Taimaka - Acute Malnutrition Treatment in Gombe State, Nigeria (November 2024) and Program Monitoring (March 2025)

Note: This page summarizes the rationale behind two GiveWell grants to Taimaka, as of November 2024 and March 2025, when we made these grants. Taimaka staff reviewed this page prior to publication.

Published: June 2025

Summary

What we think these grants will do

With these grants, we expect Taimaka to provide community-based management of acute malnutrition (CMAM) to treat cases of severe acute malnutrition (SAM) in three LGAs in Gombe state, Nigeria, and to collect data on key indicators, including prevalence of malnutrition, coverage of malnutrition treatment, and mortality rates among children under 5.

Why we are recommending these grants

Why we are recommending a $4.8m grant to support Taimaka's program

• We estimate the program’s cost-effectiveness to be close to our funding bar, and it could move above it as we learn more about the program.
• We estimate that Taimaka’s program is approximately nine times as cost-effective as unconditional cash transfers. At the time we recommended this grant, our cost-effectiveness bar for recommending grants was 10x cash.

We think this grant will be reasonably cost-effective because:

• Gombe state has high child mortality rates (3.2%) and a high burden of untreated severe acute malnutrition (3%) —and we think that malnutrition treatment substantially reduces risk of death.
• Baseline malnutrition coverage in Gombe is low (1%), which means that the majority of children treated by Taimaka would not have received treatment otherwise.
• Taimaka will only treat children with severe acute malnutrition, who have a higher mortality rate than children with moderate acute malnutrition, leading to more deaths averted per dollar spent.

A sketch of our cost-effectiveness model is below:

	Nigeria, Gombe state (Taimaka)	25th-75th percentile	Cost-effectiveness over this range
Grant
Grant size to charity	$4,787,985
Cost per child	$105
Benefits from reducing mortality among malnourished children
Number of additional children receiving treatment as a result of the program
Number of severely malnourished children treated	45,424	20,000- 70,000	4-13x
Proportion of children reached who would have received government treatment in the absence of Taimaka	5%
Number of additional children receiving treatment as a result of the program	43,309
Annual mortality rate from all causes among children 6-59 months with untreated malnutrition
Annual mortality rate from all causes among children 6-59 months with untreated malnutrition - initial estimate	13.2%
Annual mortality rate from all causes among children 6-59 months with untreated malnutrition - plausibility discount	33%
Annual mortality rate from all causes among children 6-59 months with untreated malnutrition - best guess	4.3%	3%-8%	6-16x
Sensitivity checks on plausibility discount - illustrative only; change input in source sheets to test effect on CEA
Prevalence of untreated severe acute malnutrition	3%
Prevalence of untreated moderate acute malnutrition	9%
ICF - severe acute malnutrition	8.00
ICF - moderate acute malnutrition	4.18
Malnutrition treatment's effect on mortality
Reduction in all-cause mortality from receiving NGO-supported malnutrition treatment, instead of no treatment	59%	15%-70%	2-10x
Increased reduction in all-cause mortality from receiving NGO-supported malnutrition treatment, instead of standard treatment	10%
Total number of deaths averted among malnourished children	1,128
Malnutrition treatment's effect on income increases
Ratio of value generated from income increases to value generated from mortalities averted	0.20
Percentage of program impact coming from different benefits
Reduced mortality among malnourished children	83%
Income increases in later life	17%
Supplemental adjustments
Adjustment for additional program benefits and downsides	-2%
Adjustment for leverage/funging	-10%
Final cost-effectiveness estimate
Moral value of averting the death of a person under age five	118
Final cost-effectiveness in terms of multiples of GiveDirectly's unconditional cash transfer program	8.8

You can see our cost-effectiveness analysis for the program here and a simple version here.

• We are unsure about some inputs in the model, in particular, caseload, malnutrition incidence, mortality rates among children under 5, and costs in the long term. We expect to learn more about these inputs from program and survey data. We think there’s a ~30% chance we’ll think the program is above our funding bar after these updates.

