Electron-Impact Ionization of Boronfluorides BFx (x=1, 2 & 3)

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Volume-7, Issue-5, September-October 2017

International Journal of Engineering and Management Research

Page Number: 110-122

Risk Factors of Prevalence of Malaria in Case of Jimma Town, A

Statistical Analysis

Agatamudi Lakshmana Rao1, Mosisa Girma2

1,2_{Department of Statistics, College of Natural Sciences, Jimma University, Jimma, ETHIOPIA}

ABSTRACT

This research is done based on the Statistical analysis on risk factors of prevalence of malaria in case of Jimma town, Ginjo guduru kebele, Oromia. Malaria is the most deadly disease caused by Plasmodium parasites. The parasites are spread to people through the bites of infected Anopheles mosquitoes, called malaria vectors. It remains to be a major challenge to public health and socio-economic development worldwide and in sub- Saharan Africa in particular. However, there is still a paucity of information on the occurrence of malaria at the study area. The objective of this study was to investigate the prevalence of malaria and related risk factors in Jimma town Ginjo Kebele. In this research Chi-square test of independence was used to see the association between Status of malaria and the other categorical independent variables. Moreover Binary Logistic regression was used to examine the impact of predictor variables on the prevalence of Malaria in the area. Using Binary logistic regression analysis out of seven categorical predictor variables, four predictor variables, like age of patient, residence of patient, net usage and stagnant water had significant effect on the outcome variable which is status malaria patients. The results of this study revealed that the variables age, residence, stagnant of water and net usage had contributed to malaria status of a patient.

Keywords-- Malaria, risk factors, Binary logistic regression and Chi-square test of independence

I. INTRODUCTION

1.1. Background of the Study

Malaria is a mosquito-borne infectious disease affecting humans and other animals caused by parasitic protozoan’s (a group of single-celled microorganisms) belonging to the Plasmodium type. Malaria causes symptoms that typically include fever, fatigue, vomiting, and headaches. In severe cases it can cause yellow skin, seizures, coma, or death. Symptoms usually begin ten to fifteen days after being bitten. If not properly treated, people may have recurrences of the disease months later.

This partial resistance disappears over months to years if the person has no continuing exposure to malaria. This disease is most commonly transmitted by an infected female Anopheles mosquito. The mosquito bite introduces the parasites from the mosquito's saliva into a person's blood. The parasites travel to the liver where they mature and reproduce. Five species of Plasmodium can infect and be spread by humans. Most deaths are caused by P.falciparum because P. vivax, P. ovale, and P. malariae generally cause a milder form of malaria. The species P. knowlesi rarely causes disease in humans. Malaria is typically diagnosed by the microscopic examination of blood using blood films, or with antigen-based rapid diagnostic tests. Methods that use the polymerase chain reaction to detect the parasite's DNA have been developed, but are not widely used in areas where malaria is common due to their cost and complexity.

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resistance to artemisinin has become a problem in some parts of Southeast Asia.

The disease is widespread in the tropical and subtropical regions that exist in a broad band around the equator. This includes much of Sub-Saharan Africa, Asia, and Latin America. In 2015, there were 214 million cases of malaria worldwide resulting in an estimated 438,000 deaths, 90% of which occurred in Africa. Rates of disease have decreased from 2000 to 2015 by 37%, but increased from 2014 during which there were 198 million cases. Malaria is commonly associated with poverty and has a major negative effect on economic development. In Africa, it is estimated to result in losses of US$12 billion a year due to increased healthcare costs, lost ability to work, and negative effects on tourism. It remains to be a major challenge to public health and socio-economic development worldwide and in sub- Saharan Africa in particular. It causes an estimated 300 to500 million cases and 1.5 to 2.7 million deaths worldwide each year, of which 80% of the cases and 90% of the deaths occur in Sub-Saharan Africa world health organization (WHO, 2009).

