Rural Public Health Financing and Resource Allocation

Introduction & Public Health Financing Overview

Speaker: Michael Meit

So again, beginning with health departments and how they're funded—we know that various types of grants fund health departments.

• Categorical grants — these are grants that come for very specific purposes.
  
  
• Clinical services — a lot of health departments have been seeking and increasing the reimbursement they’re able to attain through clinical services.
  
  
• Block grants — which I mentioned already, and we’ll talk more about.
  
  
• Tax revenue — particularly local tax revenue, which supports local public health services.
  
  

And then each of these funding types really varies by level—so there’s local funding to support local public health, state funding to support local public health, and of course, federal funding.

What we know is that federal funding itself is a very significant proportion of health department revenue.

We did a study—it’s been over 10 years ago now—but what we found was that the majority of health department revenue is actually federal dollars, either direct federal or federal dollars that flow through states, making up 57% to 75% of total revenue.

At that time, we saw that third-party reimbursement was growing as a share of public health funding, and I think that has continued. We also saw smaller percentages from state sources, fees, fines, and other sources.

The Role of USDA, Budget Cuts & Funding Instability

One of the really important points—and I think this is becoming more important in light of the current federal budget discussions—is that the largest percentage of federal revenue for health departments is not from the CDC, which is what most people in public health assume.

It’s actually from the USDA.

So when we talk about SNAP cuts or changes to WIC programs, those really make up the largest percentage of federal revenue for health departments. And those cuts will be felt very hard, in addition to any cuts from CDC or other agencies.

Other things we know:

• Federal expenditures for public health ebb and flow, often based on emerging needs.
  
  
• We in public health tend to ride from one crisis to the next—Zika, Ebola, H1N1, and most recently, COVID-19. Each of these led to an influx of money, but then funding tends to decrease as the issue ebbs.
  
  
• This is not a sustainable way to fund public health, but it’s how our system has traditionally worked.
  
  

Additionally, categorical funding is problematic because it may not match local needs. Federal priorities shape the money, but they don’t always align with what communities actually need.

And a final point: all funding has decreased. Federal funding has declined, but state and local dollars have declined even more, which means health departments have become more reliant on federal funds, even though total funding has dropped.

Rural vs. Urban Health Department Funding

Now, I’m going to show you a slide a few times in different versions. This is data from NACCHO. It’s a bit outdated, but I like using it because the older dataset has a more robust rural sample. Newer data show similar trends.

Here’s what we’ve seen:

• Rural health departments have less local revenue, usually from taxes.
  
  
• They’re more reliant on state and federal flow-through funds, like categorical grants.
  
  
• They also depend more heavily on clinical revenues, like Medicare, Medicaid, and private pay.
  
  

Implications:

• Rural departments are more vulnerable to state and federal budget cuts. They don’t have as much local revenue to fall back on.
  
  
• Categorical funding reliance means they have less flexible dollars to address specific local needs.
  
  
• And due to fewer providers in rural areas, these departments are often providing clinical services themselves, not just public health services.
  
  

So while urban departments have been able to stop providing clinical services—because others in their communities can take that on—rural health departments have had to lean into clinical care. Not just as a service point, but also to generate revenue.

And that’s important when we think about things like potential Medicaid cuts—they’ll hit rural departments especially hard.

Service Provision Differences – Urban vs. Rural

Another way to look at this is via NACCHO’s Forces of Change survey and their Profile of Local Health Departments.

What we see is:

• Rural departments are more likely to provide direct clinical services.
  
  
• Urban departments focus more on population-based services.
  
  

This creates a real split in the public health system.

Health departments are heading in very different directions depending on whether they’re rural or urban. That makes it harder for us, as public health professionals, to advocate with a unified voice, because we’re doing different things depending on geography.

That’s not entirely bad. In fact, I’d argue most rural health departments are responsive to their local community’s needs. If there are no healthcare providers, someone needs to fill that gap—and the local health department does. But again, it complicates policy and messaging.

Block Grants and Rural Community Funding

Now I’m going to shift gears and talk about block grants and what that means for funding rural communities.

This comes from work we’ve done through our Rural Health Research Center, in a study we titled “Following the Money: Do Block Grant Resources Reach Rural Communities?”

What are block grants?

• Block grants are non-competitive, formula-based grants from the federal government to states.
  
  
• They’re typically used to fund health and social services.
  
  
• States and local governments like them because they’re flexible — they allow them to meet local needs more effectively than categorical grants.
  
  

But there’s a problem: these resources often end up concentrated in urban areas.

Why?

• Metrics drive decisions. If you have limited money and want to reach the most people, that pulls you toward cities.
  
  
• There’s also a belief that consolidating categorical grants into block grants actually shrinks the pie — the total block grant amount is often less than the sum of the original categorical programs.
  
