The legislature moved to address these by passing the Statewide Accelerated Public Health for Every Community (SAPHE) Act, signed into law by Gov. Charlie Baker on April 29, 2020. Comerford co-sponsored SAPHE 2.0, which Gov. Maura Healey signed in December 2024. It provides funding for local health departments; allows for the creation of a new statewide data collection system and shared services; and requires the development of uniform credentialing systems for public health workers. The new law is “next-generation” public health legislation, says Georges Benjamin, executive director of the American Public Health Association. He calls it “a model for other states seeking to provide the legislative basis for public health system improvement efforts.”

Harvard Public Health’s Maura Kelly conducted separate interviews with Comerford and with Oami Amarasingham, deputy director of Massachusetts Public Health Alliance, which helped develop SAPHE 2.0. The interviews were edited and condensed.

HPH: Why this legislation?

Comerford: There is a moral and ethical responsibility to ensure equitable services to all residents of the commonwealth. As a commonwealth, we are less strong if we have weak pockets of resilience. That weakness appeared not only where we have seen weak public health protection and public health inequities—immigrant communities, communities of color, low-income communities. We also saw real weakness in rural communities that did not have the infrastructure to launch a full-on response to the pandemic.

Amarasingham: Covid made the case clearly to every individual and to every elected official that public health infrastructure is really important. Local public health officials have been trying to solve the problem of a lack of public health infrastructure, staff, funding, and statewide minimum standards for decades. We have one state department of public health and 351 local departments of health, whereas most states have county- and regional-level departments. Each local health department has been funded by the town or municipality and many have pretty limited resources. The city of Boston has a really big public health department, but after that, every public health department is much smaller. In some places, public health departments are open a few hours a week.

HPH: Would this law have helped Massachusetts get through Covid better, with less loss of life?

Comerford: We have huge pockets without cell service here in western Massachusetts. We have almost no consistent internet service. We relied on unbelievably intrepid public health officials to go door to door [telling people about Covid, vaccines, testing, and so on]. At the time we weren’t logging our work in a way that was consistent and usable information. There were a lot of gaps in information sharing, which is terrible in a crisis. You can’t understand what you can’t measure and track. Now there has to be data collection, training, [and] the state has to help resource this.

Amarasingham: We were not in great shape to respond in the most effective, most equitable way. What public health officials had during Covid was a PDF [containing the latest statistics]. You needed someone to convert that to an Excel spreadsheet if you wanted to use the data. It was not useful for data scientists or anyone who wanted to produce something with that data. It didn’t track demographic data. When you have 351 separate entities reporting data, you want that data to be collected and compiled in a uniform way so that it can be combined and used. It was very frustrating not being able to quickly access and understand data in a rapidly evolving situation.

HPH: Public health workers and officials came under attack during Covid. Will this new law help to protect them?

Comerford: By having performance standards and requiring a credentialing process as indicated in the law, the state is raising the level of credibility associated with public health officials. They will now be better trained, better connected, better resourced. I hope that the workforce is much stronger as a result of this legislation.

HPH: MPHA helped to develop this legislation. Did you take any lessons from other states?

Amarasingham: We understand that other states have better systems for data collection. We are not talking about personal, identifiable data, [but, for instance,] how many food inspections have been done at restaurants, when, by whom, so that public health officials can have a full picture of who is responsible. The legislature appropriated nearly $100 million in ARPA [American Rescue Plan Act] funds to go towards building and maintaining a data system to integrate data collection between the local health departments and the state.

HPH: If there is a single public health challenge looming in the future for Massachusetts that this bill will help to mitigate, what is it?

Amarasingham: Almost anything you read about in the headlines has a local public health implication. So, for example, we’ve had these extreme weather events that dump a lot of water in a short period of time and overwhelm the sewer system—so then sewers get contaminated and it can be unsafe to swim on a beach or in a river. It can reach crisis level rapidly and you have to deal with it, and if you don’t have the public health infrastructure in place, then you are trying to build the infrastructure while addressing the crisis, which is what happened during Covid. And in a case like that, the toll on the human beings who work in these understaffed systems can be overwhelming, and they quit.

HPH: Did this bill pass at an especially timely moment?

Amarasingham: We know the health impacts of climate change are going to get worse. The spread of disease will get worse. [Add to that] the uncertainty of what will happen with the incoming administration—it is the right time. These challenges are bigger, they are unfolding more rapidly, and they are ongoing.

