Facilitator's Guide - Template - TEPHINETlibrary.tephinet.org/sites/default/files/document/... ·...

FACIL ITATOR’S GUIDE

Putting Data to WorkEvidence-Based Health Program Planning and Management

SUSTAINABLE MANAGEMENT DEVELOPMENT PROGRAM

Sustainable Management Development ProgramDivision of Public Health Systems and Workforce Development

Center for Global HealthU.S. Centers for Disease Control and Prevention

http://www.cdc.gov/globalhealth/SMDP/

http://www.cdc.gov/globalhealth/SMDP/

PUTTING DATA TO WORK

Putting Data to WorkCONTENTS

IntroductionSustainable Management Development Program..................................................................iiiAcknowledgements................................................................................................................. iii

Course OverviewAbout this Course................................................................................................................... ivTarget Audience..................................................................................................................... ivLearning Objectives................................................................................................................ ivMaterials and Equipment......................................................................................................... vWorkshop Schedule............................................................................................................... viClass Preparation Checklist...................................................................................................viiClassroom Setup................................................................................................................... viiInstuctor Notes...................................................................................................................... viiiIcon glossary.......................................................................................................................... ix

Course InstructionIntroduction to Putting Data to Work.......................................................................................1How Do You Use Data?..........................................................................................................2

DataKinds of Data.......................................................................................................................... 5Types of Data.......................................................................................................................... 7Data Collection........................................................................................................................ 9

Interpreting DataRatios.................................................................................................................................... 12Proportions and Percentages................................................................................................13Rates..................................................................................................................................... 15Why Use Rates?................................................................................................................... 19

Summarizing DataSummmarizing Data..............................................................................................................21Tables................................................................................................................................... 22

CONTENTS | i


Graphs.................................................................................................................................. 25Bar Charts............................................................................................................................. 32Pie Charts............................................................................................................................. 37Maps..................................................................................................................................... 39Stratification.......................................................................................................................... 43

ConclusionSummary............................................................................................................................... 47What's Next........................................................................................................................... 48Resources............................................................................................................................. 49Appendices........................................................................................................................... 50AppendiX A: Summarizing Data Scenarios..........................................................................51AppendiX B: Glossary of Terms...........................................................................................57AppendiX c: Course Evaluation Form..................................................................................60

CONTENTS | ii

IntroductionSUSTAINABLE MANAGEMENT DEVELOPMENT PROGRAM

SMDP works with ministries of health, educational institutions, nongovernmental organizations, and other partners to strengthen leadership and management skills and systems to improve public health in low resource countries.

Program StrategySMDP strengthens leadership and management skills and systems through:

Integration with country public health priorities Strategic partnerships Technical assistance and training Policy and systems development Advocacy and education Evaluation

For more information, visit http://www.cdc.gov/globalhealth/SMDP/.

ACKNOWLEDGEMENTS

Dr. Nicholas Ayebazibwe, African Field Epidemiology Network

Northwest Center for Public Health Practice

SUSTAINABLE MANAGEMENT DEVELOPMENT PROGRAM | iii

Course Overview

ABOUT THIS COURSE

The goal of this course is to enable participants to use data to make decisions and recommendations. Data can be used for planning and decision making regarding public health needs, organizational performance and monitoring and tracking evaluations.

This course will focus on quantitative data.

TARGET AUDIENCE

This course is designed for leaders and managers of health organizations, programs and operations.

LEARNING OBJECTIVES

When participants complete this course they will be able to:

Describe how data are used in health organizations and programs

Identify methods for summarizing data Explain how data analysis and interpretation can improve

decisions Prepare and apply tables, graphs, and charts such as line

graphs, bar charts, pie charts, and spot (dot) maps, for summarizing data

COURSE OVERVIEW | iv

MATERIALS AND EQUIPMENT

MATERIALS EQUIPMENT

For the Instructor: PowerPoint® Slides Participant Workbooks Summarizing Data Scenarios Course Evaluation Form Extra Pens and Pencils Calculators

For the Instructor: Flip charts Laptop LCD Projector Tape

Copies of the PowerPoint slides should not be handed out to course participants.

MATERIALS AND EQUIPMENT | v


WORKSHOP SCHEDULE

Tasks Expected Time

Instruction to Putting Data to Work: Introduction How do you Use Data? Exercise 1: Is there a Problem?

25 minutes5 minutes

10 minutes10 minutes

Data: Kinds of Data Types of Data Data Collection Exercise 2: Using a Check

Sheet

35 minutes10 minutes10 minutes5 minutes

10 minutes

Interpreting Data: Ratios Proportions and Percentages Rates Rates Example Why use Rates? Exercise 3: Calculate Ratios

and Rates

60 minutes5 minutes


10 minutes

Summarizing Data: Summarizing Data Tables Graphs Line Graphs Creating Line Graphs Bar Charts Exercise 4: Which Method do

you Use? Pie Charts Maps Stratification Exercise 5: Summarizing data

155 minutes5 minutes

10 minutes5 minutes5 minutes



Conclusion: Summary What’s Next


WORKSHOP SCHEDULE | vi

The listed times are approximates and should be verified during classroom preparation. Be sure to allot time for breaks.

CLASS PREPARATION CHECKLIST

Task Obtain and test LCD projector and personal computer

Obtain flip charts and markers: 1 for every 4-5 participants

Copy participant materials. For each participant:

Participant Workbook

Workshop evaluation form

Appendix A: Summarizing Data Exercise

Answer sheet for Summarizing Data Exercise

Obtain and test PowerPoint file (PDW-PPT)

Prepare flip charts

Put slide #12 on a flip chart to reference during that section

CLASSROOM SETUP

Arrive early to setup the classroom.

Arrange tables for groups of 6. Provide a flip chart and easel for each table of 6

participants.

MATERIALS AND EQUIPMENT | vii


Instruction NotesThe following notes refer to slides in the PowerPoint presentation: “PDW-PPT.” Suggested actions and script for the instructor are located in the Script/Key Points section of each slide. Also included are references to the slide number and page number in the participant workbook, as well as instructions on when and how to use the exercises. Use these materials as you prepare for your session to the guide you during the workshop. Be sure to refer participants to the appropriate page number in their workbook throughout the session.

Script for the facilitator to SAY is written like this.

Instructions for the facilitator to DO are written like this.

Possible answers are written like this.

