Assessment of Knowledge on Malaria Diagnosis and Prevention: Exploring the Link between Awareness and Low Malaria Transmission Levels

doi:10.21203/rs.3.rs-5338913/v1

This article evaluates the awareness of malaria diagnosis and prevention, emphasizing the link between understanding low malaria transmission and achieving elimination. A cross-sectional study was conducted among 600 staff from three medical institutions in Jos, Nigeria, between March and June 2023. Results showed that 97.33% of participants had heard of malaria, with hospital workers being the main information source (38%). Stagnant water was identified as the primary mosquito breeding site (80%), and fever with shivering/sweating was the most recognized symptom (45.33%). Notably, 66.67% of participants were unaware of low malaria transmission levels and associated elimination challenges. Insecticide-treated nets (ITNs) emerged as the best-known prevention method (41.67%), while 63.33% were unaware of malaria vaccines. The study found significant positive correlations between awareness of low malaria transmission and malaria prevention (r_s= 0.225, P=0.001) and diagnosis (r_s= 0.403, P <0.001); (r_s= 0.330, P <0.001 ) across different institutions. The findings suggest that enhancing awareness of low malaria transmission could significantly improve knowledge of malaria diagnosis and prevention strategies, ultimately supporting elimination efforts.

Biostatistics

Statistical Epidemiology

Health Policy

Infectious Diseases

Content validity

Malaria

Malaria transmission

Malaria elimination

Malaria diagnosis

Malaria prevention

Malaria is a life-threatening disease transmitted to humans by the bites of infected female Anopheles mosquitoes. It is commonly found in tropical countries. In 2021, nearly half of the world’s population was at risk of malaria, and there were an estimated 247 million cases of malaria worldwide [1, 2]. Symptoms of malaria include fever, chills, headache, and body pain.

In Nigeria, malaria is a major public health challenge, accounting for a significant proportion of illness and mortality in the Country. Nigeria accounted for about 31.3% of all malaria deaths worldwide and 25% of the malaria burden of global cases [1, 3]. The Nigerian government has to combat malaria through various strategies with malaria elimination being at the forefront in line with the WHO Global Technical Strategy for Malaria 2016–2030. The goal is to achieve global targets including reducing malaria case incidence by at least 90% by 2030, reducing malaria mortality rates by at least 90% by 2030, eliminating malaria in at least 35 countries by 2030, and preventing malaria resurgence in all malaria-free countries.

Malaria elimination as a public health goal involves interrupting local transmission of a specified malaria parasite species in a defined geographical area due to deliberate activities. It consists of a combination of prevention, diagnosis, and treatment strategies to address the underlying determinants of malaria transmission. However, one major challenge of malaria elimination is the low malaria transmission levels, which considerably affects the performance of malaria diagnostic tests [4, 5, 6, 7, 8, 9]. According to [10], proper diagnosis is a major playmaker in various approaches to reaching the malaria elimination goal. Low malaria transmission levels are attributed to key preventive practices such as the use of insecticide-treated bed nets (ITNs), indoor residual spraying (IRS), and increased access to effective malaria treatments [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21].

Despite the numerous survey studies in Nigeria targeted at malaria elimination campaigns, there still exists incomplete knowledge and awareness of malaria elimination, low malaria transmission levels, malaria diagnosis, and prevention, hence, the need for this cross-sectional study to add to existing literature. Therefore, in this study, we seek to assess the knowledge of malaria diagnosis and prevention linking the awareness of low malaria transmission levels to the cause of achieving malaria elimination in Nigeria.

2.1 Participants and Data Collection

A cross-sectional survey was conducted among the staff of Federal College of Medical Laboratory Science and Technology, Jos; Plateau State College of Health Technology, Zawan, and Federal College of Veterinary and Medical Laboratory Technology, Vom in Plateau State, Nigeria from 27/03/2023 to 30/06/2023 to gather information on the knowledge of malaria, malaria diagnosis and malaria prevention. Plateau State is located in the North Central region of Nigeria at 9^o 10ʹN 9^o 45ʹE coordinates. It covers a land mass of about 30,913 km² (11,936 square miles) and a population of about 3 million people. It has Jos as its capital. The study areas cut across Jos East L.G.A, Jos North L.G.A, and Pankshin L.G.A. The sample size was determined using the formula in [22]. The minimum sample size obtained was 384 using a 95% confidence interval with a corresponding Z-value of 1.960, 50% of expected proportion, and 5% error margin. A total sample size of 600 was considered with 200 participants recruited from each Institution (comprising 100 males and 100 females, aged from 18 to > 45, 34.47$\:\pm\:$ 7.21, 34.80$\:\pm\:$ 7.22 and 36.02$\:\pm\:$ 7.92 for FCMLST, PSCHT, and FCVMLT, respectively) using simple random sampling and purposive sampling techniques.

A structured questionnaire was randomly administered to the participants and was divided into four sections. Section A contains information on the socio-demographic characteristics of the participants. Section B covers the knowledge of malaria. Section C focuses on the knowledge towards malaria diagnosis while Section D explores knowledge towards malaria prevention. All the participants have signed an informed consent form; ethical approval was sought and obtained from the Research and Ethics committee of Federal College of Medical Laboratory Science and Technology, Jos (acceptance number 1269), and the study was carried out following ethical standards of declaration of Helsinki.

2.2 Measurement.

The information gathered on the socio-demographic characterization was based on age, education, marital status, and gender. Hearing about malaria, sources of information about malaria best known, breeding sites for mosquitoes best known, malaria parasite from mosquito bite, and symptoms of malaria best known made up the variables that provided information about knowledge of malaria. The information about malaria diagnosis relating to low levels of transmission and malaria elimination was captured using knowing about malaria elimination, awareness of low levels of malaria transmission, knowing about malaria diagnostic tests, knowing which diagnostic test is considered adequate, and the need for higher sensitive tests. The information about malaria prevention was based on knowing about malaria prevention, the malaria prevention method best known, which prevention method is considered effective, malaria prevention as a good strategy for malaria elimination, and awareness of malaria vaccines [23].

2.3 Statistical Techniques

Content Validity Ratio (CVR), Proportion of Agreeing Experts (PAE), and Critical Validity Index (CVI) by [24] were employed to assess the content validity based on the evaluation of the eight subject matter experts. Percentages were applied to measure the prevalence of responses. Logistic regression was utilized to gauge the associations between demographics and knowledge of malaria diagnosis, knowledge of malaria prevention, and low levels of malaria transmission. The associations between low levels of malaria transmission, malaria diagnosis, and malaria prevention were measured by Spearman’s rank correlation. All the analyses were carried out using SPSS version 20.

2.3.1 Content Validity Ratio

The content validity ratio (Lawshe) is one of the earliest and most widely used methods for quantifying content validity [25];

$$\:CVR=\:\frac{{N}_{e}-\:\frac{N}{2}}{\frac{N}{2}},$$

1

where:

$\:{N}_{e}$ = the number of experts indicating that it is essential

$\:N$ = Number of experts evaluating

With the ratio index $\:-1\:\le\:CVR\:\le\:1$ and the following criteria:

$\:{N}_{e}<\:\raisebox{1ex}{$1$}\!\left/\:\!\raisebox{-1ex}{$2$}\right.N$ Then CVR < 0

$\:{N}_{e}=\:\raisebox{1ex}{$1$}\!\left/\:\!\raisebox{-1ex}{$2$}\right.N$ Then CVR = 0

$\:{N}_{e}>\:\raisebox{1ex}{$1$}\!\left/\:\!\raisebox{-1ex}{$2$}\right.N$ Then CVR > 0

A CVR value of -1 signifies a perfect disagreement while a CVR value of 1 signifies an ideal agreement.

