Tag: statistics

JCO Clin Cancer Inform. 2025 Nov;9:e2500122. doi: 10.1200/CCI-25-00122. Epub 2025 Nov 7.

ABSTRACT

PURPOSE: We developed and validated a dual-purpose, open-access Rural-Urban Commuting Area (RUCA) tool to standardize geographic coding for cancer disparities research, addressing National Institutes of Health (NIH) Helping to End Addiction Long-term (HEAL) Initiative Common Data Element requirements while supporting institutional catchment area analyses.

METHODS: This web-based tool¹⁶ integrates US Department of Agriculture RUCA codes with census tract data and electronic health record systems, meeting NIH HEAL Initiative Findable, Accessible, Interoperable, and Reusable (FAIR) data ecosystem requirements. We implemented the tool using Wake Forest Cancer Center’s 2023 registry data (n = 21,219) and conducted systematic comparison with county-level Rural-Urban Continuum Code (RUCC) classifications using 18,714 cancer cases across 336 ZIP codes, focusing on breast, colon, and lung cancers to demonstrate enhanced geographic granularity.

RESULTS: Among 21,219 patients with cancer, 19.51% (n = 4,140) resided in rural areas, with 4.81% (n = 1,022) in the most rural census tracts (RUCA codes 7-10). Comparative analysis revealed 9.4% disagreement between RUCA and RUCC classifications, affecting 1,765 patients. Twenty-eight ZIP codes classified as rural by RUCA were located within metropolitan counties according to RUCC, encompassing 109 patients with cancer who would be misclassified using county-level measures. As a separate use case, integration with NIH HEAL Initiative standardized rurality data collection across 15 research studies.

CONCLUSION: The RUCA tool addresses critical gaps in geographic data standardization by providing census tract-level precision that county-level classifications miss. This dual-application framework aligns institutional catchment analyses with national standardization efforts, identifying 109 patients with cancer who would be misclassified as urban residents using traditional county-level approaches, thereby enhancing targeted interventions for rural cancer care access.

PMID:41202192 | DOI:10.1200/CCI-25-00122

JCO Clin Cancer Inform. 2025 Nov;9:e2500086. doi: 10.1200/CCI-25-00086. Epub 2025 Nov 7.

ABSTRACT

PURPOSE: Partial nephrectomy has been advocated as the preferred surgical approach for small kidney tumors over total nephrectomy. However, partial nephrectomy is associated with increased perioperative risk. Estimating renal function after nephrectomy can facilitate personalized patient counseling, guide surgical approach, and identify patients who could benefit from perioperative interventions. Existing prediction models have several limitations including the lack of external validation or a user-friendly tool or application, and most have used traditional statistical methods.

METHODS: We used data from two academic medical institutions and machine learning (ML) methods to develop and externally validate renal function after nephrectomy-machine learning (RFAN-ML), a model to estimate long-term renal function after partial or total nephrectomy. Boruta feature selection was used to select four routinely available clinical features, specifically age, BMI, preoperative renal function, and nephrectomy type. In the training set of 1,932 patients, we compared six ML regression models representing a set of both ensemble and nonensemble ML algorithms and optimized for root mean squared error (RMSE). This model was evaluated in a test set of 1,995 patients, and the best performing model was selected as RFAN-ML.

RESULTS: We compared RFAN-ML with existing renal function prediction benchmarks and found that RFAN-ML outperformed or had competitive performance with benchmarks on RMSE (16.6 [95% CI, 15.6 to 17.5]), R², and mean absolute error.

CONCLUSION: We developed and externally validated RFAN-ML, a ML model to predict renal function after nephrectomy, and have deployed our model online. RFAN-ML has the potential to improve the care and outcomes in patients with kidney tumors by informing personalized patient counseling and guiding surgical planning.

PMID:41202191 | DOI:10.1200/CCI-25-00086

PLoS One. 2025 Nov 7;20(11):e0336111. doi: 10.1371/journal.pone.0336111. eCollection 2025.

