Skip to main content
Erschienen in: Malaria Journal 1/2019

Open Access 01.12.2019 | Research

Knowledge of malaria prevention among pregnant women and non-pregnant mothers of children aged under 5 years in Ibadan, South West Nigeria

verfasst von: Kelechi Elizabeth Oladimeji, Joyce Mahlako Tsoka-Gwegweni, Elizabeth Ojewole, Samuel Tassi Yunga

Erschienen in: Malaria Journal | Ausgabe 1/2019

Abstract

Background

Adequate knowledge of malaria prevention and control can help in reducing the growing burden of malaria among vulnerable groups, particularly pregnant women and children aged under 5 years living in malaria endemic settings. Similar studies have been conducted but with less focus on these vulnerable groups. This study assessed knowledge of malaria prevention and control among the pregnant women and non-pregnant mothers of children aged under 5 years in Ibadan, Oyo State, South West Nigeria.

Methods

In this cross sectional study, data on socio-demographic, clinical and knowledge on malaria prevention was collected using interviewer administered questionnaires from consenting study participants attending Adeoyo maternity hospital between May and November 2016. Data was described using percentages and compared across the two maternal groups in the study population. Knowledge scoring from collected data was computed using the variables on causes, symptoms and prevention of malaria and thereafter dichotomised. Multivariate analyses were used to assess the interactive effect of socio demographic and clinical characteristics with malaria knowledge. Level of statistical significance was set at p < 0.05.

Results

Of the 1373 women in the study, 59.6% (818) were pregnant women while 40.4% (555) were mothers of children aged under 5 years. The respondents mean age was 29 years ± 5.2. A considerable proportion of both the pregnant women (n = 494, 60.4%) and the non-pregnant mothers of children aged under 5 years (n = 254, 45.8%) did not have correct knowledge on malaria prevention measures based on our assessment threshold (p < 0.001). Having a tertiary level education was associated with better knowledge on malaria (4.20 ± 1.18, F = 16.80, p < 0.001). Multivariate analyses showed that marital status, educational attainment, gravidity, and HIV status were significantly associated with knowledge of malaria prevention and control.

Conclusion

The findings indicate that socio-demographic factors such as marital and educational status greatly influence knowledge on malaria prevention and control measures. Key health stakeholders and authorities need to implement strategies and direct resources to improve the knowledge of mothers on malaria prevention and control. This would stem the tides of malaria related deaths among pregnant women and children aged under 5 years.
Abkürzungen
ACT
artemisinin-based combination therapy
BREC
biomedical research ethics committee
GTS
global technical strategy
HIV
human immunodeficiency virus
IDI
in-depth interviews
IPT
intermittent preventive treatment
IPTp
intermittent preventive treatment of malaria in pregnancy
ITN
insecticide treated nets
IVM
integrated vector management
KAP
knowledge, attitudes, and practices
LLIN
long-lasting insecticide-treated nets
NMSP
national malaria strategic plans
SPSS
statistical package for social sciences
WHO
World Health Organization

Background

Malaria is a major public health problem in ninety-one countries world-wide with sub-Sahara Africa bearing 80% of the disease burden [1]. Malaria remains endemic in Nigeria where the parasitic disease disproportionately affects children aged under 5 years and pregnant women compared to the rest of the population groups [26]. In pregnancy, malaria increases the risk of maternal anaemia, spontaneous abortions, stillbirths, premature deliveries, intra-uterine growth retardation and low birth weight babies, and these are all important causes of infant mortality [7]. Also, more than 70% of all malaria deaths occur in children aged under 5 years [4, 8]. The scope of malaria control is changing worldwide with more emphasis on community and individual participation. Health education can improve participation in malaria control, when such education is designed to address gaps in the knowledge, attitudes and practice of individuals in the communities [4, 9]. Nigeria has implemented three national malaria strategic plans (NMSP) till date, and is presently implementing a fourth NMSP (2014–2020). This fourth NMSP aims to achieve pre-elimination status and reduce malaria-related deaths to zero by 2020 [10].
Evidence from malaria knowledge, attitudes, and practices (KAP) studies reported that misconceptions on malaria transmission and risk factors still exist with adverse impact on malaria control programmes [11, 12]. Findings from a study conducted by Singh et al. in rural areas of Northern Nigeria revealed that although knowledge about malaria prevention measures was high (90%), it was poorly reflected in their practices (16%) [13]. Another study by Adebayo et al. [14] assessed the knowledge of malaria prevention among mothers of children aged under 5 years and pregnant women in a rural community in Southwest Nigeria. This latter study also found poor knowledge and utilization of malaria prevention measures among majority of the caregivers in the rural study area [14]. Considering the vulnerability of both children aged under 5 years and pregnant women to malaria [10, 15], this study aimed to determine the knowledge of malaria prevention and management among pregnant women and non-pregnant mothers of children aged under 5 years seeking health care at one of the main secondary maternity hospitals in Ibadan, Nigeria. Only few studies have assessed knowledge on malaria prevention among mothers in hospital-based setting. This study sought to fill this gap and provide new insights on the depth of knowledge gaps. The findings will help to improve implementation of integrated malaria control strategies. It will also be essential in establishing epidemiological and behavioural baseline indicators to evaluate and improve progress by malaria control programmes.

Methods

Ethics statement

Prior to data collection, ethical approval was obtained from the Oyo state ministry of health ethics committee (IRB AD13/479/1035) in Nigeria and from the biomedical research ethics committee (BREC- BE199/16) of the University of KwaZulu-Natal, South Africa. Study participants voluntarily signed written informed consent forms without any incentives. They consented because they believed their responses would contribute to increased effective control of malaria. The participants were also assured of confidentiality. The data collection tool was translated to both Yoruba, which is the dominant local language, and English language.

