Annual Healthy Georgia Report looks at public health in the Peach State

How healthy are Georgians are compared to citizens across the southeast and U.S.

Feb 6, 2025

3 min

The fourth edition of the “Healthy Georgia: Our State of Public Health” report has been released by the Institute of Public and Preventive Health in Augusta University’s School of Public Health.

Within the 64 pages of the report is a snapshot of how healthy Georgians are compared to citizens across the 12 states that make up the Southeastern Region (Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia) and the entire United States. The 2025 edition addresses 31 health topics and has been expanded this year to include multimorbidity; long COVID-19; HIV, chlamydia, gonorrhea and syphilis infection rates; opioid and methamphetamine drug use; suicides; and vaping.

Biplab Datta, PhD, assistant professor in the Department of Health Management, Economics, and Policy in SPH, heads up the team of IPPH faculty and staff who create the report each year. Datta credits Jen Jaremski, research associate, and Kit Wooten, public health analyst, with handling much of the work of bringing the report to life. Together, Jaremski and Wooten collected and organized all of the needed assets and organized the 64-page document, preparing it for print and the web.

“Every year we strive to present data in a way that policymakers may find helpful in making policy choices,” Datta said. “There are several new topics that were added to this year’s report and some of those are concerning for the state of Georgia, particularly the communicable diseases like HIV, chlamydia, gonorrhea and syphilis. High prevalence rates of these conditions in Georgia, compared to the rest of the U.S. and the Southeastern Region, warrant attention of the public health community.”

Georgia has the second-highest rate of HIV infections in the U.S., fourth-highest rates of gonorrhea, sixth-highest for chlamydia and 13th for syphilis.

Something that is also new in this year’s report is a comparison of numbers from 2019, or before the COVID-19 pandemic began, compared to after the pandemic for certain conditions.

Also coming out of the pandemic, the report looks at how long COVID has affected Georgians, with the state ranking 24th in the nation for rates of long COVID. According to the U.S. Centers for Disease Control and Prevention, long COVID is defined as a chronic condition that occurs after COVID-19 infection and is present for at least three months.

On top of looking at comparisons between Georgia and the Southeast and the nation, Datta noted a clearer picture is starting to emerge concerning the difference in urban and rural areas within the state.

“For several chronic conditions, like hypertension, diabetes and multimorbidity, we clearly see a striking difference between rural and urban residents of Georgia,” Datta said.

Hypertension affects 44.1% of adults in rural Georgia compared to 35% in urban areas, while diabetes affects 17.5% of adults in rural Georgia compared to 12.3% of those in urban areas. Hypertension and diabetes are major risk factors for cardiovascular disease, which affects 12.2% of adults in rural areas compared to 8.3% of adults in urban areas of Georgia.

“Hypertension and diabetes are the major risk factors for heart disease, which is the leading cause of death in the U.S. and worldwide, so these are some concerning numbers to see,” Datta said.

Multimorbidity, which is when a person has multiple chronic conditions, including obesity, asthma, chronic obstructive pulmonary disease, depression, kidney disease, diabetes, hypertension, high cholesterol, cardiovascular disease, cancer, skin cancer and arthritis, affects 57.4% of adults in rural Georgia compared to 49% of adults in urban areas of the state. These rates are significantly lower than the rest of the Southeast but on par with the rest of the country.

Read the complete report

When comparing Georgia to the U.S. national average, adults in Georgia have lower rates of cancer and methamphetamine use but higher rates of childhood asthma and adult obesity. At the same time, rates of adult asthma and adult obesity among Georgians were comparable to the averages seen in the Southeast. Interestingly, while adult health insurance coverage was significantly lower than the U.S. national and Southeast Regional averages, the child health insurance coverage in Georgia was comparable to both national and regional averages.

The Healthy Georgia Report is the only report of its kind in the state

Looking to know more or connect with Biplab Datta, PhD? Then let us help. Dr. Datta is available to speak with media regarding this important topic. Simply click on his icon now to arrange an interview today.

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Biplab Datta, PhD

Assistant Professor, Department of Health Management, Economics, and Policy

Dr. Datta focuses on biopsychosocial determinants of health and the scope of psychosocial attributes in healthcare management.

Biopsychosocial Determinants of HealthHealth EconomicsPopulation HealthNoncommunicable DiseasesPsychosocial Wellbeing

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Aug 20, 2026

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AU Hosts Congressional Hearing on 'Building an AI-Ready America'

