Robust Estimation Research Articles

Traditional and Contemporary Measures of Recreational Fishing Activity to Inform Sustainable Management of Blue Swimmer Crab (Portunus armatus) Fisheries in Western Australia

ABSTRACTRecreational fishing is a popular activity that has many social and economic benefits. Monitoring has traditionally centred around measuring fishing effort and catch. However, contemporary measures, such as fisher demographics and behaviours, which may be linked to social and economic objectives, are now recognised as necessary to meet expectations that all aspects of resource use are considered for fisheries management. The challenge for recreational fisheries is how to regularly provide information across a growing diversity of metrics, and at a scale relevant to specific fisheries, with limited resources. The Blue Swimmer Crab (Portunus armatus) is one of the most commonly recreationally harvested species in Western Australia. This species served as a case study to explore if data from five broad‐scale phone‐diary surveys between 2011–2012 and 2020–2021 could be adapted to meet expanded information needs. Twelve discrete fisheries represented small spatial‐scale areas for fisheries management, with some variation in management arrangements (i.e., bag limits). Robust estimates of traditional and contemporary measures could be produced for three fisheries: Peel‐Harvey Estuary, Swan‐Canning Estuary and Geographe Bay. Traditional measures of participation, fishing effort, total catch and harvest from 2011–2012 to 2020–2021 were generally consistent for Swan‐Canning Estuary but decreased for Peel‐Harvey Estuary and Geographe Bay. The proportion of harvest from recreational and commercial fishing varied from mixed use (Swan‐Canning Estuary, Peel‐Harvey Estuary) to recreational only (Geographe Bay). Variation in contemporary measures was also evident, with recreational fishing largely undertaken by local residents in Swan‐Canning Estuary, while participation was more uniform between local and non‐local residents in Peel‐Harvey Estuary and Geographe Bay. We found that broad‐scale surveys could be successfully adapted to meet the growing need for information on contemporary measures to support a holistic approach to sustainable fisheries management for small spatial‐scale recreational fisheries.

Technology-Based Substance Use Interventions for Emerging Adults and College Students: A Systematic Review and Meta-Analysis

Abstract Objective To synthesize randomized controlled trial evidence on technology-based interventions’ (TBIs) effectiveness for substance use among emerging adults (EA)/college students (CS). Methods Nine electronic databases were searched. Two reviewers independently screened studies, extracted data, and assessed evidence quality. We used robust variance estimation in meta-regression for effect size synthesis and moderator analysis. Results Based on 130 studies, the overall between-group effect size was 0.23 (95% CI = 0.18, 0.28). The effect sizes for comparing TBIs with no treatment, standard care, and non-technology interventions were 0.25 (CI = 0.19, 0.31), 0.23 (CI = 0.15, 0.32), and 0.12 (CI = -0.02, 0.25), respectively. Older participants showed significantly larger effect sizes, and interventions using multiple technologies had larger effects than smartphone-based ones. Conclusion TBIs are effective in reducing substance use in EA/CS, with outcomes comparable to non-technology interventions and advantages over no treatment and standard care. Future research should address drug-related outcomes, multi-technology approaches, age-appropriate designs, and cultural diversity.

Robust Lifetime Estimation with Small Sample Sized High Speed Railway ETCS Component Data

Railway maintenance and efficient operation has been an important issue for a safe rail traffic. When an unexpected malfunction occurs on various components on the train or on the railway system, it may result in unscheduled maintenance which may cause the rail traffic to stop. In this paper, we study the random failure model of some frequently malfunctioning high-speed railway equipment based on the statistical analysis of the real data of failure records in Turkey. Popular distribution functions and parameter estimation methods have been used considering that the data has a small sample size, and it may contain outliers. In this study, we showed that for the case of a few numbers of failure data, the L-moments method gives effective results when there exists no outlier and the robust-M method gives effective results when there exists an outlier or outliers.

