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IPTI: A DECISION SUPPORT TOOL FOR SUBSAHARAN AFRICA

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IPTI: A DECISION SUPPORT TOOL FOR SUBSAHARAN AFRICA

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WHO > Bulletin of the World Health Organization > Bulletin of the World Health Organization > Volume 88: 2010 > Volume 88, Number 11, November 2010, 797–876

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Intermittent preventive treatment for malaria in infants: a decision-support tool for sub-Saharan Africa

Ilona Carneiro a, Lucy Smith a, Amanda Ross b, Arantxa Roca-Feltrer a, Brian Greenwood a, Joanna Armstrong Schellenberg a, Thomas Smith b & David Schellenberg a

a. Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, England.
b. Department of Public Health and Epidemiology, Swiss Tropical Institute, Basel, Switzerland.

Correspondence to Ilona Carneiro (e-mail: ilona.carneiro@lshtm.ac.uk).

(Submitted: 28 September 2009 – Revised version received: 05 March 2010 – Accepted: 10 March 2010 – Published online: 10 May 2010.)

Bulletin of the World Health Organization 2010;88:807-814. doi: 10.2471/BLT.09.072397

Introduction

The burden of severe forms of Plasmodium falciparum malaria is concentrated in young children and a recent pooled analysis showed that this is even more pronounced for malaria leading to death than for less severe forms of the disease.1 The targeted provision of insecticide-treated nets to pregnant women2 and children under 5 years of age3 has helped protect those at an increased risk. Measures that target the very young may provide a useful additional strategy for malaria control.

Intermittent preventive treatment involves the administration of a therapeutic dose of an antimalarial drug at predefined times regardless of an individual’s infection status. The effect of administering intermittent preventive treatment in infants (IPTi) at the time of routine vaccination delivered through the Expanded Programme on Immunization (EPI) has been evaluated in several randomized controlled trials.410 A pooled analysis of the results of the first six trials of sulphadoxine-pyrimethamine49 showed an overall protective efficacy of 30% (95% confidence interval, CI: 20–39) against clinical malaria, 38% (95% CI: 13–56) against hospital admission for infection with malaria parasites, 23% (95% CI: 10–34) against all-cause hospital admission and 21% (95% CI: 8–33) against anaemia in the first year of life.11 One trial in an area of very high drug resistance to sulphadoxine–pyrimethamine showed that such treatment had no effect, although the long-acting drug mefloquine had a protective efficacy of 38% against clinical malaria.10 The strategy of administering IPTi using an efficacious, long-acting antimalarial drug therefore has merit. A recent World Health Organization (WHO) consultation document recommended that IPTi with sulphadoxine–pyrimethamine (IPTi-SP) be considered under certain epidemiological conditions in which the drug combination is effective.12

There has been some debate about where IPTi should be introduced because the burden of clinical malaria extends beyond infancy and, in some settings, infection is concentrated in only a few months of the year.1315 It is not feasible to carry out large-scale randomized controlled trials to determine the effectiveness of IPTi against severe outcomes in a wide range of different settings. Alternative methods are therefore needed to determine where IPTi is likely to be most beneficial. In this study, we attempted to solve this problem using two approaches: a secondary analysis of existing research data and a stochastic mathematical model.

As the epidemiology of malaria is complex and heterogeneous and even varies over small distances, it is difficult to develop a universal malaria control policy that is appropriate to all situations in a given country. Nevertheless, to simplify logistics, malaria control programme managers plan their activities at a national or subnational level, taking into account local variations in malaria epidemiology where possible.

Here we present evidence from our research into the applicability of IPTi under a range of epidemiological conditions. These findings have been developed as a decision-support tool (available at: http://ipti.lshtm.ac.uk) to help policy-makers decide where to implement IPTi.

Methods

Age pattern analysis

A pooled analysis of available data on the age pattern of outcomes of P. falciparum malaria was undertaken and has been described elsewhere.1 In brief, a systematic literature review was used to determine the age distribution of patients with clinical malaria, of those admitted to hospital with malaria parasites (i.e. those in whom malaria infection was confirmed after hospital admission for severe symptoms) and of those whose death was diagnosed as due to malaria by verbal autopsy. Data were collected in 21 sub-Saharan African countries from a total of 61 research sites. Each site was categorized as having a low, medium or high intensity of malaria transmission according to whether the entomological inoculation rate (EIR) was < 10, 10–100 or > 100 infective bites per person per year, respectively, as determined from temporally matched and georeferenced data, or whether the georeferenced prevalence of malaria parasites in children aged under 5 years was < 25%, 25–60% or > 60%, respectively. Each site was also categorized according to whether or not malaria transmission was markedly seasonal: the cut-off criterion was that at least 75% of clinical episodes occurred within a period of 6 months or less throughout the year, as previously described.16 Where local data were unavailable, experts with local knowledge were consulted.

