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Modelling the Early Life-Course: A micro-simulation model for policy-makers

COMPASS ColloquiumWellington, Aug 3 2012

Barry Milne, Roy Lay-Yee, Jessica Thomas, Janet Pearson, Oliver Mannion, Martin von Randow, Peter DavisCOMPASS Research CentreUniversity of Auckland New Zealandwww.compass.auckland.ac.nz

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ndOverview

Modelling the Early Life-Course• Rationale; Process; Framework; ScenariosDemonstration

Testing Scenarios

Validation

Conclusions

Where to next2

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ndThe Rationale

Modelling the Early Life-Course (MEL-C)

1. Goals … what are we trying to do?Develop a software application as a decision-support tool for policy-making

2. Purpose … why are we doing it?To improve policymakers’ ability to respond to issues concerning children and young people

3. Methods … how are we doing it?By building a computer simulation model with data from existing longitudinal studies to quantify the underlying determinants of progress in the early life course 3

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ndThe Process

1. We start with a sample of people• Real or synthetic• A birth cohort of children (Christchurch Health & Development Study,

CHDS) with individual attributes at the start (n=1265, born 1977)

2. We then apply statistically-derived rules that allow us to create a ‘virtual cohort’ (synthetic data) to age 13• A sample of children with typical biographies over the life-course• With allowance for variation around the average (via random

allocation)

3. We then can simulate what might happen if policy were to change • Impact on outcomes when we alter features in our synthetic data set

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Child characteristics• (age)• gender• ethnicity

Parental characteristics• age at birth of child• ethnicity• education level

Socio-economic position• SES at birth of child• (single-parent status at birth)

Parental employmente.g. employed / welfare dependent

Psychosocial factorse.g. family functioning: change of parents, change of residence

Health service usee.g. GP visits, hospital admissions,hospital outpatient attendances

Educatione.g. Reading ability

Social/Justicee.g. Conduct disorder

Structural level Intermediate level Outcome

Other factorse.g. perinatal factors

Behavioural factorse.g. parental smoking

Family/household characteristicse.g. single-parent status, number ofchildren, household size

Material circumstancese.g. housing: accommodation type, owned/rented, number of bedrooms

The Framework

The

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ndScenario testing

Test “what if” scenarios• Projection into the future; alternative settings• Simulate impact of policy change• Child development policy impacts not typically tested

using micro-simulation

Important role of policy reference group• Engage key people from government agencies• Adopt a partnership approach• Use their expertise to get better model & policy-relevant

scenarios6

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ndSimulation tool -Demonstration

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ndSimulation tool -Demonstration

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ndScenarios to test

1. Are children in households where both parents are working better off?

2. How does smoking in pregnancy affect later outcomes?

3. How can we improve early literacy, school achievement and reduce failure in the job market?

4. How does single parenting affect later conduct problems?

5. What interventions have impact on later (health, wealth, social, education, justice) outcomes for Māori, Pacific or low-socio-economic status groups? 11

The

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ndScenarios to test

1. Are children in households where both parents are working better off?

2. How does smoking in pregnancy affect later outcomes?

3. How can we improve early literacy, school achievement and reduce failure in the job market?

4. How does single parenting affect later conduct problems?

5. What interventions have impact on later (health, wealth, social, education, justice) outcomes for Māori, Pacific or low-socio-economic status groups? 12

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ndScenario 5: Good outcomes for population subgroups I

Total (yrs1-13) Low SES Medium SES High SESBase 6.62 4.81 3.52Scenario 4.42 3.88 3.22Base - Scenario 2.20 0.97 0.30

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Total (yrs1-13) European Māori PacificBase 4.83 6.08 6.55Scenario 3.78 4.67 4.29Base - Scenario 1.05 1.41 2.26

Emergency Department / Outpatient attendancesFactors changed

• Increased: parental education, breastfeeding• Decreased: welfare receipt

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Mean (yrs8-13) Low SES Medium SES High SESBase 61.8 65.8 73.0Scenario 69.0 72.1 77.2Scenario - Base 7.2 6.3 4.2

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Mean (yrs8-13) European Māori PacificBase 67.0 62.3 59.1Scenario 73.0 69.1 67.1Scenario - Base 6.0 6.8 8.0

Scenario 5: Good outcomes for population subgroups II

ReadingFactors changed

• Increased: parental education, maternal warmth, birth weight, breastfeeding

• Decreased: welfare receipt

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Mean (yrs7-10) Low SES Medium SES High SESBase 5.9 5.2 4.5Scenario 4.5 3.9 3.5Base - Scenario 1.4 1.3 1.0