ICON GLOSSARY

SPECIFIC SLIDE FOR USE DURING THE EXPLANATION

FLIPCHART USE

SUPPLEMENTAL INFORMATION TO HELP PERFORM A TASK MORE EASILY

WORKSHOP SCHEDULE | viii

SMALL GROUP EXERCISE

QUESTIONS FOR FACILITATOR TO ASK PARTICIPANTS

VIDEO PRESENTATION

MATERIALS AND EQUIPMENT | ix

Course InstructionINTRODUCTION TO PUTTING DATA TO WORK

Expected Time: 25 minutes_______________________________________________________________________

Introduction WORKBOOK CLOSED

5 minutes

SCRIPT/KEY POINTS Welcome the participants and introduce yourself

Introduce dignitaries (allow them to speak)

Explain housekeeping items, such as break times, restroom locations, etc.

Explain the workbook and how they will use it to complete exercises and take notes.

How Do You Use Data?Workbook Page 1

10 minutes

SCRIPT/KEY POINTS Think about the many decisions that health managers make in

a day. Some of them are simple and straightforward, and some of

them are not as simple as they may appear! And then there are decisions that managers know will have

significant impact on the population they serve, on their workforce or on the way work gets done.

As a manager concerned with the health of your community and the population your program serves, as well as the performance of your organization, timely and accurate analysis of data can inform technical as well as administrative decisions.

Ask the participants, can you think of any ways that you use data to make your decisions? Instruct the participants to write their answers in their workbook on page 1.

As a large group, ask the participants to share their answers.(write their responses on the flip chart)

Exercise 1: Is There A Problem?Workbook Page 2

10 minutes

SCRIPT/KEY POINTS Let’s go through a scenario. Imagine that you are a medical

director of a district hospital and part of your job is to manage resources. Each month your employees submit receipts for their fuel usage.

Instead of just approving the bills, you can study the data they provide.

Collecting and analyzing these simple data will allow you to better track and understand trends in fuel usage.

Consider the following graph which tracks fuel usage over a year for all employees of the district hospital.

As a large group discussion, ask the participants the following questions. Write their responses on a flip chart to possibly use later during the stratification portion of this session. – What is the graph telling you about fuel usage?


Answer: Fuel usage was lowest between April and June. It reached its highest peaks in February, July and September. There was a sharp decline in fuel use between February and April and also between September and December. Fuel use continuously declined after the high peak in September.

– Is there a problem with usage?Answer: We cannot tell if there is a problem with fuel usage because there is not enough information to answer that question. The graph shows fluctuation in fuel use but it doesn’t indicate that something is wrong. We won’t know anything unless we continue to monitor the trends and ask questions to find out what is going on.

Without data, you can only guess what is not working, how to correct it and whether your efforts at change were effective.

While your guesses may be informed by your experience, this approach is not likely to be successful, especially in the long term.

Only data can provide the guidance you will need. But collecting the data is only one component of program management and planning.

Accurately analyzing and interpreting data over time will help you identify areas that need your attention and assist to prioritize health problems.

Ask the participants, as the medical director of this district hospital, what are some other things you would want to keep track of besides fuel usage? Possible answers: Waiting times at clinics, how often are vehicles in the shop for repairs or maintenance

HOW DO YOU USE DATA? | 3

DATA


Kinds of DataWorkbook Page 4

10 minutes

SCRIPT/KEY POINTS You probably have access to more data than you realize. If you work in a hospital or clinic, the patient admissions and

medical records can provide valuable information to help you manage your programs.

Similarly, data that is collected about employees may be useful for managing human resources.

Just as in our example of fuel usage, something as simple as an invoice can provide you valuable data for program management and planning.

There are different kinds of data. Health data, for example, can focus on individuals or entire populations.

Individual: A classic example of individualized data is a patient’s medical

record. Each record is devoted exclusively to one person and contains

information about his or her unique illnesses, injuries, behaviors, etc.

The data are used primarily to improve the health of that one individual.

In public health the focus is primarily on populations (communities, cities, districts, provincials).

We are mostly concerned with population-based data because it will tell us about the overall health of that population.

Analyzing these data can inform policy and help guide public health programs and recommendations.

For example, if data indicated that there are fewer residents in


our community wearing motorcycle helmets than in other communities, we can consider interventions to increase helmet use.

Management: Then there are program and operation data, which are also

valuable for making decisions and recommendations. These data enable you to monitor and evaluate the use and

distribution of resources, such as staff, supplies, and equipment.

Examples of management data include the fuel usage example mentioned earlier, as well as human resources and budget information.

HOW DO YOU USE DATA? | 4

Types of DataWorkbook Page 5

10 minutes

SCRIPT/KEY POINTSGenerally, there are two types of data: quantitative and qualitative.

Quantitative Data: Quantitative data provide the what, who, when, and where.

They are measurable and tangible. Quantitative data involves the counting of people, behaviors,

conditions, or other discrete events; classifying those events into categories; and using math and statistics to answer questions.What Numbers of deaths can be used to identify leading

causes of death

Who Numbers of smokers and nonsmokers by gender can be used to determine whether men are more likely to smoke than women

When Keeping track of hospital admissions can identify when more staff may be needed

Where Comparing the proportion of women who began prenatal care after the first trimester in various districts will provide an indication of where access to prenatal services may be a problem

Qualitative Data: Qualitative data, on the other hand, can be used to explain the

why and the how. Qualitative data involve observing people in selected places

and listening to discover how they feel and why they might feel that way.

For example, a focus group of teenage girls could provide valuable insights concerning why they do or do not use contraceptives.

Interviews in a local clinic might indicate how people feel about


the clinic’s services. Quantitative data and qualitative data often interact and

complement each other. Using both types of data can give you a better picture of the situation.

Quantitative data will typically give you the numbers or measurable details (size of population, staff turnover rates, etc.), while qualitative data will help you understand why something is happening.

For example, quantitative data can tell us that clinic wait times are higher in one district as compared another. However, it is through qualitative data that we can get a better understanding of why the wait times are higher.

Perhaps the clinic has very few staff serving a large population, it double books appointments or patients are typically running late so they miss the appointment time and have to wait even longer. This detail is not typically found through quantitative data.

In this course, we will focus almost entirely on quantitative data.

Ask the participants to think about that data that crosses your path each day, i.e. fuel usage, hospital admissions, etc.

Can you think about any data that crosses your path each day? What data are available to you?(allow time for a brief discussion and write responses on a flip chart)

TYPES OF DATA | 7


Data CollectionWorkbook Page 6

5 minutes

SCRIPT/KEY POINTS There are many ways to collect useful data. You may already be collecting data formally through a

surveillance system or with a management system such as financial management or human resources information system.

You can start your own data collection with a simple tool.

Counts: Counts are the simplest measure of occurrence. A count is just the actual number of events in the population of

interest, such as identifying how many individuals have a specific illness such as HIV.

You can use counts in management as well, such as counting the number of times an employee calls in sick to work each month.

Counts are commonly used for program planning and monitoring.

Having a count by itself is helpful in planning things like the amount of vaccines or other medications to order, but counts alone do not provide context.

