Tag: statistics

Gastroenterol Nurs. 2025 May-Jun 01;48(3):153-160. doi: 10.1097/SGA.0000000000000874. Epub 2025 May 28.

ABSTRACT

Colonoscopy is a primary diagnostic method for colorectal cancer screening. Ensuring completeness is critical for its effectiveness. The aim of this study is to explores patient and procedure-related contributors to incomplete colonoscopy in a Danish high volume endoscopy unit. A population-based register study was conducted, using data from electronic health records from a Danish hospital was analyzed, covering all colonoscopies performed between July 2015 and August 2019. The primary outcome assessed was the completeness of the index colonoscopy, with incomplete cases further classified based on the causes for incompleteness that were assessed and documented in real-time by the endoscopist. Data also included patient demographics and comorbidities, and profession of the endoscopist. Among 33,128 colonoscopies, prevalence of incomplete colonoscopies was 6.55%, with inadequate bowel preparation as the leading cause (60.3%). Men were more prone to inadequate bowel preparation, while procedural pain, non-passability, and stenosis were associated with women. Physician endoscopists exhibited higher incomplete colonoscopy rates compared to nurse endoscopists, and patients with higher comorbidity scores were more likely to have incomplete colonoscopy. This study highlights the prevalence of incomplete colonoscopy and recognizes modifiable risk factors like inadequate bowel preparation and procedural pain. Findings underscore the need for personalized interventions, stressing ongoing endoscopist education and targeted strategies to improve colonoscopy effectiveness.

PMID:40439900 | DOI:10.1097/SGA.0000000000000874

Eur J Prev Cardiol. 2025 May 29:zwaf304. doi: 10.1093/eurjpc/zwaf304. Online ahead of print.

ABSTRACT

AIMS: There is an overabundance of cardiovascular disease (CVD) risk-prediction models applicable to patients with Type 2 diabetes (T2D), but most of them still require external validation. Our aim was to assess the performance of 18 CVD risk scores in a Spanish cohort of patients with T2D.

METHODS AND RESULTS: The CARdiovascular Risk in patients with DIAbetes in Navarra (CARDIANA) cohort, which includes 20 793 individuals with T2D and no history of CVD, was used to externally validate 13 models developed in patients with T2D [Action in Diabetes and Vascular Disease (ADVANCE), Atherosclerosis Risk in Communities, Basque Country Prospective Complications and Mortality Study risk engine, Cardiovascular Healthy Study, Diabetes Cohort Study, DIAL2, DIAL2-extended, Fremantle, Kaasenbrood, Swedish National Diabetes Register (NDR), PREDICT1-diabetes, SCORE2-diabetes, and Wan] and 5 models developed in the general population (ASCVD, PREVENT-basic, PREVENT-full, QRISK2, and SCORE2). Harrell’s C-statistic and calibration plots were used as measures of discrimination and calibration, respectively. There were 991 incident CVD events within 5 years of follow-up, resulting in a cumulative incidence of 5.0% (95% confidence interval 4.7-5.3). Discrimination ability was moderate for all the models, with SCORE2-diabetes, NDR, PREDICT1-diabetes, PREVENT-full, Wan, ADVANCE, and both DIAL2 models showing the highest C-index values. All models showed good calibration, although most of them required recalibration, with the exception of ADVANCE-, DIAL2-, and SCORE2-related models.

CONCLUSION: In our context, models derived for or adapted to diabetes patients, as well as models derived in the general population but incorporating diabetes-related metabolic measures (such as Hb1Ac) as predictors, demonstrated better performance than the others. DIAL2, DIAL2-extended, SCORE2-diabetes, and ADVANCE showed optimal calibration even without recalibration, which implies greater applicability, especially for SCORE2-diabetes and ADVANCE because of their simplicity.

PMID:40439899 | DOI:10.1093/eurjpc/zwaf304

Planta. 2025 May 29;262(1):10. doi: 10.1007/s00425-025-04703-3.