Comerford: The Office of Senator Jo Comerford
Amarasingham: Mario Quiroz / Courtesy of MPHA

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The federal government has bet big on the concept with a new nationwide network called Insight Net, which links academic disease modelers with public health practitioners. The network comprises 13 research consortia with participants in 24 states and is funded with up to $262 million from the Centers for Disease Control and Prevention (CDC). Insight Net members are piloting analytical techniques that combine novel data sources to guide surveillance and inform decision-making during outbreaks. The end goal is to create something akin to a National Weather Service for disease.

Such capacity would be a game-changer for state and local health authorities and for hospitals. At the height of the COVID-19 pandemic, “we were not good at forecasting the demand,” says Douglas Sawyer, chief academic officer of MaineHealth, that state’s biggest hospital system. “We didn’t steer the ship, so to speak, with high fidelity as we wish we could have. We couldn’t prepare and shift resources in thoughtful ways.”

Many hospitals struggled with the crush of patients who needed intensive, isolated care. Because hospitals could not accurately predict the size of impending infection waves, many delayed or canceled routine health care such as physicals or cancer screenings. Meanwhile, Covid care sites built or converted by federal and state authorities ended up being largely unneeded.

These forecasting issues had serious financial consequences for hospitals—and serious health consequences for the public. Insight Net’s progress toward closing that information gap has been steady and marked by small but important victories—as well as plenty of reminders that even the best forecasts are only as good as the data that feed them.

Forecasting more than the next crisis

By linking people working in public health directly with disease modelers, the CDC aims to fix the ad hoc approach it used for pandemic forecasting, which was panned from almost the start. In the summer of 2020, a critique in Foreign Affairs labeled the CDC’s approach “an arbitrary assortment of academics” reacting on the fly and asserted no one today would handle hurricane response in that fashion. In 2021, the CDC tapped Caitlin Rivers, one of the article’s coauthors and an epidemiologist at Johns Hopkins Bloomberg School of Public Health, as the first associate director of its new Center for Forecasting and Outbreak Analytics (CFA). In 2023 the CFA established Insight Net, and Rivers, who had returned to Johns Hopkins, became director of its node in the network (Dylan George, her Foreign Affairs coauthor, is the CFA’s current director). Their core message, then and now: Disease forecasting shouldn’t be improvised.

Policymakers and the public put their trust in major storm alerts, according to George, because the weather service is “applying the best models in an operational context on a day in and day out basis, cranking out results,” George says. “And then you have local meteorologists interpret those results for people to actually make decisions.” That process establishes a track record of monitoring and communicating forecasts, including their uncertainty, even when the weather is calm, sunny, and mild. “We’ve tried to pattern after that,” George adds.

That has meant investing in a dedicated program for disease forecasting, with formal working relationships between modelers and federal, state, and local health officials. It also means the CFA has been keen to demonstrate how modeling can help public health practice and communication. For instance, it has tapped data from the National Wastewater Surveillance System, launched by the CDC in 2020, to improve localized forecasts of Covid hospital admissions. It also helped the Chicago Department of Public Health confront a March 2024 measles outbreak at a temporary migrant shelter housing more than 1,400 people. As public health workers began vaccinating and screening shelter residents to identify and isolate the sick, department leaders reached out to the CFA, which rapidly refined a model of measles to mimic the outbreak’s timeline of infection, symptom onset, and recovery, which Chicago health officials could then use to predict its future course.

The model didn’t influence the department’s interventions, which were already underway. But it did reassure officials they’d correctly identified their patient zero: Outbreak simulations that assumed earlier, undetected infections generated far different case data from what was observed. The forecasts also helped set expectations for the outbreak’s severity by providing a range of potential case numbers and dates when infections would peak and subside. After a couple weeks of continuous updating with data on new measles cases, the model predicted there would be between 57 and 65 cases and the final rash would appear on April 16. In the end, the outbreak lasted about two months and infected 57 people.

“It really helped our own planning, and our thinking about staffing,” says Stephanie Gretsch, an epidemiologist at the Chicago Department of Public Health. “It was also incredibly helpful for communicating with our city agency partners responsible for housing and schooling; and the hospitals we asked to help isolate infected residents, to give them a sense of how long we thought this was going to last.”

After the outbreak, Chicago public health officials used the modeling to quantify the value of its interventions. Outbreak simulations where responses did not include mass vaccination or active case-finding efforts suggested it would have lasted seven weeks longer and more than quadrupled the number of infections. This finding suggests that modeling hypothetical scenarios might offer a tool for easing heightened skepticism of public health interventions and investments, say several Insight Net partners.

