10.6092/issn.2038-3460/17567
The Crossing Boundaries intends to open a dialogue between Science and Technology Studies, Social Studies of Health and the emerging Data Journalism perspective. It explores major issues at stake in contemporary practices of producing and sharing data, with a focus on the COVID-19 pandemic.
application/pdf
2038-3460
17567
1
en
Elisabetta Tola
https://creativecommons.org/licenses/by-nc-sa/2.5/
Tecnoscienza – Italian Journal of Science & Technology Studies
data journalism
Data Circulation in Health Landscapes
Text.Serial.Journal
https://tecnoscienza.unibo.it/
13
Crossing Boundaries
2022-07-07
2023-06-30
2022-07-07
2023-10-26
97-127
TS
Elisabetta Tola
2022/07/07
10.6092/issn.2038-3460/17567
97
Formicablu
2038-3460
1
data journalism
en
127
Data Circulation in Health Landscapes
https://tecnoscienza.unibo.it/article/download/17567/16417
13
                                                                         Crossing Boundaries

Data Circulation in Health Landscapes
    Niccolò Tempini                          Antonio Maturo               Elisabetta Tola
         University of Exeter                    University of Bologna         Formicablu




        Abstract: The crossing boundaries intends to open a dialogue between Sci-
        ence and Technology Studies, Social studies of Health and the emerging Data
        Journalism perspective. It explores major issues at stake in contemporary prac-
        tices of producing and sharing data, with a focus on the COVID-19 pandemic.

        Keywords: health data; platforms; risks; pandemic; data journalism.

        Submitted: April 4, 2022 – Accepted: June 10, 2022

        Corresponding author: Niccolò Tempini, Department of Sociology, Philos-
        ophy and Anthropology, University of Exeter & Alan Turing Institute, Byrne
        House FF8, Exeter (UK), n.tempini@exeter.ac.uk




Pandemic Data Circulation

Niccolò Tempini, University of Exeter


Introduction
    We have been two years in the COVID-19 (C19) pandemic, and many
interesting patterns have emerged that are worth discussing. I can only at-
tempt to touch on a few of them, which are related to the practices and
flows of health data. It was interesting to see, as the pandemic ensued, how
many different kinds of data were mobilised. And many different social
actors got involved in the use of data, for many different purposes.
    Data were put in circulation in ways and speed that were unforeseen,
from both public and private sector companies. But data circulate well in
some directions, and less so in others. Overwhelmed perusing a constantly
moving panoply of numbers, charts and assessments on the state of the
pandemic, it is easy to miss out that some data are not flowing well at all,
and that others should perhaps stay where they are.

    TECNOSCIENZA
    Italian Journal of Science & Technology Studies
    13 (1) pp. 71-95 - ISSN 2038-3460
    www.tecnoscienza.net                                   2022

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98                                                           Tecnoscienza – 13 (1)


UK: “State of Play”
    The UK has been widely regarded as one country where the state has
been most willing to play with various sorts of data experimentation. A
report by the Alan Turing Institute on data science and artificial intelli-
gence in the “age” of COVID-19 (von Borzyskowski et al. 2021) highlights
how, in many ways, the pandemic was an exceptionally propitious oppor-
tunity for all sorts of innovation and experimentation with data to occur.
As it started reacting to the C19 outbreak, on 17th March 2020 the Govern-
ment mandated healthcare data custodians at NHS (National Health Ser-
vice) Digital to support access and processing of health data by authorised
organisations for purposes of pandemic response (Secretary of State for
Health and Social Care 2020). The relaxation of regulatory standards, with
a mandate for various public agencies to put the data in motion, was the
first “booster” for the circulation of data. But it was not only national
healthcare system data that were quickly mobilised. Private companies in-
cluding the likes of Silicon Valley giants Google and Apple also offered
select access to proprietary data, albeit to a limited extent. This created the
space for various teams and organisations to intervene and offer their ser-
vices as to how such an unprecedented and all-encompassing mobilisation
could be achieved. Datasets that could matter for the coordination of re-
sponse were myriad, as were the indicators to closely watch to monitor how
things unfold. Government decision makers procured from private com-
panies a set of analytics computational infrastructures to manage the for-
mer, and analytics dashboards for watching the latter. One of the most im-
portant contracts, worth more than 12.5 million GBP (Gov. UK 2020), was
awarded to Palantir – a secretive Silicon Valley company familiar to con-
troversies thanks to its propensity sourcing contracts from military, intelli-
gence and border control agencies involved in politically questionable mis-
sions. Consultancies McKinsey, Deloitte and Faculty AI also contributed.
On top of Palantir’s computational infrastructure NHS Digital could then
launch a “COVID-19 Data Store”, a repository of datasets available to
agencies involved in the pandemic response: “NHS COVID-19 Data Store
brings together and protects accurate, real-time information to inform strate-
gic and operational decisions in response to the current pandemic in one
place.” (NHS England 2022a) The datasets included in the Data Store are
rather disparate and come in many different formats, as a collection which
could be potentially accessed by many different users for many different
purposes would be (NHS England 2022b). There are data such as raw
NHS medical helpline call data; NHS staff absence reports; mobility data
from Google and Apple; Enterprise Resource Planning data for healthcare
system management; patient demographics; counts of online and video
consultations; personal protective equipment stocks and purchases; and
even self-report symptom data collected by volunteers for the COVID-19
Symptom Study using a citizen science app called “ZOE”. These data are


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Crossing Boundaries                                                        99

not limited to tables of numerical and/or structured text values. They are
also lists, lookup tables, summaries of historical data, and records that “de-
scribe”.
    Thanks to several dashboards and analytics features which draw dispar-
ate data sources together in “actionable” summaries of visualisations,
charts and numbers, decision makers in government should be equipped
with the best real time intelligence to take complex decisions: “These dash-
boards are designed to help senior national and regional officials to make
policy and strategic decisions in response to COVID-19” (NHS England
2022a). Perusing Palantir’s contract we learn that the system comprises of
three main interfaces: the Strategic Decision Makers Dashboard; Recovery
of Critical Services; and Early Warning System. We also learn that these
technologies might have a scope and longevity that reach farer than the
pandemic alone: they should help to “coordinate national response to
COVID-19 and EU Exit” and provisions are made to allow the Government
to transition this system from the pandemic use to “general business-as-
usual monitoring” (if Palantir’s software-as-a-service contract is renewed
past expiry). The Early Warning System interface seems the most ambi-
tious, sporting an “Explainability and Trust Overview” feature displaying
forecasts generated by the models of a private third party (the consultancy
Faculty AI, founded by a physicist) using NHS data, 111 medical helpline
data, and Google and Apple mobility data among others. And so, the time
when governing comes to resemble a session of Sim City (or Chile’s Cyber-
Syn room of cybernetic government, discussed by Medina 2015) might be
finally here. Those who sit at the fence of government action and have lim-
ited information to go by might have many questions about this “system of
systems”. One may wonder, for instance, who is interested in knowing
counts of tele-care consultations? How many of the people calling a medi-
cal helpline or logging their symptoms through the ZOE app would imag-
ine that their data could show up on a government dashboard, and what
would they think if they knew? Are self-report data from the ZOE COVID
Symptom Study app displayed on any dashboard, and who looks at it?
    Those around the Data Store are not the only movements of health data
between public and private sector that are currently noteworthy in the UK.
The General Practice Data for Planning and Research (n.b. GPDPR – not
GDPR) is a policy unveiled in the Spring of 2021 that, resurrecting the
ambitions of defunct “care.data framework” (Vezyridis and Timmons
2017), mandates NHS Digital to create a centralised repository of general
practitioner healthcare data, the access to which should be sold to private
sector companies on a cost-recovery basis. It is the latest of a series of at-
tempts to enable the permanent circulation of national healthcare system
data in the UK private sector and boost its valorisation. As researchers at
the Ada Lovelace Institute observe (Machirori and Patel 2021), the scheme
was introduced with notable disregard for public engagement through a
“method” that could be described as “decide, announce and defend”: if


