Gosling, RD; Ghani, AC; Deen, JL; von Seidlein, L; Greenwood, BM; Chandramohan… · 2018. 7. 18. · Chandramohan et al. [4] Macete et al [5] Kobbe et al. [6] Mockenhaupt et al

Gosling, RD; Ghani, AC; Deen, JL; von Seidlein, L; Greenwood, BM;Chandramohan, D (2008) Can changes in malaria transmission inten-sity explain prolonged protection and contribute to high protectiveefficacy of intermittent preventive treatment for malaria in infants?Malaria Journal, 7. ISSN 1475-2875 DOI: https://doi.org/10.1186/1475-2875-7-54

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Open AccessResearchCan changes in malaria transmission intensity explain prolonged protection and contribute to high protective efficacy of intermittent preventive treatment for malaria in infants?Roly D Gosling*1, Azra C Ghani2, Jaqueline L Deen3, Lorenz von Seidlein1, Brian M Greenwood1 and Daniel Chandramohan1

Address: 1Department of Infectious and Tropical Disease, London School of Hygiene and Tropical Medicine, London, UK, 2MRC Centre for Outbreak Analysis & Modeling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK and 3International Vaccine Institute, Seoul, South Korea

Email: Roly D Gosling* - [email protected]; Azra C Ghani - [email protected]; Jaqueline L Deen - [email protected]; Lorenz von Seidlein - [email protected]; Brian M Greenwood - [email protected]; Daniel Chandramohan - [email protected]

* Corresponding author

AbstractBackground: Intermittent preventive (or presumptive) treatment of infants (IPTi), theadministration of a curative anti-malarial dose to infants whether or not they are known to beinfected, is being considered as a new strategy for malaria control. Five of the six trials usingsulphadoxine-pyrimethamine (SP) for IPTi showed protective efficacies (PEs) against clinical malariaranging from 20.1 – 33.3% whilst one, the Ifakara study, showed a protective efficacy of 58.6%.

Materials and methods: The possible mechanisms that could explain the differences in thereported PE of IPTi were examined by comparing output from a mathematical model to data fromthe six published IPTi trials.

Results: Under stable transmission, the PE of IPTi predicted by the model was comparable withthe observed PEs in all but the Ifakara study (ratio of the mean predicted PE to that observed was1.02, range 0.39 – 1.59). When a reduction in the incidence of infection during the study wasincluded in the model, the predicted PE of IPTi increased and extended into the second year of life,as observed in the Ifakara study.

Conclusion: A decrease in malaria transmission during the study period may explain part of thedifference in observed PEs of IPTi between sites and the extended period of protection into thesecond year of life observed in the Ifakara study. This finding of continued benefit of interventionsin settings of decreasing transmission may explain why rebound of clinical malaria was absent in thelarge scale trials of insecticide-treated bed nets.

BackgroundIntermittent preventive treatment of infants (IPTi) is theadministration of a curative anti-malarial dose to infants,

whether or not they are known to be infected, at specifiedtimes to prevent malaria [1]. IPTi delivered through theEPI programme was first shown to successfully prevent

Published: 3 April 2008

Malaria Journal 2008, 7:54 doi:10.1186/1475-2875-7-54

Received: 14 December 2007Accepted: 3 April 2008

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malaria in infants in 2001 [2]. Three doses of sulphadox-ine-pyrimethamine (SP) given to Tanzanian infants livingin an area of perennial transmission at the time of vacci-nation with DPT2, DPT3 and measles vaccines reducedthe incidence of clinical malaria and anaemia during thefirst year of life by 59% and 50% respectively. Further-more, protection against clinical episodes of malaria per-sisted into the second year of life [3]. In contrast, innorthern Ghana, where malaria transmission is intenseand highly seasonal, SP-IPTi gave only 25% protectionagainst clinical malaria and 35% protection against hospi-tal admissions with anaemia during the first year of lifeand no protection during the second year [4]. A similarlevel of protection against clinical malaria during the firstyear of life was seen in Mozambique but no protectionagainst anaemia was detected in this study [5]. Further tri-als of SP-IPTi conducted in areas of Ghana [6,7] andGabon [8] with differing epidemiological patterns ofmalaria have given similar results to those observed inGhana and Mozambique. The results from the first studyin Tanzania therefore appear at odds with those from thelater studies.

A number of explanations for the differences in protectiveefficacy (PE) of IPTi against clinical malaria between siteshas been suggested including the intensity of transmissionand consequent malaria incidence, the pattern of antima-larial resistance, the administration of iron and the use ofadditional control measures, specifically insecticide-treated nets (ITN) [9]. This paper, using data from the sixSP-IPTi randomized placebo-controlled trials reported sofar, explored the association between resistance to SP, ITNcoverage and malaria transmission intensity in each studysite. The observed PE of IPTi against clinical malaria isexamined using a mathematical model which mimics the

acquisition and loss of parasites to predict the PE expectedin the six trial settings.

