Reducing Anemia through Food Fortification at Scale

Our proposed project will leverage the existing state-run public distribution system (PDS) to provide fortified rice using a domestically manufactured rice grain (Fortified Rice Kernels, or FRK). FRKs are grains made from rice flour, highly enriched with iron and other micronutrients (such as Vitamin A, B9 and Vitamin B12), and shaped like a conventional grain of rice. It is mixed into conventional rice in specified ratios (1g of FRK 100g of conventional rice) and is almost indistinguishable from conventional rice in appearance and taste.

The Tamil Nadu PDS already provides up to 20 kilograms of rice at no cost to all households in the state, with an additional 15 kilograms to households identified as particularly poor. This is done through a widespread network of Fair Price Shopes (FPS). Our proposed project (implementation and accompanying evaluation) will be conducted as a cluster-randomized controlled trial, with baseline and follow-up surveys conducted 12-15 months apart. In the interim, 110 Treatment FPS will be randomly assigned to receive rice fortified with UR, and another 110 randomly assigned Control FPS will continue receiving conventional rice. We plan to conduct the study in one sub-district of Tamil Nadu with a population of about 1/2 million people.

In cluster, we will randomly select 40 households to participate in the baseline and follow-up surveys. These surveys will collect information about dietary patterns and socio-economic status – and importantly, they will also collect objectively measured health indicators (biomarkers), enabling us to test directly for anemia and micronutrient deficiencies.

To implement the intervention we will use the Government pre-existing PDS system. Every household in Tamil Nadu has a card that entitles it monthly to a pre-specified quantity of rice (and other goods) from a specific location that belongs to a network of so called Fair Price Shops (FPS). These FPS in turn receive rice from government-run warehouses (called godowns) where rice is delivered from a central procurement system and then bagged.

Our treatment and control interventions will be implemented as follows:

Arm 1 (Treatment): PDS rice fortified with Ultra Rice. All households receiving rice from the PDS will receive fortified rice instead of conventional PDS rice. Because a given FPS only receives rice from a single upstream distributor (godown) it should be straightforward to ensure that fortified rice reaches the appropriate treatment FPS and only those FPS.

Arm 2 (Control): Conventional PDS Rice. FPS assigned to this arm will continue receiving conventional (non-fortified) PDS rice. This experimental arm therefore represents the status quo and serves as a control group against which any improvements observed in the treatment group will be gauged.

2017-08-15

2018-11-30

i. Rapid Diagnostic Tests (RDT) will be used to measure Hemoglobin concentrations in80 individuals tested in each FPS catchment area (total sample size 17600 women and children). Hemoglobin (Hb) will be tested with the HemoCue analyzer, a portable, accurate system for measuring Hb. Hb measures will be used to calculate prevalence of anemia among the target population of women and young children. The test requires less than 0.5 ml of blood and delivers results in approximately 15 minutes. Transferrin Receptor (TfR); and (3) C-Reactive Protein (CRP) will also be measured. TfR is a good indicator of iron stores in the body, and this marker is expected to improve following food fortification. CRP is useful both to detect the presence of inflammation and to identify cases where measured TfR is likely to give a biased account of iron stores, given that TfR becomes elevated in the presence of acute and sometimes chronic infections; thus, TfR will be corrected for inflammation using CRP.
ii. Anthropometric data for children, specifically for height and weight for children that are 2 years and older will be collected.
iii. Cognitive outcomes for children using Development Milestones Checklist instrument, as well as school enrolment and attendance data for older children will be measured.
iv. Employment status, hours worked, productivity, and earnings will be measured for working age adults.
v. Household socio-economic outcomes will be measured, using well-established recall methods to measure consumption data at the household level, with a particular emphasis on consumption of rice both from the PDS and from other sources.
vi. Data from twenty-four (24) hour recalls to measure consumption of fortified rice and estimation of contribution of fortified rice to iron requirements for women and children will be collected.

This project (implementation and accompanying evaluation) will be conducted as a cluster-randomized controlled trial, with baseline and follow-up surveys conducted 12 months apart in the same individuals. In the interim, 110 Treatment FPS will be assigned randomly to receive rice fortified with iron and other micronutrients, and another 110 Control FPS will be assigned randomly to conventional PDS rice. The control arm therefore represents the status quo and serves as a control group against which any improvements observed in the treatment group will be gauged.

