Cost-effectiveness analysis of Lafiya Nigeria intervention

Global Health and DevelopmentGlobal Health Interventions

Oct. 23

Editorial Note

This document is intended to explain some of the modeling decisions we made in assessing the cost-effectiveness of Lafiya Nigeria’s intervention, along with some considerations for scaling and potential model extensions. We additionally provide explanation of uncertainty in some of the model parameters, which we use to inform our uncertainty analysis.

Due to the limited time allotted for this model-building exercise, we do not provide a comprehensive report describing the model and its findings. This research was conducted over a brief period (less than three weeks) by one senior researcher and one senior research manager, so we caution that some model inputs may not use the most recent or applicable data available.

We have tried to flag major sources of uncertainty in the report and are open to revising our views as more information becomes available. We are grateful for the invaluable input of our interviewees, and for the support and data that Klau Chmielowska and Céline Kamsteeg of Lafiya Nigeria provided throughout the project.

Summary and Key Takeaways

We model the near-term cost-effectiveness (2024-2025) of Lafiya Nigeria’s (n.d.) intervention, in which they upskill trained female health workers to provide family planning counseling and distribute injectable contraceptives (Sayana Press, or DMPA-SC) to women in remote parts of rural northern Nigeria.
We estimate that the cost-effectiveness of the intervention is $19 per unintended pregnancy averted at the beginning of 2024, falling to $7 per unintended pregnancy by the end of 2025 when approximately calibrating the model to the current and planned rate of intervention scaling.

We provide additional cost-effectiveness estimates, including cost per DALY averted (maternal, child, total), death averted (maternal, child, total), couple-year protection, additional contraceptive user, unsafe abortion averted, and $1 increase in women’s income, which can be found here.
Our crude estimate of the effect of the intervention on women’s income suggests that increasing the income of women serviced by Lafiya Nigeria by $1 cost $1.20 at the beginning of 2024, and will decline to $0.20 by the end of 2025. Reproducing GiveWell’s approach to understanding these benefits in terms of multiples of cash transfers, we find that the intervention’s impact on income increases from approximately 11x cash transfers at the beginning of 2024, increasing to 53x cash transfers by the end of 2025.

We conduct uncertainty analysis using a Monte Carlo simulation to gain insight into the effect of uncertainties from 15 inputs/factors on our cost-effectiveness estimates (Figure 1).

Looking at the effect of uncertainty on cost per unintended pregnancy averted, in Q1 2024 the 90% highest density interval (HDI) falls in the range of $17-$20 per unintended pregnancy averted (mean cost-effectiveness is ~$18). This uncertainty narrows over time, with a 90% HDI between $3.50 and $4.30 in Q4 2025 (mean cost-effectiveness is ~$4).
Uncertainties affect our cost-effectiveness estimation for other modeled outcomes similarly, except for the intervention’s impacts on income in multiples of cash where significant uncertainty in this estimate results in quite wide HDIs, getting wider over time with mean cost-effectiveness estimates growing from 10.8x (90% HDI 4.7x, 17.5x) in Q1 2024 to 49.9x (90% HDI 22.2x, 81.8x) in Q4 2025.
Overall, our uncertainty analysis did not show significant variation in estimates and therefore increases our confidence in the findings of the model.

Figure 1: Uncertainty analysis summary statistics for multiple outcomes. Points represent the mean of the Monte Carlo simulation (as do numbers on the outside of the ring), with error bars representing the 90% HDI. Numbers on the inside of the ring represent the year and quarter for each estimate.

Model structure, inputs, and assumptions

We model the near-term cost-effectiveness (2024-2025) of Lafiya Nigeria’s (n.d.) intervention, in which they upskill trained female health workers to provide family planning counseling and distribute injectable contraceptives (Sayana Press, or DMPA-SC) to women in remote parts of rural northern Nigeria.

This document is a supplement to our cost-effectiveness model. The notes below are intended to explain some of the modeling decisions we made, along with some considerations for scaling and potential model extensions that we or others might make given more time and/or data. Due to the limited time allotted for this model-building exercise, we do not provide a comprehensive report describing the model and its findings, but instead provided a live walkthrough of the model to Lafiya Nigeria during the project timeframe.

Dynamic model with quarterly inputs

Dynamic:

Allows model users to account for expected changes in the landscape and for intervention scaling, and to better calibrate the model to data that is or becomes available.

Quarterly:

Coverage from the Sayana Press contraceptive method lasts for 13 weeks, so that women are encouraged to receive an injection every three months (i.e., quarterly).
Allows for calibration of the model against Lafiya’s monthly data.¹

Model to 2025:

Lafiya Nigeria (LN) plans to operate solely in rural (or at most peri-urban) Nigeria for the next 1-1.5 years.
LN believes they will be operating independently of the government in the near term, and the model does not account for any possible changes to impact or costs resulting from integration with the Nigerian Ministry of Health (i.e., LN’s longer term plan for scaling the intervention).

Supply-side issues

We had originally intended to include adjustments for the possibility of supply-side issues (i.e., stockouts and wastage). However, while contraceptive supply in the country is often problematic, LN has not experienced any supply-side issues and does not expect to over the period that we have modeled for this exercise.

A counterpoint to this expectation came from an anonymous expert on women’s health innovations. They said that wholesalers like the one selling to LN are often receiving their supplies from different partners—in this case, their assumption is that the United Nations Population Fund (UNFPA) has procured the doses and donated them to the public sector, and the product is leaking to the wholesaler from the public sector. These wholesalers are “sometimes stocked out and have to scramble to find another commodity,” which is a risk LN faces.