For each study, the age distribution of patients aged 0–10 years with each malaria-related outcome was calculated. Data from sites classified as being in the same intensity and seasonality categories were grouped together to form a 3 × 2 matrix. The five principal and most widely used continuous probability distributions were fitted to the age distributions for each outcome and each of the six malaria transmission categories using maximum likelihood methods. The probability distribution that best fitted the data was taken to represent the age profile of the outcomes, and was presented graphically.1

The percentage of cases of each malaria-related outcome in children under 10 years of age (excluding neonates among those whose death was diagnosed as due to malaria) that would be targeted by IPTi was calculated by integrating the area under the probability distribution curve for infants between 3 and 12 months of age. This was done because this age range covers those children who would be most likely to receive IPTi-SP in practice, since, in all the trials, IPTi-SP was administered with the third dose of the diphtheria–tetanus–pertussis combined vaccine (DTP3) and with measles immunization to children approximately 3 and 9 months of age, respectively. The range was extended to 12 months to take into account variations in the actual age at which IPTi-SP was given and because its protective effect has been reported to last 1 to 2 months after each dose.17,18

Stochastic model

A stochastic mathematical model was developed to predict the impact of IPTi because research data on some malaria-related outcomes and epidemiological settings were incomplete: in particular, none of the IPTi-SP trials discussed above was powered to detect an effect on mortality, and EPI coverage and health-system functioning were relatively good in the trial settings, but not necessarily elsewhere. Moreover, IPTi may have consequences for the development of immunity over time. The model developed here was used to predict the likely impact of IPTi-SP in different settings up to 10 years after its introduction.

A comprehensive, individual-based, stochastic model of malaria epidemiology had been developed previously.19 Briefly, malaria-related outcomes in a simulated population are updated in 5-day time steps in a process that takes into account variables representing new infections, parasite density (i.e. the number of parasites per microlitre of blood), acquired immunity to malaria, uncomplicated and severe episodes of malaria, direct and indirect malaria-related mortality and infectivity to mosquitoes (i.e. the likelihood that a mosquito feeding on an infected human will itself become infected). The model also took into account the action of sulphadoxine-pyrimethamine, as determined by Hastings and Watkins,20 and the final model was validated21 using the results of the six IPTi-SP trials.49

The model was used to explore factors that influence the effectiveness of IPTi. Since applying the model required substantial computer processing time, only a limited number of scenarios was modelled for each estimated age pattern. The aim was to predict the number of cases of each malaria-related outcome that could be averted in each scenario for use in an Internet-based decision-support tool. The intensity of malaria transmission explored in the scenarios used EIRs of 1, 10, 100 and 200 infective bites per person per year, and the numbers of cases averted are presented in overlapping ranges for low (EIR = 1–10), medium (EIR = 10–100) and high (EIR = 100–200) transmission intensity. It was assumed that antimalarial drug doses were given with DTP2, DTP3 or measles vaccinations in infants under 12 months of age according to national EPI schedules in countries in sub-Saharan Africa.22 The following assumptions were made for all settings: 4% of clinical cases23 and 48% of severe cases24 (i.e. cases that should be admitted to hospital) were treated effectively in each 5-day interval; the drug combination used was sulphadoxine-pyrimethamine, and the prevalence of the dhfr double and triple mutations, which are markers for sulphadoxine-pyrimethamine drug resistance, was low at 10% and 10%, respectively. Since IPTi is expected to have the greatest impact when a long-lasting effective drug is administered,21 in line with trial findings,10 model values for treatment effectiveness lay towards the upper bound of what could be expected.

Decision-support tool

An algorithm for predicting the effect of IPTi-SP was developed using the results of the two approaches described above (Fig. 1). First, when using the tool, the country and the first administrative level (i.e. the highest subnational administrative division) are selected from a comprehensive list of all countries in sub-Saharan Africa. The intensity and seasonality of malaria transmission are then categorized using published data, if available. In addition, the user can alter the data presented, thereby enabling the decision-support tool to incorporate alternative or additional information. Next, the IPTi-SP administration schedule is selected from the DTP2, DTP3 and measles infant immunization schedule for the country concerned.22 Where available, recent national and subnational estimates of the percentage of infants receiving three doses of DTP vaccine2527 are presented as a proxy measure of how well the EPI is functioning. Finally, the user enters the expected level of IPTi coverage and the effectiveness of IPTi is estimated as a direct proportion of the effectiveness of 100% coverage.

