PIHOA-SPC-WHO-FNU-RAPID-CDC Joint EpiTech-Data for Decision Making Training Program · ·...

1 DDM2: Palau May 2014

PIHOA-SPC-WHO-FNU-RAPID-CDC

Joint EpiTech-Data for Decision Making Training Program

Data for Decision Making II: Basic Applied Epidemiology and Data Analysis

Meeting Report

Palau, May 2014

Location: Palau Community College, Republic of Palau, Western Pacific

Dates: 26-31 May 2014

Additional Sponsors: Training Programs in Epidemiology and Public Health Interventions Network

(TEPHINET), Palau Ministry of Health, CDC Center for Chronic Disease Prevention & Health Promotion

Course Goals:

1) Reinvigorate of EpiNet teams in the USAPI through building of knowledge and skills of EpiNet

team members

2) Strengthen capacity of teams through developing non-communicable disease surveillance plans

at the jurisdiction level

3) Obtain academic course credit for students toward formal credentials in applied epidemiology

through Fiji National University

4) Build a mid-level Epi-tech track to complement advanced applied epidemiology training to be

offered within a Pacific SHIP/FETP fellowship program

Facilitators:


George Kahlil, Centers for Disease Control and Prevention (CDC); Sevil Huseynova World health

Organization (WHO); Kate Hardie, Response and Analysis for Pacific Infectious Diseases (RAPID); Beverly

Paterson, Response and Analysis for Pacific Infectious Diseases (RAPID); Adam Roth, Secretariat of the

Pacific Community (SPC); Damian Hoy, Secretariat of the Pacific Community (SPC); Haley Cash, Palau

Ministry of Health; Dawn Fitzgibbons, Palau Ministry of Health; Mark Durand, PIHOA; Thane Hancock,

PIHOA;

Participants:

30 Participants from the U.S. Associated Pacific Islands including CNMI, Guam, Palau, Yap, Chuuk,

Pohnpei, Kosrae, RMI, and American Samoa

[See attached Participant List]

Background:

In 2010 and 2012, the Declaration of Health Emergency for NCDs and the heightened threats of dengue

and influenza outbreaks in the Northern Pacific demonstrated a need to improve the epidemiologic

capacity of the region, both for outbreak prone diseases (via EpiNet teams) and for other public health

surveillance officers, including those responsible for NCDs.

The EpiTech-Data for Decision Making series for the Pacific is a collaborative project of PIHOA, the

Secretariat for the Pacific Community (SPC), the Centers for Disease Control and Prevention (CDC), the

World Health Organization (WHO), the Fiji National University (FNU) and AusAID-funded Response and

Analysis for Pacific Infectious Diseases Project (RAPID) of Hunter-New England Health District.

A version of Module 1 (of 5) of CDC’s Data for Decision Making curriculum was used for this workshop.

This module was recently updated by technical staff from SPC and RAPID, and had been pilot tested in

previous workshops. This was the fourth time that Module 1 has been delivered in the Pacific in the past

12 months.

The Data for Decision Making curriculum is a set of 5 courses (four instructional and one independent

project) that originated with a set of US Centers for Disease Control and Prevention workshop modules

and was converted to an accredited applied epidemiology curriculum in the early 2000s through the

then Fiji School of Medicine, Department of Public Health (by Dr. Naren Singh of the then Fiji School of

Medicine, and Dr. Mike O’Leary, of WHO). In 2013, a revision of the course package was undertaken in

partnership by a group of epidemiologists from Fiji National University School of Medicine, Nursing, and

Health Sciences Department of Public Health (FNU), Secretariat for the Pacific Community, PIHOA,

WHO, RAPID project of Hunter-New England Health District/Univ. of Newcastle, WHO, and CDC.

Students must formally register with FNU for each course. Course faculty will have appointments with

FNU (as adjunct faculty for epidemiologists working for regional associations). Student performance will

be measured against course learning objectives through end of course examinations and student

projects. Full time faculty at FNU will perform final assessment of student based on examinations and


projects, and award course credit. Students who pass all 5 courses will be awarded a post-graduate

certificate in applied epidemiology from FNU. With 4 additional courses students can be awarded a post

graduate diploma and with an additional 4 courses, a Master’s degree. Courses passed can also count

toward Bachelor degrees for those who do not yet have a BA or BS.