ABSTRACT

The trans-theoretical model of behavior change (TTM) is widely used to assess an individual’s readiness to perform the new behavior and categorizes the behavior change into five stages: “pre-contemplation, contemplation, preparation, action, and maintenance.” This study focuses on assessing smoking cessation counseling practices (SCC) among Jordanian healthcare providers (HCPs) across various settings using the TTM. A cross-sectional study was conducted among HCPs (i.e., pharmacists, nurses, physicians, and dentists) working in private and public healthcare settings, using an online self-administered questionnaire. A total of 443 HCPs were included. One-third of HCPs reported asking patients if they smoked at their “first visit only.” Only 24.2% advised every patient to stop smoking, while 17.6% went beyond to assist smokers to make quit attempts, and (16.5%), assessed the willingness of the patients to quit and arrange follow-up quit attempts (10.6%). Only 28.4% of HCPs received training on SCC techniques. The majority of HCPs had a moderate level of confidence in performing SCC practices. HCPs in the private sector were more likely to be active in SCC practices than those in the public sector. Dentists and physicians were more involved in SCC practices than nurses and pharmacists. The study found a significant relationship between HCPs’ stage of change, self-efficacy, and performing SCC practices. This study affords a better understanding of the HCPs’ SCC practices. HCPs are found not to fully perform the “5 As” guidelines in their practices. Future efforts should focus on training and developing education programs that encourage HCPs to perform SCC practice.

PMID:41202117 | DOI:10.1371/journal.pone.0336111

PLoS One. 2025 Nov 7;20(11):e0335445. doi: 10.1371/journal.pone.0335445. eCollection 2025.

ABSTRACT

The coral microbiome can strongly influence coral health, development, and resilience. While larval settlement is fundamental for coral restoration efforts using assisted larval propagation, post-settlement survival remains a major challenge. The study of lab-bred Orbicella faveolata settlers (LBOFS) microbiome has been proposed due to its potential role in coral adaptation processes. However, there is limited information about LBOFS bacterial communities and comparisons between different growth conditions and life-cycle stages have not been conducted. Using 16S rRNA high-throughput sequencing, we analyzed the structure and composition of LBOFS-associated bacteria and compared them to those from outplanted LBOFS and wild settlers. We also compared the microbiomes of settlers to adult colonies. The LBOFS bacterial community was composed of 4224 ASVs with the Orders Kiloniellales, Rhodobacterales, Cytophagales, Cyanobacteriales, and Flavobacteriales being the most abundant across the samples, with a rare biosphere consisting of 44.6% relative abundance. A Principal Coordinates Analysis and a PERMANOVA indicated significantly different bacterial community structures based on settler growth conditions and life-cycle stage. Linear discriminant analysis Effect Size analysis identified specific taxa whose differential abundances contributed to the observed differences. For settler growth conditions, the differences were mainly due to the Order Cyanobacteriales for LBOFS, SAR202 clade for outplanted settlers, and Microtrichales for wild samples. Statistical analysis of functional prediction showed significant differences only in nitrogen fixation for LBOFS. For life-cycle stage, LEfSe revealed that the Orders Cytophagales and Cyanobacteriales exhibited the highest differential abundances in adults and settlers, respectively. Functional prediction revealed that nitrogen fixation and oxygenic photoautotrophy were more enriched in settlers, whereas nitrate reduction and anaerobic chemoheterotrophy were more enriched in adults. This study highlighted the bacterial taxa and predicted metabolic processes that could potentially contribute to coral settler functioning, providing a valuable baseline for future research to enhance their survival rates using probiotics.

PMID:41202107 | DOI:10.1371/journal.pone.0335445

PLoS One. 2025 Nov 7;20(11):e0336255. doi: 10.1371/journal.pone.0336255. eCollection 2025.

ABSTRACT

BACKGROUND: Adverse pregnancy outcomes continue to pose a significant global public health challenge, especially in low- and middle-income countries. Although preconception care (PCC) interventions are advised to address this problem, their adoption remains inadequate, supported by scarce evidence particularly in conflict-impacted areas such as Tigray, Ethiopia, where rates of poor outcomes like neural tube defects are notably higher than in other regions. This study investigates the experience of pregnant women regarding the use of PCC in the Tigray, northern Ethiopia.

METHODS: A community-based cross-sectional study was conducted from July 31 to August 16, 2024, involving 764 pregnant women in their first or second trimester. Participants were consecutively enrolled from clusters until the predetermined sample size was achieved. Data were collected through interviewer-administered questionnaires in accordance with World Health Organization, and Centers for Disease Control and Prevention, and national guidelines. PCC uptake was measured as the receipt of any service component (screening, counseling, or management) during healthcare consultations. We used SPSS version 27.0 to analyze PCC uptake and its associated factors. Descriptive and binary logistic regression statistics were used in the analysis. Finally, data was presented using text, tables, and figures as appropriate.

