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Background: Late gadolinium enhancement (LGE) cardiac MRI is the method of choice in revealing the presence of myocardial scarring, but its availability remains limited in clinical practice. Purpose: To assess myocardial scarring in patients with autoimmune rheumatic diseases (ARDs) using contrast‐free cardiac MRI with a radiomics model. Study Type: Retrospective. Population: One hundred ninety‐two patients (mean age, 41 years ± 15, 62 men) with or without ARDs, grouped into a training set of 153 patients and a testing set of 39 patients. Field Strength/Sequence: 3.0 T/ cine imaging with a balanced steady‐state free precession sequence, T1 mapping with a modified Look‐Locker inversion recovery sequence, and LGE imaging with a phase‐sensitive inversion recovery gradient echo sequence. Assessment: LGE assessment was the reference standard for identifying myocardial scarring. Based on motion features extracted from cine images and tissue characterization features extracted from native T1 maps, a fully automated radiomics model with T1, cine MRI, or combined inputs was developed. Statistical Tests: Logistic regression model was used to detect myocardial scarring using contrast‐free cardiac MRI parameters. Receiver operating characteristic curves were analyzed to assess the accuracy, sensitivity, and specificity in detecting myocardial scarring. Sensitivities of the models were further assessed in patients with various myocardial scarring proportions. Z‐statistic and dice coefficient were assessed to compare the performance. P‐values <0.05 were considered significant. Results: The multivariable regression model exhibited an accuracy of 85.3%, a sensitivity of 93.5%, and a specificity of 50.0%. The radiomics model with T1 and cine MRI input exhibited an accuracy of 75.7%, a sensitivity of 60.9%, and a specificity of 85.5%. Moreover, the radiomics model showed a sensitivity of 90.9% among patients with >25% myocardial scarring. Data Conclusions: The proposed radiomics model allowed for the identification of myocardial scarring similar to LGE, but on contrast‐free cardiac MRI in patients with ARDs. Evidence Level: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to assess performance in the Brazilian Lung Cancer Registry Database by using the parsimonious EuroLung risk models for morbidity and mortality. Methods: The EuroLung1 and EuroLung2 models were tested and evaluated through calibration (calibration plot, Brier score, and the Hosmer-Lemeshow test) and discrimination (ROC AUCs), in a national multicenter registry of 1,031 patients undergoing anatomic lung resection. Results: The evaluation of performance in Brazilian health care facilities utilizing risk-adjustment models, specifically EuroLung1 and EuroLung2, revealed substantial miscalibration, as evidenced by calibration plots and Hosmer-Lemeshow tests in both models. In terms of calibration, EuroLung1 exhibited a calibration plot with overlapping points, characterized by a slope of 1.11 and a Brier score of 0.15; the Hosmer-Lemeshow test yielded a statistically significant p-value of 0.015; and the corresponding ROC AUC was 0.678 (95% CI: 0.636-0.721). The EuroLung2 model displayed better calibration, featuring fewer overlapping points in the calibration plot, with a slope of 1.22, with acceptable discrimination, as indicated by a ROC AUC of 0.756 (95% CI: 0.670-0.842). Both models failed to accurately predict morbidity and mortality outcomes in this specific health care context. Conclusions: Discrepancies between the EuroLung model predictions and outcomes in Brazil underscore the need for model refinement and for a probe into inefficiencies in the Brazilian health care system. [ABSTRACT FROM AUTHOR]

To investigate the usefulness of multistate models (MSM) for determining colorectal cancer (CRC) recurrence rate, to analyse the effect of different factors on tumour recurrence and death, and to assess the impact of recurrence for CRC prognosis. Observational follow-up study of incident CRC cases disease-free after curative resection in 2006–2013 (n = 994). Recurrence and mortality were analyzed with MSM, as well as covariate effects on transition probabilities. Cumulative incidence of recurrence at 60 months was 13.7%. Five years after surgery, 70.3% of patients were alive and recurrence-free, and 8.4% were alive after recurrence. Recurrence has a negative impact on prognosis, with 5-year CRC-related mortality increasing from 3.8% for those who are recurrence-free 1-year after surgery to 33.6% for those with a recurrence. Advanced stage increases recurrence risk (HR = 1.53) and CRC-related mortality after recurrence (HR = 2.35). CRC-related death was associated with age in recurrence-free patients, and with comorbidity after recurrence. As expected, age≥75 years was a risk factor for non-CRC-related death with (HR = 7.76) or without recurrence (HR = 4.26), while its effect on recurrence risk was not demonstrated. MSM allows detailed analysis of recurrence and mortality in CRC. Recurrence has a negative impact on prognosis. Advanced stage was a determining factor for recurrence and CRC-death after recurrence. [ABSTRACT FROM AUTHOR]

BACKGROUND: It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF. Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e', >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization. RESULTS: Beyond atrial size, the 3D LA models revealed roof dilation as the main feature found in masked HFpEF (diagnosed during exercise-stress only) preceding a pattern shift to overall atrial size in overt HFpEF (diagnosed at rest). Characteristics of the 3D model were integrated into the LA HFpEF shape score, a biomarker to characterize the gradual remodeling between noncardiac dyspnea and HFpEF. The LA HFpEF shape score was able to discriminate HFpEF (n=34) to noncardiac dyspnea (n=34; area under the curve, 0.81) and was associated with a risk for atrial fibrillation occurrence (hazard ratio, 1.02 [95% CI, 1.01-1.04]; P=0.003), as well as cardiovascular hospitalization (hazard ratio, 1.02 [95% CI, 1.00-1.04]; P=0.043). CONCLUSIONS:' LA roof dilation is an early remodeling pattern in masked HFpEF advancing to overall LA enlargement in overt HFpEF. These distinct features predict the occurrence of atrial fibrillation and cardiovascular hospitalization. [ABSTRACT FROM AUTHOR]