LN is working with independent suppliers, and each Lafiya Sister has two to three months’ worth of supply at any given point in time. Program officers maintain reserve stocks to replenish Lafiya Sisters’ supply if needed. In the event a dose expires, LN can exchange them for unexpired doses free of charge.
We note that the model may need to be adjusted to account for supply-side issues if the program is integrated into existing health programming from the Ministry of Health.

Non-health benefits

Several sources suggest that wider access to family planning information and resources in sub-Saharan Africa would have broader benefits beyond child and maternal health, and we broadly think such arguments carry weight (i.e., we think several of these benefits are feasible or even likely, but we have not critically investigated the evidence base). Some such benefits could include:

Education benefits²
Household wealth benefits³
Female empowerment and societal contributions⁴
Economic growth and improved government spending⁵
Stronger national institutions⁶
Climate change resilience and food security⁷

We spent most of a day researching and attempting to incorporate effects on one such benefit—household wealth—into our cost-effectiveness model.

We started by spending about 2 hours looking into the available literature and models to try to identify existing conventions for modeling these benefits, including scanning through Founders Pledge’s BOTEC for Family Empowerment Media as well as MSI’s modeling (Weinberger et al., 2023). We did not quickly find any sources that attempt to model the cost-effectiveness of family planning interventions on household wealth, though we did not conduct an exhaustive search.
From a quick scan of the literature on Google Scholar, we identified Canning and Schultz (2012) as a source containing an estimated treatment effect of a program similar to LN’s.

The authors conduct a comparative analysis of villages sharing similar characteristics in Bangladesh (1997-1996) that did (treatment group) or did not (control group) receive a program wherein community health workers offered reproductive health services to married women of reproductive age, including offering contraceptives.
They found women’s monthly earnings were 40% higher in treated villages than in control villages in 1996 (controlling for age and schooling) and had 25% “more physical assets per adult in their household.”⁸

The authors mention a similar program in Ghana (“The Navrongo Project”) starting in 1993, but note that long-term follow-up studies have not been completed; we did not have time to search deeply for any subsequent studies that identify effects on household income, though a quick search did not suggest one exists.⁹

While we make significant adjustments to the treatment effect using internal and external validity discounts in the model, we would have liked to interview David Canning to get a deeper understanding of the state of the literature on this subject with the aim of identifying an estimate in which we would feel more confident.

Assumptions

We make several assumptions in the model (see column titled “Type” to identify them). We provide explanations for these assumptions directly in the model (see column titled “Notes”), so we use this section to mention assumptions that we are making implicitly by omitting certain elements from the model.
We assume that non-adopters of Sayana Press also do not adopt an alternative contraceptive method following a visit with a Lafiya Sister.¹⁰

Klau Chmielowska mentioned in our interview that women who do not want to use Sayana Press can be referred to MSI clinics to seek out other contraceptives, although Celine said that women who would prefer an alternative (e.g., due to side effects) are rare.

We do not include a discount rate in the model, under the assumption that the most relevant values to LN pertain to the current quarter (we model to the end of 2025); if LN plans to calculate the net present value of the program’s benefits into the future, it would need to consider an appropriate discount rate and model accordingly.
To calculate the maternal and child health outcomes, we largely replicated MSI’s approach—which is widely used in the family planning space—and incorporated several inputs from MSI collated sources.

However, we judged that MSI’s approach to calculating the ‘percent of unintended pregnancies that end in miscarriage,’ which relied on a ratio of miscarriages per abortion calculated from a 1992 paper, would be improved if replaced with a more recent direct estimate for Nigeria from the Guttmacher Institute (Guttmacher Institute, 2015).
We could not find an equivalent updated input for ‘Percent of unintended pregnancies that end in stillbirth,’ so we instead calculated this input using MSI’s input for ‘stillbirth per 1000 live births,’ which is not specific to unintended pregnancies. We therefore implicitly assume that this ratio is the same in intended and unintended pregnancies. We find it plausible that this ratio may differ between these groups, so we are relatively uncertain in this assumption.
We did not calculate unintended pregnancies averted in the same way that MSI does, which is outside the scope of our model (see Weinberger et al., 2023, pp. 27-30). Instead, we estimate the number of women receiving contraception from Lafiya Nigeria this quarter (both existing clients and new adopters) that would counterfactually not have been using contraception, and we multiply this figure by MSI’s global default pregnancy rate of 44%.

One potential issue with our calculation is that in a counterfactual world without LN, a significant percentage of LN’s clients would have become pregnant and would therefore be less likely to have unintended pregnancies in the subsequent quarters (e.g., if their pregnancy went to term, they would not get pregnant again for nine months, or three subsequent quarters, though the significant abortion rate for unintended pregnancies along with the percent of pregnancies ending in miscarriages suggest that several women could still become pregnant again much sooner).
We do not attempt to account for these nuances in our model but instead focus on the renewed possibility of unintended pregnancy for each client in each quarter. That is, a woman who receives contraception in Q1 2024 has a 44% chance of becoming pregnant and does not. She then continues using in the following quarter, and again has a 44% chance of becoming pregnant in Q2 2024 were it not for receiving contraception.

