Advances in Medicine
Volume 2014 (2014), Article ID 531074, 8 pages
http://dx.doi.org/10.1155/2014/531074

Research Article

Malaria, Typhoid Fever, and Their Coinfection among Febrile Patients at a Rural Health Center in Northwest Ethiopia: A Cross-Sectional Study

Meseret Birhanie,¹ Belay Tessema,² Getachew Ferede,¹ Mengistu Endris,² and Bamlaku Enawgaw³

¹Department of Medical Parasitology, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, P.O. Box 196, Gondar, Ethiopia
²Department of Medical Microbiology, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
³Department of Hematology & Immunohematology, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

Received 3 July 2014; Accepted 17 September 2014; Published 1 October 2014

Academic Editor: Spilios Manolakopoulos

Copyright © 2014 Meseret Birhanie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Malaria and typhoid fever are major public health problems in tropical and subtropical countries. People in endemic areas are at risk of contracting both infections concurrently. Objectives. The study was aimed at determining the prevalence and associated risk factors of malaria, typhoid, and their coinfection among febrile patients. Methods. A cross-sectional study was conducted on 200 febrile patients suspected for malaria and/or typhoid fever from April to May, 2013, at Ayinba Health Center, Northwest Ethiopia. Blood samples were collected for blood culture, Widal test, and blood film preparation. Data were analyzed using SPSS version 20 statistical software. Results. The prevalence of malaria was 36.5% (). Among these 32 (43.8%), 30 (41.1%) and 11 (15.1%) were positive for P. falciparum, P. vivax, and mixed infections, respectively. The seroprevalence of typhoid fever was 38 (19%), but 1 (0.5%) with blood culture. Malaria typhoid fever coinfection was 13 (6.5%). 2–5-year-old children and poor hand washing habit were significantly associated with malaria and typhoid infection, respectively (). Conclusions. The prevalence of malaria and typhoid fever was found high. Further studies should be done on the other determinants of malaria and typhoid fever coinfection in different seasons and different study areas.

1. Introduction

Malaria is one of the febrile illnesses and the most common fatal disease in the world caused by one or more species of plasmodium. These are Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, Plasmodium malariae, and Plasmodium knowlesi. Approximately half of the world population is at risk of malaria. Most of malaria cases and deaths occur in sub-Saharan Africa. According to the World malaria report 2011, there were about 216 million cases of malaria and an estimated 655,000 deaths in 2010 [1].

Malaria is the most communicable disease in Ethiopia and it accounts for about 30% of the overall disability adjusted life years lost. Approximately 68% (54.2 million) of the total population of 78 million lives in malaria risk areas. P. falciparum and P. vivax are the dominant species of malaria in Ethiopia, with 60% and 40% relative frequencies, respectively. Plasmodium falciparum is a predominant species in endemic areas and causes complicated disease and death in the country [2].

Typhoid fever (enteric fever) is a systemic prolonged febrile illness caused by certain Salmonella serotypes. Salmonella enterica serotype typhi (S. typhi) and Salmonella enterica serotype paratyphi (S. paratyphi A, S. paratyphi B, and S. paratyphi C) are species that cause typhoid fever. S. typhi is the most common serotype of salmonella that causes typhoid fever [3–6]. The estimated total number of world typhoid fever episode in 2010 was 13.5 million [5]. Poor disposal of human excreta, poorly equipped latrine with water facility, poor hand washing habit, and untreated water usage are the main cause of transmission of typhoid fever in developing countries [4, 5].

Malaria and typhoid fever are a major public health problem in tropical and subtropical countries caused by very different organisms, protozoa and Gram negative bacilli, respectively, and transmitted via different mechanisms [5–8]. People in endemic areas are at a risk of contracting both infections concurrently [9, 10]. There is a considerable overlap of signs and symptoms of malaria and typhoid fever [2, 11–14]. Thus the similarity of clinical features of both diseases leads to misdiagnosis and mistreatment of the febrile patients [11, 15]. So, reliable diagnostic method is important for effective management of cases to reduce misuse and wastage of drugs [11, 14–16]. So far, the prevalence of malaria, typhoid fever, their coinfection, and associated risk factorswere not well studied in Ethiopia. This study was conducted to determine the prevalence of malaria, typhoid fever, and their coinfection among febrile patients.

