DHS ANALYTICAL 

STUDIES 61

Using Household Survey Data 

to Explore the Effects of

Improved Housing Conditions

on Malaria Infection in

Children in sub-Saharan Africa

August 2016

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Lia Florey and Cameron Taylor of ICF International.

DHS Analytical Studies No. 61 

Using Household Survey Data to Explore the Effects 

of Improved Housing Conditions on Malaria Infection 

in Children in Sub-Saharan Africa

Lia Florey 

Cameron Taylor 

ICF International 

Rockville, Maryland, USA 

August 2016 

Corresponding author: Lia Florey, The Demographic and Health Surveys (DHS) Program, ICF 

International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. Phone: +1 301-572-0922; fax: 

+1 301-407-6501; E-mail: lia.florey@icfi.com 
Acknowledgment: 

The authors would like to thank Fred Arnold, Matt Lynch, and Yazoume Ye for careful review and expert 

advice for this paper. 

Editor: Bryant Robey 

Document Production: Natalie La Roche 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#AIDOAA-C-13-00095). 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 International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA. 

Phone: +1 301-407-6500; Fax: +1 301-407-6501; E-mail: reports@dhsprogram.com; Internet: 

www.dhsprogram.com. 

Recommended citation: 

Florey, Lia, and Cameron Taylor. 2016. Using Household Survey Data to Explore the Effects of Improved 

Housing Conditions on Malaria Infection in Children in Sub-Saharan Africa. DHS Analytical Studies No. 

61. Rockville, Maryland, USA: ICF International.
iii 

Contents 

Tables ................................................................................................................................................................... v 

Figures ................................................................................................................................................................. v 

Abstract ............................................................................................................................................................. vii 

1. Introduction ........................................................................................................................................... 1 

2. Methods .................................................................................................................................................. 3 

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

2.2. Study Population ....................................................................................................................... 4 

2.3. Study Variables ......................................................................................................................... 4 

2.4. Analysis ..................................................................................................................................... 9 

3. Results .................................................................................................................................................. 11 

3.1. Descriptive Analyses ............................................................................................................... 11 

3.2. Multivariable Analyses ............................................................................................................ 17 

4. Discussion ............................................................................................................................................. 27 

5. Conclusion ............................................................................................................................................ 31 

References .......................................................................................................................................................... 33 

v 

Tables 

Table 1. Housing characteristics summary .............................................................................................. 6 

Table 2. Unimproved and improved housing materials summary ........................................................... 7 

Table 3. Summary of explanatory variables ............................................................................................ 8 

Table 4. Countries and surveys included in the analysis ....................................................................... 10 

Table 5. Unimproved and improved floor, wall, and roof by malaria parasitemia status for 

children age 6-59 months ........................................................................................................ 16 

Table 6. Traditional and modern housing type by malaria parasitemia for children age 

6-59 months ............................................................................................................................. 17 

Table 7. Unadjusted and adjusted associations between malaria parasitemia for children age 

 6-59 months and improved floor ............................................................................................ 18

Table 8. Unadjusted and adjusted associations between malaria parasitemia for children age 

 6-59 months and improved wall ............................................................................................. 20

Table 9. Unadjusted and adjusted associations between malaria parasitemia for children age 

6-59 months and improved roof .............................................................................................. 22

Table 10. Unadjusted and adjusted associations between malaria parasitemia for children age 

6-59 months and modern house ............................................................................................... 24

Figures 

Figure 1. Countries included in analysis ................................................................................................... 3 

Figure 2. Timeline of available survey data .............................................................................................. 4 

Figure 3. Model questions for housing characteristics .............................................................................. 5 

Figure 4. The spatial distribution of Plasmodium falciparum (Pf) malaria stratified by endemicity 

class in 2010 based on Pf prevalence rates among children age 2-10 ....................................... 9 

Figure 5. Range of malaria parasitemia prevalence in children age 6-59 months .................................. 11 

Figure 6. Percentage of households with improved floor, by country .................................................... 12 

Figure 7. Percentage of households with improved walls, by country .................................................... 12 

Figure 8. Percentage of households with improved roof, by country ..................................................... 13 

Figure 9. Percentage of households with a modern house, by country ................................................... 13 

Figure 10. Parallel plot of country-specific floor, wall, and roof values compared with the pooled 

multi-country median values ................................................................................................... 14 

Figure 11. Distribution of housing characteristics in the multi-survey pooled sample ............................. 15 

Figure 12. Pooled adjusted odds ratios of malaria parasitemia and improved floor ................................. 19 

Figure 13. Pooled adjusted odds ratios of malaria parasitemia and improved wall .................................. 21 

Figure 14. Pooled adjusted odds ratios of malaria parasitemia and improved roof .................................. 23 

Figure 15. Pooled adjusted odds ratios of malaria parasitemia and modern house ................................... 25 

vii 

Abstract 

In the past decade, malaria control strategies in sub-Saharan Africa have focused on the use of insecticidetreated nets (ITNs), indoor residual spraying (IRS), and prompt diagnosis and treatment in combatting 

malaria. Improved housing can act as a barrier, preventing mosquito entry into homes, and thereby serving 

as a supplement to insecticides and antimalarial drugs. Evidence of the effectiveness of improved housing 

on malaria control in endemic tropical countries has been mounting; however, few studies have been 

attempted on a large scale or have produced findings likely to be generalizable to a broad population. This 

analysis examines data from 29 nationally representative Demographic and Health Surveys (DHS surveys) 

and Malaria Indicator Surveys (MIS surveys) from 21 malaria endemic countries in sub-Saharan Africa. 

Logistic regression was used in both survey-specific and pooled meta-analyses to assess whether improved 

flooring, wall, and roofing construction materials protected against malaria infection. The models were 

adjusted to control for potential confounders. This study shows that improved house construction may be 

an effective malaria control intervention, as it is associated with reduced risk of malaria infection in young 

children; however, the direction and strength of effect varied by household feature and by setting. Results 

corroborate findings from other studies that show improved housing as an important predictor of malaria. 

Findings suggest that investments in improved housing may contribute to sustainable development goals 

by conferring protection against malaria in addition to other socioeconomic benefits. 

1 

1. Introduction 

The investments made in insecticide-treated net (ITN) distribution, indoor residual spraying (IRS), and 

prompt diagnosis and treatment of malaria infections have prevented 633 million malaria cases since 2001 

and have averted 4.3 million deaths (WHO 2015a). Despite these significant gains, 214 million cases of 

malaria occurred worldwide last year (2015) and 438,000 people died of the disease (WHO 2015b). In the 

World Health Organization (WHO) document “Action and Investment to Defeat Malaria 2016–2030: For 

a Malaria-Free World,” the Roll Back Malaria Partnership outlined goals of a 90% reduction in malaria 

incidence and malaria mortality from the 2015 levels by 2030 (WHO 2015b). Achieving these goals will 

demand not only sustained investment in current interventions but also will require innovative and 

collaborative efforts beyond the existing approaches. 

Further reductions in malaria morbidity and mortality may require complementary interventions that do not 

rely on insecticides, given growing evidence of resistant mosquitoes (Cohen et al. 2012). To date, vector 

resistance to at least one class of insecticides has been reported in over two-thirds of malaria endemic 

countries, with pyrethroid resistance being the most prevalent (WHO 2012). Given the current reliance on 

pyrethroids for vector control, widespread resistance could reduce current gains in malaria control by over 

half (WHO 2012). Parasite resistance to antimalarial drugs is also a significant problem threatening the 

long-term effectiveness of medication-based interventions. Artemisinin resistance in the Greater Mekong 

region is a prime example (WHO 2016). 

Due to the indoor, night-biting behaviors of malaria vectors, interventions that prevent mosquito entry into 

homes have proved an effective supplement to insecticides and antimalarial drugs for malaria control. After 

the role of the mosquito vector in malaria transmission was identified in 1897, interventions focused on 

reduction of vector populations or vector-human interactions such as screening. Historical evidence of the 

role of housing improvements and improved socioeconomic conditions in successful malaria control exists 

from the United States and Europe (Bruce-Chwatt and de Zulueta 1980; Celli 1901; Garcia-Martin 1972; 

Hackett and Missirolli 1932). Army barracks in Pakistan, India, and Spain experienced enormous declines 

in malaria incidence after installing screens (Anderson, Simpson, and Stephens 2014). In many parts of the 

world, screening played a major role in the elimination of malaria (Lindsay, Emerson, and Charlwood 

2002). 

More recently, evidence has been mounting of the effectiveness of improved housing on malaria control in 

endemic tropical countries. Studies have shown protective effects of ceilings and closed eaves, as well as 

screened windows. In The Gambia, installation of ceilings reduced house entry by Anopheles gambiae by 

59-80% (Lindsay et al. 2003). A similar intervention in western Kenya installed papyrus mat ceilings below 

open eaves of traditional homes with a small ITN fixed into the ceiling as a decoy. Results show 76%-82% 

reductions in indoor A. gambiae populations and 86% reductions in A. funestus compared with controls 

(Atieli et al. 2009). Kirby and colleagues found a 59% reduction in A. gambiae s.l. in houses with full 

screens and a 47% reduction in houses with screened ceilings in The Gambia. Significant reductions in 

anemia prevalence were also observed among children in the intervention houses (Kirby et al. 2009). 