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100                                                         Tecnoscienza – 13 (1)


the policy is rammed through fast enough, it might survive the public back-
lash and the government would get its way. Once again, public backlash
might prove sticky enough. The introduction of the plan has been delayed
and there has been a sizeable opt-out, as noted by Cori Crider, director of
Foxglove Legal, in a recent expert consultation by the Ada Lovelace Insti-
tute (2021). Regardless the exact timings, the repeated attempts of consec-
utive UK governments to enable private sector use of patient records
demonstrate a long-term determination, which predates and will outlast
the pandemic, to get health data to circulate more widely and loosely and
for many more purposes than the performance of health care; and for the
government to allow private data platform developers to embed themselves
in the infrastructure of the state and its governance activity. As also high-
lighted by the Ada Lovelace Institute, the UK Government fancies the op-
portunity to turn the relaxation of data circulation regulations introduced
during the pandemic into a standard for the future regulatory regime, so as
to favour faster and broader circulation. The same kind of pattern has been
observed in yet another front of government development of pandemic
technology infrastructure, that of contact-tracing apps. As Rob Kitchin has
noted (Kitchin 2020), in order to develop contact-tracing apps many coun-
tries desperate to curb the spread of C19 resorted to working with organi-
sations that have been at the centre of scandals or polemics in “normal”
time because they develop controversial techniques, technologies or ser-
vices of population surveillance. Contact-tracing collaborations includes
organisations such as NSO Group, who sells weapons-grade spyware to
illiberal and autocratic governments accused of repressing dissidents and
opponents, and has worked with Israel on their app. More notoriously,
tech giants and mobile monopolists Google and Apple, who rushed to offer
a common Bluetooth-based stack for automating contact tracing in An-
droid and iOS phones, used their privileged position of mobile gatekeepers
to make an impactful contribution (not without privacy implications – see
Kitchin 2020).

Translating Private Technology to Public Infrastructure
    In respect to organisational operations and decisions, data seem to cir-
culate well indeed. Many more private organisations have been taking part
in the C19 data craze, often with much display aimed at “covid-washing”
their reputation (Kitchin 2020), keen to be seen as generous tech wizards
rather than greedy data harvesters. After all, one might say, there is a point
in letting these companies collecting so much data about the public, if they
respond to the call when their help is needed. But are in particular those
organisations, who outside of “pandemic time” have been at the centre of
many ethical controversies over the ways in which they generate and use
data, that have rushed to the forefront of more and less consequential


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Crossing Boundaries                                                        101

efforts to help. It reveals a key assumption as to the ways in which techno-
logical, organisational and methodological frameworks originally devel-
oped for watching and manipulating consumer behaviour through digital
technology have been seen as translatable to the context of social distancing
restrictions and other emergency rules. The double-edgedness of these in-
itiatives is easy to surmise. For instance, data broker Experian, who sells
individual data at population scale after collecting them through a vast net-
work of business relationships and repackaging them in the form of value-
added marketing demographics, studied the distribution of C19’s socio-
economic impacts. Besides the potential to help public health response,
one should remember the knowledge thus generated is likely to have value
for marketing demographics too; and so, the first beneficiary of this effort
might well be Experian itself. At least in a rhetorical sense, these frame-
works have proved translatable: research into individual attitudes towards
contact-tracing apps (Lucivero et al. 2021) shows that many believe the
impact of intensive data collection and cross-dataset linking is negligible
since the lives of ordinary individuals are already intensively surveilled, and
for much less of a reason.

    With so much “help” on offer, the pandemic has certainly reaffirmed
the central role of private technology in the coordination of society’s reac-
tion to emerging events. But should it? A piece on the Harvard Business
Review (Balsari et al. 2020) suggests otherwise. A “tidal wave of data” is
sloshing around all corners, but not much of it might be “any good”. Many
datasets made available are incomplete in ways that are not-randomly dis-
tributed across society, but rather, reflect socio-economic inequalities. If
the disadvantaged are less well represented in datasets used to coordinate
pandemic response, expect the inequality to be drawn on. And so, the au-
thors suggest, while many tech organisations are busy offering up datasets
and expertise on linkage, hosting and analytics, there is not enough engage-
ment with subject matter experts. Many models are produced with exper-
tise that is translated from being involved in the solution of problems other
than the medical, but rather, rooted in mathematics, physics, or operations
management expertise, among others; other innovations, such as auto-
mated contact-tracing, are live experiments. What a contrast with the ex-
hortations of data analytics and visualisations leader Tableau, who encour-
ages users to start “your own analysis” (Tableau 2022). Cloud-computing
giant, Amazon Web Services, offers a suite of data and computational in-
frastructure resources to help and “provide these experts with the data and
tools needed to better understand, track, plan for, and eventually contain and
neutralize the virus that causes COVID-19” (Amazon Web Services 2020).


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102                                                          Tecnoscienza – 13 (1)


Experts can “use AWS or third-party tools to perform trend analysis, do key-
word search, perform question/answer analysis, build and run machine learn-
ing models, or run custom analyses to meet their specific needs.”

The Challenges that Remain
    While some data might have been circulated in and out, and across,
government quite well, other data were not circulating equally well. As the
Alan Turing Institute’s report observes (von Borzyskowski et al. 2021), a
number of challenges were experienced by the community of data science
and artificial intelligence researchers striving to make an impact through
the production of new knowledge about C19. Certain kinds of data can be
difficult to access because of governance issues – the Ada Lovelace Insti-
tute points out that current governance processes were often too slow and
required too much of too few data custodians (Ada Lovelace Institute
2021); but also because they are more difficult to generate than others.
Data on some pandemic response measures and their impacts, such as non-
pharmaceutical interventions (e.g., social distancing and face masks), were
not sufficiently available. Local council and administration decision makers
complained not enough data were made available to them. Uneven quality
and representation in population datasets further raised concerns of ine-
quality in the response to the pandemic. Unequal vulnerability to pandemic
response measures would also lead to mistrust and uneven participation
and compliance in various undertakings, such as active installs of contact-
tracing apps, or symptom self-reporting in citizen science studies such as
ZOE COVID Symptom Study. While datasets were over-produced in a
scramble to help, attention slipped over quality and methodological issues
such as sampling (von Borzyskowski et al. 2021). While new problems are
often resolved more quickly the more open and participative is the search
for a solution, there is a way in which the eventually ensuing chaos brings
about new problems in the meantime. The ubiquitous discussion of statis-
tics and data in all kinds of public reporting further amplified concerns
over interpretation and communication. Last but not least, Alan Turing
Institute researchers complain about their relationship with government
decision makers. They found it difficult to understand if the expert
knowledge that they were generating through many studies was getting any
attention by policy makers. Researchers who are well connected could have
government’s ear and access data that others could not. As we have seen,
government decision-makers were providing themselves with cutting-edge
analytics technology from private firms the likes of Palantir. It is as if they
wanted to lock themselves up in the button room with the latest tech gear,


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Crossing Boundaries                                                       103

leaving other experts outside who kept insisting they could help. The ap-
peal of translating all-powerful consumer surveillance infrastructure might
have been more powerful than working with experts the old way.

From Normal to Pandemic Time, and Back
    From this quick sketch, it should be possible to get a feel for a complex
and protracted situation involving many kinds of initiatives, data practices,
actions, claims, and contexts of use. Talk about data can be at different
levels (Rosenberg 2013; Leonelli 2016; Tempini 2020): as digital object
stored on computer systems, as epistemic product of an empirical scientific
investigation or of activist projects, and as a rhetorical device that can be
waved around (as in the press conferences that saw Boris Johnson so fre-
quently argue that the UK government “just” followed the data). This
makes the analysis of data practices and movements complex. Some of the
stakes of data practices and movements from “pandemic time” will be
played out in the future, and in such a cacophony of data practices and
claims it is easy to lose sight of a few big trends that have been driving all
things data. But there is perhaps enough to see that the circulation of data
during the pandemic was uneven and dependent on many factors including
the organisational and technological infrastructures datasets are managed
with; and to suspect that the pandemic crisis, like many other crises, won’t
be let go to waste, and instead, will allow private infrastructure to be
wedged further underneath society and its spaces. In times of emergency a
general mobilisation of all sectors and actors might feel like the only intel-
ligent thing to do, and so many private sector organisations all scrambled
to see what they could offer to government and research community. But
in all things infrastructural, there is a strong sense in which the past will
become the present, and the present will become the future; because infra-
structures are built over long time and sit on top of even longer-evolving
methodological and cultural frames, path dependencies are deemed and
continued through the material shaping of systems and practices (Star and
Ruhleder 1996; Hanseth, Monteiro, and Hatling 1996; Hanseth 1996;
Bowker and Star 1999).