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Aug 17, 2026

2 min

The Real Risk of AI in the Classroom Isn't the Tool, It's the Shortcut

Artificial intelligence has moved from novelty to daily habit in classrooms across the country, and the debate over what that means for learning shows no sign of slowing down. Some schools have built AI into their curriculum. Others have banned it outright. Caught in the middle are students trying to figure out where the technology actually helps and where it quietly gets in the way. Trent Kays, PhD, an assistant professor and Director of Professional Writing and Rhetoric at Augusta University, has spent time researching exactly that question, and he recently sat down with FOX54 News to talk through what he's seeing firsthand. Kays, who also serves as a faculty affiliate in Women's and Gender Studies and specializes in digital rhetoric and machine writing, says students are already using AI for far more than quick homework help, from brainstorming and studying to building out entire lesson plans on their own. The bigger concern, according to Kays, isn't misuse in the obvious sense. It's what he calls cognitive offloading, students handing the thinking itself over to the machine. "Students are essentially using AI to do their thinking for them, which is not something that they should do," he said. It's part of why he keeps AI out of his first- and second-year courses entirely: those early classes are where students build the critical thinking skills they'll need before a tool can actually help. "They have to be able to think critically first before they can ask a tool to help them." Accuracy is its own problem. AI tools have gotten sharper, but Kays points out they still fabricate information regularly, including sources that were never real to begin with, something students are far less likely to catch than an instructor who actually reads the citations. None of this means Kays thinks AI belongs outside the classroom. He describes it as a potential process partner, something that can push a student's thinking further rather than replace it, as long as the structure around it is clear. As he put it, what schools need now is to "craft clear guidelines and structures for students to follow so that they know how to use it and how to use it well." Read the full interview and FOX54's coverage here: AI in the Classroom: Augusta University Professor Explains the Benefits and Risks Dr. Trent Kays is available for interviews on AI in education, digital rhetoric, and responsible AI use in academic settings. If you're covering similar stories, click on this icon to schedule time with him.

Aug 25, 2026

3 min

Rethinking how data meets the atom

Quantum computing is often framed as a hardware race with attention fixed on machines that promise amazing speed. But a foundational mathematical problem remains unresolved. No complete framework yet exists for converting massive amounts of data generated by fields such as healthcare, disease surveillance, financial transactions and climate monitoring into units a quantum machine can process. “What exactly we are proposing is this: data is continuous,” said Arni S.R. Srinivasa Rao, PhD, professor and director of the Laboratory for Theory and Mathematical Modeling, Division of Infectious Diseases, Medical College of Georgia at Augusta University. “That means the generation of the data is continuously happening. And how to slice it down into pieces, that is what it is about. The slicing down of the data into smaller pieces, not too small pieces, but smaller enough for the computers to handle them in a meaningful way.” Rao and Steven G. Krantz, a professor of mathematics at Washington University in St. Louis, published their findings on “The Role of Quantization in Quantum Computing” in the July/August 2026 issue of SIAM News, published by the Society for Industrial and Applied Mathematics. “Quantization is a mathematical method, nothing to do with quantum computing,” Rao said. “But these two we are trying to mix together.” The problem with just chopping it up Classical computers store information as bits, switches that are either off or on, zero or one. Quantum computers use qubits, which can hold combinations of both states simultaneously, which is why they can theoretically process vastly more information at once. But qubits operate at the atomic level. Getting that data into a quantum machine requires breaking it down into atomic-scale pieces first. The math for doing that well, Rao and Krantz argue, does not yet exist in any complete form. The hard part is “slicing” it right. “Not slicing in a random, arbitrary way,” Rao said. “If you slice it in a way which is arbitrary, then you might miss, you might necessarily slice it where no information is required. Information is broken into pieces rather than combining the information. That’s what the mathematics is playing a role here.” Their research proposes using an ensemble of quantization techniques rather than a single method, with different mathematical tools matched to the different layers inside a dataset. A disease surveillance file might bundle together demographic data, income levels, infection status and housing type, for example. Each layer has its own structure. The partitioning, they argue, should flex to fit the problem rather than forcing every dataset through the same process. Mixing two worlds that don’t quite line up The second major contribution is a formally defined measure the authors call “atomic uncertainty.” It quantifies the mismatch between quantized data and the atomic structure that data is supposed to travel through inside a quantum processor. The mismatch exists because atomic models, the diagrams of electrons and nuclei that physics students study, are approximations. The actual behavior of electrons inside an atom follows probability distributions. There is inherent randomness in there that scientists have not resolved. Planets are called spheres, and textbooks draw them as smooth perfect balls, Rao explained, but their surfaces are cratered and irregular. The sphere is a useful model, not an accurate description. Atomic structure works the same way. When quantized data does not fit cleanly into that imperfectly understood atomic structure, the gap becomes computational error. At small scale, manageable. At the scale of real-world data inputs, those errors can compound into results that are simply wrong. “We are not trying to fix the error. We are trying to see how big the error is,” Rao said. This study illustrates the concept with a disease-modeling example, building a dataset that layers population age, income bracket, infection status and housing type. When the data is quantized and compared against a model atomic structure, the spaces do not fully align. That leftover mismatch is the atomic uncertainty. In a real quantum system processing actual disease data, it does not disappear but shows up as wrong answers about how a disease spreads. The next step, Rao said, requires industry partners to take the framework and build hardware around it. “Designing a computer is different from the actual making a computer,” Rao said. “We designed the method of slicing the larger, huge continuous data into smaller digestible computer bits, that’s called qubits. The industrialists have to build it. Like designing a mobile phone is different from actually making a mobile phone.”

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