders nominated (policy makers and planners) in spite of the endorsement of the senior investigators. It may be that the views expressed in this report are only partially representative and that those most difficult to ‘pin down’ may have something qualitatively different to add. Further work needs to be done to investigate how best to engage people in key roles for child development and how to make it easier for them to allocate time to projects such as this. 8. Future research As the new 27 month child health surveillance contact is rolled-out across Scotland there is a clear opportunity to conduct further research with much larger datasets. As we not yet at the stage of having fully integrated systems it would be useful to conduct another data linkage exercise following the first year of data collection. Glasgow City Education Services is continuing to gather SDQs as part of the transition documentation at school entry which presents an ideal opportunity to conduct cross-sectional and ultimately longitudinal data linkage within this population. 9. Dissemination The Phase 1 report (Appendix 2) has already been distributed to the project steering group and the respondents from Phase 1 and 2. It forms an appendix to this report which will also be distributed to the project steering group and the respondents in Phases 1 and 2. We will request that it is passed on through people’s existing networks to whoever might have an interest. It will also be placed on our project website (http://www.gla.ac.uk/researchinstitutes/healthwellbeing/research/mentalhealth/project s/psf/researchactivities/child) once approved by the SCPHRP.

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We plan to arrange a dissemination event for children’s services managers and policy makers for autumn 2013. This will include a presentation of the report findings, but will focus on discussion and generation of ideas regarding optimal implementation of the findings. We hope to attract delegates with regional and national remits as well as academic colleagues. We plan to present the research at a number of conferences in 2013. An abstract has already been accepted for the Scottish School of Primary Care conference (April 2013) and we intend to submit an abstract to the 2013 Scottish Health Informatics Programme conference, which this year has ‘Exploiting Existing Data for Health Research’ as its theme. Prof Wilson will include the findings within presentations at the following conferences: - Holyrood Early Years Conference: A Positive Start to Parenting, 12th March 2013; - North Lanarkshire multiagency conference for senior managers (public services), 21st March; - NSPCC - A safer childhood, a brighter future conference, 24th-25th April 2013; - Association for Child and Adolescent Mental Health (Scotland) meeting, May 10th; - European Child Health Conference in Dublin? - Infant mental health and wellbeing conference, Bergen, 25th - 26th September 2013; - Growing Up in Scotland and Scandinavia conference, November 2013. Finally, we plan to present the Phase 3 findings in a peer-reviewed publication within 2013. 10. Research workers Funding was granted for a 0.4 WTE Research Fellow for 24 months. Tracy Ibbotson was employed as Research Fellow from April 2010 until May 2011. Lucy Thompson took over the Research Fellow post from January 2012 to January 2013. Word count (Sections 1-10 excluding tables and footnotes): 4571

Acknowledgements We wish to thank the project steering group members as well as all the respondents for Phase 1 and Phase 2 for taking time out of their busy schedules to take part in this project. Thanks to Andy Duffy at ISD for facilitating the data linkage for Phase 3, and to Louise Marryat at the University of Glasgow for input to the Phase 3 analysis. Project Steering Group: Phil Wilson (chair & PI) Rachael Wood (co-investigator) Lucy Reynolds (co-investigator) Lucy Thompson (co-investigator) Matt Forde (co-investigator) Michele McClung (co-investigator) John Butcher (Glasgow City Education Services) Julie Mullin (NHSGGC Child Health Surveillance services) John O’Dowd (NHSGGC Child and Maternal Public Health) Please see Appendices on Part 2

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Full Report: Enhancing information systems to support children’s health and development