- Open Access
Individual and household factors associated with ownership of long-lasting insecticidal nets and malaria infection in south-central Ethiopia: a case–control study
Malaria Journal volume 16, Article number: 402 (2017)
A recent considerable decline in malaria morbidity and mortality in Ethiopia is likely to be followed by changes in the practice of effective preventive measures and malaria risk factors. This study aimed to identify determinants of long-lasting insecticidal nets (LLINs) ownership and risk of malaria infection.
A matched case–control study of 191 case and 377 control households was conducted between October 2014 and November 2015 in Adami Tullu district in south-central Ethiopia. Cases were microscopy or rapid diagnostic test confirmed malaria patients identified at three health centers and nine health posts, and matched on age with two neighbourhood controls. Information was collected on socio-demographic factors, house structure, knowledge on malaria and ownership of LLINs. The logistic regression model was used to determine predictors of LLINs ownership and malaria infection.
All cases were infections due to either Plasmodium falciparum (71.2%) or Plasmodium vivax (28.8%). About 31% of the study households had at least one LLINs. Significant determinants of LLINs ownership were household’s head malaria knowledge [adjusted odds ratio (AOR) = 2.47, 95% confidence interval (CI) 1.44–4.22], educational status [read and write (AOR = 6.88, 95% CI 2.30–20.55), primary education or higher (AOR = 5.40, 95% CI 1.57–18.55)], farmer respondent (AOR = 0.35, 95% CI 0.17–0.76), having ≥ 3 sleeping areas (AOR = 6.71, 95% CI 2.40–18.77) and corrugated roof type (AOR = 2.49, 95% CI 1.36–4.58). This study was unable to identify important risk factors of malaria infection with regard to sex, household wealth index, house structure, ownership of LLINs, keeping livestock inside house, staying overnight outdoor or having malaria during the last 6 months.
Household socio-economic status, educational status and knowledge on malaria were important predictors of LLINs ownership. Households with farmer respondents were less likely to own LLINs. Addressing these factors could improve household’s ownership of LLINs. The importance of factors associated with malaria infection was less evident in the current low transmission setting, and necessitates further epidemiological study.
A considerable decline in malaria morbidity and mortality in Ethiopia has been observed since 2005 [1, 2]. Scaling-up of effective anti-malarial interventions such as long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS) and case management using artemether–lumefantrine (AL, Coartem®) are the main reasons for the decline . According to the Ethiopia Malaria Indicator Surveys (EMIS), malaria prevalence was low and estimated at 0.9% in 2007, 1.3% in 2011 and 0.5% in 2015 [4,5,6]. Nevertheless, malaria remains one of the 10 leading public health problems in the country, accounting for 3.5% of outpatient consultations, 2% of hospital admissions and 1% of inpatient deaths in 2014/2015 .
Ownership of LLIN and its proper use by the community is one of the key determinants for the success of malaria intervention. However, household’s ownership and utilization of LLINs has been low in Ethiopia despite efforts made to improve its universal coverage. The EMIS showed that the proportion of ownership of at least one LLIN was 66% in 2007, 55% in 2011 and 64% in 2015 [4,5,6]. Several studies on community acceptance and use of LLINs in Ethiopia have revealed that various cultural, behavioural and socio-economic factors influence ownership and use of LLINs [8,9,10].
Various environmental, household and individual factors are associated with malaria risk. Studies reported that housing types and domestic animals staying in the house , number of LLINs in the household and use of preventive methods [12, 13], age of individuals and houses with holes  and household wealth status  were significantly associated with malaria risk.
Ethiopia has adopted malaria elimination strategy and the target to eliminate the disease in selected low transmission areas was set for 2020 . The strategy requires a reliable evidence to improve understanding of malaria risk factors to inform proper targeting of anti-malarial interventions. As a result, understanding individual and household factors that determine malaria incidence and use of effective preventive measures such as LLINs and IRS is very crucial.
However, the conventional community-based cross-sectional surveys, such as malaria indicator surveys or prospective cohort study designs, would not be an efficient way to identify malaria cases or to assess risk factors because of the very large number of participants needed to be recruited. To study rare infections or diseases, the case–control methods are often used in identifying risk factors, but have been less frequently applied in malaria research during the last decade. This matched case–control study was undertaken in a predominantly rural population in south-central Ethiopia to investigate individual and household factors associated with LLINs ownership and malaria infection.