Counting events or cases does not give any indication of the problem in relation to the size of the population.

For example, 76 cases of guinea worm in the capital city of Gaborone would be very different from 76 cases in a small rural village in Botswana.

Check Sheet: One way to organize counts is through the use of a check

sheet. A check sheet is a table or form that is used to systematically register data as it is collected.

Check sheets can be used to indicate how often an event occurs.

TYPES OF DATA | 8


DATA COLLECTION | 9


Exercise 2: Using A Check SheetWorkbook Page 7

10 minutes

SCRIPT/KEY POINTS The check sheet on page 7 in your workbook shows the

number of appointments that were missed at 5 different clinics over a 7 week period. Each tally represents one missed appointment.

Click to show: For example, in week 1 there was 1 missed appointment at clinic D.

TYPES OF DATA | 8


Ask the participants to turn to page 7 in their workbook to complete the following task.

Using the check sheet, fill in the missing data for week 7 using the following information: 3 people from Clinic C missed appointments and 1 from Clinic D.

Answer: 3 tallies for clinic C and 1 tally for clinic D, all for week 7

Use the counts from each clinic to add up the total number of missed appointments over the 7 week period.Answer: The counts for total number of missed appointments are highlighted in yellow:

DATA COLLECTION | 10

Location

Missed Appointments

Wee

k 1

Wee

k 2

Wee

k 3

Wee

k 4

Wee

k 5

Wee

k 6

Wee

k 7

Tota

ls

Clinic A

Clinic B

Clinic C

Clinic D

Clinic E

Totals # people who missed appointments

6 2 26 5 6 5

Total # appointments booked for week

50 65 45 52 61 49 60 382

Percent missed 12.0 3.1 57.8 9.6 9.8 10.2

Location

Missed Appointments

Wee

k 1

Wee

k 2

Wee

k 3

Wee

k 4

Wee

k 5

Wee

k 6

Wee

k 7

Tota

ls

Clinic A 8

Clinic B

7

Clinic C

16

Clinic D

12

Clinic E

11

Totals # people who missed appointments

6 2 26 5 6 5 4 54

Total # appointments booked for week

50 65 45 52 61 49 60 382

Percent missed 12.0 3.1 57.8 9.6 9.8 10.2


INTERPRETING DATA


RatiosWorkbook Page 8

5 minutes

SCRIPT/KEY POINTSTurn to the flip chart that displays slide #12 Measuring the frequency of events, such as disease or health

events, is at the heart of public health surveillance and resource allocation.

And measuring results is at the heart of good leadership and management.

The most commonly used measures for assessing frequency are ratios, proportions, percents and rates.

The next series of slides will introduce you to these various frequency measures, which are all commonly used in public health.Ratios

A ratio is a comparison of two different things; it is a numerical expression that indicates the relationship between two or more things.

The two numbers are generally separated by a colon, but can also be expressed as a fraction.

For example, the ratio of missed appointments between Clinic A (8) and Clinic B (16), is 8:16 or 8/16. We can reduce this to 1:2.

So for every missed appointment at Clinic A, there are 2 at Clinic B.



Proportions and PercentsWorkbook Page 8

10 minutes

SCRIPT/KEY POINTS

Proportions A proportion is a ratio in which the numerator is included in the

denominator; it is a numerical expression where a part is compared to the whole.

A proportion can be expressed as a fraction or a decimal. For example, we can express the relationship of Clinic A to the

total number of missed appointments as a proportion. The proportion of missed appointments in Clinic A compared

to the group is 8 divided by 54 or 0.15.

Clinic A 8

= = 0.15

Total Missed 54

To be more easily interpreted proportions are often multiplied by 100, 1000 or 100,000, so we can also express this fraction as 15 per 100 or 150 per 1000.

When multiplied by 100, proportions can also be reported as percentages, so we can say that the percentage of missed appointments in Clinic A is 15%.

As you can see, proportions and percents are essentially the same measure.

Percents What makes a percentage different is that it standardizes the

data, which means it makes it easier to compare to similar data obtained from a different sample. Such as when comparing the percent of missed appointments from the different clinics.

However, you have to be careful when calculating and interpreting percentages if the total number or sample size is



small. Therefore, it is recommended that the number or count

always be given together with the percentage.

Let’s illustrate this through an example. In the following table Clinic A appears to have the best percentage of pregnant women whose partner gets tested. But are they really doing a considerably better job of getting partners tested?

If we look at that total number of pregnant women from each clinic we see that clinic A has a much smaller population than clinic C.

Clinic % Pregnant women whose partner gets tested

# of pregnant women whose partner gets tested

A 50% 3 out of 6

B 21% 38 out of 181

C 17% 121 out of 712

INTERPRETING DATA | 13


RatesWorkbook Page 9

15 minutes

SCRIPT/KEY POINTS A rate is often a proportion, with the added dimension of time.

Rates measure the frequency at which an event occurs over a period of time.

In health, a rate can represent the burden of disease or other health related outcome during a specific time period.

In management, a rate might represent the number of times a pharmacy runs out of medicine or a vehicle breaks down over a period of time such as months or years.

Are you at risk? Before we go on to look at rates in more detail, let’s discuss a

term you’ll often hear in assessing health: “Risk or Persons at risk.”

Risk is the probability or likelihood that an event will occur, such as the likelihood that an individual will become ill or die, within a stated period of time.

Persons at risk are people in the total population to whom the event or health condition being studied could have happened. Usually this is everyone living in a specific geographic area during the time period of interest.

The term "persons at risk" defines the denominator for the calculation of rates. It alludes to the number of persons potentially capable of experiencing the event or outcome of interest.

Under certain conditions, however, we exclude some persons from the total population. For example, when looking at those at risk for contracting polio, we would exclude all those who have had the vaccination from our population.

Rates allow us to compare the risk of health events across



different groups of people, places and times. The general formula for calculating rates is shown below: Number of persons experiencing the event Number of persons “at risk” of x K experiencing the event over a specified time period The number or persons who actually experience the event of

interest make up the numerator. The denominator includes the number of persons potentially

capable of experiencing the event, or population at risk, over a specific time period.

K represents a standard unit of the population (per 100, 1,000, or 100,000)

Both the numerator and denominator must represent the same time and place.

Example:

In looking at this table of surveillance data, if you have 1000 people in your community and 20 cases of a disease, that could be a lot more significant than if you have 1 million people in your community and 20 cases of a disease.

When we divide the number of cases by the total population for each city, you can see that the rate in city A is much greater than the rate in city B – 1,000 times greater.

In this example, to convert our decimals to whole numbers, we use a K of 100,000; the result is in the far right column.

