Note: This is an active and ongoing project - This page is likely to be updated. Last update Dec 2, 2024
We have $1000 to donate, a team, and a 30-day plan to make difference. Can we do it? Resources like money and time are often limited. Can data guide us to the most effective solutions?
We think so.
Next steps:
Impact Project Overview
Goals and Objectives
We believe that our short-term actions and outcomes will lead to medium- and long-term goals and, ultimately, will impact maternal mortality in the U.S.
Long-Term Impact (Our Hope)
▼Reduce maternal mortality in the U.S.
Medium-Term Goals (Our Assumption)
↑↑ Increase $$ for organizations doing important work.
↑↑ Increase capacity for organizations.
↑↑ More people reached, more services provided.
Main Objective, Short-Term (An Outcome We Can Achieve)
Obj 1. Donate. In 30 days, donate a total of $1000 to 3 to 5 organizations doing evidence-based work in key📍places where maternal mortality is believed to be high.
Additional Objectives (Yes, We Can Achieve This Too)
Obj 2. Grow our impact project portfolio by adding newly identified organizations to a maternal mortality portfolio for future donations.
Obj 3. Increase our knowledge in how to run an engaging data-guided donation process.
The Plan
Main Objective
In 30 days, donate a total of $1000 to 3 to 5 organizations doing evidence-based work in key📍places where maternal mortality is believed to be high.
Inventory
Here's what we have as inputs:
💰 - Money - $1,000 to donate
⏰ - Time - 30 days
👥 - Team - 20+ volunteers
🧰 - Tools - Free online data tools such as 🛠️ - U.S. Vital records data, CDC Wonder, Google Sheets, 📊 Datawrapper
Action Steps
How did we find these organizations? Our activities included 7 steps:
Prep Phase
Data Phase
Solutions Phase
Final Step
Step #7. Donate to those organizations
Please see our methods for each step below.
Caveats and Limitations
This project is meant as a demonstration of how data may guide decisions when resources are limited. A donation is a small way to help in the short term; however, maternal mortality is a challenge that will take a long-term, coordinated effort of people, programs, organizations, and policies. Regarding the analytic methods, small geographies and sparse data may be unreliable. A deeper, more robust peer-reviewed analysis, paired with expert and community consultation, is warranted.
Data are only clues and cannot tell us exactly what is happening within communities. We hope this project empowers others and sparks a desire to further explore this topic. There are many ways to approach this challenge. For instance, organizations were prioritized based on programs and location. Future work may focus on the quality of organizations and/or a quantified impact (e.g., number of people served by a program, anticipated costs saved by a policy, etc.).
How We Took a Data-Guided Approach to Maternal Mortality - Our Methods
The project began with selecting a topic. We explored the data and found organizations working within key places. We will be donating to these organizations.
Step #1. Decide topic
What do we want to have an impact on? We decided to reduce maternal mortality.
Maternal mortality is unacceptably high. - World Health Organization, 2024
Definitions. Maternal mortality is the death of a person while pregnant or within 42 days of the pregnancy from any cause related to or aggravated by the pregnancy or its management (National Center for Health Statistics, 2024).
How did we choose this topic? BroadStreet focuses on improving health through prevention and conducted an initial topic screen, followed by a secondary topic screen to see if addressing the topic was practical (NACCHO, 2024).