2. Methods

2.1. Study Area, Population, and Design

The study was conducted from April 2013 to May 2013 at Ayinba Health Center, Dembia district, Northwest Ethiopia. The altitude of the district ranges between 1,750 and 2,100 m above sea level. It has a population of more than 300,000 and the majority of its population depends on subsistence agriculture. The district is malarious (mainly P. vivax and P. falciparum) and covers an area of 1,270 Km². It is 27 km away from ancient city Gondar. All febrile patients (age ranged 2–80 years) suspected for malaria and/or typhoid fever who had not taken antimalarial drug and/or antibiotics within 2 weeks were included. Patients with underlying diseases were excluded from the study.

2.2. Specimen Collection and Processing

Data on the sociodemographic and clinical characteristics of the study participants were collected using a pretested structured questionnaire by interview. After interview, 10 mL blood sample was collected from adult patients by experienced laboratory technologist. Then, 7 mL blood was inoculated immediately to 45 mL Brain Heart Infusion broth. Similarly, 3-4 mL blood was collected from children and 1.5–2 mL blood was inoculated to 9 mL broth to isolate S. typhi and S. paratyphi. Both thick and thin blood films were prepared for malaria diagnosis and slide agglutination test was done for typhoid fever screening using somatic (O) and flagellar (H) antigens kits of S. typhi (TYDAL, Lab Care Diagnostics (India)). Antibody titration was performed for slide reactive samples. Antibody titer of ≥1 : 80 against O and H antigen of S. typhi was taken as a cut of value based on the manufacturer instruction. The blood smear was read at the health center by laboratory technicians and the result was reported. All blood films were reread by experienced microscopist at the University of Gondar Hospital laboratory who was blinded to initial results. Discrepancies occurred in the result by the two readers were solved by using the third experienced microscopist.

2.3. Statistical Analysis

The data was cleaned, edited, checked for completeness, entered to Epi Info version 3.5.3, and exported to SPSS version 20 for analysis. Chi-square and odds ratio (OR) by logistic regression were calculated to determine associated factors. value <0.05 was considered statistically significant.

2.4. Ethical Consideration

Ethical clearance was obtained from University of Gondar, School of Biomedical and Laboratory Sciences research and ethical committee. Permission was obtained from Dembia Woreda Health Office and Ayinba Health Center. Written informed consent was obtained from each of the volunteer study subjects or guardian of children. Positive results were given for nurses working in the health center for treatment according to the national treatment guideline.

3. Results

Sociodemographic Data. A total of 200 febrile patients suspected for malaria and/or typhoid fever were included in this study. About 60% of the study participants were males. The mean age was 24.24 ± 13.4 years and majority of the patients (41.5%) were within the age range of 12–25 years, and most of the patients were farmers (71.5%), rural residents (89%), and illiterate (61.5%) (Table 1).

Table 1: Sociodemographic data of study subjects in Ayinba Health Center, Northwest Ethiopia, April to May 2013.

Variables
Frequency
Percentage

Sex

    Male
120
60.0

    Female
80
40.0

Age in years

    2–5
12
6.0

    6–11
25
12.5

    12–25
83
41.5

    26–45
66
33.0

    ≥46
14
7.0

Residence

    Rural
178
89.0

    Urban
22
11.0

Education

    Illiterate
123
61.5

    Read and write
20
10.0

    Primary school
40
22.0

    Secondary school
7
3.5

    College/university
6
3.0

Occupation

    Farmer
152
76.0

    Merchant
7
3.5

    Civil servant
3
1.5

    Housewife
2
1.0

    Daily laborers
4
2.0

    Students
32
16.0

Prevalence of Malaria. Malaria was the most prevalent disease in the study area. From the total 200 febrile patients 73 (36.5%) were malaria positive. Of them, 32 (43.8%) were positive for P. falciparum, 30 (41.1%) were positive for P. vivax, and the remaining 11 (15.1%) were positive for both P. falciparum and P. vivax. The positivity rates of P. falciparum and P. vivax were 51.2% and 48.8%, respectively (Figure 1).