2.3.2 Proportion Agreeing Essential (PAE)

PAE accounts for the proportion of experts who rated an item as essential out of the total number of experts who rated the item. It is represented using the formula:

PAE = $\:\frac{{N}_{e}}{N}$, (2)

where $\:{N}_{e}$ is the number of experts indicating an item as essential, and $\:N$ is the total number of experts rating the items [24].

2.3.3 Content Validity Index (CVI)

The CVI of an instrument can be obtained using the formula

$$\:CVI=\frac{1}{n}\:\sum\:_{i=1}^{n}{CVR}_{i}$$

3

where $\:n$ is the total number of items. Including CVI as part of instrument assessment is highly recommended and CVI should be calculated using all final items on the instrument [24, 26, 27].

2.3.4 Multivariate Logistic Regression

Multivariate Logistic Regression is a statistical technique for modeling the association between categorical or continuous independent variables and several categorical dependent variables. It is useful for assessing the relative importance of factors that determine outcomes.

Let $\:\pi\:\left(X\right)$ be the probability of outcome events given the values $\:{X}_{1}$, $\:{X}_{2}$, $\:{X}_{3}$,…, $\:{X}_{p}$ or the probability that a case is in a particular category.

$$\:\pi\:\left(X\right)=\:\frac{{e}^{{\beta\:}_{0}+\:{\beta\:}_{1}{X}_{1}+\:{\beta\:}_{2}{X}_{2}+\:{\beta\:}_{3}{X}_{3}+\dots\:+{\beta\:}_{p}{X}_{p}}}{1+\:{e}^{{\beta\:}_{0}+\:{\beta\:}_{1}{X}_{1}+\:{\beta\:}_{2}{X}_{2}+\:{\beta\:}_{3}{X}_{3}+\dots\:+{\beta\:}_{p}{X}_{p}}}\:$$

,

$$\:logit\left[\pi\:\left(X\right)\right]=ln\left[\frac{\pi\:\left(X\right)}{1-\pi\:\left(X\right)}\right]\:{\beta\:}_{0}+\:{\beta\:}_{1}{X}_{1}+\:{\beta\:}_{2}{X}_{2}+\:{\beta\:}_{3}{X}_{3}+\dots\:+{\beta\:}_{p}{X}_{p}$$

4

,

where

$\:logit\left[\pi\:\left(X\right)\right]$ is a logit transform that changes $\:\pi\:\left(X\right)$ from 0 to 1 to $\:-\:\infty\:\:to+\infty\:$

X = the whole set of independent variables $\:{X}_{1}$, $\:{X}_{2}$, $\:{X}_{3}$,…, $\:{X}_{p}$

$\:{\beta\:}_{0}$ = the constant

$\:{\beta\:}_{i}$ = the coefficient of independent variables for $\:i=\text{1,2},3,\dots\:,p$

2.3.5 Odds Ratio (OR)

The odds ratio estimates the change in the odds of membership in the target group for a one-unit increase in the predictor. It can be derived from the logistic regression in Eq. (4) as follows:

for a change of $\:g$ units, the odds ratio is given as

ORs = $\:{e}^{{g\beta\:}_{i}}$, (5)

for any variable $\:{X}_{i},\:i=\text{1,2},3,\dots\:,p\:.$

OR values are significant when the 95% Confidence Interval (CI) does not include 1:

A value < 1 implies that the factor is negatively associated with the outcome thus signifying the percentage decrease in the odds (OR).

A value > 1 implies that the factor is positively associated with the outcome. Signifying the percentage increase in the odds (OR) (See [28, 29]).

2.3.6 Correlation Analysis

Correlation is a statistical tool for assessing the possible linear association between two variables. The correlation coefficient gives the strength of the linear relationship between two variables. It is a dimensionless quantity that takes values of -1(perfect negative correlations) to + 1(perfect positive correlations), with 0 indicating no relationship. The stronger the correlation, the closer the correlation coefficient comes to $\:\pm\:1$ [30].

Spearman’s rank correlation: measures the strength and direction of association between two ranked variables when the relationship does not conform to linearity assumption. It is most suitable for continuous or ordinal data [31].

$$\:{r}_{s}=1-\:\frac{6\sum\:{d}_{i}^{2}}{n({n}^{2}-1)}$$

6

,

$\:{r}_{s}$ = Spearman’s rank correlation coefficient

$\:{d}_{i}$ = Difference between the two ranks of each observation

n = Number of observations.

3.1 Technical Validation

The relevant variables for the questionnaire were drawn from literature, and [32]. 8 subject matter experts validated the contents and their evaluations were analyzed using Lawshe’s method [24, 26, 33, 34]. According to [24], the minimum value for content validity ratio (CVR) for 8 experts is 0.75. As shown in Table 1, the indicators of the knowledge of malaria, knowledge of malaria diagnosis, and knowledge of malaria prevention had content validity ratios of mostly 0.75 and 1.00 which are valid values of CVR for the evaluated items to be retained. The proportion of experts agreeing was found to be greater than 50%. The content validity index for the instrument was estimated to be 0.95 as presented in Table 2 indicating an excellent content validity (see [23] for more detail about all supporting data).

Table 1

Results of Experts Content Evaluation Rating Assessment
Variable	Item	$\:{N}_{e}$	$\:{N}_{i}$	$\:{N}_{u}$	PAE	CVR	Remark
Knowledge of Malaria
	Item 1	8	0	0	1.00	1.00	Valid
	Item 2	8	0	0	1.00	1.00	Valid
	Item 3	8	0	0	1.00	1.00	Valid
	Item 4	8	0	0	1.00	1.00	Valid
	Item5	8	0	0	1.00	1.00	Valid
Knowledge of malaria Diagnosis
	Item 6	8	0	0	1.00	1.00	Valid
	Item 7	8	0	0	1.00	1.00	Valid
	Item 8	8	0	0	1.00	1.00	Valid
	Item 9	7	1	0	0.88	0.75	Valid
	Item10	7	1	0	0.88	0.75	Valid
Knowledge of Malaria Prevention
	Item 11	8	0	0	1.00	1.00	Valid
	Item 12	8	0	0	1.00	1.00	Valid
	Item 13	8	0	0	1.00	1.00	Valid
	Item 14	8	0	0	1.00	1.00	Valid
	Item 15	7	1	0	0.88	0.75	Valid

$\:{N}_{e}$ = the number of experts stating that an item is essential. $\:{N}_{i}$ = the number of experts indicating an item is important but not necessary. $\:{N}_{u}$ = the number of experts indicating that an item is not necessary. PAE = Proportion Agreeing Essential. CVR = Content Validity Ratio.

Table 2

Content Validity Index Evaluation
Variable	Item	Content Validity Ratio	Content Validity Index
Knowledge
of Malaria	Item 1	1.00	0.95
	Item 2	1.00
	Item 3	1.00
	Item 4	1.00
	Item5	1.00
Knowledge of Malaria Diagnosis
	Item 6	1.00
	Item 7	1.00
	Item 8	1.00
	Item 9	0.75
	Item10	0.75
Knowledge of Malaria Prevention
	Item 11	1.00
	Item 12	1.00
	Item 13	1.00
	Item 14	1.00
	Item 15	0.75

3.2 Data Records

The description of the socio-demographics of all the participants is presented in Table 3. Six hundred participants took part in the study, two hundred each from the Federal College of Medical Laboratory Science and Technology, Jos, Plateau State (FCMLST), Nigeria, Plateau State College of Health Technology, Zawan (PSCHT), and Federal College of Veterinary and Medical Laboratory Technology, Vom, Plateau State (FCVMLT). The gender was distributed so that 100 males and 100 females came from each institution. 34.17% of the total participants’ ages ranged from 25–31 years and constituted the majority. Similarly, a total of 73% of the participants had a tertiary education while a total of 83.33% were married.