Challenges to scaling (not modeled)

An anonymous expert suggested there may be some challenges to scaling, including:

Stockouts (whether currently with the wholesaler providing LN’s commodities, or at scale)
Reliance on upskilling, which means reliance on a supply of health workers being available that have been trained by others; unclear whether these providers exist at a national scale

We also imagine that additional factors—such as increased coordination—could complicate intervention implementation, which we do not capture in our modeling.

Potential future model extensions

There are several potential additions/extensions to the model that Lafiya could consider in the future, we mention some of these in this section.
Additional benefits (e.g., autonomy, education, subjective well-being, environment, broader economic benefits)
Allow for different assumptions or impacts based on women’s age (e.g., adolescent vs. not), marital status, ethnicity, or other demographic
Given the appropriate data, incorporate nuance around differential increases in protection between ‘new adopters’ and women who switch from a different contraceptive method. However, it is difficult to conceptualize the change in protection in terms of CYP and there is a lack of data on women who were using at the time of the Lafiya Sister visit
Effect of increasing the self-injection rate on cost-effectiveness. There are several caveats:

Unclear impact on cost-effectiveness, though we anticipate it would improve
Presume decreases in burden on Lafiya Sisters, which would allow for visits with more clients, and would outweigh increases in upfront costs for the additional Sayana Press doses
Differential compliance (and associated implications for protection) for women who choose to self-inject

An anonymous expert mentioned that increasing self-injection rates may be quite promising, and that rates of self-injection in Nigeria are 40%.
They said adherence after 12 months for women with the intention of using contraception for the next 12 months is significantly higher for women who self-inject than for those who receive injections from a health worker.

In Malawi, Senegal, and Uganda, self-injection led to 73%, 80%, and 81% continuation at the 12-month mark, whereas clinical injection led to 45%, 70%, and 65% continuation, respectively. In other words, self-injection led to an increase in continuation after a year of using the injectable contraceptive of 28, 10, and 16 percentage points in Malawi, Senegal, and Uganda, respectively.
Their recollection is that this data comes from three RCTs in which women were randomized into receiving health worker administration or self-injection, and they shared the following information in follow-up communications.

Table 1: Rates of 12-month continuous use, by method of injection

	12-month continuous use
	Health worker	Self-injection	Difference
Malawi (Burke et al., 2018)	45	73	+28
Uganda (Cover et al., 2018)	65	81	+16
US (Kohn et al., 2017)	54	69	+15
Senegal (Cover et al., 2019)	70	80	+10

Self-injection rates (proportion of DMPA-SC visits for self-injection) in LN states (Q4 2023 data):

Jigawa: 12%
Kebbi: 16%
Sokoto: 16%
National: 40%

Improve data collection and modeling around discontinuation

On the final day of the project, we received a response from Maryam Kaoje suggesting that “About 50% of the women discontinue the method for wanting to get pregnant again and 40% discontinue the method because they don't want to continue to use any method of contraception and 10% want to test another method” (interview with Maryam Kaoje).
The model currently assumes women either discontinue while still in need (which would include women who prefer to seek out another method), or discontinue due to the desire to get pregnant.
We do not include women who simply no longer want contraception, and Kaoje suggests this may be a sizable fraction (40%) of the women who discontinue.
An update to the model could attempt to reduce uncertainty around discontinuation and its implications for impact.

Going forward, we would suggest that LN use costs in Naira where possible, so that updates to the exchange rate apply throughout the model. Currently several costs are hard-coded in USD (e.g., cost per worker trained).
Switching to a longer-release version of Sayana Press will increase cost-effectiveness all else equal, since it would require fewer doses and increase the productivity of Lafiya Sisters in reaching more women.

We hope the model can be helpful in informing this decision.
We are unclear whether there is a cost difference between the short vs. long-release versions and, as LN noted, whether there might be knock-on effects to other parts of the model (e.g., rate of discontinuation).

Uncertainty analysis

To generate estimates of uncertainty for cost-effectiveness calculations, we conducted a Monte Carlo simulation using the programming language R. Monte Carlo simulation refers to using random sampling as a means of including uncertainty in a calculation. Several inputs to the cost-effectiveness model were deemed to be sufficiently uncertain that they would be best included in the model not as single numerical values, but as a distribution of possible values that reflect the range of possibilities for that input.

The calculations in the uncertainty analysis are equivalent to the calculations in the spreadsheet, except that in each case where the input was deemed to be uncertain, we draw a random sample from the corresponding distribution. For this analysis, we drew 5,000 samples for each uncertain input. The final cost estimates therefore reflect the distribution of cost-related outcomes when taking into account the uncertainty we attributed to each of the different inputs. We then plot this overall distribution, as well as providing summary statistics (e.g., the mean, median, and modal values, as well as the interval for the 90% most likely values or ‘highest density interval’ and 90% central values or ‘equal tailed interval’).

In the section below, we describe our uncertainties and present the distributions of values that were used for the uncertain inputs. In the subsequent subsection, we present the outputs for the Monte Carlo simulation.

Input uncertainty

We and/or Lafiya are currently uncertain about a number of inputs in the model, and we selected several of these for inclusion in our uncertainty analysis based on the extent of our uncertainty as well as information provided by LN. Based upon literature review and discussions with the Lafiya team, and with an eye to keeping the values within relatively conservative ranges, we suggested some upper and lower bounds on these uncertain parameters along with a shape of the distribution of their most likely values. We then used these intuitive suggestions to select corresponding distributions of values (primarily using truncated normal, PERT, and uniform distributions). Below we describe these distributions as well as the considerations that led us to select them.

