Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Prevalence and associated risk factors of malaria among adults in East Shewa Zone of Oromia Regional State, Ethiopia: a cross-sectional study

BMC Public HealthBMC series – open, inclusive and trusted201718:25

https://doi.org/10.1186/s12889-017-4577-0

Received: 9 January 2017

Accepted: 6 July 2017

Published: 17 July 2017

Abstract

Background

Malaria is one of the most important causes of morbidity and mortality in sub-Saharan Africa. The disease is prevalent in over 75% of the country’s area making it the leading public health problems in the country. Information on the prevalence of malaria and its associated factors is vital to focus and improve malaria interventions.

Methods

A cross-sectional study was carried out from October to November 2012 in East Shewa zone of Oromia Regional State, Ethiopia. Adults aged 16 or more years with suspected malaria attending five health centers were eligible for the study. Logistic regression models were used to examine the effect of each independent variable on risk of subsequent diagnosis of malaria.

Results

Of 810 suspected adult malaria patients who participated in the study, 204 (25%) had microscopically confirmed malaria parasites. The dominant Plasmodium species were P. vivax (54%) and P. falciparum (45%), with mixed infection of both species in one patient. A positive microscopic result was significantly associated with being in the age group of 16 to 24 years [Adjusted Odds Ratio aOR 6.7; 95% CI: 2.3 to 19.5], 25 to 34 years [aOR 4.2; 95% CI: 1.4 to 12.4], and 35 to 44 years [aOR 3.7; 95% CI: 1.2–11.4] compared to 45 years or older; being treated at Meki health center [aOR 4.1; 95% CI: 2.4 to 7.1], being in Shashemene health center [aOR = 2.3; 95% CI: 1.5 to 4.5], and living in a rural area compared to an urban area [aOR 1.7; 95% CI: 1.1 to 2.6)].

Conclusion

Malaria is an important public health problem among adults in the study area with a predominance of P. vivax and P. falciparum infection. Thus, appropriate health interventions should be implemented to prevent and control the disease.

Keywords

Malaria Prevalence Diagnosis Oromia Ethiopia

Background

Ethiopia is one of the malaria-epidemic prone countries in Africa. Malaria is prevalent in over 75% of the country’s area, with 68% of the total population being at risk [13]. The disease was responsible for about 12% of outpatient consultations and 10% of health facility admissions, and represents the largest single cause of morbidity [4, 5]. In 2010, there were more than four million clinical and confirmed malaria cases [6]. Ethiopia is at a high risk of epidemics of malaria due to climate and topography. Broad range of epidemics happen every 5–8 years in some areas due to climatic fluctuations and drought-related nutritional emergencies [79]. P. falciparum and P. vivax are the two predominant malaria parasites in the country, accounting for 60–70% and 30–40% of infections, respectively [10], transmitted by inoculation by mosquitos (Anopheles species including Anopheles arabiensis) [9].

As per the National Strategic Plan, the four major intervention strategies that are being applied in the country to combat malaria include early diagnosis and prompt treatment, selective vector control that involves the use of indoor residual spraying (IRS) and insecticide treated nets (ITNs) and environmental management [11]. Since 2007, malaria control interventions have been scaled-up and significantly reduced the prevalence of malaria [3]. Nevertheless, malaria is still among the leading causes of outpatient visits and hospital admissions in the country [4]. There is a scarcity of information on the prevalence of malaria among suspected malaria patients attending health facilities and the associated individual and household factors. Such information provides both a measure of the pre-test probability of a positive result as well as geographical and personal risk factors for having a positive diagnosis of infection for patients who present with symptoms of malaria. The World Health Organization (WHO) considers that when the slide positivity rate of all febrile patients with suspected malaria is less than 5%, then this region may be considered as transitioning into a state of pre-elimination of malaria infection [11].

Methods

Study setting and population

This study was conducted from October to November 2012 among adults aged 16 years or above who were attending five health centers in East Shewa zone of Oromia Regional State, Ethiopia. The main aim of the study was to determine the impact of health beliefs on time to presentation [12], and data were also collected on knowledge about malaria. East Shewa zone is malaria endemic area located in the Great Rift Valley in southeast Ethiopia. The climate is regarded as tropical, and this area contains a number of large lakes in a lowland setting. Based on the 2007 national census, East Shewa zone had a total population of 1,356,342; of whom 51% were men and 49% women [13]. The zone has three hospitals, 18 health centers and 296 health posts.

