RISK-BASED ASSESSMENT AND MAPPING OF MALARIA DISTRIBUTION IN RURAL KWARA STATE
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Date
2017-03
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: de-infinity vision ent. : https://www.researchgate.net/publication/316885219
Abstract
Despite the possibility of being preventable, malaria has a high level
of mortality and morbidity and is the world's most prevalent parasitic
disease. It is caused by infection with single-celled parasites of the
genus Plasmodium, which is transmitted by the bite of female
Anopheles mosquitoes. In Nigeria, statistics shows that malaria
accounts for 25% of the under-five mortality, 30% of childhood
mortality and 11% of maternal mortality (Okonko et al., 2009). All
Nigerians are at risk of malaria and the problem is compounded by
the increasing resistance of malaria to hitherto cost-effective drugs
and insecticides (Okonko et al., 2009). Describing in detail the spatial
and temporal variation in transmission and disease risk is
fundamental to epidemiological understanding and control of
malaria. Risk maps are, by definition, outcomes of models of disease
transmission based on spatial and temporal data. These models
incorporate, by varying degrees, epidemiological, entomological,
climate and environmental information. Decades of experience
conrm that successful malaria control depends on accurate
identication and geographical reconnaissance of high-risk areas
(Carter et al., 2000). In the past, malaria risk maps at different
geographical levels were largely based on expert opinion based on
limited data, crude climate isolines with no clear and reproducible
numerical denition. In recent years, the availability of new data
sources such as remote sensing (RS) and mapping tools, such as
computerized geographic information systems (GIS) for quantitative
analysis of spatial data, have provided an unprecedented amount of
information and increased capability to describe, predict and
communicate risk and outcome of interventions (Berquist, 2001).
Measures that might be mapped include categories of endemicity
(e.g. unstable, mesoendemic or holoendemic), vector density and
capacity, entomological inoculation rate (EIR) and incidence of
disease. However, although malaria endemicity can vary widely over
only short distances, most of these measures have only been studied
in a few, widely separated localities. In general, results from different
sites differ.
Our corporate effort in this research work focuses on utilization of
Geographic Information Systems (GIS) hardware, software and
training to map the incidence/prevalence of malaria over some
geographic area. The GIS map incorporate physical environmental
risk variables such as vegetation covers, rivers, pond and streams,
housing and drainage pattern, ecological and topographical lay-out,
built up status of the settlements and vectorial interphase and
interactions with potential host communities could serve as an
environmental model to predict Malaria distribution in selected
settlements neighbourhood of Kwara State University, Malete,
Nigeria. In this study, Apodu settlement showed a high vulnerability
index due its dense vegetation and the presence of impounded water
in the dam. Apodu and Elemere had the highest malaria vulnerability
index within 300m radius. That is, the vulnerability index increases
as one moves away from the center of the settlement. Futhermore, the
Fulani who stay some few meters away from the centre of settlements
were more at risk. However, Gbugudu was at highest risk at 100 m
buffer (60%) but the vulnerability index decreases as one move away
from the settlement centre. The absence of thick vegetation and
presence numerous cultivated farmlands on the eastern part could
have been possible explanation for this reduction in vulnerability
index (Appendix 2).
Over the years, one of the challenges in malaria management,
particularly among children, is inaccurate diagnosis of the condition
(Olukosi et al., 2015). Clinical diagnosis of malaria without
laboratory support may lead to malaria misdiagnosis and
maltreatment (Oladosu and Oyibo, 2013). The focus here is to
examine past trends through available medical records, as well as the
present situation with the possibility of correlating current malaria
incidence / prevalence among the population in order to calculate
populations at risk, malaria parasite stage distribution by settlement,
age-group and occupation. The goal with these studies is to see if any
obvious patterns exist, but the study neither found evidence of
existing interventions nor medical records. We conducted routine
screening to scale-up malaria diagnosis comparing Rapid Diagnostic
Tests (RDTs) and Giemsa Microscopy techniques. The RDT results
revealed a 37% malaria prevalance in all three communities
combined while Light microscopy recorded 48% positivity rate.
However, with Light microscopy, Apodu community had a higher
infected to non-infected persons ratio at 58.7% to 41.3% than
Gbugudu and Elemere, both of which showed lower prevalence rates
at 30.3% and 46.2% respectively (Appendix 1; Table2 & 7). Rings
and trophozoite stages were the two most pronounced stages detected
under the light microscope. Most (56.9%) of the malaria cases were
found to have parasites at the “Ring Stage”, while the others (43.1%)
had progressed to the “Trophozoite Stage”. Out of One hundred and
thirty five (135) individual that were screened and diagnosed for
Malaria, (44/135) 32.6% Yoruba and (21/135) 15.6% Fulani were
positive respectively (Appendix1). We therefore infer that both
techniques could be employed to detect malaria infection, however,
Giemsa microscopy method demonstrated higher sensitivity and
effectiveness over RDT for being able to resolve and detect low
parasitaemia, symptomatic and asymptomatic cases. Results
obtained from this study conrm that the microscopy method
remains the reference standard and a better diagnostic tool for malaria
diagnosis in the laboratory than the RDTs in limited resources
endemic zones.
Results from this study indicate that the degree of malaria
parasitaemia in the three settlement correlates directly with the
remote sensing data (Appendix 1&2).
We recommend more appropriate land utilization and engagement,
environmental sanitation and consistent re-training of household
leaders (fathers, mothers and grandparents) and caregivers on causal
factors and prevention of malaria in the rural communities under
studied to be able to effectively assess the effect of intervention
program provided. The essence would be for the villagers to take
responsibility for ownership and be able to self-apply and manage
these approaches.