Information CycleData Handling in Information Cycle:
Collection and Collation
University of OsloDepartment of Informatics
Oslo - 2007Facilitator: Gertrudes Macueve
11th April 2007
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
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
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.
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
Information Cycle
Data converted to information
What do we do with What do we do with it?it?
How do we present How 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 at Quality at every stageevery stage
EDSEDS
Data Handling in the Information Cycle:
1. Data collection
The starting point…Feeding the information cycle
Collection
InputRaw data
PresentingInterpreting
USEANALYSIS Processing
OutputINFORMATION
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
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
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
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
Essential data set at each level
• Standardised• Usefulness• Address the needs
of all stakeholders• User-friendly• Dynamic
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
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
Example: data collected at PHC facilitiesSpecial 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
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
Data collection tools
A. Client Record Cards
B. Tally Sheets
C. Registers
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
Road to Health card
Family planning consultation card
B. Tally sheets• Easy way of counting identical events that do not
have to be followed-up (e.g. headcounts, children weighed)
C. Registers• Record data that need follow-up over long periods
such as ANC, immunisation, FP, TB
Assessment of data collection Assessment of data collection toolstools
(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)
Data collection ToolsData collection Toolscriteria 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
Data Collation
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
Common collation problems
• Incorrect grouping of data
• Data are incorrectly added
• Missing data forms
• Double counting of data
Data collation practical methodsUnities method
Disease Cases Frequency
Cholera III 3Accidents I 1
Malaria IIII IIII IIII 15
Diarrhea 12IIII IIII II
Data collation practical methodsRectangles method
Disease Cases Frequency
Cholera 3Accidents 1
15
12
Malaria
Diarrea
Data collation practical methodsZeros Method (Tally sheet)
Disease Cases FrequencyMalaria 00000 00000 00000 00000 00000 15Diarrea 00000 00000 00000 00000 00000 12Cholera 00000 00000 00000 00000 00000 3Accidents 00000 00000 00000 00000 00000 1
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