statistical analysis on household factors influencing annual episodes of malaria

International Journal of

Science, Engineering and Innovative Research Volume 6, December 2015

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STATISTICAL ANALYSIS ON HOUSEHOLD FACTORS INFLUENCING ANNUAL

EPISODES OF MALARIA

K.O. Obisesan

Department of Statistics

University of Nigeria

A.S. Adelanwa

Total Quality Management Department

University College Hospital, Ibadan

Abstract

Malaria is responsible for about 66 per cent of all clinic visits in Nigeria. It accounts for 25%

of under-5 mortality, 30% childhood mortality and 11% maternal mortality. At least 50% of

the population will have at least one episode of malaria annually. Moreover, environment

dictates the incidence and prevalence of diseases all over the world and if timely action is not

taken, it may lead to diseases. Three (3) out of six (6) major towns in Ido local government

area are considered and accumulated one hundred and ninety one (191) individuals as

respondents using haphazard non probability sampling technique for selection. The obtained

data through questionnaire was presented on frequency table and charts while inferential

statistics were analysed using dummy variables in regression. It was revealed that majority of

the respondents suffered from one or more incidences of malaria in a year, where female had

the higher percentage of the incidence and there was high incidence of malaria among the adult

ages 30years and above. The qualitative predictor variable in regression analysis revealed

significant relationship between annual episode of malaria and number of members of

household, toilet type, absent ceiling, building type, disposable site and source of domestic

water. The ANOVA, F – test was significant for all predicted factors. Conclusively, in the view

of the discovery, it was therefore recommended that people need awareness on densely

populated area / household are more prone to experience more episodes of malaria incidence

than sparsely populated one, encouragement on utilization of closed domestic water system

instead of open system to avoid reservoir for mosquito, enlightenment on type toilet used and

avoid absence ceiling to prevent being a breeding site for mosquitoes, government to stage

more campaign against malaria especially for adult not for children under 5year alone and



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create a task force officer/ sanitary inspectors to checkmate sanitation of our environment to

avoid unkempt toilet habit which serves as breeding site for mosquitoes.

1 Introduction

Malaria is a disease caused by a parasite that is transmitted by an Anopheles mosquito.

The symptoms include fever, chills, headaches, muscle aches and general malaise (similar to

flu symptoms). This disease is prevalent in tropical or sub-tropical climates [8]. In Nigeria,

malaria causes the deaths of an estimated 250,000 children under the age of five every year.

Malaria is responsible for about 66 per cent of all clinic visits in Nigeria. Health workers are

sometimes forced to work overtime, and doctors and nurses can be on duty for over 12 hours a

day [21].

Malaria transmission can be reduced by preventing mosquito bites by distribution of

inexpensive mosquito nets and insect repellents, or by mosquito-control measures such as

spraying insecticides inside houses and draining standing water where mosquitoes lay their

eggs [15]. Mosquito nets help keep mosquitoes away from people and greatly reduce the

infection and transmission of malaria [22]. However, the inexpensive mosquito nets are not a

perfect barrier. Insecticides Treated Nets(ITNs) have been shown to be the most cost-effective

prevention method against malaria and are part of WHO’s Millennium Development Goals

(MDGs), but less than 2% of children in urban areas in Sub-Saharan Africa are protected by

ITNs and this process poses a significant logistical problem in rural environment [10].

In Nigeria, the burden of malaria is well documented and has been shown to be a big

contributor to the economic burden of disease in communities where it is endemic and is

responsible for annual economic loss of 132 billion Naira [19], [5] and [13]. It is estimated that

300, 000 deaths occurring each year, 60% of outpatient visits and 30% hospitalizations are all

attributable to malaria [6] and [18]. The disease is particularly virulent among pregnant women

and children under 5 years of age, due to their low levels of immunity. Also [20] indicated a

strong correlation between malaria and poverty has also long been recognized. Not only does

malaria thrive in poverty but it also impedes economic growth and keeps households in poverty.

