DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2020 No. 169

Mahamadi Tassembedo

Soumaila Coulibaly

Boukary Ouedraogo

August 2020

This document was produced for review by the United States Agency for

International Development.

Factors Associated with the Use of

Insecticide-Treated Nets: Analysis of the

2018 Burkina Faso Malaria Indicator Survey

DHS Working Paper No. 169 

Factors Associated with the Use of Insecticide-Treated Nets: 

Analysis of the 2018 Burkina Faso Malaria Indicator Survey 

Mahamadi Tassembedo1,2

Soumaila Coulibaly1,3

Boukary Ouedraogo1,4

Institut de Formation et de Recherche en Science Sociale et de l’Education 

Wemtenga, Ouagadougou, Burkina Faso 

ICF 

Rockville, Maryland, USA 

August 2020 

1

 Institut de Formation et de Recherche Interdisciplinaires en Sciences de la Santé et de l’Education 

(IFRISSE) 

2

 Direction du suivi Évaluation et de la capitalisation, Ministère de la santé, Burkina Faso 

3 Centre National de Recherche et de Formation sur le Paludisme (CNRFP) /Institut National de Santé 

Publique (INSP), Burkina Faso 

4 Direction des systèmes d’information en santé, Ministère de la santé, Burkina Faso 

Corresponding author: Mahamadi Tassembedo; phone: +226 76007602; email: 

tassembedomahamadi@gmail.com 
Acknowledgments: We are very grateful to USAID for funding our research project. We acknowledge 

and thank the high-quality team of the ICF International and the DHS Fellows Program. Particular thanks 

to Shireen Assaf for her availability and precious time for giving us advice, and her commitment and 

valuable contributions that helped us produce a quality work. We are grateful for the extensive review by 

Cameron Taylor, and appreciate all the hard work and time devoted to this publication. We are grateful to 

our institution, Institut de Formation et de Recherche Interdisciplinaires en Sciences de la Santé et de 

l’Education (IFRISSE), whose support made this paper possible. 

Editor: Diane Stoy 

Document Production: Chris Gramer 

This study was implemented with support from the United States Agency for International Development 

(USAID) through The DHS Program (#720-0AA-18C00083). The views expressed are those of the authors 

and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. For additional information about The DHS Program contact: 

DHS Program, ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: +1 301-572-0950; 

fax: +1 301-572-0999 or +1 301-407-6501; email: reports@dhsprogram.com; Internet: 

www.dhsprogram.com. 

Recommended citation: 

Tassembedo, Mahamadi, Soumaila Coulibaly, and Boukary Ouedraogo. 2020. Factors Associated with the 

Use of Insecticide-Treated Nets: Analysis of the 2018 Burkina Faso Malaria Indicator Survey. DHS 

Working Paper No. 169. Rockville, Maryland, USA: ICF. 
iii 

CONTENTS 

TABLES ........................................................................................................................................................ v

FIGURES ...................................................................................................................................................... v

ABSTRACT ................................................................................................................................................ vii

1 INTRODUCTION .............................................................................................................................. 1

1.1 Research question .............................................................................................................. 2 

2 DATA AND METHODS ................................................................................................................... 3 

2.1 Data ..................................................................................................................................... 3 

2.2 Variables ............................................................................................................................. 3 

2.2.1 Outcomes variables ............................................................................................... 3 

2.2.2 Independent variables ............................................................................................ 3 

2.3 Statistical analysis ............................................................................................................... 3 

3 RESULTS ........................................................................................................................................ 5 

3.1 Characteristics of the study population ............................................................................... 5 

3.2 Bivariate analysis ................................................................................................................ 6 

3.3 Multivariate analysis ............................................................................................................ 8 

4 DISCUSSION ................................................................................................................................. 11 

5 CONCLUSIONS ............................................................................................................................. 13

REFERENCES ............................................................................................................................................ 15 

v 

TABLES 

Table 1 Description of the de facto sample used in the analysis among households 

with at least one ITN .................................................................................................. 5 

Table 2 Crosstabulation of variables used in the analysis and slept under an ITN last 

night among households with at least one ITN .......................................................... 7 

Table 3 Crosstabulation of malaria prevalence with region and place of residence 

among households with at least one ITN ................................................................... 8 

Table 4 Adjusted odds ratios for slept under an ITN last night among households with 

at least one ITN .......................................................................................................... 9 

