Journal of Tropical Medicine

Journal of Tropical Medicine / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 2316375 | https://doi.org/10.1155/2019/2316375

Martin Amogre Ayanore, John Tetteh, Asiwome Ameko, Wisdom Kudzo Axame, Robert Kaba Alhassan, Augustine Adoliba Ayanore, Victor Mogre, Seth Owusu-Agyei, "Reproductive-Age Women’s Knowledge and Care Seeking for Malaria Prevention and Control in Ghana: Analysis of the 2016 Malaria Indicator Survey", Journal of Tropical Medicine, vol. 2019, Article ID 2316375, 17 pages, 2019. https://doi.org/10.1155/2019/2316375

Reproductive-Age Women’s Knowledge and Care Seeking for Malaria Prevention and Control in Ghana: Analysis of the 2016 Malaria Indicator Survey

Academic Editor: Aditya Prasad Dash
Received29 Oct 2018
Accepted20 Jan 2019
Published12 Feb 2019

Abstract

Introduction. Malaria is a major cause of morbidity and mortality worldwide, requiring individual and environmental level controls to prevent its adverse morbidity effects. This study examined reproductive-aged women’s knowledge and care-seeking practices for malaria prevention and control in Ghana. Methods. The 2016 Ghana Malaria Indicator Survey data for reproductive-age women was analysed (n=5,150). Multilevel mixed-effects logistic regression model was used to determine factors associated with reproductive-aged women’s knowledge and care-seeking practices for malaria. Results. 62.3%, 81.3%, and 64.6% knowledge levels on causes, signs/symptoms, and prevention of malaria were found, respectively, among respondents. Age, wealth and educational status, religion, region, and place of residence (rural) were found to significantly influence respondents’ knowledge of causes, signs/symptoms, and care-seeking practices for malaria. A 15% differential among Insecticide Treated Nets (ITNs) awareness and use was found. Increasing age (≥35 years) was associated with increasing knowledge of malaria. Regional variations were observed to significantly influence knowledge of malaria treatment. Conclusion. Though ownership of ITNs and knowledge of malaria prevention were high, it did not necessarily translate into use of ITNs. Thus, there is a need to intensify education on the importance and the role of ITNs use in the prevention of malaria.

1. Introduction

In Ghana, malaria is a major cause of death and other socioeconomic losses due to morbidity and social, economic, and health implications [15]. Aggregate estimates from the district health information data show that about 2000 deaths in Ghana are attributable to malaria, with approximately 48% of these case fatalities affecting children under 5 years [6]. Plasmodium falciparum accounts for 80-90% of malaria reported morbidity issues in Ghana, particularly among pregnant women and children under 5 years [7]. The National Malaria Control Programme (NMCP) which became operational in 1999 sets up the modalities to reduce the burden of malaria nationally [8].

Malaria control interventions such as the introduction of Insecticide Treated Nets (ITNs), Indoor Residual Spraying (IRS), and Rapid Diagnostic Tests (RDTs) which form part of key strategies in Ghana have contributed significantly to malaria control in Ghana [9]. World Health Organization (WHO) advocates a mix of strategies to combat malaria, from an increased awareness of malaria causes and prevention, improvements in surveillance, and advances in new vaccine control mechanisms [10]. A new strategic plan (2015-2020) against malaria in Ghana provides a renewed framework to reduce malaria-related mortality rates by 75% in 2020 [2, 9, 11].

Pregnant women and children under 5 years remain vulnerable in the epidemiology of the malaria disease worldwide. According to national estimates from the 2016 Ghana Malaria Indicator Survey (GMIS) report, about 54.3% of women of reproductive age (15-49 years) suffer from malaria-related morbidities [12]. Though there are recent Knowledge, Attitudes, and Practices (KAP) studies on malaria in Ghana, most are limited to district and regional level data [1315]. In reviewing national level studies on malaria morbidity in Ghana, we found only recent reports from implementing partners of the 2016 GMIS. No study was found to have applied the 2016 GMIS data to examine knowledge and treatment practices among a specific population group such as women of reproductive age. The 2016 GMIS serves as a useful database to further understand the clinical and nonclinical aspects of malaria and address its morbidity concerns in Ghana. To scale up efforts towards malaria eradication, a heightened awareness of knowledge and treatment practices among varied subpopulation groups in Ghana is relevant, an identified gap that this study seeks to close in Ghana. The study examined reproductive-age women’s knowledge and care seeking for malaria prevention and control in Ghana. Since reproductive-age women’s productive lives affect other household member health needs [16], addressing reproductive-age knowledge and care seeking for malaria prevention is important to the overall promotion of health and well-being at the family level. Policy and future study suggestions presented in this study are useful for improving control strategies in Ghana and in malaria endemic areas in other countries in Sub-Saharan Africa (SSA).

2. Materials and Methods

2.1. Data

Data from the 2016 GMIS were analysed. The survey was implemented by partners made up of Ghana Statistical Service (GSS), National Public Health Reference Laboratory (NPHRL), DHS Program, PATH-MACEPA, the Malaria Consortium, Center for Disease Control (CDC), and the Carter Centre. The survey collected data on the epidemiology of malaria and household behaviours regarding malaria prevention and treatment in Ghana. Information collected included ownership and use of Insecticide Treated Nets (ITNs), Indoor Residual Spraying (IRS) with insecticides, treatments for fevers in children under 5 years of age, malaria and its prevention among pregnant women, and biomarker test for anaemia and malaria among pregnant women and children. For the purpose of this study, knowledge and care seeking related variables on malaria among reproductive-age women (15-49 years) were analysed.

2.2. Independent Variables

We included twelve (12) independent variables, after partial least squares regression (PLS) analysis was conducted to remove any variables that may have correlated effects in the final regression. Independent variables included in the final model were age, wealth index, spatial location (region), place of residence, ownership of bed net, sleep under mosquito bed, pregnancy status, type of mosquito bed net(s) slept under which were used in the analysis as presented in the original data file. Educational status was recategorized into three levels: no education, primary, and secondary, while religion was recategorized into 4 responses: Christianity, Islam, Traditional, and no religion. Parity status was recategorized into 4 responses: nulliparous 1-2 births, 3-4 births, and 5+ births. Type of toilet facility at household type was recategorized into 4 groups (flush toilet, pit latrine, open defecation, bucket type, and not a Dejure).