Rate of injectable discontinuation while in need of protection

In our interview with an anonymous expert, they mentioned that they think a Nigeria-wide estimate is likely to underestimate this input for rural areas. While we use the Nigeria estimate in the model, we think this value is a lower bound and that the real value is plausibly quite a lot higher for the women that LN is reaching.
Suggested uncertainty bound: [35, 90]
Suggested uncertainty distribution: left-skewed (peaking at 70)
Resulting distribution: PERT(min = 35, mode = 70, max = 90, shape = 4)

Reduction in rate of discontinuation while in need due to LN distribution model

Discontinuation is likely to be due in some part to lack of access or lack of information, both of which LN’s distribution model aims to address. We therefore assume that Lafiya's model reduces the discontinuation rate by some proportion, which is captured in this input.
An anonymous expert thinks such an adjustment would be “modest” but non-zero since “most women discontinue for reasons outside of access,” including spousal opposition, side effects, and wanting to get pregnant (the last of which we capture separately in the model).
The expert mentioned that higher rates of self-injection tend to improve continuation, which suggests access is still an important consideration.
Suggested uncertainty bound: [5, 35]
Suggested uncertainty distribution: normal ~ (20, 6.5)
Resulting distribution: Truncated normal(min = 5, max = 35, mean = 20, sd = 6.5)

Rate of injectable discontinuation due to desire to get pregnant

LN survey data on “Reasons you decided to use Sayana-Press” suggest that 47% (i.e., 8,032 out of 16,972 from January-April of 2024) of those surveyed decided to use Sayana Press for birth spacing reasons. A healthy birth spacing interval is generally considered ~2 years. Since eight injections would provide protection for two years, and we assume the duration since last pregnancy will be approximately uniformly distributed over a two-year time period across women in the sample, we assume that on average ⅛ (12.5%) of women discontinue from quarter to quarter due to the desire to get pregnant.
However, we are highly uncertain about this input, so we create a wide uncertainty bound of ~90% in either direction.
Suggested uncertainty bound: [1, 24]
Suggested uncertainty distribution: normal ~ (12.5, 4)
Resulting distribution: Truncated normal(min = 1, max = 24, mean = 12.5, sd = 4)

% increase in health workers trained each quarter

We include an assumption that the number of workers trained each quarter will scale, and we have approximately calibrated the percentage increase in number of workers trained each quarter to support the data on the number of trained Lafiya Sisters in Q1 and Q2 of 2024 that LN shared with us.
This number is currently static at 20%, though we think it could increase as LN scales if they rapidly expand to more states (albeit with possibly different returns, given the model is currently calibrated to productivity data in states in which LN currently operates). It could also decrease, for instance if they remain in the states in which they currently operate and there are fewer trained health workers available to upskill, or if there are fewer trained health workers in other states to upskill.
We guess that this rate of increase could be up to or beyond 100% in future quarters and years, though for the timeframe of the model (through 2025) our best guess is that this rate of increase could be as high as 50% or as low as -10%.
Suggested uncertainty bound: [-10, 30]
Suggested uncertainty distribution: left-skewed (peaking at 20)
Resulting distribution: PERT(min = -10, mode = 20, max = 30, shape = 4)

% of previous contraception users with current unmet demand

According to LN's surveys in 2024, only 31.4% of respondents are new to contraception, though the question asked in Lafiya's surveys pertains to use of contraception any time prior to the survey (i.e., not necessarily at the time of the visit with the Lafiya Sister).
We are uncertain about the extent to which women who had previously used contraception in their lifetime were using contraception prior to switching to Sayana Press. This input in the model allows for uncertainty analysis until a data-supported approach becomes available.
An anonymous expert’s best guess is that the number of current contraception users among women who had previously used any contraceptive method in their lifetime would be “really low”

They sent a paper (Adedini et al., 2023) analyzing Nigeria Demographic and Health Survey (DHS) data that suggests that among low- and middle-income countries (LMICs), the modern contraceptive prevalence rate in Nigeria is relatively very low at 12% (2% in Sokoto State in the North West), and the discontinuation rate is relatively high at 41%.
The discontinuation rate for all contraceptive methods in Nigeria is highest in the North West at 50.1% (see Adedini et al., 2023, p. ix).
Finally, 90.0% of women in rural Nigeria were not currently using contraception when they were surveyed (National Population Commission and ICF, 2019, p. 141, Table 7.4).

An anonymous expert said that DHS Nigeria should have within-subject data on whether a woman has used contraception before and whether they are using it now, which can inform this estimate. They also mentioned that DHS 2023 is complete, but not yet released, so we would suggest taking a look when the new data is released to see if it can help to reduce uncertainty in relation to this model input.

We did not have time to seek access to within-subject data to assess differences in the rate of non-use for women who have and have not ever used contraception, but given the expert’s intuition about current contraception usage being “really low” even among women who have previously used contraception, we assume the two rates are the same or roughly similar in the model.