Malaria is a life-threatening caused by

Plasmodium parasite infection. Malaria is the most deadly, and it predominates in Africa. The problem of malaria is very severe in Ethiopia where it has been the major cause of illness and death for many years. According to records from the Ethiopian Federal Ministry of Health, 75% of the country is malarious with about 68% of the total population living in areas at risk of malaria that is more than 50 million people are at risk from malaria, and four to five million people are affected by malaria annually. The transmission of malaria in Ethiopia depends on altitude and rainfall with a lag time varying from a few weeks before the beginning of the rainy season to more than a month after the end of the rainy season. Epidemics of malaria are relatively frequent involving highland or highland fringe areas of Ethiopia, mainly areas 1,000-2,000 meters of above sea level. Malaria transmission peaks bi-annually from September to December and April to May, coinciding with the major harvesting seasons. This has serious consequences for Ethiopia’s subsistence economy and for the nation in general. Major epidemics occur every five to eight years with focal epidemics as the commonest form. Early diagnosis and prompt treatment is one of the key strategies in controlling malaria. For areas where laboratory facilities are not available, clinical diagnosis is widely used.

There are four types of human malaria: Plasmodium falciparum, Plasmodium Vivax, Plasmodium malaria and Plasmodium Ovale. Among the four species, Plasmodium falciparum is by far the most aggressive species, distributed globally especially common in Africa (WHO, 2009). In Ethiopia, Plasmodium falciparum is the dominant species followed by P. Vivax and these two species accounts for 60% and 40% of all malaria cases

respectively. P. malaria accounts only for less than 1% of cases and is restricted in distribution. But, P.ovale is rarely reported (MOH, 2008). However, the relative frequency of the species varies from place to place and from season to season. For instance, in Oromia region plasmodium, falciparum accounts for 49% of confirmed malaria cases, P.vivaxwere 22% and 11% mixedinfection. However; P.vivax falciparum is responsible for most hospital and health center admissions, morbidity and mortality (Oromia region report, 2000). Prevention and control activities of malaria in Ethiopia are implemented as guided by the National Strategic Plan to ultimately reduce the burden of malaria to level where it is no longer a public health problem. There are four major intervention strategies that are being applied in Ethiopia to combat malaria were: early diagnosis and prompt treatment, selective vector control that involves use of indoor residual spraying (IRS), insecticide-treated mosquito nets (ITNs) and environmental management ministry of health (MOH, 2008).

1.2. Statement of the problem

Prevalence of malaria is the major global health problem and causes the most serious form of the disease, and it is common in developing countries, particularly in sub-Saharan Africa like Ethiopia. There are many factors that contribute to prevalence of malaria like seasonal variability, altitude due to parasite was change to complicated, problems of society awareness and etc. However, the factors themselves and the health care way to manage or prevent the effect of these factors are not well known by the community. Similarly there is gap in health service and people face lack of health insurance and skilled medical care in the study area. Also there is no clear statistical methodology and quantitative research applied using modern and appropriate statistical models on these factors in the study area. Considering the above listed and other problems, this study is motivated to contribute in identifying those important factors which play role to incidence of malaria using appropriate statistical method particularly, binary Logistic regression Model.

The core research questions are:

 What factors enhance the prevalence of malaria in the study area?

 What factors are highly related to the prevalence of malaria in the study area?

 In which residents the prevalence of malaria high; rural or urban residents?

 In which sex and age groups this disease more prevalent?

II. METHODOLOGY

2.1. Descriptions of the Study Area

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The town has altitude 1740m above sea level and has latitude and longitude 7.39 N and 36.50 E respectively and with annual maximum and minimum temperature of 28c and 13.2c respectively and with annual average rain fall of 1520mm.Gunjo guduru Keble is located in Jimma town Jimma zone of Oromia regional states. Our study is carried out on Jimma town Gunjo guduru Keble case of malaria prevalence and also town has favorable air condition . 2.2. Method of data collection

The data for this study will be collected (obtained) from Jimma University Specialized Hospital from hospital’s registry, patient’s card and log book. The data will intended to use in this research is secondary. Secondary data will be the major source of this study. This secondary data used to achieve the objective of the study and to get reliable information from the target population (group)

2.3. The Study variable

In this study, the variables included in the study

are dependent and independent variables. 2.3.1Dependent variable

The response or dependent variable in this study is the binary response variable which is named as presence or not presence

2.3.2 Independent variables are the following:- Sex of patients

 Age interval in year  Season in months

 Types of malaria species diagnosed  Residence of patients

 Stagnant water well(mosquito comfortable zone)  Is society uses internet?

