DHS ANALYTICAL 

STUDIES 52

Increasing equity of 

Insecticide-Treated Net 

Ownership in Sub-Saharan 

Africa from 2003 to 2014

September 2015

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

Cameron Taylor, Lia Florey, and Yazoume Ye of ICF International.

DHS Analytical Studies No. 52 

Increasing Equity of Insecticide-Treated Net 

Ownership in Sub-Saharan Africa from 2003 to 2014 

Cameron Taylor1

Lia Florey1 

Yazoume Ye2

ICF International 

Rockville, Maryland, USA 

September 2015 

1

 The DHS Program, ICF International 

2

 MEASURE Evaluation, ICF International 

Corresponding author: Cameron Taylor, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 

20850; phone: +1 301-572-0277; email: Cameron.Taylor@icfi.com. 
Acknowledgments: The authors would like to thank Ana Franca Koh for careful review and expert advice 

for this paper. 

Editor: Diane Stoy 

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 (#AID-OAA-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: 

The DHS Program, ICF International, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; phone: 

301-407-6500; fax: 301-407-6501; email: reports@dhsprogram.com; Internet: www.dhsprogram.com. 

Recommended citation: 

Taylor, Cameron, Lia Florey, and Yazoume Ye. 2015. Increasing Equity of Insecticide-Treated Net 

Ownership in Sub-Saharan Africa from 2003 to 2014. DHS Analytic Studies No. 52. Rockville, Maryland, 

USA: ICF International. 
iii 

Contents 

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

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

Preface ....................................................................................................................................................... vii 

Abstract ........................................................................................................................................................ ix 

Executive Summary ..................................................................................................................................... xi 

1. Background ...................................................................................................................................... 1 

1.1. Insecticide-Treated Nets (ITNs) ......................................................................................... 1 

1.2. Malaria Inequities ............................................................................................................... 2 

1.3. Malaria Endemicity ............................................................................................................. 2 

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

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

3. Results .............................................................................................................................................. 9 

3.1. Descriptive Analysis ........................................................................................................... 9 

3.2. Country-level Equity Analysis .......................................................................................... 10 

3.3. Pooled Equity Analysis ..................................................................................................... 16 

4. Discussion ...................................................................................................................................... 19 

5. Limitations ..................................................................................................................................... 21 

6. Conclusions .................................................................................................................................... 23 

Appendix ..................................................................................................................................................... 25 

References ................................................................................................................................................... 29 

v 

Tables 

Table 1. Countries included in country-level equity analysis .................................................................. 4 

Table 2. Countries included in pooled equity analysis ............................................................................ 5 

Table 3. Household ITN ownership, concentration index value, and 95% CIs, by survey ................... 11 

Table 4. Summary of equity changes from baseline to endline surveys ................................................ 14 

Table A.1. List of the surveys included in this report, with dates and sample sizes.................................. 25 

Table A.2. Percentage of households with at least one insecticide-treated net, by wealth quintile 

and survey ................................................................................................................................ 26 

Table A.3. Percentage of households with at least one insecticide-treated net, including 95% 

confidence intervals, by survey ............................................................................................... 27 

Figures 

Figure 1. Countries with survey data included in analyses ................................................................ 3 

Figure 2. Timeline of ITN/LLIN strategies and survey data points ................................................... 6 

Figure 3. Illustration of the use of the concentration index as a measure of equity ........................... 7 

Figure 4. Proportion of households with at least one ITN by country and survey year ..................... 9 

Figure 5. Proportion of households with at least one ITN by wealth quintile .................................. 10 

Figure 6. Change in concentration index from baseline to endline survey by country .................... 12 

Figure 7. Proportion of households with at least one ITN by concentration index .......................... 15 

Figure 8. Pooled ITN equity (15 countries) ..................................................................................... 16 

Figure 9. Pooled equity by high malaria transmission risk (PfPR2-10>40%) .................................... 17 

Figure 10. Pooled equity by intermediate/low malaria transmission risk (PfPR2-10 0.1%-40%) ........ 18 

vii 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to analyze DHS data and provide findings that will be useful 

to policymakers and program managers in low- and middle-income countries. DHS Analytical Studies serve 

this objective by providing in-depth research on a wide range of topics, typically including several countries 

and applying multivariate statistical tools and models. These reports are also intended to illustrate research 

methods and applications of DHS data that may build the capacity of other researchers. 

The topics in the DHS Analytical Studies series are selected by The DHS Program in consultation with the 

U.S. Agency for International Development. 

It is hoped that the DHS Analytical Studies will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries. 

Sunita Kishor 

Director, The DHS Program 

ix 

Abstract 

An increase in funding for malaria control in the past decade has resulted in a dramatic increase in ITN 

ownership and use in sub-Saharan Africa (SSA). There is a need to ensure equal access to the benefits of 

ITNs across socioeconomic groups. Using data from the Demographic Health Surveys (DHS) and Malaria 

Indicator Surveys (MIS), this study assesses change in disparity in ITN ownership among households from 

different socioeconomic groups between baseline (2003-2008) and endline (2009-2014) periods. The 

analysis compared Lorenz Concentration Curve (C-Curve) and Index (C-Index) values over time to assess 

changes in economic equity both at the individual country-level and pooled across all countries. Results 

show evidence of increasing equity in household ownership of at least one ITN by household wealth 

between baseline and endline periods. In 14 of 19 nineteen countries analyzed, ITN ownership either 

became more equitable or maintained equity between baseline and endline. Pooled analyses substantiated 

findings that the rapid and significant increase in ITN ownership has reduced or removed the previous bias 

that favored wealthy households. Findings from this study support the hypothesis that increased ITN 

coverage has been accompanied by reduced socioeconomic inequity in ITN ownership over the past 10 

years. 

xi 

Executive Summary 

Background 

Insecticide-treated bed nets (ITNs) are highly effective in reducing malaria morbidity and mortality [1]. 

The increase in funding for malaria control in the past decade has resulted in a dramatic increase in ITN 

ownership and use in many countries in sub-Saharan Africa (SSA). With the shift in programmatic focus 

from target populations to universal coverage, there is a need to ensure equal access and use of ITNs for all 

subpopulations regardless of their socioeconomic status. This report examines the change in equity of ITN 

ownership among malaria endemic countries in SSA from 2003-2014. 