Table 3: Socio-demographic Characteristics of Participants

Variables	FCMLST (%)	PSCHT (%)	FCVMLT (%)	Total (%)
Age
18–24	5 (2.5)	5 (2.5)	7(3.5)	17 (2.83)
25–31	75 (37.5)	70 (35)	60(30)	205 (34.17)
32–38	60 (30)	60 (30)	55(27.5)	175(29.17)
39–45	40 (20)	50 (25)	53(26.5)	143 (23.83)
> 45	20 (10)	15 (7.5)	25(12.5)	60 (10)
Education
Primary	0 (0)	0 (0)	0(0)	0 (0)
Secondary	50 (25)	60 (30)	52(26)	162(27)
Tertiary	150(75)	140 (70)	148(74)	438 (73)
Marital Status
Single	30 (15)	35 (17.5)	25(12.5)	90 (15)
Married	167 (83.5)	163 (81.5)	170(85)	500 (83.33)
Divorce	3 (1.5)	2 (1)	5(2.5)	10 (1.67)
Gender
Male	100(50)	100(50)	100(50)	300(50)
Female	100(50)	100(50)	100(50)	300(50)

3.3 Knowledge of Malaria

Regarding the knowledge of malaria, about 97.33% of the participants had heard about malaria and only 2.67% of participants claimed not to have heard about malaria. 38% of the participants identified hospital workers as the best-known source of information about malaria followed by the TV, 25.83%, radio (17.5%), friends/neighbours (10%), and social media (8.67%). 80% of the participants selected stagnant water as the best-known mosquito breeding site, followed by dirty areas (15%) and bushes (5%). The awareness that the parasite from mosquito bites was 97.67% and 2.33% were unaware that parasites from mosquitoes cause malaria. Malaria symptoms selected as best known: Fever with shivering/sweating, 45.33%, Body pains/weakness (22.5%), Headache (11.67%), Chills/dizziness (11.67%), and Loss of appetite (8.83%)[See Table 4].

Table 4: Knowledge of Malaria among Participants

Variables	FCMLST (%)	PSCHT (%)	FCVMLT (%)	Total (%)
Have you heard of Malaria?
Yes	195 (97.5)	194 (97)	195 (97.5)	584 (97.33)
No	5 (2.5)	6(3)	5 (2.5)	16 (2.67)
Source of Information about malaria best known
TV	55 (27.5)	50 (25)	50 (25)	155 (25.83)
Radio	35 (17.5)	30(15)	40(20)	105(17.5)
Social Media	20 (10)	20 (10)	20(10)	60 (10)
Friends/Neighbours	15 (7.5)	20(10)	17(8.5)	52 (8.67)
Hospital / Health Workers	75 (37.5)	80 (40)	73(36.5)	228 (38)
Breeding Sites for Mosquitoes best known
Dirty Areas	30 (15)	35 (17.5)	25(12.5)	90 (15)
Stagnant Water	160 (80)	150 (75)	170(85)	480 (80)
Bushes	10 (5)	15 (7.5)	5(2.5)	30(5)
Malaria is caused by parasite through mosquito bite
Yes	195 (97.5)	194 (97)	197(98.5)	586(97.67)
No	5 (2.5)	6(3)	3(1.5)	14 (2.33)
Symptoms of Malaria
Fever with shivering/sweat	90 (45)	87 (43.5)	95(47.5)	272 (45.33)
Body pains/weakness	45 (22.5)	50 (25)	40(20)	135(22.5)
Headache	25 (12.5)	25 (12.5)	20(10)	70 (11.67)
Loss of Appetite	25 (12.5)	20 (10)	25(12.5)	70 (11.67)
Chills/dizziness	15 (7.5)	18 (9)	20(10)	53 (8.83)

3.4 Knowledge of Malaria Diagnosis

On the diagnosis of malaria, 8.67% of the participants had extremely low knowledge of malaria diagnosis, low (11.33%), moderate (20%), high (32.5%), and extremely high (27.5%). 66.67% of the participants were unaware of low levels of malaria transmission and the major challenges of malaria elimination while only 33.33% had the awareness. There was a significant difference in the awareness of low levels of malaria transmission and major challenges among the Institutions. Microscopy tests were popular and well known to the participants with about 37.5%, followed by rapid diagnostic tests (RDTs) with 23.75%, and polymerase chain reaction diagnostic tests (PCRDTs) with 13.75%. 66.67% of the participants claimed that the diagnostic tests were inadequate compared to 33.33% who considered the tests adequate. The need for higher sensitive diagnostic tests was 75% while 25% did not see any need for higher sensitive tests [See Table 5].

Table 5: Knowledge of Malara Diagnosis among Participants

Variables	FCMLST (%)	PSCHT (%)	FCVMLT (%)	Total (%)
How much do you know about malaria diagnosis?
1 Extremely low	15(7.5)	S20 (10)	17(8.5)	52 (8.67)
2	15 (7.5)	25(12.5)	28(14)	68 (11.33)
3	50 (25)	30 (15)	40(20)	120 (20)
4	70 (35)	65 (32.5)	60(30)	195 (32.5)
5 Extremely high	50 (25)	60(30)	55(27.5)	165 (27.5)
Aware of low levels of transmission/major challenges?
Yes	80 (40)	70 (35)	50(25)	200 (33.33)
No	120 (60)	130 (65)	150(75)	400(66.67)
Best known Malaria diagnostic test?
Microscopy	130 (65)	120 (60)	127(63.5)	277 (37.5)
RDTs	45 (22.5)	50 (25)	53(26.5)	148 (23.75)
PCRDTs	25 (12.5)	30 (15)	20(10)	75 (13.75)
Are the tests above adequate?
Yes	60 (30)	80 (40)	60 (30)	200 (33.33)
No	140 (70)	120 (60)	140 (70)	400(66.67)
Need for higher Sensitive diagnostic test?
Yes	150 (75)	145 (72.5)	155(77.5)	450 (75)
No	50 (25)	55 (27.5)	45(22.5)	150 (25)

3.5 Knowledge of Malaria Prevention

On the prevention of malaria, 5.33% of the participants had extremely low knowledge of malaria prevention, low (4.83%), moderate (35.83%), high (30.83%), and extremely high (23.1%). The best-known malaria prevention methods to the participants were insecticide-treated nets (ITNs) (41.67%), antimalarial drugs (27.5%), Indoor residual spraying (18.33%), and use of mosquito repellent (12.5%). The participants considered ITNs as the most effective malaria method (55%), followed by antimalaria (25.83%), Indoor residual spraying (12.5%), and the use of mosquito repellent (6.67%). Malaria prevention as a good strategy for malaria elimination 85% and 15% responded otherwise. 63.33% of the participants were unaware of malaria vaccines while 36.67% were aware of malaria vaccines [See Table 6].