Decision Matrix | Initial topic screen |
|---|---|
Is maternal mortality ... | |
a leading cause of death? | ❌ No, not in top 5. In the U.S., complicated pregnancy is ranked as the 7th leading cause of death for females ages 15 to 34 (2018 - 2022) |
a cause of years of potential life lost (YPLL)? | ❌ No, not in top 10. Complicated pregnancy was ranked #16 for YPLL and accounts for less 1% of YPLL in the U.S. for females under 65-years-old (2018 - 2022). |
a growing concern? | ✔️ Yes, 📈 maternal mortality is increasing in the U.S. (ACOG, 2024; Healthy People 2030, 2024) and there is evidence of persisting disparities (Hoyert, 2021) |
preventable? | ✔️ Yes, it is estimated that over 80% of pregnancy-related deaths are preventable (Susanna Trost et al, 2022). Improvement is possible: The U.S. rates are far above other high-income nations (The Commonwealth Fund, 2024) |
aligned with Healthy People 2030 objectives? | ✔️ Yes, Healthy People 2030 aims to "reduce maternal deaths" and this is a Leading Health Indicator (indicator code: MICH‑04) (Healthy People 2030, 2024) |
timely/urgent? | ✔️ Yes, many recent policy changes are happening in the U.S. and we believe maternal mortality has cultural relevance and is timely. |
important to us as an organization? | ✔️ Yes, this topic is well-aligned with the interests of the BroadStreet Institute community |
Initial topic screen summary. While maternal maternal mortality is, thankfully, a relatively rare event in the U.S., the reasons for selecting it were 4-fold: Maternal mortality is (a) a preventable cause of death that is on-the-rise, (b) is a timely/urgent topic, (c) is well-aligned with Healthy People 2030 and (d) is important to our organization.
Secondary topic screen
The secondary topic screen assesses practicality and the likelihood that we can complete all 7 steps of this impact project.
Decision Matrix | Secondary topic screen |
|---|---|
Can we find📍places where maternal mortality is high? Are there "good" data available on this topic? | ✔️ Yes, "good" data are available on maternal mortality. [Note 1] |
Do good interventions exist? Is there a high level of evidence that preventive strategies work? | ✔️ Yes, high levels of evidence exist for maternal mortality interventions and prevention programs. [Note 2] |
Can we make a difference? Do we believe that our short-term actions have the potential to impact maternal mortality in the long term? | ✔️ Yes, we believe (i.e. assume) that by donating to organizations doing evidence-based strategies, we will increase their capacity and the reach of their services. [Note 3] |
Additional Notes on Secondary Topic Screen
[Note 1] What is "good" data exactly?
👍 Publicly available for free download
👍 Available nationwide or nearly nationwide
👍 Available at the county-level (for targeting resources)
[Note 2]❓What exactly are "levels of evidence"? Here are example definitions:
⬇️ Low levels of evidence = under 5 years of research and most studies are low quality studies and/or opinion-papers.
⬆️ High level of evidence = 20+ years of research and "studies of studies" have been published in the peer-reviewed literature. "Studies of studies" include reviews, systematic reviews, and meta-analysis.
[Note 3] What's the downside of assuming we can make a difference
Assumptions can be tested through an evaluation process. Future follow-up to evaluate the impact of organizations to whom we donate would ensure that our assumptions are met. For now, our assumptions are untested.
Step #2. Set Geographic Scope
Q. Where do we want to focus our efforts?
A. The United States.
This is a value-based decision. As of 2024, the BroadStreet team preferentially focuses within the United States. There are a few reasons for this.
Firstly, we were founded in Milwaukee and most of our team is currently located within the U.S.
Secondly, our team has expertise in health-related data systems of the U.S.
And finally, we hope that our techniques are used as a framework that can be improved upon by others who are working in different places.
What's the downside of selecting this geography? We recognize that there are places with high Maternal Mortality Rates around the globe (GiveWell, 2024) and a dollar may go further in other countries (Giving What We Can, 2024). We encourage everyone to research global issues and would certainly be open to expanding our scope in the future if the circumstances were right (i.e. If we had an in-house expert in data systems outside the U.S.).
Step #3. Where is maternal mortality high in the U.S.?
In order to know where maternal mortality is high, we must measure it.
How do we measure maternal mortality?
Maternal deaths. The number of maternal deaths is defined by the World Health Organization as the death of a woman during pregnancy or within 42 days of ending a pregnancy. The death must be caused by or worsened by the pregnancy or its management.