Malaria is the third leading cause of outpatient department (OPD) visits (36%) in East Shewa Zone. The study participants were adult patients aged 16 years or above who presented with malaria symptoms who gave blood for microscopic blood film examination at five health centers (Modjo, Meki, Batu, Bulbula, and Shashemene), each health center representing five woredas (districts). Patients who were mentally retarded, critically ill, or unwilling were excluded from the study.

Study design and data collection

The study design was a cross-sectional study. Quantitative data were collected using pre-tested structured questionnaires, containing questions on socio-demographic characteristics, and knowledge and perception about malaria, specifically developed and applied for data collection using the local language. The interview took place after a blood sample was drawn by finger prick. The trained laboratory technicians administered the questionnaire after obtaining informed consent from an individual with malaria symptoms. The recruitment of the study participants into the study at each site sequentially continued until the required sample size for each health center was completed.

Blood was collected by experienced laboratory technicians from the finger of patients. Then smears were prepared according to the WHO protocol [14]. Parasitemia and species was determined from thick and thin smear [15], respectively. Microscopic examination of thick films using high power magnification for the presence of parasites and parasite species identification using thin films under 1000× oil immersion objective was done. A minimum of 100 consecutive fields were counted in the thick blood film before a slide was classified as negative [16].

Statistical analysis

Data were entered using EPI INFO version 3.5.1 software package (CDC, Atlanta, GA, USA) and analyzed using SPSS version 16 (SPSS, Chicago, IL, USA) (Additional file 1). Initial analysis was done using Chi-squared test and subsequent analysis was performed by logistic regression after adjustment for potential confounding variables presented in Table 4. The data were originally collected for a study of malaria and concern about HIV testing [12] among 810 adults (16 years or above). Hence, there is no formal power calculation as this is a secondary analysis of these data. The original sample size was proportionally allocated to each health center considering the total number of suspected malaria patients tested during the previous three months (June–August, 2012) [12].

Results

Characteristics of the study participants

Eight hundred thirty eight individuals were approached and a total of 810 (97%) suspected malaria patients attending the health centers participated in the study, with 59% of patients from urban areas and 41% from rural areas. The median age of the patients was 27 years (ranging 16 to 80). 35% of participants had attended school to grade nine or above, while 30% had had no formal education (Table 1).
Table 1

Socio-demographic characteristics of the study participants

Variables

Health center

Total, n (%)

Modjo

Meki

Batu

Bulbula

Shashemene

Residence

 Rural

50

58

61

119

46

334 (41%)

 Urban

119

117

119

28

93

476 (59%)

Sex

 Female

70

92

86

81

68

397 (49%)

 Male

99

83

94

66

71

413 (51%)

Age

      

 15–24

78

46

83

48

58

313 (39%)

 25–34

58

80

66

48

47

299 (37%)

 35–44

24

44

24

37

13

142 (17%)

  > 45

9

5

7

14

21

56 (7%)

Educational status

 No formal education

33

61

39

76

38

247 (30%)

 Grade 4

28

33

26

7

9

103 (13%)

 Grade 5–8

32

35

47

42

17

173 (21%)

  > Grade 8

76

46

68

22

75

287 (35%)

Marital status

 Married

99

103

93

105

60

460 (57%)

 Single

70

58

80

41

74

323 (40%)

 Others

0

14

7

1

5

27 (3%)

Religion

 Muslim

15

44

84

125

57

325 (40%)

 Christian

154

131

96

22

82

485 (60%)

Occupation

 Farmer

35

69

48

85

11

248 (31%)

 House wife

25

30

26

15

24

120 (15%)

 Daily laborer

27

8

24

2

6

67 (8%)

 Gov. employee

21

14

16

5

17

73 (9%)

 NGO employee

28

2

16

0

51

97 (12%)

 Trader

5

24

6

4

3

42 (5%)

 Student

28

28

44

36

27

163 (20%)

Type of roof

 Thatched

32

36

34

64

17

183 (23%)

 Corrugated iron

137

139

146

83

122

627 (77%)