Objective: To estimate the prevalence of long COVID among Western Australian adults, a highly vaccinated population whose first major exposure to the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) was during the 2022 Omicron wave, and to assess its impact on health service use and return to work or study. Study design: Follow‐up survey (completed online or by telephone). Setting, participants: Adult Western Australians surveyed 90 days after positive SARS‐CoV‐2 test results (polymerase chain reaction or rapid antigen testing) during 16 July – 3 August 2022 who had consented to follow‐up contact for research purposes. Main outcome measures: Proportion of respondents with long COVID (ie, reporting new or ongoing symptoms or health problems, 90 days after positive SARS‐CoV‐2 test result); proportion with long COVID who sought health care for long COVID‐related symptoms two to three months after infection; proportion who reported not fully returning to previous work or study because of long COVID‐related symptoms. Results: Of the 70 876 adults with reported SARS‐CoV‐2 infections, 24 024 consented to contact (33.9%); after exclusions, 22 744 people were invited to complete the survey, of whom 11 697 (51.4%) provided complete responses. Our case definition for long COVID was satisfied by 2130 respondents (18.2%). The risk of long COVID was greater for women (v men: adjusted risk ratio [aRR], 1.5; 95% confidence interval [CI], 1.4–1.6) and for people aged 50–69 years (v 18–29 years: aRR, 1.6; 95% CI, 1.4–1.9) or with pre‐existing health conditions (aRR, 1.5; 95% CI, 1.4–1.7), as well as for people who had received two or fewer COVID–19 vaccine doses (v four or more: aRR, 1.4; 95% CI, 1.2–1.8) or three doses (aRR, 1.3; 95% CI, 1.1–1.5). The symptoms most frequently reported by people with long COVID were fatigue (1504, 70.6%) and concentration difficulties (1267, 59.5%). In the month preceding the survey, 814 people had consulted general practitioners (38.2%) and 34 reported being hospitalised (1.6%) with long COVID. Of 1779 respondents with long COVID who had worked or studied before the infection, 318 reported reducing or discontinuing this activity (17.8%). Conclusion: Ninety days after infection with the Omicron SARS‐CoV‐2 variant, 18.2% of survey respondents reported symptoms consistent with long COVID, of whom 38.7% (7.1% of all survey respondents) sought health care for related health concerns two to three months after the acute infection. [ABSTRACT FROM AUTHOR]

Background Stroke is an acute cerebrovascular disease in which blood fails to flow into the brain, resulting in damage to brain tissue, with high rates of disability, death, and recurrence, and most of the related studies in Ningxia region are clinical analyses of strokes, which lack trend analysis of stroke hospitalization in Ningxia in recent years. Objective To analyze the disease prevalence characteristics and distribution of stroke hospitalized patients in Ningxia, and provide a basis for the development of comprehensive prevention and treatment measures for stroke. Methods The case home page data of 55 hospitals of traditional Chinese medicine (TCM) and western medicine in Ningxia region were collected, and the data discharged from 2013 to 2019 with a primary diagnosis of stroke [International Classification of Diseases (ICD) -10 coded as I60-I63] were screened and analyzed. SPSS 24.0 software was applied to analyze the differences in gender and age using the chi-square test, and a geographic information system (GIS) was applied to analyze the distribution of stroke hospitalization rate data across Ningxia from 2013 to 2019 using global spatial auto correlation analysis. Results A total of 190 634 stroke patients were finally included. The hospitalization rate of stroke in Ningxia region showed an increasing trend year by year from 2013 to 2019 (hospitalization rates of 24.302/10 000, 30.045/10 000, 34.949/10 000, 39.397/10 000, 44.049/10 000, 47.617/10 000, and 52.944/10 000, respectively, χ2trend=5.982, P=0.014) , and the hospitalization rates were higher during the cold season of each year, with a significant peak in March and a slight decline thereafter. Among the stroke inpatients in Ningxia from 2013 to 2019, there were 167 194 cases of ischemic stroke and 23 440 cases of hemorrhagic stroke; there was significant difference between ischemic stroke and hemorrhagic stroke patients in gender and age (χ2=473.533, 7 518.543, P

Objectives: To project how many minimal trauma fractures could be averted in Australia by expanding the number and changing the operational characteristics of fracture liaison services (FLS). Study design: System dynamics modelling. Setting, participants: People aged 50 years or more who present to hospitals with minimal trauma fractures, Australia, 2020–31. Main outcome measures: Numbers of all minimal trauma fractures and of hip fractures averted by increasing the FLS number (from 29 to 58 or 100), patient screening rate (from 30% to 60%), and capacity for accepting new patients (from 40 to 80 per service per month), and reducing the proportion of eligible patients who do not attend FLS (from 30% to 15%); cost per fracture averted. Results: Our model projected a total of 2 441 320 minimal trauma fractures (258 680 hip fractures; 2 182 640 non‐hip fractures) in people aged 50 years or older during 2020–31, including 1 211 646 second or later fractures. Increasing the FLS number to 100 averted a projected 5405 fractures (0.22%; $39 510 per fracture averted); doubling FLS capacity averted a projected 3674 fractures (0.15%; $35 835 per fracture averted). Our model projected that neither doubling the screening rate nor reducing by half the proportion of eligible patients who did not attend FLS alone would reduce the number of fractures. Increasing the FLS number to 100, the screening rate to 60%, and capacity to 80 new patients per service per month would together avert a projected 13 672 fractures (0.56%) at a cost of $42 828 per fracture averted. Conclusion: Our modelling indicates that increasing the number of hospital‐based FLS and changing key operational characteristics would achieve only moderate reductions in the number of minimal trauma fractures among people aged 50 years or more, and the cost would be relatively high. Alternatives to specialist‐led, hospital‐based FLS should be explored. [ABSTRACT FROM AUTHOR]

Objective To investigate the rules of gout patients attending the hospital by analyzing their time series data, and to provide a reference for the prevention and treatment of gout in medical and health departments. Methods Time series data were collected from the gout patients who attended a grade A tertiary hospital in Qingdao from 2013 to 2018, and a descriptive analysis was performed for the data including the time of patients attending the hospital, age, and sex. An Autoregressive Integra-ted Moving Average (ARIMA) model was established based on the time series data of gout patients in 2013—2018, and the data of gout patients from January to October, 2019, were used for the extrapolation validation of this model. The X-11 method was used to analyze seasonal factors, long-term trends, and random fluctuations. Results Among the gout patients attending the hospital in 2013—2018, male patients accounted for 94.68% and female patients accounted for 5.32%; in terms of age composition, the patients aged 30-39 years, 40-49 years, 50-59 years, and >60 years accounted for 21.50%, 21.67%, 19.74%, and 23.32%, respectively. The ARIMA model established based on time series data of the gout patients in 2013—2018 was ARIMA(0,1,1)×(0,1,1)12, with an AIC value of 674.89 and an SBC value of 679.05, and the extrapolation verification of the predictive effect of this model showed an MAE value of 86.28 and an MAPE value of 7.64%. The X-11 method for seasonal stability (F=27.81,P0.05) showed that the time series of gout patients had stable seasonality and was not affected by time. The number of patients attending the hospital in July and August each year was higher than the mean value, and that in February was lower than the mean value; the number of patients was stable in the other months. Conclusion The ARIMA model is effective with stable prediction results, and combined with the seasonal factors and long-term trend extracted by the X-11 method, it can better explain the rules of gout patients attending the hospital and thus provide a reference for health authorities and medical institutions to formulate the prevention and control policies for gout, conduct health education, and allocate human resources.

ABSTRACT BACKGROUND: Although studies have examined the relationship between variables associated with active aging and quality of life (QoL), no studies have been identified to have investigated the effect of a structural model of active aging on QoL in a representative sample of older people in the community. OBJECTIVE: To measure the domains and facets of QoL in older people and identify the effect of the structural model of active aging on the self-assessment of QoL. DESIGN AND SETTING: This cross-sectional analytical study included 957 older people living in urban areas. Data were collected from households using validated instruments between March and June 2018. Descriptive, confirmatory factor, and structural equation modeling analyses were performed. RESULTS: Most older people self-rated their QoL as good (58.7%), and the highest mean scores were for the social relationships domain (70.12 ± 15.4) and the death and dying facet (75.43 ± 26.7). In contrast, the lowest mean scores were for the physical domains (64.41 ± 17.1) and social participation (67.20 ± 16.2) facets. It was found that active aging explained 50% of the variation in self-assessed QoL and directly and positively affected this outcome (λ = 0.70; P < 0.001). CONCLUSION: Active aging had a direct and positive effect on the self-assessment of QoL, indicating that the more individuals actively aged, the better the self-assessment of QoL.