Table 6: Knowledge of Malaria Prevention among Participants

Variables	FCMLST (%)	PSCHT (%)	FCVMLT (%)	Total (%)
How much do you know about malaria prevention?
1 Extreme low	10 (5)	11 (5.5)	11(5.5)	32 (5.33)
2	12 (6)	9(4.5)	8(4)	29 (4.83)
3	70 (35)	72 (36)	73(36.5)	215 (35.83)
4	62 (31)	58 (29)	65(32.5)	185 (30.83)
5 Extremely high	46 (23)	50 (25)	43(21.5)	139 (23.1)
Malaria prevention method best known
Insecticide-treated nets (ITNS)	80 (40)	80 (40)	90(45)	250 (41.67)
Indoor residual spraying	35 (17.5)	40(20)	35 (17.5)	110 (18.33)
Use of mosquito repellent	25 (12.5)	25 (12.5)	25 (12.5)	75 (12.5)
Antimalarial Drugs	60 (30)	55(27.5)	50(25)	165 (27.5)
Which Malaria prevention method is considered effective
ITNS	110 (55)	100 (50)	120(60)	330 (55)
Indoor residual spraying	25 (12.5)	30(15)	20(10)	75 (12.5)
Use of mosquito repellents	15 (7.5)	15 (7.5)	10(5)	40(6.67)
Antimalarial Drugs	50 (25)	55 (27.5)	50 (25)	155 (25.83)
Malaria Prevention as a Good Strategy for Malaria eliminations?
Yes	160 (80)	170 (85)	180(90)	510 (85)
No	40 (20)	30 (15)	20(10)	90 (15)
Aware of any malaria vaccines?
Yes	80 (40)	75 (37.5)	65(32.5)	220 (36.67)
No	120 (60)	125 (62.5)	135(67.5)	380 (63.33)

3.6 Relationship between Socio-demographic Characteristics and Knowledge of Malaria Diagnosis

Table 7 presents the relationship between knowledge of malaria diagnosis and socio-demographic characteristics using multivariate logistic regression. Extremely low knowledge of malaria diagnosis was deployed as the reference category. For FCMLST: The level of knowledge of malaria diagnosis was not significantly associated with gender: Low (OR = 0.455, 95% CI = 0.096–2.158, P = 0.322), moderate (OR = 0.763, 95% CI = 0.214–2.721, P = 0.676), high (OR = 1.785, 95% CI = 0.502–6.347, P = 0.371), extremely high (OR = 1.388, 95% CI = 0.374–5.146, P = 0.624); The knowledge of malaria diagnosis was significantly associated with age at moderate, high and extremely high levels as follows: Moderate (OR = 3.613, 95% CI = 1.106–11.803, P = 0.033), High (OR = 5.544, 95% CI = 1.704–18.039, P = 0.004), extremely high (OR = 5.315, 95% CI = 1.615–17.495, P = 0.006) but not significantly associated with age at low level (OR = 2.709, 95% CI = 0.643–11.422, P = 0.175); The level of education was only significantly associated with extremely high level of knowledge of malaria diagnosis: Low (OR = 0.169, 95% CI = 0.028–1.018, P = 0.052), moderate (OR = 2.012, 95% CI = 0.529–7.644, P = 0.305), high (OR = 2.491, 95% CI = 0.645–9.620, P = 0.185), extremely high (OR = 4.801, 95% CI = 1.053–21.901, P = 0.043); The level of knowledge of malaria diagnosis was not significantly associated with marital status: Low (OR = 0.439, 95% CI = 0.077–2.498, P = 0.353), moderate (OR = 2.817, 95% CI = 0.635–12.500, P = 0.173), high (OR = 2.638, 95% CI = 0.608–11.457, P = 0.195), extremely high (OR = 2.829, 95% CI = 0.591–13.551, P = 0.193).

For PSCHT: The level of knowledge of malaria diagnosis was no significantly associated with gender: Low (OR = 0.812, 95% CI = 0.222–2.961, P = 0.752), moderate (OR = 0.632, 95% CI = 0.174–2.293, P = 0.486), High (OR = 0.667, 95% CI = 0.198–2.245, P = 0.513), extremely high (OR = 1.622, 95% CI = 0.437–6.028, P = 0.470); The level of knowledge of malaria diagnosis was not significantly associated with age: Low (OR = 1.158, 95% CI = 0.547–2.455, P = 0.701), moderate (OR = 0.450, 95% CI = 0.200 -1.014, P = 0.054), high (OR = 0.649, 95% CI = 0.313–1.348, P = 0.247), extremely high (OR = 0.538, 95% CI = 0.247–1.171, P = 0.118) ; The level of education was significantly associated with the level of knowledge of malaria diagnosis: Low (OR = 4.748, 95% CI = 1.015–22.217, P = 0.048), moderate (OR = 7.245, 95% CI = 1.500 -34.981, P = 0.014), high (OR = 15.753, 95% CI = 3.548–69.939, P < 0.001), extremely high (OR = 138.505, 95% CI = 18.690–1026.426, P < 0.001) ; The level of knowledge of malaria diagnosis was not significantly associated with marital status: Low (OR = 1.714, 95% CI = 0.288–10.200, P = 0.554), moderate (OR = 1.347, 95% CI = 0.263–6.891, P = 0.721), high (OR = 1.711, 95% CI = 0.350–8.352, P = 0.507), extremely high (OR = 2.704, 95% CI = 0.461–15.844, P = 0.270).

For FCVMLT: The low and extremely high levels of knowledge of malaria diagnosis were significantly associated with gender while the moderate and high levels were not significant: Low (OR = 9.705, 95% CI = 2.240-42.061, P = 0.002), moderate (OR = 2.774, 95% CI = 0.946–8.134, P = 0.063), high (OR = 1.260, 95% CI = 0.423–2.242, P = 0.679), extremely high (OR = 5.827, 95% CI = 1.920-17.683, P = 0.002); The level of knowledge of malaria diagnosis was not significantly associated with age: Low (OR = 1.239, 95% CI = 0.685–2.455, P = 0.478), moderate (OR = 0.985, 95% CI = 0.616–1.575, P = 0.948), High (OR = 1.191, 95% CI = 0.743–1.910, P = 0.467), extremely high (OR = 1.049, 95% CI = 0.641–1.171, P = 0.848); The level of education was significantly associated with the high and extremely high levels of knowledge of malaria diagnosis with exception to low and moderate levels: Low (OR = 0.377, 95% CI = 0.101–1.403, P = 0.146), moderate (OR = 1.647, 95% CI = 0.617–4.398, P = 0.319), high (OR = 8.359, 95% CI = 3.548–25.638, P < 0.001), extremely high (OR = 12.854, 95% CI = 2.726–25.638, P < 0.001); The level of knowledge of malaria diagnosis was significantly associated with marital status at extremely high level but not significant at low, moderate and high levels: Low (OR = 0.420, 95% CI = 0.091–1.944, P = 0.267), moderate (OR = 1.147, 95% CI = 0.329–3.997, P = 0.830), high (OR = 3.914, 95% CI = 0.918–16.681, P = 0.065), extremely high (OR = 5.177, 95% CI = 1.093–24.518, P = 0.038).