Maternal Mortality Rate (MMR). Defined as number of maternal deaths during a given time period per 100,000 live births during the same time period (Note: The World Health Organization calls this "Maternal Mortality Ratio")
How do we find data on maternal mortality?
National and state-level rates can be found in reports (Hoyert, 2021). This geography was believed to be too vast for targeting resources.
County-level rates can be calculated with queries of vital records. Data for smaller geographies may be missing or unreliable and must be interpreted with caution.
How do we define "high maternal mortality"?
There are different ways to answer the question: "Where is maternal mortality high?" Three definitions were used. All definitions used maternal deaths and 5-year estimates (2018-2022) for counties in the U.S.:
Definition 1. Highest number (#) of maternal deaths
Definition 2. Highest Maternal Mortality Rate (MMR)
Definition 3. Highest MMR for non-Hispanic Black or African American populations
Materials. National Vital Statistics System, Multiple Cause of Death (Provisional) Data, queried on CDC WONDER Online Databases.
Methods. Maternal deaths defined as total number of deaths (ICD-10 codes: A34, O00–O95, and O98–O99) for the U.S. and by county during a 5-year timespan 2018-2022. Queried for all races and ethnicities and for non-Hispanic Black or African American populations in June and November 2024. Maternal Mortality Rate was calculated as number of maternal deaths for every 100,000 live births (2018-2022). Counties were ranked by (1) total number of deaths (2) Maternal Mortality Rate overall (3) Maternal Mortality Rate overall for non-Hispanic Black or African American (NHB) populations.
Missing data. Due to maternal mortality being, thankfully, a rare event, 97% of counties (n=3,144) have missing or unreported data for maternal deaths. Data were suppressed and not reported by the query system for numbers were 10 or less. Most U.S. counties (97%, n=3,144) had missing or suppressed data for maternal deaths. Deaths for all races and ethnicities were reported for 92 counties. When combined, these 92 counties accounted for 1,687 (39%) of the total 4,295 maternal deaths in the U.S. from 2018 to 2022. This means that over 60% of deaths were not included in the analysis. Deaths by race and ethnicity had even sparser data. Data were available for non-Hispanic Black of African American populations for 24 counties in the United States. Counties with existant data were ranked (highest to lowest number or rate), graphed, and mapped.
Definition 1. Highest number (#) of maternal deaths
The top 3 places with the highest number of deaths were:
Number of deaths per county is expected to be high in places with many births. Therefore, counties with high populations are expected to have the highest numbers of deaths. In order to balance this, Maternal Mortality Rate (deaths per 100,000 live births) was also calculated and compared to the U.S. rate (23.2). The top 3 counties with the highest number also had comparatively high Maternal Mortality Rates. For instance, regions like Cook County (#4) (i.e. Chicago, IL) and Los Angeles County (#5) had many deaths, but, due to a high number of births, overall rates were average or low.
Definition 2. Highest Maternal Mortality Rate (MMR)
Counties with available data were by ranked Maternal Mortality Rate overall. Rates were compared the the national average of 23.2 deaths for 100,000 live births.
Where was maternal mortality highest in in the U.S. in 2018 to 2022?
Definition 3. Highest Maternal Mortality Rates for non-Hispanic Black or African American populations
When Maternal Mortality Rates were ranked by highest rates for non-Hispanic Black or African American (NHB) populations, different counties emerged.
#1 New York County, NY
147 deaths per 100,000 births in NHB populations
High Disparities. Maternal Mortality rates were 6x higher rates for non-Hispanic Black or African American populations compared to all races and ethnicities in New York county.
#2 Caddo Parish, LA
130 deaths per 100,000 births in NHB populations
#3 Essex County, NY
88 deaths per 100,000 births in NHB populations
Results Summary
Depending upon how "high maternal mortality" was defined, different counties emerged.
Data caveats and limitations. It is important to note that maternal mortality estimates and rates may be unreliable, especially when numbers are reported by small geographies, like counties, and by race and ethnicity. Death record coding methods may vary by state and region and the time period included the period of time associated with the COVID-19 pandemic.