RESULTS: In this study, the overall uptake of PCC services was 7.2%. All participants in the current pregnancy were exposed to at least one risk factor for adverse pregnancy outcomes. Factors such as women’s decision-making power, having information about PCC, HIV screening during the current pregnancy, and perceived susceptibility to preconception risks showed a statistically significant positive association with the uptake of PCC services.

CONCLUSION: The uptake of PCC services was very low. Addressing the low uptake of PCC services requires a multifaceted strategy, including public health campaigns via media and social forums, strengthened health extension programs, and the integration of a reproductive life plan tool to improve health-seeking behavior among women.

PMID:41202106 | DOI:10.1371/journal.pone.0336255

PLoS Comput Biol. 2025 Nov 7;21(11):e1013691. doi: 10.1371/journal.pcbi.1013691. Online ahead of print.

ABSTRACT

Understanding microbial interactions is fundamental for exploring population dynamics, particularly in microbial communities where interactions affect stability and host health. Generalized Lotka-Volterra (gLV) models have been widely used to investigate system dynamics but depend on absolute abundance data, which are often unavailable in microbiome studies. To address this limitation, we introduce an iterative Lotka-Volterra (iLV) model, a novel framework tailored for compositional data that leverages relative abundances and iterative refinements for parameter estimation. The iLV model features two key innovations: an adaptation of the gLV framework to compositional constraints and an iterative optimization strategy combining linear approximations with nonlinear refinements to enhance parameter estimation accuracy. Using simulations and real-world datasets, we demonstrate that iLV surpasses existing methodologies, such as the compositional LV (cLV) and the generalized LV (gLV) model, in recovering interaction coefficients and predicting species trajectories under varying noise levels and temporal resolutions. Applications to the lynx-hare predator-prey, Stylonychia pustula-P. caudatum mixed culture, and cheese microbial systems revealed consistency between predicted and observed relative abundances showcasing its accuracy and robustness. In summary, the iLV model bridges theoretical gLV models and practical compositional data analysis, offering a robust framework to infer microbial interactions and predict community dynamics using relative abundance data, with significant potential for advancing microbial research.

PMID:41202104 | DOI:10.1371/journal.pcbi.1013691

Environ Sci Technol. 2025 Nov 7. doi: 10.1021/acs.est.5c06700. Online ahead of print.

ABSTRACT

Estuaries and adjacent waters are highly productive ecosystems, but are increasingly stressed by urbanization and climate change. Understanding long-term shifts in fish communities is critical for sustainable management, yet remains limited by scarce historical data. Here, we applied quality-controlled sedimentary DNA (sedDNA) metabarcoding, combining contamination prevention, stringent data filtering, and statistical calibration, to reconstruct ca. 100 years of fish diversity dynamics in the Pearl River Estuary (PRE) area, southern China. The monitored sedDNA data sets revealed that changes in fish communities in the PRE can be categorized into four distinct historical phases: the 1930s-1950s, 1950s-1970s, 1970s-1990s, and 1990s-2020s. Taxonomic and functional richness peaked around the 1970s but declined sharply thereafter. Small-bodied and omnivorous species gradually gave way to larger-bodied and warm-water species, reflecting a shift in trophic and habitat preferences over time. Invasive species, such as Oreochromis niloticus and Coptodon zillii, became increasingly dominant, whereas indigenous species markedly declined. Multivariate analyses showed that urbanization primarily affected taxonomic diversity, while climate drivers shaped functional traits and community structure, with invasive species acting as key mediators of ecological disruption. Overall, these results offer new insights into the century-scale fish diversity dynamics under compounded urbanization and climatic pressures, and highlight sedDNA as a powerful tool for reconstructing historical biomonitoring records.

PMID:41202103 | DOI:10.1021/acs.est.5c06700

Microb Genom. 2025 Nov;11(11). doi: 10.1099/mgen.0.001555.

ABSTRACT

Pseudomonas aeruginosa uses multiple type VI secretion systems (T6SSs) to manipulate eukaryotic cells, kill competing microbes and take up nutrients. Bacterial strains are known to differ in their T6SS apparatus and the toxic effector proteins responsible for killing. The ability to eliminate competitors has been repeatedly demonstrated in lab studies, but much less is known about effector genotypes during infection. We used comparative genomics to test for the presence and absence of T6SS effector genes in over 450 clinical P. aeruginosa isolates from people with cystic fibrosis in Copenhagen (Denmark) and complemented these findings with data of 52 isolates from people with cystic fibrosis in London (UK). We found natural variation in the occurrence and combination of effector genes. Patients were typically infected with isolates that differ in their effector gene sets but show no statistically significant association between the number of effector genes and chronic infection. Isolates with a pair of T6SS effector and immunity genes and isolates without these genes, which would be expected to kill each other based on existing work in the laboratory, were found on the same individual. Taking the isolates’ phylogeny and sampling times into account, we identified five putative loss events of effector genes during infection. Although the impact of our findings for infected individuals will require further investigation, we demonstrate the extent of strain-level variation in T6SS effector genes in clinical isolates.