• We think helping Taimaka grow and learning about their work could open up significant grantmaking opportunities in the next 5-10 years. Once we add these benefits we estimate the program to be 13x, though this is a very rough estimate which should not be taken literally.
• We have a positive qualitative impression of Taimaka and we think they’ll be a valuable thought partner as we keep refining our understanding of the cost-effectiveness of malnutrition treatment.

More detail on the case for the grant below.

Main reservations about the $4.8m grant to support Taimaka's program

• We're unsure about the extent to which malnutrition treatment decreases mortality, due to limited evidence on this.
• We’re unsure about the extent to which Taimaka’s growth will open up grantmaking opportunities in the next few years, partly because we are uncertain about the effectiveness of malnutrition treatment, partly because we are uncertain about Taimaka’s ability to grow.
• Taimaka adds to government provision rather than supporting existing government programs, and treats malnutrition cases through health workers rather than qualified nurses. As a result, we have some open questions about the effect Taimaka’s program might have on quality of care and coverage of other health services, but we expect to be able to track this through program and administrative data.

More detail on our reservations below.

Why we are recommending an $0.5m grant to fund monitoring for Taimaka's program

• We think this grant will reduce uncertainties about key inputs in our cost-effectiveness estimates for Taimaka, including prevalence, coverage of malnutrition treatment before and after Taimaka enters an area, and mortality rates among children under 5.
• The grant will also support Taimaka staff to scope additional data collection projects, which could further improve our understanding of the cost-effectiveness of Taimaka's program, as well as the cost-effectiveness of CMAM more broadly.

Main reservation about the $0.5m monitoring grant

• High cost and capacity requirements. Our monitoring plan is relatively intensive due to the large number of surveys, which trades off against time Taimaka might otherwise spend on program expansion (more).

The organization

Taimaka was founded in 2019,1 and initially focused on providing smallholder farmers in Nigeria with post-harvest loans to address food insecurity.2 In 2021, as a result of an evaluation of its previous work, Taimaka shifted to working on malnutrition treatment. 3 Taimaka currently operates in two LGAs in Gombe state,4 and has admitted around 5,000 patients from October 2023 to October 2024.5

The intervention

Acute malnutrition refers to excessive thinness for one's height and/or the presence of nutritional edema, or swelling caused by excess fluid retention in tissues.6 Acute malnutrition is believed to raise the risks of developmental delays and death from infectious disease.7

Community-based management of acute malnutrition (CMAM) identifies and treats cases of malnutrition primarily on an outpatient basis. Cases are identified by measuring childrens' mid-upper-arm circumference (MUAC), or by measuring their weight and height to calculate a weight-for-height Z score (WHZ), a measure of nutritional status. Treatment includes specific therapeutic foods and standard medications, including a short course of antibiotics for children with SAM. For more information about the treatment of acute malnutrition, see our report here.

At the outpatient centers it runs, Taimaka hires health workers to assess and treat malnourished children, or refer them to inpatient facilities if they need more specialized care.8 Taimaka covers the cost of ready-to-use therapeutic food (RUTF), drugs, and tests, and for inpatient care, additionally covers transport costs and meals for caregivers.9 At inpatient facilities, Taimaka hires its own nursing staff and provides additional stipends to some government staff at each facility.10 Taimaka also pays stipends to government health workers who conduct screening to identify malnourished children.11

Does malnutrition treatment work?

Our primary outcome of interest is the impact of CMAM on all-cause mortality in children 6-59 months old with malnutrition, relative to no treatment. We have not found direct estimates of this outcome, since it is widely considered unethical to study children with malnutrition without providing treatment.12

We use historical observational data on the mortality rate of children with untreated malnutrition, relative to children without malnutrition, to estimate the mortality rate of children with untreated malnutrition and the impact of CMAM on mortality.13 Additional inputs into our cost-effectiveness calculations include current local all-cause mortality rates and the prevalence of malnutrition, as well as several adjustments to account for key limitations of these estimates.14

This estimation method has major limitations but suggests that SAM treatment reduces all-cause mortality by about 60%.15 Paired with the program’s highly plausible mechanism of action,16 we believe CMAM is very likely to avert child mortality, but we are uncertain about the size of the effect. This is discussed in greater detail in our malnutrition intervention report.