FIGURES 

Figure 1 Percentage of children age 6-59 months who tested positive for malaria by 

microscopy ................................................................................................................. 2

vii 

ABSTRACT 

Sleeping under an insecticide-treated net (ITN) is recommended by the World Health Organization to 

protect against mosquito bites. Individuals who sleep under an ITN reduce their contact with mosquitoes 

through the combination of a physical barrier and an insecticidal effect, which reduces the incidence of 

malaria. The 2016-2020 Burkina Faso National Malaria Strategic Plan aims to have at least 90% of the 

population, 100% of children under age 5, and 100% of pregnant women sleep under an ITN. To help 

achieve this goal, this analysis examines the factors that are associated with the use of ITN nets by using 

data from the 2017-18 Burkina Faso Malaria Indicator Survey (MIS). The analysis examines individual, 

household, and community-level factors associated with ITN usage. According to the 2017-18 Burkina Faso 

MIS, 58% of individuals in households that own at least one ITN reported that they slept under an ITN on 

the night before the survey. The use of ITNs was significantly associated with individual, household, and 

community-level variables that included age, gender, age of household head, number of sleeping rooms, 

wealth, malaria prevalence, residence, and region. The results highlight areas of intervention at the 

individual, household, and community levels that can increase ITN use. Targeting these areas will make it 

possible to improve the fight against malaria, which is the leading cause of mortality and morbidity in 

Burkina Faso. 

KEY WORDS: Burkina Faso, malaria, insecticide-treated nets, malaria indicator survey 

1 

1 INTRODUCTION 

Malaria is a disease caused by parasites that are transmitted to people though the bites of infected female 

Anopheles mosquitoes (World Health Organization 2020). Malaria is a major cause of mortality and 

morbidity in developing countries. According to the World Health Organization (WHO), there were 228 

million cases of malaria and 405,000 deaths from malaria worldwide in 2018, with 93% of cases and 94% 

of deaths occurring in the African Region (World Health Organization 2019). 

One of the core malaria interventions recommended by WHO to protect against mosquito bites is the use 

of insecticide-treated nets (ITNs). Sleeping under an ITN is effective in reducing the incidence of malaria 

by reducing contact with mosquitoes through the combination of a physical barrier and an insecticidal effect 

(Fegan et al. 2007; Habluetzel et al. 1997; Lengeler 2004). Across sub-Saharan Africa (SSA), there has 

been an increased focus on the scale-up and distribution of ITNs with the goal of having every household 

at risk of malaria transmission and every person within that household protected by an ITN (Kilian, 

Wijayanandana, and Ssekitoleeko 2010; Willey et al. 2012). Countries have achieved high ITN coverage 

levels by using net distribution channels such as community delivery, routine health services, and outreach 

activities (Taylor, Florey, and Ye 2017). This investment in ITN distribution has increased the proportion 

of people in malaria-endemic areas sleeping under an ITN from 29% in 2010 to 50% in 2018 (World Health 

Organization 2019). 

Studies have shown that the major driver of ITN use is access to an ITN, because a person cannot use an 

ITN unless one is available (Graves et al. 2011; Koenker and Kilian 2014; Olapeju et al. 2018). However, 

individual, household, and community-level factors also influence ITN usage. At the individual level, 

factors that influence net use include age, gender, education, degree of control over household decisionmaking, ITN preferences, malaria knowledge and beliefs, and risk perception (Baume and Marin 2007; 

Chuma et al. 2010; Dunn, Le Mare, and Makungu 2011; Eisele et al. 2009; Hwang et al. 2010; Koenker 

and Yukich 2017; Olapeju et al. 2018; Toé et al. 2009; Wiseman et al. 2007). Household-level determinants 

of ITN include household size, household composition, number of sleeping rooms, and intrahousehold 

sleeping arrangements (Dunn, Le Mare, and Makungu 2011; Iwashita et al. 2010; Keating et al. 2005; 

Olapeju et al. 2018; Toé et al. 2009; Wiseman et al. 2007). At the community level, factors such as residence, 

environmental conditions, and malaria seasonality have also been shown to influence ITN usage (Graves 

et al. 2011; Koenker et al. 2019; Wiseman et al. 2007). 

