reflection
Hemants4692
Week3DataPresentationInterpretationandUse.pptLearning objectives
Participants will be able to:
Understand different ways of summarizing data
Choose the right table/graph for the right data and audience
Ensure that graphics are self-explanatory
Create graphs and tables that are attractive
Speaker notes
By the end of this session, participants should be able to: [READ BULLETS]
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Data Presentation, Interpretation and Use
Speaker notes
By the end of this session, participants should be able to: [READ BULLETS]
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Do you present yourself like this?
Speaker notes
Do you present yourself like this? [HAVE AUDIENCE ANSWER QUESTION.]
Why would you not present yourself like this? Do you think this man is taken seriously? What do you think would happen if he tried to speak to someone in the Ministry of Health about some information related to a BCC campaign? Would he even be let in?
So, if you know that you would not be taken seriously if you presented yourself like this, then . . .
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So why would you present your data like this?
Speaker notes
Why would you present your data like this? Would most people be able to get the message from this data if it was presented in this STATA output? [ALLOW COMMENTS]
No, it is too busy and it is difficult to interpret.
The way you present your data can greatly affect how usable the data will be.
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Or this?
Speaker notes
And why would you present your data like this? Can anyone tell me what some problems may be with this chart?
POSSIBLE ANSWERS
No title
No axis labels
The colors are difficult to read. (You should never put a dark color on a dark background.)
The green color is too bright.
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This is Better!
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Use of ITNs in Zambia
Speaker notes
What is improved in this slide compared to the last one? (other than the data points themselves)
POSSIBLE ANSWERS
Title
Axis labels
Data labels
The colors are easy to read.
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Chart1
| % of children under 5 who slept under an ITN last night | % of children under 5 who slept under an ITN last night | % of children under 5 who slept under an ITN last night |
| % of women 15-49 who slept under an ITN last night | % of women 15-49 who slept under an ITN last night | % of women 15-49 who slept under an ITN last night |
Sheet1
| 2001-02 DHS | 2007 DHS | Column1 | |
| % of children under 5 who slept under an ITN last night | 7.3 | 28.5 | |
| % of women 15-49 who slept under an ITN last night | 8 | 28.2 | |
| To resize chart data range, drag lower right corner of range. |
Effective presentation
Clear
Concise
Actionable
Attractive
Speaker notes
Regardless what communication formats you use, the information should be presented in a clear, concise way with key findings and recommendation that are actionable.
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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
Speaker notes
An appropriate amount of information will be determined by your audience and format.
Policymakers may do better with direct and concise summaries of key points, whereas the scientific community will want more detail.
On a PowerPoint slide, try to limit to six lines with no more than six words per line, balance text with graphics, and make sure that there are not too many slides.
One way to ensure that you create consistent materials is to decide on a template for the document/presentation/graph, etc., before you produce it. You can then give these guidelines to the different people involved in the process, and then only have to do minor formatting at the end.
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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
Speaker notes
The two main ways of summarizing data are by using tables and charts or graphs.
A table is the simplest way of summarizing a set of observations. A table has rows and columns containing data which can be in the form of absolute numbers or percentages, or both.
Graphs are pictorial representations of numerical data and should be designed so that they convey at a single look the general patterns of the data. Generally, the data in a table is in the form of percentages. Although they are easier to read than tables, they provide less detail. The loss of detail may be replaced by a better understanding of the data.
Tables and graphs are used to
Convey a message;
Stimulate thinking; and
Portray trends, relationships, and comparisons.
The most informative graphs are simple and self-explanatory.
Tables can be good for side-by-side comparisons, but can lack visual impact when used on a slide in a presentation.
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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
Speaker notes
To make the graphic as self explanatory as possible there are several things to include:
- Every table or graph should have a title or heading
- The x- and y-axes of a graph should be labeled, include value labels such as a percentage sign, include a legend
- Cite the source of your data and put the date when the data was collected or published
- Provide the sample size or the number of people to which the graph is referring
- Include a footnote if the graphic isn’t self-explanatory
These points will pre-empt questions and explain the data. In the next several slides, we’ll see examples of these points.
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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
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Rikki Welch (RSW) - edit
Speaker notes
All text should be readable. Try to avoid having text in less than 25 point font. There are exceptions, of course (especially when creating and using graphs and charts), but try to make sure that everything is readable from the back of the room.
Use no more than 2 typefaces per presentation. In PowerPoint slides, a sans serif typeface can be more readable than a serif typeface.
Nothing in your slides should be superfluous (no extra doodads for decoration).
Limit the use of animations and other special effects. Use them sparingly, if at all.
Ideally, there should be no more than 6 lines per slide, with six words per line.
Resist the urge to add too many slides.
A light background with dark text (such as this one) will show up better a light-filled room than a slide with a dark background and light text.
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Choosing a Title
A title should express
Who
What
When
Where
Speaker notes
A title should most of the time express who, what, when, and where.
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Tables: Frequency distribution
| Year | Number of cases |
| 2000 | 4 216 531 |
| 2001 | 3 262 931 |
| 2002 | 3 319 339 |
| 2003 | 5 338 008 |
| 2004 | 7 545 541 |
| 2005 | 9 181 224 |
| 2006 | 8 926 058 |
| 2007 | 9 610 691 |
Speaker notes
Frequency distribution is a set of classes or categories along with numerical counts that correspond to each one such as number cases in a given year.
What should be added to this table to provide the reader with more information?
POSSIBLE ANSWERS
Better labels-What type of cases? Malaria cases
Title
reference
Source of text on tables and graphs: Pagano M and Gavreau K. Principles of Biostatistics. 1993.
