The Beneficial Effects of Preschool Attendance

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Assignment 1 – Epidemiology for Public Health – SP10, 2012 1

EPIDEMIOLOGY FOR PUBLIC HEALTH TM5515

ASSIGNMENT ONE

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Assignment 1 – Epidemiology for Public Health – SP10, 2012 2

Read and discuss the article attached

D’Onise K, Lynch JW, McDermott RA, Esterman A. The beneficial effects of

preschool attendance on adult cardiovascular disease risk. ANZJPH 2011; 35(3):

278-283.

1. What was the purpose of the study? (1 mark)

2. Classify the study design (study type, directionality, timing). Was the study

design appropriate to address the purpose of the study? Justify your answer. (3

marks)

3. Describe the sampling process(s) and the sample as detailed as possible. What

was the sample size? (2 marks)

4. Who was the target population? (1 mark)

5. In the Methods section and also in Figure 1, the authors describe the

participants who remained in the study and the ones who were excluded. Whatkind of bias was potentially introduced into the study because of the

recruitment and participation process? (0.5 marks)

6. In Discussion, page 282, 5th paragraph (“These results may not be

generalisable……”) the authors discussed the issues surrounding their

selection process. What is the resulting direction of the bias suggested by the

authors? Do you agree with their discussion of the issue and their conclusion?

Why or why not? Justify your answer. (2.5 marks)

7. Describe how the study factor was assessed? What kind of bias was potentially

introduced into the study because of the way the study factor was assessed?

Speculate on the direction of the bias. Justify your answer. (3 marks)

8. What were the outcome measures? Describe how the outcome measures were

assessed. When was outcome assessed in relation to the study factor? What

kind of bias was potentially introduced into the study because of the way the

outcome was assessed? (3 marks)

9. In the context of the study, would you consider “age”, “parental alcohol

consumption”, and “level of education” to be potential confounders? Discuss

each of the three variables separately. (3 marks)

10. In Results, Table 3, page 281, the authors presented their main findings. Please

explain the Model 3 results for “Physical activity” and “Fruit”. What do theseresults tell you? Please also interpret the 95% CI. (3 marks)

11. Are the authors able to contribute to the question of causality between

attending a KU kindergarten and cardiovascular disease risk? If so, in what

way? Explain your answer. (3 marks)



278 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 2011 vol. 35 no. 3 © 2011 The Authors. ANZJPH © 2011 Public Health Association o Australia

Cardiovascular disease (CVD),which is responsible or 17% o

the total burden o disease in

Australia, remains a challenge or population

health despite the many recent advances in

prevention, identifcation and management o

established disease.1 Social actors continue

to play an important role in the aetiology o

CVD, with social disadvantage rom early

inancy (and possibly rom in utero or

earlier) through to adulthood potentially

having an important inuence on knowncardiovascular risk actors and CVD.2-4 While

a proportion o the excess CVD risk or low

socioeconomic position (SEP) groups is

unexplained, there is evidence that the well

known liestyle related risk actors tend to

co-occur more requently in people who are

socially disadvantaged.5-7

There is some evidence that the social,

physical and biologica l environment in

childhood inluences the development

o CVD, which points to the potential

or interventions in childhood having

benefcial eects on CVD in adulthood.8

Early childhood educational interventions

(ECDIs) which involve a combination o

educational, health and social services or

children in addition to parenting programs

or home visiting, are thought to enhance

child development.9,10 The success o these

interventions is thought to be due in part to

being set in early childhood, which is thought

to be a sensitive period in development.The benefts attained in childhood provide

a turning point that sets the children on

positive social trajectories either through a

pathway or chains o risk model.11,12Through

this improved socioeconomic trajectory,

ECDIs may in turn reduce the prevalence o

various liestyle related risk actors or CVD

and possibly also reduce the probability

o clustering o these risk actors. Some

evidence or this potential o preschool

programs comes rom a small number o studies conducted in the United States,13-15

with indications o benefts into adulthood

(up to age 40) on smoking and physical

activity, but no benefts on ruit and vegetable

consumption or binge alcohol use. These

studies are however limited by being mostly

resource intensive interventions in highly

socially disadvantaged US populations, with

relatively crude measures o health outcomes

and so may be o limited generalisability.

