Data Quality: Missing Data (PPT slides)

Ethics and Integrity
in Data Use and
Management
John E. Sidle, M.D., M.S.
May 10, 2010
This module was recorded at the health informatics-training course – data management series offered by the Regional East African
Centre for Health Informatics (REACH-Informatics) in Eldoret, Kenya. Funding was made possible by NIH’s Fogarty Center and a
(REACH-Informatics)
grant from the Rockefeller Center. The training was held at the Academic Model Providing Access to Healthcare (AMPATH), a
(AMPATH),
USAID-funded program, supported by the Regenstrief Institute at Indiana University. The modules were created in collaboration with
University.
the School of Informatics at IUPUI.
IUPUI.

Content licensed under Creative Commons Attribution-Share Alike 3.0 Unported

Objectives
Examine a brief history of ethics in research
and data management
Discuss pertinent principles of ethics in data
management
Discuss the concept of “data integrity” and
ethics in data management
Discuss applications for data management
personnel in ethical use of data

Outline
Definitions
Ethical Principles in Bioethics
Guidelines and Regulations
Data Integrity
Applications for ethics and maintenance of
data integrity

Definitions

In·teg·ri·ty n. 1. Strict adherence to a standard of value
or conduct. 2. Personal honesty and independence. 3.
Completeness: unity 4.Soundness

Eth·ic n. 1. A principle of right or good conduct. 2. A
system of moral values. 3. ethics (sing. In number). The
branch of philosophy dealing with the rules of right
conduct.

Source: Webster’s II New Riverside Dictionary, Based on the Webster’s II New College
Dictionary (1996)

Ethical Principles
Beneficence
 Minimize harm
 Maximize Benefits
Respect of Persons (Autonomy)
 Informed voluntary consent
 Vulnerable subjects must be protected
Justice
 Equity in distributing risks and benefits between
populations
 “fairness” in dealing with research participants
 Equity between institutions and research partners

Clinical Data vs. Research Data:
Are the Ethics Different?

Privacy and Confidentiality
Informed Consent vs. Implied Consent
Data Integrity / Data Quality
Data Security and Storage

Ethical Guidelines (for Research)
Declaration of Helsinki
 ethical standard used by the International Committee
of Medical Journal Editors
 guidelines govern all medical research
CIOMS Guidelines
 Council for International Organizations of Medical
Sciences
 developed guidelines in collaboration with WHO
Belmont Report
National Guidelines (Kenya)

Regulations
US: Code of Federal Regulations Title 45,
Part 46 (45CFR46)
FDA– 21CFR50 and 56
NIH--“he who has the gold makes the rules”
HIPAA—related both to clinical records and
use of subject data in research

45CFR46
US Code of Federal Regulations Title 45, Part
46 (45CFR46)
 Human Subjects Protections
 IRB requirements
 Protection of Vulnerable Subjects

Human subject
 Human subject means a living individual about whom
an investigator (whether professional or student)
conducting research obtains:
(1) data through intervention or interaction with the
individual, or
(2) identifiable private information.

Privacy/Confidentiality
Applies to both clinical and research data

Major concern for patients when it comes
to electronic records and data

Must be safeguarded by all members on
the team

HIPAA
Health Insurance Portability and
Accountability Act of 1996 (HIPAA), HHS
issued regulations entitled Standards for
Privacy of Individually Identifiable Health
Information.
For most covered entities, compliance with
these regulations, known as the Privacy
Rule, was required by April14, 2003.

HIPAA and Privacy Rule
The Privacy Rule, at 45 CFR parts 160 and 164,
establishes a category of health information,
defined as protected health information (PHI),
which a covered entity may only use or disclose
to others in certain circumstances and under
certain conditions.
Usually requires an individual to provide signed
permission, known as an Authorization before a
covered entity can use or disclose the
individual's PHI for research purposes.
Need for authorization may be waived in some
instances by an IRB (and a Privacy Board)

Individually Identifiable Information
and Protected Health Information
Includes any subset of health information, including
demographic information, that identifies the individual (or
there is a reasonable basis to believe that the information
can be used to identify the individual).
1. Name
2. All elements of dates except Year.
3. SSN
4. Driver's License Number
5. Geographic subdivisions smaller than a State.
6. URL's and IP's
7. Vehicle Identifiers including VIN and License #
8. Phone numbers

PHI
Individually identifiable information becomes PHI when it
is created or received by a covered entity:
 US health plans
 US health care clearinghouses
 US health providers that transmit electronic health information
A researcher is not a covered entity unless he/she is
also a provider within a covered entity.
Research is also governed by HIPAA if data is obtained
from a covered entity.
IRB may on occasion waive the HIPAA restrictions on
use of PHI

