REVIEW

Estimating the Number of Insecticide-TreatedNets Required by African Households to ReachContinent-wide Malaria Coverage TargetsJohn M. Miller, MPHEline L. Korenromp, PhDBernard L. Nahlen, MDRichard W. Steketee, MD

USE OF INSECTICIDE-TREATED

mosquito nets (ITNs) forprevention of malaria inAfrica is one of the pillars of

the Roll Back Malaria (RBM) partner-ship, especially for children youngerthan 5 years and pregnant women,populations that are particularly vul-nerable to malaria. In trials, ITNs havebeen shown to reduce all-cause mor-tality in children younger than 5 yearsacross malaria-endemic countries inAfrica.1,2 In addition, ITNs have beenshown to reduce morbidity and adversebirth outcomes in pregnant women.3-5

Monitoring ITN coverage in Afri-can countries is central to evaluating theprogress of malaria control under theRBM partnership. At the African Sum-mit on Roll Back Malaria in April 2000,African heads of state committed them-selves to ensuring that by 2005, at least60% of those at risk of malaria, par-ticularly pregnant women and chil-dren younger than 5 years, would ben-efit from the protection of ITNs.6 By2005, very few African countries re-ported progress toward these targets,7

despite which the World Health Orga-nization agreed to raise coverage to 80%by 2010.8,9 Although progress has beenslow, achieving these targets is aided by

increased funding and operational sup-port provided through RBM partners.In particular, initiatives such as theGlobal Fund to Fight AIDS, Tubercu-losis and Malaria (GFATM), the USPresident’s Malaria Initiative, the WorldBank Malaria Booster Programme, in-tegrated-ITN campaigns through theMeasles Initiative, and the Bill andMelinda Gates Foundation–sup-ported Malaria Control and Evalua-

Author Affiliations: PATH Malaria Control and Evalu-ation Partnership in Africa (MACEPA), National Ma-laria Control Centre, Lusaka, Zambia (Mr Miller); De-partment of Epidemiology, Tulane University Schoolof Public Health and Tropical Medicine, New Or-leans, La (Mr Miller); The Global Fund to Fight AIDS,Tuberculosis and Malaria, Geneva, Switzerland (DrsKorenromp and Nahlen); Erasmus MC, UniversityMedical Centre Rotterdam, Department of PublicHealth, Rotterdam, the Netherlands (Dr Koren-romp); and PATH MACEPA, Batiment Avant Centre,Ferney-Voltaire, France (Dr Steketee).Corresponding Author: John M. Miller, MPH, PATHMACEPA, PostNet PO Box 370, P/bag E10, Lusaka,Zambia (jmiller@path.org).

Context African countries are scaling up malaria interventions, especially insecticide-treated nets (ITNs), for which ambitious coverage targets have been set.

Objective To estimate how many ITNs are available in African households that areat risk of malaria and how many ITNs are needed to reach targets for use by childrenyounger than 5 years and pregnant women.

Data Sources Primary sources of data were the Multiple Indicator Cluster Surveys II,the Demographic and Health Surveys, or other nationally representative or large-scale household surveys that measured household possession and use of nets or ITNsamong children younger than 5 years.

Data Extraction Data from 42 household surveys between 1999 and 2006 on netand ITN coverage (either household possession or use) and average numbers of netsand ITNs per household were compared with populations and households at risk. Dataare included for 43 sub-Saharan African countries.

Data Synthesis For the median survey year 2003, the population-weighted meanproportion of households possessing at least 1 ITN was 6.7% (range among coun-tries, 0.1%-71.0%) and was 23.8% (range, 5.0%-91.2%) for any type of net. Basedon an average of 0.13 ITNs per household, we estimated that 53.6 million nets, ofwhich 16.7 million were ITNs, were available in households at risk of malaria. Be-tween 130 million and 264 million ITNs are required in 2007 to reach the 80% cov-erage target for about 133 million children younger than 5 years and pregnant womenliving in 123 million households in risk areas; the exact number depends on usage pat-terns (best estimate, assuming 55% of owned ITNs are used by the target groups,192 million ITNs).

Conclusion To achieve the targeted ITN usage rates, numbers of ITNs available toAfrican households must be dramatically increased.JAMA. 2007;297:2241-2250 www.jama.com

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tion Partnership in Africa (MACEPA),along with national governments, areaccelerating ITN distribution to achievehigh coverage in several areas.10-15

Progress is also reinforced by improve-ments in net manufacturing technol-ogy, thanks to which conventional ITNsthat required at least annual re-treatment are being replaced with moredurable and longer-lasting insecti-cidal nets (LLINs), whose residual in-secticidal activity lasts throughout theirexpected life span16-18 without need forre-treatment.

Two indicators are used commonlyfor monitoring mosquito net cover-age: (1) household possession of nets,which indicates the extent to which dis-tribution channels are enabling highcoverage, and (2) use of nets by chil-dren younger than 5 years, which cor-relates with protection and, therefore,is a more direct predictor of epidemio-logical impact.19 Between 1999 and2004, nationally representative house-hold surveys provided the first cover-age assessments for 36 sub-Saharancountries in Africa. From these sur-veys, only 3% of children younger than5 years and 2.8% of pregnant womenslept under ITNs.7 These unaccept-ably low coverage estimates are par-tially explained by very low availabil-ity of nets within African households,with only 18% of households possess-ing 1 or more nets (insecticide-treatedor untreated).

The purpose of this article is to es-timate the total number of nets andITNs currently available at the house-hold level in sub-Saharan Africa and thenumber needed to achieve the 80% cov-erage target for children younger than5 years and pregnant women. We com-pared survey data on current net andITN coverage with estimates of popu-lation and households at malaria risk.