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Percent contribution of reported malaria cases by year between 2000 and 2007, Kenya
Source: WHO, World Malaria Report 2009
Tables: Relative frequency
| 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 |
Speaker notes
In this table, we already had the total number of observations (or n) in the second column but we added a title and the source of the data. Note that this table includes both a title and a reference. The citation is one area where it is acceptable to have typeface that is fairly small in relation to the rest of the text. You do want to have the citation on the slide so that people can know where the data is from if they want that information, but the citation is not the most important part of the slide. You want to draw attention to the data, not the citation itself.
We also added relative frequencies to this table. Relative frequency is the percentage of the total number of observations that appear in that interval. It is computed by dividing the number of values within an interval by the total number of values in the table then multiplying by 100. It is the same as computing a percentage for the interval.
To analyze this table, we should look at the relative frequencies. What do they tell us? There is an increasing trend in the number of reported malaria cases and in the relative frequency of cases.
Does this mean that there is an increase in malaria cases? What would this say about our programs?
It is important to take into account what we already know when interpreting these data. We know that since 2000 there has been an increased effort towards malaria control. During this time period, the quality of treatment has improved and the quality of routine information systems has improved.
When taking this knowledge into account how would we interpret these data?
From 2000-2007, the number of reported malaria cases increased. This may not reflect an actual increase in cases, but an increase in care seeking and reporting. Due to improved case outcomes seen after the introduction of ACTs in Kenya in 2004, individuals with fever began to seek care at formal medical facilities at higher rates. Furthermore, the routine information system improved during this period of time and thus reported more complete information.
Source of text on tables and graphs: Pagano M and Gavreau K. Principles of Biostatistics. 1993.
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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
Speaker notes
We’re going to review the most commonly used charts and graphs in Excel/PowerPoint. Later we’ll have you use data to create your own graphics which may go beyond those presented here.
Bar charts are used to compare data across categories.
A histogram looks similar to a bar chart but is a statistical graph that represents the frequency of values of a quantity by vertical rectangles of varying heights and widths. The width of the rectangles is in proportion to the class interval under consideration, and their areas represent the relative frequency of the phenomenon in question A histogram is a histogram, not just because the bars touch. In the bar graph bars in a bar graph can touch if you want them to ... but they don't have to. Touching bars in a bar graph doesn't mean anything.
In a histogram, however, the bars must touch. This is because the data elements we are recording are numbers that are grouped, and form a continuous range from left to right. There are no gaps in the numbers along the bottom axis. This is what makes a histogram.
Line graphs display trends over time, continuous data (ex. cases per month)
Pie charts show percentages or the contribution of each value to a total. When there are more than 4 categories it is best to go to a bar chart so that it is readible
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Bar chart
Speaker notes
In this bar chart we’re comparing the categories of data which are any net or ITN.
What should be added to this chart to provide the reader with more information?
Add a title and data labels. You could also add the source of the data but it isn’t necessary if all of your tables and graphs are derived from the same source/dataset.
On the next slide we see how the graph has been improved and is now self-explanatory.
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Bar Chart
Source: Quarterly Country Summaries, 2008
Speaker notes
Note that this chart has a title, axis labels , data labels, and a source. It is best if you limit the bars to 4-8 to keep it readable, especially if it is to be used in a PowerPoint presentation.
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Stacked bar chart
% Children <5 with Fever who Took Specific Antimalarial, 2007-2008
Speaker notes
A stacked bar chart is often used to compare multiple values when the values on the chart represent durations or portions of an incomplete whole, such as the percentage of children taking each type of medication for fever when not all children received medication at all.
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Histogram
Speaker notes
This is a histogram. At first glance, histograms look a lot like bar charts. Both are made up of columns and plotted on a graph.
However, there are some key differences. The major difference is in the type of data presented on the x (horizontal) axis. With bar charts, each column represents a group defined by a categorical variable. This variable could be types of sports, different football teams, health facilities, or provinces. These are all categories.
A histogram presents quantitative variables; the groups on the chart are always made up of numbers or something that could be turned into numbers. This could be age, height, weight, the number of minutes women wait in a queue, years, or months of the year. These groupings are sometimes called “bins.” The bin label can be a single value or a range of values. For example, you could split out the time spent waiting in line by the minute (5 minutes, 6 minutes, 7 minutes) or you could split it into chunks (less than 5 minutes, 6-10 minutes, 11-15 minutes).
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Bar Chart v. Histogram
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Data fabricated for illustration
Speaker notes
The columns in a typical bar chart can be arranged however you want to arrange them, alphabetically, by height, or the order in which you received the data—it doesn’t really matter. No matter which column comes first in this presentation, the idea presented does not change.
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Chart1
| 22 | 15 | 29 |
Sheet1
| Northwestern | Copperbelt | Central | |
| 22 | 15 | 29 | |
| To resize chart data range, drag lower right corner of range. |
Bar Chart v. Histogram (cont.)
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Data fabricated for illustration
Rikki Welch (RSW) - edit
Speaker notes
The order of the columns in a histogram is very specific, and the columns cannot be rearranged. The columns are arranged from low to high. A bar chart does not have a “high” end and a “low” end. A histogram does. You can see on this chart that the data is “skewed” toward the high end. It would NOT make sense to rearrange the columns on this chart.