    Time was of the essence, and the rapidity with which private data-in-
tensive businesses deployed a panoply of initiatives to share and analyse
data of all kinds bears witness to the strategic dynamism and translatability
of data platforms. Response to the pandemic was characterised by lack of
time, and those who control data infrastructures were in a good position to
enter the frame of pandemic response efforts and reap financial and


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104                                                          Tecnoscienza – 13 (1)


reputational benefits. Infrastructure evolution in “normal” time has often
been seen as challenged by “infrastructural inertia” (Star and Ruhleder
1996; Bowker and Star 1999), that is the way in which infrastructures
swamp change and make material legacy. In “pandemic time”, instead, it
seems as if infrastructures were key to enable movement and change. All
time had been suddenly sucked out. Being able to re-deploy and re-pur-
pose data infrastructures and methods was a chief way to buy time. This
might not need to be a contradiction. Infrastructural inertia is likely to be
observed as an infrastructural reaction when the change that is being car-
ried out is proactive or transformative – a move away from the current ways
of doing. The change and re-organisation that the pandemic time required
was essentially reactive: when surprised and unprepared to unexpected de-
velopments, the current ways of doing might be the only available to effect
change. Infrastructures and methods that are re-deployable, transferable
and extendable are quickly whisked into new positions.

    One could wonder why should we be concerned about all this? That is
because once time is “normal” again, infrastructural inertia can kick in
again. Dislodging private technology infrastructure, and the practices asso-
ciated with it, that was deeply embedded in the government machinery
back in pandemic time will become ever more difficult. As Sharon points
out (Sharon 2020), running pandemic response with the technology pro-
vided by private corporations will “increase our dependency on them for the
provision of (public) services, and they make themselves necessary passage
points for the adequate functioning of these sectors” in the future. Continued
reliance on any infrastructure makes it invisible and undermines the imag-
ination of technological-organisational-political alignments that respond to
different values, priorities and logics. Complex and consequential infra-
structures, and their developmental inertia, help to ensure the past, from
before the pandemic, is carried over to what comes after. They make some
of the linkages that thread together the before-during-after of pandemic
times.

    Update 9th June 2022
    This morning the Financial Times is breaking with reporting suggesting
the Government is planning to award a giant contract for the provision of a
data analytics “operating system for the NHS” and Palantir is devoting enor-
mous resources to win the contract. Privacy activists who have exposed Pal-
antir’s penetration in state infrastructure since 2021 point out the same kinds
of concerns I have been repeating here. It turns out worrying developments
might be moving even faster than we might have worried.


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Crossing Boundaries                                                              105

   “Palantir gears up to expand its reach into UK’s NHS”, 2022. Financial
Times. https://on.ft.com/3xaqnsw


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    Science and AI in the Age of COVID-19: Reflections on the Response of the UK’s
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    ring Institute.

                                          ***

Polysocial Risk Scores and Behavior-Based Health Insur-
ance: Promises and Perils

Antonio Maturo, University of Bologna


Cotton Balls, Zinc Supplements and Predictive Analytics

    Once upon a time, a long time ago, around 2010, an irate father walked
into a Target store on the outskirts of Minneapolis. He asked to speak with the
manager, and upon their arrival, he waved coupons and vouchers in their face:

      “My daughter got this in the mail!” he said. “She’s still in high school, and
      you’re sending her coupons for baby clothes and cribs? Are you trying to
      encourage her to get pregnant?”1


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Crossing Boundaries                                                              107

   The manager apologized profusely and stammered that he had no idea
how this could have happened.
   A few days later, the same manager called the father to apologize again,
but something happened:

    On the phone, though, the father was somewhat abashed. “I had a talk with
    my daughter,” he said. “It turns out there’s been some activities in my house
    I haven’t been completely aware of. She’s due in August. I owe you an apol-
    ogy.” (ibid.)

    What led to this bewildering encounter was a new office in that retail
location, where a mysterious new practice had been implemented: Predic-
tive Analytics. A sudden change in the young woman’s shopping patterns
had been noticed, signaled through her loyalty card, sparking an unantici-
pated chain reaction. Back in 2010, retailers had just started to collect inti-
mate details about consumption habits. They had noted that:

    Women on the baby registry were buying larger quantities of unscented lotion
    around the beginning of their second trimester. Another analyst noted that
    sometime in the first 20 weeks, pregnant women loaded up on supplements
    like calcium, magnesium and zinc. Many shoppers purchase soap and cotton
    balls, but when someone suddenly starts buying lots of scent-free soap and
    extra-big bags of cotton balls, in addition to hand sanitizers and washcloths,
    it signals they could be getting close to their delivery date.2

    Because of this shift in purchasing habits, the young woman’s preg-
nancy had been made apparent in her data-double, even before her social
identity.
    This incident occurred over ten years ago, while paper mail was still the
main form of promotion. In the meantime, self-tracking has exploded, gen-
erating enormous amounts of data, especially physiological and behavioral
data. In addition, sophisticated algorithms can monitor the time we spend
on a site, the physical places we visit, and the likes we place. By monitoring
our credit cards, it is possible to know what we eat and how many calories
we ingest. Especially in the context of COVID-19, unseen sensors can rec-
ognize who is running a temperature in a train station. In the field of health,
therefore, there is not only big data but thick data: data that can tell us
about our health from a clinical, physical and social point of view.
    In this datafication of health, perhaps the two most disruptive and cut-
ting-edge developments are “Polysocial Risk Scores” and “Behavior-Based
Health Insurance”. These two areas, in some ways, overlap since the risk
score is the basis of health insurance, of which Behavior-Based is the most
advanced kind. Surrounding both are big players with keen interests and
high expectations. Both Polysocial Risk Scores and Behavior-Based Health
Insurance share close attention to social aspects of health, and both are


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driven by the need to predict possible (individual) futures on the basis, of
course, of quantification (e.g., datafication). Surrounding these develop-
ments, beyond innovative possibilities, are clear doubts and concerns
about their implications and consequences in terms of social justice.

The Polysocial Risk Score

    In order to understand what Polysocial Risk Scores consist of, it is help-
ful to underline the main features of the Polygenic Risk Score, which in
some ways acts as its prototype.
    The Polygenic Risk Score estimates the risk that a person has of devel-
oping a disease from his or her genes. More precisely, the Polygenic Risk
Score represents the total number of genetic variants that an individual has
to assess their heritable risk of developing a particular disease since multi-
ple genetic mutations and their interactions cause most diseases.
    At first glance, Polysocial Risk Scores can be seen as the sociological
version of the Polygenic Risk Scores, with the idea of the Polysocial Risk
Score being developed in the context of the social theory of social determi-
nants of health.
    Social determinants of health are the factors that affect a person’s
health, namely education, income, type of work, type of housing, neighbor-
hood, social cohesion, and others. These determinants affect health
through lifestyles, health literacy, and access to care. Epidemiologists and
health sociologists have repeatedly confirmed the influence of social con-
text and social determinants on physiology. The determinants of health are
strongly intertwined, e.g., how income influences health and how it, in
turn, is affected by education; how the weight of income and how the
weight of education affects a person’s health, and how much, in turn, the
weight of education on the possibility of acquiring higher income.
    Therefore, the challenge is to weigh and estimate the conditioning of
social determinants and their interactions with individual health. However,
to date:

      Most efforts to precisely quantify the influence of individual social determi-
      nants of health have failed, largely because the causal pathways are numer-
      ous, interconnected, and complex. (Figueroa et al. 2020, 1553).

    The enormous amount of data that can now be acquired on people’s
health could mark a turning point for developing precise estimates of indi-
vidual risk of becoming ill. Notwithstanding, one would have to arrive at a
Polysocial Risk Score for each disease or health outcome, even in this case.
One person would then have several Polysocial Risk Scores. Nevertheless,
compared to the Polygenic Risk Score, there is a considerably more turbu-
lent level of complexity:



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    One key difference is that unlike polygenic risk scores, which are not dy-
    namic because the scores are based on an individual’s genes, polysocial risk
    scores may change if an individual’s social circumstances change. (ibid.)