Other observational studies have shown protective effects of improved house construction. A study in Sri 

Lanka showed that residents of houses with completed construction, brick or plaster walls, and a tiled roof 

had decreased malaria incidence compared with residents of the poorest type of housing (Gamage-Mendis 

et al. 1991). In another Sri Lankan study, malaria incidence was 2.5 times higher among residents of poorly 

constructed homes (defined as being incomplete and/or having mud walls and coconut palm thatch roofs 

compared with plastered brick walls and tiled or corrugated iron roofs) (Gunawardena 1998). In Uganda, 

children living in houses with metal roofs and brick or concrete walls had 56% lower odds of malaria 

compared with those in houses with traditional thatched roofs, mud walls, and open eaves (Wanzirah et al. 
2 

2015). Synman and colleagues showed that living in a modern house (non-earth floors, non-thatched roofs 

and non-mud walls) was associated with a reduction in malaria incidence of almost one-half compared with 

living in a traditional house (Snyman et al. 2015). Indoor vector populations have also been shown to be 

significantly lower in houses with higher-quality construction. In a study in Tanzania, for example, houses 

constructed with the highest quality (as determined by a nine-component score) had significantly lower 

vector density and lower malaria incidence than the lowest-quality houses (Liu et al. 2014). Also, Wanzira 

and colleagues observed a 52% reduction in the human-biting rate in modern houses compared with 

traditional houses (Wanzirah et al. 2015). 

Individual elements of housing construction have also been shown to affect vector densities and to be 

associated with malaria risk. Sealed walls (often brick) and metal roofs in particular have been associated 

with reductions in indoor vector populations (Ernst et al. 2006; Lindsay et al. 2003; Lwetoijera et al. 2013; 

Sintasath et al. 2005; Wanzirah et al. 2015; Yé et al. 2006), as well as lower malaria incidence (Coleman et 

al. 2010; Gamage-Mendis et al. 1991; Mmbando et al. 2011; Roberts and Matthews 2016; Yé et al. 2006) 

The Roll Back Malaria Vector Working Group recently reviewed the evidence on housing and malaria and 

concluded that strong evidence exists supporting the protective effect of ‘modern’ housing in many tropical 

countries. The consensus report issued by the group highlighted the importance of closed eaves, ceilings, 

and window and door screening, as well as construction features such as metal roofs and improved or 

finished walls, as examples of housing conditions conferring protection against malaria (Vector Control 

Working Group-Roll Back Malaria 2015). 

Few studies have examined the effect of housing conditions on malaria risk in large samples or across 

countries. One recent meta-analysis by Tusting and colleagues estimated the odds of malaria infection 

associated with modern housing combining results of case-control, cross-sectional, and cohort studies 

(Tusting et al. 2015). The studies included in this meta-analysis come from a wide range of countries 

spanning multiple continents and a wide range of study years (from 1939 to 2015), which likely contributed 

to the significant heterogeneity and limited the generalizability of findings. In order to produce more 

representative and reliable results, the present study makes use of publicly available, nationally 

representative data from Demographic and Health Surveys (DHS surveys) and Malaria Indicator Surveys 

(MIS surveys). Both surveys are an untapped resource for analysis of the links between housing type and 

malaria infection. They include standard survey questions on housing construction with questions about 

floor, wall, and roof materials. Most MIS surveys and many DHS surveys in malaria-endemic countries 

also contain data on malaria infection status in young children; the datasets include results of rapid 

diagnostic tests (RDTs) and sometimes results of microscopy readings of blood slides to detect malaria 

parasites in children age 6-59 months. DHS and MIS surveys use standard methods to calculate 

socioeconomic indicators and other variables that may be important confounders of the association between 

housing and malaria infection. Exploring these resources, the present study looks at national-level 

associations between house construction and malaria infection in children age 6-59 months across a wide 

range of countries from sub-Saharan Africa, controlling for potential confounders. 
3 

2. Methods 

2.1. Data 

This analysis uses data from the DHS and MIS surveys, which are nationally representative, populationbased household surveys. All survey data are available at www.dhsprogram.com. The analysis examines 

21 malaria endemic countries in sub-Saharan Africa using 29 DHS and MIS surveys with data on malaria 

parasitemia status in children age 6-59 months and housing characteristics such as type of flooring, wall, 

and roofing materials (Figure 1). 

Figure 1. Countries included in analysis
4 

The time period for this analysis is from 2007 to 2015 (Figure 2). The first DHS/MIS survey that included 

malaria parasitemia testing was in 2006, but it was not until the following year that a survey included both 

parasitemia data and data on housing characteristics such as floor, wall, and roofing materials. 

Figure 2. Timeline of available survey data

2.2. Study Population 

The study population for the analysis is children age 6-59 months who stayed in surveyed households the 

night before the survey who were tested for malaria parasitemia. Malaria parasitemia testing in this 

population was conducted by microscopy and rapid diagnostic test (RDT). 

2.3. Study Variables 

2.3.1. Outcome: Parasite prevalence 

The definition of parasite prevalence is the number of children age 6-59 months with malaria infection 

detected by a rapid diagnostic test or microscopy out of the total number of children age 6-59 months tested 

for malaria parasites by rapid diagnostic test or microscopy. The parasite prevalence among children age 

6-59 months is an indicator of malaria burden within populations and provides a guide to the level of malaria 

transmission. All countries studied used malaria microscopy values for the analysis except Cameroon DHS 

2011, Ghana DHS 2014, and Tanzania HMIS 2007-08. These countries only tested for malaria parasitemia 

using RDT. The type of RDT varied across surveys according to the official RDT guidelines for the country 

at the time of the survey implementation. 

It is important to note that parasite prevalence can fluctuate dramatically throughout the course of a year 

with the seasonal patterns of malaria transmission, and thus the timing of a survey in relation to peak 

transmission may influence values of the indicator. Seasonality may also influence sleeping behaviors 

(sleeping outdoors to avoid heat, going indoors late, leaving windows open, etc.) thus it may confound the 

association between housing characteristics and risk of malaria infection. MIS surveys are conducted during 

peak malaria transmission, which tends to occur within 4-6 weeks of the peak rainy season, due to 

fluctuations in mosquito populations. 

2.3.2. Explanatory variables 

Key predictors: Housing characteristics 

Housing variables used in the analysis examine the main materials used for the construction of floors, walls, 

and roofs among the surveyed households. Each housing material is divided into categories of natural, 

rudimentary, and finished. The interviewer observes and records housing characteristics at the beginning 

of the household questionnaire. If a variety of different materials is used for either the floor, wall, or roof 
5 

(i.e., both vinyl and carpet flooring), the interviewer records the material that covers the largest area. At the 

start of the survey, questionnaires are reviewed and updated to include country specific text/descriptions if 

needed. Figure 3 shows the model questionnaire for DHS/MIS housing characteristics. Readers will note 

the absence of questions on other aspects of housing that could influence indoor vector populations, such 

as the presence of open or closed eaves, screening of windows or doors, and presence of ceilings. This 

information is not routinely collected in DHS/MIS surveys and therefore is not included in the analysis. 

Figure 3. Model questions for housing characteristics
6 

Table 1 summarizes the different types of housing characteristics found across the countries included in the 

analysis. The survey questionnaire classifies housing characteristics as natural, rudimentary, and finished. 

Table 1. Housing characteristics summary 

Flooring Types Wall Types Roof Types 

Natural 

Earth, sand, clay, mud No wall No roof 

Dung Cane/palm/trunks Grass/thatch/palm leaf 

 Dirt Sod 

 Mud and sticks Straw 

 Tin/cardboard/paper/ bags 

Thatched/straw 

Rudimentary 

Tablets/wood planks Bamboo with mud Rustic mat 

Palm, bamboo Stone with mud Palm/bamboo 

Mat Uncovered adobe Wood planks 

Adobe Plywood Cardboard 

 Cardboard Tarpaulin, plastic 

 Reused wood 

 Trunks with mud 

 Unburnt bricks 

 Unburnt bricks with plaster 

 Unburnt bricks with mud 

Finished

Parquet, polished wood Cement Metal 

Vinyl, asphalt strips, floor mat, Stone with lime/cement Wood 

Linoleum Bricks Calamine/cement fiber 

Ceramic tiles, mosaic Cement blocks Ceramic tiles 

Cement Covered adobe Cement 

Carpet Wood planks/shingles Roofing shingles 

Stone Burnt bricks with cement Asbestos/slate roofing sheets 

Bricks 
7 

Table 2 shows the categorization of unimproved and improved housing materials. In this analysis natural 

and rudimentary wall and roofing types are considered unimproved, while only natural flooring is 

considered unimproved. For improved materials, rudimentary and finished flooring are categorized as 

improved, while improved walls and roofs are only those listed under the finished category. The improved 

categories for floor, wall, and roof types are used throughout the analysis. 

Table 2. Unimproved and improved housing materials summary

Flooring Types Wall Types Roof Types 

Unimproved Materials 

Earth, sand, clay, mud No wall No roof 

Dung Cane/palm/trunks Grass/thatch/palm leaf 

 Dirt Sod 

 Mud and sticks Straw 

 Tin/ cardboard/ paper/ bags Rustic mat 

Thatched/straw Palm/bamboo 

Bamboo with mud Wood planks 

Stone with mud Cardboard 

Uncovered adobe Tarpaulin, plastic 

Plywood 

 Cardboard 

 Reused wood 

 Trunks with mud 

 Unburnt bricks 

 Unburnt bricks with plaster 

 Unburnt bricks with mud 

Improved Materials 

Tablets/wood planks Cement Metal 

Palm, bamboo Stone with lime/cement Wood 

Mat Bricks Calamine/cement fiber 

Adobe Cement blocks Ceramic tiles 

Parquet, polished wood Covered adobe Cement 

Vinyl, asphalt strips, floor mat, Wood planks/shingles Roofing shingles 

Linoleum Burnt bricks with cement Asbestos/slate roofing sheets 

Ceramic tiles, mosaic 

Cement 

Carpet 

Stone 

Bricks 

The analysis also includes comparison of housing characteristics by a composite measure: Modern housing 

is defined as having improved floor, wall, and roof construction, while traditional housing is a composite 

of unimproved floor, wall, and roof construction. 

Covariates: All potential confounders 

For the purpose of this analysis, variables found in the literature related to parasitemia and housing 

characteristics were reviewed and included, based on data availability (Table 3). The DHS wealth index is 

a survey-specific measure of the relative economic status of households based on an analysis of household 

assets and service amenities at a particular point in time. The asset-based wealth index places individual 

households on a continuous scale of relative wealth generated by using principal components analysis. 