South Australian

Kindergarten Union

preschoolsThe Kindergarten Union (KU) managed

preschools in SA rom 1906 to 1985, and

until the 1970s, the KU managed the vast

majority o preschool services in SA.17

The preschools were initially established to

enhance the social, emotional, physical and

The benefcial eects o preschool attendance

on adult cardiovascular disease risk

Abstract

Objective : To assess the eect o

South Australian Kindergarten Union

participation on adult cardiovascular

behavioural risk actors.

Methods : Using a retrospective cohort

design, this study examined the eect

o attendance at a Kindergarten Union

preschool rom 1940 to 1972 on

behavioural risk actors or cardiovascular

disease in adults 34-67 years.Dichotomous outcomes were analysed

using a generalised linear model (Poisson

distribution) with robust variance estimates.

Outcomes with more than two categories

were analysed with a multinomial logistic

model.

Results : There was a benefcial eect o

preschool on high physical activity relative

to sedentary and on ever smoking, but

a negative eect on ruit consumption.

Preschool attendance was not associated

with alcohol risk or vegetable consumptionunder traditional criteria, however the point

estimate or vegetable consumption was in

the benefcial direction. The point estimates

rom the multinomial model suggested a

step-wise decreasing risk or preschool

attendees to have less risk o experiencing

multiple behavioural risk actors (e.g. risk o

fve risk actors or preschool participants

compared with non-participants).

Conclusions and implications:

Attendance at a Kindergarten Union

preschool was associated with a reducedrisk o two and an indication o beneft in a

third behavioural risk actor in adulthood.

This study provides some evidence or the

potential health beneft o interventions

outside o the health sector to prevent

cardiovascular diseases, which are

strongly associated with lielong social

disadvantage.

Key words : early intervention (education),

child development, cardiovascular disease

Aust NZ J Public Health. 2011; 278-83

doi: 10.1111/j.1753-6405.2010.00661.xSubmitted: November 2009 Revision requested: May 2010 Accepted: October 2010

Correspondence to: Dr Katina D’Onise, Sansom Institute or Health Research, University oSouth Australia, City East Campus, North Terrace, Adelaide, South Australia 5000;e-mail: katina.d’[email protected]

Katina D’Onise

Sansom Institute or Health Research, University o South Australia

John W. Lynch

Sansom Institute or Health Research, University o South Australia and

Department o Social Medicine, University o Bristol, United Kingdom

Robyn A. McDermott, Adrian Esterman

Sansom Institute or Health Research, University o South Australia

Social Determinants o Health Article



2011 vol. 35 no. 3 AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH 279 © 2011 The Authors. ANZJPH © 2011 Public Health Association o Australia

cognitive development o children who were living in poverty,

with an emphasis on educational services.18 In the initial years o

the KU, preschools were established in suburbs with high levels

o poverty and were ree or socially disadvantaged children,

expanding to middle class suburbs by the 1940s. Attendance was

primarily through geographic proximity to an existing centre.

The preschool program enrolled children between two and

fve years old, or hal or ull days, or up to fve days a week.

The program involved direct educational services or children,

parenting services, home visiting and health screening and reerral

or specialist services when required.19

The KU preschools included a number o eatures o high

quality. All preschool directors and teachers were required to have

a recognised early childhood development qualifcation. Standards

developed by the Australian Pre-School Association were adopted

across all preschools, which included a child-sta ratio limit20 and

standards or building and playground design.19 As the number

o preschools increased, a ‘Pre-school Adviser’ was appointed in 1945 to assist the preschools to adhere to the standards and

curriculum set by the KU.19

This study aimed to assess the eect o Kindergarten Union

(KU) attendance in SA on single cardiovascular behavioural risk

actors and their clustering. Investigating the potential or these

interventions in Australia is timely given the renewed ocus o

ederal and state governments on early childhood education,

both in terms o quality and increased access or disadvantaged

groups.21 Ethics approval or the study was granted by the

University o South Australia Human Research Ethics Committee.