Figure 1: Frequency of malaria among febrile patients at Ayinba Health Center Northwest Ethiopia, April to May 2013.

Prevalence of Typhoid Fever. Of the total study subjects, 38 (64.4%) patients had antibody titers of ≥1 : 80 for both O and H antigens; of them, 7 (18.4%) had ≥ 1 : 320 titers. Different gram negative organisms were grown on the blood culture but there was only one growth of S. typhi (Table 2).

Table 2: The frequency distribution of titration result among slide agglutination test result in febrile patients at Ayinba Health Center, Northwest Ethiopia, April to May 2013.

Widal titer
Frequency
Percentage

No agglutination
5
8.5

1 : 20
6
10.2

1 : 40
10
16.9

1 : 80
14
23.7

1 : 160
17
28.8

≥1 : 320
7
11.9

Total
59
100.0

≥1 : 80 titer taken as positive titer.

3.1. Prevalence of Malaria and Typhoid Fever Coinfection

The titration result showed that the prevalence of coinfection was 13 (6.5%). Of them 8 (61.5%) were coinfected with P. falciparum, 3 (23.1%) were with P. vivax, and 2 (15.4%) were with mixed infection. The prevalence of coinfection using blood culture was 1 (0.5%).

3.2. Risk Factors Associated with Malaria, Typhoid Fever, and Their Coinfection

The age group 2–5 years old were significantly associated with malaria . The chi square analyses showed that this age group was significantly associated with malaria . There was no significant association of malaria and typhoid fever with sex, residence, occupation, and educational background (Table 3). Clinical features were not significantly associated with malaria and typhoid fever (Table 4).

Table 3: Prevalence of malaria and typhoid fever and their coinfection in relation to sociodemographic characteristics among febrile patients at Ayinba Health Center, Northwest Ethiopia, from April to May, 2013.

Variables
        Malaria
        Typhoid fever
        Coinfection

Positive (%)
Negative (%)

value
Reactive (%)
Nonreactive (%)

value
Positive (%)
Negative (%)

value

Sex

    Male
47 (39.2)
73 (60.8)
0.92
0.337
20 (16.7)
100 (83.3)
1.06
0.30
9 (7.5)
111 (92.5)
0.49
0.482

    Female
26 (32.5)
54 (67.5)
18 (22.5)
62 (77.5)
4 (5.0)
6 (95.0)

Age

    2–5
8 (66.7)
4 (33.3)
10.2
0.04
3 (25.0)
9 (75.0)
0.91
0.92
3 (25.0)
9 (75.0)
8.26
0.08

    6–11
8 (32.0)
17 (68.0)
6 (24.0)
19 (76.0)
2 (8.0)
23 (92.0)

    12–25
35 (42.2)
48 (57.8)
15 (18.1)
68 (81.9)
5 (6.0)
78 (94.0)

    26–45
20 (30.3)
46 (69.7)
13 (19.7)
53 (80.3)
3 (4.5)
63 (95.5)

    ≥46
2 (14.3)
11 (85.7)
2 (14.3)
12 (85.7)
0 (0.0)
14 (100)

Residence

    Rural
68 (38.2)
110 (61.8)
0.02
0.155
34 (19.1)
144 (80.9)
0.01
0.91
12 (6.7)
166 (93.3)
0.16
0.69

    Urban
5 (22.7)
17 (77.3)
4 (18.2)
18 (81.8)
1 (4.5)
21 (95.5)

Education

    Illiterate
44 (35.8)
79 (64.2)
1.36
0.851
24 (19.5)
99 (80.5)
0.18
0.99
8 (6.5)
115 (93.5)
1.19
0.879

    Read and write
8 (40.0)
12 (60.0)
4 (20.8)
16 (80.0)
1 (5.0)
19 (95.0)

    Primary school
17 (38.6)
27 (61.4)
8 (18.2)
36 (81.8)
3 (6.8)
41 (93.2)

    Secondary school
3 (42.9)
4 (57.1)
1 (14.3)
6 (85.7)
1 (14.3)
6 (85.7)