Burkina Faso uses ITNs as the principal tool for malaria prevention. The 2016-2020 National Malaria 

Strategic Plan includes three approaches for ensuring that ITNs are available to the entire population: 

(1) free distribution of ITNs via nationwide campaigns, (2) free distribution of ITNs through routine 

antenatal care and expanded programs on immunization at all public health facilities, and (3) the sale of 

ITNs by the private sector (USAID 2019). In 2011, the National Malaria Control Program, in collaboration 

with its partners, implemented the first national campaign for the mass distribution of ITNs. This was 

followed by three other mass distribution campaigns in 2013, 2016, and 2019 (USAID 2019). To achieve 

universal ITN coverage, the national strategy aimed to provide enough ITNs to cover all residents of the 

household. The indicator that evaluates this strategy is the percentage of households with at least one ITN 

for every two people who stayed in the household the night before the interview. The percentage of 

households with at least one ITN for every two persons increased from 19% in the 2010 Burkina Faso 
2 

Demographic and Health Survey (DHS) to 33% in the 2017-18 Burkina Faso Malaria Indicator Survey 

(MIS). Another focus of malaria prevention in Burkina Faso is the promotion of ITN use throughout the 

country. The 2016-2020 National Malaria Strategic Plan aims to have at least 90% of the population, 100% 

of children under age 5, and 100% of pregnant women in Burkina Faso sleep under an ITN (Ministère de 

la Santé 2016). 

Burkina Faso is a landlocked Sahel country located in the center of West Africa. Malaria remains a major 

public health issue and is endemic throughout the country (USAID 2019). The percentage of children age 

6-59 months who tested positive for malaria by microscopy, according to the 2017-18 MIS, ranges from 

7% in the Centre Region to 39% in the Sud Oust Region (see Figure 1). Malaria is seasonal across the 

country, with peak malaria season occurring from June through October. In Burkina Faso, the duration of 

the rainy season varies across the country with variances in seasonal malaria transmission based on 

geographic zones. In the north, the rainy season can last up to 3 months, while in the central zone it lasts 

up to 6 months, and in the south, 9 months (USAID 2019). 

Figure 1 Percentage of children age 6-59 months who tested positive for malaria by microscopy 

1.1 Research question 

Our central research question is to determine the factors associated with the use of ITN nets in the 

population. This includes identifying high-risk areas in term of malaria prevalence, as well as factors at the 

individual, household, and community levels. 

Studying the factors that determine net use can lead to a better understanding of the actions that can increase 

net use in the general population and in specific areas of the country. These targeted actions will make it 

possible to improve the fight against malaria, which is the leading cause of mortality and morbidity in 

Burkina Faso. 

The results of this study will provide important answers on net use, and will inform decisions on distribution 

campaigns, the development of net replacement strategies, and the development and deployment of tools 

that include behavior change communication activities. 
3 

2 DATA AND METHODS 

2.1 Data 

Data from the Burkina Faso 2017-18 MIS were used in the analysis. The MIS is a household survey of a 

representative sample of respondents. The MIS collected data on the availability and use of ITNs in 

households, and several other malaria indicators. 

External data were obtained from the Malaria Atlas Project (MAP) (Malaria Atlas Project 2015) to 

determine the variable malaria prevalence in the analysis (described below). Since the Burkina Faso 2017-

18 MIS collected GPS coordinates of the clusters, we were able to merge the external data with the DHS 

data at the cluster level. 

2.2 Variables 

2.2.1 Outcomes variables 

Our dependent variable is a binary variable of whether an individual living in a household that owned at 

least one ITN had slept under an ITN the night before the survey. In addition, only de facto individuals that 

lived in households with at least one ITN were included in the analysis. This resulted in a sample size of 

27,299 individuals or 27,333 after applying sample weights. 

2.2.2 Independent variables 

The choice of independent variables (or explanatory variables) was based on the data in the literature and 

was defined at the cluster, household, and individual level. One of the main variables of interest is malaria 

prevalence in 2015 estimated by the MAP. The variable is defined as the average parasite rate of 

plasmodium falciparum (PfPR) in children between the ages of 2 and 10 within the 2 km (urban) or 10 km 

(rural) buffer that surrounds the DHS survey cluster location. This variable is used as a proxy to assess the 

malaria risk areas. 

The remaining variables in the analysis include other cluster level variables of region and place of residence 

(urban/rural), that would also determine risk areas. The household level variables include wealth quintiles, 

number of household members (1-3 4-6, 7-9, 10+), number of sleeping rooms (0-1, 2, 3, 4, more than 4 

rooms), and age of household head in years (less than 30, 30-39, 40-49, 50-59, and 60+). The individuallevel variables include age in years (0-10, 11-19, 20-29, 30-39, 40-49, 50+), sex, and relationship to 

household head (wife/husband, son/daughter, and others). 

2.3 Statistical analysis 

This paper included descriptive analysis of the data, bivariate analysis to assess the association between the 

outcome and the independent variables, and multivariate logistic models to assess the magnitude of the 

associations after including controls. Three logistic models were fit to include each of the three cluster-level 

variables (malaria prevalence, region, and place of residence) separately. This is due to the high association 

of these three variables with one another. The multivariate models do not include the variables of number 
4 

of household members and relationship to household head because these were highly correlated with the 

number of sleeping rooms and age of the individual, respectively. 

