Data Presentation,
Interpretation and Use
Learning objectives
Participants will be able to:
1. Understand different ways of summarizing data
2. Choose the right table/graph for the right data
and audience
3. Ensure that graphics are self-explanatory
4. Create graphs and tables that are attractive
Do you present yourself like this?
So why would you present your
data like this?
Or this?
100
80
60
Any net
LLIN
40
20
0
This is Better!
Use of ITNs in Zambia
6
Effective presentation
•
•
•
•
Clear
Concise
Actionable
Attractive
Effective presentation
• For all communication formats it is important
to ensure that there is:
– Consistency
• Font, Colors, Punctuation, Terminology, Line/
Paragraph Spacing
– An appropriate amount of information
• Less is more
– Appropriate content and format for
audience
• Scientific community, Journalist, Politicians
Summarizing data
• Tables
– Simplest way to summarize data
– Data is presented as absolute numbers or
percentages
• Charts and graphs
– Visual representation of data
– Usually data is presented using percentages
Points to remember
• Ensure graphic has a title
• Label the components of your graphic
• Indicate source of data with date
• Provide number of observations (n=xx)
as a reference point
• Add footnote if more information is
needed
Tips for Presenting Data in PowerPoint
• All text should be readable
• Use sans serif fonts
– Gill Sans (sans serif)
– Times New Roman (serif)
•
•
•
•
Use graphs or charts, not tables
Keep slides simple
Limit animations and special effects
Use high contrast text and backgrounds
11
Choosing a Title
• A title should express
– Who
– What
– When
– Where
Tables: Frequency distribution
Year
2000
2001
2002
2003
2004
2005
2006
2007
Number of cases
4 216 531
3 262 931
3 319 339
5 338 008
7 545 541
9 181 224
8 926 058
9 610 691
Tables: Relative frequency
Percent contribution of reported malaria cases by year between 2000 and 2007, Kenya
Year
Number of malaria cases (n)
Relative frequency (%)
2000
4 216 531
8
2001
3 262 931
6
2002
3 319 339
7
2003
5 338 008
10
2004
7 545 541
15
2005
9 181 224
18
2006
8 926 058
17
2007
9 610 691
19
Total
51 400 323
100.0
Source: WHO, World Malaria Report 2009
Use the right type of graphic
• Charts and graphs
– Bar chart: comparisons, categories of data
– Histogram: represents relative frequency of
continuous data
– Line graph: display trends over time,
continuous data (ex. cases per month)
– Pie chart: show percentages or
proportional share
Bar chart
100
80
60
40
20
0
Any net
LLIN
Bar Chart
Household Ownership of at Least 1 Net or ITN, 2008
100
80
Percent
77
70
66
60
57
56
40
46
45
38
20
0
Country 1
Country 3
Source: Quarterly Country Summaries, 2008
Country 4
Country 5
Any net
LLIN
Stacked bar chart
% Children <5 with Fever who Took Specific Antimalarial, 2007-2008
ACT
Amodiaquine
Chloroquine
2007
9
20
Year
26
Quinine
Sulfadoxine-Pyrimethamine
Other
2008
36
0
20
9
40
11
Percent
60
80
100
Histogram
Percent contribution of reported malaria cases by year
between 2000 and 2007, Kenya
20
18
16
Percent
14
12
10
Relative Frequency
8
6
4
2
0
2000 2001 2002 2003 2004 2005 2006 2007
Bar Chart v. Histogram
Data fabricated for illustration
20
Bar Chart v. Histogram (cont.)
Data fabricated for illustration
21
Age
Population Pyramid: Country Z, 2008
80+
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
15-19
10-15
5-10
<5
Female
Male
15
10
5
0
Percent
5
10
15
Line graph
Number of Clinicians* Working in Each Clinic During Years 1-4, Country Y
6
Number of clinicians
5
4
Clinic 1
3
Clinic 2
Clinic 3
2
1
0
Year 1
Year 2
*Includes doctors and nurses.
Year 3
Year 4
Caution: Line Graph
Number of Clinicians* Working in Each Clinic During Years 1-4, Country Y
Number of clinicians
6
5
4
Year 1
3
Year 2
2
Year 3
Year 4
1
0
Clinic 1
*Includes doctors and nurses.