Individuals are ranked according to the standardized score of the household in which they reside, and then 

the sample is divided into quintiles. Each survey has a single asset index that is relevant only for that 

population during the time period of the survey. The DHS wealth indices are not comparable across 

countries or over time; they are a cross-sectional measure of relative wealth (Rutstein 2004). Residence is 

defined as whether a household is located in a rural or urban area. The child’s age and sex (male or female) 

were also included in the model. Child’s age is available as a continuous variable from 6-59 months based 
8 

on the date of birth and the date of interview. For use in the analyses, this variable was divided into five 

categories: 6-11 months, 12-23 months, 24-35 months: 36-47 months and 48-59 months. ITN usage (yes/no) 

is defined as sleeping under an ITN the night before the survey and IRS (yes/no) is categorized as the 

household being sprayed against mosquitoes in the 12 months preceding the survey. Since IRS spraying is 

not national policy in all countries, questions on IRS were not included in all surveys; 21 of the 29 surveys 

included in the analysis contained questions on IRS spraying. All households in surveys without IRS 

questions were treated as though they did not benefit from this intervention (coded as no). 

Table 3. Summary of explanatory variables 

Variable Type Details of Measurement 

Key Predictors 

Improved Floor Categorical with two categories: yes/no 

Improved floor categorized as having a rudimentary 

(i.e., tablets, mat, adobe) or finished floor (i.e., 

parquet, carpet, cement, bricks) 

Improved Wall Categorical with two categories: yes/no 

Improved wall categorized as having a finished wall 

(i.e., covered adobe, bricks, cement blocks, wood 

planks) 

Improved Roof Categorical with two categories: yes/no 

Improved roof categorized as having a finished roof 

(i.e., metal, wood, ceramic tiles, cement, roofing 

shingles) 

Modern House Categorical with two categories: yes/no Composite variable of improved floor, improved wall, 

improved roof 

Covariates 

Wealth Index Categorical with five categories Asset-based principal component analysis 

Residence Categorical with two categories Household classified as being in an urban or rural 

area 

Child’s Age Categorical with five categories (months): 

6-11, 12-23, 24-35, 36-47, 48-59 Based on date of birth and date of interview 

Child’s Sex Categorical with two categories: male, 

female Collected from women’s interview 

ITN Categorical with two categories: yes/no ITN used the night before the survey 

IRS Categorical with two categories: yes/no Household was sprayed against mosquitoes in the 

last 12 months 

Malaria 

Endemicity 

Categorical with three categories: no 

malaria/low risk, intermediate risk, and 

high risk 

Categorized using Malaria Atlas Project (MAP) 

PfPR2-10 values 

Malaria endemicity levels were created using data from the 2010 Malaria Atlas Project (MAP). MAP 

provides a spatial data layer of age-standardized PfPR2-10, describing the estimated proportion of children 

age 2-10 in the general population that are infected with P. falciparum at any one time, averaged over the 

12 months of 2010 (Gething et al. 2011). DHS and MIS data include geospatial data for the location of the 

approximate center of each cluster, thereby permitting linkage of MAP data to survey clusters; thus, all 

residents of a cluster from the DHS or MIS survey data were assigned the same malaria risk value based on 

corresponding MAP data. MAP PfPR2-10 cut-offs (≤5%, 5%-40%, and >40%) were used to categorize 

malaria into no malaria/low risk, intermediate risk, and high risk (Figure 4). DHS and MIS cluster locations 

are displaced to ensure participant confidentiality. Urban clusters are displaced by 0-2 kilometers and rural 

clusters by 0-5 kilometers, with 1% of rural clusters displaced between 0-10 kilometers. Due to this 

displacement, linking the survey data with MAP data is not exact. 
9 

Figure 4. The spatial distribution of Plasmodium falciparum (Pf) malaria stratified by endemicity 

class in 2010 based on Pf prevalence rates among children age 2-10 

Source: Malaria Atlas Project (Gething et al. 2011) 

2.4. Analysis 

All analyses were conducted using Stata 14. Household survey data were adjusted for survey design, 

clustering, and sample weights. The study included a country-level descriptive analysis of housing 

characteristics and logistic regression analysis. Logistic regression was used in both a survey-specific and 

a pooled meta-analysis to assess whether improved flooring, wall, and roofing types were protective against 

malaria. Specific inclusion criteria for the descriptive and regression analysis included: 

1. Countries must have had a survey that included parasitemia testing via microscopy or RDT on 

children under age 5. 

2. The survey must have collected data on all three housing characteristics (flooring, wall, and roofing 

materials). 

3. Survey datasets and GIS coordinates for survey clusters were publicly available before June 2016. 

In total, 29 surveys in 21 countries were selected for inclusion in the analysis (Table 4). These 29 surveys 

included 10,288 clusters, of which 31% were categorized in the high-risk category for malaria 
10 

(PfPR2-10>40%), 44% in the intermediate-risk category (5%-40%), and 20% in the low-risk category (≤5%). 

Four percent of the household clusters were dropped from the analysis due to missing GPS coordinates. 

Table 4. Countries and surveys included in the analysis 

Country Survey Dates of Fieldwork 

Angola MIS 2011 01/2011- 05/2011 

Benin DHS 2011-12 12/2011- 03/2012 

Burkina Faso DHS 2010 05/2010- 01/2011 

Burkina Faso MIS 2014 09/2014- 10/2014 

Burundi MIS 2012 11/2012- 01/2013 

Cameroon DHS 2011 01/2011- 08/2011 

Congo Democratic Republic DHS 2013-14 08/2013- 02/2014 

Cote d’Ivoire DHS 2011-12 12/2011- 05/2012 

Ghana DHS 2014 09/2014- 12/2014 

Guinea DHS 2012 06/2012- 10/2012 

Kenya MIS 2015 07/2015- 08/2015 

Liberia MIS 2009 12/2008- 03/2009 

Liberia MIS 2011 09/2011- 12/2011 

Madagascar MIS 2011 03/2011- 06/2011 

Madagascar MIS 2013 04/2013- 06/2013 

Malawi MIS 2012 04/2012- 05/2012 

Malawi MIS 2014 05/2014- 06/2014 

Mali DHS 2012-13 11/2012- 02/2013 

Mozambique DHS 2011 06/2011- 11/2011 

Nigeria MIS 2010 10/2010- 12/2010 

Rwanda DHS 2010 09/2010- 03/2011 

Rwanda DHS 2014-15 11/2014- 04/2015 

Senegal DHS 2010-11 10/2010- 04/2011 

Senegal DHS 2012-13 09/2012- 06/2013 

Tanzania HMIS 2007-08 10/2007- 02/2008 

Tanzania HMIS 2011-12 12/2011- 05/2012 

Togo DHS 2013-14 11/2013- 04/2014 

Uganda MIS 2009 11/2009- 01/2010 

Uganda MIS 2014-15 12/2014- 01/2015 

Descriptive analyses were conducted on all available survey data to examine distributions of parasitemia 

and housing characteristics. Confidence intervals of 95% (95% CI) were calculated around each prevalence 

estimate. A parallel plot of the percentage of households with improved floor, wall, and roof materials 

plotted against the pooled median values was created to compare distributions of these three variables across 

countries. 

The study used unadjusted and adjusted logistic regression models to assess whether improved flooring, 

wall, and roofing types were protective against malaria infection. The adjusted models control for ITN use, 

IRS spraying in the past 12 months, household wealth status, age of child, sex, and malaria endemicity, in 

separate survey-specific analyses as well as in a meta-analysis. The association between malaria parasitemia 

and the three housing characteristics were examined first for each country and survey year independently 

and then in a meta-analysis using pooled data accounting for random effects at the survey level (using the 

metan macro in Stata 14.1). Tests for heterogeneity (I2

 tests) were generated to verify that the model 

sufficiently controlled for differences between surveys. 
11 

3. Results 

3.1. Descriptive Analyses 

3.1.1. Malaria parasitemia prevalence 

The prevalence of parasitemia in children age 6-59 months ranged from 1% in Rwanda DHS 2010 to 62% 

in Burkina Faso DHS 2010, among surveys conducted between 2007 and 2015 in which RDT or microscopy 

measured malaria parasitemia (Figure 5). 

Figure 5. Range of malaria parasitemia prevalence in children age 6-59 months 

3.1.2. Housing characteristics 

Figures 6-8 present the percentage of households with improved floor, wall, and roofing materials. The 

percentage of households with improved floors ranged from 12% in Burundi MIS 2012 to 93% in Ghana 

DHS 2014 (Figure 6). The percentage of households with improved walls ranged from 20% in Mozambique 

DHS 2011 to 72% in Ghana DHS 2014 (Figure 7). The percentage of households with an improved roof 

ranged from 21% in Madagascar MIS 2011 to 100% in Rwanda DHS 2014-15 (Figure 8). The percentage 

of households classified as having modern construction (improved floor, wall, and roof) ranged from 10% 

in Burundi MIS 2012 to 70% in Ghana DHS 2014 (Figure 9). 

1 2 3 3 4 5 6

9 10

17 17 17 20 22

26 27 29

30

32

33 35 36 36

42 44

45 45

52

62

0

10

20

30

40

50

60

70

80

90

100
12 

Figure 6. Percentage of households with improved floor, by country 

Note: Improved floor = Rudimentary and Finished materials, everything except for earth, sand, clay, mud and dung. 

Figure 7. Percentage of households with improved walls, by country 

Note: Improved walls = Finished materials, such as cement, stone, bricks, and wood planks or shingles. 