Syphilis is one target of the Insight Net consortium at the University of Utah. The disease, resurgent in the U.S., can infect a fetus during pregnancy and cause serious medical complications, including miscarriage, stillbirth, and infant death. The goal is to “address the issues and show how bad this problem could get if trends continue,” says principal investigator Matthew Samore, a professor of medicine and the division chief of epidemiology at the University of Utah. “We also want to get a deeper understanding of how STIs like this are spreading through different populations…and to calculate how much benefit do we get by investing in more intensive screening and contact tracing.” By helping establish the extent of the risk, the models could bolster requests to fund more screening and treatment of groups with high infection rates, such as people in prison.

The modeling could also improve disease forecasting dashboards used by the public to assess health threats. The Massachusetts Department of Public Health (MDPH) has dashboards that track severe respiratory illnesses statewide, but delays in data reporting from local hospitals limit their usefulness. In 2024, MDPH worked with the Insight Net researchers at the University of Massachusetts Amherst and the University of Texas at Austin to build models filling in those gaps, allowing it to add recent emergency room visits and hospital admissions due to Covid, RSV, and influenza broken down by demographics. Such small-scale adoptions are needed both to validate disease forecasting and to build trust in the models, says Meagan Burns, a senior informatics epidemiologist at MDPH. “These tools are very cool, but they’re also very new,” she says.

People in Massachusetts also are getting a look at disease forecasts as part of their weather news. In February, meteorologists at Boston’s CBS affiliate, WBZ-TV, began adding localized disease data visualizations to their weather reports. These are put together by the Insight Net team based at Johns Hopkins and arranged through a collaboration with the American Meteorological Society. The first one featured a colorful chart showing that emergency room visits due to COVID-19 were declining steadily from their post-Christmas peak. The original plan was to do weekly check-ins on infectious respiratory illnesses, but as the weather warmed, infection numbers plummeted and stayed low.

“There were several weeks where there wasn’t a whole lot to talk about with Covid or the flu,” says meteorologist Terry Eliasen, executive producer of WBZ’s weather team. While viewers might find sunny weather forecasts useful, there didn’t seem to be much news value in “sunny” public health numbers. So WBZ skipped a few weeks. Then Eliasen asked the Johns Hopkins team what else it could do. Over the summer, researchers responded with data visualizations related to outbreaks of norovirus and eastern equine encephalitis, as well as the risk of heat-related illnesses.

This quick shift in focus drew praise as a sign that the university-based modelers at Insight Net are serious about partnering with public health practitioners and communicators. The CFA worked with the Council of State and Territorial Epidemiologists (CTSE) on the legal and logistical issues of data-sharing, and to see what forecasting tools might be useful to its members. The two organizations convened a series of meetings with state and local health officials to ask what uses they might have for forecasting tools and whether there were specialized techniques they’d like. That was especially useful, says Janet Hamilton, the CSTE’s executive director. “We need to have enough time to talk to the modelers to say, ‘That’s a great model but it doesn’t help me. It doesn’t answer my questions.’”

Fixing public health data: everything, everywhere, all at once

Disease threats do not yet have the color-coded, real-time tracking maps the National Weather Service uses for potential hurricanes. Of course, there are no satellite images of developing disease threats, which not only are propelled by unique (and often mutating) biology, but also have to account for something that’s even harder to predict—human behavior. Several Insight Net forecasters are trying to meet this massive data challenge by mixing traditional data sources such as infection rates with the digital breadcrumbs of human activity like searches for symptoms, social media posts, and trends in medication purchases.

People spread diseases when they travel and gather, notes Alessandro “Alex” Vespignani, a physicist and computational scientist at Northeastern University whose lab models large-scale complex systems. He and his team are part of an Insight Net research consortium with Maine’s major hospital systems, MaineHealth and Northern Light Health, which are working on a pilot project to weave human mobility data into disease models. They draw on aggregated and anonymized mobile device location data, databases of global flight schedules, and traces of pathogens found in wastewater sampled from municipal sources and from international flights for analysis by the Boston biotech company Ginkgo Bioworks.

