Introduction
Public Health Informatics is the systematic application of information and computer
sciences to public health practice, research, and learning, as it has been quoted
in the Medical Subject Heading information since 2013. A new section on this specific
topic has been integrated in the IMIA Yearbook in 2015. An overview of the area covered
by Public Health Informatics is presented in the Dixon et al. paper [[1]]. In 2016, the term Precision Public Health started to appear in several publications
[[2], [3]]. In parallel to precision medicine, it is concerned with providing the right intervention
to the right population at the right time [[2]]. The idea is to take advantage of the development of technologies including health
information technology to improve the assessment of population health and prevention
interventions and policies. Surveillance of epidemics and community health issues
are obvious examples that are pinpointed in publications [[2], [3]]. New technologies and big data should help accelerate the detection of epidemics
in a timely and accurate manner by accessing laboratory, satellite, and phone data,
tracking population movements, and integrating all data for making more precise estimations.
Modelling the risk of epidemics in well-defined areas could help in targeting interventions
for preventing epidemics. Hence, although public health informatics covers a large
spectrum of applications, the surveillance of epidemics using recently available web-based
and other tools, that could be referred to as precision epidemiology or digital epidemiology,
constitutes a recurrent topic in the literature. In addition, numerous papers have
been published and present methods to optimize and analyze internet data for various
infectious agents, as it is done with Google flu[1]. This “classical epidemiology of infectious disease” using new digital tools should
eventually be useful for public agencies and for the surveillance of other diseases.
But public health informatics is covering many other areas of research including communication.
Using newly available tools and especially web-based ones should be of benefit to
the public health informatics community.
Paper Selection
A comprehensive literature search was performed using two bibliographic databases,
Pubmed/Medline (from NCBI, National Center for Biotechnology Information), and Web
of Science® (from Thomson Reuters). The papers had to be journal articles, excluding
all other kinds of papers (such as comments, letters, case reports, etc.), written
in English, and having an abstract. The following keywords were selected for the query:
public health informatics or at least one of “public health, epidemiology, disease
outbreaks, registries, epidemiologic study characteristics, epidemiological monitoring,
population surveillance, public health surveillance, sentinel surveillance, public
health practice, organizational policy, planning techniques”, and at least one of
“medical records systems, computerized, computing methodologies, signal processing,
computer-assisted, mathematical computing, computer simulation, expert systems, fuzzy
logic, knowledge bases, neural networks (computer), medical informatics, medical informatics
computing, medical informatics applications, decision support techniques, community
networks, databases as Topic, information dissemination, health information systems”
or “techniques such as Fourier, cyclic analysis, neural networks, data sources as
Internet, social network, knowledge bases, computerized medical record system, and
telemedicine”.
The search was targeted at public health and epidemiology papers that involve computer
science or the massive amount of web-generated data. References addressing topics
of other sections of the Yearbook, such as those related to interoperability between
data providers or clinical research were excluded from our search. The study was performed
at the beginning of January 2017, covering the year 2016. A total of 807 references
were returned.
Articles were separately reviewed by the two section editors, and were first classified
into three categories: “keep”, “discard”, or “leave pending”. Then, the two lists
of references were merged, yielding 73 references that were retained by at least one
reviewer or classified as “leave pending” by both of them. The two section editors
jointly reviewed the 73 references and drafted a consensual list of 14 candidate best
papers. All pre-selected 14 papers were then peer-reviewed by editors and external
reviewers (at least four reviewers per paper). Three papers were finally selected
as best papers ([Table 1]). A content summary of these selected papers can be found in the appendix of this
synopsis. Lamy, et al., [[4]] describe the entire selection process.
Table 1
Best paper selection of articles for the IMIA Yearbook of Medical Informatics 2017
in the section ‘Public Health and Epidemiology Informatics’. The articles are listed
in alphabetical order of the first author’s surname.
|
References
|
Topic
|
-
Kite J, Foley BC, Grunseit AC, Freeman B. Please Like Me: Facebook and Public Health
Communication. PLoS One 2016;11(9).
|
Prevention
|
-
Sharpe JD, Hopkins RS, Cook RL, Striley CW. Evaluating Google, Twitter, and Wikipedia
as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative
Analysis. JMIR Public Health Surveill 2016 20;2(2).
|
Surveillance
|
-
Tran A, Trevennec C, Lutwama J, Sserugga J, Gély M, Pittiglio C, Pinto J, Chevalier
V. Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable
Areas for Rift Valley Fever Transmission in Eastern Africa. PLoS Negl Trop Dis 2016;10(9).
