The study, published in BMC Public Health, examined inpatients admitted to the First Affiliated Hospital of Henan Medical University between January 2018 and December 2025. The research team, led by Danxia Ji and Kang Li with corresponding author Jianhua Zhao, identified rare disease cases using ICD-10 diagnostic codes mapped to China’s two national rare disease catalogs, released in 2018 and 2023. Because rare diseases are scattered across virtually every medical specialty and often lack standardized coding, building a reliable case series from routine hospital data is a methodological challenge in itself. By anchoring the search to the official national lists, the investigators aimed to capture a clinically meaningful and policy-relevant cohort rather than an arbitrary assortment of uncommon diagnoses.

What emerged was a dataset of 3,496 rare disease inpatient cases, representing 0.38 percent of all hospitalizations at the center during the study window. That fraction may sound trivial, but the trajectory tells a different story. The annual count of rare disease admissions rose consistently across the eight-year period, reaching a peak of 1,098 cases in 2025, which alone accounted for 31.4 percent of the entire eight-year total. In other words, nearly a third of all rare disease hospitalizations recorded over the study period occurred in its final year. The authors used ARIMA time-series modeling to project the trend forward, and the model predicts a continued upward climb in 2026, suggesting the observed growth is not a statistical fluke but a sustained pattern.

Part of that increase likely reflects genuine growth in case volume, but the authors and the study design also point to the role of improved recognition. China’s publication of the 2018 national rare disease list gave hospitals a shared framework for identifying and coding these conditions, and the 2023 expansion of that list broadened the diagnostic net further. A tertiary referral hospital in a densely populated province is precisely the kind of institution where patients with long-unexplained symptoms eventually arrive, so rising admission counts may partly represent patients who previously circulated through the system without a rare disease label ever being attached to their records.

The demographic profile of the cohort was broad in every sense. Men accounted for 55.6 percent of cases and women 44.4 percent, and the mean age was 49.56 years with a strikingly large standard deviation of 24.52 years. That spread means the cohort ranged from children to the very elderly, underscoring a central difficulty of rare disease medicine: these conditions do not respect age boundaries, and a hospital service designed around common adult or pediatric disease patterns will struggle to accommodate such heterogeneity. Geographically, the cohort was heavily regional, with 98.1 percent of cases coming from just five cities and Xinxiang, the hospital’s home city, predominating. That concentration reflects both the catchment area of a provincial tertiary center and the reality that patients with complex, rare conditions tend to funnel toward the highest tier of care available to them.

By disease category, the burden was dominated by two organ systems. Nervous system diseases made up 29.1 percent of cases and respiratory system diseases 21.4 percent, together accounting for roughly half of all rare disease hospitalizations. Among individual diagnoses, idiopathic pulmonary fibrosis was the single most frequent condition, with 745 cases representing 21.3 percent of the cohort. This chronic, progressively scarring lung disease is a well-known driver of repeated hospitalizations in the later stages of illness, and its prominence here illustrates how a handful of relatively better-recognized rare conditions can shape the overall inpatient picture even when hundreds of other diagnoses contribute smaller numbers.

The economic findings are among the most consequential for policy. The highest mean total hospitalization cost was recorded for perinatal rare diseases, at 35,800 Chinese yuan on average with a standard deviation of 26,037 yuan, reflecting the intensive neonatal and obstetric care these cases demand. Tumor-related rare diseases followed closely, with mean costs of 20,353 yuan and a standard deviation of 19,955 yuan, a figure inflated by oncology workups, targeted therapies, and prolonged stays. The wide standard deviations in both categories reveal how uneven the financial hit can be even within a single diagnostic group: some patients incur modest costs while others generate bills many times the average. For households in a province where a large share of the population relies on the Urban-Rural Resident Basic Medical Insurance scheme, out-of-pocket exposure to costs of this magnitude can be catastrophic, which is precisely why the study’s authors frame their data as an evidence base for healthcare policy rather than a purely clinical exercise.

Readmission patterns added another layer of nuance. Among the top 15 diseases ranked by readmission rate, ANCA-associated vasculitis, an autoimmune condition in which the body’s own antibodies attack small blood vessels, had the highest rate at 83.5 percent, meaning the vast majority of affected patients returned to the hospital after an index admission. At the other extreme, multiple system atrophy, a progressive neurodegenerative disorder, had the lowest readmission rate among the group at 9.7 percent, a figure that likely reflects the relentless, end-stage trajectory of that disease rather than successful outpatient management. Between those poles, the readmission landscape varied enormously, and the authors used wavelet analysis to test whether hospitalizations clustered in predictable monthly cycles. They found no significant periodicity, indicating that rare disease admissions do not follow seasonal rhythms the way respiratory infections do, and therefore cannot be anticipated with calendar-based staffing or bed planning.

