erpho: a whistle-stop tour of public health intelligence [email protected]

ChildPublicHealthIntelligenceTeam

Erpho: a whistle-stop tour of public health intelligence

[email protected]


A short talk about populations


Why are we interested in populations?



Any ideas?



• Populations show us the make-up of an area. Without understanding an area we cannot know what to commission (buy) for that area.




• Without information on the population we cannot understand some of the issues within that area.




• Without information on the population we cannot understand some of the issues within that area.

• And we will not know how to effectively tackle these issues, and plan for the future.


Know your population - how would you describe these populations?

http://erphodev.hyperspheric.com/PracProf/ProfileChart.aspx?pct=5GC&prac=E81631&chart=POPULATION



http://erphodev.hyperspheric.com/pracprof/ProfileChart.aspx?pct=5P2&prac=E81626&chart=POPULATION



http://erphodev.hyperspheric.com/PracProf/ProfileChart.aspx?pct=5PQ&prac=D82004&chart=POPULATION


• How would the different age structures in the three areas have an impact on Midwifery?

• What is missing?

• Why might it have an impact on midwifery?

Questions


Comparison of the ethnic diversity

62%

3%

5%1%

29%

White MixedBlack or Black British Chinese or Other Ethnic GroupAsian or Asian British

94%

1% 1%2% 2%

White MixedBlack or Black British Chinese or Other Ethnic GroupAsian or Asian British


Other aspects to consider

• Deprivation



• Deprivation• Rurality



• Deprivation• Rurality• Access



• Deprivation• Rurality• Access• Local resources



• Deprivation• Rurality• Access• Local resources• Qualitative information


Data.....or......


Data.....or......

more simply ......some of the terms and phrases you may wish to know

about and keep in mind


Key terms

We shall now look at the following:• Numerator• Denominator• Proportion (as a percentage)• Rate• Prevalence• Incidence

These cover the majority of terms you will come across and are intended to ease you in to looking at and understanding just what the data are showing.


Numerator

• The numerator is a count of something e.g. number of obese people in the UK (goes above the line).


Numerator

AB

Which one is the Numerator?


Denominator

• The denominator is the number we divide the numerator by, e.g. population of the UK (goes below the line).


Denominator

AB

Which one is the Denominator?


Proportion• Proportion: number in a subgroup of the

population (numerator) divided by the whole population (denominator)




• It is often expressed as a percentage




• It is often expressed as a percentage

• E.g. Proportion obese children in Bedfordshire in 2008 is the number of obese children in Bedfordshire in 2008 divided by the 2008 child population


98 children in a school of 650 get the winter vomiting bug in one month – what proportion of the school succumbs?

1. 10%2. 12%3. 15%4. 20%


98 children in a school of 650 get the winter vomiting bug in one month – which figure is the numerator?

1. 98 sick children2. 650 school

population


The East of England has an Asian population of 253,000.To measure the proportion of Asians in the East of England what Denominator do we need?

1. The UK population figure

2. The East of England population figure

3. The world population4. The population of

Cambridge


We want to know how many Obese people there are in CambridgeshireWe have the total population for Cambridgeshire What will the numerator be?

1. The total amount of obese people in the UK

2. The total amount of obese children in the East of England

3. The total amount of obese people in Cambridgeshire

4. The City of Cambridge population


Rate

• Is a number per population per unit time


Rate

• Is a number per population per unit time

• Often expressed per 1,000, per 10,000 or per 100,000


Rate• Is a number per population per unit time

• Often expressed per 1,000, per 10,000 or per 100,000

• E.g. The rate of injury admissions for under 17s in Cambridgeshire is the total number of injury admissions for children 0-17 in Cambridgeshire divided by the total child population (0-17) of Cambridgeshire multiplied by the ‘per’ multiplier.


