Demographic and socio-economic factors affecting bed net ownership, usage, and malaria transmission among adult patients seeking healthcare in two Ghanaian urban cities

doi:10.21203/rs.3.rs-2369701/v1

The most cost-effective malaria prevention and control strategy is the use of a bed net. However, several factors affect the ownership and usage of bed nets among the adult population. Hence, this study aimed to examine socio-demographic factors affecting bed net ownership, usage and malaria transmission among adult patients seeking healthcare in two Ghanaian urban cities. Structured questionnaires were administered to a total of 550 participants to ascertain their ownership and usage of the bed nets. Afterwards, finger prick blood samples were collected for malaria microscopy. Crude and adjusted prevalence ratios (PR) and their respective 95% CIs were calculated, using Poisson regression with robust standard errors, to show associated variables in bivariate and multivariate analyses respectively. About 53.3% (n = 293) of participants owned a bed net but only 21.5% (n = 118) slept under it the previous night. Those married were 2.0 (95% CI: 1.7–2.5) and 2.4 (95% CI: 1.6–3.5) times more likely to own and use a bed net respectively than those never married. Also, pregnant women were 1.3 (95% CI: 1.1–1.6) and 1.8 (95% CI: 1.3–2.5) times more likely to own and use a bed net respectively than non-pregnant. Even though income levels were not associated with bed net ownership and usage, students were 0.4 (95% CI: 0.2–0.6) and 0.2 (95% CI: 0.1–0.5) times less likely to own and use bed net respectively compared to formally employed persons. The overall malaria prevalence rate was 7.8%. Malaria-negative patients were 1.6 (95% CI: 1.2–2.0) and 2.4 (95% CI: 1.4–4.1) times more likely to own and use bed nets respectively than malaria positive. Patients with tertiary education recorded the lowest malaria prevalence (3.5%, n = 4). None of those with a monthly income >$300 recorded a case of malaria. On the contrary, majority 83%, n/N = 25/30) of the malaria-positive patients earned ≤ $150. Hence, in designing malaria control programmes for the urban adult populations, income, occupation, educational and marital status should be considered in order to improve bed net usage and decrease malaria prevalence.

Bed net

Malaria

Socio-Economic

Adult

Urban

Ghana

Ghana records about 2% and 3% of global malaria cases and deaths respectively, making it part of the 15 countries in the world with the heaviest malaria burden [1]. Although most malaria cases and deaths occur in children, a considerable number of adults also get infected and experience the associated morbidity and mortality [2, 3, 4]. Infected adults may serve as a reservoir for vulnerable groups such as children under five years and pregnant women by carrying low-density parasites for long periods [5]. It has been debated for decades as to whether malaria is a consequence of or a cause of poverty [6]. Nonetheless, there is a strong consensus that its impact is ferocious especially on people with low socioeconomic status as they are least able to afford medical treatment and preventive measures [7].

Among the various malaria prevention and control strategies implemented in endemic areas, available evidence suggests that the use of a bed net is the most cost-effective [8]. For this reason, Ghana's National Malaria Control Program (NMCP) does embark on a mass distribution campaign every other year to promote ownership and use of Insecticide Treated Nets (ITNs) [9]. However, the success of such mass distribution campaigns varies from region to region due to limited staffing, logistical challenges and the short time required to complete the campaign [10]. For effective protection against malaria by bed nets, the WHO recommends that at least 80% of household members should own and use bed nets [11]. Even the use of an untreated bed net provided it is in relatively good condition, can reduce malaria transmission [12, 13]. High household ownership of bed nets immediately after a successful mass distribution campaign turns to decline within two years as some nets are lost or damaged [14]. Also, high bed net ownership does not automatically lead to high utilization [15]. The self-reported reasons for not utilizing a bed net when available include discomfort due to heat, the smell of the net and difficulty in hanging [16].

The main objective of the bed net distribution initiative is to prevent transmission among vulnerable groups such as people living in rural areas, pregnant women and children under five years [17, 18]. Hence, there is less free bed net available for urban households with only healthy adults residing in them. The situation could worsen if households are unable to purchase bed nets from the open market due to their low socio-economic status [19]. Nonetheless, adults living in urban areas are prone to malaria [20], therefore they must be considered when designing and implementing malaria control strategies. Although some previous studies have reported on the relationship between socio-demographic factors and bed net ownership, usage and malaria transmission among vulnerable sub-populations [4, 17, 21, 22], such information is lacking among the urban adult population. This paper seeks to examine socio-economic and demographic factors affecting bed net ownership, usage and malaria transmission among adult patients seeking healthcare in two Ghanaian urban cities.

Study area

This hospital-based cross-sectional study was conducted at Bremang Seventh-Day Adventist Hospital, Suame Municipal, Ashanti Region and Sunyani Municipal Hospital, Sunyani, Bono Region. Suame Municipal is an urban town with an estimated adult (≥ 18 years old) population of 81,774 [23] and shares boundaries with Kumasi Metropolitan, the capital town of the Ashanti Region (Fig. 1). Sunyani is the capital town of the Bono Region with an estimated adult (≥ 18 years old) population of 116,157 [23]. Sunyani Municipal Hospital is a primary health care facility specialized in providing quality health care to clients in and around Sunyani (Fig. 1). The working population of the selected cities is mainly made up of people belonging to the middle class with the majority of them depending on small to medium business ventures as a source of livelihood.