Study design and setting

Using a cross sectional study design, this survey was conducted between May and November 2016. The study recruitment site was the Adeoyo Maternity Hospital located in Ibadan North East-Oyo state, Nigeria. The elevation of the study area lies between 64 and 414 mm (Fig. 1). The study setting and site have been described in another publication [16]. The hospital is situated in the semi-urban community of Yemetu-Adeoyo in Ibadan. This facility is one of the oldest of its kind in Nigeria (opened in 1927) that provides both primary and secondary level maternal and child health care [17].

Study population and eligibility criteria

A multi stage sampling technique was employed with the aim of ensuring that the study population was representative of pregnant women and non-pregnant mothers of children aged under 5 years in the study area. The first stage involved identification of the geographical area and the second stage involved selection of the specific health facility from a list of facilities within the identified geographical area. In the third stage, participants were randomly selected from the selected health facility. The study population included consenting pregnant women and mothers of children under 5 years old attending the study site for health care. Mothers who were residents in Ibadan and regular attendees of the study site for health care were eligible to participate in the study. Criteria for inclusion into the study was that the women had to be either pregnant or have at least one child who is less than 5 years old.

Data collection

A semi-structured interviewer administered questionnaire was used to collect data from the consenting study participants. The variables and measurements collected included socio demographic data such as age, socio-economic status; clinical characteristics such as human immunodeficiency virus (HIV) status, gravidity status, blood group; and questions assessing the participants’ awareness and extent of knowledge on malaria symptoms, prevention and management.

Data analysis

Overall knowledge score was computed by aggregating the knowledge related variables (1) awareness of malaria (2) knowledge of cause of malaria (3) knowledge of breeding sites for mosquito (4) knowledge of three or more symptoms of malaria (5) knowledge of when malaria mosquito feeds (correct knowledge when at night), and (6) knowledge of malaria prevention knowledge (which include chemoprophylaxis, insecticide treated nets (ITN) and environmental sanitation). The knowledge variables were recoded to binary level such that respondents with correct option in the knowledge variables were coded 1 while not having correct knowledge was coded 0. Knowledge score was computed as the sum of the six knowledge variables, with 0 as the least possible score and 6 as highest possible score. Increasing score indicated better malaria knowledge. Subsequently, the median of the composite score was used as the cut-off to classify knowledge level as either poor or good. Individuals who scored less than the median of knowledge score were categorized as having poor knowledge while scoring within the exact median cut off and above were classified as having good malaria knowledge.
Categorical variables were presented as numbers and percentages; numerical variables were presented as means and standard deviation to describe the study population by their socio demographic and clinical characteristics. To assess the level of relationship and interaction between malaria knowledge score and the respondents’ socio demographic and clinical characteristics, analytical statistics involving Chi square and analysis of variance was carried out. Multivariate linear analysis was further performed to determine predictors of malaria knowledge. Level of statistical significance was set at p < 0.05. Analyses were performed using Statistical Package for the Social Sciences software (SPSS) version 25, Chicago, IL.

Results

Table 1 presents results on the socio-demographic and clinical characteristics of the study respondents. Of the 1373 women in the study, 59.6% (818) were pregnant women whereas 40.4% (555) were non-pregnant mothers of children aged under 5 years. Mean age of respondents in the study was 29 years ± 5.2 years old. Mean age of the pregnant women in the study was 28.9 ± 5.21 while mean age of non-pregnant mothers of children aged under 5 years was 30.0 ± 5.14. The most predominant age group was 25–34 years of age (pregnant women: 71.3% vs non-pregnant mothers of children aged under 5 years: 66.8%). The most predominant socio economic class among both maternal groups were the lower upper class (60.4% for the pregnant women and 61.4% among non-pregnant mothers of children aged under 5 years). Married respondents were the majority in the study across both maternal groups (pregnant women: 89.4% vs non-pregnant mothers of children aged under 5 years: 95.5%). A larger proportion of the mothers had attained secondary education more than the less educated mothers, and this distribution was similar in both maternal groups (Table 1).
Table 1
Socio-demographic and clinical distribution by maternal group
 
Maternal group
Total N (1373)
Pregnant women N (%)
Non-pregnant mothers of children aged under 5 years N (%)
Age group
 < 24
128 (15.6)
79 (14.2)
207
 25–34
583 (71.3)
371 (66.8)
954
 35+
107 (13.1)
105 (18.9)
212
Socio-economic status
 Lower class
140 (17.2)
62 (11.2)
202
 Lower middle class
119 (14.6)
100 (18.0)
219
 Lower upper class
492 (60.4)
341 (61.4)
833
 Upper class
63 (7.7)
52 (9.4)
115
Marital status
 Never married
30 (3.7)
12 (2.2)
42
 Married
731 (89.4)
530 (95.5)
1261
 Separated/widowed
57 (7.0)
13 (2.3)
70
Education
 No formal education
76 (9.3)
21 (3.8)
97
 Primary
40 (4.9)
41 (7.4)
81
 Secondary
384 (46.9)
325 (58.6)
709
 Tertiary
318 (38.9)
168 (30.3)
486
Religion
 Christianity
338 (41.3)
229 (41.3)
567
 Islam
459 (56.1)
325 (58.6)
784
 Traditional worshiper
21 (2.6)
1 (0.2)
22
Status of residence
 Owned
209 (25.6)
118 (21.3)
327
 Not owned
597 (73.0)
414 (74.6)
1011
 Others
12 (1.5)
23 (4.1)
35
Gravidity status
 Prime-gravid
275 (33.6)
275
 Multi-gravid
543 (66.4)
555 (100.0)
1098
Parity
 No child
275 (33.6)
275
 One child
250 (30.6)
135 (24.3)
385
 Two Children
165 (20.2)
174 (31.4)
339
 Three or more children
128 (15.6)
246 (44.3)
374
HIV status
 Positive
12 (1.5)
8 (1.4)
20
 Negative
603 (73.7)
442 (79.6)
1045
 Not known
203 (24.8)
105 (18.9)
308
Blood group
 A
290 (35.5)
184 (33.3)
474
 B
133 (16.3)
131 (23.7)
264
 AB
51 (6.2)
66 (12.0)
117
 O
342 (41.9)
171 (31.0)
513
Genotype
 AA
574 (70.4)
366 (65.9)
940
 AS
190 (23.3)
122 (22.0)
312
 AC
41 (5.0)
49 (8.8)
90
 SS
10 (1.2)
18 (3.2)
28
With regards to the clinical characteristics of respondents, about a third of the pregnant women were primegravida (33.6%) while the rest were multigravidae (66.4%). There were about 1.5% of pregnant women and 1.4% among the non-pregnant mothers of children aged under 5 years who self-reported that they HIV positive. Also, 24.8% and 18.9% of the pregnant women and non-pregnant mothers of children aged under 5 years did not know their HIV sero-status, respectively. With regards to the blood group of the respondents blood group ‘AB’ was less common (6.2% vs 12%, in pregnant and non-pregnant mothers of children aged under 5 years, respectively). Conversely, the predominant genotype was ‘AA’ (pregnant women: 70.4% vs non-pregnant mothers of children aged under 5 years: 65.9%) followed by ‘AS’ (pregnant women: 23.3% vs non-pregnant mothers of children aged under 5 years: 22%), ‘AC’ (pregnant women: 5% vs non-pregnant mothers of children aged under 5 years: 8.8%) and ‘SS’ (pregnant women: 1.2% vs non-pregnant mothers of children aged under 5 years: 3.2%).