METHODSData Sources

The primary sources of data on mos-quito net coverage used in the analysiswere the Multiple Indicator ClusterSurveys II (MICS)20 and the Demo-graphic and Health Surveys (DHS),21 2

well-established international house-hold survey instruments used in manydeveloping countries for monitoringand evaluating a variety of healthinterventions. Between 1999 and2001, MICS surveys conducted in 24malaria-endemic African countries,with support from the United NationsChildren’s Fund, included questionson the use of mosquito nets and ITNsamong children younger than 5 years.Multiple Indicator Cluster Surveys arenationally representative householdsurveys, sampled through a 2-stagecluster design with a sample size ofapproximately 5000 households. TheDHS surveys are nationally representa-tive household surveys that focus onreproductive and child health. Demo-graphic and Health Surveys are orga-nized through national ministries ofhealth, national statistics bureaus, andthe MEASURE DHS program as imple-mented by ORC Macro, Calverton,Md. Typically, DHS consist of inter-views with approximately 8000women aged 15 to 49 years living inhouseholds that are sampled in amultiple-stage cluster design.

The survey instruments have beenextensively validated through pretest-ing between 1998 and 2003 and fur-ther standardized through interna-tional collaboration coordinatedthrough the RBM Monitoring andEvaluation Reference Group (MERG)Household Survey Task Force. Theseinstruments represent the gold stan-dard for assessing global targets for ITNcoverage and are used by most coun-tries for reporting on the United Na-tions’ Millennium Development Goals.22

Between 1998 and 2006, 22 DHS sur-veys in African countries asked ques-tions related to household net posses-sion or use.

Where surveys were not availablefrom DHS or MICS, we consideredother nationally representative esti-mates or malaria-specific surveys as-sessing net and ITN coverage, which in-cluded recent surveys from Eritrea,Somalia, Togo, and Zambia.23-26 TheMay-June 2006 survey in Zambia wasthe first application of the standard-

ized RBM MERG Malaria Indicator Sur-vey, following the DHS questionnairemodel, which assesses coverage of ma-laria interventions along with malariaparasite and anemia prevalence.7 Fi-nal reports from the MICS, DHS, andRBM MERG Malaria Indicator Sur-veys include details of response ratesand estimate precision, and the re-sults reflect weighted, nationally rep-resentative estimates to account for de-sign effect of cluster and householdselection.

Current Number of Nets

The total number of mosquito nets(treated or untreated) was calculatedfrom the proportion of households thatpossessed at least 1 net or ITN, the av-erage household size, the number ofhouseholds in the country, and the av-erage number of nets or ITNs perhousehold. The proportion of house-holds that possessed at least 1 mos-quito net (irrespective of whether in-secticide-treated) was known from 21nationally representative or other sur-veys; similar data for ITNs specificallywere available from 17 surveys. For theremaining countries with survey dataavailable, household possession was es-timated from reported use by childrenyounger than 5 years, based on a pre-viously established correlation for Afri-can countries between 1999 and 200219:(1) percentage of net use in childrenyounger than 5 years=−4.2%�0.875�(percentage of households possessing1 or more nets) (R2=0.89; P�.001) and(2) percentage of ITN use in childrenyounger than 5 years=0.55�(percent-age of households possessing 1 or moreITNs) (R2=0.97; P�.001).

For countries with no estimate ofpossession or use in children youngerthan 5 years, we used as a nearest-neighbor approximation the averagefrom possession estimates (either di-rectly observed or estimated from netuse) from all neighboring countrieswith a survey estimate available. Forcountries where surveys had mea-sured net possession but not ITN pos-session, the latter was estimated fromthe former using the mean ratio in pos-

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session of nets vs ITNs from all sur-veys that had measured both of theseindicators.

The total number of householdswithin a country was estimated fromaverage household size and totalpopulation. For 34 countries, the mostrecently available nationally represen-tative survey provided the averagehousehold size. For countries with noavailable estimate of average house-hold size, the median value of docu-mented median household sizes fromthese 34 other countries (5.1) wasused. Total population, populationyounger than 5 years, and femalepopulation aged 15 to 49 years wereabstracted from the World PopulationProspects Population Database foreach country in Africa, for the year inwhich the corresponding net coveragesurvey was conducted or the year2003.27 The medium variant assump-tions of changing mortality, fertility,and migration for total population sizewere used.

Required Number of Nets

Not all households in sub-SaharanAfrica are at risk of malaria, nor do allhouseholds have pregnant women orchildren younger than 5 years. The pro-portion of households with childrenyounger than 5 years and with womenaged 15 to 49 years was reported in 22MICS surveys. We estimated the pro-portion of households with pregnantwomen from the proportion of house-holds with women aged 15 to 49 yearsand the ratio of pregnant women to allwomen aged 15 to 49 years. The latternumber was derived from numbers ofpregnancies, estimated from numbersof live births (projection for interval2000-2005 from the World Popula-tion Prospects Population Database27

plus the number of maternal deathsduring pregnancy27,28). For countrieswith no MICS data available, the me-dian value of all other countries (ie,61.8% of households with children �5years and 86.7% with women aged15-49 years) was used.

The aforementioned relationship be-tween household ITN possession and

use among children younger than 5years19 was further used to derive thetotal required number of ITNs from theresulting total households.