The grants

$4.8m grant to support Taimaka's program

We are recommending a $4,787,985 grant to Taimaka to support malnutrition treatment in up to three LGAs in Gombe state, Nigeria. With this grant, Taimaka expects to expand its work from 4 to between 8 and 15 outpatient centers, and from 2 to 4 inpatient centers.17 A detailed budget breakdown is available here.

$0.5m grant monitoring grant

We are also recommending a $483,054 grant to Taimaka to conduct surveys on several key indicators (prevalence of malnutrition, coverage of malnutrition treatment, and mortality rates among children under 5) as well as scope additional data collection opportunities. A budget summary is available here.

The case for these grants

Cost effectiveness and learning value

We estimate that this program’s cost-effectiveness is close to our funding bar, and that it could move above it as we learn more about the program. We estimate that Taimaka’s program is approximately nine times as cost-effective as unconditional cash transfers ("9x cash"), the benchmark we use for cost-effectiveness. At the time we recommended this grant, our cost-effectiveness bar for recommending grants was 10x cash. Given the program's proximity to the bar and our uncertainties about key inputs, we think there's a reasonable (roughly 30%) chance we'll think the program is above our funding bar as a result of learning more about key inputs from program and survey data, and/or if Taimaka’s cost per child decreases as the program scales.

The section below details key inputs to our most recent cost-effectiveness analysis for Taimaka's program.

Key inputs in the cost-effectiveness model

Caseload

Taimaka estimates they will treat roughly 53,000 SAM cases over the three-year grant. Taimaka estimates this on the basis of the number of total outpatient facilities it expects to run each year, the number of cases per outpatient facility at steady state (2,500), and ramp-up time.

We then apply a 15% discount to this number to estimate the number of individual children treated (the discrepancy between the number of cases and children is due to relapse, i.e., the same child being treated for acute malnutrition multiple times). This leads us to an estimate of ~45,000 children treated over 3 years.

Mortality rates among targeted children

Our estimate is based on two inputs: a main estimate, and a ceiling analysis, which puts a plausibility cap on the main estimate.

Main estimate:
Our main model estimates 13% annual all-cause mortality rates among children with untreated SAM. This is based on the following:

• Our best guess is that the all-cause mortality among 6-59 month-old children with the age distribution we see among CMAM admissions in Gombe state is 2.7%. We calculate this as follows:
  • We estimate the mortality rate for children under 5 in 2021 in Gombe state to be 3.2%, using the following approach: we put 40% weight on the GBD 2021 estimate for mortality in Gombe, 20% weight on the UN IGME estimate, and 40% weight on a 2021 Nigeria MICS survey. For more details about why put weight on multiple sources of mortality data, see here.
  • We add a 59% adjustment, to account for the percent of under-5 deaths that occur in the 6-59 month bracket, based on data from IHME and DHS.
  • We adjust mortality on the basis of age distribution in the program, based on program data from Taimaka and DHS data on the percent of mortality among children 6-59 months occurring within different age brackets.
• We estimate the mortality ratio of malnourished children as compared to non-malnourished children to be ~6:1 for SAM and ~3:1 for MAM, based on several observational studies conducted in the 1980s and 1990s.
  
• We estimate the prevalence of untreated malnutrition among 6-59 month-old children to be ~13%, based on data shared by Taimaka.

Ceiling analysis: In order to sense-check our estimates, we developed a ceiling analysis to estimate the maximum plausible mortality rates for malnourished children. This is based on:

• Overall mortality rates among 6-59 month-old children (see above, though we do not use estimates adjusted for age distribution of the program)18
• The prevalence of untreated malnutrition (see above)
• An estimate of the maximum plausible percentage of deaths per year among children ages 6-59 months that occur among children who are malnourished at a given time. In other words, of all the children ages 6-59 months who die in a year, the number who would show up as malnourished if a survey were conducted at one point in time.
  • We subjectively guess that a maximum of 90% of the deaths that occur in children 6-59 months annually occur among untreated malnourished children.
  • We use this to estimate the maximum plausible percentage of deaths that occur among children who are malnourished at each point in time, to match the prevalence estimates. To do so, we divide 90% by the incidence correction factor (ICF), a value used to convert the number of current cases into an estimate of total cases per year. We calculate the ICF on the basis of the Alliance for International Medical Action (ALIMA)'s best guess for their work in Kaita, Nigeria.

