This is Andrew Martin, a Senior Research Associate at GiveWell, writing from GiveWell's EA Forum account.
We at GiveWell would like to thank Akash Kulgod for his deep engagement with our analysis of iron fortification programs in India in his Change Our Mind contest entry. We appreciate his work, which led us to investigate some questions about iron fortification programs in India that we had not previously considered.
Below, we discuss Akash's arguments, the additional research we've conducted in response to the issues he raised, and our current views.
- GiveWell has granted roughly $9.5 million to support iron fortification programs in India so far, and may consider additional grants going forward.
- In his post, Akash argues that GiveWell is overestimating the cost-effectiveness of iron fortification in India—his arguments imply that the program may only be around 4x as cost-effective as cash transfers, compared to GiveWell's current estimate of 9x.
- Several of Akash's arguments focus on external validity—the extent to which existing evidence on how to define anemia and the impact of iron fortification on reducing anemia applies to populations in India. A summary of Akash’s claims, their potential impact on cost-effectiveness, and our assessment of those claims is below. Relevant calculations are in this spreadsheet. We've currently concluded against updating our cost-effectiveness analysis for iron fortification programs in India based on Akash's specific arguments, but we do think that continued consideration of external validity issues for iron fortification programs in India is warranted.
- In his post, Akash argues:
- Anemia prevalence in India may be overestimated in our models, due to the hemoglobin cutoffs we use. This argument is based on a 2021 paper by Sachdev et al. that claims cutoffs for defining anemia based on hemoglobin levels should be lower for India than those adopted by WHO, which are based on data from countries in North America and Europe. Sachdev et al. claim that using their recommended hemoglobin concentration cutoffs for 0 to 19 year-olds in India would result in a drop of anemia prevalence of 19 percentage points. If we followed this recommendation, our anemia prevalence estimate for 0 to 19 year-olds in India would drop from 44% to 25% and our estimate of anemia YLDs (Years Lived with Disability) would be cut in half. We estimate that, taken at face value, this concern would lower the cost-effectiveness of iron fortification programs in India by roughly 54%.
- The proportion of anemia cases in India caused by iron deficiency may be overestimated in our models. This is based on research indicating that areas with higher levels of infectious disease may have a lower than average proportion of anemia caused by inadequate iron intake (and a higher proportion caused by inflammation as a response to infections). This would limit the share of anemia burden that is addressable by iron fortification programs. Akash did not specify how much this concern might impact cost-effectiveness.
- There may be harms of iron fortification GiveWell isn't accounting for, such as higher risk of type 2 diabetes and non-alcoholic fatty liver disease. This is based on research showing an association between blood iron levels and these conditions. We make a rough guess that these concerns, taken at face value, would lower cost-effectiveness by 10%.
- There may be ethical considerations or reputational risk related to iron fortification in India. Akash notes that GiveWell might be perceived as interfering with individuals’ personal choices on whether to consume iron-fortified food or not, which is especially concerning given the additional harms of iron fortification he lists above.
- In response to Akash's concerns, we reviewed the literature he cited, and also spoke with three iron and anemia experts and a person familiar with public perceptions of food fortification in India.
- Based on our research, our current bottom line is:
- We don’t think we should lower cost-effectiveness to account for the potential for lower hemoglobin cutoffs for anemia in Indian populations: We spoke to an anonymous iron and anemia expert who was critical of the methodology of Sachdev et al. 2021, since the sample studied may not have been fully healthy (which could explain their lower hemoglobin concentrations). He also noted that he thinks there are not clear theoretical explanations for why anemia cutoff thresholds would differ by race or nationality. This makes us think there aren't compelling enough reasons to disagree with the current WHO guidelines.
- We don't think that we should lower our cost-effectiveness estimates to account for a larger share of anemia being driven by infection, rather than iron deficiency. Based on a shallow review, we don't think this is a greater concern among populations reached by Fortify Health's programs in India compared to the settings of the trials we're relying on to estimate the impact of iron fortification on anemia (Brazil, India, Sri Lanka, Pakistan, and the Philippines). Additionally, we have seen evidence from trials of iron supplementation that iron interventions can have a large impact on anemia prevalence in India, which suggests that iron deficiency is an important cause of anemia (at least among the specific populations studied in the trials).
- We don’t think we should update our cost-effectiveness estimates due to risk of type 2 diabetes and non-alcoholic fatty liver disease. We think the evidence for iron fortification causing increased risk of these diseases is fairly weak, based on a light review of the evidence and discussions with an iron and anemia expert.
- We think the reputational risk of funding iron fortification in India is low. We guess the risks of iron fortification are very low relative to the benefits, which we think limits ethical concerns. We also spoke with someone who we believe has expertise in public perceptions of food fortification in India and believe, based on that conversation, that reputational risk to GiveWell of funding these programs seems low.
- Our main uncertainties about these conclusions are below:
- For our bottom line on hemoglobin cutoffs and its impact on our model, we rely heavily on the views of experts we spoke with. It’s possible that we would update our view if we reviewed the underlying literature in more depth ourselves or spoke to additional experts, including the authors of Sachdev et al. 2021.
- There is limited available evidence on the proportion of anemia caused by iron deficiency, both among Fortify Health's participants and among participants in the trials we rely on to estimate the impact of iron fortification on anemia.
- It may be possible to collect additional data to inform our views on the issues Akash raised. We have not explored how feasible it would be to do this, how much it would update our views, and how much this research would cost.
- Continued research on external validity: We agree with Akash on the importance of considering external validity. We expect to continue to research external validity issues when considering future grants to iron fortification programs in India—we list some potential research questions below.
GiveWell has made three grants to Fortify Health for its work on iron fortification of wheat flour in India: $0.3 million in 2018, $1 million in 2019, and $8.2 million in 2021. We may also consider making additional grants to iron fortification programs in India and other countries going forward.
In our intervention report, we discuss the evidence of effectiveness for iron fortification programs. One of the primary benefits of iron fortification in our cost-effectiveness model is a reduction in morbidity due to anemia, a condition in which hemoglobin concentrations in blood are lower than normal, leading to fatigue, dizziness, weakness, and other symptoms. Our intervention report also discusses the evidence for short-term cognitive benefits of iron fortification among children and adults with anemia.
In his post, Akash raises a set of criticisms of GiveWell's model for iron fortification in India:
- Anemia prevalence in India may be overestimated
- The proportion of anemia cases in India caused by iron deficiency may be overestimated
- There may be harms of iron fortification GiveWell isn't accounting for
- There may be ethical considerations or reputational risk related to iron fortification in India that GiveWell isn't accounting for
We decided to conduct additional research and write a public response based on the importance of the issues Akash raised. The first three issues could have an impact on our cost-effectiveness estimates, and the final issue could also be relevant for GiveWell's future grantmaking decisions.