The 5 courses are as follows:

DDM1- Outbreak Surveillance and Response

DDM2- Basic Applied Epidemiology and Data Analysis

DDM3- Advanced Applied Epidemiology and Data Analysis

DDM4- Public Health Surveillance

DDM5- Surveillance Project or Research Project

Work Shop Details:

The workshop was conducted at the Palau Community College Conference Hall.

In order to achieve the goals the following objectives and methods were utilized:

Objectives:

1. Define epidemiology, uses and outline the general principles

2. Describe basic measurements in epidemiology and calculate basic indices of health status.

3. Describe and explain the various measures of disease frequency- measures of morbidity,

mortality, including performing adequate descriptive epidemiology,

4. Describe the concepts of life expectancy, age specific life expectancy, years of potential life lost,

disability adjusted life years and person time rates, and age-adjusted rates

5. Outline concepts in public health demography

6. Describe various sources of data and their limitations

7. Describe methods of scientific enquiry in epidemiology i.e. the concept of study designs in

epidemiology; key features and applications, particularly those study designs used commonly in

field epidemiology

8. Explain the principles of data organization, analysis and presentation

9. Introduction to electronic file management

10. Undertake the following processes in Excel: save file, add filter, sort, basic formulae, cleaning

data, dealing with missing data/data entry errors/outliers, constructing tables/graphs, inserting

tables/graphs in word and PowerPoint

11. To understand how to communicate the results of data analysis

12. To understand the differences between Excel and Epi-Info

13. To become familiar with Epi-Info and some of the following functions: create database

(questionnaire); enter data/ create filters or check codes; import data from excel; analyze data;

present data or report using relevant tools; inserting tables/graphs in word and PowerPoint.

Methods:


Multiple teaching methods were utilized to deliver the course content and achieve the identified

objectives. These included:

1) Lectures:

a. Course Background and Expectations

b. General Principles of Epidemiology

c. Measures in Epidemiology and Demography Part I

d. Measures in Epidemiology and Demography Part II

e. Measures in Epidemiology and Demography Part III

f. Data sources

g. BRFSS and NCD STEPs Survey Comparisons

h. Data Dissemination and Use

i. Study Design

j. Introduction to Epi-Info

2) Group work/exercises

a. Excel Refresher I

b. Excel Refresher II

c. Cleaning, analyzing and reporting on own data set

d. Mapping/developing NCD surveillance plan or framework for each jurisdiction

3) Group presentations- evaluation

a. Presentation and constructive critique of jurisdictions NCD surveillance plan

b. Presentation on own data project

4) Written examination- evaluation

a. 90 minute written exam

b. 90 minute retest


Agenda:


Workshop Evaluation:

Participant evaluation of the workshop was conducted through a form developed and analyzed by Bev

Paterson. Her summary findings are:

The DDM2 course run in Palau was very well received by participants.

“I'm so happy that I've learned a lot from this course and will apply it to my work.”

All areas of the course rated highly as did the different formats used in the course. For each of

the key taught areas (excel, data, calculations and own data) there was a participant self-

assessed improvement during the course. Key areas identified as particularly useful by

participants were excel (pivot tables and filters, data analysis – mean, mode and median), data

cleaning (identifying and documenting errors, making a working copy) and data analysis (rates,

percentages and proportions). Key aspects of the course likely to be used in the workplace

included data collection, cleaning, quality, analysis and interpretation.

Course

Which were the most useful parts of the course to you?

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 n/a

Excel

Data analysis

Own data

Epi concepts

Surveillance plan


Which formats used in this course were most useful in helping you learn the concepts?

Excel

How would you rate your Excel skills?

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 n/a

Lectures

Individual work

Group work

Facilitators workingthrough problems

Roaming faciliators

1 2 3 4 5 n/a

Excel pre-course 1 3 10 8 2 3

Excel post-course 0 1 3 15 6 2

0

2

4

6

8

10

12

14

16

Excel pre-course

Excel post-course


What were the key things you learnt in Excel that you would use in your workplace?