Rate ExampleCalculate the crude rate of Injury Admissions to

hospital for all children (0-17) in 2011 in the PCTs below:

PCT Number of Admissions

in 2011

2011 mid year

population estimate

Rate of Admission per 100,000 population

Bedfordshire 4444 407,000

Cambridgeshire 5933 597,400

Luton 2667 188,800


Answer

PCT Number of Admissions

in 2011

2011 mid year

population estimate

Rate of Admission per 100,000 population

Bedfordshire 4444 407,000 1092

Cambridgeshire 5933 597,400 993

Luton 2667 188,800 1413


Prevalence

• Prevalence is the number of individuals in a population who have the disease at a specific point in time


Prevalence

• Prevalence is the number of individuals in a population who have the disease at a specific point in time

• Usually expressed as a proportion of the population at risk

Prevalence = Total number of cases at a given time Total population at that time


Incidence• Incidence is the number of new cases of

disease that develop in a population of individuals at risk during a specified time period


Incidence• Incidence is the number of new cases of

disease that develop in a population of individuals at risk during a specified time period

• Incidence measures the rate at which new events occur

Incidence = Number of new cases in period of time Population at risk


Example

High incidence and low prevalence e.g. influenza

January February March

1st February

Cases of flu in class 4J. Class size: 20


ExampleHigh prevalence and low incidence e.g.

asthma

MarchFebruary

Cases of asthma in class 4J. Class size: 20

January

1st February


Incidence and prevalence model

Incidence

Prevalence

Deaths

Cured

Got better


Working with imperfect data: Understanding the limits of routine data

and how to deal with them

ChildPublicHealthIntelligenceTeam We live in a world of imperfect data.....


In a perfect world....

• The right data• At the right geographical or

organisational level• At the right time• Of sufficient reliability • With the appropriate comparisons

√√√√√


In the real world...

• The right data• At the right geographical or

organisational level• At the right time• Of sufficient reliability • With the appropriate comparisons

(√)

X

?

(√)

(√)


What should you do?

Nothing because

we ca

n’...

Use lo

ts of re

source

s to...

Use so

me reso

urces t

o tr...

Make

the best

decisions..

.

It will

depend on the ci

...

20% 20% 20%20%20%1. Nothing because we can’t take action if the data is not perfect

2. Use lots of resources to make the data perfect

3. Use some resources to try to improve the data

4. Make the best decisions you can based on the imperfect data

5. It will depend on the circumstances

0

30


Can we make the data perfect?• Often relatively minor changes can be made to improve the

quality of the data

• However, to achieve perfection may require a lot of resources

• Can the additional expenditure be justified? Will it make a difference to your decisions?

• There is likely need to balance the desire for perfect data quality against the resources needed to achieve it.

• Can you think of some examples where it would be important to allocate resources to ensure accurate, reliable data and where sometimes allocating lots of resources may be not be justified?


Working with data in the real world

• Data will never be perfect• Often have to make decisions based on

imperfect dataBUT:• Should try to minimise imperfections and

improve data quality as far as is possible & practical

• Be aware of the imperfections and recognise the implications for decision making


UNDERSTANDING WHAT THE DATA ARE TELLING YOU


Example: In 2009/10 the number of children aged under 1 year admitted to hospital with gastroenteritis in PCT A was 125.

What does this mean?

Loca

l service

s are cle

arly ...

Loca

l service

s are doing ..

Can’t tell

33% 33%33%1. Local services are clearly failing, something must be done.

2. Local services are doing an excellent job, no change is needed.

3. Can’t tell

0

30


Understanding the data• Understanding what the data are telling us

about the local population generally means making comparisons:– Between populations and places– Over time– Against targets or standards

• This sets the data in context and allows us to make decisions about whether action is needed


Example: In 2009/10, 22 infants aged under 1 years in every 1000 were admitted to hospital for gastroenteritis in PCT A

compared to 26 in every 1000 in PCT B. What does this mean?

Resource

s should be re

al...

The admission ra

te is hi...

