The researchers analyzed patient-level records from routine tuberculosis case management in Liangshan, Sichuan Province. The source workbook contained 521 tuberculosis/HIV records, from which the investigators selected 340 episodes with a closed status. They excluded 153 episodes that were still under treatment and 28 re-registered episodes, analyzing the closed cohort over an administrative observation and censoring window from 20 October 2024 to 30 June 2026. Treatment initiation was defined as time zero, and elapsed days from the recorded initiation date formed the time scale. Follow-up ended at the earliest recorded treatment-stop date or 30 June 2026. The cohort was overwhelmingly composed of Yi patients, who represented 332 of 340 participants, or 97.6%, and 260 participants, or 76.5%, were male. Mean age was 41.04 years, with a standard deviation of 10.89 years, and the median age was 41 years with an interquartile range of 36–47 years.

Among the 340 episodes, 27, or 7.9%, ended in the composite unfavorable outcome; 303, or 89.1%, ended in cure or treatment completion; and 10, or 2.9%, were administratively censored. The unfavorable group comprised 22 records coded as non-tuberculosis death, three transfers to multidrug-resistant tuberculosis treatment, one treatment failure and one loss to follow-up. Thus, non-tuberculosis deaths accounted for 81.5% of unfavorable outcomes. The source field contained no separately coded category for tuberculosis-related death, so the analysis did not estimate all-cause mortality and could not distinguish the mortality burden attributable to tuberculosis. Transfer to multidrug-resistant treatment was classified as unfavorable because it ended the observed episode, not because the investigators interpreted it as biological treatment failure.

The Aalen–Johansen estimator showed that most of the unfavorable cumulative incidence accrued early. It was 4.41% at 3 months, with a 95% confidence interval of 2.35% to 6.77%; 6.47% at 6 months, with an interval of 3.82% to 9.12%; 7.65% at 9 months, with an interval of 5.00% to 10.60%; and 7.96% at 12 months, with an interval of 5.30% to 10.91%. By 3 months, 55.5% of the 12-month unfavorable cumulative incidence had already accumulated. In parallel, the cumulative incidence of cure or treatment completion rose from 0.29% at 3 months to 14.71% at 6 months, 55.73% at 9 months and 81.83% at 12 months. Pointwise 95% confidence intervals for these cumulative-incidence functions were generated from 2,000 individual-level bootstrap resamples. The analysis did not perform formal pairwise or Gray-type comparisons between the cumulative-incidence functions.

The statistical approach addressed a central feature of tuberculosis treatment data: favorable and unfavorable outcomes compete for the opportunity to occur. In an earlier analysis, cure and treatment completion were treated as censoring observations in Kaplan–Meier and Cox analyses. The authors explained that this practice can alter the estimand and overestimate absolute event probability because it effectively redistributes some probability among participants who remain under observation. Their reanalysis instead used Fine–Gray subdistribution hazard models to examine associations with the cumulative-incidence estimand. A prespecified three-variable sensitivity model included age, sex and patient source recoded as tracing versus all other sources, while a cause-specific hazards model tested whether the principal estimates were directionally consistent. The full model contained 14 parameters and was treated as exploratory because only 27 unfavorable outcomes occurred.

Older age produced the most consistent exploratory association. In the full 14-parameter model, each 10-year increase in age was associated with a 1.54 times higher subdistribution hazard, with a 95% confidence interval of 1.02 to 2.32 and a p value of 0.039. The corresponding association in the parsimonious Fine–Gray model was 1.70, with an interval of 1.18 to 2.43 and a p value of 0.004. A cause-specific hazards model yielded a similar estimate of 1.73, with an interval of 1.20 to 2.50 and a p value of 0.003. These results identify an association within the analyzed cohort, not an independent causal effect or a validated clinical prediction rule. The surveillance extract did not include measurements that could test proposed mechanisms such as physiological reserve, comorbidity, polypharmacy, delayed presentation or treatment toxicity, so the analysis cannot determine why older age was associated with the outcome.

