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BACKGROUND
Although the major components of data science have existed for many years, the term has
rapidly grown in prominence in the last decade. This reflects the confluence of several
important trends in science, including the prevalence of big data, the development of
computational approaches to analysis, and recognized need for reproducibility in research.
Meanwhile, public health has always emphasized rigorous study design and data collection,
appropriate analysis methods, interdisciplinary research, and the ethical use of data. The
emergence of public health data science reflects a dialog between these disciplines and promises
transformative, lasting innovations in the use of data to advance population health.
In January 2020, the Mailman School of Public Health at Columbia University hosted the
inaugural Data Science in Public Health Summit to examine the issues described in this
commentary. With participants from over 60 schools and programs in public health, the
Summit provided a forum for discussion and debate about the role of data science in the
future of public health. In her opening remarks, Dean Linda Fried of the Mailman School
reiterated the constant mission of public health–“to use science to raise the floor and the
ceiling of health for everyone”–while recognizing the shifting landscape and dynamic
challenges we face. A keynote address focused on innovative uses of data science methods
to advance health, while a series of panels featuring leading experts discussed key research,
educational, and ethical issues surrounding data science within the field of public health. The
Summit was recorded, and all presentations and panel discussions can be viewed at https://
tinyurl.com/yavhmlcf.
This commentary is contains our views on the present and future of public health data science.
These views were shaped by the discussions at the Summit, but may not reflect the opinions of all
Summit participants.
Edited by:
Raquel Lucas,
University Porto, Portugal
*Correspondence:
Jeff Goldsmith
ajg2202@cumc.columbia.edu
Yifei Sun
ys3072@cumc.columbia.edu
Linda P. Fried
lpf8787@cumc.columbia.edu
Jeannette Wing
wing@columbia.edu
Gary W. Miller
gm2815@cumc.columbia.edu
Kiros Berhane
ktb2132@cumc.columbia.edu
Received: 08 February 2021
Accepted: 01 April 2021
Published: 26 April 2021
Citation:
Goldsmith J, Sun Y, Fried LP, Wing J,
Miller GW and Berhane K (2021) The
Emergence and Future of Public Health
Data Science.
Public Health Rev 42:1604023.
doi: 10.3389/phrs.2021.1604023
Public Health Reviews | Owned by SSPH+ | Published by Frontiers April 2021 | Volume 42 | Article 1604023 1 ——————–
Public Health Reviews
COMMENTARY
published: 26 April 2021
doi: 10.3389/phrs.2021.1604023
DEFINING PUBLIC HEALTH DATA SCIENCE
In less than a decade, “data science” has transformed from a
niche term describing a growing phenomenon to a broad
concept invoked in almost every setting. Universities and
research institutions have departments, centers, or
institutes focusing on data science, either as a stand-alone
discipline or a field spanning many traditional domains. Data
science is becoming a popular undergraduate major, and
graduate programs in the area have expanded rapidly to
meet demand. Bootcamps, short courses, and certificate
programs—in person and online, and through academic
and non-academic platforms—have proliferated outside of
traditional degree programs. Data scientists (identified by
virtue of their formal training or demonstrated skillsets)
are among the most sought-after members in the
workforce, and the need for data scientists is expected to
grow rapidly in coming years [1].
Despite the sudden prominence of data science [2], there
continues to be debate over how the field should be defined.
There are competing, and sometimes conflicting,
perspectives; typically these include a combination of
proficiency in computer science, grounding in statistics,
and understanding of the relevant substantive domain, but
views differ in the relative importance given to each [3]. In
some instances, it has been argued that “data science” simply
rebrands existing fields like statistics or computer science.
Because of this indefinition, even researchers with clear
quantitative expertise can be hesitant to present themselves
as “data scientists”: doing so may seem like claiming mastery
of an amorphous and constantly—changing collection of
skills.
Our view is that data science has gained traction as an
overarching term due to the convergence of several trends
over the last decade or more. These trends are: increased data
availability and complexity, which can result in “big data”
settings; development of computational methods, especially
those for prediction; advances in computational
infrastructure, such as cloud computing and GPU clusters,
that enable the processing of massive datasets; growing
concerns about scientific rigor and the reproducibility of
research findings; and a recognition that new advances will
result from interdisciplinary research and collaboration.
