Examining the social determinantsof children’s developmental health:protocol for building a pan-Canadianpopulation-based monitoring systemfor early childhood development

Martin Guhn,1 Magdalena Janus,2 Jennifer Enns,3 Marni Brownell,3 Barry Forer,1

Eric Duku,2 Nazeem Muhajarine,4 Rob Raos2

To cite: Guhn M, Janus M,Enns J, et al. Examining thesocial determinantsof children’s developmentalhealth: protocol for buildinga pan-Canadian population-based monitoring systemfor early childhooddevelopment. BMJ Open2016;6:e012020.doi:10.1136/bmjopen-2016-012020

▸ Prepublication history forthis paper is available online.To view these files pleasevisit the journal online(http://dx.doi.org/10.1136/bmjopen-2016-012020).

Received 22 March 2016Accepted 24 March 2016

For numbered affiliations seeend of article.

Correspondence toDr Martin Guhn;martin.guhn@ubc.ca

ABSTRACTIntroduction: Early childhood is a key period toestablish policies and practices that optimise children’shealth and development, but Canada lacks nationallyrepresentative data on social indicators of children’swell-being. To address this gap, the Early DevelopmentInstrument (EDI), a teacher-administered questionnairecompleted for kindergarten-age children, has beenimplemented across most Canadian provinces over thepast 10 years. The purpose of this protocol is todescribe the Canadian Neighbourhoods and Early ChildDevelopment (CanNECD) Study, the aims of which areto create a pan-Canadian EDI database to monitortrends over time in children’s developmental health andto advance research examining the social determinantsof health.Methods and analysis: Canada-wide EDI recordsfrom 2004 to 2014 (representing over 700 000children) will be linked to Canada Census and IncomeTaxfiler data. Variables of socioeconomic status derivedfrom these databases will be used to predictneighbourhood-level EDI vulnerability rates byconducting a series of regression analyses and latentvariable models at provincial/territorial and nationallevels. Where data are available, we will measure theneighbourhood-level change in developmentalvulnerability rates over time and model thesocioeconomic factors associated with those trends.Ethics and dissemination: Ethics approval for thisstudy was granted by the Behavioural Research EthicsBoard at the University of British Columbia. Studyfindings will be disseminated to key partners, includingprovincial and federal ministries, schools and schooldistricts, collaborative community groups and the earlychildhood development research community. Thedatabase created as part of this longitudinalpopulation-level monitoring system will allowresearchers to associate practices, programmes andpolicies at school and community levels with trends indevelopmental health outcomes. The CanNECD Studywill guide future early childhood development actionand policies, using the database as a tool for formativeprogramme and policy evaluation.

INTRODUCTIONThe early years of childhood are a key devel-opmental period. Early experiences becomebiologically embedded, shaping physiologicalpathways that have lifelong protective or det-rimental effects on health, well-being, learn-ing and behaviour.1–3 Many young childrenin Canada are affected by physical, mental,cognitive and social–emotional challenges,which, in turn, are associated with negativeoutcomes later in life, such as poor health,school failure, delinquent behaviour andunemployment.4–8 But even though chil-dren’s developmental trajectories are stronglyinfluenced by early experiences, their out-comes are not set in stone. Investment inearly childhood has been shown to have sub-stantial benefits on health and social

Strengths and limitations of this study

▪ The Canadian Neighbourhoods and Early ChildDevelopment (CanNECD) Study will use whole-population pan-Canadian data to monitordevelopmental health trajectories in the childpopulation. By creating a national database ofEarly Development Instrument (EDI) results, wecan sample more than 700 000 kindergarten-agechildren.

▪ The study provides opportunity for a broad over-view of children’s developmental vulnerabilityacross Canada and over time while also allowingmore in-depth analyses of the socioeconomicdeterminants of health and well-being.

▪ Unique neighbourhood-level data linkages willallow us to develop measures that can be com-pared across all Canadian provinces.

▪ Gaps or changes in data collection in some pro-vinces and years (eg, the 2011 Census) maypotentially be important limitations.