Knowledge about the causes, symptoms and prevention of malaria

Table 2 shows the distribution of variables related to knowledge about malaria disaggregated according to maternal grouping. There was a low proportion of respondents who were not aware of malaria, less than one-tenth among the pregnant women (7%) and even lower among non-pregnant mothers of children aged under 5 years (2.9%). and this was statistically significant, p < 0.05. Almost half proportion of both the pregnant and the non-pregnant mothers of children aged under 5 years did not have knowledge on the breeding sites of mosquitoes (47.1% vs 49.7%, respectively), however this finding was not significant (p > 0.05). Majority of the participants had low knowledge of malaria symptoms and was only able to identify a maximum of 2 or less symptoms of malaria (74% among pregnant mothers and 69% among non-pregnant mothers of children aged under 5 years), the difference in the proportion was on the edge of being statistically significant with p = 0.051. Across both maternal groups, about a third of the respondents reported insecticide treated nets (ITN) as common method of malaria prevention. Similarly, another one-third reported insecticide spray as common prevention methods for malaria. The proportion which reported the correct prevention knowledge for malaria to include ITN, environmental sanitation and chemotherapy such as artemisinin-based combination therapy (ACT), were 39.6% among the pregnant women and 54.2% among non-pregnant mothers of children aged under 5 years, p < 0.001.
Table 2
Respondents awareness and knowledge of malaria
 
Pregnant women n (%)
Non-pregnant mothers of children aged under five years n (%)
Total N (1373)
Chi square value
p value
Awareness about malaria
 Yes
759 (93.0)
539 (97.1)
1298
11.028
0.001
 No
57 (7.0)
16 (2.9)
73
  
Causes of malaria
 Mosquito
697 (85.2)
480 (86.5)
1177
12.312
0.031
 Contaminated food
8 (1.0)
8 (1.4)
16
  
 Living in dirty environment
34 (4.2)
32 (5.8)
66
  
 Too much sunlight or heat
4 (0.5)
5 (0.9)
9
  
 Don’t know
66 (8.1)
22 (4.0)
88
  
 Stress
9 (1.1)
8 (1.4)
17
  
Correct knowledge on cause of malaria
 Mosquito bites
697 (85.2)
480 (86.5)
1177
0.442
0.506
 Causes not mosquito bites
121 (14.8)
75 (13.5)
196
  
Breeding sites of mosquitoes
 Stagnant water
433 (52.9)
279 (50.3)
712
0.940
0.332
 Other sites/factors not related to breeding sites
385 (47.1)
276 (49.7)
661
  
Symptoms of malaria
 Cold
281 (34.5)
254 (45.8)
535
18.122
0.000
 Fever
369 (45.1)
265 (47.7)
634
0.926
0.336
 Headache
350 (42.8)
330 (59.5)
680
36.767
0.000
 Vomiting
75 (9.2)
57 (10.3)
132
0.462
0.497
 Weakness
167 (20.4)
69 (12.4)
236
14.805
0.000
 Dizziness
36 (4.4)
25 (4.5)
61
0.008
0.927
 Nausea
6 (0.7)
6 (1.1)
12
0.461
0.497
 Loss of appetite
42 (5.1)
31 (5.6)
73
0.134
0.714
 Bitter mouth taste
56 (6.8)
38 (6.8)
94
0.000
0.999
 Convulsion
7 (0.9)
9 (1.6)
16
1.684
0.194
 Diarrhoea
6 (0.7)
7 (1.3)
13
0.982
0.332
 Joint pain
54 (6.6)
47 (8.5)
101
1.691
0.193
 Coloured/yellowed eye
10 (1.1)
5 (0.9)
15
0.316
0.574
 Coloured/yellowed urine
6 (0.7)
1 (0.2)
7
0.316
0.574
Knowledge on symptoms of malaria
 0–2 correct symptoms
525 (74.0)
358 (69.0)
883
3.812
0.051
 Three correct symptoms or more
184 (26.0)
161 (31.0)
345
  
When does mosquitoes feed
 Wrong knowledge as other times
300 (36.7)
240 (43.2)
540
5.979
0.014
 Correct knowledge as night
518 (63.3)
315 (56.8)
833
  
Malaria preventive methods
 Insecticide spray
305 (37.3)
205 (36.9)
510
55.885
0.000
 Chemoprophylaxis
15 (1.8)
11 (2.0)
26
  