Study Design

Sampling took place between January and September 2021. After participants gave their informed consent, a structured questionnaire was administered to collect data. The questionnaire was initially pretested and difficult-to-understand questions were rephrased for easy understanding. The questionnaire collected data on demographic characteristics (sex, age, ethnicity and marital status), socioeconomic status (education, occupation, income and house ownership) and bed net ownership and usage. Bed net usage was defined as sleeping under a bed net the previous night preceding the study. After administering the questionnaire, finger prick blood samples were collected from all participants for thick and thin blood smears.

Malaria Microscopy

Malaria microscopy was done according to the procedure described by Haggaz et al. [24]. In summary, thin and thick blood smears were prepared on the same microscope slide using approximately 2 µl and 6 µl of capillary blood respectively and stained with 10% Giemsa for 10 minutes. Afterwards, each slide was observed under the ×100 oil immersion objective lens of a light microscope by two independent and experienced laboratory scientists who were blinded to each other's results. The thick smears were evaluated for the presence of Plasmodium parasites. At least 100 high-power fields had to be examined before a smear was considered negative. Parasite densities were recorded as a ratio of parasites to 200 WBC in thick films (or to 500 WBC, if the parasite count was less than 100 per 200 WBC), assuming a leukocyte count of 8000 cells/µl. When more than 300 parasites were seen per high power field on the thick film, then a thin blood smear was used for parasite estimation. The species identification of all positive thick smears was done using their corresponding thin smears. When disagreement occurred, a third independent reader was employed. All microscopists had a level A expertise according to the National Competency Assessment in Malaria Microscopy (NCAMM). Parasite densities (parasite/µl of whole blood) were calculated using the assumed WBC counts of 8.0 × 10⁹/L.

Parasite densities = (Number of parasites counted/WBC counted) ×Assumed WBC counts.

Ethical approval

The protocol for this study was reviewed and approved by the Committee on Human Research, Publication and Ethics at the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana (CHRPE/AP/612/19).

Data analysis

Descriptive statistics for sociodemographic variables and bed net ownership and usage were done using absolute frequencies and their corresponding percentages. Crude and adjusted prevalence ratios (PR), and their respective 95% CIs were calculated, using Poisson regression with robust standard errors, to show associated variables in bivariate and multivariate analyses respectively [25]. The dependent variables were own or do not own bed net; use or do not use a bed net and malaria positive or negative. Demographic and socio-economic characteristics were the independent variables considered in the regression analysis. The geometric mean of parasite density and their corresponding 95% confidence interval (CI) were used to calculate the population measures of malaria parasitaemia. Because of the exploratory nature of this study, no p values were calculated; instead, confidence intervals (CIs) were used [26]. R software (version 4.1.1) was used to perform all statistical analyses [27]. The sandwich package (version 3.0-0) was used to compute robust estimators of the Poisson regression. The figure which shows the prevalence of bed net ownership, usage and malaria transmission among pregnant and non-pregnant women and males was plotted using ggplot2 package (version 3.3.5). QGIS software, version 3.24.0 [28] was used to draw a map to show the geographical location of the two study sites.

Socio-economic and demographic characteristics of study participants

In Table 1, the socio-economic and demographic characteristics of study participants are presented. Out of the 550 participants, 332 (60.4%) were from Suame and 218 (39.6%) from Sunyani. Females accounted for most (76.5%, n/N = 421/550) of the study participants. Also, more than half of the participants (52.5%, n/N = 289/550) were between the ages of 18 – 30. While 49.5% (n/N = 272/550) of the participants were married, 37.6%, (n/N = 207/550) had never married at the time of the survey. About 20.9% (n/N = 115/550) had attained tertiary level of education while those with no education were only 4.7% (n/N = 26/550). Most (68.7%, n/N = 378/550) of the participants were either formally or informally employed with 17.5% (n/N = 96/550) being unemployed. Only 4.0% (n/N = 15/378) of those employed earn more than $300 per month with the majority (78.3%, n/N = 294/378) earning $150 or less. About 47.1% (n/N = 259/550) of the study participants lived in a house owned by their household head.