Data

How would you rate your data cleaning skills?

0 5 10 15 20

Data cleaning

Filters

Sort

Charts

Tables

Pivots

Saving Files

Coding

Data dictionary

Shortcuts

Mean, median, mode

Equations

Sums

Age groups

Track changes

1 2 3 4 5 n/a

Data pre-course 2 7 10 5 1 2

Data post-course 0 1 3 14 6 2

0

2

4

6

8

10

12

14

16

Data pre-course

Data post-course


What were the key things you learnt about data cleaning that you will use in your workplace?

Calculations

How would you rate you’re your ability to calculate rates?

0 1 2 3 4 5 6 7 8

Identify errors

Finding outliers

Makes analysis easier

Format table using sort/ filter

Interpretation of data

Identify your audience

Data dissemination

Data scope

Type of data error

Screen data

Diagnose errors

Fix/edit errors

Data cleaning process

Document errors and changes

Filtering outliers

Use data dictionary

Make a working copy

Create codes

Importance of data cleaning

1 2 3 4 5 n/a

Calculation pre-course 0 3 10 8 4 2

Calculation post-course 0 1 4 16 4 2

0

2

4

6

8

10

12

14

16

18

Calculation pre-course

Calculation post-course


What were the key things that you learnt about any calculations that you will use in your workplace?

Own data

How would you rate your understanding of your own data?

0 2 4 6 8 10

Rates

Percentages/ proportions

Dividing

Fractions

Means, mode, median

Ratio

Indicators

Prevalence

Incidence

Attack rate

All calculations

Using excel bar to show totals

Use populations

Numerator/ denominator

Ranges

Sum/if functions in excel

Reference variables

1 2 3 4 5 n/a

Own data pre-course 3 7 7 7 0 3

Own data post-course 0 0 6 11 7 3

0

2

4

6

8

10

12

Own data pre-course

Own data post-course


What are the key things that you've learnt about your own data that you will use in your workplace?

Continued uncertainty

Is there anything that was covered in the course that you’re still unsure about?

0 2 4 6 8 10 12

Comparisons

The data

Incidence and prevalence

See the whole picture

Know where to start

Improve data

Variables

Excel

Understand data from an epi perspective

Data analysis and interpretation

Data screening

Save a working copy

Unique identifier

Filtering/sorting

Data collection, cleaning and data quality

Rates

Coding

Telling the story of the data/presenting data

0 1 2 3 4

Epinfo

Excel

Presentations

Comparing and analysing different data sets

Study design

Data interpretation

Everything

Epidemiological concepts

Which graphs to use

Calculating rates

Variance (standard deviation)

How to use the data for policy change


Participant Outcomes:

All participants were evaluated on three sets of criteria— the presentation of the analysis of their own

data, the presentation on their NCD surveillance plan, and a written exam. The analysis of the

participant grades was performed by Adam Roth.

Total Number of Participants: 30

Mean Score: 79%

Range: 66%-95%

Mode: 74%

Grading breakdown and evaluation criteria:

1) Own Data Presentation (20%)

Presentation of results/findings of analysis own data set

Mean Score: 86%

Range: 70%-96%

Evaluation criteria:

1) Correctly name what indices their own data describe or could describe: ratio,

rate, proportion, mortality rate, cancer incidence/prevalence etc.

2) Presents appropriate descriptive epidemiology: relevant basic analysis of data

main variables by age group, sex etc.; were graphs and tables correctly designed

and labeled

3) Identifies sources of data and limitations: describe what source of data is used,

its construction and purpose and limitations

4) Provides proper description of the study design behind the data: it's benefits

and limitations

5) Organization and presentation of data: Was the analysis and presentation clear

and understandable while identifying the quality and importance of the data

6) Were the results presented in an informative and concise way

7) Future data plan: Is it realistic and relevant


2) NCD Surveillance Plan Presentation (20%)

Develop written draft NCD surveillance plan to share with jurisdiction leadership