Malaria Risk

Estimates of populations living in areasat risk of malaria transmission were de-rived from the Mapping Malaria Riskin Africa (MARA/ARMA) project.29 Thedefinition of a population at risk of ma-laria was based on a continental distri-bution model of climate suitability formalaria transmission that, for all coun-tries in middle Africa, produced the fol-lowing risk categories: no malaria risk,epidemic malaria risk, and endemic ma-laria risk.30 Southern African coun-tries (Botswana, Namibia, South Africa,Swaziland, and Zimbabwe), which havea different pattern of malaria transmis-sion, were categorized as areas of anymalaria risk or no malaria risk.31 MARAcombined this model with a popula-tion distribution map from interpo-lated human settlement to determinethe number of people at any malaria risk(which includes both epidemic and en-demic malaria) or endemic malariarisk.32 The population totals were ag-gregated to the national level to deter-mine a fixed percentage of populationat risk in each country.33

To apply the national-level cover-age estimates to percentage popula-tions at risk, we assumed that cur-rently available nets are equallydistributed across risk and nonriskareas. This was thought to be reason-able because in many surveyed coun-tries, net coverage is equally high orhigher in urban (less malarious) areasthan in rural (more malarious) areas.34

The sensitivity of outcomes to this as-sumption was assessed in a sensitivityanalysis.

RESULTSCurrent Number of Nets

TABLE 1 presents the reported andestimated household possession ofany nets and ITNs by country. Of the48 countries in sub-Saharan Africa, 43were included in the analysis. Djibouti,Cape Verde, Lesotho, and the island

nations of Seychelles and Mauritiuswere excluded because the risk ofmalaria transmission is minimal, ab-sent, or confined to small focal areas.Sudan was considered part of sub-Saharan Africa. Twenty-three coun-tries had nationally representative ormultidistrict survey estimates (18 DHS,1 AIDS Indicator Survey,35 and 4 ma-laria-specific23-26) of household posses-sion of nets between 1999 and 2006 thatwere included in the analysis; 19 coun-tries had nationally representative ormultidistrict survey estimates (15 DHSand 4 malaria-specific surveys) ofhousehold possession of ITNs. A fur-ther 16 countries had nationally rep-resentative surveys (15 MICS and 1DHS) measuring use among childrenyounger than 5 years of nets and ITNs,from which we estimated householdpossession. For countries where mul-tiple surveys were available, prefer-ence was given to estimates based on anationally representative sample de-sign and to those that were more re-cent. For 3 countries (Botswana, Libe-ria, and South Africa), no estimate ofeither net or ITN possession or usagewas available, and we used a nearest-neighbor approximation.

Across the 43 countries in sub-Saharan Africa, the estimated overallpopulation-weighted proportions ofhouseholds that possessed any net(s)or ITN(s) were 23.8% and 6.7%, re-spectively (Table 1). For the mediansurvey year 2003, a total of 31.9 mil-lion households were estimated to haveat least 1 mosquito net and 9.4 millionhouseholds to have at least 1 ITN insub-Saharan Africa.

To convert households possessing 1or more nets (or ITNs) to total num-ber of nets, we multiplied the re-ported households with 1 or more netsby the average number of nets perhousehold. Of the available nationallyrepresentative surveys, 15 includedquestions on the average number of netsor ITNs a household possessed. Fromthese surveys, a clear correlationemerged between the average numberof nets and ITNs per household andthe proportion of households with at

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Table 1. Estimates of Household (HH) Possession of Mosquito Nets and ITNs in 43 Sub-Saharan African Countries

Country (Source of Data on NetPossession or Use)