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Chart1
| 0-10 |
| 11-20 |
| 21-30 |
| 31-40 |
| Over 40 |
Sheet1
| Series 1 | |
| 0-10 | 2 |
| 11-20 | 3 |
| 21-30 | 10 |
| 31-40 | 30 |
| Over 40 | 55 |
| To resize chart data range, drag lower right corner of range. |
Population Pyramid: Country Z, 2008
Speaker notes
This is a population pyramid. It is basically two histograms presented side by side. On the right you can see males and on the left you see females. The bins shown are five-year age categories. Population pyramids are useful for presenting descriptive data about your population of interest or study population. On your disc, you will find a template for producing a population pyramid. All that you need is the data on age and sex and this excel worksheet will automatically produce a pyramid.
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Line graph
*Includes doctors and nurses.
Number of Clinicians* Working in Each Clinic During Years 1-4, Country Y
Speaker notes
A line graph should be used to display trends over time and is particularly useful when there are many datapoints. In this case we have 4 datapoints for each clinic.
By adding a label to the y-axis, a title and a footnote. In some settings, clinicians may only mean doctors but to be clear the footnote let’s the reader know that in this case we are referring to both doctors and nurses.
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Chart1
| Year 1 | Year 1 | Year 1 |
| Year 2 | Year 2 | Year 2 |
| Year 3 | Year 3 | Year 3 |
| Year 4 | Year 4 | Year 4 |
Sheet1
| Clinic 1 | Clinic 2 | Clinic 3 | |
| Year 1 | 4.3 | 2.4 | 2 |
| Year 2 | 2.5 | 4.4 | 2 |
| Year 3 | 3.5 | 1.8 | 3 |
| Year 4 | 4.5 | 2.8 | 5 |
| To resize chart data range, drag lower right corner of range. |
Caution: Line Graph
Number of Clinicians* Working in Each Clinic During Years 1-4, Country Y
*Includes doctors and nurses.
Speaker notes
What is wrong with this line graph? If you look closely you can see that the X axis should be years, but instead it is clinics. Make sure that the right data is always charted on the axes, or else you may end up with a graph that cannot be interpreted like this one.
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Chart1
| Clinic 1 | Clinic 1 | Clinic 1 | Clinic 1 |
| Clinic 2 | Clinic 2 | Clinic 2 | Clinic 2 |
| Clinic 3 | Clinic 3 | Clinic 3 | Clinic 3 |
Sheet1
| Clinic 1 | Clinic 2 | Clinic 3 | |
| Year 1 | 4.3 | 2.4 | 2 |
| Year 2 | 2.5 | 4.4 | 2 |
| Year 3 | 3.5 | 1.8 | 3 |
| Year 4 | 4.5 | 2.8 | 5 |
| To resize chart data range, drag lower right corner of range. |
Pie chart
Speaker notes
A pie chart displays the contribution of each value to a total. In this chart, the values always add up to 100.
What should be added to this chart to provide the reader with more information?
What should be changed about this chart to make it more readible?
POSSIBLE ANSWERS
The color scheme, which is currently too bright
The title should be more specific and indicate whether these are numbers or percentages.
If these are percentages, that should be listed on the data and the n, or number of cases should be indicated to provide context.
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Chart1
| 1st Qtr |
| 2nd Qtr |
| 3rd Qtr |
| 4th Qtr |
Sheet1
| Females | |
| 1st Qtr | 59 |
| 2nd Qtr | 23 |
| 3rd Qtr | 10 |
| 4th Qtr | 8 |
| To resize chart data range, drag lower right corner of range. |
Pie chart
N=257
Percentage of all confirmed malaria cases treated by quarter, Country X, 2011
Speaker notes
A pie chart displays the contribution of each value to a total. In this case we used the chart to show contribution of each quarter to the entire year. For example, the first quarter contributed the largest the percentage of enrolled patients.
To improve the understanding of the pie chart, we’ve added a more descriptive title and added value labels. On the previous chart, we couldn’t tell if the values are numbers or percentages. Adding the sample size let’s us know the total number of observations. For example
It is also important to have charts that are attractive, easy to look at and easy to read. The chart on the previous page was so colorful that it was distracting, the colors were so bright that it was hard to look at the chart, let alone read it. While these colors are not the most interesting, they let the reader focus on the chart. The last chart was an exaggeration, but be sure to make sure that you do not make the same mistake on a smaller level.
Limit the slices to 4-6. For extra pizzazz, contrast the most important slice either with color or by exploding the slice.
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Chart1
| 1st Qtr |
| 2nd Qtr |
| 3rd Qtr |
| 4th Qtr |
Sheet1
| Females | |
| 1st Qtr | 59% |
| 2nd Qtr | 23% |
| 3rd Qtr | 10% |
| 4th Qtr | 8% |
| To resize chart data range, drag lower right corner of range. |
How should you present…
Prevalence of malaria in 3 countries over a 30 year period?
Data comparing prevalence of malaria in 10 different countries?
Data on reasons why individuals not using ITNs (out of all individuals surveyed who own an ITN and are not using it)?
Distribution of patients tested for malaria by parasite density
Speaker notes
How should you present the following data?
1. Line graph
2. Bar Chart
3. Pie Chart
4. Histogram
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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
Speaker notes
In summary, [READ BULLETS]
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Creating Graphs
Speaker notes
Now that we know a little bit about the main types of graphs, we are going to try our hand at making some in Excel. We are including a few helpful hints in this section on more advanced graphing. If you are already very good at making graphs in Excel, please help your neighbors complete the task after you are finished with your work.