|
Surveillance
|
Outlook and Conclusion
A substantial number of short-listed papers were about digital surveillance of infectious
diseases. Several compared the sources of information among Google, Twitter, and Wikipedia
[[5]–[9]] for various infectious diseases such as Middle East Respiratory Syndrome Coronavirus
(MERS-CoV) in Korea, bubonic plague outbreak in Madagascar, chicken pox caused by
varicella zoster virus (VZV), and Influenza in United States. Of note, one work presented
an open-source system gathering tweets on symptoms associated with influenza-like
illness (ILI) [[8]], and another used Twitter for avian influenza risk surveillance [[9]]. The work performed by Sharpe et al., is very well done and particularly interesting because it adds an unconventional
source of information: the accesses to Wikipedia pages on Influenza [[5]]. In addition to the objective of detecting outbreaks as early as possible, the
same approaches could be applied to look at the impact of vaccination programs [[10]]. Besides web-based data, information extracted from cloud-based electronic health
record (EHR) databases can also be used for real-time surveillance of influenza-like
illnesses [[11]]. In the past, it was not possible to use medical records for tracking epidemics
because of the time lag due to the availability of data. Today, the easy access to
EHRs makes their use for real time surveillance possible.
Another example of digital surveillance is active surveillance using short message
service (SMS), or text messages, as described by Caceres et al., [[12]] in the context of the recent Ebola epidemics for daily reporting of zero cases.
They have shown that such surveillance was feasible and may be rapidly implemented
even in low resource countries.
Surveillance of infectious diseases is also performed through geographic information
systems (GIS). Tran et al., [[13]] present an adaptation of a geographic knowledge-based method [[14]] to identify areas for Rift Valley fever transmission in Eastern Africa. Allen et al., found a statistically significant correlation between influenza outbreaks using
the social media platform Twitter and techniques from GIS for the thirty most populated
cities in the United States during the 2013–2014 influenza season, compared with national,
regional, and local influenza outbreak reports [[15]]. A visual analytics GIS-based decision support system for early infectious diseases
outbreak detection was applied in Pakistan, using real-time streaming data from emergency
departments [[16]].
GISs are also used for chronic diseases as Laranjo et al., demonstrate for type 2 diabetes [[17]]. Akil et al., used GISs to show that geographic location besides socioeconomic status may contribute
to the high rates of Salmonella in Mississippi [[18]].
Besides surveillance, a paper about communication has been selected as one of this
year best paper [[19]]. This study aims at identifying the features of Facebook posts that are associated
with higher user engagement on Australian public health organizations’ Facebook pages.
Appendix: Content Summaries of Selected Best Papers for the 2017 IMIA Yearbook, Section
‘Public Health and Epidemiology Informatics‘
Kite J, Foley BC, Grunseit AC, Freeman B
Please Like Me: Facebook and Public Health Communication
PLoS One 2016;11(9)
This study aimed at reviewing the use of Facebook by Australian public health organisations
to identify features of posting activity that are associated with user engagement,
which authors define as likes, shares, or comments. Authors selected 20 eligible pages
relevant to selected public health issues through a systematic search and coded 360-days
of posts for each page. The health issues were: smoking, healthy diet, physical activity/sedentariness,
overweight/obesity, alcohol, sexual health, illicit drug use, skin cancer, aboriginal
health. Posts were coded by: post type (photo, text only, game, poll/ quiz, app, link,
event, or video), communication technique employed (informative, call-to-action, instructive,
positive emotive appeal, fear appeal, testimonial, humor), and use of marketing elements
(e.g., branding, use of mascots, etc.). Negative binomial regressions were used to
assess associations between post characteristics (post type, communication technique,
and marketing elements as categorical independent variables), and user engagement
(respectively, number of likes, shares, and comments as the outcome variables). The
results showed that video posts produced the greatest amount of user engagement, although
an analysis of a subset of the data suggested that this might be a reflection of the
Facebook algorithm, which governs what is and is not shown in user newsfeeds and appears
to prefer videos over other post types. Posts that featured a positive emotional appeal
or provided factual information attracted higher levels of user engagement, while
conventional marketing elements, such as sponsorships and the use of persons of authority,
generally discouraged user engagement, with the exception of posts that included a
celebrity or a sportsperson. Further research could assist in understanding whether
engagement with public health-related pages on Facebook actually leads to the achievement
of public health goals. This study has shown that in order to increase the chances
of achieving public health goals, content providers must encourage engagement and
adapt to the Facebook algorithm in order to maximize message exposure, while also
ensuring that the content is of high quality.
