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A Failure to Rescue: How Predictive Modeling Can Rewrite the Story of Congenital Syphilis

News Desk by News Desk
November 14, 2025
in Health
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A Failure to Rescue: How Predictive Modeling Can Rewrite the Story of Congenital Syphilis
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By KAYLA KELLY

Every semester I have the privilege of guiding nursing students through their maternal and pediatric clinicals. At the beginning of the semester, their enthusiasm is contagious. They share stories about witnessing their first delivery, helping a new mother with breastfeeding, and practicing developmental assessments on pediatric patients. As the semester progresses, I see their demeanor shift. “You were right, we took care of another congenital syphilis baby today.” Their reflections on the clinical day are a mixture of emotions: frustration, anger, and sadness, as they watch fragile infants fighting an infection that no child should ever have to endure.

When I first tell my nursing students that they will likely care for infants born with syphilis during their clinical rotations, they look at me with wide-eyed disbelief. “Didn’t we cure syphilis in the 1950’s?” some ask. A few of my students usually recall hearing about the Tuskegee Study, but most have no idea that we are still fighting (and losing) a battle against congenital syphilis in the United States today.

Congenital syphilis occurs when a mother transmits the infection to her infant during pregnancy or delivery. It is almost entirely preventable with timely screening and treatment, yet the number of cases continues to rise at an alarming rate. Between 2018 and 2022, the United States experienced a 183% increase in congenital syphilis cases, rising from 1,328 cases to 3,769. This national trend was mirrored at the state level, with Texas reporting 179 cases in 2017 and 922 in 2022. During those five years, the rate of infants born with congenital syphilis in Texas rose from 46.9 to 236.6 per 100,000 live births, a sharp increase that necessitates action.

Texas now has one of the highest congenital syphilis rates in the country, despite having one of the most comprehensive prenatal screening laws. According to the Texas Department of State Health Services, policy mandates syphilis screening at three points during pregnancy:

(1) at the first prenatal visit

(2) the third trimester (but no earlier than 28 weeks)

(3) at delivery

But herein lies the problem: What happens when a woman never attends prenatal care? How do we reach those who never step into an OB/GYN office during pregnancy? Screening laws only protect those who are able to access care. In 2022, over 1/3 of Texas mothers whose infants were diagnosed with congenital syphilis did not receive any prenatal care. Each of these cases represents a failure of our current medical system, a system that should be protecting the most vulnerable yet remains unable to reach those who need it most.

Socioeconomic and systemic barriers often limit healthcare access for vulnerable populations and communities. Congenital syphilis disproportionately affects infants born to mothers who experience limited healthcare access, housing instability, poverty, maternal drug use, and inadequate prenatal care. Many women also avoid or delay prenatal care due to stigma, fears of judgment from healthcare providers, or concerns about being tested for substance abuse.

Imagine if, instead of depending solely on women to attend prenatal appointments for screening, we could identify who is most at risk for delivering an infant with congenital syphilis the moment they interact with any part of the healthcare system. By leveraging existing electronic health record (EHR) data and artificial intelligence (AI), we could build predictive models capable of forecasting maternal and infant health outcomes.

These models could incorporate things like prenatal care utilization, zip code, and other clinical data. Patients flagged as high-risk within the EHR could automatically trigger a nurse navigator referral for further assessment and care coordination. Rather than limiting syphilis screening to obstetric visits, this approach would identify high-risk patients at any point of contact: the emergency department, primary care, behavioral health, substance use treatment, or community outreach clinics.

Predictive models have already proven successful in improving other clinical outcomes such as sepsis, diabetes, and even preterm birth. We already have the EHR systems and the data needed. We just have to develop and apply the model. These success stories demonstrate that with data analytics and AI, improving congenital syphilis outcomes is not only possible, but within reach.

Currently, both U.S. and Texas policies focus on syphilis screening requirements during prenatal visits. But what about the women who never attend traditional prenatal care? How do we protect their infants from congenital syphilis? We must critically evaluate our approach and develop policies that evolve with the realities of today’s healthcare system.

Many pregnant women seek care in emergency rooms or urgent care clinics for unrelated issues such as UTIs, fevers, or coughs. Each of these encounters represents an opportunity for healthcare providers to intervene and prevent the transmission of congenital syphilis. Policies should be updated to require screening at every healthcare encounter for pregnant women who have not met existing screening guidelines, and to ensure follow-up for those identified as high-risk within 48 hours.

Once high-risk patients are identified through predictive modeling, geomapping can help public health professionals effectively target outreach efforts. This tool creates visual maps that can reveal clusters of infections and highlight hotspots where testing, education, and community resources should be focused. This approach is commonly used by health departments to allocate resources to where they are needed most.

Funding to build and integrate predictive modeling into EHR systems could come from state and public health grants. Once developed, the ongoing cost of maintaining the model would be minimal compared to the rising costs of congenital syphilis. The average hospitalization cost for an infant born with congenital syphilis is approximately $56,802, which is nearly four times higher than an infant without congenital syphilis. Preventing even a small number of cases would quickly offset the cost of the investment required to develop and implement this model.

The drastic rise in congenital syphilis cases represents a failure of our health system, a failure defined by missed opportunities for prevention. While AI can never replace the human element of compassionate caregiving, it can provide us with the data needed to make a lasting impact within vulnerable populations and improve maternal-infant health outcomes.

Remaining stagnant under our current ineffective policies borders on negligence. To have the available technology and not use it is, in many ways, a failure to rescue. But the marriage of technology and compassion can change this story’s ending. I think of my students’ faces, the frustration and disbelief in their eyes. I wish I could tell them this will be the last time they see a baby born with congenital syphilis, but unless things change, this is only the beginning.

Kayla Kelly, MSN, RN, CPN is a nursing instructor and PhD student at the University of Texas at Tyler

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Tags: CongenitalFailureModelingPredictiverescuerewritestorySyphilis
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