Statistical predictions for numbers of CPP and LAC for the West Midlands Local Authorities, based on their level of deprivation

Darrell Harman & Chandan Kaur, November 2013

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Why this study was carried:

Directors of Children’s Services have a statutory duty to ‘address’ needs within their areas.

“The DCS is responsible for securing the provision of services which address the needs of all children and young people, including the most disadvantaged and vulnerable, and their families and carers.”

“The LMCS is responsible for ensuring that the needs of all children and young people, including the most disadvantaged and vulnerable, and their families and carers, are addressed.”

Source: Statutory guidance on the roles and responsibilities of the Director of Children’s Services and the Lead Member for Children’s Services. April 2013

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Impact of the economic recession

The economic recession has caused a situation where need (deprivation) has increased since 2008

At same time, as a result of the Coalition Government’s Deficit Programme, Council budgets are reducing

There is an even greater need to understand how increased need translates into demand for social care

Results may help Children’s Services to understand if:o their level of demand = predicted demando they are overspending or underfunded?

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Extensive research on the relationship between deprivation and social care demand (several cited in Jan 2011 report)

NSPCC Child protection research briefing between Poverty and child maltreatment (2008) – research evidence showed there is an association between poverty and an increased risk of child maltreatment, particularly neglect and physical abuse.

Sidebotham, P. and Heron, J. (2006): Child maltreatment in the “children of the nineties”. A cohort study of risk factors - found that the biggest risk factor to CPP listing was Parental Poverty. (10:1 odds ratio, domestic abuse 2:1)

Bywaters, P. (2013) ‘ Inequalities in Child Welfare’ – clear relationship between the child’s chances of being looked after or being subject to a CPP with the overall deprivation in the LA in which they lived.

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Why JSA and not English Indices of deprivation?

IDACI, IMD (2010) is actually based on 2008 data – so takes no account of the worst depression since the 1930s.....

JSA counts are available for each month, a month later and at a small area level, more real time and released in a timely fashion.

JSA is a good proxy of need as it is part of IMD and IDACI calculations

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West Midlands as a region - context

2nd highest unemployment rate in Great Britain!

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Total "CPP and LAC Population Rate" and JSA rates for the West Midlands Region

Total CPP+LAC Rate

West Midlands JSA Rate

Baby P case: highnational media profile

Mar 09: Publication of Lord Laming Report

Apr 08: PubllicLaw Outline changes

CPP/LAC increased well before Baby P impact and soon after recession commenced.

P/t jobs and Welfare Reform effect and LoS

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A generic children’s services system

Increasing Cost

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This statistical model is based on the real system and flows

The above system can be mathematically solved by a series of difference equations if all counts/rates are known:

e.g. if t= the count/rate at the end of month or year

and ∆t = the count at the end of the following month or year:

CPP(t+ Δt) - CPP(t) = (CiNOUT to CPPIN)Δt + CPPINΔt -CPPOUTΔt – (CPPout to LACIN)Δt LAC(t+ Δt) – LAC(t) = LACINΔt - LACOUTΔt + (CPPout to LACIN)Δt

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Characteristics of a useful predictive model

i) Firstly the predictor variables have to be statistically significant with the variable that is being predicted i.e. in this case, CPP and LAC

ii) Secondly the model has to have high predictive power i.e. R-squared is high.

(iii) The 95% confidence limits of the mean predicted variable are narrow enough to be useful and provide LAC ranges so more realistic for councils.

Our statistical model meets these three tests= useful predictive model!

What does statistically significant mean? Data showed that there is a relationship between the predictor(s) and predicted variable, which could not be explained by chance alone.

High R-squared = the model formulated with predictor variables can explain a large degree of the variation in LAC or CPP.

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Causation

• It is important to remember that a high correlation coefficient or a high R-sq does not prove causation. In other words, a strong correlation with Unemployment rates does not mean that if parents become unemployed that their children will require support from Children’s Social Care Services; it only applies at population levels.

• One explanation could be that unemployment can further increase the risk of pre-existing family dysfunction / stresses (e.g. family breakdown, domestic violence) eventually leading to a crisis. Research supports this hypothesis.

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Model 1: National I52 councils

Multiple regression model developed based on snap-shot data (31st March 2013) from 152 LAs in England.

Multiple regression model = more than one predictor variable used JSA rates 31st March 2013, Proportion of under 5s within under 18s

population, Proportion of Asians within local population (negative coefficient)

Snap-shot data taken from DfE 31st March 2013 statutory returns, 2011

Census and www.nomisweb.co.uk for JSA rates.

Outcome: Predictions for LAC populations were the most useful from this method.

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Model 1 LAC mean predictions with 95% CI from Multiple regression model

Because JSA rates have stabilised but p/t work and benefit sanctions, have increased need is likely to be closer to upper 95% confidence interval

* LA has UASC

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JSA based model could be underestimating risk…

Due to economic structural changes (increased under-employment and p/t work) and benefit sanctions;

– The combined affects of all 2010-2013 reforms excluding Universal Credit leave Lone Parents worse off by £32.67pw (7.8%), and couples with children by £43.86 (5.6%)

– 1 in 10 workers is ‘under employed’, increasing by 42% across all occupations in four years to 3.3 million

– E.g. Between October 2013 and June 2013, 7,590 JSA claimants in Walsall had their benefits sanctioned

– Data match CiN with Benefit cap data and target support

It maybe that JSA is becoming a less accurate predictor of need, so make use of the upper 95% confidence intervals

Child Poverty Strategy – a corporate response is needed to mitigate LAC costs pressures on council.