All analyses considered the survey sampling design and sampling weights, and used Stata version 16. 
5 

3 RESULTS 

3.1 Characteristics of the study population 

Table 1 summarizes the characteristics of the study population. For the outcome variable, 58% of the sample 

reported that they slept under an ITN on the night before the survey. The sample includes a relatively young 

population with more than half (58%) of the sample under age 20. Most were either the son or daughter of 

the household head (53%). Most household heads were age 40-49 (27%) and age 30-39 (26%). At the 

household level, most households had two or three rooms (34% and 27% respectively) and between four to 

six household members (39%). The sample was predominantly rural (82%), from the regions of Centre 

Ouest (13%), Boucle du Mouhoun (12%), and Hauts-Bassins (11%). In terms of malaria prevalence, 

according to the 2015 MAP estimates, a quarter lived in areas with a prevalence of 45-60%, with the 

majority living in areas with a prevalence of 35-45% (39%). Only 9% of the sample lived in areas with a 

malaria prevalence of 0-24%. According to the malaria prevalence variable, the total number of individuals 

is less than the total for the remaining variables. There were some clusters with missing information for this 

variable and this resulted in 2,478 unweighted individuals with missing values for this indicator. 

Table 1 Description of the de facto sample used in the analysis among households with at least 

one ITN 

Variable % 95% C.I. N 

Slept under an ITN last night 

Yes 58.4 [56.8,60.0] 15,964 

No 41.6 [40.0,43.2] 11,369 

Age in years 0-10 34.6 [33.8,35.5] 9,464 

11-19 23.6 [23.0,24.3] 6,456 

20-29 13.0 [12.4,13.6] 3,539 

30-39 11.7 [11.3,12.2] 3,208 

40-49 7.1 [6.8,7.4] 1,927 

50+ 10.0 [9.4,10.6] 2,731 

 

Sex of household member 

Male 49.1 [48.5,49.8] 13,430 

Female 50.9 [50.2,51.5] 13,903 

Relationship to household head Head 17.0 [16.6,17.4] 4,642 

Wife/husband 18.5 [18.1,18.8] 5,045 

Son/daughter 52.6 [51.7,53.6] 14,384 

Others 11.9 [11.0,12.9] 3,256 

 

Age of household head 

Less than 30 8.9 [7.9,10.1] 2,444 

30-39 25.6 [24.1,27.2] 6,991 

40-49 27.3 [25.7,28.9] 7,460 

50-59 18.5 [16.9,20.2] 5,054 

60+ 19.7 [17.6,21.9] 5,371 

Number of rooms for sleeping 0-1 room 13.4 [12.2,14.7] 3,666 

2 rooms 33.7 [31.8,35.6] 9,181 

3 rooms 26.7 [24.8,28.7] 7,286 

4 rooms 14.7 [13.2,16.4] 4,019 

More than 4 rooms 11.5 [9.8,13.4] 3,128 

 

Continued...

 
6 

Table 1 Description of the de facto sample used in the analysis among households with at least 

one ITN (continued)

Variable % 95% C.I. N 

Number of household members 

1-3 9.2 [8.4,10.1] 2,512 

4-6 38.6 [36.7,40.6] 10,556 

7-9 29.0 [27.3,30.7] 7,923 

10 or more 23.2 [21.0,25.5] 6,341 

Wealth quintile Lowest 18.3 [16.0,20.7] 4,996 

Second 20.1 [18.6,21.6] 5,493 

Middle 20.6 [18.9,22.4] 5,623 

Fourth 21.2 [19.2,23.5] 5,805 

Highest 19.8 [17.6,22.3] 5,416 

 

Malaria prevalence* 

0-24% 9.1 [7.0,11.8] 2,270 

25-34% 26.9 [21.9,32.4] 6,674 

35-44% 38.7 [31.9,45.9] 9,617 

45-60% 25.3 [20.0,31.4] 6,284 

Place of residence Urban 18.5 [16.2,21.1] 5,053 

Rural 81.5 [78.9,83.8] 22,280 

 

Region 

Boucle du Mouhoun 12.1 [10.5,13.9] 3,310 

Cascades 4.1 [3.3,5.2] 1,131 

Centre 8.8 [7.6,10.3] 2,411 

Centre Est 6.9 [5.5,8.5] 1,877 

Centre Nord 8.8 [7.4,10.3] 2,398 

Centre Ouest 12.6 [8.8,17.7] 3,438 

Centre Sud 3.9 [3.3,4.5] 1,055 

Est 8.3 [7.3,9.4] 2,266 

Hauts-Bassins 11.2 [9.6,13.1] 3,068 

Nord 9.2 [8.2,10.3] 2,513 

Plateau Central 4.7 [4.1,5.3] 1,280 

Sahel 6.2 [4.8,8.1] 1,702 

Sud Ouest 3.2 [2.7,3.8] 885 

Total 27,333 

Note: * Due to missing values, the total for this variable is 24,845. 

