Post on 13-Jan-2016
Using Information for Health Management; Part II- Health Information Systems Strengthening
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Learning objectives
– the information cycle; tools and processes for turning data into action
– the relationship between data use and data quality
– hierarchy of standards / essential data set– common reasons for compromised data quality,
and various counter measures– different information products for
communicating different meanings
Information cycle; from data to action
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Presentation
• What do you want to communicate?
• Different information products for different data & meanings
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Preparing for Presentationessential prerequisites
CorrectComplete submission by all (most) reporting facilities / units
Consistent data within normal ranges clear definitions / standards
Timely
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)
• 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
Tables
A nice table
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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
Number of children per family in Maputo, 2005
Source: Statistics & Planning Directorate, 2005
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
Graph Elements
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200
400
600
800
1000
1200
Jan Feb Mar Apr May Jun
num
bers
Graph 1: Clinic Alpha -PHC Headcount, 2001
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 – must be appropriate
Source: Notes:
Five rules for graphs
1. Never put too much information in the graph. KEEP IT SIMPLE
2. Be careful about mixing different activities: stick to one group of people, diseases or service
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 axes5. Where possible, draw a target line or reference
point to show where you are aiming at
Type of graphs
Continuous data – histograms– line graphs– scatter graphs
Discrete Data– bar graphs – pie charts
Line graph
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100
200
300
400
Jan Feb Mar Apr May Jun
accurate, can show changes in the relationships between two variables
displays trends over time
useful if more than one data item is used
PHC headcount under 5 years old, Manyara Clinic, 2001
Bar graph versus Line graph which one is best?
Line graph, with two dependent variables
Line graph, for cumulative coverage
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
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
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
Pie chart good to show relative proportions
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Only for data that adds up to a total (100%)
Bar graph, simple
Clinic Alpha : Attendance 2001
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100
200
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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
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
it displays the quantities, but it also shows the relative proportions of the categories to each other and to the wholeBUT hard to estimate the value of the variables at the top
Population pyramids
highlight the differences in age distribution between males and females as well as proportional age categories
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!
BADGRAPHS!
…gone fishing…
GapMinder
some inspiration…
Hans Rosling's 200 Countries, 200 Years, 4 Minutes
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?
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Interpret information to find causes
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Low PHU Deliveries
TBAs holding on clients
Low community sensitization
High fees for deliveries
Staff attitude
Can’t afford fees
Men not involved
No proper orientation
Low educational
level
Staff shortage
Staff not motivated
Problem with sharing of
fees
Cultural beliefs
Family trust in the TBAs
Community norms
Bye laws not instituted
Patients refusal to go to PHU
Long distance
Irregular supervision
Difficult terrain
Can’t afford travel
Data quality bias?1st Dose VS Population <1yr
Correlating two data sources
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Take action: Underweight children
• Public campaign:”You must weigh your
child every month to make sure it grows properly”
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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
Targets
Example Targets
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Targets should be SMART
Specific capturing 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
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
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