“Our models are like a layer cake,” Vespignani says, with each layer creating a virtual “business as usual world” the modelers use for outbreak simulations. Layers are only added if they significantly improve the model’s predictions or extend the timeline for an accurate forecast. For instance, the lab found that it could accurately forecast greater Boston hospital admission rates three weeks ahead of time by adding mobility and proximity data derived from about 82,000 mobile phones, compared to just two weeks using conventional public health data such as statewide Covid test results. That extra week for planning is “a big deal for hospitals” for scheduling staff and procedures, says Samuel Scarpino, director of Northeastern University’s Institute for Experiential AI and a member of the Insight Net team. Since hospitals aim for 90 percent capacity, even a slight uptick in the need for beds can complicate care.

This fall, the lab will tap retrospective data from Maine’s Covid hospitalization numbers to try to replicate that forecasting capability. It’s also planning to use the mobility-enhanced models to forecast hospitalizations for flu, RSV, and Covid at individual Maine hospitals for the winter of 2024-25. If these efforts are successful, Scarpino hopes to scale the models for use nationwide.

The Insight Net initiative also faces the labyrinthine way the U.S. gathers and shares core public health data such as test results and hospital records. Reducing those obstacles is a key target of the CDC’s Data Modernization Initiative, launched in 2019 to promote things like electronic case reporting, interoperability among different data collection systems, and standardized data use agreements between state, tribal, local and territorial, and federal health authorities. But the data pipeline’s bottlenecks aren’t simply technical and legal, according to infectious disease experts such as Jennifer Nuzzo, an epidemiologist who directs Brown University’s Pandemic Center. They also involve whether we’re asking the right questions about disease threats to get the data we need. “It’s great for us to invest in analytic approaches that can help us tell what could happen in the future,” says Nuzzo. “But what I want to see is a better utilization, analysis, and visualization of the data that we have to tell us what’s happening today.”

For instance, the fragmented efforts to track the H5N1 bird flu virus in the U.S. have drawn a chorus of concern from public health leaders and researchers. Earlier this year, the virus leapt from wild birds to more than 100 million poultry in 49 states as well as other domesticated species, including dairy cows and, more recently, pigs. A small but growing number of people have also been infected (mostly farm workers, but not all). Tracking the virus requires coordination among multiple federal agencies, including the Department of Agriculture, the Food and Drug Administration, and the CDC, as well as states that vary widely in the ways they test animals, people, and bulk milk tanks. The only federally mandated H5N1 screening is for lactating dairy cows being moved across state lines.

Thus far, most humans with bird flu have had minor symptoms, and there’s no evidence of the virus spreading from person to person, which could trigger a pandemic. But the risk increases with flu season, because different viruses infecting the same host can swap genes (known as genetic reassortment) and evolve into something new and more dangerous. If pandemics were hurricanes, having the avian flu virus circulating in cows along with regular flu infections in humans would be akin to a low pressure system in the Caribbean—it could dissipate, but it could also develop into huge trouble for the mainland United States. Nuzzo says we could better predict the outcome if we focused more on targeted surveillance about emerging health threats.

“An awareness of what’s happening this week, and last week, is the starting point for trying to figure out what’s going to happen in the next few weeks and beyond,” says Roni Rosenfeld, a professor of machine learning, language technologies, computer science, and computational biology in the School of Computer Science at Carnegie Mellon University and a cofounder of the Delphi Research Group, a global network of disease modelers working with Insight Net. “So, already before the pandemic, we shifted much of our effort to what I call situational awareness—being aware of what’s happening right now at as fine a geographic, pathogenic, syndromic, and demographic granularity as possible.”

Dylan George, director of the CFA, agrees that disease forecasts will require better raw data and more proactive surveillance. He argues now is the time to strengthen partnerships between researchers and public health practitioners, to build trust and a shared language, and to smooth frictions that can cripple effective collaboration during a crisis. The ultimate test of success for Insight Net, he says, will be seeing them in action:

“If a bunch of state and local health department folks are saying, ‘These forecasting tools are helping me do my job better,’ then I know that we deserve to live another day.”

Illustration: Mary Delaware / Source images: Adobe Stock

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Camey Christenson, who worked with Father Joe’s Villages at the time, remembers conversations with the leaders building the new health information exchange (HIE). “They said, ‘Simmer down! We have enough to focus on with actually sharing health data,’” she recalls.

So her group joined with others and built what they called a “community information exchange” (CIE) and won a $1 million grant from San Diego’s Alliance Health Care Foundation to roll it out under 211 San Diego, the organization that runs a hotline providing information on health, disaster, and community services.