Clinic 2
Clinic 3
Pie chart
Malaria Cases
8
10
1st Qtr
2nd Qtr
3rd Qtr
23
59
4th Qtr
Pie chart
Percentage of all confirmed malaria cases treated by quarter, Country X, 2011
8%
10%
1st Qtr
2nd Qtr
23%
N=257
59%
3rd Qtr
4th Qtr
How should you present…
1. Prevalence of malaria in 3 countries over a 30 year
period?
2. Data comparing prevalence of malaria in 10
different countries?
3. Data on reasons why individuals not using ITNs (out
of all individuals surveyed who own an ITN and are
not using it)?
4. Distribution of patients tested for malaria by
parasite density
Summary
• Make sure that you present your data in a consistent
format
• Use the right graph for the right data and the right
audience
• Label the components of your graphic (title, axis)
• Indicate source of data and number of observations
(n=xx)
• Add footnote for more explanation
Creating Graphs
Learning objectives
1. Understand basic chart terminology
2. Create charts in PowerPoint using data in
Excel
3. Give a description of the data presented in
each chart
Pie Chart
Status of Lost Net Among Households that Lost Any Nets
1% 1%
12%
Net was sold
Net was given away to relatives
Net was given away to others
Material used for other purpose
86%
Source: MEASURE Evaluation, Retention, Use and Achievement of “Universal Access” Following the
Distribution of Long Lasting Insecticide Treated Nets in Kano State, Nigeria, 2009
Individual Work: Bar Chart
Parasite Prevalence among Children under Five in
Tanzania, 2008
30
Percent
25
20
15
Parasite Prevalence
10
5
0
6-11
12-23 24-35 36-47 48-59
Age in Months
Source: Tanzania HIV and Malaria Indicator Survey, 2008
Secondary Axis
1000
800
600
400
200
Number of Confirmed Malaria Cases
Number of Confirmed Malaria Deaths
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
0
Number of Confirmed Malaria Deaths
180000
160000
140000
120000
100000
80000
60000
40000
20000
0
2000
Number of Confirmed Malaria Cases
Confirmed Malaria Cases and Deaths, Country X
2000-2010
Data Interpretation
Analysis vs. Interpretation
• Analysis: describing data with tables, graphs, or
narrative; transforming data into information
• Interpretation: adding meaning to information
by making connections and comparisons and by
exploring causes and consequences
Has the Program Met its Goal?
Use of Nets or ITN by Children <5 yrs of Age, 2008
100
Target >80%
80
60
20
ITN
48
40
25
29
29
Country 4
Country 5
0
Country 1
Country 3
Interpreting Data
 Does the indicator meet the target?
 What is the programmatic relevance of the finding?
 What are the potential reasons for the finding?
 How does it compare? (trends, group differences)
 What other data should be reviewed to understand
the finding (triangulation)?
 Conduct further analysis
Practical
• Question:
– Are ANC clinics in country X reaching their coverage
targets for IPTp?
• Data Source:
– Routine health information
Data Source
General ANC Registers
Code
Variables
1.
New ANC clients
2.
Group pre-test counseled
3.
Individual pre-test counseled
4.
Accepted HIV test
5A.
HIV test result - Positive
5B.
HIV test result – Negative
5C.
HIV test result - Indeterminate
6 A.
Post-test counseled - Positive
6 B.
Post-test counseled – Negative
8A.
ARV therapy received – Current NVP
9.
IPTp-1
10.
IPTp-2
 Which of these variables are
relevant to answer your
question?
 Which elements will be
included in your numerator
and which in your
denominator?
Answers:
1) New ANC clients, IPTp-1
2) New ANC clients =Denominator,
IPTp-1 and IPTp-2= Numerator
IPTp Coverage-Facility Performance
Number of ANC clients receiving IPTp
Code
Variables
Facility 1
Facility 2
Facility 3
Facility 4
Facility 5
9.
IPTp-1
536
1435
39
969
862
10.
IPTp-2
372
542
38
452
780
 Question:
Among the five facilities, which one performed better?