Not sure

33% 33%33%1. Resources should be reallocated to tackle the higher rate of admissions in PCT B

2. The admission rate is higher in PCT B compared to PCT A but the reasons for the difference are not clear. We need more information before reallocating resources.

3. Not sure

0

30


Decisions about services are often based on differences between population health data.

Differences can arise because:

• there are real differences between populations or services

• because of random chance

Which should decisions about services be based on?


These data are fictional. Would you ever expect to see data like this – even if the health of the

population has not changed?

23.6 23.6 23.6 23.6 23.6

0

5

10

15

20

25

30

2006/7 2007/8 2008/9 2009/10 2010/11

Rate

per

100

0 in

fant

s un

der 1

yea

rInfants aged under 1 years admitted to hospital for gastroenteritis in

Southwood (fictional) , rate per 1000 infants

These data are very unlikely. All systems (and data) will vary just due to chance


This is much more likely. Although the rate at which infants are admitted to hospital for gastroenteritis has not really changed, you would still expect a bit of variation – just due to chance. How do you decide when changes in data are due

to real differences or due to chance?

23.821.9 22.5

24.722.4

0

5

10

15

20

25

30

2006/7 2007/8 2008/9 2009/10 2010/11

Rate

per

100

0 in

fant

s un

der 1

yea

rInfants aged under 1 years admitted to hospital for gastroenteritis in

Southwood (fictional) , rate per 1000 infants


• Statistical analysis can help us decide whether differences are due to chance

• The commonest approach is the use of confidence intervals

• Confidence intervals use information on the probability of events occurring to measure how things may vary just by chance.


Confidence intervals• The rate of hospital admission for gastroenteritis in

infants under 1 years in PCT A was 22 per 1000 with 95% confidence limits of 19-27 per 1000.

• This means that we are 95% confident that, taking random fluctuations into account, the underlying rate of hospital admission for gastroenteritis in infants under 1 years in PCT A was between 19 and 27 per 1000 infants.


• Admission rate for gastroenteritis in infants aged under 1 year in PCT A in 2009/10 was 22 per 1000 infants with 95% confidence limits of 19 to 27 per 1000.

• 22 per 1000 (95% CI 19 to 27).


Confidence intervals can help us decide whether differences are due to chance or not

22 26

0

5

10

15

20

25

30

35

PCT A PCT B

Rate

per

100

0 in

fant

s un

der 1

yea

rRate of hospital admission for infants aged under 1 year

(Source: ChiMat data atlas)

The rate of admission in PCT B is higher than PCT A but could the difference due to chance?


Do the confidence intervals make it easier to decide whether the difference could be due to chance?

22 26

0

5

10

15

20

25

30

35

PCT A PCT B

Rate

per

100

0 in

fant

s un

der 1

yea

r

Rate of hospital admission for infants aged under 1 year with 95% confidence intervals (Source: ChiMat data atlas)

We can not exclude the possibility that the difference between PCT A and PCT B is due to chance.

In which case, should resources be re-allocated?


Using confidence intervals to help identify local priorities Statistical

analysis can be used to identify what are the most important local priorities for health.

The red dots on this health profile show those health issues that are worse than the England average and are unlikely to be due to chance.


Summary – working with imperfect data

• Population health data will never be perfect

• Improving data quality will improve decision making

• But often (usually) we have to make decisions based on imperfect data

• We can take some steps to help us make decisions based on imperfect data

• Being aware of the imperfections is an important first step


Questions?


