Millicent
Ele
An
Environmental and Public Health Law Consultant;
Lecturer,
Faculty of Law, University of Nigeria;
Doctoral Candidate in Law, University of Aberdeen, Scotland, UK.
This
is an abridged version of the full article published in 2015:
Millicent
Ele “The International Health Regulations Mandate on the Development and
Maintenance of Core Surveillance Systems: Prospects and Challenges of Meeting
the 2016 Deadline in Africa” (2015) 34(4) Journal of Medicine and Law 569-594
Disease
surveillance is primary and indispensable towards effective prevention and
control of infectious diseases. It provides needed information for prompt
detection of outbreaks and the initiation of response actions. An effective
disease surveillance system must be able to produce valid, representative data
in a timely manner; and these would be promptly analysed for early detection of
outbreaks. The International Health Regulations (IHR 2005 or the Regulations)
came into force on 15th June 2007 and mandates all Member States to develop,
strengthen, and maintain core capacity for disease surveillance and response at
most by June 2016. With no specific provision for funding, it is a challenge
for low-resourced African countries (including Nigeria) to meet this deadline.
It is equally a global concern because infectious diseases know no boundaries
and can spread to other nations and continents in a matter of hours and days.
Therefore, developing an effective surveillance system for African countries is
an imperative.
Traditional
surveillance that relies on exact diagnosis and laboratory confirmation is
usually very slow. This is because data collection and reporting take a long
winding route from the time the patient gets to the hospital and the doctor
orders the laboratory test to the time the test result comes back and is
reported if reportable. This journey could take days or even weeks especially
in Africa where the bulk of the population lives in rural areas with few
hospitals and inefficient transportation systems. Time is usually of the
essence in infectious disease detection and control and a day or two could mean
a lifetime in controlling the spread of such diseases. Additionally, even where
a laboratory test was ordered and the results come back indicating a reportable
disease, the doctor may fail to log in the report either because he is not
aware of any obligation to do so or because the infrastructure to enable such
reporting is not in existence.
Syndromic
surveillance is a surveillance technique that monitors, and analyses routinely
collected automated disease-indicator data for early signs of outbreak of
diseases. These data sources may include for instance, pharmacy records of
sales of medications, ambulance dispatch records, outpatient records, records
of hospital emergency departments, requests for laboratory tests in public and
private hospitals and health centres, etc., captured electronically, and
capable of real-time analysis for early signs of disease outbreaks in a
community. Syndromic surveillance could also be done using appropriate internet
applications to mine the web, gather, and sort through disease indicator data
and outbreak information in real-time, in order to detect possible outbreaks of
disease before the actual identification of the pathogenic organism. Under this
category are the use of initiatives like the Global Public Health Intelligence
Network (GPHIN), HealthMap, and EpiSPIDER, etc. However, due to the diverse
nature, sources, and huge volumes of information collected through these
techniques, it is difficult to classify and analyse the data without the
possibility of losing vital materials. Therefore, improved algorithm for data
classification and analysis is needed. Alternatively, a more targeted and
well-structured form of data collection needs to be adopted so as to simplify,
quicken and possibly standardise the analytical process. This could be achieved
by the use of modern technologies like mobile phones and mobile devices
equipped with appropriate applications (apps), and standard forms for gathering
and reporting health events and outbreaks in a community. A good demonstration
of such mobile phone app was the Open Data Kit (ODK) Collect application used
for data collection and reporting for contact tracing in Nigeria during the
Ebola outbreak response in 2014. So, rather than wait for laboratory confirmation
of diseases before reporting them or collect desperate information via the web,
members of the community will gather and supply outbreak information in a
pre-determined format with their mobile phones and devices.