PMID:41201843 | DOI:10.1099/mgen.0.001555

Proc Natl Acad Sci U S A. 2025 Nov 11;122(45):e2515667122. doi: 10.1073/pnas.2515667122. Epub 2025 Nov 7.

ABSTRACT

We propose a mathematical framework that we call quantum, higher-order Fourier analysis. This generalizes the classical theory of higher-order Fourier analysis, which led to many recent advances in number theory and combinatorics. We define a family of “quantum measures” on linear transformations on a Hilbert space, that reduce in the case of diagonal matrices to the uniformity norms introduced by Timothy Gowers. We show that our quantum measures and our related theory of quantum higher-order Fourier analysis characterize the Clifford hierarchy, an important notion of complexity in quantum computation. In particular, we give a necessary and sufficient analytic condition that a unitary is an element of the [Formula: see text] level of the Clifford hierarchy.

PMID:41201827 | DOI:10.1073/pnas.2515667122

JAMA Health Forum. 2025 Nov 7;6(11):e253986. doi: 10.1001/jamahealthforum.2025.3986.

ABSTRACT

IMPORTANCE: Black, Indigenous, and Latino communities are disproportionately affected by the US overdose epidemic. Structural inequalities, encompassing social, economic, and infrastructural dimensions, have been increasingly theorized as fundamental drivers of these disparities.

OBJECTIVE: To investigate whether there is an association between neighborhood-level structural racism and opioid-involved overdose deaths in an urban area.

DESIGN, SETTING, AND PARTICIPANTS: This ecological serial cross-sectional study of 796 census tracts (2017-2019) and 792 census tracts (2020-2022) in Chicago, Illinois, used a geospatial and intersectional analytic approach. A quasi-Poisson spatial regression was conducted to examine associations between neighborhood-level structural racism and census tract-level opioid-involved overdose deaths before the COVID-19 pandemic (2017-2019) and during the COVID-19 pandemic (2020-2022). Eigenvector spatial filtering was used to control for residual spatial autocorrelation. Population density was also accounted for in the regression model. Two structural racism indicators (historical redlining and contemporary racialized economic segregation) were combined to develop an index that captures 4 distinct neighborhood intersectional groups of racism over an 80-year period. Average marginal effect calculations were also performed to support the interpretability of the findings. Data were analyzed from February 19, 2024, to July 3, 2025.

EXPOSURE: A combined measure of 2 structural racism indicators (historical redlining and contemporary racialized economic segregation).

MAIN OUTCOMES AND MEASURES: Overdose deaths were aggregated to census tracts; the main outcome measure was the number of overdose deaths at the census tract-level.

RESULTS: The total sample sizes were 796 census tracts before the COVID-19 pandemic (2017-2019) and 792 census tracts during the COVID-19 pandemic (2020-2022). As defined by the study’s combined measure of structural racism, census tracts with high levels of racism in the past and/or present showed statistically significantly higher number of fatal overdoses compared with tracts with low levels of racism both in the past and present. Just before the COVID-19 pandemic (ie, 2017-2019), tracts with high sustained levels of structural racism past and present had, on average, over 2 more fatal overdoses per tract compared with sustained advantaged tracts (average marginal effect, 2.60; 95% CI, 2.02-3.19; P < .001). During the COVID-19 pandemic (2020-2022), tracts that were advantaged in the past but experienced high present-day segregation had, on average, almost 4 more fatal overdoses per tract compared with sustained advantaged tracts (average marginal effect, 3.81; 95% CI, 1.94-5.68; P < .001). The overall burden of overdose death was higher for all neighborhood groups during the pandemic compared with before the pandemic.

CONCLUSIONS AND RELEVANCE: These findings provide preliminary evidence that structural racism could be a root cause of opioid-involved overdose deaths. Future research is needed to identify mechanisms linking structural racism to overdose deaths and to develop effective policies and programs to reduce fatal overdose rates.

PMID:41201811 | DOI:10.1001/jamahealthforum.2025.3986