> For the complete documentation index, see [llms.txt](https://research.ambitiousimpact.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://research.ambitiousimpact.com/bemonc/simulation-based-training-for-emergency-obstetric-and-newborn-care/5-neglectedness-and-geographic-assessment.md). # 5 Neglectedness and geographic assessment ## 5.1 Neglectedness *Our neglectedness analysis has confirmed the presence of several large and effective actors in the space, complicating geographic selection and the assessment of counterfactuals. However, we believe that there is still considerable space for a new organization, as there are many countries and regions where simulation-based training isn’t commonly practiced.* ### Actors delivering this intervention **We carried out a staged assessment of actors we could identify, attempting to understand the space.** We identified a longlist of 60 organizations, and prioritized six for deep review[^1]. * [**Jhpiego**](https://jhpiego.org/) is an international NGO operating across many health areas in addition to maternal and neonatal health (including HIV, malaria, immunization, adolescent health, and primary care). Jhpiego's presence reduces neglectedness in the specific geographies where they actively run MNH training programs. However, their broad mandate, relatively high costs, and uneven geographic coverage leave substantial room for a focused, lower-cost actor doing LDHF simulation training at primary facilities. * [**PRONTO International**](https://prontointernational.org/) is a nonprofit that works to improve the safety and quality of childbirth, particularly in resource-limited healthcare settings. They have worked in 17 countries through 2022. PRONTO’s work is very close to what we envisage for this charity, but differs in its funding model, being primarily dependent on time-limited grants as opposed to ongoing presence and fundraising. As such, the organization has exited some countries it has worked in, potentially creating gaps that an AIM-incubated charity could fill. * [**Laerdal Global Health**](https://laerdalglobalhealth.com/)’s work is directly relevant to a potential new BEmONC-training charity. Their program, the Safer Births Bundle of Care, trains and equips healthcare workers to manage the leading causes of newborn and maternal deaths. The program has scaled to over 150 health facilities in Tanzania. * [**International Federation of Gynecology and Obstetrics (FIGO)**](https://www.figo.org/) is probably not a direct competitor to a new BEmONC training charity. Their model is upstream advocacy and leadership work through national OBGYN societies, not facility-level training of frontline cadres. * [**United Nations Population Fund (UNFPA)**](https://www.unfpa.org/) is a major, well-funded actor across the priority geographies but largely seems to operate upstream of where a new BEmONC training charity would intervene. Most of their training work is pre-service—accrediting midwifery schools and supporting students through diploma and degree programs—rather than ongoing skill reinforcement for midwives already working in facilities. This likely improves the baseline quality of newly-deployed midwives in countries like Nigeria, Ghana, Côte d'Ivoire, Guinea, and Uganda, which matters for setting counterfactuals, but does not appear to crowd out facility-level LDHF training of staff already in post. ### Attention and funding: USAID funding cuts and their impact on neglectedness **The recent downturn in overseas development assistance—particularly the dismantling of USAID—has had a substantial impact on maternal and neonatal health programs.** This downturn may lead to a more severe unmet burden, increasing the counterfactuals of cheaper and more efficient efforts. Estimates of the impact suggest a constrained space for action and funding. For instance, an internal USAID memo projected service disruption for 16.8 million pregnant women annually. The WHO has surveyed its country offices and found almost half reported moderate to severe disruptions, and reports facility closures, loss of healthcare workers, and supply chain disruption [(KFF, 2025)](https://www.kff.org/global-health-policy/the-trump-administrations-foreign-aid-review-status-of-u-s-global-maternal-and-child-health-efforts/). **There are several implications for a new organization:** * **The implementer landscape has been materially reduced.** A large share of the organizations that previously delivered USAID-funded MNH work in LMICs have lost their major funding source. Per KFF, 86% of USAID's maternal and child health (MCH) awards were terminated, totalling hundreds of cancelled projects across the implementer ecosystem. Their current operational footprints in BEmONC-relevant work are significantly smaller than they were 18 months ago. * **The case for non-USAID-funded BEmONC work is stronger now than two years ago.