The differences between data collection and collation.

1. Information Cycle
Data Handling in Information Cycle:
Collection and Collation
University of Oslo
Department of Informatics
Oslo - 2007
Facilitator: Gertrudes Macueve
11th April 2007

2. Learning objectives (1)
• Define what is data and what is
information
• Identify the different stages of the
information cycle
• Explain how to handle data
• Recognize the difference between
collecting data and gathering data
• Identify data collection tools

3. Learning objectives (2)
• Explain the need for flexibility and
standardization in data collection
• Explain the rationale for use of an
essential dataset
• Explain the correlation between data
elements and indicators
• Define what is data collation
• Indicate common data collation methods
and problems

4. Data and information
• Data
– observations and measurements about the world,
e.g.
– Representation of observations or concepts suitable
for communication, interpretation, and processing by
humans or machines.
– May or may be not useful to a particular task.
• Information
– facts extracted from a set of data (interpreted data),
Meaningful and useful
– Data brought together in aggregate to demonstrate
facts;
– It is useful to a particular task.

5. Information CycleInformation Cycle
What do we
collect?
What do we do with
it?
How do we present
it?
How do we use
it?
Quality
information

6. Information Cycle
Data converted to
information
What do we do withWhat do we do with
it?it?
How do we presentHow do we present
it?it?
How do we use it?How do we use it?
data sources &
tools
Process &
Analysis
Reports & graphs
Interpretation of
information
Good quality
data
What do we collect?What do we collect?
Decision-making
for effective
management
feedbackfeedback
Stages
Tools
Outputs
Quality atQuality at
every stageevery stage
EDSEDS

7. Data Handling in the Information
Cycle:
1. Data collection

8. The starting point…
Feeding the information cycle
Collection
Input
Raw data
Presenting
Interpreting
USEANALYSIS
Processing
Output
INFORMATION

9. Data collection
• Two ways to obtain data
1. Collect data: Physical counting of elements
2. Gather data: if data have already been
collected; Requirements:
• The definitions of the data are the same as
ours
• The format in which the data are collected, is
the same
• Data are collected reasonably accurately
• We are able to negotiate access to the data

10. Data collection/gathering
guiding principles
• WHO health care workers at all levels
• WHAT Essential Data Set
• WHEN daily – collated weekly & processed monthly
• WHERE work sites, facilities, districts (info filter)
• HOW data sources (tally sheets, registers etc)
• WHY To monitor progress towards goals & targets
To Plan new policies and changes
To evaluate current services
To assist health management processes

11. What data elements should be
collected?
• Can provide useful information (affecting the
management decisions)
• Cannot be obtained elsewhere
• Are easy to collect
• Do not require much work or time
• Can be collected relatively accurately
 ESSENTIAL DATA SET based on indicators
reflecting the health status of the community

12. Essential data set
MUST
KNOW
The % of children
under one year who
are fully immunised
Drop out rate DPT 1-
3; measles coverage The % of children
under two years who
are fully immunised
Other programme
vaccines given

13. Essential data set at each level
• Standardised
• Usefulness
• Address the needs
of all stakeholders
• User-friendly
• Dynamic

14. Where do we get data from?
• Routine data collection
– Routine health unit and community data
• Activity data about patients seen and
programmes run, routine services and
epidemiological surveillance; e.g.
• Semi-permanent data about the population
served, the facility itself and staff that run it
– Civil registration

15. Where do we get data from?
• Non-routine data collection
– Surveys
– Population census (headcounts
proportion/facility catchment’s area)
– Quantitative or qualitative rapid assessment
methods

16. Example: data collected at PHC facilities
Special
programme
activities
• Mental & reproductive health
• Child health & nutrition
• HIV/AIDS, STI and TB
• Chronic diseases
Routine Service
Activities
• Minor ailments
• Non-priority activities
Epidemiological
surveillance
• Notifiable diseases
• Environmental health
Administrative
Systems
• Infrastructure, equipment
• Human resources
• Drugs, transport, labs, finances, budget, staff
Population • Census: age, sex, place
• Births & deaths registration

17. Requirement for data collection:
Standardised definitions
• Essential standardised definitions of both
data elements and indicators:
– To ensure comparability between different
facilities, districts and provinces
– To ensure comparability across years

18. Data collection tools
A. Client Record Cards
B. Tally Sheets
C. Registers

19. A. Client Record Cards
• Record details of the client’s interaction
with the health service, e.g.:
– Health facility record system (traditional)
Associated with misfiling and loss vs
– Client-held record system (Road to Health
Card, Child Health Booklet, Women’s Health
Book, TB patient treatment card);
Associated with efficiency of the individual
concern, suitable for mobile community

20. Road to Health card

21. Family planning consultation card

22. B. Tally sheets
• Easy way of counting identical events that do not
have to be followed-up (e.g. headcounts, children weighed)

23. C. Registers
• Record data that need follow-up over long periods
such as ANC, immunisation, FP, TB

24. Assessment of data collection toolsAssessment of data collection tools
(Using SOURCE criteria)(Using SOURCE criteria)
 conduct an information audit of all tools – type & number
 S simple – ease of use (layout)
 O overlap – duplication (no overlap)
 U useful for – indicators (relevance)
 R relevance
 C clear – ease of use (layout)
 E effective – decisions used for (purpose)

25. Data collection ToolsData collection Tools
criteria for appropriatenesscriteria for appropriateness
TOOL PURPOSE LAYOUT RELEVANCE OVERLAP
How many?
•client cards
• tally sheets
• registers
• reports
Effective
decision-making
for:
•Public health
• Management
• Supervision/
support
•monitoring
• evaluation
Simple,
Clear,
Easy to
understand
•Priority actions
•No useless data
•Missing actions
evident
Useful for:
• Output/
Outcome/imput/
Process
• coverage/
Quality
• incidence/
prevalence
• no Overlap
with other
forms
• What
• When
• Where
• Why
• How

26. Data Collation

27. 1. summarising data from the same data
elements but from different sources
2. summarising data from the same
source but over a period of time.
Ways of collating data

28. Common collation problems
• Incorrect grouping of data
• Data are incorrectly added
• Missing data forms
• Double counting of data

29. Data collation practical methods
Unities method
Disease Cases Frequency
Cholera III 3
Accidents I 1
Malaria IIII IIII IIII 15
Diarrhea 12IIII IIII II

30. Data collation practical methods
Rectangles method
Disease Cases Frequency
Cholera 3
Accidents 1
15
12
Malaria
Diarrea

31. Data collation practical methods
Zeros Method (Tally sheet)
Disease Cases Frequency
Malaria 0000000000000000000000000 15
Diarrea 0000000000000000000000000 12
Cholera 0000000000000000000000000 3
Accidents 0000000000000000000000000 1