These trends are not unique to data science, but their
integration and consolidation under a single term, however
broad, reflects an understanding of their interconnectedness
and is a real shift in the scientific landscape. Data science is
better understood as a new conceptual perspective on
scientific work than as a collection of specific skills or as a
single discipline focused on prediction methods, and may
result in a transformational change in the conduct of scientific
research. It will fall to public health researchers and
practitioners to shape and translate this perspective in the
service of advancing population health.
Against this backdrop, we propose a definition to frame our
discussion:
“Public health data science is the study of formulating
and rigorously answering questions in order to advance
health and well-being using a data-centric process that
emphasizes clarity, reproducibility, effective
communication, and ethical practices.”
This includes elements of hypothesis generation; study design;
data collection, data storage, manipulation, processing; methods
development and application for appropriate analysis;
dissemination; and translation. Our definition draws on
recognizable elements of existing disciplines within and
outside of public health, and includes the specific goal of
protecting and promoting health. This definition is
accompanied by a connotation, so that “data science” implies
a perspective that is shaped by the emergence of big data,
prediction algorithms, computational approaches,
reproducibility, and interdisciplinary work. This connotation,
as much as any specific definition, helps explain the sudden
valence of data science.
Public health is well situated not simply to react to the
emergence of data science, but to lead in the ongoing
evolution of this dynamic new field. Data have been the
foundation of public health’s mission: to understand the
burden of disease, disability and injury and the opportunity to
improve health across the full life course, to recognize solutions to
disparities, to infer causal mechanisms, and to provide evidence
for the impact of interventions. Public health researchers are
trained to think critically about the appropriateness of a study’s
design to evaluate scientific hypotheses, and to interrogate the
measurement and sampling processes that produce observations.
Public health research is inherently interdisciplinary and
collaborative, drawing from quantitative and qualitative
expertise across domains to effect change in the health of
populations. Crucially, public health is concerned with the
ethical questions that surround data and prediction
algorithms, and the impact these can have on exacerbating
disparities in health outcomes. These long-standing public
health competencies are clearly relevant to the future of data
science.
DATA SCIENCE IN PUBLIC HEALTH
RESEARCH AND PRACTICE
Innovative Data Science Research Methods
can Extend Public Health’s Reach
Data relevant to public health has grown in scale and complexity,
and will continue to do so for the foreseeable future. Now-common
observations on individuals that involve large quantities of data
already include genomic and other information available through
biosamples; exposure to mixtures of environmental pollutants;
lifestyle behaviors measured continuously through wearable
devices; detailed health care histories from electronic records; and
the social media, search queries, digital records of on- and off-line
transactions, and similar elements that make up an individual’s
digital footprint. These data sources have emerged in parallel to the
Public Health Reviews | Owned by SSPH+ | Published by Frontiers April 2021 | Volume 42 | Article 1604023 2
Goldsmith et al. Public Health Data Science
development of new analytical strategies and capabilities, including
statistical or machine learning methods, prediction algorithms, and
deep neural networks. Rich data and complex methods promise to
reshape the questions public health researchers can ask and the ways
in which those questions can be answered. To capitalize on this
potential, it is necessary to synergistically combine data science
approaches with the public health science perspectives.
There is an apparent conflict between the need for interpretable
models in public health and the “black box” approaches often
associated with methods in data science. In the long term,
“explainable AI,” or artificial intelligence methods that yield
interpretable predictions, may bridge this divide; development in
this direction is underway [4]. More immediately, however, we see
an opportunity to adapt new approaches to existing public health
problems. For example, targeted maximum likelihood estimation
provides estimates of causal effects using data from observational
studies by building on ensemble prediction methods [5], and
automated variable selection has been used to identify the
predictors most associated with health outcomes from a large
collection of features [6]. We also argue that the combination of
computational methods and big data has increased the ability to
generate new hypotheses by discovering patterns hidden by noise,
scale, and complexity. Indeed, data-driven approaches to identifying
important subgroups, using techniques like clustering, principal
components analysis, and t-distributed stochastic neighbor
embedding (t-SNE), are common even in the absence of
statistical inference or clear interpretations. By understanding
when and how cutting-edge approaches can be used, public
health scientists will ensure that rich data are being used to their
fullest potential.
