Foreword: Big Data and its Application in Health Disparities Research

Foreword

Guest Editors: Eberechukwu Onukwugha, MS, PhD1; O. Kenrik Duru, MD, MS2; Emmanuel Peprah, PhD3 We developed this themed is-

tives; 2) a clinical trial perspective

sue out of a desire to understand

on whether Big Data will increase

the relevant issues that arise when

or decrease disparities within the

utilizing Big Data to document,

health care system; 3) results from

explain and address health care dis-

an investigation of how differential

parities. The term, Big Data, has

linkage by race can affect health

been coined to define the large and

care disparities research using the

complex datasets that are character-

linked data; 4) results from a study

ized by volume (large number of

of Hispanic residential isolation

records), variety (multiple sources),

and the relationship with Atten-

velocity (frequent, and not neces-

tion Deficit Hyperactivity Disorder

sarily uniform, updates) and value

health service utilization; 5) a scop-

(unique and powerful insights). We

ing review of opportunities for Big

sought manuscripts from diverse

Data science to address health dis-

settings including academia, health

parities as well as some challenges.

care systems, contract research or-



ganizations, and government. The

Big Data from a modern view-

contributed manuscripts offer an

point, the problem of analyzing

important, baseline snapshot of

large amounts of data to under-

current perspectives on Big Data

stand the content of said data, draw

and health disparities. Specifically,

reasonable conclusions, and make

the articles in this special section

meaningful inferences is an age-old

provide: 1) an overview of recent ef-

problem requiring the development

forts to improve workforce diversity

of novel tools. Big Data problems

and training through the National

existed even before the invention

Institutes of Health Big Data to

of the 20th century computer. For

Knowledge (BD2K) diversity initia-

example, it was predicted that it

The articles presented in this special issue advance the conversation by describing the current efforts, findings and concerns related to Big Data and health disparities. They offer important recommendations and perspectives to consider when designing systems that can usefully leverage Big Data to reduce health disparities. We hope that ongoing Big Data efforts can build on these contributions to advance the conversation, address our embedded assumptions, and identify levers for action to reduce health care disparities. Ethn Dis. 2017;27(2):6972; doi:10.18865/ed.27.2.69.



Keywords: Big Data, Health Disparities Pharmaceutical Health Services Research Department; University of Maryland School of Pharmacy 2 Division of General Internal Medicine; Geffen School of Medicine, University of California Los Angeles 3 Center for Translation Research and Implementation Science (CTRIS); National Heart, Lung, and Blood Institute; National Institutes of Health 1

Address correspondence to Eberechukwu Onukwugha, MS, PhD; Pharmaceutical Health Services Research Department, University of Maryland School of Pharmacy, 220 Arch Street, Baltimore, MD 21201; 410.706.8981; [email protected] edu

Ethnicity & Disease, Volume 27, Number 2, Spring 2017

Although we are familiar with

69

Foreword: Big Data and Health Disparities - Onukwugha et al would take more than eight years

over, characterization of pleiotropic

ignoring best practices for the use

to hand-tabulate all the data col-

effects based on integration of vari-

and interpretation of either clinical

lected in the 1880 US census, and

ous Big Data types will be critical to

trial or observational data can limit

it would take more than 10 years

identifying molecular pathways that

our ability to identify and address

to analyze data from the 1890 cen-

underlie disease and health. Using

health disparities. Most EHRs are

sus.1 The US Census Bureau needed

Hollerith’s example, we note that

currently constructed to maximize

a major technological advance to

these challenges provide the oppor-

billing revenue; increasing the ca-

analyze and produce accurate data

tunity to develop solutions that can

pacity of these datasets to collect

to inform policy development at

address the vexing problems posed

and merge clinically relevant data

the government level. To accom-

by Big Data in the modern era.

elements will be an important step

plish this task, Herman Hollerith



Currently, 87% of physicians

toward building the infrastructure

automated the process and devel-

and 80% of non-Federal acute

needed to tackle health disparities.