Patients identified through tracing also had elevated estimates in selected analyses. Compared with direct presentation, tracing had an unadjusted subdistribution hazard ratio of 5.45, with a 95% confidence interval of 1.63 to 18.29 and a p value of 0.006. In the full exploratory model, the adjusted estimate was 3.12, with an interval of 0.81 to 11.93 and a p value of 0.097, making it inconclusive in that specification. The parsimonious model gave a subdistribution hazard ratio of 4.73, with an interval of 1.89 to 11.86 and a p value of 0.001, while the cause-specific model gave a hazard ratio of 4.62, with an interval of 1.84 to 11.59 and a p value of 0.001. The investigators did not calculate a crude cumulative incidence stratified by source, so these regression estimates should not be recast as a directly measured difference in absolute risk. They also did not measure possible explanations involving previous disengagement, delayed presentation, disease severity, mobility, stigma or structural barriers.

Associations involving incomplete documentation were much less stable. Five of seven patients whose fixed-dose-combination field was missing and all three patients whose bacteriological result was unavailable experienced unfavorable outcomes. The full model consequently produced an adjusted subdistribution hazard ratio of 5.41 for missing fixed-dose-combination status, with a 95% confidence interval of 1.48 to 19.77, and 79.77 for unavailable bacteriology, with an interval of 11.63 to 547.26. These extreme estimates arose from very small cells and should not be interpreted as effects of treatment formulation or microbiology. The source system did not document why the fields were missing. Early deterioration, death, diagnostic workflows, treatment interruption or service processes could all be related to incomplete records, creating information bias and possible reverse causation. No imputation was performed because the missingness mechanism was unknown. Missing and unavailable values were retained as explicit data-completeness categories rather than treated as biological exposures.

Several design and modeling limitations constrain the findings. The retrospective cohort included only 27 unfavorable events but 14 parameters in its full model, making overfitting, wide confidence intervals, separation and unstable p values likely. The composite endpoint was clinically heterogeneous and primarily reflected mortality rather than a balanced combination of failure, loss to follow-up and transfer. Routine cause-of-death coding may be inaccurate, and the analysis could not include a separate tuberculosis-related death category. The data also lacked CD4 count, HIV viral load, antiretroviral therapy timing and adherence, body mass index, anemia, opportunistic infections, tuberculosis site and severity, drug susceptibility, adverse reactions and socioeconomic measures. The nearly entirely Yi population from one prefecture further restricts generalizability, while selection effects, residual confounding and errors in event dates or classifications remain possible.

The proportional-hazards assessment did not settle whether a constant effect was appropriate over time. A joint interaction test that incorporated uncertainty in the estimated censoring weights suggested non-proportionality, principally involving sex, with a Wald statistic of 9.096, 3 degrees of freedom and a p value of 0.028. An inverse-information sensitivity calculation for the same model did not support that conclusion, producing a statistic of 2.536 and a p value of 0.469. Because only three women experienced unfavorable outcomes, these asymptotic tests were unstable, and proportional subdistribution hazards could not be regarded as established. Overall, the analysis demonstrates how a competing-risk framework can estimate absolute outcome probability while recognizing that favorable treatment completion permanently ends the relevant risk period. Its age and tracing associations warrant evaluation in larger linked cohorts with more events, fuller clinical data, prespecified models, outcome-specific analyses and external validation; the current results do not establish causal effects, clinical prediction performance or a generalizable treatment-failure rate.

Subject of Research: Competing-risk estimation of tuberculosis treatment outcomes among people living with HIV in Liangshan, China

Article Title: Cumulative incidence and factors associated with unfavorable tuberculosis treatment outcomes among people living with HIV in Liangshan, China: a competing-risk analysis

Article References: Yang, N., Li, T., Li, Y., & Xia, L. (2026). Cumulative incidence and factors associated with unfavorable tuberculosis treatment outcomes among people living with HIV in Liangshan, China: a competing-risk analysis. Frontiers in Public Health, 14, Article 1958022. https://doi.org/10.3389/fpubh.2026.1958022

Image Credits: AI Generated

DOI: 10.3389/fpubh.2026.1958022

Keywords: tuberculosis, HIV, competing risks, cumulative incidence, Liangshan, treatment outcomes, Fine–Gray model, retrospective cohort, mortality, survival analysis, Cumulative, incidence

The study screened adult ICU patients by rectal culture at admission and weekly from July 2022 through August 2025. Rectal CRE colonization was identified in 287 of 3,221 ICU admissions, or 8.9%. Colonization was present at admission in 88 cases (30.7%) and acquired during the ICU stay in 199 (69.3%). Sixteen infections were detected on the colonization date and evaluated separately, leaving 271 admissions for the primary analysis of infection occurring after colonization.

