OBTAINING CONSENTS FOR ACCESS TO HEALTH AND OTHER RECORDS:

What Matters for Different Individuals & Can We Use It to Improve Consenting Rates?

V. Joseph Hotz | L. Kristin Newby | Vladislav Slanchev | Ashley Dunham

Duke Colloquium on Data and MedicineDecember 10th, 2015

Enhancing Studies with Admin Records

• Increasing Interest in Enhancing Health & Behavioral Studies with Participants’ Admin Records

• Types of Administrative Data Records

– EHRs for health conditions and disability status from health care providers, government programs & other systems

– Wage & Earnings data from social and unemployment insurance systems– Housing wealth, mortgages, foreclosures from private sector data providers, e.g., Zillow, CoreLogic– Credit score data from private companies (see Israel et al, PNAS, 2014, on financial risks and risk of CVD)

• Promise & Potential for Admin Records in such Studies

– Usually more accurate data than self-reports.– Can reduce data collection costs – both interview time, need for follow-ups – & respondent burden– Can facilitate new research, e.g., interplay of health and economic conditions (e.g., Israel et al. 2014)– Can provide ways of dealing with non-response &/or attrition in studies

Enhancing Studies with Admin Records

• Increasing Interest in Enhancing Health & Behavioral Studies with Participants’ Admin Records

• Types of Administrative Data Records

– EHRs for health conditions and disability status from health care providers, government programs & other systems

– Wage & Earnings data from social and unemployment insurance systems– Housing wealth, mortgages, foreclosures from private sector data providers, e.g., Zillow, CoreLogic– Credit score data from private companies (see Israel et al, PNAS, 2014, on financial risks and risk of CVD)

• Promise & Potential for Admin Records in such Studies

– Usually more accurate data than self-reports.– Can reduce data collection costs – both interview time, need for follow-ups – & respondent burden– Can facilitate new research, e.g., interplay of health and economic conditions (e.g., Israel et al. 2014)– Can provide ways of dealing with non-response &/or attrition in studies

Enhancing Studies with Admin Records

• Increasing Interest in Enhancing Health & Behavioral Studies with Participants’ Admin Records

• Types of Administrative Data Records

– EHRs for health conditions and disability status from health care providers, government programs & other systems

– Wage & Earnings data from social and unemployment insurance systems– Housing wealth, mortgages, foreclosures from private sector data providers, e.g., Zillow, CoreLogic– Credit score data from private companies (see Israel et al, PNAS, 2014, on financial risks and risk of CVD)

• Promise & Potential for Admin Records in such Studies

– Usually more accurate data than self-reports.– Can reduce data collection costs – both interview time, need for follow-ups – & respondent burden– Can facilitate new research, e.g., interplay of health and economic conditions (e.g., Israel et al. 2014)– Can provide ways of dealing with non-response &/or attrition in studies

Studies that motivated this project• MURDOCK Study

– Sample: individuals living in Cabarrus County, NC, and the surrounding region– Admin Records sought: Consent to obtain participants’ EHRs (& biospecimens) for Health & biospecimen Registry– Scientific Goals: “Identify linkages between major diseases and disorders and to defeat leading causes of illness &

death.”

• Add Health & Add Health Parent Studies– Sample: Nationally representative sample of children (adolescence to adulthood) & their parents– Admin Records sought: Consents to obtain Disability (SSDI & SSI), Medicare & Medicaid, Earnings history (SSA)

records, & Housing records (CoreLogic)– Scientific goals: “Examine role & importance of intergenerational linkages in Health, Cognition, Family

Relationships & Social & Economic Attainment for health and well-being of each generation.”

• Precision Medicine Initiative – Sample: 1 million volunteers across U.S.– Admin Records sought: Consent to provide EHRs, along with biospecimens & behavioral data, and undergo physical

exam. Health & biospecimen Registry– Scientific Goals: Support “research at the intersection of human biology, behavior, genetics, environment, data

science and computation … to produce new knowledge with the goal of developing more effective ways to prolong health and treat disease.”