Additional items to consider


From the Marmot review:


0

2

4

6

8

10

12

14

161

101

201

301

401

501

601

701

801

901

1001

1101

1201

1301

1401

1501

1601

1701

1801

1901

2001

2101

2201

2301

2401

2501

2601

2701

2801

2901

3001

3101

3201

3301

3401

3501

3601

3701

3801

3901

4001

4101

4201

4301

4401

4501

4601

4701

4801

4901

5001

5101

5201

5301

5401

5501

5601

5701

5801

5901

6001

6101

6201

6301

6401

6501

6601

6701

% lo

w b

irthw

eigh

t birt

hs

Low birthweight births, % of total births, MSOAs in England 2003-7, ranked by levels of child poverty

Lowest povertyHighest poverty

Source: Small area data for JSNA, APHO http://www.apho.org.uk/RESOURCE/VIEW.ASPX?RID=87735 based on ONS birth data and IDACI 2007

http://www.apho.org.uk/RESOURCE/VIEW.ASPX?RID=87735


Geography

• Population and geography go hand in hand


Geography


• Geography and data collection go hand in hand


Geography


• Geography and data collection go hand in hand

• What are some of the types of political geography you know?


Postcodes and health data

• From a health data perspective it all starts with the patient postcode.

• Incorrect or missing postcode means that the patient can not be assigned to the correct higher geography (LSOA, ward etc).

• Therefore will not contribute to any health related rates or numbers.

• One full postcode contains around 15 households. Postcode use in health and social care is very important to get right.


PCT level

• PCTs have a geographical boundary which reflects the population they serve (although there are caveats with this owing to the movement of people cross boundaries)

• PCTs are often co-terminus with county boundaries or unitary authorities.

• PCT population size is approx 330,000• 152 in total but changing. • Many types of data are presented at PCT level in

order to aid commissioning of services


Local Authority level

• Two tier administrative system in England• Top Tier –County Council (planning, adult and

children social care, performance, health)• Second tier – District, City, Borough ... (leisure,

refuse, council tax etc)• Mid system – Unitary Authority – usually centred

around larger population areas i.e. Luton or Peterborough – combine the role of both top and secondary tier

• Like PCT-level many data are at this level


Ward• District / division of a town • Electoral purposes – very political and subject to

change• Contain between 40-100 households • Census data can be found at ward level• Ward level data can have issues with small

numbers and therefore some data is suppressed <5 or removed completely

• EMPHO Teenage Pregnancy ward mapping

http://www.empho.org.uk/THEMES/teenagepregnancy/tp11.aspx


LSOA / MSOA

• Lower Layer SOAs typically contain 4 to 6 OAs with a population of around 1,500

• Middle Layer SOAs on average have a population of 7,200

• Created to not change (unlike Wards)• Types of data at these levels are:

Index of Multiple DeprivationJSNA core dataset (MSOA)Health Inequality data (MSOA)


Data issues

• Can we identify any issues with data at these levels?


The new NHS

• The NHS is changing with the Health and Social Care bill recently passed in parliament.


The new NHS


• PCTs (the current commissioners of services) are to be abolished.


The new NHS


• PCTs (the current commissioners of services) are to be abolished.• Replaced by GP Commissioning or Commissioning consortia Groups.

The aim is to pass the budget to GPs who can commission on a much more local focus.


The new NHS

• The NHS is changing with the Health and Social Care bill currently being passed.



• Each CCG is made up of a number of GP practices linked together.


The new NHS

• The NHS is changing with the Health and Social Care bill currently being passed.



• Each CCG is made up of a number of GP practices linked together.• Public Health is moving to Local Authorities as a separate directorate,

link with the wider determinants of health.


The new NHS





link with the wider determinants of health.• Increased privitisation of the NHS.


The new NHS





link with the wider determinants of health.• Increased privatisation of the NHS. • Hospital trusts being given ‘Foundation’ status.


Some resources which may be of use

• Health profiles

http://www.apho.org.uk/resource/view.aspx?RID=50202



• Practice profile

http://www.apho.org.uk/PracProf/ChoosePCT.aspx?sha=Q35&yr=2009



• Child health profile

http://www.chimat.org.uk/resource/view.aspx?RID=102390



• Inequality profile

http://www.erpho.org.uk/viewResource.aspx?id=18853


Health Needs Assessment (HNA)

The following is a brief look at Health Needs Assessment.


Why are we interested in populations (pt.2)?