Akash's post references the 2019 version of GiveWell's cost-effectiveness analysis of Fortify Health—we also published a more recent cost-effectiveness analysis for our 2021 grant to Fortify Health. For this post, we are also making a new CEA version public here. Our preliminary discussion of the updates we've made between the 2021 CEA and the current version is here.
Anemia prevalence and disease burden in India
The case for GiveWell's grants to Fortify Health relies on estimates of the prevalence and disease burden of anemia in India. Our cost-effectiveness model uses estimates of anemia prevalence and Years Lived with Disability (YLDs), a measure of disease burden, from the Institute for Health Metrics and Evaluation (IMHE)'s Global Burden of Disease (GBD) project. GBD estimates the prevalence of anemia at 44% for 0 to 19 year-olds and at 42% across all ages in India as of 2019.
The World Health Organization (WHO) sets commonly used standards (including by GBD) for diagnosing anemia, based on concentrations of hemoglobin in blood samples. As Akash notes in his post, WHO hemoglobin cutoffs for defining anemia have been largely unchanged since 1968, and were based on a set of studies among primarily white populations in North America and Europe (though cutoffs distinguishing mild, moderate, and severe anemia were updated more recently, and the 1968 cutoffs were later validated in surveys of multi-ethnic populations in the United States).
Our understanding is that WHO's original process for defining the anemia cutoff was to identify the fifth percentile of hemoglobin concentrations in otherwise healthy individuals in the available studies. In his post, Akash points to Sachdev et al. 2021, which uses data from India's 2019 Comprehensive National Nutrition Survey (CNNS) to propose new hemoglobin concentration cutoffs for 0-19 year-olds in India using a similar methodology to the original WHO process. Sachdev et al. 2021 excludes CNNS data from individuals with some identified health issues and calculates the fifth percentile of hemoglobin concentrations among the remaining sample.
Sachdev et al. 2021's process leads to lower hemoglobin concentration cutoffs for anemia in 0-19 year-olds in India than the WHO standards. Sachdev et al. conclude that if their cutoffs were used, estimates of anemia prevalence among the full CNNS sample of 0 to 19 year-olds in India would drop by 19.2 percentage points. If we applied this finding to the IHME estimate for anemia prevalence among 0 to 19 year-olds in India, it would drop from 44% to 25%. Akash also includes some rough estimates of expected declines in anemia prevalence among adult populations in his post, extrapolating from the findings of Sachdev et al. 2021.
This is important because a large drop in anemia disease burden estimates in India would lead to substantial reductions in our cost-effectiveness estimates for iron fortification programs. We estimate three main benefits of iron fortification programs in our CEA: anemia morbidity averted (46% of total benefits), cognitive benefits for children (5%), and cognitive benefits for adults (49%). Our estimates of anemia morbidity averted are sensitive to baseline anemia YLDs—if baseline YLDs are cut in half (as Akash estimates as the impact of applying the Sachdev et al. 2021 hemoglobin concentration cutoffs), our estimate of the benefits of iron fortification for averting anemia morbidity would be cut in half as well. Updating hemoglobin cutoffs for anemia could also affect our estimates of cognitive benefits, since we only apply cognitive benefits to the subset of the population that is anemic at baseline.
Our research process and updated views
To investigate the concerns raised by Akash about anemia prevalence in India, we:
- Reviewed Sachdev et al. 2021.
- Spoke with and commissioned a written report from an iron and anemia expert who wishes to remain anonymous.
A summary of our updated views:
- Following our conversations with an anonymous iron expert, we are somewhat skeptical about the case for lowering hemoglobin concentration thresholds for Indian populations compared to WHO standards. This is primarily because Sachdev et al. 2021 may not have eliminated all individuals with health conditions affecting hemoglobin concentrations from its reference sample. This would mean that lower observed hemoglobin concentrations in the study may have been caused by health conditions, rather than reflecting lower normal concentrations among Indian populations.
- We are unsure whether applying Sachdev et al.'s proposed hemoglobin threshold cutoffs would result in declines in anemia YLDs proportional to declines in anemia prevalence. This could mean that using the Sachdev et al. 2021 thresholds would only have a small impact on cost-effectiveness. Sachdev et al.'s proposed hemoglobin concentration thresholds may primarily affect individuals currently identified as having mild anemia, which make up only around 5% of anemia YLDs due to mild anemia's small disability weight. We have been unable to investigate this issue further because Sachdev et al. do not propose updated thresholds for defining moderate and severe anemia.
- We are also unsure whether it would be appropriate to apply updated anemia thresholds to our estimates of cognitive benefits, since it is our understanding that the original studies we rely on for estimating cognitive benefits used WHO's hemoglobin concentration cutoffs.
- We do not currently plan to update our iron fortification CEA on the basis of this criticism, but we are open to revising our views based on additional evidence or updates to WHO recommendations going forward.
Should hemoglobin concentration thresholds for defining anemia be lowered for India?
The anonymous iron and anemia expert we spoke to expressed skepticism about lowering hemoglobin concentration thresholds for defining anemia based on the findings of Sachdev et al. 2021:
For our cost-effectiveness model, we are interested in the impact of iron fortification on alleviating the morbidity associated with anemia symptoms. An argument in favor of lowering anemia thresholds in India would imply that individuals in India experience less severe anemia symptoms at a given hemoglobin concentration than other populations—Sachdev et al. 2021 does not present a clear theoretical justification for why we should expect this to be the case.
The group studied in Sachdev et al. 2021 may not have been a fully healthy reference population—individuals with reported exposure to infections in the past two weeks were excluded, but infections may impact hemoglobin concentrations for longer than two weeks. This is a concern because infections may be fairly common in the population studied. The reference population not being fully healthy could explain the lower hemoglobin concentrations found in Sachdev et al. 2021, rather than the explanation that healthy Indian populations normally have lower hemoglobin concentrations than other healthy populations.
Addo et al. 2021 uses a similar methodology to Sachdev et al. 2021 across 25 countries to estimate the fifth percentile of hemoglobin concentrations among survey participants identified as healthy based on biomarker measurements. The study finds high heterogeneity in the fifth percentile of hemoglobin concentrations in populations identified as healthy across countries, ranging from 7.9 g/dL in Pakistan to 11.2 g/dL in the United States for preschool-aged children and from 8.8 g/dL in Gujarat, India to 12.1 g/dL in the United States for women aged 15-49. The anonymous iron and anemia expert we spoke with interprets these results as indicating that post-hoc exclusion of unhealthy individuals based on biomarker data, as implemented in Addo et al. 2021 and Sachdev et al. 2021, fails to fully eliminate individuals with low hemoglobin concentrations due to inflammation.
