Urban-rural disparities in medical care, including in home healthcare, persist globally. With aging populations and medical advancements, demand for home health services rises, warranting investigation into home healthcare disparities. Our study aimed to (i) investigate the impact of rurality on home healthcare quality, and (ii) assess the temporal disparities and the changes in disparities in home healthcare quality between urban and rural home health agencies (HHAs), incorporating an analysis of geospatial distribution to visualize the underlying patterns. This study analyzed data from HHAs listed on the Centers for Medicare and Medicaid Services website, covering the period from 2010 to 2022. Data were classified into urban and rural categories for each HHA. We employed panel data analysis to examine the impact of rurality on home healthcare quality, specifically focusing on hospital admission and emergency room (ER) visit rates. Disparities between urban and rural HHAs were assessed using the Wilcoxon test, with results visualized through line and dot plots and heat maps to illustrate trends and differences comprehensively. Rurality is demonstrated as the most significant variable in hospital admission and ER visit rates in the panel data analysis. Urban HHAs consistently exhibit significantly lower hospital admission rates and ER visit rates compared to rural HHAs from 2010 to 2022. Longitudinally, the gap in hospital admission rates between urban and rural HHAs is shrinking, while there is an increasing gap in ER visit rates. In 2022, HHAs in Mountain areas, which are characterized by a higher proportion of rural regions, exhibited higher hospital admission and ER visit rates than other areas. This study underscores the persistent urban-rural disparities in home healthcare quality. The analysis emphasizes the ongoing need for targeted interventions to address disparities in home healthcare delivery and ensure equitable access to quality care across urban and rural regions. Our findings have the potential to inform policy and practice, promoting equity and efficiency in the long-term care system, for better health outcomes throughout the USA.

OBJECTIVE: The ileum is the most commonly affected segment of the gastrointestinal tract in Crohn\'s disease (CD). We aimed to determine whether disease location affects response to filgotinib, a Janus kinase (JAK) inhibitor, in patients with moderate-to-severely active Crohn\'s disease (CD) and applying appropriate methods to account for differences in measuring disease activity in the ileum compared to the colon.
METHODS: This post-hoc analysis of data from the FITZROY phase 2 trial (NCT02048618) compared changes in the Crohn\'s Disease Activity Index (CDAI) and Simple Endoscopic Score for Crohn\'s Disease (SES-CD) amongst patients with ileal-dominant and isolated colonic CD treated with 10 weeks of filgotinib 200 mg daily or placebo. A mixed effects model for repeated measures was used to test whether ileal disease responded differently than colonic disease, by evaluating for effect modification using the interaction term of treatment assignment-by-disease location.
RESULTS: Numerically greater proportions of patients with isolated colonic disease compared to ileal-dominant CD achieved clinical remission (CDAI <150, 75.9% vs. 41.6%) and endoscopic response (SES-CD reduction by 50%, 52.5% vs. 15.5%) at Week 10. However, after adjusting for baseline disease activity by disease location and within-patient clustering effects, there was no significant difference in treatment response by disease location (mean difference in ΔCDAI between ileal-dominant vs. isolated colonic disease +9.24 [95% CI: -87.19, +105.67], p=0.85; mean difference in ΔSES-CD -1.93 [95% CI: -7.03, +3.44], p=0.48).
CONCLUSIONS: Filgotinib demonstrated similar efficacy in ileal-dominant and isolated colonic CD when controlling for baseline disease activity and clustering effects.

OBJECTIVE: Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based \"Automated Visual Evaluation\" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment VIA performance.
METHODS: Prospective study.
METHODS: Eight public health facilities in Zambia.
METHODS: A total of 8204 women aged 25-55.
METHODS: Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive.
METHODS: Area under the receiver operating curve (AUC); sensitivity; specificity.
RESULTS: As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden\'s index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91).
CONCLUSIONS: These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).

Cadmium (Cd) is a heavy metal and natural element found in soil and crops with increasing concentrations linked to phosphate fertilizers and sewage sludge applied to crop lands. A large fraction of older US men and woman have documented Cd exposure. Cd exposure has proven health concerns such as risk of lung cancer from inhalation and impaired renal function; however, growing evidence suggests it also influences bone and muscle health. Given that low levels of Cd could affect bone and muscle, we have designed prospective studies using the two largest and most detailed US studies of bone health in older men and women: the Osteoporotic Fractures in Men Study and the Study of Osteoporotic Fractures. We are investigating the association of urinary cadmium (U-Cd), as a surrogate for long-term Cd exposure, with bone and muscle health. Building off suggestive evidence from mechanistic and cross-sectional studies, this will be the first well-powered prospective study of incident fracture outcomes, bone loss, and muscle loss in relation to U-Cd, an established biomarker of long-term Cd exposure. The following is a proposed protocol for the intended study; if successful, the proposed studies could be influential in directing future US policy to decrease Cd exposure in the US population similar to recent policies adopted by the European Union to limit Cd in fertilizers.

BACKGROUND: The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data.
METHODS: We sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods.
RESULTS: Of 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method.
CONCLUSIONS: Our empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

In the COMET trial of patients with late-onset Pompe disease, greater improvement in upright forced vital capacity (FVC) % predicted was observed with avalglucosidase alfa (AVA) vs alglucosidase alfa (ALGLU) (estimated treatment difference: 2.43%). The pre-specified mixed model repeated measures (MMRM) analysis demonstrated non-inferiority of AVA (P = 0.0074) and narrowly missed superiority (P = 0.063; 95% CI: -0.13-4.99). We report superiority of AVA in two post-hoc analyses that account for an extreme outlier participant with low FVC and severe chronic obstructive pulmonary disease at baseline: MMRM excluding the outlier (P = 0.013) and non-parametric analysis of all data with repeated measures analysis of covariance (P = 0.019).