As long as you multiply the rates for cities A and B by the same multiple of 10, you will still have a valid comparison.

For example, in city B, we can say that the rate of disease in this population was 2 per 100,000, or for every 100,000 people in the population, two cases of disease were identified during


Cases Total population

# cases Total pop.

Rate

20 ÷ 1,000 0.02 2,000 per

100,000

20 ÷ 1,000,000 0.00002 2 per

100,000

the time period of interest.

Rates ExampleWorkbook Page 10

15 minutes

SCRIPT/KEY POINTS On way to decide what to use as your K is to look at what you

are comparing to. For example, if national data are described in terms of ‘per 100,000’, use this as your unit, for easier comparison.

Let’s work through an example. The table below shows the number of deaths from HIV/AIDS among women from Guatemala and Panama for the year of 2002.

Country HIV/AIDS Deaths Female Population

Panama 114 1,573,289 Guatemala 167 6,342,703

Source: World Health Organizationhttp://www.who.int/entity/healthinfo/statistics/bodgbddeathdalyestimates.xls

As a large group, use the table to calculate the rates of death from HIV/AIDS in these populations, also called the mortality rate.– What is the mortality rate from HIV/AIDS per 100,000

women in Panama?

Answer: (114/1573289 ) * 100,000 = 7.2 per 100,000 per year

– What is the mortality rate from HIV/AIDS per 100,000 women in Guatemala?

Answer: (167/6342703) * 100,000 = 2.6 per 100,000 per year

http://www.who.int/entity/healthinfo/statistics/bodgbddeathdalyestimates.xls

http://www.who.int/entity/healthinfo/statistics/bodgbddeathdalyestimates.xls


– Based on the rates we have calculated which country has a higher rate of women dying from the disease?

Answer: Panama

– Which country has the higher number of women dying from HIV/AIDS?

Answer: Guatemala. Since we are just asking about a count and not a rate, then as the table shows, Guatemala has more women dying from HIV/AIDS. However, when calculating the rates, we are able to take the population size into account. Panama is approximately ¼ the size of Guatemala, so even though the count of women dying is smaller, since the population is so small the rate is higher.

As you can see from the example with Panama and Guatemala, the greatest benefit in using rates is that it is a valid way to make comparisons.


Why use Rates?Workbook Page 10

5 minutes

SCRIPT/KEY POINTS Rates allow us to make comparisons between groups of

people, such as different age groups, or locations that have different population sizes, such as districts versus villages or urban versus rural areas.

With rates, you can identify groups in the community with an elevated risk of disease. With this information, risk factors can be examined and interventions targeted to high-risk groups.

You can also use rates for managing operations and resources. For example, you can calculate the rate of employee turnover

or the vaccination coverage rates across provinces in your country.

You can also calculate rates for hospital admissions to determine whether rates are increasing or decreasing for certain diseases.

Rates can help play an important role in improving organizational performance across key indicators.

Exercise 3: Calculate Ratios and RatesWorkbook Page 11

10 minutes

SCRIPT/KEY POINTS Now we’ll put into practice some of the things we just learned. Ask the participants to refer back to the check sheet on

page 7 in your workbook to answer the following questions.– What is the ratio of total missed appointments between

Clinic C and Clinic D?

Answer: Total no. of missed appointments at Clinic C=16Total no. of missed appointments at Clinic D =12Ratio =16:12 or 4:3. For every 4 missed at Clinic C, 3 are missed at Clinic D.

– What is the proportion of missed appointments in Week 5 for Clinic E?

Answer: Total no. of missed appointments in week 5 at Clinic E =2. Total no. of missed appointments in week 5 = 6. Proportion=2/6=1/3 or 0.33 or 33%

– What is the rate of missed appointments over the 7 week period?

Answer: Rate = total no. of missed appointments for all the clinics divided by the total no. of appointments booked for the 7 week period of time. This number is then multiplied by some standard population unit, let’s say 1000 for this eg. Therefore the rate = 54/382 x 1000=141. So there are 141 missed appointments per 1000 appointments booked.

SUMMMARIZING DATA


Summarizing DataWorkbook Page 12

5 minutes

SCRIPT/KEY POINTS Data represent observations that require organization in order

to provide useful information. Each piece of data is like a puzzle piece: on its own, it doesn’t

tell you much, but when it is put with the rest of pieces and they are arranged systematically, it can be part of a meaningful picture.

To organize data and information, we can present our findings in the form of tables, graphs, charts, and maps.

Organization and presentation of data are essential for several reasons.– First of all, organizing data is a necessary step in the

analysis of that data. In other words, in order to make sense of the data that you have collected, you need to organize the information in a meaningful way.

– Second, organizing and presenting data allow you to explore patterns and trends in the data, as well as to be able to identify variations in trends.

– Third, and perhaps most importantly, organizing and presenting data is an extremely useful way to communicate and share information with others.

Basic Methods for Summarizing Data This section will provide a basic introduction to tables, graphs,

charts and maps as a means of organizing and presenting data.

We will review examples of these figures and their use in public health, planning, management and evaluation.

We will also learn how to create some of these figures.

TablesWorkbook Page 13

10 minutes

SCRIPT/KEY POINTS Instructor click before reading: In the very simplest

terms, a table is a set of data that is organized into rows and columns.

Click before reading: Tables are very useful for organizing and displaying quantitative data. They allow us to demonstrate a number of patterns or differences between groups, depending on what data are included in the table. Almost any quantitative information can be organized into a table.

Click before reading: This is an example of a 2X2 table, which is a simple way to summarize two variables, when each variable has two distinct values.

This table describes the follow-up status of a group of men with and without diabetes who were participating in a follow-up study using a medical examination survey between 2005 and 2010.

Click before reading: Every row and column in a table should have corresponding labels that are clear and concise. The first row shows the number of diabetic men and the second row shows the number of non-diabetics. The first column shows the number of deceased men and the second column shows the number of men who are still alive.

Click before reading: Sometimes each row and column is accompanied by totals. The third row shows the total numbers for columns 1 and 2. The third column shows the total numbers for rows 1 and 2.

Click before reading: If any codes, abbreviations, symbols, or data sources are used, these should always be explained by a footnote.


A successfully and correctly structured table should be self-explanatory, meaning that it is easily understandable and capable of standing alone.Ask the participants: looking at the table, can you tell me how many diabetic men are living? Answer: 89.

The check sheet that we saw in the previous exercise is another example of a table.

We can use the information in a table to create a line graph, bar chart, pie chart or map. We will practice how to do so in this section.

Because large complicated tables can be overwhelming for the reader, when summarizing a large number of variables try creating two or three small tables instead.

SUMMARIZING DATA | 22

Continuous vs. Discrete 5 minutes

SCRIPT/KEY POINTS Before we talk about the next method for organizing and

presenting data, let’s talk a little bit about the data we are using.