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Learning objectives
Understand basic chart terminology
Create charts in PowerPoint using data in Excel
Give a description of the data presented in each chart
Speaker notes
By the end of this session, participants should be able to: [READ BULLETS]
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Pie Chart
Source: MEASURE Evaluation, Retention, Use and Achievement of “Universal Access” Following the Distribution of Long Lasting Insecticide Treated Nets in Kano State, Nigeria, 2009
Speaker notes
Please open the file called graphs from the data presentation folder on your cd. We are going to use the data there to create this and the other charts and graphs in this session. For all of these charts, I want you to try to duplicate the chart shown in the PPT slide exactly. This is not to say that this chart is perfect; however, trying to copy this exactly will allow you to explore some of the chart making functionality in Excel.
Go over making this chart with the participants. Show them how to do it using the standard chart layouts in Excel (this is layout 6 in Excel 2007) and also how to adjust aspects such as the legend, data labels and colors of the chart using the layout tab.
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Individual Work: Bar Chart
Source: Tanzania HIV and Malaria Indicator Survey, 2008
Speaker notes
Please now try to create this chart on your own. You may not know how to add the confidence intervals. If that is the case, please finish the other aspects of the chart and I will then give you a demonstration of how to add the CI.
They will need to create this chart in excel and export it to PPT. It should look almost exactly like this chart and include the error bars which they will need to be instructed on. Each participant has the data needed to create this chart in an excel file in the folder for this module.
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Secondary Axis
Speaker notes
Please now try to create this chart on your own. If you do not know how to create a secondary Y-axis, please finish the other aspects of the chart and I will then give you a demonstration of how to add the CI. You use secondary axes to be able to chart numbers that have very different scales on the same graph. In this case, there are a lot more malaria cases than deaths. If you charted them on the same axis, you would see a flat line at the bottom for the deaths.
They will need to create this chart in excel and export it to PPT. It should look almost exactly like this chart. Each participant has the data needed to create this chart in an excel file in the folder for this module.
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Data Interpretation
Speaker notes
Now that we know how to present our data, we need to be sure that we are interpreting our findings properly.
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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
Speaker notes
Analysis is summarizing the data and turning it into information. Data on its own is generally not useful for the decision-making process. Analysis will vary in complexity. Most data analysis is quite simple, but some is much more complicated and requires a great deal of expertise.
Interpretation is the process of making sense of the information. What does it mean for your program?
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Has the Program Met its Goal?
Speaker notes
In many cases we need to interpret data to assess the performance of our programs and identify areas that are doing well and others which are underperforming. In this case, our target is to have 80% of children under five sleep under an ITN every night.
Have we met our goal? How can you tell?
Answer
No, the goal has not been met. Country 3 is doing the best but has only reached a little more than half of the goal for ITN.
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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
Speaker notes
When interpreting data we may ask these questions: What is the relevance of the unmet target for the program? Is it because we are not meeting our coverage or efficiency goals? Is our quality of care poor? What could be causing this? How are we doing in comparison with other clinics? Districts?
What are the potential reasons for the finding? Do data quality issues play a role in what we are observing? What other data should be reviewed to understand the finding (triangulation)? Is there a need to donduct further analysis?
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Practical
Question:
Are ANC clinics in country X reaching their coverage targets for IPTp?
Data Source:
Routine health information
*
Speaker notes
Now we are going to consider how we could answer the following question:
Are ANC clinics reaching their coverage targets for IPTp? We will answer this question using routine health information.
Data Source
General ANC Registers
- 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
| 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 |
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Speaker notes
Which of these variables are relevant to answer your question? We’re going to focus on elements 1, 9 and 10. Which elements will be included in your numerator and which in your denominator?
IPTp Coverage-Facility Performance
Number of ANC clients receiving IPTp
- Question:
Among the five facilities, which one performed better?
- Answer:
Cannot tell because we don’t know the denominators
| 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 |
Speaker notes
Here we have the data on IPTp-1 and 2 to assess facility performance. Among the five facilities, which one performed better?
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IPTp Coverage-Facility Performance
Number of ANC clients receiving IPTp
Question: Now, you have the denominators, which of these facility performed better?
Response: Facility 5
| 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 |
| 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% |
Speaker notes
Now, you have the denominators, which of these facility performed better? We can see that it was actually facility 5.
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Are facilities reaching coverage targets?
Target-80%
* National coverage target for pregnant women receiving IPTp-2 is 80%.
*
Speaker notes
Here is the same information presented as a chart. We need to use this information to determine, or interpret, whether or not facilites are reaching their coverage targets. Let’s assume that the national coverage target for pregnant women receiving IPTp is 80%. Are the facilities reaching the coverage target? What else can we interpret from this information?
Possible answers
Facility 1 needs to do a better job following up and increase IPTp coverage a bit.
Facility 2 does a better job with IPTp-1 coverage than IPTp-2, but needs to increase coverage of both.
Facility 3 does a good job administering IPTp-2 to patients that receive the first round, but they need to increase initial coverage and maintain follow-up.
Facility 4 does a good job with IPTp-1 coverage, but this falls of with IPTp-2. Is this loss to follow-up, or are they not administering IPTp-2 when patients return?
Facility 5 can be seen as a model and we could investigate their best practices for use in other programs
This information does not tell you why coverage is at these levels. You would have to investigate further, but you can see which facilities you need to work with.