 Any bed net
44 (5.4)
8 (1.4)
52
  
 Insecticide-treated nets
289 (35.3)
274 (49.4)
563
  
 Drinking traditional concoction
5 (0.6)
1 (0.2)
6
  
 Keeping environment neat and clean
20 (2.4)
16 (2.9)
36
  
 Others
140 (17.1)
40 (7.2)
180
  
Malaria prevention knowledge
 Has correct knowledge on chemotherapy, insecticide-treated nets and environmental sanitation
324 (39.6)
301 (54.2)
625
28.520
0.000
 Does not have correct knowledge
494 (60.4)
254 (45.8)
748
  
There was no significant difference in malaria knowledge score between pregnant women and non-pregnant mothers of children aged under 5 years in the study (Table 3). There was also no statistical difference in knowledge score between the age groupings of the respondents. Significantly, knowledge on malaria was higher among respondents who were of the lower middle class (4.10 ± 1.28) and lower upper class (4.10 ± 1.26) than the lower class (3.73 ± 1.66), F = 4.43, p < 0.001. Knowledge score was also highest among the never married women (4.31 ± 1.52, F = 30.2, p < 0.001) compared with the other like the married group (1.08 ± 1.26, F = 30.2, p < 0.001). Educational status of the mothers was also associated with knowledge of malaria as mothers who had secondary (4.07 ± 1.28) and tertiary education (4.20 ± 1.18) as their highest educational qualification showed significantly better knowledge about malaria than those with no formal education (3.38 ± 1.84) and primary education (3.38 ± 1.79), F = 16.80, p < 0.001. The clinical characteristics of the women such as gravidity status, HIV status, blood group and genotype showed significant relationship with malaria knowledge (Table 3). Women with more than a single child had better knowledge of malaria. Respondents whose HIV sero-status, was either positive (4.35 ± 0.88) or negative (4.14 ± 1.21) had higher mean knowledge score about malaria than those who did not know their HIV status (3.63 ± 1.71), p < 0.001.
Table 3
Association between selected socio-demographic and clinical characteristics with respondents’ knowledge on malaria
 
Mean
Standard deviation
Number
F-statistic
p value
Maternal grouping
 Pregnant women
3.80
0.47
292
2.48a
0.116
 Mothers of under-five
3.87
0.50
171
  
Age group
 < 24
4.12
1.27
207
1.506
0.222
 25–34
3.98
1.41
954
  
 35+
4.13
1.16
212
  
Socio-economic status
 Lower class
3.73
1.66
202
4.431
0.004
 Lower middle class
4.10
1.28
219
  
 Lowe upper class
4.10
1.26
833
  
 Upper class
3.95
1.38
115
  
Marital status
 Never married
4.31
1.52
42
30.725
0.000
 Married
4.08
1.26
1261
  
 Separated/widowed
2.83
2.13
70
  
Education
 No formal education
3.38
1.84
97
16.808
0.000
 Primary
3.38
1.79
81
  
 Secondary
4.07
1.28
709
  
 Tertiary
4.20
1.18
486
  
Gravidity status
 Prime-gravida
3.45
1.74
275
64.18a
0.000
 Multigravida
4.17
1.20
1098
  
HIV status
 Positive
4.35
0.88
20
17.691
0.000
 Negative
4.14
1.21
1045
  
 Not known
3.63
1.71
308
  
Blood group
 A
4.04
1.37
474
7.294
0.000
 B
3.70
1.56
264
  
 AB
4.06
1.26
117
  
 O
4.17
1.21
513
  
Genotype
 AA
4.10
1.26
940
2.9
0.034
 AS
3.86
1.60
312
  
 AC
3.89
1.35
90
  
 SS
3.86
1.24
28
  
at-test
Table 4 presents the post hoc analysis performed to show where the difference in mean for sub-groups significantly associated with knowledge score in Table 3 occurred. The post hoc analysis also shows significant association between selected socio-demographic and clinical characteristics with patients’ knowledge on malaria (Table 4). There was significant association between socio-economic status of the women in the study and their malaria knowledge score. The significant differences were between the lower class and the lower middle class; also between lower class and lower upper class. There was also significant difference between: women who had primary education compared to women who had secondary and tertiary education; women who had secondary education compared to women who had no formal and primary education. 
Table 4
Post Hoc analysis for significant association between socio-demographic and clinical characteristics with knowledge on malaria score
 
Mean difference (I − J)
Sig.
95% confidence interval
Lower bound
Upper bound
(I) Socio-economic status
(J) Socio-economic status
    
Lower class
Lower middleclass
− .3678*
0.026
− 0.7041
− 0.0314
Lower upper class
− .3682*
0.003
− 0.6386
− 0.0978
Upper class
− 0.2152
0.516
− 0.6179
0.1876
Lower middleclass
Lower class
0.3678*
0.026
0.0314
0.7041
Lower upper class
− 0.0004
1
− 0.2622
0.2614
Upper class
0.1526
0.756
− 0.2444
0.5497
Lower upper class
Lower class
0.3682*
0.003
0.0978
0.6386
Lower middleclass
0.0004
1
− 0.2614
0.2622
Upper class
0.153
0.66
− 0.19
0.4960
Upper class
Lower class
0.2152
0.516
− 0.1876
0.6179
Lower middleclass
− 0.1526
0.756
− 0.5497
0.2444
Lower upper class
− 0.153
0.66
− 0.496
0.1900
(I) Marital status
(J) Marital status
    
Never married
Married
0.2263
0.521
− 0.2614
0.7139
Separated/widowed
1.4810*
0
0.8742
2.0877
Married
Never married
− 0.2263
0.521
− 0.7139
0.2614
Separated/widowed
1.2547*
0
0.873
1.6364
Separated/widowed
Never married
− 1.4810*
0
− 2.0877
− 0.8742
Married
− 1.2547*
0
− 1.6364
− 0.8730
(I) Education
(J) Education
    