Table 7: Association between Socio-demographic Characteristics and Knowledge of Malaria Diagnosis

	Low				Moderate			High			Extremely High
	OR	95% CI		P	OR	95% CI	P	OR	95% CI	P	OR	95% CI	P
FCMLST
Gender	0.455	0.096, 2.158		0.322	0.763	0.214, 2.721	0.676	1.785	0.502, 6.347	0.371	1.388	0.374, 5.146	0.624
Age	2.709	0.643, 11.422		0.175	3.613	1.106, 11.803	0.033	5.544	1.704, 18.039	0.004	5.315	1.615, 17.495	0.006
Level of Education	0.169	0.028, 1.018		0.052	2.012	0.529, 7.644	0.305	2.491	0.645, 9.620	0.185	4.801	1.053, 21.901	0.043
Marital Status	0.439	0.077, 2.498		0.353	2.817	0.635, 12.500	0.173	2.638	0.608, 11.457	0.195	2.829	0.591, 13.551	0.193
PSCHT
Gender	0.812	0.222, 2.961	0.752		0.632	0.174, 2.293	0.486	0.667	0.198, 2.245	0.513	1.622	0.437, 6.028	0.470
Age	1.158	0.547, 2.455	0.701		0.450	0.200, 1.014	0.054	0.649	0.313, 1.348	0.247	0.538	0.247, 1.171	0.118
Level of Education	4.748	1.015, 22.217	0.048		7.245	1.500, 34.981	0.014	15.753	3.548, 69.939	< 0.001	138.505	18.690, 1026.426	< 0.001
Marital Status	1.714	0.288, 10.200	0.554		1.347	0.263, 6.891	0.721	1.711	0.350, 8.352	0.507	2.704	0.461, 15.844	0.270
FCVMLT
Gender	9.705	2.240, 42.061	0.002		2.774	0.946, 8.134	0.063	1.260	0.423, 3.755	0.679	5.827	1.920, 17.683	0.002
Age	1.239	0.685, 2.242	0.478		0.985	0.616, 1.575	0.948	1.191	0.743, 1.910	0.467	1.049	0.641, 1.717	0.848
Level of Education	0.377	0.101, 1.403	0.146		1.647	0.617, 4.398	0.319	8.359	2.726, 25.638	< 0.001	12.854	3.472, 47.582	< 0.001
Marital Status	0.420	0.091, 1.944	0.267		1.147	0.329, 3.997	0.830	3.914	0.918, 16.681	0.065	5.177	1.093, 24.518	0.038

Reference category is Extremely low

3.7 Relationship between Demographic and Knowledge of Malaria Prevention

Table 8 presents the relationship between knowledge of malaria prevention and socio-demographic characteristics using multivariate logistic regression. Extremely low knowledge of malaria diagnosis was deployed as the reference category. For FCMLST: The level of knowledge of malaria prevention was not significantly associated with gender: Low (OR = 0.971, 95% CI = 0.177–5.325, P = 0.973), moderate (OR = 0.771, 95% CI = 0.197–3.017, P = 0.708), high (OR = 1.073, 95% CI = 0.244–4.720, P = 0.926), extremely high (OR = 1.127, 95% CI = 0.244–5.198, P = 0.878); The knowledge of malaria prevention was not significantly associated with age: Low (OR = 1.620, 95% CI = 0.404–6.501, P = 0.496), moderate (OR = 1.427, 95% CI = 0.474–4.293, P = 0.527), high (OR = 1.611, 95% CI = 0.521–4.984, P = 0.408), extremely high (OR = 1.905, 95% CI = 0.606–5.991, P = 0.270); The level of education was only significantly associated with high and extremely high levels of knowledge of malaria prevention: Low (OR = 0.263, 95% CI = 0.016–4.346, P = 0.351), moderate (OR = 5.153, 95% CI = 0.882–30.105, P = 0.069), high (OR = 95.766, 95% CI = 10.524- 871.452, P < 0.001), extremely high (OR = 106.482, 95% CI = 7.737–95.766, P < 0.001); The level of knowledge of malaria prevention was not significantly associated with marital status: Low (OR = 2.142, 95% CI = 0.216–21.229, P = 0.515), moderate (OR = 1.255, 95% CI = 0.233–6.765, P = 0.791), high (OR = 1.055, 95% CI = 0.161–6.907, P = 0.955), extremely high (OR = 1.255, 95% CI = 0.171–9.177, P = 0.823).

For PSCHT: The level of knowledge of malaria prevention was not significantly associated with gender: Low (OR = 0.642, 95% CI = 0.103–4.001, P = 0.635), moderate (OR = 0.892, 95% CI = 0.241 -3.300, P = 0.864), high (OR = 0.687, 95% CI = 0.183–2.583, P = 0.578), extremely high (OR = 1.210, 95% CI = 0.294–4.982, P = 0.792); The level of knowledge of malaria prevention was not significantly associated with age: Low (OR = 0.374, 95% CI = 0.107–1.307, P = 0.123), moderate (OR = 0.545, 95% CI = 0.253–1.172, P = 0.120), high (OR = 0.855, 95% CI = 0.400–1.826, P = 0.685), extremely high (OR = 0.496, 95% CI = 0.219–1.120, P = 0.092); The level of education was not significantly associated with the level of knowledge of malaria prevention with exception to extremely high level: Low (OR = 0.600, 95% CI = 0.075–4.830, P = 0.631), moderate (OR = 2.143, 95% CI = 0.544–8.432, P = 0.276), high (OR = 3.904, 95% CI = 0.969–15.731, P = 0.055), extremely high (OR = 65.374, 95% CI = 6.083–702.538, P = 0.001); The level of knowledge of malaria prevention was not significantly associated with marital status: Low (OR = 1.472, 95% CI = 0.152–14.275, P = 0.739), moderate (OR = 2.214, 95% CI = 0.400 -12.257, P = 0.363), high (OR = 2.704, 95% CI = 0.454–16.089, P = 0.274), extremely high (OR = 5.808, 95% CI = 0.807–41.783, P = 0.081).

For FCVMLT: The levels of knowledge of malaria prevention were not significantly associated with gender: Low (OR = 2.063, 95% CI = 0.255–16.669, P = 0.497), moderate (OR = 1.116, 95% CI = 0.281–4.438, P = 0.876), high (OR = 0.957, 95% CI = 0.228–4.018, P = 0.953), extremely high (OR = 1.237, 95% CI = 0.273–5.607, P = 0.783); The knowledge of malaria prevention was significantly associated with age at low level but not significant at moderate, high and extremely high levels: Low (OR = 0.153, 95% CI = 0.036–0.641, P = 0.010), moderate (OR = 0.766, 95% CI = 0.419–1.398, P = 0.385), high (OR = 0.900, 95% CI = 0.478–1.696, P = 0.385), extremely high (OR = 1.025, 95% CI = 0.520–2.019, P = 0.944); The level of education was significantly associated with the high and extremely high levels of knowledge of malaria prevention but not significant at low level: Low (OR = 1.886, 95% CI = 0.181–19.642, P = 0.595), moderate (OR = 7.263, 95% CI = 1.504–35.066, P = 0.014), high (OR = 26.301, 95% CI = 4.968- 139.232, P < 0.001), extremely high (OR = 95.630, 95% CI = 11.474–797.005, P < 0.001); The knowledge of malaria prevention was significantly associated with marital status at low level but not significant at moderate, high and extremely high levels: Low (OR = 17.885, 95% CI = 1.271–251.701, P = 0.033), moderate (OR = 3.066, 95% CI = 0.662–14.195, P = 0.152), high (OR = 2.475, 95% CI = 0.483–12.682, P = 0.277), extremely high (OR = 4.196, 95% CI = 0.652–27.001, P = 0.131).