This leads to a -67% plausibility adjustment to the main mortality estimate, reducing our estimate of annual all-cause mortality rates among children with SAM to ~4%.

Effect of malnutrition treatment on mortality

To estimate the impact of malnutrition on mortality in our CEA, we use observational studies conducted in the 1980s and 1990s to generate a mortality ratio that represents the comparison of mortality rates given average WHZ/MUAC before vs. after malnutrition treatment. The WHZ/MUAC inputs come from a literature review of the impact of malnutrition programs on WHZ/MUAC, adjusted for differences in recovery rates between government-only and NGO-supported malnutrition programs. This method estimates that SAM treatment reduces mortality by about 70%. We then apply a 20% discount to account for internal and external validity concerns, which leads to an estimate of roughly 60% mortality reduction for SAM treatment.

Development effects

We use our seasonal malaria chemoprevention (SMC) estimates as a benchmark for the ratio between mortality and development effects (~30%). We then apply a 65% adjustment to account for the fact that we expect the ratio between development effects and mortality effects to be larger for SMC than CMAM, since prevention interventions act earlier in the “causal chain” and are therefore likely causing development benefits for a higher proportion of people whose deaths aren't averted, compared to treatment interventions.

Learning value

While our current best guess is that this program is below our funding bar, we think there’s a 30% chance it could end up above the 10x bar in the long term, since:

• We have a number of open questions about program inputs in our model (including malnutrition prevalence, coverage, under-5 mortality and caseload), to which our cost-effectiveness estimate is moderately sensitive.
• Taimaka’s costs might decrease at scale, since it may be possible for Taimaka to find cheaper ways of sourcing RUTF (e.g., by coordinating with other providers to guarantee higher demand).

We expect to learn more about these questions through program data (costs and caseload) and monitoring data (prevalence, coverage, and under-5 mortality).

Potential to open up additional RFMF

We think helping Taimaka grow and learning about its work could open up significant additional room for more funding (RFMF) in the next 5-10 years. We estimate this grant's cost-effectiveness to be 13x when optionality benefits are included, though this is a very rough estimate which should not be taken literally.

The key inputs in our estimate are:

• The probability that we conclude the program is above our bar after this grant, which we expect to be 50% if our bar is set at 8x the cost-effectiveness of cash transfers, 30% for a 10x bar, and 10% for a 12x bar.
• Room to absorb additional funding over the next ten years, which we model on the basis of three scenarios, weighted as follows
  • (70%) Best guess - this is based on Taimaka’s best-guess projections, which estimate that Taimaka will treat 110k annual cases in 5 years, and 405k annual cases in 10 years. We then discount this by 30% to account for risk of bias.
  • (15%) Pessimistic guess - this assumes Taimaka cannot absorb additional funding
  • (15%) Optimistic guess - this is based on Taimaka’s optimistic projections, and assumes Taimaka will treat 140k annual cases in 5 years, and 810k annual cases in 10 years

Overall, we think Taimaka has large opportunities for growth: our best guess is that their program could absorb $25m/year in 10 years, and in the optimistic scenario this could be up to $70m/year. This means helping Taimaka grow at this early stage and learning more about the cost-effectiveness of its program could have significant optionality value.

Qualitative considerations

We think Taimaka would be a good thought partner as we seek to better understand malnutrition as an intervention area. Our impression so far is that they are very engaged with our work, responsive to questions, and interested in evidence generation on our key uncertainties.

Risks and reservations

Reservations about the $4.8m grant to support Taimaka's program

Our main reservations about this grant are:

We're uncertain about the risk of death among malnourished children and the effect malnutrition treatment has on mortality.

We do not have any direct evidence of the mortality rates of untreated children with malnutrition. This is different from most other child health programs we support, where we have direct estimates of the program’s effectiveness, usually from a number of randomized controlled studies. We instead rely on historical observational studies to inform our estimate. You can read more in our intervention report on community-based management of acute malnutrition (CMAM).