MethodsDataData are from IPTi trials conducted in Manhiça (Mozam-bique), Lambarene (Gabon), Ifakara (Tanzania) and Nav-rongo, Kumasi and Tamale (Ghana). Detaileddescriptions of the study population, methodology andoutcome in each study included in this analysis have beenpublished elsewhere [2-8]. A summary of the studydesigns and their epidemiological background is shownin Table 1. The model output was compared to dataderived from the IPTi Consortium's Statistical WorkingGroup (SWG) Report of September 2007. The SWG usedcommon definitions for time at risk and for an episode ofclinical malaria across all six studies. For time at risk achild treated for clinical malaria was censured for 21 daysin order to prevent double counting of cases and to allowfor any prophylactic effect of the antimalarial. A case ofclinical malaria was defined as measured fever or historyof fever with any parasitaemia of P. falciparum (definitionof duration of history of fever differed between studies: forIfakara and Manhica studies it was 24 hours and forremaining studies it was 48 hours). In this paper, all refer-ences to the PE of IPTi refers to the PE against episodes ofclinical malaria up to 12 months of age based on inci-dence rates of multiple episodes of clinical malaria, nottime to first or only episodes.

The relationship between the observed PE of IPTi and thefollowing potential determinants of PE were explored:resistance to SP; estimated ITN coverage (% of the studypopulation reporting use of ITN); and malaria transmis-sion intensity (mean incidence of malaria per child per

Table 1: Study characteristics of SP-IPTi efficacy trials

Study parameter Schellenberg et al. [2, 3]

Chandramohan et al. [4]

Macete et al [5] Kobbe et al. [6] Mockenhaupt et al. [7]

Grobusch et al [8]

Trial, country Ifakara, Tanzania Navrongo, Ghana Manhica, Mozambique Kumasi, Ghana Tamale, Ghana Lambaréné, GabonRecruitment year(s) 1999–2000 2000–2002 2002–2004 2003–2005 2003 2004–2005EIR/year 29 418 38 400 NA 50Transmission Perennial moderate Highly seasonal high Perennial with

seasonal peaks moderate

Perennial high Perennial with seasonal peaks high

Perennial with seasonal peaks low-

moderateIn vivo SP resistance by day 14%

31 (1999–2000) [10] 22 (2004) [11] 21 (2001) [12] NA 14 (2002) [14] 21 (2004) [13]

Use of bed nets, % placebo/SP treated (untreated)

67/68 17/19 0/0 (14/15) 20/20 estimate (39/38)

<1% 5/5 (80/80)

Iron supplementation Yes Yes None None None NoneAges at dosing, months

2, 3, 9 (at time of DPT2, DPT3 &

measles)

3, 4, 9, 12 (at time of DPT2, DPT3 &

measles + extra at 12 months)

3, 4, 9 (at time of DPT2, DPT3 &

measles)

3, 9, 15 (at time of DPT3 & measles + extra at 15 months)



No. of children enrolled, placebo/active

351/350 = 701 1,242/1,243 = 2,485 755/748 = 1,503 535/535 = 1,070 600/600 = 1,200 595/594 = 1,189

Study design Individual randomization

Cluster randomization

Individual randomization






year in the placebo group). Day-14 parasitological andclinical failure rates were used to define resistance becausefive out of the six IPTi trials had published this informa-tion within two years of conducting the IPTi trial [10-14].One site in Ghana, Kumasi, did not have data on day 14parasitological and clinical failure of SP and therefore theestimate from Tamale, relatively close geographically, wasused.

Mathematical modelAn age-structured model (Figure 1) was developed to rep-resent the acquisition of malaria infection and clinical dis-ease and the development of immunity in the studycohort of infants between the ages of two and 24 months.The modelling exercise only examines the specific cohortas studied in the trials, thus age and calendar times areequivalent and the model output is expressed in terms ofthe age of the children. At any point in time children canbe in one of two states – uninfected and susceptible tonew infection S(a) or infected with parasites which canremain asymptomatic or can become symptomatic, A(a).It is assumed that the rate of acquisition of new infectionsis determined by the force of infection in the study area,λ(a), which may vary through time (and hence by age).Once infected and in the asymptomatic state, childrenreturn to the susceptible state through one of three routes.First, they may become a clinical case and receive an effec-tive treatment. It is assumed that in clinical trial settings,every case of malaria detected was adequately treated andparasites cleared. Secondly, they may receive antimalarialtreatment for asymptomatic parasitaemia eg IPTi. Finally,they may remain asymptomatic and recover naturally.Symptomatic cases of malaria that are not detected by sur-veillance systems will remain in the asymptomatic state in

the model until they die of severe disease or their immuneresponse clears parasites. The model does not incorporatechildren leaving the asymptomatic pool by death, assum-ing this will be a very small number because most of thecases would be detected in time to receive effective treat-ment in a trial setting.