2.3. Dependent Variables

Two main outcome dichotomous variables were assessed as presented in Table 1: (1) overall knowledge of malaria causes, signs, symptoms, and prevention and (2) care-seeking practice assessed by respondent’s awareness that malaria is covered by health insurance. In order to get an index variable to represent each level of the outcome measure on knowledge (causes, signs, symptoms, and prevention), mean scores were estimated for knowledge of causes, signs, and symptom/prevention. All mean scores below average mean score were considered poor knowledge while means scores from mid-point upwards as good knowledge (see Table 2). Care-seeking practice was assessed using the single variable, awareness that malaria is covered by health insurance.


Index variablesVariables description

Knowledge of causes of malariaThirteen (13) dichotomous variables with responses on eating and work habits, personal and environmental hygiene, bites from mosquitos, houseflies and hereditary factors, and other causes were used. Average scores were created on this item.

Knowledge of signs and symptomsTen (10) dichotomous variables that assessed respondents’ views on body fevers, vomiting, appetite, body weakness, cough/chills, urine colour, bitterness in mouth and other signs and symptoms. An ordinal scale was created to assess good and poor knowledge.

Knowledge of prevention/protection from malariaEleven (11) dichotomous variables that collected information on respondents use of ITNs, use of mosquito repellents and sprays, personal and environmental hygiene, use of protective cloths, and household strategies to prevent mosquito bites. An ordinal scale was created to measure good and poor knowledge.

Treatment awareness under NHISA single dichotomous variable that measured respondent’s awareness that treatment for malaria was covered under national health insurance.

Source: extracted from the GMIS 2016 data file for women.

LevelKnowledge assessment scores
CauseSigns and SymptomsProtection
Score=0-6Score=0-9Score=0-7

Poor0-10-20-1
Good2-53-92-7
Mean (SD)1.97(0.93)2.5(1.16)2.01(0.96)

2.4. Statistical Analysis

Descriptive profile of respondents was determined first, followed by multilevel mixed-effects logistic regression model to assess binary or binomial responses with suppress constant term from the fixed-effects equation. Multilevel logistic models (MLLM) were used to examine the influence of the effect of independent variables on dependent variables, using suppress constant term from the random-effects equation and restricted maximum likelihood estimation with displaying constraints.

MLLM was appropriate for this research due to the cluster sampling design adopted in the MIS data collection process. The MLLM allows one to account for the clustering of socioeconomic characteristics within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes [17] which in our case is knowledge of causes, signs and symptoms, and prevention of malaria. Stata version 14 was used to analyse all data. Statistical significance was considered at α=0.05.

3. Results

3.1. Sociodemographics of Respondents

A total of 5,150 women were surveyed during the 2016 MIS, 964 participants (19%) were within 15-19 years, 880 (17%) within 25-29 years, and 419 (8%) within 45-49 years. On wealth status, 1440 participants (28%) were in the poorest wealth quintile, 916 participants (18%) were within the middle wealth index, and 912 participants (18%) were in the richest wealth index. The Northern region recorded the highest (12%) while Upper West region recorded the lowest (9%) of survey respondents. More than half (57%) of the respondents were educated above secondary school level with 25% without any formal education. More than half of respondents (54%) lived in rural areas. Nulliparous women constituted 30% of respondents, while multiparas (3+births) women constituted 40.8%. The correlation coefficients of ever-experience for malaria, knowledge of malaria, knowledge of signs and symptoms, and knowledge of protection of malaria are presented in Table 3.


VariableEMKCMKSSMKPM

EM1
KCM0.0611
KSSM0.12850.30671
KPM0.05270.45280.38321

Note: EM=episodes of malaria; KCM=knowledge of causes of malaria; KSSM= knowledge of signs and symptoms of malaria; KPM= knowledge of protection of malaria.
3.2. Knowledge of Malaria Causes and Preventions

About 6 out of 10 reproductive-aged women were aware ITNs can prevent malaria (see Table 4). However, only 47% slept under ITN the night prior to survey. About half (51%) of the respondents reported the use of pit latrines as toilet type at the household level. Open defecation was practiced by 27% of the sampled population. Regarding knowledge of causes of malaria, 37% were found to have poor knowledge while 62% had good knowledge. About 8 out of 10 women, however, had good knowledge of signs and symptoms of malaria. About 35.4% had poor knowledge while 65% had good knowledge of prevention practices. Overall, 60% of respondents registered as members of the national health insurance scheme.


Demographic variableFrequencyPercentageDemographic variableFrequencyPercentage
N=5150%N=5150%

Age in 5-year groupsSlept under ITNs night prior to survey
15-1996418.7No2,72753.0
20-2484416.4Yes2,42347.1
25-2988017.1Currently pregnant
30-3479915.5No/unsure4,79993.2
35-3967513.1Yes3516.8
40-4456911.1Type of mosquito bed net(s) slept under
45-494198.1No net2,72753.0
Wealth indexTreated net2,38446.3
Poorest1,44028.0Both treated and untreated nets10.0
Poorer94618.4Only untreated nets380.7
Middle91617.8Type of toilet facility
Richer93618.2Flush toilet1,03020.0
Richest91217.7Pit latrine2,62350.9
RegionOpen defecation1,37126.6
Western4518.8Bucket toilet110.2
Central5029.8Not a Dejure1152.2
Greater Accra56010.9Age of household head in groups
Volta55310.7Below 20180.4
Eastern4659.020-2963912.4
Ashanti56310.930-391,32325.7
Brong Ahafo5009.740-491,44828.1
Northern59111.550-5993118.1
Upper east52810.360-694348.4
Upper west4378.570+3186.2
Educational levelDon’t know390.8
No education1,28324.9Covered by health insurance
Primary91817.8No2,05739.9
Secondary2,94957.3Yes3,09360.1
ReligionAware that malaria is covered under the NHIS
Christianity3,77473.3No1,04920.4
Islam1,14722.3Yes4,10179.6
Traditional/spiritualist1282.5
No religion1012.0Knowledge on causes of Malaria
Place of residencePoor knowledge193937.7
Urban2,36946.0Good Knowledge321162.3
Rural2,78154.0
Parity (living children)Knowledge on signs and symptoms of Malaria
Nulliparous1,54029.9Poor knowledge96518.7
1-21,51029.3Good Knowledge418581.3
3-41,22523.8
5+87517.0
Have mosquito bed netKnowledge on protection of Malaria
No88317.2Poor knowledge182235.4
Yes4,26782.9Good Knowledge332864.6

