Write and submit a 2 page single spaced summary of a critical assessment of the article. Include purpose of the study, conclusions, limitations if any. Then write your review of the article and provid

Correspondence to Prof Arnaud Chiolero,Population Health Laboratory (#PopHealthLab), Department of Community Health,University of Fribourg, 1700Fribourg, Switzerland; [email protected]

Received 16 November 2019 Revised 15 January 2020Accepted 29 February 2020

©Author(s)(ortheiremployer(s)) 2020. Re-usepermitted under CC BY-NC.No commercial re-use. See rights and permissions. Published by BMJ.

To cite: Chiolero A, Buckeridge D. J Epidemiol Community Health2020; 74:612 –616.

Glossary for public health surveillance in the age of

data science

Arnaud Chiolero ,1,2,3,4 David Buckeridge 4

ABSTRACT

Public health surveillance is the ongoing systematic

collection, analysis and interpretation of data, closely

integrated with the timely dissemination of the resulting

information to those responsible for preventing and

controlling disease and injury. With the rapid

development of data science, encompassing big data and

arti ficial intelligence, and with the exponential growth of

accessible and highly heterogeneous health-related data,

from healthcare providers to user-generated online

content, the field of surveillance and health monitoring is

changing rapidly. It is, therefore, the right time for a short

glossary of key terms in public health surveillance, with an

emphasis on new data-science developments in the field.

PURPOSE OF THIS GLOSSARY

‘Only describe, don ’t explain ’, attributed to Ludwig

Wittgenstein

Public health surveillance is the ongoing systema-

tic collection, analysis and interpretation of data,

closely integrated with the timely dissemination of

the resulting information to those responsible for

preventing and controlling disease and injury. 1It is

a core element of public health practice, through

routine monitoring and reporting systems, and of

population health science —the science that informs

public health and prevention strategies —through

observational evidence. 2More specifically, surveil-

lance aims to provide health decision-makers with

timely and useful information to set priorities, to

identify the need for interventions and to evaluate

the effects of interventions. 3It is related to public

health research but differs in its purposes ( figure 1 ):

research aims to increase general knowledge while

surveillance aims to provide information for deci-

sion and action in public health. 1

With, on the one hand, the rapid development

of data science, encompassing big data and arti-

ficial intelligence (AI), and, on the other hand,

the exponential growth of accessible and highly

heterogeneous health-related data, from electro-

nic medical records used by healthcare providers

to user-generated online content, 4-6the field of

surveillance and health monitoring is changing

rapidly with a widening scope of application, an

increasing depth and new methods. It is,

therefore, the right time for a glossary for public

health surveillance and monitoring, with an

emphasis on new data-science developments. 7

We do not aim to cover the whole field of sur-

veillance but rather focus on how data science is

changing methods and concepts, going from data

generation and collection to information dissemi-

nation for decision-making ( figure 2 ).

ABERRATION DETECTION

In public health, aberration detection is the identifi-

cation of anomalous events or patterns in data, with

a clinical or public health potential relevance, that is,

statistical signals in surveillance data that may be of

epidemiological importance. 8A major challenge, of

growing importance with the use of highly hetero-

genous types of surveillance data, is to account for

random variability and measurement error, which

makes it difficult to tease out the ‘signal ’upon which

the decision to intervene is based from the ‘back-

ground ’noise. 9Traditionally, outbreak detection

and infectious disease surveillance have relied on

reports from clinicians and laboratories. At the

turn of the century, surveillance expanded to con-

sider prediagnostic or syndromic data, such as the

Figure 1 Health data and related information are

used, on one hand, to increase general knowledge,

which corresponds traditionally to a public health

research activity. On the other hand, they are also key

for guiding decisions and actions by stakeholders in

public health, which corresponds to public health sur-

veillance activities. The knowledge produced by

research is eventually used to improve public health

surveillance.

Figure 2 Steps in the data processing of public health surveillance, from data generation and collection to information

dissemination for decision-making.

