Using Information for Health Management; Part II

1. Using Information for Health Management; Part II - Health Information Systems Strengthening

2. Information cycle; from data to action

3. Presentation • What do you want to communicate? • Different information products for different data & meanings

4. Preparing for Presentationessential prerequisites Correct Complete • submission by all (most) reporting facilities / units Consistent • data within normal ranges • clear definitions / standards • Timely

5. Presenting Information Tabular:frequency distribution table Graphs:Histogram, Line diagrams, Scatter plot, Bar chart, Pie chart, population pyramids Numerical:  Measures of Typicality or Center: mode, median, mean  Measures of Variability (or Spread): range, variance, standard deviation  Measures of Shape: skewness, kurtosis • Proportions, rates, ratios Maps:geographical representation (GIS)

6. Tables • Beware information overload: • easy to produce – difficult to use • Ideally should contain: • Few rows • Few categories/columns • Uses: • assess quality • trends over time • make comparisons • pick up outliers, gaps

7. A nice table Number of children per family in Maputo, 2005 Source: Statistics & Planning Directorate, 2005

8. GRAPHS(a visual representation of data) Advantages: • Information is instantly conveyed • Data presented clearly and simply • Can expose relationships and patterns • Detect trends over time • Can be used to emphasise information

9. Graph Elements 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 Y X Source: Notes: Scale – must be appropriate

10. Five rules for graphs • Never put too much information in the graph. KEEP IT SIMPLE • Be careful about mixing different activities: stick to one group of people, diseases or service • 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 • Select scales that fit the entire graph on both axes • Where possible, draw a target line or reference point to show where you are aiming at

11. Type of graphs Continuous data • histograms • line graphs • scatter graphs Discrete Data • bar graphs • pie charts

12. Line graph PHC headcount under 5 years old, Manyara Clinic, 2001 • accurate, can show changes in the relationships between two variables • displays trends over time • useful if more than one data item is used

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

14. Line graph, with two dependent variables

15. Line graph, for cumulative coverage

16. 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

17. Pie chart good to show relative proportions Only for data that adds up to a total (100%)

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

19. Bar graph, stacked it displays the quantities, but it also shows the relative proportions of the categories to each other and to the whole BUT hard to estimate the value of the variables at the top

20. Population pyramids as an example of a histogram highlight the differences in age distribution between males and females as well as proportional age categories

21. 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 • Too high ink-to-data ratio (e.g. 3D graphs) Don’t trust the computer!

22. BADGRAPHS!

24. …gone fishing…

26. some inspiration… GapMinder Hans Rosling's 200 Countries, 200 Years, 4 Minutes

27. Action • Interpreting the information • Take into account data quality bias • Plan action and interventions • Prioritze resources • Set well-defined targets • How is the action going to be evaluated?

28. Linking Information and Planning 3. Evaluating Options 4. Creating Strategic Initiatives 2. Sense Making 1. Performance Measurement 6. Enacting Strategies 5. Rehearsing Strategies Direction Setting

29. Comprehensive Performance Management Framework Children vaccinated 1. Program stages Community Demand for Immunisation Decreased incidence of measles Vaccines available Cold chain maintained 2. Program indicators 1 2 5 6 3 4 FMoH 3. Program performance for each indicator at each level of hierarchy SMoH LGA Facility Community Causal factors L3 Staff establishment Defined Appropriate for scope of services Not recently reviewed Causal factors L2 HR Causes Staff est defined Funding available Authority to appoint Causal factors L1 HR Causes Finance Equipment Logistics Community 4. Causal factor analysis Strategic plan: Causal factors can be aggregated up from lowest level of the hierarchy to obtain a general consensus of contributing factors Strategic plan is based on the obstacles that are identified, and grouped by causal factors; Strategic plan can be focussed on specific issues at each level of the hierarchy 5. Strategic plan to address obstacles

31. Indicators • Grouped in reporting groups around CoC processes • Comparison across • Indicators/OU or • OU’s/indicator • ID of hi/lo performers • Assessment of Causal Factors 2. Sense Making 1. Performance Measurement

32. http://hispsa.org/DHIS14_ndoh5 • admin:district

33. Data quality bias?1st Dose VS Population <1yr

34. Correlating two data sources

35. Targets state exactly what has to be achieved, by whom and by when a realistic point at which to aim to reach a goal turning organizational goals into operational numbers

36. Example Targets

37. Targets should be SMART Specificcapturing changes in situation concerned Measurable able to be easily quantified Appropriate fit to local needs, capacities and culture Realistic can be reached with available resources Time bound to be achieved by a certain time

38. Summary • Data quality is an issue at all steps of the information cycle • The best way to improve data quality is to use the data • Indicators (rates, ratios) are much more useful than raw data • Indicatros can be compared across time and space • Different information products serve different needs • Targets should be set for all action