 Answer:
Cannot tell because we don’t know the denominators
IPTp Coverage-Facility Performance
Number of ANC clients receiving IPTp
Code
Variables
Facility 1
Facility 2
Facility 3
Facility 4
Facility 5
1
New ANC Clients
744
2708
105
1077
908
9.
IPTp-1
536
1435
39
969
862
10.
IPTp-2
372
542
38
452
780
Question: Now, you have the denominators, which of these
facility performed better?
Indicator
Facility 1
Facility 2
Facility 3
Facility 4
Facility 5
% of new ANC clients who receive
IPTp-1 in the past year
72%
53%
37%
90%
95%
% of new ANC clients who receive
IPTp-2 in the past year
50%
20%
36%
42%
86%
Response: Facility 5
Are facilities reaching coverage
targets?
Percent of ANC Clients Receiving IPTp in Select Facilities
100
Target-80%
Percent
80
60
40
IPTp-1
20
IPTp-2
0
1
2
3
4
5
Facility
* National coverage target for pregnant women receiving IPTp-2 is 80%.
Additional Questions
• Which facility is performing better/worse than
expected?
• What is the trend over time for these facilities?
• How would you assess each facility’s performance
based on the data?
• What other data or information should you consider
in providing recommendations or guidance to the
facilities?
Data Dissemination
Learning Objectives
By the end of this session, participants will be
able to identify:
1. The purpose of dissemination
2. Dissemination issues and concerns
3. Strengths and weaknesses of different
communication formats
4. The main components of a dissemination plan
Dissemination Framework
Results
Dissemination
Source: MEASURE DHS
Informed
Informed
User
Decisions
Improved
Programs/
Policies
Purpose of Dissemination
• Disseminating data can help potential
users:
– Understand current health status
– Reach decisions based on quality data
– Make changes to existing health
programs and policies
– Take other actions to improve health
outcomes
Plan Materials Carefully
• Use different formats if possible, including:
– Print materials
• HIS Reports, Success story, Posters, Key findings, Fact
Sheet, Press Report
–
–
–
–
PowerPoint presentations
CD-ROMS with datasets
Videos
Online media
Focus on a Specific Audience
• Create different materials for different users:
– Meet the audience’s needs
– Translate materials into local languages
– Produce reports on specific topics
• Impact
• LLINs
• Case Management
• IPTp
– Match the medium to the audience
Make Sense of the Data
• Help users make sense of the data:
– Add policy recommendations and conclusions
– Highlight key points
– Break down findings by categories of interest
• Province
• Education
• Wealth
– Use maps and graphics to convey information
Put Findings in Context
• Put survey findings in context:
– Show trends over time
– Make comparisons with other countries in
the region
– Link findings with national or regional
programs and policies
Appropriate and Attractive Presentation
• Provide an appropriate amount of information
– Less is more
– Try to identify the most important pieces of
information and avoid overwhelming the user
with too much data
• Make materials appealing to look at whenever
possible
• Balance text and graphics
– Use pictures and graphs
How much is enough information?
In Tanzania, P. falciparum malaria, which is spread by the anopheles mosquito, is the leading cause of death among children under the age of five years. Young children have
increased susceptibility to symptomatic malaria as they have not yet acquired immunity to the malaria parasite.
Pregnant women are also especially vulnerable because their immunity to the parasite is suppressed during pregnancy and the parasite often sequesters itself in the
placenta – leading to both maternal morbidity due to anemia and low birth weight deliveries.
Mosquitoes need standing water to breed. Therefore, there are more mosquitoes in the environment (and thus higher malaria transmission) during the rainy season than
during the dry season. There are two rainy seasons in Tanzania: from October through January and from March through May (Figure 2).
Malaria control efforts in Tanzania focus on the following three interventions to prevent malaria among women and children under five years of age including:
Bednets
Used correctly, bednets offer protection from mosquito bites and thereby reduce the transmission of malaria. While all bednets can protect the people sleeping under them,
insecticide-treated nets (ITN) are especially effective because they both block the mosquito bite and kill any mosquitoes that land on the net. Pilot studies promoting
ITNs have shown increased child survival and reduced anemia among children under five years of age, as well as reduced maternal morbidity and low birth weight
deliveries.