Ignoring mortality from other causes and migration, themodel without interventions can be expressed by the fol-lowing equation:

where N is the fixed population size, r(a) is the age-dependent rate of natural clearance of parasitaemia andc(a) is the age-dependent rate of development of clinicaldisease which is then treated.

In endemic areas the risk of developing clinical diseasedecreases with exposure to infection but rates of parasitae-mia remain almost constant in early childhood. Themodel incorporates functions that mimic the develop-ment of immunity so that as children age the rate at whichthey develop clinical disease decreases and the rate atwhich they clear parasites increases. For simplicity, themodel assumes that both immunity functions are linearlydependent on the expected number of malaria infectionsat age a:

δ αδα

λ α α χ α ρ α αΑ Ν Α Α( ) = ( )( − ( )) − ( ( ) + ( )) ( ) (1)

E I aN

a S a da

a

[ ( )] ( ’) ( ’) ’= ∫1

0

λ (2)

The asymptomatic parasite pool modelFigure 1The asymptomatic parasite pool model.



The rate of development of clinical disease is given by thelogistic function

where ϕ is the rate of development of clinical disease inthe absence of immunity and α1 and α2 are parameterswhich determine the number of infections after which fullimmunity to clinical disease occurs. The rate of naturalclearance of parasites is assumed to be linear within therange of interest and hence is given by

r(a) = min(ωE [I(a)], 1)) (4)

where 1/ω is the mean number of infections after whichfull parasite immunity is obtained and it was assumedthat at full immunity parasites are cleared after a mean ofone day.

A generic maternal protective function which acts toreduce the force of infection following birth was incorpo-rated. Maternal protection is complicated and multifac-eted [15], incorporating both biological immunity as wellas behavioural factors that limit exposure. Given the pau-city of data with which to determine an appropriate func-tion, the following factors were used, which act on theforce of infection up to six months of age: 0.05, 0.15, 0.4and 0.8 at age 2, 3, 4 and 5 months of age respectively,which represent a gradual loss of immunity.

The model was numerically evaluated as difference equa-tions in 1-month time-steps using Excel.

Incorporation of IPT and ITNTo compare the model results to the trial data the twointerventions were incorporated. Firstly, use of ITNs isincluded by reducing the force of infection in the groupassigned to ITNs by a factor θ:

The model only examines the personal protection gainedfrom an ITN and does not examine any other effects, suchas effects on transmission.

IPT use across all age-groups modelled (two to 24months) is assumed to act in three ways, clearing parasitesin a proportion (1-σ) of the population, ie the treatmenteffect, prophylaxes against new infection (factor σ reduc-ing the force of infection) and reducing the rate of devel-opment of clinical disease by the same factor. As themodel is defined in monthly time-steps, the parameter σ

can be interpreted as the PCR uncorrected day 28 Ade-quate Clinical and Parasitological Response (ACPR)which measures the proportion that will clear parasitae-mia and be protected against new infection 28 days posttreatment. For simplicity, it was assumed that the ACPRacts with equal efficacy to clear parasites, to protect againstnew infection and prevent development of disease.

Equation (6) is applied to the model only for monthswhen IPTi doses are given. For months in those childrenwho used ITNs and receive IPT equations (5) and (6) arecombined.

For the modelling exercise, the coverage of ITNs (reportedownership) in each trial site was used because the use ofITN at the individual level was not available for all trials.The expected incidence of clinical disease in each trial arm(placebo and IPT) is therefore calculated as a weightedcombination of the model predictions with and withoutITNs. The protective efficacy of IPT predicted by the modelis calculated as 1-relative risk = 1-clinical incidence in IPTigroup/clinical incidence in placebo group.

Model parametersIt is assumed that that on average after 5 infections ofmalaria an individual is totally protected against clinicaldisease and that after 50 infections an individual can clearparasites rapidly. A sensitivity analysis for these parame-ters can be found later in this paper. Parameters thatreproduce these patterns are given in Table 2. The force ofinfection, λ(a) was assumed to be constant for the base-line scenarios. At a later stage the modelling exercise usedscenarios in which the force of infection was decreased inearly childhood due to maternal protective factors andseparately allowed to increase or decrease linearly as thechildren aged reflecting changes in transmission overtime. The force of infection was initially estimated directlyas the mean incidence of clinical malaria in the placebogroup. This approximation was based on the observationthat the age-specific incidence of clinical malaria is high-est in infants, the IPTi target age group and that the agegroup with the highest incidence should have the lowestimmunity. The observed data from the Navrongo study(the only full dataset available to the study team) wascompared with the model estimates of clinical disease,which were found to be half of that observed in the study.Thus, the mean incidence of clinical malaria in the pla-cebo group was multiplied by a factor of two to estimatethe force of infection. Entomological Inoculation Rates(EIR) were not used as a measure of transmission in themodel for two reasons: EIRs measured concurrently withthe IPTi trials were not available for most sites and in the

c ae E I a

( )[ ( )]