The hope, Christenson says, was to start “blowing the roofs off the silos of information.” Providers could give better care—and save everyone money—if they could see the rest of the “ecosystem” in which their clients lived. Providers could also see whether clients were following through on referrals, which organizations had capacity for more clients, and what interventions worked.

San Diego launched the first version of the CIE in 2014; today, it maintains records for around 380,000 people, or more than 10 percent of the county’s population. The CIE helps coordinate care across 138 partners, ranging from all the area’s major health insurers to LGBTQ organizations to the YMCA. The system can also alert providers to crisis moments, such as a client entering the shelter system or winding up in the emergency department.

Over time, the CIE has become more integrated with the HIE that originally excluded it. Today, health care providers can access the CIE’s records through a special tab in the HIE dashboard. (In 2022, San Diego 211 took over the local HIE, which now shares the same board and CEO as the CIE.)

The community information exchange is already improving outcomes. One 2017 study run by the University of California, San Diego found that enrollment in the CIE significantly reduced emergency medical transports among seniors, saving an estimated $1.3 million in medical costs across San Diego County over a three-year period.

The San Diego community exchange is a model of what a CIE can be, and it has sparked a spread of the concept. It coordinates and provides tools and mentorship for a national network of more than 30 other places building their own exchanges. San Diego 211 actually trademarked the term “Community Information Exchange” to head off vendors using the term in a misleading way, Christenson said; other similar efforts across the country use names like “community referral networks.”

One effort it’s assisting is in Chicago, where an exchange is being built by the Illinois Public Health Institute (IPHI), with support from the Chicago Department of Public Health and the Cook County Bureau of Economic Development. As in San Diego, Chicago’s exchange is initially focusing on people experiencing housing insecurity, but IPHI is building the system so it can include other populations. It’s also making it HIPAA compliant so that it’s prepared from the outset to handle medical data as it grows. Because the Chicago metropolitan area is not currently served by an HIE, the CIE will be the first data tool capable of capturing information about individuals who use the health care system the most. Officials expect to deploy the exchange next year.

Health care institutions are already key stakeholders. They want the CIE to succeed in large part because it has the potential to drive down costs. In particular, care organizations anticipate savings by diverting high-use individuals—many of whom are unhoused—from their emergency departments, says Waldo Mikels-Carrasco, who oversees the CIE program at IPHI and previously helped run a health information exchange in neighboring Indiana.

The 2010 Affordable Care Act pushed health care providers and insurers to shift their care approach to improving population health—a recognition, at least in part, that keeping patients healthy means they use less care, which is less expensive than solely focusing on patients after they fall ill. Many hospitals responded by employing case managers and community health managers to refer patients to services. But there was no way to track who was being sent where, nor to record what interventions worked. Many small social service organizations were overwhelmed, and hospitals quickly found they didn’t have the tools to make a population health approach work.

“All these nonprofits out there, little community-based organizations that are totally understaffed. They’re totally not set up to get a ton more referrals, and certainly not to provide data back,” says Mikels-Carrasco. A CIE, on the other hand, can show case managers which organizations have capacity for referrals and how well various interventions work.

Changes the Biden administration made to waivers under Medicaid’s Section 1115, which allows states to pilot new care strategies, also promise to bring more funding to such interventions if better records are kept on them. The Biden administration encouraged waivers that address health-related social needs, allowing states to reimburse providers for services like housing or food assistance that could keep Medicaid recipients healthier.

“If we could get information back, we could show that [a referral] helped the person, and then … CMS will reimburse us,” Mikels-Carrasco says.

The Robert Wood Johnson Foundation has provided funding to many data-sharing initiatives, including the ones in San Diego and Chicago. Hilary Heishman, a deputy director at the foundation, says because these exchanges can be built by a community from the ground up, they address many of the trust issues that hamper other health information initiatives. That can make them a tool for underserved communities to gather data on their own needs much faster than a top-down system could detect them. “Public health needs sources of information that are more driven by the community that they’re trying to support and serve,” Heishman says. Grassroots organizations are using CIEs to find ways to “use data to tell the story of how our community is doing and where the opportunities are, where the challenges are.”

These data initiatives can be a key tool for finding “patterns in the noise,” she says. And that’s “what public health should be doing all the time.”

Source images: Adobe Stock

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Targeting infectious diseases like influenza or lead exposure would have been easier: State laws required reporting those illnesses to public health officials. But asthma is a chronic disease that health care providers weren’t required to report. How could caseworkers find the children they wanted to help?

