The study, published as an open-access research article in BMC Endocrine Disorders, drew on the Korea National Health and Nutrition Examination Survey, known as KNHANES, specifically the fourth wave conducted between 2007 and 2009. The investigators, led by Ziran Qiu of the Department of Breast and Thyroid Surgery at Loudi Central Hospital in Hunan, China, together with colleagues including corresponding author Na Jin, focused on postmenopausal women, a group in which both menopausal hormone therapy and thyroid disease converge with unusual frequency. Thyroid disorders, which range from underactive and overactive gland function to nodules and autoimmune inflammation, are markedly more common in women than in men, and their prevalence rises with age, making the postmenopausal population a natural setting for asking whether exogenous hormones tip the balance.

The analytical sample comprised 3,974 postmenopausal women, of whom 582 reported a history of using female hormone medication. The exposure was deliberately simple: a questionnaire item recording whether the participant had ever taken female hormone drugs. Importantly, the survey did not capture whether use was current, which formulation was taken, at what dose, or for how long, a limitation that shapes how the results can be interpreted. The outcomes were equally grounded in self-report: thyroid disorders diagnosed by a doctor served as the primary endpoint, while current thyroid disorder, lifetime thyroid disorder, and currently treated thyroid disorder served as secondary endpoints. This reliance on questionnaire-defined disease means the study measures the burden of recognized and recorded thyroid illness rather than laboratory-confirmed thyroid dysfunction in the moment.

The headline numbers are striking. Weighted prevalence of doctor-diagnosed thyroid disorder was 10.87 percent among women with a hormone therapy history versus 6.87 percent among women without one. Current thyroid disorder showed a similar gap, 6.79 percent versus 3.79 percent, as did lifetime thyroid disorder at 10.95 percent versus 6.96 percent and treated thyroid disorder at 4.88 percent versus 2.95 percent. In every case, women reporting prior hormone medication use carried roughly a one-and-a-half to nearly two-fold heavier burden of thyroid disease on the raw, survey-weighted scale.

Because raw differences in observational data can easily reflect age, body weight, income, reproductive history, or the simple fact of seeing doctors more often, the team turned to survey-weighted logistic regression, the standard tool for estimating associations in complex national surveys. Their models adjusted for demographic, socioeconomic, metabolic, reproductive, and survey-year factors. To guard against the statistical trap of cherry-picking significant results across multiple tests, they applied Benjamini-Hochberg correction, computing q values that control the expected proportion of false discoveries across the four outcomes. After adjustment, a history of female hormone therapy remained associated with doctor-diagnosed thyroid disorder with an adjusted odds ratio of 1.52 and a 95 percent confidence interval of 1.06 to 2.16, with a q value of 0.031. The estimate for current thyroid disorder was even stronger, at an odds ratio of 1.75 with a confidence interval of 1.09 to 2.83, and lifetime thyroid disorder yielded an odds ratio of 1.50 with a confidence interval of 1.06 to 2.14, both also surviving the false discovery correction at q equal to 0.031. The estimate for currently treated thyroid disorder was directionally similar but less statistically secure, at an odds ratio of 1.60 with a confidence interval spanning 0.96 to 2.68 and a q value of 0.071.

Recognizing that any single modeling choice can swing results, the researchers ran an unusually thorough battery of sensitivity analyses. When they expanded the covariate set further, the primary estimate attenuated to an odds ratio of 1.38 with a confidence interval of 0.96 to 2.00, no longer excluding the possibility of no association. When they adjusted for healthcare contact, an attempt to account for the possibility that hormone therapy users simply interact with the medical system more and therefore get diagnosed more often, the association held at an odds ratio of 1.46 with a confidence interval of 1.01 to 2.10. Restricting the analysis to narrower age bands, however, produced estimates that crossed the null, suggesting the signal may be sensitive to the age composition of the sample. Adding gravidity, the number of times a woman had been pregnant, to the adjustment left the estimate essentially unchanged at an odds ratio of 1.52 with a confidence interval of 1.05 to 2.20. Exploratory analyses of treatment duration returned heterogeneous results, offering no consistent picture of whether longer exposure carried greater risk.

What might connect ovarian hormone preparations and the thyroid gland biologically? The mechanistic story is plausible but unresolved. Estrogen influences thyroid physiology in several documented ways: it can stimulate thyroglobulin, the protein scaffold on which thyroid hormones are synthesized, and it modulates immune activity in a gland where autoimmune disease, particularly Hashimoto’s thyroiditis, is disproportionately common in women. Hormone therapy could also plausibly promote thyroid growth or nodule formation in a gland already rich in estrogen receptors. Conversely, the association could run in the opposite causal direction: women with thyroid symptoms such as fatigue, weight change, or mood disturbance might seek menopausal symptom relief through hormone therapy, or their thyroid conditions might simply be diagnosed around the same life stage. Residual confounding by health-seeking behavior, physician vigilance, or unmeasured metabolic traits remains a live possibility in any cross-sectional design.