Suggested uncertainty bound: [70, 100]
Suggested uncertainty distribution: left-skewed (peaking at 90)
Resulting distribution: PERT(min = 70, mode = 90, max = 100, shape = 4)

Percent of abortions that are unsafe

We are uncertain about the percentage of abortions that are unsafe in (rural) Nigeria. We have seen estimates for Western Africa as high as 85% that are from over a decade ago (as used in LN’s own CEA), and more recent global numbers that are much lower (e.g., 45% according to WHO, 2024).
While PMA2020 focuses on states outside of the areas where LN is operating (according to an anonymous expert), we use their estimate of 60% given it falls between the two aforementioned estimates, and that it is an estimate specifically in Nigeria. However, we think it is plausible that the true value is between 50% and 80%.
Suggested uncertainty bound: [50, 80]
Suggested uncertainty distribution: right-skewed (peaking at 60)
Resulting distribution: PERT(min = 50, mode = 60, max = 80, shape = 4)

Percent of unintended pregnancies that end in stillbirth

We are also uncertain about whether the percentage of pregnancies that end in stillbirth is the same for intended versus unintended pregnancies, as we currently assume in the model. Our best guess is that stillbirth rates for unintended pregnancies would be equal to or higher than the average for all pregnancies (e.g., due to women with demand for contraceptives being more likely to be at risk of health complications due to recent births, or due to other known maternal health issues that might lead them to use contraception).
We also have seen an estimate for Nigeria from UNICEF (2020) that is almost double the estimate in MSI’s default data (i.e., 4.3% versus 2.3%), though we use MSI’s default data in our model.
Suggested uncertainty bound: [2.3, 6.3]
Suggested uncertainty distribution: normal ~ (4.3, 0.66)
Resulting distribution: Truncated normal(min = 2.3, max = 6.3, mean = 4.3, sd = .66)

Estimated average monthly earnings for women in rural Nigeria (Naira)

An estimate of mean income earned by rural Nigerian women in a study in Kwara State was 15,344.65 Naira per month (Falola et al., 2020).¹¹ Asset ownership among women in Kwara State is between that in Sokoto State (slightly higher) and those in Jigawa and Kebbi States (lower), and women’s asset ownership in all of these states is quite low (between 0-15%; National Population Commission and ICF, 2019, p. 394).
Estimated median rural Nigerian household's consumption from 2018-2019, with inflation adjustments to 2020, was 71,810 Naira (Anker Research Network and Global Living Wage Coalition, 2020, p. 5, Figure 1)
While we use a point estimate for monthly income of 15,000 Naira, we think it is possible this value could be as low as 0 and as high as 20,000 Naira.
Suggested uncertainty bound: [0, 20,000]
Suggested distribution: left-skewed, peaking at 15,000
Resulting distribution: PERT(min = 0, mode = 15000, max = 20000, shape = 4)

Internal validity discount

We introduce an internal validity discount for the treatment effect in the study we identified (Canning & Schultz, 2012) on the effects of family planning exposure and access on income.
While the non-RCT setting introduces the possibility of bias in the study’s estimates (e.g., selection bias), a very brief scan through the paper suggests the analysis is at least somewhat econometrically sophisticated, and treatment and control areas were similar based on important observable information: “Before the programme began, according to a 1974 census, all 141 villages had similar surviving fertility (ie, child-to-woman ratios), average schooling, and housing characteristics” (Canning & Schultz, 2012, p. 2).
Suggested uncertainty bound: [0, 90]
Suggested distribution: right skewed, peaking at 20
Resulting distribution: PERT(min = 0, mode = 20, max = 90, shape = 4)

External validity discount

Canning and Schultz (2012) has several dissimilarities with the context of interest. It takes place in Matlab, Bangladesh (i.e., a different continent and culture), and evaluates the results of a decades-old policy.

“In the district of Matlab, Bangladesh, outreach family planning programmes were set up in 71 of 141 villages from 1977 to 1996” (Canning & Schultz, 2012, p. 2).

At the same time, similar to northern Nigeria, the context in the study is also a rural area of a LMIC characterized by limited female labor force participation. Additionally, the intervention seems somewhat similar to that of Lafiya Nigeria, with health workers visiting women of childbearing age and offering family planning services, including access to contraception.

“Community health workers were trained to visit the homes of all married women of childbearing age in the outreach programme villages every 2 weeks to offer them various contraceptives and child and maternal health services and supplies, with some additional services provided after 1982” (Canning & Schultz, 2012, p. 2).

Therefore, some ways in which the generalizability seems strong is in the rural LMIC context and in the limited economic opportunity for women; moreover, the program does less targeting of women who seem to be the most likely candidates for uptake, indicating that a treatment effect among the women Lafiya Sisters reach could be even stronger (i.e., due to higher likelihood of adoption).
However, there are some ways in which the intervention is quite different: the intervention is (i) available only to married women (who may be more or less likely to be educated, or employed), (ii) provides more regular access to family planning services, and (iii) offers more contraceptive methods, which are immediately available to recipients (though our understanding is that injectables are by far the preferred contraceptive method in rural Nigeria, and a LN partnership with MSI means women in targeted areas can fairly easily access other contraceptives as well).
We are not confident that this study is the most generalizable study to our context of interest, but we did not conduct an exhaustive literature search due to time constraints.
Suggested uncertainty bound: [10, 70]
Suggested distribution: uniformly distributed
Resulting distribution: Uniform(min = 10, max = 70)

Cost per health worker trained

While the cost of training per Lafiya Sister is currently $120, LN expects to bring this cost down to $107 due to provision of more localized training as the program scales, primarily reducing transportation costs.
Suggested uncertainty bound: [105, 120]
Suggested distribution: exponential,¹² peaking at 120
Resulting distribution: PERT(min = 105, mode = 120, max = 120, shape = 3)
Note: The resulting distribution here is intentionally conservative, with only ~5% of values being $112 or less, and a median value of ~$117. Very few samples from the distribution reach $107.