How much would lowering hemoglobin concentration thresholds affect cost-effectiveness?
Akash estimates the impact of applying the Sachdev et al. 2021 hemoglobin concentration cutoffs would be to cut baseline anemia YLDs in India in half, which appears to be roughly proportional to the decline in anemia prevalence with the Sachdev et al. cutoffs. Cutting anemia YLDs in half leads to a 54% reduction in our cost-effectiveness estimate.
We are uncertain about the assumption that reductions in anemia YLDs would be roughly proportional to reductions in anemia prevalence because Sachdev et al. 2021 does not propose new thresholds for defining moderate and severe anemia. In this spreadsheet, we compare the prevalence of mild, moderate, and severe anemia in India with the number of YLDs attributed to mild, moderate, and severe anemia, according to GBD data. Across all ages, mild anemia accounts for nearly 50% of anemia cases, but only accounts for 5% of YLDs attributable to anemia. This difference is due to the disability weight for mild anemia being very low (0.004). Moderate anemia has a disability weight (0.052) that is 13 times higher than the weight for mild anemia, and severe anemia's disability weight (0.149) is ~37 times higher.
WHO's hemoglobin levels to diagnose anemia at sea level (grams per deciliter)
|Children 6-59 months of age
|11.0 or higher
|10.0 to 10.9
|7.0 to 9.9
|Lower than 7.0
|Children 5-11 years of age
|11.5 or higher
|11.0 to 11.4
|8.0 to 10.9
|Lower than 8.0
|Children 12-14 years of age
|12.0 or higher
|11.0 to 11.9
|8.0 to 10.9
|Lower than 8.0
|Non-pregnant women (15 years of age and above)
|12.0 or higher
|11.0 to 11.9
|8.0 to 10.9
|Lower than 8.0
|11.0 or higher
|10.0 to 10.9
|7.0 to 9.9
|Lower than 7.0
|Men (15 years of age and above)
|13.0 or higher
|11.0 to 12.9
|8.0 to 10.9
|Lower than 8.0
We have not been able to create our own estimate of changes in anemia YLDs under Sachdev et al.'s proposal because Sachdev et al. 2021 does not include updated thresholds for defining moderate and severe anemia. If thresholds for moderate and severe anemia remained relatively unchanged, the impact of adopting Sachdev et al. 2021's hemoglobin concentration thresholds for defining anemia might be a large reduction in anemia prevalence with a much smaller reduction in anemia YLDs, since the majority of cases re-defined as non-anemic would be mild cases. We expect that this would have a fairly small impact on our cost-effectiveness estimate, since mild anemia only accounts for 5% of anemia YLDs. It is possible, however, that implementing a proposal like Sachdev et al.'s would also involve re-defining cutoffs for moderate and severe anemia. We would be open to investigating this issue further if we encounter proposals for updating hemoglobin concentration thresholds for moderate and severe anemia.
Impact on cognitive benefits
Updating anemia prevalence estimates could also have an impact on our estimates of the cognitive benefits of iron fortification programs since, based on our evidence review, we only apply those benefits to individuals with anemia at baseline. We include reductions in cognitive benefits in our estimate of the impact of Akash's criticisms on our cost-effectiveness model. We are uncertain, however, whether this adjustment would be appropriate even if we were to adopt Akash's recommendation, since it would mean that we'd be using different definitions of anemia for populations reached by iron fortification programs today and individuals who participated in the research we rely on for estimating cognitive impacts.
Implications for our cost-effectiveness model
We think the arguments laid out by the anonymous iron and anemia expert we consulted with seem reasonable, but we remain open to changing our views based on new evidence. We also would plan to revisit this issue if WHO updates its guidelines—we expect that this would be a strong signal that expert views on this issue have shifted.
The proportion of anemia cases in India caused by iron deficiency
Iron deficiency is considered to be the most common cause of anemia globally. Other causes of anemia include other micronutrient deficiencies, diseases including malaria and parasitic infections, inflammation, and genetic disorders. Underlying causes of anemia are an important issue for our cost-effectiveness analysis, since we expect that iron fortification programs would only be able to potentially address anemia caused by iron deficiency. (Fortify Health's fortification programs also include vitamin B12, which we expect also has some impact on anemia, though much smaller than the impact of iron.)
In his post, Akash points to research that suggests that areas with high levels of infectious disease may have a lower than average proportion of anemia caused by inadequate iron intake (and a higher proportion caused by inflammation as a response to infections). Akash also notes that the CNNS data (discussed in the section above) has also been analyzed to estimate the proportion of 0 to 19 year-olds in India with iron deficiency—Kulkarni et al. 2021 estimates the prevalence of iron deficiency in India at ~32% for 1 to 4 year-olds, ~30% for adolescent girls, with lower rates (11%-15%) for adolescent boys and 5 to 9 year-olds.
Akash doesn't suggest a specific adjustment to GiveWell's CEA for iron fortification programs in India, but notes that he believes that GBD may be overestimating the contribution of iron deficiency to anemia.
Our research process
We agree with Akash that this is an important issue—we had conducted some internal research on this topic before seeing Akash's post (which was not reflected in the public version of the CEA Akash reviewed).
We briefly reviewed the papers Akash referenced in this section of his post (Engle-Stone et al. 2017 and Kulkarni et al. 2021). We also reviewed Petry et al. 2016, a systematic review of nationally-representative surveys measuring biomarkers of iron deficiency and anemia, focused on preschool-aged children and women of reproductive age.
We spoke with and received written feedback on this topic from the anonymous iron and anemia expert mentioned above. We also reviewed Hurrell 2022, a review article on inflammation and iron absorption.
Our current views
We do not currently think that we should apply a downward adjustment to our CEA to account for the proportion of anemia cases caused by iron deficiency being lower than commonly estimated in India, for the following reasons:
For our cost-effectiveness analysis of Fortify Health, our primary concern is the extent to which the proportion of anemia caused by iron deficiency among Fortify Health's target population differs from the populations studied in the trials we're relying on to estimate the impact of iron fortification on anemia. If the impact of iron fortification on anemia were similarly affected by infectious disease prevalence in both Fortify Health's context and the context of the trials, we don't think it would be appropriate to apply an additional downwards adjustment to our cost-effectiveness estimate for Fortify Health.