ABSTRACT

A model predicting the level of resistance of basil to downy mildew was developed. The model integrates plant age, genetic background, sporulation, disease intensity, pathogen races, and environmental data at an early stage of disease. These results can be used to select and develop new basil cultivars and accelerate the time needed in breeding for basil downy mildew resistance. Basil downy mildew (BDM) caused by the oomycete Peronospora belbahrii emerged as a global threat, rapidly becoming the most devastating disease of sweet basil (Ocimum basilicum) and other Ocimum spp. worldwide. Despite advancements in understanding its biology and epidemiology, and the availability of approved fungicides and management strategies, BDM remains economically destructive and an ongoing risk to basil production worldwide. Recently, the development and introduction of resistant cultivars have emerged as crucial tools in BDM management and the emergence of new BDM races creates new challenges to controlling this disease. The present study aimed to provide growers and breeders with insights into the survival capabilities of resistant basil cultivars under varying genetic backgrounds, pathogen races, growth stages, and various environmental conditions. Through a series of lab and field experiments, we evaluated the response of multiple resistant sources and their lineages to various isolates of P. belbahrii across different locations, using multiple indices to assess their resistance. Entries carrying the R genes Pb1/Pb2 exhibited complete resistance across all races, growth stages, and environmental conditions. Those harboring the R-gene Pb2 showed similar resistance levels, with minor variability due to growth stage. Responses of Pb1 plants varied with pathogen race, displaying full resistance to race 0 at all growth stages but displaying susceptibility to race 1. Plant cultivars possessing MRI resistance genes and their recombinant inbred lines (RIL’s) exhibited variable responses to pathogen attacks, ranging from high tolerance to complete susceptibility. Some MRI RIL’s showed high resistance similar to Pb2 entries. Pb0 cultivars and ‘Eleonora’ (unknown background) were susceptible to all races and growth stages in all experiments. Comprehensive analysis across all genetic backgrounds revealed a significant correlation (R = 0.73) between disease intensity (D.I) at the seedling stage under controlled conditions and D.I in adult plants under field conditions. Principal Component Analysis (PCA) across six experiments indicated that the primary components influencing disease outcomes were the accession, race, and growth stage, explaining 65%, 22%, and 7% of the variability, respectively. A prediction model based on the statistical parameters residual (%) and root-mean-square error (RMSE) demonstrated strong predictability, particularly regarding pathogen sporulation and daily disease development rates. The model predicted resistance probabilities with R² values of 0.81, 0.91, and 0.93 at the second, third, and final disease score readings, respectively, significantly earlier (~ 14-21 days post-infection) than traditional assessments (~ 42 days). These findings demonstrate that resistance in basil entries against current pathogen races can be effectively assessed within weeks of disease onset, facilitating more timely and informed management decisions for growers and providing an important tool for plant breeders in search of improved BDM resistance.

PMID:40439895 | DOI:10.1007/s00425-025-04703-3

Health Serv Res. 2025 Jun;60(3):e14422. doi: 10.1111/1475-6773.14422. Epub 2024 Dec 29.

ABSTRACT

OBJECTIVE: To evaluate the data consistency and quality of Medicaid claims data on opioid use disorder (OUD) treatment, benchmarking the Transformed Medicaid Statistical Information System Analytic Files (TAF) against metrics reported by the Medicaid Outcomes Distributed Research Network (MODRN), which analyzed data obtained directly from 11 state Medicaid agencies.

DATA SOURCES AND STUDY SETTING: The primary data source was TAF claims for the years 2017 and 2018, limited to non-dual, full-benefit Medicaid beneficiaries aged 12-64 in one of the 11 states in our study sample.

STUDY DESIGN: Using TAF data, we replicated performance on the following five OUD quality metrics reported by MODRN: number of enrollees receiving any OUD medication, receipt of behavioral health counseling, completion of urine drug screens, receipt of any opioid analgesic fill, and receipt of any benzodiazepine fill.

DATA COLLECTION/EXTRACTION METHODS: Access to TAF data was facilitated through the Chronic Conditions Warehouse.

PRINCIPAL FINDINGS: There were 11% fewer Medicaid enrollees with OUD in TAF compared to MODRN (912,478 vs. 1,034,412). Patient characteristics were largely similar across the two datasets, with the exception of more missing race information in TAF (20.9% vs. 7.1%). Across the 11 states, performance on the six quality measures was similar. For example, the rate of use of any OUD medication in 2018 was 57.1% in MODRN and 58.6% in TAF. However, there were important discrepancies in the TAF data in individual states for single years.

CONCLUSIONS: TAF data may undercount patients with OUD, but otherwise exhibited consistency with MODRN benchmarks, suggesting suitability of TAF for research on OUD treatment. Our results highlight several data quality issues with TAF that researchers who use these data should be aware of, including reporting of race and ethnicity.