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functioning in adulthood.9 10 Thus, early childhoodoffers a window of opportunity that promises the greatest‘return on investment’ in establishing policies and prac-tices that optimise children’s developmental health andhelp them succeed later in life.11

Measuring early childhood developmentCurrent data on children’s well-being that are availableat local, regional, provincial and national levels, and overtime, are essential for constructing and adapting socialpolicies related to developmental health, and translatingknowledge into action. Until recently, however, Canadahas lacked nationally representative data on social indica-tors of children’s developmental health. To address thisgap in Canada’s ability to holistically monitor child devel-opment trends over time and explore variability acrossjurisdictions, data collection initiatives have been imple-mented across most Canadian provinces and territoriesover the past 10 years using a common tool, the EarlyDevelopment Instrument (EDI).12–15 The EDI is a kin-dergarten teacher-administered questionnaire that mea-sures children’s developmental health across five coredomains: physical health and well-being; social compe-tence; emotional maturity; language and cognitive devel-opment; and communication skills and generalknowledge (table 1). EDI data, collected for each childindividually, are reported at aggregate levels to provide

an assessment of developmental vulnerability in a popu-lation. EDI developmental vulnerability rates may beinterpreted as an estimate of the proportion which,without additional support, may experience future aca-demic and societal challenges and lower levels ofwell-being. In Canada, the average rate of developmentalvulnerability in at least one area of development is 26%,with a range between 17% and 36% for provinces andterritories where these data are available.16

Developmental health outcomes are not equally dis-tributed in our society, but rather follow socioeconomicgradients. Children who are born to teen mothers, arein families receiving income assistance or are involvedwith child welfare services are up to four times morelikely to be vulnerable than children who are not in anyof these subgroups.17 Previous research has shown thatvulnerabilities in children’s developmental health differby as much as 10-fold across neighbourhoods, with vul-nerability rates ranging from less than 5% to above50%.18 Neighbourhoods, defined as discrete geograph-ical units that share social, cultural, demographic and/or socioeconomic characteristics, appear to have animportant influence on children’s development, healthand well-being. Neighbourhood effects are complex andmay include, for example, social-interactive effects (suchas social cohesion and family dynamics), geographicaleffects (such as proximity to child-family resources) and

Table 1 EDI domains and variables available from aggregated data

EDI domains EDI subdomainsPhysical health and well-being Physical readiness for the school day, physical independence, gross and

fine motor skills

Social competence Overall social competence, responsibility and respect, approaches to

learning, readiness to explore new things

Emotional maturity Prosocial and helping behaviour, anxious and fearful behaviour,

aggressive behaviour, hyperactive and inattentive behaviour

Language and cognitive development Basic literacy, interest in literacy/numeracy and memory, advanced

literacy, basic numeracy

Communication skills and general knowledge No subdomains

Demographic variables EDI domain variables

Unique neighbourhood code Average domain score in each of the five domains

Neighbourhood name Percentage vulnerable in each of the five domains

Province Percentage vulnerable in one or more domains

Implementation Percentage missing in each of the five domains

Percentage male/female

Percentage missing sex EDI subdomain variables

Average age in years Mean scores

Percentage of children with special needs Percentage meeting few or none of the developmental expectations

Percentage of children with multiple challenges Percentage meeting some of the developmental expectations

Percentage with/without/do not know/missing

Aboriginal status

Percentage meeting almost all or all of the developmental expectations

Percentage missing

Percentage of children with/without/missing status

for English or French as a second language EDI collection variables

Number of EDIs aggregated

Percentage of EDIs collected via paper/electronic system

EDI, Early Development Instrument.