612 Chiolero A, Buckeridge D. J Epidemiol Community Health 2020; 74:612 –616. doi:10.1136/jech-2018-211654

Glossary count of patients visiting an emergency room 5 (see also

Syndromic surveillance ). With the growth in volume and variety

of accessible surveillance data, aberration detection methods

have evolved from the analysis of time series of case counts to

the complex modelling of individual-level surveillance cases with

covariates drawn from multiple sources 5,8; it is also applied

beyond the field of human infectious diseases.

BIG DATA AND DATA SCIENCE

Big data refers to the massive amount of data that is more and more

easily accessible through the digitalisation of all aspects of health,

healthcare and related areas. 10 It is characterised by its variety,

volume and velocity —the ‘3Vs ’.11 Multiple sources of data have

become usable for public health su rveillance, for example, mobile

phones, online searches, social media, credit card transactions,

wearable and ambient sensors, electronic health records (EHRs),

medico-administrative records and pharmacy sales. While public

health monitoring relies traditionally on well-defined and high-

quality data, effective use of big data for surveillance requires new

analytical methods such as data mining and data visualisation; data

science is becoming mainstream in public health, integrating knowl-

edge and skills from informatics and biostatistics. One major chal-

lenge in the analysis of big data is to account for the low quality, the

poor data consistency across setting and time and the lack of meta-

data (see also Source population and selectivity bias ). The question-

able ‘veracity ’(the fourth ‘V’) of big data refers actually to its poor

quality and high noise. Of critical importance is to go from big to

‘ smart ’data, that is, data that can be transformed into information.

While the development of big data and related data-science meth-

ods opens the way to data-informed or data-driven healthcare and

public health, 12it also raises major concerns about privacy protec-

tion (see also Ethics of public health surveillance and privacy protec-

tion ). At the policy level, the use of big data for surveillance raises

issues of access and benefit sharin g, accountability and transparency

and quality and safety. 13,14

DATA, INFORMATION, KNOWLEDGE AND WISDOM PYRAMID

The data, information, knowledge and wisdom (DIKW) pyramid is

a framework to help understand the hierarchal relationships from

data to wisdom. 15It has gained importance in public health mon-

itoring, with the growing use of all types of data for surveillance

activities, notably to highlight that data do not speak by itself and

need to be transformed to become information, for example, in the

form of health indicators, 16,17with the latter having to be contex-

tualised to become knowledge and eventually wisdom, for example,

to inform health policy decisions 18 (see also figure 2 ). The DIKW

pyramid also highlights that surveillance is not the mere collection

and analysis of data, but a complex multilayer activity at the core of

public health decision-making process, allowing evidence-informed

policy-making 19(see also Evidence based and data-informed public

health ). Recently, it has been proposed to review this pyramid, by

deemphasising the notion of wisdom and by adding ‘evidence ’

between information and knowledge (DIEK) 20; evidence emerges

through the comparison of information and is used to build action-

able knowledge for public health.

DATA MINING

The discovery of patterns in large data sets by drawing on a range of

methods from engineering, computer science and statistics is called

data mining (see also Big data and data science ). These methods are

applied in an automated or semiautomated manner, usually with no

a priori specification of the pattern to be detected. In a health mon-

itoring context, some methods used for detecting aberrations or out-

breaks can be considered data mining methods 5(see also Aberration

detection ). Mining EHRs aims to gather information from unstruc-

tured narrative data 21(see also Electronic medical record ).

DATA VISUALISATION

Data visualisation has always been an important tool of public health

surveillance. However, with the growth in available data and the

improvement in statistical tools, da ta exploration through visualisa-

tion has gained importance for surveillance and monitoring activities.

The field has evolved with contributions of computer science merging

scientific visualisation, information visualisation and visual analytics,

making visualisation an important part of surveillance data

analyses 22; it is a powerful tool to understand complex multilayer

data, which are not easily captured by simple indicators. It has a major

impact on how temporal and spatial analyses are conducted and

reported. The production of continuously updated maps and atlas

of diseases and risk factors has become possible by leveraging big data,

thereby strengthening the surveillance of numerous conditions, nota-

bly of infectious diseases. 23 Visualisation of healthcare outputs

through maps has also become a standard tool for health services

research aiming to address unwarranted variation in healthcare. 24

Data visualisation is also gaining importance for displaying complex

longitudinal data from EHRs 25(see also Electronic medical record ).