Clifford S. Mitchell, director of the office that led the program, first looked to Medicaid claims data since the asthma program was intended for low-income kids. But that data was only reported every six months, and then it had to go to a vendor to be analyzed—which meant as much as a year may have passed between a child’s visit to the hospital and the first outreach by a caseworker. By then, contact information for the families often no longer worked. If their children hadn’t just had an asthmatic crisis, parents often saw no need for a caseworker to visit their home. “We [had] a conundrum, because we have this service,” Mitchell remembers, “but we can’t really efficiently get to the people who are most likely to receive the service favorably and ask for it.”

He found a solution in a system that wasn’t created for public health at all: a health information exchange, initially set up in the mid-2000s to organize and share patients’ electronic health records between their health care providers. Maryland’s health information exchange, known as CRISP, received hospitalization reports every night. It could identify Mitchell’s target patients in real time.

It took some effort to work out technical and privacy issues to integrate CRISP’s data into systems used by the state health department and county-level case workers, and the program slowed dramatically during the COVID-19 pandemic. But on September 8, 2022, information about children eligible for the asthma program began moving automatically from the CRISP database to the county caseworkers who could reach out to families. Caseworkers were suddenly enrolling about 25 percent of children the system flagged for the program—a success rate roughly ten times higher than when they were working with Medicaid claims records.

“I am one lucky guy,” Mitchell says. He has the real-time ability to track an illness and deploy an intervention that most public health workers only dream about. He is also working in a state that has taken steps to make it especially easy to adapt a health information exchange (HIE) built primarily for health care providers so that it serves a public health function.

Maryland is one of 43 states that rely on HIEs for at least one public health function, according to a 2022 study published by the Civitas Networks for Health, a national collaborative of organizations working on HIEs. Providers, payers, and public health officials are gradually recognizing the whole health system, public and private, can benefit if they share tools with one another.

For many years, there have been two entirely separate conversations about data modernization in health care—one among public health agencies used to working with disease registries based on reporting requirements imposed on health care providers, and a parallel conversation among doctors, hospitals, and health plans working to streamline patient care. There’s a growing recognition, especially after the pandemic, that both communities are wrestling with the same question, and there are many ways they could work together—to make practitioners’ lives easier and to improve the nation’s health.

Using CRISP for poor kids with asthma would have seemed far-fetched when it began taking shape in about a decade earlier, because it was originally created to shuffle the medical paperwork of seniors. The data-sharing initiative spawned from the frustrations of John C. Erickson, the founder and then-chairman of a chain of elder care facilities called Erickson Senior Living. He didn’t like that when nursing home residents went to the hospital, the hospital’s doctors “would know nothing about them,” recalls David Horrocks, then Erickson’s chief information officer, “and then they come back, and our docs would know nothing about what happened in the hospital.”

Most information-driven industries were then rapidly adopting technology, like email, that’s now commonplace; medicine was still in the Stone Age, with practitioners relying on paper records and fax machines. Erickson thought there had to be a better way. In 2006, he enlisted Maryland’s three largest hospital systems to launch pilot programs to digitally share seniors’ medical records, and he put up $5 million of his own money to get it off the ground. Horrocks, as Erickson’s tech chief, was tasked with building the platform. Over several years, CRISP was developed.

It coalesced as lawmakers were trying to drag the health care industry toward the latest innovations of the information age. A major shot in the arm came as part of the economic stimulus package signed by President Barack Obama in 2009, which included the Health Information Technology for Economic and Clinical Health Act (HI TECH for short). The legislation codified a position in the Department of Health and Human Services (HHS) to coordinate health technology efforts and appropriated $35 billion over ten years to encourage the adoption of electronic health records—and HIEs to share records between providers.

None of these efforts paid much attention to public health; policymakers were focused on making providers more efficient, bringing down the cost of care, and improving patient outcomes. The federal government did not specify what form the HIEs should take, so they evolved in many different ways. Some were for-profit companies, some were independent nonprofits, and some sat inside state or county agencies. Some covered entire states while others were organized at a regional level or only included a subset of providers. Some were more focused on networking hospitals; others were more focused on serving health insurance companies. Collaborations between different stakeholders were often uncomfortable, because they required competing providers to share data with one another, as well as greater transparency between providers and insurers. In some states, privacy laws had to be modified to facilitate information exchange. And in some parts of the country, these challenges were so insurmountable that they are still not served by an HIE.