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institutional effects (the distribution of power and thepolitical economy, including the quality of schools in theneighbourhood).19 The underlying mechanisms thatmay explain the associations between neighbourhoodcharacteristics and child development outcomes are,however, not well understood, and methods that allowresearchers to account for the complex associationsbetween health determinants and child developmentoutcomes are under investigation by our team20–23 andothers.24–26

Building a pan-Canadian population-based monitoringsystem for early childhood developmentAustralia and Canada are the only countries in theworld that have collected population-based data onchildren’s early development, akin to a census.27

Comparable data on the five core areas of early child-hood development using the EDI exist for 12 out of 13Canadian provinces and territories. The use of thiscommon tool across Canada enables linking of childdevelopment data and Census-derived socioeconomicneighbourhood-level characteristics, and facilitates theanalysis of developmental health patterns acrossCanadian jurisdictions. To this end, we have identifiedfive key research questions that will guide the project:1. Which (combinations of) socioeconomic variables

are most strongly associated with developmentalhealth outcomes at the neighbourhood level?

2. To what extent do the important social determinantsand steepness of the social gradients of developmen-tal health differ between jurisdictions (ie, healthregions, provinces, rural vs urban) across Canada?

3. In what ways do the important social determinantsand steepness of the social gradients of developmen-tal health differ across subpopulations?

4. What are the sociodemographic characteristics andprogrammes that are associated with ‘off-diagonal’neighbourhoods, or neighbourhoods that consistentlyshow significantly higher or lower developmentalhealth outcomes than would have been predicted bytheir socioeconomic status (SES)?

5. How do patterns of neighbourhood-level develop-mental health vary over time, and to what extent areincreasing or decreasing trends associated with spe-cific social and demographic contextual factors?Thus, the purpose of this protocol is to describe the

creation of a pan-Canadian EDI database as part of theCanadian Neighbourhoods and Early Child Development(CanNECD) Study. The database will be used tomonitor developmental health trajectories of the childpopulation, and by addressing the research questions, todemonstrate how the database will advance researchexamining the social determinants of developmentalhealth. Establishing nationwide EDI data linkages willallow us to identify social determinants of childhooddevelopmental health and the programmes and policiesthat work best for maximising healthy child develop-ment and family functioning.

METHODS AND ANALYSISData sources and variablesEarly childhood developmental health dataData on early childhood developmental health havebeen collected with the EDI at population level since2003/2004 in 12 of 13 Canadian provinces and territor-ies, forming the database constructed for the CanNECDStudy. Jurisdictions that have participated in EDI datacollection have been provided government and/or foun-dation funding to have all kindergarten teachers com-plete the 103-item EDI questionnaire for each of theirkindergarten children in the middle of the kindergartenyear.13 The Canadian EDI database contains EDI datafrom 2003/2004 to 2013/2014, spanning 12 of the 13Canadian provinces and territories. The database cur-rently comprises 798 298 children of kindergarten age(figure 1). In some provinces, the EDI data have beencollected in ‘waves’, where a subset of the provincialpopulation was sampled each year until each neighbour-hood within the jurisdiction had been sampled to com-plete full provincial coverage. EDI data are collected forindividual children and are then aggregated accordingto geographic or demographic criteria (eg, by school,neighbourhood, school district or province/territory),and reported either as mean item scores for each of theEDI’s developmental domains or subdomains, or as vul-nerability scores (ie, percentage of vulnerable children)for each aggregate unit. The EDI score vulnerability clas-sifications for each domain are based on a cut-off scoreat the 10th percentile for a nationally representative nor-mative sample of Canadian children.13 The psychomet-ric properties of the EDI have been validated extensively,including studies using multilevel covariance analysisand differential item functioning, showing that the EDIhas high predictive validity for later school achieve-ment.14 15 28 Table 1 lists the variables in the EDIincluded in the CanNECD Study EDI database accessibleto Canadian researchers via this project.

Neighbourhood-level SES dataSmall area-level data on SES and demographic informa-tion will come from the 2006 and 2011 Canadian Censusand 2005 and 2010 Income Taxfiler databases. The smal-lest geographic areas for which population and dwellingcounts are available are dissemination blocks (DBs). Thearea of a DB is equivalent to a city block bounded byintersecting streets, and these cover all the territory ofCanada. Together, the Census and Income Taxfiler data-bases contain more than 1500 neighbourhood-level demo-graphic and SES variables, including information on, forexample, income, poverty, wealth, employment, educa-tion, immigration, donations, family composition, transi-ence/residential stability, ethnicity and age distributions.