Number of Lafiya Sisters a PO [program officer] manages

Céline Kamsteeg said that LN arbitrarily selected 50 as the maximum number of Lafiya Sisters a program officer should manage. However, she mentioned that program officers have expressed a capacity and desire to manage more Lafiya Sisters (without significant additional compensation). LN is deciding whether to increase this number by some amount, up to 100 total.
Suggested uncertainty bound: [50, 100]
Suggested distribution: exponential distribution, peaking at 50
Resulting distribution: PERT(min = 50, mode = 50, max = 100, shape = 3)

Contingency

Céline Kamsteeg also mentioned that the organization has tended to underspend relative to its budget, but maintains a contingency in the event of unforeseen changes.
We include this contingency, adding 10% of all delineated costs in the total cost. However, we think it is reasonable to assume that cost-effectiveness is actually higher than modeled if the organization does not spend this contingency.
Suggested uncertainty bound: [0, 10]
Suggested distribution: uniformly distributed
Resulting distribution: Uniform(min = 0, max = 10)

Price per dose

LN is under contract to receive Sayana Press for $0.27 per dose, though the existing pricing agreement for sale of Sayana Press in LMICs between Pfizer, the Children’s Investment Fund Foundation, and the Gates Foundation sets a price of $0.85 per dose, per our interview with an anonymous expert.
In early communication with LN, they mentioned that: “In our previously developed CEA, we calculate with a price per injection of $0.85 + 10% delivery, totaling $0.94 per dose. This is a price ceiling and thereby a very conservative estimation. In the last year, we have procured DMPA-SC for $0.30 per dose including delivery, due to a favorable exchange rate and agreements with the supplier. We are medium confident that the price will remain around $0.30 for the next year, but have lower confidence it will remain $0.30 for the next 10 years.”
Since the horizon of our model only extends to the end of 2025, we therefore provide uncertainty modeling for two scenarios: one where the price per dose is $0.27 + 10% delivery for both 2024 and 2025, and one where the price per dose is $0.27 + 10% delivery for 2024 increasing to $0.85 + 10% delivery for 2025.

Exchange rate

Note that we considered an uncertain input for the USD/Naira exchange rate, though we encountered a challenge in that it might also require modeling how possible devaluation of the Naira could have knock-on effects on wages and prices. Without including such impacts, adding the possibility of exchange rate changes might unreasonably bias estimates towards higher cost effectiveness due to greater dollar purchasing power. These considerations were beyond the scope of this report, but we highlight that changes in the exchange rate or general valuation of the Naira remain a source of uncertainty.
For context, we reviewed the historical conversion chart of USD to NGN (Wise, n.d.). The exchange rate was highly stable until last year, and since then the dollar has strengthened against the Naira significantly and become rather unpredictable.

Plotted distributions for each of the uncertain inputs are in the appendix.

Effects of uncertainty on our cost-effectiveness estimates

We conduct two versions of our uncertainty analysis to account for uncertainty in the price per dose of Sayana Press from 2025 onward. Specifically, we assume that LN either maintains its currently contracted price for Sayana Press of $0.27 per dose, or that the price increases to $0.85 in line with the price agreement between Pfizer and the Gates Foundation. Unless explicitly stated, we use the version of the uncertainty analysis where the price per dose remains $0.27 + 10% delivery for the entire horizon of the model.

We first look into the effect of uncertainty on cost per unintended pregnancy averted, assuming the exchange rate and the cost per dose remain the same. The mean cost-effectiveness figure is about $18 per unintended pregnancy averted in Q1 2024, while the 90% highest density interval (HDI)¹³ falls in the range of about $17-$20. The HDIs become increasingly narrow through time as cost-effectiveness improves¹⁴: for instance, by Q4 2024, the intervention averts an unintended pregnancy for about $6 with a 90% HDI between $5.40 and $6.40, and by Q4 2025 cost-effectiveness improves to about $4 with a 90% HDI between $3.50 and $4.30. Our uncertainties affect our cost-effectiveness estimation for other modeled outcomes similarly, with the exception of the intervention’s impacts on income in multiples of cash, which are shown in Figure 2 below.

Figure 2: Uncertainty analysis for cost-effectiveness of impacts on women’s income, in multiples of “cash”

As noted, there is significant uncertainty in this estimate in the model, so we see quite wide HDIs for this outcome, and they get wider over time, as shown in Table 2 below. For instance, mean cost-effectiveness estimates grow from 10.8x with 90% HDI [5.0x, 17.9x] in Q1 2024 to 49.9x with 90% HDI [23.1x, 83.6x] in Q4 2025. However, the impact of uncertainty will only be decision-relevant if the static model suggests that cost-effectiveness falls short of or exceeds a donor’s threshold for impact—leading to a decision on whether to allocate funds to the intervention—but the uncertainty analysis calls this conclusion into question.

For instance, let’s say there is a donor who (i) trusts our modeling, (ii) cares about improving income in LMICs, (iii) shares GiveWell’s ‘bar’ of 10x cash transfers, and (iv) will donate funds to Lafiya Nigeria if the lower bound of the 90% HDI is above this bar. The model spreadsheet suggests this intervention exceeds the 10x bar through all periods in the model, and increases through time, so the donor’s decision is to allocate funds to LN. The uncertainty analysis may not change the donor’s decisions, apart from perhaps to delay the donor’s decision to support LN to Q3 2024, when the lower bound of the 90% HDI exceeds 10x and the donor can therefore be confident that the intervention indeed meets their threshold for cost-effectiveness. Note that increasing the cost per dose in 2025 also does not change the donor’s decision, since the lower bound of the 90% HDI is still 11.6x in Q1 2025.