3.3. Associations regarding Knowledge of Causes of Malaria among Respondents (Adjusted)

Table 5 presents results of knowledge of causes of malaria and their associations among respondents. Relative to women aged 15-19 years, women aged 20-24 and 30-49 years were significantly associated with women with good knowledge of the causes of malaria. Women in richer wealth quartile were significantly associated with women with good knowledge of causes of malaria, compared to women in richest wealth quartile. Women in 6 regions (Western, Central, Volta, Northern Upper East, and Upper West) were significantly associated with those with good knowledge of causes of malaria, compared to women in the Greater Accra region. Women with no education had a significant association with good knowledge of causes of malaria compared to women educated to secondary and above level. Women in rural areas had a significant association with good knowledge of causes of malaria, compared to urban women. Surprisingly, noneducated women were significantly associated with women with good knowledge of causes of malaria, compared with women educated at secondary level.


DemographicsKnowledge of cause of malaria f (%)COR (95 C.I) p-valueAOR(95 C.I)p-value
Poor knowledge=0
(n=1939)
Good knowledge =1 (n=3211)Total=5150Model 1Model 2

Age in 5-year groups
15-19408(21.0)556(17.3)964(18.7)RefRef
20-24315(16.3)529(16.4)844(16.4)1.23(1.02-1.49)1.23(1.01-1.50)
25-29350(18.1)530(16.6)880(17.1)1.11(0.92-1.34)1.14(0.94-1.38)
30-34296(15.3)503(15.7)799(15.5)1.25(1.02-1.51)1.37(1.12-1.67)
35-39225(11.6)450(14.0)675(13.1)1.47(1.20-1.80)1.69(1.35-2.11)
40-44198(10.2)371(11.6)569(11.1)1.37(1.10-1.70)1.57(1.24-1.98)
45-49147(7.6)272(8.5)419(8.1)1.36(1.07-1.72)1.57(1.22-2.03)

Wealth index
Poorest659(34.0)781(24.3)1440(28.0)0.61(0.51-0.72)1.04(0.78-1.37)
Poorer372(19.2)574(17.9)946(18.4)0.79(0.66-0.96)1.13(0.88-1.45)
Middle314(16.2)602(18.8)916(17.8)0.99(0.81-1.19)1.16(0.92-1.47)
Richer284(14.7)652(20.3)936(18.2)1.18(0.97-1.44)1.25(1.01-1.55)
Richest310(16.0)602(18.8)912(17.7)RefRef

Region
Western216(11.1)235(7.3)451(8.8)0.38(0.29-0.49)0.41(0.31-0.54)
Central194(10.0)308(9.6)502(9.8)0.55(0.42-0.71)0.59(0.44-0.77)
Greater Accra144(7.4)416(13.0)560(10.9)RefRef
Volta256(13.2)297(9.3)553(10.7)0.40(0.31-0.52)0.44(0.34-0.59)
Eastern140(7.2)325(10.1)465(9.0)0.80(0.61-1.06)0.81(0.61-1.09)
Ashanti153(7.9)410(12.8)563(10.9)0.93(0.71-1.21)0.93(0.71-1.22)
Brong Ahafo155(8.0)345(10.7)500(9.7)0.77(0.59-1.01)0.84(0.63-1.11)
Northern255(13.2)336(10.5)591(11.5)0.46(0.36-0.59)0.67(0.50-0.91)
Upper east222(11.5)306(9.5)528(10.3)0.48(0.37-0.62)0.68(0.50-0.93)
Upper west204(10.5)233(7.3)437(8.5)0.40(0.31-0.52)0.54(0.40-0.74)
Educational level
No education595(30.7)688(21.4)1283(24.9)0.60(0.52-0.68)0.67(0.55-0.79)
Primary340(17.5)578(18.0)918(17.8)0.88(0.75-1.02)0.92(0.78-1.08)
Secondary1004(51.8)1945(60.6)2949(57.3)RefRef

Place of residence
Urban739(38.1)1630(50.8)2369(46.0)RefRef
Rural1200(61.9)1581(49.2)2781(54.0)0.60(0.53-0.67)0.77(0.65-0.89)

Slept under mosquito bed net
No973(50.2)1754(54.6)2727(53.0)1.20(1.07-1.34)1.04(0.91-0.17)
Yes966(49.8)1457(45.4)2423(47.1)RefRef

P < 0.05, p < 0.01, p < 0.001. Ref: denotes reference group.
3.4. Associations regarding Knowledge of Signs and Symptoms of Malaria

Age, wealth index, region, educational status, place of residence, household toilet type, and religion were significantly associated with women with good knowledge of signs and symptoms of malaria (see Table 6, model 2). Increasing age showed increasing odds on knowledge of signs and symptoms of malaria as presented in model 2. Women aged 35-39 years [aOR=2.23, 95% CI: 1.68-2.95], 40-44 years [aOR=2.16, 95% CI: 1.59-2.89], and 45-49 years [aOR=2.34, 95% CI: 1.68-3.24] were more likely to have good knowledge of the signs and symptoms of malaria compared to women aged 15-19 years. Increasing wealth also showed increased odds for knowledge of signs and symptoms. However, women in poorest and poorer wealth groups were significantly associated with women with good knowledge of signs and symptoms, compared to women in richest wealth group. Women from Western, Ashanti, Northern, Upper East, and Upper West regions were significantly associated with women with good knowledge of signs and symptoms of malaria, compared to women in the Greater Accra region.