Step #4. Why do we think maternal mortality is high in some places?
Our team used a prevention logic model framework, created and simplified into a schematic of possible root causes. Policies and other "upstream" factors such as poverty may also impact factors at multiple levels and are not included in the schematic of the framework for simplification purposes.
Within the 📍places with highest maternal mortality, we sought various indicators on
Populations with higher risk: Maternal age 35 years and over
Health Behaviors: Tobacco use during pregnancy
Health Risk Factors: Pregnancies with 1 or more health risk factors
Access Clinical Care, Affordable: No insurance/self-pay, State Medicaid policy
Access Clinical Care, Nearby: Ob/Gyn provider rate (per 100,000 population)
Quality of Care: No Prenatal care
Indicators provided clues about what may be happening in counties where maternal mortality was believed to be high. We found that each community was unique and had a different health profile and data story. Leon County, for example, had relatively healthy pregnancies at baseline. However, rates of health insurance were low, there was a a low number of Ob/Gyn providers, and, perhaps unsurprisingly, there was a relative lack of prenatal care. Across multiple counties, access to care was an important issue, especially in places with pregnancies with higher risk factors. Policies relating to affordability of care were also reviewed. For instance, Florida, Texas, and other states did not adopt a Medicaid expansion, which would have qualified more people for insurance coverage during the study timeframe (2018-2022) (Kaiser Family Foundation, 2024). Further exploration is needed into additional indicators such as low-risk C-section rates and maternal demographics. We explored only a partial list of potential indicators.
Step #5. What works? Are there any evidence-based strategies that might work to improve maternal mortality?
✔️ Yes, evidence-based solutions are believed to exist. A few evidence-based solutions relating to health and healthcare includes:
Optimize health before the pregnancy. Reduce baseline heart disease risk and ensure those who are able to get pregnant are healthy and heart disease risk factors, such as blood pressure control. Risk factors should be prevented, screened, and treated (U.S. Department of Health and Human Services).
Connect to healthcare during the pregnancy. Pregnant persons should be connected to primary care, prenatal, postpartum, and any recommended specialty care visits; follow recommended screenings and risk reduction strategies such as no tobacco use during pregnancy; and be educated in the warning signs of complications. (Surgeon General, 2021)
Improve quality of care. Reduce low-risk cesarean delivery rate, because, in general, cesarean deliveries increase the likelihood of maternal morbidity (U.S. Department of Health and Human Services).
Improving health and healthcare requires many things such as optimal policies, health insurance coverage, and access to affordable, nearby, high quality care. Additional factors, such as education and family support, may also be part of the prevention model (Collier, Molina, 2020; Wang, Glazer, Howell, Janevic, 2021).
Step #6. Which organizations are doing those evidence-based strategies?
In June and November 2024, our team did an online search to find organizations meeting the following criteria and nominated 1 to 2 organizations per 📍place. Then, each organization was reviewed based on criteria within a Decision Matrix.
Priority criteria
✅ Organizations were operating in 📍 key places where maternal mortality was believed to be high.
✅ Organizations were running programs that were evidence-based solutions.
✅ Most importantly, organizations were non-profits to which we could donate (i.e. not government agencies)
Additional criteria set by steering committee
☑️ A donation, if given, could be reasonably assumed to be going to the program AND region of interest (i.e. it would not go to another country or state)
☑️ Organizations were at least 1 year old and have filed IRS 990s for 501(c)3
☑️ Organizations had an annual report in which we could see the number of people served
❓IF we had additional questions, were uncertain about programs, we reached out to the organization and placed the organization in our portfolio "queue" or backlog for consideration once our questions were answered.
There were 3 organizations that met all criteria. Organizations for which we had additional questions, we are still investigating and may donate to in the future.