    College
1 (16.7)
5 (83.3)
1 (16.7)
5 (83.3)
0 (0.0)
6 (100)

Occupation

    Farmer
52 (35.4)
95 (64.6)
8.54
0.20
29 (19.7)
118 (80.3)
2.90
0.82
9 (6.1)
138 (93.8)
2.14
0.906

    Merchant
2 (28.6)
5 (71.4)
2 (28.6)
5 (71.4)
1 (14.3)
6 (85.7)

    Civil servant
1 (33.3)
2 (66.7)
0 (0.0)
3 (100.0)
0 (0.0)
3 (100)

    Housewife
0 (0.0)
2 (100.0)
0 (0.0)
2 (100.0)
0 (0.0)
4 (100)

    Daily laborers
0 (0.0)
4 (100.0)
1 (25.0)
3 (75.0)
0 (0.0)
2 (100)

    Student
14 (43.8)
18 (56.2)
6 (18.8)
26 (81.2)
3 (9.4)
29 (90.6)

: number; : Chi square.

Table 4: Prevalence of malaria and typhoid fever infection in relation to clinical features in febrile patients at Ayinba Health Center, Northwest Ethiopia, from April to May 2013.

Clinical features
        Malaria
        Typhoid fever
        Coinfection

Positive (%)
Negative (%)

value
Reactive (%)
Nonreactive (%)

value
Positive (%)
Negative (%)

value

Fever

    Continuous
27 (34.2)
52 (65.8)
0.30
0.58
15 (19)
64 (81)
0.00
0.99
4 (5.1)
75 (94.9)
0.44
0.5

    Intermittent
46 (38.0)
75 (62.0)
23 (19)
98 (81)
9 (7.4)
112 (92.6

Headache

    Yes
66 (36.1)
117 (63.9)
0.18
0.68
34 (18.6)
149 (81.4)
0.24
0.62
10 (5.4)
174 (94.6)
4.29
0.06

    No
7 (41.2)
10 (58.8)
4 (23.5)
13 (76.5)
3 (16.7)
13 (83.3)

Joint pain

    Yes
55 (36.2)
97 (63.8)
0.03
0.87
28 (18.4)
124 (81.6)
0.14
0.71
8 (5.3)
144 (94.5)
1.59
0.44

    No
18 (37.5)
30 (65.5)
10 (20.8)
38 (79.2)
5 (10.4)
43 (89.6)

Vomiting

    Yes
33 (38.8)
52 (61.2)
0.34
0.56
13 (15.3)
72 (84.7)
1.32
0.25
4 (4.7)
81 (95.3)
0.78
0.38

    No
40 (34.8)
75 (65.2)
25 (21.7)
90 (78.3)
9 (7.8)
106 (92.2)

Chill/rigor

    Yes
63 (38.0)
103 (62.0)
0.89
0.35
29 (17.5)
137 (82.5)
1.48
0.22
10 (6.0)
156 (94.0)
0.36
0.55

    No
10 (29.4)
24 (70.6)
9 (26.5)
25 (73.5)
3 (8.8)
31 (91.2)

Fatigue

    Yes
70 (38.5)
112 (61.5)
3.36
0.07
33 (18.1)
149 (81.9)
0.99
0.32
12 (6.6)
170 (93.4)
0.03
0.86

    No
3 (16.7)
15 (83.3)
5 (27.8)
13 (72.2)
1 (5.6)
17 (94.4)

Nausea

    Yes
49 (36.6)
85 (63.4)
0.00
0.98
29 (21.6)
105 (78.4)
1.84
0.18
9 (6.7)
125 (93.3)
0.03
0.86

    No
24 (36.4)
42 (63.6)
9 (13.6)
57 (86.4)
4 (6.1)
62 (93.9)

Diarrhea

    Yes
27 (37.0)
46 (63.0)
0.01
0.91
19 (25.7)
55 (74.3)
3.40
0.06
6 (8.2)
67 (91.8)
0.56
0.45

    No
46 (36.2)
81 (63.8)
19 (15.1)
107 (84.9)
7 (5.5)
120 (94.5)

Constipation

    Yes
14 (40)
21 (60)
0.05
0.83
6 (17.1)
29 (82.9)
0.49
0.82
4 (11.8)
30 (88.2)
1.87
0.17

    No
59 (35.8)
106 (64.2)
32 (19.4)
133 (80.6)
9 (5.4)
157 (94.6)

: number; : Chi square.