2.4. Method of data analysis

Both descriptive and inferential statistical methods would be used.

2.4.1. Descriptive statistics

Different statistical techniques like tables, graphs and percentage, mean, media are used in this study.

2.4.2. Inferential statistics

There are different kinds of statistical models. Among them chi-square, logistic regression and multiple linear regression are most popular once. In this study since the response variable, that is presence/not status of malaria presence is a dichotomous variable so the effect of explanatory variables on the dependent variable can be investigated using logistic regression model that can be formulated under the classical or the Bayesian set up. We considered binary logistic regression analysis to determine the factors that cause the prevalence of malaria.

2.5 Goodness of Fit of the Model

The goodness of fit or calibration of a model measures how well the model describes the response variable. Assessing goodness of fit involves investigating how close values predicted by the model with that of observed values (Bewick et al, 2005).

III. RESULTS AND DISCUSSION

As already stated, the main objective of this study has been to investigate the prevalence of malaria in Jimma town Ginjo Guduru kebele using binary logistic regression model. The first part of this section deals with descriptive statistics and, the second part deals with binary Logistic Regression model.

For this study, the data was obtained from Jimma University Specialized Hospital from clinical registry patient’s card and log book. The data of size 383 were obtained from record reviews of all malaria patients’ admitted to the clinic. In this paper 5% level of significance was used to investigate the significance of the variable.

3.1. Descriptive Statistics

The result displayed on table below show percentage and frequency of malaria prevalence status with respect to each category together with, Chi-square, p-value and degrees of freedom. The tests were intended to test the association between each explanatory variables and status of malaria prevalence (negative or positive). Jimma University Specialized Hospital from clinical registry, patient’s card and log book, April 2017).

Table 3.1: summary of descriptive statistics for the study variables

variable category frequency percent

Sex Female _Male 222 ₁₆₁ 58 ₄₂

Season winter 93 24.3

spring 115 30

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MSp Vivax 211 55.1

Falciprium 172 44.9 Residence Urban 207 54

Rural 176 46

Stagnant

Of water No

198

51.7

Yes 185 48.3

Net usage no 157 40.2

yes 229 59.7

sAge child 165 43.1

adult 111 29

old 107 27.7

The output on Table 3.1 shows the proportions of prevalence of malaria, frequency distribution, with respect to each category of the categorical explanatory variables. From the above summary table we can see that among 383 status of malaria patients considered in the analysis, are 42% males and 58% females.

The above table indicates that out of 383 status of malaria patients considered in the analysis, about 46% are

come from rural areas and about 54% of them are come from urban areas.

From the above table we can see that out of 383 status of malaria patients considered in the analysis, 51.7 of them get stagnant of water and rest 48.3 are not get stagnant of water.

Fig.3.1.This bar-chart shows age of malaria status

As shown clearly on the bar chart above that 165(43.08%) of the malaria status are child, 111 (28.98%)

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Type of malaria species

Fig.3.2.This bar-chart shows age of malaria status

As shown on the above bar chart 211(55.09%) of them are cached by vivax malaria species and 172(44.91) is cached by falciprium malaria species.

Residence of malaria status

Figure 3.3 this pie chart shows residence of malaria status

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Sex of malaria status

Figure 3.4 this pie chart shows sex of malaria status.

As the pie chart above shows, 42.04% (161) of the malaria status are male and 57.96% (222) of the malaria status are female.

3.2 Inferential statistics

Inferential statistics is statistical method deals with making inference or conclusion about

population based on data obtained from a limited number of observations that come from the population.