Association between socio-demographic characteristics and bed net ownership and usage

A slight majority (53.3%, n/N = 293/550) of the study participants owned at least one bed net; however, only 21.5% (n/N = 118/550) slept under it the night before the survey. The ownership and usage of the bed net varied by study site. More participants (64.7%, n/N = 141/218) from Sunyani than Suame (45.8%, n/N = 152/332) own a bed net (PR = 1.4; 95% CI: 1.2 – 1.7). Similarly, subjects from Sunyani were 1.9 (95% CI: 1.3 – 2.6) times more likely to use a bed net than their counterpart from Suame. More females owned (PR = 1.5; 95% CI: 1.2 – 1.9) and used (PR = 2.0; 95% CI: 1.2 – 3.2) bed net that males. Among the various age groups, the prevalence of bed net use was highest (PR = 1.9, 95% CI: 1.3 – 2.7) in the 31 – 40 years age group when compared to the reference age group of 18 – 30 years. Even though majority (79.5%, n/N = 437/550) of the study participants were Akans, the Mole Dagbani ethnic groups were 1.3 (95% CI: 1.1 – 1.6) and 1.5 (95% CI: 1.0 – 2.1) times more likely to own and use bed net that the Akan ethnic group. Those married were 2.0 (95% CI: 1.7 – 2.5) and 2.4 (95% CI: 1.6 – 3.5) times more likely to own and use a bed net respectively than those who had never married. The crude and adjusted prevalence ratios of socio-economic and demographic characteristics associated with bed net ownership and usage are further presented in Table 2.

The ownership of bed net was 0.7 (95% CI: 0.6 – 0.8) times lower among participants with secondary school education and below compared to those with primary education and below. Nonetheless, these two groups equally used bed net (PR = 0.7; 95% CI: 0.5 – 1.0). Those informally employed (PR=1.0; 95%, CI: 0.8 – 1.3) or unemployed (PR = 0.9; 95% CI: 0.7 – 1.2) had similar prevalence of bed net ownership compared to formally employed participants. However, students were 0.4 (95% CI: 0.2 – 0.6) and 0.2 (95% CI: 0.1 – 0.5) times less likely to own and use bed net respectively compared to formally employed persons. Income levels were not associated with bed net ownership and usage. Nonetheless, those who lived in a house owned by their household head recorded a higher prevalence of bed net usage than their counterpart living in a rented apartment (PR = 1.3, 95% CI: 1.1 – 1.8) (Table 2).

Socio-economic risk factors for malaria

The overall malaria prevalence was 7.8% (n/N = 43/550), the prevalence was similar in Suame (7.5%, n/N = 25/332) and Sunyani (8.3%, n/N=18/218) (PR = 0.9; 95% CI: 0.5 – 1.6). Females accounted for majority (62.8%, n = 27) of the malaria positive cases compared to males (37.2%, n = 16) (PR = 1.7; 95% CI: 1.1 – 2.7)). Similarly, almost half (46.5%, n = 20) of malaria-positive adults were between the ages of 18 – 25 years. Plasmodium falciparum constituted the majority (97.6%, n = 42) of the infections, with Plasmodium ovale being responsible for only one (2.3%) case. Malaria-negative individuals were 1.6 (95% CI: 1.2 – 2.0) and 2.4 (95% CI: 1.4 – 4.1) times more likely to own and use bed nets respectively than malaria-positive patients.

The relationship between malaria infection and socio-economic variables is displayed in Table 3. While those with tertiary education (3.5%, n/N = 4/115) recorded the lowest prevalence of malaria the highest prevalence was recorded by participants with no formal education (15.4%, n/N = 4/26). However, in the various educational levels, the highest geometric mean parasite density of 12449 (95% CI: 6983 – 22196) parasites/µL was observed among participants with only primary education. The prevalence of malaria varied based on the nature of their occupation. Students (13.2%, n/N =10/76) followed by those informally employed (8.4%, n/N = 25/296) recorded the highest infection rate and geometric mean parasite densities. Among participants who were employed, none of those earning a monthly income >$300 recorded a case of malaria; contrary, the majority 83%, n/N = 25/30) of the malaria-positive patients earned ≤ $150 and recorded a high geometric mean parasite density of 5820 (95% CI: 3516 – 9634) parasites/µL.

Malaria prevalence and bed net ownership and usage among pregnant women

Out of the 421 female participants that were sampled, 124 (29.5%) were pregnant with an age range of 18 – 49 years. Figure 2 shows the prevalence of bed net ownership, usage and malaria prevalence among pregnant and non-pregnant women and males. Pregnant women were 1.3 (95% CI: 1.1 – 1.6) and 1.8 (95% CI: 1.3 – 2.5) times more likely to own and use a bed net respectively than non-pregnant patients. However, the prevalence of malaria infection in pregnant women and non-pregnant individuals was similar (PR = 0.8; 95% CI: 0.4 – 1.7).

Our results show that bed net ownership (53.3%) in households of adults attending the hospitals sampled in the two cities was lower than the proposed universal coverage mark of 80% and above [11]. The current bed net ownership rate is similar to results from a recent nationwide survey which reported that 52% of households have at least one ITN for every two people [9]. Suame which is closer to a bigger city, Kumasi, recorded lower bed net ownership and usage compared to Sunyani. Other studies conducted in smaller towns and rural areas have reported bed net ownership closer to the universal coverage mark [17, 21]. Urban areas have high population density which is usually heterogeneous in terms of ethnicity, religion, occupation and socioeconomic backgrounds. Hence, mass bed net distribution campaigns in bigger cities are likely to face more logistical challenges. This could explain the disparities in bed net ownership and usage observed among the two cities in the current study and other earlier studies conducted in rural areas.

