Visual Presentation

For categorical data, pie charts can be effective in their simplicity for portraying relative frequencies.

Pie chart to show percentages of each smoking status in a sample of 211 women.

Females, n=211

18.0%

23.7%

.9%

13.7%

43.6%

Reg Cigarette smoker

Ex-smoker

Occasional smoker

Ex-occasional smoker

Never smoked

You can try various things to spice up the chart.

The inclination and distortion that produce the 3D effects make it difficult to interpret the data.

In the view below, the Hazelnuts (4%) and the Cashews (8%) look the same.

You should avoid using such distortions.

When both variables are continuous it is helpful to produce a graph, called a scatterplot, of one against the other.

Fasting blood glucose

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Fasting blood glucose

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Axes for any continuous variable on any graph should be chosen with a suitable scale and, importantly, should make it very clear if a “false origin” is used.

Relationship between Mortality from All Causes All Ages (DSR 1999 and 2001 pooled) and Deprivation (IMD2000) for Local Authorities in the East Midlands

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Deprivation (IMD2000 average of ward scores per local authority)

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Scatter Plots

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Peasron correlation coefficient= -0.37

Is this a real trend ?How might we check ?

Table 1. Percentage of people in England and Wales rating theirhealth as “not good”: data from Census 2001

 Males Females

  Age-standardised proportion Age-standardised proportion

White British 7.8% 8.0%

White Irish 10.2% 9.3%

Other White 7.4% 7.8%

     

Mixed 9.8% 10.4%

     

Indian 8.7% 12.0%

Pakistani 13.5% 17.1%

Bangladeshi 13.9% 15.5%

Other Asian 8.7% 10.5%

     

Black Caribbean 10.0% 12.2%

Black African 6.8% 8.4%

Other Black 10.1% 11.9%

     

Chinese 5.6% 6.2%

Any other ethnic group 8.2% 8.0%

     

All ethnic groups 7.9% 8.2%

Tabular Presentation: Crosstabs

Local Authority England East Midlands ONS Cluster ONS Cluster DescriptionCorby CD 142.9 124.0 121.2 134.2 Manufacturing TownsDaventry CD 127.0 124.0 121.2 113.4 Prospering Smaller TownsEast Northamptonshire CD 102.6 124.0 121.2 113.4 Prospering Smaller TownsKettering CD 97.4 124.0 121.2 113.4 Prospering Smaller TownsNorthampton CD 130.8 124.0 121.2 125.6 New and Growing TownsSouth Northamptonshire CD 107.1 124.0 121.2 113.4 Prospering Smaller TownsWellingborough CD 120.9 124.0 121.2 125.6 New and Growing Towns

Mortality from all cancers (ICD10 C00-C97): Directly age-standardised rates (DSR) Less than 75 years 2001-03 (Pooled)

Tables clearly give actual numbers involved. They can be colour coded to represent statistical significance.Very hard to misinterpret a table.However with a large numbers of data can get overwhelming.

Bar charts are similar to histograms, but are used to represent data classified into categories. The widths of blocks are always equal and there are spaces between the blocks.

Group

UNCLASSIFIEDNORMALIGTNEW DIABETES

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Bar chart showing the number of people in each of four groups.

Clustered and stacked bar charts are used to describe two or more categorical variables simultaneously.

Group

UnclassifiedNormalIGTNew Diabetes

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Female

Group

UnclassifiedNormalIGTNew Diabetes

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Female

Male

Clustered bar chart showing the percentage in each group within each gender.

Stacked bar chart showing the number of people of each gender, within each of four groups.

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NE NW Y&H EM WM EE L SE SW

65 and over

35-64

16-34

Under 16

100% stacked vertical bar chart

Difficult to make comparisons among the second, third, or subsequent segments in a stacked bar because judgment is not being made from a common base.

Which region has largest proportion of males 35-64?

3d barchart

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What % of abortions in England at gestation< 10 weeks?

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What % of abortions at 13+ gestation in East Midlands?

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GESTATION 10-12WEEKS

GESTATION 13+ WEEKS

ENGLAND EAST MIDLANDS

What % of abortions at 13+ gestation in East Midlands?

What % of abortions in England at gestation< 10 weeks?