We're also highly uncertain about our method for estimating the effect that malnutrition treatment has on mortality. As above, we discuss this in more detail in our intervention report on community-based management of acute malnutrition (CMAM).

These contribute to a high level of uncertainty about our cost-effectiveness estimate for malnutrition treatment that we do not expect to resolve with these grants, although we hope to make progress on these uncertainties in partnership with Taimaka going forward.

We have some open questions about Taimaka’s model

The effects of health worker displacement on coverage of other health services

Taimaka's program model involves hiring its own staff to focus on malnutrition treatment, and having them work in government health clinics alongside government health staff.19 At the start of this grant investigation, we were concerned that a potential harm of Taimaka's program could be "poaching" qualified health workers who might otherwise work in the government health system directly, lowering the coverage of other health services.

We spoke with two government officials in Gombe state and a nutrition advisor at an international NGO about this issue. None of them seemed concerned about the possibility of Taimaka hiring staff who might otherwise work directly for the government.20

We also expect to be able to estimate the effect of Taimaka's expansion on the provision of other health services by using facility data before and after Taimaka enters an area.21

The effects of using staff with lower health qualifications on the quality of malnutrition care

Taimaka’s staffing model is somewhat different from usual CMAM staffing. Typically, NGO-run outpatient clinics are staffed by nurses.22 In Taimaka’s programs, patients are seen by “triage officers” (TOs).23 TOs are qualified and licensed Community Health Extension Workers (CHEWs), which is a Nigerian medical position designed to offer primary healthcare services in primary healthcare centers and community settings. CHEWs hold a diploma in community healthcare and are licensed by a national body, though this is a lower qualification than is required for nurses.24 Taimaka provides TOs with additional malnutrition-specific training when these staff are hired, along with a digital app that guides TOs through the diagnosis and treatment of each patient. TOs are supervised by registered nurses, who provide direct input on particularly difficult cases. Complicated cases are referred to inpatient care and managed by doctors and nurses as well.25

It is possible that using health staff with lower qualifications will negatively impact the quality of malnutrition care. Overall, we do not think this is likely, since:

• We would expect lower effectiveness or quality of care to be reflected in recovery rates, and Taimaka reports that more than 90% of the patients they treat recover (in 2024, 92.9%, ignoring non-responses.26 )
• Taimaka has reasonably strong M&E processes, including MUAC back-checks during and after discharge, which make us reasonably confident in their reported recovery rates.27
• Health workers in OTPs are supervised by registered nurses, who provide direct input on particularly difficult cases28
• Taimaka’s treatment and staffing protocols are reviewed and approved by the Gombe State Ministry of Health and the Gombe State Primary Healthcare Development Agency. They provide reports to and share our program data with these same bodies. These key bodies (the Ministry of Health, the Primary Healthcare Development Agency, and the Hospital Services Management Board (for inpatient care) also conduct supervisory trips to Taimaka’s facilities to monitor delivery.29

We’re unsure about the extent to which Taimaka’s growth will open up additional funding in the next years

Our estimate of optionality benefits is based on very uncertain inputs, including the likelihood that this program will meet our bar, and Taimaka’s ability to scale.

Our uncertainty about the likelihood of this program meeting our funding bar is due to our uncertainties about the effectiveness of CMAM in general and Taimaka in particular, discussed above.

We are highly uncertain about Taimaka’s ability to scale. Taimaka is currently quite small, and has only admitted ~5k patients from October 2023 to October 2024.30 This gives us limited information on Taimaka's ability to scale, or the speed at which the program could scale.

Reservations about the data collection grant

• High capacity requirements: Taimaka will conduct 16 surveys over two years in order to measure prevalence and coverage twice a year across the facilities where it operates. The large number of surveys will help us assess changes in indicators over time, including coverage before and after Taimaka enters an area and seasonal variation in malnutrition risk.31 This approach will be relatively intensive, which trades off against time Taimaka might otherwise spend on program expansion. We are unsure the costs are worth the benefits. To maintain flexibility, we are recommending a two-year monitoring grant, and plan to re-assess in the next couple of years.
• Low precision: The surveys Taimaka will conduct are not powered to estimate all-cause mortality with high precision, which is a key uncertainty in our cost-effectiveness analysis.32 Surveys with greater statistical power would require significant additional capacity, and we think it’s best to free up Taimaka's time to focus on expansion.