This study aimed to investigate household factors influencing the annual episodes of malaria

among people living in Ido local governments Area, Oyo State. Specifically, the study is to

investigate the prevalence of malaria in Ido local government area of Oyo State, examine the



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sex and age group with high incidence of the malaria, and household components with

significant relationship with annual episodes of malaria.

WHO, (2014) [24] reported that commonly, the disease is transmitted by the bite of an

infected female Anopheles mosquito. This bite introduces the parasites from the mosquito's

saliva into a person's blood. The parasites then 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. Carabolla, (2013)

[2] opined that 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.

Malaria occurs mostly in poor tropical and subtropical areas of the world. In many of

the countries affected by malaria, it is a leading cause of illness and death. In 2010, [4] reported

that 3.4 billion people live in areas at risk of malaria transmission in 106 countries and

territories. An estimated 91% of deaths in 2010 were in the African Region. In 2012, an

estimated 627,000 people died of malaria and it caused 207 million clinical episodes - most

were young children in sub-Saharan Africa. Within the last decade, increasing numbers of

partners and resources have rapidly increased malaria control efforts [23].

1.1 Malaria in Nigeria

Jimoh et al., (2007) [13] reported that malaria is the 3rd leading cause of death for

children under five years worldwide, after pneumonia and diarrheal disease. Nigeria bears up

to 25 percent of the malarial disease burden in Africa, hence contributing significantly to the

one million lives lost per year in the region, which mostly consists of children and pregnant

women. Malaria in Nigeria is endemic and constitutes a major public health problem despite

the curable nature of the disease. Malaria-related deaths account for up to 11 percent of

maternal mortality. Additionally, they contribute up to 25 percent of infant mortality and 30

percent of under-5 mortality, resulting in about 300,000 childhood deaths annually. The disease

overburdens the already-weakened health system: nearly 110 million clinical cases of malaria

are diagnosed each year, and malaria contributes up to 60 percent of outpatient visits and 30

http://en.wikipedia.org/wiki/Anopheles

http://en.wikipedia.org/wiki/Circulatory_system

http://en.wikipedia.org/wiki/Plasmodium_falciparum

http://en.wikipedia.org/wiki/Plasmodium_vivax

http://en.wikipedia.org/wiki/Plasmodium_ovale

http://en.wikipedia.org/wiki/Plasmodium_malariae

http://en.wikipedia.org/wiki/Plasmodium_knowlesi

http://en.wikipedia.org/wiki/Blood_film

http://en.wikipedia.org/wiki/Antigen

http://en.wikipedia.org/wiki/Malaria_antigen_detection_tests

http://en.wikipedia.org/wiki/Polymerase_chain_reaction

http://en.wikipedia.org/wiki/Polymerase_chain_reaction

http://en.wikipedia.org/wiki/DNA

http://en.wikipedia.org/wiki/Endemic_%28epidemiology%29



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percent of admissions. Malaria also exerts a huge social and economic burden on families,

communities, and the country at large, causing an annual loss of about 132 billion naira in

payments for treatment and prevention as well as hours not worked.

Malaria is a major public health problem in Nigeria where it accounts for more cases

and deaths than any other country in the world. Malaria is a risk for 97% of Nigeria’s

population. The remaining 3% of the population live in the malaria free highlands. There are

an estimated 100 million malaria cases with over 300,000 deaths per year in Nigeria. This

compares with 215,000 deaths per year in Nigeria from HIV/AIDS. Malaria contributes to an

estimated 11% of maternal mortality

1.2 Malaria Transmission in Nigeria

The seasonality, intensity, and duration of the malaria transmission season vary

according to the five ecological strata that extend from the South to the North. These include

mangrove swamps, rain forest, guinea-savannah, Sudan-savannah, and Sahel-savannah. The

duration of the season decreases as one moves from the South to the North, being perennial in

duration in most of the South but lasting three months or less in the northeastern region

bordering Chad. The geographic location of Nigeria makes the climate suitable for malaria

transmission throughout the country. It is estimated that up to 97 percent of the country’s more

than 150 million people risk getting the disease. The remaining 3 percent of the population who

live in the mountains in southern Jos (the Plateau State) at an altitude ranging from 1,200 to

1,400 metres, are at relatively low risk for malaria.