The study’s technical toolkit deserves attention because it signals a shift in how hospital epidemiology is being done. Beyond standard descriptive statistics, the team applied wavelet analysis, a signal-processing method that decomposes time-series data into different frequency components to detect periodic structure, and ARIMA modeling, a classical autoregressive integrated moving average framework for forecasting counts from their own past values. Neither method is exotic by the standards of time-series research, but applying them to rare disease admission data is relatively novel, and the combination allowed the authors to separate trend from noise and to generate a quantitative forecast for 2026 rather than a vague expectation of continued growth.

The limitations of a single-center retrospective design are real and the authors do not disguise them. The data capture only patients who were hospitalized at one tertiary institution in northern Henan, so the findings cannot be generalized to the whole province or country, and cases managed entirely in outpatient clinics or smaller hospitals are invisible here. Coding practices may also have shifted over the eight-year window, particularly after the national catalogs changed how clinicians label rare diagnoses. Still, the value of the work lies in its granularity. By quantifying who is admitted, with what conditions, at what cost, and how often they come back, the study gives regional health planners in China something they have historically lacked: concrete numbers on which to base decisions about rare disease care capacity, insurance coverage, referral networks, and specialist training. As China’s national rare disease policy framework matures, studies of this kind, replicated across other regions and centers, will be essential to turning a list of rare disease names into a functioning system of care for the millions of patients those names represent.

Subject of Research: Characteristics and hospitalization burden of rare disease inpatients at a tertiary hospital in northern Henan, China, from 2018 to 2025

Article Title: Characteristics and hospitalization burden of rare disease inpatients in Northern Henan, 2018–2025: a single-center retrospective observational study

Article References: Ji, D., Li, K., Wang, N., Chen, Z., Zhai, X., Ren, R., & Zhao, J. (2026). Characteristics and hospitalization burden of rare disease inpatients in Northern Henan, 2018–2025: a single-center retrospective observational study. BMC Public Health. https://doi.org/10.1186/s12889-026-29790-z

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29790-z

Keywords: rare diseases, hospitalization burden, tertiary hospital, Henan, ICD-10 coding, idiopathic pulmonary fibrosis, ANCA-associated vasculitis, readmission rates, ARIMA model, wavelet analysis, healthcare costs, BMC Public Health

The research, published in Supportive Care in Cancer, surveyed 100 patients with confirmed multiple myeloma at the German tertiary center over a 12-month period. Using a mixed-methods design, the team combined structured questionnaires with free-text narratives, then integrated the two strands to see where they agreed, complemented each other, or diverged. The central concept under investigation was the treatment-free interval, or TFI: a stretch of time between active anti-myeloma treatment contacts during which no scheduled anti-myeloma therapy is given. Crucially, the researchers were careful to define TFIs as periods that could still include monitoring visits, laboratory tests, supportive-care contacts, and urgent reassessment—so a treatment-free interval is not the same as going off the radar.

The headline finding is striking. Nearly half of the participants—49 of 100, or 49.0 percent—rated the importance of intervals between inpatient treatments at the maximum of 10 on a 10-point scale, while only 9 percent rated them at zero. When patients explained in their own words why these breaks mattered, a consistent picture emerged: physical and emotional recovery, reconnecting with family, restoring daily routines, social activities, travel, and a sense of autonomy. For many respondents, the hospital was not simply a place of healing but an environment that displaced them from their own lives. The median self-reported number of inpatient days in the preceding year was 31, meaning that for a typical patient in this cohort, an entire month had been spent inside the hospital.

What made hospital stays so stressful? The most frequently cited stressor was separation from family, reported by 27 percent of participants. Treatment-related symptoms and side effects came next at 23 percent, followed by fatigue at 17 percent, food quality at 15 percent, waiting times at 14 percent, and shared rooms at 12 percent. Communication gaps, climate control problems, and lack of physical activity rounded out the list. These are, in many respects, modifiable factors—details of the care process rather than the disease itself—and they point toward concrete improvements that hospitals could implement without touching a single treatment protocol.

The ripple effects of treatment encounters extended far beyond the ward. One third of participants reported difficulties managing their households, often because of frequent admissions, cognitive strain, or stress, although two thirds managed to avoid major backlogs through planning, delegation, and online tools. Social relationships suffered for 32 percent of respondents, mainly through cancelled plans, infection precautions, and exhaustion. Among the 42 participants who were not retired or early retired, more than half—54.8 percent—reported work-related or role-related limitations, with flexible scheduling and supportive workplace policies described as key mitigating factors. The picture that emerges is of an illness whose treatment footprint touches nearly every domain of daily life.

Yet the study’s most nuanced finding concerns monitoring. While patients clearly valued breaks from active treatment, they did not want to lose touch with their medical team. Regular check-ups were described as reassuring by 77 percent of participants and as burdensome by only 13 percent, with the remainder undecided or neutral. In the qualitative narratives, this reassurance was tied to predictable scheduling, clear communication, and rapid access to the clinic when symptoms appeared. In other words, valuing intervals between treatments and valuing continued follow-up were not mutually exclusive—patients wanted both breathing room and a safety net.