Nonetheless, among pregnant women, we reported high bed net ownership comparable to the universal coverage mark. Also, pregnant women were more likely to use bed nets compared to non-pregnant women. These women are usually given bed nets for free or at a subsidised cost during the antenatal clinic (ANC) [29] and educated on the benefits of using them [30, 31]. The reason why not all pregnant women who own bed net use it could be difficulty in hanging the net, discomfort resulting from heat and smell of the net [16]. The frequency of bed net ownership and usage was higher among married participants than never married. This could be attributed to better decision-making by married individuals through consultation with their partner and also their responsibility of protecting other family members against infections [21, 32].

The findings of this study showed that ownership and use of bed net in the city setting is influenced by some socio-economic variables. Those with a secondary school level of education and above were less likely to own a bed net; however, educational level did not affect bed net utilization. Similarly, a recent study did not find significant disparities in bed net utilization by educational status [17]. The current study and a similar one conducted in Cameroon [31] showed no association between income and bed net ownership and utilisation. Inequities in bed net ownership are decreased by free mass-distribution campaigns. During such campaigns, the poor and least educated are the ones who mostly avail themselves [29], since middle-income Ghanaians prefer and are willing to pay the commercial price for bed net with certain design features that meet their preferences [33, 34]. We found that compared to those who were formally employed, adult students were less likely to own and use a bed net. Similarly, a study conducted in Nigeria reported that the majority of Secondary school students knew about bed nets; nonetheless, only less than half use them [35]. The lack of adequate knowledge of the biology and behaviour of mosquitoes have been identified as the reason why adult students do not use bed net since they are unable to see the link between bed net use and malaria control [36].

The overall malaria prevalence of 7.8% recorded by our study shows that malaria is still a public health concern among the adult population seeking healthcare in the two cities sampled. Studies conducted among a similar population in Ethiopia [2] and Uganda [37] recorded comparable prevalence. However, other studies have reported a higher (≥14%) frequency of malaria in the adult population in Ghana [3, 4], Nigeria [20, 38] and Kenya [39]. This variation might be due to geographical differences; since malaria is more prevalent in rural areas [40, 41]. Also, one study that recorded a higher prevalence was conducted only on suspected febrile patients [38]. Plasmodium falciparum was the dominant Plasmodium species responsible for the majority of the infections, this finding is in line with several studies conducted in Africa [2, 4, 22, 38, 39]. Contrastingly, in Asia, Plasmodium vivax is the dominant malaria species [42, 43]. Climate variation and its effect on parasite life cycle and adaptation could explain why falciparum is dominant in our study area.

We observed that those who own or used bed nets were less likely to be infected with malaria. The current finding is in agreement with several other studies which also observed significantly lower malaria prevalence among participants who used bed nets [44, 45, 46]. Even though we found that ownership of bed nets was associated with lower infection, an earlier study had reported that ITN campaigns which often lead to increased ownership do not reduce malaria prevalence over time since utilization turn to decrease with years of ownership [19]. Also, our study suggests that lack of education and low income were associated with an increased prevalence of malaria. Similarly, other studies have linked low educational status [7, 47, 48] and income [7, 49] with high malaria prevalence. Indeed, education and income increase one’s ability to access and implement health-related guidelines. High-income earners have the ability to buy indoor residual sprays and insecticide-treated nets (ITNs) which may help to decrease their contact with mosquito vectors.

Our study had some limitations. Due to the cross-sectional nature of the study, sampling was not continuously done throughout the year hence we were unable to show seasonal relationships. Also, this study did not differentiate between the different types of bed nets such as untreated bed nets, ITNs and long-lasting insecticide nets (LLINs) owned and used by participants. There were no independent observations carried out to validate the responses on bed net usage and socio-economic status provided by participants. Irrespective of the aforementioned limitations, our study identified socio-economic factors associated with bed net ownership, utilization and malaria transmission among adult patients seeking healthcare in two Ghanaian urban cities, thus the findings can be generalized to similar settings.

Despite, malaria being endemic in the urban adult population in Ghana, bed net coverage and usage which serves as a malaria prevention and control strategy remains low. Bet net coverage and usage were relatively high among pregnant women but low among students. Hence, bed net distribution campaigns and education on the benefits of using it should not only be limited to the ANC but outpatient departments (OPD) and tertiary schools as well. Age, marital status, educational level and type of occupation were related to bed net ownership and usage. Also, bed net usage, educational status and income were associated with malaria infection in the population. Therefore, in designing Malaria Control Programmes for the urban adult population these factors ought to be considered in order to improve bed net usage and decrease malaria prevalence.

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Funding statement

There was no specific fund for this research

Acknowledgements

The authors hereby express their profound gratitude to all the participants who took part to this study. We also thank the entire laboratory team of Bremang Seventh-Day Adventist Hospital, Suame Municipal, Ashanti Region and Sunyani Municipal Hospital, Sunyani, Bono Region.