Fig. 1. Flowchart illustrating the use of a decision-support tool for predicting the effect of intermittent preventive treatment in infants (IPTi) for malaria in different scenarios in sub-Saharan Africa

 

DTP3, third dose of diphtheria–tetanus–pertussis combined vaccine; EPI, Expanded Programme on Immunization.a Malaria transmission intensity is categorized as low, medium or high according to whether the entomological inoculation rate is < 10, 10–100 or > 100 infective bites per person per year, respectively.b The selected data are described as “robust” when based on screened epidemiological data.c The selected data are described as “indicative” when based on additional unscreened data provided by the user.d The selected data are described as based on “user perception” if not based on epidemiological data.e Malaria transmission is categorized as seasonal when at least 75% of clinical episodes occur within a period of 6 months or less.

For the scenario specified by the user, the decision-support tool provides graphical information on the predicted age distributions of patients with clinical malaria, of those admitted to hospital with malaria parasites and of those who will die due to malaria. In addition, the percentage of cases of each malaria-related outcome in children under the age of 10 years that would be targeted by the IPTi strategy is estimated. Further, the stochastic model produces estimates of the predicted number of cases of each malaria-related outcome that would be averted if IPTi-SP were implemented, while taking into account the expected treatment programme coverage and using the assumptions about health-system coverage, the effectiveness of treatment and the level of drug resistance described above.

Results

Age pattern analysis

The percentage of cases of malaria-related outcomes occurring in children aged under 10 years that would be targeted by IPTi was estimated from the age patterns of clinical P. falciparum malaria cases, of hospital admissions with malaria parasites and of deaths diagnosed as due to malaria observed in sub-Saharan Africa. Episodes of clinical malaria are fairly evenly distributed throughout childhood and IPTi could be expected to target 2–17% of these episodes, depending on the transmission setting. Given the pooled estimate of 30% for the protective efficacy of IPTi-SP against clinical malaria,11 IPTi-SP could prevent approximately 3–5% of childhood cases of clinical malaria where the intensity of transmission is high (i.e. EIR > 100 infective bites per person per year).

Typically, the age distribution of patients admitted to hospital with malaria parasites is skewed towards younger children and this becomes more pronounced as the intensity of transmission increases. Consequently, IPTi could target 9–29% of these cases, depending on the transmission setting. Given the pooled estimate of 38% for the protective efficacy of IPTi-SP against this outcome,11 IPTi could prevent 3–11% of admissions with malaria parasites during childhood, depending on the epidemiological setting.

Deaths diagnosed as due to malaria were heavily concentrated in very young children and, although no data were available to predict the age pattern with a low intensity of transmission, there appeared to be a shift towards younger ages as the intensity of transmission increased. Consequently, IPTi could target 17–41% of all childhood deaths diagnosed as due to malaria in settings with a medium-to-high intensity of transmission. To date, the protective efficacy of IPTi against death due to malaria or any other cause is unknown, so we were unable to estimate the percentage of deaths that could be averted by IPTi.

Stochastic model

The results of the stochastic model have been presented in detail elsewhere.21 The model predicted that the number of episodes of malaria averted by IPTi would increase when IPTi coverage increased, when treatment coverage by the local health system decreased and when drugs that were more effective or had a longer prophylactic period were introduced.21 In addition, the number of cases averted was higher when IPTi doses were timed so that the prophylactic periods did not overlap. When the model was run to predict the number of cases averted in infants aged under 12 months with the assumptions and conditions described above, the predictions concurred with estimates derived from the analysis of the age distribution of cases reported in studies and indicated that the impact of IPTi would be proportionately greater on more severe outcomes. There was very little impact on the intensity of transmission, suggesting that the age pattern of malaria cases would not change as a direct result of implementing IPTi. Further, IPTi was predicted to have a beneficial effect in all scenarios: 85–1382 cases of clinical malaria could be averted per 1000 infant–years with 100% IPTi coverage, depending on the dosing schedule and the intensity and seasonality of malaria transmission. Correspondingly, 4–61 hospital admissions for infection with malaria parasites and 1–19 deaths in individuals with malaria could be averted per 1000 infant–years with 100% IPTi coverage. The predicted impact of IPTi increased linearly with coverage (data not shown).

Decision-support tool

The decision-support tool provides predictions for each specified scenario in two parts. The first is a graph of the expected age pattern of clinical malaria cases, cases admitted to hospital with malaria parasites and deaths diagnosed as due to malaria for the one category of the six intensity and seasonality categories derived from study data, that best matches the specified scenario (Fig. 2). In addition, the percentage of cases of each malaria-related outcome that would be targeted by IPTi is listed. The second part is a table derived by the stochastic model detailing the predicted number of cases of each outcome that could be averted in infants aged under 12 months in the specified scenario, that is, according to the specified epidemiological setting and IPTi dosing schedule and coverage and using the underlying assumptions described above (Fig. 3).