  

Our Study

We did a mixed methods study:

• Document reviews and quantitative analyses to track dollars.
  
  
• Interviews with state and local stakeholders to understand how money gets allocated.
  
  

We looked at five block grant programs across three federal agencies:

• CDC
  
  
• SAMHSA
  
  
• Administration for Children and Families
  
  

Key Findings

Here’s maybe the biggest takeaway—and it’s pretty shocking:

These block grant programs were developed in the 1970s and ’80s, and their funding formulas are still based on population demographics from that time.

Let that sink in: we’re allocating federal dollars today based on what states looked like 40 to 50 years ago.

And these formulas have never been updated.

Why? Because changing them literally takes an act of Congress. And nobody’s asking. If you change the formula, you’ll create winners and losers, and someone’s going to be unhappy. So if nobody demands a change, there’s no political incentive to make one.

Key point: If we think the formulas are outdated and unfair, we need to start advocating for change.

A More Realistic Opportunity: The State Level

While changing the federal formulas is tough, we do think there’s a policy opportunity at the state level.

States have the ability to allocate their block grant funds internally, and that’s where advocacy can really make a difference.

So to summarize:

• We should push for more accurate federal formulas (even if it’s politically difficult).
  
  
• Meanwhile, we can work with states to make sure the money that does come in is distributed equitably within the state.
  
  

Recommendations for Improving Block Grant Distribution

These are the recommendations we came up with from our study:

1. Integrate or expand mechanisms to support more effective distribution of block grants.
   
   
2. Review outdated funding formulas (again, this will require advocacy and political will).
   
   
3. Set base or floor funding amounts — ensure that every state gets at least enough to serve its rural communities adequately.
   
   
4. Advocate for rural carve-outs — designate a minimum amount of funding that must go to rural communities, so they’re not consistently overlooked.
   
   
5. Rural providers and organizations should engage with state agencies — advocate for better allocation within the state based on rural needs.
   
   
6. Include rurality in state needs assessments — make it part of the criteria that influences funding decisions.
   
   
7. Build the capacity of rural organizations so they can effectively apply for and manage block grant funds, especially in states that use competitive processes.
   
   

Area-Level Vulnerability Indices (ALVIs)

Now I want to shift gears again and talk about area-level vulnerability indices, which we often refer to as ALVIs.

We’ve done a lot of work in this area, and I’ll walk you through some of the studies and key insights.

What Are ALVIs?

These are tools like the:

• Area Deprivation Index (ADI) – used by CMS
  
  
• Social Vulnerability Index (SVI) – used by CDC
  
  

But we’ve actually identified over 50 of these indices.

Here’s the big thing I want you to take away:

None of these indices have ever been validated.

That’s pretty shocking. These were developed through expert consensus — smart people in a room identifying what they believe to be important indicators of vulnerability or deprivation — and then combining them into a single score.

Sounds good in theory.

But in practice, we don’t know how well they work, especially for prioritizing communities for funding or interventions.

Our Work with Milbank Memorial Fund

We conducted a study for the Milbank Memorial Fund, and developed a policy brief based on that research.

We evaluated several of the most widely used indices, including:

• ADI (Area Deprivation Index)
  
  
• SVI (Social Vulnerability Index)
  
  
• And others used to guide federal and state decisions
  
  

Let me walk you through some examples from the study.

EXAMPLE 1: Area Deprivation Index (ADI)

• Originally developed at HRSA
  
  
• Maintained at the University of Wisconsin
  
  
• Composite of 17 measures of deprivation
  
  

It’s been used in research on:

• Mortality
  
  
• Healthcare utilization
  
  
• Surgery outcomes
  
  
• Cardiovascular disease
  
  
• And more
  
  

Here’s what it looks like for Tennessee:

• The top map is ADI at the state level
  
  
• The bottom map is the national version
  
  
• Red areas = most disadvantaged
  
  

When you compare these maps to Tennessee’s rural counties (shown in light blue on a separate map), there’s decent alignment — the rural areas generally match up with higher deprivation.

Example 2: Social Vulnerability Index (SVI)

• Developed by CDC for emergency preparedness
  
  
• Composite of 16 measures across four domains
  
  
• Originally meant to help identify areas that need more support before, during, and after disasters
  
  

But over time, it has been repurposed for many other uses — including healthcare, vaccination prioritization, and funding allocations.

 Important: The original intent of an index matters. A tool created for disaster planning might not work well for other applications.

Here’s what SVI looks like:

• Top map shows county-level SVI in Tennessee
  
  
• Bottom map shows census tract-level SVI
  
  
• Darker blue = higher vulnerability
  
  

But when you overlay Tennessee’s rural counties, something odd happens — rural areas look less vulnerable in this index.