Presentation of NCD surveillance plan

Mean Score: 74%

Range: 70%-94%

Evaluation criteria:

i. Plan and address essential elements of NCD surveillance

1. Youth risks

2. Adult risks

3. NCD mortality

ii. Specific NCD plan:

1. Which surveys to be done

2. How often

3. Who is responsible for what

iii. Describe important NCD indicators and data sources

iv. Understand difference between survey and routine data

v. Present and discuss a NCD surveillance plan, understanding the concepts of:

1. timeliness

2. validity

3. resource constraints

vi. Good and clear presentation with clear messages.

vii. A clear distribution plan and products.

viii. Realistic understanding of external assistance needed

ix. The importance of buy in and endorsement

3) Written Exam (60%)

90 minutes

Collection of multiple choice, short answer, calculations and matching

Mean Score: 72%

Range: 42%-96%

11 students took retest (90 minute exam made up of all new questions)

i. Mean Score for Retest: 76%

ii. Range: 63%-89%


Conclusions/Next Steps:

The workshop appears to have been successful in achieving its four main goals.

1) Reinvigorate of EpiNet teams in the USAPI through building of knowledge and skills of EpiNet team

members

This was the first time DDM2 was provided. The participants demonstrated continued

enthusiasm for EpiNet that was demonstrated in DDM1. There were several participants who

had not yet attended DDM1, but were successful in obtaining the knowledge and skills targeted

in this training.

Each participant worked in a group to clean, evaluate, analyze, display and present public health

data. Examples of these presentations included: BMI rates in Yapese youth, prevalence of NCD


risk factors in CNMI, measles outbreak in Kosrae, and health screening results from Pohnpei.

These all helped build knowledge and skills in applied epidemiology and data analysis.

2) Strengthen capacity of teams through developing non-communicable disease surveillance plans at the

jurisdiction level

All teams were successful in creating an NCD surveillance plan tailored for their jurisdiction. This

included a written draft proposal to be shared with health leadership upon returning from the

workshop. Additionally each team created and delivered a PowerPoint presentation detailing

their NCD plan. These presentations can also be utilized by the teams when returning to their

work settings.

3) Obtain academic course credit for students toward formal credentials in applied epidemiology through

Fiji National University

All participants were given the opportunity to register with FNU to receive course credit.

Paperwork was completed and submitted to FNU with the assistance of PIHOA. Guidance was

provided by FNU during the development of the training and evaluation to help ensure the

course meets all accreditation requirements. FNU is currently reviewing work submitted to

determine if credit can be given to passing participants.

4) Build a mid-level Epi-tech track to complement advanced applied epidemiology training to be offered

within a Pacific SHIP/FETP fellowship program

The workshop was the first time DDM2 was provided as part as the Epi-Tech track of training. It

reaffirms that the DDM series delivers the appropriate knowledge and skills for training EpiNet

team members. A cohort of USAPI EpiNet team members is developing from which individuals

for further training can be identified.

The training was also successful in bringing together the key partners in Pacific public health and

epidemiology. The close collaboration and commitment between these partners to develop,

deliver, and evaluate the training is extraordinary. During the training it benefits the

participants to have all partners involved, and after the training, strengthens communication

and cooperation in regional public health work. It is a model that should be utilized more

frequently.

Next Steps:

1) Partners -PIHOA, WHO, CDC, SPC, RAPID, FNU- together to:

a. continue to collaborate and pilot test revisions of other DDM modules,

b. deliver the other 3 DDM modules to EpiNet team members,

c. provide further input for Pacific FETP (SHIP) development and be ready to provide

mentoring to SHIP fellows once the program is up and running,


2) Follow-up with FNU on the accreditation of the courses and registration of participants,

3) Support other deliveries of DDM2 in the Pacific region as requested by PICTs,

4) Continue to expand epidemiology competencies in NCD surveillance and response; consider

development of a companion manual to the Pacific Outbreak Manual for NCDs,

5) Check back with jurisdiction EpiNet teams in late August to determine progress on developing,

revising or maintaining NCD surveillance systems.

Submitted:

W. Thane Hancock

July 26, 2014

PIHOA-SPC-WHO-FNU-RAPID-CDC Joint EpiTech-Data for Decision Making Training Program · ·...

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