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Model 1 useful for predicting LAC only

Model 1 had low predictive power for predicting – CPP populations, – LAC admissions and CPP listings.

This was similar to what was found when using IDACI previously.

Poor predictive power due to poor correlation. Why would this happen, compared to LAC population high

predictive power? – Preventative work more effective at lower levels of risk– LAC admissions volatile (large sibling groups) – Risk appetite varies by councils (see LAC/CPP ratio variance see next slide)– Throughput of CPP is higher than LAC.

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Ratio of LAC to CPP by council over time

Are LA risk thresholds influenced by volumes of LAC and CPP? As volumes increased across region, the ratio has reduced – is this related to budget pressures?

LAC:CPP ratio, 31st March 2002 2007 2012 2013

Birmingham 2.22 1.67 1.49 1.67Coventry 1.76 2.70 1.37 1.19

Dudley 2.89 3.48 3.16 3.02

Herefordshire, County of 2.85 3.09 1.44 1.00Sandwell 3.84 4.17 1.75 1.87Shropshire 1.67 1.16 1.14 1.14Solihull 1.21 4.00 1.70 1.51

Staffordshire 1.75 1.93 2.13 1.73

Stoke-On-Trent 3.55 2.93 1.56 1.35

Telford and Wrekin 2.00 1.70 1.36 2.25Walsall 2.63 2.61 2.14 2.29Warwickshire 1.50 1.52 1.27 1.26

Wolverhampton 3.36 2.96 2.65 2.69Worcestershire 2.82 1.65 1.32 1.48

Average LAC:CPP ratio 2.25 2.09 1.65 1.66WMids LAC + CPP counts 9,735 10,785 13,599 14,355

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LA variation (31st March 2013) from West Midlands average regionalLAC to CPP ratio

Lower risk threshold - "Risk adverse"

Higher risk threshold - "Manage Risk"

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Model 2: West Mids LAs only, over time

Simple linear regression models using 14 West Midlands LAs data over time (end of year CPP and LAC rates vs JSA rates from 2001 to 2013). For example:

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Methodology

Due to the large variation in JSA rates; 14 LA’s allocated into either a ‘Higher Deprivation’ band or ‘Lower Deprivation’ band.

Outcome: predictions for LA’s good for; LAC population and CPP populations including CPP listings and LAC admissions. and Predictive power ranged from 47% to 87%!

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Predictions for LAC from Models 1 and 2

Blue: >>10% difference outside range on both modelsYellow: outside ranges but <=10% difference outside range on at least one modelGreen: inside range on at least one model

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If your council is outside of predicted ranges on both models:

You may plan to change your LAC and/or CPP numbers.or You may wish to explain them based on local evidence;

(i) Technical – wrong coding in statistical returns(ii) Interpretational – e.g. of indicators(iii) Situational e.g. large number of UASC or higher level of drug misuse(iv) Operational - were thought to have the most influence and was

seen to be more in local authority control:• How children’s services were resourced and delivered, • the extent of inter-agency working, • professional expertise and management support • and the availability of family support services. Oliver et al (2001) reasons for variance in CPP or LAC numbers not

accounted for by deprivation:

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Evidence from London

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2004 2005 2006 2007 2008 2009 2010 2011 2012

Rate

Children looked after rate, per 10,000 children aged under 18

Tower Hamlets Statistical Neighbours England

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Findings from a London Study, Feb 2013:

http://www.londoncouncils.gov.uk/policylobbying/children/publications/lacanalysis.htm

• Proactive approaches to reduction usually work• Focus on flows (especially early discharges)• Lower caseloads are linked to lower LAC; av. for inner London

was 17 cases.• Spend needs to be sufficient and focussed• Early help is as yet unproven• Senior managers commented on a direct correlation with rise

of LAC numbers following either a poor inspection or a high profile Serious Case Review.

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Conclusions

Statistical models can provide estimates for predicted demand based on current levels of deprivation.

JSA is a good proxy measure of deprivation – captures impact of economic recession more fully.

Two statistically significant models developed predict LAC and CPP– Model 1: National snap shot data – predicts LAC

well.– Model 2: Regional data – predicts LAC but CPP

populations, CPP listings and LAC admissions as well.

Deprivation

Need

Demand

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Conclusions cont’d

Structural economic change and benefit capping/sanctioning means demand more likely to be nearer upper 95% CI of Models 1 and 2.

If your council is outside these predicted ranges then you may wish to

- Understand and explain variance due to local factors. Perform a systems ‘health check’

OR- Increase/reduce LAC and Examine LAC/CPP ratio- Ensure a corporate Child Poverty reduction targets targets CiN

families