DemographicsKnowledge of signs and symptoms f (%)COR (95 C.I) p-valueAOR (95 C.I) p-value
Poor knowledge=0 (n==965)Good knowledge=1 (n=4185)Total=5150Model 1Model 2

Age in 5-year groups
15-19230(23.8)734(17.5)964(18.7)RefRef
20-24163(16.9)681(16.3)844(16.4)1.31(1.04-1.64)1.35(1.04-1.72)
25-29151(15.7)729(17.4)880(17.1)1.51(1.20-1.90)1.66(1.27-2.13)
30-34140(14.5)659(15.8)799(15.5)1.47(1.17-1.87) 1.84(1.41-2.41)
35-39113(11.7)562(13.4)675(13.1)1.56(1.21-2.00) 2.23(1.68-2.95)
40-4498(10.2)471(11.3)569(11.1)1.51(1.16-1.96)2.16(1.59-2.89)
45-4970(7.3)349(8.3)419(8.1)1.56(1.16-2.10)2.34(1.68-3.24)

Wealth index
Poorest374(38.8)1066(25.5)1440(28.0)0.30(0.24-0.39) 0.49(0.34-0.72)
Poorer216(22.4)730(17.4)946(18.4)0.36(0.28-0.47) 0.54(0.38-0.76)
Middle139(14.4)777(18.6)916(17.8)0.60(0.45-0.79) 0.76(0.54-1.06)
Richer148(15.3)788(18.8)936(18.2)0.57(0.43-0.75) 0.66(0.48-0.89)
Richest88(9.1)824(19.7)912(17.7)RefRef

Region
Western149(15.4)302(7.2)451(8.8)0.34(0.25-0.46) 0.47(0.34-0.67)
Central111(11.5)391(9.3)502(9.8)0.59(0.43-0.81) 0.90(0.63-1.29)
Greater Accra80(8.3)480(11.5)560(10.9)RefRef
Volta58(6.0)495(11.8)553(10.7)1.42(0.99-2.04)2.70(0.81-4.04)
Eastern83(8.6)382(9.1)465(9.0)0.77(0.55-1.07)1.07(0.74-1.55)
Ashanti63(6.5)500(12.0)563(10.9)1.32(0.93-1.88)1.54(1.06-2.23)
Brong Ahafo80(8.3)420(10.0)500(9.7)0.88(0.63-1.22)1.43(0.99-2.07)
Northern120(12.4)471(11.3)591(11.5)0.65(0.48-0.89)2.09(1.41-3.11)
Upper east118(12.2)410(9.8)528(10.3)0.58(0.42-0.791.72(1.16-2.56)
Upper west103(10.7)334(8.0)437(8.5)0.54(0.39-0.75) 1.50(1.01-2.23)

Educational level
No education358(37.1)925(22.1)1283(24.9)0.41(0.35-0.49) 0.41(0.33-0.51)
Primary199(20.6)719(17.2)918(17.8)0.58(0.48-0.70) 0.62(0.50-0.76)
Secondary408(42.3)2541(60.7)2949(57.3)RefRef

Religion
Christian656(68.0)3118(74.5)3774(73.3)1.13(0.96-1.34)1.01(0.82-1.22)
Islam221(22.9)926(22.1)1147(22.3)RefRef
Traditional/spiritualist41(4.3)87(2.1)128(2.5)0.51(0.34-0.75) 0.57(0.38-0.87)
Other religions47(4.9)54(1.3)101(2.0)0.27(0.18-0.42) 0.31(0.21-0.59)

Place of residence
Urban327(33.9)2042(48.8)2369(46.0)RefRef
Rural638(66.1)2143(51.2)2781(54.0)0.54(0.46-0.62) 0.89(0.73-1.08)

Currently pregnant
No885(91.7)3914(93.5)4799(93.2)1.31(1.01-1.69)1.26(0.96-1.67)
Yes80(8.3)271(6.5)351(6.8)RefRef

P < 0.05, p < 0.01, p < 0.001. Ref: denotes reference group.

Across regions, women in the Volta and Northern regions had significant and high odds for good knowledge of signs and symptoms, compared to women in the Greater Accra region (aOR=2.70, 95% CI 0.81-4.04) and (aOR=2.09, 95% CI 1.41-3.11), respectively. Uneducated women and those educated to the primary level were significantly associated with women with good knowledge of signs and symptoms of malaria. Women who belong to traditional and other religious affiliations were also significantly associated with women with good knowledge of signs and symptoms, compared to women who belong to the Islamic religion.

3.5. Associations regarding Malaria Prevention

Women aged 20-39 years were significantly associated with women with good knowledge of malaria prevention, compared with women aged 15-19 years in regression model 2. Increasing wealth showed increasing odds regarding good knowledge of malaria prevention. In Table 7 (model 1), women in richer wealth index were significantly associated with those with good knowledge of prevention of malaria, compared to women in the richest wealth index. However, in model 2 (adjusted), women in the richer wealth index were not significant regarding good knowledge of malaria prevention. Women in 2 regions (Western and Volta) were significantly associated with women with good knowledge of malaria prevention, compared to women in the Greater Accra region. Relative to women in urban areas, women in rural areas were significantly associated with those with good knowledge of malaria prevention. Women who belong to other religious status were significantly associated with those with good knowledge, relative to women who belong to Islamic religion. Uneducated women and those educated at primary level were significantly associated with women with good knowledge of malaria prevention, compared to higher educated women.


