Tag: statistics

Cancer Epidemiol. 2025 Apr 21;97:102818. doi: 10.1016/j.canep.2025.102818. Online ahead of print.

ABSTRACT

BACKGROUND: Leukemia ranks as the 11th most prevalent cancer globally, contributing significantly to the cancer burden. Despite its rising impact, recent epidemiological data on leukemia in South Africa remain limited. This study investigates the incidence, mortality trends, and disease burden of leukemia in South Africa from 2015 to 2019.

METHODS: Leukemia incidence data were obtained from the South African National Cancer Registry, and mortality data from Statistics South Africa for 2015-2019. Age-standardized incidence and mortality rates were calculated using mid-year population data and the Segi world standard population for standardization. The burden of disease was quantified using Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and Disability-Adjusted Life Years (DALYs). Rates were compared by age, sex, year, and province.

RESULTS: There were 2 001 new cases of leukemia and 1 244 deaths reported, with an incidence rate of 4.11 per 100,000 and mortality rate of 3.01 per 100,000 population. The male-to-female ratio was 1.1:1 and the mean age was 38 years at diagnosis and 53 at death. Acute myeloid leukemia was the most common type of leukemia in South Africa. Gauteng had the highest age standardized incidence rate (4.92 per 100,000), and the Western Cape had the highest age standardized mortality rate (3.98 per 100,000). In 2019, leukemia accounted for 6 309 DALYs, with a decline in age standardized DALY (-1.04 %) and YLL (-4.7 %) rate, respectively.

CONCLUSION: This study provides up-to-date incidence and mortality data, expressing the burden of leukemia in South Africa. The age-standardized mortality and DALY rates showed favorable patterns over the study period. However, the incidence rates showed an increase, which may reflect the progressive aging and growth of the population. These findings highlight the need for sustained efforts to improve leukemia detection, treatment access, and healthcare quality in South Africa.

PMID:40262221 | DOI:10.1016/j.canep.2025.102818

Hum Reprod. 2025 Apr 22:deaf062. doi: 10.1093/humrep/deaf062. Online ahead of print.

ABSTRACT

STUDY QUESTION: Is there an increased risk of immunological diseases among endometriosis patients, and does a shared genetic basis contribute to this risk?

SUMMARY ANSWER: Endometriosis patients show a significantly increased risk of autoimmune, autoinflammatory, and mixed-pattern diseases, including rheumatoid arthritis, multiple sclerosis, coeliac disease, osteoarthritis, and psoriasis, with genetic correlations between endometriosis and osteoarthritis, rheumatoid arthritis, and multiple sclerosis, and a potential causal link to rheumatoid arthritis.

WHAT IS KNOWN ALREADY: The epidemiological evidence for an increased risk of immunological diseases among women with endometriosis is limited in scope and has varied in robustness due to the opportunity for biases. The presence of a biological basis for increased comorbidity across immunological conditions has not been investigated. Here we investigate the phenotypic and genetic association between endometriosis and 31 immune conditions in the UK Biobank.

STUDY DESIGN, SIZE, DURATION: Phenotypic analyses between endometriosis and immune conditions (17 classical autoimmune, 10 autoinflammatory, and 4 mixed-pattern diseases) were conducted using two approaches (8223 endometriosis, 64 620 immunological disease cases): (i) retrospective cohort study design to incorporate temporality between diagnoses and (ii) cross-sectional analysis for simple association. Genome-wide association studies (GWAS) and meta-analyses for those immune conditions that showed phenotypic association with endometriosis (1493-77 052 cases) were conducted.