= +

+ϕ α

α α1 1

1 2(3)

dA ada

a N A a c a r a A a( )

( ) ( )( ( )) ( ( ) ( )) ( )= − − − +1 θ λ

(5)

dA ada

a N A a c a r a A a A a( )

( ) ( )( ( )) (( ) ( ) ( )) ( ) ( )= − − − − + −1 1σ λ σ σ

(6)



studies with EIRs there was no common methodology.Secondly, these large cohort studies had enrolled childrenfrom a large geographical area over several years (two tofour years). Thus a single measure of EIR would not sufficeto represent the whole study area or the whole studyperiod.

The proportion of infected children becoming sympto-matic and treated in the absence of immunity wasassumed to be 90% in one month. This was derived froma study of asymptomatic parasitaemia in 6–59 month oldchildren in a moderate malaria setting in Kampala,Uganda [16]; in this population 50% of children withasymptomatic parasitaemia developed clinical malariaafter 30 days. As the Ugandan study was undertaken inpartially immune children we assumed a higher rate ofdevelopment of disease. Clinical malaria cases areassumed to recover within a month post treatment, twicethe average terminal half-life of the antimalarials used fortreatment and rejoin the susceptible population. Deathsand migrations were not included in the model.

For those children receiving IPT, it is assumed that treat-ment, prophylaxis, and prevention of developing clinicaldisease effects of SP will be equally affected by the PCRuncorrected day 28 ACPR of SP. Day 28 PCR uncorrectedACPR is a measure of both the treatment and prophylacticeffect combined (it includes both recrudescence's and re-infections) and is more likely to represent the effects of thedrugs when used for prevention as opposed to treatment.The sensitivity analysis for how changes in ACPR affect PEis shown in the in the results section. Briefly, as drug resist-ance increases PE declines. The day 28 ACPR was onlyavailable for 2 sites, the sites with the highest and lowestresistances at day 14, namely Ifakara [10] and Tamale [14]respectively. The extrapolation from day 14 to 28 efficacy

for the 3 studies [11-13] without day 28 ACPR is the midpoint between these two studies.

For those children using ITNs, we assume that the protec-tive efficacy of an ITN is 0.5 [17]. The model parametersare summarised in Table 2.

Sensitivity analysisA sensitivity analysis of how ACPR, ITN coverage andimmunity functions affects predicted PE was carried out.

ResultsAssociation between IPTi protective efficacy and various factorsFigure 2 shows the relationship between PE of IPTi andresistance to SP, estimated ITN coverage, and malariatransmission intensity in each study site. The Ifakara studysite had the highest IPTi PE (59%) despite having thehighest resistance to SP (31% day-14 parasitological andclinical failure rate). This site also had the highest ITN cov-erage (67%). Resistance to SP was 14 – 22% and ITN cov-erage was 0 – 20% in the other five sites.

Protective efficacy of IPTi in stable transmission settingsThe model predicted a similar pattern across the six trials,with a transient decline in incidence among the groupsreceiving IPTi (with and without ITN coverage) as well asa generally lower incidence among groups with ITNs(with and without IPTi). Using the Navrongo study as anexample (IPTi doses given at 3, 4, 9 and 12 months ofage), Figure 3 shows the models prediction of monthlyincidence of clinical disease cases in groups with andwithout ITNs (Figure 3A), the combined model weightedby ITN coverage (Figure 3B) and observed data from theNavrongo [18] study for comparison (Figure 3C). In thosetrial settings with higher incidence the model generates a

Table 2: Summary of Model Parameters and Symbols

Parameter Description Parameter Symbol Value

Definitions for equationsSusceptible population at age (a) S(a) N/AAsymptomatic population at age (a) A(a) N/AAge-dependent rate of natural clearance of parasitaemia at age (a) r(a) N/AAge-dependent rate of development of clinical disease which is then treated at age (a) c(a) N/AProtective Efficacy PE N/AFixed parametersRate of development of clinical disease in the absence of immunity φ 0.9Mean number of infections after which full immunity to clinical disease occurs µ 5Parameters which determine the number of infections after which full immunity to clinical disease occurs α1 and α2 1Mean number of infections after which full parasite immunity occurs ω 50Protection offered by ITN use θ 0.5Variable parameters between sitesForce of infection λ(a) See table 3Drug action reducing the force of infection σ See table 3



Protective efficacy of IPTi at 12 months of age compared to estimated resistance to SP at Day 14, ITN coverage and incidence of malaria in placebo groupsFigure 2Protective efficacy of IPTi at 12 months of age compared to estimated resistance to SP at Day 14, ITN cover-age and incidence of malaria in placebo groups.