Fig. 2. Example of a graph of expected malaria-related outcomes in children produced by a decision-support toola for predicting the effect of intermittent preventive treatment in infants (IPTi) for malaria in different scenarios in sub-Saharan Africa

 

EIR, entomological inoculation rate; MARA, Mapping Malaria Risk in Africa.a Decision-support tool and references presented in this screenshot are available at: http://ipti.lshtm.ac.uk

Fig. 3. Example of a table produced by a decision-support toola for predicting the effectb of intermittent preventive treatment in infants (IPTi) for malaria in different scenarios in sub-Saharan Africac

 

DTP2, second dose of diphtheria–tetanus–pertussis combined vaccine; DTP3, third dose of diphtheria-tetanus-pertussis combined vaccine; EIR, entomological inoculation rate.a Decision-support tool and references presented in this screenshot are available at: http://ipti.lshtm.ac.ukb In terms of the number of cases of malaria-related outcomes in children that could be averted by treatment.c The epidemiological scenario considered corresponds to moderately intense malaria transmission (i.e. an EIR of 10–100 infective bites per person per year) with no marked seasonality of transmission. The lower EIR is 10 infective bites per person per year and the higher EIR is 100 infective bites per person per year.

Discussion

The intensity of transmission is known to influence the age pattern of severe P. falciparum malaria cases and there is evidence that it also affects other malaria-related outcomes.1 In addition, there is increasing evidence that this pattern changes as the intensity of transmission declines,13,28 though malaria-related deaths still tend to be concentrated in the very young.29 The changing epidemiology of malaria makes it difficult to predict the potential impact of new control strategies. Moreover, as transmission intensity declines, the heterogeneity of malaria infection is likely to increase and the need for subnational policy-making will grow. The decision-support tool described here is intended to meet that need by enabling malaria control programme managers in sub-Saharan Africa to assess the potential benefits of IPTi under different epidemiological conditions and with varying levels of treatment coverage.

The methodologies used to produce the predicted age pattern of malaria-related outcomes and the predicted number of cases averted have several limitations, which have been discussed separately elsewhere by Carneiro et al.1 and Ross et al.,21 respectively.

Predictions for low-transmission settings (i.e. an EIR of 1–10 infective bites per person per year) are particularly uncertain. In this situation, local foci of malaria transmission are common and the overall age distribution of infected individuals depends on whether malaria occurs in small pockets with a high intensity of transmission or more uniformly across the population. In addition, EIR estimates are notoriously imprecise, especially below 5 infective bites per person per year.

Where the intensity of transmission is very high (i.e. an EIR > 200 infective bites per person per year), the stochastic model predicts that the burden of disease averted decreases as the EIR increases. The factors contributing to this unexpected trend are currently being investigated. Possible factors include the relationships between the disease rate and age and the intensity of transmission in the data sets30,31 initially used in devising the model.32,33 However, this trend will have little effect on decisions about the effectiveness of IPTi because predicting the impact of treatment at low EIRs is more critical.

The main limitation of the decision-support tool lies in the difficulty in determining the appropriate category for the intensity of transmission in the selected first administrative level in a specific country, since the EIR varies significantly over space and time and few robust data are available for most settings. We also used the parasite prevalence in children, which has a log-linear relationship with the EIR,34,35 to classify the intensity of transmission. Broad categories for the intensity of transmission were used in both the analyses and the decision-support tool to avoid giving the impression that intensity could be determined precisely for a given site while still allowing some general and consistent patterns to be discerned. Given these caveats, the decision-support tool was designed to be sufficiently flexible to allow the user to incorporate local knowledge or temporal changes by including additional data on the EIR or parasite prevalence or by altering the categories allocated by the tool for the intensity and seasonality of transmission. Work is ongoing on improving the accuracy of the allocated intensities of transmission using parasite prevalence maps from the Malaria Atlas Project.36,37 Another limitation is that the model predictions took into account only a selected number of input variables for each setting. Variations in factors such as the level of drug resistance and health system coverage will also have an influence. Future development of the decision-support tool will enable users to alter the prevalence of drug resistance and local health system costs.