Study setting and population
The study was conducted in Oromia Regional State in Ethiopia in “Adami Tullu Jiddo Kombolcha” district, commonly known as “Adami Tullu” district. The district is located in the Great Rift Valley, with a projected population size of 173,000 people for the 2014 based on the 2007 census . It is administratively divided into 48 kebeles, the smallest of local government. A kebele is further divided territorially into the garee (team or group), comprised of 40–90 households. The district is characterized by a low annual rainfall, with the total annual precipitation of 700 mm that peaks to 250 mm during July and August . The average minimum and maximum annual temperature is 14.5 to 27.7 °C, respectively. The main ethnic group inhabiting the area is Oromo, and the majority of the population lives in rural areas. The economy of the area primarily depends on subsistence farming and livestock rearing. In 2014, there were one hospital, nine health centres and 43 health posts in the district. Each kebele is intended to have one health post staffed with two Health Extension Workers (HEWs). There were also a number of private health facilities serving the community.
Malaria is the leading cause of morbidity and mortality in the district, transmission being peaking shortly after the end of the rainy season during July–August. Transmission is seasonal, and characterized with several epidemics recorded in the past . Both Plasmodium falciparum and Plasmodium vivax account for almost all of the infections, while Anopheles arabiensis is the dominant malaria vector in the area [19, 20]. The use of LLINs and IRS in addition to malaria case treatment has been the major malaria prevention and control tools used in the area. A free distribution of LLINs by the District Health Office undertakes 2–3 years to all households in malarious localities, and IRS operation undertakes annually during July–August in the targeted kebeles. Malaria diagnosis and treatment is provided free-of-charge in the public sector. Artemether–lumefantrine and chloroquine are the first-line anti-malarial drugs for the treatment of uncomplicated falciparum and vivax malaria, respectively, based on diagnosis with microscopy at health centres and rapid diagnostic tests (RDTs) at health posts .
Study design and sample size estimation
This study used a matched case–control design to investigate associations between malaria and individual/household risk factors. The study was undertaken as part of a larger cluster-randomized controlled trial (MalTrials) project that aimed to investigate whether the combination of LLINs with IRS will enhance the protective benefits of both interventions against malaria . Each case was matched with two neighbourhood healthy controls based on age and same neighborhood or village. The matching was not done exactly on age. Instead the matching variable “age” was categorized into < 5 years old and with 10-year intervals (± 5 years) for those ≥ 5 years old.
Using two population proportions matched case–control sample size formula, the number of cases and controls required for the study was calculated assuming 95% confidence level, 80% power and 5% non-response rate with a 1:2 ratio of case to controls . Taking household’s ownership of at least one LLIN as a major exposure variable associated with malaria, the probabilities of exposure among households of controls (Po) and cases (P1) were assumed to be 0.3 and 0.19, respectively. Finally, a minimum of 185 cases and 370 controls were estimated for the study. The calculated sample size was allocated to the study health centres and health posts based on the flow of malaria patients during the preceding months. The sample size was also assumed to adequately satisfy the analysis of the association between household’s ownership of LLINs and associated factors.
Identification of cases and controls
Three health centres and nine health posts in the district were included in the study. Cases were defined as patients confirmed with malaria parasitaemia (P. falciparum or P. vivax) at the health posts or health centres using RDTs or microscopy, respectively. Cases were enrolled into the study consecutively from the health facilities until the allocated sample size was attained. Cases with signs of severity or from distant kebeles or areas were excluded.
Controls were healthy individuals selected from neighboring households nearest to the malaria cases and matched on age. They were considered to be healthy if they perceived healthy or the parents/guardians perceived the child to be healthy, and if they acknowledged no symptoms of malaria such as fever and had not been diagnosed and treated for malaria within a 2-week time prior to the presentation of the matched case at the health facility. However, they were not ascertained for malaria parasitaemia using either RDTs or microscopy due to financial and logistic constraints.
When more than two controls were available in the selected household, two were selected randomly. If there was no eligible control in the household, the next nearest neighboring household was selected. In this study, a household was defined as any unit headed by a male or female with his/her dependents who shared a common source of food and/or income and permanently resided in the area.