PARTICIPANTS/MATERIALS, SETTING, METHODS: Comprehensive phenotypic association analyses were conducted in females in the UK Biobank. GWAS for immunological conditions were conducted in females-only and sex-combined study populations in UK Biobank and meta-analysed with existing largest available GWAS results. Genetic correlation and Mendelian randomization (MR) analyses were conducted to investigate potential causal relationships. Those immune conditions with significant genetic correlation with endometriosis were included in multi-trait analysis of GWAS to boost discovery of novel and shared genetic variants. These shared variants were functionally annotated to identify affected genes utilizing expression quantitative trait loci (eQTL) data from GTEx and eQTLGen databases. Biological pathway enrichment analysis was conducted to identify shared underlying biological pathways.

MAIN RESULTS AND THE ROLE OF CHANCE: In both retrospective cohort and cross-sectional analyses, endometriosis patients were at significantly increased (30-80%) risk of classical autoimmune (rheumatoid arthritis, multiple sclerosis, coeliac disease), autoinflammatory (osteoarthritis), and mixed-pattern (psoriasis) diseases. Osteoarthritis (genetic correlation (rg) = 0.28, P = 3.25 × 10-15), rheumatoid arthritis (rg = 0.27, P = 1.5 × 10-5) and multiple sclerosis (rg = 0.09, P = 4.00 × 10-3) were significantly genetically correlated with endometriosis. MR analysis suggested a causal association between endometriosis and rheumatoid arthritis (OR = 1.16, 95% CI = 1.02-1.33). eQTL analyses highlighted genes affected by shared risk variants, enriched for seven pathways across all four conditions, with three genetic loci shared between endometriosis and osteoarthritis (BMPR2/2q33.1, BSN/3p21.31, MLLT10/10p12.31) and one with rheumatoid arthritis (XKR6/8p23.1).

LIMITATIONS, REASONS FOR CAUTION: We conducted the first female-specific GWAS analyses for immune conditions. Given the novelty of these analyses, the sample sizes from which results were derived were limited compared to sex-combined GWAS meta-analyses, which limited the power to use female-specific summary statistics to uncover the shared genetic basis with endometriosis in follow-up analyses. Secondly, the 39 genome-wide significant endometriosis-associated variants used as instrumental variables in the MR analysis explained approximately 5% of disease variation, which may account for the nominal or non-significant MR results.

WIDER IMPLICATIONS OF THE FINDINGS: Endometriosis patients have a moderately increased risk for osteoarthritis, rheumatoid arthritis, and to a lesser extent, multiple sclerosis, due to underlying shared biological mechanisms. Clinical implications primarily involve the need for increased awareness and vigilance. The shared genetic basis opens up opportunities for developing new treatments or repurposing therapies across these conditions.

STUDY FUNDING/COMPETING INTEREST(S): We thank all the UK Biobank and 23andMe participants. Part of this research was conducted using the UK Biobank Resource under Application Number 9637. N.R. was supported by a grant from the Wellbeing of Women UK (RG2031) and the EU Horizon 2020 funded project FEMaLe (101017562). A.P.M. was supported in part by Versus Arthritis (grant 21754). H.F. was supported by the National Natural Science Foundation of China (grant 32170663). N.R., S.A.M., and K.T.Z. were supported in part by a grant from CDMRP DoD PRMRP (W81XWH-20-PRMRP-IIRA). K.T.Z. and C.M.B. reported grants in 3 years prior, outside the submitted work, from Bayer AG, AbbVie Inc., Volition Rx, MDNA Life Sciences, PrecisionLife Ltd., and Roche Diagnostics Inc. S.A.M. reports grants in the 3 years prior, outside this submitted work, from AbbVie Inc. N.R. is a consultant for Endogene.bio, outside this submitted work. The other authors have no conflicts of interest to declare.

TRIAL REGISTRATION NUMBER: N/A.

PMID:40262193 | DOI:10.1093/humrep/deaf062

J Med Internet Res. 2025 Apr 22;27:e69425. doi: 10.2196/69425.

ABSTRACT

BACKGROUND: Although mobile health (mHealth) interventions have demonstrated effectiveness in modifying 1 or 2 health-risk behaviors at a time, there is a knowledge gap regarding the effects of stand-alone mHealth interventions on multiple health risk behaviors.