Chart1
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 4 |
| 5 | 5 |
Sheet1 (2)
| Figure 2. Household Ownership of at Least 1 Net or ITN, 2008 | ||||||||||||||
| Country 1 | Country 2 | Country 3 | Country 4 | Country 5 | CI | |||||||||
| Any net | 56 | 63 | 77 | 66 | 70 | 64 | 75 | |||||||
| THMIS | NIMR | PSI | NMCP | IHI/LS | ||||||||||
| LLIN | 38 | 29 | 45 | 57 | 46 | 40 | 52 | |||||||
| 80 | 80 | 80 | 80 | 80 | ||||||||||
| Total | 46 | 65 | 56 | |||||||||||
| Use of Nets or ITN by Children <5 yrs of Age, 2008 | ||||||||||||||
| Country 1 | Country 2 | Country 3 | Country 4 | Country 5 | CI | |||||||||
| Any net | 35 | 54 | 74 | 48 | 48 | 41 | 56 | |||||||
| THMIS | NIMR | PSI | NMCP | IHI/LS | ||||||||||
| ITN | 25 | 32 | 48 | 29 | 29 | 22 | 36 | |||||||
| Total | 46 | 65 | 56 | |||||||||||
| Use of Nets or ITNs by Pregnant Women, 2008 | ||||||||||||||
| THMIS | NMCP | IHI/LS | CI | |||||||||||
| Any net | 36 | 52 | 39 | 31 | 47 | |||||||||
| ITN | 26 | 30 | 19 | 13 | 27 | |||||||||
| Total | 46 | |||||||||||||
| Use of IPTp by Pregnant Women, 2008 | Use of IPTp by Pregnant Women, 2008 | |||||||||||||
| THMIS | IHI/LS | 1 | 2 | 3 | 4 | 5 | ||||||||
| IPTp-1 | 57 | 50 | 47 | 54 | IPTp-1 | 72 | 53 | 37 | 90 | 95 | ||||
| IPTp-2 | 30 | 26 | 23 | 29 | IPTp-2 | 50 | 20 | 36 | 42 | 86 | ||||
| Total | 46 | 56 | ||||||||||||
| % Children <5 with Fever who Took Specific Antimalarial, 2008 | ||||||||||||||
| 2008 | 2007 | |||||||||||||
| Sulfadoxine-Pyrimethamine | 2 | 2 | ||||||||||||
| Chloroquine | 0.5 | 0.5 | ||||||||||||
| Amodiaquine | 11 | 20 | ||||||||||||
| Quinine | 9 | 9 | ||||||||||||
| ACT | 36 | 26 | ||||||||||||
| Other | 3 | 0.5 | ||||||||||||
| % Children <5 with Fever Who Took Specific Antimalarial within Same or Next Day, 2008 | ||||||||||||||
| THMIS | NMCP | |||||||||||||
| Sulfadoxine-Pyrimethamine | 0.5 | 1 | ||||||||||||
| Chloroquine | 0 | 0 | ||||||||||||
| Amodiaquine | 12 | 4 | ||||||||||||
| Quinine | 6 | 5 | ||||||||||||
| ACT | 13 | 13 | ||||||||||||
| Other | 3 | 0.5 | ||||||||||||
| Percent Overall malaria prevalence and overall anemia prevalence | ||||||||||||||
| THMIS | NMCP | IHI/LS | CI | THMIS CI | ||||||||||
| Parasitemia | 18 | 14 | 11 | 8 | 14 | 16 | 20 | |||||||
| Anemia (HB <8 g/dL) | 8 | 6 | 3 | 3 | 4 | 7 | 9 | |||||||
| Months | Parasitaemia | HB <8 g/dl | ||||||||||||
| 6-11 | 9 | 0 | 11 | |||||||||||
| 12-23 | 14 | 0 | 12 | |||||||||||
| 24-35 | 20 | 0 | 8 | |||||||||||
| 36-47 | 20 | 0 | 5 | |||||||||||
| 48-59 | 22 | 0 | 3 | |||||||||||
| Mainland | 18 | 0 | 8 | |||||||||||
| Zanzibar | 1 | 0 | 5 | |||||||||||
| 2001 | 2003 | 2005 | 2008 | |||||||||||
| Artemisinin Mono | 0 | |||||||||||||
| ACT | 3 | 57 | ||||||||||||
| Quinine | 16 | 19 | 16 | 18 | ||||||||||
| Chloroquine | 54 | 3 | 1 | 0 | ||||||||||
| Amodiaquine | 2 | 22 | 32 | 20 | ||||||||||
| Sulfadoxine-Pyrimethamine | 28 | 57 | 48 | 5 | ||||||||||
| Net was sold | 1 | |||||||||||||
| Net was given away to relatives | 68 | |||||||||||||
| Net was given away to others | 9 | |||||||||||||
| Material used for other purpose | 1 |
Sheet1 (2)
| Net was sold |
| Net was given away to relatives |
| Net was given away to others |
| Material used for other purpose |
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?
Speaker notes
Here are some other questions that we might want to ask to help interpret this information and identify how to improve performance.
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Data Dissemination
Speaker notes
It is not enough to know how to collect, present and interpret your data. These data will not help to improve programs if your keep it to yourself. The next step that you need to take is dissemination.
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Learning Objectives
By the end of this session, participants will be able to identify:
The purpose of dissemination
Dissemination issues and concerns
Strengths and weaknesses of different communication formats
The main components of a dissemination plan
Speaker notes
By the end of this session, participants should be able to: [READ BULLETS]
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Dissemination Framework
Source: MEASURE DHS
Speaker notes
Effective dissemination should create informed users (the center block in the decision framework), who can then make informed decisions that ultimately lead to improved health.
The goal of dissemination is to provide accurate and up-to-date information for evidence-based decision-making. Evidence-based decisions lead to better programs and, ideally, better health outcomes.