No. ofHHs,

�1000

MeanHH Size,

No. ofPeople

No. (%) ofHHs With�1 Net,�1000

MeanNo. ofNets,

per HH

No. (%) ofHHs With�1 ITN,�1000

MeanNo. ofITNs

per HH

Netsin HHs,�1000

ITNsin HHs,�1000

Angola (MICS, 2001) 2815 5.1 a 463 (16.5)b 0.29c 118 (4.2)b 0.10c 815 273Benin (DHS, 2001) 1428 5.2 574 (40.2) 0.74c 193 (13.5) 0.27c 1058 381Botswana 351 5.1a 106 (30.2)d 0.55c 40 (11.5)d 0.23c 193 81Burkina Faso (DHS, 2003) 1910 6.5 772 (40.4) 0.70 88 (4.6) 0.10 1337 191Burundi (MICS, 2000) 1426 4.6 111 (7.8)b 0.12c 34 (2.4)b 0.06c 177 91Cameroon (DHS, 2004) 3341 4.8 675 (20.2) 0.30 47 (1.4) 0.05c 1002 154Central African Republic (MICS, 2000) 771 4.9 309 (40.1)b 0.74c 21 (2.7)b 0.07c 570 54Chad (DHS, 2004) 1750 5.4 1123 (64.2) 1.20c 25 (1.4)e 0.05c 2094 81Comoros (MICS, 2000) 111 6.3 51 (46.4)b 0.86c 19 (17.0)b 0.33c 95 37Congo (DHS, 2005) 769 5.2 581 (75.5) 1.50 62 (8.0) 0.10 1154 77Democratic Republic of Congo (MICS, 2001) 8017 6.4 1466 (18.3)b 0.32c 103 (1.3)b 0.04c 2599 351Côte d’Ivoire (MICS, 2000) 2699 6.2 426 (15.8)b 0.28c 54 (2.0)b 0.06c 746 154Equatorial Guinea (MICS, 2000) 75 6.0 17 (22.4)b 0.40c 1 (1.3)b 0.04c 30 3Eritrea (MSS, 2003) 844 4.8 770 (91.2) 1.46 600 (71.0) 1.31c 1232 1108Ethiopia (DHS, 2005) 18 436 4.2 1051 (5.7) 0.07 627 (3.4) 0.02 1291 369Gabon (DHS, 2000) 254 5.0 38 (14.8)b 0.26c 3 (1.3)d 0.04c 66 11Gambia (MICS, 2000) 261 5.1a 138 (52.9)b 0.98c 70 (26.9)b 0.51c 256 133Ghana (DHS, 2003) 5303 4.0 933 (17.6) 0.26 170 (3.2) 0.08c 1379 417Guinea (DHS, 2005) 1541 6.1 413 (26.8) 0.67 8 (0.5) 0.03c 1037 46Guinea-Bissau (MICS, 2000) 270 5.1a 219 (81.1)b 1.52c 37 (13.5)b 0.27c 411 72Kenya (DHS, 2003) 7613 4.3 1660 (21.8) 0.40 206 (2.7) 0.10 3045 761Liberia 638 5.1a 138 (21.6)d 0.39c 53 (8.2)d 0.17c 247 109Madagascar (DHS, 2004) 3938 4.6 1532 (38.9) 0.72c 340 (8.6) 0.18c 2819 700Malawi (DHS, 2004) 2866 4.4 1201 (41.9) 0.70 785 (27.4) 0.40 2006 1146Mali (DHS, 2001) 2263 5.3 1231 (54.4) 1.01c 33 (1.4) 0.05c 2287 106Mauritania (DHS, 2004) 514 5.8 288 (56.0) 1.00 1 (0.1) 0.40 514 206Mozambique (DHS, 2003) 3888 4.9 692 (17.8) 0.31c 292 (7.5) 0.16c 1225 611Namibia (DHS, 2000) 389 5.1 51 (13.1) 0.23c 19 (5.0)d 0.11c 88 43Niger (MICS, 2000) 1997 5.9 475 (23.8)b 0.43c 37 (1.8)b 0.05c 856 107Nigeria (DHS, 2003) 25 182 5.0 2972 (11.8) 0.23 554 (2.2) 0.06 5792 1524Rwanda (DHS, 2005) 1965 4.6 358 (18.2) 0.30 289 (14.7) 0.20c 589 393Sao Tome and Principe (MICS, 2000) 28 5.1a 15 (53.4)b 0.99c 12 (41.7)b 0.78c 27 22Senegal (DHS, 2005) 1340 8.7 509 (38.0) 0.90 271 (20.2) 0.40 1206 536Sierra Leone (MICS, 2000) 893 5.1a 198 (22.2)b 0.40c 24 (2.7)b 0.07c 355 63Somalia (MSS, 2005) 1629 5.1a 228 (14.0) 0.24c 33 (2.0) 0.06c 396 93South Africa 11 171 4.2 1230 (11.0)d 0.19c 470 (4.2)d 0.10c 2077 1083Sudan, North (MICS, 2000) 4985 6.6 1555 (31.2)f 0.57c 36 (0.7)f 0.03c 2840 168Swaziland (MICS, 2000) 203 5.1a 10 (5.0)b 0.07c 0.4 (0.2)b 0.02c 15 4.8Togo (MSS, 2005) 1138 5.4 747 (65.6) 1.22c 711 (62.5) 1.16c 1392 1318Uganda (AIS, 2005) 6003 4.8 1561 (26.0) 0.50 43 (0.7)e 0.03c 3002 201Tanzania (DHS 2004) 7679 4.9 3555 (46.3) 0.90 1735 (22.6) 0.40 6911 3072Zambia (MIS, 2006) 2244 5.2 1124 (50.1) 0.90 996 (44.4) 0.78 2020 1750Zimbabwe (DHS, 1999) 2970 4.2 303 (10.2) 0.17c 771 (3.9)d 0.09c 507 1464Mean, population-weighted 5.2 23.8 0.44 6.7 0.13Median 5.1 26.0 0.50 3.9 0.10Total 143 908 31 867 9373 57 754 18 371Abbreviations: AIS, AIDS Indicator Survey21; DHS, Demographic and Health Surveys21; ITN, insecticide-treated net; MICS, Multiple Indicator Cluster Surveys II20; MIS, Malaria Indicator

Surveys23; MSS, malaria-specific survey.24-26

a In the absence of data, the median household size (5.1 people) across countries with data points (from the DHS) was used instead.bEstimated from reported use in children younger than 5 years according to (1) percentage of mosquito net use in children younger than 5 years = −4.2% � 0.875 � (percentage of

households possessing �1 mosquito nets) and (2) percentage of ITN use in children younger than 5 years = 0.55 � (percentage of households possessing �1 ITNs).19

cApproximated from the relationship between the proportion of households with at least 1 net or ITN and the corresponding mean nets or ITNs per household (Figure 1).d In the absence of survey data, estimated as the mean among neighboring countries.eThe surveys from Chad (DHS, 2004) and Uganda (AIS, 2005) did not report on the treatment status of nets available in households. We therefore estimated ITN possession based on

the relative net vs ITN coverage from the previous surveys (MICS, 2000, and DHS, 2000-2001, respectively) available for these countries.fData reflect only northern Sudan.

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least 1 net or ITN (FIGURE 1). Thisrelationship was subsequently used toestimate the average number of nets andITNs in households for surveys that hadnot directly measured the ITNs. Forcountries with no measure of percent-age of households with at least 1 ITN,this measure was estimated using theaverage ratio in possession of nets vsITNs observed from surveys that hadmeasured both of these indicators. Mul-tiplying the total number of house-holds and the estimated number of netsand ITNs per household for each coun-try, a total of 57.8 million nets and 18.4million ITNs were estimated to be pre-sent in households in these countries(Table 1). Of these, we estimated that16.7 million ITNs were located in areasat any malaria risk and 15.2 millionITNs were located in areas at endemicrisk (TABLE 2).