The numerical data we will be talking about for our graphs and charts falls into 1 of 2 categories: continuous and discrete.

Continuous data can assume any value within a range, such as height, weight, temperature or even time.

One general way to tell if data are continuous is to ask yourself if it is possible for the data to take on values that are fractions or decimals. If your answer is yes, this is usually continuous data.

Discrete data have a fixed number of possible values, such as gender, race, marital status and trained and untrained staff.

Discrete data usually occurs when we are counting something, using whole numbers. For example we can’t have half a person.

GraphsWorkbook Page 14

5 minutes

SCRIPT/KEY POINTS A second popular means of organizing and presenting data is

through the use of graphs. In essence, a graph is a way to show numerical data visually by

using a system of coordinates. Like tables, graphs are capable of showing patterns, trends,

similarities and differences in the data. In general, creating a graph requires that a very specific format

be followed.

This is an example of a basic line graph. The graph shows the number of reported cases of measles in the United States between the 1950 and the 1990.

Instructor click before reading. The x-axis, the horizontal line, is used to show the values of the method of classification. The method of classification is the criteria that you are using to order the information. For example, if you want to examine rates of a disease over time, you would use the x-axis to show units of time (e.g. days, months or years).


Along the x-axis of this graph, you see that the intervals are divided up by years, beginning with 1950, and then moving at 5-year intervals through time, all the way to 1990. So, in this graph, time is the method of classification.

Instructor click before reading. The y-axis, the vertical line, is used to show a frequency measure—how much or how many.

In this graph the y-axis depicts the number of reported cases of measles per 1,000 people, with a range from 0-900. The number of reported cases, therefore, represents the frequency measure.

Click before reading. In creating a line graph, we plot using a set of two coordinates, an x coordinate and a y coordinate. In this graph the x-coordinate is the year and the y-coordinate is the number of reported cases measles per 1,000 people that occurred during that particular year.

Ask the participants, in looking at our graph, what observation can you make about the reported case of measles over the 40 year period?

Answer: From the 1950s through the early 1960s, reported cases of measles in the United States occurred in large numbers. However, as you can see from the graph, when a vaccine was licensed in 1963, the number of reported cases of measles/1,000 people began to decline on a steady basis, falling off to almost 0 reported cases by the late 1980s.

It can be easier to understand and remember the details of a particular data set from looking at a graph as opposed to reviewing a table. Also, grid lines are optional. For a clean look use a faint color or delete the lines completely.


Creating Line GraphsWorkbook Page 15

15 minutes

SCRIPT/KEY POINTS Line graphs are useful for showing patterns or trends over

some variable, usually time. They are used to represent continuous data.

Remember, continuous data can assume any value within a range, such as height, weight, temperature or time.

Line graphs are also useful for comparing two or more sets of data in the same graph.

Now, let’s go through some general tips for creating a line graph.

These tips are also useful when creating other types of graphs and charts, so you may want to keep them handy.

Example: In our example, we would like to track the number of staff

members who have been hired to work at district health facilities from 1975 to 2010.

When creating a line graph, you first mark off each axis at equal intervals.

Here you see the X and Y axes, without any intervals marked. Instructor, click to show the intervals: All we do is mark

off equal intervals along the X and the Y axes. Before you start marking though, you will need to take into

account some of the tips covered on the next few slides.

Let’s think about a reasonable interval. You should use a scale on the x-axis that matches the intervals used when collecting the data, e.g. days, weeks, months, or years). For example, if you collected the data over days, then use an x-axis scale that is also based on days.

In our example, we would like to graph data from 1975 to 2010. While weekly or monthly reporting may have occurred, it is reasonable since the information spans such a long time period to present these data in years. Let’s think about a


reasonable interval. Ask the audience, do you want to show each year on the

graph over the 35 year period (1-year-intervals)? Or mark an interval every certain number of years?

Discuss with the participants that every year from 1975 to 2010 will make the x-axis crowded and hard to read. Having intervals that are too far apart, such as 10 years or 15 years, makes it hard to see which year the data being presented correspond to. Also a consideration is the tendency for us to want to look at things in increments of 1, 5, or 10. In this case, showing every 5 years (with a tic-mark at every year) is a reasonable interval.CLICK to show the axis with every 5 years shown.


You can see that the x-axis, which represents time, has been clearly divided into 7 equal intervals of 5 years each, beginning in 1975 and ending in 2010. This is easy to read and understand.

Go to the next slide: It is a smart idea to make the x-axis longer than the y-axis—this helps to ensure that the line graph appears to be positioned horizontally. In addition, you should always start your y-axis with 0.

To determine the range of values that you need to use on the y-axis, identify the largest value that you intend to plot, and then round the value off to a number that is just slightly


larger. This way your range of values on the y-axis will be slightly

higher than the largest value you will be plotting. In our example, the lowest number of staff members reported

in one year during the time period is about 500, and the highest number reported during one year is about 8300.

After rounding off, our range of values can extend from either 0 to 8500 or from 0 to 9000.

Before we decide our maximum value we should decide which interval we want to use. The interval used can help determine the maximum value displayed. For example, if we use an interval of 500, the maximum value could be 8500; but if we use an interval of 1000, the maximum value would be 9000.

In this case, the easiest way of showing staff members would be to display the y-axis going from 0 to 9,000 staff, by intervals of 1000. Intervals of 500 could work, but may make the axis too crowded.

CLICK to show the y-axis drawn by intervals of 1000 and with a maximum value of 9000.

After determining that our intervals will be 1000, you can see, the y-axis begins with 0 and goes up to 9000.


Inform the participants that they may have some back-and-forth in this process. Sometimes after you have chosen your maximum value and intervals, you need to go back and play with the number of intervals or the spacing.

Explain to the participants that because of the large range, the detail for the exact number of staff members cannot be shown. Instead, the reader will have a sense of “about” how many staff members were present each year, and the pattern (increasing or decreasing). Generally having 8-15 intervals on the y-axis is appropriate. So, after all that work, you can plot the data on your line graph. Here is the completed graph of number of staff members hired to work in district health facilities from 1975 to 2010.

Now that you have a better understanding of how to create a line graph, let’s review what this graph is telling us.

Ask the participants the following question and ask for volunteers to answer.– What does the data tell you?

Suggested answer: Between 1975 and 1996, the number of staff members remained between 4000 and 6000 with the lowest peaks between 1979 and 1990. There was a sharp rise in the number of staff members in 1991 with the highest number seen around 2001.

Bar ChartsWorkbook Page 16

10 minutes

SCRIPT/KEY POINTS Another method of organizing and presenting data is through

the use of bar charts. Bar charts take data from a simple table and create a visual

display of the data. Bar charts provide an easy way to compare the relative magnitude of different values.