A
new community-based syndromic surveillance system called the “Call-in system
of syndromic surveillance” is built on the above idea. Under this system,
data collection is outsourced to a distributed group in the community and the
data collected are reported to a centre which could be a hospital, health centre
or a designated section of the ministry of health. These data are tallied and analysed
for early detection of outbreaks like Lassa fever Cholera, Zika virus etc. This
system is event-based and as such is supported by the World Health Organisation
(WHO), and the International Health Regulations (2005). The Regulations
embraced a comprehensive syndrome-oriented approach that would take care of
both known and unknown infectious diseases. It defined ‘surveillance’ as the
systematic ongoing collection, collation and analysis of data for public health
purposes and the timely dissemination of public health information for
assessment and public health response. This operational definition can be
actualised where there is a system set up for continuous collection and
collation of data, and analysis of the same in real time; where the result of
such data analysis is promptly disseminated to relevant agencies and
institutions for rapid response and control actions.
For
well over a decade now and mostly in developed countries, logs and records of
automated disease-indicators like records of hospital emergency departments,
nurse advice lines, data from poison control centres, school/personnel
absenteeism, ambulance dispatch records, pharmacy records of sales of
medications, etc., have been used for syndromic surveillance. For instance, an
unusual or unseasonable spike in sale of certain medications at the pharmacy will
be an indication of an outbreak of the disease the medication is meant to cure
or control. In 2003 following the severe acute respiratory syndrome (SARS)
epidemic, it was discovered that two months before the outbreak, there was a
spike in the sale of an anti-viral herbal medication used in treating flu-like
symptoms in the Guangdong province of China from where SARS originated. If this
pharmacy sale had been tracked for purposes of syndromic surveillance, perhaps,
SARS would have been detected earlier and the epidemic prevented. In the same
vein, spikes in school/personnel absenteeism, records of hospital emergency
room on certain diseases, or an unusually large influx of calls on nurse advise
lines on the same or similar symptom/syndrome have been used as outbreak
indicators. Unfortunately, in developing countries like Nigeria, most of these
data sources are either not available, or not usually in electronic forms. They
are commonly kept manually in notebooks with attendant laxity and
inconsistencies due to human error. Besides, such manual information can
neither be transmitted automatically nor aggregated in real-time for outbreak
detection. This is a handicap for most developing countries which may want to
join the trend in using automated data sources for syndromic surveillance.
However,
today, there is extensive use of mobile phones and applications even in
developing countries. There is equally a broad agreement in literature and
practice that these mobile devices could be used to report incidents of disease
outbreaks, collect health related data for surveillance purposes, and reach patients
in remote locations in order to offer medical advice and diagnosis. This
practice is becoming increasingly popular in Africa especially because of its
advantage of taking healthcare services to the hard-to-reach areas and getting
health related information from there, for surveillance and planning purposes.
For instance, the government of Kenya has introduced and piloted several mobile
devices for health services and disease surveillance. Also, in Rwanda, in 2013,
an electronic integrated disease surveillance and response (eIDSR) system was
built and launched in all district hospitals and health centres in the country.
This system is used to report potential outbreaks of diseases like Cholera,
Ebola, Lassa fever, etc., as well as help health workers contain the spread of
diseases. With this eIDSR, the users can collect timely information from the
field via the web and mobile phones and electronically transmit them to all
health facilities at the same time. This has reportedly helped to improve timeliness,
accuracy and completeness of reporting, and helped officials detect outbreaks
rapidly, investigate them and mount a quick response within the country.
Also,
some African countries have used select members of the community for delivering
basic health services. For instance, in Rwanda, community health workers (CHWs)
are simply select members of the community assigned to designated geographic
area for basic health services delivery. There is also the “Nyateros of Gambia”
(Friends of the eye) who because of the prevalence of eye disease, Trachoma (or
Ocular Chlamydia trachomatis infection), are employed to deliver basic eye care
services in their community. In the Call-in system of syndromic surveillance,
select members of the community are trained for surveillance purposes and early
detection of infectious diseases. Implementation of this in Nigeria could lead
to early detection, prompt reporting, as well as early investigation, and rapid
response to outbreaks like Lassa fever.