How does all this translate to
practice?
Electronic health information (clinical or research)
 Can improve quality and safety of medical care
(beneficence)
 Is key to showing outcomes in research on health and
health practices (beneficence)
 Confidentiality and Privacy can be lost (breach of
autonomy and beneficence)
 Can be used without a patient’s knowledge or consent
(breach of autonomy)
 Might exclude some populations (breach of justice)

Data Integrity
The assurance that data is accurate, correct and
valid.
Accuracy and consistency of stored data,
indicated by an absence of any alteration in data
between two updates of a data record. Data
integrity is imposed within a database at its
design stage through the use of standard rules
and procedures, and is maintained through the
use of error checking and validation routines.
Exact duplication of the sent data at the
receiving end, achieved through the use of error
checking and correcting protocols.
Assurance that the data are unchanged from
creation to reception.

Data Integrity (2)
Process to maintain data integrity depends
on:
 Collection (accurate representation)
 Data transfer (accurate recording and transfer
of data)
 Storage and Security (preventing loss of data)
 Sharing of Data
 Use of data (analysis)

Data Integrity (3)
Fabrication and Falsification of data are one of
the most serious challenges to data integrity
Human error also contributes to loss of data
integrity
Concern about research misconduct was a
primary motivation for a 1990 conference on
data management sponsored by the US
Department of Health and Human Services.
Conference summarized the many ways in
which the conduct of research depends on
responsible data management.

Data Integrity (4)
Responsible research begins with
experimental design and protocol approval
It involves recordkeeping in a way that
ensures accuracy and avoids bias
It guides criteria for including and
excluding data from statistical analyses
It entails responsibility for collection, use,
and sharing of data.

Data Integrity (5)
Everyone with a role in research has a
responsibility to ensure the integrity of the data.
The ultimate responsibility belongs to the
principal investigator, but the central importance
of data to all research means that this
responsibility extends to anyone who:
 helps in planning the study
 collecting the data
 analyzing or interpreting the research findings
 publishing the results of the study
 maintaining the research records.

Data Collection and Integrity
Because data collection can be repetitious, time-
consuming, and tedious there is a temptation to
underestimate its importance.
Those responsible for collecting data must be
adequately trained and motivated
They should employ methods that limit or
eliminate the effect of bias
They should keep records of what was done by
whom and when

Analysis and Selection of Data
The use of statistical methods varies widely
among research disciplines and also clinical
programs (reporting)
It is a laudable ideal to analyze and report all
data
Because it is not possible to report everything
that has been done, researchers must make
decisions about which studies, data points, and
methods of analysis to present.

Analysis and Selection of Data (2)
Must critically evaluate the reasons for
inclusion or exclusion of data, the
measures taken to avoid bias, and
possible ways in which bias may
nonetheless influence data selection
Must clearly document how the data were
obtained, selected, and analyzed--
especially if the methods are unusual or
potentially controversial

Retention of Data
What should be retained?
It may be impractical to store
extraordinarily large volumes of primary
data.
At minimum, enough data should be
retained to reconstruct what was done.
How long should clinical records be
retained?

Sharing of Data
This is considered an important part of
responsible research.
De-identified data should be shared so
that others can verify your conclusions or
analysis
Sharing of personal patient information is
NOT a good practice as noted in Privacy
sections earlier.

Data Security
Limiting Access
 Locked Paper Records Offices
 Limiting access to Paper or Electronic records
to appropriate personnel
 Password Protection of electronic records
 Defined privileges for electronic data users
 Firewalls to prevent outside access
Regular Backups and proper archiving

Ownership of Data
Who owns the data that is generated?
 Patient?
 Institution?
 Funder?
 Investigator?
 Publisher?

Ethics in Publication—General
guidelines pertinent to data
Research should strive to answer specific
questions—not just collect or mine data
Statistical issues (sample size) are an
important part of design to ensure that the
research data is likely to answer the
question
IRB approval is required when using
human subjects, human tissues, or
medical records

Publication Ethics and Data
Management/Data Analysis

Data should be appropriately analyzed

Inappropriate analysis is not necessarily
ethical misconduct

Fabrication or falsification of data is
always ethical misconduct

Publication Ethics and Data
Management/Analysis
Sources and methods of obtaining and
processing data should be disclosed
Data exclusions should be explained in full
Methods used to analyze data should be
explained in detail
Post hoc analysis of subgroups is
acceptable as long as this is disclosed
Data Bias should be discussed in all
publications of data or analysis

THE WORK IS PROVIDED UNDER THE TERMS OF THIS
CREATIVE COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE
WORK IS PROTECTED BY COPYRIGHT AND/OR OTHER APPLICABLE
LAW. ANY USE OF THE WORK OTHER THAN AS AUTHORIZED UNDER
THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED.

Data Quality: Missing Data (PPT slides)

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