1.3 Malaria in Oyo State

Gbadegesin (2013) [9] in Oyo State, the state’s Commissioner for Health at the

inauguration of the 2013 World Malaria Day Celebration said, ‘NO fewer than six million

attacks of malaria occur yearly’. Furthermore, he said malaria had serious health and socio-

economic impact, reiterated that six out of every 10 cases of patients attended to at the state

hospitals were as result of malaria. Children from age zero to four years have at least two to

four attacks in a year, while half of the adult population have at least one attack in a year.

To curb deaths from malaria, there is need for individuals to seek appropriate medical

attention and embark on self-help activities such as use of insecticide-treated nets and keep



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their environment clean to prevent mosquitoes. The government was committed to reducing

the malaria prevalence by 50 per cent yearly and so exceed the Millennium Development Goal

target. To achieve this, the state was in collaboration with its malaria implementing partners,

distributed 890,000 long-lasting insecticide-treated nets (LLIN); supplied malaria-designated

health care facilities with 300,000 doses of antimalarial drugs and 80,000 rapid diagnostic

tools, as well as 100 per cent malaria coverage for pregnant women.

1.4 Household Components

1.4.1 Drains, Ditches and Gutters

While agriculture provides the most productive urban vector breeding sites, drains and

ditches may provide more common habitats. [3] reported in a study in Dar es Salaam, Tanzania,

there were three times more anopheline-positive drains and ditches compared to agricultural

breeding sites, and anopheline presence was much more likely in drains that were blocked. [1]

reported that blockages are often due to poor sanitation and lead to reduced water flow and

accumulation of stagnant water pools which are ideal for mosquito breeding. Gutters provide

a similar breeding site for mosquitoes in both the wet and dry seasons and were specifically

noted by a recent study in Abeokuta, Nigeria.

1.4.2 Tyre Tracks

Tyre tracks were the second most-cited artificial vector breeding site. In Malindi,

Kenya, they accounted for as much as 29% of all water bodies that were positive for mosquitoes

[12]. Tyre tracks are more common in areas of high socioeconomic status, which tend to house

more vehicle owners while still having roads of sufficiently poor quality to lead to the

formation of potholes, tyre tracks, and other artificial breeding sites.

1.4.3 Swimming Pools

In another study in Malindi, unused swimming pools were found to provide a

particularly productive habitat for Anopheles immature stages [12]. Of the 250 habitats

identified in the study, 66 were swimming pools, and these were found to have the highest

abundance of Anopheles mosquitoes. Hotel workers, tourists, and domestic workers may be at

heightened risk of malaria transmission in areas with an abundance of unused pools.

1.4.4 Water Pipes



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Klinkenberg et al. (2008) [16] reported that water pipes can lead to breeding site

formation in a variety of ways, most frequently when they are broken and pools of water collect.

[11] stated that pipes often break as a result of poor installation or quality, clay soil expansion

and contraction, construction work, and as an opportunity to procure free water for sale or

consumption. Water sources that are further away from pipes are more likely to be anopheline

positive because water flow from nearby pipes may disturb the water surface, reducing the

breeding site quality [12]. Artificial water storage containers can also serve as breeding sites,

and car washing has been found to provide excellent habitats for larval development [14].