Data were collected between October 2014 and November 2015. Blood samples were collected from patients visiting health posts and health centers by the health workers as part of the routine malaria diagnosis and treatment. At the health posts, malaria diagnosis was made by the HEWs from blood samples collected from finger pricks using multi-species RDTs. The RDTs were the CareStart® Pf/Pv combo test (Access Bio, Inc., Somerset, NJ, USA), which were individually packaged with an alcohol swab, lancet, capillary tube, buffer and test device. At health centres, thick and thin blood smears from finger pricks were collected by laboratory technicians following standard procedures and stained with Giemsa. Malaria parasitaemia was determined by examination of thick blood smears under oil immersion using a microscope. The Plasmodium species was determined on the thin blood smear, and anti-malarial treatment was performed following the national guidelines . Artemether–lumefantrine (20/120 mg, Coartem®) and chloroquine (150 mg base) were given if RDT or microscopy was positive for P. falciparum or P. vivax, respectively.
Data on sex, age, result of malaria diagnosis, type of anti-malarial treatment and residential address were collected for each case identified at the health facilities using a logbook prepared for this study. Residential addresses were used to locate the cases. Trained field staff conducted a population-based house-to-house visits to follow-up the cases at their home in the community and find suitable neighborhood controls within a 2-week time from the date of diagnosis at the health facilities.
On finding the case and identifying the matching controls, information on demographics, housing, socio-economic characteristics and possible risk factors for malaria were collected using a pre-tested structured English questionnaire translated into the local Afan Oromo language. The head of each case and control household was interviewed about the knowledge of malaria, availability and number of LLINs in the household. If the presence of LLINs were reported, interviewers asked permission to enter the house, count and observe the nets. Finally, information on household assets such as radio, television, mobile telephone, refrigerator, table, chair, kerosene lamp, an electric stove or electricity, bicycle, motorcycle, cart, car, or land were collected to determine household wealth index.
All questionnaires were manually checked for completeness and the data entered into a computer using SPSS version 23 (SPSS, Chicago, IL, USA). Descriptive analysis was conducted using SPSS, while all bivariate and multivariate analyses were performed using STATA version 13 (College Station, TX, USA). Bivariate and multivariate unconditional logistic regression models were used to calculate the crude odds ratios (COR) and adjusted odds ratios (AOR) with 95% confidence intervals (CIs) for any association between household’s ownership of LLINs and possible predictors. Ownership was defined as having at least one LLIN. A conditional logistic regression (CLR) model was performed to determine the association between the risk factor and malaria infection, using the clogit command in STATA .
In the bivariate analysis, independent variables that were associated with the dependent variable at P < 0.25  were included in the multivariate regression model to identify those significant factors associated with the outcome variable, adjusting for other variables, and the level of significance was set at P < 0.05. Since the matching was done on “age” variable, this variable was not included in the multivariate CLR model for malaria risk assessment. The SURVEY (SVY) command in STATA was used to account for the clustered nature of the data with kebele as a primary sampling unit in all bivariate and multivariate models.
Malaria knowledge score
Knowledge of malaria was defined based on the responses of the head of each case and control household to nine closed-ended questions on malaria. The knowledge score was constructed using “yes” and “no” responses: (1) Fever is the main symptom of malaria illness, (2) IRS kills mosquitoes, (3) IRS prevents malaria, (4) IRS is a chemical sprayed on the interior walls of a house, (5) IRS is an effective means for malaria prevention, (6) Re-plastering makes IRS ineffective, (7) Sleeping under LLINs every night prevents malaria, (8) Malaria is a preventable, and (9) treatable disease. Each question contributed one point to the overall knowledge score. The knowledge scores were finally categorized into binary exposure variable using the median score (low knowledge < median and high knowledge ≥ median) as a predictor for ownership of LLIN or malaria risk. The low levels of knowledge constituted 44% of the respondents while the high knowledge levels represented 56% of the study participants.