Objective: To simulate the impact on population mental health indicators of allowing people to book some Medicare‐subsidised sessions with psychologists and other mental health care professionals without a referral (direct access), and of increasing the annual growth rate in specialist mental health care capacity (consultations). Design: System dynamics model, calibrated using historical time series data from the Australian Bureau of Statistics, HealthStats NSW, the Australian Institute of Health and Welfare, and the Australian Early Development Census. Parameter values that could not be derived from these sources were estimated by constrained optimisation. Setting: New South Wales, 1 September 2021 – 1 September 2028. Main outcome measures: Projected mental health‐related emergency department presentations, hospitalisations following self‐harm, and deaths by suicide, both overall and for people aged 15–24 years. Results: Direct access (for 10–50% of people requiring specialist mental health care) would lead to increases in the numbers of mental health‐related emergency department presentations (0.33–1.68% of baseline), hospitalisations with self‐harm (0.16–0.77%), and deaths by suicide (0.19–0.90%), as waiting times for consultations would increase, leading to disengagement and consequently to increases in adverse outcomes. Increasing the annual rate of growth of mental health service capacity (two‐ to fivefold) would reduce the frequency of all three outcomes; combining direct access to a proportion of services with increased growth in capacity achieved substantially greater gains than an increase in service capacity alone. A fivefold increase in the annual service growth rate would increase capacity by 71.6% by the end of 2028, compared with current projections; combined with direct access to 50% of mental health consultations, 26 616 emergency department presentations (3.6%), 1199 hospitalisations following self‐harm (1.9%), and 158 deaths by suicide (2.1%) could be averted. Conclusion: The optimal combination of increased service capacity growth (fivefold) and direct access (50% of consultations) would have double the impact over seven years of accelerated capacity growth alone. Our model highlights the risks of implementing individual reforms without knowledge of their overall system effect. [ABSTRACT FROM AUTHOR]

Background Severe multiple trauma prevalence has been increasing recently, which has become the leading cause of labor force loss. Early and rapid assessment of patients' conditions will greatly affect their prognosis, which could be significantly supported by a concise and effective visual scoring system. Objective To identify and Screen the prognostic factors of severe multiple trauma, and use them to develop two nomograms, then improve them, and verify their clinical application values. Methods Patients with severe multiple trauma were recruited from the general ICU and EICU, Suzhou Ninth People's Hospital, including 321 treated during December 2015 to December 2020 (model group) , and 136 treated during January to August 2021 (validation group) . General data at admission and clinical data within 24 hours of admission were retrospectively collected. Prognosis (successful or unsuccessful treatment result) was assessed at discharge. Prognostic factors of severe multiple trauma were Screened using univariate and LASSO regression, and used to develop models using multivariate Logistic regression with restricted cubic splines, then based on this, two nomograms were developed, and their calibration accuracies were estimated using the bootstrap approach and decision curve analysis (DCA) . The receiver operating characteristic (ROC) analysis with associated AUC values was used to estimate the prognostic value of two nomograms in severe multiple trauma. External verification of the nomograms was carried out in the validation group to evaluate their clinical application values. Results (1) In the model group, successful and unsuccessful treatment results occurred in 244 and 77 cases, respectively. LASSO regression with multivariate Logistic regression analyses showed that age (OR=1.028) , Glasgow Coma Score (GCS) (OR=0.616) , arterial lactate (OR=1.202) , platelet count (OR=3.888) and Injury Severity Score (ISS) (OR=1.104) were associated with the prognosis of severe multiple trauma (P

Abstract Background Hospital outbreaks of multidrug resistant Pseudomonas aeruginosa are often caused by Pseudomonas aeruginosa clones which produce metallo-β-lactamases, such as Verona Integron-encoded Metallo-β-lactamase (VIM). Although different sources have been identified, the exact transmission routes often remain unknown. However, quantifying the role of different transmission routes of VIM-PA is important for tailoring infection prevention and control measures. The aim of this study is to quantify the relative importance of different transmission routes by applying a mathematical transmission model using admission and discharge dates as well as surveillance culture data of patients. Methods We analyzed VIM-PA surveillance data collected between 2010 and 2018 of two intensive-care unit (ICU) wards for adult patients of the Erasmus University Medical Center Rotterdam using a mathematical transmission model. We distinguished two transmission routes: direct cross-transmission and a persistent environmental route. Based on admission, discharge dates, and surveillance cultures, we estimated the proportion of transmissions assigned to each of the routes. Results Our study shows that only 13.7% (95% CI 1.4%, 29%) of the transmissions that occurred in these two ICU wards were likely caused by cross-transmission, leaving the vast majority of transmissions (86.3%, 95% CI 71%, 98.6%) due to persistent environmental contamination. Conclusions Our results emphasize that persistent contamination of the environment may be an important driver of nosocomial transmissions of VIM-PA in ICUs. To minimize the transmission risk from the environment, potential reservoirs should be regularly and thoroughly cleaned and disinfected, or redesigned.

Purpose: To predict the probability of azoospermia without a semen analysis in men presenting with infertility by developing an azoospermia prediction model. Materials and Methods: Two predictive algorithms were generated, one with follicle stimulating hormone (FSH) as the only input and another logistic regression (LR) model with additional clinical inputs of age, luteinizing hormone, total testosterone, and bilateral testis volume. Men presenting between 01/2016 and 03/2020 with semen analyses, testicular ochiodemetry, and serum gonadotropin measurements collected within 120 days were included. An azoospermia prediction model was developed with multi-institutional two-fold external validation from tertiary urologic infertility clinics in Chicago, Miami, and Milan. Results: Total 3,497 participants were included (n=Miami 946, Milan 1,955, Chicago 596). Incidence of azoospermia in Miami, Milan, and Chicago was 13.8%, 23.8%, and 32.0%, respectively. Predictive algorithms were generated with Miami data. On Milan external validation, the LR and quadratic FSH models both demonstrated good discrimination with areas under the receiver-operating-characteristic (ROC) curve (AUC) of 0.79 and 0.78, respectively. Data from Chicago performed with AUCs of 0.71 for the FSH only model and 0.72 for LR. Correlation between the quadratic FSH model and LR model was 0.95 with Milan and 0.92 with Chicago data. Conclusions: We present and validate algorithms to predict the probability of azoospermia. The ability to predict the probability of azoospermia without a semen analysis is useful when there are logistical hurdles in obtaining a semen analysis or for reevaluation prior to surgical sperm extraction. [ABSTRACT FROM AUTHOR]