Consent: Obtaining Access to Admin Records

• Obtaining individuals’ consents for collection & use of admin records key issue– Common Rule & legislation (HIPAA, CIPSEA) requires obtaining informed or passive consent

for admin records, especially health records. – Proposed revisions to Common Rule will allow for prospective consent for health records, but

informed consent still required

• Obtaining consents in Population-Representative Studies not easy

Consent: Obtaining Access to Admin Records

• Obtaining individuals’ consents for collection & use of admin records key issue– Common Rule & legislation (HIPAA, CIPSEA) requires obtaining informed or passive consent

for admin records, especially health records. – Proposed revisions to Common Rule will allow for prospective consent for health records, but

informed consent still required

• Obtaining consents in Population-Representative Studies not easy

Consent Rates for Linking Admin Records

*conditional on participation in the study

Survey Citation Type of Admin Data Consent rate (%)*Health and Retirement Survey (USA) HRS website Medicare 80

National Health Interview Survey Field Test (USA)

Dahlhamer and Cox (2007) Government health records 68

Australian Longitudinal Survey on Women's Health Young et al. (2001) Medicare 49British Household Panel Survey Knies et al. (2002) Health data and NHS registration records 41

English Longitudinal Study of Ageing (Wave 1, UK) Banks et. Al (2005) Benefits and tax records 78

Health and Retirement Survey (2008; USA) Sakshaug et al. (2012) Social Security Records 68

Improving Survey Measurement of Income and Employment Household Survey (UK) Jenkins et al. (2006) Employer records 68

Consent: Obtaining Access to Admin Records

• Obtaining individuals’ consents for collection & use of admin records key issue– Common Rule & legislation (HIPAA, CIPSEA) requires obtaining informed or passive consent

for admin records, especially health records. – Proposed revisions to Common Rule will allow for prospective consent for health records, but

informed consent still required

• Obtaining consents in Population-Representative Studies not easy

• For Volunteer Samples, provision of consent may be criterion for sample inclusion

• Access to health records (& consent requirements) is a feature of “debate” over use of Voluntary vs. Population-Representative Sampling Plan in studies like PMI.– But this is not a very useful debate!

Consent: Obtaining Access to Admin Records

• Obtaining individuals’ consents for collection & use of admin records key issue– Common Rule & legislation (HIPAA, CIPSEA) requires obtaining informed or passive consent

for admin records, especially health records. – Proposed revisions to Common Rule will allow for prospective consent for health records, but

informed consent still required

• Obtaining consents in Population-Representative Studies not easy

• For Volunteer Samples, provision of consent may be criterion for sample inclusion

• Access to health records (& consent requirements) is a feature of “debate” over use of Voluntary vs. Population-Representative Sampling Plan in studies like PMI.– But this is not a very useful debate!

Consent: Obtaining Access to Admin Records

• Obtaining individuals’ consents for collection & use of admin records key issue– Common Rule & legislation (HIPAA, CIPSEA) requires obtaining informed or passive consent

for admin records, especially health records. – Proposed revisions to Common Rule will allow for prospective consent for health records, but

informed consent still required

• Obtaining consents in Population-Representative Studies not easy

• For Volunteer Samples, provision of consent may be criterion for sample inclusion

• Access to health records (& consent requirements) is a feature of “debate” over use of Voluntary vs. Population-Representative Sampling Plan in studies like PMI.– But this is not a very useful debate!

Project’s Research Questions

• What considerations influence individuals’ willingness to consent to providing health & other admin records for research purposes?– Data confidentiality? Intrusiveness? Challenges to privacy? Benefits from research?

• Can we develop predictive models of who is likely to consent?– What can we measure that is predictive?

• If we can reliably predict likelihood of consent, can we develop “adaptive strategies” for improving rates of consent?– Exploit insights from behavioral economics while abiding by ethical principles for

protecting human subjects

Project’s Research Questions

• What considerations influence individuals’ willingness to consent to providing health & other admin records for research purposes?– Data confidentiality? Intrusiveness? Challenges to privacy? Benefits from research?

• Can we develop predictive models of who is likely to consent?– What can we measure that is predictive?

• If we can reliably predict likelihood of consent, can we develop “adaptive strategies” for improving rates of consent?– Exploit insights from behavioral economics while abiding by ethical principles for

protecting human subjects

Project’s Research Questions

• What considerations influence individuals’ willingness to consent to providing health & other admin records for research purposes?– Data confidentiality? Intrusiveness? Challenges to privacy? Benefits from research?

• Can we develop predictive models of who is likely to consent?– What can we measure that is predictive?

• If we can reliably predict likelihood of consent, can we develop “adaptive strategies” for improving rates of consent?– Exploit insights from behavioral economics while abiding by ethical principles for

protecting human subjects

What Can Predict the Consent Decision

• Socio-demographics of the individual• Measures of Personality Traits

• Willingness to answer sensitive survey questions– Personal income and health conditions

• Privacy-related Actions and Experiences– Has she ever been a victim of identity theft?– Has she ever provided her full SSN online?