0

10

20

30

40

50

60

70

0 20 40 60 80

IPT

i P

rote

cti

ve

Eff

ica

cy (%

)

Estimated ITN coverage (%)

b)

0

10

20

30

40

50

60

70

0 0.2 0.4 0.6 0.8 1 1.2 1.4

IPT

i P

rote

cti

ve

Eff

ica

cy (%

)

Incidence rates in placebo group (episodes per chi ld per year)

c)

Ifakara, Tanzania Mahnica, Mozambique

Navrongo, Ghana Lambarene, Gabon

Kumasi, Ghana Tamale, Ghana

0

10

20

30

40

50

60

70

0 10 20 30 40

IPT

i P

rote

cti

ve

Eff

ica

cy (%

)

Day 14 parasitological and cl inical fai lure rates (%)

a)



small increase in the numbers of cases shortly after each

Asymptomatic pool model prediction of monthly clinical cases per child year at risk from the Navrongo, Ghana IPTi study (A) by intervention group and ITN use by age with stable transmission (B) the prediction weighted by ITN coverage and (C) the actual incidence by age in the placebo and IPTi groups from the Navrongo study [18] (by kind permission of Tropical Medicine and International Health, Blackwell Publishing)Figure 3Asymptomatic pool model prediction of monthly clinical cases per child year at risk from the Navrongo, Ghana IPTi study (A) by intervention group and ITN use by age with stable transmission (B) the prediction weighted by ITN coverage and (C) the actual incidence by age in the placebo and IPTi groups from the Nav-rongo study [18] (by kind permission of Tropical Medicine and International Health, Blackwell Publishing). Arrows indicate time of IPTi dosing.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 2 4 6 8 10 12 14 16 18 20 22 24

Ep

iso

de

s p

er

pe

rso

n y

ea

r

Age (months)

b)

Incidence of Clinical Cases of malaria in placebo arm

Incidence of Clinical Cases of malaria in IPTi arm

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 2 4 6 8 10 12 14 16 18 20 22 24

Ep

iso

de

s p

er

pe

rso

n y

ea

r

Age (months)

a)

Placebo Placebo+ITN

IPTi IPTi+ITN

c)



IPTi dose due to delayed acquisition of immunity whichcontinues into the second year of life (the rebound effect).

The predicted PE of IPTi in the six sites with the observeddata are shown in Table 3. The mean ratio between modelto actual PE was 1.02 (range 0.39 – 1.59) with the pre-dicted PE lying within the 95% confidence interval fromthe trial data in all but the Ifakara study. The main differ-ences between the Ifakara and the other studies were thehigh ITN coverage and the higher resistance to SP. Thereseems no obvious explanation to why IPTi should bemore effective with higher drug resistance. However, highITN coverage may have an effect on transmission. Thusthe effect of changing transmission on the PE predicted bythe model is further explored.

Protective efficacy of IPTi in changing transmission settingsTable 4 shows the change in both PE and the effect ofdelayed immunity (rebound effect) predicted by themodel for Ifakara under four scenarios with changingtransmission during the study period with both maternalimmunity function removed and included – (a) increas-ing at a rate of 25% per month, (b) increasing at a rate of5% per month, (c) stable, (d) a decline of 5% per monthand (e) a decline of 25% per month. In the scenarios ofchanging transmission, PE is dominated when the effectof IPTi is most efficacious, ie when transmission is high-est. In the output of the model without the maternalimmunity function (Figure 4A) the overall PE measuredby the trial is dominated by its early efficacy with the firsttwo doses of IPTi. Thus, because most of the efficacy ispredicted to occur during the months in which doses aregiven (at 2, 3 and 9 months of age in this trial), the meas-ured overall PE is high. Conversely, if transmission isincreasing then the overall measure of PE will be domi-nated by what is happening during the later months whendoses are no longer being given and hence a lower overallprotective efficacy will be estimated. When including the

maternal immunity function (Figure 4B) the 3rd dose ofIPTi given at nine months of age has the dominant effecton the PE. Changes in transmission affect the extent towhich rebound or prolonged protection are observed inthe months following the IPTi doses independently ofmaternal immunity. If overall transmission is beingreduced, this in turn reduces the probability of infectiononce the direct protection afforded by IPTi is removed andhence reduces the potential for the rebound effect to beobserved and increases the probability of observing pro-longed protection.