DemographicsKnowledge of malaria prevention f (%)COR (95 C.I) p-valueAOR (95 C.I) p-value
Poor knowledge (n=1822)Good knowledge (n=3328)Total=5150Model 1Model 2

Age in 5-year groups
15-19352(19.3)612(18.4)964(18.7)RefRef
20-24256(14.1)588(17.7)844(16.4)1.32(1.09-1.61)1.44(1.15-1.81)
25-29298(16.4)582(17.5)880(17.1)1.12(0.93-1.36)1.32(1.03-1.68)
30-34288(15.8)511(15.4)799(15.5)1.02(0.84-1.24)1.33(1.01-1.75)
35-39241(13.2)434(13.0)675(13.1)1.04(0.84-1.27)1.50(1.12-2.01)
40-44220(12.1)349(10.5)569(11.1)0.91(0.74-1.13)1.30(0.96-1.77)
45-49167(9.2)252(7.6)419(8.1)0.87(0.69-1.10)1.30(0.94-1.81)

Wealth index
Poorest711(39.0)729(21.9)1440(28.0)0.27(0.22-0.32) 0.49(0.37-0.67)
Poorer372(20.4)574(17.3)946(18.4)0.40(0.33-0.50) 0.70(0.53-0.92)
Middle308(16.9)608(18.3)916(17.8)0.52(0.42-0.64) 0.74(0.57-0.95)
Richer242(13.3)694(20.9)936(18.2)0.75(0.60-0.93)0.93(0.73-1.17)
Richest189(10.4)723(21.7)912(17.7)RefRef

Region
Western184(10.1)267(8.0)451(8.8)0.39(0.29-0.51) 0.56(0.41-0.75)
Central172(9.4)330(9.9)502(9.8)0.51(0.39-0.67) 0.79(0.59-1.07)
Greater Accra118(6.5)442(13.3)560(10.9)RefRef
Volta277(15.2)276(8.3)553(10.7)0.27(0.20-0.35) 0.42(0.32-0.56)
Eastern119(6.5)346(10.4)465(9.0)0.78(0.58-1.04)0.15(0.84-1.57)
Ashanti148(8.1)415(12.5)563(10.9)0.75(0.57-0.99)0.88(0.66-1.18)
Brong Ahafo148(8.1)352(10.6)500(9.7)0.63(0.48-0.84) 1.02(0.75-1.38)
Northern250(13.7)341(10.3)591(11.5)0.36(0.28-0.47) 1.11(0.80-1.53)
Upper east211(11.6)317(9.5)528(10.3)0.40(0.31-0.52) 1.17(0.84-1.62)
Upper west195(10.7)242(7.3)437(8.5)0.33(0.25-0.44) 0.93(0.67-1.29)

Educational level
No education677(37.2)606(18.2)1283(24.9)0.32(0.28-0.36) 0.43(0.35-0.51)
Primary373(20.5)545(16.4)918(17.8)0.52(0.44-0.61) 0.63(0.53-0.74)
Secondary772(42.4)2177(65.4)2949(57.3)RefRef

Religion
Christian1236(67.8)2538(76.3)3774(73.3)1.36(1.20-1.56) 1.14(0.97-1.34)
Islam458(25.1)689(20.7)1147(22.3)RefRef
Traditional/spiritualist66(3.6)62(1.9)128(2.5)0.62(0.43-0.90)1.22(0.83-1.79)
Other religions62(3.4)39(1.2)101(2.0)0.42(0.28-0.63) 0.55(0.35-0.86)

Place of residence
Urban609(33.4)1760(52.9)2369(46.0)RefRef
Rural1213(66.6)1568(47.1)2781(54.0)0.45(0.40-0.50) 0.79(0.67-0.92)

Living Children
No child460(25.3)1080(32.5)1540(29.9)1.92(1.62-2.28) 1.18(0.89-1.58)
1-2511(28.1)999(30.0)1510(29.3)1.60(1.35-1.90) 0.97(0.77-1.22)
3-4457(25.1)768(23.1)1225(23.8)1.38(1.15-1.64) 1.03(0.84-1.25)
5+394(21.6)481(14.5)875(17.0)RefRef

P < 0.05, p < 0.01, p < 0.001. Ref: denotes reference group.
3.6. Associations regarding Care Seeking for Malaria Treatments

Women in 7 regions (Western, Volta, Eastern, Brong Ahafo, Northern, Upper East, and Upper West) showed significant association with women who knew malaria treatment was covered under national health insurance, compared to women in the Greater Accra region (see Table 8). Women in the Upper East and Brong Ahafo regions had the highest odds of women who knew that malaria treatments were covered under health insurance, compared to women in the Greater Accra region (aOR=3.73, 95% CI 2.55-5.44) and (aOR=3.41, 95% CI 2.43-4.79), respectively. Relative to women in urban areas, women in rural areas were significantly associated with women who knew malaria treatment was covered under the national health insurance scheme. Nulliparous women were significantly associated with women who knew malaria treatment was covered under national health insurance scheme, compared to women with parity level of 5 and above. Relative to women who slept under mosquito net, women who did not sleep under any mosquito net were significantly associated with those with knowledge that malaria treatments are covered under the national health insurance scheme.


Demographics Aware that malaria is covered by insurance f (%)COR (95 C.I) p-valueAOR (95 C.I) p-value
No=1049Yes=4101Total=5150Model 1Model 2
N (%)N (%)N (%)

Wealth index
Poorest226(21.5)1214(29.6)1440(28.0)1.76(1.43-2.16) 0.92(0.66-1.27)
Poorer169(16.1)777(19.0)946(18.4)1.51(1.20-1.88) 1.02(0.76-1.35)
Middle190(18.1)726(17.7)916(17.8)1.25(1.00-1.56)0.99(0.76-1.28)
Richer239(22.8)697(17.0)936(18.2)0.96(0.77-1.18)0.89(0.71-1.13)
Richest225(21.5)687(16.8)912(17.7)RefRef

Region
Western105(10.0)346(8.4)451(8.8)1.71(1.29-2.26) 1.46(1.08-1.97)
Central146(13.9)356(8.7)502(9.8)1.26(0.97-1.63)1.05(0.78-1.39)
Greater Accra191(18.2)369(9.0)560(10.9)RefRef
Volta65(6.2)488(11.9)553(10.7)3.89(2.84-5.31) 3.57(2.55-4.99)
Eastern98(9.3)367(9.0)465(9.0)1.94(1.46-2.57) 1.66(1.23-2.24)
Ashanti157(15.0)406(9.9)563(10.9)1.34(1.03-1.72)1.17(0.91-1.52)
Brong Ahafo59(5.6)441(10.8)500(9.7)3.87(2.80-5.34) 3.41(2.43-4.79)
Northern96(9.2)495(12.1)591(11.5)2.67(2.02-3.53) 2.98(2.11-4.19)
Upper east69(6.6)459(11.2)528(10.3)3.44(2.53-4.68) 3.73(2.55-5.44)
Upper west63(6.0)374(9.1)437(8.5)3.07(2.23-4.23) 3.12(2.15-4.53)

