RESUMO
Introduction:Geographical Information System (GIS) has proven to be very useful for large scale mapping of ecosystems, land use and cover, disease prevalence, risk mapping and forecasting. GIS establish relationship or link between vector borne diseases and associated environmental factors thereby providing explanation for spatial distribution pattern, possible causes of diseases outbreak andimplications on the community.Aims and Objectives:Our approach in this study was to define and identify areas and places that are exposed to Malaria risk through proximity analysis and to compare geospatial risk with laboratory diagnosed malaria epidemiology. Methodology:Garmin GPS was used to capture the geographic coordinates of six (6) selected settlements and overlaid with georeferenced and processed satellite images in the study area. GIS modeling was performed on risk factors using weighted overlay technique to produce malaria risk map. A total of One hundred and thirty-five (135) vulnerable individuals were diagnosed for Malaria with light Olympus microscope and rapid diagnostic kit (RDT). Data were entered and analyzed using R-Package for Statistical Computing and Graphics.Results:Proximity to malaria risk follows relatively the order Apodu > Central Malete > Elemere > KWASU Campus > Gbugudu. Apodu being the largest place with proximity to malaria risk, within 500m radius. The risk index increases as one move away from the center of the settlement. The possible explanation for this high risk could be the presence of pond / lake in Apodu. This is a good breeding site for mosquito couple with dense vegetation as one move away from the centre of the settlements. Unlike Apodu, Gbugudu was at medium risk at 100m buffer (60%) but the risk index decreases as one move away from the settlement centre. The absence of thick vegetation and presence of numerous open farms and partly cultivated farmlands on the eastern part could have been responsible for reduction in risk index. Dense vegetation and ponds were observed within Apodu, while Central Malete was built up with dense vegetation are possible reasons for the high-risk index, while settlements within 1 km radius around KWASU campus recorded lower risk index possibly dueto low vegetation. The geospatial malaria risk analysis correlates with the laboratory-based test results. RDT kits and light microscopy results showed Apodu having the highest malaria prevalence with 46% and 58.7% followed by Elemere 41% and 30.3% respectively. When calculating prevalence by aggregating results across all communities, Apodu still had the highest malaria prevalence for the whole region. RDT and light microscopy results combined for all communities had Apodu with malaria prevalence of 21.48% and 27.4% followed by Elemere with 11.85% and 12.5% respectively. Gbugudu had the least malaria prevalence within the region with 3.7% and 7.4% respectively.Discussion and Conclusion:Findings of this study showed dense vegetation and ponds within Apodu, Elemere and Central Malete served as good breeding site for mosquitoes and were responsible for the high-risk index at these areas. Settlements within 1 km radius around KWASU campus recorded lower index possibly due to low vegetation. Results from this study indicate that the degree of malaria parasitaemia in the three major settlements correlates directly with the remote sensing data
RESUMO
Background:As a foremost disease of public health significance, malaria has wrecked untold havoc among children and pregnant women in developing countries with Africa and Nigeria being worst affected. Objective:This study was aimed at documenting the prevalence and socio-demographic differences in the pattern of malaria infection among asymptomatic primary school children in Gombe state, Nigeria. Methodology:Using a cross sectional study design 745 pupils aged 6 to 15 years were assessed from March to June 2019. The subjects were selected from 12 public and 6 private schools in 6 Local Government Areas (LGAs) of the state, using multi-stage sampling technique. Malaria rapid diagnostic test (mRDT) kitswere deployed in screening the subjects for malaria parasites positivity or otherwise. Data was analyzed using IBM SPSS version 21 analytical software. And the findings were presented in tables and charts. Proportions were compared using chi square, with alpha significance level set at 0.05. Results: The mean age of the participants was 9.96±2.26 and 379 (50.9%) of them were boys while the rest 366 (49.1%) were girls (about 1:1 male-female sex ratio). A total of 101 (13.6%) of the 745 school children screened yielded positive to malaria rapid diagnostic test (mRDT), with children from the public primary schools being significantly (p=0.000) more affected than their counterparts in the private schools (18.2% vs. 4.1%). Though male children were more affected by malaria, the difference was not statistically significant (15.0% vs. 12.0%, P=0.229). Similarly, significantly higher proportion (p=0.000) of pupils from the rural schools were affected and were about three times more at risk of malaria (OR=3.362, CI: 2.169-5.212) than their urban counterparts (22.4% vs. 7.9%). Low academic performance was found to be associated with malaria infection (p=0.012). Other socio-demographic factors associated with malaria infection among the pupils include; senatorial district, family size, source of parental care, tribe etc. However, from the evidence of this study there was no significant association between the prevalence of malariainfection and birth order, age group and gender of the school children.Conclusion: In view of the high and disproportionate distribution of the burden of malaria among the subjects, it is hereby recommended that concerned authorities should include primary schools in the design and implementation of malaria control/ elimination programmes