Bar charts are best used for comparing data with discrete categories, meaning that the data have a fixed number of possible values, like marital status and blood type.

Remember, the opposite of discrete data are continuous, which are often used for line graphs.

Click to show. Here is an example of a bar chart. It represents the average clinic wait times for various districts in India in 2008.

As you can see, this particular bar chart uses horizontal bars, as opposed to vertical bars, and the bars are arranged in increasing order—they get longer (and thus the values that they represent get larger) as you move up the y-axis from


bottom to top. The bars in a bar chart may be organized horizontally or vertically—it depends on how you want to communicate the data.

The horizontal axis of the graph represents the amount of time patients had to wait in minutes and is divided up in increments of 10 with the range being 0 to 70. The vertical axis represents the various districts: Nalgonda, Adilabad, and so on. This graph tells the observer that, in 2008, the average wait time was the longest in clinics in the Nalgonda district.

Note that similar to a line graph, in a bar chart, one axis is used to show the method of classification, or how you are ordering the data, and the other axis is used for the frequency measure. However, with a bar chart it does not matter which axis shows which--it just depends on how you would like your chart to look, i.e. vertically-oriented, horizontally-oriented, etc.

Now that you have mastered the line graph, let’s move on to the simple bar chart.

The bars that comprise bar charts have several important characteristics. Firstly, the bars in a bar chart may be organized horizontally or vertically—it’s up to you.

All bars in a simple bar chart should be of equal width, and separated from one another visually.

Each individual bar in a bar chart represents one value of the variable being displayed. For example, let’s imagine that you collect data on the occurrence of 5 different diseases over a 5 year period and then construct a chart to display your data. In your bar chart, there would be 5 bars, and each individual bar would correspond to a different disease.

The length and/or height of each bar in your chart is proportional to the frequency of the event in that particular category. So, in a vertical bar chart, for example, if there were more observed cases of Disease A as opposed to Disease B in the 5 year time period, the bar representing Disease A would be taller than the bar representing Disease B.

Examples: Click to show. Here is an example of a vertical bar chart.

This chart shows the number of reported polio cases worldwide from 1990 to 1995.


Each individual bar represents one value of the variable being displayed. So for this bar chart, the variable is the year. There are 6 bars, and each individual bar corresponds to a different year.

The length and/or height of each bar in a bar chart is proportional to the number of reported polio cases.

According to this bar chart, the highest amount of reported polio cases occurred in 1990 and has steadily declined since then.

Tip: Some people refer to bar charts as bar graphs.

Exercise 4: Which Method Do You Use?Workbook Page 17

10minutes

SCRIPT/KEY POINTS Let’s go through an exercise to check in on what we just

learned. Scenario: As a district director of health in Dangme West

district in Ghana, you acquired a mobile clinic in the last year to improve health coverage in your area. You wish to track the number of trips that the mobile unit has made to various parts of the district in order to determine if coverage needs are being met.

As a large group, inform the participants that they will be using the below table to answer the question.

This table shows the names of sub-districts and the number of trips that the mobile unit has made to each place.

Sub-districts Number of trips

Dodowa 5

Prampram 3

Osudoku 1

Ningo 2

Ask the participants, how would you graphically represent this information? Explain your answer.Suggested answer: since the data here is discrete, it would be make sense to have a bar graph that shows the names of towns on the x-axis and the number of trips on the y-axis. A line graph would not be appropriate in this case because we are not looking at data with respect to some sort of continuous or time variable.

Pie ChartsWorkbook Page 18

10 minutes

SCRIPT/KEY POINTS Pie charts are frequently used to show how part of something

relates to the whole. Pie charts are useful for showing the component parts of a

single group or variable. The basic design is a circle, the shape of a pie, and the

components, or slices of the pie, are usually percentages of the different categories of the variable.

Pie charts are a way to effectively present percentages in which the “slices” of the pie add up to 100%.


Example:Let’s look at a pie chart.

The whole pie represents the total number of accidental deaths among children aged 1 to 19 years between 2005 and 2010. From this pie chart we can see that traffic accidents were the leading cause of death among 1–19 year olds during the 5 year time period. Although these data could certainly be presented in a table or using a bar graph, note how easy it is to make comparisons between the causes of death using the pie chart in this example.

When a single variable is being presented (such as accidental deaths), and the information you want to convey is how parts relate to the whole (like how the causes of accidental deaths relate to the total number of deaths), you should consider using a pie chart to display your data.

Let’s say we wanted to determine what percentage of children died from drowning, using raw data.

We would divide the number of deaths from drowning by the total number of deaths overall and multiply that number by 100. That is how we obtained the 8% shown in the chart.

You can use this process to obtain the percentages for all the other causes of death among children.

Use different colors or different patterns to represent each component of the pie. This makes it easier to read and distinguish one component from another.


MapsWorkbook Page 19

15 minutes

SCRIPT/KEY POINTS Maps are a way to visually display data, in this case

geographic, or spatial, data. Of course, you could display the same data on a table or bar

chart, but maps are a much easier way of taking in spatial or geographic patterns.

They are also a better alternative in cases when you may have too many geographic units to easily display on a chart.

Mapping your data can provide a quick visual overview. It is a particularly powerful tool for looking at clusters of diseases or events to determine where the health outcome is occurring most frequently.

Different geographic levels are used to answer that question: province, district, or village. These geographic areas can be grouped by specific characteristics, such as rural and urban.

Mapping can also be used for management purposes. For example, you can use a map to show the distribution of health laboratories in a certain city.

There are many types of maps that can be used to organize and present data.

The more common ones include spot maps, area maps, and maps created through use of geographic information systems or GIS. We will focus on spot maps.

Spot maps are created by placing a dot or some other symbol on a geographic site where some event occurred.

One of the most famous spot maps was Dr. John Snow’s tracking of cholera deaths in London in September 1854.

You can also use spot maps to track operations information, such as location of ambulances in a district or the location of VCT centers in a certain district or province.

Let’s look at an example. The following spot map shows a region in South Africa. The circles represent the clinics that offer HIV/AIDS counseling and testing. The triangles represent


persons that have been recently diagnosed with HIV/AIDS. We can look at the distribution of patients across the region in relation to the number and location of clinics. This helps provide a visual representation of where resources are and helps us analyze whether we need to makes changes.

An important point about spot maps is that they DO NOT take into account the size of the population at risk. In other words, they do not show the risk of the event occurring in a particular place.

A heavy clustering of dots in one particular geographic area may simply mean that more people are living in that area, not necessarily that there is a higher rate.