The authors are explicit about the limits of their evidence. Because the exposure and outcomes were both self-reported and captured at a single point in time, the study cannot establish temporality, let alone causality. It cannot distinguish whether hormone therapy preceded the thyroid disorder, followed it, or merely co-occurred with it. The questionnaire also could not identify current users or treatment details, blurring any dose or duration effects. Yet the consistency of the association across three of four endpoints, its survival of false discovery correction, and its persistence under healthcare-contact adjustment give the finding enough weight that it should not be dismissed as statistical noise. At the same time, the attenuation under expanded adjustment and the null results in age-restricted analyses are honest reminders that the true effect, if any, is likely modest and context-dependent.

For clinicians and the millions of postmenopausal women weighing hormone therapy, the practical takeaway is one of measured vigilance rather than alarm. The study’s authors state plainly that their findings do not support universal thyroid screening for women with a hormone therapy history, nor do they justify changing menopausal hormone therapy decisions solely on the basis of that history. Standard practice, in which thyroid function is evaluated when symptoms or clinical findings warrant it, remains the appropriate course. What the research does provide is a well-powered, nationally representative East Asian data point in a literature long dominated by Western cohorts, and a clear template for the kind of study needed next: prospective, longitudinal designs with pharmacy-verified hormone exposure, laboratory-measured thyroid function, and careful sequencing of treatment and diagnosis. Until such evidence arrives, the association between hormone therapy history and thyroid disorder burden in postmenopausal Korean women stands as an intriguing signal, a question mark that modern endocrinology will now be pressed to resolve.

Subject of Research: The association between self-reported menopausal hormone therapy history and thyroid disorder prevalence among postmenopausal Korean women in the KNHANES IV survey.

Article Title: Self-reported female hormone therapy history and thyroid disorder burden among postmenopausal Korean women: a cross-sectional KNHANES IV study

Article References: Qiu, Z., Long, H., Li, R., Yang, J., Cao, W., Chen, S., & Jin, N. (2026). Self-reported female hormone therapy history and thyroid disorder burden among postmenopausal Korean women: a cross-sectional KNHANES IV study. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02540-3

Image Credits: AI Generated

DOI: 10.1186/s12902-026-02540-3

Keywords: menopausal hormone therapy, thyroid disorder, postmenopausal women, KNHANES, cross-sectional study, South Korea, endocrinology, odds ratio, thyroid screening, estrogen, women's health, observational study

The research, led by Sarah N. Kunz of Harvard Medical School and Beth Israel Deaconess Medical Center together with colleagues at Stanford University School of Medicine and the California Perinatal Quality Care Collaborative, examined data from California on very low birth weight, preterm infants born before 37 weeks of gestation who were less than 28 days old. The study period spanned 2012 through 2018, a window that captures a mature era of regionalized perinatal care in the nation’s most populous state. California offers a particularly valuable setting for this kind of analysis because of its sheer scale and the richness of its linked birth cohort records, which allow researchers to follow infants from birth through any subsequent acute transport between hospitals with unusual precision.

The design of the study was a retrospective cohort analysis, meaning the investigators looked backward at records that had already been generated by routine clinical care. Their outcome of interest was acute inter-hospital transport, the urgent movement of a newborn from one hospital to another, typically because the receiving institution can provide a level of neonatal intensive care that the discharging hospital cannot. These transports are among the highest-stakes events in neonatal medicine. A tiny infant, often weighing less than a carton of milk, is placed in a portable incubator, connected to a transport ventilator, monitored continuously, and driven or flown across traffic and distance to a destination intensive care unit. Every minute of that journey carries physiologic risk, and the quality of the stabilization before departure can determine whether the infant arrives in stable condition or in crisis.

To isolate the effect of race and ethnicity on transport likelihood, the team calculated odds ratios comparing each racial and ethnic group against non-Hispanic White infants. The comparison groups included non-Hispanic Black infants, infants classified as Asian American, Native Hawaiian, and Pacific Islander, often abbreviated AANHPI, and Hispanic infants. Critically, the models did not stop at this simple comparison. The investigators controlled for a battery of confounding factors organized into four domains: the characteristics of the hospital network in which the birth occurred, hospital-level factors such as the level of the neonatal intensive care unit, maternal sociodemographic characteristics, and infant clinical characteristics that reflect how sick the baby was at the time a transport decision would be made.

The central finding was striking in its specificity. Asian American, Native Hawaiian, and Pacific Islander infants were significantly less likely to be acutely transported than non-Hispanic White infants, with an odds ratio of 0.85 and a p-value of 0.01 after full risk adjustment. An odds ratio of 0.85 translates to roughly a 15 percent lower odds of transport for this group compared with their White counterparts, once all measured sources of confounding have been removed from the equation. In other words, this was not a difference explained by where these infants happened to be born, by the capabilities of their birth hospitals, by their gestational age or birth weight, or by the illness severity recorded in their charts. Something in the system itself appeared to be operating differently for these infants.