MethodsData

The North West Adelaide Health Study (NWAHS) is a

longitudinal representative cohort study o adults over 17 years

old, randomly selected rom the northern and western metropolitan

regions o Adelaide using the electronic telephone directory.22

Within each household, the person with the last birthday aged over

17 years was selected or interview. Exclusion criteria included not

having the capacity to participate (intellectual, illness), living in a

residential institution and being unable to communicate in English.

The sample was recruited rom November 1999 to July 2003.

The 4060 participants represented 49.4% o those who were

eligible to participate. Data were collected by questionnaire,

Computer Aided Telephone Interview (CATI) and clinic

attendance in stage 1 (years 1999-2003) and stage 2 (2004), and

a telephone ollow up CATI was conducted in 2007 when details

o preschool attendance were collected.

Study population

Figure 1 outlines the process or selecting the study population.

Participants in the 2007 telephone ollow up survey in the NWAHS

(n=2996, 74% o baseline population) who lived in SA as children

and were born during the years 1937-1969 were included in thestudy. Application o the inclusion criteria led to a reduction in

sample size rom 2,996 to 1,490, and “don’t know” responses on

preschool attendance reduced the sample size urther to 1,395.

Retired people were excluded rom the income variable (163,

explained below) and additional missing data (169) led to a fnal

analytic sample o 1,063.

Kindergarten Union attendance

Participants were asked in the 2007 telephone ollow up study to

recall i they had attended preschool (Did you attend kindergarten

or preschool?), and the age at which they attended (How old were

you when you frst started kindergarten or preschool?). People who

reported attending preschool at age fve (n=147) and six (n=12)

were re-categorised as not having attended preschool as school

entry generally occurred by fve years in SA and also to reect the

evidence that suggests that intervention beore age fve is important

or long term eects. People who indicated that they did not know

i they went to preschool were considered to be missing (n=95 ater

application o the inclusion criteria). This group was less likely

to have gained a Bachelor’s degree (prevalence ratio (PR) 0.44,

95% CI 0.18-1.06), and more likely to be in the lowest income

category (PR 1.7, 95% CI 0.83-3.54) than the non-preschool group.

Behavioural risk factors

Physical activity questions were taken rom the National Health

Survey (NHS) collected in stage 2 (2004), which asked about the

intensity, requency and length o leisure time physical activity in

the previous two weeks. Categories or sedentary, low, moderate

and high exercise level were constructed as described in the

NHS.23,24 Missing data on physical activity were replaced with data

collected at stage 1 (9.2%). Alcohol intake was measured using sel

report o usual requency o intake and usual number o standard

drinks rom stage 2 data collection with missing data replaced

with data collected in stage 1 (0.1%). The dierent risk categorieswere based on the NHMRC recommendations in 200125 which

was current at the time o the NWAHS data collection (Table 1).

Figure 1: Selection o study population.

Study population – 4,050

Participated in 2007

Telephone Follow-up survey - 2,996

Met inclusion criteria - 1,490

No missing data on preschool attendance - 1,395

No missing data on covariates/outcome - 1,063

Social Determinants o Health Benefcial eects o preschool attendance on adult health