Data linkageDefining geographic neighbourhood boundariesTo systematically examine associations between earlychildhood development outcomes and demographic

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and socioeconomic neighbourhood characteristics, thisproject has devised a systematic neighbourhood bound-ary definition process to create contiguous, discreteneighbourhood units for the analysis of the Canada-wideEDI data. Accordingly, neighbourhood boundaries aredesigned to optimally portray geographic and socio-economic variability across neighbourhoods. We havedeveloped a set of criteria (box 1) and designed a seriesof steps to establish neighbourhood boundaries (box 2).Throughout this process, we consulted with representa-tives from government departments and agencies (suchas education and child health) and community organisa-tions to align neighbourhood boundaries with those thatare being used for local governance and communityplanning. We also consulted with academic groups con-ducting early childhood development research in eachprovince, where available, to ascertain that neighbour-hood boundaries meet scientific criteria for locallymeaningful neighbourhood effects research. In add-ition, a criterion pertaining to the minimum number ofEDI records of 50 per neighbourhood was based on pre-vious EDI reliability research. Where a neighbourhood

unit has more than 400–600 EDI records, it will befurther divided to maximise the opportunity to displayvariance while prioritising pre-existing neighbourhoodboundaries, where applicable. Larger rural areas will bemaintained as individual units using Canada Census sub-divisions, which exist across all provinces and are a proxyfor rural municipality boundaries, as long as they meetthe EDI record population size criteria (minimum 50records). Where EDI record density is low, we will useCensus subdivisions as the largest spatial unit. At theend of the neighbourhood definition process, eachneighbourhood will be described by assigning it a name(eg, MB0020 for Manitoba neighbourhood number 20)and a label (eg, Dauphin).

Integrating EDI and neighbourhood dataIn order to obtain a semicustom profile of Census vari-ables from Statistics Canada, we will create a correspond-ence file in Excel with three columns. The first columnwill list each of the approximately 479 000 DBs inCanada (using the official Statistics Canada DB name),while the second and third columns will list the corre-sponding neighbourhood name and label we assign,respectively. Separate correspondence files will becreated for 2006 and 2011, as there were minor changesin the DBs between those Census years. These two fileswill then be sent to Statistics Canada, where analysts willattach the values of over 2000 requested semicustomCensus variables to each DB, and then calculate apopulation-weighted aggregate for each variable for eachof the pan-Canadian neighbourhood units. The result-ing, custom-built data file with over 2000 Census variableswill be sent back to the project team in Beyond 20/20 fileformat, for each of the 2058 neighbourhoods, stratifiedby Census year (2006 or 2011). Owing to the fact the

Figure 1 EDI records collected across Canada by year.

Box 1 Criteria for defining neighbourhood boundaries

▸ Must have a minimum of 50 EDI records per unit (and main-tain 50 records over time, if applicable)

▸ Results should be verified with local contacts, where possible▸ Should have no more than 400–600 EDI records per unit▸ Must nest within Statistics Canada Census Divisions▸ Should use local ‘neighbourhood’ or other applicable adminis-

trative boundaries, where possible▸ Spatial units must be made up of the smallest viable geo-

graphic unit (DBs) that allows for linking with other sourcesof data (ie, Census data, Income Taxfiler data)

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Canadian government replaced the 2011 long-formCensus with the National Household Survey (NHS), wewill request two files for 2011: one with the variables fromthe short-form Census, and one with the NHS variables.A similar process for creating a correspondence file

will be employed for the Income Taxfiler variables.However, the Income Statistics Division of StatisticsCanada requires postal codes (rather than DBs) for thecorrespondence files. As such, we will send StatisticsCanada Excel files containing all postal codes inCanada, the corresponding neighbourhood name andthe associated label for 2005 and 2010. Statistics Canadaanalysts will calculate the values of the requested

Income Taxfiler variables for each postal code, and thenaggregate the population-weighted results up to the levelof the pan-Canadian neighbourhoods.At the Offord Centre for Child Studies at McMaster

University, we will aggregate individual EDI data to theneighbourhood level using the geocoded pan-Canadianboundaries. The EDI variables can then be merged withthe Census and Income Taxfiler variables at systematic-ally defined neighbourhood levels, using the neighbour-hood variable name as the linking variable.