    Where the Polygenic Risk Score is static, the Polysocial Risk Score is
(would be) dynamic. Moreover, the same social determinants have differ-
ent weights in different social contexts. Indeed, and methodologically it is
even worse with some social determinants being part of the context itself
(e.g., social capital and social cohesion).
    As Figueroa and collogues (2020) illustrate, it is necessary to constantly
collect, aggregate, and mobilize data from different domains regarding
people’s quality of life and sociodemographic data Polysocial Risk Scores
need to be periodically updated. Above all, it is necessary to relate these
“external” data to people’s state of health, to their “internal” health data,
and to their physiology.
    Moreover, as scores are elaborated and processed by algorithms, in
some cases, health data may result in biases and, in worst cases, social dis-
crimination. As summarized by Leslie et al.:

    AI systems can introduce or reflect bias and discrimination in three ways:
    in patterns of health discrimination that become entrenched in datasets, in
    data representativeness, and in human choices made during the design, de-
    velopment, and deployment of these systems (2021, 1).

    Thus was the case of genetic data, as in the U.S, most genome-wide
association study-based polygenic risk scores have been based on popula-
tions of European descent, neglecting the health of other ethnic minorities.

Pricing Risk: Behavior-Based Health Insurance

    Creating the Polysocial Risk Score would be something between mira-
cle and mirage, yet this does not mean that attempts have not been made.
On the contrary, the health analytics industry is a rapidly developing sector
in the digital firms of Silicon Valley and the biotech industry of the Boston
Area, with the American health insurance agencies leading the charge to-
wards the construction of health risk scores, with the latter being interested
in knowing the health status of their members. Moreover, actors that has
most influenced this orientation of health insurance, at least according to
some scholars, has been a legal provision contained in the Affordable Care
Act (ACA), approved in 2010. As Liz McFall points out:

    The ACA alternative introduced a “behavioural” approach (...) including
    new responsibilities to pay a “fair share” of the costs of the entire pool and
    be “as healthy as you can.” The responsibility to be healthy is promoted by
    the provision of access to preventative care and treatments for chronic,


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      preventable disease. (...) This emphasis on behavioral responsibility is a
      great fit with data-driven healthcare innovations including wearable self-
      tracking devices and apps. (Mc Fall 2019, 60).

     This provision has operated in “association” with other factors, primar-
ily technology. As McFall (2019) and Schüll (2016) point out, digital tech-
nology and the ACA have been presented as a “dynamic duo” working
together, and

      compelling insurers, health care providers and consumers to cut costs (...)
      shifting the management of chronic conditions like diabetes and heart dis-
      ease away from hospitals and doctors and into the hands of patients them-
      selves (Schüll 2016, 318).

   If over a decade ago the office of a chain store was able to learn of a
customer’s pregnancy through her purchases of hygiene products, what
can health insurers know about us today? What could insurance “provid-
ers” learn when they are given access to sociodemographic data, clinical
data, genetic predisposition, and, more importantly, lifestyle data (not
simply “lifestyle data” as in whether individuals are smokers or vegetarians,
but all digital activities and data-doubles)? Moreover, some digital plat-
forms have already identified rich sets of data points for proxies of social
determinants of health:

      individual purchasing behavior, consumer engagement with advertising, in-
      surance claims, sentiment, and expression in online forums, credit histories,
      and online social networks (Rowe 2021, 4).

    This data, in turn, is coupled with the mundane data generated by per-
sonal FitBits, generously gifted by health insurance agencies (Maturo and
Moretti, 2018).
    Before the spread of digital social networks, Christakis and Fowler
(2010) wrote that social friend networks greatly influence personal deci-
sions. Christakis and Fowler showed through animated sociograms based
on accurate longitudinal research how certain behaviors may be “conta-
gious”. Not only does a person have a high probability of gaining weight if
their friend does, but also if their friend’s friend does, this can be further
applied to divorce and smoking cessation. Today these analyses are im-
mensely easier given the ease with which big data can be collected and pro-
cessed. The predictive potential delivered to insurance agencies is enor-
mous, leading to correlation taking the place of causation, with the latter
becoming an obsolete 20th-century category (Anderson 2008).
    Raschel Rowe (2021) has done thorough research on the platform
“Opioid360”, a platform that combines browser histories, credit, insur-
ance, social media, and traditional survey data to sell the service of risk


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calculation in population health. Created as a tool that would support over-
worked clinicians to see invisible signs of potential addiction in their pa-
tients, Opioid360 paved the way for broader applications to prevent
chronic diseases. Most importantly:

    By extending digital phenotyping imaginaries, Opioid360’s presentation
    appealed to the notion that comprehensive personal data can offer behav-
    ioral science the precision that genomics has offered to identify rare diseases
    (Rowe, 2021, 4).

    In their analysis of Vitality health insurance, McFall et al. (2020) make
clear that:

    Behaviour is Vitality’s core brand value and its policies provide incentives
    to customers to meet behavioral targets, share their data with the company
    and share their progress on social media (McFall et al. 2020, 7).

     The big switch that many health insurers have made is to link insurance
premiums and access to specific policies to the constant digital monitoring
of physical activity (InsurTech). In theory, through self-tracking, the pre-
mium costs could fluctuate every day, in connection with our physical
states, instead of once a year. The extension of insurance surveillance to
other aspects of our lives through the datafication of health raises big ques-
tions about social justice.
     The encouragement of certain behaviors opens an extended reflection
on the empowerment of the individual. In social studies of health, it is well
known how social context affects a person’s health and that certain social
factors such as income make adherence to healthy lifestyles relatively easy
for some people, while for others practically impossible.
     When I arrive at around 8 o’clock outside my department, I often meet
one of the ladies who clean the offices – being female, visibly overweight,
doing an extremely physical job (maybe she has a disease or seeks satisfac-
tion in food?). She gets up at 4.45 a.m. to start work before 6 a.m. When
she greets me at 8 a.m., she lights a cigarette with her South Italian accent
before getting into the car. She inhales in big puffs as if it were a prize, a
seal, or as we say today in the field of gamification, an award for the work
done. However, it is not her avatar who is smoking, unfortunately. Her face
is tired, and she is in a hurry – maybe she will light another one soon: she
has to go to the other side of the town to do some more cleaning, and there
is a lot of traffic by then. Just before entering the department, out of the
corner of my eye, I see a colleague of mine jogging through the beautiful
palm trees on our campus.




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Algorithmic Forecasting and Insurance Customization

      According to Barry and Carpentier (2020), insurance can be defined as

      the transformation of unknown individual uncertainty, or chance, into a
      measurable aggregate risk. Technically, it consists of pooling uncertainty
      and applying the law of large numbers (Barry and Carpentier 2020, 3).

    In this way, the occurrence of catastrophic events for one person was
remedied by adding small amounts set aside by all. Through statistical pre-
dictions, it is relatively easy to predict that a certain number of insured
people will fall ill without knowing who exactly. At least until now, insur-
ance has been based on the concept of socialized actuarialism. However,
as early as 1996, O’Malley glimpsed the advance of privatized actuarialism,
a more refined approach based on:

      a technology of governance that removes the key concept of regulating in-
      dividuals through collectivistic risk management and places the responsi-
      bility for risk management back on the individual (O’Malley 1996, 197).

    Thus, whereas traditional insurance was based on prediction (i.e., ag-
gregate predictions at the macro level), the new behavior-based insurance
is based on forecasting (i.e., attention to the individual’s future at the mi-
cro-level). This mode of insurance makes policyholders more responsible
for their daily actions and health. However, many scholars question
whether, technically, behavior-based insurance can still be considered in-
surance. Based on the distinction between individual fairness and social
fairness, Cevolini and Esposito, effectively summarize how the ancient
principle of solidarity can be undermined by new insurance policies:

      Algorithmic prediction could radicalize the principle of segmentation, cul-
      minating in the extreme case of “segments of one.” This would almost au-
      tomatically mean the end of the risk-pooling on which the principle of risk-
      sharing is based (Cevolini and Esposito 2020, 4).

    The end of risk-pooling carries significant implications as to whether
Polysocial Risk Scores have the potential to become a central tool in
healthcare. In this regard, a crucial issue here concerns what would happen
if Polysocial Risk Scores are calculated and accredited by institutions.
    Considering that constructed indicators tend to become objective enti-
ties, Polysocial Risk Scores can be employed in different contexts and by
different actors; from public health departments, government officials,


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technology companies, investors, and private insurance companies (Ne-
resini 2015). In a world that is increasingly computerized, quantified, and
managed by algorithms, health scores could be mobilized for a variety of
purposes. Some of these uses could be noble and others less so:

    Health risk scores are not only useful for immediate patient classification
    or public health program planning, they are also useful to investors seeking
    to leverage or hedge their risk exposure. (Rowe 2021, 9).