Low risk drinkers were the reerence category given non-drinkers

represent a potentially heterogeneous group o ex-drinkers and

never-drinkers. Ever smoking was measured using a sel report o

ever having smoked and parental smoking was taken rom report

o smoking in a parent or guardian when the respondent was 4

years old. Fruit and vegetable intake collected at the 2007 interview

were analysed to indicate the general quality o the diet, given a

diet high in vegetables and ruit is thought to reduce the risk o

CVD.26 Questions were taken rom the National Nutrition Survey,

on how many serves o either ruit or vegetable are usually eaten

a day. These two questions were ound to be a reliable indicator

o the results obtained rom the 24-hour recall.23

To determine whether preschool attendance could reduce the

total number o co-occurring risk actors, an index was created

that summed each o the behavioural risk actors. A score o

one was assigned or an alcohol intake o moderate or high risk,

physical activity o sedentary or low, being an ever smoker, and

less than two serves a day o ruit or less than fve serves a day o

vegetables, such that a score o fve indicated high risk and zero

low behavioural risk. Due to small numbers, a risk actor index o

zero or one was collapsed into one low risk category.

Indicators of childhood

socioeconomic position (SEP)

Childhood SEP was measured using report o ather’s main

lietime occupation27-29 (substituted or mother’s main lietime

occupation i brought up in a maternal single parent household),

coded as manual or non-manual,30 report o periods o at least

six months o parental unemployment, or being brought up in a

sole parent household. An index was created that summed these

variables such that zero indicated no marker o disadvantage and

three indicated a maximal marker o disadvantage (category two

or three were collapsed to one category due to small numbers).

Adult height which reects aspects o the early nutritional and

socioeconomic environment31 and has the advantage o being

precisely measurable, was used as an additional indicator o

childhood disadvantage.

Indicators of adult SEP

Education was categorised into our mutually exclusive

categories (leaving school up to 15 years, leaving school ater 15 years, attainment o a trade or diploma and attainment o a

Bachelor’s Degree or higher) using sel reported educational

attainment rom stage 2. Three gross household income categories

were constructed rom the six collected in stage 2 (0-$40,000,

$40,001-80,000, $80,001-over $100,000), excluding people who

were retired.

Statistical analysis

Dichotomous outcome variables were analysed using ageneralised linear model (Poisson distribution) with robust

variance estimates, with resulting prevalence ratios (PR,

prevalence o disease in exposed versus prevalence in unexposed)

or the eect estimate. This model was chosen over a log binomial

generalised linear model as the latter ailed to iterate to a solution

in many instances. The commonly used logistic regression

model or dichotomous outcomes was not considered as the

outcomes were relatively common (i.e., > 10-20%) and a measure

approximating the relative risk was preerred to an odds ratio to

enhance the interpretability o the results.32 Similarly, ordinal

variables (physical activity, alcohol risk and the behavioural risk actor index) were analysed using multinomial logistic regression

Table 1: Alcohol risk levels and classifcation*

(standard drinks).

Risk category

Men Women

Average

per day

Amount

per week

Average

per day

Amount

per week

Non-drinkers 0 0 0 0

Low risk drinker 4 28 2 14

Moderate riskdrinker

5-6 29-42 3-4 15-28

High risk drinker ≥7 ≥43 ≥5 ≥29

*NHMRC 2001 guidelines, numbers indicate the upper limit o the category

Table 2: Descriptive analysis o participant

demographic characteristics in the North West

Adelaide Health study, 1999-2007.

Preschoolattendees

No preschoolattendance

n=476 % or s.d. n=587 % or s.d.

Age (years, s.d.) 45.3 7.6 51.1 7.7

Female (%) 262 55.0 318 54.2

Year o birth

1937-1949 (%) 69 14.5 202 34.4

1950-1959 (%) 158 33.2 245 41.7

1960-1969 (%) 249 52.3 140 23.9

Child SEP*

0 (%) 192 40.3 190 32.4

1 (%) 262 55.0 343 58.4

2/3 (%) 22 4.6 54 9.2

Adult height (cm, s.d.) 169.8 9.0 168.8 9.5

Education

Let school ≤15 years (%) 36 7.6 91 15.5

Let school >15 years (%) 157 33.0 199 33.9

Trade/certifcate/diploma(%)