Authors' contributions

E.K.P. and L.A.O. conceived the research. E.K.P. drafted the manuscript. E.B., A.D. and B.H.A. were responsible for the laboratory analysis. A.K. was responsible for the acquisition of the data. E.K.P. did the data analysis. All authors read and approved the submitted final version of the manuscript.

Who. "World malaria report 2020: 20 years of global progress and challenges." World malaria report. (2020): 1-151.
Fekadu M, Yenit MK. Ayenew Molla Lakew. "The prevalence of asymptomatic malaria parasitemia and associated factors among adults in Dembia district, northwest Ethiopia, 2017." Archives of public health 76, 1 (2018): 1–6.
Paintsil E, Kobina AY, Omari-Sasu MG, Addo. and Maxwell Akwasi Boateng. "Analysis of haematological parameters as predictors of malaria infection using a logistic regression model: A case study of a hospital in the Ashanti Region of Ghana." Malaria Research and Treatment 2019 (2019).
Heinemann M, Phillips RO, Vinnemeier CD, Rolling CC, Egbert Tannich, and Thierry Rolling. "High prevalence of asymptomatic malaria infections in adults, Ashanti Region, Ghana, 2018." Malaria journal 19, no. 1 (2020): 1–7.
Gonçalves BP, Melissa C, Kapulu P, Sawa WM, Guelbéogo AB, Tiono L, Grignard W, Stone, et al. Examining the human infectious reservoir for Plasmodium falciparum malaria in areas of differing transmission intensity. Nat Commun. 2017;8(1):1–11.
Teklehaimanot, Mejia P, A. Malaria and poverty. Ann N Y Acad Sci. 2008;1136(1):32–7.
Degarege A, Fennie K, Degarege D, Chennupati S. and Purnima Madhivanan. "Improving socioeconomic status may reduce the burden of malaria in sub Saharan Africa: A systematic review and meta-analysis." PloS one 14, no. 1 (2019): e0211205.
Lindsay SW, Thomas MB, Kleinschmidt I. Threats to the effectiveness of insecticide-treated bednets for malaria control: thinking beyond insecticide resistance. The Lancet Global Health. 2021;9(9):e1325–31.
GSS GHS. "Ghana Statistical Service (GSS), Ghana Health Service (GHS), and ICF. 2017." Ghana Malaria Indicator Survey (2016).
Linn S, Yi TM, Maung JP, Tripathy. Hemant Deepak Shewade, Swai Mon Oo, Zaw Linn, and Aung Thi. "Barriers in distribution, ownership and utilization of insecticide-treated mosquito nets among migrant population in Myanmar, 2016: a mixed methods study. Malar J. 2019;18(1):1–16.
World Health Organization. "WHO Recommendations for Achieving Universal Coverage with Long-lasting Insecticidal Nets in Malaria Control. 2013." Geneva: World Health Organization (2014).
Clarke SE, Bøgh C, Brown RC, Pinder M, Gijs EL, Walraven, Steve W. Lindsay. "Do untreated bednets protect against malaria? Trans R Soc Trop Med Hyg. 2001;95(5):457–62.
Russell TL, Dickson W, Lwetoijera D, Maliti B, Chipwaza J, Kihonda J, Derek Charlwood TA. Smith, et al. Impact of promoting longer-lasting insecticide treatment of bed nets upon malaria transmission in a rural Tanzanian setting with pre-existing high coverage of untreated nets. Malar J. 2010;9(1):1–14.
Mboma ZM, Festo C, Lorenz LM, Dennis J, Massue WN, Kisinza J, Bradley, Jason D. Moore, et al. The consequences of declining population access to insecticide-treated nets (ITNs) on net use patterns and physical degradation of nets after 22 months of ownership. Malar J. 2021;20(1):1–13.
Mensah EA. and Francis Anto. "Individual and community factors associated with household insecticide-treated Bednet usage in the Sunyani West District of Ghana two years after mass distribution." Journal of environmental and public health 2020 (2020).
Manu G, Boamah-Kaali EA, Febir LG, Ayipah E. Seth Owusu-Agyei, and Kwaku Poku Asante. "Low utilization of insecticide-treated bed net among pregnant women in the middle belt of Ghana." Malaria research and treatment 2017 (2017).
Kanmiki E, Wedam JK, Awoonor-Williams JF, Phillips SP, Kachur, Sabastian F, Achana J, Akazili, Ayaga A Bawah. "Socio-economic and demographic disparities in ownership and use of insecticide-treated bed nets for preventing malaria among rural reproductive-aged women in northern Ghana." PloS one 14, no. 1 (2019): e0211365.
Bennett A, Smith SJ, Yambasu S, Jambai A, Alemu W, Kabano A, Thomas P. Eisele. "Household possession and use of insecticide-treated mosquito nets in Sierra Leone 6 months after a national mass-distribution campaign. PLoS ONE. 2012;7(5):e37927.
Hamre KES, Ayodo G, Hodges JS, Chandy C. John. "A mass insecticide-treated bed net distribution campaign reduced malaria risk on an individual but not population level in a highland epidemic-prone area of Kenya. Am J Trop Med Hyg. 2020;103(6):2183.
Awosolu O, Bunmi ZS, Yahaya, Meor Termizi Farah Haziqah, Iyabo Adepeju Simon-Oke, and Comfort Fakunle. "A cross-sectional study of the prevalence, density, and risk factors associated with malaria transmission in urban communities of Ibadan, Southwestern Nigeria." Heliyon 7, no. 1 (2021): e05975.
Nyavor K, Delali M, Kweku I, Agbemafle W, Takramah I, Norman E, Tarkang. Fred Binka. "Assessing the ownership, usage and knowledge of insecticide treated nets (ITNs) in malaria prevention in the hohoe municipality, Ghana." Pan Afr Med J 28, no. 1 (2017).
Dao F, Djonor SK, Ayin CTeye-Muno, Adu GA, Sarfo B, Nortey P. Kwadwo Owusu Akuffo, and Anthony Danso-Appiah. "Burden of malaria in children under five and caregivers’ health-seeking behaviour for malaria-related symptoms in artisanal mining communities in Ghana. Parasites & vectors. 2021;14(1):1–11.
Ghana Statistical Service. "Ghana 2021 Population and Housing Census: General Report Volume 3N.". (2021).