Methods 

Using data from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS), the 

study assesses the change in disparity in ITN ownership among households from different socioeconomic 

groups in both country-level and pooled multi-country analyses. Socioeconomic status is assessed with 

survey-specific assets-based wealth indices categorized into quintiles. Countries included in the analysis 

had at least two national household surveys with ITN data between 2003 and 2014. The analysis compared 

Lorenz Concentration Curve (C-Curve) and Index (C-Index) values over time to assess changes in 

economic equity. 

Results 

Study results show evidence of increasing equity in household ownership of at least one ITN by household 

wealth between baseline and endline periods. In 14 of 19 nineteen countries analyzed, ITN ownership either 

became more equitable or maintained equity between baseline (2003-2008) and endline (2009-2014). In 

Senegal and Madagascar, ITN ownership favored the poorest households at both baseline and endline while 

in Mozambique, Angola, and Niger, the inequity in ITN ownership that favored wealthy households 

remained stable or increased. Multi-country pooled analyses substantiated findings that the rapid and 

significant increase in ITN ownership has reduced or removed the previous bias that favored wealthy 

households. 

Conclusions 

Since 2003, there has been tremendous improvement in the scale up of ITN ownership across SSA. 

However, with the shift in programmatic focus from high-risk target groups to universal coverage, there is 

a need to ensure equal access to ITNs for all subpopulations regardless of socioeconomic status. Findings 

from this study support the hypothesis that increased ITN coverage, achieved through mass distribution 

campaigns, has reduced socioeconomic inequity in ITN ownership over the past 10 years. 

1 

1. Background 

1.1. Insecticide-Treated Nets (ITNs) 

Individual use of a bed net treated with insecticide is one of the best possible forms of personal protection 

in malaria-endemic areas. In addition to the physical barrier of the net that reduces human-vector contact 

at the individual level, the insecticide impregnated in the nets kills or reduces the longevity of mosquitoes 

and thereby prevents transmission. This overall reduction in transmission provides a “community effect” 

by which even those residents not sleeping under a net have increased protection from malaria infection 

[2-5]. 

In April 2008, Roll Back Malaria (RBM) officially launched the universal coverage campaign with the goal 

of reducing 2000 levels of malaria deaths by 50% by 2010 and reducing malaria deaths to near zero by 

2015 [6]. The target for this campaign was that by 2010, 80% of those at risk from malaria should be using 

locally appropriate vector control such as ITNs and indoor residual spraying (IRS) [7]. Since the launch of 

RBM’s universal coverage campaign, countries have achieved high ITN coverage levels by utilizing a 

variety of distribution channels such as community delivery, routine services, or outreach activities. The 

end goal was having every household at risk of malaria transmission and every person within the household 

protected by an ITN [8, 9]. 

Prior to the launch of universal coverage of ITNs in 2008, many distribution strategies focused on 

populations at high risk of adverse health outcomes due to malaria, typically young children and pregnant 

women. The ITN policies frequently included distribution to children under age 5 during routine 

vaccination campaigns and distribution to pregnant women during antenatal care visits advertised via social 

marketing. This distribution approach benefited children and pregnant women who had access to health 

services. In addition, ITNs can be purchased either at health facilities or in the private market. Historically, 

those with greater access to health facilities (urban populations) were more likely to own ITNs than those 

with low access (rural populations) [5, 10]; less poor households were more likely to own ITNs than the 

poorest households [5, 11, 12]. With the shift from targeted distribution to universal coverage, malaria 

control programs have adopted mass distribution campaigns with the potential to significantly increase ITN 

ownership and provide more equitable coverage of high-risk groups [13-19]. 

Currently, most National Malaria Control Program (NMCP) policies include mass ITN distributions 

designed to reach every household in malarious areas and provide one ITN for every two household 

members or “full population coverage”. This assumes that nets are available to 100% of the population and 

that at least 80% of those with access will use the net each night [20]. The campaigns are recommended 

every three years based on the longevity of the nets and the cost effectiveness of conducting a mass 

distribution as compared to a targeted net replacement [20]. While rapid scale up of ITNs has been 

successful in achieving high population coverage, questions remain about the sustainability of gains in the 

post scale up phase. After highly effective mass distribution campaigns, additional nets are almost 

immediately needed for mop-up (for those missed by the distribution) and to address demographic changes 

(immigration, births, deaths) and replacement of older nets [21]. While mass distribution campaigns are 

implemented predominately through a top-down and centrally-driven approach, it is likely that the “keepup” or maintenance process will be driven from the district or community level (bottom-up) [20]. Proper 

implementation of continuous or maintenance distribution mechanisms are essential for achieving and 

maintaining equity in ITN coverage. 
2 

1.2. Malaria Inequities 

For decades, equity in health has been a major tenet of global development organizations, such as the World 

Health Organization (WHO) and the World Bank, whose policies aimed to decrease the poor-rich gap as a 

priority. The WHO defines health inequity as “inequality with respect to health determinants, access to the 

resources needed to improve and maintain health or health outcomes [22].” Unfortunately, many diseases, 

such as malaria, are not distributed equitably among populations; malaria disproportionately afflicts poor, 

rural populations, with pregnant women and young children at highest risk of severe morbidity and 

mortality [5, 23-30]. The many underlying causes of this inequitable distribution include factors such as 

climate, infrastructure, socioeconomic conditions, access to health facilities, and availability of 

commodities. 

Addressing those inequities that are actionable, such as the availability of commodities, has been the 

cornerstone of malaria control efforts for the more than a decade. Early assessments of ITN intervention 

distributions showed low intervention coverage levels [31] and high levels of inequity with poor households 

disproportionately disadvantaged [5, 12, 32]. Logically, increasing efforts to reach these high burden 

populations would seem to be the solution; however, several researchers have concluded that meaningful 

reductions in the malaria burden will require coverage of entire populations, not just those at highest risk 

[19]. Thus, the new adoption of universal ITN coverage policies should, in theory, reduce health inequities 

related to malaria. This study provides an opportunity to assess progress made towards these reductions 

during the several years after widespread implementation of universal coverage programs. 

1.3. Malaria Endemicity 

Patterns of malaria transmission are influenced by a myriad of factors such as those that operate on small 

spatial and temporal scales. Thus, disease distribution can be heterogeneous in space as well as over time. 

While access to malaria control interventions is obviously necessary for use, perceptions of risk also play 

a role [33, 34]. Many studies investigating ITN use have shown patterns of seasonal use correlated with 

periods of mosquito nuisance or periods of intense heat [35-38]. Past studies have shown that malaria 

endemicity and seasonality factors should be considered when examining drivers of ITN ownership [39]. 