• Needs / service assessment– Size, age, ethnicity, fertility, inward & outward

migration




migration• Current and future need (5-10 years

ahead)– What is coming, how do we prepare




migration• Current and future need (5-10 years

ahead)– What is coming, how do we prepare

• Projected changes– How does the above change – modelling tells

us only so much


A Systematic & Comprehensive HNA

Comprehensive Health Needs Assessment

BUSINESS CASE. SERVICE SPECIFICATION. TENDER. CONTRACT

Defining and Understanding Your Population

Demographics

Health Problems

Uncertainties

Inequalities

Evidence relevant to those health problems & population

Policy relevant to those health problems & population

What are the current services?

Identify Service Gaps &

Select Priorities for

Implementation

Stakeholder views on health problems & population

Quantify

Benefits, RisksFinances

Staffing, IT & Estate

Assess

Context of QIPP

Value for moneyAffordabilityAcceptability

Spine Charts Provide a Good Summary


Define the population• Geographic location e.g. living in deprived

neighborhoods or housing estates

• Settings e.g. schools, prisons, workplaces

• Social experience e.g. asylum seekers, specific age groups, ethnicity, sexuality, homelessness

• Experience of a particular medical condition e.g. mental illness, diabetes, respiratory disorders.






Demographics

Health Problems

Uncertainties

Inequalities






Implementation


Quantify



Assess

Context of QIPP




Collect and interpret various sources of data• Broad categories of data:

Demography Other wider determinants of health Prevalence of risk factors Disease frequency and prevalence Depends on defined population

• Consider comparisons: Between different groups in the population Between different locations in the geography External comparisons (national, international, from the evidence)

• Spine charts often provide useful summaries• CHImat NA tool

http://www.chimat.org.uk/resource/view.aspx?QN=CHMT2






Demographics

Health Problems

Uncertainties

Inequalities






Implementation


Quantify



Assess

Context of QIPP




Stakeholder Engagement• Who to ask

Perceptions and expectations of the profiled population (public, patients, carers)

Perceptions of professionals providing the services Perceptions of managers of commissioner/ provider organisations Relevant national, regional or local priorities

• Consider using formal qualitative techniques: Interviews Focus groups


Stakeholder Engagement• What to ask (in addition to your own research)

– Relevant policies– Current services (often very time consuming)– Views on health needs

• How this can help?– Provide evidence about a population on which to plan services and

address health inequalities– An opportunity to engage with specific populations and enable

them to contribute to targeted service planning and resource allocation

– Provides an opportunity for partnership working and developing creative and effective services


Example Sources of Evidence

• Scratching the surface (eg Oxford have an entire MSc devoted to this)– Do a literature search yourself– Use an accredited source with a transparent process

for collating and evaluating evidence: eg Cochrane, National Institute of Health and Clinical Excellence. NHS Evidence ‘Kitemark’

– Beware of evidence reviews or recommendations where the process by which recommendations are made is not transparent






Demographics

Health Problems

Uncertainties

Inequalities






Implementation


Quantify



Assess

Context of QIPP




Data sources on the internet• Area Health profiles

– Good overview of health of local population (all age) www.apho.org.uk• Child health profiles

– Overview of health of local child population ww.chimat.org.uk• ChiMat data atlas

– Good source of wide range of data relating to mothers and children www.chimat.org.uk

• erpho– Good source of child health data for the East of England– http://fingertips.erpho.org.uk/childhealth

• National Obesity Observatory data atlas – For information on adult and child healthy weight and obesity

www.noo.org.uk• ONS Neighbourhood Statistics

– range of information at small area level http://www.neighbourhood.statistics.gov.uk/

http://www.apho.org.uk/

http://www.chimat.org.uk/

http://www.chimat.org.uk/

http://fingertips.erpho.org.uk/childhealth

http://www.noo.org.uk/

http://www.neighbourhood.statistics.gov.uk/

erpho: a whistle-stop tour of public health intelligence [email protected]

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