Knowledge about malaria

Seven hundred ninety (97%) of the patients believed that malaria is a major health problem in the study areas. The most commonly cited malaria symptoms included feeling cold (82%), headache (76%), fever (69%), vomiting (53%), sweating (48%), and loss of appetite (49%) (Table 2). The causes of malaria were reported to be mosquito bite by 759 (94%) individuals, hunger by 276 (34%) individuals, eating maize stalk by 199 (25%) individuals, and eating immature sugar cane in 196 (24%) individuals. 803 (99%) of the patients believed that malaria is a preventable disease.
Table 2

Malaria knowledge and household ownership of ITNs among the study participants

Variables

Health center

Total, n (%)

Modjo

Meki

Batu

Bulbula

Shashemene

Symptoms of malaria

 Fever

151

141

97

126

46

561 (69%)

 Feeling cold

143

136

131

138

117

665 (82%)

 Headache

127

87

125

136

137

612 (76%)

 Vomiting

81

82

105

75

84

427 (53%)

 Joint pain

84

52

56

31

9

232 (29%)

 Loss of appetite

114

136

66

53

32

401 (49.5%)

 Muscle pain

69

20

8

10

5

112 (14%)

 Nausea

85

47

11

41

50

234 (29%)

 Sweating

87

134

11

37

118

387 (48%)

Malaria is preventable

 Yes

169

172

177

146

139

803 (99%)

 No

0

3

3

1

0

7 (1%)

Household ownership of ITNs

 Yes

72

82

101

90

62

407 (50%)

 No

97

93

79

57

77

403 (50%)

Number of ITNs owned

 1

21

28

48

25

24

146 (36%)

 2

44

40

34

48

29

195 (48%)

 3

7

13

12

15

7

54 (13%)

 4

0

1

6

2

2

11 (3%)

Frequency of night slept under ITNs in the last 15 days

 All nights

39

28

50

56

41

214 (52%)

 Sometimes

24

35

22

19

21

121 (30%)

 Only few night

0

1

3

4

0

8 (2%)

 None of the nights

9

18

26

11

0

64 (16%)

Household ownership of ITNs

Fifty percent of patients with suspected malaria had any mosquito nets/ITNs in their household that can be used while sleeping. Out of those who had mosquito nets/ITNs 195 (48%) had two, 146 (36%) had only one, and 54 (13%) had three mosquito nets/ITNs. In response to a question asked about the frequency of nights slept under mosquito nets/ITNs in the last fifteen days; 214 (52%) reported all nights, 121 (30%) sometimes, and 64 (16%) none of the nights. 241 (59%) individuals reported sleeping under mosquito net/ITNs in the night prior to presentation to the health center (Table 2).

Prevalence of malaria parasites in the study population

Two hundred four (25%) individuals in the study population had microscopically confirmed malaria parasites in their blood sample. Among those who had a positive laboratory test result, the dominant Plasmodium species were P. vivax 111 (54%), followed by P. falciparum 92 (45%), the remaining one (0.5%) showed mixed infections of P. falciparum and P. vivax (Table 3).
Table 3

Prevalence of malaria among the study participants

Variables

No. of patients

No. positive slides (%)

Positive for P. f (%)

Positive for P. v (%)

Health center

 Modjo

169

30 (18%)

16 (53%)

14 (47%)

 Meki

175

70 (40%)

41 (59%)

29 (41%)

 Batu

180

43 (24%)

16 (37%)

26 (60%)

 Bulbula

147

19 (13%)

6 (32%)

13 (68%)

 Shashemene

139

42 (30%)

13 (31%)

29 (69%)

Residence

 Rural

334

96 (29%)

47 (49%)

48 (50%)

 Urban

476

108 (23%)

45 (42%)

63 (58%)

Sex

 Female

397

95 (24%)

43 (45%)

51 (54%)

 Male

413

109 (26%)

49 (45%)

60 (55%)

Age

 15–24

313

93 (30%)

40 (43%)

52 (56%)

 25–34

299

75 (25%)

37 (49%)

38 (51%)

 35–44

142

32 (22%)

14 (44%)

18 (56%)

  > 45

56

4 (7%)

1 (25%)

3 (75%)

Type of roof

 Thatched

183

56 (31%)