Overall, our uncertainty analysis generally increases our confidence in the findings of the model, since defining and explicitly introducing uncertainty into our estimation approach does not introduce significant or particularly meaningful variation in our estimates. The distribution of potential cost-effectiveness figures for all outcomes from our Monte Carlo simulations—including the mean, mode, and median cost-effectiveness along with 90% uncertainty intervals based on these simulations—for both ‘cost per dose’ scenarios are available in the appendix.

Table 2: Summary of cost-effectiveness in Q1 2024 and Q4 2025 (in two cost-per-dose scenarios)

							90% HDI
Outcome	Year	Quarter	Cost per dose	Mean	Median	Mode	Lower	Upper
Cost per DALY averted	2024	1	$0.27	9.28	9.25	9.12	8.45	10.04
	2025	4	$0.27	1.96	1.96	1.96	1.76	2.16
	2025	4	$0.85	2.82	2.81	2.79	2.54	3.07
Cost per additional contraceptive user	2024	1	$0.27	7.82	7.8	7.82	7.28	8.4
	2025	4	$0.27	2.3	2.26	2.22	1.82	2.75
	2025	4	$0.85	3.29	3.25	3.18	2.65	3.95
Cost per child DALY averted	2024	1	$0.27	10.89	10.86	10.71	9.95	11.83
	2025	4	$0.27	2.31	2.3	2.3	2.07	2.54
	2025	4	$0.85	3.31	3.3	3.28	2.99	3.62
Cost per child death averted	2024	1	$0.27	920.87	918.15	905.8	841.61	1000.31
	2025	4	$0.27	194.95	194.06	194.42	174.69	214.8
	2025	4	$0.85	279.61	278.62	276.97	252.75	305.76
Cost per couple year protection	2024	1	$0.27	31.27	31.21	31.26	29.12	33.59
	2025	4	$0.27	9.18	9.04	8.88	7.27	10.99
	2025	4	$0.85	13.17	12.98	12.73	10.61	15.82
Cost per death averted	2024	1	$0.27	748.48	746.08	736.09	681.94	809.91
	2025	4	$0.27	158.46	157.79	157.92	142.12	174.43
	2025	4	$0.85	227.27	226.53	225.53	204.59	247.57
Cost per dollar increase in client income	2024	1	$0.27	1.81	1.49	1.08	0.55	3.09
	2025	4	$0.27	0.39	0.32	0.23	0.12	0.66
	2025	4	$0.85	0.56	0.46	0.33	0.17	0.95
Cost per maternal DALY averted	2024	1	$0.27	62.59	62.35	61.8	57.02	67.91
	2025	4	$0.27	13.25	13.19	13.1	11.87	14.63
	2025	4	$0.85	19	18.92	18.73	17.08	20.71
Cost per maternal death averted	2024	1	$0.27	3999.3	3983.98	3949.21	3643.58	4339.5
	2025	4	$0.27	846.67	842.68	836.86	758.71	934.65
	2025	4	$0.85	1214.34	1208.75	1196.71	1091.47	1323.38
Cost per unintended pregnancy averted	2024	1	$0.27	18.4	18.34	18.25	17.03	20.02
	2025	4	$0.27	3.9	3.88	3.84	3.5	4.27
	2025	4	$0.85	5.59	5.57	5.55	5.11	6.11
Cost per unsafe abortion averted	2024	1	$0.27	62.58	62.34	62.17	51.78	72.56
	2025	4	$0.27	13.25	13.19	13.22	10.99	15.65
	2025	4	$0.85	19	18.93	18.87	15.58	22.06
Cost-effectiveness in multiples of 'cash'	2024	1	$0.27	10.75	10.31	7.75	4.7	17.47
	2025	4	$0.27	49.9	47.8	35.55	22.21	81.85
	2025	4	$0.85	34.76	33.29	24.88	14.95	56.37

To maintain model credibility through time, we suggest that Lafiya Nigeria create quarterly versions of the model to resolve uncertainties and update inputs that resolve differently through time than the model suggests. Tracking updates to these changes is key to updating the uncertainty analysis, so we would suggest documenting any such changes clearly should LN wish to engage in further uncertainty analysis going forward. We are open to sharing the R code for the uncertainty analysis or to discuss working with our team to make updates if that is the case.

Acknowledgments

Rethink Priorities’ Greer Gosnell and Jamie Elsey wrote this report under the supervision of Tom Hird. Rethink Priorities is a research and implementation group that identifies pressing opportunities to make the world better. We act upon these opportunities by developing and implementing strategies, projects, and solutions to key issues. We do this work in close partnership with foundations and impact-focused nonprofits. We thank Lafiya Nigeria for commissioning and funding this research report. The views expressed here are not necessarily endorsed by Lafiya Nigeria. We are grateful for the invaluable input of our interviewees, and for the support and data that Klau Chmielowska and Céline Kamsteeg of Lafiya Nigeria provided throughout the project.

We invite you to explore more RP research via our database and stay updated on new work by subscribing to our newsletter.

