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Presenting informationcommunicating meaning

Health Management Information Systems

João Carlos de Timóteo Mavimbe

Oslo, April 2007

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Presenting Presenting informationinformation

LEARNING OUTCOMES:

By the end of the session you should be able to:

Understand the purposes and basic principles of data presentation

Present data in simple tables

Select appropriate graph types to present the various types of data

Build appropriate graphs for display of data

Develop skills in proper presentation of information

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The information cycle: Presenting Information

Collection

InputRaw data

PresentingInterpreting

USEANALYSIS Processing

Tables, Graphs, Population, Maps

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Preparing for Presentationessential ingredients: 3 C + 1 Tessential ingredients: 3 C + 1 T

Correct good quality data

Complete submission by all (most) reporting facilities

Consistent data within normal ranges

reflects community shifts

clear definitions

Timely

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Presenting information

What information is presented?

Why is information presented?

How is information presented?

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What What “information“information” is ” is presented?presented?

Analysed data (mainly)

Collated data (sometimes)

Raw data (rarely) 

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Why is Why is information information presentedpresented??

To promote understanding and facilitate interpretation:

Appropriate interpretations what linkages are possible? (correct, logical, sensible) may answer important questions may result in action Possible interpretations are context dependent (population, health, service status) depend on data quality should depart from data definitions

 

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Why is Why is information information presentedpresented??

To share knowledge with whom?

To provide feedback to whom?

 

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How is information How is information presented?presented?

Three ways of presenting data:

1. Tabular: frequency distribution table

2. Graphs: Histogram, Line diagrams, Scatter plot, Bar chart, Pie chart

3. Numerical:

Measures of Typicality or Center: mode, median, mean

Measures of Variability (or Spread): range, variance, SD

Measures of Shape: skewness, kurtosis

Proportions, rates, ratios

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Types of data

Data

Qualitative, Non- Numerical or Categorical

Discrete

Quantitative or Numerical

Discrete

Continuous

They determine the most appropriate tool for presenting data.

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Data

Quantitative(Numbers)

Qualitative(Characteristics)

Discrete Continuous

Discretecategories/ kinds

counts measures

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Numerical Data

Continuous – they are measurablemeasurable Examples:

Age of patients in years or months Weight of newborn in grams

Discrete – they are countedcounted (possible values are distinct or separate): Examples:

The size of a family expressed as the number of children

The number of days since the begining of a disease

units of measurement

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Non-numerical Data

They are the qualitative description of categories of a characteristic.

Examples:The gender of a patient is recorded as

“male” or “female”;The list of diagnoses in a health center;

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Exercise:Mark with in the blank spaces

Data Quantitative Qualitative  Discrete Continuous Discrete

Number of beds per HC      Bed ocupation      Addresses of patients      Number of children      Patient temperature in ºC      Cost of a drug presciption      Population of a village      Age of patients in years      Number of broken vials      Health area             

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BEDS

Number of bedsType of bedHeight of the bed

(from mattress to floor)

– an example of how a single data element may provide different types of data.

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Tables: saying it with figures

TitleColumn

Row Cell

Source: Comments: Date:___/___/___

Table No.

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TablesTables Beware information overload:

easy to produce – difficult to use

Ideally should contain:

Few rows

One category

Uses:

assess quality

trends over time

make comparisons

pick up outliers, gaps

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Tables

Number of Children Frequency %0 7 6,71 10 9,62 15 14,43 25 24,04 21 20,25 10 9,66 6 5,87 5 4,88 2 1,99 3 2,9

Total 104 100,0

Table 1: Number of children per family in Maputo, 2005

Source: Statistics & Planning Directorate, 2005

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GRAPHS: talking with pictures(…a visual representation of data)

Advantages: Information is instantly conveyed Data are presented clearly and simply Can expose relationships and patterns Detect trends over time Can be used to emphasise information

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Graph ElementsGraph ElementsGraph 1: Clinic Alpha -PHC Headcount, 2001

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200

400

600

800

1000

1200

Jan Feb Mar Apr May Jun

num

bers

PHC Headcount

X

Y

Title – descriptive clinic name, what is graphed and the time period

Y axis – must ALWAYS be labeled

Y axis label

X axis – label if appropriate

Key or legend – used if more than one element graphed

Scale – be appropriate

Source: Notes:

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Golden rules for graphs

1. Never put too much information in the graph. KEEP IT SIMPLE.2. Never mix different activities: stick to one group of people or

diseases or services.3. Label your graph: always have a clear heading, easily read

labels on the axes, and a legend which explains each of the lines or bars.

