MALARIA ELIMINATION IN ZANZIBAR - Soper Strategies

DATA COLLECTION METHODS
A robust surveillance system on Zanzibar will need to incorporate
both passive and active approaches to detecting and reporting
malaria cases. Passive and active case detection have been defined
as follows (Teutsch and Churchill, 2000):
�� Passive case detection–system where data are routinely
received by a central health authority based on a set of rules/
laws that require a healthcare provider or health facility to
report certain diseases or conditions on an ongoing basis and
at specific intervals (weekly, monthly, annually).
�� Active case detection–system where data are regularly pursued
by a central health authority at periodic intervals, often with the
intent to validate the representativeness of a passive surveillance
system. An active surveillance system will likely provide more
complete reporting, and can identify asymptomatic individuals,
but it is more labor intensive and thus more costly to operate as
compared to passive case detection.
There is relatively little central guidance on both of these
approaches for malaria elimination. This lack exists because
they are highly context dependent and must be adapted to each
country and area.
Passive Case Detection (PCD)
A strong passive case detection system is the cornerstone of
any approach to surveillance–if new malaria cases identified at
health facilities are not being adequately reported and followed
up, elimination will not be achieved. In Zanzibar, the passive
case detection system will have to be substantially improved to
ensure that a high proportion of infections are detected (both
symptomatic and asymptomatic) and reported to the central level
with the required speed (e.g., initially within 24 hours but once
at or near zero local transmission as soon as possible). From the
results of the simulations described in the Technical Feasibility
Chapter we know that as long as preventive measures have an
effective coverage of 75%:
�� Between 70-80% of infections have to be promptly identified
and treated for passive case detection alone to be sufficient to
avoid second-generation infections.
�� Passive case detection that identifies around 50% of all
infections–a detection rate only slightly higher than the
present day–will need to be complemented with active case
detection that screens about 100 households neighboring
each newly identified case.
Achieving the required detection levels is influenced by a range of
factors: the fraction of infections that will become symptomatic,
the people’s health seeking behavior for fever, testing rates at
the facility, and the sensitivity of the test used. The operational
requirements for the surveillance system to achieve the above
mentioned detection levels are described below.
In addition, the Zanzibar MOHSW will need to strengthen the
central units that record and analyze cases that are reported by
increasing human resources, improving skills, and obtaining
appropriate technology.
2 | Operational Feasibility
Current System
There are currently two systems for passive detection of malaria
cases on Zanzibar: the Health Management Information System
(HMIS) and a vertical Malaria Early Epidemic Detection System
(MEEDS) through sentinel sites (52 sites operational in 2009).
The HMIS is managed by a specific unit in the MOHSW that
was established in 2001 and strengthened through funding from
DANIDA. The HMIS serves as the primary clinical services
monitoring system for the MOHSW and fits within the larger
context of health sector M&E efforts in Zanzibar. Data from
peripheral facilities are collected by the HMIS focal point at the
district level. Aggregated data are compiled at the district level
and sent to the HMIS unit in the MOHSW.
Malaria information collected as part of the HMIS is supposed
to be reported quarterly through District Health Management
Teams (DHMT) and provincial offices. The cases reported are
both (parasitological) confirmed and non-confirmed based on
clinical diagnosis and therefore include non-malarious fever
cases. Although most facilities have diagnostic capacity (RDT
and/or microscopy) and the results are supposed to be captured
in the laboratory reports, the HMIS is currently not providing
this information to the ZMCP. The following indicators are
theoretically available through routine HMIS:
�� Out Patient Department monthly malaria cases (both
confirmed and non-confirmed)
�� Laboratory data (total slides, positive films for malaria
parasites, hemoglobin tests)
�� Malaria and severe anemia admissions and deaths
Unfortunately, the information collected through the HMIS
system is not routinely passed on to the different control programs
within the MOHSW. The ZMCP therefore decided to collect
malaria data for their strategic planning using 6 health facilitybased
sentinel surveillance sites (HFBSS). These sites are part of
the normal HMIS and do not report different information as such.
The main difference is that the HFBSS provide regular, timely,
high-quality malaria-related laboratory, morbidity, and mortality
data, an important pillar for the ZMCP strategic decision. The
MEEDS (see below) now provides these data regularly, and the
HFBSS will most probably be scaled down and even stopped in
the near future.
The second existing malaria surveillance system, malaria
early epidemic detection system (MEEDS), is connected to
a broader effort by the MOHSW to strengthen infectious
disease surveillance. It is funded by PMI and uses mobile phone
technology to provide weekly malaria data to a central server
where they are automatically analyzed. It provides aggregate
data (for under fives and over fives) on total number of patients
seen at the facility, total numbers tested and numbers testing
positive. The MEEDS is so far the only operational component
of the planned Integrated Diseases Surveillance and Response
(IDSR) that aims at providing health workers with additional
epidemiological information on key diseases to assist in decisionmaking
on and implementation of interventions As the MEEDS
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