Bayesian Credible Interval Research Articles

Forecasting cognitive functioning: an artificial intelligence approach with Dynamic Bayesian Networks

Abstract Background Several potentially modifiable risk factors are associated with subjective cognitive decline (SCD). However, developmental patterns of these risk factors have not been used before to forecast later SCD. Practical tools for the prevention of cognitive decline are needed. We examined multifactorial trajectories of risk factors, and their associations with SCD using an artificial intelligence approach to build a score that forecasts later SCD. Methods Five repeated surveys (2000-2022) of the Helsinki Health Study (n = 8960, 79% women, aged 40-60 at Phase 1) were used to build dynamic Bayesian networks (DBN) for estimating the odds of SCD. A score-based approach was implemented for learning DBN using the quotient normalized maximum likelihood criterion. The model was used to predict SCD based on the history of consumption of fruit and vegetables, smoking, alcohol consumption, leisure-time physical activity, body mass index, and insomnia symptoms, adjusting for sociodemographic covariates. Results Of the participants, 31-48% reported decline in memory, learning, and concentration in 2022. Physical activity was the strongest predictor of SCD in a 5-year interval, with an odds ratio of 0.76 (95% Bayesian credible interval 0.59-0.99) for physically active compared to inactive participants. Alcohol consumption showed a U-shaped relationship with SCD. Other risk factors had minor effects. Conclusions A new online risk score tool was developed that enables individuals to inspect their own risk profiles, as well as explore potential targets for interventions and their estimated contributions to later SCD. Dynamic decision heatmap was presented as a communication tool to be used at healthcare consultations. Key messages • Potentially modifiable health-related behaviours such as physical activity can contribute to subjective cognitive decline. • Artificial intelligence can be applied to discover the complex interactions between risk factors and subjective cognitive decline, to support preventive healthcare at individual and population levels.

The Development and Piloting of an Early Youth-Engagement (EYE) Model to Improve Engagement of Young People in First Episode Psychosis Services: A Mixed Methods Study.

Psychosis is associated with significant health and societal costs. Early intervention in psychosis services (EIP) are highly effective in promoting recovery, yet substantial proportions of young people disengage. The current study aimed to develop and evaluate a novel engagement intervention in EIP services. A qualitative investigation of facilitators and barriers to engagement in 68 first episode psychosis patients, family members and young people, and a Delphi consultation with 27 regional and national youth and psychosis service leads informed the development of the intervention. A mixed-methods feasibility-pilot study then compared engagement outcomes in 298 EIP service users in two cohorts: standard EIP versus standard EIP plus the novel early youth-engagement (EYE) intervention. A qualitative study explored intervention experiences in 22 randomly selected service users, carers and clinicians. A process evaluation explored delivery. Disengagement was 24% in the standard EIP cohort compared to 14.5% in the standard EIP plus EYE intervention cohort. A 95% Bayesian credibility interval revealed a 95% probability that the true reduction in disengagement lay somewhere between 0% and 18%. The number needed to treat was 11, 95% CI [5, 242]. Use of the EYE resources was associated with engagement. Qualitiative feedback supported effects on communication, social network engagement, service user goals, mental health and well-being outcomes. The EYE intervention was designed from a service user, young person and carer perspective. Both qualitative and quantitative data support impacts on engagement. We now need to evaluate effectiveness, cost-effectiveness and implementation in a multi-site randomised controlled trial.

AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification-Decoding Lateral Epicondylitis in Forearm Muscles.

Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase-amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject's label prediction. Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries-Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. This study's implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders.

Improving Inference Within Freshwater Community Studies: Accounting for Variable Detection Rates of Amphibians and Fish.