OBJECTIVE: This study aimed to estimate the 2- and 4-month effectiveness of an mHealth intervention (LIFE4YOUth) targeting alcohol consumption, diet, physical activity, and smoking among Swedish high school students, compared with a waiting-list control condition.

METHODS: A 2-arm parallel group, single-blind randomized controlled trial (1:1) was conducted from September 2020 to June 2023. Eligibility criteria included nonadherence to guidelines related to the primary outcomes, such as weekly alcohol consumption (standard drinks), monthly frequency of heavy episodic drinking (ie, ≥4 standard drinks), daily intake of fruit and vegetables (100-g portions), weekly consumption of sugary drinks (33-cL servings), weekly duration of moderate to vigorous physical activity (minutes), and 4-week point prevalence of smoking abstinence. The intervention group had 16 weeks of access to LIFE4YOUth, a fully automated intervention including recurring screening, text message services, and a web-based dashboard. Intention-to-treat analysis was conducted on available and imputed 2- and 4-month self-reported data from participants at risk for each outcome respectively, at baseline. Effects were estimated using multilevel models with adaptive intercepts (per individual) and time by group interactions, adjusted for baseline age, sex, household economy, and self-perceived importance, confidence, and know-how to change behaviors. Bayesian inference with standard (half-)normal priors and null-hypothesis testing was used to estimate the parameters of statistical models.

RESULTS: In total, 756 students (aged 15-20, mean 17.1, SD 1.2 years; 69%, 520/756 females; 31%, 236/756 males) from high schools across Sweden participated in the trial. Follow-up surveys were completed by 71% (539/756) of participants at 2 months and 57% (431/756) of participants at 4 months. Most participants in the intervention group (219/377, 58%) engaged with the intervention at least once. At 2 months, results indicated positive effects in the intervention group, with complete case data indicating median between-group differences in fruit and vegetable consumption (0.32 portions per day, 95% CI 0.13-0.52), physical activity (50 minutes per week, 95% CI -0.2 to 99.7), and incidence rate ratio for heavy episodic drinking (0.77, 95% CI 0.55-1.07). The odds ratio for smoking abstinence (1.09, 95% CI 0.34-3.64), incidence rate ratio for weekly alcohol consumption (0.69, 95% CI 0.27-1.83), and the number of sugary drinks consumed weekly (0.89, 95% CI 0.73-1.1) indicated inconclusive evidence for effects due to uncertainty in the estimates. At 4 months, a remaining effect was observed on physical activity only.

CONCLUSIONS: Although underpowered, our findings suggest modest short-term effects of the LIFE4YOUth intervention, primarily on physical activity and fruit and vegetable consumption. Our results provide inconclusive evidence regarding weekly alcohol consumption and smoking abstinence.

TRIAL REGISTRATION: ISRCTN Registry ISRCTN34468623; https://doi.org/10.1186/ISRCTN34468623.

PMID:40262133 | DOI:10.2196/69425

J Alzheimers Dis. 2025 Apr 22:13872877251325725. doi: 10.1177/13872877251325725. Online ahead of print.

ABSTRACT

Strategies that may modify Alzheimer’s disease (AD) and other dementia disorders are being developed. To maximize the benefits of these strategies, it is critical that indicators suggesting neurocognitive decline are identified as early as possible. ‘Precision neurocognition’ is a heuristic that seeks to develop methodologies capable of identifying subtle behavior(s) that may flag emerging AD and other dementia related syndromes. Recent research suggests that digital neuropsychological assessment technology may be the platform that can realize the goals of precision neurocognition, i.e., the early detection of neurocognitive difficulties that are prognostic for mild cognitive impairment (MCI) and dementia. Past research associating 100% correct or statistically within normal limits responding using neuropsychological tests with time-based parameters obtained while participants undergo assessment is reviewed. Recent research with community dwelling and memory clinic participants examined test scores obtained using commonly available neuropsychological tests. This research extracted a number of discrete latency measures that clearly dissociate between groups, despite final test scores that are either 100% correct or statistically within normal limits. In sum, past research using digitally administered neuropsychological tests suggests that the goals of precision neurocognition as related to the early identification of neurodegenerative illness may be realized via an analysis of time derived, process-based behavior using digital assessment technology. Latency or time-based parameters as described in recent research could form the basis of a range of neurocognitive biomarkers for identifying people at risk for developing AD, other dementing disorders, and MCI.