Household wealth index
The relative wealth index was constructed for each household using the Principal Components Analysis (PCA) method in SPSS version 23 based on a combination of household and asset variables [23, 24]. Overall, 14 variables were subjected to PCA. However, variables such as ownership of bed and watch were given weights < 0.25 and excluded from the final model. The final PCA model for 12 items revealed the presence of three components with eigenvalues exceeding 1, explaining 21.81, 16.43, and 13.55% of the variance, respectively. The value of Keiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.651 and the Bartlett’s Test of Sphericity value reached statistical significance (P < 0.001). This method gave the greatest weight to ownership of livestock, herds, or farm animal (0.829), land used for agriculture (0.793), ownership of mobile phone (0.608), number of sleeping areas in the household (0.552), ownership of bank account (0.550), radio (0.540), television (0.475), number of rooms (0.466), electricity (0.434), ownership of bicycle (0.356) and cart (0.321), and separate kitchen (0.291). Finally, the households were categorized into three socio-economic levels based on the PCA scores: lower (poorest) (33.3%), middle (33.8%) and upper (least poor) (32.9%).
The study was approved by the Institutional Review Board of the College of Health Sciences of Addis Ababa University and the National Ethical Committee of the Federal Ministry of Science and Technology in Ethiopia as part of a larger cluster-randomized controlled trial that aimed to investigate whether the combination of LLINs with IRS will enhance the protective benefits of both interventions against malaria . All cases and controls including their households were informed about the purpose of the study, data collection procedures, potential benefits and harms, and confidentiality issues. Oral informed consent was obtained from all older children and adults, while verbal assent was obtained from the parents or caregivers/guardians of young children (< 15 years old). Cases were treated for malaria as per the national guidelines .
Characteristics of the respondents and households
A total of 191 case and 377 control households were included in the main analysis of the data. Six cases and 14 controls were excluded from the analysis due to incomplete information or matching problems. Table 1 shows characteristics of the respondents (or household heads) and their households included in the study. More than 98% of the respondents were males, with the majority (77.4%) being between 25 and 44 years of age. The majority were married (96.8%), Muslims (91%), Oromo ethnic group (98.4%) and farmers (90.3%). About 55% of the respondents replied that they could read and write. The average household size of the cases and controls was 4.6 per case household and 4.4 per control household. By household wealth index, there was no difference between case and control households.
Characteristics of the cases and controls
A total of 191 cases of malaria and 377 controls were included in the final data analysis. Cases were identified from 27 kebeles and the number varied from three to 14. All cases were traced and nobody or household from the cases or controls refused to take part in the study. About 52% (n = 109) of the cases were identified at the health centres, while 42.9% (n = 82) cases were identified from the health posts. All cases were infections due to either P. falciparum (71.2%, n = 136) or P. vivax (28.8%, n = 55). About 45% (n = 85) and 48.3% (n = 182) were males among cases and controls, respectively. The mean (± standard deviation) age was 15.46 (± 11.5) years for cases and 15.37 (± 11.8) years for controls. About 35% (n = 66) cases and 38.5% (n = 145) controls were less than 10 years of age.
Household’s ownership of LLINs and its association with other factors
About 31% (n = 174) of the total interviewed households (29.8% case households and 31% control households, P = 0.77) had at least one functional LLINs that could be used while sleeping and almost all nets were PermaNet® 2.0. From the total net owning households, a total of 341 LLINs were identified, where 30% (n = 52) of the households had one net, 46% (n = 80) had two, and 24% (n = 42) had three or more nets. Only 24.3% (n = 138) of the total respondents reported that they slept under LLINs the previous night. Particularly, 16.4% (n = 93) of the respondents reported they always or frequently slept under a net whilst 13.4% (n = 76) reported they occasionally slept under nets.
Based on the bivariate analysis, a strong association was found between ownership of LLINs and educational status of the head of household, knowledge about malaria (COR = 1.97, 95% CI 1.12–3.48), number of sleeping spaces in the household and household wealth index (Table 2). Households with respondents who could read and write or with primary or higher education were more likely to own LLINs. In addition, households with corrugated iron roofs were associated with increased ownership of LLINs (COR = 2.67, 95% CI 1.37–5.21). Similarly, households in the higher wealth quantile were more likely to own LLINs compared to households in the lower wealth category.