185 38.9 222 37.8

Bachelor’s degree (%) 98 20.6 75 12.8

Income

0-$20 000 (%) 49 10.3 78 13.3

$20,001-40,000 (%) 95 20.0 128 21.8

$40,001-60,000 (%) 113 23.7 174 29.6

$60,001-80,000 (%) 99 20.8 110 18.7

$80,001-100,000 (%) 57 12.0 59 10.1

>$100,000 (%) 63 13.2 38 6.5s.d.- standard deviation

*Child SEP: Number o markers o disadvantage rom manual paternal occupation, 6+ months o parental unemployment, sole parent

household

D’Onise et al. Article




so that the outcome (a relative risk ratio, RRR) would approximate

a relative risk estimate.

The association between preschool attendance and each o the

outcomes was assessed in sequential regression models. Model 1

adjusted or age at stage 2 clinic ollow up and gender, and model

2 urther adjusted or child SEP and adult height, actors which

may have inuenced the chance o preschool participation. For

ever smoking analyses, parental smoking was included in model

2. Model 3 urther adjusted or educational attainment and adult

income which were hypothesised to mediate the eect o preschool

on adult health behaviours.

ResultsThere were 476 people who reported attending preschool and

587 who did not attend preschool. The average age o preschool

attendees was younger (45.3 years) than non-attendees (51.1 years,

Table 2). There was a similar distribution o males and emales

across the comparison groups, however the preschool group came

rom a slightly more advantaged childhood SEP (40.3% compared

with 32.4%). A higher proportion o preschool attendees had a

Bachelor’s degree and were in the higher income groups than the

non-attendees.

Multivariable analyses

Results or the multivariable analysis are presented in Table

3. There was an eect o preschool on physical activity, with a

greater probability o being in any physical activity group relative

to sedentary. The eect was greatest or being in the high physical

activity group, which was the only category in which the 95%

confdence interval (CI) did not include the null (PR 1.99, CI

1.19-3.35). Preschool attendance appeared to be associated with

a reduced risk o ever smoking (PR 0.86, CI 0.77-0.97) in the

ully adjusted model, but a negative eect on ruit consumption

(PR 0.85, CI 0.73-0.99). The eect o preschool attendance on

vegetable consumption was in the positive direction but the 95%

Table 3: Multivariable analysis o the eect o Kindergarten Union preschool attendance on behavioural risk actors

in the North West Adelaide Health study, 1999-2007.

n Model 1 Model 2 Model 3

Eect estimate 95% CI Eect estimate 95% CI Eect estimate 95% CI

Physical Activity n=1052 (RRR)

Sedentary 298 1.0 1.0 1.0

Low physical activity 388 1.29 0.93-1.80 1.26 0.91-1.76 1.24 0.89-1.74

Moderate physical activity 273 1.37 0.96-1.96 1.32 0.92-1.90 1.26 0.87-1.81

High physical activity 93 2.22 1.34-3.67 2.07 1.24-3.45 1.99 1.19-3.35

Fruit n=1,062 (PR)

<2 serves a day 621 1.0 1.0 1.0

≥2 serves a day 441 0.88 0.75-1.02 0.86 0.74-1.01 0.85 0.73-0.99

Vegetable n=1060 (PR)

<5 serves a day 976 1.0 1.0 1.0

≥5 serves a day 84 1.51 0.96-2.37 1.46 0.93-2.29 1.41 0.90-2.19

Smoking n=1040 (PR)

Never smoker 464 1.0 1.0 1.0

Ever smoker 576 0.85 0.76-0.95 0.86 0.76-0.97 0.86 0.77-0.97

Alcohol risk o harm n=1027 (RRR)

Low risk 679 1.0 1.0 1.0Moderate risk 167 0.85 0.59-1.23 0.83 0.57-1.21 0.87 0.60-1.27