Intermittent Preventive Treatment in Pregnancy
Intermittent preventive treatment in pregnancy (IPTp) reduces placental malaria and anemia in pregnant women as well as the incidence of low birth weight deliveries. The
regimen for IPTp recommended by the World Health Organization (WHO) is two to three doses of sulfadoxine-pyrimethamine (SP) given to pregnant women after
quickening (the first fetal movements felt by the mother) in the second and third trimesters during routine antenatal care visits. As resistance to SP is growing in
much of sub-Saharan Africa, researchers are investigating the efficacy of this drug for IPTp and exploring the safety of other more effective medications for this
purpose.
Prompt and Effective Treatment
To reduce morbidity and mortality from malaria, young children should be treated as soon as symptoms (usually fever) appear. Moreover, it is important that they receive
the correct medication. In much of sub-Saharan Africa, the malaria parasite has developed resistance to older medications such as chloroquine, amodiaquine and
sulfadoxine-pyrimethamine. Consequently, Tanzania has changed its treatment guidelines to recommend treatment with artemisinin-based combination therapies
(ACTs).
President’s Malaria Initiative. 2008. Malaria in Tanzania. Available online at: http://www.fightingmalaria.gov/countries/profiles/tanzania.html
D’Alessandro, U. et al. 1995. Mortality and morbidity from Malaria in Gambian children after introduction of an impregnated bednet program. Lancet, 345(8948), 479-483.
Schulman, C.E., and E.K. Dorman. 2003. Importance and prevention of malaria during pregnancy. Transactions of the Royal Society of Tropical Medicine and Hygiene, 97.
Schellenberg, J.R. et al. 2001. Effect of large-scale social marketing of insecticide-treated nets on child survival in rural Tanzania. Lancet, 357 (9264), 1241-1247.
Ter Kuile, F.O., et al. 2003. Reduction of malaria during pregnancy by permethrin-treated bed nets in an area of intense perennial malaria transmission in western Kenya.
American Journal of Tropical Medicine and Hygiene, 68 (Suppl. 4) 50-60.
Roll Back Malaria, World Health Organization. 2003. Reducing the burden of malaria in pregnancy. Available online at:
http://www.who.int/malaria/rbm/Attachment/20040713/MeraJan2003.pdf
World Health Organization. 2008. The World Malaria Report, 2008. Available online at: http://malaria.who.int/wmr2008/malaria2008.pdf
Components of a Dissemination Plan
1.
2.
3.
4.
5.
6.
Project overview
Dissemination goals and objectives
Target audiences
Key messages
Sources/messengers
Dissemination activities, tools, timing, and
responsibilities
7. Budget
8. Evaluation Plan
Source: Canadian Health Services Research Foundation
Dissemination Planning Matrix
Activity
Target
Audience
Present results
at partner
meetings
Partner
organizations
Present results
at health
conferences
Publish results in
peer-reviewed
journals
Alert media
about the above
activities
Present results
to community
members
Scientific
Community
Tools
Person
Responsible
Timing
Powerpoint
Presentation,
Full report
(Printed,
electronic)
Poster
Jane
September 2014
John
November 2014
Scientific
Community
Article
John
December 2013
General
population
Interview, news
segment
Alice
December 2013
Community
members
Oral
presentation
with interactive
exercises
Alice
June 2013
Engage in Capacity-building
• Combine dissemination with capacity-building:
– Help users understand context and
terminology
– Train users to read tables and charts
– Provide exercises on using data
– Always ask users to consider implications of
the information for programs and policy
Dissemination Issues/Concerns
• Data Literacy
– Understanding terminology
– Understanding concepts of sampling errors,
confidence intervals
– Reading tables
– Comparing multiple data sources
• National and regional data vs district planning
• Timing of dissemination vs national planning
cycle
Dissemination Issues/Concerns
• Getting information out of the capital city
• Extending dissemination beyond the
immediate post-release period
• Difficulty tracking and monitoring use
Tracking Information Use
Learning objectives
By the end of this session, participants will be
able to identify:
1. Methods of tracking data and information use
2. Opportunities for improving data production and
use
3. Opportunities for feedback mechanisms
4. Points where analysis & data could support