Meanwhile, we recognize prediction accuracy as an important
goal in itself. Identifying signals from massive and dynamic data,
such as those at risk of death by suicide using data from social media
posts and other sources, for example, would provide an avenue for
timely intervention even without an interpretable model. Overall,
through a focus on prediction accuracy, precision public health may
seek “the right intervention for the right population at the right time”
to improve health, in contrast to precision medicine’s patient- and
treatment-centered outlook. In this framework, identifying optimal
interventions and the right target population can be more important
than interpreting model results. Initiatives like the NIH’s “All of
United States,” the United Kingdom Biobank, and other resources
that make complex data available at large scale, may make predictive
algorithms attractive or even necessary analytic approaches to
facilitate precision public health research [7, 8]. Prediction
accuracy is complementary to interpretability: it may not be the
highest priority in many settings, but should be understood
nonetheless.
Clearly, data science for public health will rely on
interdisciplinary teams to make advances–no single researcher,
or even a research team comprised of members of a single
discipline, will have the requisite breadth of expertise needed
to solve problems in this environment. Successful teams will be
well-versed in the behavioral, social, or biological determinants of
the health outcome of interest; understand the complex systems
that describe the determinants and outcomes; recognize the
potential for opaque methods to propagate bias inherent in
underlying data; and rely on quantitative experts to identify
and implement appropriate analytic strategies. Increasingly,
teams will integrate expertise in bioinformatics, computer
science, engineering, and other quantitative domains that have
been, to date, infrequent partners in public health research. As a
consequence, institutions that adopt incentives to promote team
science and actively seek to bridge silos of expertise will be leaders
in public health data science; external groups, particularly those
that fund research, should encourage this through initiatives that
reward interdisciplinary work.
Data Science Tools will Improve Current
Public Health Practice
The popularity of advanced computational methods and
prediction algorithms doesn’t mean the end of long-standing
methods in public health research: odds ratios are in no danger of
becoming obsolete. Data science emerged during a time of
growing concern regarding the “reproducibility crisis” in
science, and as a partial response emphasizes the adoption of
tools for data analysis, project management, and collaboration
that encourage transparent and reproducible research [9, 10].
These tools are code—and computer-centric, and are as relevant
to “small data” studies that use traditional analytic techniques as
they are to big data settings.
As anyone who has worked with data knows, there are many
steps and a good deal of work between data collection and
obtaining evidence from those data. “Data wrangling” involves
importing, restructuring, cleaning, and otherwise organizing data
for analysis. Exploratory data analysis can include computation of
numeric summaries, visualization, and initial evaluation of
hypotheses. Formal analysis of (ideally pre-specified) scientific
hypotheses, the results of which are summarized and
disseminated, follows these initial steps. This process is often
non-linear and iterative: underlying data might be updated, for
example, necessitating re-analysis. In practice, working with data
can be time-consuming, ad-hoc, and even undervalued.
There is an emerging consensus that the processes supporting data
analysis also require meritorious science themselves. Approaching
data analytic work in a way that is deliberate, coherent, and consistent
across projects has several benefits. A shared definition for“tidy data”
allows researchers to focus on producing evidence by reducing the
cognitive demand required to understand the structure of individual
datasets. Using a common analysis workflow for all projects facilitates
collaboration across members of a research team. Clear project
organization, including raw data, cleaned data, code, and output,
as well as tracked versions of these, can help promote reproducibility
through transparency and structure. “Good enough practices” for
working with software and data formalize this perspective and are
accessible to the vast majority of public health researchers and
analysts [11].
These strategies have become an understood element of doing
data science in practice, and don’t depend on the use of specific
software. However, some computational environments foster
reproducibility directly rather than relying on proactive users. As
an example, the R packages collectively known as the “tidyverse”
implement tools designed around a shared philosophy for data
Public Health Reviews | Owned by SSPH+ | Published by Frontiers April 2021 | Volume 42 | Article 1604023 3
Goldsmith et al. Public Health Data Science
structures, and are intended to focus researchers on extracting
evidence rather than wrestling with datasets [12]. Meanwhile, the
RStudio integrated development environment facilitates project
organization, data analysis, reproducible reporting, and version
control; this organization has also taken steps to foster a
supportive and inclusive user community. These tools for data
science, and similar infrastructure in other statistical
programming languages, are successful when they make
transparency and reproducibility the default behavior.
Adopting the tools of data science can strengthen the
reproducibility of public health science, although we caution that
these are not a panacea. By themselves, these tools don’t ensure
appropriate study designs for the creation of datasets, interrogate
potential biases in data sources, or assess the biological plausibility or
public health relevance of findings. Just as public health research can
be strengthened through the thoughtful adoption of data science
practices, we believe data science will be improved by incorporating
fundamental public health perspectives.