For the risk analysis, time zero was defined as the first documented CRE colonization date. The researchers used competing-risk methods to estimate cumulative incidence, treating ICU death and live ICU discharge before infection as competing events. No evaluated factor was independently associated with subsequent infection in the Fine-Gray model. Prior colistin exposure was borderline but not statistically significant, with a subdistribution hazard ratio of 1.53, a 95% confidence interval of 0.99–2.37 and p = 0.057.

Mortality was higher among patients who developed subsequent infection than among those who did not: 71.2% versus 55.4% (p = 0.007). This was an observed association in a retrospective cohort and does not establish that subsequent infection caused the higher mortality. The findings are limited by the single-center design, retrospective data and the screened adult ICU population, and the available report is an abstract of an accepted article subject to later revision rather than a full assessment of the study.

Subject of Research: Prevalence, timing, outcomes and risk factors for subsequent CRE infection after rectal colonization in adult ICU patients

Article Title: Rectal colonization with Carbapenem-resistant Enterobacterales in intensive care unit patients: prevalence, clinical outcomes, and risk of subsequent infection

Article References: Yüce Yıldırım, D., Şenol, G., Yiş, R., & Gülfidan, Ö. G. (2026). Rectal colonization with Carbapenem-resistant Enterobacterales in intensive care unit patients: prevalence, clinical outcomes, and risk of subsequent infection. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14445-6

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14445-6

Keywords: CRE colonization, rectal culture, intensive care units, subsequent infection, carbapenem resistance, Enterobacterales, cumulative incidence, Fine-Gray model, retrospective cohort, patient mortality, Rectal, colonization

The study’s design reflects a pragmatic response to a structural gap in Japanese health data. Unlike the United States, where donor outcomes can be tracked through linked national records such as the Scientific Registry of Transplant Recipients, Japan has no routine individual-level linkage between its transplant registry and its dialysis registry. The researchers therefore had to construct their estimate from aggregate cross-tabulations, combining the Japanese Renal Data Registry’s relatively new item on donor history among incident dialysis patients with published counts of living-donor transplants stretching back to 1999. This period approach, which applies the dialysis rates observed in 2019 through 2024 to every interval since donation, is a well-established epidemiological technique, but it carries an inherent assumption: that the risk of kidney failure in a given decade after donation is similar across donation cohorts. The authors checked this assumption and found no meaningful differences in rates between donation cohorts, which lends some support to the approach even though it cannot be verified with certainty.

The statistical machinery behind the headline figure deserves attention because it illustrates how epidemiologists handle uncertainty when perfect data are unavailable. The team used cause-specific hazards calculated on death-censored donor-years, meaning that years in which a donor died were removed from the denominator so that the estimate reflects the risk of dialysis among living donors rather than being diluted by deaths. These hazards were then converted into a cumulative incidence using the Aalen–Johansen estimator, which properly accounts for death as a competing risk — a donor who dies before developing kidney failure can never reach that outcome, and ignoring this would overstate the dialysis risk. The authors then produced three estimates that bracket the truth: an unadjusted figure of 0.5 percent, a death-censored net estimate of 1.3 percent, and a primary adjusted estimate of 1.1 percent that allocates interval-unknown cases proportionally and imputes donor cases among patients whose donor status was never recorded. The honesty of this bounding approach is notable; rather than presenting a single number as definitive, the authors explicitly state that the resulting range of roughly 0.5 to 2 percent is not a confidence interval and that values above 2 percent cannot be excluded if the sensitivity of the registry item falls below 0.6.