Our current view based on a shallow review is that levels of infectious disease prevalence seem similarly moderate in both Fortify Health's context and the context of the trials.
Based on a brief literature review, the only systematic analysis of anemia, iron deficiency, and infectious disease prevalence we were able to identify was Petry et al. 2016. Petry et al. 2016 finds that the proportion of anemia associated with iron deficiency in preschool-aged children and women of reproductive age is lower in countries with high "inflammation exposure" due to infectious diseases, in line with Akash's arguments. Petry et al. 2016 also creates a country-level "inflammation-exposure index" based on the prevalence of infectious diseases (e.g., malaria, schistosomiasis) and other factors.
We compare Petry et al. 2016's inflammation-exposure index ratings for the settings of trials included in Field et al. 2021 and India in this spreadsheet. Our impression is that the index ratings for India and the countries where trials included in Field et al. 2021 took place (Brazil, India, the Philippines, Pakistan, and Sri Lanka) are similar, with "Low" or "Medium" ratings for preschool-aged children and women of reproductive age (with the exception of one "High" rating for preschool-aged children in the Philippines). For comparison, inflammation-exposure ratings for many countries in sub-Saharan Africa are "High" or "Very High."
We also note that GBD's estimates of the proportion of anemia caused by dietary iron deficiency in India and in the countries included in Field et al. 2021 are very similar, with GBD's estimate for India being 63% and a weighted average of trial locations being 62% across all ages.
Kulkarni et al. 2021, discussed in Akash's post, reports estimates of the proportion of 0 to 19 year-olds in India with iron deficiency, but doesn't directly report on the proportion of anemia caused by iron deficiency. After an initial review, it doesn't appear clear to us that Kulkarni et al. 2021 presents evidence that GBD is overestimating the proportion of anemia caused by iron deficiency. In this spreadsheet, we note that the results of Kulkarni et al. 2021 do not seem to be inconsistent with the possibility that at least ~60% of anemia cases are caused by iron deficiency in among 0 to 19 year-olds in India.
If a low proportion of anemia in India were caused by inadequate iron intake, we'd expect iron supplementation programs among those populations to have a limited impact on anemia prevalence. The anonymous iron and anemia expert we spoke to discussed two iron supplementation trials in India that have found large reductions in anemia:
- Mehta et al. 2017, a cluster RCT of iron supplementation in 18 to 35 year-old women, which found a large impact of iron supplementation: "The anemia prevalence at 90 d was lower for intervention (29.2%) than for control participants (98.6%) (OR: 0.007; 95% CI: 0.001, 0.04)."
- Bharti et al. 2015, a cluster-based RCT of women and adolescent girls comparing directly observed home-based daily iron treatment (DOHBIT) with unsupervised self-treatment, found that the DOHBIT group had significantly lower prevalence of anemia at the end of the trial: (16.8% vs 35.3%, RR = 0.47 [95% CI = 0.33-0.65].
We have seen limited evidence overall on the proportion of anemia cases caused by iron deficiency, both among populations reached by Fortify Health and among populations studied in the trials included in Field et al. 2021. Additional evidence could convince us that the proportion of anemia cases caused by iron deficiency among Fortify Health's program participants is lower than among populations studied in Field et al. 2021, in which case a downward adjustment to our cost-effectiveness estimates might be appropriate.
We have not carefully reviewed the methodology in Petry et al. 2016 for creating an "inflammation-exposure index." We may follow up with additional research comparing infectious disease burden between populations studied in Field et al. 2021 and populations reached by Fortify Health, based on the methodology of Petry et al. 2016 or other sources.
Kulkarni et al. 2021 reports on iron deficiency prevalence and anemia prevalence, but doesn't directly report on the proportion of anemia caused by iron deficiency. Our understanding is that not all cases of iron deficiency (defined by measurements of iron biomarkers) are accompanied by anemia (defined by hemoglobin concentrations). We are unsure about the extent of overlap between iron deficiency cases and anemia cases reported in Kulkarni et al. 2021 (see this spreadsheet).
We have not conducted a recent systematic search for trials of iron supplementation among populations in India, for comparison with the two trials the anonymous iron and anemia expert shared with us. We are also uncertain about the extent to which the two trials we've seen are representative of populations reached by Fortify Health—the two trials mentioned above only include adolescent girls and women.
Additional potential negative impacts of iron fortification
Akash notes that there may be additional potential negative impacts of iron fortification beyond those that we've already identified, namely an increased risk of type 2 diabetes and non-alcoholic fatty liver disease.
Below are brief summaries of the two papers Akash linked on these topics:
Liu et al. 2020 is a meta-analysis of 12 non-experimental (case-control and cohort) studies. The authors find a positive association between median and high serum ferritin concentrations (a measure of iron in blood) and type 2 diabetes risk.
Mayneris-Perxachs et al. 2021 is a non-experimental study that finds a positive association between serum ferritin concentrations and liver fat accumulation in obese subjects. To assess these potential risks of iron fortification programs, we lightly reviewed the papers Akash linked and consulted with an anonymous iron expert.
Based on our review, we think that the current evidence for iron fortification causing increased risk of type 2 diabetes or non-alcoholic fatty liver disease appears fairly weak. The main reason is that these non-experimental studies aren't able to establish that high serum ferritin concentrations cause increased type 2 diabetes risk or liver fat accumulation—it may also be the case that the causation is reversed, or that a third factor influences both serum ferritin concentrations and disease risk. The anonymous iron expert we spoke with noted that he believes that the most likely explanation for the association between high serum ferritin and metabolic disease or liver disease is "reverse causality."
We agree with Akash that it's worth taking risks of iron fortification seriously, and we may continue investigating potential negative impacts of the intervention as we consider additional grants going forward. We are also open to revising our views on the impact of iron fortification on type 2 diabetes and non-alcoholic fatty liver disease based on new evidence.
Ethical considerations and reputational risk
In his post, Akash raises ethical concerns and notes potential reputational risk involved in funding iron fortification programs in India. Quoting from his post:
- "Multiple pieces critical of iron fortification have been published recently in leading Indian newspapers (The Hindu, Indian Express, Times of India). International philanthropy/charities already tend to be viewed with a degree of hostility in India and it's possible EA gets dragged under the bus because of its support for iron fortification."