By using logistic regression analysis, bed net usage, impregnation of the bed net with chemicals, and history of travel to malaria endemic areas were not significantly associated risk factors of malaria (Table 5). But hand washing habit was significantly associated with typhoid fever infection (, OR = 2.893, 95% CI = 1.245–6.72) (Table 6).

Table 5: Determinants associated with malaria in febrile patients at Ayinba Health Center, Northwest Ethiopia, from April to May 2013.

Variables
Positive (%)
Negative (%)
value
COR
95% CI

History of travel

    Yes
10 (37.0)
17 (63.0)
0.95
0.974
0.420–2.256

    No
63 (36.4)
110 (63.6)

1

Bed net usage

    Yes
56 (38.9)
88 (61.1)

1

    No
17 (30.4)
39 (69.9)
0.26
1.685
0.354–1.326

Impregnation of bed net

    Yes
3 (18.8)
13 (81.2)

1

    No
53 (41.4)
75 (58.6)
0.366
1.889
0.476–7.497

: number; COR: crude odds ratio; CI: confidence interval.

Table 6: Determinants associated with typhoid fever in febrile patients at Ayinba Health Center, Northwest Ethiopia, from April to May 2013.

Variables
Typhoid fever

Reactive (%)
Nonreactive (%)
value
COR
95% CI

Water source

    Tap water
13 (23.2)
43 (76.8)

1

    River
6 (27.2)
16 (72.7)
0.77
1.24
0.403–3.820

    Spring
17 (16.0)
89 (84.0)
0.26
0.632
0.281–1.418

    Well
2 (12.5)
14 (87.5)
0.36
0.473
0.095–2.235

Uncooked food feeding

    Yes
14 (21.2)
52 (78.8)
0.57
1.234
0.590–2.57

    No
24 (17.9)
110 (82.1)

1

Toilet usage

    Yes
34 (19.1)
144 (80.9)

1

    No
4 (18.2)
18 (81.8)
0.91
0.941
0.299–2.961

Hand washing habit

    Poor
11 (35.5)
20 (64.5)
0.01
2.893
1.245–6.720

    Good
27 (16.0)
142 (84.0)

1

: number; COR: crude odds ratio; CI: confidence interval.

4. Discussion

The result of this study is comparable with the reports from Akoko State, Nigeria, 37.6% [17] and Imo State, Nigeria, 39% [18]. But it is less than the reports from Sierra Leone 62.3% [8] West Gojam, Ethiopia, 62% [19] and Ibadan, Nigeria, 44.3% [20] and higher than the reports in Ebony, Nigeria, 13.2% [21], Enugu, Nigeria, 22.2% [22], Kaduna State, Nigeria, 27% [7], Sokoto, Nigeria, 17% [23], and Benin 5% [24]. The discrepancy of the results between the studies might be due to seasonal variation and difference in geographical locations.

The positivity rates of P. falciparum and P. vivax were almost similar (51.2% and 48.8%, resp.). But according to the Federal Ministry of Health report the relative frequency of P. falciparum and P. vivax was 60% and 40%, respectively [2]. There is a great difference in frequencies of two plasmodium species. The difference in the frequencies of the two species might be the result of the prevention and control measures employed in the study area that have higher impact on P. falciparum than P. vivax. In case of P. vivax the dormant stage of the parasite can relapse at any time and relatively maintain its prevalence in the community.

In this study the prevalence of malaria was higher in males 64.4% than females 35.6% but there was no statistically significant association . While other studies showed in Sierra Leone, females (53.4%) are more affected than males (46.6%) [8]. This might be due to the fact that males are sleeping outside their house for agricultural purpose and have greater chance to travel to malaria endemic area for crop cultivation or daily labor. In this study there was significant association between age and malaria . This might be due to low immune response against malaria infection, inappropriate use of bed nets, and in appropriate use of antimalarial drugs in case of children.