    Although indirectly, a strong impetus for developing health scores has
undoubtedly come from COVID-19 pandemic. The pandemic has bol-
stered the trend of health quantification through the robust joint growth
of medicalisation and digitalisation. Most importantly, COVID-19 pan-
demic has spurred surveillance. To put a long story short: 9/11 increased
police surveillance, big data stimulated capitalist surveillance, and COVID-
19 hyperbolically accelerated molecular surveillance. Molecular surveillance
can be seen as the scrupulous and precise monitoring of our physiological
motions and their instantaneous transformation into data. A panopticon of
our internal states, or more precisely: the endopticon (Maturo 2015). How-
ever, this surveilling is not perpetrated by shadowy officials of mysterious
agencies wearing thick-lensed glasses in smoke-filled rooms of some gov-
ernmental molecular surveillance departments but by algorithms them-
selves. Programs that react to numbers that exceed certain thresholds, to
parameters that measure, compare, and discriminate our physiological mo-
tions, collect our behavioral habits and read our molecules’ silent but viva-
cious lives.
    Yuval Noal Harari, the author of the successful Homo Deus, in an arti-
cle published in the Financial Times on April 19, 2020 entitled The world
after the Coronavirus, fears a dystopian scenario:

    Hitherto, when your finger touched the screen of your smartphone and
    clicked on a link, the government wanted to know what exactly your finger
    was clicking on. But with Coronavirus, the focus of interest shifts. Now the
    government wants to know the temperature of your finger and the blood-
    pressure under its skin. One of the problems we face in working out where
    we stand on surveillance is that none of us know exactly how we are being
    surveilled, and what the coming years might bring. Surveillance technology
    is developing at breakneck speed, and what seemed science-fiction 10 years
    ago is today old news.3

   Harari’s concerns reaffirm that health scores will soon be the subject of
a Black Mirror episode. Behavior-Based Insurance and Polysocial Risk



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114                                                               Tecnoscienza – 13 (1)


Score have disturbing implications, starting with the de-politicization of
health, which is no longer understood as a public and social issue but as a
business and private concern. The challenge, however, is not to assume
ipso facto Luddite or apocalyptic attitudes. It is necessary to find a catalyst
that brings health back to the center of public discourse. In a society dom-
inated by chronicity, the masses (of patients and caregivers) should become
aware of their strength.


References

Anderson, C. (2008) The end of theory: The data deluge makes the scientific method
    obsolete, in https://www.wired.com/2008/06/pb-theory/ (retrieved June 3,
    2022).
Barry, L. and Charpentier, A. (2020) Personalization as a promise: Can Big Data
    change the practice of insurance?, in “Big Data & Society”, 7 (1), pp. 1-12.
Cevolini, A. and Esposito, E. (2020) From pool to profile: Social consequences of
    algorithmic prediction in insurance, in “Big Data & Society”, 7 (2), pp. 1-11.
Christakis, N. and Fowler, J. (2011) Connected: The Surprising Power of Our Social
    Networks and How They Shape Our Life, Boston, Little Brown and Co.
Figueroa, J.F., Frakt, A.B. and Jha, A. (2020) Addressing Social Determinants of
    Health: Time for a Polysocial Risk Score, in “Journal of American Medical As-
    sociation”, 323 (16), pp. 1553-1554.
Fourcade, M. and Healy, K. (2013) Classification Situations: Life-chances in the Ne-
    oliberal Era, in “Accounting, Organizations, and Society”, 38 (8), pp. 559-572.
Leslie, D., Mazumder, A., Peppin, A., Wolters, M.K. and Hagerty, A. (2021) Does
    “AI” stand for augmenting inequality in the era of covid-19 healthcare?, in “Brit-
    ish Medical Journal”, 372, pp. 1-5.
Maturo, A. (2015) Doing Things with Numbers: The Quantified Self and the Gami-
    fication of Health, in “Eä – Journal of Medical Humanities & Social Studies of
    Science and Technology”, 7 (1), pp. 87-105.
Maturo, A. and Moretti, V. (2018) Digital Health and the Gamification of Life: How
    Apps Can Promote a Positive Medicalization, London/New York, Emerald.
McFall, L. (2019) Personalizing solidarity? The role of self-tracking in health insur-
    ance pricing, in “Economy and Society”, 48 (1), pp. 52-76.
McFall, L., Meyers, G. and Hoyweghen, I.V. (2020) Editorial: The personalisation
    of insurance: Data, behaviour and innovation, in “Big Data & Society”, 7 (2), pp.
    1-11.
Moretti, V. and Maturo, A. (2021) “Unhome” Sweet Home: The Construction of
    New Normalities in Italy during Covid-19, in D. Lupton and K. Willis (eds.),
    The COVID-19 Crisis: Social Perspectives, New York, Routledge, pp. 90-102.
Neresini, F. (2015) Quando i numeri diventano grandi: che cosa possiamo imparare
    dalla scienza, in “Rassegna Italiana di Sociologia”, 56 (3-4), pp. 405-431.



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Crossing Boundaries                                                               115

O’Malley, P. (1996) Risk and responsibility, in A. Barry, T. Osborne and N. Rose
   (eds.), Foucault and Political Reason, Chicago, University of Chicago Press, pp.
   189-207.
Rowe, R. (2021) Social determinants of health in the Big Data mode of population
   health risk calculation, in “Big Data & Society”, 8 (2), pp. 1-12.
Schu!ll, N.D. (2016) Data for life: Wearable technology and the design of self-care, in
   “BioSocieties”, 11 (3), pp. 317-333.

                                         ***

A Data Journalism Perspective on Data Circulation

Elisabetta Tola, Formicablu


Introduction
    No longer in its infancy, but not a grown-up either. That is the state of
data journalism, as far as Italian media are concerned. With very few ex-
ceptions, the practice of using data to provide sound, accurate, transparent
information is still very rarely explored at its full potential in our country.
The reasons are many, and the recent experience of the COVID-19 pan-
demic has made them clearer. The list is long – lack of the appropriate
mindset on the side of most journalists and even more of their editors and
publishers, lack of resources and skills, lack of time and reliable sources.
However, most of all, lack of data. Despite the data deluge we seem to have
experienced, with maps and graphs popping up everywhere, the reality is
that meaningful and valuable data are still very scarce. The negative im-
pacts and implications of such a greedy approach remain to be assessed.
But there are also lessons to be learned and used to improve the infor-
mation and the future journalistic work.

Data and Health: How We Learnt to Tell Stories in a Dif-
ferent Way

    The first data-driven investigations appeared in UK and US newspapers
around 2009-2010. The Guardian Datablog, founded by journalist Simon
Rogers, currently data editor at Google, was one of the earliest efforts to
introduce data in daily journalistic practice routinely. Anyone can do it –
argued Rogers in a popular piece he wrote at the time: Data journalism is
the new punk4. The combination of data available in easy-to-use formats
released by many public institutions with the ability to use a datasheet to
compile basic statistical operations and a few tools to create graphs and
maps were deemed by Rogers as the basic bricks that could build a new


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approach to journalism. Nevertheless, even at that initial step, it was al-
ready clear to all, and well highlighted in Rogers’s article, that:

      Maybe everyone can do it, but not everyone can do it well. Like so many
      other things, done well is a mix of art and science (see footnote n.4).