Data access and securityThe national repository for all individual-level EDI datais securely housed at McMaster University in Hamilton,Ontario. For this project, the neighbourhood-levelaggregated EDI, Census and Income Taxfiler data willbe hosted in the secure database system operating in thePublic Economics Data Analysis Laboratory (PEDAL) atMcMaster University. Authorised and approved usersgain access to PEDAL through a remote connectiondevice, and data are protected through a double firewallsecurity system, dual encryption and a user interfacethat prevents retrieval of data. Analyses are completedvia remote access, and only outputs that meet datasharing requirements that protect confidentiality andallow for the deduction of meaningful statistics areexported for further research purposes. PEDAL will alsoenhance our ability to link the database with publiclyavailable geographically aggregated provincial or federaldata, such as health and education records.

Analysis planThe planned analyses are designed to address each ofthe project’s five research questions.1. Developing an SES index: There are thousands of socio-

economic and demographic variables available at theneighbourhood level. Our goal is to identify a smallsubset of between 5 and 20 variables that representsan optimal compromise between maximising thevariance explained in developmental health out-comes and restricting the included components toa number that can be reasonably interpreted.The initial choice of potential variables will beinformed by several criteria, including the currentCanadian health literature relating to SES indices;29–33

an intention to cover a wide variety of theoreticallyimportant social determinants; and the aforemen-tioned desire to maximise the variance explainedacross all of our developmental health domains. Ourmethod for selecting variables as components of anSES index will be based on the existing methodo-logical approach used at the Human Early LearningPartnership at the University of British Columbia.34

The resulting SES index will be composed of the mostimportant variables to account for variation in EDIscores across all of the neighbourhoods in Canada.

2. EDI–SES relationships across different geographical areas:This research question addresses the importance of

Box 2 Neighbourhood definition process

1. Within each province, assign geographic coordinates for indi-vidual EDI records with two collection time points using thePostal Code Conversion File.▸ Use postal code if the positional accuracy is high

enough, or▸ Use school location if the positional accuracy is too low

2. Identify geographic units with a minimum of 50 records forboth points in time.

3. Erase the identified units with ≥50 records for both timepoints from next largest geography. This will ensure thatrecords within a ‘neighbourhood’ identified in step 2 are notcounted when repeating the process for the next largestgeography.

4. Repeat the process for Census Subdivisions, CensusConsolidated Subdivisions and Census Divisions. For thelargest geography (Census Divisions), skip step 3, as there isnot a larger geography to erase the results from.

5. Merge all of the resulting geographic units from steps 2, 3and 4 together to create one neighbourhood network thatcovers the entire province.

6. Identify units that still have <50 children and dissolve themwith appropriate neighbouring units:▸ If a unit with <50 records is nested completely within a

larger geographic unit, dissolve it with the surroundingunit

▸ When dissolving neighbouring features together, useother administrative boundaries and geographic featuresto determine which to group together (eg, road networks,water bodies, Census Division boundaries,Regional Health Authority boundaries, average incomeby DB and other socioeconomic factors)

Creating appropriate neighbourhood units in dense, heavily popu-lated urban areas:1. If any of the resulting units from the steps above have an EDI

record count >400–600 children (ie, densely populated urbanareas), they can be divided into smaller neighbourhood units.▸ Use other administrative boundaries (eg, city planning

neighbourhoods or school districts) or major dividinglandmarks (eg, east/west of a highway or water body), or

▸ Group urban Census geographies by similar qualities(ie, average income)

2. Match the smallest viable geographic unit with newly createdneighbourhood boundaries, and correct for any edge effects(ie, where DB boundaries and other administrative boundariesdo not line up).