Haggaz AbdElrahiumD, Leana M, Elbashir GK, Adam DA, Rayis, Adam I. Estimating malaria parasite density among pregnant women at central Sudan using actual and assumed white blood cell count. Malar J. 2014;13(1):1–5.
Barros AJD, Vânia N. Hirakata. "Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. 2003;3(1):1–13.
Rothman KJ. Disengaging from statistical significance. Eur J Epidemiol. 2016;31(5):443–4.
Team R, Core. "R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria." i>http://www</i. i>R-project</i. i>org/</i (2020).
Development Team QGIS. A. E. "QGIS geographic information system." Open source geospatial foundation project (2018).
Scates SS, Timothy P, Finn J, Wisniewski D, Dadi R, Mandike M, Khamis G, Greer, et al. Costs of insecticide-treated bed net distribution systems in sub-Saharan Africa. Malar J. 2020;19(1):1–18.
Gamble C, Ekwaru PJ, Garner P, Feiko O. Ter Kuile. "Insecticide-treated nets for the prevention of malaria in pregnancy: a systematic review of randomised controlled trials. PLoS Med. 2007;4(3):e107.
Fokam EB, Germaine F, Kindzeka L, Ngimuh, Kevin TJ, Dzi. and Samuel Wanji. "Determination of the predictive factors of long-lasting insecticide-treated net ownership and utilisation in the Bamenda Health District of Cameroon.". BMC Public Health. 2017;17(1):1–10.
Jan M, Akhtar S. An analysis of decision-making power among married and unmarried women. Stud Home Community Sci. 2008;2(1):43–50.
Kim S, Piccinini D, Mensah E. and Matthew Lynch. "Using a human-centered design approach to determine consumer preferences for long-lasting insecticidal nets in Ghana. Global Health: Science and Practice. 2019;7(2):160–70.
Alfonso Y, Natalia M, Lynch E, Mensah D, Piccinini, Bishai D. Willingness-to-pay for long-lasting insecticide-treated bed nets: a discrete choice experiment with real payment in Ghana. Malar J. 2020;19(1):1–13.
Aliyu AA. and M. Alti-Mu'azu. "Insecticide-treated nets usage and malaria episodes among boarding students in Zaria, northern Nigeria." Annals of African Medicine 8, no. 2 (2009).
Kudom AA, Ben A, Mensah. The potential role of the educational system in addressing the effect of inadequate knowledge of mosquitoes on use of insecticide-treated nets in Ghana. Malar J. 2010;9(1):1–7.
Rek J, Katrak S, Obasi H, Nayebare P, Katureebe A, Kakande E, Arinaitwe E, et al. Characterizing microscopic and submicroscopic malaria parasitaemia at three sites with varied transmission intensity in Uganda. Malar J. 2016;15(1):1–8.
Mustapha J, Olaide AU, Emeribe, Idris Abdullahi N. .". "Survey of malaria and anti-dengue virus IgG among febrile HIV-infected patients attending a tertiary hospital in Abuja, Nigeria. HIV/AIDS (Auckland NZ). 2017;9:145.
Nyamu G, Waweru JH, Kihara EO, Oyugi V, Omballa. Hajara El-Busaidy, and Victor Tunje Jeza. "Prevalence and risk factors associated with asymptomatic Plasmodium falciparum infection and anemia among pregnant women at the first antenatal care visit: A hospital based cross-sectional study in Kwale County, Kenya. PLoS ONE. 2020;15(10):e0239578.
Molina Gómez, Karen MA, Caicedo A, Gaitán M, Herrera-Varela MI, Arce AF, Vallejo J, Padilla, et al. Characterizing the malaria rural-to-urban transmission interface: The importance of reactive case detection. PLoS Negl Trop Dis. 2017;11(7):e0005780.
Ntigui MM. Chérone Nancy, Sandrine Lydie Oyegue-Liabagui, Lady Charlene Kouna, Karl Roméo Imboumy, Nathalie Pernelle Tsafack Tegomo, Alain Prince Okouga, Seinnat Ontoua, and Jean-Bernard Lekana-Douki. "Inflammatory cytokine responses in children with asymptomatic malaria infection living in rural, semi-urban and urban areas in south-eastern Gabon. Clinical & Experimental Immunology. 2021;206(3):395–409.
Howes RE, Katherine E, Battle, Kamini N, Mendis DL, Smith RE, Cibulskis J Kevin Baird, and Simon I. Hay. "Global epidemiology of Plasmodium vivax." The American journal of tropical medicine and hygiene 95, no. 6 Suppl (2016): 15.
Benavente E, Diez E, Manko J, Phelan M, Campos D, Nolder D, Fernandez G, Velez-Tobon, et al "Distinctive genetic structure and selection patterns in Plasmodium vivax from South Asia and East Africa." Nature communications 12, no. 1 (2021): 1–11.
Atieli HE, Zhou G, Afrane Y, Lee M-C, Mwanzo I, Githeko AK, Yan G. Insecticide-treated net (ITN) ownership, usage, and malaria transmission in the highlands of western Kenya. Parasites & vectors. 2011;4(1):1–10.
Omonijo A, Oluwatosin A, Omonijo HI, Okoh. and Azeez Oyemomi Ibrahim. "Relationship between the Usage of Long-Lasting Insecticide-Treated Bed Nets (LLITNs) and Malaria Prevalence among School-Age Children in Southwestern Nigeria." Journal of Environmental and Public Health 2021 (2021).
Rek J, Musiime A, Zedi M, Otto G, Kyagamba P, Rwatooro JA, Arinaitwe E, et al. Non-adherence to long-lasting insecticide treated bednet use following successful malaria control in Tororo, Uganda. PLoS ONE. 2020;15(12):e0243303.
Addai-Mensah O, Annani-Akollor ME, Fondjo LA, Sarbeng K, Anto EO, Owiredu E-W. and Shanice Nglokie Arthur. "Regular antenatal attendance and education influence the uptake of intermittent preventive treatment of malaria in pregnancy: a cross-sectional study at the university hospital, Kumasi, Ghana." Journal of Tropical Medicine 2018 (2018).
Oladimeji K, Elizabeth JM, Tsoka-Gwegweni E, Ojewole. and Samuel Tassi Yunga. "Knowledge of malaria prevention among pregnant women and non-pregnant mothers of children aged under 5 years in Ibadan, South West Nigeria." Malaria journal 18, no. 1 (2019): 1–12.
Rudasingwa G, Sung-Il C. Determinants of the persistence of malaria in Rwanda. Malar J. 2020;19(1):1–9.

