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

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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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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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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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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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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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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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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