Power calculations for different choices of parameters have been drawn up, with possibilities including 160 control clusters and 60 treatment clusters (with 40 tests per cluster); 150 control clusters and 50 treatment clusters (with 20 tests per cluster); and 120 control clusters and 60 treatment clusters (with 40 tests per cluster). Statistical power remains in the range of 70-90% even when different parameters are varied in the experimental design. Final decisions will be made keeping in mind logistical and budgetary constraints.

Randomization done in office by a computer

The unit of randomisation is FPS and associated population within the catchment area of FPS, and treatment is then clustered at the FPS level. the treatment is clustered at the Fair Price Shop (FPS) level. Every household in Tamil Nadu has a card that entitles it monthly to a pre-specified quantity of rice (and other goods) from a specific location that belongs to a network of FPS. These FPS in turn receive rice from government-run warehouses (called godowns) where rice is delivered from a central procurement system and then bagged.

Yes

As stated above, the study will follow a randomized cluster design at the Fair Price Shop (FPS) level, that is, Fair Price Shops will be randomly assigned either to the treatment or the control arm. 110 FPS will be assigned to the treatment arm, and 110 FPS will be assigned to the control arm.

In each area of the 220 FPS, 40 households will be randomly selected to participate in the baseline and endline surveys, i.e. a total of 8,800 women between ages of 15 and 45 years and 8,800 children between the ages of 6 to 59 months. These surveys will collect information about dietary patterns and socio-economic status and health indicators, enabling the direct testing of anemia and micronutrient deficiencies. In addition, a range of other outcomes looking both at cognitive measures as well as socio-economic outcomes such as employment and earnings will also be measured.

See above

Our calculations of statistical power focus on blood haemoglobin (Hb) concentrations and anemia prevalence as primary outcomes of interest. We will also study impacts on a number of other bio-markers and health indicators (in particular ferritin levels, a key indicator of iron stores), socio-economic outcomes and cognitive outcomes for children, but we base power calculations on Hb concentrations and anemia prevalence because such outcomes are particularly demanding in terms of statistical power. Using data from rural areas surveyed in the 2005-06 round of the NFHS, we find that the intra-village correlations for hemoglobin and anemia range from 0.02 for anemia among men to 0.11 for anemia among children (6-59 month old). Data from the 1998-99 round of the NFHS confirm similar estimates. The survey of randomized evaluations of food fortification with iron reported in Gera et al (2012) finds an average increase in Hb of 0.28 g/dl (95% CI: 0.28-0.56), and an average reduction in the risk of anemia of 41% (95% CI: 0.29-0.52%). Recent data from the 2012-13 District Level Household Survey (DLHS) show wide variation in the prevalence of anemia across districts in Tamil Nadu. In several districts anemia rates are 50% or higher among women of fertility age, and 60% or higher among children and we use these figures to approximate anemia prevalence at baseline. We thus calculate first the effect size (E), defined as the ratio between the minimum difference between groups that the researcher is interested in being able to estimate precisely (in the numerator) and the standard deviation of the outcome at baseline (in the denominator). If we evaluate the power of estimating a relatively small 15% reduction in anemia, the effect size for anemia among women of fertility age is 0.15 (= 0.50*0.15/sqrt(0.5*0.5)), while it is 0.18 (0.60*0.15/sqrt(0.6*0.4)) for children. We also assume conservatively that the intra-village correlation (R) will be 0.10 and that baseline Hb and anemia will explain only 10% of the variation in the outcomes (R2=0.10). Given these numbers, we propose to study 220 clusters (each cluster corresponds the catchment area for a FPS), randomly allocated to either T (n=110) or C (n=110). Using the Optimal Design software, we estimate that with R = 0.10 and R2 = 0.10, we will need 40 tests per cluster to generate a 91% probability of detecting an effect size E = 0.15 using a 95% confidence level. We also did power calculations for different choices of parameters, including 160 control clusters and 60 treatment clusters (with 40 tests per cluster); 150 control clusters and 50 treatment clusters (with 20 tests per cluster); and 120 control clusters and 60 treatment clusters (with 40 tests per cluster). Statistical power remains in the range of 70-90% even when different parameters are varied in the experimental design. Final decisions will be made keeping in mind logistical and budgetary constraints.