1.4.5 Other household factors

Better-quality housing decreases the risk of malaria as it minimizes entry points for

mosquitoes during the night. To illustrate this, a study in Gambia showed that houses with

malaria-infected children are more likely to have mud walls, open eaves, and absent ceilings

than those with uninfected children. Floors comprised of earth bricks are also associated with

lower malaria risk as inhabitants are more likely to sleep on raised beds to avoid ground

moisture, in turn eluding bites from An. gambiae mosquitoes which search for blood close to

the ground. Interestingly, a study in Burkina Faso found that electricity use was associated with

increased malaria risk, as the alternative of biomass fuel burning produces smoke that is

thought to deter mosquitoes from entering houses; however, electricity use in better-quality

housing would presumably not show this trend [25].

Fobil et al. (2011) opined hygiene, sanitation, and waste collection are key determinants

of malaria transmission which, while household responsibilities, have a community-level effect

on disease transmission. As an example, the more the households dispose of waste properly,

the lower the risk of liquid waste collecting in pools of stagnant water and forming vector

breeding sites. [17] stated that in Accra, Ghana, being connected to a toilet was found to be

even more important than waste removal in reducing community malaria mortality; however,

toilets are also potential areas of mosquito activity, and septic tanks within communities are a

potential source of vector breeding sites.

2. Materials and Methodology

A self-developed and well structured empirically related questionnaire was used. The

text items were twenty seven in number. It has three sections: A – Contain text item to elicit



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information on respondent’s socio-demographic variables. B– have items to elicit information

on respondent’s household components and C– have items to elicit information on respondent’s

malaria preventive measures. In order to ensure that the research instrument maintain

consistency in measuring what it intends to measure, a pilot study of 10% of the sample size

was carried out using 20 people from Akinyele local government area. Cronbach alpha

reliability coefficient was used to analyse data collected. For effective collection of data for

this study, the researcher employed the help of six (6) research assistants who were trained and

assessed for data collection process. A questionnaire was administered to the identified willing

and available members of household selected. The questionnaire was retrieved immediately

after duly and correctly filled. Exploratory data analysis was carried out on the collected data

to explore the salient features of the data and clean-up the error it contained. Descriptively,

qualitative data were presented on tables and charts, continuous data as mean and standard

deviations.

Inferentially, dummy variables in regression was employed whereby the episode of

incidence of malaria on individual is the response variable (Y) and the predictor variables are

age (X1), sex (X2), family income (X3), Disposable site (X4), Water drainage system (X5), Mud

wall (X6), Absent Ceiling (X7), Drinking water (X8), Domestic water (X9), toilet type (X10) and

building type (X11).

Regression Model is given by:

Y = 0 + 1X1 + 2X2 + 3X3 + 4X4 + 5X5 + 6X6 + 7X7 + 8X8 + 9X9 + ....+Ui (i)

Tool Model X1 X2 X3 X4 X5 X6 X7 X8 X9 ... X11

M1 Xi1 1 0 0 0 0 0 0 0 0

M2 Xi2 0 1 0 0 0 0 0 0 0

M3 Xi3 0 0 1 0 0 0 0 0 0

M4 Xi4 0 0 0 1 0 0 0 0 0

M5 Xi5 0 0 0 0 1 0 0 0 0

M11 Xi5 0 0 0 0 0 0 0 0 1

E(Y) for equation (i)

E(Y) = 0 + 1X1 + 2X2 + 3X3 + 4X4 + 5X5 + 6X6 + 7X7 + 8X8 + 9X9 + .... (ii)

For M1; X2 = 1, X3 =X4 =X5 =X6 =X7 =X8 =X9 =X10 =X11 = 0.

E(Y) = 0 + 1X1 + 2X2



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= 0 + 1X1 + 2(1)

= (0 + 2) + 1X1

For M2; X3 = 1, X2 = X4 =X5 =X6 =X7 =X8 =X9 =X10 =X11 = 0.