Causal Estimands and Multiply Robust Estimation of Mediated-Moderation

When studying effect heterogeneity between different subgroups (i.e., moderation), researchers are frequently interested in the mediation mechanisms underlying the heterogeneity, that is, the mediated moderation. For assessing mediated moderation, conventional methods typically require parametric models to define mediated moderation, which has limitations when parametric models may be misspecified and when causal interpretation is of interest. For causal interpretations about mediation, causal mediation analysis is increasingly popular but is underdeveloped for mediated moderation analysis. In this study, we extend the causal mediation literature, and we propose a novel method for mediated moderation analysis. Using the potential outcomes framework, we obtain two causal estimands that decompose the total moderation: (i) the mediated moderation attributable to a mediator and (ii) the remaining moderation unattributable to the mediator. We also develop a multiply robust estimation method for the mediated moderation analysis, which can incorporate machine learning methods in the inference of the causal estimands. We evaluate the proposed method through simulations. We illustrate the proposed mediated moderation analysis by assessing the mediation mechanism that underlies the gender difference in the effect of a preventive intervention on adolescent behavioral outcomes.

Outliers rejection for robust camera pose estimation using graduated non‐convexity

AbstractCamera pose estimation plays a crucial role in computer vision, which is widely used in augmented reality, robotics and autonomous driving. However, previous studies have neglected the presence of outliers in measurements, so that even a small percentage of outliers will significantly degrade precision. In order to deal with outliers, this paper proposes using a graduated non‐convexity (GNC) method to suppress outliers in robust camera pose estimation, which serves as the core of GNCPnP. The authors first reformulate the camera pose estimation problem using a non‐convex cost, which is less affected by outliers. Then, to apply a non‐minimum solver to solve the reformulated problem, the authors use the Black‐Rangarajan duality theory to transform it. Finally, to address the dependence of non‐convex optimisation on initial values, the GNC method was customised according to the truncated least squares cost. The results of simulation and real experiments show that GNCPnP can effectively handle the interference of outliers and achieve higher accuracy compared to existing state‐of‐the‐art algorithms. In particular, the camera pose estimation accuracy of GNCPnP in the case of a low percentage of outliers is almost comparable to that of the state‐of‐the‐art algorithm in the case of no outliers.

Gestational Exposure to Nonsteroidal Anti-Inflammatory Drugs and Risk of Chronic Kidney Disease in Childhood

Gestational exposure to nonsteroidal anti-inflammatory drugs (NSAIDs) may increase the risk of adverse fetal kidney outcomes. However, details regarding timing, specific NSAIDs, and long-term childhood kidney outcomes are limited. To evaluate the association between gestational exposure to NSAIDs and the risk of chronic kidney disease (CKD) in childhood. This national cohort study assessed 1 025 255 children born alive in Taiwan from January 1, 2007, to December 31, 2017, with follow-up until December 31, 2021. Children without valid maternal-child linkage and with incomplete birth information were excluded. Data analysis was performed from November 30, 2023, to April 30, 2024. Maternal prescriptions for NSAIDs from the last menstrual period to birth. The main outcome was childhood CKD, including congenital anomalies of the kidney and urinary tract and other kidney diseases. Cox proportional hazards regression models with stabilized inverse probability of treatment weighting (weighted hazard ratio [wHR]) and a robust sandwich estimator were used to estimate the relative risk of NSAID exposure in pregnancy, adjusted for newborn characteristics. This study included 163 516 singleton-born children (24.0%) whose mothers (mean [SD] age at birth of child, 31.25 [4.92] years) used at least 1 dispensing of an NSAID during pregnancy. Gestational NSAID exposure was significantly associated with a higher risk of childhood CKD (wHR, 1.10; 95% CI, 1.05-1.15). No association was observed between NSAID use and fetal nephrotoxicity in sibling comparisons. Elevated risks were revealed for exposure during the second trimester (wHR, 1.19; 95% CI, 1.11-1.28) and the third trimester (wHR, 1.12; 95% CI, 1.03-1.22) in singleton-born children. Specific NSAID exposures associated with higher CKD risk included indomethacin (wHR, 1.69; 95% CI, 1.10-2.60) and ketorolac (wHR, 1.28; 95% CI, 1.01-1.62) in the first trimester, diclofenac (wHR, 1.27; 95% CI, 1.13-1.42) and mefenamic acid (wHR, 1.29; 95% CI, 1.15-1.46) in the second trimester, and ibuprofen (wHR, 1.34; 95% CI, 1.07-1.68) in the third trimester. In this study, gestational exposure to NSAIDs was not associated with a substantial increase in the risk of childhood CKD when comparing between siblings. However, the findings underscore the need for caution when prescribing NSAIDs during pregnancy, particularly indomethacin and ketorolac in the first trimester, mefenamic acid and diclofenac in the second trimester, and ibuprofen in the third trimester, to ensure the safety of the offspring's kidneys.