3.2 Bivariate analysis 

Table 2 shows that all independent variables were significantly associated with the outcome. Individuals 

age 11-19 used ITNs the least (45%) compared to other age groups. This corresponds with sons and 

daughters using nets the least (54%). More females (63%) slept under ITN nets compared to males (54%). 

At the household level, as the number of rooms or the number of household members increase, the use of 

ITN nets decreases. Households in the highest wealth quintile had the highest ITN use (65%), with the 

remaining wealth quintiles having similar rates of use between 56% to 57%. 

Use of ITN nets was higher in urban areas (66%) compared to rural areas (57%). Use of ITNs was also the 

highest in the Cascades and Centre regions (both approximately 70%) and the lowest in the Sahel Region 

(39%). The use of ITN nets was also highest in areas that had the lowest malaria prevalence (67%), 

compared to 59% that used ITN nets in the areas of highest malaria prevalence. 
7 

Table 2 Crosstabulation of variables used in the analysis and slept under an ITN last night 

among households with at least one ITN 

Variable % 95% C.I. p-value 

Age in years <0.001 

0-10 61.2 [59.2,63.2] 

11-19 44.9 [42.8,47.0] 

20-29 64.2 [61.7,66.6] 

30-39 66.8 [63.9,69.5] 

40-49 63.6 [60.9,66.2] 

50+ 59.6 [56.6,62.5] 

Sex of household member <0.001 

Male 53.6 [51.7,55.4] 

Female 63.1 [61.3,64.9] 

 

Relationship to household head <0.001 

Head 62.3 [60.0,64.6] 

Wife/husband 74.3 [72.3,76.2] 

Son/daughter 53.8 [52.1,55.6] 

Others 48.4 [45.1,51.7] 

Age of household head <0.001 

Younger than 30 72.3 [68.9,75.4] 

30-39 63.8 [61.2,66.3] 

40-49 57.1 [54.3,59.8] 

50-59 55.2 [51.6,58.7] 

60+ 50.0 [46.4,53.5] 

Number of rooms for sleeping <0.001 

0-1 room 71.3 [68.3,74.1] 

2 rooms 63.0 [60.5,65.4] 

3 rooms 55.2 [52.6,57.7] 

4 rooms 51.6 [48.3,54.9] 

More than 4 rooms 46.1 [42.0,50.2] 

 

Number of household members <0.001 

1-3 75.2 [72.0,78.1] 

4-6 65.4 [63.2,67.5] 

7-9 54.1 [51.2,57.0] 

10 or more 45.6 [42.5,48.7] 

Wealth quintile 0.001 

Lowest 55.8 [52.7,58.8] 

Second 57.2 [54.6,59.6] 

Middle 57.4 [54.1,60.6] 

Fourth 57.1 [54.1,60.1] 

Highest 64.5 [60.9,68.0] 

 

Malaria prevalence* 0.011 

0-24% 67.3 [61.8,72.4] 

25-34% 57.4 [54.2,60.6] 

35-44% 55.8 [53.1,58.5] 

45-60% 59.2 [54.8,63.5] 

Place of residence <0.001 

Urban 66.1 [62.8,69.3] 

Rural 56.7 [54.8,58.5] 

 

Region <0.001 

Boucle du Mouhoun 58.9 [53.8,63.8] 

Cascades 69.9 [65.8,73.7] 

Centre 69.1 [63.9,73.9] 

Centre Est 62.6 [57.2,67.7] 

Centre Nord 63.4 [60.5,66.1] 

Centre Ouest 50.7 [47.3,54.0] 

Centre Sud 60.8 [53.3,67.7] 

Est 61.0 [55.8,66.0] 

Hauts-Bassins 58.3 [51.2,65.0] 

Nord 61.6 [56.7,66.3] 

Plateau Central 53.4 [46.6,60.2] 

Sahel 39.8 [34.5,45.3] 

Sud Ouest 45.4 [40.2,50.7] 

Note: * Due to missing values, the total for this variable is 24,845. 
8 

The relationship between malaria prevalence, region, and place of residence was also examined. Table 3 

shows that region and place of residence were both significantly associated with malaria prevalence. Areas 

of low malaria prevalence were more urban, with 37% of urban areas having a malaria prevalence of 0-

24% compared to only 2% of rural areas with that prevalence. In addition, 84% of the Centre Region, which 

is highly urban, had a malaria prevalence of 0-24%. In contrast, 72% of the Hauts-Bassins Region was in 

the highest malaria prevalence category of 45-60%. This was followed by Boucle du Mouhoun (57%), 

Centre Nord (46%), and Nord (42%). 