Abide Women's Health Services, serving 📍Dallas, TX
Capital Area Healthy Start Coalition, serving 📍Leon County, FL
Fund for Public Health NYC, serving 📍New York County, NY
These 3 organizations met all criteria: ✅ Serving 📍key places, ✅ doing evidence-based programs to reduce maternal mortality, ✅ donating was possible (i.e. a non-profit organization vs. government agency), ☑️ organization was at least 1 year old, ☑️ organization had an annual report with published numbers, and 🆗 our team had no additional❓questions or concerns requiring outreach and followup.
Step #7. Give a data-guided donation to organizations
In December 2024, we donated $200 to all three organizations, plus service fees. The total budget was $1000 and we held remaining funds for organizations for which we had additional questions.
Organizations for our Maternal Mortality Impact Fund Portfolio
Abide Women's Health Services, serving 📍Dallas, TX
Capital Area Healthy Start Coalition, serving 📍Leon County, FL
Fund for Public Health NYC, serving 📍New York County, NY
Addressing geographic gaps. We are still investigating ways to best serve 📍 key places that are not currently represented in our donation portfolio like Harris County and Caddo Parish. As more information comes to light, we will continue expanding our portfolio.
Recommendations for future activities. We selected organizations implementing evidence-based programs to reduce maternal mortality in 📍 key places. There are certainly more organizations to which we could donate in order to have an impact. Even though we explored possible root causes in our steps above, we did not donate based upon which root cause was being addressed. A focus of future work may include finding organizations addressing specific root causes (e.g. addressing state or national policies to improve access to care). For now, we wanted to celebrate organizations working locally in 📍 key places.
You can too! Any future donations will be distributed to organizations in our Maternal Mortality Portfolio.
Pledge to our Impact Project: Reducing Maternal Mortality in the U.S.
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Author List
Thank you to our authors, data analysts, and project contributors.
Contributors
The Maternal Mortality Impact Project was completed in several phases, the first of which occurred in the summer of 2024. Thank you to all who attended our Summer Giving Event and who helped to plan, promote, and run it!
Thank you BroadStreet Team Natalie E. Anna H. Shaunta' J. Alyssa M. Angelica M. Breyonna G.B. Diana L. Elise K. Jeny P. Marjorie B. Melody Y. Monique E. Nekhil P. Oreen C. Regina A. Sadaf S. Sandhya B. Shikha S. Valerie M. Xinbo W.
| Summer Giving Event Participants, June 2024 Aditya N. Alicia S. Aziza A. Ernesta W. Jack F. Jyoti D. Kennedi N. Olumide O. Princess M. Sowmya M. Tenzin K. Yin K. Brittany V. Elise K. Hannah C. Hui W. Melissa A. Patrick H. Raymond S. Theresa R. | Allison W. Ankur G. Arien R. Chidubem E. Ginika M. Hillary C. Kristen B. Maria M. Quincy C. Shanun R. Shelley L. Sofia J. Victory I. Vanisa T. Zain H. |
Thank you to our Donors!
Donors to the Maternal Mortality Impact Fund contribute donations to organizations in the fund's portfolio and make the donations possible. Last updated Dec 2024.
Platinum Sponsors Brian & Sandy F. Gold Sponsors The Kitterman Family Goran F. & Michelle K. Teresa Tse Mohana M. Mallory S. Anonymous Donor Anonymous Donor | Silver Sponsors Alicia S. Allison W. Arien R. Brittany V. Charlie E. Diana G. Dinika M. Ernesta W. Faisal A. Farah B. Hannah C. Hui W. Kristen B.
| Liane C. Lisa C. Maria M. Melissa A. Oreen C. Patrick H. Princess M. Quincy C. Sadaf S. Sofia J. Theresa R. Valerie M. Victory I. Yingyu P. |
Please note that findings and insights from the Maternal Mortality Impact Project do not represent the individual views or opinions of authors, contributors, or donors.
Thank you to everyone for collaborating to make this project possible!