25 (45%)

30 (54%)

 Corrugated iron

627

148 (24%)

67 (45%)

81 (55%)

Household owned at least one ITNs

 Yes

407

99 (24%)

46 (46%)

52 (52%)

 No

403

105 (26%)

46 (44%)

59 (56%)

Frequency of night slept under ITNs in the last 15 days

 All nights

188

45 (24%)

20 (44%)

24 (53%)

 Almost all nights

26

5 (19%)

3 (60%)

2 (40%)

 Sometimes

121

31 (26%)

13 (42%)

18 (58%)

 Only few night

8

2 (25%)

1 (50%)

1 (50%)

 None of the nights

64

16 (25%)

9 (56%)

7 (44%)

Sought treatment before visiting the health center

 Yes

75

18 (24%)

9 (50%)

9 (50%)

 No

735

186 (25%)

83 (45%)

102 (55%)

Number of days after illness onset

  ≤ 2 days

140

27 (19%)

9 (33%)

18 (67%)

  > 2 days

670

177 (26%)

83 (47%

93 (52%)

One individual had infection with both Plasmodium falciparum and vivax

Factors associated with malaria positivity

Among the potential determinants explored regarding the positivity for malaria age being 16 to 24, 25 to 34, and 35 to 44 years compared to an age of 45 years or more; being in Meki or Shashemene compared to Modjo health centers; living in a rural residence compared to living in an urban area were significantly associated with positive test result for malaria. Compared to those aged 45 years or more, those who were in the age group of 16 to 24 years [Adjusted OR (aOR) = 6.7; 95% CI (2.3 to 19.5)], those who were in the age group of 25 to 34 years [aOR =4.2; 95% CI (1.4 to 12.4)], those who were in the age group of 35 to 44 years were more likely to have positive test result for malaria [aOR =3.7; 95% CI (1.2 to 11.4)] as compared to those in the age group of above 44 years. Those who were living in rural areas were more likely to have positive test result for malaria [aOR =1.7; 95% CI (1.1, 2.6)] as compared to those who were living in urban area (Table 4).
Table 4

Factors associated with test positivity for malaria

 

Test positivity

Variables

Negative

Positive

Crude OR (95% CI)

Adj. OR (95% CI)

Health center

 Modjo

139

30

1

1

 Meki

105

70

3.1 (1.9, 5.1)

4.1 (2.4,7.1) **

 Batu

137

43

1.5 (0.9, 2.5)

1.7 (0.9, 2.9)

 Bulbula

128

19

0.7 (0.4, 1.3)

0.6 (0.3, 1.2)

 Shashemene

97

42

2.0 (1.2, 3.4)

2.6 (1.5, 4.5)*

Residence

 Rural

238

96

1.4 (0.9, 1.9)

1.7 (1.1, 2.6)*

 Urban

368

108

1

1

Sex

 Female

302

95

0.9 (0.6, 1.2)

0.8 (0.6, 1.1)

 Male

304

109

1

1

Age

 15–24

220

93

6.0 (1.9, 15.6)

6.7(2.3, 19.5)*

 25–34

224

75

4.4 (1.5, 12.4)

4.2(1.4, 12.4)*

 35–44

110

32

3.8 (1.3, 11.3)

3.nn(1.2, 11.4)*

  > 45

52

4

1

1

Type of roof

 Thatched

127

56

1.4 (0.9, 2.1)

1.5 (0.9, 2.3)

 Corrugated iron

479

148

1

1

Household ownership of ITNs

 No

298

105

1.1 (0.8, 1.5)

0.9 (0.6, 1.3)

 Yes

308

99

1

1

Sought treatment before visiting the health center

 Yes

57

18

0.9 (0.5, 1.6)

0.6 (0.4, 1.2)

 No

549

186

1

1

Number of days after illness onset

  ≤ 2 days

269

84

0.9 (0.6, 1.2)

1.3 (0.9, 1.9)

  > 2 days

337

120

1

1

*Significance level of <0.05, **Significance level of <0.001

Discussion

This study provides information regarding the prevalence of a positive diagnosis of malaria and its associated risk factors among adults with suspected malaria in malaria endemic areas located in the Great Rift Valley of southeast Ethiopia. This study has demonstrated that in a population of individuals with malaria symptoms, the prevalence of malaria was 25.2%, of which P. vivax and P. falciparum accounts for 54% and 45%, respectively. The present study depicts that being in the productive age group, living in Meki or Shashemene areas, and living in rural areas are risk factors for malaria infection in this population.