Because of heterogeneous transmission among countries and limited resources, national malaria control 

programs have historically focused interventions on high transmission areas, which are frequently 

concentrated in rural locales. However, with the increase in funding for malaria control and the policy shift 

to universal coverage, many countries are now including areas of intermediate and low risk in their control 

programs. This shift in focus could influence equity of ITN ownership by increasing the number of less 

poor households that receive ITNs. 
3 

2. Methods 

2.1. Data

The following data come from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys 

(MIS), which are nationally representative, population-based household surveys that collect information on 

a wide range of demographic and maternal and child health indicators. All survey data are available at 

www.dhsprogram.com. This analysis focused on malaria-endemic countries in SSA that have conducted 

nationally representative household surveys which collected data on malaria interventions between 2003 

and 2014 (Figure 1). Baseline surveys were conducted between years 2003-2008 and endline surveys 

between years 2009-2014. We used the terms baseline and endline to describe the two time periods but it 

should be noted that each survey was independently sampled. See Annex Table A.1 for further survey 

details. 

Figure 1. Countries with survey data included in analyses 
4 

Analysis included a country-level equity analysis as well as a pooled equity analysis. Specific inclusion 

criteria for the country-level equity analysis included: 

1. Countries must have had two surveys conducted from 2003-2014 with accessible data. 

2. One survey must have been conducted between years 2003-2008 (baseline) and the other survey 

conducted between years 2009-2014 (endline). 

3. All surveys must have included data on ITN ownership via a bed net roster in the household 

questionnaire. 

In total 19 countries (45 surveys) were selected for country level equity analysis (Table 1). 

Table 1. Countries included in country-level equity analysis 

Country 

Baseline Surveys 

(2004-2008) 

Endline Surveys 

(2008-2013 ) 

Angola 2006-07 MIS 2011 MIS 

Benin 2006 DHS 2011-12 DHS 

Burkina Faso 2003 DHS 2010 DHS 

Cameroon 2004 DHS 2011 DHS/MICS 

Congo 

(Brazzaville) 2005 DHS 2011-12 DHS 

Congo(DR) 2007 DHS 2013-14 DHS 

Guinea 2005 DHS 2012 DHS/MICS 

Madagascar 2008-09 DHS 2011 MIS 

2013 MIS 

Malawi 2004 DHS 2010 DHS 

2012 MIS 

Mali 2006 DHS 2012-13 DHS 

Mozambique 2007 MIS 2011 DHS 

Niger 2006 DHS 2012 DHS 

Nigeria 2008 DHS 2010 MIS 

2013 DHS 

Rwanda 2005 DHS 2010 DHS 

2013 MIS 

Senegal 2005 DHS 

2008-09 MIS 2010-11 DHS 

Sierra Leone 2008 DHS 2013 DHS 

Tanzania 2004-05 DHS 

2007-08 THMIS 2011-12 THMIS 

Uganda 2006 DHS 2009 MIS 

2011 DHS 

Zimbabwe 2005-06 DHS 2010-11 DHS 

The pooled equity analysis examines households by malaria risk. To classify households into a risk zone, 

it is necessary to obtain the global positioning system (GPS) location of household clusters. In the pooled 

analysis, countries and surveys were included if the GPS location data for the surveyed clusters were 

publically available. Specific inclusion criteria for the pooled country-level equity analysis included: 
5 

1. Country/survey was included in country-level analysis. 

2. Countries had a survey in the baseline and endline period with publicly available GPS data. 

3. If a country had more than one survey with GPS data in a time period, only one survey (either the 

earliest survey or the latest survey) was included in the analysis. 

In total, 15 countries (30 surveys) were included for analysis (Table 2). 

Table 2. Countries included in pooled equity analysis 

Country 

Baseline Surveys 

(2004-2008) 

Endline Surveys 

(2008-2013 ) 

Angola 2006-07 MIS 2011 MIS 

Burkina Faso 2003 DHS 2010 DHS 

Cameroon 2004 DHS 2011 DHS/MICS 

Congo(DR) 2007 DHS 2013-14 DHS 

Guinea 2005 DHS 2012 DHS/MICS 

Madagascar 2008-09 DHS 2013 MIS 

Malawi 2004 DHS 2012 MIS 

Mali 2006 DHS 2012-13 DHS 

Nigeria 2008 DHS 2013 DHS 

Rwanda 2005 DHS 2010 DHS 

Senegal 2005 DHS 2010-11 DHS 

Sierra Leone 2008 DHS 2013 DHS 

Tanzania 2007-08 THMIS 2011-12 THMIS 

Uganda 2006 DHS 2011 DHS 

Zimbabwe 2005-06 DHS 2010-11 DHS 

2.1.1. Indicator 

The outcome of interest in this analysis is the proportion of households with at least one ITN which 

measures household ITN ownership. During the interview with the head of household or other household 

representative, questions focused on the availability of mosquito nets for use while sleeping and whether 

each net has been treated with insecticide. This indicator provides a measure for household ownership of 

an ITN. The indictor reflects the extent to which ITN programs have reached all households or, conversely, 

the proportion of households not yet reached [40]. While many older surveys (pre-2003) included questions 

on possession of bed nets, most did not include a bed net roster with detailed questions on the number, 

types, and characteristics of nets within the household. Without this information, it is not possible to 

determine which bed nets within the household were treated with insecticide. For this reason, surveys 

occurring before 2003 could not be included in the analysis. 

The proportion of households with at least one ITN was calculated for each survey in the analysis. To test 

for significant changes in ITN ownership between baseline and endline surveys, 95% percent confidence 

intervals (95% CI) were calculated with non-overlapping intervals used as evidence of significant 

differences between surveys. 
6 

2.1.2. Timing of ITN campaigns 

The timing of national ITN distribution campaigns can greatly influence ITN ownership estimates in 

nationally representative surveys. Information on the timing of national level ITN distributions in relation 

to DHS surveys can be difficult to obtain, particularly for surveys conducted between 2003 and 2008, before 

mass campaigns to all age groups were common mechanisms of distribution. Figure 2 outlines the timing 

of ITN policies/strategies as reported in the World Malaria Report 2014 [41]as well as the dates of data 

collection of the DHS and MIS surveys in the analysis. This figure presents a general overview of when 

policies/strategies were enacted in relation to the fieldwork dates for the surveys. However, this should be 

interpreted with caution since policy enactment does not always equate to the timing of implementation in 

the field; there are often delays between the time when a policy becomes official and when the infrastructure 

and funding are available for implementation. For many countries included in the analysis, the policy shift 

from targeted distribution to universal ITN distribution campaigns occurred during this period which 

included free distribution of nets to all age groups. 