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geographic context in children’s developmental out-comes through investigation of how well the SESindex performs across different geographic units(neighbourhoods). There will almost certainly bemarked differences in the amount of varianceexplained by the index, and in the steepness of thesocial gradients for each index component, whencomparing one province/territory with another,urban neighbourhoods with rural ones and perhapsprovincial/territorial capitals with other cities. We willexplore and describe these varying results, test theinfluence of non-index variables on observed gradi-ents and explanatory power, and try to develop theor-etical explanations for the observed patterns. Therelative predictive strength across provinces of child-related policy variables (from Taxfiler), for example,may provide important insights into social determi-nants at the societal level.

3. EDI–SES relationships across subpopulations of children:Just as we expect to find interesting geographic differ-ences in the performance of the SES index, it is aslikely that the influence of SES on children’s develop-mental health will be a function of the child context.The EDI does capture these context variables tosome extent; they include child gender, child specialneeds status and English (or French) as a second lan-guage (ESL/FSL) status. Therefore, it is possible, formost neighbourhoods, to calculate neighbourhood-level vulnerability rates for these subpopulations.These vulnerability rates could then be used toexplore how EDI–SES relationships vary across thesesubpopulations, both in the strength of the relation-ship and the steepness of the social gradients.Similarly to Research Question 2, exploring thesechild-contextual differences will help us develop the-oretical explanations for their role in overall develop-mental health.

4. EDI–SES relationships in off-diagonal neighbourhoods:Our SES index will be composed of a small numberof theoretically relevant SES variables that collectivelyoptimise variation accounted for in developmentalvulnerability. This means that, for most neighbour-hoods, there will be a close correspondence betweenthe actual vulnerability rate and the vulnerability ratepredicted by the SES index. These are the‘on-diagonal’ neighbourhoods. However, there will besome neighbourhoods (which we label ‘off-diagonal’)where the actual vulnerability rate is substantiallyhigher or lower than predicted from the index.There is no standard definition of ‘substantiallyhigher or lower’, but in one jurisdiction, an off-diagonal neighbourhood has been defined as onethat is among the 20% of neighbourhoods with thegreatest discrepancy (half higher, half lower) betweenactual and predicted vulnerability, consistently overtwo time points.35 These off-diagonal neighbour-hoods are particularly interesting because they repre-sent places where the most important determinants

of vulnerability are not the typical ones (ie, thosethat were included in the index). By looking moreclosely at these neighbourhoods, we can exploreother potentially important determinants of chil-dren’s health (particularly if these determinants areamong those available from Census or IncomeTaxfiler data). One potential strategy would be tocompare off-diagonal neighbourhoods with theiron-diagonal counterparts that have been matchedgeographically and on components of the SES index.Comparisons would be made on other available SESvariables. Future research could focus on identifyingpotential important non-SES variables (eg, socialcapital) that could be captured in sufficient numbersto provide reliable neighbourhood-level estimates.

5. Change over time in EDI–SES relationships: Five pro-vinces and two territories have collected two or morewaves of EDI data (figure 1), allowing forchange-over-time analyses at the aggregate (eg, neigh-bourhood, school district or province) level. Ourresearch team has previously developed a microsimu-lation technique that models the reliability ofneighbourhood-level EDI vulnerability rates, takinginto account the number of children in a givenneighbourhood.28 Using a generalisability theoryapproach to measurement reliability, we are able toestimate the size of a ‘critical difference’ for EDI vul-nerability rates, for any aggregate unit size. In otherwords, we can calculate the threshold for a statisticallysignificant neighbourhood-level increase or decreasein EDI vulnerability rates over two time points. Wewill use this methodology to describe broad patternsof developmental vulnerability in the four jurisdic-tions, and explore regional differences in these pat-terns. We will also conduct analyses to examine theSES factors that are associated with particular EDIvulnerability trends over time. We will use latent classmodelling to create a classification of neighbour-hoods according to their patterns of change overtime (eg, consistently low or high average develop-mental health outcomes; increasing or decreasingtrends of EDI vulnerability rates over time). Then, wewill statistically model which socioeconomic anddemographic predictors are associated with each ofthe different patterns. Our analyses will thus explorethe extent to which the trends over time reflectchanges in the respective jurisdictions’ child popula-tion and/or social context.

