1. 1 Presenting information communicating meaning Health Management Information Systems João Carlos de Timóteo Mavimbe Oslo, April 2007

2. 2 Presenting information 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

3. 3 The information cycle: Presenting Information Collection Input Raw data Presenting Interpreting USE ANALYSIS Processing Tables, Graphs, Population, Maps

4. 4 essential ingredients: 3 C + 1 T Preparing for Presentation essential 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

5. 5 Presenting information What information is presented? Why is information presented? How is information presented?

6. 6 What “information” is presented?  Analysed data ( mainly )  Collated data (sometimes)  Raw data (rarely)

7. 7 Why is information presented? 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

8. 8 Why is information presented? To share knowledge  with whom? To provide feedback  to whom?

9. 9 How is information 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

10. 10 Types of data Data Qualitative, Non- Numerical or Categorical Discrete Quantitative or Numerical Discrete Continuous They determine the most appropriate tool for presenting data.

11. 11 Data Quantitative (Numbers) Qualitative (Characteristics) Discrete Continuous Discrete categories/ kinds counts measures

12. 12 Numerical Data measurable Continuous – they are measurable  Examples: Age of patients in years or months Weight of newborn in grams counted Discrete – they are counted (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

13. 13 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;

14. 14 Exercise: Mark with in the blank spaces DataQuantitativeQualitative 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

15. 15 BEDS Number of beds Type of bed Height of the bed (from mattress to floor) – an example of how a single data element may provide different types of data.

16. 16 Tables: saying it with figures Source:Comments: Date:___/___/___ Table No.

17. 17 Tables  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

18. 18 Tables Table 1: Number of children per family in Maputo, 2005 Source: Statistics & Planning Directorate, 2005

19. 19 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

20. 20 Graph Elements 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:

21. 21 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.

22. 22 Types of graphs They follow the types of data available: Data Quantitative (Numbers) Qualitative (Characteristics) Discrete Continuous Discrete categories/ kinds counts measures

23. 23 Type of graphs Continuous data  histograms  line Graphs  scatter Graphs Discrete Data  bar graphs  pie charts

24. 24 Graphs for sets of continuous data a) histograms b) line graphs c) cumulative line graphs

25. 25 Line graph  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

26. 26 Bar graph versus Line graph which one is best?

27. 27 Line graph, with 2 dependent variables Remember to remove the silly gray background to improve contrast! The larger the font, less detail will be shown in the axes

28. 28 Line graph, for cumulative coverage

29. 29 Line 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

30. 30 Graphs for sets of discrete data a) pie charts b) bar graphs

31. 31 Bar graph, simple displays data over time or can compare 2 or more different facilities / districts / regions / years

32. 32 Bar graph, stacked 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.

33. 33 Pie chart or circle graph 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

34. 34 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

35. 35 Graphs- population pyramids they may highlight the differences in age distribution between males and females as well as proportional age categories

36. 36 The Facility Map

37. 37 GRAPHS YOU SHOULD NOT BUILD!

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39. 39 …gone fishing…

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