oped a storing and information

hospitals in the United States use



processing system in the 1880s.2

single-system electronic health re-

data such as genetic information



Data standardization, computa-

cords (EHRs), in addition to the

and patient-reported health out-

tion analysis, methodology, statis-

development of aggregated Clini-

comes have even greater potential to

tical approaches, and data storage

cal Data Repositories (CDRs) and

address health disparities. However,

all represented challenges that Hol-

other massive databases.6 It is pos-

exciting opportunities such as algo-

lerith had to solve in addressing his

sible that novel uses of Big Data

rithms for machine learning using

Big Data problem. We have simi-

will finally reduce or eliminate stub-

large amounts of data from smart-

lar challenges in the modern era.

bornly persistent racial/ethnic and

phone health apps, or programs

As an example, the development

socioeconomic disparities in health

that analyze genetic mutations in a

of genome-wide association stud-

outcomes. The articles in this issue

large patient population to unlock

ies (GWAS) required the creation

discuss the current data, privacy,

triggers for cancer growth, will re-

of novel analytical pipelines that

and larger societal environments

quire patients to consent to use

combined statistical analysis with

and illustrate how we can make

of their data or ideally provide it

computing power. These GWAS

progress toward this goal. Canner

themselves. Because of longstand-

analytical pipelines have had lim-

et al describe the ongoing efforts to

ing concerns about historical abuse

ited integration power because of

increase workforce diversity within

at the hands of medical leaders as

the utilization of linear univari-

the field of biomedical data science.

noted by Zhang et al, minority

ate methodology. Higher order in-

Creative projects that combine pre-

patients may be particularly suspi-

teraction studies on large GWAS

existing public databases, as illus-

cious about sharing their data with

utilizing multivariate analysis are

trated by Miller et al and Pennap et

researchers or other entities. There

needed but currently computation-

al, can provide important insights

are plenty of present-day concerns

ally intensive and thus limited by

in the delivery and outcomes of

as well that justify these serious

the availability of hardware.4 More-

care. As noted by Heller & Seltzer,

concerns – the number of individu-

3

70

5

Ethnicity & Disease, Volume 27, Number 2, Spring 2017

Datasets built on personalized

Foreword: Big Data and Health Disparities - Onukwugha et al als whose personal health informa-

as: What are the source experienc-

will require that we re-think how we

tion was affected by hacking/secu-

es, triggers or events that result in

generate, store, and protect patient

rity breaches of network servers or

health disparities? What are the per-

data and will require the leadership

EHRs jumped 10-fold from 2014

sonal, health system and geographic

to implement solutions to the exist-

to 2015, exceeding 110 million

barriers to improved individual

ing challenges with Big Data. The

people.6 Health systems are already

health behaviors? What are the le-

articles represented in this issue ad-

somewhat wary of sharing patient

vers for action needed to address the

vance the conversation by describ-

data to create the massive databases

identified health disparities? These

ing the current efforts, findings and

needed to advance this work due to

data also can be used to deepen our

concerns related to Big Data and

a perceived loss of competitive ad-

understanding of heterogeneity and

health disparities. They offer impor-

vantage. At the same time, the po-

the implications for how we use Big

tant recommendations and perspec-

tential for data breaches will further

Data. To the extent that “Models

tives to consider when designing

dampen enthusiasm if the scientific

are opinions embedded in mathe-

systems that can usefully leverage

community cannot provide assur-

matics”7 we will need to examine our

Big Data to reduce health dispari-

ance to patients that their data will

embedded assumptions regarding

ties. We hope that ongoing Big Data

be safeguarded. Reversing each of

the health care system we desire be-

efforts can build on these contribu-

these trends will require intensive

fore we can conduct a complete and

tions to advance the conversation,

efforts to demonstrate the value to

inclusive investigation of the health

address our embedded assump-

patients of sharing their individual

disparities that persist within this

tions, and identify levers for action

data and to health systems of shar-

system. Big Data is not immune to

to reduce health care disparities.

ing their aggregate data, as well as

these embedded assumptions. For

boosting data security safeguards

example, if we assume that qual-

Acknowledgements

using the most effective technol-

ity indices (and their contributing

ogy and eliminating all “weak links”

subdomains) can be applied to sub-

within these shared data networks.

populations in which they have not

The contents of this article are those of the authors and do not necessarily represent the official view of the National Institutes of Health. E.P. is a staff member of the National Heart, Lung, and Blood Institute, National Institutes of Health.