    It was almost immediately apparent that data journalism could be par-
ticularly useful when applied to issues that, while being of high public in-
terest, are particularly difficult to interpret and understand, such as health
or environmental ones. “Not all data journalism has to bring down the gov-
ernment – it’s often enough for it to shine a light in corners that are less
understood, to help us see the world a little clearer.” That is Rogers, again
(see footnote n.4).
    One of the first iconic data investigation in the health domain was the
initiative Dollars for Docs, a long series of online articles and a rich database
built in 2015 and maintained until 2019 by the US online magazine
“ProPublica”5. According to the methodological scaffold in which the ar-
ticles are framed, the story started in 2009 when seven drug companies
were required by a court to release details of their payments to doctors and
teaching hospitals in the US. The story grew big in time and lasted over 10
years until, in 2019, the magazine stopped updating the database. At that
point, the database included information on payments made by over 2000
companies to more than 1 million doctors and 1200 teaching hospitals for
a total of over 12 billion dollars of payments. The payments included di-
verse categories, such as promotional speaking, consulting, meals, travels
and royalties. A tool was developed that allowed any reader to search for
their doctor and check whether a company had somehow paid him or her,
when, how often, for what and how much. This may put your next prescrip-
tion in a different light, wrote the editor Stephen Engelberg in one of the
first commentaries accompanying the launch of the Dollars for Docs inves-
tigation6. Furthermore, the investigation and the data enacted a collabora-
tive approach where ProPublica started co-producing articles and in-depth
analyses with other media, local and national, to make sure that the data-
base could be exploited to its total capacity and highlight many stories of
local interest to citizens living in different cities and states. In many cases,
it spurred actual investigations on malpractice and wrongdoing, which
would have been very difficult to undertake without those data.
    Shortly after that, Wired Italy launched the Dove ti curi [Where do you
go for your health care, n.d.r.] investigation, based on the first release of
data by the Agenas, the Italian National Agency for Regional Health Sys-
tems. Agenas produces a periodic report on health care quality in all 1200
Italian public hospitals. These data were made available for the first time
to journalists and professionals in the health sector in 2012. The following
year, Wired Italy decided to publish the entire dataset in a searchable for-
mat and many articles explaining the meaning of those data. It was an


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Crossing Boundaries                                                               117

absolute novelty in the landscape of health journalism in our country. The
investigation is, unfortunately, no longer available online. However, it re-
mains a landmark in the way health data could and should be used in ena-
bling people to make informed decisions on where to go to be assisted,
which hospitals are better for one or the other type of treatment, and which
are absolutely to avoid because their rate of mortality is a dangerous outlier.
Thus, those data would be precious also to researchers and professionals
in the health sector for assessing and comparing the performances of the
different health centres. They can show how organisational choices can
have a positive impact in terms of outcomes, and they can help re-modulate
or eliminate the situations where the outcomes are adverse, reducing suf-
fering and saving lives in the end.

Interlude: How Data Enter the Journalistic Practice and
Become Stories

   Far from being magic, data journalism is done following a very straight-
forward methodology:

    Data journalism begins in one of two ways: either you have a question that
    needs data or a dataset that needs questioning. Whichever it is, the compi-
    lation of data is what defines it as an act of data journalism. (Bradshaw
    2011).

    The first step is finding and compiling the data. One can do this by
finding a ready-to-use spreadsheet online, but also by using advanced
scraping techniques to get data from online pages and databases, by ex-
tracting numbers from .pdf or other formats into a table, or by pulling the
data using an API or finally collecting them manually, either by observa-
tions, surveys, questions, investigations. Once the database is available, it
needs to be cleaned, filtered, combined, and analysed. Even with a ready-
to-go table, such as the ones that can be downloaded from any open data
repositories, as the data warehouses of the National Institutes of Statistics
or the international organizations (such as FAOSTAT, World Bank Open
Data or OECD data), the numbers are not ready to be used to write a story.
This is even more true if the data are raw, and the database has been built
from scratch. In this latter case, there is the need to run some statistical test
to make sure that the data are significant with respect to the initial hypoth-
esis. When the dataset has been validated, there are more operations to go
through. We need to be sure that there are no mistakes, duplications, mis-
spellings, missing information and so on, and we can achieve this step by
means of running tools that highlight those errors and allow corrections in
order not to misinterpret the final result.




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    At this point, a journalist will look at a data set in a different way than
a researcher: the questions that come to mind when using data to craft an
investigation are motivated by the interest in finding an angle, an explana-
tion, an interpretation for a story that will have an impact on people’s view
of a certain subject. Journalists look for outliers to see if there is a moment
in time, or a situation compared to others, that could be explained by ex-
ternal factors. Or they look for trends comparing situations that might give
rise to a wider view on a certain phenomenon. Therefore, it is particularly
important to be very accurate in the cleaning step since outliers are often
simply the results of errors, generated either during the data collection or
the data entry into the database. Comparisons in time or across different
geographical situations, for instance, can only be made if the data are con-
sistent, if the same methodology has been used to collect them in the da-
taset.
    Furthermore, data need to be put in context, with the appropriate
metadata explaining the methodology and the significance behind the data
collection and organisation. In the journalism domain it is possible to use
data collected in different ways, if those are the only one available, but the
extent and meaning of those discrepancies have to be made clear to the
readers in order to be truly informative and not misleading.
    For a journalist an interesting set of data can also be a dataset that is
missing a key information: at that point, the question is why that piece of
information is not available. Sometimes the story can be, as a matter of fact,
in the missing data, since that absence is telling something about ineffi-
ciency, malpractice, opacity and much more. Finally, in order to tell a full
story, the concerned database might need to be combined with other data,
such as demographic ones, historical, environmental. By addressing spe-
cific questions through the database, the journalist might see if there are
interesting correlations, i.e., identifying factors that might influence or af-
fect a certain trend. For instance, The hunger profiteers, a recent investiga-
tion published by Lighthouse Reports, a European collaborative investiga-
tive journalistic effort, has focused on the dramatic increase in food prices
in recent months7. The current narrative, both by media and by many key
actors and public institutions is that this increase has a lot to do with the
Russian invasion of Ukraine and its impact on grain production and trade.
And yet, looking more closely to the food price index estimated by Food
and Agriculture Organization of the United Nations and comparing that
with the global production of cereals in the last few years and the global
demand, it becomes obvious that the price skyrocketed well before the
Russian invasion and it does not seem to be linked to production nor de-
mand but rather to other external factors. Investigating further and getting
hold of the documents published on the main cereal trade exchange mar-
kets, those in Paris and in Chicago, the reporters exposed the role of in-
vestment funds and of speculative maneuvers on the price of cereals. These
speculation have, as a primary effect, that of generating food insecurity for


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Crossing Boundaries                                                      119

millions of people. Of course, more data are needed to consolidate this
interpretation, but this investigation shows exactly what the power and role
of data journalism is, that of connecting and exposing data and facts of
public interest. These stories are then often better explained using charts
and visualisations, but that is not always the case, and sometimes charts can
also lie (see Cairo 2019). In conclusion, doing a story with data requires
profound respect for the data and the way they are collected and analysed.
This also may require an effort in spending some time asking the appropri-
ate questions to the database once it has been created or obtained. On the
contrary, inaccurately using the data can lead to a wrong story, or no story
at all.

Covering an Emergency in a Data Void

    Fast forward ten years, there has been a steady increase in the number
of civic activists and data journalists that bring data into the information
flow. In some cases, by collaborating with a local or national media. In oth-
ers by doing their work independently online, on different platforms, and
organised more or less informally. There are collaborative efforts, small
communities of data journalists helping each other, training courses. Many
people have learned how to manage a datasheet, perform the basic checks
and operations, and convert the data into meaningful graphs, charts, and
maps. In these over 10 years, we have gone from simple maps and graphs
showing the data in an interactive way so that the readers could select the
information they most needed or wanted to see, to very elaborate data vis-
ualizations that have become, in the worst cases, more focused on the aes-
thetic and decorative aspects than on the informative ones. Recent investi-
gations see a return of simple graphs that prove to be easier to access and
interpret. Data journalism units and teams have been organised in small
and big media outlets in many countries, and this practice has now been
integrated in the journalistic practice so that maps, charts, dashboards are
produced on quite a regular basis. In Europe many collaborative networks
have been working on data, such as the “European Data Journalism Net-
work” that includes news outlets from many European countries, such as
“Investigate Europe”: a collective effort publishing investigations, often
data-driven, in different languages. There have been massive global data
investigations such as the well-know “Panama papers” published by the
“International Consortium of Investigative Journalists”, that have been
awarded the Pulitzer Prize and have seen more than 400 journalists and
investigators from over 80 countries cooperating together. But also, more
regional efforts, such as for example that of “Grand theft Europe”, coor-
dinated by the “German outlet Correctiv”, where 63 journalists from 30
countries worked to expose the largest tax fraud in Europe perpetrated by
criminal organisations, or “Don’t miss the train”, coordinated by “Journal-
ism ++” and the “EDJNet”.