High risk 66 1.00 0.58-1.72 0.97 0.56-1.69 1.01 0.58-1.77

Non-drinker 115 1.22 0.79-1.88 1.23 0.80-1.90 1.25 0.80-1.93

Behavioural risk actor index n=1012 (RRR)

0/1 risk actors 108 1.0 1.0 1.0

2 risk actors 237 0.78 0.48-1.28 0.81 0.50-1.33 0.82 0.50-1.35

3 risk actors 354 0.75 0.47-1.20 0.78 0.49-1.25 0.83 0.52-1.34

4 risk actors 227 0.67 0.41-1.10 0.71 0.43-1.17 0.75 0.45-1.26

5 risk actors 86 0.50 0.27-0.93 0.53 0.28-0.99 0.57 0.30-1.08

Model 1: adjusted or age, gender. Model 2: model 1 + child SEP, adult height (or ever smoking also included parental smoking). Model 3: model 2 + educational

attainment, adult income.Behavioural risk actor index: Score o one or each o an alcohol intake o moderate or high risk, physical activity o sedentary or low, being an ever smoker, and

less than two serves a day or less than fve serves a day o vegetables

Eect estimates: RRR – relative risk ratio, PR – prevalence ratio, 95% CI – 95% confdence interval

Missing data: physical activity 11, ruit consumption 1, vegetable consumption 3, smoking 23, alcohol 36, behavioural risk actor index 51.





confdence interval included the null (PR 1.41, CI 0.90-2.19).

For all but the smoking outcome, addition o the child and adult

SEP variables attenuated the association between preschool and

the outcome slightly.

The eect sizes or alcohol drinking risk were small and low in

precision. There was a null eect (small eect size with a wide

CI) on being a moderate or high risk drinker compared with a

low risk drinker and possibly a greater probability o being a non-

drinker (PR 1.26, CI 0.81-1.96). Addition o adult SEP increased

the magnitude o the eect estimates slightly across high risk and

non-drinker categories.

While all o the 95% confdence intervals or the behavioural

risk actor index crossed the null, the point estimates or the

BRF index suggested an increased protective eect against an

increasing number o risk actors or preschool attendees (e.g. risk

o fve risk actors PR 0.57, CI 0.30-1.08). Addition o the child

and adult SEP in the models slightly attenuated the eect sizes.

Discussion

This study ound that preschool attendance resulted in a more

avourable cardiovascular behavioural risk actor profle, or three

o fve examined risk actors examined individually but also in

an index independent o age, gender, and SEP in childhood and

adulthood. These results extend the evidence on the eects o

preschool programs, fnding some benefts into late adulthood o

attendance at a multi-site, universal, community intervention in a

country outside o the US. While the benefts seen were modest,

it is noteworthy that such benefts were demonstrated over the

ollow up period spanning up to 65 years.These fndings are generally consistent with those rom other

studies. Comprehensive ECDIs have been shown to enhance

exercise participation (measured dichotomously)13,33 and reduce

the risk o ever smoking16,33,34 as was seen in participants o the

KU. The results o this study, however, dier rom those o the

small randomised US studies Project CARE and Abecedarian

that ound no dierence between participants and the control

group in dietary actors (a ‘good’ diet was defned as consuming

ruit and/or vegetables once or twice within the past 24 hours).13

This study ound a negative eect o preschool attendance on

ruit consumption but a suggestion o beneit or vegetableconsumption, which was an unexpected inding. A possible

explanation is that the SEP variables measured in this study did not

predict ruit consumption but did predict vegetable consumption

and so any SEP eect through preschool attendance may not have

an eect on ruit consumption. It may also reect error in the

measurement and categorisation o ruit consumption. The results

on alcohol consumption in this study did not ollow a clear pattern

o beneft or risk, which is in contrast to the Perry Preschool project

and Abecedarian studies that both ound preschool increased the

risk o alcohol binge drinking, but measured dierently than here.