A significant number of P. falciparum cases occur in Ethiopia during the peak malaria transmission mainly in October. The national figure of 30%–40% of malaria cases in Ethiopia is due to P. vivax [10]. In contrast, in this study the prevalence of P. vivax is higher than P. falciparum. Likewise, P. vivax was the main causative agent of malaria in Oromia Regional State of Ethiopia, which accounted for 60% of slide-positive cases [3]. A study conducted in East Shewa indicated a proportion of 53% for P. falciparum and 47% for P. vivax [17]. The higher proportion of P. vivax in our study is consistent with studies conducted in other parts of Ethiopia [16, 1820], which indicates trend shift of species composition. Conversely, the dominance of P. falciparum was indicated by other studies conducted in different parts of Ethiopia [2123]. This could be explained by the fact that the prevention and control activities of malaria in Ethiopia [20] mainly focus on P. falciparum as it is deadlier than P. vivax [24]. Other possible reasons might be climate variability or that P. vivax might have developed resistance for Chloroquine.

Appropriate utilization of ITNs is one of the key interventions for the prevention of malaria [3]. In the present study, 50% of households had at least one ITN. Similarly, according to a malaria indicator survey conducted in 2011, 55% of households residing in malaria-prone areas of Ethiopia owned at least one mosquito net (of any type), and Oromia was found to have the lowest net ownership (44%) [3]. It is estimated that 42% of households in Africa owned at least one ITN in mid-2010 [25]. Moreover a study conducted in Eastern Ethiopia indicated an ITN ownership of 62% [26]. To the contrary, a study conducted in malaria epidemic prone areas of Ethiopia indicated that the overall ITN distribution was 98% [27]. The difference for this high value compared to our data could be explained by the reason that the present study is not a household survey which might have underestimated it. On the other hand, 41% of households without a single ITN represent a public health concern which needs to be addressed. The mean possession of bed net of 1.82 per household reported in our study is consistent with the report (1.73 /household) from study conducted in Ghana [28]. However, it is by far higher than the findings of malaria indicator survey conducted in 2011 (mean 0.7 /household) [3]. The ITN utilization of our study is high as compared to the study conducted in Eastern Ethiopia (21.5%) [26].

The use of representative samples with a high response rate of 97% is the strength of the present study, however it has some limitations. This study is a facility based survey. Therefore, it does not represent the situation in the whole population but it already provides reliable important data. Data collection relied on information given by the interviewees. Practices such as presence, type and use of ITN could not be verified by direct observation. Moreover, the diagnosis of malaria did not include PCR (Polymerase Chain Reaction). As this was a pragmatic study in a real-life rural environment, blood film was available to diagnose malaria infection, rather than rapid diagnostic testing which has a higher sensitivity [29]. On top of that, microscopic tests of malaria were done by the laboratory technicians in the different settings who didn’t get training about the determination of test positivity which could have led to bias due to interpersonal variation. However, these details reflect the ‘real-world’ nature of our data, that were based on usual clinical practice, and do not necessarily invalidate our findings.

Conclusions

In conclusion, findings of this study indicate that malaria is an important public health problem among adults in East Shewa with the predominance of P. vivax and P. falciparum; and being in the productive age group, living in Meki or Shashemene, and living in rural areas, were risk factors for malaria infection. According to WHO when the slide positivity rate of all febrile patients with suspected malaria is less than 5%, the country could consider transitioning into “pre-elimination” [11]. Therefore, a test positivity rate of 25% at health facility level indicates that malaria is a major burden in the zone, which is not in line with the national strategic plan for malaria prevention control and elimination in Ethiopia. Moreover, there is a gap regarding the mosquito nets/ITNs ownership and utilization. Hence, more focus should be given to environmental sanitation as well as the consistent utilization of ITNs should be promoted by health workers and health extension workers in particular. In addition, the number of mosquito nets/ITNs supplied to households should be increased in order to assure adequate mosquito nets/ITNs ownership in each household. Further study using direct observation at sleeping time rather than reported use is important to assess ownership proper utilization of ITNs. Special attention should be given to those living in the rural area of the zone. Furthermore, there was an increased risk of malaria infection among the younger age group as well as among those living in Meki and Shashemene areas which needs a further investigation.