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    Contrary to most other countries, with few exceptions Italian media still
do not have a data team embedded in the newsroom. While the main in-
terest of data journalism is finding new stories, new angles, and new or
better explanations in the data by analysing them thoroughly, most Italian
media still use the data without questioning their quality. Data are turned
into something decorative, a chart here and a map there to catch the eye.
Even worst, data might be cherrypicked to support a theory, a thesis, or an
argument. Of course, there are numerous exceptions and truly thorough
investigations done mainly by freelancers, and published on Italian media
outlets. “Infodata”, the data journalism section of Il Sole 24 Ore, or Wired
Italy, are among the few ones to do data-driven journalism on a regular
basis.
    At the beginning of the COVID-19 pandemic, things changed dramat-
ically. Before then, any investigation by media usually used the data from
months or even years before. Most public institutions were (and are) still
very far from releasing the raw data as a flowing stream as soon as they
collect them. Hiding behind the idea that non-experts cannot access the
data because they would not know how to read them, institutions demand
time to clean, polish and harmonise the data before making them available
to the public. At the end of February 2020, after news broke about the first
Italian COVID-19 case, it soon became evident to many people from dif-
ferent professional environments that fresh data were badly needed. We
quickly moved into one restriction after another, without having a deep
understanding of what was happening and without the numbers to support
many decisions. In this respect, the only guiding North Star was the daily
bulletin of the Italian Civil Protection Department, released every evening
at 6 p.m., listing the number of total cases by province or region. We knew
nothing about the testing scale, the availability of tests, the registration of
new cases compared to the previous days, as well as the testing capability.
We knew nothing of the hospital capacity, how many beds could be given
to the patients suffering COVID-19. For days, and then weeks, the only
thing that majority of media did was publish the bulletin as it was, a .pdf
table, with no further information.

Journalists Filling the Void: Bottom-up Data Related Practice

    The first ones trying to have more data were the journalists working at
local media outlets in the most hit cities, Bergamo, Brescia, Varese, in the
region of Lombardy. Tomaso Bassani, deputy editor-in-chief of “Varese
News”; Isaia Invernizzi, at that time reporter at “Eco di Bergamo”; Cristina
Da Rold, freelance data journalist at “InfoData – Il Sole 24 Ore” and her
colleague Riccardo Saporiti, who is also writing for Wired Italy; the team
at “Il Giornale di Brescia”. These journalists, to mention a few of the most
active ones, immediately started looking at those data posing the same
questions discussed above: What does this data mean?; How were they


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Crossing Boundaries                                                          121

collected?; What is left out of the databases that are being shaped to monitor
the COVID-19 pandemic?. More and more journalists and activists in the
fields of open data and transparency have started asking public institutions
for better data and detailed information, so as to be able to compare data
coming from the different cities and regions. They also launched collective
efforts to get the data from the local health agencies. Around them and
together with them, different grassroots associations became vocal actors
in reclaiming more data: the association “OnData”, advocating for open
data for over a decade; the independent “Gimbe Foundation”, working on
evidence-based medicine; the collective “DataNinja” group on Facebook,
where many data journalists discuss daily the problems encountered in
working with data. For all of them, the main point was the impossibility to
inform their audiences and communities helpfully because the numbers of-
fered in the daily bulletin were meaningless. Tomaso Bassani (deputy edi-
tor-in-chief of the newspaper Varese News), for instance, started building
a longer-term series, creating his own database, collecting the data daily
and showing the trend in the mid and long term instead of offering just the
day-to-day numbers. On March 4th 2020, OnData opened a repository on
GitHub to collect, in a machine-readable format, the data published by the
Civil Protection in .pdf, so journalists and activists could at least build their
tables and do some analysis. At the same time, journalists campaigned to
ask the Ministry of Health and the Civil Protection for the release of all the
data in an open format, machine-readable, with less aggregated data
needed to perform local analysis. Finally, the Civil Protection adopted the
same attitude and created a GitHub repository to publish their daily data
in an open format and with an open license. Since then, thousands of peo-
ple have used it, showing its potential for scrutinizing the COVID-19 pan-
demic.
    In those early weeks, journalists were among the first ones to complain
about the lack of data. Those working in smaller towns were asked many
questions directly from their readers regarding the actual scale of the emer-
gency. They put up an enormous endeavor to find out more data using the
old method by calling public hospitals, the regional health agencies, and
the local health agencies, thus shaping databases from scratch. Their stories
highlight the complete absence of an institutional culture of transparency
for what concern the release and use of data. Some local health agencies or
governments understood the importance of disclosing the extent of the
emergency, even if only to gain support from the population. But many
preferred to remain silent, hiding behind the fact that they were dealing
with an emergency and did not have the time and resources to work on the
data. One of the major problems was that the Italian Regions are formally
in charge of locally addressing and regulating relevant issue health system.
And each region works differently. We have nineteen administrative re-
gions and two autonomous provinces, meaning 19 regional health systems
and 2 provincial ones. And not only the health systems are very diverse, but


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122                                                         Tecnoscienza – 13 (1)


the way data are collected differs, and the results are not always compara-
ble. Therefore, aggregating them in one table is meaningless, and it can
hardly lead to any conclusion.
    Besides journalists, researchers became highly interested in the data
too. Given the scale of the COVID-19 pandemic, this was a unique oppor-
tunity to make in-depth data analyses at many different levels: perfor-
mances of the health system; comparisons, trends, correlations to under-
stand if the spread of the virus could also be worsened by other factors,
such as environmental ones, among many others. Even local administrators
and authorities can benefit greatly from the access to the data with the aim
to assess the evolving situation and the measures to be enforced. An exam-
ple can elucidate this point. The Italian schools remained closed for
months, even in areas where the cases remained very low during the first
and the second wave. The absence of data regarding the impact of the
COVID-19 pandemic within the teaching system is probably still one of
the less acceptable outcomes of the entire story. Studies published after the
first lockdown, such as the “OpenPolis”8 series on educational poverty or
the report by “Save the children”9 on the same topic, showed a consistent
increase in learning and educational inequalities worsened by the complete
unpreparedness of the Italian school system to use digital schooling appro-
priately and equitably. Beyond any wishful thinking, there is no doubt that
our defeat to protect the most vulnerable groups of the youngest genera-
tions and to offer them a viable opportunity to attending school could have
been mitigated if we had known better how the virus was spreading in
schools. Reality is that even the institutions that should be on the forefront
of data collection seem to lack either suitable methodologies and standards
or a set of procedures in place to make those data promptly available to
researchers as well as to the public, as it should be granted within an open
democracy. Particularly, in the case of health data and of school-related
data, each Italian region is responsible for the monitoring of the situation,
for the data collection and for the communication of those data both to the
central state authorities, such as the Ministry of Health or that of Educa-
tion, and to the citizens through the websites.
    This fragmentation has been used to justify, for instance, the inexist-
ence of a complete database of all Italian public schools on the Ministry of
education website. Only in 2019, after more than seven years of public re-
quests, campaigns and investigations, those data have been finally made
available. Therefore, what happened during the COVID-19 pandemic is
not a surprise, but it is still unacceptable.
    The Civil Protection daily bulletin failed to provide an accurate picture
about the real death toll of the pandemic. Indeed, many mayors of local
villages and towns, particularly in the most hit places (such as the province
of Bergamo), highlights the low reliability and the scarce heuristic power
of the official statistics on the progress of the pandemic.



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Crossing Boundaries                                                                  123

    With no fresh mortality data available from ISTAT – traditionally re-
leased only every three months – it was difficult to make comparisons with
average mortality for specific geographic areas. That is why the local news-
paper “L’Eco di Bergamo” launched its own investigation. Supported by a
data science startup, the journalists designed a survey to collect the data
from the local administrations, one by one. The results were distressing. In
an online newspaper article published in L’Eco di Bergamo, the journalist
Isaia Invernizzi argues:

    What the official figures don’t say. They don’t say that in March 2020 more
    than 5.400 people have died in Bergamo province, 4.500 of which due to
    Coronavirus. Six times more than the previous year. Of only 2.060 of them,
    the «official» certified deaths caused by COVID-19 in the local hospitals
    (data as at yesterday), we know everything: age, gender, pre-existing condi-
    tions. We do not know anything about the other 2.500. Many of them are
    old people, who died at home or in assisted residential homes. In spite of
    the unmistakable symptoms, as recorded by physicians and relatives, they
    were never tested for the disease. On their death certificate you can just
    read: interstitial pneumonia10.