Relapse anxiety, meanwhile, proved to be a powerful counterweight. Only 7 of 100 participants explicitly said they would accept a higher relapse risk in exchange for longer treatment-free intervals. This small minority stands in sharp contrast to the majority, for whom the fear of the myeloma returning outweighed the appeal of extended breaks. Preferred minimum interval lengths were similarly heterogeneous: among 99 respondents, 39.4 percent preferred intervals longer than eight weeks, 23.2 percent preferred less than four weeks, and 19.2 percent fell in the four-to-eight-week range, with 18.2 percent leaving the question unspecified. The explanations patients gave referenced recent treatment intensity, recovery needs, travel logistics, family and work responsibilities, relapse anxiety, and personal circumstances—a reminder that there is no single schedule that fits all.

The timing of this research is significant because the therapeutic landscape of multiple myeloma is changing in ways that make treatment-free intervals newly relevant. Finite-course therapies such as CAR T-cell therapy can create prolonged periods without scheduled anti-myeloma drug administration in responding patients, with long-term remission data now available for products like ciltacabtagene autoleucel and idecabtagene vicleucel. Bispecific antibodies are also increasingly being delivered through outpatient step-up dosing pathways, and prophylactic tocilizumab has been shown to reduce, though not eliminate, cytokine release syndrome during teclistamab initiation. But the authors are emphatic that finite-course treatment should not be equated with low-burden or low-risk care. CAR T-cell therapy carries risks including cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, cytopenias, infections, hypogammaglobulinemia, delayed neurological syndromes, and rare fatal complications, and patients still require surveillance, prophylaxis, immunoglobulin replacement, and supportive-care visits.

The study’s limitations deserve honest acknowledgment. It was a single-center, cross-sectional survey at an academic hospital, so its findings may not generalize to community settings or health systems where care is delivered differently—indeed, some treatments that require admission in Germany are handled on an outpatient basis elsewhere. The number of patients approached and reasons for nonparticipation could not be reconstructed, so a participation rate could not be calculated and nonresponse bias cannot be assessed. The self-reported inpatient-day item was not validated against administrative records, and its wording may not have consistently excluded outpatient or day-case encounters. Current regimen, administration route, treatment line, response status, cytogenetic risk, and minimal residual disease status were not captured in sufficient detail for treatment-specific analysis, and the study-specific questionnaire did not undergo formal psychometric validation. Some archived summary values did not reconcile with the cohort denominator and were omitted rather than imputed—a transparency measure that reflects the team’s methodological caution.

What the study ultimately delivers is a descriptive foundation, not a prescription. The authors explicitly state that their findings do not establish the safety or clinical appropriateness of longer treatment-free intervals and should not be interpreted as support for interrupting evidence-based therapy, which remains central to myeloma care. Lenalidomide maintenance after autologous stem-cell transplantation, for example, has demonstrated progression-free and overall survival benefits in meta-analyses. But the research does make a compelling case for preference-sensitive scheduling: prospective multicenter studies should now examine how patient goals, treatment burden, disease risk, care setting, and monitoring preferences can be woven into follow-up models whose safety and feasibility are rigorously evaluated. In the meantime, the patient-generated improvement proposals—single rooms and quieter conditions, better food, shorter waits, flexible appointments, adequate staffing, empathetic communication, and access to physiotherapy, yoga, or light exercise—offer hospitals an immediately actionable agenda. For a disease in which patients may live many years with treatment, the message from Würzburg is clear: how care is delivered matters almost as much as what is delivered, and the patients best positioned to judge that are the ones living it.

Subject of Research: Patient preferences for treatment-free intervals, monitoring trade-offs, and supportive care in multiple myeloma

Article Title: Patient preferences for treatment‑free intervals in multiple myeloma: mixed‑methods insights on monitoring trade‑offs and supportive care improvements

Article References: Fleischer, A., Barakat, M., Peter, J., Kadel, S.-K., Riedhammer, C., Strunz, P.-P., Mersi, J., Waldschmidt, J., Kortüm, K. M., Einsele, H., Maatouk, I., & Rasche, L. (2026). Patient preferences for treatment‑free intervals in multiple myeloma: mixed‑methods insights on monitoring trade‑offs and supportive care improvements. Supportive Care in Cancer, 34(10), Article 1050. https://doi.org/10.1007/s00520-026-11255-z

Image Credits: AI Generated

DOI: 10.1007/s00520-026-11255-z

Keywords: multiple myeloma, treatment-free intervals, patient preferences, monitoring, supportive care, mixed-methods survey, CAR T-cell therapy, bispecific antibodies, lenalidomide maintenance, hospitalization burden, relapse risk, outpatient follow-up