Research into freshwater communities often aims to link patterns of species distribution in ponds with underlying biotic factors. However, errors with species detection (e.g. false negatives) may underestimate distribution and bias assessments of community structure. Occupancy models that account for imperfect detection offer a solution to this problem. Here, we used three methods (call/visual encounter surveys, dip-netting and newt trapping) to survey amphibians and fish (potential amphibian predators) at 100 ponds in an urbanised landscape in Hungary over one breeding season. We estimated species detection probabilities for amphibians (all life stages combined) and fish using occupancy models to gain insight into amphibian-fish relationships and other survey-specific variables. We detected nine amphibian and 20 fish species. There were relatively low but variable estimated probabilities of detection for amphibians (mean: 0.320, 95% Bayesian credible interval: 0.142-0.598), with three species having detection rates < 0.1. Probabilities of detection peaked in the middle of the breeding season and increased with survey effort. Detection probabilities of five species were negatively associated with the detection of fish at a pond, while there were positive relationships between detection and emergent vegetation cover. We found no substantial differences in detection rates among the three survey methods. The probability of detecting fish was much higher than for amphibians (0.588, 0.503-0.717) but was lower at ponds with high emergent vegetation where amphibian detection was higher. Our results underscore the importance of accounting for the imperfect detection of both response organisms and potentially interacting species in aquatic community studies. We recommend applying multi-species occupancy models to enable inference for both common and rare species at ponds in landscapes subjected to human disturbances.

Bayesian Lower and Upper Estimates for Ether Option Prices with Conditional Heteroscedasticity and Model Uncertainty

This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower and upper estimates for prices of Ether options, that is, options written on Ethereum, with conditional heteroscedasticity and model uncertainty. Specifically, a discrete-time generalized conditional autoregressive heteroscedastic (GARCH) model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum, and Bayesian nonlinear expectations are adopted to introduce model uncertainty, or ambiguity, about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov’s principle is employed to change probability measures for introducing a family of alternative GARCH models and their risk-neutral counterparts. The Bayesian credible intervals for “uncertain” drift and volatility parameters obtained from conjugate priors and residuals obtained from the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD. Comparisons with a model incorporating conditional heteroscedasticity only and a model capturing ambiguity only are presented.

Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis.

Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies.

Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.

Different estimation techniques and data analysis for constant-partially accelerated life tests for power half-logistic distribution

Partial accelerated life tests (PALTs) are employed when the results of accelerated life testing cannot be extended to usage circumstances. This work discusses the challenge of different estimating strategies in constant PALT with complete data. The lifetime distribution of the test item is assumed to follow the power half-logistic distribution. Several classical and Bayesian estimation techniques are presented to estimate the distribution parameters and the acceleration factor of the power half-logistic distribution. These techniques include Anderson–Darling, maximum likelihood, Cramér von-Mises, ordinary least squares, weighted least squares, maximum product of spacing and Bayesian. Additionally, the Bayesian credible intervals and approximate confidence intervals are constructed. A simulation study is provided to compare the outcomes of various estimation methods that have been provided based on mean squared error, absolute average bias, length of intervals, and coverage probabilities. This study shows that the maximum product of spacing estimation is the most effective strategy among the options in most circumstances when adopting the minimum values for MSE and average bias. In the majority of situations, Bayesian method outperforms other methods when taking into account both MSE and average bias values. When comparing approximation confidence intervals to Bayesian credible intervals, the latter have a higher coverage probability and smaller average length. Two authentic data sets are examined for illustrative purposes. Examining the two real data sets shows that the value methods are workable and applicable to certain engineering-related problems.

Confidence Intervals for the Mean and Difference of Means of Birnbaum-Saunders Distributions with Application to Wind Speed Data

This paper proposes the confidence intervals for the mean and difference of means of Birnbaum-Saunders (BirSau) distributions based on the Bootstrap confidence interval (BCI), Percentile bootstrap confidence interval (PBCI), Generalized confidence interval (GCI), Bayesian credible interval (BayCrI) and the highest posterior density (HPD). The simulation study used R statistical software to evaluate the coverage probabilities and average lengths. The concerning results of the mean suggest that HPD is the recommended method for constructing confidence intervals in the BirSau distributions, except for small sample sizes where the GCI method proves more efficient. For the difference of means, PBCI emerges as the preferred way to construct confidence intervals, except in some cases where small sample sizes with the HPD method are more efficient. Moreover, the average lengths of these proposed confidence intervals decreased as both sample size and shape parameters increased. To illustrate the effectiveness of the suggested confidence intervals, we applied them to wind speed datasets collected in Ayutthaya and Ratchaburi provinces, Thailand.

Long-Term Effect of Temperature Increase on Liver Cancer in Australia: A Bayesian Spatial Analysis.