TRAINING PUBLIC HEALTH DATA
SCIENTISTS
Public health is, of course, the most important component of
public health data science, and training will continue to
emphasize the time-proven and science-driven fundamental
principles that underlie public health reasoning. Proficiency in
the core competencies of epidemiology, biostatistics, and other
disciplines that support public health will ensure that public
health data scientists are able to understand the burden of
disease and determinants of health; to think critically about
study design, sampling, and measurement; and to quantify the
evidence that data provides in support of hypotheses.
The distinct areas of data science described in the previous
section—cutting-edge methods in statistical learning and
computer science and the computational infrastructure that
underlies data science work—suggest changes to existing
training programs in public health. First, traditional analysis
methods should be complemented by instruction in more
recent computational techniques, and these should be
integrated into other core public health disciplines. Second,
practices for transparency and reproducibility in data analysis
should be taught explicitly and expected in all data-centric
training components; many students will need at least basic
proficiency in a programming language like R or Python.
Supporting these, project-based learning has several benefits in
general; in the context of data science, this approach can
encourage a collaborative, interdisciplinary perspective.
Learners at different stages or with different backgrounds will
have unique objectives for training in public health data science.
Introductory courses targeting undergraduates, MPH students, and
others with similar needs should focus on drawing distinctions
between statistical methods used to make inferences and
prediction-oriented computational tools, and understanding when
each is appropriate; introducing concepts for reproducibility in
research; and communicating effectively with quantitative experts.
Others may need more explicit technical training in modern
methods or in implementing those methods in novel settings,
and could get this training from traditional coursework or from
bootcamps focusing on particular topics. Because data science is
relatively new, options for continuing education emphasizing
practical skills may appeal to members of the public health
workforce. In all of these domains, forming partnerships with
computer scientists, engineers, and other disciplines with strength
in data science will improve training in public health data science.
AVENUES FOR PUBLIC HEALTH
CONTRIBUTIONS TO DATA SCIENCE
Data science is a developing field that draws on, and attempts to
formalize, themes that are established components of quantitative
research. Meanwhile, public health has always relied on a rigorous
understanding of study design, data collection, and sources of
bias; the principled use of quantitative methods; expertise in the
content areas of health that can guide theory formation and
testing; an interdisciplinary and translational approach; and a
commitment to the ethical and responsible use of data. These
strengths are as relevant now as ever, and offer clear directions for
public health leadership in data science.
Public Health Perspectives are Broadly
Relevant
There is an inherent idealism among public health researchers
and practitioners—members of this community have chosen to
work in a field that is dedicated to improving the health and lives
of populations—and that idealism is present in every analysis,
study, and intervention. This is increasingly mirrored by a
commitment to effecting positive change in the data science
community. The Data Science Institute at Columbia
University, for example, emphasizes the use of “Data for
Good” to reflect the Institute’s dedication to benefitting society
through responsible data science approaches. Other groups share
similar goals, and members of the data science community are
focused on medicine, health, inequality, and justice.
We are supportive of this trend, and look forward to more fully
integrating a public health perspective into data science. Research on
human subjects imposes clear responsibilities that the community
takes very seriously, including respect for persons, beneficence, and
justice, among others. From a practical perspective, public health
researchers are required to learn from data that can be messy and
imperfect, and have been trained to question whether an observation
is a valid measure for a construct of interest, if selection biases create
a mismatch between the sample and target population, and if the
study design allows for rigorous statements about the hypotheses of
interest. A shift toward complex data and analytic approaches makes
these considerations more critical than ever; indeed, we worry that
there are risks associated with the use of data science to address
health-related questions in the absence of these considerations.
Training in data science should include a public health
component, emphasizing core competencies in epidemiology,
biostatistics, and ethics, and interdisciplinary data science teams
should include experts in public health. This is critical for data
Public Health Reviews | Owned by SSPH+ | Published by Frontiers April 2021 | Volume 42 | Article 1604023 4
Goldsmith et al. Public Health Data Science
scientists and teams interested in health, but relevant beyond this
domain. Indeed, a public health perspective is important in settings
that are not obviously health-related: the potential for bias in deep
learning algorithms used to screen job applicants; the concern that
social media recommendation systems can lead to information silos;
and the possible use of facial recognition and other biometric
surveillance mechanisms by law enforcement. In these areas and
others, data scientists should understand the processes that give rise
to available data, how those processes can shape conclusions, and
what data uses might lead to just outcomes. We believe a close
collaboration between data science and public health will advance
this perspective.