The Impact of Greenhouse Gas Risks on Stock Market Returns: An Application on G7 Countries

This study investigates the impact of greenhouse gas risks on stock market returns. While carbon dioxide, methane, and nitrogen gas emissions are taken as greenhouse gases; the stock market benchmark indices of the G7 countries, which are defined as the seven most developed countries in the world, are taken as the basis for the stock market. Due to data limitations, the scope of the current study has been set as the 2000-2020 time period and the panel data analysis method has been applied. In this study, endogeneity and multicollinearity problems, cross-sectional dependence, and homogeneity/heterogeneity tests are tested respectively, and the model is estimated by performing unit root analysis in line with the findings obtained. The results of the analyses indicate that there are no endogeneity and multicollinearity problems among the variables used in the study, there is cross-sectional dependence, the variables are stationary at level I(0), there are problems of autocorrelation in the panel and it is appropriate to estimate the model with the fixed effects model. As a result of the estimation with the robust model estimator that solves the problem of autocorrelation, it is found that carbon dioxide and methane have a negative effect on stock market return, while nitrogen gas has a positive effect on stock market return. These findings suggest that investors reflect their concerns about climate change to stock markets through greenhouse gas emissions.

Application of robust estimation methods in real estate valuation

Motives: Conducting real estate appraisals in well-developed markets presents a multitude of data analysis challenges. Some property price data may contain outliers that can significantly affect the valuation process and, as a result, the estimated value. In the case of real estate valuation regression models, estimation is most often based on the least squares method, where outliers are taken into account just like the rest of the data. Eliminating or minimizing the influence of outliers can lead to more reliable estimation results. Such problems can be solved by implementing robust regression methods. Aim: The main goal is to determine whether robust estimation methods using M-estimators with multiple regression models can provide more accurate estimates of property values. Results: Research has shown that multiple regression models using robust regression methods can be applied to estimate property values. The use of different types of M-estimators allows for the objective elimination of outliers through algorithms that operate on the entire data set. The calculations are carried out iteratively, and at each iteration step the residuals are verified and the observations are re-weighted. The following M-estimators were considered: Huber, Hampel, Tukey, Faire, Cauchy and Welsch. The reference point was the estimation results from the ordinary least squares method (OLS). All analysed M-estimators led to an increase in the coefficient of determination value and a decrease in standard estimation errors. Each algorithm detected the outliers. The valuation results for the selected properties were also more reliable. The results obtained depend on the characteristics of the data, and the choice of the best estimator may vary across different property markets. The selection of the best estimator may even vary within the same local property market, where the valuer makes subjective assessments of the location or other attributes.

An Improved SOGI-Higher-Order Sliding Mode Observer-Based Induction Motor Speed Estimation

Abstract This article presents a novel adaptive gain tuning second-order generalised integrator (SOGI)-higher-order sliding mode (HOSM) observer for robust speed estimation for an induction motor’s entire speed range. This article introduces a hyperbolic tangent function and a varying gain exponent that ensures accurate speed estimation under noisy conditions and significantly reduces chattering observed in conventional sliding mode observers (SMOs). The robustness of the proposed speed estimation method is verified through simulations conducted on MATLAB/Simulink R2024a developed by MathWorks, demonstrating its capability to effectively track the motor’s actual speed even under varying load torque conditions, parameter variations and additional sensor noise. The proposed approach’s superiority and robustness were compared with the conventional SOGI-frequency locked loop (FLL) and super twisting algorithm (STA) SMO.

