Quality and methodological heterogeneity of COVID-19 vaccine safety studies focusing on the myocarditis safety signal: A systematic review, meta-analysis and meta-regression.

Ankle plantar flexor spasticity is a common post-stroke impairment that may influence functional performance and responsiveness. However, descriptive responsiveness patterns across spasticity severity levels remain insufficiently explored. This study aimed to descriptively examine functional changes and responsiveness across ankle plantar flexor spasticity severity categories in individuals with subacute stroke. In this prospective observational cohort study with a 4-week follow-up, 58 participants were categorized based on ankle plantar flexor spasticity severity using Modified Ashworth Scale (MAS) scores (MAS=0, MAS=1-1+, and MAS≥2). Functional performance was assessed using the 10-M Walk Test (10mWT), 6-Minute Walk Test (6MWT), Timed Up and Go (TUG), 5-Times Sit-to-Stand Test (5-Times STS), Figure-of-8 Walk Test (F8WT), and Four Square Step Test (FSST) at baseline and after 4weeks. Responsiveness was quantified using effect size (ES) and standardized response mean (SRM), and subgroup findings were descriptively examined across MAS categories. All outcome measures showed changes over 4weeks, with moderate-to-large responsiveness observed across tests. Responsiveness estimates appeared numerically greater in the MAS=0 subgroup, whereas more variable estimates were observed in participants with spasticity (MAS≥1). Similar patterns were observed across gait-related and functional mobility measures. Responsiveness estimates varied across spasticity levels, suggesting that responsiveness, as a measurement property, may differ according to impairment severity. These findings provide preliminary descriptive information regarding responsiveness across spasticity severity categories. Given the exploratory design and descriptive subgroup analyses, the findings should be interpreted cautiously.

Abstract Population size of an endangered species and its trend are two key parameters that define its conservation status, and eventually determine the need for and design of future conservation actions. The assessment of its conservation status can be misled by various sources of uncertainty, if these not properly considered in monitoring programmes. We analysed the potential consequences of variations and uncertainty in estimates of the population size of the narrow endemic species for assessing their conservation status, using Petrocoptis grandiflora Rothm. as a model species. After accounting for semantic uncertainty, we found that the published estimates of P. grandiflora population size in different dates, based on density and area, are highly variable. Differences in the estimated population size by site were up to five-fold, and all the estimates differed from a complete census across its distribution area carried out in this study. The trend in population size based on two previously published estimates (2009 and 2017) was highly negative, and led to unfavourable-inadequate assessment of the conservation status. However, the trend in population size based on two partial counts (2007 and 2023) was stable, and would have led to a favourable assessment. Furthermore, after eliminating spatial uncertainty, we found that Natura 2000 coverage for the species is less than 50%. Given the profound consequences that different uncertainty sources could have for conservation management if unaccounted for, we conclude that the population size of endangered chasmophytes should be determined by tailored methods, optimised for the particular objectives of each monitoring programme.

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Health is a direct determinant of human well-being and an essential input to economic productivity and societal resilience. In the Economic Community of West African States, persistent deficits in equitable access to healthcare and inclusive human capital development are caused by socioeconomic inequalities, financial barriers, and a fragmented healthcare system. These factors lead to low economic productivity and a continuous cycle of poverty and also violate people’s fundamental right to health and education. This research examined the impact of healthcare accessibility on inclusive human capital development among ECOWAS countries using a time series data from 1980 to 2023. Human capital theory is the theoretical framework for this study. The variables used are the inequality human development index (IHDI) (dependent variable) and other independent variables, such as education expenditure (EDEx), public health expenditure (PHEx), real GDP per capita (GDPp), poverty headcount ratio, and institutional quality (INSQ). An instrumental variables (IV) estimator of dynamic panel models based on the System Generalized Method of Moments (SGMM) was used in the estimation. The study found that public health expenditure, GDP per capita, and institutional quality have a negative and significant impact on the inclusive development of the region due to structural corruption lag, high administrative overhead and systemic leakages. Education expenditure and poverty headcount ratio have a negative and insignificant impact. This study recommends, amongst others, that access to health care such as county or community hospitals, mobile clinics, and Ward ambulance services should be provided to increase health accessibility. The government can also promote inclusive growth policies, such as free or sponsored access to education and health care, to ensure that economic gains reach the poor in ECOWAS countries.