PMID:40262110 | DOI:10.1177/13872877251325725

J Alzheimers Dis. 2025 Apr 22:13872877251333613. doi: 10.1177/13872877251333613. Online ahead of print.

ABSTRACT

BackgroundThere is growing evidence that cardiorespiratory fitness (CRF) mitigates the likelihood of dementia caused by Alzheimer’s disease and may underlie the cognitive benefits observed from aerobic exercise. Previous evidence further demonstrates neurodegeneration is the biological substrate for cognitive deterioration and younger brain age may protect the brain from the deleterious effects of neurodegeneration. However, little is known about the relationships between CRF, brain age, and neurodegeneration in older adults with amnestic mild cognitive impairment (aMCI).ObjectiveThe aim of this cross-sectional study was to examine associations between CRF, brain age, and neurodegeneration among individuals with aMCI, using baseline data from the Aerobic exercise and Cognitive Training (ACT) trial, which examined the cognitive effects and underlying mechanisms of a 6-month ACT in older adults with aMCI.MethodsCRF was measured with peak oxygen uptake (VO_2peak), from a symptom-limited peak cycle-ergometer test. Brain age and hippocampal volume were obtained from structural magnetic resonance imaging. Brain age was estimated using brainageR. Descriptive statistics and bivariate correlations were assessed. Linear regression models were used to analyze the relationships between CRF, brain age, and hippocampal volume, while adjusting for covariates. All analyses were conducted using R (version 4.3.2).ResultsThe sample (N = 141) averaged 73.66 ± 5.78 years of age, 16.91 ± 2.89 years of education, 27.46 ± 5.15 in BMI, and 23.49 ± 2.16 on Montreal Cognitive Assessment, with 53% male and 92.2% White. The mean brain age was 72.37 ± 7.89 years with 3157.31 ± 449.35 mm³ hippocampal volume. No association was found between CRF, brain age, and hippocampal volume.ConclusionsFuture studies need to explore other brain indicators related to CRF.Trial RegistryClinicalTrials.gov, https://clinicaltrials.gov/study/NCT03313895, NCT03313895, October 18, 2017.

PMID:40262108 | DOI:10.1177/13872877251333613

PLoS One. 2025 Apr 22;20(4):e0319383. doi: 10.1371/journal.pone.0319383. eCollection 2025.

ABSTRACT

The US COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications. As such, the Forecast Hub is a critical centralized resource to promote transparent decision making. While the Forecast Hub has provided valuable predictions focused on accuracy, there is an opportunity to evaluate model performance across social determinants such as race and urbanization level that have been known to play a role in the COVID-19 pandemic. In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. We hope this work will encourage COVID-19 modelers and the CDC to report fairness metrics together with accuracy, and to reflect on the potential harms of the models on specific social groups and contexts.

PMID:40262087 | DOI:10.1371/journal.pone.0319383

PLoS One. 2025 Apr 22;20(4):e0318167. doi: 10.1371/journal.pone.0318167. eCollection 2025.