Erectile dysfunction and associated factors among patients with diabetes in Public hospitals of Harari region, Eastern Ethiopia: modified poisson regression model

BackgroundErectile dysfunction is a common problem among patients with diabetes, often going undiagnosed and having a significant negative impact on their health. This condition necessitates accurate evaluation and early intervention. However, there is an inconsistent explanation of factors and limited evidence on the prevalence of erectile dysfunction among patients with diabetes in eastern Ethiopia. Thus, this study aimed to determine the prevalence of erectile dysfunction and its associated factors among patients with diabetes in public hospitals in the Harari Region, eastern Ethiopia.MethodsA cross-sectional study was conducted involving 339 randomly selected participants. Data were collected using the Kobo Toolbox with standardized questionnaires administered by interviewers, along with a review of the patients’ medical records. Data analysis was performed using Stata version 14. The Poisson regression model with robust variance estimate was fitted to examine the association of the independent variables and erectile dysfunction. An adjusted prevalence ratio (APR) with 95% confidence intervals was reported. Statistical significance was declared at the p-value < 0.05.ResultThe prevalence of erectile dysfunction was 78.28% (95% CI 73–83). Age 35–45 years [APR = 1.38; 95% CI: 1.03–1.84], and > 45 years [APR = 1.58; 95% CI 1.17–2.13], depression [APR = 1.41; 95% CI 1.06–1.86], current khat use [APR = 1.14; 95% CI 1.00-1.28], low social support [APR = 1.70; 95% CI 1.09–2.65] and medium social support [APR = 1.79; 95% CI 1.16–2.78] were factors significantly associated with erectile dysfunction.ConclusionsMore than three-fourths of the participants had erectile dysfunction. Significant factors associated with erectile dysfunction included age 35 years and above, depression, current hat use, and low and medium social support level. The management of erectile dysfunction should be integrated into routine medical care in diabetic follow-up clinics, with special attention for those participants aged 35 years and above, who have depression, currently use Khat, and have low-level social support.

A computational model for angular velocity integration in a locust heading circuit.

Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We present a model for angular velocity integration in a desert locust heading circuit, applying concepts from early theoretical work on heading circuits in mammals to a novel biological context in insects. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360° heading direction representation and is updated by neuromodulatory angular velocity inputs. Our computational model was implemented using steady-state firing rate neurons with dynamical synapses. The circuit connectivity was constrained by biological data, and remaining degrees of freedom were optimised with a machine learning approach to yield physiologically plausible neuron activities. We demonstrate that the integration of heading and angular velocity in this circuit is robust to noise. The heading signal can be effectively used as input to an existing insect goal-directed steering circuit, adapted for outbound locomotion in a steady direction that resembles locust migration. Our study supports the possibility that similar computations for orientation may be implemented differently in the neural hardware of the fruit fly and the locust.

Nutritional Counseling Based on Mindful Eating for the Eating Behavior of People Living with Overweight and Obesity: A Randomized Clinical Trial.

This study aimed to evaluate the effect of a counseling approach based on Mindful Eating on the eating behavior of individuals living with overweight and obesity. A 16-week randomized clinical trial was conducted to compare an online group intervention (IG) with individual online nutritional counseling (TAU). Volunteers (n = 61), aged 18 or older with BMI ≥ 25 kg/m2, were randomly assigned to the two groups and invited to participate in eight biweekly sessions. Eating behavior was assessed using the Three-Factor Eating Questionnaire (TFEQ-21), and anthropometric measures were collected through self-report. Comparison between the groups was performed using generalized linear models and Poisson models with robust variance estimators. A sensitivity analysis was conducted to evaluate the group-by-time interaction. The study was approved by the UFCSPA Ethics Committee. At baseline, the eating behavior domain with the highest mean score was emotional eating (EE) in both groups. At the end of the study, all domains decreased, while an increase in cognitive restraint (CR) was observed in the TAU group, showing a difference between the groups (-23.1; 95%CI -37.7 to -8.5; p = 0.004). A group-by-time interaction difference was noted only in the CR domain, explained by the change in the TAU group for the CR comparison before and after treatment and by the difference between the IG and TAU groups at the end of the treatment period. A Mindful Eating approach may aid in managing dysfunctional cognitive restraint, a key component for sustainable excess weight treatment. The protocol can be found on ClinicalTrials.gov (NCT05845411).