Although none of the trials on the administration of chemoprophylaxis to infants3842 and none of the IPTi trials were powered to detect an effect on mortality in infants, one chemoprophylaxis trial in the Gambia reported a statistically significant reduction in mortality in children with malaria aged 1–4 years (P = 0.03).38 Our stochastic model predicted that 1 to 19 deaths associated with malaria could be averted per 1000 infant-years over a range of different epidemiological settings under the conditions defined above and given 100% coverage with IPTi. The higher figure in the range is indicative and will be affected by changes in IPTi coverage, dosing schedule, health system coverage and the level of drug resistance but is likely to represent the upper bound of what could be expected. It is difficult to compare this estimate of deaths averted with estimates of the deaths linked to other interventions as different age groups and denominators are often used. However, the number of infant deaths due to acute lower respiratory infection averted by pneumonia case management has been estimated to be 10.7 per 1000 live births per year,43 and the number of all-cause child deaths averted by the use of insecticide-treated bednets, at 5.5 per 1000 protected children aged under 5 years per year.44

The number of indirect deaths and, to a lesser extent, the number of severe malaria episodes predicted by the model partly rely on functions that represent the age-dependent risk of comorbid conditions. Comorbidity is assumed to weaken the host so that clinical malaria becomes severe. In the model, an indirect death was defined as a death that would not have occurred in the absence of prior malaria exposure but that was associated with a terminal illness which would not have been diagnosed as malaria by a competent physician.32 In settings where health-care provision is good, the pattern of comorbid conditions may be quite different from that assumed in our model.

Both the percentage and number of cases that could be averted by IPTi estimated by the model will be lower than comparable estimates for similar interventions that target children over a wider age range. The strategy of seasonally administering intermittent preventive treatment in children aged under 5 years (IPTc) provides almost continuous chemoprophylaxis during the period of peak transmission and has proven highly effective against clinical malaria.4548 However, the main concern with IPTc, and with any extension of IPTi beyond infancy, is the logistical complexity of delivering treatment in a sustainable and cost-effective manner. Studies of the efficacy of IPTc indicate that the cost would be considerably higher than for IPTi, which can take advantage of the health-care infrastructure that already exists for EPI and hence be a highly cost-effective addition to existing malaria control interventions.49 It is unclear whether IPTi and IPTc must be mutually exclusive or whether they can be complementary in areas with highly, but not exclusively, seasonal transmission.

Recently, there has been a move away from targeting specific age groups towards universal coverage for malaria interventions.50 While our data support this trend for areas where the intensity of transmission is low, there is clearly still a role for targeting infants using interventions that can reduce the number of malaria-associated deaths. Consequently, IPTi may still form a highly cost-effective component of malaria control strategies in large swathes of Africa where access to curative services is poor at present and will remain so for years to come.

 

Acknowledgements

We thank Jamie Griffin for his statistical advice, John Aponte for comments on the manuscript, Nicolas Maire and Diggory Hardy for developing the modelling software architecture and the many volunteers who made their computers available to malariacontrol.net. We are grateful to Simon Hay and the Malaria Atlas Project team (http://www.map.ox.ac.uk) for their ongoing collaboration to improve the accuracy of the allocated intensities of malaria transmission, Lesong Conteh for her work on the cost-effectiveness component of the model, and Andrew Kirkpatrick, Dan Forys and Mark Stevenson for developing the decision-support tool web page.

Funding:

This project was funded by the Bill & Melinda Gates Foundation through the IPTi consortium. Ilona Carneiro is also funded by the Department for International Development (UK) through the TARGETS Communicable Disease Consortium. No funding sources had any role in the study design, data collection, data analysis, data interpretation, or writing of this manuscript.

Competing interests:

None declared.

References

1.     Carneiro I, Roca-Feltrer A, Griffin JT, Smith L, Tanner M, Schellenberg JA, et al., et al. Age-patterns of malaria vary with severity, transmission intensity and seasonality in sub-Saharan Africa: a systematic review and pooled analysis. PLoS One 2010; 5: e8988- doi: 10.1371/journal.pone.0008988 pmid: 20126547.

2.     Mulligan JA, Yukich J, Hanson K. Costs and effects of the Tanzanian national voucher scheme for insecticide-treated nets. Malar J 2008; 7: 32- doi: 10.1186/1475-2875-7-32 pmid: 18279509.

3.     Grabowsky M, Nobiya T, Selanikio J. Sustained high coverage of insecticide-treated bednets through combined Catch-up and Keep-up strategies. Trop Med Int Health 2007; 12: 815-22 doi: 10.1111/j.1365-3156.2007.01862.x pmid: 17596247.

4.     Schellenberg D, Menendez C, Kahigwa E, Aponte J, Vidal J, Tanner M, et al., et al. Intermittent treatment for malaria and anaemia control at time of routine vaccinations in Tanzanian infants: a randomised, placebo-controlled trial. Lancet 2001; 357: 1471-7 doi: 10.1016/S0140-6736(00)04643-2 pmid: 11377597.