ABSTRACT

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML) models and conventional survival analysis techniques, to identify consistent key predictors of breast cancer survival outcomes. Methods This study employs data-driven survival modeling approaches to predict breast cancer survival, including survival-specific methods such as the Cox Proportional Hazards (CPH) model, Random Survival Forests (RSF), and Cox Proportional Deep Neural Networks (DeepSurv), as well as machine learning models like Random Forests (RF), XGBoost, Support Vector Machines (SVM) with an RBF Kernel, and LightGBM. The dataset, sourced from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program, comprises 4,024 women diagnosed with infiltrating duct and lobular carcinoma breast cancer between 2006 and 2010. To ensure interpretability across all models, the Shapley Additive Explanation (SHAP) method was applied to RSF, DeepSurv, Random Forests (RF), and XGBoost. This enabled the identification of key predictors influencing breast cancer survival, highlighting consistent factors across models while uncovering unique insights specific to each approach. Results The performance of survival-specific and ML models were evaluated using the Concordance index (C-index), Integrated Brier Score (IBS), mean accuracy, and mean AUC. The CPH model achieved a C-index of 0 . 71 ± 0 . 015 and an IBS of 0 . 08 ± 0 . 006, while RSF demonstrated slightly better discriminatory power with a C-index of 0 . 72 ± 0 . 0117. DeepSurv performed comparably, with a C-index of 0 . 71 ± 0 . 0095 and an IBS of 0 . 09 ± 0 . 0008. Both Cox and RSF models achieved the lowest IBS (0 . 08), indicating accurate survival probability predictions over time. For ML models, RF achieved a mean AUC of 0 . 74 ± 0 . 0021, and XGBoost with a mean AUC 0 . 69 ± 0 . 0183, reflecting fair discriminatory ability but not accounting for censoring in survival data. SHAP analysis for the top-performing models highlighted the extent of lymph node involvement, Regional Node-Positive (number of affected lymph nodes), tumor grade (cell abnormality and growth rate), progesterone status, and age as key predictors of breast cancer survival outcomes. Conclusions While ML models like XGBoost and RF can effectively identify important predictors and patterns in breast cancer outcomes, survival-specific methods such as the Cox model, RSF, and DeepSurv provide essential capabilities for handling time-to-event data and censoring, making them more suitable for accurate survival predictions. The primary objective of including ML models in this analysis was to leverage their interpretability in identifying key variables alongside survival-specific models, rather than to directly compare their performance against survival models. By examining both ML and survival models, this research highlights the complementary strengths of each approach. This study contributes to the integration of artificial intelligence in healthcare, emphasizing the value of data-driven survival modeling techniques in supporting healthcare professionals with accurate, personalized, and actionable insights for high-risk patients. Together, these approaches enhance the precision of survival predictions, paving the way for more informed clinical decision-making and improved patient care.

PMID:40262081 | DOI:10.1371/journal.pone.0318167

PLoS One. 2025 Apr 22;20(4):e0319775. doi: 10.1371/journal.pone.0319775. eCollection 2025.

ABSTRACT

The complexities of stock price data, characterized by its nonlinearity, non-stationarity, and intricate spatiotemporal patterns, make accurate prediction a substantial challenge. To address this, we propose the DCA-BiLSTM model, which combines dual-path convolutional neural networks with an attention mechanism (DCA) and bidirectional long short-term memory networks (BiLSTM). This model captures deep information and complex dependencies within time-series data. First, wavelet packet decomposition extracts high- and low-frequency features, followed by DCA for robust deep feature extraction, and finally, BiLSTM models bidirectional dependencies. Validated on datasets from Yahoo Finance, including Apple, Google, Tesla stocks, and the Nasdaq index, the model consistently outperforms traditional approaches. The DCA-BiLSTM achieves an [Formula: see text] of 0.9507 for Apple, 0.9595 for Google, 0.9077 for Tesla, and 0.9594 for the Nasdaq index, with significant reductions in error metrics across all datasets. These results demonstrate the model’s robustness and improved predictive accuracy, offering reliable insights for stock price forecasting.

PMID:40262076 | DOI:10.1371/journal.pone.0319775

JCO Clin Cancer Inform. 2025 Apr;9:e2400253. doi: 10.1200/CCI-24-00253. Epub 2025 Apr 22.