Vertical Bar Charts

Horizontal Bar ChartMortality from all causes, females, East Midlands LAs, 2002-2004 pooled

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Rutland UABlaby CD

Melton CDSouth Kesteven CD

Derbyshire Dales CDSouth Northamptonshire CD

Rushcliffe CDHarborough CD

Broxtowe CDOadby and Wigston CD

Daventry CDCharnwood CD

Hinckley and Bosworth CDWellingborough CD

East Northamptonshire CDWest Lindsey CD

Newark and Sherwood CDSouth Holland CD

North Kesteven CDLeicestershire,

Gedling CDENGLAND

East Lindsey CDKettering CD

EAST MIDLANDSBoston CD

Northampton CDDerby UA

Erewash CDTrent SHA

North West Leicestershire CDAmber Valley CD

South Derbyshire CDChesterfield CD

North East Derbyshire CDHigh Peak CDMansfield CD

Bassetlaw CDLincoln CD

Ashfield CDNottingham UA

Corby CDLeicester UABolsover CD

Directly age-standardised rates (DSR)

Spurious ranking somebody's got to be at the bottom

Encourages comparison when perhaps not justified

95% CI’s arbitrary

No consideration of multiple comparisons

SMR < 75 Circulatory Disease: Bassetlaw PCT and CAS Wards: 5 year average 1999-2003; SMR = 100 is East Midlands average

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Dot plot with Confidence intervals

Not a standard graph format in excel

Gives a better visual representation of the width of confidence intervals, useful when CI’s are wide.

Line charts display the same information as bar charts except that the line connects the midpoints of tops of corresponding bars. Line charts are preferable to bar charts

when there are many categories or a continuous variable.

Length of stay as an inpatient

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Line chart showing trend of frequencies of days spent in hospital. This connects mid-points of the top of bars for the corresponding bar chart, with straight lines.

Life Expectancy at Birth Target = by 2010 increase LE to 78.6 years for men and to 82.5 years for women

Line Chart

LE Gap = 5 years

Useful for looking at trends of time

Not using a consistent scale leads to misrepresentation of trends.

Histograms are used to depict numeric variables. The areas of rectangles represent numbers of cases. If the widths of the rectangles are all the same, then the height of the blocks is proportional to the observed frequencies.

Histogram showing the distribution of the number of days stayed in hospital. Short lengths of stay (less than 4 days) were more common than longer ones.

Length of stay as an inpatient

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Histogram• Means of summarising the frequency distribution

of a single variable eg. Age distribution of North Notts residents off Exeter system

• Use where you havent already got the frequency distribution provided

• Easiest to do if have stats package Like SPSS or can use Data Analysis Tools add-in in Excel

• Alternatively use Access to get frequency count and export into Excel and do bar chart

Histogram: Age Distribution of 1% random sample off N Notts Exeter

Histogram

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Figure 10 Histogram with normal-curve and statistics for cholesterol measurements.

Cholesterol

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Std. Dev = 1.45

Mean = 6.6

N = 357.00

useful for illustrating the shape of the distribution of a batch of numbers

Figure 10 Histogram with normal-curve and statistics for cholesterol measurements.

Cholesterol

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Mean = 6.6

N = 357.00

Mean=6.6

useful for illustrating the shape of the distribution of a batch of numbers

Figure 10 Histogram with normal-curve and statistics for cholesterol measurements.

Cholesterol

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Mean = 6.6

N = 357.00

Mean=6.6Mode

useful for illustrating the shape of the distribution of a batch of numbers

Figure 10 Histogram with normal-curve and statistics for cholesterol measurements.

Cholesterol

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Mean = 6.6

N = 357.00

Mean=6.6ModeMedian6.4

useful for illustrating the shape of the distribution of a batch of numbers

Boxplots are graphical representations which clearly show the quartiles and extremes or outliers of a set of data in a compact manner.

Boxplots showing the distribution of 2-hour blood glucose among males and females.

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2 Hour Blood Glucose

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Boxplots are graphical representations which clearly show the quartiles and extremes or outliers of a set of data in a compact manner.

Boxplots showing the distribution of 2-hour blood glucose among males and females.