Robust estimation of Shear Wave group velocity for ultrasound elastography based on frequency domain analysis.

Shear wave elastography (SWE) is of great significance in measuring the elasticity and in evaluating mechanical properties of biological tissues. The elasticity of biological tissues can be reflected by measuring the propagation velocity of shear waves. Therefore, accurate estimation of shear wave velocity is crucial. In this study, we proposed a robust estimation method based on a cyclic shifting algorithm (CSA) for measuring shear wave group velocity in homogeneous media. To validate the utility of the algorithm, we conducted accuracy analysis and robustness analysis with different noise levels in the digital phantom used for the standardization of shear wave velocity by Quantitative Imaging Biomarker Alliance (QIBA) of the Radiological Society of North America. The estimated shear wave velocities (SWV) of the elastic digital phantoms with Young's moduli 3 kPa, 6 kPa, 15 kPa, 30 kPa are 1.0156 m/s, 1.4065 m/s, 2.1875 m/s, 3.1250 m/s, respectively. When adding Gaussian white noise with 0 dB, the relative errors of the estimated SWVs are 2.5%, 4.8%, 11.8%, 20.5%, respectively. The estimated SWVs in the gelatin phantom with gelatin concentration of 7% and 10% are 2.0442 m/s and 3.1237 m/s. Compared with the existing two representative estimation algorithms, the estimation algorithm proposed in this paper has a higher anti-noise performance due to effective energy accumulation in the frequency domain. The proposed SWV method based on CSA in frequency domain is a robust shear wave group velocity estimation method, which seems to be a useful tool in homogeneous media for ultrasound elastography.

Shared visuo-tactile interactive perception for robust object pose estimation

Shared perception between robotic systems significantly enhances their ability to understand and interact with their environment, leading to improved performance and efficiency in various applications. In this work, we present a novel full-fledged framework for robotic systems to interactively share their visuo-tactile perception for the robust pose estimation of novel objects in dense clutter. This is demonstrated with a two-robot team sharing their visuo-tactile scene representation which then declutters the scene using interactive perception and precisely estimates the 6 Degrees-of-Freedom (DoF) pose and 3 DoF scale of a target unknown object. This is achieved with the Stochastic Translation-Invariant Quaternion Filter (S-TIQF), a novel Bayesian filtering method with robust stochastic optimization for estimating the globally optimal pose of a target object. S-TIQF is also deployed to perform in situ visuo-tactile hand-eye calibration, since shared perception requires accurate extrinsic calibration between the two different sensing modalities, tactile and visual. Finally, we develop a novel active shared visuo-tactile representation and object reconstruction method employing a joint information gain criterion to improve the sample efficiency of the robot actions. To validate the effectiveness of our approach, we perform extensive experiments across standard datasets for pose estimation, as well as real-robot experiments with opaque, transparent and specular objects in randomised clutter settings and comprehensive comparison with other state-of-the-art approaches. Our experiments indicate that our approach outperforms state-of-the-art methods in terms of pose estimation accuracy for dense visual and sparse tactile point clouds.