Plans for follow up

We expect to receive further information about expansion in new facilities and caseload from program data. We plan to ask Taimaka to share yearly monitoring reports, including survey methodology and results. We also plan to explore additional data collection opportunities with Taimaka to improve our understanding of the effectiveness of its program, and of CMAM more generally. We expect to make a decision on whether to renew our support for Taimaka's program in 2026.

Our Process

• We updated our CMAM cost-effectiveness analysis to include Taimaka-specific inputs
• We had several conversations and a number of email exchanges with Taimaka
• We talked to four local stakeholders about Taimaka’s plan and risk of diverting capacity from the government

Predictions relevant to the grant

Confidence	Prediction	By time	Resolution
65%	Taimaka expands as follows 2025: from 4 to 6 OTPs and from 2 to 3 ITPs across the 2 LGAs they already operate in 2026: add two more OTPs and one additional ITP in a new LGA	End of 2026	-
65%	Taimaka meets its caseload targets of 2025: 14,120 2026: 19,160 2027: 20,160	End of 2027	-
30%	Taimaka decreases cost per child treated by at least 10%	End of 2027	-
50%	We decide to make an additional grant by end of 2026	End of 2026	-

Sources

Document	Source
Conexus Medstaff, Conexus Partners with Taimaka in Support of Providing Critical Malnutrition Care to Nigerian Children, 2024	Source (archive)
David Roodman, On the association between anthropometry and mortality in children, 2022	Source
Frison, Checchi, and Kerac 2015	Source (archive)
GiveWell, CEA of Taimaka's CMAM Program, 2024	Source
GiveWell, Community-Based Management of Acute Malnutrition	Source
GiveWell, Malnutrition Treatment CEA, 2023	Source
GiveWell, Malnutrition Treatment CEA, Combined protocol ALIMA, 2023	Source
HealthDirect, "Fluid retention."	Source (archive)
Olofin et al. 2013	Source
Taimaka, 2025-2027 Budget Summary	Source
Taimaka, Overview of How Taimaka's CMAM Program Works, 2024	Source
Taimaka, Public Performance Dashboard	Source
Taimaka, Who We Are	Source (archive)
UNICEF, WHO, World Bank, "Joint child malnutrition estimates — levels and trends," 2020	Source
WHO, "WHO child growth standards and the identification of severe acute malnutrition in infants and children," 2009	Source

• 1

  Justin Graham, Taimaka, comments on a draft of this page, April 2025 (unpublished).

• 2

  "In our first year, our vision for cost-effective impact led us to focus on a microfinance program – providing smallholder farmers in Nigeria with post-harvest loans to tackle food insecurity among some of the most vulnerable communities." Taimaka, Who We Are.

• 3

  "In 2021, we ran a randomized control trial on this program in partnership with researchers at the University of California – Berkeley to evaluate its impact in line with our commitment to impact. After this comprehensive evaluation, we decided that our approach did not meet our own standards for using every dollar to do the most good possible, leading us to pivot to focus solely on our malnutrition work, which we began in early 2021." Taimaka, Who We Are.

• 4

  "We currently deliver treatment in two local government areas (LGAs), Funakaye and Yamaltu/Deba, of Gombe State, Nigeria." Taimaka, Overview of How Taimaka's CMAM Program Works, 2024, p. 1.

• 5

  According to their public program performance dashboard, Taimaka admitted 4,996 cases of severe acute malnutrition and 5,483 total cases between October 1, 2023 and October 1 2024. See the dashboard here.

• 6
  • “Acute malnutrition is a major public health issue in low-income countries. It includes both wasting and edematous malnutrition, but the terms wasting and acute malnutrition are often used interchangeably." Frison, Checchi, and Kerac 2015, Abstract.
  • "Wasting refers to a child who is too thin for his or her height. Wasting is the result of recent rapid weight loss or the failure to gain weight." UNICEF, WHO, World Bank, "Joint child malnutrition estimates — levels and trends," 2020, p. 2.