Figure 2. Timeline of ITN/LLIN strategies and survey data points 

Data Source: World Malaria Report Country Profiles 2014 [41] and DHS Program Website www.dhsprogram.com 
7 

2.1.3. Wealth quintiles 

Wealth is a household characteristic that often has a large effect on health [42]. The DHS wealth index is 

designed to measure economic well-being of households independently from health and education[43]. The 

DHS wealth index is a survey-specific measure of the relative economic status of households based on 

analysis of household assets and service amenities at a particular point in time. This measure is particularly 

valuable in countries which lack reliable data on income and expenditures [44]. The wealth index places 

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

analysis. Wealth quintile ranking indicates relative rather than absolute economic status of the household 

[42]. To compare the influence of wealth on various population and health indicators, DHS separates all 

interviewed households into five wealth quintiles (lowest, second, middle, fourth, and highest ) [42, 44, 

45]. The wealth index is presented in the DHS Final Reports and included in survey datasets as a 

background characteristic. 

As the economic status of the population in a country improves and household services and assets become 

more widely available and affordable, a household in the top quintile in an earlier survey could rank in the 

bottom quintile in a later survey [43]. In another words, wealth status is not directly comparable across 

surveys and countries. This study, however, is not affected by this limitation because it focuses on the 

disparities in ITN ownership between the wealthy and the poor only within each survey. Regardless of the 

survey period or economic variability in country, if ITN ownership is equitable, those concentrated in the 

lowest wealth quintile should have ownership of ITNs equal to the individuals in the richest wealth quintile. 

For this reason, this report will not be using a comparative wealth index but will instead calculate equity 

based on the household wealth quintile for each particular survey. 

2.1.4. Equity calculation 

We used the Lorenz Concentration Curve (CCurve) and the Lorenz Concentration Index (CIndex) to assess equity in household ITN 

ownership across household wealth quintiles. The 

C-Curve graphically presents the degree of 

socioeconomic related inequality in a health 

variable and the C-Index provides quantification of 

this measure [46, 47]. The x-axis of the 

concentration curve graphs the cumulative 

percentage of the sample, ranked by wealth, 

beginning with the poorest [46]. On the y-axis the 

concentration curve graphs the cumulative 

percentage of the health variable/intervention 

corresponding to the cumulative percentage of the 

distribution of wealth [46]. C-Index values range 

between -1 to 1. A value of 0 suggests no difference 

in ownership and use among different 

socioeconomic groups. A concentration index of 

more than 0 suggests that the outcome is more 

prevalent among the rich. Conversely, a negative index indicates that the outcome is more concentrated 

among the poor. In context of the C-Curve, the dashed 45-degree line represents equity whereby the health 

outcome is equally distributed among all wealth quintiles. If the concentration curve is below the equity 

line (C-index>0), the health outcome is concentrated in richer households; if the concentration curve is 

above the equity line, the health outcome is concentrated in poorer households (C-index<0) (Figure 3). The 

Figure 3. Illustration of the use of the 

concentration index as a measure of equity 
8 

C-Index provides a measure of equity across all five quintiles that is relatively independent of the overall 

level of coverage [12, 48]. 

Analyses were conducted with Stata13 software using the concindc command for calculating the C-Index 

and standard errors to measure economic inequalities in ITN ownership. The concentration curves were 

produced with the clorenz command. Ninety-five percent confidence intervals (95%) were calculated for 

C-Index values. Non-overlapping intervals were used as evidence of significant differences in equity 

between surveys. 

It is important to note that for binary variables (such as ITN ownership), the minimum and maximum 

potential C-Index values depend on the mean ITN ownership in the country. As the mean increases, the 

range of potential values of the C-Index is reduced; this places bounds on the possible values of the C-Index 

[48]. Since the bounds of the concentration index are narrower for higher means, it is suggested that when 

making comparisons across countries with different means, it is important to express the concentration 

index as a fraction of the relevant bound [48]. To account for these issues, Erreygers [49] and Wagstaff [48] 

have developed corrections to the C-Index for bounded binary variables; however, these have been 

challenged by further research that shows that there are actually few technical differences between the 

indices [50]. No corrections were applied in these analyses since the goal was not to compare/rank C-Index 

values by country. 

2.1.5. Malaria endemicity 

In order to explore if equity in ITN ownership varies by level of malaria transmission, the multi-country 

pooled equity analysis categorized all survey clusters into categories of malaria risk. Household clusters 

were assigned a “malaria transmission risk zone” based on Plasmodium falciparum parasite prevalence 

rates among children to 2-10 years (PfPR2-10) from the Malaria Atlas Project (MAP). The MAP provides a 

spatial data layer that describes the estimated proportion of 2-10 year olds in the general population infected 

with Pf at any one time, averaged over the 12 months of 2010 [51]. The DHS and MIS data include 

geospatial data for the location of the centroid of each cluster; this permits linkage of survey clusters to 

MAP data. Using the centroids, all households in a cluster from the DHS or MIS survey data were assigned 

the same malaria risk value based on corresponding MAP data[52]. Standard MAP PfPR2-10 cut-offs were 

used for analysis: 

• No Risk: PfPR2-10 <0.1% 

• Low Risk: 0.1% > PfPR2-10 ≤ 5% 

• Intermediate Risk: 5%> PfPR2-10 ≤ 40% 

• High Risk: PfPR2-10 >40% 

Out of the 346,272 pooled household clusters located in 15 countries, 50% were categorized in the highrisk category (PfPR2-10>40%), 36% in the intermediate risk category (≥5%-40%), 10% in the low risk 

category (0.1%-5%) and 4% in the no risk category (<0.1%). Clusters located in areas with no risk of 

malaria (PfPR2-10<0.1%) were excluded from analyses because populations in these areas would not be 

targeted by ITN distribution campaigns. Due to small sample size in the low risk group, intermediate and 

low risk categories were combined to comprise 46% of the pooled household clusters; 50% of the pooled 

household clusters comprised the high risk group. 
9 

3. Results 

3.1. Descriptive Analysis 

In all countries, except Angola, there was a statistically significant increase in ITN ownership between the 

baseline and endline surveys. The greatest improvement in ITN ownership was seen in both Tanzania and 

Rwanda where ownership increased by 68 percentage points. Smaller improvements in ITN ownership 

between baseline and endline surveys were seen in Angola (7%, non-significant increase) and Madagascar 

(11% increase) (Figure 4). See Table A.3 for 95% confidence intervals. 