Robustness assessment of regressions using cluster analysis typologies: a bootstrap procedure with application in state sequence analysis

BackgroundIn standard Sequence Analysis, similar trajectories are clustered together to create a typology of trajectories, which is then often used to evaluate the association between sequence patterns and covariates inside regression models. The sampling uncertainty, which affects both the derivation of the typology and the associated regressions, is typically ignored in this analysis, an oversight that may lead to wrong statistical conclusions. We propose utilising sampling variation to derive new estimates that further inform on the association of interest.MethodsWe introduce a novel procedure to assess the robustness of regression results obtained from the standard analysis. Bootstrap samples are drawn from the data, and for each bootstrap, a new typology replicating the original one is constructed, followed by the estimation of the corresponding regression models. The bootstrap estimates are then combined using a multilevel modelling framework that mimics a meta-analysis. The fitted values from this multilevel model allow to account for the sampling uncertainty in the inferential analysis. We illustrate the methodology by applying it to the study of healthcare utilisation trajectories in a Swiss cohort of diabetic patients.ResultsThe procedure provides robust estimates for an association of interest, along with 95% prediction intervals, representing the range of expected values if the clustering and associated regressions were performed on a new sample from the same underlying distribution. It also identifies central and borderline trajectories within each cluster. Regarding the illustrative application, while there was evidence of an association between regular lipid testing and subsequent healthcare utilisation patterns in the original analysis, this is not supported in the robustness assessment.ConclusionsInvestigating the relationship between trajectory patterns and covariates is of interest in many situations. However, it is a challenging task with potential pitfalls. Our Robustness Assessment of Regression using Cluster Analysis Typologies (RARCAT) may assist in ensuring the robustness of such association studies. The method is applicable wherever clustering is combined with regression analysis, so its relevance goes beyond State Sequence Analysis.

Handling Multicollinearity and Outliers: A Comparative Study of Some One and Two–Parameter Estimators Using Real-Life Data

It is evident that when data suffers the problem of multicollinearity, the traditional least square is incapacitated and unreliable. Hence, needs to use bias estimator such as Ridge estimator, Liu estimator among others. Also, presence of outliers is another treat and to tackle this challenge is the use of robust regression estimators which include M, MM, LTS, LMS, LAD, LQS and S estimators. However, the presence of the two anomalies may be inevitable. Several estimators have been combined to handle the problems simultaneously. Therefore, this study compared and contrasted some robust one and two-parameter estimators using some real-life data sets. Mean Square Error (MSE) was used as criterion to select the best estimator. Some of the robust estimators were found to be inconsistent in addressing the twin problems. However, across all the data set employed in the study, the results revealed that robust Modified Ridge Type (MRT) in M, MM and LTS did well using minimum MSE

A Two-Parameter Ridge Estimator for Handling Extreme Multicollinearity Problems in Logistic Regression

This paper introduces a robust two-parameter ridge estimator that is customized for logistic regression models, which tend to be sensitive to extreme multicollinearity problems. Inflated standard errors and unreliability in the results stem from the problem of multicollinearity characterized by high correlations among predictor variables in logistic regression model. Traditional approaches like the Maximum Likelihood Estimator (MLE) and one-parameter ridge-type estimators often perform poorly under these settings, thus calling for the development of more robust approaches. This new proposal is called New Biased Two Parameter (NBTP), which extends the ridge regression framework by introducing additional biasing parameters customized for an extreme multicollinearity problem. The paper combines a theoretical analysis with extensive Monte Carlo simulations and real application to Pena data. It demonstrates that the new estimator, New Biased Two Parameter (NBTP), provides much more stable and accurate parameter estimates than previous methods. The results underscore the importance of using robust estimation methods within logistic regression, especially when multicollinearity may be widespread in fields such as medical research, finance, and the social sciences.

Expanding the Landscape of Aging via Orbitrap Astral Mass Spectrometry and Tandem Mass Tag (TMT) Integration.