Table 8: Association between Socio-demographic Characteristics and Knowledge of Malaria Prevention

	Low			Moderate			High			Extremely High
	OR	95% CI	P	OR	95% CI	P	OR	95% CI	P	OR	95% CI	P
FCMLST
Gender	0.971	0.177, 5.325	0.973	0.771	0.197, 3.017	0.708	1.073	0.244, 4.720	0.926	1.127	0.244, 5.198	0.878
Age	1.620	0.404, 6.501	0.496	1.427	0.474, 4.293	0.527	1.611	0.521, 4.984	0.408	1.905	0.606, 5.991	0.270
Level of Education	0.263	0.016, 4.346	0.351	5.153	0.882, 30.105	0.069	95.766	10.524, 871.452	< 0.001	106.482	7.737, 1465.455	< 0.001
Marital Status	2.142	0.216, 21.229	0.515	1.255	0.233, 6.765	0.791	1.055	0.161, 6.907	0.955	1.255	0.171, 9.177	0.823
PSCHT
Gender	0.642	0.103, 4.001	0.635	0.892	0.241, 3.300	0.864	0.687	0.183, 2.583	0.578	1.210	0.294, 4.982	0.792
Age	0.374	0.107, 1.307	0.123	0.545	0.253, 1.172	0.120	0.855	0.400, 1.826	0.685	0.496	0.219, 1.120	0.092
Level of Education	0.600	0.075, 4.830	0.631	2.143	0.544, 8.432	0.276	3.904	0.969, 15.731	0.055	65.374	6.083, 702.538	0.001
Marital Status	1.472	0.152, 14.275	0.739	2.214	0.400, 12.257	0.363	2.704	0.454, 16.089	0.274	5.808	0.807, 41.783	0.081
FCVMLT
Gender	2.063	0.255, 16.669	0.497	1.116	0.281, 4.438	0.876	0.957	0.228, 4.018	0.953	1.237	0.273, 5.607	0.783
Age	0.153	0.036, 0.641	0.010	0.766	0.419, 1.398	0.385	0.900	0.478, 1.696	0.745	1.025	0.520, 2.019	0.944
Level of Education	1.886	0.181, 19.642	0.595	7.263	1.504, 35.066	0.014	26.301	4.968, 139.232	< 0.001	95.630	11.474,797.005	< 0.001
Marital Status	17.885	1.271, 251.701	0.033	3.066	0.662, 14.195	0.152	2.475	0.483, 12.682	0.277	4.196	0.652, 27.001	0.131

Reference category is Extremely low

3.8 Correlation between Awareness of Low Levels of Malaria Transmission and Knowledge of Malaria Diagnosis and Prevention

Table 9 shows the correlation between awareness of low levels of malaria transmission and malaria diagnosis and prevention. Deduced from Spearman’s correlation test; for FCMLST: a positive correlation was found between awareness of low levels of malaria transmission and malaria diagnosis ($\:{r}_{s}=\:0.\:136,\:\text{P}=0.055\:$), and a significant positive correlation between low levels of malaria transmission and malaria prevention ($\:{r}_{s}=\:0.\:225,\:\text{P}=0.001$). Further findings from multivariate logistic regression analysis showed that improved awareness of low levels of malaria transmission could positively boost knowledge of malaria diagnosis (OR = 1.350; 95% CI = 1.041–1.751) and knowledge of malaria prevention (OR = 1.664; 95% CI = 1.234–2.243). That is to say, the participants with improved awareness of low levels of malaria transmission have 35.0% higher odds of having increased knowledge of malaria diagnosis and 66.4% higher odds of having increased knowledge of malaria prevention. For PSCHT: a significant positive correlation was found between awareness of low levels of malaria transmission and malaria diagnosis ($\:{r}_{s}=\:0.\:403,\:\text{P}<0.001\:$), and a nonsignificant positive correlation between awareness of low levels of malaria transmission and malaria prevention ( $\:{r}_{s}=\:0.005,\:\text{P}=0.944$). Further findings from multivariate regression analysis showed that improved awareness of low levels of malaria transmission could positively boost knowledge of malaria diagnosis (OR = 2.387; 95% CI = 1.684–3.384) and knowledge of malaria prevention (OR = 1.083; 95% CI = 0.808–1.452). By implication, participants with improved awareness of low levels of malaria transmission have 138.7% higher odds of having increased knowledge of malaria diagnosis and 8.3% higher odds of having increased knowledge of malaria prevention. For FCVMLT: a positive significant correlation was found between low levels of malaria transmission and malaria diagnosis ($\:{r}_{s}=\:0.\:330,\:\text{P}<0.001\:$), and a nonsignificant positive correlation between low levels of malaria transmission and malaria prevention ($\:{r}_{s}=\:0.\:117,\:\text{P}=0.099$). Further findings from multivariate regression analysis showed that improved awareness of low levels of malaria transmission could positively boost knowledge of malaria diagnosis (OR = 2.008; 95% CI = 1.445–2.792) and knowledge of malaria prevention (OR = 1.371; 95% CI = 0.987–1.904). These findings imply that participants with improved awareness of low levels of malaria transmission have: 100.8% higher odds of having increased knowledge of malaria diagnosis and 37.1% higher odds of having increased knowledge of malaria prevention.

Table 9

**Relationship between Knowledge of Low Levels of Malaria Transmission and Knowledge of Malaria Diagnosis and Prevention**
	FCMLST				PSCHT				FCVMLT
	Correlation		OR (95%)	P	Correlation		OR(95%)	P	Correlation		OR (95%)	P
	$\:{r}_{s}$	P	OR (95%)	P	$\:{r}_{s}$	P	OR(95%)	P	$\:{r}_{s}$	P	OR (95%)	P
Malaria Diagnosis	0. 136	0.055	1.350 (1.041, 1.751)	0.024	0.403	< 0.001	2.387 (1.684, 3.384)	< 0.001	0. 330	< 0.001	2.008 (1.445, 2.792)	<0.001
Malaria Prevention	0.225	0.001	1.664 (1.234, 2.243)	0.001	0.005	0.944	1.083 (0.808, 1.452)	0.592	0.117	0.099	1.371 (0.987, 1.904)	0.060

Reference category: No

Our study is concerned with assessing the knowledge of malaria diagnosis and prevention linking the awareness of low levels of malaria transmission to the cause of malaria elimination in Nigeria.

Most respondents heard about malaria and identified hospital workers as the best-known source of information similar to prior studies [17, 21, 35], followed by TV, radio, friends/neighbours, and social media. In contrast, the study of [36] identified friends/neighbours as the most frequently mentioned group for malaria-related information while [37] identified TV as the major source of information. The participants selected stagnant water as the best-known mosquito breeding site, followed by dirty areas and bushes [38, 39, 40]. The awareness that mosquito bites cause malaria was common among the participants in agreement with the studies [37, 40, 41, 42]. Malaria symptoms selected as best known: Fever with shivering/sweating, body pains/weakness, headache, Chills/dizziness, and Loss of appetite as reflected in different studies that the most characteristic symptom of malaria is fever. Other common symptoms include headaches, pains, chills, Loss of appetite, and so forth [12, 13, 18, 35, 43].

The participants had increasing knowledge of malaria diagnosis with the majority unaware of low levels of malaria transmission and the major challenges of malaria elimination. The gap in the awareness of low levels of malaria transmission and the major challenges of malaria elimination has remained the oxygen pipe in the spread of malaria. According to [44], very low transmission and higher concentrations of malaria can have important consequences for elimination efforts. Microscopy tests were popular and well-known to the participants, followed by rapid diagnostic tests (RDTs), and polymerase chain reaction diagnostic tests (PCRDTs). Most participants claimed the diagnostic tests were inadequate and opined the need for more sensitive diagnostics. The studies of [45, 46] showed that RDTs were more sensitive than microscopy. In contrast, the studies of [4, 8] picked that RDTs were inadequate for low levels of malaria transmission and recommended the need for sensitive diagnostic tools. The study of [7] emphasized that the availability of sensitive, specific, and quantitative molecular diagnostic tests for malaria is becoming increasingly important as control strategies seek to eliminate asymptomatic infections that serve as reservoirs for low levels of malaria transmission.

