of each intervention will be estimated for education outcomes and health outcomes in separate multiple regression models. An interaction term will be included to identify any synergistic effects between the two interventions.

The survey data will be collected over two years – see section 10. Development and testing of survey instruments will take place in 2008. Baseline data collection and intervention activities will commence in January 2009, with follow-up surveys planned in 2010 and 2011. The study will be conducted by the Kenya Medical Research Institute in collaboration with the Division of Malaria Control in the Ministry of Public Health and Sanitation, University of Nairobi, the London School of Hygiene and Tropical Medicine, and Harvard University.

# **ii) Outcome measures**

# **Primary outcomes:**

1. Prevalence of anemia

2. Education achievement assessed by a battery of tests of reading, writing and arithmetic

# **Secondary outcomes:**

3. Prevalence of malaria parasitemia 4. Concentration as assessed by classroom-based tests of sustained attention 5. Classroom behavior as assessed by teacher ratings of children’s inattentive and hyperactive-impulsive behaviors.

6. School attendance as assessed by class attendance registers 7. Examination results as assessed by government examination scores

# **iii) Study sites**

The study will take place in rural primary schools in Kwale district, a hot and relatively dry area on the Kenyan coast. Continuous precipitation supports intense year-round transmission, with two seasonal peaks in malaria cases reflecting the bimodal rainfall pattern, with the heaviest rainfall typically occurring between April and June, with a smaller peak in October and November each year. Most malaria is caused by _Plasmodium falciparum_ . A recent survey among 25 schools in 2008 found that the prevalence of malaria infection among schoolchildren in Kwale district was 9% (ranging from 1-28% by school), and 20% (3-37% by school) of children are anaemic (Brooker and Mwandawiro, unpublished data). Helminth infections are also common with 38% of schoolchildren estimated to be infected with hookworm and 41% infected with _Schistosoma haematobium_ .

Kwale district also has some of the poorest educational indicators in the country, with the district having the worst KCPE examination scores for the last three years running.

**iv) Study populations**

Children enrolled in participating schools will be assessed for the following eligibility criteria:

1. **Inclusion criteria**

- a. Pupil enrolled at participating schools in standards 1-5

- b. Provision of informed consent from parent or guardian

- c. Provision of assent by student

# 2. **Exclusion criteria**

- a. Pupils unwilling to participate in the study

- b. Known allergy or history of adverse reaction to study medications

- c. Known or suspected sickle-cell trait. These children will be referred to testing and/or clinical management as per national guidelines

v) **Sample size determination** Power analysis was adjusted for clustering (Hayes & Bennett, 1999) and focused on two main outcomes of interest: the percentage of children who are anaemic (hemoglobin <110 g/L); and education achievement assessed by a battery of tests of reading, writing and arithmetic.

For anemia, assuming a baseline prevalence of 20% (as found in the 2008 school surveys in Kwale), an intraclass correlation of between 0.025 and 50 children sampled per school, a sample size of 21 schools with a total of 1,050 children of all ages in each arm was estimated to provide 80% power to detect a 25% reduction in anemia in the intervention group compared to placebo at 5% level of significance. This equates to 84 schools and 4,200 children.

For educational achievement tests, an overall sample size of 100 schools with 50 children per school (5,000 children overall) is sufficient to detect an effect size of 0.2 standard deviation (SD) assuming an intra-class correlation of 0.2 (ICC varied from 0.1 to 0.2 with mathematics and literacy tests in Class 2 in 210 schools in Western Kenya; ICC is expected to be lower in Class 1) and a correlation between baseline and final test scores of 0.7. This sample size is sufficient to detect an effect size of 0.15 SD for concentration tests, which have a lower intra-class correlation (0.07 in the previous study in Bondo).

# **vi) Randomization and treatment assignment and allocation**

Schools will be randomly assigned to one of the four intervention groups using a pre-defined stratified randomization procedure. A computer generated randomization list will be created by a member of the project who will not be directly involved in the conduct of the study. Sealed copies of the original randomization lists and documentation of the procedure used to generate the lists will be stored in KEMRI offices in Nairobi. Prior to the onset of the study, sealed copies of the randomization lists will be distributed to the member of the study team responsible for treatment allocation.

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