During
the 2014 Ebola outbreak in West Africa (mostly in Liberia, Sierra Leone, Guinea
and Nigeria), a text message was sent on 17th August 2014, by the Federal
Ministry of Health, Nigeria, through MTN to all subscribers, asking them to
help prevent the spread of Ebola by reporting any suspected case. The message
gave the phone number and the government email address to which these messages
would be sent. Though such reporting is clearly unscientific, one must agree
that when many of such reports flow in from a particular area or village, the
Ministry is bound to investigate further to determine whether an outbreak is
actually occurring.
The
Call-in system of syndromic surveillance presents a more
structured arrangement in the sense that each select member of the public is
assigned specific geographic area in their community to cover and will be
instructed to collect and send outbreak information to a designated health
centre or hospital. They would be taught what to look for and a standardised
way of reporting it. The advantages of engaging the community in this way is
that they would help to quickly point out where outbreak is likely occurring
for prompt investigation and urgent laboratory confirmations. It is therefore a
good supplement to traditional surveillance for early detection of outbreaks;
faster control and containment of infectious diseases.
Like
all syndromic surveillance, the Call-in system is intended to alert public
health officials of possible outbreaks leading to further investigation.
Generally, if incoming reports show an increase/unseasonable spike in a
particular syndromic group; a manifestation of an unknown syndrome/disease; or
an event that is either hazardous to health or could create a potential for
disease; then a response is triggered. This response, depending on the kind of
disease or event could vary from mere preliminary investigation, to emergency
control measures if the disease is highly infectious.
Under
the Call-in system, preliminary investigation starts with call-backs to
participants, health centres/hospitals from where the reports were originally
submitted. This is a way to also cross-validate the information and ensure that
it is not a fluke. Depending on the outcome of this preliminary investigation
(e.g., if the suspicion of outbreak is sustained), detailed reporting including
location of the victims/source population, age, gender, occupation, date and
time of the onset of symptom, and its severity etc., may be required so that
the sick could easily be tracked for control and treatment protocols.
Some
of the challenges to this system include: Poor understanding of how to operate
the apps; difficulties in actual disease detection through a syndromic approach
and understanding case definitions; also training the participants may not
always produce the assurance that they will know how to use the disease
surveillance applications or correctly recognise reportable disease syndromes;
there may be problems of how to appropriately fashion incentives to
participants in order to improve the willingness to participate and sustain
enthusiasm in reporting; analysis of the data may pose a problem at the
implementation level due to a dearth of equipment for instant analysis,
qualified manpower and enabling environment like steady power supply and other
ancillary and supporting technology.
The
challenges anticipated to arise in the operation of the Call-in system much
like any other smart phone-based system of data collection and analysis could
be cured with intensive training and retraining of the participants. This
systematic training will improve the quality and accuracy of the system with
respect to outbreak detection and reporting. To improve the willingness to
participate and sustain enthusiasm in reporting, some incentives in form of
bonus airtime minutes, free medical screening and some monetary stipends should
be given to the participants. However, full time CHWs have to be paid. In
Rwanda, performance-based financing (PBF) is used for this purpose and may be
adopted by Nigeria with necessary modifications. Availability of funding both
for equipment purchase and maintenance, training of the workforce and payment
of remuneration, etc., is a big issue. This may be improved by intense advocacy
in support of the system so that the government would increase funds allocation
for healthcare and disease surveillance. Appeal for funds should also be made
to international organisations, and corporations.
In
conclusion, syndromic surveillance for infectious diseases outbreak alert and
response must be taken seriously if we must remain a step ahead of any
pandemic. Nigeria should invest in the training of CHWs and health
professionals to act as health vigilant eyes and ears for reportable
diseases/syndromes and health events in their communities. Also, Nigeria needs
sustained capacity building in health personnel and services in order to make
healthcare accessible to the vast majority of the people and supply the
workforce for surveillance activities.
The
Call-in system’s community-sourcing paradigm will help to energise community
participation, improve public vigilance and situational awareness leading to
early detection of outbreaks like Lassa fever in the community. It will also be
an effective way for Nigeria and other low-resourced African countries to meet
the IHR’s deadline for the development and maintenance of core surveillance
systems. The adoption of this system or at least a variant of it is therefore,
recommended for Nigeria.
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