• This analysis included 99 tumors in 92 patients who were treated with 25 Gy in one fraction to lung tumors on a prospective clinical trial. • We observed modest rates of low-grade chest wall pain for small volume, peripheral tumors treated with single fraction SABR. • Dosimetric parameters of D 2cc and V 11Gy were most highly correlated with chest wall pain and can be used to estimate risk. • Proximity to the chest wall may not be a hard contraindication for single fractionation when using highly conformal, image-guided techniques with sharp dose gradients. Concerns over chest wall toxicity has led to debates on treating tumors adjacent to the chest wall with single-fraction stereotactic ablative radiotherapy (SABR). We performed a secondary analysis of patients treated on the prospective iSABR trial to determine the incidence and grade of chest wall pain and modeled dose–response to guide radiation planning and estimate risk. This analysis included 99 tumors in 92 patients that were treated with 25 Gy in one fraction on the iSABR trial which individualized dose by tumor size and location. Toxicity events were prospectively collected and graded based on the CTCAE version 4. Dose-response modeling was performed using a logistic model with maximum likelihood method utilized for parameter fitting. There were 22 grade 1 or higher chest wall pain events, including five grade 2 events and zero grade 3 or higher events. The volume receiving at least 11 Gy (V 11Gy) and the minimum dose to the hottest 2 cc (D 2cc) were most highly correlated with toxicity. When dichotomized by an estimated incidence of ≥ 20 % toxicity, the D 2cc > 17 Gy (36.6 % vs. 3.7 %, p < 0.01) and V 11Gy > 28 cc (40.0 % vs. 8.1 %, p < 0.01) constraints were predictive of chest wall pain, including among a subset of patients with tumors abutting or adjacent to the chest wall. For small, peripheral tumors, single-fraction SABR is associated with modest rates of low-grade chest wall pain. Proximity to the chest wall may not contraindicate single fractionation when using highly conformal, image-guided techniques with sharp dose gradients. [ABSTRACT FROM AUTHOR]

Background: A previous National Institute for Health and Care Research study [Harrison DA, Ferrando-Vivas P, Shahin J, Rowan KM. Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients. Health Serv Deliv Res 2015;3(41)] identified the need for more research to understand risk factors and consequences of critical care and subsequent outcomes. Objectives: First, to improve risk models for adult general critical care by developing models for mortality at fixed time points and time-to-event outcomes, end-stage renal disease, type 2 diabetes, health-care utilisation and costs. Second, to improve risk models for cardiothoracic critical care by enhancing risk factor data and developing models for longer-term mortality. Third, to improve risk models for in-hospital cardiac arrest by enhancing risk factor data and developing models for longer-term mortality and critical care utilisation. Design: Risk modelling study linking existing data. Setting: NHS adult critical care units and acute hospitals in England. Participants: Patients admitted to an adult critical care unit or experiencing an in-hospital cardiac arrest. Interventions: None. Main outcome measures: Mortality at hospital discharge, 30 days, 90 days and 1 year following critical care unit admission; mortality at 1 year following discharge from acute hospital; new diagnosis of end-stage renal disease or type 2 diabetes; hospital resource use and costs; return of spontaneous circulation sustained for > 20 minutes; survival to hospital discharge and 1 year; and length of stay in critical care following in-hospital cardiac arrest. Data sources: Case Mix Programme, National Cardiac Arrest Audit, UK Renal Registry, National Diabetes Audit, National Adult Cardiac Surgery Audit, Hospital Episode Statistics and Office for National Statistics. Results: Data were linked for 965,576 critical care admissions between 1 April 2009 and 31 March 2016, and 83,939 in-hospital cardiac arrests between 1 April 2011 and 31 March 2016. For admissions to adult critical care units, models for 30-day mortality had similar predictors and performance to those for hospital mortality and did not reduce heterogeneity. Models for longer-term outcomes reflected increasing importance of chronic over acute predictors. New models for end-stage renal disease and diabetes will allow benchmarking of critical care units against these important outcomes and identification of patients requiring enhanced follow-up. The strongest predictors of health-care costs were prior hospitalisation, prior dependency and chronic conditions. Adding pre- and intra-operative risk factors to models for cardiothoracic critical care gave little improvement in performance. Adding comorbidities to models for in-hospital cardiac arrest provided modest improvements but were of greater importance for longer-term outcomes. Limitations: Delays in obtaining linked data resulted in the data used being 5 years old at the point of publication: models will already require recalibration. Conclusions: Data linkage provided enhancements to the risk models underpinning national clinical audits in the form of additional predictors and novel outcomes measures. The new models developed in this report may assist in providing objective estimates of potential outcomes to patients and their families. Future work: (1) Develop and test care pathways for recovery following critical illness targeted at those with the greatest need; (2) explore other relevant data sources for longer-term outcomes; (3) widen data linkage for resource use and costs to primary care, outpatient and emergency department data. Study registration: This study is registered as NCT02454257. Funding details: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 39. See the NIHR Journals Library website for further project information.

ABSTRACT Objectives: to analyze factors associated, directly and indirectly, with lower social support of older adults, according to sex. Methods: a cross-sectional study, with 941 older adults from a health micro-region in Minas Gerais. Descriptive and trajectory analyzes were carried out (p

Objective To explore the diagnostic value of combining pediatric appendicitis score (PAS) with high-frequency ultrasound for appendicitis types in children over 5 years of age. Methods Retrospective analysis was performed for 394 hospitalized appendicitis children fulfilling the inclusion criteria from January 2018 to December 2020. There were 270 boys and 124 girls with a mean age of (8. 94 ± 2. 28) years. Depending on intraoperative finding and the final histology, patients were classified into two groups of simple appendicitis (n = 113) and non-simple appendicitis (n = 281). The effects of general data, PAS and ultrasonic images were compared between two groups. Fisher's stepwise discriminant analysis was employed for establishing a discriminant function by applying significant variables. Then the diagnostic efficacy of this model was verified by self-test, cross-validation and validation group. Results Univariate analysis indicated that 13 factors were statistically different between two group (P < 0. 05) . Seven parameters of PAS, illegibility of wall layers, count of leucocyte, duration of symptoms, periappendiceal fluid, enlarged appendix and thickened periappendiceal fat were crnsidered ultimately. The discriminant function equation was Y1 = - 14. 446 - 1. 312X3 + 1. 102X5 + 0. 481X2 + 0. 065X1 - 1. 884X6 + 4. 046X4 + 3. 878X7, Y2 = - 36. 834 + 1. 509X3 + 4. 297 X5 + 0. 687 X2 + 0. 110X1 -0. 167X6 + 3. 432X4 + 0. 874X7. The results obtained by self-test and cross-validation all indicated that the accuracy rate of discriminant function was 94. 7% (373/394); The results verified by validation group showed that the accuracy rate of discriminant function was 94. 4% (84/89). Conclusion Combining PAS with high-frequency ultrasound is useful in distinguishing the types of appendicitis in children aged above 5 years. Thus clinicians may formulate re111onahle treatments for acute appendicitis in children. [ABSTRACT FROM AUTHOR]

A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely in practice. However, the DeLong method as implemented in popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify the DeLong algorithm using ranks and extend it to accommodate missing data by using a mixed model approach for multivariate data. Simulation results demonstrate the validity and efficiency of our procedure for data missing at random. We illustrate our proposed procedure in SAS, Stata, and R using the original DeLong data., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component-based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component-based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly-shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems., (© 2024 John Wiley & Sons Ltd.)