5.     Chandramohan D, Owusu-Agyei S, Carneiro I, Awine T, Amponsa-Achiano K, Mensah N, et al., et al. Cluster randomised trial of intermittent preventive treatment for malaria in infants in area of high, seasonal transmission in Ghana. BMJ 2005; 331: 727-33 doi: 10.1136/bmj.331.7519.727 pmid: 16195288.

6.     Macete E, Aide P, Aponte JJ, Sanz S, Mandomando I, Espasa M, et al., et al. Intermittent preventive treatment for malaria control administered at the time of routine vaccinations in Mozambican infants: a randomized, placebo-controlled trial. J Infect Dis 2006; 194: 276-85 doi: 10.1086/505431 pmid: 16826474.

7.     Grobusch MP, Lell B, Schwarz NG, Gabor J, Dornemann J, Potschke M, et al., et al. Intermittent preventive treatment against malaria in infants in Gabon–a randomized, double-blind, placebo-controlled trial. J Infect Dis 2007; 196: 1595-602 doi: 10.1086/522160 pmid: 18008242.

8.     Kobbe R, Kreuzberg C, Adjei S, Thompson B, Langefeld I, Thompson PA, et al., et al. A randomized controlled trial of extended intermittent preventive antimalarial treatment in infants. Clin Infect Dis 2007; 45: 16-25 doi: 10.1086/518575 pmid: 17554695.

9.     Mockenhaupt FP, Reither K, Zanger P, Roepcke F, Danquah I, Saad E, et al., et al. Intermittent preventive treatment in infants as a means of malaria control: a randomized, double-blind, placebo-controlled trial in northern Ghana. Antimicrob Agents Chemother 2007; 51: 3273-81 doi: 10.1128/AAC.00513-07 pmid: 17638703.

10.  Gosling RD, Gesase S, Mosha JF, Carneiro I, Hashim R, Lemnge M, et al., et al. Protective efficacy and safety of three antimalarial regimens for intermittent preventive treatment for malaria in infants: a randomised, double-blind, placebo-controlled trial. Lancet 2009; 374: 1521-32 doi: 10.1016/S0140-6736(09)60997-1 pmid: 19765815.

11.  Aponte JJ, Schellenberg D, Egan A, Breckenridge A, Carneiro I, Critchley J, et al., et al. Efficacy and safety of intermittent preventive treatment with sulfadoxine-pyrimethamine for malaria in African infants: a pooled analysis of six randomised, placebo-controlled trials. Lancet 2009; 374: 1533-42 doi: 10.1016/S0140-6736(09)61258-7 pmid: 19765816.

12.  Report of the technical consultation on intermittent preventive treatment in infants (IPTi), Technical Expert Group on Preventive Chemotherapy. Geneva: World Health Organization; 2009. Available from: http://malaria.who.int/docs/IPTi/TEGConsultIPTiApr2009Report.pdf [accessed 23 September 2009].

13.  O’Meara WP, Breman JG, McKenzie FE. The promise and potential challenges of intermittent preventive treatment for malaria in infants (IPTi). Malar J 2005; 4: 33- doi: 10.1186/1475-2875-4-33 pmid: 16033653.

14.  Greenwood B. Review: Intermittent preventive treatment–a new approach to the prevention of malaria in children in areas with seasonal malaria transmission. Trop Med Int Health 2006; 11: 983-91 doi: 10.1111/j.1365-3156.2006.01657.x pmid: 16827699.

15.  Chandramohan D, Webster J, Smith L, Awine T, Owusu-Agyei S, Carneiro I. Is the Expanded Programme on Immunisation the most appropriate delivery system for intermittent preventive treatment of malaria in West Africa? Trop Med Int Health 2007; 12: 743-50 doi: 10.1111/j.1365-3156.2007.01844.x pmid: 17550471.

16.  Roca-Feltrer A, Schellenberg JR, Smith L, Carneiro I. A simple method for defining malaria seasonality. Malar J 2009; 8: 276- doi: 10.1186/1475-2875-8-276 pmid: 19958535.

17.  Cairns M, Carneiro I, Milligan P, Owusu-Agyei S, Awine T, Gosling R, et al., et al. Duration of protection against malaria and anaemia provided by intermittent preventive treatment in infants in Navrongo, Ghana. PLoS One 2008; 3: e2227- doi: 10.1371/journal.pone.0002227 pmid: 18493597.

18.  May J, Adjei S, Busch W, Gabor JJ, Issifou S, Kobbe R, et al., et al. Therapeutic and prophylactic effect of intermittent preventive anti-malarial treatment in infants (IPTi) from Ghana and Gabon. Malar J 2008; 7: 198- pmid: 18828899.