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Mean (yrs7-10) European Māori PacificBase 5.1 5.6 6.9Scenario 3.9 4.4 4.2Base - Scenario 1.2 1.2 2.7

Scenario 5: Good outcomes for population subgroups III

Conduct problemsFactors changed

• Increased: parental education

• Decreased: parental changes, harsh punishment

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ndConclusions- Results

No one factor has large effect; need combination of factors to have substantial impact

Need to focus on changing a number of factors (or targetting children with multiple risks)Important lesson for policy makers: no one “silver bullet” that will solve a lot of problems

Both social and background/biological factors had an impact

Not all factors easily modified

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ndValidation

‘Quantitative’ validationRun simulations with different base data, but still using transition probabilities derived from CHDSSimulate reading using DHDS dataSimulate conduct problems using DHDS dataSimulate hospital admissions using NMDS data

‘Qualitative’ validationHow do our results compare to what the literature suggests?

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ndQuantitative Validation- Reading

Simulated Burt scores for DHDS ages 8-13; compared to actual DHDS data ages 9, 11, 13

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Age SimulationMean

DHDSMean (n=952)

8 45.7

9 54.3 53.9

10 63.9

11 72.0 72.4

12 78.8

13 84.3 84.1

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ndQuantitative Validation - Conduct problems

Simulated conduct scores for DHDS ages 6-10; compared to actual DHDS data ages 7 & 9

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Age SimulationMean

DHDS Mean (n=951)

6 0.166

7 0.164 0.125

8 0.149

9 0.159 0.125

10 0.164

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ndQuantitative Validation- Conduct problems

Simulating too high (by 0.25-0.31SD). Are group differences simulated accurately? SES

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Simulation DHDS

SES High Med Low High Med Low

7 0.144 0.171 0.167 0.103 0.125 0.138

% increase 20% 17% 21% 35%

9 0.128 0.167 0.173 0.094 0.123 0.150

% increase 31% 36% 31% 60%

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ndQuantitative Validation- Conduct problems

Are group differences simulated accurately? Ethnicity

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Simulation DHDS

Ethnicity European Non-European

European Non-European

7 0.165 0.161 0.126 0.116

% decrease 2% 8%

9 0.161 0.151 0.127 0.112

% decrease 7% 11%

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ndQuantitative Validation - Hospital admissions

Simulated hospital admissions for NMDS ages 1-5; compared to actual NMDS admissions ages 1-5 (all NMDS births for January 2001)

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Age SimulationMean

NMDS Mean (n=4323)

Discrepancy

1 0.281 0.281 Not simulated

2 0.131 0.114 -13%

3 0.098 0.062 -37%

4 0.090 0.047 -48%

5 0.112 0.037 -67%

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ndQuantitative Validation - Hospital admissions

Admissions for 1-5 year olds decreased markedly since 1970s, so simulation based on CHDS cohort estimates performs poorly. What about group differences? SES

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Simulation NMDSSES High Med Low High Med Low2 0.099 0.141 0.138 0.109 0.100 0.130% increase 42% 39% -8% 19%3 0.076 0.107 0.099 0.054 0.062 0.067% increase 41% 30% 15% 23%4 0.068 0.094 0.098 0.046 0.039 0.058% increase 37% 44% -16% 24%5 0.091 0.118 0.116 0.046 0.035 0.035% increase 30% 28% -24% -24%

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ndQuantitative Validation - Hospital admissions

What about group differences? Ethnicity

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Simulation NMDSEthnicity Euro Pacific Māori Euro Pacific Māori2 0.107 0.117 0.140 0.106 0.165 0.129% increase 9% 31% 55% 22%3 0.077 0.070 0.106 0.057 0.064 0.089% increase -9% 37% 12% 56%4 0.076 0.072 0.089 0.044 0.090 0.050% increase -5% 17% 105% 14%5 0.092 0.088 0.115 0.035 0.056 0.041% increase -4% 25% 60% 17%

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ndQualitative Validation- Literature Review

Lifecourse literature reviewed for factors associated with outcomes (age 0-18) under domains of:

Health – respiratory infections, obesity, asthma, dental health, metabolic syndrome (hypertension, diabetes)Education – cognitive development, school performance, school readinessSocial/Justice – externalizing (conduct, delinquency, crime), internalizing (depression, anxiety), attention (ADHD), suicide, alcohol, drugs, smoking, risky sex

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ndQualitative Validation - Reading

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Factor Direction of effect

Reference Simulation says...