  “Fluid retention is also called oedema or water retention. It occurs when parts of the body swell due to a build-up of trapped fluid. The fluid gets trapped and makes the area swollen or puffy.” HealthDirect, "Fluid retention."

• 7
  • "Restricted growth as a result of inadequate nutrition and infections is an important cause of morbidity and mortality in infants and children worldwide. . . . Several prospective studies have shown associations of undernutrition with increased risk of various disease outcomes, and reduced survival, in children." Olofin et al. 2013, Introduction.
  • “Stunting is the devastating result of poor nutrition in-utero and early childhood. Children suffering from stunting may never attain their full possible height and their brains may never develop to their full cognitive potential. Globally, 144.0 million children under 5 suffer from stunting. These children begin their lives at a marked disadvantage: they face learning difficulties in school, earn less as adults, and face barriers to participation in their communities.” UNICEF, WHO, World Bank, "Joint child malnutrition estimates — levels and trends," 2020, p. 2.

• 8

  "During treatment, some patients will present with complications severe enough that they require a higher-level of care…In these cases, we transfer patients from our OTPs to our inpatient care centers (ITPs)." Taimaka, Overview of How Taimaka's CMAM Program Works, 2024, p. 2.

• 9

  Taimaka, answers to questions from GW, October 24, 2024 (unpublished)

• 10

  "While we work out of existing government facilities, we hire our own staff at OTPs to assess patients, write prescriptions, and administer drugs and therapeutic foods. We also pay additional stipends to a few government staff at each facility to handle tasks that correspond to their existing responsibilities." Taimaka, Overview of How Taimaka's CMAM Program Works, 2024, p. 1.

• 11

  "Patients are sourced through three methods: (1) active screening by mid upper arm circumference (MUAC) in designated communities surrounding our clinics by government community health workers to whom we provide additional training and pay an extra stipend…" Taimaka, Overview of How Taimaka's CMAM Program Works, 2024, p. 1.

• 12

  “The assessment of the risk of death associated with different degrees of wasting can be carried out only by community based longitudinal studies with a follow up of untreated malnourished children. This can be analysed only from a limited number of existing studies. For ethical reasons, these observational studies cannot be repeated, as an effective community-based treatment of severe acute malnutrition is now possible.” WHO, "WHO child growth standards and the identification of severe acute malnutrition in infants and children," 2009, p. 4, footnote 1.

• 13

  This work was conducted by GiveWell senior advisor David Roodman and is described in the following report: David Roodman, On the association between anthropometry and mortality in children, 2022.

• 14

  See the additional inputs into our calculations in our CEA here.

• 15

  Percent mortality reduction is calculated by taking the inverse of the mortality ratios in table 10 of Roodman 2022 and subtracting them from 1. For example, in Nigeria, the mortality ratio for NGO-supported malnutrition treatment vs. no treatment is 1.68 for MAM and 3.36 for SAM:

  • Mortality reduction from MAM treatment: 1 - (1 / 1.68) = 0.40 (0.60 relative risk of mortality with MAM treatment)
  • Mortality reduction from SAM treatment: 1 - (1 / 3.36) = 0.70 (0.30 relative risk of mortality with SAM)

  David Roodman, On the association between anthropometry and mortality in children, 2022, table 10, p. 45.

• 16

  "Malnutrition treatment has a highly plausible mechanism of action. Low body energy stores and nutritional deficiencies increase the risk of death from infectious diseases. [Ready-to-use therapeutic food] RUTF addresses deficiencies of energy and essential nutrients, while antibiotics treat infections and may also work through less well-understood mechanisms. We also believe the standard care that is typically provided at initiation of CMAM, such as screening and treatment for malaria and administration of preventative vaccines, is likely to be beneficial. Overall, we have a strong prior that CMAM will avert deaths among malnourished children to some extent." GiveWell, "Community-Based Management of Acute Malnutrition (CMAM)"

• 17

  Justin Graham, Taimaka, comments on a draft of this page, April 2025 (unpublished).