Figure 4. Proportion of households with at least one ITN by country and survey year 

The distribution of ITN ownership by household wealth quintile is seen graphically in Figure 5. The x-axis 

shows the proportion of households with at least one ITN and the y-axis the survey years for each country. 

Patterns in ITN ownership by household wealth are represented by colored dots with purple representing 

the lower wealth quintiles (more poor) and green dots representing the higher wealth quintiles (less poor) 

populations. Patterns in ITN ownership by household wealth have changed between baseline and endline 

surveys. ITN ownership in baseline surveys was more frequently concentrated in the highest (richest) 

quintiles. In the endline surveys we see less evidence of inequality with a smaller range of ITN ownership 

estimates between wealth quintiles (the purple and green dots are closer together). In some countries, 

endline surveys show inequity in the opposite direction, with households in the lower wealth quintile with 

the highest coverage of ITN ownership (Congo (Brazzaville) and Senegal, for example). One notable 

exception to the pattern of increasing equity between the poorest and the least poor households over time 

is seen in Angola. Unlike patterns in other countries, the baseline survey in Angola shows higher ITN 
10 

ownership among the poorest wealth quintiles as compared to the least poor. This pattern reversed by the 

endline survey in which the least poor households owned more ITNs than the poorest. Wealth index values 

can be found in the Annex Table A.2. 

Figure 5. Proportion of households with at least one ITN by wealth quintile 

3.2. Country-level Equity Analysis 

At the country level, concentration curves and C-Index values show improvements in equity of ITN 

ownership between baseline and endline surveys (Figure 6 and Table 3). For Burkina Faso, Democratic 

Republic of Congo (DR Congo), Malawi, Rwanda, and Uganda, we see significant improvements in equity 

between the baseline (2003-2008) and endline (2009-2014) surveys. During the baseline survey the 

concentration curve falls far below the equity line (C-Index>0), which indicates that ITN ownership is 

concentrated in the richer quintiles; by the endline survey, these countries’ concentration curves had 

significantly improved (C-Index scores were closer to zero with non-overlapping 95% confidence intervals 

compared to values from baseline surveys). In Benin, Cameroon, Mali, and Tanzania, equity in ITN 

ownership across wealth quintiles (C-Index=0) had been achieved by the time of the endline survey. 

Congo (Brazzaville), Guinea, Nigeria, Sierra Leone, and Zimbabwe had inequitable ITN distributions that 

favored richer households at baseline; at the endline survey, ITN ownership was more concentrated in 

poorer households (concentration curve located above the equity line and C-Index<0 with non-overlapping 

95% confidence intervals). 

Mozambique maintained levels of inequity from baseline to endline surveys that favored wealthier 

households. Madagascar and Senegal maintained levels of inequity from baseline to endline surveys that 

favored the poorest households. Angola and Niger showed increased inequity in household ITN ownership 
11 

between wealth quintiles from baseline to endline surveys that favored the wealthiest households (Table 3. 

Household ITN ownership, concentration index value and 95% CIs, by survey and Figure 6). Table 4 shows 

a summary of equity changes from baseline to endline surveys categorized by country. 

Table 3. Household ITN ownership, concentration index value, and 95% CIs, by survey 

Country 

Survey 

Type 

Year of 

Survey 

Concentratio

n Index 

95% Confidence Interval 

Low High 

Angola MIS 2006-07 0.05 0.01 0.08 

MIS 2011 0.17 0.15 0.18 

Benin DHS 2006 0.23 0.21 0.24 

DHS 2011-12 0.00 -0.01 0.00 

Burkina Faso DHS 2003 0.45 0.40 0.49 

DHS 2010 0.06 0.05 0.06 

Cameroon DHS 2004 0.25 0.17 0.33 

DHS/MICS 2011 0.02 0.00 0.03 

Congo 

(Brazzaville) 

DHS 2005 0.15 0.10 0.20 

DHS/MICS 2011-12 -0.11 -0.12 -0.09 

Congo (DR) DHS 2007 0.26 0.23 0.30 

DHS 2013-14 0.03 0.03 0.04 

Guinea DHS 2005 0.28 0.21 0.35 

DHS/MICS 2012 -0.03 -0.04 -0.02 

Madagascar 

DHS 2008-09 -0.04 -0.04 -0.03 

MIS 2011 -0.06 -0.07 -0.05 

MIS 2013 -0.03 -0.04 -0.02 

Malawi 

DHS 2004 0.29 0.28 0.31 

DHS 2010 0.11 0.10 0.11 

MIS 2012 0.06 0.04 0.07 

Mali DHS 2006 0.02 0.01 0.03 

DHS 2012-13 0.01 0.00 0.01 

Mozambique MIS 2007 0.05 0.02 0.08 

DHS 2011 0.04 0.03 0.05 

Niger DHS 2006 0.00 -0.02 0.01 

DHS 2012 0.09 0.08 0.10 

Nigeria 

DHS 2008 0.18 0.16 0.20 

MIS 2010 -0.08 -0.09 -0.06 

DHS 2013 -0.06 -0.06 -0.05 

Rwanda 

DHS 2005 0.36 0.33 0.38 

DHS 2010 0.04 0.03 0.04 

MIS 2013 0.02 0.02 0.03 

Senegal 

DHS 2005 -0.03 -0.05 0.00 

MIS 2008-09 -0.09 -0.10 -0.08 

DHS 2010-11 -0.11 -0.12 -0.10 

Sierra Leone DHS 2008 0.05 0.04 0.07 

DHS 2013 -0.02 -0.03 -0.01 

Tanzania 

DHS 2004-05 0.41 0.39 0.43 

THMIS 2007-08 0.22 0.20 0.23 

THMIS 2011-12 -0.01 -0.01 0.00 

Uganda 

DHS 2006 0.11 0.09 0.14 

MIS 2009 0.01 -0.01 0.03 

DHS 2011 0.02 0.02 0.03 

Zimbabwe DHS 2005-06 0.19 0.15 0.22 

DHS 2010-11 -0.06 -0.08 -0.04 

 
12 

Figure 6. Change in concentration index from baseline to endline survey by country 

(Continued...) 
13 

Figure 6. − Continued
14 

Table 4. Summary of equity changes from baseline to endline surveys 

Equity Summary Countries 

Baseline: ITN ownership concentrated in richer households 

Endline: ITN ownership closer to equity but still concentrated in richer 

households 

Burkina Faso 

Congo (DR) 