Sensitivity analysisThe results of the sensitivity analysis of ACPR are shownin Table 5. PE increases when the ACPR is high (i.e. thereis little resistance) as the IPT effect is greatest under thisscenario and visa versa. Varying ITN coverage from 0–100% had little effect on predicted PEs of the trials (rangeof variation of PE from baseline (results shown in Table3): 0–0.6%). Increasing the mean number of infections tobecome immune against clinical disease from 5 to 10reduced predicted PE but the magnitude was small (rangeof variation of PE from baseline: 0.3–0.8%). Varying theeffect of number of infections to get anti-parasite immu-nity also had little effect on predicted PE (range of numberattacks required to get anti-parasite immunity: 20–100,range of variation of PE from baseline: 0–0.3%). Thematernal immunity function greatly affected predicted PE.Table 6 shows the effect on PE when (a) no immunity ispredicted, (b) the models fixed non-parametric form isused (baseline) and (c) a function of maternal immunityagainst severe disease published elsewhere [19]. Withoutmaternal immunity PE is enhanced.

DiscussionThe high PE of IPTi found in the Ifakara study and a sim-ilar preventive trial using amodiaquine in north-easternTanzania [20] triggered a series of IPT trials in other Afri-can study sites to investigate this potentially promising

Table 3: Modelled and actual protective efficacy to 12 months of age in each IPTi trials

Input Output

Study Site Age of SP IPTi administration

(months)

Mean incidence in placebo group (epi-

sodes per person year) (λ(a)/2)

ITN coverage

(%)

Estimated*** cross sectional

prevalence parasitaemia at

start of study (%)

Estimated Day 28 ACPR for SP (σ × 100)

Model estimate of PE

(%)

Actual PE of IPTi (% 95% CI)

Ratio model:study

Ifakara 2,3 and 9 0.54 67 4.5 60 23.0 58.8 (40.8–71.3) 0.39Navrongo 2,4,9 and 12 1 18 8.3 65* 31.9 29.3 (17.7–39.5) 1.09Mahnica 3,4 and 9 0.71 0 5.9 65* 32.0 20.1 (2.1–34.9) 1.59Kumasi 3,9 and 15 1.27 20 10.6 69** 25.9 20.9 (8.9–31.3) 1.24Tamale 3,9 and 15 0.93 1 7.8 69 24.9 33.3 (20.7–43.8) 0.75

Lamberene 3,9 and 15 0.16 5 1.3 65* 23.7 22.0 (-25.4–51.5) 1.08

* Estimated from Day 14 ACPR, ** Estimated from Tamale data.*** Starting Cross sectional parasite prevalence (asymptomatic infected children) estimated from mean monthly incidence in placebo group Incidence and PE figures from IPTi Consortium Statistical Working Group Report September 2007 (Aponte JJ et al. In preparation)



method of malaria control. Subsequent published trialsshowed a much lower efficacy of IPTi than was observedin Ifakara [2]. To explain these differences in efficacybetween sites some observers have focussed on the differ-ences in drug resistance to SP between the sites. However,this explanation does not appear plausible because thesite with the highest PE had the highest SP resistance (Fig-ure 2). In response to this observation, it has been sug-gested that there may be an immunisation effect of SP, the"Leaky Drug" theory [3,21]. The hypothesis is that a par-tially effective drug allows for low level and persisting par-asitaemia and thus allowing prolonged stimulation of theimmune system resulting in the extended period of pro-tection as seen in the Ifakara site. This model-based anal-ysis provides an alternative explanation, namely that theexceptionally high ITN coverage in Ifakara decreasedtransmission and boosted the observed PE of IPTi. HighITN coverage was recognised as a potential explanation ofdifferences in PE between the Manhica and Ifakara studies[9]. Ifakara District is known to have experienced a 10 foldreduction in transmission around the study period (forexample, the EIR in 1995 was recorded as 300 and by2001 had fallen to 29). Although the EIR estimates camefrom different places within the district there was areported change in the epidemiology of clinical diseaseduring this time period [22]. In addition many other stud-ies have shown the mass effect on transmission of highITN coverage [17]. The model suggests that changing thetransmission intensity affects both the PE and the lengthof protection and thus gives a plausible explanation forthe difference in results between study sites. Anothermodelling exercise focussing on the mechanism of IPTi(Ross A., manuscript in preparation) has confirmed thisfinding. No clear decrease was seen in the mean incidenceof clinical malaria in the placebo arm of the Ifakara studyfrom the published data from the first [2] to the second[3] year, going from 0.43 to 0.42 episodes per person yearat risk. The model predicts that over the first year of the

study transmission must fall by at least 22% per month tobe within the 95% confidence limits of the PE observed.Whilst this seems unlikely, the pattern of transmissionfaced by the cohort may have changed within the observa-tion period and affected the observed PE. To test thehypothesis derived from this model the data will need tobe examined by looking at monthly incidence in eachgroup by age in the Ifakara study.

The model shows that PE mainly depends on the level ofmalaria transmission during the few months which IPTidoses are administered and the length of follow up andtransmission intensity when IPTi is not given. To maxim-ise PE IPTi should be given during high malaria transmis-sion and follow up should be short when malariatransmission is low. Supportive evidence for this is dem-onstrated in the extended analysis of the Navrongo study[18] and an IPT seasonal study where antimalarials weregiven in Senegal, West Africa during the malaria seasonswith a short follow up of 13 weeks [23]. In this study effi-cacy against clinical malaria was 86%.