E(Y) = 0 + 1X1 + 3X3

= 0 + 1X1 + 3(1)

= (0 + 3) + 1X1

3. Analysis and Result

3.1 Data Visualization

Table 1 reveals that out of 191 respondents, 56 (29.3%) were residing at Apata, 60(31.4%)

were residing at Ido town and 75(39.3%) were residing at Apete area. The proportion was in

line with the population of the area. In Apata, Ido local government area covers only a sectional

part, Ido is a town but more rural than urban because majority of the land were used as

farmland. Apete is a settlement with more enlightening personnel including students than

illiterate due to its nearness to high institution of learning. 171 (89.5%) of the respondents are

Yoruba indicating Yoruba ethnic dominated area. Fifty six percent were Muslims among whom

53.9% polygamous home. Slightly above half of the respondents 50.8 percent had between

N10,000 and N30,000 as their family average monthly income. There were 53.4% female in

the study. The mean age of the respondents was 24years (S.D = 17), the minimum was 1 year

and maximum 70years.



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Table 1: Demographic Characteristics of the Respondents (N = 191)

Variable Response Frequency (n) Percentage (%)

Location Apata 56 29.3

Ido 60 31.4

Apete 75 39.3

Tribe Yoruba 171 89.5

Igbo 20 10.5

Religion Christianity 84 44.0

Islam 107 56.0

Family Type Monogamy 88 46.1

Polygamy 103 53.9

Amount of family

income (Monthly

average)

Below 10,000 7 3.7

10,000 – 30,000 97 50.8

31,000 – 50,000 29 15.2

51,000 – 70,000 19 9.9

71,000 – 90,000 2 1.0

Above 90,000 37 19.4

Sex Male 89 46.6

Female 102 53.4

Age group (yrs) 1 – 5 21 11.0

6 – 10 25 13.1

11 – 15 29 15.2

16 – 20 22 11.5

21 – 25 22 11.5

26 – 30 11 5.0

Above 30 61 31.9

Episode of Malaria

in a year

None 13 6.8

1 46 24.1



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2 61 31.9

3 22 11.5

4 17 8.9

5 and above 32 16.8

Figure 1 reveals that out of 191 respondents, only 6.8% did experience any incident of malaria

in a year, 24.1% experienced it once, 31.9% experience it twice, 11.5% experienced it three

times in a year, 8.9% experienced it four times a year and 16.8% experienced it five time and

the more. Figure 2 reveals that 145(75.9%) of the respondents used well water for domestic

household and 46(24.5%) utilized borehole water. Also, 79(41.4%) of the respondents drink

well water and 112(58.6%) drink borehole/sachet water. Figure 3 above shows that 75% of the

respondent’s disposable site is near while 25% disposable site is far from house they live.

Figure 4 above shows that 102(53.4%) of the respondents uses water closet, 50(26.2%) uses

pit latrine and 39(20.4%) uses bush. Figure 5 reveals that 87% of the respondents are running

open water drainage while 13% are running closed water drainage. Figure 6 shows that

137(71.7%) of the respondents were living in face-to-face type of building, 49(25.7%) were

living in flat while 5(2.6%) were living in duplex. Figure 7 reveals that 16% of the respondents’

houses are with mud while 84% without mud. Figure 8 reveals that 68(35.6%) of the

respondents were with absent ceiling houses and 123(64.4%) were without absent ceiling.

Figure 9 shows that 70% of the respondents electricity source was from government, 25% were

both government and self powered and 5% were only self powered.