- "I'm worried about the ethics of fortification with respect to the way it takes away agency from the dietary choices of people. This seems okay if we can be extremely confident that the choices being pushed have minimal risks of harm (a priori seems true for chlorination of water and vitamin A supplementation) but iron doesn't seem to as clear-cut. It also seems important that GiveWell consults with medical and public health researchers from respective countries where interventions are being assessed to catch country-specific considerations."
We agree that these are important issues, and we thank Akash for raising them. Below are our current responses to these concerns:
Individual agency in dietary decisions:
We think that the issue of individual agency has limited relevance to most of the iron fortification programs we've supported in India so far. Fortify Health has primarily focused its work on partnering with specific millers who sell wheat flour in the open market. Our understanding is that Fortify Health's partner mills use labels to indicate that their flour is fortified, and that individuals who would prefer to buy unfortified flour remain able to purchase other brands.
However, Fortify Health also partners with mills producing flour for consumption in schools and in India's public distribution system (PDS), a social safety net program. The concern about limiting individuals' ability to choose whether or not to consume fortified flour appears more relevant to these programs than Fortify Health's open market fortification.
We acknowledge that the issue of food fortification and individual agency in dietary decisions is complex, but we currently don't expect this to be a determining factor in our grant decisions. Akash's concern appears to be motivated in part by the perception of increased risk of type 2 diabetes and non-alcoholic fatty liver disease due to iron fortification, which we are less convinced of. Our overall view is that the risks of iron fortification are very low relative to the benefits, which we think limits ethical concerns.
We spoke with Advait Deshpande, a food technologist who has worked on scaling up food fortification programs in eastern states in India, to learn more about public perceptions of food fortification in India. Our overall view following the conversation was that the reputational risk to GiveWell of funding iron fortification programs in India is low.
Some highlights from our conversation:
General concerns about fortification: Advait noted that food fortification in India is a somewhat contentious issue in some circles, but that the debate tended to focus on whether fortification is driven by corporate interests or genuine concern for public health, more so than concerns about limiting individual choice. Advait also emphasized that implementing food fortification (especially rice fortification) is a major priority of the national government over the next few years.
International philanthropy: Advait told us that there are Indian government regulations about the influence of funding from foreign sources through NGOs, but that this didn't present a risk as long as the organizations we fund in India are properly registered and following appropriate regulations. Fortify Health has shared documents with us providing details on its legal structure and registration in India.
Continued research on external validity
We expect to continue to research external validity issues when considering future grants to iron fortification programs in India—some potential areas for research are listed below:
To what extent does consumption of tea (or other iron absorption inhibitors) reduce iron absorption in Fortify Health's populations, as compared to groups studied in the wheat flour fortification RCTs?
To what extent does consumption of iron absorption enhancers (e.g., ascorbic acid - i.e., vitamin C) enhance iron absorption in Fortify Health's populations, as compared to groups studied in the wheat flour fortification RCTs?
How is iron-fortified food distributed across different individuals at the household level? Is iron-fortified food disproportionately consumed by non-anemic household members?
We have also granted funding to Evidence Action for iron and folic acid supplementation programs in India: a $0.3 million grant in 2018, a second $3.4 million grant in 2018, and a $0.8 million grant for an impact evaluation in 2019. ↩︎
- The cost-effectiveness model linked from our intervention report is here.
- "Anaemia is a condition in which the number of red blood cells or the haemoglobin concentration within them is lower than normal. Haemoglobin is needed to carry oxygen and if you have too few or abnormal red blood cells, or not enough haemoglobin, there will be a decreased capacity of the blood to carry oxygen to the body’s tissues. This results in symptoms such as fatigue, weakness, dizziness and shortness of breath, among others." WHO, "Anaemia"
"YLD is an abbreviation for years lived with disability, which can also be described as years lived in less than ideal health. This includes conditions such as influenza, which may last for only a few days, or epilepsy, which can last a lifetime. It is measured by taking the prevalence of the condition multiplied by the disability weight for that condition. Disability weights reflect the severity of different conditions and are developed through surveys of the general public." IHME, "About GBD" ↩︎
See the most recent version of our CEA here (not previously published). As of June 2023, we have not yet updated our intervention report for iron fortification to account for our CEA updates. The published version of our CEA that Akash referenced in his post is here. ↩︎
For comparison, global anemia prevalence rates are 28% for 0 to 19 year-olds and 23% for all ages. IHME, GBD Results tool, Rate of anemia from all causes, 2019 ↩︎
- For example, for children 6-59 months of age, a hemoglobin concentration of 110 grams per liter or higher is defined as non-anemic, 100-109 g/l is defined as mild anemia, 70-99 g/l is defined as moderate anemia, and lower than 70 g/l is defined as severe anemia. WHO, Haemoglobin concentrations for the diagnosis of anemia and assessment of severity, 2011, p. 3, Table 1.
- "Anaemia is defined by decreased blood concentration of haemoglobin. We estimated unique, continuous distributions of elevation-adjusted haemoglobin concentrations (g/L), anaemia prevalence, and years lived with disability (YLDs) by severity and 37 underlying causes of anaemia annually from 1990 to 2021 for 204 countries and territories, 21 GBD regions, male and female sexes, and 25 age groups (0–6 days, 7–27 days, 1–5 months, 6–11 months, 12–23 months, 2–4 years, 5–94 years in five-year age bins, and ≥95 years). Anaemia severity levels (mild, moderate, and severe) were defined using specific haemoglobin concentration thresholds that vary by age, sex, and pregnancy status (table)...Published WHO thresholds were used for males and females aged 6 months and older; thresholds for those younger than 6 months were imputed as described in the appendix (p 6)." GBD 2021 Anemia Collaborators 2023, p. 715.
- "The anaemia cut-offs presented in Table 1 were published in 1968 by a WHO study group on nutritional anaemias, while the cut-offs defining mild, moderate and severe anaemia were first presented in the 1989 guide Preventing and controlling anaemia through primary health care and then modified for pregnant women, nonpregnant women, and children less than five years of age in The management of nutrition in major emergencies. The overall anaemia cut-offs have been unchanged since 1968, with the exception that the original age group of children 5-14 years of age was split, and a cut-off of 5 g/l lower was applied to children 5-11 years of age to reflect findings among non-iron deficient children in the USA. Although these cut-offs were first published in the late 1960s, they have been included in numerous subsequent WHO publications and were additionally validated by findings among participants in the Second National Health and Nutrition Examination Survey (NHANES II) who were unlikely to have iron deficiency based on a number of additional biochemical tests." WHO, Haemoglobin concentrations for the diagnosis of anemia and assessment of severity, 2011, p. 3.