** With 86% of USAID MCH awards terminated, gaps have opened in countries that previously had heavy US-funded MNH programming. A privately-funded charity could potentially fill some of these gaps. * **The counterfactual maternal mortality has likely shifted upward.** We believe the modelled projection of 510,000 additional maternal deaths by 2040 means the marginal value of a new BEmONC training charity is higher now than it would have been before the cuts, simply because the baseline has worsened. * **Supply chains for BEmONC-relevant commodities may be disrupted.** The WHO ([2025](https://www.who.int/news/item/07-04-2025-aid-cuts-threaten-fragile-progress-in-ending-maternal-deaths-un-agencies-warn)) warned that supply chains for commodities including treatment for pre-eclampsia or hemorrhage may be disrupted as a result of the cuts. The charity will need to carefully assess whether the commodities needed for BEmONC are reliably present in the clinics and hospitals it works with. **The situation remains fluid.** The US senate has restored some grants and activities, and it is possible that a future administration restores significant MCH funding, or that Congress continues to protect MCH from the deepest cuts. However, even in optimistic scenarios, restored funding would take years to ramp back up—implementing organizations have already laid off staff, closed country offices, and lost institutional capacity that is not quickly rebuilt. So even if the political picture shifts, the operational gap in MNH implementation in LMICs is likely to persist for several years. ## 5.2 Geographic assessment *Several large SSA and South Asian countries may be good targets for this intervention (e.g., Nigeria, Ethiopia, Pakistan) as well as some smaller countries (e.g., Chad, Sierra Leone). Our geographic model is only indicative; other factors, such as the number of facilities that the nonprofit could reach, and the percentage of births that happen in facilities could be deciding factors.* **We built a simple** [**geographic assessment model**](https://docs.google.com/spreadsheets/d/14Bj3SJVXxzQWVZ2mw9buNOQ0oZEiNfUUa6-q2yCqWYs/edit?gid=835461868#gid=835461868) **to help us identify potential priority countries based on the potential for impact and cost-effectiveness.** We strongly encourage founders not to anchor too heavily on the list of countries and instead treat the model results as primary suggestions. We fully expect to have missed some promising countries and that further investigation will show some of those included here to be less promising than the model suggests. **The model is a weighted factor model (WFM) combining three variables: Two proxies for the burden of mortality that this charity may be able to avert and one proxy for costs.** Table 5 summarizes the variables, their assigned weights, reasoning for inclusion, and data sources. **Table 5: Variables used in the geographic WFM.** | Variable | Data source & manipulation | Strengths / weaknesses | Weight | | -------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------ | | **The purchasing power parity (PPP) price level ratio** | World Bank (2024, 2023 where 2024 unavailable). Z-scored across countries; inverted so that lower price levels score higher. | Proxy for the purchasing power of philanthropic dollars in-country. Imperfect: aggregates national price levels rather than the costs of clinical training inputs specifically. | 30% | | **Neonatal mortality** [**rate**](#user-content-fn-2)[^2] **(deaths per 1,000 live births)** | UN IGME (most recent year). Z-scored across countries; higher rates score higher. | Direct burden proxy for the population BEmONC training is designed to help (mothers and newborns around the time of delivery). Rates capture intensity of need rather than total scale. | 45% | | **Total number of neonatal deaths per year** | IHME Global Burden of Disease (2023). Z-scored across countries and capped at z = 3 to prevent India, China, and Nigeria from dominating purely on population size; higher counts score higher. | Captures the scale of the opportunity (total averted deaths achievable). Capping the z-score reflects that a direct-delivery training charity is unlikely to operate at full national scale in the very largest countries. | 25% | **Table 6 lists the top candidate countries based on this model.** After applying our tractability filters—excluding fragile and conflict-affected states and contexts where AIM is unlikely to be able to incubate a charity—the top three candidate countries are Pakistan, Nigeria, and Ethiopia. We see a meaningful step-down in the total score after Ethiopia (from 1.58 to 1.35 for India), and a second, larger step-down after the top six to seven candidates. **Table 6: Top candidate countries for scaling BEmONC training, ranked by composite WFM score.