The demographic profile of Japanese donors makes these findings particularly relevant to counselling conversations in that country. More than 40 percent of living donations in Japan come from spouses, who are unrelated donors, and roughly half of donors are aged 60 or older at the time of donation. This age structure differs substantially from that of the United States, where donors tend to be younger at donation. The comparison with American data illustrates this point vividly: the net Japanese estimate of 1.3 percent at 20 years is 2.7 times the US figure of 0.49 percent, but when the comparison is restricted to donation before age 60, the ratio narrows to 1.7, and it falls further when Japanese estimates are compared against white or unrelated American donors specifically. In other words, much of the apparent gap between the two countries is explained by the simple fact that Japanese donors are older, and kidney failure risk rises steeply with both age and time since donation. This is a crucial nuance for anyone interpreting the numbers: the elevated Japanese figure is not necessarily evidence that Japanese donors face intrinsically higher risks, but rather that the population being measured is older on average.

The authors are also candid about the limits of comparing donors with the general population. Donors are selected for good health, so their baseline risk should sit well below that of the general population, which includes people with diabetes and chronic kidney disease — conditions that account for a large share of dialysis initiations in Japan as elsewhere. American data suggest donors have roughly 0.3 times the lifetime kidney failure risk of the general population, so a standardized incidence ratio near 1, which is what the Japanese analysis found at 1.23, would already imply some excess risk relative to what selection alone would predict. Yet the authors caution that this ratio depends heavily on the accuracy of the donor-history item in the dialysis registry: with 41 percent of incident dialysis patients having no recorded donor status, the standardized incidence ratio falls below 1 if the positive predictive value of the recorded item reaches 0.8. The data therefore cannot settle whether Japanese donors carry an excess risk or whether donor selection in Japan is less stringent than elsewhere — a question made more pointed by the observation that nearly a quarter of recent Japanese donors have hypertension, most of it treated. The authors’ recommendation to focus on absolute risk rather than ratios is a methodologically sound one for counselling purposes.

Several of the limitations the authors describe are worth unpacking for readers who want to understand what the estimate can and cannot capture. Misclassification runs in both directions: six of the 94 recorded donors had a documented kidney transplant, suggesting their kidney failure may have occurred in the transplanted organ or after re-transplantation rather than in the remaining native kidney, and two had an incompatible primary disease. Excluding these cases lowers the estimate slightly to 1.0 percent. More troubling is the reverse direction — donors recorded as non-donors are undetectable by design, and the registry has no code for kidney failure occurring in a solitary kidney, so some genuine donor cases may be buried in the “other” primary disease category, where donor history was 14 times as frequent as in diabetic nephropathy. The registry also counts only dialysis, meaning donors who received a pre-emptive transplant, died before reaching dialysis, or chose conservative management of advanced kidney disease are missed entirely. This last omission is particularly consequential for older donors, among whom conservative management is more common, so the 1.6 percent figure for donation at age 60 or older likely understates the true burden of severe kidney disease in that group.

The finding that over half of recorded dialysis initiations occurred more than 20 years after donation carries significant implications for how the numbers should be communicated. The exploratory extension of the analysis, using transplant denominators going back to 1971, suggests a cumulative risk of about 2.2 percent at 30 years — roughly one in 50 donors. Because today’s donors are older than the cohorts whose second and third post-donation decades the study could observe, the 20-year figure probably understates the lifetime risk that current donors will actually face. The authors’ standardization of recent age-specific estimates to the current donor age mix yields 1.3 percent at 20 years, supporting this interpretation. For prospective donors, the practical message is that kidney failure risk after donation is a moving target that grows with advancing age, and that a counselling conversation framed only around a 20-year horizon may substantially understate the relevant lifetime picture, particularly for someone donating in their sixties.

What the study does not claim is equally important. The authors emphasize that these figures represent the absolute risk of dialysis after donation, not the risk attributable to donation itself. Disentangling how much kidney failure among donors would have occurred anyway, had they not donated, requires comparison against a carefully matched non-donor population, which the aggregate registry data cannot provide. The authors also note that the figures do not by themselves change who may be eligible to donate; rather, they equip prospective Japanese donors with a concrete, nationally derived number for the consent process and support the case for monitoring kidney function well beyond 20 years after donation, especially in older donors. Registry linkage, which the authors identify as the path forward, would enable precise age- and sex-specific estimates and resolve the uncertainties that currently force the analysis to rely on bounding assumptions. Until then, the Japanese figures join those from the United States and Norway as part of a small but growing international evidence base, offering donors in each country numbers calibrated to their own population’s demographics and health system rather than imported from abroad.

