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Comparison of Factors Associated With Preterm and Term Birth at University College Hospital Ibadan Oyo State

This retrospective case-control study at University College Hospital Ibadan identified that a history of preterm birth, multiple gestations, preeclampsia, inadequate antenatal care, and low socioeconomic status are significant risk factors associated with preterm delivery compared to term birth.

Original authors: Monisola Anike Popoola, Chizoma Millicent Ndikom, Margaret Akinwaare, Oluwasegun Caleb Idowu, Olumide Emmanuel Olufayo, Imran Oludare Morhason-Bello

Published 2026-07-24
📖 5 min read🧠 Deep dive

Original authors: Monisola Anike Popoola, Chizoma Millicent Ndikom, Margaret Akinwaare, Oluwasegun Caleb Idowu, Olumide Emmanuel Olufayo, Imran Oludare Morhason-Bello

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the human body as a magnificent, self-regulating factory. Inside this factory, a tiny, intricate project is underway: building a new human being. Normally, this project has a strict deadline—about 37 weeks. If the factory finishes the project on time, the baby is "term," meaning it's fully stocked with all the necessary parts to survive and thrive in the outside world. But sometimes, the factory hits an emergency button and ships the project out early, before the 37-week mark. This is called "preterm birth." While the factory might be trying to save the day by rushing the exit, the baby often arrives with unfinished wiring or missing components, leading to serious health struggles or even tragedy. Scientists are like the factory inspectors, constantly trying to figure out why the emergency button gets pressed. They know that in some parts of the world, this happens far too often, and they are hunting for the specific triggers—like a faulty alarm system, a power outage, or a structural weakness—that cause the early departure. Understanding these triggers is the key to keeping the project running until the perfect moment of completion.


This research paper acts as a detailed investigation into that very factory floor, specifically at the University College Hospital (UCH) in Ibadan, Nigeria. The researchers, a team of doctors and scientists, decided to play detective by looking backward through the hospital's records from January 2018 to December 2022. They gathered a massive lineup of 380 mothers: 190 who had babies too early (the "cases") and 190 who had babies right on schedule (the "controls"). To make sure the comparison was fair, they matched the two groups like twins, ensuring they had similar ages, family sizes, and whether they had booked their hospital appointments in advance.

The team then pored over the medical files, looking for clues that might explain why some babies left the factory early. They treated the medical records like a treasure map, searching for specific "X's" that marked the spot of trouble. What they found was a collection of very clear, very loud warning signs.

The most dramatic clue they uncovered was the history of the mother herself. If a woman had a baby too early in the past, she was a staggering 7.1 times more likely to have another early delivery when looking at the final predictive model. It's as if the factory had a previous glitch that, once it happened, made the same glitch significantly more likely to happen again. This wasn't just a small bump in the road; the odds were overwhelmingly in favor of it happening again.

Other factors also lit up the warning lights. Women who had a previous Cesarean section (a surgical delivery) were about 2.3 times more likely to have a preterm birth. Similarly, if a mother didn't get enough check-ups during her pregnancy (fewer than four visits), her risk jumped to 2.9 times higher. It seems that skipping the factory inspections leaves the system vulnerable to surprises.

The study also highlighted that the "social environment" of the factory mattered. Mothers with lower family incomes faced a risk nearly 2.9 times higher. However, while mothers with lower levels of education initially appeared at risk, the final analysis showed this was not a statistically significant predictor in this specific group. It suggests that while resources play a huge role, the link between education and early delivery in this study was less definitive than other factors.

But the most intense alarms were triggered by specific medical emergencies that happened during the pregnancy. The researchers found that:

  • Multiple gestations (twins, triplets, etc.) made a preterm birth 8.8 times more likely. It's like trying to build two complex projects in one factory at the same time; the system gets overwhelmed and rushes the finish line.
  • Pregnancy-induced hypertension (high blood pressure caused by the pregnancy) increased the risk by 6.7 times.
  • Premature rupture of membranes (when the water breaks too early) made a preterm birth 4.8 times more likely.
  • Pre-eclampsia (a dangerous condition involving high blood pressure and organ damage) and multiple gestations were also linked to a risk about 5 to 5.5 times higher.
  • Conditions like sepsis (a severe infection) and bleeding during pregnancy doubled or tripled the chances of an early delivery.

The researchers were very careful to note that while they found these strong links, they didn't find that things like the mother's age, marital status, or religion made a difference in this specific group of women. The "factory" seemed to run the same regardless of those factors, but the medical and social stressors were the real troublemakers.

In the end, the study concludes that preterm birth in this setting isn't a random accident. It is often the result of a perfect storm of medical complications and a lack of support systems. The authors suggest that if we can fix the "leaks" in the system—by ensuring mothers get enough prenatal care, managing high blood pressure, and supporting families with lower incomes—we might be able to keep more babies inside the factory until they are truly ready to be born. The data is clear: these factors are potent predictors, and addressing them could save many lives.

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