Malaria: Causes, symptoms, and treatment

Malaria is an infectious disease , caused by parasites that invade red blood cells. The disease is transmitted from parasite to humans via a plasmodium parasite of an infected mosquito. Despite being preventable and curable, malaria is a life-threatening disease that must be promptly managed. According to the World Health Organization (WHO), i n 2018, malaria caused an estimated 228 million cases and 405, 000 deaths globally. Developing countries such as Africa have the highest disease burden globally. The Africa region accounts for approximately 93 % of malaria cases and 94 % deaths globally. It was established that children who are below the age of five years are the most vulnerable and affected by Malaria. In 2018, 67 percent of children below the age of 5 years died as a result of malaria infections globally.   

The disease is transmitted by a female  Anophele s mosquito , an insect with about 400 species and 30 major vectors. The mosquitoes are active and bite during the dusk and dawn. Three factors determine the intensity of transmission ; the human host, parasite and environment. The female mosquitoes bite the human being to get a blood meal to nurture the eggs. Each of the mosquito species has its preferred habits that are distinct for others. But the Anopheles mosquitoes prefer the aquatic habitats and lay and hatch eggs in tropical countries during rainy periods.   

Areas with longer mosquito lifespans endure mosquito bites over prolonged periods as they target human beings. When the mosquito has a l o nger lifespan , it develops a strong human-biting habit , hence, explaining why Africa accounts for about 90 percent of the world's total malaria cases. Further, the transmission is affected by the climatic conditions that threaten their survival . F or example, temperature, rainfall pattern, and humidity. Mostly, the transmission is at a peak when it is rainy season.  

Therefore, preventing and eradicating malaria infection is the ultimate goal of the WHO. It recommended that all people at the risk of malarial should be protected with an effective malaria vector control. Lee et al. (2018) conducted a study to examine the relationship between malaria infection and other demographic factors. They revealed that there is a significant association between malaria incidence and demographic factors such as geographical region, age, income, and health status. Further, Miazgowicz et al. (2018) established that the anopheles mosquitos survive and are dominant is rainy seasons. At this period , there are more cases of malaria recorded in tropical areas such as Africa. 

This study aims to examine i f there is a relationship linking malaria infections with age and regions. The study will employ the quantitative technique. The logistic regression will be employed to estimate the effect of the age and region on malaria infections ( Gasso, 2019 ) . This model is used when the dependent variable is binary or dichotomous (Yes or No). There are three variables of interest in this study of the maria infections, age, and region. The dependent variable is the malaria infection measured on “yes” if there if infection and “ N o” when there is no infection. The independent variables are the age in years and the region that is nominal , measured based on geographical regions.  

Research question  

Does geographical region and age predict Malaria infections? 

Hypothesis  

H0: There is no association linking malaria infection with age and region. 

H1: There is an association linking malaria infection with age and region. 

The Malaria infection is nominal/ categorical (Yes/ No), age is interval/quantitative and the region is nominal. 

Logistic R egression  

Table 1 :Descriptive Statistics for Region 

REGION
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1	83	29.6	29.6	29.6
	2	71	25.4	25.4	55.0
	3	74	26.4	26.4	81.4
	4	52	18.6	18.6	100.0
	Total	280	100.0	100.0

Table 2 : Descriptive Statistics for Malaria 

MALARIA
		Frequency	Percent	Valid Percent	Cumulative Percent
Valid	0	182	65.0	65.0	65.0
	1	98	35.0	35.0	100.0
	Total	280	100.0	100.0

Variables in the Equation
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 0	Constant	-.619	.125	24.410	1	.000	.538

Table 3 : Logistic Regression 

Variables in the Equation
		B	S.E.	Wald	df	Sig.	Exp(B)
Step 1 a	AGE	-.010	.009	1.173	1	.279	.990
	REGION	-.100	.116	.748	1	.387	.905
	Constant	.043	.499	.007	1	.932	1.044
a. Variable(s) entered on step 1: AGE, REGION.

The Wald chi-square = 24.410,  p  = 0.000. Ince the p-value is smaller compared to alpha = 0.05, we reject the null hypothesis. Hence, we deduce that there is a significant association linking malaria infection with age and region.  

The logistic regression equation is, 

Log (Malaria) = 0.043 – 0.100*Region - 0.010*Age 

When malaria infection is on the logit scale, it implies that there is a relationship linking age and region with malaria. For every one-unit decrease in the region, we expect a 0.100 decrease in the log-odds of malaria infection, holding age constant. The odds ratio is 0.905, implying that where a person comes from region = 1, the odds of having malaria is 0.905 ( Midi, Sarkar & Rana, 2010 ) . Also, for every one-unit decrease in age, we expect a 0.01 decrease in the log-odds of malaria infection while holding region constant. Its odd ratio is 0.990, suggesting that old people are more likely to be infected with malaria.  

References 

Gasso, G. (2019). Logistic regression. https://moodle.insa-rouen.fr/pluginfile.php/7984/mod_resource/content/6/Parties_1_et_3_DM/RegLog_Eng.pdf 

Lee, E. H., Miller, R. H., Masuoka, P., Schiffman, E., Wanduragala, D. M., DeFraites, R., ... & Hickey, P. W. (2018). Predicting risk of imported disease with demographics: G eospatial analysis of imported Malaria in Minnesota, 2010–2014.  The American Journal of Tropical Medicine and Hygiene ,  99 (4), 978-986. 

Miazgowicz, K. L., Mordecai, E. A., Ryan, S. J., Hall, R. J., Owen, J., Adanlawo, T., ... & Murdock, C. C. (2019). Mosquito species and age influence thermal performance of traits relevant to malaria transmission.  bioRxiv , 769604. 

Midi, H., Sarkar, S. K., & Rana, S. (2010). Collinearity diagnostics of binary logistic regression model.  Journal of Interdisciplinary Mathematics ,  13 (3), 253-267.