In the final multivariate model, five of the seven explanatory variables including respondent’s educational status, farming occupation (AOR = 0.35, 95% CI 0.17–0.76), higher knowledge about malaria (AOR = 2.47, 95% CI 1.44–4.22), and number of sleeping areas in the household and corrugated iron roof (AOR = 2.49, 95% CI 1.36–4.58) were significantly associated with ownership of LLINs after adjusting for all other explanatory variables in the model (Table 3). Households with respondents who could read and write or with primary/higher education showed more than fivefold higher odds of LLINs ownership. Households with ≥ 3 sleeping areas had significantly higher odds of LLINs ownership (AOR = 6.71, 95% CI 2.40–18.77). However, no significant association was found between household wealth index and LLINs ownership after adjusting for other explanatory variables in the final multivariate model.
Association between malaria infection and other factors
Table 4 shows individual and household risk factors associated with malaria infection in the bivariate matched analysis. Among the 15 independent individual and household variables assessed in the matched bivariate analysis, none of them were significantly associated with malaria infection. Case households were more likely to have family size of ≥ 5 but not statistically significant (COR = 1.57, 95% CI 0.99–2.48). A reported history of staying overnight outdoor in the previous month (COR = 1.91, 95% CI 0.94–3.88) and lack of reporting mosquito biting during the last month (COR = 0.36, 95% CI 0.11–1.11) were associated with malaria infection, but not statistically significant. Sex and other variables with P < 0.25 in the matched bivariate analysis were included in the multivariate CLR model (Table 5). However, none of the variables included in the model were statistically significantly associated with malaria infection after adjustment for all other variables except history of mosquito biting during the last month with borderline statistical significance (AOR = 0.33, 95% CI 0.11–1.04).
This study showed that only 31% of households owned at least one LLIN, and 21.5% of the net owning households had at least two nets. Educational status of the heads of households, occupation, knowledge about malaria, type of roof and number of sleeping areas in the household were independent predictors of ownership of LLINs. No statistically significant association with malaria infection was found in this study with regard to sex, household size, keeping livestock in the household, opening windows on retiring to sleep, history of malaria illness during the last 6 months, and staying overnight outdoor in the last month.
In the current study area, ownership of LLINs was very low despite efforts towards the universal net coverage by the Ministry of Health with free mass distribution and replacement of worn out nets through routine distribution . The Ethiopian malaria prevention and control strategy aims to achieve universal coverage of all households at risk of malaria with a minimum of one net per two persons . A study undertaken in the current study area in 2013 showed 27% ownership of LLINs by households . One potential reason for the low level of LLINs ownership could be seasonality and low prevalence of malaria since people tend to own and use LLINs less likely during low transmission season . Several studies demonstrated low prevalence and incidence of malaria in Ethiopia [5, 6, 20].
In this study, household heads higher educational level was associated with ownership of LLINs. Studies have shown that higher educational level and malaria knowledge positively influencing ownership of nets by households [10, 26,27,28]. This could be due to the fact that more educated and knowledgeable people were able to access information about the importance and benefits of mosquito nets, leading to better practices to protect and retain the received nets.
In the current study, household wealth index was not statistically associated with ownership of LLINs. A study conducted in south-central Ethiopia  reported similar findings. One possible explanation for the lack of association between LLINs ownership and socio-economic level would be that individuals do not buy additional nets for their households and hence are more likely to depend on free distribution or replacement of LLINs. In contrast, higher ownership of LLINs amongst households with higher socio-economic status has been observed previously in Ethiopia  and Tanzania .
According to the 2014/15 national malaria data, P. falciparum accounted for about 64% of all malaria cases, followed by P. vivax (about 36%) , whilst P. falciparum represented about 77% of confirmed cases in the 2011 EMIS report . All malaria infections identified in this study were due to either P. falciparum (71%) or P. vivax (29%). A health facility-based study conducted in 2012 in the same geographic zone showed that P. vivax and P. falciparum accounted for 57 and 41% of cases, respectively . However, in a longitudinal community-based study conducted in 2013, P. vivax (85%, n = 33) outweighed P. falciparum (15%, n = 6) although the overall number of confirmed cases was small . Heterogeneity between and within villages has previously been reported and is particularly a feature of malaria epidemiology in Ethiopia [32,33,34,35].