19.  Smith T, Killeen GF, Maire N, Ross A, Molineaux L, Tediosi F, et al., et al. Mathematical modeling of the impact of malaria vaccines on the clinical epidemiology and natural history of Plasmodium falciparum malaria: Overview. Am J Trop Med Hyg 2006; 75: 1-10 pmid: 16931810.

20.  Hastings IM, Watkins WM. Tolerance is the key to understanding antimalarial drug resistance. Trends Parasitol 2006; 22: 71-7 doi: 10.1016/j.pt.2005.12.011 pmid: 16406706.

21.  Ross A, Penny M, Maire N, Studer A, Carneiro I, Schellenberg D, et al., et al. Modelling the epidemiological impact of intermittent preventive treatment against malaria in infants. PLoS One 2008; 3: e2661- doi: 10.1371/journal.pone.0002661 pmid: 18628828.

22.  WHO immunization surveillance, assessment and monitoring: immunization schedule. Geneva: World Health Organization; 2007. Available from: www.who.int/immunization_monitoring/data/data_subject. [accessed 5 August 2009].

23.  Maire N, Aponte JJ, Ross A, Thompson R, Alonso P, Utzinger J, et al., et al. Modeling a field trial of the RTS,S/AS02A malaria vaccine. Am J Trop Med Hyg 2006; 75: 104-10 pmid: 16931821.

24.  Goodman CA, Coleman PG, Mills A. Economic analysis of malaria control in sub-Saharan Africa. Geneva: Global Forum for Health Research; 2000.

25.  Measure DHS. Demographic and Health Surveys. Calverton: Measure DHS; 2007. Available from: http://www.measuredhs.com/ [accessed 10 June 2009].

26.  United Nations Children's Fund. Multiple indicator cluster surveys. New York: UNICEF; 2009. Available from: http://www.childinfo.org/mics.html [accessed 10 June 2009].

27.  World Health Organization, United Nations Children's Fund. WHO–UNICEF time series estimates of DTP3 coverage. Geneva: WHO, UNICEF; 2009. Available from: http://www.who.int/immunization_monitoring/en/globalsummary/timeseries/tswucoveragedtp3.htm [accessed 4 May 2010].

28.  Ceesay SJ, Casals-Pascual C, Erskine J, Anya SE, Duah NO, Fulford AJ, et al., et al. Changes in malaria indices between 1999 and 2007 in The Gambia: a retrospective analysis. Lancet 2008; 372: 1545-54 doi: 10.1016/S0140-6736(08)61654-2 pmid: 18984187.

29.  Schellenberg D, Menendez C, Aponte J, Guinovart C, Mshinda H, Tanner M, et al., et al. The changing epidemiology of malaria in Ifakara Town, southern Tanzania. Trop Med Int Health 2004; 9: 68-76 doi: 10.1046/j.1365-3156.2003.01161.x pmid: 14728609.

30.  Trape J-F, Rogier C. Combating malaria morbidity and mortality by reducing transmission. Parasitol Today 1996; 12: 236-40 doi: 10.1016/0169-4758(96)10015-6 pmid: 15275204.

31.  Marsh K, Snow RW. Malaria transmission and morbidity. Parassitologia 1999; 41: 241-6 pmid: 10697862.

32.  Ross A, Maire N, Molineaux L, Smith T. An epidemiologic model of severe morbidity and mortality caused by Plasmodium falciparum. Am J Trop Med Hyg 2006; 75: 63-73 pmid: 16931817.

33.  Smith T, Ross A, Maire N, Rogier C, Trape JF, Molineaux L. An epidemiologic model of the incidence of acute illness in Plasmodium falciparum malaria. Am J Trop Med Hyg 2006; 75: 56-62 pmid: 16931816.

34.  Beier JC, Killeen GF, Githure JI. Short report: entomologic inoculation rates and Plasmodium falciparum malaria prevalence in Africa. Am J Trop Med Hyg 1999; 61: 109-13 pmid: 10432066.

35.  Hay SI, Rogers DJ, Toomer JF, Snow RW. Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, Internet access and review. Trans R Soc Trop Med Hyg 2000; 94: 113-27 doi: 10.1016/S0035-9203(00)90246-3 pmid: 10897348.

36.  Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, Noor AM, et al., et al. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 2009; 6: e1000048- doi: 10.1371/journal.pmed.1000048 pmid: 19323591.

37.  Gething PW, Patil AP, Hay SI. Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation. PLoS Comput Biol 2010; 6: e1000724- doi: 10.1371/journal.pcbi.1000724 pmid: 20369009.

38.  Menon A, Snow RW, Byass P, Greenwood BM, Hayes RJ, N’Jie AB. Sustained protection against mortality and morbidity from malaria in rural Gambian children by chemoprophylaxis given by village health workers. Trans R Soc Trop Med Hyg 1990; 84: 768-72 doi: 10.1016/0035-9203(90)90071-L pmid: 2096501.