One major change is the possibility of tailoring visualisation surveil-

lance output to users ’needs through interactive data visualisation. 22

ETHICS OF PUBLIC HEALTH SURVEILLANCE AND PRIVACY

PROTECTION

In 2017, the WHO issued international ethics guidelines on public

health surveillance. 26,27Surveillance activities raise ethical issues due

to data collection methods, notably when the identity of individuals

is recorded. More broadly, it is necessary to account for the balance

between the protection of privacy and the benefits at a population

level. With the development of surveillance based on the analyses of

medicoadministrative, 6social media or geospatial mobile phone

data, and with growing linkage possi bilities, individual privacy pro-

tection has become a major concern. The increasing sophistication

and broadening possibilities for data linkage put at risk data manage-

ment transparency and accountability. 13,14The new European Union

General Data Protection Regulation (GDPR) is the current legal

framework for the collection of personal data in European

countries 18; it aims notably to give citizens more control over their

own data and to harmonise data protection across Europe. The

broad principles of GDPR include having a legitimate basis for data

collection, purpose limitation, transparency, as much privacy and

data minimisation as possible and accountability for all data use. 18

ELECTRONIC MEDICAL (EMR) OR HEALTH RECORD (EHR) AND

PERSONAL HEALTH RECORD (PHR)

The increasing adoption of electronic records to manage medical

and health data creates new opportunities for public health

monitoring. 28 An electronic medical record (EMR) is used to

integrate, manage and analyse patient data collected in a clinical

context, often within one clinic or institution. An EHR is

intended to have a broader scope, encompassing all health-

related data over the life course. A related concept is a personal

health record (PHR), which is an EHR controlled by a patient. In

all cases, these records are useful for population monitoring to

the extent that they record concepts and health events in

a consistent and unambiguous manner (eg, through the use of

data standards and ontology 29), which enables different systems

to exchange data, or interoperate 30 (See also Interoperability ).

Major challenges remain such as how to define the denominators

for events extracted from EHR. 31

Chiolero A, Buckeridge D. J Epidemiol Community Health 2020; 74:612 –616. doi:10.1136/jech-2018-211654 613

Glossary EVIDENCE-BASED AND DATA-INFORMED PUBLIC HEALTH

At the crossroad between population health science 2and applied

public health research, public health surveillance is a core ele-

ment of evidence-based public health ( figure 3 ).32Indeed, popu-

lation assessment, production of indicators and reports and

evaluations are typical activities and outcomes of public health

surveillance. Monitoring the literature is also an integral part of

surveillance, for example, to allow comparison and benchmark-

ing or to challenge measurement and definition of indicators. In

the age of data science, the management of surveillance data and

information has gained importance in the evidence-based public

heath cycle, with the policy-making process becoming not only

evidence based but also data informed if not data driven.

Evidence-based public health should also guide how surveillance

system is designed 33(see also Population health record ).

FORECASTING

Data collected through surveillance are often analysed to identify

important changes in population health. Inference about change

requires an estimate of the expected state of population health,

which is obtained through forecasting, or predicting future popu-

lation health status using data collected in the past. Many meth-

ods are available for forecasting, from a simple average of

historical values to multivariate time-series methods. 34

Forecasting of expected values is a critical step in routine surveil-

lance for outbreaks and is also used to estimate the future burden

from chronic diseases and other prevalent conditions. The accu-

racy of a forecast usually decreases as the length of the horizon

increases and is usually evaluated by comparing forecasts to

actual values once data become available. Because the perfor-

mance of predictive models depends on the quality and stability

(across eg, time and space) of data, forecasting methods must

adapt to the relatively low quality and selectivity of big data (see

also Source population and selectivity bias ).

INTEROPERABILITY

Increasingly, public health surveillance draws data from a wide

range of sources and makes information available to many stake-

holders. This acquisition of data and dissemination of information

has traditionally been a manual process, but as volumes continue

to grow, automation of data and information exchange becomes

necessary. Such automation requires the definition and adoption

of standards that indicate clearly how information systems should

interact with one another or interoperate. The term semantic

interoperability is used to define the ability for one information

system to receive data from another system and to reliably process

this data to produce information. 35 For e xa mp le, m es sa gin g

standards such as Health Level Seven and Fast healthcare

Interoperability Resource allow public health surveillance systems

to interoperate with laboratory systems and information exchange

standards such as Statistical Data and Metadata Exchange allow

public health systems to interoperate with web-based systems to

automate the dissemination of population-based indicators.