For example, in our spot map there is a heavy clustering of patients in the central part of the region. That could mean


Clinics that offer HIV/AIDS counseling

Persons with HIV/AIDS

therefore there are more cases.

Ask the participants: What information can you gather from looking at this map?

Answer: What the map tells us is that the clinics appear to be located outside of the range of newly diagnosed patients. Looking at this map could help plan better locations for the clinics where they will be more accessible to its patients.

Let’s go through an example of how to make a spot map. This is a map of Guatemala. To turn it into a useful tool, we need to put data on it.

Instructor click before reading. Various symbols are used to represent events you are studying.

Click before reading. These symbols are laid over a geographic map of an area and can indicate disease clustering or occurrence, populations at risk, locations of hospitals, health centers, etc. Click again here. Because each case is shown as a dot, this is a dot map. For the sake of comparison, let’s look at an area map.

This slide shows a great example of an area map.


This particular map was taken from the World Health Organization, and uses a darker blue color to depict those countries that reported at least one outbreak of yellow fever between 1985 and 1999. The lighter shade of blue is used to identify those countries considered to be at risk of experiencing yellow fever in their population.Notice that this map does not show individual cases of disease as a dot map does. Instead, it illustrates the differences in risk in different geographic areas.


StratificationWorkbook Page 20

10 minutes

SCRIPT/KEY POINTS Another way to organize or present data is to stratify it. When

looking at the data you have collected, sometimes it is helpful to break it down into smaller groups. This is called stratification.

Stratification is a technique used to divide a set of data into meaningful subgroups (strata) of data.

Think about what kind of subgroups might have a meaningful influence on the data results. They could include, for example, age, gender, time, or geographical location.

Let’s think back to our check sheet on page 7 of your workbook. The following bar chart shows the weekly percentage of people missing ART appointments from all the clinics combined. Ask the participants, would it be helpful to know the percentages from each clinic separately?

In this example, if you would want to see the data for each clinic, you would stratify by clinic.

Once we stratify the percent of people missing appointments we


can analyze the data and ask more specific questions to determine if there is a problem.

As a large group, ask the participants the following questions.

In this case stratification by clinic helps get more information from the data. It allows you to ask questions such as

- Why does clinic B have 100% attendance, except during week 3?

- Why has clinic A improved in week 3, while all others are worse?

- Why did clinics B, D and E experience higher non-attendance in week three? Are they in a similar geographic location? What else could link them?

In Summary: The purpose of organizing and presenting data is to analyze it,

to explore patterns and trends, and to communicate information to others

Data can be organized through the creation of tables, graphs, charts, and maps

2-variable tables can illustrate the number of people who share a certain characteristic/set of characteristics which can provide valuable information about possible or probable relationships between two variables

Line graphs are useful for showing patterns or trends over SUMMARIZING DATA | 43

some variable, usually time Bar charts are used to display countable or discrete data, such

as race or gender, and make it easy to see differences among the categories

Pie charts are useful for showing the component parts of a single group or variable

Maps are an excellent way to display geographic information, and make it easier to identify geographical patterns in data

Spot (dot) maps use dots or other symbols to show geographic distribution of an events or a disease/condition

Exercise 5: Summarizing DataWorkbook Page 22

60 minutes

SCRIPT/KEY POINTS Split the participants into small groups. Give each group a scenario (located in the Appendix on

pages 51-56), flip chart sheet and markers to use. Inform the groups that they will be using the given

scenarios to…– Determine which data summary method to use.– Summarize the data using your recommended method.– Present your results to the class. (approximately 3

minutes/presentation) Debrief: Allow each group to share their answer with

the class, saying what their diagram represents and holding it up for others to see.

Alternative Option: Allow each group to hang their diagrams on the board/walls to allow classmates to walk around and view them.

At the end of the exercise hand out the answer key.

CONCLUSION


SummaryWorkbook Page 23

5 minutes

SCRIPT/KEY POINTS Knowing how to read, understand and interpret data specific to

your community will help you better understand your community’s health needs.

Just as important, careful data analysis can help make you a more successful manager as you make important decisions regarding your workforce, your equipment and your long-term plans.

Regular analysis of data can help you detect problems that need addressing or successes that should be replicated.

Data can be organized through the creation of tables, graphs, charts, and maps.

What’s NextWorkbook Page 24

5 minutes

SCRIPT/KEY POINTS Think about the data you have available to you. How can it

help you make stronger management and public health policy decisions?

Practice data analysis and presentation and share your reports with colleagues.

To continue to improve your skills in using data for decision making, research these additional training topics:– Values Based Decision Making– Introduction to Biostatistics– Program Planning– Process Improvement– Project Management– Employee Performance Management

RESOURCES

For more information about Putting Data to Work, consult these reference materials:

Centers for Disease Control and Prevention (CDC) Office of Workforce and Career Development Principles of Epidemiology in Public Health Practice (3rd ed.). Atlanta.

Page RM, Cole GE, Timmreck RC. (1995). Organizing Epidemiologic Data. In J. Burns (Ed.). “Basic Epidemiological Methods and Biostatistics-A Practical Guidebook”. Boston: Jones and Bartlett Publishers.

PowerPoint presentation-Tables, graphs and diagrams, Barbara Schimmer, 2006 (based on EPIET material). Available at: http://www.epinorth.org/dav/C7358EDE63.ppt

FOCUS Volume 3, Issue 6. Data Analysis: Simple Statistical Tests. Available at: http://nccphp.sph.unc.edu/focus/vol3/issue6/3-6DataTests_issue.pdf

Kipp A, Porter K. “Graph Talk with Aaron and Kim.” Epid759, Spring 2007. University of North Carolina School of Public Health.

Northwest Center for Public Health Practice: http://www.nwcphp.org/

http://www.nwcphp.org/

http://nccphp.sph.unc.edu/focus/vol3/issue6/3-6DataTests_issue.pdf

http://www.epinorth.org/dav/C7358EDE63.ppt

APPENDICES

Appendix A: Summarizing Data Scenarios.....................................51

Appendix B: Glossary of Terms......................................................57

Appendix C: Course Evaluation Form............................................60

APPENDIX A: SUMMARIZING DATA SCENARIOS

Group1

Let’s refer to the check sheet that we used to show missing appointments over a 7 week period in the previous section. A portion of the table has been reproduced here for your convenience.

Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7

Clinic C

Clinic D

Clinic E

How can you graphically represent this information to show the number of missed appointments for clinics C, D and E over the 7 week period? Which clinic had the highest number of missed appointments? Can you draw any other conclusions?

Group 2

The table below shows the HIV status for patients who visit clinics in a certain region. Individuals with an unknown HIV status cannot remember their status or their records cannot be found.