- "Appropriate guidelines for measurement of haemoglobin and definition of anaemia are crucial for both clinical and public health medicine, but require consideration of the range of complexities across different populations. Haemoglobin thresholds to define anaemia were first proposed by WHO in 1959. Current thresholds recommended by WHO for men, women, young children, and pregnant women (table) were first proposed in 1968 after technical meetings of a group comprising clinical and public health experts working with data from five studies of predominantly white populations in Europe and North America (appendix). Data from other countries, races, and ages (ie, infants, young children, adolescents, and elderly people) were not available to the panel." Pasricha et al. 2018, p. e60.
- "The current WHO cutoff levels were derived from mainly White adults but were validated in a multiethnic sample from a single country (US)." Addo et al. 2021, p. 8.
- Addo et al. 2021 cites Looker et al. 1997 for the claim that the WHO cutoffs were validated against a multiethnic sample in the United States. We have not reviewed the original study.
"More than 95% of normal individuals are believed to show haemoglobin levels higher than the values given, which are appropriate for all geographic areas; however, the values must be modified for persons who reside at higher altitudes." WHO, Nutritional Anemias, 1968, pp. 9-10. ↩︎
"For this population-based study, we constructed age-specific and sex-specific haemoglobin percentiles from values reported for a defined healthy population in the CNNS, which used rigorous quality control measures during sample collection and in the laboratory analyses. To obtain a healthy population, we excluded participants with iron, folate, vitamin B12, and retinol deficiencies; inflammation; variant haemoglobins (haemoglobin A2 and haemoglobin S); and history of smoking. We considered age-specific and sex-specific 5th percentiles of haemoglobin derived for this healthy population as the study cutoff to define anaemia. We compared these with existing WHO cutoffs to assess significant differences between them at each year of age and sex for quantifying the prevalence of anaemia in the entire CNNS sample." Sachdev et al. 2021, p. e822. ↩︎
"Between Feb 24, 2016, and Oct 26, 2018, the CNNS survey collected blood samples from 49 486 individuals. 41 210 participants had a haemoglobin value, 8087 of whom were included in our study and comprised the primary analytical sample. Compared with existing WHO cutoffs, the study cutoffs for haemoglobin were lower at all ages, usually by 1–2 g/dL, but more so in children of both sexes aged 1–2 years and in girls aged 10 years or older. Aanemia prevalence with the study cutoffs was 19·2 percentage points lower than with WHO cutoffs in the entire CNNS sample with valid haemoglobin values across all ages and sexes (10·8% with study cutoffs vs 30·0% with WHO cutoffs)." Sachdev et al. 2021, p. e822. ↩︎
See the most recent version of our CEA here (not previously published). The published version Akash referenced in his post is here. See this section of the CEA for the proportional breakdown of the three main benefits. ↩︎
See "Anemia morbidity averted" section here. If "Adjusted annual anemia YLDs per person, all ages" is reduced by 50%, "Annual anemia YLDs per person (all ages) averted by iron fortification" is reduced by 50% as well. For details on our views on the evidence for iron fortification, see our intervention report. ↩︎
"It is uncertain why Indians should have lower Hb thresholds than essentially any other population: inherited red cell disorders (globin gene mutations for example) are common in India, but need to be diagnosed for clinical reasons. The authors argue that the reason relates to having a muscle-thin but high adipose body composition; this hypothesized physiologic variance should currently be considered a cause of anemia until it has been confirmed the effect on Hb is not pathological." Report for GiveWell by an anonymous iron and anemia expert, p. 5. ↩︎
"Hemoglobin concentrations are extremely sensitive to inflammation, and if a person becomes anemic, it can take time to recover from the anemia. Individuals were excluded if they reported a chronic illness or a known acute infection (/fever) were excluded; however, it is still highly likely that some of the individuals included still had had an illness or infection which, even if it had resolved, still caused reduced hemoglobin and hence anemia, which may not have recovered by the time the blood testing had occurred. Indeed, the authors mention this in the Discussion: 'Fourth, even though exposure to infections in the preceding 2 weeks was an exclusion criteria, lingering post-infectious altered erythropoiesis could not be ruled out.' This is a critical point in a setting where there is a high prevalence of infections. Malaria was not assessed in this study." Report for GiveWell from an anonymous iron and anemia expert, p. 5. ↩︎
"The survey was done in a way to be representative of the Indian population; the Indian middle class is not the middle class of the West; some definitions (eg Pew) are that this is a group earning $10-20/day, i.e. having risen out of poverty… the numbers are not well defined. The population living a lifestyle akin to the West is much smaller. The majority of participants in this cross-sectional survey designed to assess diseases of poverty are not well off." Report for GiveWell from an anonymous iron expert, p. 4. ↩︎
"It is crucial to identify healthy populations for reference studies since the goal is to identify the 5th centile – so if the population below the 5th centile is not all ‘healthy’ then these thresholds will be reduced." Report for GiveWell from an anonymous iron expert, p. 5. ↩︎
"To define the healthy population, persons with iron deficiency (ferritin <12 ng/mL for children or <15 ng/mL for women), vitamin A deficiency (retinol-binding protein or retinol <20.1 μg/dL), inflammation (C-reactive protein >0.5 mg/dL or α-1-acid glycoprotein >1 g/L), or known malaria were excluded. Survey-specific, pooled Hb fifth percentile cutoffs were estimated. Among individuals with Hb and sTfR data, Hb-for-sTfR curve analysis was conducted to identify Hb inflection points that reflect tissue iron deficiency and increased erythropoiesis induced by anemia." p. 1.
"In this cross-sectional study, data were collected and evaluated from 30 household, population-based nutrition surveys of preschool children aged 6 to 59 months and nonpregnant women aged 15 to 49 years during 2005 to 2016 across 25 countries. Data analysis was performed from March 2020 to April 2021." p. 1.
"To define the healthy population, persons with iron deficiency (ferritin <12 ng/mL for children or <15 ng/mL for women), vitamin A deficiency (retinol-binding protein or retinol <20.1 μg/dL), inflammation (C-reactive protein >0.5 mg/dL or α-1-acid glycoprotein >1 g/L), or known malaria were excluded. Survey-specific, pooled Hb fifth percentile cutoffs were estimated. Among individuals with Hb and sTfR data, Hb-for-sTfR curve analysis was conducted to identify Hb inflection points that reflect tissue iron deficiency and increased erythropoiesis induced by anemia." p. 1. ↩︎
- Addo et al. 2021, Figure 1, p. 7.
- "There was low intersurvey variance when analyzing individual-level Hb data of 39 325 apparently healthy individuals, but high interstudy heterogeneity from meta-analysis highlighting the limitation of meta-analyses to directly address this study objective." Addo et al. 2021, p. 9.