• 18

  Note that we use the mortality rate for all children 6-59 months rather than mortality rates adjusted for the age distribution of CMAM admissions. This is because (1) this adjustment is highly sensitive to the prevalence of malnutrition; (2) we do not know prevalence rates in children 6-59 months with the age distribution of CMAM admissions, (3) we expect prevalence for those ages to be higher, since we would guess this is what explains higher admissions. For reference, increasing both mortality rates and prevalence by the same factor leaves the adjustment unchanged.

• 19

  "While we work out of existing government facilities, we hire our own staff at OTPs to assess patients, write prescriptions, and administer drugs and therapeutic foods." Taimaka, Overview of How Taimaka's CMAM Program Works, 2024, p. 1.

• 20

  Conversations with stakeholders, August 2024 and October 2024 (unpublished)

• 21

  We discussed this possibility with Taimaka in Taimaka, answers to questions from GW, October 24, 2024 (unpublished)

• 22

  This is based on our impression from other CMAM programs we've funded with the Alliance for International Medical Action (ALIMA) and the International Rescue Committee (IRC).

• 23

  Taimaka, answers to questions from GW, October 24, 2024 (unpublished)

• 24

  "CHEWs hold a Diploma in community health, having completed a three-year program, primarily at Schools of Health Technology. The CHEW curriculum is focused on community diagnosis and treatment of minor ailments and diseases in preparation for community outreach and assistance at PHCs, including monitoring of labour and delivery." Adepoju et al. 2021

• 25

  Taimaka, answers to questions from GW, October 24, 2024 (unpublished)

• 26

  See Taimaka's public dashboard here. .8368 recovered / (1-.0997 non-response) = 0.929.

• 27

  "Each child is assigned a paper admission card that follows them through the program, logging their details on admission and at each weekly follow-up visit. This admission card is linked to the child’s digital record through a scannable barcode that contains a unique identification number… About 50% of these records will be non-randomly selected and reviewed by outpatient program managers in the course of their normal programmatic duties… About 10-20% of these records are then randomly selected for review by our M+E staff to look for problems in data entry that need to be trained on/corrected and for discrepancies from digital records…"
  "triage officers fill out paper ledgers of therapeutic foods and medications disbursed to each child.
  All of these ledgers are checked weekly against therapeutic food stocks and digital records of what was prescribed…"
  "Photos of each child are taken at admission and each weekly visit. Every visit is reviewed the day after it happens by an M+E staff member, checking to make sure the child pictured matches the child shown at initial admission."
  Taimaka, answers to questions from GW, October 24, 2024 (unpublished)

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  Taimaka, answers to questions from GW, October 24, 2024 (unpublished)

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  Taimaka, answers to questions from GW, October 24, 2024 (unpublished)

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  According to their public program performance dashboard, Taimaka admitted 4,996 cases of severe acute malnutrition and 5,483 total cases between October 1, 2023 and October 1 2024. See the dashboard here.

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  We expect these comparisons to help us make progress on our understanding of the effectiveness of Taimaka's program because:

  • Coverage rates before and after Taimaka enters an area will feed into our understanding of Taimaka's effect on coverage, which we use to estimate how many children treated by Taimaka would not have received treatment otherwise. See this section of our CEA.
  • Prevalence at multiple times of year: we think malnutrition prevalence is likely seasonal, due to variation in staple crop yields over the course of the year, which means that multiple data points on prevalence each year will likely be more representative. We use malnutrition prevalence to estimate mortality rates among malnourished children. See this section of our CEA.

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  The surveys funded by this grant will be a combination of SMART surveys (which measure prevalence and mortality) and SQUEAC surveys (which measure coverage). SMART (Standardized Monitoring and Assessment of Relief and Transitions) surveys provide statistically significant estimates for overall malnutrition prevalence, but do not provide statistically significant estimates for all-cause mortality.
  We estimate the all-cause mortality rate for children under 5 in Gombe state is 2.74%, and the SMART surveys will be powered to detect mortality with a precision of +/- 1.10 percentage points.
  Taimaka, Coverage - Sample Size Calculations (unpublished).

  • Sample size for prevalence and coverage: Taimaka estimates a sample for prevalence of ~400-500 children per site, and coverage will be ~50-70 SAM children per site. Taimaka, answers questions on M&E from GiveWell, March 10, 2025 (unpublished)