Nevin Manimala

BMC Pediatr. 2026 Jul 28;26(1):687. doi: 10.1186/s12887-026-07003-1.

ABSTRACT

BACKGROUND: Estimating the total number of children with a life-limiting, complex chronic condition or serious illness has been identified as an important first step in determining how many children might need palliative care in a country or region. Relevant estimates have not been calculated for Ireland, an exercise complicated by the country’s lack of a fully operational unique patient identifier in healthcare data.

METHODS: This study estimated prevalence of life-limiting conditions amongst children aged 0-19 in Ireland in 2019 and 2024, demonstrating methods in the absence of a unique identifier. Baseline hospital-based prevalence calculated as (estimated) number of inpatients with a life-limiting diagnoses per 10,000 population aged 0-19 in Ireland, 2019 (pre-Covid) and 2024 (most recent year available). Number of inpatients were estimated from discharge-level data under 3 alternative scenarios (E1, E2, E3) adjusting for multiple admissions in the data. In sensitivity analysis, adjusted prevalence estimates further corrected for sources of under-counting in the data.

RESULTS: Baseline estimated total prevalence of life-limiting conditions amongst children aged 0-19 in Ireland between 34.8 (E3) and 50.0 (E1) per 10,000 population in 2019; between 32.3 (E3) and 44.5 (E1) per 10,000 population in 2024. In sensitivity analysis, correcting for potential under-counting in the data, adjusted estimated prevalence: between 46.4 (E3) and 66.6 (E1) per 10,000 population in 2019; and between 43.0 (E3) and 59.3 (E1) per 10,000 population in 2024. Prevalence was higher for males than for females, and highest in the age group < 1 for all estimation scenarios.

CONCLUSIONS: These are the first estimates of prevalence of life-limiting conditions amongst children in Ireland that have been calculated using Irish-specific hospital data. The estimates are in line with prevalence from other similar-income countries based on studies using similar definitions and methodologies. The findings demonstrate how careful application of transparent methods to routinely collected administrative hospital activity data can yield important findings even in the context of data limitations.

PMID:42509542 | DOI:10.1186/s12887-026-07003-1

BMC Gastroenterol. 2026 Jul 27;26(1):475. doi: 10.1186/s12876-026-05156-y.

ABSTRACT

BACKGROUND: Inflammatory markers are routinely used in the evaluation of inflammatory bowel disease (IBD). Red blood cell distribution width (RDW) has previously been proposed as a potential biomarker in the evaluation of IBD. This meta-analysis and systematic review challenges the utility of RDW in IBD evaluation.

METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines. A search strategy was formulated based on Medical Subject Headings (MeSH) terms and other relevant medical terms. A comprehensive search of PubMed, Web of Science, and Scopus was performed up to 4th of September 2025. Data were extracted for identifying the role of RDW in IBD detection and its association with disease activity. Statistical analysis employed a random-effects model, with subgroup analysis performed according to different levels of disease activity.

RESULTS: RDW was significantly elevated in patients suffering from Crohn’s disease (CD) (MD = 2.2, p < 0.001) and ulcerative colitis (UC) (MD = 1.36, p < 0.001) compared to healthy controls. Both diseases showed significantly higher RDW in active disease versus remission (CD: MD = 1.29, p < 0.001; UC: MD = 1.11%, p < 0.001). For differentiating active CD from remission, an RDW cut-off > 14% showed a sensitivity of 0.86 and specificity of 0.78 (AUC = 0.83). RDW levels were also significantly higher in active CD compared to active UC (MD = 0.59, p < 0.005).

CONCLUSION: RDW levels were markedly increased in patients with IBD compared to healthy controls. Furthermore, RDW showed a consistent association with both disease activity and severity. Given its wide availability, low cost, and reliable diagnostic performance, RDW could represent a practical and valuable biomarker for evaluating inflammatory bowel disease.

PMID:42509537 | DOI:10.1186/s12876-026-05156-y

J Racial Ethn Health Disparities. 2026 Jul 27. doi: 10.1007/s40615-026-03127-3. Online ahead of print.

