Spatial features of malaria in the lowland and nearby highland areas of Taraba State, Nigeria

Abstract

Malaria is a dilemma among inhabitants of the lowland and scarcely among those of the highland in Nigeria. The infection in the lowland and the highland was determined as well as a relationship between the infection and the environmental and climatic factors in Taraba State, Nigeria. The polymerase chain reaction was used to amplify the deoxyribonucleic acid (DNA) of Plasmodium species. The statistical tests were at probability p ≤ 0.05. Plasmodium falciparum infection was significantly higher in the lowland,106 (72.6 %) than in the highland, 58 (38.6 %) (χ² = 40.350; p = 0.000). Males and females on the lowland had significant infection, 49 (80.3 %) (χ²= 21.921; p = 0.000) and 57 (67.1 %) (χ²= 14.329; p = 0.000) respectively. In the lowland, the age groups, 6–15 years and 16–50 years respectively had higher infection with 60 (75.9 %) (χ²= 14.329; p = 0.000) and 31(67.4 %) (χ²= 10.580; p = 0.001). Households with two (2) and five (5) individuals in the lowland significantly had infection of 31(64.6 %)(χ² = 8.861; p = 0.003) and 53(85.5 %)(χ² = 33.034; p = 0.000) respectively. In the lowland, males with the low hemoglobin (< 11.5 g/dL) significantly had 16 (22.5 %)(χ² = 12.933; p = 0.051), while females on the highland with low hemoglobin significantly had malaria infection of 48 (47.0 %) (χ²= 14.421; p = 0.022). The low altitude, rainfall and temperature significantly had spatial autocorrelation predictive models for malaria infection with respective Odd Ratios (OR) of 3.521 (%95CI: 2.045 - 6.092; p = 0.000), 7.986 (%95CI: 4.489 - 14.209; p = 0.000) and 4.402 (%95CI: 2.687 - 7.212; p = 0.000). The males, females, age groups between 6 and 15 years as well as 16–50 years significantly had higher infection in the lowland. The low altitude, high temperature and rainfall significantly predicted malaria infection in the lowland. The appropriate distribution of long-lasting treated nets as well as the use of geographic information systems are recommended respectively to prevent and predict malaria infection in other areas.