The findings of the current study show lack of association between malaria risk and household factors such as house structure, keeping livestock inside house, and opening windows on retiring to bed. This is consistent with another study in south-central Ethiopia that showed lack of association between housing structure and malaria incidence . Another study in Yemen reported lack of association between open window and malaria risk . In contrast, opening windows when retiring to bed or the presence of openings in the walls during peak transmission season was strongly associated with increased risk of malaria in South Africa  and, therefore, closing windows or openings in the walls can act as a protective barrier to prevent mosquitoes from entering houses.
The study findings show lack of association between ownership of LLINs and risk of malaria, which is probably due to the similar level of net coverage among case and control households. Another possible explanation for this lack of association can be attributed to low usage of nets in the area due to low transmission of malaria coupled with relatively high IRS coverage. A study in South Africa showed a lack of association between malaria risk and ownership of nets . However, a study in Tanzania has demonstrated strong association between higher community LLINs coverage and reduced risk of malaria .
In the current study, respondent’s knowledge about malaria was not statistically significantly associated with malaria risk. The possible explanation that could be put forward is that the level of knowledge among the cases and controls was similar. However, it is likely to argue that increased level of knowledge on malaria is associated with reduced risk of malaria. One possible explanation could be that people who have a high level of knowledge are in a better position to protect themselves against malaria.
It is clear that household socio-economic status plays an important role in malaria prevention because people with higher income have more resources to access personal protective measures against malaria. However, in the present study it could not be determined whether households in the lower wealth category were at increased risk of malaria, consistent with other studies that reported lack of association between household wealth and malaria risk [14, 39]. In contrast, a case–control study undertaken in South Africa showed that households in the higher wealth categories had a lower risk of malaria than households in the poorer categories . This is also consistent with other studies that showed strong association between higher household socio-economic status and reduced risk of malaria [12, 38, 40].
Several potential limitations may affect the findings of this study. The study was based on malaria cases identified at health facilities, controls were not tested for malaria, the findings may be affected by three sources of bias (selection, recall and information bias), and the study did not evaluate a key outcome of LLINs use. This study used a neighbouring household as a control and the selected households might have the same background variables as the cases. This could explain some of the findings, for example, that there was no association between malaria infection and wealth. Furthermore, almost all the study participants for assessment of the knowledge on malaria were male respondents and may not reflect the women’s perspective. Finally, given that this study was conducted in a low transmission setting with low LLINs coverage, the findings may not be generalizable to other settings with high malaria incidence. However, as the aim of the study was to identify potential factors associated with ownership of LLINs and malaria infection, the findings could still serve as an important input to inform proper targeting of anti-malarial interventions.
Household ownership of at least one LLIN was very low and it is far from being universal. This study confirmed that educational status, occupation, knowledge about malaria and house structure were important predictors of ownership of LLINs. This study warrants a population-based study to identify effective measures by which universal household ownership of LLINs can be achieved and sustained. The importance of individual and household risk factors associated with malaria infection was less evident in the present study in comparison with similar studies reported elsewhere. Further studies are needed to generate more evidences, particularly in low transmission settings similar to the current study area.
Aregawi M, Lynch M, Bekele W, Kebede H, Jima D, Taffese HS, et al. Time series analysis of trends in malaria cases and deaths at hospitals and the effect of antimalarial interventions, 2001–2011, Ethiopia. PLoS ONE. 2014;9:e106359.
Otten M, Aregawi M, Were W, Karema C, Medin A, Bekele W, et al. Initial evidence of reduction of malaria cases and deaths in Rwanda and Ethiopia due to rapid scale-up of malaria prevention and treatment. Malar J. 2009;8:14.
MOH. National malaria guidelines. Addis Ababa: Federal Democratic Republic of Ethiopia Ministry of Health; 2012.
MOH. Ethiopia national malaria indicator survey 2007. Addis Ababa: Federal Democratic Republic of Ethiopia Ministry of Health; 2008.
MOH. Ethiopian Health and Nutrition Research Institute and Partners. Ethiopia national malaria indicator survey 2011. Addis Ababa: Federal Democratic Republic of Ethiopia Ministry of Health; 2011.