Required Number of Nets

For the median survey year 2003, weestimated that in sub-Saharan Africa,133.4 million children younger than 5years and pregnant women are livingat risk of malaria; 118.5 million of theselive in areas of endemic risk (Table 2).These children younger than 5 yearsand pregnant women live in an esti-mated 86.4 million households at riskof malaria, of which 75.0 million house-holds are at endemic risk.

The required number of ITNs for useby 80% of target populations does notdirectly follow from these populationsizes, since this depends on behav-ioral usage patterns: are all owned netsin use and, if so, by pregnant womenand/or children younger than 5 yearsrather than by other household mem-bers? How many women and youngchildren sleep under the same net? Also,the extent to which net distribution canbe targeted to households in risk areas(how many owned and used nets arein households at actual malaria risk)and, more specifically, to householdswith pregnant women and children, willmake a difference. A previous analysisof 12 African DHS surveys found that,on average, in only 55% of house-holds that possessed 1 or more ITNs

were children younger than 5 years(among) the reported net users(R2=0.97; P�.001).19

If we assume that all ITNs couldbe effectively targeted to householdswith pregnant women and childrenyounger than 5 years in areas at riskand that 55% of nets owned by thesehouseholds would be used by thosetarget groups, at a minimum 119.4million ITNs would be needed toensure that 80% of pregnant womenand children younger than five yearsat risk slept under them during themedian survey year 2003. In theabsence of effective targeting to house-holds with young children and/orpregnant women, this same usage tar-get would require 175.2 million ITNsfor 2003. Assuming instead that ITNswill get equally distributed over areasat risk and not at risk, the numberrequired for use by 80% of young chil-dren and pregnant women would be203.9 million. And, as a maximum, afixed 2 ITNs for all households in riskareas, regardless of whether they havepregnant women and/or childrenyounger than 5 years, would require atotal of 240.9 million ITNs.

The minimum, mean, and maxi-mum estimates of 119.4 million, 175.2million, and 240.9 million required

ITNs from the median survey year 2003used in the analysis would correspondto 130.2 million, 192.1 million, and264.1 million ITNs in 2007, respec-tively, given population growth(FIGURE 2). Assuming that nets wearout after 4 years,18,36 these numberswould correspond to at least 37 mil-lion additional ITNs every year toachieve the 80% ITN coverage target forthe African continent by 2010. Figure 2also shows what increase in net avail-ability might be expected over time,based on the observed increase be-tween 2000 and 2003.

Sensitivity Analysis

We assessed the sensitivity of esti-mated numbers of nets and ITNspresent in households against severalassumptions (TABLE 3).

Under the assumption that net distri-bution has until now been effectively tar-geted so that all currently available mos-quito nets and ITNs are present inmalaria risk areas, the estimated num-bers of nets and ITNs in areas at any riskof malaria increase slightly, from 53.6million to 57.8 million and from 16.7million to 18.4 million, respectively.

Not all surveys identify a treated netin the same manner. Recent DHS sur-veys (�2005) defined ITNs as those

Figure 1. Correlation Between the Proportion of Households With at Least 1 Net andAverage Number of Nets per Household

1.8

0.6

1.2

1.0

0.8

1.4

1.6

0.4

0.2

0

0 2010 30 40 50 60 70 80 90Households With at Least 1 Net, %

Mea

n N

o. o

f Net

s pe

r H

ouse

hold

ITNsAny Nets

Best Fit for Any NetsBest Fit for ITNs

Open circles indicate data points for any nets from available nationally representative surveys separately forboth urban and rural areas; filled circles, data points for insecticide-treated nets (ITNs) from available nation-ally representative surveys for both urban and rural areas; dashed line, best fit for average number of nets perhousehold predicted from the proportion of households with at least 1 net (y=1.9288x; R2=0.95); solid line,best fit for average number of ITNs, including nets obtained or re-treated in the 6 or 12 months before thesurvey, per household predicted from the proportion of households with at least 1 ITN (y=1.8199x; R2=0.97).

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Table 2. Numbers of ITNs Currently Available and Required in Households (HHs) in 43 Sub-Saharan African Countries*

Country

Populationat Risk

of Malaria, %HHs in Areas at Risk,

�1000

ITNs Currently Availablein Areas at Risk,

�1000†

HHs With Children �5 yand/or Pregnant Women

in Areas at Risk,�1000

Children �5 yand Pregnant WomenLiving in Areas at Risk,

�1000

Angola 99 (90) 2782 (2546) 270 (247) 1916 (1754) 3507 (3210)

Benin 100 (100) 1428 (1428) 381 (381) 983 (983) 1616 (1616)

Botswana 32 111 25 43 96

Burkina Faso 100 (100) 1910 (1910) 191 (191) 1322 (1322) 3054 (3054)

Burundi 85 (68) 1209 (963) 77 (61) 802 (639) 1218 (970)

Cameroon 98 (94) 3273 (3126) 150 (144) 1896 (1811) 3039 (2902)

Central African Republic 100 (100) 771 (771) 54 (54) 520 (520) 764 (764)

Chad 100 (96) 1750 (1688) 81 (79) 1221 (1178) 2253 (2173)

Comoros‡ 100 111 37 108 140

Congo 100 (100) 769 (769) 77 (77) 535 (535) 918 (918)

Democratic Republicof Congo

94 (92) 7552 (7343) 330 (321) 5622 (5466) 10 882 (10 581)

Côte d’Ivoire 100 (100) 2699 (2699) 154 (154) 1998 (1998) 3407 (3407)

