4. Discussion
This study is a first attempt at reporting the uses of and perspectives
on animal information systems and risk analysis tools by professionals
from all around the world. Similar surveys have been conducted but
referring specifically to a single animal health information system
(i.e. analysing the WAHIS database only or giving general summary of
animal health platforms) (OIE, 2017; OIE, 2020; FAO, 2011). To date,
attitudes towards the use of these platforms have not been
discussed/analysed. However, this same point highlights a limitation of
this study only in that it gives only a general picture of the
constraints and attitudes. This is because there is too wide a
variability of i) animal health information systems (national and
international) as well as ii) risk analysis tools, to be able to provide
details. A larger study, specific to these systems or tools would be
required.
The response rate to this study could be considered as overestimated
given the fact that a snowball strategy was used. The anonymous form of
the survey forbids quantification of the number of experts who could
have been added to the survey using the network of the original set of
identified professional (snowball strategy) (Lupo et al., 2016).
Although a low response rate was achieved, this strategy provided a good
representation of professionals who used the systems as the survey was
specifically sent to focal points responsible for notifying animal
diseases. Moreover, the years of experience in the field and age of the
respondents were well represented as well as international location and
area of professional activities of the respondents. The sample
population carried out their professional activities from a broad range
of nations, which gives a good general picture of the uses of these
systems at an international level. The survey also captured the
international activities of the respondents, probably at different
levels or at international organizations. The responses were analysed as
a group (i.e. without dividing it into subgroups by professionals’
provenance) and did not compare relationship between issues or
restraints and regions (i.e. differences in term of animal health
institutionalisation or data accessibility). It is important to
highlight that the snowball strategy was done using specific focal, thus
there may have been differences in issues by regions. The sample would
have differed if the snowball strategy for example was used in an
institution of a university in the United States. The sample would have
represented more the United States universities, which may have arisen
other issues or restraints.
Most of the respondents in this survey were employed in governmental
institutions, research and universities with animal and public health
being the field of competency most represented. Academia and
governmental institutions were the places where animal health
information systems and risk analysis tools are most used, most likely
them having easier access to these tools and a level of understanding of
using the tools.
As for the utility of animal health information systems, it is important
to consider that an animal health information system is only as good as
the data it contains (OIE, 2020). This survey highlighted that the
degree of frequency of animal health systems use and the information
type found in them was not related. According to the gathered expert
opinion, prevention of disease occurrence is more important than
treatment. This study highlighted how professionals give important focus
to the type of information related to i) disease incursion and ii)
epidemiological characteristics of diseases (i.e. information on
cases/incidence, the evolution/spread of the disease). The latter
informs actions to limit the introduction of a disease into a country
free from the infection.
Data sources were more needed than used, showing there is a lower access
to data sources than required. However, there were no questions to know
if there were issues in having a knowledge (i.e. understanding the known
databases, mechanisms of extraction, obtaining information) or due to
limitations in technology.
Although, there is accessibility to certain data (e.g. by officially
demanding access to international organizations) respondents showed that
access to databases on public and animal health, access limitation are
still high (Bellet et al., 2012; EFSA, 2020; Humblet et al., 2016).
Additionally this access can be hindered by the limited knowledge of
computer science (translating PDF or HTLM format to EXCEL or text using
text mining) or heavy manual work required by the conversion as stated
by Humblet et al. (2016). Although raw tabulated data (e.g. EXCEL and
TEXT files) are more appropriate for risk assessment, these sources are
not often available and sometimes difficult to access (e.g. restricted
or paying access) (EFSA, 2020).
Preferred forms of data where Excel and PDF, but as stated by Humblet et
al. (2016), the main forms of data they found were PDF and HTLM files.
Although raw tabulated data (e.g. EXCEL and TEXT files) are more
appropriate for risk assessment, these sources are not often available
and sometimes difficult to access (e.g. restricted or paying access)
(Bellet et al. 2012).
Data availability and accessibility are crucial for epidemiological
analysis. Availability of the data, more than its accessibility, is the
main issue for experts and research scientists/assessors. The data
format plays a key role in the feasibility and rapidness of data
management and analysis. The HTML format allows easier management of
data than PDF files because it is more appropriate for data extraction;
PDF data are better adapted to consulting only (Bellet et al., 2012).
Additional training skills and collaborations though multidisciplinary
disciplines could help in overcoming the issues on accessibility to the
right form of data and also its availability.
Harmonization of animal health systems, in regard to data collection and
accessibility is encouraged, to provide useful and reliable data, both
at the national and the international levels for both animal and human
health.