MACHINE LEARNING, ARTIFICIAL INTELLIGENCE

AI can be defined in terms of human intelligence, such that any

machine that can act like a human is displaying AI. 36The ability of

a machine to perform any intellectual task is called Artificial

General Intelligence or Strong AI and is thought to require

a range of skills, such as natural la nguage processing, knowledge

representation, automated reasoning, machine learning, computer

vision and robotics. Each of these skills is the subject of considerable

research in AI, employing different connectionist (ie, data driven) or

symbolic (ie, using logic and symbols) approaches. Recent algorith-

mic advances have enabled profound gains in the performance of

neural networks for machine learning. 37In epidemiology and pub-

lic health surveillance, machine learning is used as one tool to

execute causal inference analysis, diagnosis and prognosis studies,

genome-wide association studies, geospatial applications or

forecasting. 38Such machine learning methods also have the poten-

tial to advance aberration detection. 5

POPULATION HEALTH RECORD

The International Organization for Standardization (ISO) has

defined a population health record (PopHR) as a system analogous

to an EHR but containing aggregated and usually deidentified data

for public health and other epidemiological purposes. 39 The con-

cept of the PopHR was subsequently developed further, noting that

its primary purpose is to support efficient and effective public health

practice, that it should be based on an explicit population health

framework and that it should make available indicators that docu-

ment the current status and influences of the health of a defined

population. 40While PopHR systems have yet to be adopted widely

in public health practice, researchers have developed and imple-

mented demonstration systems, 33 along with formal ontologies to

support information integration in a PopHR. 41

PRECISION PUBLIC HEALTH

Precision public health is inspired by precision medicine with the

idea that a better use of all types of data, encompassing geogra-

phy, physical and sociodemographic characteristics, as well as

health behaviours and biomarkers, at a local or community

scale, would help design specific public health policy for a given

population, and be more effective than general policy. 42,43Some

have argued that the term is problematic, causing confusion with

the precision medicine movement and focusing attention on

individual diagnosis and treatment. 44,45 Others have suggested

that precision public health merely rebrands modern public

health surveillance activities and adds little value. 45

SECONDARY USE OF DATA

Surveillance activities are relying increasingly on the use of data

not specifically collected for that purpose, including data a priori

not related to health. 46,47The secondary use of data is not new in

surveillance, but it has grown in importance and depth, leading to

a paradigm shift in surveillance. Indeed, the classical approach is

(1) to define or choose the health problem for which surveillance

is necessary, (2) to define and collect the data needed and (3) to

analyse data to address your problem. Along this approach,

‘ designed data ’specifically tailored to address surveillance goals

Figure 3 Public health surveillance is a central element of evidence-

based public health. Inspired by Brownson et al 2009. 32

614 Chiolero A, Buckeridge D. J Epidemiol Community Health 2020; 74:612 –616. doi:10.1136/jech-2018-211654

Glossary are used. The more contemporary data-driven approach is (1) to

collect data from multiple source without knowing a priori what

will be done with this data and (2) to analyse data to see if they

could help solve surveillance problems. With this approach,

‘ organic data ’not specifically tailored for surveillance are used

(see also Big data ).48 Designed and organic data have specific

advantages and disadvantages. On the one hand, validity and

reliability of designed data are often documented. Further,

designed data collection processes are defined and the ethical

and legal frameworks for collection are explicit; the lack of

such clear frameworks for organic data is a major current issue

(see also Ethics of public health surveillance and privacy protec-

tion ). On the other hand, resources needed to collect designed

data are larger than for organic data. Also, the reporting delay can

be shorter with organic data compared with designed data.

However, the source population of organic data can be tricky to

identify (see also Source population and selectivity bias ).31

SOURCE POPULATION AND SELECTIVITY BIAS

Public health surveillance aims to gather information on the

health-related characteristics of a specific population, which

mostoftenisagroupofpeoplelivinginagivenlocation.