No formal education
Primary
− 0.0013
1
− 0.5164
0.5139
Secondary
− .6905*
0
− 1.061
− 0.3200
Tertiary
− .8140*
0
− 1.1946
− 0.4334
Primary
No formal education
0.0013
1
− 0.5139
0.5164
Secondary
− .6892*
0
− 1.0906
− 0.2878
Tertiary
− .8128*
0
− 1.2235
− 0.4020
Secondary
No formal education
0.6905*
0
0.32
1.0610
Primary
0.6892*
0
0.2878
1.0906
Tertiary
− 0.1235
0.392
− 0.3251
0.0780
Tertiary
No formal education
0.8140*
0
0.4334
1.1946
Primary
0.8128*
0
0.402
1.2235
Secondary
0.1235
0.392
− 0.078
0.3251
(I) HIV status
(J) HIV status
    
Positive
Negative
0.2132
0.76
− 0.4951
0.9214
Not known
0.7201
0.052
− 0.0038
1.4441
Negative
Positive
− 0.2132
0.76
− 0.9214
0.4951
Not known
0.5070*
0
0.3036
0.7104
Not known
Positive
− 0.7201
0.052
− 1.4441
0.0038
Negative
− .5070*
0
− 0.7104
− 0.3036
(I) Blood group
(J) Blood group
    
A
B
0.3389*
0.006
0.0731
0.6047
AB
− 0.024
0.998
− 0.3813
0.3333
O
− 0.1357
0.389
− 0.3562
0.0848
B
A
− .3389*
0.006
− 0.6047
− 0.0731
AB
− 0.3629
0.072
− 0.7473
0.0215
O
− .4746*
0
− 0.7367
− 0.2124
AB
A
0.024
0.998
− 0.3333
0.3813
B
0.3629
0.072
− 0.0215
0.7473
O
− 0.1117
0.85
− 0.4663
0.2429
O
A
0.1357
0.389
− 0.0848
0.3562
B
0.4746*
0
0.2124
0.7367
AB
0.1117
0.85
− 0.2429
0.4663
(I) Genotype
(J) Genotype
    
AA
AS
0.2368*
0.037
0.0098
0.4637
AC
0.21
0.493
− 0.1732
0.5933
SS
0.2418
0.787
− 0.4244
0.9080
AS
AA
− .2368*
0.037
− 0.4637
− 0.0098
AC
− 0.0267
0.998
− 0.4423
0.3889
SS
0.005
1
− 0.6802
0.6903
AC
AA
− 0.21
0.493
− 0.5933
0.1732
AS
0.0267
0.998
− 0.3889
0.4423
SS
0.0317
1
− 0.7199
0.7834
SS
AA
− 0.2418
0.787
− 0.908
0.4244
AS
− 0.005
1
− 0.6903
0.6802
AC
− 0.0317
1
− 0.7834
0.7199
In the multivariate linear regression analysis to examine the predictors of malaria knowledge, socio-demographic factors including marital status, education, gravidity status and the clinical factor HIV status remained significant with malaria knowledge (Table 5).
Table 5
Multivariate linear model of factors associated with knowledge of malaria
 
Unstandardized regression coefficient (95% CI)
95% CI
Standard error
Standardized coefficient
t-statistic
Lower bound
Upper bound
Age
− 0.004
− 0.018
0.009
0.007
− 0.02
− 0.60
Wealth status
0.03
− 0.051
0.117
0.04
0.02
0.77
Marital status
− 0.47
− 0.724
− 0.205
0.13
− 0.10
− 3.51***
Education
0.16
0.072
0.252
0.05
0.10
3.52***
Gravidity status
0.67
0.474
0.859
0.10
0.20
6.80***
HIV status
− 0.32
− 0.478
− 0.16
0.08
− 0.10
− 3.93***
Blood group
0.04
− 0.014
0.092
0.03
0.04
1.44
Genotype
− 0.08
− 0.175
0.022
0.05
− 0.04
− 1.52
Maternal grouping
− 0.14
− 0.291
0.017
0.08
− 0.05
− 1.75
R2 = 0.050, F for change in R2 = 2.328, p = 0.011, * p < .05, ** p < 0.01; *** p < 0.001