While some evidence has potentially linked climate change to carcinogenic factors, the long-term effect of climate change on liver cancer risk largely remains unclear. Our objective is to evaluate the long-term relationship between temperature increase and liver cancer incidence in Australia. We mapped the spatial distribution of liver cancer incidence from 2001 to 2019 in Australia. A Bayesian spatial conditional autoregressive (CAR) model was used to estimate the relationships between the increase in temperature at different lags and liver cancer incidence in Australia, after controlling for chronic hepatitis B prevalence, chronic hepatitis C prevalence, and the Index of Relative Socio-economic Disadvantage. Spatial random effects obtained from the Bayesian CAR model were also mapped. The research showed that the distribution of liver cancer in Australia is spatially clustered, most areas in Northern Territory and Northern Queensland have higher incidence and relative risk. The increase in temperature at the lag of 30 years was found to correlate with the increase in liver cancer incidence in Australia, with a posterior mean of 30.57 [95% Bayesian credible interval (CrI): 0.17, 58.88] for the univariate model and 29.50 (95% CrI: 1.27, 58.95) after controlling for confounders, respectively. The results were not highly credible for other lags. Our Bayesian spatial analysis suggested a potential relationship between temperature increase and liver cancer. To our knowledge, this research marks the first attempt to assess the long-term effect of global warming on liver cancer. If the relationship is confirmed by other studies, these findings may inform the development of prevention and mitigation strategies based on climate change projections. https://doi.org/10.1289/EHP14574.

Technical note: Posterior uncertainty estimation via a Monte Carlo procedure specialized for 4D-Var data assimilation

Abstract. Through the Bayesian lens of four-dimensional variational (4D-Var) data assimilation, uncertainty in model parameters is traditionally quantified through the posterior covariance matrix. However, in modern settings involving high-dimensional and computationally expensive forward models, posterior covariance knowledge must be relaxed to deterministic or stochastic approximations. In the carbon flux inversion literature, (Chevallier et al., 2007) proposed a stochastic method capable of approximating posterior variances of linear functionals of the model parameters that is particularly well suited for large-scale Earth-system 4D-Var data assimilation tasks. This note formalizes this algorithm and clarifies its properties. We provide a formal statement of the algorithm, demonstrate why it converges to the desired posterior variance quantity of interest, and provide additional uncertainty quantification allowing incorporation of the Monte Carlo sampling uncertainty into the method's Bayesian credible intervals. The methodology is demonstrated using toy simulations and a realistic carbon flux inversion observing system simulation experiment.

Aquatic invasive species exhibit contrasting seasonal detectability patterns based on environmental DNA: Implications for monitoring

Abstract Aquatic invasive species (AIS) are a global threat to freshwater biodiversity and ecosystem services. Documenting AIS prevalence at broad spatial scales is critical to effective management and early detection. However, conventional monitoring for AIS is costly and is rarely applied at the resolution and scale required for effective management. Monitoring of AIS using environmental DNA (eDNA) has the potential to enable broadscale surveillance at a fraction of the cost of conventional methods, but key questions must first be addressed related to how eDNA detection probability varies among environments, seasons, and multiple species with different life histories. To quantify spatiotemporal variation in the detection probability of AIS using eDNA sampling, we surveyed 20 lakes with known populations of four aquatic invasive species: common carp (Cyprinus carpio), rusty crayfish (Faxonius rusticus), spiny waterflea (Bythotrephes longimanus), and zebra mussels (Dreissena polymorpha). We collected water samples at 10 locations per lake, five times throughout the open water season resulting in a total of 1,000 water samples. Quantitative polymerase chain reaction was used with species‐specific assays to determine the presence of each species' eDNA in water samples. With Bayesian occupancy models, we quantified the effects of lake and site characteristics and Julian date on eDNA detection probability. The probability of eDNA detection varied seasonally, and the seasonal variation was species‐specific and related to species life histories. Zebra mussel eDNA was generally the most detectable among the species we targeted, and detection probability peaked in midsummer when only six water samples were required to achieve a 95% probability of detection (80% Bayesian credible interval: 3–12 samples). Spiny waterflea eDNA detections also peaked in mid to late summer, but were overall the most difficult species to detect, requiring 160 samples for a 95% probability of detection (80% Bayesian credible interval: 67–1,616 samples). Common carp eDNA was most detectable in the spring and rusty crayfish eDNA was most detectable in the early autumn, corresponding to key life history events. Sampling for eDNA during the optimal time of the year for each species decreased the number of samples required to reach a 95% probability of detection by an order of magnitude or more. Our results are relevant for decision makers interested in using eDNA as a multi‐species monitoring tool and highlight the importance of life history in the efficacy of eDNA monitoring.