Table 3 Crosstabulation of malaria prevalence with region and place of residence among 

households with at least one ITN 

Malaria prevalence 

p-value 

0-24% 25-34% 35-44% 45-60% 

Variable % 95% C.I. % 95% C.I. % 95% C.I. % 95% C.I. 

Place of residence <0.001 

Urban 37.4 [29.7,45.8] 23.6 [16.9,31.8] 31.8 [22.4,43.0] 7.2 [1.9,23.6] 

Rural 2.4 [1.1,4.9] 27.6 [21.9,34.3] 40.4 [32.4,48.8] 29.6 [23.4,36.8] 

Region <0.001 

Boucle du Mouhoun 0.0 0.0 43.0 [21.3,67.8] 57.0 [32.2,78.7] 

Cascades 0.0 59.7 [38.0,78.2] 37.0 [18.9,59.7] 3.3 [0.4,21.4] 

Centre 83.7 [59.3,94.8] 16.3 [5.2,40.7] 0.0 0.0 Centre Est 0.0 100.0 0.0 0.0 Centre Nord 0.0 20.2 [6.1,49.6] 33.7 [14.4,60.7] 46.1 [21.4,72.8] 

Centre Ouest 0.0 15.1 [5.3,36.4] 84.9 [63.6,94.7] 0.0 Centre Sud 33.9 [14.9,60.0] 50.7 [27.8,73.3] 15.4 [5.2,37.6] 0.0 Est 3.3 [0.4,21.0] 34.8 [17.3,57.8] 55.7 [32.6,76.6] 6.1 [0.8,34.4] 

Hauts-Bassins 0.0 0.0 28.4 [15.0,47.1] 71.6 [52.9,85.0] 

Nord 0.0 8.8 [1.2,43.5] 49.0 [28.7,69.6] 42.2 [22.9,64.3] 

Plateau Central 6.2 [0.8,35.1] 93.8 [64.9,99.2] 0.0 0.0 

Sahel 0.0 52.6 [21.1,82.2] 27.6 [8.1,62.3] 19.7 [3.4,63.5] 

Sud Ouest 0.0 0.0 70.5 [43.9,88.0] 29.5 [12.0,56.1] 

Total 9.1 [7.0,11.8] 26.9 [21.9,32.4] 38.7 [31.9,45.9] 25.3 [20.0,31.4] 

3.3 Multivariate analysis 

Table 4 summarizes the adjusted odds ratios (AOR) estimates from the three models fit for the outcome 

variable. Model 1 includes the malaria prevalence variable without region or place of residence. Model 2 

includes the region variables without the malaria prevalence and place of residence variables, and Model 3 

the place of residence variable without the malaria prevalence and region variables. All models had the 

same controls. 

At the individual level, individuals age 11-19 had 50% lower odds of using an ITN compared to individuals 

age 0-10 in all three models. Those age 40-49 had 20% greater odds of ITN use compared to individuals 

age 0-10. Females had 50% greater odds of using ITNs compared to males in all three models. 

At the household level, heads of household age 30 and above had between 30-40% lower odds of using an 

ITN compared to heads of households younger than age 30. The odds of ITN use decrease with an increasing 

number of rooms. Households that have more than four rooms had 60% lower odds of ITN use compared 

to households with 0-1 rooms in all three models. In general, we find that the odds of ITN use increases 

with increasing wealth quintile. 
9 

In Model 1, the odds of ITN use were significantly lower in areas with 25%-34% and 35%-44% malaria 

prevalence compared to areas with 0-24% prevalence (30% lower odds for both categories). In addition, 

there was no significant difference in ITN use between areas with the highest malaria prevalence of 45%-

60% and areas with the lowest prevalence of 0%-24%. 

In Model 2, individuals who live in the regions of Boucle du Mouhoun, Centre Ouest, Hauts-Bassins, 

Plateau Central, Sahel, and Sud Ouest had significantly lower odds of ITN use compared to the Centre 

Region. The highest disparities were found in Sahel and Sud Ouest with 70% and 60% lower odds, 

respectively, compared to the Centre Region. 