Equatorial Guinea 99 (98) 74 (73) 3 (3) 51 (50) 97 (96)

Eritrea 99 (92) 836 (778) 1098 (1022) 567 (528) 863 (803)

Ethiopia 64 (40) 11 731 (7320) 235 (146) 8145 (5082) 10 976 (6848)

Gabon 96 (96) 245 (245) 11 (11) 163 (163) 223 (223)

Gambia 100 (100) 261 (261) 133 (133) 159 (159) 258 (258)

Ghana 100 (100) 5303 (5303) 417 (417) 3528 (3528) 3749 (3749)

Guinea 100 (100) 1541 (1541) 46 (46) 1065 (1064) 2071 (2071)

Guinea-Bissau 100 (100) 269 (269) 72 (72) 236 (236) 332 (332)

Kenya 78 (53) 5921 (4066) 592 (407) 4042 (2776) 4658 (3199)

Liberia 100 (100) 638 (638) 109 (109) 447 (447) 770 (770)

Madagascar 96 (89) 3786 (3508) 673 (623) 2735 (2534) 2853 (2570)

Malawi 99 (97) 2844 (2771) 1138 (1109) 1969 (1918) 2949 (2873)

Mali 100 (99) 2262 (2242) 106 (105) 1507 (1556) 2971 (2945)

Mauritania 100 (65) 513 (336) 205 (134) 351 (230) 176 (115)

Mozambique 100 (100) 3882 (3870) 610 (608) 2641 (2632) 3995 (3983)

Namibia 41 159 18 106 159

Niger 100 (97) 1995 (1938) 107 (104) 1581 (1536) 3136 (3048)

Nigeria 100 (100) 25 181 (25 176) 1524 (1523) 17 201 (17 197) 26 758 (26 752)

Rwanda 67 (53) 1309 (1041) 262 (208) 701 (557) 1345 (1070)

Sao Tome and Principe‡ 100 28 22 14 27

Senegal 100 (100) 1340 (1340) 536 (536) 1044 (1044) 2349 (2349)

Sierra Leone 100 (100) 893 (893) 63 (63) 560 (560) 1040 (1040)

Somalia 99 (20) 1624 (327) 92 (18) 1130 (227) 2052 (413)

South Africa 20 2209 214 1434 1244

Sudan 99 (74) 4922 (3692) 166 (125) 4922 (3692) 6009 (4508)

Swaziland 77 155 4 83 152

Togo 100 (100) 1138 (1138) 1318 (1318) 993 (993) 1278 (1278)

Uganda 93 (90) 5586 (5413) 187 (181) 3991 (3868) 6891 (6678)

Tanzania 96 (93) 7381 (7153) 2953 (2861) 5013 (4858) 7648 (7411)

Zambia 99 (96) 2223 (2156) 1734 (1682) 1373 (1332) 2560 (2484)

Zimbabwe 84 2501 1232 1663 1957

Total 91 (74) 123 105 (106 730) 16 701 (15 237) 86 442 (74 972) 133 442 (118 460)

80% target NA NA NA NA 106 754 (94 768)Abbreviations: ITN, insecticide-treated net; NA, not applicable.*Data are estimates for any (endemic or epidemic) malaria risk; data in parentheses are for endemic malaria risk.†Assumes that ITNs are equally distributed over risk and nonrisk areas.‡Comoros and Sao Tome and Principe are not included in the continental malaria distribution and risk analysis from the Mapping Malaria Risk in Africa project. We assumed 100% of the

populations of these countries to be at risk of malaria.

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obtained or re-treated with insecticidein the 12 months preceding the sur-vey, whereas most DHS surveys con-ducted in 2003 or 2004 reported netsas insecticide-treated only if they wereobtained or re-treated in the 6 monthspreceding the survey, a more strict defi-nition. The revised, less strict defini-tion corresponds better with opera-tional re-treatment efforts, such as thoseconducted during focused annual childhealth weeks or with mass immuniza-tion campaigns, as are common in manyof these countries. Surveys with thenew, less strict definition had, on av-erage, about 0.16 more ITNs per house-hold. While this may be a result ofcountry-specific distribution success ortiming of measurement, adjusting re-sults from surveys reporting the morestrict definition by this difference leadsto a higher estimate: 22.1 million ITNsavailable within all malaria risk areas.Using a fixed median number of netsper household (0.44 for any net and0.13 for ITNs; Table 1) instead of coun-try-specific estimates did not greatlyaffect the total number of nets and ITNsin malaria risk areas (estimates 53.6 and16.2 million, respectively).

Availability of ITNs in South Africa,Botswana, and Liberia, countries withno available survey reports of house-hold possession of nets or ITNs, mayin reality be lower that what we as-

sumed based on data from neighbor-ing countries because of recent inter-nal conflict and health service deliveryinterruption in Liberia37 and the rela-tively low burden of malaria and reli-ance on indoor residual spraying inSouth Africa and Botswana.38 Chang-

ing these assumptions did not, how-ever, appreciably affect the continen-tal estimates (Table 3).