Discussion
ASK participants to define evidence-based decision-making.
Answer: There are many definitions, but essentially it means that decisions are based on scientific evidence or data, not personal opinions or observations.
Evidence-based decision-making has several advantages.
- It is easy to justify, since decisions can be explained and backed up with data.
- It can protect decision-makers from accusations of fraud and bias.
- It leads to transparency in decision-making, which is important for buy-in from other people involved.
TELL participants that problems can occur at different stages in the dissemination process.
The first major problem arises early in the process, during the dissemination step. Simply getting information to potential users can be challenging.
The second major problem arises later, when users try to make informed decisions. Users may find it difficult to understand and apply the survey results to their decisions.
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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
Speaker notes
Disseminating data can help potential users by providing them with information to understand current health status, reach decisions based on quality data, make changes to existing health programs and policies, and 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
Speaker notes
TELL participants that print materials are the most common way to disseminate results. If funding permits, however, it is helpful to use other kinds of materials in addition. For example, some projects prepare PowerPoint presentations of findings and maks those presentations available in the country. OCDs can be distributed to a wide audience. The more ways in which information is made available, the more likely that information is to reach a wide audience and be used.
Videos are an effective way to disseminate survey findings because they can include visuals of the country and interviews with women and men. This helps give survey data a human face and makes the information more compelling. However, video production can be expensive and time-consuming.
As online technologies become more widely available in Africa, new ways are emerging to disseminate information electronically.
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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
Speaker notes
TELL participants that whenever possible, dissemination products should be tailored to a specific audience and its needs. Policymakers, for example, do not have time to read long documents. For this audience, policy briefs that frame the data in the context of policy are a highly effective dissemination tool.
Translating materials into local languages improves comprehension of the information, indicates respect for the culture, and reaches additional audiences.
Even if your project collects data on a large number of topics, not every publication needs to address every topic. Focusing on just one area, such as coverage or impact, can make materials more useful for people working in those fields.
Matching the media to the audience makes it more likely that the intended audience will have access to the message. For example, CD-ROMs are good for technical experts with access to computers, but print materials and videos are a better way to reach religious leaders.
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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
Speaker notes
EXPLAIN that dissemination materials are most useful if they draw conclusions, summarize major points, and highlight key ideas. This lets the materials do most of the work for the user. It may also be better to leave out some of the results in order to make sure that the major points stand out. This is better than flooding people with so much information that they feel overwhelmed and cannot absorb it.
A good way to present information is to categorize it by characteristics, such as wealth, education, province, and region.
Maps are particularly persuasive and easy to understand. They are more compelling than words because they present geographic differences so clearly.
Other graphics—including bar graphs, line graphs, and pie charts—allow the eye to grasp large amounts of information and to see trends more easily than in written text or tables.
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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
Speaker notes
EXPLAIN that people want to see data presented in context. For example, a policymaker who is not familiar with malaria will have trouble making sense of the bare fact that Zambia’s malaria parasite prevalence is 10.2%. This number will be much more meaningful if it is placed in a larger context—for example, if a policy brief shows how the rate has changed over time or whether it is higher or lower than Zambia’s neighbors. Linking a finding to a specific program or policy also makes survey results more understandable and more applicable.
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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
Speaker notes
When presenting your information in both text and graphical format, it is important to provide an appropriate amount of information. While we may be tempted to present all of our findings, this may result in the loss of our core message due to information overload. Remember that less is more. Focus on 3-5 key points depending on the length of your presentation or document.
You should also make sure that materials are appealing to look at whenever possible and to balance text and graphics by including pictures and graphs.
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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
Speaker notes
So how much is enough information? Is this enough. Clearly too much is presented here to exaggerate the point; however, many presentations and documents may feel this crowded with information and overwhelming to us when we are faced with compelittle time.
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Components of a Dissemination Plan
Project overview
Dissemination goals and objectives
Target audiences
Key messages
Sources/messengers
Dissemination activities, tools, timing, and responsibilities
Budget
Evaluation Plan
Source: Canadian Health Services Research Foundation
Speaker notes
Developing a dissemination plan is a key part of the collaborative research planning process. Although
the decision makers and researchers working together won’t know the results of the research until it’s
completed, working through an initial dissemination plan can help your team focus the project and
identify key audiences. When the research results come in, you’ll be ready to flesh out key messages,
review and finalize the plan, and then implement it.
Following is a list of some of the key elements that should be included in a dissemination plan. While
this is not a detailed guide to developing a dissemination plan, it provides a good overview of some of
the most critical things that should be considered.
1. Project overview
Describe the current environment or context that provides the impetus for the research being
undertaken — what is your research aiming to clarify or change? Who is or should be interested in
the results?
Briefly sketch out the research project and its objectives. How will it address the context or
challenges you have identified?
2. Dissemination goals
What are you hoping to achieve by disseminating this research? You may have a single long-term
goal, such as a change in a policy, practice, or even culture, but make sure to also include any
supporting or shorter-term goals.
3. Target audiences
These are the groups you want to reach with your research results — and who you will target in
your dissemination activities. Be as specific as you can — who are the people who can use this
research?
You may want to divide your list into primary audiences (more important) and secondary
audiences (less important) and allocate dissemination efforts according to audience importance.
4. Key messages
In your first stab at a dissemination plan, you won’t be able to develop specific key messages
because you won’t know the results of your research project. However, you can plan broadly
around what you anticipate the content will be.