More broadly, a population is a group of people sharing

a characteristic, such as a medical condition or treated in

specific healthcare facilities. 2,49 With some types of big data,

one difficulty is to define the source population from which

this data have emerged; completeness or representativeness

of the supposedly source population cannot be ensured due

to the non-probabilistic character of this data, resulting

from the selectivity of people from which data are

recorded. 50,51 Routinely collected data are often event

basedratherthanpopulationbased,withnoinformation

on the individuals who did not experience the event, 46 and

the link between the event and the individual can be diffi-

cult to establish. Further, the source population can change

very rapidly, for example, for sales, online and any other

user-generated data, and in an unpredictable manner. As

a result, denominators cannot be easily computed, and infer-

ence beyond the study population is problematic, due to

a selectivity bias (see also Secondary use of data ).

Selectivity bias is a term used to highlight the challenge of

identifying and defining the source population per se of big

data; it differs from selectio n bias which refers usually to

a sampling issue, making the d ata used for the analysis

problematic for inference to the source or target population.

SURVEILLANCE BIAS

Many conditions and health-related events under surveillance are

sensitive to the modality and intensity of detection activities, for

example, several types of cancer, thromboembolism or postopera-

tive infections. 52,53 Surveillance bias occurs when such conditions

are sought with differential intensity across populations or over

time, or according to care setting and patient characteristics. 54,55

As a result, the difference in the frequency (incidence, prevalent) of

the condition may not reflect a change in the risk of this condition,

but instead a difference in the frequency of detection. For instance,

between-hospital differences in th e frequency of thromboembolism

following hip surgery can reflect between-hospital differences in

postsurgery screening activities (large number of cases identified in

hospitals with intense screening activities vs low number in other

hospitals), rather than any difference in the quality of care. 55

A related concept is the ‘streetlight effect ’which occurs when

surveillance activities are not c oncentrated on what matters, but

on what is measurable, even if it is not relevant.

SYNDROMIC SURVEILLANCE

Case definitions based on syndromes can enhance the sensitivity

and timeliness of surveillance. Around the turn of the millen-

nium, surveillance of syndromes was implemented on a large

scale by applying automated algorithms to clinical data. 56 The

automated detection of syndromes in clinical data and by auto-

mated statistical analysis to detect aberrations in the frequency of

syndromes are defining characteristics of syndromic

surveillance 57 (see also Aberration detection ). Although an early

motivation for syndromic surveillance was rapid outbreak detec-

tion, the use of non-specific, prediagnostic data can make it

challenging to detect a signal quickly with an acceptable rate of

false alerts. 58Nonetheless, due to their potential to provide real-

time information about population health, syndromic surveil-

lance systems routinely contribute to situational awareness in

many public health systems and are often deployed for mass

gathering events.

CONCLUSION

Data-science and newly accessible data are driving innovation in

methods for public health surveillance and monitoring, offering

new opportunities. However, disappointment is also to be

expected due to the challenge in extracting value from healthcare

data which often lack consistent structure and clear meaning. 59

Fostering the ability of primary data providers to improve the

structure and semantics of the data they collect can make it easier

to obtain meaningful information and, eventually, knowledge

from these data. Stronger semantic interoperability between

health information systems 35and more consistent data structure

will be essential to help moving from big to smart data, that is,

data that can be used to produce information, and to transform

health systems which are currently data rich but information

poor into systems which are data and information rich. 60

Finally, while many resources are directed towards data collec-

tion and processing, the resources and expertise needed to make

these data truly useful for surveillance, namely background

knowledge on public health and on the processes generating the

data, 6are critical more than ever in an age of data science;

knowledge brokers are needed to bridge data science, health

monitoring and public health.

Contributors AC and BD both drafted the paper and reviewed it before submission.

Funding The authors have not declared a speci fic grant for this research from any funding agency in the public, commercial or not-for-pro fit sectors.

Competing interests None declared.

Patient consent for publication Not applicable.

Provenance and peer review Commissioned; externally peer reviewed.

Open access This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properlycited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

ORCID iD Arnaud Chiolero http://orcid.org/0000-0002-5544-8510

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