Malawi 

Rwanda 

Uganda 

Baseline: ITN ownership concentrated in richer households 

Endline: ITN ownership equitably distributed 

Benin 

Cameroon 

Mali 

Tanzania 

Baseline: ITN ownership concentrated in richer households 

Endline: ITN ownership concentrated in poorer households 

Congo 

(Brazzaville) 

Guinea 

Nigeria 

Sierra Leone 

Zimbabwe 

Baseline: ITN ownership concentrated in richer households 

Endline: Maintained inequity at baseline levels (no significant change) 

Mozambique 

Baseline: ITN ownership equitable or concentrated in richer 

households 

Endline: Worsening inequity favoring richer households 

Angola 

Niger 

Baseline: ITN ownership concentrated in poorer households 

Endline: ITN ownership concentrated in poorer households 

Madagascar 

Senegal 

Figure 7 shows a scatter plot of the concentration index by ITN ownership for all surveys included in the 

country-level analysis. The figure shows a decline in the variation of concentration index values as ITN 

coverage increases. This trend is not surprising since high ITN coverage levels are difficult to reach without 

improving coverage in the poorest households; any ITN ownership levels greater than 80% require at least 

part of the population in the lowest wealth quintile to own ITNs. Comparing the trends in baseline and 

endline surveys, we see that surveys that took place from 2009-2014 tend to have higher ITN ownership 

and greater equity in ITN ownership as compared to surveys from 2003-2008. 
15 

Figure 7. Proportion of households with at least one ITN by concentration index 

Note: ITN Universal Coverage of 80% is based on the Global Strategic Plan 2005-2015 
16 

3.3. Pooled Equity Analysis 

Analysis of all clusters of surveys from the 15 countries included in the multi-county pooled analysis found 

a significant reduction in wealth inequality between baseline (C-Index 0.11, 95% CI: 0.10; 0.11) and endline 

surveys (C-Index 0.00, 95% CI: =0.01; 0.00). In surveys conducted between 2003-2008, the concentration 

curve falls far below the equity line (C-Index>0); this indicates that ITN ownership was concentrated in the 

richer quintiles, although by 2008-2014 the pooled concentration curve was just at the equity line (CIndex<0) (Figure 8). 

Figure 8. Pooled ITN equity (15 countries) 
17 

When restricting to clusters located in the high malaria transmission (PfPR2-10>40%) risk zones, we see a 

similar pattern with ITN ownership concentrated in higher wealth quintiles from 2003-2008 (C-Index 0.07, 

95% CI: 0.06; 0.08) and transitioning to a more equitable distribution in 2009-2014 (C-Index 0.00, 95% 

CI: 0.00; 0.01) (Figure 9). 

Figure 9. Pooled equity by high malaria transmission risk (PfPR2-10>40%) 
18 

However, when restricting to clusters in the intermediate/low malaria transmission risk zones (PfPR2-10 = 

0.1%-40%), we see that ITN ownership was concentrated in higher wealth quintiles from 2003-2008 (CIndex = 0.14, 95% CI: 0.13; 0.14) and in poorer households in 2009-2014 (C-Index = -.01, 95% CI: -0.02;-

0.01) (Figure 10). 

Figure 10. Pooled equity by intermediate/low malaria transmission risk 

(PfPR2-10 0.1%-40%) 

In comparisons of the intermediate/low malaria transmission risk zones (Figure 10) to high malaria 

transmission risk zones (Figure 9), we see that there was greater wealth inequality in the baseline surveys 

of the intermediate risk zones (C-Index 0.14) as compared to those located in the high risk zone (C-Index 

0.07).This difference was significant at the 95% confidence level. Although both risk zones achieved equity 

by the endline survey, there was a greater increase in equity between baseline and endline surveys in the 

intermediate/low malaria transmission risk zones (a difference of 0.15 C-Index values) as compared to the 

high risk zones (a difference of 0.07 C-Index values). 
19 

4. Discussion 

The study results show evidence of increasing equity in household ownership of at least one ITN by 

household wealth between baseline and endline periods. In 14 of 19 countries analyzed, ITN ownership 

either became more equitable or maintained equity between baseline (2003-2008) and endline (2009-2014). 

However, in Senegal and Madagascar, ITN ownership favored the poorest households at both baseline and 

endline. In Mozambique, Angola, and Niger, inequity in ITN ownership that favored wealthy households 

remained stable or increased. Pooled analyses substantiated the findings that the rapid and significant 

increase in ITN ownership has not favored the wealthiest households in most settings. 

Although the overall trends are clear, the reason for the trends is not as straightforward. In countries with 

very high levels of household ITN ownership, the chances of equitable distribution are inherently higher (if 

every household owns an ITN then distribution will necessarily be equitable). Thus, the recent influx of 

funding for malaria control and the subsequent investment of mass ITN distribution campaigns have likely 

contributed to improved equity by attaining high levels of coverage. However, a study by Njau and 

colleagues [53] found little evidence of improved equity in ITN ownership in the comparisons of regions 

of Angola, Uganda, and Tanzania that had experienced targeted bed net distribution despite significant 

improvements in coverage. The authors concluded that targeted net distribution strategies may not be 

sufficient for addressing socioeconomic inequities in access to bed nets and that further analyses were 

needed to assess equity after the implementation of mass distribution campaigns. 

The timing of mass net distributions in relation to the collection of survey data may influence observed 

trends. Mozambique had a mass ITN distribution campaign the same year as the last DHS. If the survey 

data were collected before the campaign, we would not expect to see the effects reflected in the equity 

estimates from the survey. Angola’s trend of increasing inequity in ITN ownership could be partially due 

to the timing of campaigns and an insufficient ITN supply for the population at risk. In 2011, for example, 

the same year as the baseline survey, ITNs were distributed free of charge but only in specific municipalities 

where the NMCP’s implementing partners were present. Reasons for the trend in Niger are less clear but 

could be due to geographic and infrastructural challenges with accessing households equally during 

distribution campaigns. A 2005 ITN distribution campaign linked to a mop-up polio immunization 

campaign found that the large, widely dispersed, migrant population and the desert terrain made it difficult 

to reach all targeted individuals [54]. This suggests that, in addition to policy decisions, the geographic and 

epidemiologic diversity in malarious countries may influence equity of interventions. 