Sharpe JD, Hopkins RS, Cook RL, Striley CW
Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using
Bayesian Change Point Analysis: A Comparative Analysis
JMIR Public Health Surveill 2016 20;2(2)
Traditional influenza surveillance relies on the reports provided by health care providers
of influenza-like illness (ILI) syndromes. It primarily captures individuals who seek
medical care and misses those who do not interact with the health care system, and
this surveillance method is limited by relatively dated technology and by delays of
up to one to two weeks between the occurrence of the illness event and the dissemination
of surveillance information. Syndromic surveillance includes the use of novel data
sources such as emergency department records and prescription sales to enhance traditional
surveillance systems. Recently, nontraditional data sources, particularly Web-based,
have been applied to public health surveillance, as there is a growing number of people
who search, post, and tweet about their illnesses before seeking medical care. This
so coined ‘digital epidemiology’ can be less expensive, timelier, and can expand detection
by increasing the range of health events that can be detected. Existing research has
shown some promise of using data from Google, Twitter, and Wikipedia to complement
traditional surveillance for ILI, but none compared the three of them. The objective
of this study is to comparatively analyze Google Flu Trends , Twitter, and Wikipedia
by examining which best corresponds with Centers for Disease Control and Prevention
(CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI
data as a previous research found it to be least influenced by high media coverage
as compared with Google and Twitter. Publicly available, deidentified data were collected
from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012–2015 influenza
seasons. Bayesian change point analysis was used to detect seasonal changes, or change
points, in each of the data sources. Change points in Google, Twitter, and Wikipedia
that occurred during the exact week, the preceding week, or the week after the CDC’s
change points were compared with the CDC data as the gold standard. All analyses were
conducted using the R package “bcp” version 4.0.0 in RStudio. In addition, sensitivity
and positive predictive values (PPV) were calculated for Google Flu Trends, Twitter,
and Wikipedia. During the 2012–2015 influenza seasons, a high sensitivity of 92% and
a PPV of 85% were found for Google Flu Trends. A low sensitivity of 50% and a low
PPV of 43% were found for Twitter. Wikipedia had the lowest sensitivity of 33% and
lowest PPV of 40%. Limitations: 1) Bayesian change point analysis assumes time series
data are distributed normally, which may not be the case with public health surveillance
data, 2) for the analysis of Wikipedia views, only the “Influenza” article was used
for analysis, excluding other articles on influenza medications and influenza strains.
The authors assumed that all the views of the English-language Wikipedia “Influenza”
article were done by US users when some may have come from users in other English-speaking
countries where the influenza season is very different, 3) the Google Flu Trends data
were fitted to match CDC data, 4) data duplication could be an issue with each data
source used in this study, 5) Internet users are younger than the general U.S. population.
Of the three Web-based sources, Google had the best combination of sensitivity and
PPV in detecting Bayesian change points in influenza-related data streams. Findings
demonstrated that change points in Google Flu Trends, Twitter, and Wikipedia data
occasionally aligned well with change points captured in CDC ILI data, yet these sources
did not detect all changes in CDC data and should be further studied and developed.
Tran A, Trevennec C, Lutwama J, Sserugga J, Gély M, Pittiglio C, Pinto J, Chevalier
V
Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable
Areas for Rift Valley Fever Transmission in Eastern Africa
PLoS Negl Trop Dis 2016;10(9)
Rift Valley fever (RVF), a mosquito-borne disease affecting ruminants and humans,
is one of the most important viral zoonoses in Africa. The RVF virus (RVFV) is transmitted
from ruminant to ruminant by mosquitoes. Different climatic, environmental, and socio-economic
factors may impact the transmission of the virus. The objective of the present study
was to develop a geographic knowledge-based method to map the areas suitable for RVF
amplification and RVF spread in four East African countries, namely, Kenya, Tanzania,
Uganda, (three countries which have been historically affected by RVF), and Ethiopia
(where the disease has never been reported but which shares borders with infected
countries), and to assess the predictive accuracy of the model using livestock outbreak
data from Kenya and Tanzania. Risk factors and their relative importance regarding
RVF amplification and spread were identified from a literature review. The data were
imported into a geographic information system (GIS) and processed to produce standardized
spatial risk factor layers, namely a mosquito index (suitability for RVF mosquito
vectors), sheep density, goat density, cattle density, proximity to markets, road
density, railways density, proximity to water bodies, proximity to wildlife national
parks. A numerical weight was calculated for each risk factor using an analytical
hierarchy process. The corresponding geographic data were collected, standardized,
and combined based on a weighted linear combination to produce maps of the suitability
for RVF transmission. The accuracy of the resulting maps was assessed using RVF outbreak
locations in livestock reported in Kenya and Tanzania between 1998 and 2012 and the
ROC curve analysis. Results confirmed the capacity of the geographic information system-based
multi-criteria evaluation method to synthesize available scientific knowledge and
to accurately map (AUC = 0.786; 95% CI [0.730–0.842]) the spatial heterogeneity of
RVF suitability in East Africa. Some areas may be at-risk without having experienced
outbreaks in past years. The identification of these areas is essential for implementing
risk-based surveillance and reducing the impact of RVF human and animal outbreaks
in the coming years (until 2016, Uganda and Ethiopia remained free from outbreaks,
but these two countries are highly vulnerable to the disease). This approach provides
users with a straightforward update of the maps according to data availability and
contributes to the further development of scientific knowledge.