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Figure 1: Annual Episode of Malaria Attack

Figure 2: Type of Domestic and Drinkable water used by the respondents

0

10

20

30

40

50

60

70

None 1 time 2 times 3 times 4 times 5 times and above

Fre

qu

en

cy

0

20

40

60

80

100

120

140

160

Domestic Water Drinkable Water

WellBorehole



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Figure 4: Respondent’s Toilet Type

Figure 5: Respondent’s Water Drainage

75%

25%

Near

Far

0

20

40

60

80

100

120

Water Closet Pit Latrine Bush

102

5039

Water Closet

Pit Latrine

Bush

87%

13%

Open

Closed

Figure 3: Nearness of Respondent’s Disposable Site



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Figure 6: Respondent’s Building Type

Figure 7: Respondent’s House with mud Wall

0

20

40

60

80

100

120

140

Face-to-face Flat Duplex

137

49

5

Face-to-face

Flat

Duplex

16%

84%Yes

No



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Figure 8: Respondent’s house without Ceiling

Figure 9: Respondent Electricity Source

0

20

40

60

80

100

120

140

Yes No

68

123

Yes

No

5%

70%

25%Self

Govt.

Self/Govt



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Model Summary

Model R

R

Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square Change F Change df1 df2 Sig. F Change

1 .571a .326 .277 2.163 .326 6.589 13 177 .000

a. Predictors: (Constant), Abse_Ceil_Yes, Age, Fem, Well1, Tiolet, Near, Mono, Open, Mud_No, Well2, No_HH,

Fam_Inc, Building

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 400.840 13 30.834 6.589 .000a

Residual 828.343 177 4.680

Total 1229.183 190

a. Predictors: (Constant), Abse_Ceil_Yes, Age, Fem, Well1, Tiolet, Near, Mono, Open, Mud_No, Well2, No_HH, Fam_Inc, Building

b. Dependent Variable: Episode

Coefficientsa

Model

Unstandardized Coefficients Standardized Coefficients

t Sig. B Std. Error Beta

1 (Constant) -1.792 1.499 -1.195 .234

Poly -.605 .494 -.119 -1.224 .223

Fam_Inc -.143 .161 -.090 -.890 .375

No_HH .238 .114 .191 2.094 .038

Male .009 .319 .002 .028 .978

Age .007 .009 .047 .739 .461

Far .968 .433 .165 2.233 .027

Toilet 1.165 .294 .364 3.966 .000

Closed -1.018 .816 -.135 -1.247 .214

Building 1.040 .300 .391 3.471 .001

Mud_Yes .590 .638 .085 .925 .356

Abse_Ceil_No 2.044 .415 .386 4.924 .000

Borehole2 -.103 .431 -.020 -.239 .811

Borehole1 -1.153 .445 -.194 -2.590 .010

a. Dependent Variable: Episode

3.2 Findings



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The R-squared is (0.326) indicating the factors considered could only explain 32.6% of the

causes of annual episode of malaria. The ANOVA indicating the combination of the factors

with F-value (6.589) was significant having p-value (0.0001).

The factors that were significantly influencing the annual episode of malaria include:

- Number of household member

- Disposable site

- Toilet type

- Building type

- Absence ceiling

- Source of domestic water

4 Discussion and Conclusion

The number of the household living together in the same house thus constitute high rate

of malaria for the members may be due to the fact that the belongs of the dwellers will be much

and that will serves as hidden place for the mosquitoes that gained access into the room.

Disposable site is another influencing factor which could be as a result in being near

the house. It will contribute towards high risk of insects leading to unhygienic environment.

The insects such mosquitoes will find their reservoir for surviving and breeding their new ones.

This is in line with Fobil et al. (2011) opined that hygiene, sanitation, and waste collection are

key determinants of malaria transmission which, while household responsibilities, have a

community-level effect on disease transmission. As an example, the more the households

dispose of waste properly, the lower the risk of liquid waste collecting in pools of stagnant

water and forming vector breeding sites. Another finding of Adeleke et al (2008) reported that

blockages are often due to poor sanitation and lead to reduced water flow and accumulation of

stagnant water pools which are ideal for mosquito breeding.