ABSTRACT

The prevalence and impact of mental health challenges among racially and ethnically diverse populations have received growing attention in recent years. This study investigates the factors associated with mental health service utilization and unmet mental health care needs among minoritized adults in the United States. Drawing on data from the 2021-2022 National Health Interview Survey (NHIS), an annual survey of the U.S. civilian, noninstitutionalized population that understands public health conditions, this study included a sample of Asian, Black/African American, and Hispanic participants (N = 16,245). A secondary data analysis was conducted using a multinomial logistic regression framework to examine how predisposing, enabling, and need-based factors influence patterns of mental health service use, guided by Andersen’s Behavioral Model of Health Services Use. Findings highlight persistent disparities, showing that Black and Hispanic individuals who reported living with disabilities were more likely to forgo mental health services due to cost. Additionally, Asian older adults were less likely to report receiving mental health services. Meanwhile, Asian young adults who had never been married and who experienced anxiety had the highest odds of delaying mental health care. Together, these findings underscore the importance of culturally responsive strategies to improve access and equity in mental health services for minoritized individuals with intersecting social identities, as well as the need to strengthen training for culturally competent providers with attention to cultural diversity, acculturation processes, language proficiency, and advocacy.

PMID:42509521 | DOI:10.1007/s40615-026-03127-3

J Racial Ethn Health Disparities. 2026 Jul 27. doi: 10.1007/s40615-026-03126-4. Online ahead of print.

ABSTRACT

INTRODUCTION: American Indian/Alaska Native (AI/AN) communities experience persistent oral health disparities shaped by a range of social determinants of health (e.g., social, economic, and healthcare access factors). However, associations between demographic and socioeconomic characteristics and oral health outcomes among AI/AN populations remains understudied. The objective of this study was to conduct an exploratory analysis of demographic and socioeconomic factors and oral health outcomes among the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) population.

METHODS: Demographic and general health variables were assessed through the TBRFSS. Exposure variables included age, sex, income, employment status, and dentist office type. The nine independent, ordinal outcome variables included: difficulty with chewing, difficulty with speech, dry mouth, felt anxious, felt embarrassment, avoided smiling, reduced social activities, problems sleeping, and experienced pain. Multivariable ordinal logistic regressions were conducted to produce proportional ORs and 95% CIs.

RESULTS: A total of 379 responses from participants who identified as AI/AN were included for analysis. Amongst those 379 participants, most identified as female (62% n = 224), had healthcare coverage (92%, n = 343), were Oklahoma residents (78%, n = 295), and were employed full-time (60%, n = 220). Multivariable ordinal logistic regression analyses revealed a statistically significant association between household income and oral health for seven out of the nine outcomes. The age group categories, income, and sex varied in association with the outcomes.

DISCUSSION: Understanding factors associated as it relates with oral health among AI/AN communities is an integral component of addressing health inequities.

PMID:42509519 | DOI:10.1007/s40615-026-03126-4

J Racial Ethn Health Disparities. 2026 Jul 27. doi: 10.1007/s40615-026-03134-4. Online ahead of print.

ABSTRACT

Prostate cancer remains the most prevalent malignancy and a leading cause of cancer-related mortality among men in Puerto Rico. This study evaluates regional disparities in prostate cancer incidence across 76 contiguous municipalities on the main island and characterizes areas exhibiting statistically significant spatial clustering. Using spatial analytical techniques-including Moran’s I and Getis-Ord Gi*-within Geographic Information Systems (GIS), we analyze data from the Puerto Rico Central Cancer Registry (RCCPR) spanning 2016 to 2022. The findings reveal a non-random spatial distribution of prostate cancer incidence, with consistent clustering patterns observed throughout most of the study period, except in 2019. Notably, cold-spot regions were persistently identified in the northwest and western municipalities, while elevated incidence rates were concentrated in clusters located in the southern and eastern regions. These results underscore the presence of enduring geographic disparities in the prostate cancer burden across Puerto Rico.

PMID:42509516 | DOI:10.1007/s40615-026-03134-4

Histochem Cell Biol. 2026 Jul 27;164(1):65. doi: 10.1007/s00418-026-02520-7.