Linking Opinions Shared on Social Media About COVID-19 Public Health Measures to Adherence: Repeated Cross-Sectional Surveys of Twitter Use in Canada.

The effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised. The objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter. Repeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination. Data from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6% (95% CI 20.1%-21.2%) of Canadians, of whom 29.9% (95% CI 28.6%-31.3%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11% (95% CI 10.6%-11.3%) of Canadians reported poor adherence to mask-wearing, and 10.8% (95% CI 10.4%-11.2%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95% BCI 3.38-4.80 and OR 7.31, 95% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non-Twitter users, and those who tweeted in support were less likely (OR 0.47, 95% BCI 0.31-0.64 and OR 0.96, 95% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non-Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95% BCI 3.40-4.85) and 0.20 (95% BCI 0.10-0.32), respectively, compared to non-Twitter users. Opinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors.

Classical and Bayesian estimation of multicomponent stress–strength reliability with power Lindley distribution under progressive first-failure censored samples

This research article presents a methodology for estimating the multicomponent reliability based on progressively first-failure censored samples, where the underlying distribution of stress and strength is modelled using the power Lindley distribution. The classical and Bayesian estimation methods are utilized for estimating the multicomponent reliability. In the Bayesian estimation, the Markov Chain Monte Carlo approximation method is employed to obtain the posterior mean under a generalized entropy loss function. Various intervals including asymptotic confidence, bootstrap-p confidence, bootstrap-t confidence, Bayesian credible, and highest posterior density credible intervals are computed. A simulation study is conducted to evaluate the performance of the proposed methodology. Also, two different real data applications are presented to illustrate the practicality of the approach.

Adjustment for sparse data bias in odds ratios: Significance to appraisal of risk of diabetes due to occupational trichlorfon insecticide exposure

BackgroundBias away from the null in odds ratios (OR), aggravated by low power, is a well-known phenomenon in statistics (sparse data bias). Such bias increases in presence of selection of “significant” results on the basis of null hypothesis testing (effect size magnification, ESM). ObjectivesWe seek to illustrate these issues and adjust for suspected sparse data bias in the context of a reported more than doubling of the odds of new onset type 2 diabetes in presence of occupational trichlorfon insecticide exposure reported in the Agricultural Health Study. MethodsWe performed ESM analysis on the crude ORs extracted from the contingency table in the published report, which is done by simulating selected OR given a posited true OR. Next, we applied easily accessible methods that adjust for sparse data bias to the extracted contingency tables, including data augmentation, bootstrap, Firth's regression, and Bayesian methods with weakly informative priors. ResultsDuring the ESM analysis, we observed that there was a reasonable chance that a “statistically significant” OR of around 2.5–2.6 would be observed for true OR of 1.2. Adjustment for sparse data bias revealed that Bayesian methods outperformed alternative approaches in terms of yielding more precise inference, while not making unjustified distributional assumptions about estimates of OR. The OR in the original paper of about 2.5–2.6 was reduced on average to OR of 1.9 to 2.2, with 95% (Bayesian) credible intervals that included the null. DiscussionIt is reasonable to adjust ORs for sparse data bias when the reported association has societal importance, because policy must be informed by the least biased estimates of the effect. We think that such adjustment would lead to a more appropriate evaluation of the extent of evidence on the contribution of occupational exposure to trichlorfon pesticide to risk of new onset diabetes.

Estimation of multi-stress strength reliability under progressive first failure censoring using generalized inverted exponential distribution

We investigate the estimation of multi-stress strength reliability under progressively first failure censoring for the generalized inverted exponential distribution using classical and Bayesian approaches. Classical estimation employs maximum likelihood estimators with the Expectation-Maximization algorithm to handle incomplete data. Bayesian estimation utilizes Markov Chain Monte Carlo (MCMC) techniques and Tierney-Kadane's approximation to approximate the posterior distribution. Furthermore, A generalized entropy loss function is employed to measure estimation accuracy. Finally, Validation is provided through Monte Carlo simulations and real-world applications. Findings highlight the robustness and accuracy of Bayesian methods, particularly with informative priors, and underscore the strengths of MCMC techniques and Bayesian Credible Intervals in specific aspects of estimation.