Toilet type was significantly associated with annual episode of malaria. This may be as

a result of dampness of the toilet facility available without being covered which can serve as a

reservoir/breeding site for mosquitoes causing malaria. This supported the finding of

Impoinvil, et al., (2008) reported that unused swimming pools were found to provide a

particularly productive habitat for Anopheles immature stages. Mourou et al. (2010) stated that

in Accra, Ghana, being connected to a toilet was found to be even more important than waste

removal in reducing community malaria mortality; however, toilets are also potential areas of



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mosquito activity, and septic tanks within communities are a potential source of vector

breeding sites.

Building type is an influencing factor of annual episode of malaria which shared the

same view with number of members of household.

Absence ceiling in a building is an affluence of annual episode of malaria. It

consequently create abode for mosquitoes in the house. Yamamoto et al. (2010) report that

houses with malaria-infected children are more likely to have mud walls, open eaves, and

absent ceilings.

Source of domestic water is an influencing factor of annual episode of malaria. The

study showed that the majority of the respondents depend on borehole which is not owned by

the household and this warrant them having more containers to reserve the water for domestic

use and consequently, it serves as mosquitoes reservoir. This finding supported report of

Keating et al., (2003) that artificial water storage containers can also serve as breeding sites,

and car washing has been found to provide excellent habitats for larval development.

Conclusively, study aimed at investigating significant factors influencing annual

episodes of malaria. Therefore, it concluded that female were prone to more episode of malaria

than male. Malaria episode was on increase among age 30years and above. Specifically, the

study discovered that factors like number of household member, Disposable site, Toilet type,

Building type, Absence ceiling and Source of domestic water are factors influencing factors of

annual episode of malaria which the stakeholder needs to work on.



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References

[1] Adeleke, M. A. Mafiana, C. F. Idowu, A. B. Adekunle, M. F.and Sam-Wobo S. O.,

(2008). “Mosquito larval habitats and public health implications in Abeokuta, Ogun

State, Nigeria,” Tanzania Journal of Health Research, vol. 10, no. 2, pp. 103–107

[2] Caraballo H (2013). "Emergency department management of mosquito-borne illness:

Malaria, dengue, and west nile virus". Emergency Medicine Practice 16 (5).

[3] Castro MC, Kanamori S, Kannady K, Mkude S, Killeen GF and Fillinger U (2010).

“The importance of drains for the larval development of lymphatic filariasis and malaria

vectors in dares salaam, United Republic of Tanzania,” PLoS Neglected Tropical

Diseases, vol. 4, no. 5, article e693

[4] Centre for Disease Control (CDC, 2010) Centers for Disease Control and Prevention

1600 Clifton Rd, MS A-06, Atlanta, GA 30333

[5] Federal Ministry of Health (FMOH, 2005). National Antimalarial Treatment Policy.

FMOH, National malaria and Vector Control Division, Abuja, Nigeria 2005

[6] Federal Ministry of Health (FMOH, 2009). Strategic Plan 2009-2013 "A Road Map for

Malaria Control in Nigeria. Nigeria and National Malaria Control Programme (NMCP).

Abuja, Nigeria. 2009.

[7] Fobil JN, Kraemer A, Meyer CG, et al. (2011). “Neighborhood urban environmental

quality conditions are likely to drive malaria and diarrhea mortality in Accra, Ghana,”

Journal of Environmental and Public Health, vol. 2011, Article ID 484010, 10 pages

[8] Freudenrich Craig, (2011). Mosquito Bites, Diseases and Protection. Discovery

Communication. http://www.animals.howstuffworks.com//

[9] Gbadegesin (2013) © Copyright 2004-2014 African Newspapers of Nigeria Plc | All

Rights Reserved | Site Designed by Tribune Web Team

[10] Hawley WA, Philips-Howard PA, ter Kuile FO (2003). “Community-wide effects of

permethrin-treated bed nets on child mortality and malaria morbidity in western

Kenya”. Amj. Trop. Med. Hyg. 68 (4 Suppl): 121-7, April.