12

15 17

19 21 22

24 26 26 27 27

35

40

41

42

44 44

48 50 51

52 60

63

69

78

83 86 88

93

0

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50

60

70

80

90

100

20

21 24

27

27 28 28 28

32 32 34

37 37 38

38 40 40 41

46 47 49

52 54

55 56 58

62

68

72

0

10

20

30

40

50

60

70

80

90

100
13 

Figure 8. Percentage of households with improved roof, by country 

Note: Improved roof = Finished materials, such as metal, wood, ceramic tiles, cement, roofing shingles and slate roofing sheets. 

Figure 9. Percentage of households with a modern house, by country 

Note: Modern house = Improved floor, improved walls and improved roof. 

21 23

30 31

36

42

49 51

53

60 60

67

68 68 70

72 73 74 74 76 77 78 79 83 84 85

91 94 100

0

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30

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90

100

10 13 13 15 15 16 17 17 17 17

19 22 22 22 24

24 28 28

34 34 38 40 42 42

51 52 54

59

70

0

10

20

30

40

50

60

70

80

90

100
14 

The pooled median percentages for the three types of improved housing characteristics were: floor (42%), 

wall (38%), and roof (70%). As Figure 10 shows, across the three types of housing characteristics, more 

countries had a higher percentage of improved roof materials than of improved wall and flooring materials. 

Figure 10 also shows the differences in the type of housing construction across countries comparing the 

country-specific values for floor, wall, and roof with the pooled median values. Some countries (Benin, 

Cameroon, Cote d’Ivoire, Ghana, Senegal, and Togo) have values higher than the pooled median values for 

all three housing variables, while other countries (DRC, Mali, Tanzania 2007-08 and Uganda 2009) have 

below-median values for all three housing variables. 

Figure 10. Parallel plot of country-specific floor, wall, and roof values compared with the pooled 

multi-country median values 

Median Values: Floor (42%); Wall (38%); Roof (70%) 
15 

The distribution of housing characteristics in the pooled, multi-survey sample is summarized in a Venn 

diagram in Figure 11. Of the approximately 128,000 children age 6-59 months who stayed in interviewed 

households the night before surveys and who had a malaria parasitemia test result, more than a quarter 

(28%) lived in houses with traditional construction, defined as unimproved floor, walls, and roof. Another 

quarter of the study population (27%) lived in houses with modern construction, defined as improved floor, 

walls, and roof. Eighteen percent of children in the study lived in houses with improved roofs but 

unimproved walls and floor. Seven percent of the study population lived in houses with only improved 

floors, and 3% with only improved walls. 

Figure 11. Distribution of housing characteristics in the multi-survey pooled sample 

3.1.3. Housing characteristics and malaria parasitemia 

Table 5 shows the prevalence of positive malaria parasitemia tests in children by the type of floor, wall, 

and roof construction in the child’s house. In a large majority of surveys, the prevalence of positive malaria 

tests was higher among children living in houses with unimproved floor, wall, and roof construction 

compared with children living in houses with improved construction characteristics. This is the case 

regardless of the level of parasitemia prevalence. 
16 

Table 5. Unimproved and improved floor, wall, and roof by malaria parasitemia status for children age 6-59 months 

 FLOOR WALL ROOF 

Malaria 

Prevalence 

(%) 

Number 

of 

Children

Unimproved Floor Improved Floor Unimproved Wall Improved Wall Unimproved Roof Improved Roof 

Country/Survey % 95% CI % 95% CI % 95% CI % 95% CI % 95% CI % 95% CI Angola MIS 2011 13.7 [10.0,18.5] 2.6 [1.6,4.2] 13.2 [9.6,17.9] 2.8 [1.7,4.8] 15.3 [9.2,24.3] 8.9 [6.3,12.6] 9.9 3,362 Benin DHS 2011-12 38.3 [35.2,41.5] 22.2 [20.1,24.6] 35.2 [32.5,38.0] 21.8 [19.4,24.4] 32.6 [28.8,36.6] 27.3 [25.1,29.7] 28.6 3,648 Burkina Faso DHS 2010 69.6 [67.3,71.7] 50.3 [46.9,53.7] 67.3 [65.0,69.5] 52.6 [48.5,56.7] 70.5 [68.2,72.7] 53.5 [50.3,56.7] 61.8 6,245 

Burkina Faso MIS 2014 53.0 [49.5,56.5] 35.6 [31.8,39.7] 51.9 [48.6,55.1] 28.6 [24.5,33.2] 56.8 [52.3,61.1] 41.9 [38.7,45.3] 45.3 5,753 

Burundi MIS 2012 18.7 [14.9,23.2] 5.8 [2.5,12.7] 23.0 [18.3,28.5] 7.9 [5.8,10.6] 25.7 [19.7,32.8] 14.6 [11.4,18.4] 17.3 3,820 

Cameroon DHS 2011 34.8 [31.6,38.1] 25.4 [22.4,28.6] 35.1 [31.3,39.1] 26.2 [23.5,29.1] 36.7 [32.6,41.1] 27.6 [25.1,30.3] 30.0 5,414 

DRC DHS 2013-14 23.6 [21.2,26.5] 12.4 [9.5,16.0] 24.0 [21.3,26.9] 15.9 [13.1,19.2] 25.6 [22.7,28.8] 14.8 [12.0,18.2] 21.8 7,457 

Cote d’Ivoire DHS 2011-12 27.2 [21.8,33.3] 14.4 [12.0,17.1] 24.2 [19.8,29.1] 12.8 [10.6,15.4] 27.8 [21.8,34.7] 14.2 [12.1,16.6] 17.2 3,255 

Ghana DHS 2014 60.7 [51.4,69.2] 34.2 [30.6,37.9] 54.4 [48.7,59.9] 28.5 [25.0,32.3] 61.7 [52.4,70.2] 33.3 [29.9,36.9] 36.0 2,529 

Guinea DHS 2012 54.5 [50.1,58.8] 33.1 [29.1,37.3] 54.1 [49.1,59.1] 34.0 [30.0,38.3] 51.1 [45.5,56.7] 40.7 [36.7,44.7] 43.9 3,234 

Kenya MIS 2015 6.7 [4.8,9.2] 2.8 [1.7,4.5] 6.5 [4.7,8.8] 2.5 [1.4,4.5] 6.4 [3.9,10.3] 4.7 [3.6,6.1] 5.0 3,073 

Liberia MIS 2009 37.6 [32.9,42.5] 23.1 [18.8,28.0] 36.6 [32.0,41.4] 21.1 [17.1,25.7] 35.6 [30.8,40.6] 29.8 [24.7,35.5] 31.7 4,260 

Liberia MIS 2011 33.8 [29.9,38] 18.3 [14.9,22.4] 32.8 [29.0,36.8] 15.6 [12.0,20.1] 33.1 [28.5,38] 23.9 [20.3,28.0] 26.5 2,941 

Madagascar MIS 2011 2.9 [1.6,5.2] 7.0 [5.4,9.2] 8.3 [6.3,10.7] 1.1 [0.6,2.0] 7.5 [5.7,9.8] 1.8 [0.9,3.6] 6.3 6,212 

Madagascar MIS 2013 8.6 [4.8,15.0] 9.1 [7.0,11.8] 10.3 [7.8,13.5] 4.1 [2.7,6.4] 10.3 [7.8,13.5] 4.8 [2.4,9.4] 9.0 5,564 

Malawi MIS 2012 31.1 [26.4,36.3] 11.9 [8.2,17.1] 31.6 [26.4,37.4] 23.1 [18.9,27.9] 33.0 [27.9,38.5] 14.8 [11.3,19.1] 27.5 2,186 

Malawi MIS 2014 38.6 [31.1,46.8] 16.2 [10.2,24.9] 39.7 [31.7,48.2] 27.6 [20.4,36.3] 40.2 [32.0,49.0] 22.7 [15.9,31.3] 32.9 2,041 

Mali DHS 2012-13 58.9 [55.8,61.9] 31.2 [27.1,35.7] 59.4 [56.3,62.5] 24.8 [21.1,28.9] 64.0 [60.4,67.4] 40.2 [36.4,44.1] 51.6 4,699 

Nigeria MIS 2010 46.5 [40.5,52.6] 37.8 [32.7,43.1] 47.2 [41.9,52.7] 33.8 [29.1,39.0] 50.9 [43.9,57.9] 37.5 [33.5,41.7] 41.8 5,227 

Mozambique DHS 2011 42.2 [38.2,46.2] 25.2 [22.0,28.8] 39.8 [36.3,43.4] 15.8 [12.5,19.9] 44.4 [40.6,48.2] 13.6 [11.3,16.3] 35.1 4,863 

Rwanda DHS 2010 1.5 [1.0,2.1] 0.8 [0.3,2.1] 1.8 [1.2,2.7] 0.9 [0.5,1.4] 2.6 [1.2,5.3] 1.3 [0.9,1.8] 1.4 4,046 

Rwanda DHS 2014-15 2.6 [2.0,3.5] 0.6 [0.2,1.8] 2.9 [2.2,3.8] 0.5 [0.2,1.4] 0.0 [0.0,0.0] 2.2 [1.7,3] 2.2 3,534 

Senegal DHS 2010-11 4.0 [2.7,5.9] 2.0 [1.3,3.2] 3.9 [2.5,6.1] 2.2 [1.5,3.3] 5.3 [3.6,7.8] 1.8 [1.2,2.9] 2.8 3,717 

Senegal DHS 2012-13 6.6 [4.6,9.5] 1.1 [0.7,1.7] 5.1 [3.6,7.2] 1.0 [0.6,1.5] 7.0 [4.7,10.3] 1.2 [0.8,1.9] 2.8 5,407 

Tanzania HMIS 2007-08 20.3 [17.9,22.9] 6.4 [4.7,8.7] 21.6 [18.9,24.6] 9.1 [7.2,11.4] 21.5 [18.9,24.3] 13.0 [10.4,16.0] 17.4 6,276 

Tanzania HMIS 2011-12 4.8 [3.9,5.9] 1.8 [1.2,2.6] 5.0 [4.0,6.3] 2.5 [1.8,3.4] 5.8 [4.5,7.6] 2.8 [2.2,3.5] 4.0 7,340 

Togo DHS 2013-14 48.8 [43.0,54.6] 34.3 [31.2,37.7] 49.1 [45,53.2] 25.3 [22.4,28.5] 49.8 [43.7,55.9] 33.5 [30.2,37.0] 36.4 2,994 

Uganda MIS 2009 50.6 [45.4,55.8] 25.1 [20.4,30.4] 48.9 [43.9,54] 33.0 [24.4,42.9] 57.9 [53.2,62.3] 35.9 [29.7,42.7] 44.7 3,532 

Uganda MIS 2014-15 24.5 [21.2,28.1] 8.5 [6.2,11.5] 22.3 [18.9,26.1] 17.0 [13.5,21.3] 29.0 [25.0,33.4] 15.9 [12.7,19.7] 20.2 4,419 

CI = Confidence interval
17 

Table 6 shows the prevalence of positive malaria parasitemia tests in children by type of housing. The 

prevalence of positive malaria tests was higher among children living in houses classified as having 

traditional construction characteristics (unimproved floor, wall, and roof) than among children living in 

modern houses (improved floor, wall, and roof types). This is the case regardless of the level of parasitemia 

prevalence. 