In clinical settings with no commonly accepted standard-of-care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power-based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top-ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top-ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top-ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this article, we propose a Bayesian individual-level model for small-area estimation of survey-based health indicators. A categorical likelihood is used at the first level of the model hierarchy to describe the ordinal data, and spatial dependence among small areas is taken into account by using a conditional autoregressive distribution. Post-stratification of the results of the proposed individual-level model allows extrapolating the results to any administrative areal division, even for small areas. We apply this methodology to describe the geographical distribution of a self-perceived health indicator from the Health Survey of the Region of Valencia (Spain) for the year 2016., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Semiparametric probabilistic index models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of generalized pairwise comparisons (GPC). As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this article, we show that the parameters of the probabilistic index model can be estimated using generalized estimating equations, for which adjustments exist that lead to estimators of the sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for example, the net treatment benefit or the success odds., (© 2024 John Wiley & Sons Ltd.)

Ecological momentary assessment (EMA), a data collection method commonly employed in mHealth studies, allows for repeated real-time sampling of individuals' psychological, behavioral, and contextual states. Due to the frequent measurements, data collected using EMA are useful for understanding both the temporal dynamics in individuals' states and how these states relate to adverse health events. Motivated by data from a smoking cessation study, we propose a joint model for analyzing longitudinal EMA data to determine whether certain latent psychological states are associated with repeated cigarette use. Our method consists of a longitudinal submodel-a dynamic factor model-that models changes in the time-varying latent states and a cumulative risk submodel-a Poisson regression model-that connects the latent states with the total number of events. In the motivating data, both the predictors-the underlying psychological states-and the event outcome-the number of cigarettes smoked-are partially unobservable; we account for this incomplete information in our proposed model and estimation method. We take a two-stage approach to estimation that leverages existing software and uses importance sampling-based weights to reduce potential bias. We demonstrate that these weights are effective at reducing bias in the cumulative risk submodel parameters via simulation. We apply our method to a subset of data from a smoking cessation study to assess the association between psychological state and cigarette smoking. The analysis shows that above-average intensities of negative mood are associated with increased cigarette use., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non-parametric Bayesian measurement error-corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time-varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model-based grouping of the functional covariate following measurement error correction. This grouping of the measurement error-corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device-based measures of physical activity and body mass index in adults in the United States., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

We present a trial design for sequential multiple assignment randomized trials (SMARTs) that use a tailoring function instead of a binary tailoring variable allowing for simultaneous development of the tailoring variable and estimation of dynamic treatment regimens (DTRs). We apply methods for developing DTRs from observational data: tree-based regression learning and Q-learning. We compare this to a balanced randomized SMART with equal re-randomization probabilities and a typical SMART design where re-randomization depends on a binary tailoring variable and DTRs are analyzed with weighted and replicated regression. This project addresses a gap in clinical trial methodology by presenting SMARTs where second stage treatment is based on a continuous outcome removing the need for a binary tailoring variable. We demonstrate that data from a SMART using a tailoring function can be used to efficiently estimate DTRs and is more flexible under varying scenarios than a SMART using a tailoring variable., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Preeclampsia is a pregnancy-associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time-to-event setting of "semi-competing risks" in which a non-terminal event of interest is subject to the occurrence of a terminal event of interest. The semi-competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non-terminal event, which we call "clinically immediate" and "non-immediate" terminal events. Though such phenomena arise in many clinical contexts, to-date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty-based illness-death model with a binary submodel to distinguish risk of immediate terminal event following the non-terminal event. The model admits direct dependence of the terminal event on the non-terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions., (© 2024 John Wiley & Sons Ltd.)

Risky-prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other "risky" drugs is a national public health concern. We use a novel data framework-a directed network connecting physicians who encounter the same patients in a sequence of visits-to investigate if risky-prescribing diffuses across physicians through a process of peer-influence. Using a shared-patient network of 10 661 Ohio-based physicians constructed from Medicare claims data over 2014-2015, we extract information on the order in which patients encountered physicians to derive a directed patient-sharing network. This enables the novel decomposition of peer-effects of a medical practice such as risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky-prescribing in patients of peer-physicians. Estimated peer-effects were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer-effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis-specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. These findings suggest network-based interventions for reducing risky-prescribing., (© 2024 John Wiley & Sons Ltd.)

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT., (© 2024 John Wiley & Sons Ltd.)

Background: It is difficult to detect the outbreak of emergency infectious disease based on the exiting surveillance system. Here we investigate the utility of the Baidu Search Index, an indicator of how large of a keyword is in Baidu's search volume, in the early warning and predicting the epidemic trend of COVID-19., Methods: The daily number of cases and the Baidu Search Index of 8 keywords (weighted by population) from December 1, 2019 to March 15, 2020 were collected and analyzed with times series and Spearman correlation with different time lag. To predict the daily number of COVID-19 cases using the Baidu Search Index, Zero-inflated negative binomial regression was used in phase 1 and negative binomial regression model was used in phase 2 and phase 3 based on the characteristic of independent variable., Results: The Baidu Search Index of all keywords in Wuhan was significantly higher than Hubei (excluded Wuhan) and China (excluded Hubei). Before the causative pathogen was identified, the search volume of "Influenza" and "Pneumonia" in Wuhan increased with the number of new onset cases, their correlation coefficient was 0.69 and 0.59, respectively. After the pathogen was public but before COVID-19 was classified as a notifiable disease, the search volume of "SARS", "Pneumonia", "Coronavirus" in all study areas increased with the number of new onset cases with the correlation coefficient was 0.69 ~ 0.89, while "Influenza" changed to negative correlated (r s : -0.56 ~ -0.64). After COVID-19 was closely monitored, the Baidu Search Index of "COVID-19", "Pneumonia", "Coronavirus", "SARS" and "Mask" could predict the epidemic trend with 15 days, 5 days and 6 days lead time, respectively in Wuhan, Hubei (excluded Wuhan) and China (excluded Hubei). The predicted number of cases would increase 1.84 and 4.81 folds, respectively than the actual number of cases in Wuhan and Hubei (excluded Wuhan) from 21 January to 9 February., Conclusion: The Baidu Search Index could be used in the early warning and predicting the epidemic trend of COVID-19, but the search keywords changed in different period. Considering the time lag from onset to diagnosis, especially in the areas with medical resources shortage, internet search data can be a highly effective supplement of the existing surveillance system., (© 2024. The Author(s).)