These efforts are necessary in order

been formally tested, any errors in

References

for Big Data to usefully support clin-

this assumption will be embedded

ical and population health research.

in models that utilize Big Data to



With access to the increased vol-

derive quality measures and then

ume, velocity and variety offered by

perpetuated when these measures

Big Data, we have to ask whether

are adopted across institutions.

there is value in the use of these data



to address health care disparities. In

ination of health disparities using

order to represent value, these data

Big Data is an admirable goal and

will need to support the investiga-

one that we believe is achievable.

tion of fundamental questions such

Sustained progress toward this goal

The reduction and ultimate elim-

Ethnicity & Disease, Volume 27, Number 2, Spring 2017

1. United States Census. Tabulation and Processing. https://www.census.gov/history/ www/innovations/technology/tabulation_ and_processing.html Accessed February 14, 2017. 2. da Cruz, F. Columbia University Computing History. http://www.columbia.edu/cu/ computinghistory/hollerith.html Accessed February 14, 2017. 3. Williams NB. The rise of Big Data: Trends and opportunity for the lab. Clinical Laboratory News. March 2014. https://www.aacc. org/publications/cln/articles/2014/march/bigdata Accessed February 20, 2017. 4. Goudey, B, Abedini M, Hopper JL, et al. High performance computing enabling exhaustive analysis of higher order single

71

Foreword: Big Data and Health Disparities - Onukwugha et al nucleotide polymorphism interaction in Genome Wide Association Studies. Health Inf Sci Syst, 2015. 3(Suppl 1 HISA Big Data in Biomedicine and Healthcare 2013 Con): S1-S3. https://doi.org/10.1186/2047-2501-3S1-S3. 5. Yang C, Li C, Wang Q, Chung D, Zhao H. Implications of pleiotropy: challenges and op-

portunities for mining Big Data in biomedicine. Frontiers in Genetics. 2015 Jun 30;6:229. doi: 10.3389/fgene.2015.00229. eCollection 2015. 6. Office of the National Coordinator for Health Information Technology, Health IT Dashboard. https://dashboard.healthit.gov/ quickstats/quickstats.php

7. Zhang, C. Q&A Cathy O’Neil, author of ‘Weapons of Math Destruction,’ on the dark side of big data. LA Times. December 30, 2016. http://www.latimes.com/books/jacketcopy/la-ca-jc-cathy-oneil-20161229-story. html Accessed: January 30, 2017.

Big Data articles in this issue of Ethnicity & Disease, Spring 2017 In the Rising Era of Big Data, Small Steps are Key Nina Heller, Jonathan H. Seltzer Racial and Ethnic Differences in a Linkage With the National Death Index Eric A. Miller, Frances A. McCarty, Jennifer D. Parker Hispanic Residential Isolation, ADHD Diagnosis and Stimulant Treatment among Medicaid-insured Youth Dinci Pennap, Mehmet Burcu, Daniel J. Safer, Julie Magno Zito Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century Xinzhi Zhang, Eliseo J. Pérez-Stable, Philip Bourne, Emmanuel Peprah, Kenrik Duru, Nancy Breen, David Berrigan, Fred Wood, James S. Jackson, David W.S. Wong, Josh Denny Enhancing Diversity in Biomedical Data Science Judith E. Canner, Archana J. McEligot, María-Eglée Pérez, Lei Qian, Xinzhi Zhang

72

Ethnicity & Disease, Volume 27, Number 2, Spring 2017

Foreword: Big Data and Its Application in Health Disparities Research.

The articles presented in this special issue advance the conversation by describing the current efforts, findings and concerns related to Big Data and...
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