Maryland was fertile ground for building an especially robust HIE. The state already had a unique payment model in which an independent commission appointed by the governor sets payment rates for hospitals. The administration of then-Governor Martin O’Malley helped get all the state’s hospitals on board with CRISP, in 2009, and provided funding to keep it going. Maryland made a major change to its payment system in 2014 that allowed hospitals to keep more revenue by keeping patients healthier and using fewer services. That in turn meant hospitals needed better data tools to track their patients, and CRISP was ready to fill this need.

CRISP gave every hospital and provider a tracking dashboard and even real-time alerts when a patient went to the ER or was admitted to the hospital. Over time, these tools expanded to gather data from medical and mental health case managers. “If there’s somebody who can get help so they’re not admitted every week to the hospital, going from ER to ER, finding out who that person is and helping them becomes a really good thing for the health care system,” says Joshua Sharfstein, a medical doctor and professor at Johns Hopkins Bloomberg School of Public Health, who was Maryland’s health secretary as this system was being created in 2014.

For public health workers, CRISP can map hospitalizations for conditions like asthma to identify hot spots with much more precision than previous tools—and with actionable information. CRISP also runs the prescription drug monitoring platform for the state Department of Health. Its role is so central to Maryland’s health system that it is recognized as what’s known as a Health Data Utility, bedrock infrastructure that provides health information to many players in the state’s health care and public health ecosystem.

HIEs like CRISP, if widely deployed, could radically improve the practice of public health in the US, says Craig Behm, CRISP’s CEO. For instance, heat maps are a mainstay of public health. But they identify very broad geographic hotspots, which Behm says often doesn’t tell public health workers very much they don’t already know. “The [bigger] problem is, ‘What are the compounding factors and health related social needs and different outcomes? And where do people get care? Those details are available!… We need to go deeper now and we can.”

That level of information is available with CRISP. But “public health has been so undervalued and underfunded for so long,” Behm says, “Public health [officials are] having trouble even understanding what modern infrastructure could do for them.”

The COVID pandemic made painfully clear the high cost of underinvestment in public health data infrastructure. Federal budget allocations also show what a low priority such resources have been for policymakers. In 2009, for example, Congress appropriated $35 billion to advance data tools for providers and insurers. But it didn’t earmark money for CDC data modernization until 2019 and has only appropriated a total of $1.35 billion for the effort over the last four years.

The COVID pandemic made clear just how useful CRISP could be for public health, Behm says. Very early in the pandemic, CRISP was processing massive amounts of data every night, tracking case reports, hospitalizations, and deaths. Lab workers were sending incomplete demographic data to local Departments of Health, but CRISP could match the health department registries with its own data set, tracking in real time essential data like the race and ethnicity of infected Marylanders. When public health workers needed phone numbers to do contact tracing, Behm recalls, “I was like, ‘Well, great! I have all the phone numbers in the world!”

In the scramble to respond to COVID, other states’ public health workers also turned to HIEs as partners—often in ways they never had before. HIEs were glad to help, but those relationships became strained as the crisis passed, says Jolie Ritzo, Civitas’s vice president for strategy. “The challenges feel bigger than they were,” Ritzo says.

HHS is now steering the next evolution in the health information ecosystem, laying out standards and certifications for national information exchanges that will allow records to follow a patient from one HIE to another. At the same time, the CDC is leading its own Data Modernization Initiative and has signed contracts for hundreds of millions of dollars with Palantir to build a data system of its own.

A major open question is how these two efforts will interact. Many HIE leaders hope that existing data exchange infrastructure can link the information networks many providers already use regularly with upgraded data systems for public health officials. Erica Galvez, CEO of Manifest Medex, California’s largest HIE, and a former official in the health technology office at HHS, says she believes many public health departments will look to existing HIEs as an “on ramp” to the nationwide network.

Claudia Williams, who was Manifest Medex’s founding director, says HIEs have already done a lot of work to manage the complicated relationships between stakeholders at the local, regional, and state levels. It would be in everybody’s interest, she thinks, for the CDC to try to engage with the HIE infrastructure that exists rather than trying to develop an entirely separate information system.

“The better place to start is to strengthen and incentivize what goes on at the state level,” says Williams, who also served as senior advisor of health innovation and technology in the Obama administration and is now the chief social impact officer of UC Berkeley’s School of Public Health.