ABSTRACT

Pregnancy induces marked metabolic and physiological adaptations in the maternal liver to maintain systemic homeostasis and support fetal development. Nuclear factor erythroid 2-related factors 1 and 2 (Nrf1 and Nrf2) are Cap’n’Collar (CNC) transcription factors that regulate antioxidant response element (ARE)-dependent gene expression and play essential roles in redox homeostasis and cellular stress responses; however, their gestational stage-dependent intracellular distribution in the maternal liver remains insufficiently characterized. This study investigated the immunohistochemical expression and subcellular localization of Nrf1 and Nrf2 in the maternal rat liver during mid and late gestation. Liver samples were obtained from non-pregnant control rats and pregnant Wistar albino rats on gestational days (GD) 13, 18, and 20 (n = 6 per group). Immunohistochemistry was performed using anti-Nrf1 and anti-Nrf2 antibodies, and staining intensities in nuclear and cytoplasmic compartments were quantified using Fiji (ImageJ). Nuclear-to-cytoplasmic (N/C) ratios were also calculated. Nrf1 immunoreactivity showed gestational stage-dependent changes, with stronger nuclear and cytoplasmic staining at GD13 and reduced staining at GD18 and GD20, accompanied by lower N/C ratios during mid gestation. In contrast, Nrf2 nuclear immunoreactivity remained relatively stable throughout most of gestation, with a significant decrease observed only between GD18 and GD20, whereas cytoplasmic immunoreactivity increased at GD13 and GD18 and declined at GD20. Statistical analysis demonstrated significant gestational stage-related differences in nuclear immunoreactivity, cytoplasmic immunoreactivity, and the N/C ratio for Nrf1. For Nrf2, significant differences were also observed in nuclear immunoreactivity, cytoplasmic immunoreactivity, and the N/C ratio, although pairwise differences in nuclear immunoreactivity were limited to GD18 and GD20. These findings demonstrate distinct gestational stage-dependent patterns of Nrf1 and Nrf2 immunoreactivity in the maternal liver, suggesting differential involvement of these transcription factors in maternal hepatic adaptation during pregnancy.

PMID:42509511 | DOI:10.1007/s00418-026-02520-7

Environ Sci Pollut Res Int. 2026 Jul 27. doi: 10.1007/s11356-026-38077-0. Online ahead of print.

ABSTRACT

Floods are among the most devastating disasters, posing significant risks to communities and infrastructure, particularly in tropical regions where hazard mapping is often constrained by limited data availability. This study applies an integrated geospatial and machine learning (ML) approach to improve flood hazard assessment in a mountainous tropical region of Aceh Jaya, Indonesia, with the aim of evaluating model performance and identifying dominant causative factors linked to spatially targeted mitigation strategies. A set of ten flood causative factors, together with historical flood inventory data, was analyzed using four ML algorithms: Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Generalized Linear Model (GLM). Flood hazard maps were classified into five levels, ranging from very low to very high susceptibility. High to very high hazard zones cover approximately 14-22% of the study area and are primarily concentrated in low-elevation downstream areas. Model evaluation using Area Under the Curve (AUC), True Skill Statistics (TSS), correlation, and deviance indicates that RF achieves the highest predictive performance (AUC = 0.983; TSS = 0.92). The results consistently identify elevation as the dominant controlling factor, underscoring the influence of terrain-driven hydrodynamic processes. The coherence between model outputs, underlying physical mechanisms, and observed spatial patterns strengthens the basis for delineating flood hazard zones and informing mitigation priorities. This integrated approach enhances the applicability of the study by supporting evidence-based decision-making and enabling more targeted flood risk management in data-limited tropical regions, with a transferable framework that can be readily applied to similar mountainous settings.

PMID:42509508 | DOI:10.1007/s11356-026-38077-0

Surg Endosc. 2026 Jul 27. doi: 10.1007/s00464-026-13123-7. Online ahead of print.

ABSTRACT

BACKGROUND: Appendectomy is a leading pediatric surgical procedure, yet the impact of missing data handling on clinical conclusions remains unexplored in pediatric surgery. This study aimed to identify current reporting practices in the pediatric appendectomy literature and quantify how different missing-data strategies influence the identification of postoperative infectious-complication risk factors.

METHODS: A systematic review of pediatric appendectomy literature from 2023 was conducted across PubMed and Web of Science to assess missing data reporting. Subsequently, a retrospective analysis of the National Surgical Quality Improvement Program-Pediatric (NSQIP-P) database (2015-2022) was performed on 142,129 cases. Five strategies were compared: available case analysis, threshold-based exclusion, complete case analysis, simple imputation, and multiple imputation. Multivariable logistic regression models identified risk factors for five infectious outcomes, including combined infectious complications, organ space infections, deep incisional infections, superficial surgical site infections, and wound dehiscence.

RESULTS: The literature review identified 116 eligible articles, of which 54.3% failed to mention missing data. Only 7.3% used advanced statistical techniques, while 86.4% reported that a method relied on complete-case analysis. In the NSQIP-P cohort, 39.3% of records had missing values, primarily in height and race. While core predictors like American Society of Anesthesiologists (ASA) class and operative duration remained stable in multivariate logistic regression analysis, variables such as preoperative white blood cell count and anthropometric measurements fluctuated in significance depending on the handling method. Complete case analysis was the most conservative, identifying the fewest significant predictors.