4. Select scales that fit the entire graph on both axes.5. Where possible, draw a target line or reference point to show

where you are aiming at.

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Types of graphs They follow the types of data available:

Data

Quantitative(Numbers)

Qualitative(Characteristics)

Discrete Continuous

Discretecategories/ kinds

counts measures

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Type of graphs

Continuous data histograms line Graphs scatter Graphs

Discrete Data bar graphs pie charts

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Graphs for sets of continuous data

a) histogramsb) line graphsc) cumulative line graphs

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Line graphLine graph

0

100

200

300

400

Jan Feb Mar Apr May Jun

accurate, can show minute changes in the relationships between 2 major variables

displays trends over time

can be useful if more than one data item is used

Graph 2: PHC headcount under 5 years old, Manyara Clinic, 2001

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Bar graph versus Line graph which one is best?

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Line graph, with 2 dependent variables

Cases of Malaria and Diarrhoea among children, year 2000, Marrupa district

020406080

100120

case

s

Plasmodium +Diarrhoea

Remember to remove the silly gray background to improve contrast!

The larger the font, less detail will be shown in the axes

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Line graph, for cumulative Line graph, for cumulative coveragecoverage

Clinic Alpha : EPI : Cumulative Coverage of Children Fully Immunised 2000

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20

40

60

80

100

%

Monthly Immunisation Cumulative Immunisation

Monthly Immunisation 4 5.3 6.2 3.8 5.6 7.3 6.8 7 5.9 6.7 7.5 5.8

Cumulative Immunisation 4 9.3 15.5 19.3 24.9 32.2 39 46 51.9 58.6 66.1 71.9

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Target line

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Line graph, for cumulative coverageLine graph, for cumulative coverage

Simple and effective monitoring tool

Used when targets are set for a year i.e. immunization, antenatal coverage, etc.

Each month, data is graphed individually and also added to the previous month

A target is set, a target line is drawn and progress is monitored with respect to the target line

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Graphs for sets of discrete data

a) pie chartsb) bar graphs

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Bar graph, simple Bar graph, simple Clinic Alpha : Attendance 2001

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100

200

300

400

500

600

700

800

Jan Feb Mar Apr May Jun

num

bers

PHC Headcount under 5 years PHC Headcount 5 years and over

• displays data over time or can compare 2 or more different facilities / districts / regions / years

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Bar graph, stacked Bar graph, stacked Clinic Alpha : Attendance 2001

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200

400

600

800

1000

1200

Jan Feb Mar Apr May Jun

num

bers

PHC Headcount under 5 years PHC Headcount 5 years and over

• has the advantages of a circle graph: it displays the quantities, but it also shows the relative proportions of the categories to each other and to the whole.

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Pie chart or circle graphPie chart or circle graph

Clinic Alpha : Headcount distribution Jan 20017%

62%

31%

PHC Headcount under 5 years

PHC Headcount 5-59 years

PHC Headcount 60 years and over

• best type of graph for showing the relative proportions of different categories to each other and to the whole

• can be used when exact quantities are less important than the relative sizes of the parts

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Common faults with graphs No title No labels for the variables No units of measurement (or incorrect units!) No scale markings (or just too many!) Inappropriate scale choice – data points

should be evenly represented Incorrect choice of independent (x-axis) and

dependent (y-axis) variables No legends when needed

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Graphs- population Graphs- population pyramidspyramids

• they may highlight the differences in age distribution between males and females as well as proportional age categories

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The Facility Map

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GRAPHS YOU

SHOULD NOT

BUILD!

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…gone fishing…

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