Ethics and disseminationEthics approval for the CanNECD Study methodologywas granted by the Behavioural Research Ethics Boardat the University of British Columbia. Participant confi-dentiality is protected as the EDI, Census and IncomeTaxfiler data for this project are aggregated to theneighbourhood level, and hosted in a secure databasesystem.

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The study investigators have significant experience inknowledge translation and will ensure that the resultsare accessible and usable to researchers and policy-makers. Substantial energy has been devoted to buildingthe relationships required to ensure that the research isstrongly connected with action. Currently, the results ofeach wave of EDI implementation are disseminated toeach participating community and school districtthrough a detailed reporting system that can be custo-mised to specific knowledge users. The EDI outcomeshave been incorporated by governments and agencies atvarious levels as a broad index of child well-being atschool entry, or to investigate the association ofcommunity-level variables with child development forprogramme improvement.36 These activities have had aprofound influence on the understanding of develop-mental health across broad audiences.We will use a number of diverse approaches to meet

our knowledge translation goals, including maintainingan online platform for the dissemination of knowledgegenerated by the project; creating new and engagingways of visualising complex information to potentialusers, such as neighbourhood-level geographicalmapping; reporting results in lay language to key part-ners, including provincial and federal ministries, schooldistricts, schools and collaborative community groups;publishing peer-reviewed research articles; presentingfindings at academic conferences; and maintaining astrong emphasis on continued community engagementand relationship-building to understand the need ofknowledge users, and to ensure that new knowledge isbeing appropriately translated and disseminated. Thenew database will provide important national context tothe existing local, regional and provincial reporting.

DISCUSSIONThe CanNECD Study brings together, for the first time,all of the Canadian EDI data collected at the populationlevel since 2004, creates neighbourhood-level aggregatescores according to a systematic set of neighbourhooddefinition criteria and links the aggregate EDI vulner-ability rates to a comprehensive suite of Census andTaxfiler data. In the long term, the longitudinalpopulation-level monitoring network will allow us toassociate practices, programmes and policies at schooland community levels with trends in developmentalhealth outcomes. Moreover, each of the institutionsrepresented by the members of the research team hasestablished consulting or collaborative links with relevantprovincial governments. Thus, our project is well posi-tioned to guide future early childhood developmentaction and policies at a scale not possible thus far, byusing the database as a tool for formative programmeand policy evaluation.Promoting positive early childhood development is a

priority for Canada, but comprehensive data werelacking until the implementation of the EDI in the

2000s. Health-related research, practices and policies aremost beneficial when they are informed by data that arerepresentative at the population level, rather thanrelying on information limited to certain places or sub-populations. Understanding of the determinants ofdisease onset and progression has benefited greatlyfrom studies based on cross-jurisdictional, population-based databases; the same benefit would accrue fromconducting pan-Canadian research on the social deter-minants of children’s developmental health in the earlyyears. Specifically, by establishing a representative data-base on children’s developmental health trajectoriesthat is analysable at local, provincial and pan-Canadianlevels, we can identify areas of developmental strengthand needs across place, subpopulation and time. Theanswers to our research questions will provide highlycontextualised information on as complete a populationof Canadian children as pragmatically possible, and thesocial and economic factors that are associated with posi-tive or negative developmental health outcomes. Theseanswers can then be used at a variety of levels, fromhelping local early-year stakeholders in their decision-making and practices to guiding and evaluating effectivesocial policies at the provincial/territorial and nationallevels.The use of pan-Canadian data in the CanNECD Study