PMID:40438998 | DOI:10.1111/1475-6773.14422

Biomark Med. 2025 May 29:1-15. doi: 10.1080/17520363.2025.2511466. Online ahead of print.

ABSTRACT

INTRODUCTION: MicroRNA (miRNA) single nucleotide polymorphisms (miRNA-SNPs) have been associated with pediatric acute lymphoblastic leukemia (ALL). However, since the results of these individual studies have been inconsistent, we performed a meta-analysis to help establish a statistical significance for the association between miRNA-SNPs and pediatric ALL risk. We also analyzed whether they confer susceptibility across country-specific studies by using different genetic models.

METHODS: Articles published from 2001 to 2023 were collected from PubMed and Google Scholar databases. Through MetaGenyo, the association between miRNA- SNPs and pediatric ALL risk was calculated by pooled odds ratio [ORs] and 95% CI. A subgroup analysis of pooled ORs in country-specific studies was also performed.

RESULTS: Based on the inclusion and exclusion criteria, 14 studies were analyzed to extract data on miR146 rs2910164, miR-196a2 rs11614913, miR-612 rs12803915 and mir-499 rs3746444. While the pooled data analysis did not reveal any association, a subgroup analysis demonstrated country-specific differences in allele frequencies of all the four miRNAs in various genetic models, implying ethnicity-based risk.

CONCLUSION: Our results suggested that miRNA-SNPS can still be considered as a potential risk factor to be explored in more populations.

PMID:40438964 | DOI:10.1080/17520363.2025.2511466

Mol Omics. 2025 May 29. doi: 10.1039/d5mo00018a. Online ahead of print.

ABSTRACT

The progressive illness known as chronic kidney disease (CKD) can often be challenging to diagnose in its early stages with conventional diagnostic approaches such as serum creatinine and albumin assessment. Early-stage CKD (stages G1-G3) is defined by a GFR of ≥30 mL min^-1/1.73 m², which indicates normal to moderately reduced kidney function with or without symptoms of impaired kidney function. Identifying possible biomarkers for early detection and personalised treatment, as well as physiological changes linked to early CKD-an area that has not been fully investigated before-is the goal of the study to address this gap. We performed a metabolomic analysis using ¹H NMR on 115 human serum samples (24 healthy controls and 91 patients with early-stage CKD). MetaboAnalyst 6.0 was used for data pre-processing and statistical analyses (PCA, PLS-DA, OPLS-DA, ANOVA, and Wilcoxon Mann-Whitney test). Strong differentiation between CKD stages was achieved by random forest modelling. The KEGG database was used to perform pathway enrichment, and ROC analysis was used to evaluate the diagnostic value of important metabolites. Across CKD stages, significant changes were observed in ten different metabolites: myo-Inositol, glycerol, pyruvate, carnitine, phenylalanine, tyrosine, histidine, TMAO, 2-hydroxyisobutyrate, and 3-hydroxyisobutyrate (p < 0.05, VIP > 1). AUC values > 0.7 from ROC curves demonstrated its potential for diagnosis. Pathway analysis revealed significant dysregulation in the metabolism of inositol phosphate, tyrosine, histidine, and pyruvate, and biosynthesis of phenylalanine, tryptophan and tyrosine. This comprehensive metabolomics investigation identified potential early-stage CKD biomarkers in addition to significant metabolic abnormalities. These findings could help provide individualized care for early CKD management.

PMID:40438956 | DOI:10.1039/d5mo00018a

Arthritis Care Res (Hoboken). 2025 May 29. doi: 10.1002/acr.25576. Online ahead of print.

ABSTRACT

OBJECTIVE: This systematic review and meta-analysis aims to evaluate the participation of historically marginalized populations in systemic lupus erythematosus (SLE) clinical trials conducted in the US.

BACKGROUND: SLE, a complex autoimmune disease characterized by a dysregulated immune response leading to inflammation and tissue damage in multiple organ systems, exhibits a mortality rate four times higher in historically marginalized populations compared to the general population. It is essential for clinical trials to accurately represent the disease population to effectively evaluate treatment modalities. However, the current trial design lacks appropriate representation of historically marginalized populations, limiting the generalizability of results. Our study addresses this research gap by evaluating the participant demographics in SLE clinical trials.