In Model 3, individuals who live in rural areas had 20% lower odds of sleeping under an ITN net the night 

before the survey compared to individuals who live in urban areas. 

Table 4 Adjusted odds ratios for slept under an ITN last night among households with at least 

one ITN 

 Model 1 Model 2 Model 3 

Variable AOR 95% C.I. AOR 95% C.I. AOR 95% C.I. 

Age in years 

0-10 (Ref.) 1 

11-19 0.5*** 0.5 - 0.6 0.5*** 0.5 - 0.6 0.5*** 0.5 - 0.6 

20-29 0.9* 0.8 - 1.0 0.9* 0.8 - 1.0 0.9** 0.8 - 1.0 

30-39 1.1* 1.0 - 1.3 1.1* 1.0 - 1.3 1.1* 1.0 - 1.3 

40-49 1.2*** 1.1 - 1.4 1.2*** 1.1 - 1.4 1.2*** 1.1 - 1.4 

50+ 1.1 1.0 - 1.3 1.1 1.0 - 1.3 1.1 1.0 - 1.3 

Sex of household member Male (Ref.) 1 1 1 

Female 1.5*** 1.4 - 1.6 1.5*** 1.4 - 1.6 1.5*** 1.4 - 1.6 

 

Age of household head 

Younger than 30 (Ref.) 1 1 1 

30-39 0.7** 0.6 - 0.9 0.7** 0.6 - 0.9 0.7** 0.6 - 0.9 

40-49 0.6*** 0.5 - 0.8 0.7*** 0.6 - 0.9 0.6*** 0.5 - 0.8 

50-59 0.7** 0.5 - 0.9 0.7* 0.6 - 0.9 0.7** 0.5 - 0.9 

60+ 0.6*** 0.4 - 0.7 0.6*** 0.5 - 0.7 0.6*** 0.4 - 0.7 

Number of rooms for sleeping 0-1 room (Ref.) 1 1 1 

2 rooms 0.7** 0.6 - 0.9 0.7*** 0.6 - 0.8 0.8* 0.6 - 0.9 

3 rooms 0.6*** 0.5 - 0.7 0.5*** 0.4 - 0.6 0.6*** 0.5 - 0.7 

4 rooms 0.5*** 0.4 - 0.7 0.4*** 0.3 - 0.6 0.5*** 0.4 - 0.7 

More than 4 rooms 0.4*** 0.3 - 0.5 0.4*** 0.3 - 0.5 0.4*** 0.3 - 0.6 

 

Wealth quintile 

Lowest (Ref.) 1 1 1 

Second 1.1 1.0 - 1.3 1.0 0.9 - 1.2 1.2 1.0 - 1.4 

Middle 1.3* 1.0 - 1.5 1.2 1.0 - 1.4 1.3** 1.1 - 1.6 

Fourth 1.3** 1.1 - 1.6 1.2* 1.0 - 1.4 1.3** 1.1 - 1.6 

Highest 1.6*** 1.2 - 2.0 1.4** 1.1 - 1.7 1.4** 1.1 - 1.8 

Malaria prevalence 0-24% (Ref.) 1 - - - - 

25-34% 0.7* 0.5 - 1.0 - - - - 

35-44% 0.7* 0.5 - 0.9 - - - - 

45-60% 0.8 0.6 - 1.2 - - - - 

 

Place of residence 

Urban (Ref.) - - - - 1 

Rural - - - - 0.8* 0.6 - 1.0 

Continued... 

 
10 

Table 4 Adjusted odds ratios for slept under an ITN last night among households with at least 

one ITN (continued)

 Model 1 Model 2 Model 3 

Variable AOR 95% C.I. AOR 95% C.I. AOR 95% C.I. 

Region Boucle du Mouhoun - - 0.7* 0.5 - 1.0 - - 

Cascades - - 0.9 0.7 - 1.3 - - 

Centre (Ref.) - - 1 - - 

Centre Est - - 0.7 0.5 - 1.0 - - 

Centre Nord - - 0.9 0.6 - 1.2 - - 

Centre Ouest - - 0.5*** 0.4 - 0.7 - - 

Centre Sud - - 0.8 0.5 - 1.2 - - 

Est - - 0.8 0.6 - 1.2 - - 

Hauts-Bassins - - 0.6* 0.4 - 0.9 - - 

Nord - - 0.8 0.6 - 1.1 - - 

Plateau Central - - 0.5** 0.3 - 0.8 - - 

Sahel - - 0.3*** 0.2 - 0.4 - - 

Sud Ouest - - 0.4*** 0.3 - 0.5 - - 

Note: * p<0.05, ** p<0.01, *** p<0.001 
11 

4 DISCUSSION 

Data from the Burkina Faso 2017-18 MIS were obtained from The DHS Program. This analysis evaluated 

factors that influence ITN use in Burkina Faso. Overall, 58% of individuals slept under an ITN in 

households that own at least one ITN. This proportion can be explained by the fact that the period of the 

survey (period of low malaria transmission) does not allow for a better appreciation of the actual use of 