COMMENTAchieving high ITN coverage, definedas 80% of children younger than 5 years

Figure 2. Currently Available and Required Number of ITNs in 43 Countries in Sub-SaharanAfrica

300

100

50

150

200

250

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010Year

No.

of N

ets,

in M

illion

s

ITNsAny Nets

Required No. of ITNs

The shaded area represents the required number of insecticide-treated nets (ITNs), where (1) the minimum isthe number of households with children younger than 5 years or pregnant women living in risk areas times80% targeted coverage divided by an estimated 55% use by children younger than 5 years or pregnant womenin households possessing 1 or more nets19; (2) the best estimate (solid line) is the total number of householdsin risk areas times 80% targeted coverage divided by an estimated 55% use given household net possession;and (3) the maximum is 2 ITNs for all households in malaria risk areas, regardless of whether they containchildren younger than 5 years or pregnant women. Filled and open circles represent numbers of ITNs and anynets, respectively, that were actually available in households at risk in the respective median survey years (2003:see Table 2 and Table 3; 2000: from a separate analysis, using similar methods, that considered additionalolder surveys). Dotted line represents the trend in ITNs expected from actual ITNs available in households fromthe 2000 and 2003 analyses. Dashed line represents the acceleration in ITN distribution required to reach the2010 target from existing levels in 2003. Required numbers of nets were projected into the future based onestimated annual population growth27 and assuming constant household size, percentages of households withat least 1 child younger than 5 years or pregnant woman, crude birth rates, maternal mortality rates, and popu-lations at malaria risk.

Table 3. Sensitivity to Uncertain Assumptions of Estimated Numbers of Nets and ITNs Present in Households (HHs) in 43 Sub-Saharan AfricanCountries

Alternative Assumption Default Assumption

All Netsin HHs,�1000

All ITNsin HHs,�1000

Nets in HHsat Malaria Risk,

�1000

ITNs in HHsat Malaria Risk,

�1000

Default estimate (Table 1 and Table 2) 57 754 18 371 53 590 16 701

Currently available nets and ITNs arein malaria risk areas only

Currently available nets and ITNs areequally distributed over malaria riskand nonrisk areas

NA NA 57 754 18 371

For countries with no surveyestimates of HH possession of�1 net, assume the lowest decileof continent-wide estimates

For countries with no survey estimate(South Africa, Botswana, and Liberia),assume the average of contiguousneighboring countries for householdpossession of �1 net or ITN

57 496 18 278 53 421 16 639

All ITNs obtained or re-treated duringthe 12 months preceding thesurvey are considered ITNs

Only ITNs reported to have been obtainedor re-treated within 6 or 12 monthsare counted as ITNs*

NA 23 728 NA 22 053

Fixed No. of 0.44 nets or 0.13 ITNs(Table 1) per HH for all countries

No. of nets or ITNs per HH estimated bylinear approximation from % of HHswith nets or ITNs (Figure 1)

62 699 18 923 53 635 16 178

Abbreviations: ITN, insecticide-treated net; NA, not applicable.*Seven surveys reported results using the 6-month cutoff and 7 surveys used the 12-month cutoff.

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and pregnant women sleeping under anITN the night before the survey inter-view, requires many more ITNs than arecurrently available in African house-holds. Based on available survey databetween 1999 and 2006, we estimatedthat 18.4 million ITNs are available inhouseholds in 43 countries in sub-Saharan Africa; of these, 16.7 and 15.3million are likely to be present in areasat any malaria risk and endemic ma-laria risk, respectively. Using evi-dence that links ITN availability at thehousehold level to ITN usage amongtarget populations provides a more re-alistic estimate of the numbers of ITNsrequired to reach high continent-widecoverage targets. Furthermore, it guidesoperational ITN delivery require-ments to ensure a maximum health im-pact.

Several limitations in coverage datamake this first continent-wide esti-mate uncertain. Of particular con-cern, the surveys in Somalia and Su-dan did not include representativenational samples. In Somalia, re-stricted access due to security con-cerns makes comprehensive house-hold surveys difficult, while in Sudan,only the northern, less malarious areaof the country was included. Esti-mates will be improved and updatedwith forthcoming DHS and MICS sur-veys in additional countries that use amore standardized malaria survey ques-tionnaire, method of analysis, and tabu-lations.20,21 In addition, the RBM MERGpromotes a standardized household sur-vey method for consistent, cross-country comparison as the scale-up ofmalaria interventions progresses.7 Thismethod, based on a full net rosterwithin households, identifies the rangeof ITNs available including conven-tional nets that were recently ob-tained and/or re-treated within the pre-vious 12 months, as well as LLINs, allof which are considered insecticide-treated.

Further uncertainty relates to bi-ases in the interpretation of survey data.Many DHS and MICS surveys were con-ducted in the dry season, when mos-quito net use is typically lower (up to

5-fold39-42) than during the rainy sea-son, when most malaria transmissionoccurs. For countries where we esti-mated net possession from net use, wemay thus have underestimated thenumber of available nets.

For ITNs specifically, many oldersurveys did not inquire whether ITNshad been obtained or (re-)impreg-nated within the preceding 6 or 12months, which is, however, critical tomaintain efficacy in killing and repel-ling mosquitoes. At the time of manyof the surveys analyzed here, whenlarge-scale ITN distribution was justbeginning, most ITNs were likely tohave been recently acquired andimpregnated. However, for futureDHS, MICS, and malaria-specific sur-veys, it will be important to includequestions on time since reimpregna-tion (and washing, which influencesinsecticide disappearance43) andbranding, to identify LLINs.

In addition to nets already presentwithin households, for which house-hold surveys measure coverage, coun-tries have increasing numbers of netsavailable in the distribution systems orin the marketplace, which had not yetreached households. Notably, since2003 almost all African countries havereceived or will receive money for ma-laria control from the GFATM throughits first 5 rounds of grant proposals.10

These GFATM grants, the largest com-mitted funding source enabling ITNprocurement, include allocations for theprocurement of approximately 109 mil-lion ITNs worldwide, of which by theend of December 2006 approximately18 million had been delivered to house-holds.10 The remaining ITNs are await-ing purchase through tendering andprocurement processes, are in the pro-duction pipeline, or are awaiting dis-tribution at national or local levels overthe 5-year terms of the respective grants.In addition, the World Bank MalariaBooster program supports 9 Africancountries, with an anticipated expan-sion to 6 more, with support for ITNprocurement embedded in many coun-tries’ action plans.15 Activities of the US-based President’s Malaria Initiative, in-

cluding support for ITN procurement,are currently targeted in 7 African coun-tries.11 This concentration of fundingprovides realistic opportunities forcountries to deploy ITNs on a large scaleand to sustain high coverage over time.