METHODS: Relevant clinical trials were obtained in a comprehensive search of MEDLINE (PubMed) and Embase (Elsevier) in May of 2024. Included trials were published in the United States between January 1, 2018, and December 31, 2023. Two reviewers independently performed screening and data extraction via a standardized Google Form.

RESULTS: Having met our inclusion criteria, 18 U.S. SLE clinical trials were evaluated for participant sex, age, racial, and ethnic data. Analysis of sex/gender revealed that the included population accurately represented the disease population. Regarding race/ethnicity participation, 11/18 (61.1%) received an overall Poor rating, and none received a Good rating. Analysis revealed that 14/18 (77.8%) of studies demonstrated statistically insignificant underrepresentation of Black, Asian, and Hispanic populations. No studies reported the inclusion of older adults in their sample, suggesting a significant need for better age representation.

CONCLUSION: The results of this study reveal disparities in the representation of the SLE disease population in clinical trials, emphasizing insufficient inclusion of Black, Asian, and Hispanic/Latinx participants and the disproportionate overrepresentation of white participants. Our study highlights the need for the initiation of effective strategies to engage historically marginalized populations in SLE clinical trials. Addressing these gaps is necessary to prioritize the participation of inequitable populations, increase standardization of SLE treatments, and improve the relevance of SLE research.

PMID:40438917 | DOI:10.1002/acr.25576

J Appalach Health. 2025 May 1;7(1):63-80. doi: 10.13023/jah.0701.04. eCollection 2025.

ABSTRACT

BACKGROUND: Wearable health devices capture metrics (e.g., physical activity, ECG, sleep) that can enhance care when shared with providers. Yet, willingness to share wearable data may differ in Appalachia, where chronic disease burdens, mistrust, and limited infrastructure pose unique challenges.

OBJECTIVE: This study explored (1) which sociodemographic, health, and digital behaviors correlate with willingness to share wearable data and (2) how these insights can guide region-specific interventions in Appalachia.

METHODS: We analyzed 320 Appalachian respondents from the Health Information National Trends Survey (HINTS 6). Descriptive statistics and logistic regression models examined willingness to share wearable data. Because of small cell counts, we supplemented with a Firth (penalized) logistic regression for robustness.

RESULTS: Approximately 25.0% unweighted (27.9% weighted) were willing to share wearable data, but two-thirds did not respond or were inapplicable. The final adjusted model (n=47) revealed:Income: Higher income correlated with increased willingness (e.g., aOR=8.52e+04 for $35-49k vs.Self-Rated Health: “Good” or “very good” health was associated with higher odds of sharing than “poor” health (aOR=4406.52; p<.05).Messaging: Surprisingly, participants who never messaged providers showed greater willingness (aOR=1.93e+07; p.

CONCLUSIONS: These preliminary findings suggest that household income, perceived health, and digital behaviors influence wearable data-sharing in Appalachia, whereas national demographic trends may not apply. Future work should use larger samples, mixed methods, and region-specific approaches to address mistrust, privacy concerns, and infrastructural barriers, aiming to enhance remote patient monitoring and reduce health disparities.

PMID:40438881 | PMC:PMC12112009 | DOI:10.13023/jah.0701.04

Cureus. 2025 Apr 28;17(4):e83108. doi: 10.7759/cureus.83108. eCollection 2025 Apr.

ABSTRACT

BACKGROUND: Adolescence is a stage characterized by behaviors that pose significant risks to long-term health, and smoking is one of the most prominent risks among young people. This period is also considered a stage with an increased need for interaction and acceptance from peer groups, which can lead adolescents to take on new risks. In this context, smoking, in various forms (cigarettes, cigarillos, vape/e-cigarettes, chewing tobacco, etc.), is a major public health issue among young people. Tobacco use, perceived as a socializing factor, provides adolescents with the opportunity to integrate more easily into groups.

OBJECTIVE: The objective of this research was to identify potential changes in adolescents’ smoking behaviour during the COVID-19 pandemic, based on sociodemographic data. Additionally, we aimed to identify the existence of vulnerabilities that could lead to the later development of the smoking habit.