is one of its greatest strengths. Using whole-populationdata maximises statistical power while minimising theselection bias associated with studies based on samples,making it possible to detect important relationshipsbetween exposures and outcomes in kindergarten-agechildren at local levels and for subpopulations. Thestudy will give a broad view of children’s developmentalvulnerability across Canada while also allowing morein-depth analyses of the socioeconomic determinants ofhealth and well-being. A second unique strength of thestudy is the national comparability of the outcome andexposure measures, as well as the geographic referenceunit. Thus far, there have been no indicators of earlydevelopment available for Canadian children beyondsample-based studies, such as the National LongitudinalSurvey of Children and Youth (NLSCY), which has nowbeen discontinued. While the NLSCY measures had thepotential to explore child development in more depththan the EDI, for most jurisdictions, they were only avail-able at a provincial level, without the possibility of disag-gregation to a neighbourhood level due to small samplesizes. A third strength of our study is the purposeful useof Census and Taxfiler variables relevant to child devel-opment and customised for neighbourhoods based onthe EDI data. This ‘fit-for-purpose’ approach has rarelybeen used (although see,)33 due to a lack of child-leveldata that could be used in this way. It remains to be seenwhether the sociodemographic variables that will consti-tute the SES index we aim to develop are markedly dif-ferent from those in other existing indices constructedlargely to explain variation in adult-level outcomes.However, the potential for deeper understanding of the

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social determinants of children’s development using thisapproach is unprecedented.Although the unique linkages and use of comparable

population data in our database have a number ofadvantages, there are also limitations. First, the EDI datain the database have not been collected at the sametime point in every jurisdiction, and in many of them,the provincial ‘waves’ have been collected over2–3 years. Moreover, in several provinces, data have onlybeen collected once in the period covered by our study,thus potentially exposing these particular regions to achance of a temporal bias. Second, as the Censushappens on a regular basis every 5 years, the sociodemo-graphic data were not collected contemporaneously withthe EDI for many of the waves. In addition, as explainedabove, there was a major methodological change in theway the Census data were collected in Canada between2006 and 2011;i therefore, any analyses using the 2011cycle data will have to be interpreted with caution.Finally, while linkages to socioeconomic and demo-graphic Census data (and potentially, to policy informa-tion, and education and health records) provideconsiderable depth and breadth to studying social deter-minants of early child development outcomes, theneighbourhood-level linkages may not be able touncover sociological and psychological processes andmechanisms that could underlie associations betweenchild development outcomes and social determinants.Parallel to our study, individual-level linkages need to befurther explored to shed light on child developmentoutcomes in the socioeconomic and demographiccontext.In conclusion, we anticipate that population-based

data linkages, such as the one described in this study,comprehensive longitudinal child development studiesand policy analyses complement and inform each otherto jointly address remaining questions in the field, andto inform best practices and policy decision-making inthe area of early child development.

Author affiliations1Human Early Learning Partnership, School of Population and Public Health,University of British Columbia, Vancouver, Canada2Offord Centre for Child Studies, Department of Psychiatry and BehaviouralNeurosciences, McMaster University, Hamilton, Ontario, Canada3Manitoba Centre for Health Policy, Department of Community HealthSciences, University of Manitoba, Winnipeg, Manitoba, Canada4Department of Community Health and Epidemiology and SaskatchewanPopulation Health and Evaluation Research Unit, University of Saskatchewan,Saskatoon, Saskatchewan, Canada

Contributors The study was conceived by Clyde Hertzman, MG and MJ, whowere the coprincipal investigators on the original funded grant proposal; BFconceived the original analysis plan and wrote the analysis section of thepaper. All authors are participating in the interpretation of findings and thedrafting of manuscripts.

Funding This work was supported by an operating grant from the CanadianInstitutes of Health Research (FRN 125965).

Competing interests None declared.

Ethics approval Behavioural Research Ethics Board at the University of BritishColumbia.

Provenance and peer review Not commissioned; peer reviewed for ethicaland funding approval prior to submission.

Open Access This is an Open Access article distributed in accordance withthe Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license,which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, providedthe original work is properly cited and the use is non-commercial. See:http://creativecommons.org/licenses/by-nc/4.0/

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