• Perception and Attitudes about Privacy and Data Confidentiality– Concerned that Govt / Private Companies / University / Medical Researchers not able to prevent misuse of

the personal data they collect?

• Perceived Benefits from Research– Will research finding help individual? her family? her community?– Is main benefit from research to advance science?

What Can Predict the Consent Decision:The Consent Request

• Characteristics of the Consent Request– Opt-in vs. Opt-out– Types of records– Providing personally identifiable information for the linking– Signing a consent form– Monetary Incentives

• Framing of the request: Lessons from Behavioral Economics– Follow the herd – tell individuals prevalence of consenting (when high!)– Building Trust – share with individual something not in interviewer’s self interest– Reciprocity – tell what scientific research has already done for individual’s community

Samples for the Online Survey

• Qualtrics panel – 1,000 respondents– matching the socio-demographic characteristics of the population from which

MURDOCK participants are recruited• Participants in the MURDOCK Study

– a total pool of 11, 438 people– already agreed to provide medical records and bio specimens for MURDOCK– likely to agree to take the survey

• Non-participants in MURDOCK Study after initial contact – a total pool of 5,425 unable to contact – a total pool of 2,769 who changed their mind– high fraction may not respond

Empirical Strategies to Identify Consent Predictors

• How are the MURDOCK consenters different from (similar to) other samples?– Compare measures of attitudes, preferences, experiences and behavior

• Use hypothetical vignettes to measure likelihood of consent?– Ask respondents whether they would consent to a hypothetical linking request

Examples of Dimensions ofHypothetical Linking Requests

• Information in the preamble

• Personally identifiable information requested for matching

• Other dimensions: – Access of Govt Agencies to Linked data– Type of records – health vs. financial– Topic of the research study

Benefits to respondent’s community OR Reliability of data security arrangements

No OR Last 4 digits of SSN + data of birth

Empirical Strategies to Identify Consent Predictors

• How are those who granted access to medical records in MURDOCK Study different from respondents in other samples?– Compare measures of attitudes, preferences, experience and behavior

• Use hypothetical vignettes to measure likelihood of consent?– Ask respondents whether they would consent to a hypothetical linking request– Effect of attributes of the linking request– Effect of respondent traits

• Short randomized experiment with actual request for linking to housing records with different versions of the preamble

Example of an Experiment with Actual Request for Linking

• Administer actual request for linking to: – the sample of MURDOCK participants – MURDOCK non-consenters

• Ask respondents for permission to both:– link survey responses to neighborhood characteristics, housing admin records provide

full name and address for the linking

• Randomly assign two versions of the requestVersion A: No explanation of the benefits from linkingVersion B: Explain how health research based on that data will benefit the respondent’s community

Issues to Address

• Are there potential “sources of concern” by participants for admin records that we should try to include in our vignettes and attitude assessment?

• Are there potential “benefits” to participants for their providing admin record that we should pose to respondents?

• Are there other consenting strategies we might want to evaluate?

• Future steps• Design adaptive consenting procedures based on findings, insights from behavioral

economics, & meet Common Rule/IRB/data provider requirements • Evaluate procedures with experimental design on population-based sample

Team

Principal Investigator: V. Joseph Hotz, Principal Investigator, Add Health Parent Study; Arts & Sciences Professor of Economics, Duke UniversityCo-Principal Investigator: Vladislav Slanchev, Add Health Parent Study; Postdoctoral Associate, Duke UniversityCo-Investigator: L. Kristin Newby, Principal Investigator, MURDOCK Study; Professor of Medicine, Cardiology, Duke University School of Medicine; DCRI; DTRI, Translational Population ResearchCo-Investigator: Ashley Dunham, MURDOCK Study; Director of Operations, Translational Population Research, Duke Translational Research InstituteCollaborators / Consultants:Sunshine Hillygus, Director, Duke Initiative on Survey Methodology; Professor of Political Science, Duke UniversitySteven Snell, Post-doctoral Fellow, Duke Initiative on Survey Methodology, Duke UniversityDan Ariely, Director, Center for Advanced Hindsight; James B. Duke Professor of Psychology Behavioral Economics, Duke UniversityArie Kapteyn, Director, Center for Economic and Social Research (CESR), University of Southern California Kristen Cooksey, PhD Candidate, Sanford School of Public Policy, Duke University; Add Health Parent Study