MATERIAL AND METHODS: The research sample comprised 521 subjects aged 15-19 years, both girls and boys, from rural and urban areas in the southern (Olt) and northern (Suceava) regions of Romania. The study was cross-sectional and included data collected online through an anthropological questionnaire between April and May 2021. The study participants were enrolled in high school education conducted exclusively online during this period due to the context generated by the pandemic. The questionnaire included a series of questions related to smoking-related behaviours among adolescents, such as the age of onset of smoking temptation and the subsequent development of this habit, analyzed in relation to sociodemographic variables. Data were processed using IBM SPSS Statistics, version 26 (IBM Corp., Armonk, USA). Results: The results were obtained from the analysis of data provided by the 521 subjects in the research sample. Approximately 45% of our study subjects were tempted and tried to smoke, but only 17.3% became regular smokers and smoked daily, while 4.2% continued to be occasional smokers (3-5 cigarettes/month). It was found that there were no statistically significant differences by gender, place of origin, or geographical area in the age at which adolescents were tempted and even tried smoking. About 52.5% of the adolescents who tried to smoke did not become regular smokers, while those who became regular or occasional smokers represented 47.5%. In our sample, the vulnerable age for the subsequent establishment of regular smoking behaviour was between 13 and 14 years. Adolescents who perceived their family income as high or average reported higher tobacco consumption compared to those who perceived their family income as low.

CONCLUSIONS: Tobacco consumption habits did not seem to be influenced by the new situation created by the COVID-19 pandemic. Regardless of gender, place of origin (rural or urban), or geographical region, the age at which the temptation to smoke was most likely to develop into a habit was 14 or younger. It is noteworthy that more than half of the adolescents were not tempted to experiment with smoking. Among those who tried tobacco and continued smoking, boys showed a higher percentage than girls. Furthermore, a correlation was observed between family income and the quantity of tobacco consumed.

PMID:40438863 | PMC:PMC12119062 | DOI:10.7759/cureus.83108

Cureus. 2025 Apr 27;17(4):e83076. doi: 10.7759/cureus.83076. eCollection 2025 Apr.

ABSTRACT

BACKGROUND: Angina pectoris remains a significant public health concern, highlighting disparities in cardiovascular health influenced by demographic, socioeconomic, and geographic factors. Analyzing the prevalence of trends is crucial to addressing health inequities and informing targeted interventions. The study of National Health Interview Survey (NHIS) data from 2019 to 2023 allowed us to observe how the pandemic affected cardiovascular care utilization when it decreased in 2020 and later rebounded into 2023 while investigating shifts in reported angina prevalence rates among main groups. Angina pectoris condition-related research requires assessment of current trends for effective health inequities intervention and targeted intervention planning.

OBJECTIVE: This study aims to examine the prevalence of angina pectoris among United States (US) adults from 2019 to 2023 and across demographic, socioeconomic, and geographic factors.

METHOD: Data from the NHIS were analyzed to determine the prevalence of angina pectoris, which was identified through self-reported diagnosis or symptoms. The identification of angina pectoris in the NHIS dataset was based on self-reported physician diagnosis alongside responses to definite survey questions regarding chest pain and discomfort consistent with the symptoms of angina. Angina pectoris was identified in the NHIS dataset based on self-reported physician diagnoses and responses to specific survey questions on chest pain or discomfort consistent with angina. Stratified analyses assessed variations in prevalence across key demographic, socioeconomic, and geographic factors over a five-year period. The statistical analyses included both inferential analyses and descriptive statistics, including hypothesis testing and confidence interval estimation, to evaluate associations and divergences within the data. The prevalence of angina was evaluated across socioeconomic, demographic, and geographic groups using stratified analyses.

RESULTS: The overall prevalence of angina pectoris remained stable (1.5-1.7%) from 2019 to 2023. Higher rates were observed among males (1.8%), older adults (4.5% in those aged 75 years and older), and US-born individuals (1.6%). Disparities observed across race/ethnicity further revealed disparities, with American Indian/Alaska Native individuals (2.1%) and Black individuals (1.2%) showing distinct patterns. Geographic trends highlighted a higher prevalence in areas with high social vulnerability (1.7%). Socioeconomic disparities were notable, with lower-income individuals (<100% federal poverty level (FPL)) experiencing higher prevalence (2.8-3.1%) and elevated rates among those with lower educational attainment. Employment status influenced prevalence, with unemployed individuals showing higher rates (3.4%).

CONCLUSION: The prevalence of angina pectoris reflects persistent disparities across demographic, socioeconomic, and geographic factors. The findings highlight the need for policies that enhance access to preventive cardiovascular care, early screening, and intervention, as well as address the social determinants of health, to minimize disparities in underserved populations.

PMID:40438860 | PMC:PMC12116826 | DOI:10.7759/cureus.83076