Experiential relationship between malaria parasite density and some haematological parameters in malaria infected male subjects in Port Harcourt, Nigeria

Abstract

This study examined the experiential relationship between the parasite density and haematological parameters in male patients with Plasmodium falciparum infection in Port Harcourt, Nigeria reporting to malaria clinics. A total of one hundred and thirty-six (136) male patients were recruited. QBC haematological analysis, QBC malaria parasite specie identification and quantification and thin blood film for differential leucocytes count was used. The mean values of the haematological parameters in each quartile of parasite densities were determined using Microsoft Excel statistical package. Regression analysis was employed to model the experiential relationship between parasite density and haematological parameters. All regression relationships were tested and the relationship with the highest coefficient of determination (R2) was accepted as the valid relationship. The relationships tested included linear, polynomial, exponential, logarithmic and power relationships. The X- axis of the regression graphs stand for the parasite density while Y-axis stands for the respective haematological parameters Neutrophil count had a negative exponential relationship with the parasite density and is related to the parasite density by a polynomial equation model: ynm = -7E-07x2 - 0.0003x + 56.685.The coefficient of determination (R2) was 0.6140. This means that the rate of change of the parasitemia will depend on the initial value of the neutrophil. As the neutrophil increases, the parasitemia will tend to decrease in a double, triple and quadruple manner. The relationship between lymphocyte count, monocyte count and eosinophil count and parasite density was logarithmic and expressed by the following linear equation models: ylm = -2.371ln(x) + 37.296, ymm = 0.6965ln(x) + 5.7692 and yem = 0.9334ln(x) + 4.1718 in the same order. Their respective high coefficients of determination (R2) were 0.8027, 0.8867 and 0.9553. This logarithmic relationship means that each doubling of monocyte count and eosinophil count will cause the same amount of increase in parasitemia whereas each doubling of lymphocyte count will cause the same amount of decrease in parasitemia. The best fitting regression model for total white cell count (WBC), haemoglobin concentration, packed cell volume (PCV)(haematocrit) and mean cell haemoglobin concentration (MCHC) and parasite density was a linear model and expressed by the following linear equation models: yWBCm = 1.2314x + 8533.8, yHbm = -0.0014x + 13.004, yPCVm = -0.0046x + 41.443 and yMCHCm = -0.0008x + 32.336. Their respective coefficients of determination are 0.7397, 0.6248, 0.9758 and 0.8584. This linear relationship means that as the parasite density is increasing that there is a corresponding decrease in haemoglobin concentration, PCV and MCHC and a corresponding increase in total white cell count. The best fitting regression model between platelet count and parasite density is a power model with a very high coefficient of determination (R2=0.9938) and expressed by: yPltm = 278047x-0.122. These equation models could be very useful in areas where there may not be functional microscopes or competent microscopists and in medical emergencies.

Figure 1

Exponential Relationship between neutrophil count and parasite density (Male)

Figure 2

Logarithmic relationship between lymphocyte, monocyte and eosinophil counts and parasite density (Male)

Figure 3

Linear relationship between WBC count and parasite density (Male)

Figure 4

Linear relationship between Hb concentraion and parasite density (Male)

Figure 5

Linear relationship between PCV and parasite density (Male)

Figure 6

Linear relationship between MCHC and parasite density (Male)

Figure 7

Power relationship between platelet count and parasite density (Male)