The introduction of new ITN prod-ucts will also transform the availabil-ity and measurement of ITN coverage.To date, only 3 ITN products have beengranted LLIN status, thus conformingto the World Health Organization Pes-ticide Evaluation Scheme 20-wash cri-teria, and are identified through theirbranding in surveys.44 A new productto transform conventional nets intoLLINs, using K-O Tab 1-2-3 tablets(Bayer Vector Control) has shownpromise in evaluations and is gainingmomentum to address the needs of ex-isting net re-treatment.44,45 For under-standing insecticidal status of nets, thispresents new challenges for measure-ment 12 months after the last re-ported re-treatment.

Coverage with ITNs is clearly in-creasing, given that ITNs did not existoutside of research settings until 1997.The pace of increase in net coverage ob-served between 2000 and 2003(Figure 2) suggests that coverage in2005 was unlikely to have achieved thethen 60% African Summit on Roll BackMalaria target and that achievement of80% coverage by 2010 would requirea substantial acceleration of net distri-bution efforts. However, more rapid in-creases in net coverage have been docu-mented in specific ITN promotionprograms recently, particularly in Togo,Burundi, Tanzania, and Ghana,26,41,46,47

indicating that with increased finan-cial and human resources it is pos-sible to rapidly scale up ITN coverage.The impressive increases in (bud-geted) ITN procurements achieved dur-ing the past 3 years through interna-tional financing initiatives further justifythe impression that ITN coverage is in-creasing exponentially.

Regular representative householdsurveys of ITN possession and use areneeded in all countries to enable com-prehensive assessments and evalua-tion of progress over time and in a

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timely manner. The 2006 round ofMICS and several ongoing DHS sur-veys are due for release by mid-2007,providing much needed progressbenchmarks for many African coun-tries.

The need for ITNs is dramatic. Be-tween 2003 and 2010, at least 37 mil-lion additional ITNs will be needed inhouseholds annually to achieve the 80%ITN coverage target for the African con-tinent by 2010, assuming available netswear out after 4 years18,36 (Figure 2).With modest cost estimates for long-lasting ITN purchase (US $4.55)48 anddelivery (US $1.40),49 this representsmore than US $220 million annually toincrease household availability of ITNsfrom existing levels through rapidscale-up channels. With available fund-ing, many donor “focus” countries willlikely achieve high coverage by 2010,but gaps will remain in many coun-tries, especially in regard to maintain-ing coverage after initial rapid scale-uptargets are met.

In addition to more ITNs, efforts areneeded to promote good net usage pat-terns and regular insecticide (re-)treatment among target populations, es-pecially in areas where levels ofhousehold net possession are alreadyhigh. In 2003, the vast majority of ITNsavailable in households were not LLINs,but conventional nets that require re-treatment at regular intervals to main-tain ITN status. Over the short term, netre-treatment efforts are needed to sus-tain ITN coverage levels and help bridgethe gap between available treated anduntreated nets. As more nets are re-placed with long-lasting ITNs, the pro-portion of available nets that are ITNswill increase and reduce the need fornet re-treatment. The volume of ITNsand re-treatments will require coordi-nation among donor agencies, publichealth officials, net manufacturers, andexisting distributions systems to en-sure timely delivery and retreament toachieve the agreed targets.

Author Contributions: Mr Miller had full access to allof the data in the study and takes responsibility forthe integrity of the data and the accuracy of the dataanalysis.Study concept and design: Miller, Korenromp.

Acquisition of data: Miller.Analysis and interpretation of data: Miller, Korenromp,Nahlen, Steketee.Drafting of the manuscript: Miller, Korenromp, Nahlen.Critical revision of the manuscript for important in-tellectual content: Miller, Korenromp, Nahlen,Steketee.Statistical analysis: Miller, Korenromp.Administrative, technical, or material support: Miller,Steketee.Study supervision: Miller, Nahlen, Steketee.Financial Disclosures: None reported.Funding/Support: Mr Miller was supported througha grant from the Bill and Melinda Gates Foundationto PATH MACEPA. Dr Korenromp received financialsupport from a Van Rijn fellowship at Erasmus MC,University Medical Centre Rotterdam.Role of the Sponsors: Both PATH and the GFATM (em-ployer of Drs Korenromp and Nahlen at the time ofsubmission) reviewed the article internally and ap-proved its submission to JAMA and its publication. Eras-mus MC, University Medical Centre Rotterdam hadno part in the design and conduct of the study, in thecollection, analysis, and interpretation of the data, orin the preparation, review, or approval of the manu-script.Acknowledgment: We are grateful to Jo Lines, PhD,London School of Hygiene and Tropical Medicine, andCarlos (Kent) Campbell, MD, and Abdirahman Mo-hamed, MD, PATH MACEPA, for their thoughtful re-views and comments on earlier versions of the manu-script.

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By far the most dangerous foe we have to fight is “apa-thy”—indifference from whatever causes, not from alack of knowledge, but from carelessness, from ab-sorption in other pursuits, from a contempt bred ofself-satisfaction.

—Sir William Osler (1849-1919)

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