Discussion

Nigeria contributes the highest morbidity and mortality rates to the global burden of malaria, accounting for 25% of the global malaria cases and about 24% of global malaria-related deaths [1]. Thus, the initiative to study maternal knowledge on malaria prevention was essential in understanding the extent and impact of malaria programmatic efforts in malaria control. Women serve as role models for their families in raising awareness and participating in malaria prevention and control [18]. They are also responsible for home-based management of malaria for themselves when pregnant and among children aged under 5 years in the home [19]. In this study, findings revealed obstacles to effective malaria control despite high awareness of malaria as an illness which has been previously reported in studies conducted in South Western Nigeria [20], Northern Central Nigerian [21] and as confirmed in this study (93% among pregnant women and 97% among mothers of young children). There were knowledge gaps on; breeding sites for the vectors that transmit malaria, symptoms of malaria and malaria prevention measures. According to Killeen [22], level of knowledge on mosquito behavioural pattern (biting and resting times) and breeding sites has been associated with the severity of malaria. Killeen further explains that elimination of malaria from most endemic regions of the tropics requires vector control strategies that address residual transmission by deliberately targeting the mosquito behaviours which enable it [22].
In relation to the knowledge on malaria symptoms and preventive measures by respondents in this study, about 60% of pregnant women and 46% of non-pregnant mothers of young children did not have correct knowledge on malaria prevention. Further, there were 26% of pregnant mothers and 31% of the non-pregnant mothers of young children who correctly reported more than 3 clinical symptoms of malaria. Similar studies conducted in rural South West Nigeria [14], North Central Nigeria [9] and Burkina Faso [18] also showed low knowledge on malaria prevention measures. Conversely, the study by Singh et al. showed that high knowledge about malaria symptoms and prevention measures (90%) however; this knowledge was poorly reflected in practice (16%) [13]. Misconceptions about causes of malaria in this study although reported by few respondents include living in dirty environment, eating contaminated food, stress, and exposure to sunlight. Some studies in Nigeria and parts of Africa have also reported spurious causes of malaria such as staying for long in the sun and drinking bad water among other misconceptions on malaria [11, 21, 23, 24]. Overlapping knowledge on malaria causes, key symptoms, and prevention was observed between pregnant women and the non-pregnant mothers of children aged under 5 years in this study. In some aspects of malaria prevention, higher proportion of pregnant women was less knowledgeable about malaria, compared with the mothers of young children and vice versa. However, the differences in malaria knowledge on preventive measures between the maternal groups were not significant from the analysis of variance performed.
Level of knowledge on malaria was associated with; socio-demographic factors such as marital status, education and clinical factors like gravidity and HIV status of the mothers. Good malaria knowledge was associated with higher level of educational status of the women. In previous studies, educational status has been linked with good health awareness and health-seeking behaviour for the child [23, 25], and also improved knowledge on malaria and prevention among mothers [9, 18, 26]. Such association according to Fana et al. stresses the role education could have on the overall success in malaria control programmers in a region [26]. Another important finding was that respondents who knew their HIV status had a good knowledge of malaria compared with those who did not know their HIV status. Further, those who were HIV positive had better malaria knowledge when compared with both those were HIV negative and those who did not know their HIV status. The high knowledge of malaria among HIV positive respondents in the study might be due to the awareness of the high risk of acquiring opportunistic infections. For instance, knowledge of HIV status as reported by the study respondents reflects a higher awareness of their health status. This agrees with finding from study in Uganda by Katrak et al. where a > sixfold lower risk of infection with malaria parasites among HIV-infected participants with an undetectable viral load was seen when compared to HIV-uninfected participants [27]. Possible explanation could be because individuals who knew their HIV status tend to have good health-seeking behaviour and knowledge on malaria compared with those who do not know their HIV status.
Although the study investigated the knowledge of malaria prevention and control, and sought to find the socio-demographic and some clinical factors associated with malaria knowledge this study did not investigate the programmatic factors that may influence the knowledge of the respondents on malaria and would like to recommend this for future studies. Limitations of this study include recall bias on account of information provided by the respondents. Since the study population was hospital-based, another bias related to the limitation of this study is selection bias because this hospital based study population could have been more knowledgeable than similar population if recruited from the community. Though these limitations, this study has implications for control programmes given the findings, which highlights the knowledge gaps requiring urgent interventions targeted at mothers.

Conclusion

This study has demonstrated that pregnant women and mothers of children under 5 years are aware of malaria, but still lack comprehensive knowledge about the disease. Many mothers know some important symptoms of malaria such as fever, cold and headache. There was also some level of misconception about malaria, which needs to be totally debunked by intensifying education about malaria among mothers who are either pregnant and or caring for young ones who are more vulnerable to malaria disease. Education as a socio-demographic factor was an important predictor knowledge of malaria among mothers and so government policies should be geared towards improving citizens ‘educational statuses in order to reduce the burden of the disease in the country, especially among the most vulnerable population. Mothers need to be educated about the importance of a better health-seeking behaviour and awareness about their health status. Nigeria’s malaria strategic plan should to ensure that the knowledge cleft on malaria prevention and treatment needs to be addressed. This insight will help the policy makers to implement continuous strategic intervention including health awareness and educational programs to attain 2030 malaria goals.

Authors’ contributions

KEO and JMT conceptualized the idea and designed the study. Data collection, cleaning and analysis was conducted by KEO. Interpretation of results was done by KEO, JMT, EO, STY. KEO wrote the initial draft of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors wish to extend profound gratitude to Professor Moses Chimbari for the research capacity development and training opportunities for KEO. She is also thankful for the support and encouragement from Dr Kogieleum Naidoo at the Center for the AIDS Programme of Research in South Africa (CAPRISA). She is indeed grateful for the research enhancement platforms provided for her by UKZN and CAPRISA.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The dataset produced by the current study is available from the corresponding author upon request.
Not applicable.
The study was approved by the Oyo state ministry of health ethics committee ((IRB AD13/479/1035) in Nigeria and the biomedical research ethics committee (BREC- BE199/16), University of Kwa-Zulu Natal, South Africa. Signed informed consent was obtained from the respondents enrolled in the study. The participants were assured of the confidentiality of their information. They were pre-informed that the study findings will be presented at stakeholders meetings, conferences and finally published which could positively influence effective malaria control policy and its implementation in the future.

Funding

The University of KwaZulu-Natal College of Health Sciences postgraduate research scholarship award supported this project.