Subject of Research: Long-term risk of dialysis initiation among living kidney donors in Japan

Article Title: Risk of dialysis initiation within 20 years after living kidney donation in Japan: a period estimate from the national dialysis and transplant registries

Article References: Torigoe, K., Tsuyuki, T., Iwata, M., Otsuka, E., Tsuji, K., Yamashita, A., Torigoe, M., Kitamura, M., Muta, K., Imamura, R., & Nishino, T. (2026). Risk of dialysis initiation within 20 years after living kidney donation in Japan: a period estimate from the national dialysis and transplant registries. Transplant International, 39, Article 17852. https://doi.org/10.3389/ti.2026.17852

Image Credits: AI Generated

DOI: 10.3389/ti.2026.17852

Keywords: living kidney donation, dialysis, kidney failure, Japan, registry study, transplantation, nephrology, donor risk, end-stage renal disease, informed consent, cumulative incidence, aging donors

The research team, led by Xiaoxin Wu and Huadong Liang of Longyan First Affiliated Hospital of Fujian Medical University in China, set out to answer a question that has long troubled oncologists: how does the risk of a subsequent malignant brain tumor vary with both the age at which the first cancer was diagnosed and the length of time that has elapsed since then? Most previous studies have reported a single overall risk figure for survivors, which can obscure important patterns. By jointly analyzing age and latency, the researchers revealed structure that a summary statistic would have hidden.

To conduct the analysis, the investigators used the Surveillance, Epidemiology, and End Results, or SEER, program, a network of population-based cancer registries maintained by the National Cancer Institute. Specifically, they examined the SEER 8 registries, the original eight registries that have contributed data continuously since 1975, using the November 2025 submission covering the years 1975 through 2023. The study population consisted of 276,844 people who had been diagnosed with a first primary malignancy other than a brain or other nervous system cancer before reaching the age of 40. Together these individuals contributed 3,970,462 person-years of observation, an enormous cumulative window into the long-term health of cancer survivors.

The central statistical tool was the standardized incidence ratio, or SIR, a measure that compares the number of tumors actually observed in the survivor cohort with the number that would be expected if the cohort experienced the same age-, sex-, and calendar year-specific rates as the general population. An SIR of 1.0 indicates no excess; values above 1.0 indicate more tumors than expected. Over the entire follow-up period, 345 malignant brain and other nervous system tumors occurred in the cohort, against 236.97 expected, yielding an overall SIR of 1.46 with a 95 percent confidence interval of 1.31 to 1.62. The excess absolute risk, which translates the relative excess into additional cases per unit of population time, was 2.72 per 100,000 person-years.

That overall figure, however, concealed a steep age gradient. Among survivors whose first cancer was diagnosed between birth and age 14, the SIR was 5.20, meaning they experienced more than five times the expected number of malignant nervous system tumors. For those diagnosed at ages 15 to 19, the SIR fell to 1.84. Among survivors diagnosed at ages 20 to 29, the ratio was 1.17, and for those diagnosed at ages 30 to 39 it was 1.23, both only modest elevations. The pattern is striking: the younger the patient at the original cancer diagnosis, the greater the relative excess of later nervous system tumors, with the childhood group standing far apart from the rest.

Crucially, the researchers found that this age pattern itself changed depending on how much time had passed since the first diagnosis, a statistical interaction that was significant at a p-value of 0.0012. Latency, the interval between the first cancer and the subsequent tumor, matters because different mechanisms of tumor development unfold on different timescales. Radiation-induced tumors, for example, typically require years to decades to emerge, while other processes may act more quickly. When the analysis was restricted to intervals of ten or more years after the original diagnosis, the childhood group still showed an SIR of 4.64, with a 95 percent confidence interval of 3.22 to 6.49. In other words, the elevated relative risk among childhood cancer survivors did not fade away with time; it persisted well beyond a decade of survival.