programmatic decision making
Methods of Tracking Information Use
• Assessing coverage targets
• Key information interviews
• Meetings with staff
Information Flow
Feedback
Program
Clinical
histories,
service
statistics
Compiled
data,
some
analysis
Service Delivery
Point
Reports
Managers,
Government
, Donors
Analysts,
evaluators
Higher levels: district, province,
national
Information Use in Country X
• Local health centers and hospitals report up
through system
• However, local facilities never received full
reports
• Identified opportunities for feedback
through Information Use Map
Reasons to Assess Information Flow
• Local data not used locally
• Higher-level information does not return back
to local level
• Local data not assessed in broad context
• Little incentive to produce high-quality data
Information Use Mapping
• Purpose
– Describe existing flow of health information to
identify opportunities for improving its use
• Description
– Identifies gaps and opportunities for using
information
– Identifies opportunities for additional feedback
mechanisms
– Identifies points where analysis & data could
support programmatic decision making
Information Use Map: National HIV/AIDS Program May 2005
Data Collection
Private Clinic
N60
Government
Facility
Compilation
Storage
Analysis
Reporting
Use
Data collected in
electronic medical
records
Data collected both
electronically and
paper-based
Data collected
by paper-based
system
Reporting to
N60
headquarters
Data compiled
in monthly
reports
District
Data compiled in
quarterly reports
Regional
Data compiled in
quarterly reports
National
Reporting to
WHO 6FAM
Data stored
in national
HIV database
Data
analyzed
Annual state of
the program
report prepared
Development
of 5-year
strategic plan
Information Use Map: National HIV/AIDS Program May 2005
Data Collection
Private Clinic
NGO
Government
Facility
District
Compilation
Storage
Analysis
Client data
collected in EMRS
Clinic data
stored in EMRS
Conduct client
and clinic level
analysis
Data collected both
in EMRS and
paper-based
NGO data stored
in EMRS or in
paper records
Facility level data
stored in filing
cabinets
Client data
collected by
paper-based
system
Clinic staff
compile data in
monthly
summary reports
District level staff
compile data in
quarterly summary
reports
Reporting
Results reported
Use
to clinic
management
Use for clinic
service planning
and improvement
Conduct client
and site level
analysis
Results reported
to NGO headquarters & donor
Use for program
planning and
improvement
Conduct client
and facility level
analysis
Results reported
Use for program
planning and
improvement
Conduct district
level analysis
to facility
management
Results reported
to district
management
Use in district
program planning
and improvement
Results reported
Regional
Regional level staff
compile data in
quarterly summary
reports
Conduct regional
level analysis
National
Data stored
in national
HIV database
to regional
management
Data
analyzed
Reporting to
WHO 6FAM
More
sophisticated
analysis
conducted
Annual state of
the program
report prepared
Use in regional
program planning
and improvement
Development of
5-year strategic
plan
Use in national
program and policy
planning and
resource allocation
Key Messages
• Actual flow of data and information can reveal
barriers to improving data quality and use
• Information Use Map can highlight
intervention points
How does information flow
through your organization?
References
• Canadian Health Services Research Foundation. Developing a
Dissemination Plan. Available at:
http://www.chsrf.ca/knowledge_transfer/pdf/dissemination_
plan_f.pdf
• Laurie Liskin. “Dissemination and Data Use Tools”. MEASURE
DHS. PowerPoint Presentation. 17 June 2009
• MEASURE DHS. “Module 7: Disseminating and Using Data for
Change”. PowerPoint Presentation. Kenya, June 2010
MEASURE Evaluation is a MEASURE program project funded by
the U.S. Agency for International Development (USAID) through
Cooperative Agreement GHA-A-00-08-00003-00 and is
implemented by the Carolina Population Center at the
University of North Carolina at Chapel Hill, in partnership with
Futures Group International, John Snow, Inc., ICF Macro,
Management Sciences for Health, and Tulane University.
Visit us online at http://www.cpc.unc.edu/measure
Visit us online at http://www.cpc.unc.edu/measure.
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Data Presentation - Carolina Population Center