Educational level
No education231(22.0)1052(25.7)1283(24.9)1.26(1.07-1.49)0.82(0.66-1.01)
Primary177(16.9)741(18.1)918(17.8)1.16(0.97-1.40)0.93(0.76-1.13)
Secondary641(61.1)2308(56.3)2949(57.3)RefRef

Place of residence
Urban599(57.1)1770(43.2)2369(46.0)RefRef
Rural450(42.9)2331(56.8)2781(54.0)1.75(1.53-2.01) 1.44(1.21-1.74)

Parity (living children)
Nulliparous345 (32.9)1195 (29.1)1540 (29.9)0.73(0.59-0.90)0.78(0.61-1.00)
1-2321(30.6)1189 (29.0)1510 (29.3)0.78(0.63-0.96)0.84(0.66-1.06)
3-4231(22.0)994 (24.2)1225 (23.8)0.90(0.72-1.13)0.94(0.74-1.19)
5+152 (14.5)723 (17.6)875 (17.0)RefRef

Slept under mosquito bed net
No663 (63.2)2064 (50.3)2727 (53.0)0.59(0.51-0.68) 0.76(0.65-0.91)
Yes386 (36.8)2037 (49.7)2423 (47.1)RefRef

P < 0.05, p < 0.01, p < 0.001. Ref: denotes reference group.

4. Discussion

Reproductive-age women had good knowledge associated with the causes, signs/symptoms, and prevention of malaria. This study found a 15% differential among ITNs awareness and use among reproductive-age women Ghana. This indicates an awareness-practice gap for malaria control among reproductive-age women in Ghana, which has long been documented in other studies [18, 19]. Similarly, our finding is consistent with the 2016 GMIS report that documented a 66% of household population in Ghana having access to Long Lasting Insecticide Net (LLIN) with only 42% of households sleeping under a net the night before the survey [12]. Novel approaches for easy adoption and use of ITNs need to be piloted in future funded studies to generate evidence on scaling up ITNs use in Ghana.

In Ghana, contextual demographic factors have been found to influence the knowledge-practice nexus regarding malaria control and prevention, with households with children under 5 years and pregnant women likely to have positive attitudes and practices towards malaria prevention [14]. Community level misconceptions on malaria control strategies and beliefs affect ITN use in Ghana [20, 21]. Behaviour change communication (BCC) strategies that address community level misconceptions and social beliefs on ITNs use could improve use among all population groups in Ghana. Studies in Ethiopia, Nigeria, Tanzania, Zambia, and Zimbabwe [2226] found community and individual level factors that influence knowledge-practices differences, as found in Ghana.

4.1. Causes, Signs/Symptoms, Prevention, and Care Seeking for Malaria

Age, wealth and educational status, religion, region, place of residence (rural), and sleeping under ITN were found to significantly influence reproductive-age women’s knowledge levels regarding causes, signs/symptoms, prevention, and care-seeking practices for malaria. The study finding reflects existing evidence in Ghana [27, 28], other African countries [29, 30], and developing countries [31] of the role of individual, social, environmental, and health system determinants of malaria among varied population groups. Nonuse of ITN was found to be statistically significant and associated with poor knowledge related to care seeking among reproductive-age women in Ghana. Specifically, it was found that women who did not sleep under mosquito bed net have poor knowledge of care seeking in the study. Malaria messaging targeting particular poor behavioural attitudes in Ghana may seem low, explaining why only 41% of women (15-49 years) heard of malaria messaging in the last 6 months prior to the survey [12]. At the policy level, more targeted education programs that seek to sustain behaviour practices need to be stepped up so that health facilities and district environmental health officers can help improve women and other caregiver’s knowledge levels on malaria prevention and care seeking.

This study further posits that knowledge of the causes, signs/symptoms, and prevention of malaria among women aged 35 and older is high among reproductive-age women in Ghana. The findings show that increasing age (≥35 years) of a woman increases her knowledge levels on the causes, signs/symptoms, prevention, and care seeking for malaria, compared to young women. While this study did not draw causal links between older women knowledge and how these translate into malaria prevention strategies at the individual level, it can be posited that older women who have had previous malaria episodes have a high likelihood to know about causes and prevention strategies. Older women might have had several exposures to malaria messaging compared to younger women. It should be noted that this finding does not mean older women will necessarily translate knowledge to improved health behaviour since another study in Ghana found nondoers were more knowledgeable about malaria than doers [32].

Socioeconomic status (SES) measured through wealth status (poorest and poorer) and low education (no education) had undesirable outcomes regarding knowledge of signs/symptoms and prevention/coping mechanisms for malaria. Women in poorest and poorer wealth indices and those with no formal education were less likely to have knowledge regarding the signs and symptoms as well as malaria prevention compared to those in richest wealth group. In contrast, women with high wealth status (richer wealth quartile) were found to have a 125% likelihood of improved knowledge levels of the causes of malaria among reproductive-age women. Effects of poverty on poor knowledge of malaria from this study embody long held propositions that refer to malaria as a disease of poverty [33, 34]. However, other factors beyond poverty and socioeconomic status such as educational effects for early care seeking have been documented as vital in offsetting poverty and malaria care seeking among children in Gambia [35].

This study also found that significant regional differences exist regarding knowledge of causes, signs/symptoms, and prevention in Ghana. Regional variation of malaria among children in Ghana is reported [36], but not among reproductive-age women. Reproductive-age women in 3 regions: Ashanti, Brong Ahafo, and Eastern regions were observed to have the highest odds of association regarding poor knowledge of the causes of malaria. Reproductive-age women in Western Region were observed to have the lowest odds of association for poor knowledge of causes for malaria. The Volta and Northern regions had high odds of association regarding poor knowledge of signs/symptoms of malaria among reproductive-age women. Regional differences of knowledge-related measures among pregnant women and at the household were also reported in the 2016 GMIS report [12]. In addition, poor knowledge levels on causes and prevention of malaria were significantly associated with women who lived in rural areas. Despite that more than half of the population surveyed in the 2016 MIS was rural, majority of them were more likely to have good knowledge of care seeking with regard to malaria.