To complement the relative measures, the team performed a supplementary competing-risk analysis estimating cumulative incidence, which accounts for the fact that survivors may die of other causes before a second tumor ever appears. Starting from a two-month landmark after the original diagnosis, the 20-year cumulative incidence of a malignant brain or other nervous system tumor was 0.257 percent among childhood cancer survivors, with a 95 percent confidence interval of 0.193 to 0.341 percent. This figure puts the risk in perspective: even in the highest-risk group, roughly one in four hundred childhood cancer survivors developed a malignant nervous system tumor within twenty years. The authors emphasized that while the relative excess was substantial, the absolute excess and cumulative incidence were small.

The findings align with a substantial body of prior research on subsequent malignant neoplasms, the medical term for new primary cancers arising in survivors. Landmark analyses from the Childhood Cancer Survivor Study, a cohort of more than five-year survivors of childhood cancer treated at institutions across North America, have documented elevated risks of central nervous system tumors, with cranial radiation therapy identified as a major driver. Population-based studies from the British Childhood Cancer Survivor Study and, more recently, work published in the Journal of the National Cancer Institute on temporal trends of subsequent central nervous system malignancies have similarly confirmed that survivors of childhood cancer remain at heightened risk for decades. Studies of adolescent and young adult survivors, including the Teenage and Young Adult Cancer Survivor Study in Britain and a recent population-based analysis from Alberta, Canada, have generally found lower relative risks in those diagnosed later in life, consistent with the age gradient observed here.

Why should age at diagnosis matter so much? Several biological explanations are plausible, though the registry design of this study cannot distinguish among them. Younger tissues are generally more susceptible to the carcinogenic effects of radiation and chemotherapy, and a diagnosis early in life leaves more remaining years for latent damage to manifest. The developing nervous system may be particularly vulnerable to treatment-related DNA damage during critical periods of growth. Inherited cancer predisposition syndromes, which are more likely to be diagnosed in childhood, may also contribute to second tumor risk. The SEER registry data do not include detailed treatment information, so the study cannot directly attribute the excess to radiation dose or specific agents, a limitation the authors implicitly acknowledge by framing the work as a descriptive, population-based analysis.

The practical implications reach into survivorship care. Long-term follow-up guidelines issued by the Children’s Oncology Group already recommend surveillance tailored to treatment exposure, and the new results reinforce the importance of sustained, decades-long follow-up for childhood cancer survivors specifically, since their excess risk remained evident beyond ten years. At the same time, the low absolute risk argues against indiscriminate intensive screening of all survivors, particularly those diagnosed in adolescence or adulthood whose excess was modest. The authors note that joint assessment of diagnosis age and latency characterized patterns that the overall estimate obscured, a methodological point with resonance for other survivorship research. The study’s analysis code has been publicly archived on Zenodo and maintained on GitHub, and the underlying SEER data remain available to eligible researchers through the National Cancer Institute, allowing independent verification of the findings. For the hundreds of thousands of people who survive cancer before age 40 each year, the message is one of calibrated vigilance: the risk of a later malignant brain tumor is real and highest for those diagnosed as children, but it remains, in absolute terms, uncommon.

Subject of Research: Age- and latency-specific risk of subsequent malignant brain and nervous system tumors among survivors of cancer diagnosed before age 40

Article Title: Age- and latency-specific patterns of subsequent malignant brain and other nervous system tumors after cancer diagnosed before age 40 years

Article References: Wu, X., Liang, H., Wu, Q., & Li, X. (2026). Age- and latency-specific patterns of subsequent malignant brain and other nervous system tumors after cancer diagnosed before age 40 years. Cancer Causes & Control, 37(10), Article 177. https://doi.org/10.1007/s10552-026-02265-7

Image Credits: AI Generated

DOI: 10.1007/s10552-026-02265-7

Keywords: childhood cancer, cancer survivors, brain tumor, subsequent malignant neoplasm, standardized incidence ratio, SEER program, cancer epidemiology, latency, adolescent and young adult cancer, survivorship, nervous system tumors, cumulative incidence