Firm-level nature dependence and access to bank credit.

ABSTRACT This study evaluates the causal effect of public data openness on urban total factor productivity (TFP) and explores its mechanisms through government governance and transaction costs. Using panel data of Chinese prefecture-level cities from 2000 to 2023, we treat the launch of government data open platforms as a quasi-natural experiment and employ a multi-period difference-in-differences (DID) approach. TFP is measured via the two-step Solow residual method, with robustness verified by instrumental variable estimation, PSM-DID, placebo tests, and green TFP. Baseline regressions show that public data openness significantly enhances urban TFP. Mechanism analysis reveals that improved government governance and reduced transaction costs are the primary drivers. Heterogeneity analysis indicates stronger policy effects in non-Yangtze River Economic Belt regions and areas with lower financial development, exhibiting a ‘timely assistance’ characteristic. This study provides city-level causal evidence, identifies concrete transmission channels, expands research on data factor economic value, and offers empirical support for differentiated data openness policies and inclusive growth.

Risk stratification for unruptured intracranial aneurysms largely relies on meta-analyses that synthesize heterogeneous primary data. However, fundamental differences in study design, particularly between cross-sectional rupture-status analyses and prospective natural history cohorts, may influence the reported associations between aneurysm morphology and rupture risk. This umbrella review aimed to determine the extent to which study design affects these associations and whether the current evidence base supports clinically interpretable estimates of rupture risk. An umbrella review of systematic reviews and meta-analyses (1999-2025) was conducted across PubMed, Embase, and Scopus. Methodological quality was assessed using AMSTAR-2. Effect estimates for aneurysm size, location, and selected morphometric parameters (irregular shape, aspect ratio, size ratio) were descriptively compared between meta-analyses of prospective cohorts (incident rupture risk) and cross-sectional rupture-status studies. Seventeen meta-analyses encompassing more than 200 000 aneurysms were included. Moderate overlap among studies was observed (corrected covered area 8.9%), with high redundancy among natural history cohorts and minimal overlap in computational modelling studies. Effect estimates for morphological variables were generally larger in cross-sectional rupture-status syntheses than in prospective analyses. However, prospective data incorporating standardized quantitative morphometric assessments were limited, event rates were low, and potential selection bias constrained causal interpretation. Machine learning and radiomics studies predominantly focused on rupture status rather than time-to-event risk and rarely underwent external validation. Reported associations between aneurysm morphology and rupture vary substantially according to study design. Cross-sectional rupture-status analyses and prospective natural history studies address different clinical questions and should not be interpreted interchangeably. The current evidence base is further constrained by the scarcity of contemporary prospective cohorts integrating standardized morphometric assessment. Advancing clinically meaningful rupture risk stratification will require large-scale prospective natural history studies capable of supporting externally validated, time-to-event prediction models that incorporate morphology in a standardized and biologically interpretable framework.

Depicting patient transitions among multiple clinical states is a common objective in health services and epidemiological research. Multistate models (MSM) are the primary analytical approach used in such studies. Although the structure of an MSM is typically determined by the research questions of a specific application, models are typically complex, with multiple transition paths and a large number of parameters. This complexity introduces computational and numerical challenges in parameter estimation and scientific difficulties in model interpretation. Compounding these issues is the inherent within-subject correlation. For example, in studies of care transitions among patients receiving coronavirus disease 2019 (COVID-19) vaccines, the transition times among different states within the same subject tend to be correlated. Failing to accommodate these correlations may lead to inefficient estimation and questionable inference. In this paper, we propose a method for variable selection in MSM with correlated data by reparameterizing the likelihood function and approximating the penalty term with a smooth hyperbolic tangent function. This approach enforces sparsity in the MSM. We conducted an extensive simulation study to evaluate the accuracy of variable selection and parameter estimation. Finally, we applied the method to analyze data from an observational study of care transitions among individuals receiving COVID-19 vaccines, focusing on four health states: healthy, infection, emergency department or hospital admission, and death.