Background: By analysing the deaths of inpatients in a tertiary hospital in Hangzhou, this study aimed to understand the epidemiological distribution characteristics and the composition of the causes of death. Additionally, this study aimed to predict the changing trend in the number of deaths, providing valuable insights for hospitals to formulate relevant strategies and measures aimed at reducing mortality rates., Methods: In this study, data on inpatient mortality at a tertiary hospital in Hangzhou from 2015 to 2022 were obtained via the population information registration system of the Chinese Center for Disease Control and Prevention. The death data of inpatients were described and analysed through a retrospective study. Excel 2016 was utilized for data sorting, and SPSS 22.0 software was employed for data analysis. The statistical inference of single factor differences was conducted via χ2 tests. The SARIMA model was established via the forecast, aTSA, and tseries software packages (version 4.3.0) to forecast future changes in the number of deaths., Results: A total of 1938 inpatients died at the tertiary hospital in Hangzhou, with the greatest number of deaths occurring in 2022 (262, 13.52%). The sex ratio was 2.22:1, and there were significant differences between sexes in terms of age, marital status, educational level, and place of residence (P < 0.05). The percentage of males in the groups aged of 20 to 29 and 30 to 39 years was significantly greater than that of females (χ 2  = 46.905, P < 0.001). More females than males died in the widowed group, and divorced and married males experienced a greater number of deaths than divorced and married females did (χ 2  = 61.130, P < 0.001). The proportions of male students with a junior college and senior high school education were significantly greater than that of female students (χ 2  = 12.310, P < 0.05). The primary causes of mortality within the hospital setting included circulatory system diseases, injury, poisoning, tumours, and respiratory system diseases. These leading factors accounted for 86.12% of all recorded deaths. Finally, the SARIMA (2, 1, 1) (1, 1, 1) 12 model was determined to be the optimal model, with an AIC of 380.23, a BIC of 392.79, and an AICc of 381.81. The MAPE was 14.99%, indicating a satisfactory overall fit of this model. The relative error between the predicted and actual number of deaths in 2022 was 8.02%. Therefore, the SARIMA (2, 1, 1) (1, 1, 1) 12 model demonstrates good predictive performance., Conclusions: Hospitals should enhance the management of sudden cardiac death, acute myocardial infarction, severe craniocerebral injury, lung cancer, and lung infection to reduce the mortality rate. The SARIMA model can be employed for predicting the number of deaths., (© 2024. The Author(s).)

Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive decisions, and interactions between different individuals and species. There has been exciting recent progress in understanding multi-agent foraging behavior from cognitive, neuroscience, and statistical perspectives, but integrating these perspectives can be elusive. This paper seeks to unify these perspectives, allowing statistical analysis of observational animal movement data to shed light on the viability of cognitive models of foraging strategies. We start with cognitive agents with internal preferences expressed as value functions, and implement this in a biologically plausible neural network, and an equivalent statistical model, where statistical predictors of agents' movements correspond to the components of the value functions. We test this framework by simulating foraging agents and using Bayesian statistical modeling to correctly identify the factors that best predict the agents' behavior. As further validation, we use this framework to analyze an open-source locust foraging dataset. Finally, we collect new multi-agent real-world bird foraging data, and apply this method to analyze the preferences of different species. Together, this work provides an initial roadmap to integrate cognitive, neuroscience, and statistical approaches for reasoning about animal foraging in complex multi-agent environments., (© 2024. The Author(s).)

Background: Survey studies in medical and health sciences predominantly apply a conventional direct questioning (DQ) format to gather private and highly personal information. If the topic under investigation is sensitive or even stigmatizing, such as COVID-19-related health behaviors and adherence to non-pharmaceutical interventions in general, DQ surveys can lead to nonresponse and untruthful answers due to the influence of social desirability bias (SDB). These effects seriously threaten the validity of the results obtained, potentially leading to distorted prevalence estimates for behaviors for which the prevalence in the population is unknown. While this issue cannot be completely avoided, indirect questioning techniques (IQTs) offer a means to mitigate the harmful influence of SDB by guaranteeing the confidentiality of individual responses. The present study aims at assessing the validity of a recently proposed IQT, the Cheating Detection Triangular Model (CDTRM), in estimating the prevalence of COVID-19-related health behaviors while accounting for cheaters who disregard the instructions., Methods: In an online survey of 1,714 participants in Taiwan, we obtained CDTRM prevalence estimates via an Expectation-Maximization algorithm for three COVID-19-related health behaviors with different levels of sensitivity. The CDTRM estimates were compared to DQ estimates and to available official statistics provided by the Taiwan Centers for Disease Control. Additionally, the CDTRM allowed us to estimate the share of cheaters who disregarded the instructions and adjust the prevalence estimates for the COVID-19-related health behaviors accordingly., Results: For a behavior with low sensitivity, CDTRM and DQ estimates were expectedly comparable and in line with official statistics. However, for behaviors with medium and high sensitivity, CDTRM estimates were higher and thus presumably more valid than DQ estimates. Analogously, the estimated cheating rate increased with higher sensitivity of the behavior under study., Conclusions: Our findings strongly support the assumption that the CDTRM successfully controlled for the validity-threatening influence of SDB in a survey on three COVID-19-related health behaviors. Consequently, the CDTRM appears to be a promising technique to increase estimation validity compared to conventional DQ for health-related behaviors, and sensitive attributes in general, for which a strong influence of SDB is to be expected., (© 2024. The Author(s).)