MOH. Ethiopian Public Health Institute. Ethiopia national malaria indicator survey 2015 technical summary. Addis Ababa: Federal Democratic Republic of Ethiopia Ministry of Health; 2015.
MOH. Health and health related indicators 2007 E.C (2014/2015). Addis Ababa: Federal Democratic Republic of Ethiopia Ministry of Health; 2015.
Deressa W, Yihdego YY, Kebede Z, Batisso E, Tekalegne A. Individual and household factors associated with use of insecticide treated nets in southern Ethiopia. Trans R Soc Trop Med Hyg. 2014;108:616–24.
Graves PM, Ngondi JM, Hwang J, Getachew A, Gebre T, Mosher AW, et al. Factors associated with mosquito net use by individuals in households owning nets in Ethiopia. Malar J. 2011;10:354.
Sena LD, Deressa W, Ali A. Predictors of long-lasting insecticide-treated bed net ownership and utilization: evidence from community-based cross-sectional comparative study, Southwest Ethiopia. Malar J. 2013;12:406.
Ghebreyesus TA, Haile M, Witten KH, Getachew A, Yohannes M, Lindsay SW, et al. Household risk factors for malaria among children in the Ethiopian highlands. Trans R Soc Trop Med Hyg. 2000;94:17–21.
Graves PM, Richards FO, Ngondi J, Emerson PM, Shargie EB, et al. Individual, household and environmental risk factors for malaria infection in Amhara, Oromia and SNNP regions of Ethiopia. Trans R Soc Trop Med Hyg. 2009;103:1211–20.
Peterson I, Borrell LN, El-Sadr W, Teklehaimanot A. Individual and household level factors associated with malaria incidence in a highland region of Ethiopia: a multilevel analysis. Am J Trop Med Hyg. 2009;80:103–11.
Woyessa A, Deressa W, Ali A, Lindtjørn B. Malaria risk factors in Butajira area, south-central Ethiopia: a multilevel analysis. Malar J. 2013;12:273.
MOH. National malaria strategic plan: 2014–2020. Addis Ababa: Federal Democratic Republic of Ethiopia Ministry of Health; 2014.
Federal Democratic Republic of Ethiopia. Summary and statistical report of the 2007 population and housing census. In: Population Census Commission, Editor. Addis Ababa: UNFPA; 2008.
Deressa W, Loha E, Balkew M, Hailu A, Gari T, Kenea O, et al. Combining long-lasting insecticidal nets and indoor residual spraying for malaria prevention in Ethiopia: study protocol for a cluster randomized controlled trial. Trials. 2016;17:20.
Mengesha T, Nigatu W, Ghiorgis W, Eshete H, Balcha F, Ishii A, et al. The 1991 malaria epidemic in Ethiopia, with reference to the outbreak in central Ethiopia. Ethiop J Health Dev. 1998;12:111–4.
Abose T, Yeebiyo Y, Olana D, Alamirew D, Beyene Y, Regassa L, et al. Reorientation and definition of the role of malaria vector control in Ethiopia: the epidemiology and control of malaria with special emphasis on the distribution, behavior and susceptibility of insecticides of Anopheles vectors and chloroquine resistance in Zwai, central Ethiopia and other areas. WHO/MAL.1085. Geneva: World Health Organization; 1998.
Gari T, Kenea O, Loha E, Deressa W, Hailu A, Balkew M, et al. Malaria incidence and entomological findings in an area targeted for a cluster-randomized controlled trial to prevent malaria in Ethiopia: results from a pilot study. Malar J. 2016;15:145.
Kasiulevicius V, Sapoka V, Filipavieiute R. Sample size determination in epidemiological studies. Gerontologija. 2006;7:225–31.
Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. New York: Wiley; 2013.
Filmer D, Pritchett LH. Estimating wealth effect without expenditure data—or tears: an application to educational enrolments in states of India. Demography. 2001;38:115–32.
Vyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2016;21:459–68.
Winch PJ, Makemba AM, Kamazima SR, Lwihula GK, Lubega P, Minjas JN, et al. Seasonal variations in the perceived risk of malaria: implications for the promotion of insecticide-impregnated bed nets. Soc Sci Med. 1994;39:63–75.