Table 1: Socio-economic and demographic characteristics of study participants

	Suame, n (%)	Sunyani, n (%)	Total, n (%)
Sex
Female	245 (73.8)	176 (80.7)	421 (76.5)
Male	87 (26.2)	42 (19.3)	129 (23.5)
Age
18 – 30	180 (54.2)	109 (50.0)	289 (52.5)
31 – 40	82 (24.7)	48 (22.0)	130 (23.6)
41 – 50	37 (11.2)	25 (11.5)	62 (11.3)
51+	33 (9.9)	36 (16.5)	69 (12.6)
Ethnicity
Akan	296 (89.2)	141 (64.7)	437 (79.5)
Mole Dagbani	30 (9.0)	70 (32.1)	100 (18.1)
Other	6 (1.8)	7 (3.2)	13 (2.4)
Marital status
Married	158 (47.6)	114 (52.3)	272 (49.5)
Never married	126 (38.0)	81 (37.2)	207 (37.6)
Cohabiting	34 (10.2)	3 (1.4)	37 (6.7)
Other	14 (4.2)	20 (9.1)	34 (6.2)
Religion
Christians	311 (93.7)	183 (83.9)	494 (89.8)
Muslims	21 (6.3)	35 (16.1)	56 (10.2)
Formal Education
None	12 (3.6)	14 (6.4)	26 (4.7)
Primary	129 (38.9)	99 (45.4)	228 (41.5)
Secondary	123 (37.0)	58 (26.6)	181 (32.9)
Tertiary	68 (20.5)	47 (21.6)	115 (20.9)
Occupation
Informally employed	185 (55.7)	111 (50.9)	296 (53.8)
Formally employed	45 (13.5)	37 (17.0)	82 (14.9)
Unemployed	56 (16.9)	40 (18.3)	96 (17.5)
Student	46 (13.9)	30 (13.8)	76 (13.8)
Monthly income ($)^a
≤150	177 (77.0)	117 (79.1)	294 (77.8)
151 – 300	45 (19.5)	24 (16.2)	69 (18.2)
>300	8 (3.5)	7 (4.7)	15 (4.0)
House ownership
Rented	172 (51.8)	119 (54.6)	291 (52.9)
Household head	160 (48.2)	99 (45.4)	259 (47.1)