The participants had increasing levels of knowledge of malaria prevention. Insecticide-treated nets (ITNs) were selected as the best-known malaria prevention method followed by antimalarial drugs, Indoor residual spraying, and mosquito repellent. The participants considered ITNs the most effective malaria method, followed by antimalaria, Indoor residual spraying, and the use of mosquito repellent, and are in line with the studies of [15, 16, 19, 20]. Most participants considered malaria prevention a good strategy for elimination and were unaware of malaria vaccines similar to [3]. The studies of [47, 48, 49, 50] provided additional information on the knowledge of malaria prevention.

Our findings on the relationship between knowledge of malaria diagnosis and socio-demographic characteristics (gender, age, education, and marital status) across FCMLST, PSCHT, and FCVMLT reveal the following; FCVMLT showed considerable gender disparities in the knowledge of malaria diagnosis, whereas FCMLST and PSCHT did not. In FCMLST, age was positively related to the knowledge of malaria diagnosis aligning with studies showing increased health knowledge with age [51, 52]. Unlike the study of [53], the current research found no significant association between age and knowledge of malaria diagnosis in PSCHT and FCVMLT. The education level was only significant at extremely high knowledge level in FCMLST and strongly related to knowledge of malaria diagnosis across all levels in PSCHT consistent with [54, 55]. The association between marital status and knowledge of malaria diagnosis varied across the Institutions, with significance only in FCVMLT's extremely high knowledge level.

In this study, the findings on the relationship between knowledge of malaria prevention and socio-demographic characteristics (gender, age, education, and marital status) across FCMLST, PSCHT, and FCVMLT reveal the following; the association between gender and the knowledge of malaria prevention was not significant at all levels across the three study sites unlike prior studies [56, 57, 58]. The association between age and knowledge of prevention shows variation across the study sites, with significance only in FCVMLT's low knowledge level aligning with the studies [59 56]. The relationship between education level and knowledge of malaria prevention showed mixed results across the study sites, with significance in FCMLST’s high and extremely high knowledge levels, PSCHT’s extremely high knowledge level, and FCVMLT’s low knowledge level and are in tandem with the studies [59, 58, 56]. The association between marital status and knowledge of malaria prevention varied across the study sites, with significance only in FCVMLT's low knowledge level [58]. Elsewhere, age was not significantly associated with knowledge of malaria prevention in FCMLST and PSCHT, aligning with studies showing no age-related differences [57, 60].

Our findings showed that the awareness of low levels of malaria transmission is associated with better knowledge of malaria diagnosis and prevention across all three Institutions. The findings further revealed that improved awareness of low levels of malaria transmission is linked to enhanced knowledge of malaria prevention, although this association is stronger in FCMLST [20, 48, 49]. The relationships between low malaria transmission, diagnosis, and prevention vary across the three institutions, indicating potential differences in health behaviour, knowledge, or environmental factors.

So far, this study highlights the significance of understanding malaria diagnosis and prevention knowledge among Nigerians. We have established that awareness of low malaria transmission levels is vital to a better understanding and practice of malaria diagnosis and prevention. Moreover, the disparities in the findings of this study point to the fact that there are knowledge gaps among the participants. The identified gaps in knowledge and awareness are essential when designing interventional strategies to eliminate malaria. As a way of further studies, it is recommended that an investigation be conducted to unpack the underlying factors contributing to disparities in the knowledge of malaria diagnosis and prevention across sites, and also design campaign programs to address specific demographic needs.

Competing Interests

The author(s) declare no competing interests.

Code Availability

Raw data and codebook are available at https://doi.org/10.6084/m9.figshare.25138469.v1

Author Contributions

Nkereuwem Sunday Etukudoh: Supervision, Resources and Validation

Emmanuel Akpan and Cecilia Udo: Conceptualization, Writing-Original Draft, Formal Analysis and Visualization.

Joyce Ene Ocheola-Oki, Akanimo Essiet and Eno Chongs Mantu: Writing-Review, Editing, and Investigation.