This research introduces a novel approach to resampling periodically correlated time series using bandpass filters for frequency separation called the Variable Bandpass Periodic Block Bootstrap and then examines the significant advantages of this new method. While bootstrapping allows estimation of a statistic's sampling distribution by resampling the original data with replacement, and block bootstrapping is a model-free resampling strategy for correlated time series data, both fail to preserve correlations in periodically correlated time series. Existing extensions of the block bootstrap help preserve the correlation structures of periodically correlated processes but suffer from flaws and inefficiencies. Analyses of time series data containing cyclic, seasonal, or periodically correlated principal components often seen in annual, daily, or other cyclostationary processes benefit from separating these components. The Variable Bandpass Periodic Block Bootstrap uses bandpass filters to separate a periodically correlated component from interference such as noise at other uncorrelated frequencies. A simulation study is presented, demonstrating near universal improvements obtained from the Variable Bandpass Periodic Block Bootstrap when compared with prior block bootstrapping methods for periodically correlated time series., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Edward L. Valachovic. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Advancements in sensor technology have brought a revolution in data generation. Therefore, the study variable and several linearly related auxiliary variables are recorded due to cost-effectiveness and ease of recording. These auxiliary variables are commonly observed as quantitative and qualitative (attributes) variables and are jointly used to estimate the study variable's population mean using a mixture estimator. For this purpose, this work proposes a family of generalized mixture estimators under stratified sampling to increase efficiency under symmetrical and asymmetrical distributions and study the estimator's behavior for different sample sizes for its convergence to the Normal distribution. It is found that the proposed estimator estimates the population mean of the study variable with more precision than the competitor estimators under Normal, Uniform, Weibull, and Gamma distributions. It is also revealed that the proposed estimator follows the Cauchy distribution when the sample size is less than 35; otherwise, it converges to normality. Furthermore, the implementation of two real-life datasets related to the health and finance sectors is also presented to support the proposed estimator's significance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Iqbal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Experimental studies of wound healing often use survival analysis and time to event outcomes or differences in wound area at a specific time point. However, these methods do not use a potentially large number of observations made over the course of a trial and may be inefficient. A model-based approach can leverage all trial data, but there is little guidance on appropriate models and functional forms to describe wound healing., Methods: We derive a general statistical model and review a wide range of plausible mathematical models to describe wound healing. We identify a range of possible derived estimands and their derivation from the models. Using data from a trial of an intervention to promote ulcer healing in patients affected by leprosy that included three measurement methods repeated across the course of the study, we compare the goodness-of-fit of the models using a range of methods and estimate treatment effects and healing rate functions with the best-fitting models., Results: Overall, we included 5,581 ulcer measurements of 1,578 unique images from 130 patients. We examined the performance of a range of models. The square root, log square root, and log quadratic models were the best fitting models across all outcome measurement methods. The estimated treatment effects magnitude and sign varied by time post-randomisation, model type, and outcome type, but across all models there was little evidence of effectiveness. The estimated effects were significantly more precise than non-parametric alternatives. For example, estimated differences from the three outcome measurements at 42-days post-randomisation were - 0.01 cm 2 (-0.77, 0.74), -0.44 cm 2 (-1.64, 0.76), and 0.11 cm 2 (-0.87, 1.08) using a non-parametric method versus - 0.03 cm 2 (-0.14, 0.06), 0.06 cm 2 (-0.05, 0.17), and 0.03 cm 2 (-0.07, 0.17) using a square-root model., Conclusions: Model-based analyses can dramatically improve the precision of estimates but care must be taken to carefully compare and select the best fitting models. The (log) square-root model is strongly recommended reflecting advice from a century ago., (© 2024. The Author(s).)

Most epidemiological studies on the associations between pesticides exposure and semen quality have been based on a single pesticide, with inconsistent major results. In contrast, there was limited human evidence on the potential effect of pesticides mixture on semen quality. Our study aimed to investigate the relationship of pesticide profiles with semen quality parameters among 299 non-occupationally exposed males aged 25-50 without any clinical abnormalities. Serum concentrations of 21 pesticides were quantified by gas chromatography-tandem mass spectrometry (GC-MS/MS). Semen quality parameters were abstracted from medical records. Generalized linear regression models (GLMs) and three mixture approaches, including weighted quantile sum regression (WQS), elastic net regression (ENR) and Bayesian kernel machine regression (BKMR), were applied to explore the single and mixed effects of pesticide exposure on semen quality. In GLMs, as the serum levels of Bendiocarb, β-BHC, Clomazone, Dicrotophos, Dimethenamid, Paclobutrazole, Pentachloroaniline and Pyrimethanil increased, the straight-line velocity (VSL), linearity (LIN) and straightness (STR) decreased. This negative association also occurred between the concentration of β-BHC, Pentachloroaniline, Pyrimethanil and progressive motility, total motility. In the WQS models, pesticides mixture was negatively associated with total motility and several sperm motility parameters (β: -3.07∼-1.02 per decile, FDR-P<0.05). After screening the important pesticides derived from the mixture by ENR model, the BKMR models showed that the decreased qualities for VSL, LIN, and STR were also observed when pesticide mixtures were at ≥ 70th percentiles. Clomazone, Dimethenamid, and Pyrimethanil (Posterior inclusion probability, PIP: 0.2850-0.8900) were identified as relatively important contributors. The study provides evidence that exposure to single or mixed pesticide was associated with impaired semen quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Recent findings indicate a growing trend in data analysis within healthcare using statistical process control. However, the diversity of variables involved necessitates the expansion of new process control methodologies. This study examined control chart applications in cardiology by using generalized additive models (GAMs) to construct profiles while involving multiple healthcare variables (08). Two distinct statistics: deviation (D), and Hotelling (T2) were employed for constructing control charts: a commonly used single-variable statistic for nonparametric profiles and an innovative multivariate statistic that assesses the contribution of each element to process changes. These statistics were tested for monitoring ischemic and hemorrhagic strokes in 1-year acute stroke (369) patients at the Faisalabad Institute of Cardiology. Demographic parameters (age, gender), vascular risk factors (diabetes, family history, sleep), socioeconomic variables (smoking, location), and blood pressure are included in the model. The research includes the computation of zero-state average run length (ARL) for assessing the performance of control charts. The characteristics of the proposed profile were analyzed, such as the T2 control chart, performing better than the D chart for medium-to-large shifts (δ ≥ 0.50). On the other hand, for small δ = 0.25, the D control chart produces smaller ARL values but more significant standard deviations. While both statistics contribute to profile monitoring, T2 is more effective at identifying and tracing medium and large shifts. In conclusion, such handy tools may aid healthcare performance monitoring, especially for complicated predictor-response relationships. Monitored profiles demonstrated that GAMs are useful for healthcare analysis and process monitoring., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The aim of this study is to analyze the trend of acute onset of chronic cor pulmonale at Chenggong Hospital of Kunming Yan'an Hospital between January 2018 and December 2022.Additionally, the study will compare the application of the ARIMA model and Holt-Winters model in predicting the number of chronic cor pulmonale cases., Methods: The data on chronic cor pulmonale cases from 2018 to 2022 were collected from the electronic medical records system of Chenggong Hospital of Kunming Yan'an Hospital. The ARIMA and Holt-Winters models were constructed using monthly case numbers from January 2018 to December 2022 as training data. The performance of the model was tested using the monthly number of cases from January 2023 to December 2023 as the test set., Results: The number of acute onset of chronic cor pulmonale in Chenggong Hospital of Kunming Yan'an Hospital exhibited a downward trend overall from 2018 to 2022. There were more cases in winter and spring, with peaks observed in November to December and January of the following year. The optimal ARIMA model was determined to be ARIMA (0,1,1) (0,1,1) 12 , while for the Holt-Winters model, the optimal choice was the Holt-Winters multiplicative model. It was found that the Holt-Winters multiplicative model yielded the lowest error., Conclusion: The Holt-Winters multiplicative model predicts better accuracy. The diagnosis of acute onset of chronic cor pulmonale is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models., (© 2024. The Author(s).)