BACKGROUND: Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models.
METHODS: We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed.
RESULTS: A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods.
CONCLUSIONS: The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

OBJECTIVE: Randomized controlled trials (RCTs) with repeatedly measured continuous variables as primary outcomes are common. Although statistical methodologies for calculating sample sizes in such trials have been extensively investigated, their practical application remains unclear. This study aims to provide an overview of sample size calculation methods for different research questions (e.g., key time point treatment effect, treatment effect change over time) and evaluate the adequacy of current practices in trial design.
METHODS: We conducted a comprehensive search of PubMed to identify RCTs published in core journals in 2019 that utilized repeatedly measured continuous variables as their primary outcomes. Data were extracted using a predefined questionnaire including general study characteristics, primary outcomes, detailed sample size calculation methods, and methods for analyzing the primary outcome. We re-estimated the sample size for trials that provided all relevant parameters.
RESULTS: A total of 168 RCTs were included, with a median of four repeated measurements (interquartile range 3-6) per outcome. In 48 (28.6%) trials, the primary outcome used for sample size calculation differed from the one used in defining the primary outcomes. There were 90 (53.6%) trials exhibited inconsistencies between the hypotheses specified for sample size calculation and those specified for primary analysis. The statistical methods used for sample size calculation in 158 (94.0%) trials did not align with those used for primary analysis. Additionally, only 6 (3.6%) trials accounted for the number of repeated measurements, and 7 (4.2%) trials considered the correlation among these measurements when calculating the sample size. Furthermore, of the 128 (76.2%) trials that considered loss to follow-up, 33 (25.8%) used an incorrect formula (i.e., N∗(1+lose rate) for sample size adjustment. In 53 (49.5%) out of 107 trials, the re-estimated sample size was larger than the reported sample size.
CONCLUSIONS: The practice of sample size calculation for RCTs with repeatedly measured continuous variables as primary outcomes displayed significant deficiencies, with a notable proportion of trials failed to report essential parameters about repeated measurement required for sample size calculation. Our findings highlight the urgent need to use optimal sample size methods that align with the research hypothesis, primary analysis method, and the form of the primary outcome.

Denosumab (Dmab) is increasingly prescribed worldwide. Unlike bisphosphonates (BPs), its effect on mortality has yet to be well explored. This study examined the association between Dmab and all-cause mortality compared with no treatment in subjects with a fracture and BPs in subjects without a fracture. The study population was from the Sax Institute\'s 45 and Up Study (n = 267,357), a prospective population-based cohort with questionnaire data linked to hospital admissions (Admitted Patients Data Collection [APDC] data were linked by the Centre for Health Record Linkage), medication records (Pharmaceutical Benefits Scheme [PBS] provided by Services Australia), and stored securely (secure data access was provided through the Sax Institute\'s Secure Unified Research Environment [SURE]). The new-user cohort design with propensity-score (PS) matching was implemented. In the fracture cohort, Dmab and oral BP users were matched 1:2 to no treatment (Dmab: 617 women, 154 men; oral BPs: 615 women, 266 men). In the no-fracture cohort, Dmab users were matched 1:1 with oral BPs and zoledronic acid (Zol) users (Dmab:oral BPs: 479 men, 1534 women; Dmab:Zol: 280 men, 625 women). Mortality risk was measured using sex-specific pairwise multivariable Cox models. In the fracture cohort, compared with no treatment, Dmab was associated with 48% lower mortality in women (hazard ratio [HR] = 0.52, 95% confidence interval [CI] 0.36-0.72) but not in men. Oral BPs were associated with 44% lower mortality in both sexes (women HR = 0.56, 95% CI 0.42-0.77; men HR = 0.56, 95% CI 0.40-0.78). In the no-fracture cohort, compared with BPs, Dmab was associated with 1.5- to 2.5-fold higher mortality than oral BPs (women HR = 1.49, 95% CI 1.13-1.98; men HR = 2.74; 95% CI 1.82-4.11) but similar mortality to Zol. Dmab in women and oral BPs were associated with lower post-fracture mortality than no treatment. However, Dmab users had generally higher mortality than oral BP users in those without fractures. © 2023 American Society for Bone and Mineral Research (ASBMR).

Underwater soundscape that spans a broad frequency band shows variability consistent with contributing noise sources and ocean environment. However, increased anthropogenic activities result in noise proliferation which can harm natural marine habitat. Continuous monitoring of background sound is useful to assess such spatio-temporal variability of soundscape. Standard noise level metrics, for instance, mean (μ), 90th percentiles (90P), standard deviation (σ), and kurtosis (β), are constructed from noise field measured from three coastal stations in Eastern Arabian Sea. These metrics are found to be suitable to describe the soundscape variability with respect to season, frequency, and depth. Mean and 90P are used to compare the seasonal variations while kurtosis metrics are exercised to check the impulsive nature of composite signal. Histogram representation and probability density function (PDF) were utilized to analyze the spectral variation in soundscape with respect to season. Analysis was carried out at 500-ms temporal window in two spectral bands corresponding to traffic and wind noise fields. Seasonal analysis shows that in summer, mean noise level decreases as hydrophone depth increases, while in winter, deeper depths have higher mean value with the presence of seasonal surface duct. This implication of sound speed profile on noise field has also been confirmed using appropriate noise model.