This model also provides a coherent explanation as towhy no rebound effect would be observed in situations ofdecreasing transmission, such as Ifakara or Kenya [24,25].The delay in acquisition of immunity caused by very suc-cessful interventions such as continuous chemoprophy-laxis in infants are followed by increases in casesfollowing cessation of the intervention, the rebound effect[26-28]. In this situation of chemoprophylaxis in a singleage group there is no effect on transmission. However, inthe large ITN trials where no rebound was seen, the masseffect of the ITNs in reducing vectorial capacity led to adecrease in transmission [17]. The model predicts that inthe presence of decreasing transmission rebound parasi-taemia can disappear. Thus, although the population isimmunologically more susceptible to infection withmalaria, it is less exposed and so cases of malaria infection

Table 4: Change in Protective Efficacy and rebound effect with changes in transmission

Transmission Transmission effect per month (%)

Predicted protective efficacy over first 12 months (%)

Protection until Rebound effect

No maternal immunityFast increase 1.25 (25) 27.1 12 months YesIncreasing 1.05 (5) 29.2 12 months YesStable 1 (0) 30.6 13 months YesSlow decline 0.95 (5) 32.2 13 months NoFast decline 0.75 (25) 42.8 > 24 months NoMaternal immunity includedFast increase 1.25 (25) 22.7 12 months YesIncreasing 1.05 (5) 22.1 13 months YesStable 1 (0) 23.0 14 months YesSlow decline 0.95 (5) 22.7 14 months NoFast decline 0.75 (25) 23.1 16 months No



Model predictions of Ifakara Tanzania IPTi study without (A) and including (B) maternal immunity function with different changes in transmissionFigure 4Model predictions of Ifakara Tanzania IPTi study without (A) and including (B) maternal immunity function with different changes in transmission.

0

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Transmission increasing 25% per month- Placebo

Transmission increasing 25% per month- SP group

Transmission increasing 5% per month- Placebo

Transmission increasing 5% per month- SP group

Transmission stable- Placebo

Transmission stable- SP group

Transmission decreasing 5% per month- Placebo

Transmission decreasing 5% per month- SP group

Transmission decreasing 20% per month- Placebo

Transmission decreasing 20% per month- SP group

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reduce. In contrast, when an intervention that reducesexposure and hence immunity to malaria takes place in asite with stable malaria transmission or one in whichtransmission is increasing a rebound effect would be evi-dent. If IPT was spread across all age groups, ie as a formof mass drug administration or universal IPT (IPTu)reducing the asymptomatic pool (A(a) in the model) inthe whole population and not a small age group, an effecton transmission may be seen.

ITNs exert a steady personal protection to the individualsleeping under the net of approximately 50% [17] as longas the insecticide remains active. IPTi offers intermittentprotection which varies with the efficacy of the drug onlyat times when it is administered. Therefore it follows thatprotection with an ITN should be the primary interven-tion with IPTi as an additional strategy. The model foundthe largest difference in incidence of clinical malariabetween placebo groups without ITN compared to ITNplus IPTi. This observation suggests that combining inter-ventions must be a priority. ITN coverage has little influ-ence on the predicted PE by the model. This is because themodel defines the protection of an ITN to act to reduce theforce of infection to the proportion of those using ITNsand then calculates the overall PE weighted by this cover-age. The model assumes an additive effect of IPTi andITNs and not synergy.

As with all theoretical studies, the model has some limita-tions. The model is dependent on some key assumptionsregarding the effect of exposure on immunity. First, it isassumed that full clinical immunity was obtained afterfive infections. This figure was derived from past estimatesfor severe malaria [19] but clearly requires further data forverification. Increasing the number of infections requiredto become immune to developing clinical disease wouldresult in a smaller rebound effect and a smaller decrease intransmission would eliminate the rebound effect. Noclear evidence for rebound has been seen in the trials [29],thus the number of clinical attacks leading to immunity islikely to be more than five. Similarly, the threshold forachieving parasite immunity was arbitrarily set at 50 infec-tions. However, as the model only considers malaria inthe first 2 years of life, children are unlikely to reach thefive attacks needed to become immune to clinical disease(mean number of expected attacks in Kumasi, the highesttransmission setting, was 2.7 at 24 months of age) andeven less likely to reach the 50 attacks to give full antipar-asite immunity, the results are less sensitive to thesechoices of immune function. Whilst the choice of immu-nity functions determines the extent of the rebound effectpredicted by the model, it does not impact greatly on theprotective efficacies predicted by the model. In contrast,the assumptions made regarding maternal protection doimpact on the predicted protective efficacy (Tables 4 and6, Figure 4A and 4B). The effect of maternal protection islikely to vary by site and be influenced by levels of trans-