Nonawareness of malaria treatment covered by national health insurance was influenced by region, place of residence, and sleeping under a mosquito net. Reproductive-age women in four regions (Volta, Brong Ahafo, Upper East, and Upper West) had higher odds (>3) of not being aware malaria treatment was covered under health insurance. Additionally, rural women had higher odds of being unaware malaria treatment is covered by national health insurance. We also found reproductive-age women who did not sleep under a mosquito net had lesser odds of not being aware national health insurance covers malaria treatment.

The study has some limitations. Data used for analysis in this study is secondary and cross sectional, leaving little chance for assuming causality of associations. Secondly, malaria aetiology and outcomes may be influenced by other several factors (individual, environmental, and health system) which may not have been present however in the dataset, thus excluded from our analysis. The data is however a national representative and hence provides strong evidence of context factors that shape reproductive-age women views on malaria prevention and care seeking. The 2016 GMIS presented results at household levels for ITNs use and knowledge of causes of malaria based on individual variables. This study however estimated knowledge of outcomes based on index created from a cluster of variables. Methodologically, the importance of composite variable index to explain variations in health has been documented [37]; hence, the use of composite index in assessing knowledge related outcomes for malaria is useful.

5. Conclusions

This study found that age, wealth and educational status, religion, region, and place of residence (rural) significantly influence reproductive-age women knowledge levels regarding causes, signs/symptoms, and prevention of malaria. Older women (≥35 years) had higher likelihood of good knowledge levels of the causes, signs/symptoms, and prevention of malaria, compared to young women. Regional variations exist on knowledge levels and care seeking for malaria, with rural residents more likely to have poor knowledge and care seeking for malaria. Our study also observed a 15% differential among ITNs awareness and use among reproductive-age women Ghana. Thus, there is a need to intensify health education on malaria and the benefits of the use of mosquito nets at all levels. Furthermore, free distribution of mosquito nets should be scaled up to attain higher coverages. In addition, a collaborative effort among partners including the use of new technologies to vector imaging could help scale up efforts to improve knowledge and care seeking for malaria prevention and control in Ghana.

Abbreviation

CDC:Center for Disease Control
GMIS:Ghana Malaria Indicator Survey
GSS:Ghana Statistical Service
ITN:Insecticide Treated Nets
IRS:Indoor Residual Spraying
NPHRL:National Public Health Reference Laboratory
NMCP:National Malaria Control Programme
RDT:Rapid Diagnostic Tests
SSA:Sub-Saharan Africa
WHO:World Health Organization.

