Constructing a Data Management System

National Center for Immunization & Respiratory Diseases

Influenza Division

Regional Training Workshop on Influenza Data Management

Phnom Penh, Cambodia

July 27 – August 2, 2013

Pam KennedyAnalyst, McKing Consulting

Course Objectives

• Database Management System (DBMS)

What is it?

Essential functions

• Data collection forms

• Considerations in building a DBMS

Structure design

Data quality and control

Database Management System

• Definition

“…set of programs that enables you to store, modify, and extract information from a database,…

… provides users with tools to add, delete, access, modify, and analyze data stored in one location …

….provide the method for maintaining the integrity of stored data, running security … and recovering information if the system fails.”

• Basic database functionsDate Entry Storage

Modification Extraction

Searching Analysis

http://en.wikipedia.org/wiki/Database_management_system

Considerations in Building a DBMSEssential Functions

• How will the data be used?

Understand the study objective Types of data needed Data relationships Capture data collected from the questionnaire

and study forms

Understand the data flow

Understand what output is visualized

Ask questions – no assumptions

Data Collection Forms

• Use data collection forms as the basis of the electronic database

Identify all collection forms Understand the form sequence Understand each question and desired output

Yes/No Date field Data lists Eliminate redundant or unneeded information

Define interdependent information – Date of Birth vs. Date Of Hospital Admission

Gender vs. Pregnant

Date of Hospital Admission vs. Date of Hospital Discharge

Data Collection Forms

• Identify Data Rules

Identify variables that can be skipped – if any

If ‘Male’ then skip questions on pregnancy

Decide on variable options

Drop down lists

Yes/No fields

Option fields

Decide how to treat missing information

Not available vs. Unknown vs. Not applicable

Considerations in Building a DBMSStructure Design

• To increase effectiveness a good DBMS should have the following control functions enforced

Data access & relational functions Security

Control access rights Enforce data integrity

Relationship functions Data accuracy review process

Database salvage functions Backup and restore functions

Considerations in Building a DBMS Structure Design

• Questions to ask during design

How much data will be collected and stored?

How will data be analyzed?

Will year to year comparisons be conducted?

Will more than one person need access to data at

same time?

Where will backup data be stored?

What is Data Quality (DQ)?

• Aspects of data quality include: Accuracy

Date of birth expressed in day/months/years and not only years

Completeness Missing information

Update status Timeliness

Relevance Data relevant for the purpose of the activity

Consistency across data sources Data collection form to data management system

Reliability Recorded temperature or respiratory rates within

acceptable ranges

• Methods to ensure data quality include: Data validity checks Review procedures Limited access to enter and edit data once entered in

system• Documentation of changes/edits to system data

• Error log

• Standard operating procedures (SOPs) can aid in ensuring quality of data collected

• Data quality cannot be “fixed” one time and then left alone Will revert to poor quality if not controlled Issues will change over time

What is Data Quality (DQ)? (cont)

• Quality Control Strategy Steps Determine parameters (data) to be controlled Establish criticality and whether control is needed before (data

entry), during (data storage) or after results (reporting) are produced

Establish a specification which provides limits of acceptability – For example - range of acceptable temperatures (x to x)

Produce plans for control Specify how to achieve data quality, variation detection and removal Install a ‘validation check’ at an appropriate point in the process Collect and transmit data to location for analysis Verify the results and diagnose causes of variance Propose remedies and decide on the action needed

How to develop a Data Quality (DQ) Strategy?

http://www.transition-support.com/Quality_control.htm

• Identify possible sources of poor data quality Data capture and entry procedures Data collection tools Poor or lack of training Equipment calibration Data transfer from form to computer/site to site

• Identify the responsible person(s) Data source - surveillance and laboratory sites Data transfer/entry level

Data Quality (DQ) Actions

• Develop methods to address data quality issues Review of CIF/Lab results by a second reviewer to

check for missing information, etc. Identification of data “errors” at data entry level (missing

field, data inconsistency) Procedure to query source (sentinel site/laboratory)

to correct data “errors” identified (missing field, data inconsistency)

Random check of records Refer back to data sources (e.g. CIF/Lab report) to

correct errors originated at data entry level Double data entry

Data Quality (DQ) Actions

• Standard operating procedures are a systematic way of collecting, managing and storing data

• Standard operating procedures (SOPs) should include: Review and documentation of entire data collection

system Identification of people/team responsible for DQ Definition of roles and responsibilities for all data

collection personnel Methods to identify and address data quality

problems

Data Quality (DQ) Standard Operating Procedures

Validity Check (Example)

When you enter an invalid value, an error message prompts you to correct before allowing you to move to next item

• Validity checks help identify errors at data entry

Double Data Entry identify errors at data entry levelData Entry Check (Example)

Data Quality Verification• Suggested indicators that can be used for

surveillance systems Completeness

% of patients enrolled over total screened that meet the case definition in use (screening and enrollment logs) Example:

Total screened = 1000 # of patients enrolled = 1100 % enrolled = 110%

% of enrolled patients with CIF

% of enrolled patients with laboratory results

% of available CIF fully completed

% of completeness for key variables

Data Quality Verification (cont)

Timeliness % of CIF sent to central level within a defined time period % of Specimens sent to central laboratory within a defined

time period % of CIF entered in the database from reception within a

defined time period % of laboratory results available from reception within a

defined time period Example:

Total lab results = 1000 # lab results available within 7 days = 200 % available within 7 days = 20%

% of laboratory results sent to site from testing within a defined time period

Remember!!!• Understand the data and why you are collecting

• Collection forms should collect data you will use

• Define data rules and variable options• Document process and ensure everyone is

aware and understands process• Develop SOPs

• Data quality problems can occur at many points in the data collection process• To control data quality, you must control it at many

different points• If not controlled, data may become inaccurate and

begin to hinder its usefulness

• Questions???

For more information please contact Centers for Disease Control and Prevention1600 Clifton Road NE, Atlanta, GA 30333Telephone, 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348E-mail: cdcinfo@cdc.gov Web: www.cdc.gov

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

THANK YOU

National Center for Immunization & Respiratory Diseases

Influenza Division