CONCLUSION: Methodological transparency regarding missing data is critically lacking in pediatric surgical research. Because the choice of data-handling strategy significantly alters the identification of clinical risk factors, researchers should prioritize multiple imputation over simple exclusion methods. Journals must mandate rigorous reporting of missingness to ensure the reliability of evidence-based surgical guidelines.

PMID:42509504 | DOI:10.1007/s00464-026-13123-7

Surg Endosc. 2026 Jul 27. doi: 10.1007/s00464-026-13196-4. Online ahead of print.

ABSTRACT

BACKGROUND: Robotic cholecystectomy is increasingly adopted as an alternative to laparoscopic cholecystectomy and proposed as an entry‑level procedure in robotic training curricula, yet real‑world data on surgeon‑specific learning curves and their impact on perioperative safety remain limited. This study aimed to map the learning curve for robotic cholecystectomy across a multi-surgeon, multicenter cohort and assess associated safety outcomes.

METHODS: This retrospective cohort study analyzed the first 50 consecutive robotic cholecystectomies performed independently by each of five surgeons (n = 250) between January 2023 and June 2025. For each surgeon, the learning-curve endpoint was identified as the breakpoint of a piecewise linear regression of skin-to-skin operative time against case sequence, and the Mann-Whitney U test assessed whether operative time differed significantly between the early and late phases. A linear mixed-effects model, with surgeon as random intercept, identified independent predictors of operative time accounting for within-surgeon clustering.

RESULTS: The learning-curve endpoint ranged from 11 to 34 cases across surgeons. The breakpoint corresponded to a significant reduction in operative time for three of five surgeons (p = 0.017, p = 0.015, p < 0.001), but not for the remaining two (p = 0.853, p = 0.233). Cohort-level median operative time decreased significantly from early to late phase (66 vs 50 min; p < 0.001), while length of stay did not differ (p = 0.354). Case sequence number (- 0.72 min/case) and Nassar difficulty grade (+ 9.8 min/grade) were independent predictors of operative time (p < 0.001). No bile duct injuries occurred and severe complications occurred in 1/250 patients (0.4%).

CONCLUSIONS: Most surgeons showed a statistically confirmed reduction in operative time within their first 11-24 robotic cholecystectomies, while for others no significant improvement was confirmed. Complication rates remained low, although this cohort was not adequately powered to formally demonstrate safety equivalence across the learning process. These findings support robotic cholecystectomy as a feasible early procedure within structured robotic training pathways.

PMID:42509501 | DOI:10.1007/s00464-026-13196-4

Biol Trace Elem Res. 2026 Jul 28. doi: 10.1007/s12011-026-05265-5. Online ahead of print.

ABSTRACT

Selenium (Se) is an essential trace element for human health and longevity. However, the spatial association between soil Se content and longevity remains poorly understood at different spatial scales. Therefore, this study investigated the relationship between soil Se and longevity at both the national scale in China and at six representative smaller regions (Heilongjiang Province, Liaohe River Basin, Enshi Prefecture in Hubei, Ankang City in Shaanxi, Lianzhou City and Yingde City in Guangdong). Both traditional statistical methods (e.g., Pearson correlation, OLS regression) and spatial statistical methods (e.g., Moran’s I, SLM, SEM) were employed. The results revealed that: (1) At the national scale, soil Se and longevity exhibited a highly significant positive correlation and strong spatial clustering. High-Se/high-longevity clusters were mainly located in southern China, while northern provinces were characterized by low-Se/low-longevity clusters. SEM (R² = 0.31) outperformed OLS (R² = 0.22) and SLM (R² = 0.28), and the SEM model parameters indicated that the positive association persists after controlling for spatial error dependence. (2) At the small-scale level, a highly significant positive correlation was only identified in Lianzhou and Yingde. In Lianzhou, clusters of high Se and high longevity were concentrated in the southern region. SLM (R² = 0.87) outperformed OLS (R² = 0.72); the SLM model parameters indicated that the positive effect of soil Se remains after controlling for spatial lag dependence. (3) Spatial error dependence dominated at the broader scale, whereas spatial lag dependence was more prominent in small regions. Linearity and model performance improved substantially at the small scale. Regions with suitable Se levels showed the strongest Se-longevity associations.

PMID:42509495 | DOI:10.1007/s12011-026-05265-5