"The Addo analysis (Addo JAMA Network Open 2021) cited in the Sachdev paper demonstrates a wide heterogeneity in Hb thresholds in different countries where the authors used a simple post-hoc approach to exclude biochemical inflammation and iron deficiency; our analyses of this dataset suggests that inflammation is not being well excluded as the cutoff is negatively correlated with the prevalence of inflammation in the population...This highlights the need to ensure populations are ‘healthy’, which cannot be done without some clinical screening of populations." Report for GiveWell by an anonymous iron and anemia expert, p. 5. ↩︎
- "The YLDs from anemia is halved (conservative simplification)." From this section of Akash's post.
- Sachdev et al. report a decline of greater than 50% in anemia prevalence: "Anemia prevalence with the study cutoffs was 19·2 percentage points lower than with WHO cutoffs in the entire CNNS sample with valid haemoglobin values across all ages and sexes (10·8% with study cutoffs vs 30·0% with WHO cutoffs)." Sachdev et al. 2021, p. e822.
- See the "Summary tab" of our CEA for the percent change in overall cost effectiveness from this change. .
- See the highlighted row of the "Anemia morbidity averted" section in the "CEA [AK Anemia prevalence and YLDs]" tab of our CEA.
For WHO's hemoglobin concentration cutoffs defining mild, moderate, and severe anemia, see WHO, Haemoglobin concentrations for the diagnosis of anemia and assessment of severity, 2011, p. 3, Table 1. ↩︎
The disability weight for mild anemia is 0.004. IHME, Global Burden of Disease Study 2019 (GBD 2019) Disability Weights ↩︎
Adapted from WHO, Haemoglobin concentrations for the diagnosis of anemia and assessment of severity, 2011, p. 3, Table 1. ↩︎
- "Anaemia may be caused by several factors: nutrient deficiencies through inadequate diets or inadequate absorption of nutrients, infections (e.g. malaria, parasitic infections, tuberculosis, HIV), inflammation, chronic diseases, gynaecological and obstetric conditions, and inherited red blood cell disorders. The most common nutritional cause of anaemia is iron deficiency, although deficiencies in folate, vitamins B12 and A are also important causes." WHO, "Anaemia"
- IHME estimates that greater than 50% of anemia is caused by dietary iron deficiency globally. IHME, GBD 2019, "Anemia—Level 1 impairment," Figure 1.
"Anaemia may be caused by several factors: nutrient deficiencies through inadequate diets or inadequate absorption of nutrients, infections (e.g. malaria, parasitic infections, tuberculosis, HIV), inflammation, chronic diseases, gynaecological and obstetric conditions, and inherited red blood cell disorders. The most common nutritional cause of anaemia is iron deficiency, although deficiencies in folate, vitamins B12 and A are also important causes." WHO, "Anaemia" ↩︎
"Results: ID prevalence was higher in 1- to 4-y-old children (31.9%; 95% CI: 31.0%, 32.8%) and adolescent girls (30.4%; 95% CI: 29.3%, 31.5%) but lower in adolescent boys and 5- to 9-y-old children (11%–15%)." Kulkarni et al. 2021, p. 2422. ↩︎
"I'm unsure how to assess the impact of this section on GiveWell's cost-effectiveness model as I wasn't able to pin down the estimate for iron-deficiency's contribution to anemia in the model. Since the BRINDA method and other work were published from 2018 onwards, and the GBD's data sources are much older, about a decade prior on average, it seems likely that GBD overestimates ID's contribution to anemia. Like before, the CNNS data is restricted to age groups b/w 0-19 yrs, but intuitively should extend to adult age groups as well." From this section of Akash's post. ↩︎
We believe that Kulkarni et al. 2021 is the paper Akash is quoting from here: "This is the first study from India providing estimates of ID prevalence in a representative sample of children and adolescents at the national and state levels using multiple inflammation-adjusted ID indicators. In preschool children and adolescent girls, ID [iron deficiency] based on SF [serum ferritin] adjusted for inflammation by the modified BRINDA method was a public health problem of 'moderate' proportions (∼30%–32%), whereas in 5- to 9-y-old children (15%) and adolescent boys (11%) it was a public health problem categorized as 'mild.'" ↩︎
"The proportion of anemia associated with iron deﬁciency was lower in countries where anemia prevalence was >40%, especially in rural populations (14% for pre-school children; 16% for non-pregnant women of reproductive age), and in countries with very high inﬂammation exposure (20% for pre-school children; 25% for non-pregnant women of reproductive age). Despite large heterogeneity, our analyses suggest that the proportion of anemia associated with iron deﬁciency is lower than the previously assumed 50% in countries with low, medium, or high Human Development Index ranking." Petry et al. 2016, p. 692. ↩︎
"These indices were constructed using factors known to contribute to inﬂammation. For children, the inﬂammation-exposure score was based on (a) prevalence of presumed and conﬁrmed malaria cases; (b) schistosomiasis prevalence; and (c) an overall hygiene score based on the proportion of population using improved drinking water source and the proportion of the population using improved sanitation facilities (evenly weighted) as a proxy for the risk of enteric inﬂammation. For women, the different factors used to estimate country-speciﬁc inﬂammation-exposure were (a) prevalence of presumed and conﬁrmed malaria cases;(b) HIV prevalence in adults; (c) obesity prevalence in female adults; (d) schistosomiasis prevalence; and (e) an overall hygiene score based on the proportion of population using improved drinking water source and the proportion of the population using improved sanitation facilities (evenly weighted) as a proxy for the risk of enteric inﬂammation." Petry et al. 2016 p. 693. ↩︎
"Oral iron supplementation is very effective in a trial context in improving anemia (eg twice weekly iron reduced anemia 16.8% vs 35.3%, RR = 0.47) in Indian Adolescent girls; this is not a result inconsistent with appropriate thresholds (as anemia would not have responded if the girls weren’t anemic). Likewise, adolescents with anemia given iron-supplement-containing bars responded dramatically to the iron [anemia prevalence at 90 d was lower for intervention (29.2%) than for control participants (98.6%) (OR: 0.007; 95% CI: 0.001, 0.04).] (Mehta AJCN 2015). These changes would indicate anemia in India is indeed responsive to therapy. " Written response to GiveWell from an anonymous iron expert (unpublished). ↩︎
- Mehta et al. 2017, p. 746.