Table 6. Traditional and modern housing type by malaria parasitemia for children age 

6-59 months 

Traditional House Modern House 

Overall 

Malaria 

Prevalence 

Country/Survey % 95% CI % 95% CI (%) Number 

Angola MIS 2011 12.8 [9.3,17.3] 2.4 [1.4,3.9] 9.9 3,362 

Benin DHS 2011-12 34.4 [31.9,37.1] 20.9 [18.4,23.7] 28.6 3,648 

Burkina Faso DHS 2010 67.7 [65.6,69.8] 40.3 [36.0,44.7] 61.8 6,245 

Burkina Faso MIS 2014 51.1 [47.9,54.2] 24.6 [20.5,29.2] 45.3 5,753 

Burundi MIS 2012 18.8 [15.0,23.2] 3.5 [1.6,7.5] 17.3 3,820 

Cameroon DHS 2011 34.4 [31.3,37.6] 23.7 [20.5,27.2] 30.0 5,414 

DRC DHS 2013-14 23.5 [20.9,26.3] 11.6 [8.6,15.5] 21.8 7,457 

Cote d’Ivoire DHS 2011-12 24.8 [20.5,29.7] 11.9 [9.8,14.3] 17.2 3,255 

Ghana DHS 2014 54.4 [48.8,59.9] 27.9 [24.4,31.7] 36.0 2,529 

Guinea DHS 2012 52.2 [47.6,56.7] 31.0 [26.5,35.9] 43.9 3,234 

Kenya MIS 2015 6.2 [4.5,8.3] 2.5 [1.3,4.8] 5.0 3,073 

Liberia MIS 2009 36.4 [31.9,41.1] 19.6 [15.6,24.2] 31.7 4,260 

Liberia MIS 2011 32.4 [28.8,36.3] 14.9 [11.4,19.1] 26.5 2,941 

Madagascar MIS 2011 7.4 [5.6,9.7] 0.8 [0.3,1.8] 6.3 6,212 

Madagascar MIS 2013 9.9 [7.5,12.9] 3.3 [1.7,6.6] 9.0 5,564 

Malawi MIS 2012 30.6 [26.1,35.6] 11.7 [7.6,17.6] 27.5 2,186 

Malawi MIS 2014 37.7 [30.4,45.7] 17.1 [10.7,26.3] 32.9 2,041 

Mali DHS 2012-13 57.7 [54.7,60.7] 18.5 [14.6,23.0] 51.6 4,699 

Mozambique DHS 2011 47.1 [41.9,52.5] 33.2 [28.4,38.4] 41.8 5,227 

Nigeria MIS 2010 39.7 [36.2,43.3] 7.9 [5.8,10.5] 35.1 4,863 

Rwanda DHS 2010 1.4 [1.0,2.0] 0.9 [0.4,2.5] 1.4 4,046 

Rwanda DHS 2014-15 2.6 [2.0,3.4] 0.5 [0.1,2.0] 2.2 3,534 

Senegal DHS 2010-11 3.7 [2.6,5.3] 2.0 [1.2,3.3] 2.8 3,717 

Senegal DHS 2012-13 4.8 [3.4,6.7] 0.9 [0.6,1.4] 2.8 5,407 

Tanzania HMIS 2007-08 19.8 [17.5,22.3] 4.5 [3.1,6.3] 17.4 6,276 

Tanzania HMIS 2011-12 4.6 [3.8,5.6] 1.8 [1.2,2.9] 4.0 7,340 

Togo DHS 2013-14 48.9 [44.9,53.0] 24.6 [21.6,27.8] 36.4 2,994 

Uganda MIS 2009 49.5 [44.5,54.5] 24.5 [19.3,30.5] 44.7 3,532 

Uganda MIS 2014-15 23.8 [20.5,27.4] 8.6 [6.3,11.6] 20.2 4,419 

CI = Confidence interval 

3.2. Multivariable Analyses 

3.2.1. Improved floor 

Table 7 shows the unadjusted and adjusted logistic regression models of malaria parasitemia and improved 

flooring. In the unadjusted models significant protective effects of improved floor were seen in all countries 

except Rwanda 2010 (p-value=0.25) and Madagascar. In the Madagascar MIS 2013, improved floors were 

associated with significantly higher odds of malaria parasitemia (OR=2.5; 95% CI = 1.5-4.2), but the 
18 

association was not significant in the Madagascar MIS 2011. The adjusted models show a significant 

protective effect of an improved floor in Benin 2011-12 and Senegal 2012-13, (p-value<0.05) and a 

marginally significant protective effect in Uganda 2014-15 (p-value<0.10). Improved floors were 

associated with higher odds of malaria parasitemia in Madagascar 2011 (OR = 1.85; 95% CI = 1.2-2.9) and 

Nigeria 2010 (OR = 1.46; 95% CI = 1.0-2.1). The results of the meta-analysis (Figure 12) do not show a 

significant association between improved flooring and malaria parasitemia (OR = 0.96; 95% 

CI = 0.87-1.06). An I2

 test of heterogeneity suggests that the model represents moderate heterogeneity 

(I2

=39%, p-value <0.05) after adjusting for random effects at the survey level. 

Table 7. Unadjusted and adjusted associations between malaria parasitemia for children 

age 6-59 months and improved floor (odds ratios) 

 Unadjusted Adjusted 

Country/Survey OR 95% CI p-value OR 95% CI p-value Number 

Angola MIS 2011 0.17 [0.1,0.3] <0.005 1.32 [0.5,3.3] 0.54 3,362 

Benin DHS 2011-12 0.46 [0.4,0.6] <0.005 0.74 [0.6,0.9] 0.01 3,648 

Burkina Faso DHS 2010 0.44 [0.4,0.5] <0.005 1.04 [0.8,1.3] 0.73 6,245 

Burkina Faso MIS 2014 0.49 [0.4,0.6] <0.005 1.07 [0.9,1.3] 0.40 5,753 

Burundi MIS 2012 0.26 [0.1,0.7] <0.005 0.62 [0.3,1.5] 0.29 3,820 

Cameroon DHS 2011 0.64 [0.5,0.8] <0.005 0.99 [0.7,1.4] 0.94 5,414 

DRC DHS 2013-14 0.46 [0.3,0.6] <0.005 0.69 [0.4,1.2] 0.21 7,457 

Cote d’Ivoire DHS 2011-12 0.45 [0.3,0.6] <0.005 0.85 [0.6,1.2] 0.40 3,255 

Ghana DHS 2014 0.34 [0.2,0.5] <0.005 0.91 [0.6,1.4] 0.68 2,529 

Guinea DHS 2012 0.41 [0.3,0.5] <0.005 1.18 [0.8,1.7] 0.35 3,234 

Kenya MIS 2015 0.40 [0.2,0.7] <0.005 1.26 [0.5,3.0] 0.60 3,073 

Liberia MIS 2009 0.50 [0.4,0.7] <0.005 0.89 [0.6,1.3] 0.54 4,260 

Liberia MIS 2011 0.44 [0.3,0.6] <0.005 0.90 [0.6,1.4] 0.61 2,941 

Madagascar MIS 2011 2.52 [1.5,4.2] <0.005 1.85 [1.2,2.9] 0.01 6,212 

Madagascar MIS 2013 1.06 [0.6,1.9] 0.84 0.83 [0.5,1.4] 0.50 5,564 

Malawi MIS 2012 0.30 [0.2,0.5] <0.005 0.73 [0.4,1.4] 0.35 2,186 

Malawi MIS 2014 0.31 [0.2,0.5] <0.005 0.63 [0.3,1.4] 0.25 2,041 

Mali DHS 2012-13 0.32 [0.3,0.4] <0.005 1.21 [0.9,1.6] 0.16 4,699 

Mozambique DHS 2011 0.46 [0.4,0.6] <0.005 1.07 [0.8,1.3] 0.59 5,227 

Nigeria MIS 2010 0.70 [0.5,1.0] 0.02 1.46 [1.0,2.1] 0.03 4,863 

Rwanda DHS 2010 0.53 [0.2,1.5] 0.25 0.65 [0.1,4.2] 0.65 4,046 

Rwanda DHS 2014-15 0.24 [0.1,0.7] 0.01 2.12 [0.6,8.1] 0.27 3,534 

Senegal DHS 2010-11 0.50 [0.3,0.9] 0.02 1.01 [0.4,2.7] 0.98 3,717 

Senegal DHS 2012-13 0.16 [0.1,0.3] <0.005 0.39 [0.2,0.7] <0.005 5,407 

Tanzania HMIS 2007-08 0.27 [0.2,0.4] <0.005 0.80 [0.5,1.3] 0.38 6,276 

Tanzania HMIS 2011-12 0.36 [0.2,0.6] <0.005 0.96 [0.5,1.8] 0.91 7,340 

Togo DHS 2013-14 0.55 [0.4,0.7] <0.005 1.08 [0.8,1.4] 0.58 2,994 

Uganda MIS 2009 0.33 [0.2,0.4] <0.005 0.82 [0.6,1.2] 0.29 3,532 

Uganda MIS 2014-15 0.29 [0.2,0.4] <0.005 0.72 [0.5,1.1] 0.09 4,419 

OR = Odds ratio; CI = Confidence interval 

Adjusted ORs with a p-value less than 0.05 are bolded 

*Adjusted models control for improved walls, improved roofing, insecticide-treated net (ITN) use, indoor 

residual spraying (IRS) in the past 12 months, household wealth status, age of child, sex of child, and 

malaria endemicity 
19 

Figure 12. Pooled adjusted odds ratios of malaria parasitemia and improved floor 

3.2.2. Improved wall 

Table 8 presents results of unadjusted and adjusted logistic regression models of malaria parasitemia and 

improved walls. In the unadjusted models significant protective effects of improved walls were seen in all 

countries (p-value<0.05) except Senegal 2010-11, where there is a marginally significant protective effect. 