** | Country | WFM score | Preliminary qualitative neglectedness analysis | | ----------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Pakistan** | 2.28 | **Plausibly neglected**, particularly in provinces outside Sindh (where Laerdal worked through Survive & Thrive a decade ago). UNFPA is the main active actor and its work appears largely upstream, though we cannot rule out that in-service BEmONC training is happening at a scale not visible through desk research. | | **Nigeria** | 2.26 | **Most crowded**. Jhpiego, Laerdal, PRONTO, and UNFPA all have a documented presence. However, the USAID funding cuts have meaningfully reduced the operating capacity of Jhpiego and other major implementers, which may have opened gaps. We rate Nigeria low-medium neglectedness with high uncertainty. | | **Ethiopia** | 1.58 | **Low neglectedness**. Evidence of extensive activity by Laerdal—though this work may have wrapped up by this point, with unclear follow-up. | | **India** | 1.35 | **Medium-low neglectedness**, but the assessment is state-dependent. Jhpiego has a large presence, PRONTO works in Bihar, and the Government of India's Dakshata program has nationwide adoption with high ToC overlap. A new charity would need to assess specific states rather than treat India as one market. Bihar in particular looks saturated. | | **Chad** | 1.21 | **Appears the most neglected**. Rapid desk research surfaced no presence from Jhpiego, Laerdal, or PRONTO, and UNFPA's work in-country is upstream of the proposed ToC (EmONC tracking, midwifery associations, workforce frameworks) rather than facility-level LDHF training. | | **Guinea-Bissau** | 1.15 | **Looks neglected, though with more uncertainty**. UNFPA trained 16 maternal health mentors in June 2025 and IMVF runs a larger MNH program (PIMI) that includes EmONC content, but neither appears to deliver focused LDHF BEmONC training as its primary methodology. | | **Sierra Leone** | 1.12 | **Medium neglectedness.** Work has been done by Jhpiego, but USAID cuts seem to have undercut these efforts. | | **Côte d'Ivoire** | 1.07 | **Low-medium neglectedness.** Two major organizations are working here: Jhpiego and UNFPA. | | **DRC** | 1.07 | **Low-medium neglectedness**, based on Jhpiego’s extensive presence (but low confidence in this rating). | | **Angola** | 1.04 | **Medium-high neglectedness with medium uncertainty**. A recent UNFPA–Jhpiego training of 34 trainers, a UNICEF program in Huíla, and a UNFPA commitment to support EmONC training from 2026 all sit in the space, but most of this is either early-stage, narrow in content, or not yet started. | | **Guinea** | 1.02 | **Medium-high neglectedness.** UNFPA has a medium-sized presence here, but focuses mostly on midwife training, particularly pre-service training. | ### Further thoughts on geographic prioritization **We encourage readers to also consult the peer-reviewed (and grey) literature assessing the quality of BEmONC services by country.** While we have not been able to review this literature in any depth, there seem to be indicators available for at least some countries—such as the facility readiness score plotted in Figure 4.
Figure 4: Mean EmONC facility readiness score based on an assessment of staff training and guidelines

Figure 4: Mean EmONC facility readiness score based on an assessment of staff training and guidelines. Adapted from Sayeed et al. (2026), Figure 5.

[^1]: We started from a list of 60+ actors operating in or adjacent to this space and worked through them in tiers based on how likely each was to materially affect the neglectedness picture. 12 were reviewed at shallow depth (5–20 minutes each, typically a website scan supported by rapid desk research) and deprioritized. Reasons for deprioritization typically fell into one or more of the following categories: substantive overlap with organizations already covered in our medium-depth analyses; a theory of change that does not directly involve in-service BEmONC training (e.g., curriculum creation, equipment supply, or upstream health systems work); limited geographic scale; or programs that are no longer active. Six were investigated at medium depth (several hours each, including expert interviews where possible). [^2]: Note that, while we expect the charity to also reduce maternal mortality, we only included variables for neonatal mortality as it is roughly an order of magnitude more common, and hence it dominates in our cost-effectiveness models. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://research.ambitiousimpact.com/bemonc/simulation-based-training-for-emergency-obstetric-and-newborn-care/5-neglectedness-and-geographic-assessment.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.