The prevalence of the typhoid fever using Widal titration test was comparable with the study in Ebonyi, 21.2% [21], and Ibadan, 16.7% [20], and less than that of the reports in Sierra Leone, 31.4% [8], Kaduna State, 36.6% [7], Akoko, 73.9% [17], Lagos, 27.6% [25], Benin, 39% [24], and Imo State, Nigeria, 42% [18], but higher than the reports from West Gojam 5.8% [19] and Sokoto, Nigeria, 10.3% [23]. This might be due to the differences in Widal test kits, year of study, season, difference in cultural practices, and toilet facility. In addition, the antibody titer levels found in a healthy population may vary from time to time and in different areas, so it is difficult to establish a cut-off level of baseline antibody in a defined area and community [11].

The frequency of typhoid fever was greater in females (22.5%) than males (16.7%), but not statistically significant ; however, other previous studies in Sokoto, Nigeria, showed that the frequency of typhoid fever was 29.4% among males and 22.9% among females [23]. Females may acquire infection during food preparation, child care, and other household activities, thus increasing the frequency of typhoid fever.

In this study, the result of malaria and typhoid coinfection using Widal test is comparable with the reports of Ebonyi State 5.6% [21] but higher than the result of study in west Gojam, 2.8% [19] and lower than the reports in Bo city Sierra Leone 14.1% [8], Akoko 18.4% [17], Ibadan 12% [20], Enugu 16% [22], Kaduna 10.1% [7], Sokoto 10.3% [23], and Imo state 22% [18].

The result of the coinfection using blood culture was 0.5% and this is in agreement with the study in Ibadan 0.4% [20], Kaduna 0.5% [7], Sierra Leone 0.6% [8], and Ebony, 0.8% [21] but less than the study in Sokoto, 1.33% [23], Lagos, 19.95% [25], and Enugu, 26.6% [22]. Even though blood culture is a gold standard test for typhoid fever diagnosis, it can be affected by duration of infection, intake of antibiotics [11], and laboratory setup.

Because of the high prevalence of typhoid fever and malaria in the tropics, coinfections are common [9]. The high rate of typhoid and malaria coinfection using Widal test may be responsible for the frequent treatment of mixed infections. However, blood culture results showed that this rate of coinfection is only 0.5%. Hence, typhoid fever could cross-react with malaria using Widal test [6, 11] and lead to overdiagnosis of typhoid fever. Thus, overdiagnosis of typhoid fever leads to unnecessary exposure of patients to the side effects of antibiotics. In addition, misdiagnosis may result in delayed diagnosis and treatment of malaria and other acute febrile illnesses. This emphasized the importance of a reliable diagnostic test for typhoid fever. Study subjects, with poor hand washing habit, were more likely to be affected by typhoid fever ( value = 0.04, OR = 2.893, 95% CI = 1.245–6.72).

5. Conclusions

Malaria was the most prevalent disease among febrile patients in the study area. There was a substantial result discrepancy among Widal test and blood culture for the diagnosis of typhoid fever. Poor hand washing habit was significantly associated with typhoid fever. Further studies should be done on the other potential risk factors of malaria and typhoid fever coinfection in different seasons and different study areas. The community should be encouraged to use latrine to reduce the burden of high prevalence of typhoid fever infection in the area. The continued development of better diagnostic tools for both malaria and typhoid fever is still crucial.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Meseret Birhanie recruited the patients, collected the data, analyzed it, and wrote the draft paper. Belay Tessema conceived the study, supervised the collection of data, and revised the draft paper. Getachew Ferede ensured quality of the laboratory results and revised the draft paper. Mengistu Endris performed laboratory tests and interpreted and analyzed them along with Meseret Birhanie. Bamlaku Enawgaw interpreted the data collected and wrote the draft paper along with Meseret Birhanie. All authors read and approved the final version of the paper.

Acknowledgments

The authors would like to extend their appreciation to Ayinba Health Center professionals for their help in the collection of data and processing and analyzing of laboratory tests. The authors also want to express their great thanks to the study participants for their patience and cooperation.

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