But fewer than half the states have HIEs ready to take on full public health functions at this point, says Horrocks, who left CRISP in 2022 to build a state-wide HIE in New York. (Horrocks also coauthored the nationwide study of HIEs published by Civitas in 2022.) Still, he believes they’re the best structure for public health to rely on in the long term.

“HIEs do something that’s very unique: We hold fully identified data in trust, and that lets us put datasets together,” Horrocks said. “I don’t know who else can do that. We’re not going to trust the … the state government, and you’re certainly not going to trust CDC to hold everybody’s fully identified data. And, I don’t think you’re going to trust Google to do it, either, or Palantir. I think that’s a role that these HIEs can uniquely fill.”

Image: berCheck / Adobe Stock

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Elsewhere, the bacterial consequences of these storms are preventing access to safe drinking water. More than 150,000 households in North Carolina were still living under boil-water notices nearly a month after Helene, and the Environmental Protection Agency detected E. coli in 30 percent of 900 private wells it tested in the storm-ravaged state.

Each major hurricane that has made landfall in recent years has had infectious fallout. Florida’s previous record for flesh-eating bacterial infections came after Hurricane Ian. Hurricane Maria resulted in a spike of leptospirosis in San Juan, Puerto Rico, especially for people who lived near a large, heavily polluted canal. Hurricane Harvey sent at least 31 million gallons of raw sewage streaming into Houston’s neighborhoods and was associated with a rise in deadly invasive mold infections and alarming levels of antibiotic-resistant bacteria found in flooded homes.

It is painfully evident that our antiquated stormwater and sewage systems cannot manage the downpours, tidal surges, and rising sea levels that climate change is delivering, and this situation strongly favors bacteria. This trend disproportionately affects poor and vulnerable communities, amplifying equity and justice concerns, and the repercussions will be dire if we do not better prepare.

Properly treating wastewater and stormwater is a pillar of public health, one imperiled by our collective lack of maintenance. We must modernize sewage infrastructure in hurricane-prone regions to mitigate the risk of infections. But doing so will require years of planning and negotiating before a shovel breaks ground. Complicating matters are the millions of private septic tanks in backyards across the country that are both teeming with human waste and prone to flooding. As one expert told The Washington Post, these septic tanks are “ticking time bombs.”

We need immediate strategies to mitigate the risk of infections before, during, and after hurricanes.

One practical step is clearly communicating the risk to those in a storm’s path. Florida health officials should be commended for their efforts ahead of Helene and Milton; they emphasized the danger of exposing open cuts or sores to floodwaters. Similarly, residents mucking out their flooded homes should follow the state’s emergency management agency recommendations for keeping proper protective equipment such as gloves, boots, and respirator masks in their kits. In the pathogenic aftermath of Hurricane Maria, lack of basic safety equipment increased infection risk during clean-up efforts.

It will help to expand and enhance wastewater monitoring programs deployed to track levels of COVID-19. A team led by Anthony Maresso at Baylor College of Medicine is using novel sequencing technologies to track all known human and animal viruses in a single test, and these technologies can be adapted to include bacterial threats. The current technology is being used across Texas and has the potential to be inexpensive enough to use in low- and middle-income countries. When antimicrobial resistance detection is added, such a system can inform public health authorities in real time about a wide range of hazards, including the emergence of drug-resistant pathogens. Knowing what specific threats lurk in the sewers before those systems fail and then flood homes could enable public health agencies and local health systems to better tailor their response plans. It could also guide physicians in each locale to use the most effective antibiotics to treat infections that arise in their area.

It is imperative that clinicians in flooded areas have access to effective antibiotics, not just a few old generics that may not be appropriate for the situation at hand. If a hurricane triggers a localized outbreak of a drug-resistant pathogen, the results could overwhelm entire health systems. Project BioShield, a federal initiative aimed at supporting the development and procurement of medical technologies that could be needed in the wake of certain disasters, should be expanded to include the threat of drug-resistant infections, and we should ensure the Strategic National Stockpile can directly procure and manage advanced antibiotics and antifungals so that they can be rapidly made available to communities in need.

This year’s hurricane season isn’t over yet, and it’s likely that Florida will record more cases of flesh-eating bacteria. The patients suffering from those infections—and families who lost loved ones to them—will not soon forget the ways in which a hurricane affected their health. Neither should we.

Image: A utility hole cover bubbles open in a road flooded by the remnants of Hurricane Ida in Rutherford, New Jersey in September 2021. (Ted Shaffrey / AP Photo)

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