Bill Overview
Title: Data Mapping to Save Moms’ Lives Act
Description: This bill directs the Federal Communications Commission (FCC) to include data on certain maternal health outcomes in its broadband health mapping tool. This is an online platform that allows users to visualize, overlay, and analyze broadband and health data at national, state, and county levels. The FCC must consult with the Centers for Disease Control and Prevention to determine which maternal health outcomes should be incorporated.
Sponsors: Rep. Butterfield, G. K. [D-NC-1]
Target Audience
Population: Individuals engaged in maternal health, including pregnant individuals
Estimated Size: 25000000
- The bill primarily involves the inclusion of data related to maternal health outcomes in a broadband health mapping tool.
- The data will be used to accurately map and potentially improve maternal health outcomes.
- Individuals engaged in maternal health including pregnant individuals are likely to be directly impacted by this legislation.
- Indirect impact could be felt by healthcare providers and public health officials utilizing this data for planning and intervention purposes.
Reasoning
- The policy primarily affects individuals involved in maternal health, including pregnant individuals, healthcare providers, and public health officials.
- It could indirectly affect family members and policymakers who utilize the data for decision-making.
- The budget constraints limit the breadth of the implementation initially, focusing efforts on areas potentially with higher impact and need.
- A wide demographic and geographic distribution in the interviews was considered to cover different perspectives on the policy's potential impact.
- Not all individuals engaged in maternal health will experience significant changes in wellbeing immediately; the tool's benefits may be more evident over time as data affects policy and healthcare changes.
Simulated Interviews
Public Health Official (Atlanta, Georgia)
Age: 30 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 8/20
Statement of Opinion:
- This policy tool could significantly enhance our ability to address maternal health disparities.
- The collaboration with the CDC is crucial for accurate data incorporation.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 8 | 6 |
| Year 20 | 7 | 6 |
Nurse (Rural Mississippi)
Age: 32 | Gender: female
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 4/20
Statement of Opinion:
- Having accurate data will enable better decision-making for resource allocation.
- Initial implementation might take time, but the long-term benefits are promising.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 5 |
| Year 2 | 7 | 5 |
| Year 3 | 8 | 5 |
| Year 5 | 8 | 5 |
| Year 10 | 8 | 5 |
| Year 20 | 7 | 5 |
Expectant Mother (Seattle, Washington)
Age: 26 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 3.0 years
Commonness: 16/20
Statement of Opinion:
- Any improvement in maternal health information accessibility is welcome.
- I hope it translates to tangible improvements in healthcare service delivery.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 7 | 7 |
| Year 3 | 7 | 7 |
| Year 5 | 7 | 7 |
| Year 10 | 7 | 7 |
| Year 20 | 6 | 6 |
Family Physician (Houston, Texas)
Age: 55 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 10/20
Statement of Opinion:
- Data integration like this can highlight critical areas needing improvement.
- I see potential, but the challenge lies in effective data utilization.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 8 | 6 |
| Year 20 | 7 | 6 |
Health Data Scientist (Boston, Massachusetts)
Age: 40 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 5.0 years
Commonness: 6/20
Statement of Opinion:
- Access to refined datasets will improve predictive modeling in maternal health.
- We must ensure this tool remains user-friendly for healthcare practitioners.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 8 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 7 | 7 |
Software Developer (Denver, Colorado)
Age: 29 | Gender: other
Wellbeing Before Policy: 8
Duration of Impact: 3.0 years
Commonness: 5/20
Statement of Opinion:
- This initiative has the potential to create impactful technological innovations.
- It can enhance the effectiveness of existing health tech solutions.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 8 |
| Year 2 | 8 | 8 |
| Year 3 | 8 | 8 |
| Year 5 | 8 | 8 |
| Year 10 | 8 | 8 |
| Year 20 | 8 | 8 |
Pregnant Health Educator (Chicago, Illinois)
Age: 34 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 7.0 years
Commonness: 7/20
Statement of Opinion:
- The integration of data could illuminate systemic barriers impacting maternal health.
- I hope this leads to enhanced resource distribution.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 8 | 6 |
| Year 20 | 6 | 6 |
Healthcare Consultant (New York City, New York)
Age: 45 | Gender: male
Wellbeing Before Policy: 7
Duration of Impact: 5.0 years
Commonness: 9/20
Statement of Opinion:
- This policy paves the way for more informed policy-making in maternal health.
- Timely implementation is key to realizing its potential.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 7 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 7 | 7 |
Maternal Health Advocate (San Francisco, California)
Age: 50 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 5/20
Statement of Opinion:
- Having a clear and detailed data landscape can support our advocacy efforts.
- This is a step forward in bridging information gaps in maternal health.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 6 |
| Year 2 | 8 | 6 |
| Year 3 | 8 | 6 |
| Year 5 | 9 | 6 |
| Year 10 | 8 | 6 |
| Year 20 | 7 | 6 |
Registered Dietitian (Miami, Florida)
Age: 36 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 3.0 years
Commonness: 10/20
Statement of Opinion:
- Improved data access helps tailor nutrition programs to specific maternal needs.
- I am optimistic about the potential improvements in service delivery.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 7 | 6 |
| Year 10 | 7 | 6 |
| Year 20 | 7 | 6 |
Cost Estimates
Year 1: $20000000 (Low: $15000000, High: $25000000)
Year 2: $5000000 (Low: $3000000, High: $7000000)
Year 3: $5000000 (Low: $3000000, High: $7000000)
Year 5: $5000000 (Low: $3000000, High: $7000000)
Year 10: $5000000 (Low: $3000000, High: $7000000)
Year 100: $0 (Low: $0, High: $0)
Key Considerations
- The data integration must maintain high standards of privacy and security.
- Selection of maternal health outcomes requires expertise from the CDC to ensure relevance and accuracy.
- Coordination efforts must be efficient to avoid redundant data collection or analysis with existing health programs.