Table 5: Sensitivity analysis of effects of ACPR on models predictions of PE

Study Site Observed PE (%, 95% CI) Model PE (%)

Baseline ACPR increased to 100% ACPR reduced to 40%

Ifakara 58.8 (40.8–71.3) 23.0 37.5 15.5Manhica 20.1 (2.1–34.9) 32.0 47.4 20.2

Navrongo 29.3 (17.7–39.5) 31.9 47.3 20.1Lamebarene 22.0 (-25.4–51.5) 23.6 36.4 14.5

Kumasi 20.9 (8.9–31.3) 25.9 36.9 15.3Tamale 33.3 (20.7–43.8) 24.9 36.5 14.3

Table 6: Sensitivity analysis of maternal immunity function on models predictions of PE with (a) no maternal immunity function, (b) with fixed non-parametric function used in the paper (Baseline) and (c) function based on maternal immunity to severe disease.

Study Site Observed PE (%, 95% CI) Model PE (%)

(a) No immunity (b) Baseline (c) Alternative based on immunity to severe disease

Ifakara 58.8 (40.8–71.3) 32.4 23.0 26.4Manhica 20.1 (2.1–34.9) 38.6 32.0 34.0

Navrongo 29.3 (17.7–39.5) 38.5 31.9 34.1Lamebarene 22.0 (-25.4–51.5) 28.2 23.6 26.0

Kumasi 20.9 (8.9–31.3) 38.0 25.9 30.3Tamale 33.3 (20.7–43.8) 29.7 24.9 27.4



mission experienced by the mother during the transpla-cental passage of humoral immunity and behaviouralfactors. Further data are required to refine this function.Differences in calculating time at risk following treatmentalso bias the model to detect a higher PE. In the analysisused to produce the PEs in the studies, a child was cen-sored for 21 days after each case of malaria yet the modeluses one month time steps and so cases of malaria are cen-sored seven days longer, reducing the time at risk denom-inator. The model studies very few variables and onlythose directly affecting malaria. The differences of the PEsin the studies could be related to factors so far remainingunstudied such as HIV prevalence, socio-economic status,timing and dose of IPTi, heterogeneity of malaria trans-mission or placental infection.

What does this study mean for IPTi? This model demon-strates that during a decline in malaria transmission,which Africa is currently experiencing, IPTi can be highlyeffective and safe. Combining IPTi with ITNs results ingreater protection for an individual, further more, if highlevels of coverage of ITNs can be attained with a resultantdecrease in transmission, then there appears to be synergybetween the interventions. However, if transmission sub-sequently increases a reduction in the efficacy of IPTi (ascurrently measured by comparing incidence rates ofmalaria over a long period) can be expected. In stable con-ditions, PE does not seem to be greatly affected by levelsof transmission, however the higher the level of transmis-sion the more likely a rebound effect is to be seen. Therebound effect is equivalent to delaying of clinical cases ofmalaria to an older age which may be beneficial as olderchildren appear to develop less severe illness than infants[28]. Indeed, these observations will apply to all types ofinterventions during times of changing transmissionintensity. Currently there is a reduction of transmissionacross sub-Saharan Africa [30], thus exposure and immu-nity will be reduced leaving the possibility for outbreaksof malaria disease should transmission increase again.Drug resistance does play a role in IPTi efficacy and thisshould continue to be monitored. However, changes intransmission are likely to have a greater effect on IPTi pro-tective efficacy in the trials that have taken place with thelevels of drug resistance studied.

Competing interestsThe authors declare no conflict of interest. RG, DC and BGare members of the IPTi Consortium. The views expressedin the paper are those of the authors and not of the IPTiConsortium.

Authors' contributionsRG conceived the idea and developed the model andwrote the paper. AG developed the concept, refined themodel and wrote the paper. JD developed the model and

wrote the paper. LVS develop the concept and wrote thepaper. BG reviewed the early manuscript and wrote thepaper. DC developed the concept and model and wrotethe paper. All authors read and approved the final version.

AcknowledgementsThe authors wish to thank the following people: The IPTi Consortium par-ticularly David Schellenberg, Robert Newman, Andrea Egan, Clara Menen-dez, John Aponte, Martin Grobusch, Alexandra De Sousa, Mary Hamel, Ivo Meuller, Ilona Carneiro, Tom Smith and Amanda Ross for their helpful comments, access to the data and support; The editors of Tropical Medi-cine and International Health, Blackwell Publishing, for allowing us to repro-duce Figure 3C; Chris Drakeley, Cally Roper, Tanya Marchant, Colin Sutherland, Matthew Cairns and Michala Van Rose for their comments and encouragement; and the Kilimanjaro IPTi study team and participants for their inspiration. RG, JD and LVS are funded through grants from the Bill and Melinda Gates Foundation. AG, BG and DC are funded by their respec-tive institutions.

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