The effects of malaria epidemiology on equity of ITN ownership were investigated by pooling clusters into 

two groups stratified by low/intermediate (PfPR2-10 =0.1%-40%) and high levels (PfPR2-10>40%) of malaria 

transmission. In the pooled analysis of 15 countries, equity increased significantly in both groups; however, 

the greatest improvement in equity occurred in clusters in the low/intermediate risk zone. The observed 

results could be due to changing ITN policies between baseline and endline surveys, and more specifically, 

the rollout of free mass distributions campaigns after 2008. Before 2008, financial and logistic restraints 

caused most distribution campaigns to be targeted to high-risk populations (children under 5 and pregnant 

women) and/or high-risk regions (rural, high transmission zones). Those in poorer households in 

low/intermediate risk zones were less likely to own a net if they did not have access to health services or 

could not afford to pay for a net at market price. The shift to mass distribution campaigns would have 

improved equity by providing access to the poorest households that did not have previous access to nets. 
20 

Results that show the variation in C-Index values decreased (ITN ownership became more reliably equitable 

across wealth quintiles) as level of ITN ownership increases are not surprising; inherently, as ITN coverage 

rises (over 80%) equity will increase. Our results support the conclusion of Kilian and colleagues that equity 

can be achieved by attaining high levels of ITN coverage with a focus on universal distribution instead of 

targeted campaigns to address equity [8]. In the future, these strategies may need to be reconsidered. As 

ITN levels reach saturation and malaria prevalence declines, mass distributions may no longer be costeffective, and identification of increasingly specific target populations may be necessary. 
21 

5. Limitations 

This study presents evidence of the positive impact of universal coverage scale up on equity of ITN 

ownership. However, there are several important limitations. Data used in analyses come from crosssectional surveys which limits causal inference. Any overlap in survey implementation and ITN distribution 

may complicate the interpretation of findings. Additionally, it is important to note that this study focused 

on equity of ITN ownership but did not assess other important factors such as ITN use or ITN access. The 

use of ITN is essential for protection, except in settings of high community level ITN ownership in which 

some level of indirect protection is conferred from the insecticidal properties of the nets[4, 55]. Analyses 

of equity of ITN access (the percent of the population that could use an ITN with the assumption that one 

ITN can protect two users) would also be informative as it is a more comprehensive measure of the level 

of protection within a household. 

23 

6. Conclusions 

Analyzing data from 19 malaria-endemic countries in SSA between 2003 and 2014, this study assessed 

change in disparity of ITN ownership among different socioeconomic groups by using the Lorenz 

Concentration Curve (C-Curve) and Index (C-Index) as a measurement of economic equity over time. 

Since 2003 there has been tremendous improvement in the scale up of ITN ownership across SSA. 

However, with the shift in programmatic focus from high-risk target groups to universal coverage, there is 

a need to ensure equal access to ITNs for all sub-populations regardless of socioeconomic status. These 

findings support the hypothesis that mass ITN distribution campaigns have increased ITN coverage and 

reduced socioeconomic inequity in ITN ownership over the past 10 years. Despite the gains observed in 

coverage of ITN ownership over the past years, further improvements are still needed to reach coverage 

targets. With the combination of continued net scale up and monitoring inequities, great strides can be made 

in combating malaria across SSA countries. 

25 

Appendix 

Table A.1. List of the surveys included in this report, with dates and sample sizes 

Country 

Survey 

Type 

Year of 

Survey 

Household 

Sample Size Fieldwork 

Angola MIS 2006-07 2,599 November 2006 - April 2007 

MIS 2011 8,030 January 2011 - May 2011 

Benin DHS 2006 17,511 August 2006 - November 2006 

DHS 2011-12 17,422 December 2011 - March 2012 

Burkina Faso DHS 2003 9,097 June 2003 - November 2003 

DHS 2010 14,424 May 2010 - January 2011 

Cameroon DHS 2004 10,462 February 2004 - August 2004 

DHS/MICS 2011 14,214 January 2011 - August 2011 

Congo 

(Brazzaville) 

DHS 2005 5,879 July 2005 - November 2005 

DHS/MICS 2011-12 11,632 September 2011 - February 2012 

Congo (DR) DHS 2007 8,886 January 2007 - August 2007 

DHS 2013-14 18,171 August 2013 - February 2014 

Guinea DHS 2005 6,282 February 2005 - June 2005 

DHS/MICS 2012 7,109 June 2012 - October 2012 

Madagascar 

DHS 2008-09 17,857 November 2008 - August 2009 

MIS 2011 8,094 March 2011 - June 2011 

MIS 2013 8,574 April 2013 - June 2013 

Malawi 

DHS 2004 13,664 October 2004 - January 2005 

DHS 2010 24,825 June 2010 - November 2010 

MIS 2012 3,404 April 2012 - May 2012 

Mali DHS 2006 12,998 May 2006 - December 2006 

DHS 2012-13 10,105 November 2012 - February 2013 

Mozambique MIS 2007 5,745 June 2007 – July 2007 

DHS 2011 13,919 June 2011 - November 2011 

Niger DHS 2006 7,660 January 2006 - May 2006 

DHS 2012 10,750 February 2012 - June 2012 

Nigeria 

DHS 2008 34,070 June 2008 - October 2008 

MIS 2010 5,895 October 2010 - December 2010 

DHS 2013 38,522 February 2013 - June 2013 

Rwanda 

DHS 2005 10,272 February 2005 - July 2005 

DHS 2010 12,540 September 2010 - March 2011 

MIS 2013 4,766 February 2012 - May 2013 

Senegal 

DHS 2005 7,412 February 2005 - May 2005 

MIS 2008-09 9,291 December 2008 - January 2009 

DHS 2010-11 7,902 October 2010 - April 2011 

Sierra Leone DHS 2008 7,284 April 2008 - June 2008 

DHS 2013 12,629 June 2013 - October 2013 

Tanzania 

DHS 2004-05 9,735 October 2004 - February 2005 

THMIS 2007-08 8,497 October 2007 - February 2008 

THMIS 2011-12 10,040 December 2011 - May 2012 

Uganda 

DHS 2006 8,870 May 2006 - October 2006 

MIS 2009 4,421 November 2009 - January 2010 

DHS 2011 9,033 June 2011 - December 2011 

Zimbabwe DHS 2005-06 9,285 August 2005 - February 2006 

DHS 2010-11 9,756 September 2010 - March 2011 

 
26 

Table A.2. Percentage of households with at least one insecticide-treated net, by wealth quintile 

and survey 

 Wealth Quintiles 

Country Survey Year 

Lowest 

(Q1) 

Second

(Q2) 

Middle 

(Q3) 

Fourth 

(Q4) 

Highest

(Q5) 