Abbreviations

IRS: 

Indoor Residual Spraying

ITN : 

Insecticide Treated Net

OPD: 

Outpatient Department

PCR: 

Polymerase Chain Reaction

WHO: 

World Health Organization

Declarations

Acknowledgements

Our thanks go to the Addis Ababa University School of Public Health for supporting the study. We are grateful to the Oromia Regional Health Bureau, East Shewa Zone Health Department and respective District and Town Administration Health Offices for their support in facilitating the implementation of this study. Finally, we are very grateful for data collectors and study participants who willingly took part in this study. This study would not have been possible without their involvement. This work was supported by the University of Nottingham and Nottingham University Hospital Charity.

Funding

This work was funded by the University of Nottingham and Nottingham University Hospital Charity. The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to the data in the study and had full responsibility for the decision to submit.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Authors’ contributions

FT was involved in proposal writing, designed the study and participated in coordination, supervision and the overall implementation of the project, analyzed the data, drafted and finalized the manuscript. WD and AWF conceived the study and participated in all stages of the study and revision of the manuscript. AWF obtained funding for the study. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Research and Ethics Committee of the School of Public Health at the College of Health Sciences of Addis Ababa University and University of Nottingham. Written informed consent was obtained from each participant and confidentiality was maintained. Lastly, information and education was given to the study participants with regard to malaria signs and symptoms, early diagnosis and adequate treatment, and its prevention methods.

Consent for publication

Not applicable.

Competing interests

The authors have declared that there are no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Public Health, College of Health Sciences and Medicine, Jigjiga University
(2)
Division of Epidemiology and Public Health, University of Nottingham
(3)
Department of Preventive Medicine, School of Public Health, College of Health Sciences, Addis Ababa University