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.
Literatur
2.
Zurück zum Zitat National Malaria Elimination Programme (NMEP). Nigeria Malaria Indicator Survey 2015. Abuja, Nigeria, and Rockville, Maryland, USA: National Population Commission (NPopC), National Bureau of Statistics (NBS), and ICF International; 2016. National Malaria Elimination Programme (NMEP). Nigeria Malaria Indicator Survey 2015. Abuja, Nigeria, and Rockville, Maryland, USA: National Population Commission (NPopC), National Bureau of Statistics (NBS), and ICF International; 2016.
3.
Zurück zum Zitat Owolabi BB, Yusuf OB, Afonne C, Afolabi NB, Ajayi IO. Parametric and non parametric estimates of malaria attributable fractions among children in South West Nigeria. Am J Math Stat. 2016;6:79–85. Owolabi BB, Yusuf OB, Afonne C, Afolabi NB, Ajayi IO. Parametric and non parametric estimates of malaria attributable fractions among children in South West Nigeria. Am J Math Stat. 2016;6:79–85.
4.
Zurück zum Zitat Farid B. Challenges in the management of malaria in Nigeria: a healthcare system preview. Epidemiology (Sunnyvale). 2016;6:253.CrossRef Farid B. Challenges in the management of malaria in Nigeria: a healthcare system preview. Epidemiology (Sunnyvale). 2016;6:253.CrossRef
5.
Zurück zum Zitat Do M, Babalola S, Awantang G, Toso M, Lewicky N, Tompsett A. Associations between malaria-related ideational factors and care-seeking behavior for fever among children under five in Mali, Nigeria, and Madagascar. PLoS ONE. 2018;13:e0191079.CrossRef Do M, Babalola S, Awantang G, Toso M, Lewicky N, Tompsett A. Associations between malaria-related ideational factors and care-seeking behavior for fever among children under five in Mali, Nigeria, and Madagascar. PLoS ONE. 2018;13:e0191079.CrossRef
9.
Zurück zum Zitat Akaba GO, Otubu JA, Agida ET, Onafowokan O. Knowledge and utilization of malaria preventive measures among pregnant women at a tertiary hospital in Nigeria’s federal capital territory. Niger J Clin Pract. 2013;16:201–6.CrossRef Akaba GO, Otubu JA, Agida ET, Onafowokan O. Knowledge and utilization of malaria preventive measures among pregnant women at a tertiary hospital in Nigeria’s federal capital territory. Niger J Clin Pract. 2013;16:201–6.CrossRef
10.
Zurück zum Zitat Federal Ministry of Health (FMoH) [Nigeria] and National Malaria Elimination Programme (NMEP) [Nigeria]. National Malaria Strategic Plan 2014–2020. Abuja, Nigeria: FMoH and NMEP; 2014. Federal Ministry of Health (FMoH) [Nigeria] and National Malaria Elimination Programme (NMEP) [Nigeria]. National Malaria Strategic Plan 2014–2020. Abuja, Nigeria: FMoH and NMEP; 2014.
11.
Zurück zum Zitat Obol J, David Lagoro K, Christopher Garimoi O. Knowledge and misconceptions about malaria among pregnant women in a post-conflict internally displaced persons’ camps in Gulu District, Northern Uganda. Malar Res Treat. 2011;2011:107987.PubMedPubMedCentral Obol J, David Lagoro K, Christopher Garimoi O. Knowledge and misconceptions about malaria among pregnant women in a post-conflict internally displaced persons’ camps in Gulu District, Northern Uganda. Malar Res Treat. 2011;2011:107987.PubMedPubMedCentral
12.
Zurück zum Zitat Amusan VO, Umar YA, Vantsawa PA. Knowledge, attitudes and practices on malaria prevention and control among private security guards within Kaduna Metropolis, Kaduna State-Nigeria. Sci J Public Health. 2017;5:240–5.CrossRef Amusan VO, Umar YA, Vantsawa PA. Knowledge, attitudes and practices on malaria prevention and control among private security guards within Kaduna Metropolis, Kaduna State-Nigeria. Sci J Public Health. 2017;5:240–5.CrossRef
13.
Zurück zum Zitat Singh R, Musa J, Singh S, Ebere UV. Knowledge, attitude and practices on malaria among the rural communities in aliero, northern Nigeria. J Fam Med Prim Care. 2014;3:39–44.CrossRef Singh R, Musa J, Singh S, Ebere UV. Knowledge, attitude and practices on malaria among the rural communities in aliero, northern Nigeria. J Fam Med Prim Care. 2014;3:39–44.CrossRef
14.
Zurück zum Zitat Adebayo AM, Akinyemi OO, Cadmus EO. Knowledge of malaria prevention among pregnant women and female caregivers of under-five children in rural southwest Nigeria. PeerJ. 2015;3:e792.CrossRef Adebayo AM, Akinyemi OO, Cadmus EO. Knowledge of malaria prevention among pregnant women and female caregivers of under-five children in rural southwest Nigeria. PeerJ. 2015;3:e792.CrossRef
15.
Zurück zum Zitat Kimbi HK, Nkesa SB, Ndamukong-Nyanga JL, Sumbele IUN, Atashili J, Atanga MBS. Knowledge and perceptions towards malaria prevention among vulnerable groups in the Buea Health District, Cameroon. BMC Public Health. 2014;14:883.CrossRef Kimbi HK, Nkesa SB, Ndamukong-Nyanga JL, Sumbele IUN, Atashili J, Atanga MBS. Knowledge and perceptions towards malaria prevention among vulnerable groups in the Buea Health District, Cameroon. BMC Public Health. 2014;14:883.CrossRef
16.
Zurück zum Zitat Oladimeji KE, Tsoka-Gwegweni JM, Gengiah S, Daftary A, Naidoo K. Barriers to effective uptake of malaria prevention interventions in Ibadan, South West Nigeria: a qualitative study. Int J Comm Med Public Health. 2018;5:1304–10.CrossRef Oladimeji KE, Tsoka-Gwegweni JM, Gengiah S, Daftary A, Naidoo K. Barriers to effective uptake of malaria prevention interventions in Ibadan, South West Nigeria: a qualitative study. Int J Comm Med Public Health. 2018;5:1304–10.CrossRef
17.