Confidence Intervals for Ratio of Percentiles of Birnbaum-Saunders Distributions and Its Application to PM2.5 in Northern Thailand

In the statistical literature, the Birnbaum-Saunders distribution has garnered considerable attention as an important fatigue life distribution for engineers. This distribution finds applications in medicine, engineering, and science. A percentile denotes a threshold below which a designated percentage of scores or data points are situated. In particular cases, percentiles are the preferred choice over mean and variance, especially when dealing with skewed data. This paper investigates confidence intervals for the ratio of percentiles of Birnbaum-Saunders distributions. It discusses the generalized fiducial confidence interval, Bayesian credible interval, and the highest posterior density intervals using a prior distribution with partial information and a proper prior with known hyperparameters. To compare the performance of these confidence intervals, the study employs coverage probability and average length measurements. Monte Carlo simulation via the R package is used to calculate the coverage probability and average length. The results indicate that the highest posterior density interval using a prior distribution with partial information outperforms the other confidence intervals. Finally, the paper presents the results of the simulation study and applies them in the field of environmental sciences.

The generalized order statistics arising from three populations with the lower truncated proportional hazard rate models and its application to the sensitivity to the early disease stage

ABSTRACT In this paper, we present some results to make inference about the parameters of lower truncated proportional hazard rate models with the same baseline distributions based on three independent generalized order statistics samples. Then, especially by considering the results of the diagnostic tests for the non-diseased, early-diseased stage and fully diseased populations, we make inference about sensitivity to the early disease stage parameter. The maximum likelihood estimator, a generalized pivotal estimator and some Bayes estimators are obtained for different structures of prior distributions. The percentile bootstrap confidence interval, a generalized pivotal confidence interval and some Bayesian credible intervals are also presented. A Monte Carlo simulation study is used to evaluate the performances of the obtained point estimators and confidence/credible intervals and two competitors. We use two real datasets to illustrate the proposed methods.

Addressing the Southeast Asian snaring crisis: Impact of 11 years of snare removal in a biodiversity hotspot

AbstractUnsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger‐collected data to assess the impact of intensive snare removal on snaring levels in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% Bayesian credible interval [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas but by itself is likely insufficient to address this threat. To stop snaring in protected areas, a multifaceted approach will be necessary that combines short‐term reactive snare removal with long‐term proactive programs that address the underlying drivers behind snaring.

Geographical variation in dementia prevalence across China: a geospatial analysis

Dementia poses great health and social challenges in China. Dementia prevalence may vary across geographic areas, while comparable estimations on provincial level is lacking. This study aims to estimate dementia prevalence by provinces across China, taking into account risk factors of individual level and potential spatial correlation of provinces. In this study, 17,176 adults aged 50 years or older were included from the fourth wave of the ChinaHealth and Retirement Longitudinal Study (CHARLS 2018), covering 28 provinces, autonomous regions and municipalities. To improve provincial representativeness, we constructed provincial survey weights based on China 7th census (2020). The prevalence of dementia and 95% Bayesian credible intervals (BCIs) were estimated using a Bayesian conditional autoregressive (CAR) model with spatially varying coefficients of covariates. The weighted prevalence of dementia at provincial level in China in 2018 ranged from 2.62% (95%BCI: 1.70%, 3.91%) to 13.53% (95%BCI: 8.82%, 20.93%). High dementia prevalence was concentrated in North China, with a prominent high-high cluster, while provinces of low prevalence were concentrated on East and South China, characterized by a low-low cluster. Ordered by the median estimation of prevalence, the top 10% of provinces, include Xinjiang, Jilin, and Beijing. Meanwhile, Fujian, Zhejiang, and Guangdong rank among the last. The association between dementia prevalence and drinking, smoking, social isolation, physical inactivity, hearing impairment, hypertension, and diabetes exhibits provincial variation. Our study identifies a geospatial disparity in dementia prevalence and risk factor effects across China's provinces, with high-high and low-low clusters in some northern and southern provinces, respectively. The findings emphasize the need for targeted strategies, such as addressing hypertension and hearing impairment, in specific regions for more effective dementia prevention and treatment. National Science Foundation of China/the Economic and Social Research Council, UK Research and Innovation joint call: Understanding and Addressing Health and Social Challenges for Ageing in the UK and China. UK-China Health And Social Challenges Ageing Project (UKCHASCAP): present and future burden of dementia, and policy responses (grant number 72061137003, ES/T014377/1).