This paper concerns a particular property of the basic instrumental variable (IV) estimator that is useful for multiple-input multiple-output (MIMO) modeling problems where it is not obvious how to partition the available signals between the sets of inputs and outputs. In general, a repartitioning of the input and output signals will result in a different model compared to the original input–output choice. It is important to distinguish cases where a repartitioning results in an algebraically equivalent model and cases where the resulting model transformation is more complex and depends also on particular system and signal properties. The latter situation typically occurs when models are estimated from data. We here show that the basic IV estimator is an exception since it provides algebraically equivalent estimates regardless of true system structure, noise properties, or amount of data. This equivalence result is illustrated in two simulation examples.

ABSTRACT This study examines how ESG performance influences green innovation, focusing on its underlying mechanisms and heterogeneous effects across firms and regions. Using panel data for Chinese A‐share listed companies from 2009 to 2023, we employ a two‐way fixed effects model and use ESG fund exposure as an instrumental variable to support causal identification. Digital transformation and information disclosure quality are incorporated as mediating factors. The results show that ESG performance significantly enhances green innovation, partly through improvements in digital capabilities and information transparency. These findings are robust to alternative specifications and instrumental variable estimations. The positive effects are more pronounced for high‐quality innovation, large firms, state‐owned enterprises, firms in the decline stage, and those operating in high‐pollution, less competitive, and non‐digital industries, particularly in eastern regions. Overall, this study advances understanding of how ESG engagement promotes sustainability‐oriented transformation and provides important implications for firms and policymakers.

ABSTRACT This paper examines the impact of decentralization and partial financing of healthcare through local governments (Panchayats) in India on the health status and income of household members. We construct a healthcare and illness dataset from the nationally representative Rural Economy and Demography Survey (REDS) which is comprised of 14,841 adults from 238 villages. Using this dataset, we examine how various forms of decentralization affect the health status, health-seeking behaviour, and welfare of village residents. Choice of public healthcare is shown to have higher welfare impacts compared to other forms of healthcare providers and the out-of-pocket health expenditures. We show that individuals react not only to the supply-side measures by federal and state governments, but also to mechanisms of decentralized health governance. We also demonstrate that efficient choices concerning healthcare made by household members contribute significantly to the village-level economic activity. The traditional instrumental variable estimates are robust to post-double-selection LASSO specifications.

ABSTRACT This research explores the impact of integrating sustainability into corporate strategies on investment efficiency. Using Simon's (1994, 1995) levers of control framework, we analyze 15,136 firm–year observations from multinational firms across 11 countries (2007–2021). We construct a sustainability control system (SCS) index from Bloomberg data and company reports, and estimate investment efficiency following Biddle et al. (2009). Panel regressions with fixed effects and instrumental variable (IV) estimations show a positive link between integrating social sustainability into management control systems and investment efficiency. This link depends on cultural factors: in societies with higher power distance, the impact is diminished, while in contexts with greater uncertainty avoidance, it is strengthened. These findings indicate that cultural variations shape sustainable control systems' outcome.

Purpose This study investigates how political influence and regulatory uncertainty affect firms’ perceptions of environmental regulations as obstacles within the European Union (EU). It focuses on the role of transition risks in shaping business behavior amid well-defined environmental objectives. Design/methodology/approach The analysis draws on data from the 2019 World Bank Enterprise Survey, which includes a dedicated Green Economy module. The final dataset covers 4,366 enterprises across 10 EU countries. The study employs a logistic regression framework with Bayesian Model Averaging (BMA) for variable selection and robustness checks through instrumental variable (IV) estimation. Findings Results show that firms perceiving environmental regulations as obstacles are also more likely to report the use of political influence (e.g. informal payments or gifts). This relationship is particularly strong among large and manufacturing firms. Furthermore, regulatory uncertainty – particularly among small firms – is a significant driver of perceived regulatory burden. Firms complying with environmental policies through monitoring rather than strategic targets also report higher perceived obstacles. Practical implications The findings suggest that policy uncertainty and political dynamics play a critical role in shaping firms’ compliance behavior. Policymakers should consider these dimensions when designing environmental regulations and supporting mechanisms to ensure policy effectiveness and credibility. Originality/value This paper contributes to the literature by examining the intersection of political influence, regulatory uncertainty, and environmental compliance in a high-income regional context. It highlights that even in the EU, where climate goals are clearly established, transition risks remain a substantial concern for enterprises.