That seems off. Many of us working in rural health know those areas are highly vulnerable, just not in a way SVI captures well.

Comparing the Indices

Here’s a more analytical view.

• On the X-axis, you have the level of rurality (1 = most urban, 9 = most rural)
  
  
• On the Y-axis, the vulnerability score given by each index
  
  

Let’s focus on the SVI (dotted line):

According to SVI, the least vulnerable communities in the U.S. are the most rural and most urban.

That defies logic.

If you work in inner-city areas or isolated rural areas, you know those are some of the most at-risk communities. Yet SVI rates them as the least vulnerable.

That’s a major flaw in the tool’s design, and one that can lead to misallocated resources.

Who’s Using These Indices?

Pretty much everyone:

• Federal agencies
  
  
• State and local health departments
  
  
• State Medicaid agencies
  
  
• Philanthropic organizations
  
  
• Healthcare systems
  
  

They’re used to:

• Allocate infrastructure and public health funds
  
  
• Prioritize COVID-19 vaccinations
  
  
• Identify where to build new clinics or deploy new clinicians
  
  

In short: Big decisions are being made using unvalidated tools.

If these tools are not accurately identifying high-need communities, that’s a major policy failure.

Our Recommendations

Here’s what we recommend based on our findings:

• Think about its original purpose and whether it matches your current use case.

• Some indices work at the county level, others at the census tract,  that makes a difference.

• Should you use it as a continuous measure, or dichotomize it?

• Some metrics may add no real value to the index.

• Instead of relying on a complex index, go to the root data points that actually reflect the needs you're addressing.

• That’s a big gap if you’re trying to solve health access or outcome disparities.

• If the index doesn’t perform as expected, don’t keep using it just because it’s common.

This work, in my opinion, is some of the most important we’re doing right now. We continue to find example after example of tools being used to direct funding — and they’re not helping the communities most in need.

We hope our findings lead to smarter policy decisions and better tools moving forward.

Practical Tools to Support Rural Data and Resource Allocation

To wrap things up, I want to spend time on what I think might be the most important piece of this conversation: how to find and use data from your own community to make the case for resources.

If you’re working in rural public health, you need data to show why resources should come to your community — and that starts with community health assessments.

We want to equip you with tools that help you:

• Identify your local needs
  
  
• Justify funding requests
  
  
• Build strong, evidence-based proposals
  
  

Tool #1: Rural Health Mapping Tool

Website: ruralhealthmap.norc.org

This is a CDC-funded tool we developed that includes a wide range of data, especially around leading causes of death.

Example:

Here’s a heat map of heart disease mortality rates across the country.

• I’ve zoomed in on Tennessee in the example.
  
  
• Darker areas = higher mortality rates
  
  

You can filter by:

• Rural vs. urban counties
  
  
• Specific causes of death
  
  
• And much more
  
  

The data is organized into quartiles:

• On the national map, every county is ranked in a quartile relative to the entire country.
  
  
• But you can also switch to a state view, where counties are ranked relative to just that state.
  
  

Why does that matter?

When you're working on state-level advocacy or funding, comparing counties within your state gives you better granularity and helps highlight which areas need attention most.

Clickable County Data

Another key feature — click on any county and you'll get detailed statistics.

EXAMPLE:

• Crockett County, TN
  
  
• Heart disease mortality: 470.2 per 100,000
  
  
• When you select "View Details", you see:
  
  
  • The full list of leading causes of death
    
    
  • How those compare to state and national rates
    
    

This helps you show where your county is lagging and where targeted funding could have the most impact.

Even More Data: Food Insecurity, Demographics, and More

If you open the dropdown menus in the mapping tool, you’ll find:

• Food insecurity
  
  
• Demographic data
  
  
• Health insurance rates
  
  
• Health care access metrics
  
  

This is intended to be a one-stop shop to help you make the case for investing in your community — and to give you a competitive edge in grant writing.

Data Overlays: Seeing Relationships Visually

You can also create data overlays in the tool.

Example 1: Heart Disease + Uninsurance

• The pink bubbles show counties with higher uninsurance rates
  
  
• Many of those overlap with counties with high heart disease mortality
  
  

That visual connection makes a strong case for why your community needs both healthcare access and insurance expansion efforts.

Example 2: Heart Disease + Food Insecurity

• Again, you’ll see overlap in the places with high mortality and high food insecurity
  
  

These types of visual overlays can be very compelling in funding proposals and policy conversations.

Other Available Data

The tool also includes:

• Access to care data
  
  
• Behavioral risk factors
  
  
• Population health data
  
  

It’s all intended to help you tell a clear, data-driven story about the needs and disparities in your rural community.