- Additional methodological details: "Design: The Let’s be Well Red study was a 90-d, pair-matched, cluster-randomized controlled trial. A total of 361 nonpregnant women (age 18–35 y) were recruited from 10 sites within Mumbai and Navi Mumbai, India. All participants received anemia education and a complete blood count (CBC). Random assignment of anemic participants to intervention and control arms occurred within 5 matched site-pairs. Intervention participants received 1 iron-supplement bar (containing 14 mg Fe)/d for 90 d, whereas control subjects received nothing. CBC tests were given at days 15, 45, and 90. Primary outcomes were 90-d changes from baseline in hemoglobin concentrations and hematocrit percentages. Linear mixed models and generalized estimating equations were used to model continuous and binary outcomes, respectively." Mehta et al. 2017, p. 746.
- "In a community-based cluster randomized controlled trial, we randomly assigned clusters of anemic women and adolescent girls to either “directly observed home-based daily iron therapy” (DOHBIT; n = 524 in 16 villages) or unsupervised self-treatment at home (n = 535 in 16 villages) for a period of 90 days. Those in the DOHBIT group, when compared with those in the unsupervised self-treatment group, had significantly lower relative risk (RR) of anemia (16.8% vs 35.3%, RR = 0.47 [95% confidence interval (CI) = 0.33-0.65]; P < .0001), higher hemoglobin (Hb) rise of ≥2 g/dL (70.2% vs 42.2%, RR = 1.56 [95% CI = 1.31-1.87]; P <.0001), and nonsignificant trend for lower side effects (3.5% vs 6.7%, RR = 0.49 [95% CI = 0.22-1.08; P < .08) on intention-to-treat analyses. On linear mixed model analysis, the subjects in the intervention group demonstrated higher mean Hb levels (13.01 vs 12.32 g/dL; P < .0001) and higher adherence to iron therapy (93% vs 60%; P < .0001). DOHBIT is effective in lowering the prevalence of anemia in rural women and adolescent girls." Bharti et al. 2015, p. 1333.
- "All anemic rural women (young unmarried, pregnant, lactating, reproductive age group, as well as menopausal women) and adolescent girls aged 13 years and older were eligible for trial." Bharti et al. 2015, p. 1334.
Nicholas Kassebaum, Adjunct Associate Professor, Global Health, Institute for Health Metrics and Evaluation, University of Washington, conversation with GiveWell, November 15, 2022. (unpublished) ↩︎
"A total of 12 case–control and cohort studies were analyzed. Of the 12 studies, 11 described the correlation between serum ferritin levels and type 2 diabetes. The median and high serum ferritin concentrations were significantly associated with the risks of type 2 diabetes (odds ratio [OR] 1.20, 95% confidence interval [CI] 1.08–1.33 and OR 1.43, 95% CI 1.29–1.59, respectively). However, the low concentration was not correlated with the risk of type 2 diabetes (OR 0.99, 95% CI 0.89–1.11). No significant association was observed between serum soluble transferrin receptor and type 2 diabetes, whereas the soluble transferrin receptor-to-ferritin ratio was significantly inversely related to the risk of type 2 diabetes in the median and high ratio subgroups (OR 0.71, 95% CI 0.51, 0.99 and OR 0.65, 95% CI 0.45–0.95)." Liu et al. 2020, p. 946. ↩︎
"An overview of the study human cohorts and omics analyses pipeline can be found in Figure S1. Serum ferritin was measured in three cohorts: (a) a discovery cohort of subjects with obesity (n = 49); (b) a validation cohort of subjects with obesity from Italy and Spain (n = 628); and (c) an independent cohort of subjects with and without obesity from Spain (n = 130)...In both discovery and replication cohorts, serum ferritin increased with the severity of liver fat accumulation (Fig. 1a, b)." Mayneris-Perxachs et al. 2021, pp. 2-3. ↩︎
"In my view, this is not a relevant argument. It is very well known that elevated ferritins alone do not portend iron loading and that liver disease is the main determinant. This paper simply documents a well-recognized association between metabolic disease, liver disease, and elevated ferritin. In other words, this is likely reverse causality (high ferritin is being caused by, and is not the cause of, metabolic disease). Ferritin is stored in the liver, and metabolic conditions (especially if they drive fatty liver disease) cause ferritin to leak from hepatocytes." Anonymous iron expert, report for GiveWell, p. 6. ↩︎
"Fortify Health pays for and installs the equipment needed to fortify flour and pays for premix (which contains the iron compound that is used as a fortificant) so that its partner mills can fortify flour at no additional cost. It partners with privately-owned mills that produce flour that is sold at market prices to consumers." GiveWell, "Fortify Health — General Support (2019)" ↩︎
- "Begin partnering with mills producing for schools in Maharashtra and explore partnerships with mills producing for the public distribution system (PDS) over 5 years. Fortify Health plans to provide premix and equipment to millers serving atta in the Amravati division of Maharashtra. Fortify Health has told us that having 5 years of funding is necessary for cultivating partnerships with the government, so we have recommended committing 5 years of funding, rather than 3 years, for this government partnerships work. We have not vetted this claim from Fortify Health, and it’s possible we should instead provide funding over 3 years to start and provide the additional 2 years of funding once Fortify Health meets milestones for the number of partnerships with mills producing for schools. The cost of this component is $1.9 million over 5 years." GiveWell, "Fortify Health – Support for Expansion (December 2021)"
- See here for more information on India's Public Distribution System.
"Perspectives on fortification in India. The general public in India mostly does not hold strong views on food fortification, but it is somewhat contentious in some circles. Groups who are critical of food fortification in India tend to argue that it is driven by corporate profit-seeking, rather than genuine concern for public health." GiveWell's non-verbatim summary of a conversation with Advait Deshpande, May 19, 2023. ↩︎
"The trajectory of government-implemented fortification in India. Fortification started in 1953 but gained momentum in 2016 when the Food Safety and Standards Authority of India (FSSAI) published standards for fortified food. In 2021, Prime Minister Modi announced that by 2024 all rice provided through social safety net programs will be fortified." GiveWell's non-verbatim summary of a conversation with Advait Deshpande, May 19, 2023. ↩︎
"NGOs that receive funding from sources outside of India play a vital role in supporting fortification initiatives. As long as these NGOs adhere to the appropriate rules and regulations in India, they can operate effectively and contribute significantly to the fortification efforts, irrespective of the origin of their funding." GiveWell's non-verbatim summary of a conversation with Advait Deshpande, May 19, 2023. ↩︎
Nikita Patel, Chief Executive Officer, Fortify Health, email to GiveWell, September 18, 2023 (unpublished) ↩︎