Angola MIS 2006 36.7 18.8 17.5 12.8 14.3 

MIS 2011 7.4 9.6 16.1 34.1 32.9 

Benin DHS 2006 10.0 14 19.8 25.6 30.6 

DHS 2011 21.0 20.5 20.7 19.8 18 

Burkina Faso DHS 2003 6.0 6.7 9.1 11.1 67.1 

DHS 2010 15.5 18 19 20.9 26.6 

Cameroon DHS 2004 7.1 14.3 15.5 26.8 36.3 

DHS/MICS 2011 16.1 20.2 21.6 21.2 20.9 

Congo 

(Brazzaville) 

DHS 2005 13.3 16.5 16.5 21.1 32.6 

DHS/MICS 2011 45.7 27.9 11.4 8.4 6.6 

Congo (DR) DHS 2007 11.0 16.1 20.8 22.4 29.7 

DHS 2013 23.5 21.8 21.5 18.8 14.4 

Guinea DHS 2005 10.0 13.7 16.1 25.1 35.1 

DHS/MICS 2012 23.5 19 19.6 23.4 14.6 

Madagascar 

DHS 2008 22.6 19.5 16.1 17 24.9 

MIS 2011 25.5 17.8 14.7 15.3 26.8 

MIS 2013 18.5 16.7 15.7 15.8 33.3 

Malawi 

DHS 2004 10.1 15.3 18.8 24.8 30.9 

DHS 2010 16.4 19.4 21.1 21.8 21.3 

MIS 2012 14.5 15.8 16.9 18.5 34.2 

Mali DHS 2006 17.2 19.6 19.5 21.8 21.9 

DHS 2012 18.7 19.8 19.5 19.1 22.9 

Mozambique MIS 2007 17.1 18.4 21.5 20.7 22.3 

DHS 2011 14.2 17.5 20.4 22.3 25.7 

Niger DHS 2006 17.1 17.3 17.5 20 28.1 

DHS 2013 13.8 15.9 17 20.4 32.9 

Nigeria 

DHS 2008 10.8 16.6 22.2 25.2 25.3 

MIS 2010 20.0 18.8 21.4 20 19.7 

DHS 2013 17.0 20.6 22.9 21.1 18.5 

Rwanda 

DHS 2005 6.3 12.7 11.7 18.8 50.5 

DHS 2010 19.8 19.6 19.6 19.2 21.7 

MIS 2013 20.2 19.8 18.9 18.8 22.2 

Senegal 

DHS 2005 16.1 21.8 28.9 18.7 14.5 

MIS 2008 28.3 29.4 20.8 13.6 8 

DHS 2010 31.0 26.7 20.6 13.2 8.5 

Sierra Leone DHS 2008 17.0 16.3 18.4 22.8 25.5 

DHS 2013 20.8 18.8 19.8 22.7 18 

Tanzania 

DHS 2004 4.9 9.8 14 23.2 48 

THMIS 2007 10.7 14.2 16.9 25.7 32.4 

THMIS 2011 19.9 19.1 19.3 22.1 19.5 

Uganda 

DHS 2006 20.8 18 11.3 15.6 34.3 

MIS 2009 25.3 17.7 16.6 17.4 23 

DHS 2011 20.1 17.2 16.5 17.4 28.8 

Zimbabwe DHS 2005 13.2 17 14.3 23.2 32.3 

DHS 2010 25.0 17.9 17.8 18.7 20.6 

 
27 

Table A.3. Percentage of households with at least one insecticide-treated net, including 95% 

confidence intervals, by survey 

Country 

Survey 

Type 

Year of 

Survey 

Proportion of households 

with at least one ITN 

% LCI UCI N 

Angola MIS 2006-07 27.5 23.6 31.8 2,599 

MIS 2011 34.5 31.8 37.3 8,030 

Benin DHS 2006 24.5 23.5 25.6 17,511 

DHS 2011-12 79.8 78.7 80.8 17,422 

Burkina Faso DHS 2003 5.6 4.8 6.6 9,097 

DHS 2010 56.9 55.2 58.6 14,424 

Cameroon DHS 2004 1.7 1.4 2.1 10,462 

DHS/MICS 2011 36.4 34.6 38.2 7,133 

Congo 

(Brazzaville) 

DHS 2005 8.0 6.4 10 5,879 

DHS/MICS 2011-12 33.1 31.2 35 11,632 

Congo (DR) DHS 2007 9.2 7.9 10.7 8,886 

DHS 2013-14 69.5 67.6 72.2 18,171 

Guinea DHS 2005 3.5 3.0 4.2 6,282 

DHS/MICS 2012 47.4 45.2 49.6 7,109 

Madagascar 

DHS 2008-09 57.0 55.0 59.1 17,857 

MIS 2011 80.4 77.0 83.7 8,094 

MIS 2013 68.6 65.4 71.2 8,574 

Malawi 

DHS 2004 27.4 25.8 29 13,664 

DHS 2010 56.8 55.5 58.1 24,825 

MIS 2012 55.0 51.1 58.9 3,404 

Mali DHS 2006 50.0 47.5 52.6 12,998 

DHS 2012-13 84.4 83.1 85.6 10,105 

Mozambique MIS 2007 15.7 14.1 17.5 5,745 

DHS 2011 51.4 49.5 53.4 13,919 

Niger DHS 2006 43.0 40.4 45.6 7,660 

DHS 2012 61.3 59.4 63.1 10,750 

Nigeria 

DHS 2008 8.0 7.3 8.6 34,070 

MIS 2010 41.5 37.2 46.0 5,895 

DHS 2013 49.5 47.7 51.4 38,522 

Rwanda 

DHS 2005 14.7 13.6 15.9 10,272 

DHS 2010 82.0 80.7 83.2 12,540 

MIS 2013 82.6 81 84.2 4,766 

Senegal 

DHS 2005 20.3 18.9 21.9 7,412 

MIS 2008-09 60.4 57.5 63.3 9,291 

DHS 2010-11 62.5 59.7 66.1 7,902 

Sierra Leone DHS 2008 36.6 34.3 39.0 7,284 

DHS 2013 64.4 62.3 66.6 12,629 

Tanzania 

DHS 2004-05 22.6 20.7 24.7 9,735 

THMIS 2007-08 39.2 37.0 41.4 8,497 

THMIS 2011-12 90.9 90.1 91.8 10,040 

Uganda 

DHS 2006 15.9 14.3 17.7 8,870 

MIS 2009 46.7 42.8 50.6 4,421 

DHS 2011 59.8 57.7 61.9 9,033 

Zimbabwe DHS 2005-06 8.5 7.3 9.8 9,285 

DHS 2010-11 28.8 26.0 31.7 9,756 

29 

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