Download the PDF to view the appendix

Notes

Lafiya’s monthly data comes from their own data dashboard that, at the time of writing, contained data through April 2024. If you would like access to this data, please contact info@lafiyanigeria.org. ↩
“Since World War II, no country has gone from developing status to developed status without first reducing its population growth rate. Smaller family sizes enable couples to save a higher percentage of their income and invest some of it in education and infrastructure, leading to increased productivity of the economy, greater employment, and higher incomes” (Population Media Center, 2021). ↩
“The FPMCH program is unique because of the data available to measure improvements in community and household well-being. Over time, families in the program area were more likely than the comparison group to have higher incomes, increased home value, greater savings and assets, higher educational achievement, and improved access to water. . . . Family planning enables women to be healthier and have more equal opportunities to pursue an education, a career, and financial security. With fewer children to support, families can accumulate greater assets and invest more in their children’s health and well-being. The relationship between smaller families and greater wealth highlights the benefit of sustained investments in family planning and maternal and child health programs as an important poverty reduction strategy” (PRB, 2010). ↩
“... couples who have large numbers of children are more likely to experience financial struggles and might have to make some difficult choices about which children to send to school and support financially. Too often, girls are the ones who go without in these cases, because their education is considered less important. . . Investing in female education is a win not only for girls, but for families, society, countries, and the world as a whole. Girls who receive a full education are more likely to have lower fertility rates and are better equipped to make informed choices for themselves, take care of their families, and contribute to society in multiple ways” (Population Media Center, 2021). ↩
“Family planning helps countries free up resources to make the infrastructure investments needed to produce high-quality productive jobs, while reducing the number of future workers entering the job market. . . Reducing fertility gives countries the breathing room to invest in education and workforce development—human capital—and in the technology infrastructure to equip them to better meet the changing demands of the 21st century” (PRB, 2016).
“But family planning does more than save lives; it also saves money. For every dollar invested in reproductive health services, $2.20 is saved in pregnancy-related health-care costs. Moreover, the longer a woman waits to have children, the longer she can participate in the paid labor force, thereby boosting the economic health and prosperity of poor communities” (World Economic Forum, 2018). ↩
“Reduced fertility translates into more stable population growth rates, eased pressures on the job market, fewer unemployed youth, and as a consequence, an environment more conducive to cultivating strong democracies. Shifts in age structure from a youthful population to a more mature one helps lay the foundation for social and political stability—a cornerstone of robust national institutions” (PRB, 2016). ↩
“Population growth and climate change are currently the two greatest threats to food security in the Sahel region of Africa. The population of the countries that make up the Sahel is projected to nearly double by 2050, from 506 million to 912 million. Paired with the expected rise in temperature and increased frequency of extreme climatic events, these numbers could quickly overwhelm relief efforts. Strengthening human capital and economic stability are critical to prevent catastrophic suffering. This article recommends two evidence-based approaches that expand women’s autonomy and support their income-earning potential while building resilience to climate change. The first recommendation would be greater investments in adolescent girls’ education and autonomy, including efforts to delay marriage and childbearing. The second calls for an improvement in the availability and quality of reproductive health services, with a special focus on voluntary family planning. These interventions can increase incomes, reproductive autonomy and gender equity which build community resilience and adaptability to climate change” (Pasano et al., 2023). ↩
“Women in villages with an outreach programme reported monthly earnings in 1996 that were 40% higher than were earnings in comparison villages, holding constant for age and schooling. Women of childbearing age in the outreach programme area also seemed to be healthier and more productive if they were part of the paid labour force than were those in the comparison area that did paid work. This advantage remained after correction for characteristics of women with paid jobs. Married women in programme villages reported 25% more physical assets per adult in their household than did those in control areas, and the composition of household assets in programme villages had shifted away from livestock, which depends on the availability of child labour, towards housing and financial assets, consumer durables, and jewellery” (Canning and Schultz, 2012, p. 3). ↩
However, there is at least one study on the long-term fertility impacts (see Phillips et al., 2012), which we did not review. ↩
Céline Kamsteeg said that: “[Women] may decide [to use a different contraceptive method], if available (it frequently isn't). We don't capture what alternative method they may be using after a referral, because due to stockouts many items aren't available, so even if they were to prefer another method, it's chance whether or not they will actually receive it.” ↩
Mukaila et al. (2022) suggest a higher number (N22,561) in Kwara State, though we use the lower estimate given the relatively high poverty levels in northern Nigerian states. ↩
The term ‘exponential’ is used loosely here—not referring to the formal ‘exponential distribution’ but rather to a distribution with a ‘ramping’ up or down slope. ↩
Note we report the HDI since it represents the 90% most likely values. It is generally quite similar to the equal-tailed interval (ETI)—i.e., the set of values in the middle 90% of the distribution of simulated outcomes—in our analysis. ↩
It may seem counterintuitive that these HDIs narrow over time, especially as—if we were to look at the cost alone, or unintended pregnancies averted alone—the HDIs for their values get wider (i.e., increasing uncertainty) over time. However, over time the total program costs and program impact become increasingly highly correlated with one another: In Q1 and Q2 2024, the uncertainty is more reflective of the pure random uncertainty of the initial inputs, and the costs are relatively more fixed. In contrast, in later quarters, the increased coupling between cost and impact means that higher costs are more likely than in earlier quarters to be offset by greater impacts. Further, the program is generally more cost effective at scale, as some initially high costs (e.g., executive pay) do not increase as the program scales up, and the scale is generally greater at later time points. Consequently, in later quarters it tends to become more certain that the cost effectiveness values converge on values indicating more cost-effectiveness. ↩

Greer GosnellJamie ElseyTom Hird

Rethink Priorities