The adjusted models show a significant protective effect of improved walls in Burundi 2012, Madagascar 

2011, Tanzania 2007-08, and Togo 2013-14 (p-value<0.05) and a marginally significant protective effect 

in Rwanda 2014-15 (p-value<0.10). Improved walls were associated with higher odds of malaria 

parasitemia in Senegal 2010-11 and Uganda 2014-15 (p-value<0.05). The multi-country pooled model 

(Figure 13) does not show a significant association between improved wall construction and malaria 

parasitemia in young children (OR = 0.96; 95% CI = 0.85-1.07). An I2

 test of heterogeneity suggests that 

the model may represent substantial heterogeneity (I2

=63%, p-value <0.001), even after adjusting for 

random effects at the survey level. 
20 

Table 8. Unadjusted and adjusted associations between malaria parasitemia for 

children age 6-59 months and improved wall (odds ratios) 

 Unadjusted Adjusted 

Country/Survey OR 95% CI p-value OR 95% CI p-value Number

Angola MIS 2011 0.19 [0.1,0.3] <0.005 1.58 [0.7,3.7] 0.29 3,362 

Benin DHS 2011-12 0.51 [0.4,0.6] <0.005 0.99 [0.8,1.3] 0.93 3,648 

Burkina Faso DHS 2010 0.54 [0.4,0.7] <0.005 1.04 [0.9,1.3] 0.67 6,245 

Burkina Faso MIS 2014 0.37 [0.3,0.5] <0.005 0.95 [0.7,1.2] 0.71 5,753 

Burundi MIS 2012 0.29 [0.2,0.4] <0.005 0.37 [0.2,0.6] <0.005 3,820 

Cameroon DHS 2011 0.66 [0.5,0.8] <0.005 0.94 [0.7,1.3] 0.66 5,414 

DRC DHS 2013-14 0.60 [0.5,0.8] <0.005 1.09 [0.8,1.5] 0.60 7,457 

Cote d’Ivoire DHS 2011-12 0.46 [0.3,0.6] <0.005 1.16 [0.7,1.9] 0.54 3,255 

Ghana DHS 2014 0.33 [0.3,0.4] <0.005 0.93 [0.7,1.3] 0.63 2,529 

Guinea DHS 2012 0.44 [0.3,0.6] <0.005 1.27 [0.9,1.8] 0.20 3,234 

Kenya MIS 2015 0.37 [0.2,0.8] 0.01 0.93 [0.4,2.0] 0.86 3,073 

Liberia MIS 2009 0.46 [0.3,0.6] <0.005 0.86 [0.6,1.2] 0.36 4,260 

Liberia MIS 2011 0.38 [0.3,0.5] <0.005 0.97 [0.7,1.4] 0.87 2,941 

Madagascar MIS 2011 0.12 [0.1,0.2] <0.005 0.44 [0.2,0.9] 0.02 6,212 

Madagascar MIS 2013 0.38 [0.2,0.6] <0.005 1.06 [0.6,1.8] 0.83 5,564 

Malawi MIS 2012 0.65 [0.5,0.9] <0.005 1.13 [0.8,1.7] 0.52 2,186 

Malawi MIS 2014 0.58 [0.4,0.8] 0.01 1.25 [0.7,2.1] 0.40 2,041 

Mali DHS 2012-13 0.23 [0.2,0.3] <0.005 0.83 [0.6,1.1] 0.22 4,699 

Mozambique DHS 2011 0.28 [0.2,0.4] <0.005 0.98 [0.7,1.4] 0.90 5,227 

Nigeria MIS 2010 0.57 [0.4,0.8] <0.005 1.23 [0.9,1.7] 0.22 4,863 

Rwanda DHS 2010 0.47 [0.2,0.9] 0.03 0.61 [0.3,1.2] 0.13 4,046 

Rwanda DHS 2014-15 0.18 [0.1,0.5] <0.005 0.40 [0.1,1.2] 0.09 3,534 

Senegal DHS 2010-11 0.55 [0.3,1.0] 0.05 2.04 [1.1,3.7] 0.02 3,717 

Senegal DHS 2012-13 0.18 [0.1,0.3] <0.005 0.57 [0.3,1.2] 0.12 5,407 

Tanzania HMIS 2007-08 0.36 [0.3,0.5] <0.005 0.60 [0.4,0.9] 0.01 6,276 

Tanzania HMIS 2011-12 0.48 [0.3,0.7] <0.005 0.90 [0.6,1.4] 0.61 7,340 

Togo DHS 2013-14 0.35 [0.3,0.4] <0.005 0.76 [0.6,1.0] 0.05 2,994 

Uganda MIS 2009 0.51 [0.3,0.8] <0.005 1.54 [0.9,2.6] 0.10 3,532 

Uganda MIS 2014-15 0.72 [0.5,1.0] 0.02 1.70 [1.2,2.4] <0.005 4,419 

OR = Odds ratio 

Adjusted ORs with a p-value less than 0.05 are bolded 

*Adjusted models control for improved flooring, improved roofing, insecticide-treated net (ITN) use, 

indoor residual spraying (IRS) in the past 12 months, household wealth status, age of child, sex of 

child, and malaria endemicity 
21 

Figure 13. Pooled adjusted odds ratios of malaria parasitemia and improved wall 

3.2.2. Improved roof 

Table 9 presents results of unadjusted and adjusted logistic regression models of malaria parasitemia and 

improved roof construction. In the unadjusted models significant protective effects of improved roofs were 

seen in all countries (p-value<0.05) except Angola 2011, Kenya 2015, Liberia 2009, and Rwanda 2010. 

The adjusted models show a significant protective effect of an improved roof in Mali 2012-13, Nigeria 

2010, Senegal 2010-11, and Tanzania 2011-12 (p-value<0.05) and a marginally significant protective effect 

in Burundi 2012, Cameroon 2011, DRC 2013-14, Malawi 2012 and Uganda 2014-15 (p-value<0.10). 

Improved roofing was associated with higher odds of malaria parasitemia in Guinea 2012 (p-value<0.05). 

The pooled estimate from the meta-analysis (Figure 14) shows a significant negative association between 

improved roof construction and odds of malaria parasitemia in young children (OR = 0.90; 95% CI = 

0.81-0.99). However, an I2

 test of heterogeneity suggests that moderate heterogeneity between surveys may 

affect the pooled estimate (I2

=50%, p-value <0.05), even after adjusting for random effects at the 

survey level. 
22 

Table 9. Unadjusted and adjusted associations between malaria parasitemia for 

children age 6-59 months and improved roof (odds ratios) 