Addition o adult SEP variables resulted in slight attenuation

o all eect sizes (except or alcohol) suggesting that these

variables mediated only a minor component o the association

between preschool and the outcome assessed. This suggests either

measurement error with these SEP actors or that they do not

adequately index the mediating actors between preschool and

health. For example, it may be that cognitive or non-cognitive

actors not indexed by educational attainment and income may

urther explain the association between preschool and behavioural

outcomes, given preschool programs are thought to improve long

term outcomes mostly through cognitive gains34-37 and cognitiveactors are thought to inluence behavioural risk actors in

adulthood independently o adult SEP.38

As a retrospective cohort study, there are a number o limitations

that should be considered in the interpretation o the results.

There is a potential or residual conounding by unmeasured and/

or poorly measured background characteristics related to amily

environment, which may not have been indexed by the sel-

reported childhood actors measured in this study. Measurement

error was possibly introduced by the use o adult recall o preschool

attendance and sel report o the behavioural outcomes, however

this approach has been used in a number o studies

33,39,40

and wasound by one study to have reasonable validity.40 Furthermore,

the results in this study are consistent with the small amount o

evidence on preschool programs reported elsewhere suggesting

reasonable validity o recall o preschool attendance. The sel

report o behavioural risk actors introduced measurement error,

despite the use o reliable, validated sel-reported assessment

tools. While the eects observed in this study were generally in

the positive direction they were unable to be estimated with great

precision.

These results may not be generalisable to all people who

attended a KU preschool. There are no historical records o

KU attendance that would allow a comparison with the current

sample. Further, the retrospective design has lead to exclusion o

those KU attendees who did not remain in SA, with an unknown

eect on the results and generalisability o the study fndings. The

combination o attrition (24%), missing data and selecting a sub-

sample o age eligible participants may have introduced selection

bias into the study which may also reduce generalisability o the

results. However our purpose was to examine the associations

between preschool attendance and behavioural risk actors,

not estimate prevalence. Thus, it is not the case that selection

processes, which operate in every cohort study, necessarily bias

observed associations because the selection process would need to eect both the exposure actor and the outcome dierentially

by preschool attendance or bias to be introduced.41 Details

regarding how the KU services changed over time and in each

site are not available which also limits the ability to explore the

precise mechanisms by which the KU may have had an eect

on health outcomes. This is a limitation o any exposure such

as education, which changes its content and meaning over time,

however this is likely to be non-dierential with respect to the

outcomes examined here.

Under the assumption that the fndings reported here are causal,

the eatures o the preschool that are likely to have contributed

to these fndings are important to consider given the planned

Australia-wide expansion o ECDIs. Most o the evidence

regarding benefcial long term social outcomes is in avour o

D’Onise et al. Article




interventions that ocus on high-quality service provision and that

provide comprehensive services directly to children as well as their

amilies9,42 both o which the KU preschools were able to achieve

according to historical records. This KU study provides urther

evidence that high quality comprehensive services to children and

their amilies that ocus on optimal child development can also

lead to health benefts.

In conclusion, attendance at a KU preschool was associated

with modest eects on behavioural CVD risk actors in adulthood

in the positive direction although generally with low precision.

This study provides some evidence or the potential beneft o

the health sector engaging in interventions outside o health

services to prevent diseases such as CVD, which are strongly

associated with lielong social disadvantage. To this end, health

proessionals should collaborate in the planning, implementation

and evaluation processes o the new ederal government agenda

or early childhood education to maximise the social and health

gains rom these interventions.

AcknowledgementsKD was supported by the National Health and Medical Research

Council o Australia and the National Heart Foundation. JL was

supported by the National Health and Medical Research Council

o Australia. The authors would like to acknowledge the sta and

participants o the North West Adelaide Health Study.

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(AUST): AIHW; 2008.2. Galobardes B, Lynch J, Davey Smith G. Childhood socioeconomic circumstances

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The Beneficial Effects of Preschool Attendance

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