Abbreviations: ^a – for only those who were employed

Table 2: Socio-economic and demographic associations with the probability of bed net ownership and usage among the study participants

Variable	Bed net ownership		Bed net usage
Variable	PR (95% CI)	Adj. PR (95% CI)	PR (95% CI)	Adj. PR (95% CI)
Location
Kumasi	1
Suame	1.4 (1.2 – 1.6)	1.4 (1.2 – 1.6)	1.9 (1.4 – 2.6)	1.8 (1.3 – 2.5)
Sex
Male	1
Female	1.5 (1.2 – 1.9)	1.5 (1.2 – 1.9)	2.0 (1.2 – 3.2)	1.8 (1.3 – 3.0)
Age
18 – 30	1
31 – 40	1.6 (1.3 – 1.9)	1.6 (1.3 – 1.9)	1.9 (1.3 – 2.7)	1.9 (1.3 – 2.7)
41 – 50	1.5 (1.3 – 2.0)	1.5 (1.2 – 1.9)	1.5 (0.9 – 2.5)	1.4 (0.9 – 2.4)
≥51	1.5 (1.2 – 1.9)	1.4 (1.1 – 1.8)	1.6 (1.0 – 2.6)	1.5 (0.9 – 2.5)
Ethnicity
Akan	1
Mole Dagbani	1.3 (1.1 – 1.6 )	1.1 (0.9 – 1.4)	1.5 (1.0 – 2.1)	1.2 (0.8 – 1.7)
Other	1.1 (0.7 – 1.5)	1.0 (0.7 – 1.5)	0.9 (0.4 – 2.2)	0.8 (0.4 – 1.9)
Marital status
Never married	1
Married	2.0 (1.7 – 2.5)	2.0 (1.6 – 2.5)	2.4 (1.6 – 3.5)	2.3 (1.6 – 3.5)
Other	1.6 (1.2 – 2.2)	1.7 (1.7 – 1.4)	1.3 (0.7 – 2.4)	1.3 (0.7 – 2.5)
Education
Primary and below	1
Secondary and above	0.7 (0.6 – 0.8)	0.7 (0.6 – 0.8)	0.7 (0.5 – 1.0)	0.8 (0.5 – 1.1)
Occupation
Formally employed	1
Informally employed	1.0 (0.8 – 1.3)	1.0 (0.8 – 1.3)	0.2 (0.1 – 0.3)	0.2 (0.1 – 0.4)
Unemployed	0.9 (0.7 – 1.2)	1.0 (0.8 – 1.3)	0.8 (0.5 – 1.3)	0.8 (0.5 – 1.3)
Student	0.4 (0.2 – 0.6)	0.4 (0.2 – 0.6)	0.2 (0.1 – 0.5)	0.2 (0.1 – 0.5)
Monthly income ($)^a
≤151	1
151 – 300	0.9 (0.7 – 1.2)	1.0 (0.7 – 1.2)	1.3 (0.8 – 2.1)	1.4 (0.9 – 2.2)
>300	0.9 (0.5 – 1.5)	0.8 (0.5 – 1.4)	1.2 (0.4 – 3.1)	1.0 (0.3 – 3.0)
House ownership
Rented	1
Household head	1.0 (0.9 – 1.2)	1.1 (0.9 – 1.2)	1.3 (1.1 – 1.8)	1.3 (1.1 – 1.8)

Abbreviations: ^a – for only those who were employed

Table 3: Malaria parasite prevalence and density in relation to socio-economic variables

Socio-economic variable	Prevalence (n/N)	Geometric mean parasite density (95% CI)/µL of blood
Educational level
None	15.4 (4/26)	827 (349 – 1960)
Primary	7.0 (16/228)	12449 (6983 – 22196)
Secondary	10.5 (19/181)	5952 (3933 – 9007)
Tertiary	3.5 (4/115)	2028 (794 – 5180)
Occupation
Formally employed	6.1 (5/82)	2175 (1049 – 4511)
Informally employed	8.4 (25/296)	6066 (3951 – 9313)
Unemployed	3.1 (3/96)	2285 (390 – 13384)
Student	13.2 (10/76)	6780 (3824 – 12022)
Monthly income ($)^a
≤150	8.5 (25/294)	5820 (3516 – 9634)
151 – 300	5.2 (5/69)	2694 (1130 – 6420)
>300	0 (0/15)	NA
House ownership
Rented	7.8 (23/294)	3378 (2355 – 4844)
Household head	8.5 (20/236)	8143 (4777 - 13881)

Abbreviations: ^a – for only those who were employed; NA- Non-applicable

No competing interests reported.

Demographic and socio-economic factors affecting bed net ownership, usage, and malaria transmission among adult patients seeking healthcare in two Ghanaian urban cities

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Study area

Study Design

Malaria Microscopy

Ethical approval

Results

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1