 Unadjusted Adjusted 

Country/Survey OR 95% CI p-value OR 95% CI p-value Number

Angola MIS 2011 0.54 [0.3,1.0] 0.06 1.11 [0.6,2.1] 0.76 3,362 

Benin DHS 2011-12 0.78 [0.6,1.0] 0.02 1.18 [0.9,1.5] 0.14 3,648 

Burkina Faso DHS 2010 0.48 [0.4,0.6] <0.005 1.02 [0.9,1.2] 0.83 6,245 

Burkina Faso MIS 2014 0.55 [0.4,0.7] <0.005 1.02 [0.8,1.3] 0.86 5,753 

Burundi MIS 2012 0.49 [0.4,0.7] <0.005 0.70 [0.5,1.0] 0.07 3,820 

Cameroon DHS 2011 0.66 [0.5,0.8] <0.005 0.71 [0.5,1.0] 0.07 5,414 

DRC DHS 2013-14 0.51 [0.4,0.7] <0.005 0.67 [0.4,1.0] 0.05 7,457 

Cote d’Ivoire DHS 2011-12 0.43 [0.3,0.6] <0.005 0.81 [0.5,1.2] 0.33 3,255 

Ghana DHS 2014 0.31 [0.2,0.5] <0.005 0.90 [0.6,1.4] 0.63 2,529 

Guinea DHS 2012 0.66 [0.5,0.9] <0.005 1.92 [1.2,3.0] <0.005 3,234 

Kenya MIS 2015 0.72 [0.4,1.2] 0.18 0.89 [0.5,1.4] 0.63 3,073 

Liberia MIS 2009 0.77 [0.6,1.1] 0.12 1.25 [0.9,1.8] 0.20 4,260 

Liberia MIS 2011 0.64 [0.5,0.9] <0.005 1.15 [0.8,1.7] 0.45 2,941 

Madagascar MIS 2011 0.23 [0.1,0.5] <0.005 0.78 [0.3,1.8] 0.56 6,212 

Madagascar MIS 2013 0.44 [0.2,0.9] 0.03 1.63 [0.6,4.1] 0.30 5,564 

Malawi MIS 2012 0.35 [0.2,0.5] <0.005 0.61 [0.4,1.0] 0.07 2,186 

Malawi MIS 2014 0.44 [0.3,0.7] <0.005 1.16 [0.6,2.1] 0.62 2,041 

Mali DHS 2012-13 0.38 [0.3,0.5] <0.005 0.79 [0.6,1.0] 0.03 4,699 

Mozambique DHS 2011 0.20 [0.2,0.3] <0.005 0.65 [0.4,1.1] 0.11 5,227 

Nigeria MIS 2010 0.58 [0.4,0.8] <0.005 0.73 [0.6,1.0] 0.03 4,863 

Rwanda DHS 2010 0.49 [0.2,1.1] 0.08 0.92 [0.3,2.5] 0.87 4,046 

Senegal DHS 2010-11 0.33 [0.2,0.6] <0.005 0.54 [0.3,0.9] 0.02 3,717 

Senegal DHS 2012-13 0.17 [0.1,0.3] <0.005 0.57 [0.3,1.2] 0.14 5,407 

Tanzania HMIS 2007-08 0.54 [0.4,0.7] <0.005 1.07 [0.7,1.6] 0.74 6,276 

Tanzania HMIS 2011-12 0.46 [0.3,0.7] <0.005 0.55 [0.3,0.9] 0.02 7,340 

Togo DHS 2013-14 0.51 [0.4,0.7] <0.005 1.06 [0.8,1.4] 0.70 2,994 

Uganda MIS 2009 0.41 [0.3,0.6] <0.005 0.89 [0.6,1.3] 0.56 3,532 

Uganda MIS 2014-15 0.46 [0.3,0.6] <0.005 0.75 [0.5,1.0] 0.09 4,419 

OR = Odds ratio 

Adjusted ORs with a p-value less than 0.05 are bolded 

+ Rwanda DHS 2014-15 was not included due to lack of variation in roofing types 

*Adjusted models control for improved flooring, improved wall, insecticide-treated net (ITN) use, 

indoor residual spraying (IRS) in the past 12 months, household wealth status, age of child, sex of 

child, and malaria endemicity 
23 

Figure 14. Pooled adjusted odds ratios of malaria parasitemia and improved roof 

3.2.3. Modern house 

Table 10 presents the associations between modern house construction and malaria parasitemia from 

unadjusted and adjusted logistic regression models. In the unadjusted models significant protective effects 

of modern housing (p-value<0.05) were seen in all surveys except Rwanda 2010. The adjusted models show 

a significant protective effect of modern housing in Burundi 2012, Madagascar 2011, Mali 2012-13, 

Tanzania 2007-08 and Togo 2013-14 (p-value<0.05) and a marginally significant protective effect in 

Mozambique 2011 DHS and Senegal 2012-13 DHS (p-value<0.10). Modern housing was associated with 

higher odds of malaria parasitemia in Guinea 2012 (p-value<0.05) and Nigeria 2010 MIS (p<0.10). The 

pooled estimate from the meta-analysis (Figure 15) shows a significant protective effect of modern housing 

on odds of malaria parasitemia in young children (OR = 0.88; 95% CI = 0.78-1.00). However, an I2

 test of 

heterogeneity suggests that moderate heterogeneity between surveys may affect the pooled estimate 

(I2

=50.8%, p-value =0.001), even after adjusting for random effects at the survey level. 
24 

Table 10. Unadjusted and adjusted associations between malaria parasitemia for 

children age 6-59 months and modern house (odds ratios) 

 Unadjusted Adjusted 

Country/Survey OR 95% CI p-value OR 95% CI p-value Number

Angola MIS 2011 0.16 [0.1,0.3] <0.005 1.93 [0.9,4.3] 0.11 3,362 

Benin DHS 2011-12 0.50 [0.4,0.6] <0.005 0.96 [0.7,1.2] 0.73 3,648 

Burkina Faso DHS 2010 0.32 [0.3,0.4] <0.005 0.95 [0.8,1.2] 0.62 6,245 

Burkina Faso MIS 2014 0.31 [0.2,0.4] <0.005 1.05 [0.8,1.4] 0.74 5,753 

Burundi MIS 2012 0.16 [0.1,0.4] <0.005 0.28 [0.1,0.6] <0.005 3,820 

Cameroon DHS 2011 0.59 [0.5,0.7] <0.005 1.00 [0.7,1.5] 0.99 5,414 

DRC DHS 2013-14 0.43 [0.3,0.6] <0.005 0.65 [0.4,1.2] 0.15 7,457 

Cote d’Ivoire DHS 2011-12 0.41 [0.3,0.5] <0.005 0.88 [0.6,1.2] 0.46 3,255 

Ghana DHS 2014 0.32 [0.2,0.4] <0.005 0.90 [0.7,1.2] 0.50 2,529 

Guinea DHS 2012 0.41 [0.3,0.5] <0.005 1.73 [1.2,2.6] 0.01 3,234 

Kenya MIS 2015 0.39 [0.2,0.8] 0.02 1.04 [0.6,1.9] 0.89 3,073 

Liberia MIS 2009 0.42 [0.3,0.6] <0.005 0.84 [0.6,1.3] 0.41 4,260 

Liberia MIS 2011 0.36 [0.3,0.5] <0.005 0.91 [0.6,1.3] 0.60 2,941 

Madagascar MIS 2011 0.10 [0.0,0.2] <0.005 0.26 [0.1,0.6] <0.005 6,212 

Madagascar MIS 2013 0.31 [0.1,0.7] <0.005 1.10 [0.6,2.2] 0.79 5,564 

Malawi MIS 2012 0.30 [0.2,0.5] <0.005 0.58 [0.3,1.1] 0.11 2,186 

Malawi MIS 2014 0.34 [0.2,0.6] <0.005 0.81 [0.4,1.8] 0.60 2,041 

Mali DHS 2012-13 0.17 [0.1,0.2] <0.005 0.67 [0.5,1.0] 0.03 4,699 

Mozambique DHS 2011 0.13 [0.1,0.2] <0.005 0.61 [0.4,1.0] 0.07 5,227 

Nigeria MIS 2010 0.56 [0.4,0.7] <0.005 1.35 [0.9,1.9] 0.10 4,863 

Rwanda DHS 2010 0.66 [0.2,1.9] 0.45 0.98 [0.1,6.4] 0.98 4,046 

Rwanda DHS 2014-15 0.18 [0.0,0.7] 0.02 0.99 [0.1,9.5] 0.99 3,534 

Senegal DHS 2010-11 0.52 [0.3,1.0] 0.04 1.17 [0.5,3.1] 0.74 3,717 

Senegal DHS 2012-13 0.18 [0.1,0.3] <0.005 0.52 [0.2,1.1] 0.09 5,407 

Tanzania HMIS 2007-08 0.19 [0.1,0.3] <0.005 0.57 [0.3,1.0] 0.04 6,276 

Tanzania HMIS 2011-12 0.39 [0.2,0.6] <0.005 1.21 [0.6,2.3] 0.58 7,340 

Togo DHS 2013-14 0.34 [0.3,0.4] <0.005 0.76 [0.6,1.0] 0.03 2,994 

Uganda MIS 2009 0.33 [0.2,0.5] <0.005 1.10 [0.8,1.6] 0.61 3,532 

Uganda MIS 2014-15 0.30 [0.2,0.4] <0.005 0.97 [0.7,1.4] 0.88 4,419 

OR = Odds ratio 

Adjusted ORs with a p-value less than 0.05 are bolded 

*Adjusted models control for modern house, insecticide-treated net (ITN) use, indoor residual 

spraying (IRS) in the past 12 months, household wealth status, age of child, sex of child, and malaria 

endemicity 
25 

Figure 15. Pooled adjusted odds ratios of malaria parasitemia and modern house 

27 

4. Discussion 

This study used standardized data from DHS and MIS surveys to investigate associations between 

household construction materials and malaria infection among children age 6-59 months, controlling for 

additional household and child characteristics. The study covered 29 surveys in 21 countries in sub-Saharan 

Africa, including a range from low to high malaria transmission settings. The level of malaria control 

intervention coverage also varied among countries. Results of this analysis confirm small but significant 

protective effects of living in a house with an improved roof on the odds of malaria infection in children, 

but no association between type of wall and floor construction and odds of malaria infection. Children 

living in modern houses, defined as those with improved floors, walls and roofs, were also less likely to be 

infected with malaria than those living in other types of houses. Other factors associated with reduced odds 

of malaria in children are younger age, use of ITNs, higher household wealth, and urban residence. 

Children living in houses with improved roofs were shown to have moderately lower odds of malaria 

infection in pooled models, and the direction of effect was consistent but not significant in all surveyspecific analyses. The strongest associations between improved roofs and malaria infection were seen in 

Mali and Nigeria; improved roofs were associated with a 21% reduction in odds of malaria in the Mali 

2012-13 DHS and a 27% reduction in the Nigeria 2010 MIS. Significant associations were also observed 

in the Senegal 2010-11 DHS and the Tanzania 2011-12 MIS. Marginally significant associations with 

improved roof construction and malaria infection were observed in five other surveys. The lack of 

consistently significant associations is not unexpected given the variations in malaria transmission 

dynamics and baseline malaria prevalence across survey settings (from 1% in Rwanda to 62% in Burkina 

Faso). Using household survey data to measure this association also means that a relatively small proportion 

of the variation in malaria risk is likely to be explained by the available covariates. An opposite effect, in 

which improved roof construction was significantly associated with increased odds of malaria infection in 

children, was seen only in the Guinea 2012 DHS. Similar results were found in a study in Kenya, in which 

metal roofs were associated with increased risk of malaria (Ernst et al. 2006). The authors of the Kenya 

study hypothesized that metal roofs were commonly found in conjunction with open eaves in the study 

community, which would allow for easy vector entry into houses. Also, metal roofs may have been 

commonly found in homes with separate kitchens instead of with kitchens in the same structure with the 

sleeping areas. Kitchens produce smoke which as has been shown to be a deterrent to mosquitoes in other 

studies (Hiscox et al. 2013). Perhaps Guinea has similar common combinations of housing features. 