3.2.1 Chi-square test of independence

Table 3.2 gender * status of malaria Cross tabulation Status of malaria

Total negative positive

gender female Count 166 48 214

% within gender 77.6% 22.4% 100.0%

% within status of malaria 68.3% 34.3% 55.9%

% of Total 43.3% 12.5% 55.9%

male Count 77 92 169

% within gender 45.6% 54.4% 100.0%

% of Total 20.1% 24.0% 44.1%

Total Count 243 140 383

% within gender 63.4% 36.6% 100.0%

% of Total 63.4% 36.6% 100.0%

Hypothesis

H0:There is no association between gender and malaria status H1: There is association between gender and malaria status Level of significance α=0.05

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Table 3.3 Chi-Square Tests

Value df

Asymp. Sig. (2-sided)

Exact Sig. (2-sided)

Pearson Chi-Square _41.714a ₁ _.0570

Continuity Correctionb 40.345 1 .000 Likelihood Ratio 42.134 1 .000

Fisher's Exact Test .000 .000

Linear-by-Linear

Association 41.605 1 .000 N of Valid Casesb 383

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 61.78. b. Computed only for a χ2 table

From the above table of results of chi-square analysis the Pearson chi-square

Calculated value is 41.714 and p value=0.057 is greater than the level of significance α=0.05(5%).

Therefore we fail to reject HO and we conclude that there is no association between gender and status of malaria

Table 3.4 age * status of malaria Cross tabulation

Status of malaria

age child Count 124 41 165

% within age 75.2% 24.8% 100.0%

% of Total 32.4% 10.7% 43.1%

young Count 47 64 111

% within age 42.3% 57.7% 100.0%

% of Total 12.3% 16.7% 29.0%

old Count 72 35 107

% within age 67.3% 32.7% 100.0%

% of Total 18.8% 9.1% 27.9%

% within age 63.4% 36.6% 100.0%

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Hypothesis

H0: There is no association between age and malaria status H1: There is association between age and malaria status Level of significance α=0.05

Test statistic is Pearson chi –square value

Value df

Pearson Chi-Square 31.746a 2 .000 Likelihood Ratio 31.352 2 .000 Linear-by-Linear

Association 3.775 1 .052

N of Valid Cases 383

From the above table of results of chi-square analysis the Pearson chi-square Calculated value is 31.746 and p value=0.000 is less than the level of significance

α=0.05(5%).Therefore we reject HO and we conclude that there is association between age and status of malaria.

Table 3.6 Residence * status of malaria Cross tabulation Status of malaria

Residence urban Count 100 107 207

% within Residence 48.3% 51.7% 100.0%

% of Total 26.1% 27.9% 54.0%

rural Count 143 33 176

% of Total 37.3% 8.6% 46.0%

% of Total 63.4% 36.6% 100.0%

Hypothesis

H0:There is no association between age and malaria status H1: There is association between residence and malaria status Level of significance α=0.05

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Value df

Pearson Chi-Square 44.506a 1 .033 Continuity Correctionb 43.097 1 .120 Likelihood Ratio 46.314 1 .156

Linear-by-Linear

N of Valid Casesb 383

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 64.33. b. Computed only for a χ2 table.

α=0.05(5%).Therefore we reject H0 and we conclude that there is association between residence and status of malaria.

Table 3.8 Season * status of malaria Cross tabulation

Status of malaria

Season winter Count 52 40 92

% within Season 56.5% 43.5% 100.0%

% of Total 13.6% 10.4% 24.0%

spring Count 66 50 116

% within Season 56.9% 43.1% 100.0%

% of Total 17.2% 13.1% 30.3%

autumn Count 41 17 58

% within Season 70.7% 29.3% 100.0%

% of Total 10.7% 4.4% 15.1%

summer Count 84 33 117

% within Season 71.8% 28.2% 100.0%

% of Total 21.9% 8.6% 30.5%

% within Season 63.4% 36.6% 100.0%

% of Total 63.4% 36.6% 100.0%

Hypothesis

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H1 :There is association between residence and malaria status Level of significance α=0.05

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 8.876a 3 .031

Likelihood Ratio 8.970 3 .030

Linear-by-Linear

N of Valid Cases 383

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 21.20.