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2 Hour Blood Glucose

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Median

1st Quartile

3rd Quartile

Outliers “unusual observations”

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AreaValue

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National Range

Key

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Indicator

Life expectancy - male

Life expectancy - female

Deaths - smoking

Early deaths - heart disease & stroke

Early deaths - cancer

Infant deaths (under 1Yr)

Road injuries and deaths

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Health Profile Demo 4 - Low/High, Aligned National Averages, Box Plot With Inter-quartile (or ONS Cluster) Range

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Stat. sig. low value Stat. sig. high valueNon sig. valueNational average

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ENGLAND: GOR * ETHNIC

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A NORTH EAST

B NORTH WEST

D YORKSHIRE AND THE HUMBER

E EAST MIDLANDS

F WEST MIDLANDS

G EAST

H LONDON

J SOUTH EAST

K SOUTH WEST

B Mixed

C Asian or Asian British

D Black or Black British

E Chinese or other ethnic group

Radar Chart or Spider Chart

Allows the comparisons more than one group across a few different values

                        

                                                                                                                                             

             

Age Range Total MalesFemale

s

 

0 - 4 239013 122979 116034

5 - 9 265214 136557 128657

10 - 14 279334 143337 135997

15 - 19 260104 134049 126055

20 - 24 245101 124587 120514

25 - 29 254367 125803 128564

30 - 34 312846 153371 159475

35 - 39 325425 161507 163918

40 - 44 294160 146595 147565

45 - 49 269255 134352 134903

50 - 54 299218 150064 149154

55 - 59 250110 124810 125300

60 - 64 207432 102758 104674

65 - 69 186394 90219 96175

70 - 74 169602 78579 91023

75 - 79 143481 61883 81598

80 - 84 94157 35805 58352

85 - 89 51772 15850 35922

90 and over

25189 5753 19436

 

Totals 4172174204885

8212331

6

 

population pyramids for districts and regions comparing against the age-gender distribution for the UK as a whole: available on the ONS website under “Census 2001”

Population Pyramids

East Midlands Region

Not standard excel format

Spine charts- many different types, used for displaying lots of information of different variables

The range to the left and right is not necessarily equal

The range of clusters can be greatly distorted by one outlying point

No statistical significance

Variables with small variance from mean will be hard to spot

Maps

Statistical Process Control Charts

• Used to detect and monitor process improvement over time

• Easy to read guide which allows you to take appropriate action

• Provides an understanding of process over time and capability of achieving targets

• Distinguish between– Controlled (Chance) variation

• Stable and stays within process limits

– Uncontrolled (assignable) variation• Pattern changes over time and/or breaches control limits

Funnel Plots

• No ranking

• Data Displayed

• No preset threshold for out of control

• Visual relationship with volume

• Emphasises increased variability of smaller centres

Teenage pregnancy - Conceptions below age 18 (change in rate)CHI Indicator: Percentage change in the under-18 conception rate

between 1998 and 2001.

Funnel Plot for Teenage Pregnancy Reduction

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Target ratio

Tweaking with colourRe fe rral rate 2001-02: All s pe cialtie s

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Traffic lights on a graph

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These points in red represent mortality rates that are significantly above the regional average

These points in green represent mortality rates that are significantly below the regional average

Exercise

• List 3 advantages and 3 disadvantages to Funnel plots

X-Bar and R Chart

• Traditional chart – data points with mean and control limits

• The X-bar chart monitors the mean over time, based on the average of a series of observations, called a subgroup.

X-Bar and R Chart

• Does not allow us to monitor progress in terms of the targets set out by the DH

DoH Target to be reached by Jan 2006 in L&D

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MRSA data Mean Control Limits (2 SD) Control Limits (3 SD)

Data Source: Luton and Dunstable Hospital NHS Trust

XmR Chart: Number of MRSA BacteraemiaLuton and Dunstable Hospital NHS Trust

2001-2005

SPC chart with Targets

• Has targets instead of mean of data points• The “Action limits” are similar to control limits – are 2 and 3 SD’s from

target• Does not account for different trust sizes and/or number of patients seen

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Take action

Consider Action

Monitor

Below target

Target

Actual values

Year average

Data Source: 1 Department of Health Mandatory Bacteraemia Surveillance Scheme 2 Andre Charlett. CDSC HPA

Luton and Dunstable TrustNumber of MRSA Bacteraemias and Targets

Interpretation and use

• When are SPC methods not appropriate? – are we stretching their use?

• What if all points lie outside control limits? Is this a:– Bad measure?– Bad system?

• Where should control limits be set and how should we decide?

Observations

• Relatively simple to create – funnel plots, double square root charts etc, can all be created in Excel (spreadsheets available)

• Most people get it and like it when they do

• Quick, simple, robust,

• Funnel plots convey more information and use “real” units

BUT

• It’s the pattern that matters

• Only a screening tool – tells you where to look first