ITNs by households. Other factors can also influence net use such as the absence of a net, use of a fan, and 

use of repellents or insecticide by households during the same night. 

The results showed differences in the use of nets at the individual, household, and community levels. At the 

individual level, older children had reduced odds of using an ITN as compared to the youngest children in 

the household. Females were also more likely to sleep under an ITN as compared to males. This 

corroborates previous research that found that most households in SSA prioritize ITN use in young children 

(children under age 5) and pregnant women of reproductive age. In these households, children share 

sleeping spaces with their mothers or with other female siblings (Olapeju et al. 2018; Toé et al. 2009). Older 

children are not priorities for nets, especially when a household does not have enough nets for all members 

in the household. 

At the household level, the number of rooms for sleeping and wealth quintile were significantly associated 

with ITN use. The odds of ITN use decrease with an increasing number of rooms in households. This aligns 

with previous studies that found the main reasons for nonuse of ITNs to be lack of access to a net and not 

having enough nets for all household members within the household (Eisele et al. 2009; Hetzel et al. 2012; 

Koenker and Kilian 2014). Typically, the bigger the household with more sleeping spaces, the more people 

live in the household. This analysis only examined ITN use in households with at least one ITN, and did 

not examine if the household had adequate nets for all the household members. Future analysis will examine 

ITN access as it relates to ITN use in Burkina Faso. 

When examining ITN usage by community level, ITN use was significantly associated with malaria 

prevalence, residence, and region. While these factors are interrelated, it does highlight that there are places 

within Burkina Faso that should be prioritized for future social behavior messaging focused on increasing 

ITN usage. Previous studies have shown that over time, the Centre Region has consistently displayed high 

ITN usage, most likely reflective of the population in the Centre Region, which is concentrated in urban 

areas (Ouagadougou) (Samadoulougou et al. 2017). This analysis aligns with prior research that found rural 

areas to have lower odds of ITN use as compared to urban areas, and all other regions in the country having 

lower odds of ITN usage as compared to the Centre Region. Finally, ITN use was significantly lower in 

areas with 25-34% and 35-44% malaria prevalence compared to areas with 0-24% prevalence. In addition, 

there was no significant difference in ITN use between areas with the highest malaria prevalence (45-60%) 

and areas with the lowest malaria prevalence (0-24%). This suggests that future malaria prevention methods 

should not prioritize low-burden urban areas, but instead focus on messaging in more rural areas of mid- or 

high-level malaria prevalence. 

There are several limitations to this study. First, the study period coincides with the low malaria 

transmission period in Burkina Faso. Past studies have shown that ITN use is highly influenced by 

variations in rainfall and malaria seasonality (Koenker et al. 2019, Ouedraogo et al. 2018). This analysis 
12 

controlled for malaria endemicity, but was unable to account for variations in rainfall and malaria 

seasonality across the country. Accurately controlling for patterns in seasonal malaria transmission requires 

access to more microlevel data, which were unavailable at the time of analysis. Future analysis should 

examine this in more detail. Secondly, this analysis did not control for intrahousehold characteristics that 

dictate ITN use. The intrahousehold characteristics of household members influences net allocation in 

households with too few nets to cover all household members (Lam et al. 2014). Finally, the data analyzed 

are cross-sectional in nature and thus do not permit causal inferences. 
13 

5 CONCLUSIONS 

Use of ITNs represents one of the most viable options for reducing malaria-related morbidity and mortality. 

The results of this study have highlighted areas of intervention at the individual, household, and community 

level that can increase ITN use. It is important to consider these factors in the national net distribution and 

awareness-raising strategies for malaria control in Burkina Faso. As the country works to eliminate malaria, 

the results of this study can help reduce the incidence of malaria and prevent its resurgence. In addition, 

periodic evaluation of malaria reduction strategies will provide a framework for reliably assessing the 

effectiveness of these interventions and informing future strategies that can eliminate malaria. 

15 

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