Effective messages explain what your research results mean, why they are important, and what
action should be taken as a result. They are not simply a summary of the results. Note the wider
context if applicable — how the results fit with the body of related research on the topic.
Make messages clear, simple, and action-oriented. The style and content should be tailored for
each audience. Messages should be based on what that audience wants to know, rather than on
what you think it should hear.
5. Sources/messengers
Since using influential spokespersons to spread your messages can help ensure uptake of your
research results, identify the people or organizations that are viewed as credible with each of your
target audiences.
Then think about how you can get those people and organizations “on board” — maybe you can
partner with them in a workshop, or ask them to include an article about your research results on
their web site or in their newsletter.
6. Dissemination activities, tools, timing, and responsibilities
This is the meat of your dissemination plan. Here you describe the activities (such as briefings or
presentations) you will undertake to reach each target audience, and the tools (such as printed
materials or web sites) that will support these activities. You also set out timing (what you will do
first and when you will do it) and assign responsibilities to team members.
Successful dissemination activities go beyond traditional vehicles such as publication in scholarly
journals — look for activities that promote a two-way dialogue, not a one-way flow of information.
Face-to-face meetings or briefings are a very effective way to reach decision makers.
Make each member of your collaborative research team responsible for carrying out at least one
dissemination activity, and schedule meetings to report back and ensure commitments are being
met.
A good dissemination plan will have activities that reach each of your target audiences, taking into
account their attitudes, habits, and preferences.
7. Budget
Time and budget requirements for dissemination are frequently underestimated. Effective
dissemination involves resources and planning — think about travel, layout and printing,
translation, equipment, and space rental costs when allocating a budget for dissemination
activities. Don’t forget to include resources the individual(s) will need to do the future planning
and co-ordination of the activities you have identified!
8. Evaluation
Evaluation is most effective when it is built in from the start. Decide how you will evaluate the
success of your team’s dissemination efforts, selecting measurable criteria for each dissemination
activity. Focus less on efforts (how much you did) and more on outcomes (what was the result).
Please be clear on the difference between messages and survey/research findings
Findings= objective results
Message =results with commentary/interpretation
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Dissemination Planning Matrix
| Activity | Target Audience | Tools | Person Responsible | Timing |
| Present results at partner meetings | Partner organizations | Powerpoint Presentation, Full report (Printed, electronic) | Jane | September 2014 |
| Present results at health conferences | Scientific Community | Poster | John | November 2014 |
| Publish results in peer-reviewed journals | Scientific Community | Article | John | December 2013 |
| Alert media about the above activities | General population | Interview, news segment | Alice | December 2013 |
| Present results to community members | Community members | Oral presentation with interactive exercises | Alice | June 2013 |
Speaker notes
Here is an example of a dissemination planning matrix that can help you to think about how you will do your dissemination. Dissemination should not be an afterthought. When data collection is planned, it is important to start thinking about how the information will be disseminated in order to maximize its use.
In this matrix, you can see that there are sections on activities, target audiences, tools, person responsible and timing. This is just one example of a dissemination planning matrix. You could adapt it to fit your own needs for dissemination.
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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
Speaker notes
One way to make sure that the information that you are disseminating is understood and therefore more likely to be used is to engage in capacity-building. Dissemination can be combined with capacity-building in many ways. Some examples of how this can be done include: helping users understand context and terminology; training users to read tables and charts; and providing exercises on using data. It is important to always ask users to consider implications of the information for programs and policy. This way they can leave your dissemination event with ideas on how they plan to use the information.
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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
Speaker notes
When planning for our dissemination, we should consider several issues. Depending upon our audience, data literacy may be a concern. Our target audience may not understand the terminology; there may be issues in understanding concepts of sampling errors, confidence intervals, reading tables or comparing multiple data sources. This is why it is important for us to match the materials to the audience. Community members may have a difficult time understanding sampling errors, but then again, this is probably not the most important information for them. Data literacy can also be improved through capacity building, but you must recognize when and where to invest these resources. Improving the data literacy of program managers may be a bigger priority than teaching your study population which may not have great use for these skills.
Often times data is not available for the administrative level that concerns use. For example, large-scale national surveys generally only collect data down to the regional level. This data will not be extremely helpful for individuals conducting district planning.
It is helpful, when possible, to have data dissemination events precede the national planning cycle. This is often not possible, but doing so will increase the ability of those setting program priorities to make evidence-based decisions.
*
Dissemination Issues/Concerns
Getting information out of the capital city
Extending dissemination beyond the immediate post-release period
Difficulty tracking and monitoring use
Speaker notes
Another issue that we often face when disseminating information is the difficulty in getting information out of the capital city. In many cases, the information we are disseminating is more useful in the regions and districts than it is to those in the capital, yet the people in these areas may never receive the information. One way of dealing with this is to plan dissemination events in the multiple places.
While we can be capable of getting some attention with our information at the time that we release and initially disseminate it, extending dissemination beyond the immediate post-release period can be challenging.
Finally, it can be extremely difficult to track and monitor use of our information. This is partially because individuals who use data often do not publicize this fact, even when the information is used to make important evidence-based decision.
*
Tracking Information Use
Speaker notes
In this session we will focus on tracking information use.
*
Learning objectives
By the end of this session, participants will be able to identify:
Methods of tracking data and information use
Opportunities for improving data production and use
Opportunities for feedback mechanisms
Points where analysis & data could support programmatic decision making
Speaker notes
By the end of this session, participants should be able to: [READ BULLETS]
*
Methods of Tracking Information Use
Assessing coverage targets
Key information interviews
Meetings with staff
Speaker notes
There are several ways to know if data and information are being used. For example, are facilities or districts using the data to assess their coverage targets? Are interventions being developed to address problem areas identified by service statistics? Do you see a resulting improvement in service statistics (upward trend) as a result of these interventions? Are communication products regularly developed, shared with decision-makers and reviewed?