[11] Himeidan YE and Rayah, E. E. A. (2008) “Role of some environmental factors on the

breeding activity of Anopheles arabiensis in New Halfa town, eastern Sudan,” Eastern

Mediterranean Health Journal, vol. 14, no. 2, pp. 252–259

[12] Impoinvil DE, Keating J, Mbogo CM, Potts MD, Chowdhury RR, and Beier JC, (2008).

“Abundance of immature Anopheles and culicines (Diptera: Culicidae) in different

http://www.ebmedicine.net/topics.php?paction=showTopic&topic_id=405

http://www.ebmedicine.net/topics.php?paction=showTopic&topic_id=405



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water body types in the urban environment of Malindi, Kenya,” Journal of Vector

Ecology, vol. 33, no. 1, pp. 107–116

[13] Jimoh A., Sofola O., Petu A., Okorosobo T. (2007). Quantifying the Economic Burden

of Malaria in Nigeria Using the Willingness to Pay Approach, Cost Effectiveness and

Resource Allocation 2007, 5:6doi:10.1186/1478-7547-5-6

[14] Keating J, Macintyre K, Mbogo C, et al. (2003). “A geographic sampling strategy for

studying relationships between human activity and malaria vectors in urban Africa,”

American Journal of Tropical Medicine and Hygiene, vol. 68, no. 3, pp. 357–365

[15] Kilama W., Ntoumi F. (2009). “Malaria: a research agenda for the eradication era”.

Lancet 374 (9700): 1480-2. http/linkinghub.elsevier.com. October.

[16] Klinkenberg, E. McCall, P. J Wilson, M. D. Amerasinghe, F. P. and Donnelly, M. J.

(2008). “Impact of urban agriculture on malaria vectors in Accra, Ghana,” Malaria

Journal, vol. 7, article 151.

[17] Mourou JR, Coffinet T, Jarjaval F, et al. (2010). “Malaria transmission and insecticide

resistance of Anopheles gambiae in Libreville and Port-Gentil, Gabon,” Malaria

Journal, vol. 9, no. 1, article 321.

[18] National Population Commission. Nigeria demographic and health survey. Federal

Republic of Nigeria Abuja, Nigeria. 2008

[19] Onwujekwe O., Chima R., Okonkwo P. (2000). Economic burden of malaria illness on

households versus that of all other illness episodes: a study in five malaria holo-endemic

Nigerian communities. Health Policy, 54 : 143-159

[20] Teklehaimanot A., Mejia P. Malaria and Poverty. Ann. N.Y. Acad. Sci. (2008). 1136:

32-37 New York Academy of Sciences. doi: 10.1196/annals.1425.037

[21] UNICEF, Nigeria (2009). Partnering to roll back malaria in Nigeria’s Bauchi State.

UNICEF At a glance: Nigeria.

[22] WHO (2004). Development of laboratory testing methods for malaria rapid diagnostic

tests. Report of the WHO informal consultation on laboratory methods for quality

assurance of malaria rapid diagnostic tests. Manila, World Organization Regional

Office for the Western Pacific, VOA News, July.

[23] WHO (2013). "World Malaria Report 2013". World Health Organization. Retrieved 13

February 2014.

[24] WHO (2014): "Executive summary and key points". World Malaria Report 2013.

World Health Organization. Retrieved 13 February 2013.

[25] Yamamoto S, Louis VR, Sié V and Sauerborn R (2010). “Household risk factors for

clinical malaria in a semi-urban area of Burkina Faso: a case-control study,”

http://www.who.int/entity/malaria/publications/world_malaria_report_2013/wmr2013_no_profiles.pdf?ua=1

http://www.who.int/entity/malaria/publications/world_malaria_report_2013/wmr13_summary_key_points.pdf?ua=1



ISSN: 2412-513X

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Transactions of the Royal Society of Tropical Medicine and Hygiene, vol. 104, no. 1,

pp. 61–65

statistical analysis on household factors influencing annual episodes of malaria

Healthcare