Equally instructive was what the analysis revealed about the strongest predictors of transport overall. Hospital-level factors, most notably the level of the neonatal intensive care unit at the birth hospital, were the variables most significantly associated with whether a transport occurred. This makes biological and organizational sense. An infant born at a community hospital without a Level III or Level IV neonatal intensive care unit is far more likely to require transfer to a regional center than an infant born already inside such a center. This mechanism is the entire logic of perinatal regionalization, the organized system through which states route high-risk mothers and infants toward hospitals with the resources to care for them. Studies stretching back to the 1980s have consistently demonstrated that very low birth weight infants delivered at appropriate levels of care experience lower mortality, and meta-analyses have confirmed the survival advantage of regionalized systems.

Yet the system does not always function as designed. Prior work by some of the same investigators has documented the phenomenon of deregionalization, in which an increasing share of very low birth weight infants are born at hospitals that lack the highest levels of neonatal capability, eroding the protective effect of the regional model. Other research from the California Perinatal Quality Care Collaborative has shown that racial and ethnic disparities extend deep into the quality of care itself, with infants from minoritized groups receiving care in neonatal intensive care units that systematically deliver lower-quality services, a pattern described as racial segregation and inequality within the neonatal intensive care landscape. The new transport findings fit into this larger mosaic, suggesting that inequities are not confined to what happens inside intensive care units but also shape the very pathways by which infants move between them.

The authors’ interpretation of their findings is deliberately measured. They conclude that the differing likelihood of acute transport by race and ethnicity may reflect underlying inequities and implicit biases in the system of care. This framing is important because it locates the problem not in the decisions of any single clinician but in the accumulated, often invisible patterns of how referrals are initiated, how transport teams are dispatched, and how risk is assessed across different patient populations. Implicit bias in clinical decision-making is well documented across medicine, and neonatal transport involves rapid judgments under time pressure, exactly the conditions in which unexamined assumptions about patients are most likely to influence behavior. Whether the lower transport rate among AANHPI infants reflects under-triage, differences in referral relationships, communication barriers, or other mechanisms is a question the study was not designed to answer, but the statistically robust association demands investigation.

The implications for policy and practice are concrete. Because hospital-level factors dominate the transport equation, strengthening adherence to regionalization principles, ensuring that high-risk deliveries occur at appropriately equipped hospitals, and auditing transport decisions for racial and ethnic equity are all actionable levers. The study also underscores the value of the granular data infrastructure maintained by quality collaboratives, which made it possible to detect a disparity that would be invisible in national aggregates. For the AANHPI category in particular, the finding adds urgency to calls for disaggregated data, since this grouping bundles together populations with widely divergent risk profiles and outcomes. What the study ultimately delivers is a measurable signal that the ambulance is not the same ambulance for every baby, a finding that should resonate far beyond California and into every perinatal system that claims, as its founding promise, that the sickest infants will reach the highest level of care regardless of who they are.

The dataset underpinning the analysis came from the California Perinatal Quality Care Collaborative, a statewide initiative that collects detailed clinical information from neonatal intensive care units across California. Because these records are gathered under a data use agreement rather than released publicly, the authors note that the underlying data are available only upon reasonable request with the collaborative’s permission. This governance model is common among quality collaboratives, which balance the research value of granular clinical data against the privacy protections owed to patients and participating hospitals.

The study’s statistical approach deserves emphasis. By adjusting simultaneously for network, hospital, maternal, and infant characteristics, the investigators sought to ensure that the observed difference in transport odds was not an artifact of clustering, since infants born at the same hospital share equipment, staffing, and referral practices. This kind of multilevel risk adjustment is essential in perinatal research, where the hospital an infant is born in is itself shaped by maternal residence, insurance, and patterns of segregation that precede any clinical decision.

The findings also connect to a long line of evidence on neonatal outcomes by race and ethnicity. Prior studies have documented disparities in very preterm neonatal morbidities, differences in mortality among very preterm infants across hospitals in large cities, and variation in outcomes for infants born at less than 30 weeks of gestation over time. Systematic reviews of neonatal intensive care have catalogued racial and ethnic differences spanning access, quality, and outcomes, indicating that no single point in the care pathway is immune.

Acute transport occupies a distinctive position in this pathway because it sits at the junction of multiple institutions. A transport decision requires coordination between the referring hospital, the transport team, and the receiving center, each with its own protocols and thresholds for escalation. Disparities arising at this junction may be harder to detect than disparities within a single unit, which makes the statistical signal reported here particularly valuable for quality improvement efforts aimed at ensuring equitable access to regionalized care.

Subject of Research: Racial and ethnic disparities in acute inter-hospital neonatal transport among very low birth weight infants in California

Article Title: Differential transport patterns by race and ethnicity in very low birth weight infants

Article References: Kunz, S. N., Zitnik, M., Helkey, D., Razdan, S., Gould, J. B., Profit, J., & Zupancic, J. A. F. (2026). Differential transport patterns by race and ethnicity in very low birth weight infants. Journal of Perinatology. https://doi.org/10.1038/s41372-026-02896-3

Image Credits: AI Generated

DOI: 10.1038/s41372-026-02896-3

Keywords: very low birth weight, neonatal transport, race and ethnicity, health disparities, neonatal intensive care, perinatal regionalization, odds ratio, California, Journal of Perinatology, AANHPI, NICU level, implicit bias