Name of clinic

HIV status

Positive Negative Unknown

X 83 22 5

Y 12 47 11

Z 105 61 23

How can you graphically represent this data to show the number of positive, negative and unknown HIV statuses of individuals at each clinic? Using your graphical representation, which clinic has the highest numbers of HIV positive individuals? Can you draw any other conclusions?

Group 3

As a top official in Zimbabwe’s MOH, you are responsible for allocating funds for HIV/AIDS prevention, treatment and care to districts across the country. You wish to account for the various sources of external funding that you have received:

PEPFAR $35,000,000

Safe Blood for Africa (SBA) Foundation $5,550,000

Gates Foundation $40,000,000

The Global Fund $12,000,000

Bristol-Myers Squibb (BMS) Foundation $7,500,000

Total amount of money $100,050,000

How can you graphically represent this information? Which organization provided the greatest percentage of funds to the country? Can you draw any other conclusions?

Group 4

The data below shows the number of staff members in various departments of a district hospital. Using a graphical representation, which department has the highest percentage of staff members? Can you draw any other conclusions?

Department # of staff

Intensive Care Unit 14

Human Resources 10

Pediatrics 20

General surgery 28

Total 72

Group 5

The following table shows the number of vehicles available at health facilities in the Oromia region of Ethiopia. How would you graphically represent this data to show the number of vehicles at each facility? Use that representation to identify which health facility has the highest number of vehicles available for office use.

Name of health facility Number of vehicles available

Adama Health Center 3

Fiche Hospital 4

Gebre Guracha Health Center 1

Asella Health Center 0

Ambo Hospital 2

Adama Hospital 3

Shashamene Hospital 2

Group 6

The data below shows the daily summaries of malaria cases in health centers in the Western District of Kenya. How will you display this information to show the distribution of cases? Can you draw any conclusions?

Daily summaries of malaria cases in health centers in the Western District

Day Number of cases of malaria

1 16

2 16

3 18

4 19

5 16

6 21

7 25

8 28

9 28

10 32

11 21

12 19

13 21

14 16

APPENDIX B: GLOSSARY OF TERMS

Bar chart A visual display in which each category of a variable is represented by a bar or column; bar charts are used to illustrate variations in size among categories.

Case-fatality rate # of deaths due to a disease# of people with the disease

Cluster An aggregation of cases of a disease, injury or other health condition (particularly cancer and birth defects) in a circumscribed area during a particular period without regard to whether the number of cases is more than expected (often the expected number is not known)

Continuous variable A variable that has the potential for having an infinite number of values along a continuum (e.g., height and weight)

Demographic information

Personal characteristics of a person or group (e.g., age, sex, race/ethnicity, residence, and occupation); demographic information is used to characterize patients or populations

Denominator The lower portion of a fraction; used in calculating a ratio, proportion, or rate.

Dependent variable A variable whose values are a function of one or more other variables

Discrete variable A variable that is limited to a finite number of values

Epidemiology The study of the distribution and determinants of health conditions or events among populations and the application of that study to control health problems.

Evaluation Systematic and objective examination of activities to determine their relevance, effectiveness and impact.

Frequency The amount or number of occurrences of an attribute or health outcome among a population

Graph A visual display of quantitative data arranged on a system


of coordinates

Histogram A visual representation of the frequency distribution of a continuous variable.

Incidence Rate A measure of the frequency with which new cases of illness, injury, or other health condition occur among a population during a specified period.

# of new cases over a specified periodaverage population or cumulative person-time the

population was at risk

Independent variable An exposure, risk factor, or other characteristic being observed or measured that is hypothesized to influence an event or manifestation (the dependent variable)

Infant Mortality Rate # of deaths due to pregnancy/childbirth # of live births in the same population

Line graph A graph that displays patterns or trends by plotting the frequency (e.g., number, proportion, or rate) of a characteristic or event during some variable, usually time.

Maternal Mortality Rate

# of deaths due to pregnancy/childbirth # of live births in the same population

Morbidity Any departure, subjective or objective, from a state of physiological or psychological health and well-being.

Mortality Rate A measure of frequency of occurrence of death among a defined population during a specified time interval.

# of deathspopulation

Numerator The upper portion of a fraction

Pie chart A circular graph of a frequency distribution in which each segment of the pie is proportional in size to the frequency of corresponding category

Population The total number of inhabitants of a geographic area of the total number of persons in a particular group (e.g., the

APPENDIX B GLOSSARY | 57

number of persons engaged in a certain occupation)

Prevalence The number or proportion of cases or events or attributes among a given population

Proportion A ratio in which the numerator is included in the denominator; the ratio of a part to the whole, expressed as a decimal fraction (e.g., 0.2), a fraction (1/5) or a percentage (20%)

Rate An expression of the relative frequency with which an event occurs among a defined population per unit of time, calculated as the number of new cases or deaths during a specified period divided by either person-time or the average population

Ratio The relative size of two quantities, calculated by dividing one quantity by the other

Risk The probability that an event will occur (e.g., that a person will be affected by, or die from, an illness, injury or other health condition within a specified time or age span

Spot map A visual display of the geographic pattern of a health problem or event, in which a marker is placed on a map to indicate where each affected person lives, works or might have been exposed.

Table An arrangement of data in rows and columns; the data are usually summaries of the frequency of occurrence of an event or characteristic occurring among different groups

Variable A characteristic that may assume more than one set of values to which a numerical measure can be assigned (e.g., income, age and weight).

x-axis The horizontal axis of a rectangular graph, usually displaying the independent variable

y-axis The vertical axis of a rectangular graph, usually displaying the dependent variable (e.g., frequency—number, proportion, or rate)


APPENDIX C: COURSE EVALUATION FORM

Putting Data to Work EvaluationPlease help us improve the workshop by responding candidly to the following statements:Scale Definition: 1 – Strongly Disagree 2 – Disagree 3 – Neither Agree nor Disagree 4 – Agree 5 – Strongly AgreeCourse objectives were well communicated 1 2 3 4 5The training was built to match the way I need to do my job 1 2 3 4 5Adequate time was allotted for explanations/practice 1 2 3 4 5The training materials were well written 1 2 3 4 5Job aids are available to support what I learned 1 2 3 4 5I know where to get assistance when I return to my job 1 2 3 4 5Overall the class was satisfactory 1 2 3 4 5

What did you like most about the class?

How can we improve the class?

Do you have any additional questions regarding this topic?

If you wish us to contact you, please provide the following information:

Name Email Telephone Number

APPENDIX B GLOSSARY | 57

Facilitator's Guide - Template - TEPHINETlibrary.tephinet.org/sites/default/files/document/... ·...

Documents

Transcript of Facilitator's Guide - Template - TEPHINETlibrary.tephinet.org/sites/default/files/document/... ·...