Emmanuel Julius, Hilary Akpan, and Uduak Umondak: Methodology, Data curation

Biruksew A et al (2023) Diagnostic performance of NxTek™ Eliminate Malaria-Pf test for the detection of Plasmodium falciparum in school children with asymptomatic malaria. Malar J 22:112
WHO Fact Sheet about malaria (2023) https://www.who.int/news-room/fact- sheets/detail/malaria
Nnaji A, Ozdal MA (2023) Perception and awareness towards malaria vaccine policy implementation in Nigeria by health policy actors. Malar J 22:111
Campillo A, Daily J, Gon
$\:\stackrel{´}{a}$
Zlez IJ (2017) International survey to identify diagnostic needs to support malaria elimination: guiding the development of combination of highly sensitive rapid diagnostic tests. Malar J 16:385
Aribodor DN, Ugwuanyi IK, Aribodor OB (2016) Challenges to achieving malaria elimination in Nigeria. Am J Public Health Res 4(1):38–41
Abbas F et al (2023) Socio-demographic trends in malaria knowledge and implications for behaviour change interventions in Zanzibar. Malar J 22:39
Andolina C et al (2021) Sources of persistent malaria transmission in a setting with effective malaria control in eastern Uganda: a longitudinal, observational cohort study. Lancet Infect Dis 21:11 1568–1578. 10.1016/S1473-3099(21)00072-4
Niang M et al (2017) Substantial asymptomatic submicroscopic Plasmodium carriage during dry season in low transmission areas in Senegal: Implications for malaria control and elimination. PLoS ONE 12(8):e0182189. 10.1371/journal.pone.0182189
White NJ (2022) Severe malaria. Malar J 21:284. https://doi.org/10.1186/s12936-022-04301-8
Oyegoke OO et al (2022) Malaria diagnostic methods with the elimination goal in view. Parasitol Res 121:1867–1885. 10.1007/s00436-022-07512-9
Ibrahim AO et al (2023) Malaria infection and its associated with socio-demographics, long lasting insecticide nets usage and hematological parameters among adolescent patients in rural southwestern Nigeria. PLoS ONE 18(7):e0287723
Ravi N et al (2022) A malaria knowledge, attitudes and practice survey in a rural community in Guinea. Malar J 21:329. https://doi.org/10.1186/s12936-022-04357-6
DePina AJ et al (2019) Knowledge, attitudes and practices about malaria in Cabo Verde: a country in the pre-elimination context. BMC Public Health 19:850
Israel OK et al (2018) Caregivers' knowledge and utilization of long-lasting insecticidal nets among under-five children in Osun State, Southwest, Nigeria. Malar J 17(231):1. 10.1186/s12936-018-2383-5
Afolayan T, Tanimowo WO, Ibrahim AG, Oduwa H (2023) Assessment of the Knowledge, Attitude, Practices and Malaria Prevalence among Undergraduates. Int J Trop Dis 6:069
Chukwurah JN et al (2016) Knowledge, attitude and practice on malaria prevention and sulfadoxine-pyrimethamine utilisation among pregnant women in Badagry, Lagos State, Nigeria. Malariaworld J 7:3
Gupta RK et al (2016) A household survey to assess community knowledge, attitude and practices on malaria in a rural population of Northern India. JFMPC 5:101–107
Romay-Barja M et al (2015) Rural-urban differences in household treatment-seeking behaviour for suspected malaria in children at Bata District Equatorial Guinea. PLoS ONE 10:e0135887
Ezeigbo OR, Osuagwu MC, Ibegbulem ZO, Agomoh NG (2015) Evaluation of the Knowledge, Attitude and Practice of the Use of Insecticide-Treated Nets (ITNs) in Aba, Nigeria. Br J Med Med Res 5:57–64
Fuge TG, Ayanto SY, Gurmamo FL (2015) Assessment of knowledge, attitude and practice about malaria and ITNs utilization among pregnant women in Shashogo District, Southern Ethiopia. Malar J 14:235
Hlongwana KW, Mabaso ML, Kunene S, Govender D, Maharaj R (2009) Community knowledge, attitudes and practices (KAP) on malaria in Swaziland: a country earmarked for malaria elimination. Malar J 8:29
Sapra RL (2022) How to Calculate an Adequate Sample Size? In: How to Practice Academic Medicine and Publish from Developing Countries? Springer, Singapore
Akpan E, Essiet A, Udo C, Julius E, Akpan H (2024) Surveillance Data on Knowledge of Malaria, Diagnosis and Prevention. figshare. Dataset; https://doi.org/10.6084/m9.figshare.25138469.v1
Lawshe CH (1975) A quantitative approach to content validity. Pers Psychol 28:563–575
Wilson FR, Pan W, Schumsky DA (2012) Recalculation of the critical values for Lawshe’s content validity ratio. Meas Eval Couns Dev 45:197–210
Romero JM, Diaz CE, Faouzi NT (2023) A review of Lawshe’s method for calculating content validity in the social sciences. Front Educ 8:1271335
Gilbert GE, Prion S (2016) Making sense of methods and measurement: Lawshe’s content validity index. Clin Simul Nurs 12:530–531
Pal A (2021) Logistic Regression: Simple Primer. Cancer Res Stat Treat 4:551–554
Glonek GFV, McCullagh P (1995) Multivariate Logistic Model. J R Stat 57:533–546
Mukaka MM, Statistics Corner (2012) A guide to appropriate use of correlation coefficient in medical research. MMJ 24:69–71
Schober P, Boer C, Schwarte LA (2018) Correlation coefficients: appropriate use and interpretation. Anesth Analg 126:1763–1768
Nigeria malaria indicator survey (2021) final report. https://dhsprogram.com/pubs/pdf/MIS41/MIS41.pdf (2021)
Taherdoost H (2016) Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research. Int J Acad Res Manage, 5
Ayre C, Scally AJ (2013) Critical values for Lawshe’s content validity ratio: revisiting the original methods of calculation. Meas Eval Couns Dev 47:79–86
Koroma JM et al (2022) A cross-sectional survey on the malaria control and prevention knowledge, attitudes, and practices of caregivers of children under-5 in the western area of Sierra Leone. Trop med infect 7:120
Ahmed SM et al (2009) Knowledge on the transmission, prevention and treatment of malaria among two endemic populations of Bangladesh and their health-seeking behaviour. Malar J 8:173
Konlan DK, Amu H, Konlan KD, Japiong M (2019) Awareness and malaria prevention practices in a rural community in the Ho Municipality, Ghana. Interdiscip Perspect Infect Dis 9365823:8
Duval P, Aschan-Leygonie C, Valiente Moro C (2023) A review of knowledge, attitudes and practices regarding mosquitoes and mosquito-borne infectious diseases in nonendemic regions. Front public health 11:1239874
Bellini R et al (2020) Practical management plan for invasive mosquito species in Europe: I. Asian tiger mosquito (Aedes albopictus). Travel Med Infect Dis 35:101691. 10.1016/j.tmaid.2020.101691
Ajao AM et al (2017) Knowledge, attitude and practices about malaria among members of a university community in Kwara State, Nigeria. Anim Res Int 14:2793–2803
Obembe A, Oduola A, Popoola K, Ajao A (2014) Community knowledge and practices as regards malaria in Ilorin City: implications for the elimination plan of the national malaria elimination program. Asian Pac J Trop Dis 4:583–589
Naing PA et al (2017) Awareness of malaria and treatment-seeking behaviour among persons with acute undifferentiated fever in the endemic regions of Myanmar. Trop Med Health 45:31. 10.1186/s41182-017-0070-9
Singh R, Musa J, Singh S, Ebere UV (2014) Knowledge, attitude and practices on malaria among the rural communities in Aliero, Northern Nigeria. J Family Med Prim Care 3:39–44
Fernandez-Camacho B et al (2024) Malaria seroepidemiology in very low transmission settings in the Peruvian Amazon. Sci Rep 14:2806
Kavanaugh MJ, Azzam SE, Rockabrand DM (2021) Malaria rapid diagnostic tests: literary review and recommendation for a quality assurance, quality control and algorithm. Diagnostics 11:768
Azikiwe CCA et al (2012) A comparative laboratory diagnosis of malaria: microscopy versus rapid diagnostic test kits. Asian Pac J Trop Biomed 2:307–310
Nwaneli EI et al (2020) Malaria prevalence and its socio-demographic determinants in febrile children- a hospital-based study in a developing community in South East Nigeria. JPMH 61:E173–E180
Nalinya S, Musoke D, Deane K (2022) Malaria prevention interventions beyond long-lasting insecticidal nets and indoor residual spraying in low- and middle countries: a scoping review. Malar J 21:31
Chimezie RO (2020) Malaria hyperendemicity: the burden and obstacles to eradication in Nigeria. J Biosci Med 8:165–178
Maduka O (2018) End malaria for good: a review of current strategies and future novelties for malaria elimination in Nigeria. Malariaworld J 9:1
Li H, Tao S, Sun S, Xiao Y, Liu Y (2024) The relationship between health literacy and health-related quality of life in Chinese older adults: a cross-sectional study. Front Public Health 12:1288906
Fırat Kılıç H, Arifoğlu B, Kızılkaya N (2023) The Relationship between Successful Aging and Health Literacy in Older Adults. Perspect Psychiatr Care 1462914:7
Kotepui KU, Kotepui M, Punsawad C (2019) Knowledge, attitude, and practice related to malaria diagnosis among healthcare workers in hospitals: a cross-sectional survey. J Trop Med 1414079. 10.1155/2019/1414079
Onyinyechi OM, Nazan M, A. I. N., Ismail S (2023) Effectiveness of health education interventions to improve malaria knowledge and insecticide-treated nets usage among populations of sub-Saharan Africa: systematic review and meta-analysis. Front public health 11:1217052
Dike N et al (2006) Influence of education and knowledge on perceptions and practices to control malaria in Southeast Nigeria. Soc Sci Med 63:103–106
Ngasala B et al (2023) Malaria knowledge, attitude, and practice among communities involved in a seasonal malaria chemoprevention study in Nanyumbu and Masasi districts, Tanzania. Front Public Health 11:976354
Afai G et al (2022) Factors associated with knowledge about malaria prevention among women of reproductive age, Tete Province, Mozambique, 2019–2020. Malar J 21:76
Oladimeji KE et al (2019) Knowledge of malaria prevention among pregnant women and non-pregnant mothers of children aged under 5 years in Ibadan, South West Nigeria. Malar J 18:92
Addis D, Gebeyehu Wondmeneh T (2023) Assessment of malaria prevention knowledge, attitude, and practice and associated factors among households living in rural malaria-endemic areas in the Afar Pastoral Region of Ethiopia. Front. Public Health 11,1258594
Awoyesuku PA, Ohaka C, Ngeri B (2020) Knowledge of malaria and utilization of its preventive measures among pregnant women attending antenatal care at a tertiary hospital in Port-Harcourt, Nigeria. IJTDH 41:34–46

The authors declare no competing interests.

Assessment of Knowledge on Malaria Diagnosis and Prevention: Exploring the Link between Awareness and Low Malaria Transmission Levels

Status:

Version 1

Abstract

1. Introduction

2. Methods