Aging results in a progressive decline in physiological function due to the deterioration of essential biological processes, such as transcription and RNA splicing, ultimately increasing mortality risk. Although proteomics is emerging as a powerful tool for elucidating the molecular mechanisms of aging, existing studies are constrained by limited proteome coverage and only observe a narrow range of lifespan. To overcome these limitations, we integrated the Orbitrap Astral Mass Spectrometer with the multiplex tandem mass tag (TMT) technology to profile the proteomes of three brain tissues (cortex, hippocampus, striatum) and kidney in the C57BL/6JN mouse model, achieving quantification of 8,954 to 9,376 proteins per tissue (cumulatively 12,749 across all tissues). Our sample population represents balanced sampling across both sexes and three age groups (3, 12, and 20 months), comprising young adulthood to early late life (approximately 20-60 years of age for human lifespan). To enhance quantitative accuracy, we developed a peptide filtering strategy based on resolution and signal-to-noise thresholds. Our analysis uncovered distinct tissue-specific patterns of protein abundance, with age and sex differences in the kidney, while brain tissues exhibit notable age changes and limited sex differences. In addition, we identified both proteomic changes that are linear with age (i.e., continuous) and that have a non-linear pattern (i.e., non-continuous), revealing complex protein dynamics over the adult lifespan. Integrating our findings with early developmental proteomic data from brain tissues highlighted further divergent age-related trajectories, particularly in synaptic proteins. This study not only provides a robust data analysis workflow for TMT datasets generated using the Orbitrap Astral mass spectrometer but also expands the proteomic landscape of aging, capturing proteins with age and sex effects with unprecedented depth.

Sexual dissatisfaction and its association with health status among older adults in China: a nationwide study.

Background Most population-based sexual health research in China excludes older adults. To fill the gap, this study aims to characterise sexual dissatisfaction among people aged 50years or older from a nationwide, population-representative sample and to explore its association with physical, mental, and self-reported overall health indicators. Methods Data were collected as part of the China Family Panel Studies in 2020, led by the Institute of Social Science Survey of Peking University. Multivariable logistic regressions with robust estimators were used to investigate the association between sexual dissatisfaction and health indicators and potential demographic confounders. Results Among the 8222 partnered Chinese adults aged 50years or older (median age: 59, IQR: 54-66, 47% identified as women), 78% (6380/8222) reported being satisfied or very satisfied in their sex life. After adjusting for demographic variables, poor self-rated health status (aOR: 1.59, 95% CI: 1.42-1.77), experiencing depression symptoms (aOR: 2.02, 95% CI: 1.80-2.26), and having chronic diseases (aOR: 1.20, 95% CI: 1.07-1.36) were positively associated with sexual dissatisfaction in multivariable analyses. Among sociodemographic factors, younger age, female gender, and education level at senior high school or above were more likely to experience sexual dissatisfaction (all P Conclusion Based on our sample, more than one in five Chinese adults aged 50years or older might face sexual dissatisfaction. Comorbidities common in older age likely exacerbate sexual dissatisfaction. Greater attention to sexual satisfaction research and sexual health programs among older adults is needed with respect to gender differences and chronic disease comorbidities.

Estimating prevalence of opiate and crack cocaine use and injecting in England using mixed-effects capture–recapture models

Abstract Multiple social and health problems are associated with opiate and crack cocaine use and injecting, but the prevalence of these behaviours is unknown. We linked three sources of administrative data on individuals engaging in opiate and/or crack cocaine use (OCU) community treatment; arrests, prison, or probation; and drug-related deaths. We used probabilistic linkage to increase the number of matching records and capture-recapture analysis to estimate the unknown number of individuals not observed in any source. We fitted random effects models to data stratified by source, local authority (LA) area, age, sex, drug type, and injecting. Two-way interactions between sources and explanatory variables were included as fixed effects. We included random effects at the LA level, with random intercepts and random coefficients. The total count of opiate and/ or crack cocaine use in the financial year 2018/19 was estimated at 336,531 [95% confidence interval (CI): 311,932-360,845], corresponding to 9.44 (95% CI: 8.75-10.12) per 1,000 population in England. Three-quarters were men, and the estimate of people currently injecting drugs was 67,715 (95% CI: 64,769-70,526). Our study used random effects models to borrow strength across local authorities and produce robust prevalence estimates of OCU for England