Low birth weight

Decreases reading scores

Shenkin et al. 2004, Psychol Bull;130(6):989-1013.

Decreases reading scores

Early childhood education Improves reading

Zoritch B et al. 2000, Cochrane Database, Art: CD000564

Mitchell L et al. 2008, NZCER.Brooks-Gunn et al. 2002, Child

Dev;73(4):1052-72.

No effect

SES Poverty decreases reading scores

Engle et al. 2008, Ann N Y Acad Sci;1136:243-56.

No effect of SES, some effect of

parental education

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ndQualitative Validation- Conduct problems

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Factor Direction of effect

Reference Simulation says...

Smoking in pregnancy Increases CD Hill, J. 2002, J Child Psychol

Psychiatry; 43: 133-164. Increases CD

Parentaldrinking Increases CD

Barnow, et al. 2007, Alcohol Alcoholism 42: 623-628.

Grekin, et al. 2005, J Studies Alcohol 66: 14-22.

No effect

Emotional responsiveness Decreases CD Hoeve et al. 2009, J Abnorm

Child Psychol;37(6): 749-75. No effect

Harsh parenting Increases CDMoffitt & Caspi, 200,. Dev

Psychopathol;13: 355-75.Murray & Farrington, 2010, Can

J Psychiatry;55:633-42. Increases CD

Verbal skills Decreases CD Hill, J. 2002, J Child PsycholPsychiatry; 43: 133-164. Decreases CD

Low SES Increases CDHill, J. 2002, J Child Psychol

Psychiatry; 43: 133-164.Moffitt & Caspi, 200,. Dev

Psychopathol;13: 355-75.

No effect of SES, some effect of

parental educationEarly childhood education Decreases CD Zoritch B et al. 2000, Cochrane

Database, Art: CD000564 No effect

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ndQualitative Validation - Hospital admissions

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Factor Direction of effect

Reference Simulation says...

Number of children

Decreases health service use

Chen et al. 2006, Health Services Res 41: 895-915.

Decreases GP visits for morbidity

Single parenting

Increases health service access

Heck & Parker. 2002, Health Services Res 37: 173-187.

Decreases preventive GP visits; hospital

admissions

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ndConclusions- Validation

The goodReading – Simulated rates wellDirection of impact of changed factors

The badHealth Services – Simulated both rates and group differences poorly, especially poor for Pacific childrenImpact of early childcare education does not match literature

The hard to tellConduct problems – Simulated group differences well; hard to tell if rates are being simulated well

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ndWhere to next?

Analyse additional data

Synthetic base file

Tool Development

Deployment

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ndWhere to next?

Analyse additional dataCombine together:

• Christchurch Health and Development Study• Dunedin Multidisciplinary Health and Development Study• Pacific Islands Families Study• Te Hoe Nuku Roa Study• Other data sources as available

Analyse as integrated dataset where possible; combine estimates where notPossibility of using estimates from published studiesExtending range of outcomes and period of life-course covered

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ndWhere to next?

Synthetic base fileUsing 2006 Census data to create a representative synthetic unit record file – thanks StatsNZ!!Analyses ‘birth cohort’ from the Census (0 year-olds) using birth variables relevant to our micro-simulationPredict each variable from every other to obtain predicted scores for each variableStochastically assign ‘synthetic values’ based on predicted scores and measures of spreadSynthetic data should faithfully represent distributions and inter-relations of real data, without matching characteristics of any real individual

• Check with ‘hacker’ scenarios 32

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ndWhere to next?

Tool DevelopmentSubgroup scenariosContinuous time-dynamic parametersAbility to compare unlimited number of scenariosMacro to run a range of scenarios (i.e., programmable, not just point and click)More (and better) graphical representations of base-scenario differencesSignificance testing

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ndWhere to next?

DeploymentAvailable to users in policy making role

• Registration process with training mandatory• Caveats and pitfalls made explicit• User-support available

Remote desktop access• Less technical issues than web-based application

Users group might help future tool development

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ndQuestions

Questions?

Thanks to:MEL-C team: Peter Davis, Roy Lay-Yee, Jessica Thomas, Janet Pearson, Martin von RandowLongitudinal studies (CHDS, DMHDS, PIFS, THNR)Policy reference group

• MOH:• MSD• MinEdu• MOJ

Others (MSI, Statistics NZ, Treasury)35