Unfortunately, features such as open eaves or kitchen location were either not measured or were not 

measured consistently across the surveys. However, this association of improved roofs with increased risk 

of malaria appears to be an outlier and not the norm across malaria-endemic countries in sub-Saharan 

Africa. 

Living in houses with improved walls and improved floors was not found to be associated with reductions 

in malaria risk in pooled models. However, disaggregated survey-specific analysis did show significant 

associations in some countries. Improved walls were protective against malaria infection in the Burundi 

2012 MIS, Madagascar 2011 MIS, and Tanzania 2007-08 MIS but were associated with increased risk of 

malaria in the Senegal 2010-11 DHS. Improved floors were associated with reduced risk of malaria in the 

Benin 2011-12 DHS and the Senegal 2012-13 DHS, and with increased risk in the Madagascar 2011 MIS 

and the Nigeria 2010 MIS. The lack of consistent direction of effect and level of significance of improved 

housing characteristics across countries may be random and indicate that these features are not important 

predictors of malaria risk. However, alternative explanations are also possible. The housing-characteristic 

variables included in this analysis may simply be proxies for other unavailable data on housing conditions 

that are more directly linked to malaria transmission, such as open or closed eaves, and information on 

window screening. In addition, ideal housing conditions, in which the quality of construction prevents all 

mosquito entry into a home, and in which household residents stay within the protection offered by the 
28 

house at all times of mosquito feeding, are unlikely to be common. Thus, the level of effect of housing 

interventions on risk of malaria infection is unlikely to be strong. Unlike many other malaria interventions, 

improved housing conditions do not include insecticides that directly increase vector mortality. The use of 

nationally representative household survey data spanning an eight-year period (2007-2015) is also likely to 

weaken observed associations, as these data sources contain few proximate variables and will not capture 

a large proportion of the variation in individual-level outcomes. 

Results from models measuring the effects of modern housing suggest that the combination of improved 

roof, improved walls and improved floors may reduce the risk of malaria in young children. Significant, 

protective associations were seen in the pooled multi-survey model as well as in five of the survey-specific 

models. Similar to the models examining the effects of improved roofs on malaria risk, the odds of malaria 

infection were higher in children living in modern housing compared to other housing in only in the Guinea 

DHS 2012. These results would be expected if the effect of modern housing on malaria risk is being driven 

largely by the improved roof element of the composite measure. Reasons for the lack of a consistently 

significant protective effect are likely to include those previously mentioned (potential confounding by 

unmeasured household features such as eaves and screens, weak effects expected from cross-sectional data, 

a wide range of malaria transmission settings) as well as the use of a composite measure of modern housing. 

The distributions of the specific, individual housing features vary across countries as seen in Figure 10. The 

relative importance of modern housing on predicting malaria infection in any one survey may vary 

depending on the distribution of the individual roof, wall and floor materials in the houses that are not 

defined as ‘modern.’ The pooled, multivariable model also showed a moderate level of heterogeneity 

indicating significant variation between surveys. 

The level of protective effect of living in a modern house on odds of malaria infection was moderate, with 

children living in modern houses having 12% lower odds of malaria infection than those living in nonmodern houses. This compares to the recent meta-analysis by Tusting and colleagues in which odds of 

malaria infection were 42% lower and clinical malaria incidence was 54%-65% lower in children from 

modern homes compared to traditional homes (Tusting et al. 2015). Our more moderate estimate of effect 

is derived from a model that adjusts for the same set of core covariates in each survey, focuses on subSaharan Africa exclusively, and only includes surveys from 2007-2015. This is in contrast to the Tusting 

paper in which the mix of cohort, case-control and cross-sectional studies included in the meta-analysis 

ranged in date from 1939 to 2015 and came from Africa, Asia and South American countries. Traditional 

homes, those with unimproved roof, walls and floors, were assumed to lack closed eaves, screened doors 

and windows, and ceilings. In addition, the covariates included in adjusted models from individual studies 

varied and did not include a measure of malaria transmission levels. 

In addition to housing construction, our study found that other household factors were associated with 

malaria infection in children. Living in urban locations was protective against malaria infection even after 

controlling for house construction and household wealth quintile, all of which are strongly correlated. This 

finding suggests that urban settings confer some protection against malaria in addition to the protection 

related to modern housing and greater wealth. One possible explanation is that urban environments are less 

conducive to dense vector populations, possessing fewer viable larval habitats. It is also possible that the 

greater concentration of improved housing in urban areas may confer community-level protection by 

reducing parasite populations. Urban residents may have greater access to health care including antimalarial 

treatment for infected individuals. Effective treatment of infections would reduce the parasite population in 

the community. It is also true that baseline malaria prevalence tends to be lower in urban than rural areas. 

Malaria is not the only disease affected by improved housing construction. Other vector-borne diseases, 

such as dengue, chikungunya, leishmaniasis, Chagas disease, and Japanese encephalitis are influenced by 

housing features and by the household environment (Hiscox et al. 2013; Vector Control Working GroupRoll Back Malaria 2015; Wilson et al. 2014; World Health Organization 1997). Dirt floors put household 
29 

inhabitants at higher risk of soil-transmitted helminths such as Ascaris lumbricoides and Trichuris trichiura 

(Quintero et al. 2012). Elements of housing construction that affect ventilation are also important; having 

more windows and doors is associated with fewer airborne pathogens such as tuberculosis (Escombe et al. 

2007). Increased air flow may also be associated with higher levels of ITN use (von Seidlein et al. 2012). 

The Roll Back Malaria Vector Working Group concluded in a recent publication that “in addition to its 

impact on vector-borne diseases, improved housing quality brings other benefits to general health and 

development” and recommended modifications such as closed eaves, ceilings, and screening for reducing 

malaria and other vector-borne diseases (Vector Control Working Group-Roll Back Malaria 2015). 

Improved construction materials such as metal roofs and finished interior walls are also presented as 

potentially effective malaria control interventions in this report. 

Improved housing is closely linked with improving socioeconomic conditions. Given the rapid 

development currently underway in sub-Saharan Africa (World Bank 2016), quantifying projected 

reductions in malaria morbidity and mortality will require a firm understanding of the protective efficacy 

of housing improvements, both through the direct effects on vector-human interactions and through the 

indirect effect of improved socioeconomic conditions. In addition to allowing for housing upgrades, 

improvements in socioeconomic conditions may contribute to greater access to healthcare and to other 

malaria control interventions. Conversely, a high malaria burden can cause declines in socioeconomic status 

through the costs of treatment and through lost education and income due to illness (Breman, Alilio, and 

Mills 2004; de Castro and Fisher 2012; Gallup and Sachs 2001). These relationships define the vicious 

cycle between malaria and poverty in which the poorest populations suffer disproportionate consequences 

of the disease. 

Another advantage of improved housing construction as a malaria intervention or as an integrated vector 

control intervention is that can occur without requiring investment from governments or donors. Economic 

development is becoming more widespread in Africa, with further increases in gross domestic product 

expected over the next decade (World Bank 2016). An estimated 144 million new rural homes are expected 

to be built by 2050 (Vector Control Working Group-Roll Back Malaria 2015). These improvements should 

lead to reductions in malaria and other diseases even without additional donor intervention. 

This study has several limitations. The data used are cross-sectional and are therefore not ideal for 

measuring causal relationships. However, housing conditions are unlikely to undergo major changes in the 

short periods of time in which a malaria infection may develop, so the lack of temporal data are unlikely to 

introduce much bias. Another potential limitation of the study is that the available survey data do not include 

information on housing characteristics other than the materials used for constructing floors, walls, and roofs. 

Information relevant to vector control inside a home, such as the presence or absence of open eaves and 

window or door screening, is not available. In addition, the study did not directly control for the seasonality 

of malaria transmission. The observed associations between housing conditions and parasitemia measured 

in this study will be affected by the underlying level of parasites and of vectors in a community. The 

seasonal nature of malaria transmission in many settings may lead to substantial variation in the level of 

risk of malaria in a population through a calendar year as well as to the nighttime behaviors of individuals 

(outdoor sleeping in the heat of dry seasons, for example). Although models were not adjusted for any direct 

measure of seasonal or weather conditions, models were adjusted for malaria transmission level using MAP 

risk categories, which are modeled estimates controlling for many of these environmental variables 

including those with seasonal variation. Given the complex epidemiology of malaria, survey data are 

unlikely to explain a large proportion of the variation in malaria infection observed; thus even important 

associations may appear weak. Pooled analyses include data from surveys conducted between 2007 and 

2015. As many changes in malaria control have occurred over this period, along with substantial economic 

development, measures of effect between housing and malaria may have changed over this period. 
30 

At the same time, this study has several important advantages. DHS and MIS surveys are standardized to 

collect the same information using the same sampling and interview tools over time and across countries. 

This pertains to both housing characteristics and to other variable measurements such as malaria infection, 

age, and wealth quintile. The data are representative at the national and regional levels at a minimum, and 

include a wide range of malaria-endemic countries in sub-Saharan Africa, across a range of transmission 

intensities. 
31 

5. Conclusion 

Malaria continues to cause a significant health burden in many endemic countries in sub-Saharan Africa, 

even after a decade of substantial investment by governments and donors, and even in settings with strong 

malaria control efforts. Using standardized, high-quality, nationally representative survey data, this study 

shows that improved house construction may be an effective malaria control intervention, as it is associated 

with reduced risk of malaria in young children; however the direction and strength of effect varied by 

household feature and by setting. More definitive conclusions will require data on housing features such as 

open eaves, ceilings, and window screening from DHS and MIS surveys. 

33 

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