Risk assessment plays an important role in in risk of introduction of
animal diseases. These are mostly carried out based on available data
and an animal health information system is only as good as the data it
contains (FAO, 2011; Humblet et al., 2016). However, most of the data
required to fully evaluate the extent of a health issue, are generally
not available or non-existent. Owing to the lack of relevant data and
the very short period of time usually allowed to assess animal health
risk on particular topics, many institutions use a qualitative risk
method for evaluating animal health risks or crises (Dufour et al.,
2011). For this reason, qualitative risk assessment is more in use as
reflected in the answers of the respondents. There was no difference
between the quantitative and semi-qualitative approach used, which is to
be expected, as the semi-qualitative approach could be considered as
quantitative.
The risk assessment question following the definition by Dufour et al.,
(2011) was divided into 3 categories: release assessment (estimation of
the likelihood of a hazard being introduced in a particular zone);
exposure assessment: estimation of the likelihood of susceptible humans
or animals being exposed to the hazards) and consequences assessment:
describing the results of the release and exposure of the hazard for
humans and animals (health and/or economic consequences). Most of the
respondents worked in those 3 categories which combined produce a risk
estimation (Dufour et al., 2011). This is consistent with the three most
important features they require of a risk analysis platform: a spread
assessment, pathways of introduction of a disease up to the border and a
quick risk assessment. Further, this corresponds to the fact that
scientific panels must often make their assessment over a very short
time period, from a couple of days to a few weeks (De Vos et al., 2019;
Sharma & Baldock, 1999). Moreover, most commonly, risk assessments are
developed to assess the risk for a single disease and risk introduction
pathway (De Vos et al., 2019).
As to the perceptions on using animal health platforms or risk analysis
tools, the experts survey showed that data accessibility is key, which
was also the main issue encountered by the respondents. Difficulties in
understanding the page could be due to the fact that a page was not
adapted to the respondents’ countries’ needs and there may have been a
language barrier.
The feature that they most looked for was again data accessibility and
availability and being able to extract this information and its results.
Comments stated that certain platforms do not allow for ease of data
downloading (e.g. the data had to be copied from the page and pasted in
Excel which is time consuming and prone to errors). The display of the
information and its extraction is the main limiting feature
As previously mentioned, experts’ location and the one from where they
carried out their professional activity both widely affected the
efficiency of their interaction with the platform. No assumption on the
reason of such a limitation per country could be made from this survey.
However, both limited internet connection and knowledge on numerical
technologies can be listed. It would benefit future research to compare
the functionality of different national health systems. Experts could be
asked what the constraints of their own national health systems are and
if they know how different it can be from other national systems. Also,
they could be asked if they think that standardisation of made efforts
can help to improve the effectiveness of such systems. “One Health” is
now a goal for the scientific community. However, non-standardised
efforts, surveillance systems and collected dataset are still highly
limiting.
The user’s satisfaction for using platforms remains high suggesting that
the platform choice is not only related to the required information. The
platform functionality per se also attracts the user. A focus on
increasing the platform functionalities and customising its interface
can therefore lead to a higher usage. Providing user friendliness
remains one of the most important points to be addressed. A suggestion
could be to add to the platform a good online training course.
Global animal health information systems were the most mentioned during
the survey. The main one was the World Animal Health Information System
(WAHIS) (OIE, 2019) which is the global animal health information system
operated by the OIE to handle disease notification and reports from
member Countries. It is mandatory for members to report disease events
from the notifiable diseases list to the OIE through this system. The
second one was EMPRES-i (FAO, 2014) developed by FAO and available for
public access followed by Pro-MED which is hosted by the International
Society for Infectious Disease and is also publicly available. National
animal health information systems were also mentioned, but not
specified. Although not on a global scale, these are as important as the
international ones. Sharma and Baldock, (1999) described them as:” the
complete system responsible for handling information about the health of
livestock on a country”. Therefore, if there were better access to
these animal health information systems, it would be very useful for
research professionals in non-government institutions who would not
normally see these data due to governmental restriction or privacy
settings on its access. In addition, animal health information systems
should also be used to handle information about non-production domestic
animals (such as pets) and wildlife. This question was not asked, but
for future works, it would be interesting to know if there is such data
and how accessible the information is. This situation though can only be
applied in countries where a good surveillance system is in place and
data is collected and collated into a computerised system. Not all
countries have such infrastructure, which makes professionals rely on
global systems, particularly WAHIS.
The preference of platform does not improve the level of satisfaction.
This could be because the choice of platform is mostly focused on the
information available on it, more than finding the platform extremely
good.