References

  1. Gebreyesus AT, Deressa W, Witten KH, Getachew A, Seboxa T. Malaria. In: Berhane Y, Hailemariam D, Kloos H, editors. Epidemiology and ecology of health and disease in Ethiopia. Addis Ababa: Shama Books; 2006. p. 556–76.Google Scholar
  2. Federal Ministry of Health (FMOH). Malaria and Other Vector-borne Diseases Control Unit. Addis Ababa, Ethiopia: Federal Ministry of Health of Ethiopia; 1999.Google Scholar
  3. Federal Ministry of Health (FMOH): Ethiopia National Malaria Indicator Survey 2011: Technical Summary Ethiopia: Ministry of Health of Ethiopia; 2012.Google Scholar
  4. Federal Ministry of Health (FMOH): Health and health-related indicators, 2009/2010 Addis Ababa, Ethiopia: Ministry of Health of Ethiopia, 2011.Google Scholar
  5. C-Change, FHI 360. Essential Malaria Actions: Ethiopia December 2012; 1–7.Google Scholar
  6. World Malaria Report 2011: www.who.int/malaria/world_malaria_report_2011/en/
  7. Federal Ministry of Health (FMOH): Ethiopia National Malaria Indicator Survey 2007. Addis Ababa: FMOH; 2008: 1–98.Google Scholar
  8. Fontaine RE, Najjar AE, Prince JS. The 1958 malaria epidemic in Ethiopia. Am J Trop Med Hyg. 1961;10:795–803.View ArticlePubMedGoogle Scholar
  9. PMI. Malaria operational plan (MOP) Ethiopia. 2012:8–31.Google Scholar
  10. Carter C. Annual malaria control program review enhancing impact through integrated strategies malaria programs Ethiopia and Nigeria. Atlanta: Georgia; 2012.Google Scholar
  11. Federal Ministry of Health (FMOH). National strategic plan for malaria prevention, control and elimination in Ethiopia, 2011–2015. Addis Ababa, Ethiopia: 2010.Google Scholar
  12. Tadesse F, Deressa W, Fogarty AW. Concerns about covert HIV testing are associated with delayed presentation in Ethiopian adults with suspected malaria: a cross-sectional study. BMC Public Health. 2016;16:102. doi:https://doi.org/10.1186/s12889-016-2773-y.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Central Statistical Agency (CSA). The 2007 population and housing census of Ethiopia: Results for Oromia Regional State. Addis Ababa: 2007.Google Scholar
  14. World Health Organization (WHO). A comparative study of three rapid diagnostic tests (RDTs) for malaria diagnosis in Oromia Regional State, Ethiopia. World Health Organization, Geneva, Switzerland, 2000.Google Scholar
  15. Bekele S, Mengistu L, Abebe A, Daddi J, Girmay M, Berhanu E. Evaluation of the performance of CareStartTM malaria Pf/Pv combo and Paracheck PfR tests for the diagnosis of malaria in Wondo genet, southern Ethiopia. Acta Trop. 2009;111:321–4.View ArticleGoogle Scholar
  16. Alemu A, Abebe G, Tsegaye W, Golassa L. Climatic variables and malaria transmission dynamics in Jimma town. South West Ethiopia Parasit Vectors. 2011;4:30.View ArticlePubMedGoogle Scholar
  17. Deressa W, Chibsa S, Olana D. Treatment seeking of malaria patients in east Shewa zone of Oromia. Ethiop J Health Dev. 2003;17(1):9–16.View ArticleGoogle Scholar
  18. Tesfaye S, Belyhun Y, Teklu T, Mengesha T, Petros B. Malaria prevalence pattern observed in the highland fringe of Butajira, Southern Ethiopia: a longitudinal study from parasitological and entomological survey. Malar J. 2011;10:153.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Woyessa A, Gebre-Michael T, Ali A. An indigenous malaria transmission in the outskirts of Addis Ababa, Akaki town and its environs. Ethiop J Health Dev. 2004;18:2–7.View ArticleGoogle Scholar
  20. Chala B, Petros B. Malaria in Finchaa sugar factory area in western Ethiopia: assessment of malaria as public health problem in Finchaa sugar factory based on clinical records and parasitological surveys, western Ethiopia. J Parasitol Vector Biology. 2011;3:52–8.Google Scholar
  21. Ghebreyesus TA, Haile M, Witten KH, Getachew A, Yohannes M, Lindsay SW, et al. Household risk factors for malaria among children in the Ethiopian highlands. Trans R Soc Trop Med Hyg. 2000;94:17–21.View ArticlePubMedGoogle Scholar
  22. Karunamoorthi K, Bekele M. Prevalence of malaria from peripheral blood smears examination: a 1-year retrospective study from the Serbo health center, Kersa Woreda. Ethiopia J Infect Public Health. 2009;2:171–6.View ArticlePubMedGoogle Scholar
  23. Ramos J, Reyes F, Tesfamariam A. Change in epidemiology of malaria infections in a rural area in Ethiopia. J Travel Med. 2005;12:155–6.View ArticleGoogle Scholar
  24. Baird J. Neglect of plasmodium vivax malaria. Trends Parasitol. 2007;23:533–9.View ArticlePubMedGoogle Scholar
  25. World Health Organization (WHO), Report WM. World Health Organization, Geneva. Switzerland. 2010;2010Google Scholar
  26. Biadgilign S, Reda A, Kedir H. Determinants of ownership and utilization of insecticide-treated bed nets for malaria control in eastern Ethiopia. J Trop Med. 2012:1–7. doi:https://doi.org/10.1155/2012/235015.
  27. Animut A, Gebremichael T, Medhin G, Balkew M, Bashaye S, Seyoum A. Assessment of distribution, Knowledge and Utilization of Insecticide Treated Nets in Selected Malaria Prone Areas of Ethiopia. Ethiop J Health Dev. 2008;22(3):268–74.Google Scholar
  28. Adjei JK, Gyimah SO. Household bed net ownership and use in Ghana: implications for malaria control. Canadian Stud Popul. 2012;39:15-30. Google Scholar
  29. William MS, Charles PC, Douglas AO, Billie AJ, Charlotte MT, Susan HB, et al. Diagnostic performance of rapid diagnostic tests versus blood smears for malaria in US clinical practice. Clin Infect Dis. 2009;49(6):908–13. doi:https://doi.org/10.1086/605436.View ArticleGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement