|
Field
Last Published
|
Before
July 17, 2024 02:16 PM
|
After
April 15, 2026 12:42 PM
|
|
Field
Primary Outcomes (End Points)
|
Before
1. Composite disease burden index at endline (18 months)
2. Proportion of sick children who receive formal medical care at endline (18 months)
3. Attendance rate
|
After
1. Any healthcare utilisation
2. Healthcare utilisation conditional on need
3. Attendance rate
|
|
Field
Primary Outcomes (Explanation)
|
Before
1. Index combining the school-level prevalence of: malaria (RDT result), moderate to high worm load (stool test), anaemia (hemocue test), schistosomiasis (urine test), UTIs (urine test), diarrhoea (self-reported, past week), coughing (self-reported, past week), skin rash (self-reported, past week)
2. Proportion of children who had any symptoms (such as fever, cough, convulsions) who then receive formal medical care (e.g., receiving drugs in the school health room, being seen in the clinic) reported in household surveys
3. Rate of enrolled students absent during unannounced spot checks in schools, conducted every term over the course of the study
|
After
1. Any healthcare utilisation
The proportion of learners who sought formal care (any government health centre or hospital, or the school health room). Equals one if reported at least once during the health diary observation period.
[Time Frame: 8-week period across term 1 and term 2 2026]
2. Healthcare utilisation conditional on need
Conditional measure of healthcare use, restricted to the sample of children who have experienced at least one ‘serious’ illness episode, defined as one which requires medical attention according to WHO c-IMCI and IMAI guidelines. The outcome is the ratio of the number of illness episodes during which a child seeks medical care over the total number of serious illness episodes experienced by a child, for all children who have experienced at least one serious illness episode over the period.
[Time Frame: Eight week diary observation period]
3. Attendance rate
We assess the program’s effect on school attendance using data collected through repeated, unannounced spot checks at each school. Our primary attendance measure will be based on the sample of children enrolled in the study at baseline and confirmed to still be enrolled in their original school at the time of the visit. In this sample, the attendance rate, measured at each attendance spot check, will be the proportion of those children confirmed present in class on the day of the visit.
[Time Frame: 24 months after intervention start]
|
|
Field
Power calculation: Minimum Detectable Effect Size for Main Outcomes
|
Before
Assuming an alpha of 0.05, 80% power, an ICC of 0.10, 40 schools per arm and 200 children surveyed per school will allow us to detect, before controlling for any covariates, a reduction in absenteeism of 8 percentage points (based on average of 20% in the control group), an increase in standardized test scores by 0.20SD, a reduction in illness spells by one day (assuming duration of illness spells of 5 days (SD=5) in the control group).
|
After
We computed power calculations and concluded that our study was well-powered for the two co-primary outcomes defined above. We use a significance level of 5% and estimate minimum detectable effect (MDE) sizes with 80% power. We report MDEs based on intention-to-treat effects.
Unconditional use of health care. We expect the most direct effect of the school health programme to be an increase in the healthcare utilisation (HA1). In a pilot study carried out in October 2025, 31% of children surveyed sought care at least one. Assuming an intra-cluster correlation of 0.30 (higher than the one estimated during the pilot), we are powered to detect an increase of 10.5 percentage point in the probability of ever seeking care in the SHP arm relative to the control arm.
Conditional use of needed health care. We expect the biggest and most direct effect of the school health programme to be an increase in the utilisation of health care when needed (HA1). This outcome is conditional on a child experiencing at least one serious illness episode over the observation period. Based on results obtained from a pilot study carried out in October 2025, we expect that about 50% (i.e., approximately 12) of the 24 children followed in each school will experience at least one illness episode illness episodes warranting medical assessment over the 8-week observation window (this proportion likely under-estimates the prevalence of morbidity episodes we will observe during the data collection, which will take place during the rainy season, whereas the pilot occurred in a lower-morbidity season), and that they will seek medical care on average 70% of the time. Assuming an intra-cluster correlation of 0.35 (higher than the one estimated during the pilot), we are powered to detect a difference of 11.9 percentage point in the probability of seeking care between the SHP arm and the control arm. Appendix Table 1 shows that the range of minimal detectable effect size between 8.5-13.9 points, depending on the assumptions made about (1) the intra-cluster correlation, (2) the proportion of children seriously ill and (3) the proportion of healthcare utilisation in the control group.
These MDEs are within the range of effects documented in randomised evaluations of interventions aiming to increase utilisation of care in LMICs. Performance-based financing in Rwanda, Zimbabwe, and Cambodia have found increases in facility-based service use of 7 to 9 percentage points (Basinga, Gertler et al. 2011, Van de Poel, Flores et al. 2016), while demand-side interventions combining financial incentives with improved access have generated effects of 27 percentage points (Thornton 2008). Our intervention primarily reduces the physical distance to care for children and families in rural areas, one of the most consistently documented barriers to healthcare utilisation in sub-Saharan Africa. As such, it is closest to studies looking at demand-side interventions reducing opportunity cost and distance such as Thornton (2008), which has been shown to generate among the largest demand responses in the literature. A 10-12 percentage point MDE therefore represents a conservative benchmark: it lies above the effects found in studies relying on information provision or modest financial nudges alone, yet comfortably within the range achieved by access-enhancing interventions operating in similarly supply-constrained environments.
School attendance. We expect the school health programme to increase attendance in school (HA2). Earlier management of illnesses, school-based deworming, health education, and support to menstruating girls all help prevent common causes of absenteeism, such as untreated infections and poor menstrual hygiene, while the peer-buddy system ensures that children return to school more quickly after illness and that school staff are more attuned to learner absenteeism.
For this outcome, the unit of analysis is the child. As described in section 3.4.2, our primary measure of attendance will be measured using the panel sample. At baseline, the intra-cluster correlation (ICC) of attendance at the school level using the panel-based method was 0.039 (controlling for stratification-cell fixed effects).
In our power calculations, we test for the minimum detectable effect between the six comparisons we plan to make across treatment arms (e.g., Full Healthy Learners treatment vs. control, Full Healthy Learners treatment vs. deworming, and deworming vs. control). We calculate the minimum detectable effect size for each comparison assuming a two-sided significance level of α = 0.05, a conservative ICC of 0.05, and 80 percent power, accounting for clustering at the school level. We assume 89 Healthy Learners treatment schools (reflecting one treatment school that dropped out after baseline), 60 deworming schools, and 75 pure control schools, with an average of 58 sampled learners per school. We conduct these calculations across baseline attendance rates of 0.5, 0.6, and 0.7. Attendance rates measured in the panel survey are expected to be lower than those from government measures based on school registers due to survey attrition and the requirement that students be observed and present across multiple survey rounds.
The study is powered to detect effects on attendance of approximately 4 to 6 percentage points. The results of this can be found in Appendix Table 2. Minimum detectable effects range from roughly 5.0 to 6.3 percentage points, with smaller MDEs at higher baseline attendance rates. Across comparisons, minimum detectable effects are largest for Deworming versus Control arms and smallest for comparisons involving the Full HL Treatment, reflecting differences in the number of schools across arms.
|
|
Field
Secondary Outcomes (End Points)
|
Before
|
After
4. Index of health knowledge
5. Proportion of children with moderate or severe anaemia
6. Proportion of children who are underweight
7. Proportion of children who are overweight
8. Proportion of children with diarrhoea
9. Proportion of children testing positive for malaria
10. Menstrual hygiene management
11. Combined measure of attendance and retention
12. Attendance rate including learners transfered to other study schools
13. Cross-section attendance
14. Learning
15. Students' working memory and attention
|
|
Field
Secondary Outcomes (Explanation)
|
Before
|
After
4. Index of health knowledge
A standardized index score derived from a maximum of 10 questions covering diarrhea, malaria prevention, hygiene, and questions aligned with the program curriculum.
[Time Frame: At point of endline]
5. Proportion of children with moderate or severe anaemia
Haemoglobin concentration measured using a HemoCue photometer in the sample of children taking part in the health diaries (testing will occur at the end of the period). Anemia severity categories will be based on the WHO classification which depends on the age and sex of the child.
[Time Frame: Half of the sample captured in term 1 2026 and half in term 2 2026]
6. Proportion of children who are underweight
Using height and weight measured for all learners surveyed at endline, we will construct weight-for-age z-scores and determine the proportion of children who are underweight.
[Time Frame: Endline]
7. Proportion of children who are overweight
Using height and weight measured for all learners surveyed at endline, we will construct weight-for-age z-scores and determine the proportion of children who are overweight.
[Time Frame: Endline]
8. Proportion of children with diarrhoea
Incidence of diarrhoea - carer report of the child experiencing three or more loose or watery stools.
[Time Frame: Over the eight week follow-up period across term 1 and 2 in 2026]
9. Proportion of children testing positive for malaria
Testing for the presence of P. falciparum. The tests are performed using a rapid diagnostic test (RDT) on the sample of children taking part in the health diaries (testing will occur at the end of the period).
[Time Frame: End of health diaries in term 1 and term 2 2026]
10. Menstrual hygiene management
We examine the impact of the programme across three domains: knowledge, practices, and stigma. For each domain, we draw on questions developed and validated in prior studies (Austrian, Kangwana et al. 2021, Kansiime, Hytti et al. 2022, Hennegan, Hasan et al. 2024, Macours, Vera et al. 2024). For each sub-scale, we will construct a summary index using principal component analysis, standardized as a z-score using the mean and standard deviation of the control group for ease of interpretation of the results. In analysis, we will look at the impact of the programme on each of these domains separately, as well as combined.
[Time Frame: Endline]
11. Combined measure of attendance and retention
Proportion of children confirmed present in class on the day of the visit, out of the total number of children enrolled at the school at baseline and still alive. In this measure, we effectively treat children who may have transferred to another study school as ‘absent’, dropped out, or temporarily relocated.
[Time Frame: Five random spot checks across 18 months]
12. Attendance rate including learners transfered to other study schools
Proportion of children confirmed present in class on the day of the visit either in their original school or in another school to which they have transferred if that school is one of the study schools, out of the total number of children enrolled at baseline and still alive. Following an Intention-to-Treat principle, we will analyse participants based on the arm they were originally assigned to, even if they moved to a different study arm school.
[Time Frame: Five unannounced spot checks across 18 months]
13. Cross-section attendance
Additional measure of attendance, focusing on students in grades 1, 3, 5, and 7 at the time of the spot check. This register-based measure is defined as the proportion of children on the register who are present on the day of the spot check. The denominator will be defined by the list of children in each of the four grades who are confirmed by a school staff member as enrolled in the school on the day of the visit.
[Time Frame: Four randomised spot checks conducted across 12 months]
14. Learning
Learning is measured using individually administered numeracy, literacy, and science examinations. Items for these exams are aligned with the Zambian Ministry of Education national curriculum for Grades 1 through 7. Each assessment will be conducted one-on-one by trained enumerators at the start of the learners’ interviews. Items are developed by local curriculum experts and calibrated through pilot testing to ensure grade-appropriate difficulty. We will also borrow items from international test banks (e.g., TIMSS, EGRA, EGMA). Finally, we will classify items using Bloom’s taxonomy to test for differences in effects across higher- and lower-order thinking skills. In analysis, we will look at each domain separately as well as a combined score.
[Time Frame: Endline]
15. Students' working memory and attention
We measure students’ working memory and attention using the Digit Span task, a widely used short-term memory and working memory assessment. Enumerators orally present a sequence of digits at a rate of one per second, and students are asked to repeat the digits in the same order (Digit Span Forward) and then in reverse order (Digit Span Backward). The task measures students’ auditory attention, concentration, and working memory capacity, which are foundational components of general cognitive functioning and predictive of academic achievement. Scores are recorded as the total number of correct sequences reproduced, following standard administration and scoring procedures. Additional cognitive and non-cognitive measures may be added depending upon validation during piloting. These include Raven's Progressive Matrices, Interactive Stroop Tests, and pattern recognition tasks.
[Time Frame: Endline]
|