Singh M, Brown G, Rogerson SJ. Ownership and use of insecticide-treated nets during pregnancy in sub-Saharan Africa: a review. Malar J. 2013;12:268.
Ankomah A, Adebayo S, Arogundade ED, Anyanti J, Nwokolo E, Ladipo O, et al. Determinants of insecticide-treated net ownership and utilization among pregnant women in Nigeria. BMC Public Health. 2012;12:105.
Matovu F, Goodman C, Wiseman V, Mwengee W. How equitable is bed net ownership and utilization in Tanzania? A practical application of the principles of horizontal and vertical equity. Malar J. 2009;8:109.
Woyessa A, Deressa W, Ali A, Lindtjørn B. Ownership and use of long-lasting insecticidal nets for malaria prevention in Butajira area, south-central Ethiopia: complex samples data analysis. BMC Public Health. 2014;14:99.
Hailu A, Lindtjørn B, Deressa W, Gari T, Loha E, Robberstad B. Equity in long-lasting insecticidal nets and indoor residual spraying for malaria prevention in a rural southcentral Ethiopia. Malar J. 2016;15:366.
Haji Y, Fogarty AW, Deressa W. Prevalence and associated factors of malaria among febrile children in Ethiopia: a cross-sectional health facility-based study. Acta Trop. 2016;155:63–70.
Abeku TA, van Oortmarssen GJ, Borsboom G, de Vlas SJ, Habbema JDF. Spatial and temporal variations of malaria epidemic risk in Ethiopia: factors involved and implications. Acta Trop. 2003;87:331–40.
Abeku TA, de Vlas SJ, Borsboom GJJM, Tadege A, Gebreyesus Y, Gebreyohannes H, et al. Effects of meteorological factors on epidemic malaria in Ethiopia: a statistical modelling approach based on theoretical reasoning. Parasitology. 2004;128:585–93.
Alemu K, Worku A, Berhane Y. Malaria infection has spatial, temporal, and spatiotemporal heterogeneity in unstable malaria transmission areas in northwest Ethiopia. PLoS ONE. 2013;8:e79966.
Yeshiwondim AK, Gopal S, Hailemariam AT, Dengela DO, Patel HP. Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia. Int J Health Geograph. 2009;8:5.
Al-Taiar A, Assabri A, Al-Habori M, Azazy A, Algabri A, Alganadi M, et al. Socio-economic and environmental factors important for acquiring non-severe malaria in children in Yemen: a case–control study. Trans R Soc Trop Med Hyg. 2009;103:72–8.
Coleman M, Coleman M, Mabaso MLH, Mabuza AM, Kok G, Coetzee M, et al. Household and microeconomic factors associated with malaria in Mpumalanga, South Africa. Trans R Soc Trop Med Hyg. 2010;104:143–7.
West PA, Protopopoff N, Rowland M, Cumming E, Rand A, Drakeley C, et al. Malaria risk factors in northwest Tanzania: the effect of spraying, nets and wealth. PLoS ONE. 2013;8:e65787.
Ernst KC, Lindblade KA, Koech D, Sumba PO, Kuwuor DO, John CC, et al. Environmental, socio-demographic and behavioural determinants of malaria risk in the western Kenyan highlands: a case–control study. Trop Med Int Health. 2009;14:1258–65.
Ayele DG, Zewotir TT, Mwambi HG. Prevalence and risk factors of malaria in Ethiopia. Malar J. 2012;11:195.
My deepest gratitude goes to Mr. Alemayehu Dessalegne without his active engagement in field work and supervision, this study would not have been realized. I am indebted to Prof. Bernt Lindtjørn for his advice and support. This study was the result of the hard work and collaboration of the health workers in the health facilities and the MalTrials project (17). I am grateful to the study population who participated in this study. I also thank the supervisors and data collectors who worked under challenging conditions to successfully complete the study.
The author declares that he has no competing interests.
Availability of data and materials
Further data are available on request.
This study received financial support from the Research Council of Norway through GLOBVAC Program (Project Number: 220554).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Deressa, W. Individual and household factors associated with ownership of long-lasting insecticidal nets and malaria infection in south-central Ethiopia: a case–control study. Malar J 16, 402 (2017) doi:10.1186/s12936-017-2048-9
- Long-lasting insecticidal net