Zurück zum Zitat Ayoola OO, Gemmell I, Omotade OO, Adeyanju OA, Cruickshank JK, Clayton PE. Maternal malaria, birth size and blood pressure in Nigerian newborns: insights into the developmental origins of hypertension from the Ibadan growth cohort. PLoS ONE. 2011;6:e24548.CrossRef Ayoola OO, Gemmell I, Omotade OO, Adeyanju OA, Cruickshank JK, Clayton PE. Maternal malaria, birth size and blood pressure in Nigerian newborns: insights into the developmental origins of hypertension from the Ibadan growth cohort. PLoS ONE. 2011;6:e24548.CrossRef
18.
Zurück zum Zitat Yaya S, Bishwajit G, Ekholuenetale M, Shah V, Kadio B, Udenigwe O. Knowledge of prevention, cause, symptom and practices of malaria among women in Burkina Faso. PLoS ONE. 2017;12:e0180508.CrossRef Yaya S, Bishwajit G, Ekholuenetale M, Shah V, Kadio B, Udenigwe O. Knowledge of prevention, cause, symptom and practices of malaria among women in Burkina Faso. PLoS ONE. 2017;12:e0180508.CrossRef
19.
Zurück zum Zitat Mutegeki E, Chimbari MJ, Mukaratirwa S. Assessment of individual and household malaria risk factors among women in a South African village. Acta Trop. 2017;175:71–7.CrossRef Mutegeki E, Chimbari MJ, Mukaratirwa S. Assessment of individual and household malaria risk factors among women in a South African village. Acta Trop. 2017;175:71–7.CrossRef
20.
Zurück zum Zitat Iriemenam N, Dosunmu A, Oyibo W, Fagbenro-Beyioku A. Knowledge, attitude, perception of malaria and evaluation of malaria parasitaemia among pregnant women attending antenatal care clinic in metropolitan Lagos, Nigeria. J Vector Borne Dis. 2011;48:12.PubMed Iriemenam N, Dosunmu A, Oyibo W, Fagbenro-Beyioku A. Knowledge, attitude, perception of malaria and evaluation of malaria parasitaemia among pregnant women attending antenatal care clinic in metropolitan Lagos, Nigeria. J Vector Borne Dis. 2011;48:12.PubMed
21.
Zurück zum Zitat Olayemi I, Omalu I, Abolarinwa S, Mustapha O, Ayanwale V, Mohammed A, et al. Knowledge of malaria and implications for control in an endemic urban area of north central Nigeria. Asian J Epidemiol. 2012;5:42–9.CrossRef Olayemi I, Omalu I, Abolarinwa S, Mustapha O, Ayanwale V, Mohammed A, et al. Knowledge of malaria and implications for control in an endemic urban area of north central Nigeria. Asian J Epidemiol. 2012;5:42–9.CrossRef
22.
Zurück zum Zitat Killeen GF. Characterizing, controlling and eliminating residual malaria transmission. Malar J. 2014;13:330.CrossRef Killeen GF. Characterizing, controlling and eliminating residual malaria transmission. Malar J. 2014;13:330.CrossRef
23.
Zurück zum Zitat Shimaponda-Mataa NM, Tembo-Mwase E, Gebreslasie M, Mukaratirwa S. Knowledge, attitudes and practices in the control and prevention of malaria in four endemic provinces of Zambia. South Afr J Infect Dis. 2017;32:29–39. Shimaponda-Mataa NM, Tembo-Mwase E, Gebreslasie M, Mukaratirwa S. Knowledge, attitudes and practices in the control and prevention of malaria in four endemic provinces of Zambia. South Afr J Infect Dis. 2017;32:29–39.
24.
Zurück zum Zitat Aju-Ameh CO, Awolola ST, Mwansat GS, Mafuyai HB. Malaria related knowledge attitude and practices (MKAP) in fourteen communities in Benue state North Central Nigeria: evidence for the success of focal malaria control intervention programmes. Int J Mosq Res. 2016;55:11–4. Aju-Ameh CO, Awolola ST, Mwansat GS, Mafuyai HB. Malaria related knowledge attitude and practices (MKAP) in fourteen communities in Benue state North Central Nigeria: evidence for the success of focal malaria control intervention programmes. Int J Mosq Res. 2016;55:11–4.
25.
Zurück zum Zitat Houmsou R, Amuta E, Wama B, Hile T, Bingbeng J. Occurrence of malaria in children under five years: knowledge, attitudes and perceptions among mothers in a Nigerian semi-urban area. J Sci Res Rep. 2014;3:1127–34. Houmsou R, Amuta E, Wama B, Hile T, Bingbeng J. Occurrence of malaria in children under five years: knowledge, attitudes and perceptions among mothers in a Nigerian semi-urban area. J Sci Res Rep. 2014;3:1127–34.
26.
Zurück zum Zitat Fana SA, Bunza MDA, Anka SA, Imam AU, Nataala SU. Prevalence and risk factors associated with malaria infection among pregnant women in a semi-urban community of north-western Nigeria. Infect Dis Poverty. 2015;4:24.CrossRef Fana SA, Bunza MDA, Anka SA, Imam AU, Nataala SU. Prevalence and risk factors associated with malaria infection among pregnant women in a semi-urban community of north-western Nigeria. Infect Dis Poverty. 2015;4:24.CrossRef
27.
Zurück zum Zitat Katrak S, Day N, Ssemmondo E, Kwarisiima D, Midekisa A, Greenhouse B, et al. Community-wide prevalence of malaria parasitemia in HIV-infected and uninfected populations in a high-transmission setting in Uganda. J Infect Dis. 2016;213:1971–8.CrossRef Katrak S, Day N, Ssemmondo E, Kwarisiima D, Midekisa A, Greenhouse B, et al. Community-wide prevalence of malaria parasitemia in HIV-infected and uninfected populations in a high-transmission setting in Uganda. J Infect Dis. 2016;213:1971–8.CrossRef
Metadaten
Titel
Knowledge of malaria prevention among pregnant women and non-pregnant mothers of children aged under 5 years in Ibadan, South West Nigeria
verfasst von
Kelechi Elizabeth Oladimeji
Joyce Mahlako Tsoka-Gwegweni
Elizabeth Ojewole
Samuel Tassi Yunga
Publikationsdatum
01.12.2019
Verlag
BioMed Central
Erschienen in
Malaria Journal / Ausgabe 1/2019
Elektronische ISSN: 1475-2875
DOI
https://doi.org/10.1186/s12936-019-2706-1

Weitere Artikel der Ausgabe 1/2019

Malaria Journal 1/2019 Zur Ausgabe

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.