Public Health can and Should Lead in Ethics
for Data Science
The public health community can draw on experience in human
subjects research to lead in defining and promoting ethical and
responsible data science in a framework of unintended
consequences that could possibly harm health and wellbeing.
“Big data” create novel opportunities for research and
innovation, but pose commensurate ethical risks and
challenges [13]. Large observational datasets gathered through
passive surveillance or scraped from online sources raise
questions about representation, consent, privacy, and the
responsibilities researchers have to subjects. Data of the scale
required by many prediction algorithms are often collected
through mechanisms that can introduce bias: electronic health
records rely on engagement with the health care system, and
persistent racial disparities in cancer outcomes may be linked (or
missed due) to a lack of diversity in research subjects. By virtue of
this scale, such data can give a misleading perception of
representativeness and the illusion of fairness that, if
unexamined, can lead to erroneous conclusions and faulty
policies. The questions that public health researchers are
trained to ask about key components in the data life cycle are
foundational to the ethics of data and data science [14].
Ethical challenges persist or are introduced after data are
available. Algorithms trained on imperfect data can be
misleading whether the data are small or big, and will
perpetuate biases inherent in data unless actively prevented
from doing so. The identification of causes, rather than
associations, relies on subject-area expertise and appropriate
methods. Using results from a sample to understand effects in
a population requires understanding of selection and sampling.
Here, too, public health leadership is relevant. In interdisciplinary
teams, advocating a healthy skepticism about the quality of data
and identifying potential sources of bias is crucial, as is
proactively conducting checks on the validity of analysis
results. The skills necessary for this role should be updated for
modern data and approaches, and to the extent possible
formalized in steps that can be conducted routinely.
Ensuring complex approaches are used for ethical purposes is, we
believe, one avenue through which public health researchers will
have an impact on data science. We also argue that asking whether
these approaches are inherently ethical is a challenge the field must
address. It may not be immediately obvious to question the ethics of
algorithms, but several such questions exist. How frequently does a
method have unintended negative outcomes? Are extrapolations
that are unsupported by data masked by model complexity? Does an
algorithm rely on data that were not ethically obtained, or produce
data without subjects’ consent? Does the algorithm, through the
selection of a criterion to be optimized, contain implicit biases? The
answers to these questions may illustrate when and how (or
whether) a method should be used, and what safeguards are
necessary to ensure just outcomes.
OUTLOOK AND CONCLUSION
We have defined and discussed public health data science, but have
not precisely located this field with respect to public health or public
health’s constituent disciplines. This reflects current reality–individuals
who identify as public health data scientists often do so as a secondary
discipline, with primary expertise in epidemiology, biostatistics, health
policy, environmental health, or another area. It’s possible that this will
remain true, but the present state may also be attributable to
institutional organization around traditional fields or the ways that
organization has shaped training in the past. Over time, public health
data science may emerge as a primary domain among those for whom
the implied perspective resonates, suggesting a need for institutional
flexibility or reorganization. In any case, we want to be clear that public
health data science does not simply rebrand an existing discipline like
epidemiology or biostatistics; this view is flawed in a way that is
analogous to dismissals of data science as a new term for computer
science.
Whether the public health data science persists in a loose,
interdisciplinary form or solidifies as a distinct field is not
something we can predict, and will depend both on how existing
disciplines define themselves moving forward and the ways data
science itself evolves over time. While cannot predict the future, we
do look forward to an ongoing, mutually transformative partnership
between public health and data science that will strengthen both
disciplines and the improve ability to extract from data the
actionable insights that advance health.
AUTHOR CONTRIBUTIONS
All authors contributed to the formulation of the manuscript,
perspectives, and central ideas. JG lead the initial paper drafting;
all authors reviewed and edited the manuscript.
FUNDING
JG’s work was supported in part by R01NS097423.
CONFLICT OF INTEREST
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
Public Health Reviews | Owned by SSPH+ | Published by Frontiers April 2021 | Volume 42 | Article 1604023 5
Goldsmith et al. Public Health Data Science
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    Copyright © 2021 Goldsmith, Sun, Fried, Wing, Miller and Berhane. This is an openaccess article distributed under the terms of the Creative Commons Attribution
    License (CC BY). The use, distribution or reproduction in other forums is permitted,
    provided the original author(s) and the copyright owner(s) are credited and that the
    original publication in this journal is cited, in accordance with accepted academic
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    PHR is edited by the Swiss School of Public Health (SSPH+) in a partnership with
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