    In this case, the data making the difference were the missing ones. Be-
hind those data, Isaia Invernizzi and his colleagues found the most crucial
story and managed to give back to those neglected dead people the right
to be remembered.
    Another missing piece of information, not evident at first, was the im-
pact of COVID-19 on ordinary health care treatments. The journalist Ric-
cardo Saporiti – supported by a scholarship granted by SISSA – Scuola
Internazionale Superiore di Studi Avanzati and funded by the writer Paolo
Giordano – worked for the whole year on an investigation called Pazienti
dimenticati [Forgotten patients] (Saporiti 2021). His effort focused on the
screenings, diagnostical exams, oncological treatments or other surgeries
that have been cancelled or postponed due to the reorganisation of the
hospitals during the COVID-19 pandemic. These postponements resulted
from a political decision endorsed by the Ministry of Health in March
2020. According to Saporiti (2021):

    A decision, the judgment on which it is left to the reader, which has affected
    the national territory in a homogeneous way, in a context in which the pan-
    demic has hit the country in a way that is anything but homogeneous.

    Since data about the cancellation and/or rescheduling of ordinary care
treatment were not publicly accessible, Saporiti had to send 200 Freedom
of Information requests to Local Health Agencies and hospitals for obtain-
ing the concerned data: 57 ignored the request; 21 rejected it; 122 sent the
requested data, although not always in complete form. The request was


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124                                                                 Tecnoscienza – 13 (1)


aimed to access to data relating to surgical interventions, outpatient visits
and examinations and oncological services performed and postponed be-
tween March 1st and April 30th 2020.
   In the words of Saporiti (2021), the numbers offer only:

      a photograph, albeit partial, as detailed as possible of the impact that the
      pandemic containment policies have had on patients not affected by Sars-
      CoV-2. The effects of these postponements are still all to be assessed.

    The use of Freedom of Information requests and a thorough collection
of available data published in scientific journals and on a range of different
institutional websites were also the tools used by Davide Mancino for his
one year-long investigation, called The Big Wave (available both in Italian
and English11), on the health, economic and social impacts of the COVID-
19 pandemic in Italy.

Elusive Data and the Campaign to Free Them
    Difficulties relating to the collection and rapid release of data charac-
terized both the first (25th February 2020 – 31st May 2020) and second (10th
October 2020 − 31st December 2020) pandemic waves in Italy. During the
second wave, a new system for managing the pandemic was put in place,
where the different regions were assigned a colour, from yellow to red, de-
pending on 21 parameters, the most important one being the occupation
rate of hospital intensive care units. However, the whole set of 21 parame-
ters remained very complicated to be understood by concerned people.
There were weekly reports published by The Italian National Institute of
Health (ISS) and the Ministry of Health, but

      if the idea was that of sharing the choices with the citizens, the result is a
      very complex document, comprehensible only to professionals, between
      numbers that do not find any explanation and algorithms that refer to pre-
      vious publications (Da Rold 2020).

    Citizens can no longer be expected to trust the government and insti-
tutions simply without understanding the evaluations that assign a color to
each region corresponding to different levels of restrictions. According to
Da Rold (2020):

      Trust us is no longer sufficient. Many months of sacrifices have passed, and
      now citizens and all those who work with data demand to know the data
      behind the decisions and the risk assessment.




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Crossing Boundaries                                                                125

    November 2020 marked two critical steps on the data front. The first
was an agreement signed between the “Accademia dei Lincei” [The Lin-
cean Academy], whose President at the time was the Nobel Prize physicist
Giorgio Parisi, with the National Institute of Nuclear Physics (INFN) and
the ISS. The agreement implied that all data produced by the ISS would
be given to the Accademia dei Lincei to be made available through a new
platform. However, it was not clear which data were part of the bundle,
and it took many other months just to see the data. Researchers from other
institutions complained and criticised the decision, claiming that it would
have been more fruitful to make the data available to the entire scientific
community to multiply the research potential. There was also a growing
interest in these data outside the scientific community. This movement fi-
nally brought to the launch of the campaign Dati bene comune [Data as
Common Good], promoted by “ActionAid”, “Ondata” and “Transpar-
ency International”. The campaign was meant to foster a

    culture of open data among the Italian civil society and the public institu-
    tions and to ask the Italian government to publish open data on the man-
    agement of the COVID-19 pandemic12.

    By the end of 2021, the campaign had collected more than 50.000 sig-
natures and the support of over 275 organisations. Some results were ob-
tained, i.e., the change of license on publications and data available on the
websites of the ISS. Nevertheless, so much more needs to be done. The
campaign, not limited to COVID-19, is currently asking for the data in
compliance with the Recovery plan for Europe – NextGenerationEU and
the application of the due economic measures.

Lessons Learned, Looking Ahead
    In conclusion, the lessons to be learned are different and quite signifi-
cant. In the absence of preparedness, data are the most vital tool to support
decisions and try to assess risk. Data are the key ingredient to building a
common ground of trust and dialogue among institutions operating at dif-
ferent levels and between institutions and citizens. They are the only way
to promote accountability on all parts: to see if the political decisions are
followed through and if the results are coherent with the premises. Fur-
thermore, they serve the purpose of monitoring in real-time and adjusting
when things go wrong. Dealing with a pandemic, as much as with an eco-
nomic or an environmental crisis, requires the capacity to embrace uncer-
tainty and complexity simultaneously. It requires a sincere and transparent
attitude. The sense of frustration experienced by different stakeholders
and concerned groups of people hit by the consequences of the pandemic
could have found at least partial solace in the knowledge that the decisions



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126                                                                Tecnoscienza – 13 (1)


were taken upon precise solid data and not based on ineptitude or political
calculation.
    Finally, to answer those who think that data should only be handled by
a restricted circle of experts and not by lay people, many experts work in
different capacities and, when there is transparency, researchers, activists,
journalists can indeed, independently or collaboratively, confirm or dis-
pute calculations, interpretations and conclusions with better outcomes for
the entire community. When transparent and available to all, data cannot
easily be manipulated or misinterpreted or used to support wrong theories
and false conclusions. A democratic and responsible society is a society
where all have access to information the same way to make proper deci-
sions, be responsible citizens, and be an active part of the joint effort to
solve collective problems. Data per se are only one of the components of
information, but in a society that is so intensely data-driven data become a
very critical ingredient of a complete, transparent and honest information.
Without data there is no transparency, and without transparency democ-
racy is at risk.


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Cairo, A. (2019) How Charts Lie: Getting Smarter About Visual Information, New
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Correctiv (2019) Grand theft Europe, in https://correctiv.org/en/top-sto-
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Da Rold, C. (2020) I dati della discordia sulla pandemia in Italia, in “Le Science”,
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EDJNET, Don’t miss the train, in https://www.europeandatajournalism.eu/eng/In-
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Crossing Boundaries                                                                   127

Notes
     1
       How companies learn your secrets, New York Times, Feb. 16, 2012, available at:
https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html (retrieved June
3, 2022).
     2
       How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did, Forbes,
Feb. 16, 2012, available at: https://www.forbes.com/sites/kashmirhill/2012/02/16/how-
target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ (retrieved June 3,
2022).
     3
       The world after the Coronavirus, Financial Times, April 19, 2020, available at:
https://www.ft.com/content/19d90308-6858-11ea-a3c9-1fe6fedcca75 (retrieved June 3,
2022).
     4
       Anyone can do it. Data journalism is the new punk, in https://www.theguard-
ian.com/news/datablog/2012/may/24/data-journalism-punk (retrieved April 14, 2022).
     5
       See: https://www.propublica.org/series/dollars-for-docs (retrieved June 3, 2022)
     6
        See: https://www.propublica.org/article/editors-note-dollars-for-docs (retrieved
June 3, 2022).
     7
        See: https://www.lighthousereports.nl/investigation/the-hunger-profiteers/ (re-
trieved June 3, 2022).
     8
         See: https://www.savethechildren.it/cosa-facciamo/pubblicazioni/impatto-del-
coronavirus-sulla-poverta-educativa (retrieved May 20, 2022).
     9
       See: https://www.openpolis.it/poverta-educativa/ (retrieved May 20, 2022).
     10
        See: Coronavirus, the real death toll: 4500 victims in one month in the province of
Bergamo, in https://www.ecodibergamo.it/stories/bergamo-citta/coronavirus-the-real-
death-tool-4500-victims-in-one-month-in-the-province-of_1347414_11/              (retrieved
April 14, 2022).
     11
        See: https://www.grandeonda.it/en/ (retrieved May 20, 2022).
     12
        See: https://www.datibenecomune.it/advocacy/.




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