Tracking information use is not easy or cheap.
You can also interview stakeholders such as community-based groups and staff to find out if and how they have used the information and what impact it may have had.
*
Information Flow
Service Delivery Point
Feedback
Program
Higher levels: district, province, national
Analysts, evaluators
Managers, Government, Donors
Compiled data, some analysis
Clinical histories, service statistics
Reports
*
Speaker notes
This flowchart shows how data can effectively flow from the service delivery level to the higher levels responsible for supervision of programs (LGA, state, IP, Global Fund CCM, USG, etc.)
The service delivery points – whether a facility or community organization – are responsible for generating the primary data through clinical histories and service statistics – i.e. data based on the individual client. This individual client data is then compiled and ideally is presented to program managers, directors, and service providers within the facility or organization for their own use in programmatic decision-making as well as to review before sending the data on to higher levels.
The compiled data is sent on to higher levels where it is analyzed and compiled with data from other facilities and other organizations. Reports produced by this higher level should also be shared with service delivery points and organizations to ensure that they are familiar with how other service providers are performing. In addition, the higher level can provide guidance and advice to facilities on an individual level based on the data that they receive.
Each of these levels mentioned can make decisions based upon the primary and aggregated data from the service delivery point.
This is the ideal. In practice, the flow breaks down all the time, especially in the feedback from higher levels to program.
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
*
Speaker notes
In Country X, local health centers and hospitals sent information about the number of people they tested for Malaria, while labs sent test results. A statistician in the Health Information Unit aggregated the data and sent a quarterly report to the Ministry of Health, which in turn sent a quarterly report to the Epidemiology Center (EC) and an annual report to the Prime Minister. Trouble was, local facilities never got these reports. They could not know how they compared to other facilities, or to national trends and goals. Were they on track or not? These information gaps quickly became apparent when processes were visualized in an Information Use Map. Data were reported, but not used. Reports did not get back to the providers of source data. The mapping exercise identified ways the Health Information Unit could share its insights down the line, which would lead to mid-course improvements and an increase in malaria testing.
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
*
Speaker notes
The scenarios below are typical:
Local data are not being used locally. Oftentimes, data are tallied and reported up the levels, but are rarely analyzed and used to support mid-course corrections at the level at which they were generated. In many situations, data could be used to investigate trends over time, compare different areas, set priorities and goals for future years, compare progress against defined goals, and advocate for funding or policies.
Higher-level information does not return back to the local level. Consider the example of a family planning clinic, where data reveal a declining trend in use of oral contraception.The providers knew that women complained about the side effects, but they did not know how much the overall contraception rates were being affected.The district and regional officers knew contraception rates were declining, but could not know why. There was a need to bring these information sources and stakeholders together.
Local data are not assessed in broad context. For example, suppose 10 percent of the population in the region is expected to receive a service, and one district is only reaching 2 percent. Obviously, there is a large service coverage gap in this district—but the facilities and district office would not necessarily know it, because they may not be aware of how their service delivery rates compare to national objectives.
There is little incentive to produce high-quality data. People involved in local-level data collection efforts often do not see the purpose in collecting the data. They have a difficult time appreciating their role in the larger context of the health information chain, and as a result, spend less energy in collecting the data and in paying attention to detail.
Since there is such a large amount of money and effort being devoted to collecting data and reporting in health information systems, it only makes sense to maximize the impact of that data for real-world benefit. This is where the Information Use Mapping tool is so valuable.
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
*
Speaker notes
The Information Use Map is a flowchart framework that allows the user to:
- Create a schematic representation of the existing state of a health information system or subsystem.
- Through this visual representation, quickly identify gaps and deficiencies in that information flow.
- Identify opportunities for new feedback mechanisms to share high-level analysis and reports with lower levels of the information hierarchy.
- Identify points in the process where additional analysis and use of data could lead to improved programs.
- Prioritize recommendations and formulate an action plan to implement them.
The Information Use Map can be developed and applied at the international, regional, national, or local levels. The map can be an ongoing guideline to assess progress toward the “expected” future vision of the map. The Information Use Map can also become a standard part of an M&E system—revisited and revised at regular intervals or whenever a new survey or special study is being designed.
Key Messages
Actual flow of data and information can reveal barriers to improving data quality and use
Information Use Map can highlight intervention points
Speaker notes
We are going to move on to a small group activity. Before we do, let’s review the key messages of this session.
NOTE to facilitator: Read slide and solicit questions on the material covered.
*
How does information flow through your organization?
Speaker notes
Can anyone give me an example of how information flows through your organization. Base don the example of the information use map we just saw, can you identify areas for improvement in information flow in your program?
*
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
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0123456Year 1Year 2Year 3Year 4Number of cliniciansClinic 1Clinic 2Clinic 3
0123456Clinic 1Clinic 2Clinic 3Number of cliniciansYear 1Year 2Year 3Year 4
5923108
Malaria Cases
1st Qtr2nd Qtr3rd Qtr4th Qtr
59%23%10%8%
1st Qtr2nd Qtr3rd Qtr4th Qtr
02040608010012345PercentFacility
Percent of ANC Clients Receiving IPTp in Select Facilities
IPTp-1IPTp-2