ABSTRACT

PURPOSE: Incorporating patient-reported outcomes (PROs) into health care processes can improve engagement with patients; however, adopting PROs at scale is challenging. The aim of this study was to describe the design, development, and adoption at scale of a comprehensive PRO program for standard of care and research at a cancer center.

METHODS: Requirements for a PRO program were obtained from multiple stakeholders. Components of the program included a governance process to assure a consistent and satisfactory experience for patients completing PRO questionnaires, tools to create and manage questionnaires and related content, methods to send questionnaires to relevant patients at the appropriate time, interactive tools for patients to complete the questionnaires as part of their portal experience, and integration of PRO data into workflows for clinicians. We used descriptive statistics to assess the use of the program from 2016 to 2023.

RESULTS: From program launch (on February 1, 2016) until December 31, 2023, 189 unique questionnaires were developed (101 for standard-of-care, 70 for research, and 18 for quality improvement). Of the 432,497 unique patients who were assigned at least one questionnaire, 314,685 (73%) completed at least one. Of 5,948,464 questionnaires sent, 3,098,574 (52%) were completed. The median completion time was 2 minutes.

CONCLUSION: Large-scale adoption of PROs at a cancer center is feasible. Key considerations for success include governance processes, attention to patient experience and clinician workflow, and the ability to manage complex inclusion criteria and timing of delivery of questionnaires. These principles should be disseminated so the full potential of PROs in health care can be realized.

PMID:40262062 | DOI:10.1200/CCI-24-00253

Health Rep. 2025 Apr 16;36(4):3-13. doi: 10.25318/82-003-x202500400001-eng.

ABSTRACT

BACKGROUND: It is not clear whether the increased mortality pattern observed in a prior analysis of the Canadian Census Health and Environment Cohorts for HIV/AIDS, diabetes, prostate cancer, and uterine cancer among Black adults is reflected in incident hospitalization (a marker of severity) or the diagnosis of these diseases, nor is it clear whether disparities exist regarding early screening and survivability.

METHODS: To understand the paths that contribute to differential mortality patterns, standard Cox proportional hazard models were used to assess the incidence risk of diagnosis (uterine and prostate cancer) and incident hospitalization (HIV and diabetes) among 161,520 Black adults, compared with 6,866,070 White adults. Competing risk regression was used to evaluate the cumulative risk of death for the four disease outcomes since diagnosis or hospitalization. For the observed differential cancer mortality, mediation analysis was conducted to investigate the role of cancer diagnosis at follow-up (a proxy for delayed diagnosis that is not entirely indicative of late-stage cancer).

RESULTS: Across all examined outcomes, except for uterine cancer, Black adults had elevated incident diagnoses or hospitalizations compared with White adults. Notably, Black males demonstrated a risk of incident prostate cancer and hospitalizations from HIV and diabetes twice as high relative to White males. For Black females, the risk of incident HIV hospitalization was 12 times as high. However, Black females were 15% less likely to be diagnosed with uterine cancer, compared with White females. Cumulative mortality risk analysis showed significantly lower survivability (two times lower) among Black females diagnosed with uterine cancer, relative to White females. Delayed diagnosis mediated a marginally higher proportion of the total differential uterine cancer mortality among Black females (14.9%; 95% confidence interval [CI]: 10.5% to 23.1%), compared with White females (8.9%; 95% CI: 6.3% to 13.9%).

INTERPRETATION: This study unveils substantial parallels between heightened incidence risk and relative mortality for most of the four explored outcomes between Black and White adults in Canada. Notably, the study highlights a lower incident diagnosis of uterine cancer among Black females, despite a relatively higher uterine cancer mortality. Three in every 20 uterine cancer deaths were mediated through the time of uterine cancer diagnosis (relatively delayed in Black females), underscoring the need for targeted interventions and early detection strategies to address health disparities in this population.

PMID:40262030 | DOI:10.25318/82-003-x202500400001-eng