α=0.05(5%).Therefore we reject HO and we conclude that there is association between season and status of malaria

Table 3.10 Stagnant of water*status of malaria Status of malaria

Stagnant of water

no Count 148 50 198

% within Stagnant of water 74.7% 25.3% 100.0%

% of Total 38.6% 13.1% 51.7%

yes Count 95 90 185

% of Total 24.8% 23.5% 48.3%

% within status of malaria _100.0% _100.0% _100.0%

Hypothesis

H0:There is no association between age and malaria status

H1:There is association between stagnant of water and malaria status Level of significance α=0.05

Value df

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Linear-by-Linear

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 67.62. b. Computed only for a χ2 table

α=0.05(5%).Therefore we reject H0 and we conclude that there is association between stagnant of water and status of malaria.

Table 3.12 Msp * status of malaria Cross tabulation

Status of malaria

Msp vivax Count 151 54 205

% within Msp 73.7% 26.3% 100.0%

% of Total 39.4% 14.1% 53.5%

Falciprium Count 92 86 178

% within Msp 51.7% 48.3% 100.0%

% of Total 24.0% 22.5% 46.5%

% within Msp 63.4% 36.6% 100.0%

% of Total 63.4% 36.6% 100.0% Hypothesis

H0 :There is no association between age and malaria status H1=There is association between msp and malaria status Level of significance α=0.05

Value df

Linear-by-Linear

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Therefore we fail to reject HO and we conclude that there is no association between msp and status of malaria.

Table 3.14 Net usage * status of malaria Cross tabulation

Status of malaria

Net usage No Count 74 80 154

% within Net usage 48.1% 51.9% 100.0%

% of Total 19.3% 20.9% 40.2%

yes Count 169 60 229

% within Net usage 73.8% 26.2% 100.0%

% of Total 44.1% 15.7% 59.8%

% within Net usage 63.4% 36.6% 100.0%

% of Total 63.4% 36.6% 100.0% Hypothesis

H0:There is no association between age and malaria status H1=There is association between net usage and malaria status Level of significance α=0.05

Value df

Linear-by-Linear

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IV. CONCLUSIONS

The main objective of this study is to investigate the risk factors of the prevalence of malaria in Jimma town Ginjo Guduru using Binary logistic regression. Thus the following conclusions are made.

From the results, this study shows that the most important risk factors for the prevalence of malaria are sex, age, malaria species, Residence, stagnant of water and season. As we see from the final logit model the coefficient of all variables are positive providing for us evidence to conclude that all variables chosen (age, Residence, stagnant water and season can increase risk of the prevalence of malaria even though the level of effect (significance) differs.

Finally, the concerned body has to expand and maintain health promotions on designing appropriate interventions, tailored towards communities at high risk and effective treatment in home or community based care.

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[3] AlexanderMacedon de Oliveira, Malaria Branch, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS A-06, Atlanta, GA 30333. E-mail: [email protected]

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Alemuetal.Parasites&Vectors2012173http://www.parasit esandvectors.com/content/5/1/17

[18] AlexanderMacedon de Oliveira, Malaria Branch, Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, 1600 Clifton Rd, MS A-06, Atlanta, GA 30333. E-mail: [email protected]

[19] Am. J. Trop. Med. Hyg., 85(6), 2011, pp. 1002– 1007doi:10.4269/ajtmh.2011.11-0365

[21] Federal Ministry of Health and Ethiopian National Malaria Indicator Survey

[22] (ENMIS) (2008). Report on malaria situation in Ethiopia.

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Newyork.Mawili-Mboumba et al. Malaria Journal 2013, 12 :3

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[28] The Carter Center: Report of Malaria and Trachoma Survey in Ethiopia

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