Data Availability

The data and the analysis coding structure used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. F. Ricci, “Social implications of malaria and their relationships with poverty,” Mediterranean Journal of Hematology and Infectious Diseases, vol. 4, no. 1, Article ID 2012048, 2012. View at: Publisher Site | Google Scholar
  2. T. Awine, K. Malm, C. Bart-Plange, and S. P. Silal, “Towards malaria control and elimination in Ghana: challenges and decision making tools to guide planning,” Global Health Action, Article ID 1381471, pp. e602–e607, 2017. View at: Publisher Site | Google Scholar
  3. F. A. Asante and K. Asenso-Okyere, “Economic burden of malaria in Ghana,” World Health Organization WHO 1-81, 2003. View at: Google Scholar
  4. J. Nonvignon, G. C. Aryeetey, K. L. Malm et al., “Economic burden of malaria on businesses in Ghana: a case for private sector investment in malaria control,” Malaria Journal, vol. 15, no. 1, 2016. View at: Publisher Site | Google Scholar
  5. K. A. Koram, S. Owusu-Agyei, D. J. Fryauff et al., “Seasonal profiles of malaria infection, anaemia, and bednet use among age groups and communities in northern Ghana,” Tropical Medicine & International Health, vol. 8, no. 9, pp. 793–802, 2003. View at: Publisher Site | Google Scholar
  6. GHS. Ghana DHIMS2. 2015.
  7. PATH, WHO, GoG & G (2015). Ghana malaria vaccine technical brief summary.
  8. GSS, NMCP, NPHRL, DHS & I. Malaria Indicator Survey, 2016. Accra, Ghana: Ghana Statistical Service, National Malaria Control Programme, Demographic Health Survey, ICF Internataional. 2016.
  9. GHS. Ghana malaria programme review final report. 2013.
  10. WHO, “Malaria vector control and personal protection: report of a WHO study group,” Report of a WHO study group, Elsevier Ltd, 2006. View at: Google Scholar
  11. NMCP, An Epidemiological Profile of Malaria and Its Control in Ghana, National Malaria Control Programme, 2013.
  12. GSS, GHS & I. Ghana Malaria Indicator Survey 2016. Accra, Ghana, and Rockville, Maryland, USA: Ghana Statistical Service (GSS), Ghana Health Service (GHS) and ICF. 2017.
  13. R. Anokye and E. I. Obeng, “Knowledge of malaria and preventive practices among pregnant women attending antenatal clinic at Manhyia district hospital,” International Journal of Public Health, vol. 21, pp. 48–55, 2017. View at: Google Scholar
  14. A. Assan, A. Takian, A. A. Hanafi-Bojd, A. Rahimiforoushani, and S. Nematolahi, “Knowledge, attitude, and practice about malaria: Socio-demographic implications for malaria control in rural Ghana,” International Journal of Public Health, vol. 384, pp. 445–463, 2017. View at: Publisher Site | Google Scholar
  15. K. D. Nyavor, M. Kweku, I. Agbemafle et al., “Assessing the ownership, usage and knowledge of insecticide treated nets (ITNs) in malaria prevention in the hohoe municipality, Ghana,” Pan African Medical Journal, vol. 28, no. 1, 2017. View at: Publisher Site | Google Scholar
  16. K. H. Onarheim, J. H. Iversen, and D. E. Bloom, “Economic benefits of investing in women’s health: A systematic review,” PLoS One, vol. 11, no. 3, Article ID e0150120, 2016. View at: Publisher Site | Google Scholar
  17. P. C. Austin and J. Merlo, “Intermediate and advanced topics in multilevel logistic regression analysis,” Statistics in Medicine, vol. 36, no. 20, pp. 3257–3277, 2017. View at: Publisher Site | Google Scholar | MathSciNet
  18. W. Deressa, A. Ali, and F. Enquoselassie, “Knowledge, attitude and practice about malaria, the mosquito and antimalarial drugs in a rural community,” Ethiopian Journal of Health Development, vol. 17, no. 2, pp. 99–104, 2003. View at: Google Scholar
  19. P. J. Winch, A. M. Makemba, V. R. Makame et al., “Social and cultural factors affecting rates of regular retreatment of mosquito nets with insecticide in Bagamoyo District, Tanzania,” Tropical Medicine & International Health, vol. 2, no. 8, pp. 760–770, 1997. View at: Publisher Site | Google Scholar
  20. P. B. Adongo, B. Kirkwood, and C. Kendall, “How local community knowledge about malaria affects insecticide-treated net use in northern Ghana,” Tropical Medicine & International Health, vol. 10, no. 4, pp. 366–378, 2005. View at: Publisher Site | Google Scholar
  21. C. K. Ahorlu, S. K. Dunyo, E. A. Afari, K. A. Koram, and F. K. Nkrumah, “Malaria‐related beliefs and behaviour in Southern Ghana: Implications for treatment, prevention and control,” Tropical Medicine & International Health, vol. 2, no. 5, pp. 488–499, 1997. View at: Publisher Site | Google Scholar
  22. T. G. Fuge, S. Y. Ayanto, and F. L. Gurmamo, “Assessment of knowledge, attitude and practice about malaria and ITNs utilization among pregnant women in Shashogo District, Southern Ethiopia,” Malaria Journal, vol. 14, no. 1, p. 235, 2015. View at: Publisher Site | Google Scholar
  23. M. Kanyangarara, H. Hamapumbu, E. Mamini et al., “Malaria knowledge and bed net use in three transmission settings in southern Africa,” Malaria Journal, vol. 17, no. 1, p. 41, 2018. View at: Publisher Site | Google Scholar
  24. K. Karunamoorthi, B. Deboch, and Y. Tafere, “Knowledge and practice concerning malaria, insecticide-treated net (ITN) utilization and antimalarial treatment among pregnant women attending specialist antenatal clinics,” Journal of Public Health, vol. 18, no. 6, pp. 559–566, 2010. View at: Publisher Site | Google Scholar
  25. H. D. Mazigo, E. Obasy, W. Mauka et al., “Knowledge, attitudes, and practices about malaria and its control in rural Northwest Tanzania,” Malaria Research and Treatment, vol. 2010, Article ID 794261, 9 pages, 2010. View at: Publisher Site | Google Scholar
  26. B. S. C. Uzochukwu, E. N. Ossai, C. C. Okeke, A. C. Ndu, and O. E. Onwujekwe, “Malaria knowledge and treatment practices in Enugu state, Nigeria: A qualitative study,” International Journal of Health Policy and Management, vol. 7, no. 9, pp. 859–866, 2018. View at: Publisher Site | Google Scholar
  27. A. C. Krefis, N. G. Schwarz, B. Nkrumah et al., “Principal component analysis of socioeconomic factors and their association with malaria in children from the Ashanti Region, Ghana,” Malaria Journal, vol. 9, no. 1, 2010. View at: Google Scholar
  28. M. Kweku, W. Takramah, M. Takase, E. Tarkang, and M. Adjuik, “Factors associated with malaria prevalence among children under five years in the hohoe municipality of Ghana,” Journal of Transmitted Diseases and Immunity, 2017. View at: Google Scholar
  29. K. C. Ernst, K. A. Lindblade, D. Koech et al., “Environmental, socio‐demographic and behavioural determinants of malaria risk in the western Kenyan highlands: a case–control study,” Tropical Medicine & International Health, vol. 14, no. 10, pp. 1258–1265, 2009. View at: Publisher Site | Google Scholar
  30. E. H. Shayo, S. F. Rumisha, M. R. S. Mlozi et al., “Social determinants of malaria and health care seeking patterns among rice farming and pastoral communities in Kilosa District in central Tanzania,” Acta Tropica, vol. 144, pp. 41–49, 2015. View at: Publisher Site | Google Scholar
  31. R. K. Sharma, M. P. Singh, K. B. Saha, P. K. Bharti, V. Jain, and P. P. Singh, “Socio-economic & household risk factors of malaria in tribal areas of Madhya Pradesh, central India,” The Indian Journal of Medical Research, vol. 14, no. 15, pp. 567–575, 2015. View at: Google Scholar
  32. N. B. Crookston, K. Dearden, B. Gray et al., “Who sleeps under bednets in Ghana? A doer/non-doer analysis of malaria prevention behaviours,” Malaria Journal, vol. 51, no. 61, Article ID 1011861475, pp. 28–75, 2006. View at: Publisher Site | Google Scholar
  33. J. L. Gallup and J. D. Sachs, “The economic burden of malaria,” The American Journal of Tropical Medicine and Hygiene, vol. 64, 1-2 Suppl, pp. 85–96, 2001. View at: Google Scholar
  34. J. Sachs and P. Malaney, “The economic and social burden of malaria,” Nature, vol. 415, no. 6872, pp. 680–685, 2002. View at: Google Scholar
  35. K. Koram, S. Bennett, J. Adiamah, and B. Greenwood, “Socio-economic determinants are not major risk factors for severe malaria in Gambian children,” Transactions of The Royal Society of Tropical Medicine and Hygiene, vol. 892, pp. 151–154, 1995. View at: Google Scholar
  36. B. Kreuels, R. Kobbe, S. Adjei et al., “Spatial variation of malaria incidence in young children from a geographically homogeneous area with high endemicity,” The Journal of Infectious Diseases, vol. 197, no. 1, pp. 85–93, 2008. View at: Publisher Site | Google Scholar
  37. G. M. Antony and K. Visweswara Rao, “A composite index to explain variations in poverty, health, nutritional status and standard of living: Use of multivariate statistical methods,” Public Health, vol. 121, no. 8, pp. 578–587, 2007. View at: Publisher Site | Google Scholar

Copyright © 2019 Martin Amogre Ayanore et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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