Background: Although one objective of NHS 111 is to ease the strain on urgent and emergency care services, studies suggest the telephone triage service may be contributing to increased demand. Moreover, while parents and caregivers generally find NHS 111 satisfactory, concerns exist about its integration with the healthcare system and the appropriateness of advice. This study aimed to analyse the advice provided in NHS 111 calls, the duration between the call and ED attendance, and the outcomes of such attendances made by children and young people (C&YP)., Methods: A retrospective cohort study was carried out of C&YP (≤17) attending an ED in the Yorkshire and Humber region of the UK following contact with NHS 111 between 1 April 2016 and 31 March 2017. This linked-data study examined NHS 111 calls and ED outcomes. Lognormal mixture distributions were fit to compare the time taken to attend ED following calls. Logistic mixed effects regression models were used to identify predictors of low-acuity NHS 111-related ED attendances., Results: Our study of 348 401 NHS 111 calls found they were primarily concerning children aged 0-4 years. Overall, 13.1% of calls were followed by an ED attendance, with a median arrival time of 51 minutes. Of the 34 664 calls advising ED attendance 41% complied, arriving with a median of 38 minutes-27% of which defined as low-acuity. Although most calls advising primary care were not followed by an ED attendance (93%), those seen in an ED generally attended later (median 102 minutes) with 23% defined as low-acuity. Younger age (<1) was a statistically significant predictor of low-acuity ED attendance following all call dispositions apart from home care., Conclusion: More tailored options for unscheduled healthcare may be needed for younger children. Both early low-acuity attendance and late high-acuity attendance following contact with NHS 111 could act as useful entry points for clinical audits of the telephone triage service., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

This paper introduces the modified Kies Topp-Leone (MKTL) distribution for modeling data on the (0, 1) or [0, 1] interval. The shapes of the density and hazard rate functions manifest desirable shapes, making the MKTL distribution suitable for modeling data with different characteristics at the unit interval. Twelve different estimation methods are utilized to estimate the distribution parameters, and Monte Carlo simulation experiments are executed to assess the performance of the methods. The simulation results suggest that the maximum likelihood method is the superior method. The usefulness of the new distribution is illustrated by utilizing three data sets, and its performance is juxtaposed with that of other competing models. The findings affirm the superiority of the MKTL distribution over the other candidate models. Applying the developed quantile regression model using the new distribution disclosed that it offers a competitive fit over other existing regression models., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alghamdi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

With the rapid development of biotechnology, gene sequencing methods are gradually improved. The structure of gene sequences is also more complex. However, the traditional sequence alignment method is difficult to deal with the complex gene sequence alignment work. In order to improve the efficiency of gene sequence analysis, D2 series method of k-mer statistics is selected to build the model of gene sequence alignment analysis. According to the structure of the foreground sequence, the sequence to be aligned can be cut by different lengths and divided into multiple subsequences. Finally, according to the selected subsequences, the maximum dissimilarity in the alignment results is determined as the statistical result. At the same time, the research also designed an application system for the sequence alignment analysis of the model. The experimental results showed that the statistical power of the sequence alignment analysis model was directly proportional to the sequence coverage and cutting length, and inversely proportional to the K value and module length. At the same time, the model was applied to the system designed in this paper. The maximum storage capacity of the system was 71 GB, the maximum disk capacity was 135 GB, and the running time was less than 2.0s. Therefore, the k-mer statistic sequence alignment model and system proposed in this study have considerable application value in gene alignment analysis., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Dongjie Gao. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Multi-Arm, Multi-Stage (MAMS) clinical trial designs allow for multiple therapies to be compared across a spectrum of clinical trial phases. MAMS designs fall under several overarching design groups, including adaptive designs (AD) and multi-arm (MA) designs. Factorial clinical trials designs represent a combination of factorial and MAMS trial designs and can provide increased efficiency relative to fixed, traditional designs. We explore design choices associated with Factorial Adaptive Multi-Arm Multi-Stage (FAST) designs, which represent the combination of factorial and MAMS designs., Methods: Simulation studies were conducted to assess the impact of the type of analyses, the timing of analyses, and the effect size observed across multiple outcomes on trial operating characteristics for a FAST design. Given multiple outcomes types assessed within the hypothetical trial, the primary analysis approach for each assessment varied depending on data type., Results: The simulation studies demonstrate that the proposed class of FAST trial designs can offer a framework to potentially provide improvements relative to other trial designs, such as a MAMS or factorial trial. Further, we note that the design implementation decisions, such as the timing and type of analyses conducted throughout trial, can have a great impact on trial operating characteristics., Conclusions: Motivated by a trial currently under design, our work shows that the FAST category of trial can potentially offer benefits similar to both MAMS and factorial designs; however, the chosen design aspects which can be included in a FAST trial need to be thoroughly explored during the planning phase., (© 2024. The Author(s).)

Background: Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions., Results: We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies., Conclusions: We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field., (© 2024. The Author(s).)

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Meta-analysis is an essential tool to comprehensively synthesize and quantitatively evaluate results of multiple clinical studies in evidence-based medicine. In many meta-analyses, the characteristics of some studies might markedly differ from those of the others, and these outlying studies can generate biases and potentially yield misleading results. In this article, we provide effective robust statistical inference methods using generalized likelihoods based on the density power divergence. The robust inference methods are designed to adjust the influences of outliers through the use of modified estimating equations based on a robust criterion, even when multiple and serious influential outliers are present. We provide the robust estimators, statistical tests, and confidence intervals via the generalized likelihoods for the fixed-effect and random-effects models of meta-analysis. We also assess the contribution rates of individual studies to the robust overall estimators that indicate how the influences of outlying studies are adjusted. Through simulations and applications to two recently published systematic reviews, we demonstrate that the overall conclusions and interpretations of meta-analyses can be markedly changed if the robust inference methods are applied and that only the conventional inference methods might produce misleading evidence. These methods would be recommended to be used at least as a sensitivity analysis method in the practice of meta-analysis. We have also developed an R package, robustmeta, that implements the robust inference methods., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model's performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals' experience with COVID-19, some unaccounted-for latent variables were associated with the disease., (© 2024 John Wiley & Sons Ltd.)

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS.", (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Neuro-imaging data can often be represented as statistical networks, especially for functional magnetic resonance imaging (fMRI) data, where brain regions are defined as nodes and the functional interactions between those regions are taken as edges. Such networks are commonly divided into classes depending on the type of edges, namely binary or weighted. A binary network means edges can either be present or absent. Whereas the edges of a weighted network are associated with weight values, and fMRI networks belong to weighted networks. Statistical methods are often adopted to analyse such networks, among which, the exponential random graph model (ERGM) is an important network analysis approach. Typically ERGMs are applied to binary networks, and weighted networks often need to be binarised by arbitrarily selecting a threshold value to define the presence of the edges, which can lead to non-robustness and loss of valuable edge weight information representing the strength of fMRI interaction in fMRI networks. While it is therefore important to gain deeper insight in adopting ERGM on weighted networks, there only exists a few different ERGM frameworks for weighted networks; some of these are not directly implementable on fMRI networks based on their original proposal. We systematically review, implement, analyse and compare five such frameworks via a simulation study and provide guidelines on each modelling framework as well as conclude the suitability of them on fMRI networks based on a range of criteria. We concluded that Multi-Layered ERGM is currently the most suitable framework., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum., (© 2024 John Wiley & Sons Ltd.)