39.  Greenwood BM, Greenwood AM, Bradley AK, Snow RW, Byass P, Hayes RJ, et al., et al. Comparison of two strategies for control of malaria within a primary health care programme in the Gambia. Lancet 1988; 1: 1121-7 doi: 10.1016/S0140-6736(88)91949-6 pmid: 2896957.

40.  Greenwood BM, Greenwood AM, Smith AW, Menon A, Bradley AK, Snow RW, et al., et al. A comparative study of Lapudrine (chlorproguanil) and Maloprim (pyrimethamine and dapsone) as chemoprophylactics against malaria in Gambian children. Trans R Soc Trop Med Hyg 1989; 83: 182-8 doi: 10.1016/0035-9203(89)90635-4 pmid: 2692227.

41.  Alonso PL, Lindsay SW, Armstrong Schellenberg JR, Keita K, Gomez P, Shenton FC, et al., et al. A malaria control trial using insecticide-treated bed nets and targeted chemoprophylaxis in a rural area of The Gambia, west Africa. 6. The impact of the interventions on mortality and morbidity from malaria. Trans R Soc Trop Med Hyg 1993; 87: 37-44 doi: 10.1016/0035-9203(93)90174-O pmid: 8212109.

42.  Menendez C, Kahigwa E, Hirt R, Vounatsou P, Aponte JJ, Font F, et al., et al. Randomised placebo-controlled trial of iron supplementation and malaria chemoprophylaxis for prevention of severe anaemia and malaria in Tanzanian infants. Lancet 1997; 350: 844-50 doi: 10.1016/S0140-6736(97)04229-3 pmid: 9310602.

43.  Sazawal S, Black RE. Meta-analysis of intervention trials on case-management of pneumonia in community settings. Lancet 1992; 340: 528-33 doi: 10.1016/0140-6736(92)91720-S pmid: 1354286.

44.  Lengeler C. Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst Rev 2004CD000363- pmid: 15106149.

45.  Cissé B, Sokhna C, Boulanger D, Milet J, Bâ H, Richardson K, et al., et al. Seasonal intermittent preventive treatment with artesunate and sulfadoxine-pyrimethamine for prevention of malaria in Senegalese children: a randomised, placebo-controlled, double-blind trial. Lancet 2006; 367: 659-67 doi: 10.1016/S0140-6736(06)68264-0 pmid: 16503464.

46.  Sokhna C, Cissé B, Bâ H, Milligan P, Hallett R, Sutherland C, et al., et al. A trial of the efficacy, safety and impact on drug resistance of four drug regimens for seasonal intermittent preventive treatment for malaria in Senegalese children. PLoS One 2008; 3: e1471- doi: 10.1371/journal.pone.0001471 pmid: 18213379.

47.  Dicko A, Sagara I, Sissoko MS, Guindo O, Diallo AI, Kone M, et al., et al. Impact of intermittent preventive treatment with sulphadoxine-pyrimethamine targeting the transmission season on the incidence of clinical malaria in children in Mali. Malar J 2008; 7: 123- doi: 10.1186/1475-2875-7-123 pmid: 18611271.

48.  Kweku M, Liu D, Adjuik M, Binka F, Seidu M, Greenwood B, et al., et al. Seasonal intermittent preventive treatment for the prevention of anaemia and malaria in Ghanaian children: a randomized, placebo controlled trial. PLoS One 2008; 3: e4000- doi: 10.1371/journal.pone.0004000 pmid: 19098989.

49.  Hutton G, Schellenberg D, Tediosi F, Macete E, Kahigwa E, Sigauque B, et al., et al. Cost-effectiveness of malaria intermittent preventive treatment in infants (IPTi) in Mozambique and the United Republic of Tanzania. Bull World Health Organ 2009; 87: 123-9 doi: 10.2471/BLT.08.051961 pmid: 19274364.

50.  Roll Back Malaria Partnership. Global malaria action plan for a malaria-free world 2008. Geneva: RBM Partnership; 2008.

 

 


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To subscribe or unsubscribe from this list, pls contact kidsurvivalmalaria @gmail.com.

Do not subscribe on behalf of friends or colleagues; forward updates to them for their decision.
 
To subscribe, write from your most permanent E-mail address, not necessarily that of your current employer.
 
To subscribe to all child survival updates, pls write to kidsurvival@gmail.com

These updates are available at www.childsurvival.net


Readers of these malaria updates may also wish to visit the websites http://www.malariaworld.org/  and
http://www.allianceformalariaprevention.com/index.php
 
 

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