ABSTRACT Building upon factor decomposition to overcome the curse of dimensionality inherent in multivariate volatility processes, we develop a factor model-based multivariate stochastic volatility (fMSV) framework. We propose a two-stage estimation procedure for the fMSV model: in the first stage, estimators of the factor model are obtained, and in the second stage, the MSV component is estimated using the estimated common factor variables. We derive the asymptotic properties of the estimators, taking into account the estimation of the factor variables. Simulation experiments indicate that the fMSV model provides accurate measurements of the true underlying variance–covariance matrix, while empirical applications to portfolio allocation suggest superior forecasting performance compared to standard multivariate volatility models.

Traditional survey methods make it difficult to collect sensitive data, as respondents provide false information or deliberately refuse to answer. The current investigation presents innovative methods utilizing multi-stage randomized response models (MRDRMs) to tackle challenges in precisely quantifying sensitive numerical variables while safeguarding respondent anonymity. The MRDRMs framework, comprising two as well as three-stage mathematical models. To employ two randomized response mechanisms designed to improve privacy protection and estimation efficiency to address perceptions like social desirability and over-estimation, which are prevalent in sensitive public health data collection. The main problem addressed in this study is the difficulty of obtaining precise estimates of sensitive quantitative variables because respondents often hesitate to provide truthful answers due to privacy concerns, fear, and social desirability bias. The incorporation of substantial randomization stages enables the models to provide unbiased and precise estimates of means and sensitivity levels, thereby ensuring the reliability of the data while reducing the psychological strain on participants. Theoretical analysis, particularly simulations conducted using Monte Carlo methods, suggests that MRDRMs, in both two-stage and three-stage formats, significantly improve the precision and relative efficiency of estimates compared to conventional randomized response strategies. The empirical validation carried out via a cross-sectional survey in the districts of Faisalabad, Lahore, Multan, and Rawalpindi in Punjab, Pakistan, examined not sufficiently reported COVID-19 cases along with vaccine hesitancy, thereby affirming the practical significance of the MRDRMs methods. The findings demonstrate a substantial improvement in estimation accuracy accompanied by reduced variance compared to conventional approaches. The results highlighting the effectiveness of MRDRM-I and MRDRM-II as robust methods for sensitive data collection. The findings from this research highlight the potential of MRDRMs for enhancing the acquisition and evaluation regarding sensitive data, strengthening consistent, ethical, and tangible results in research related to public health. These improvements strengthen privacy protection and enhance the validity of responses in sensitive surveys, offering meaningful contributions to public health monitoring and evidence-based policy development at the global level. The proposed models can be applied in public health surveillance, epidemiological studies, social science research, and policy-sensitive domains requiring accurate and privacy-preserving data collection.

Purpose Despite the growing importance of employee insights shared on social media, the literature has largely overlooked their role in supply chain relationships, particularly how employees' forward-looking assessments of firms shape customers' supply chain decisions. Design/methodology/approach Based on 7,722 supplier–customer–year observations spanning 2012–2024, this study empirically examines how employee-generated business outlook ratings on Glassdoor influence supplier–customer relationship stability. To strengthen the relationship, the study employs a difference-in-differences approach, instrumental variable estimation, and a series of robustness tests. Findings Suppliers with more positive employee-generated business outlook ratings are less likely to experience the discontinuation of principal supplier–customer relationships. The results support both information asymmetry reduction and labor risk mitigation mechanisms. Customer bargaining power strengthens this effect, whereas longer-standing partnerships attenuate it. Additional analyses show that the informativeness of employee-generated business outlook ratings aligns with the wisdom-of-crowds phenomenon and varies by reviewer attributes and job functions. Originality/value This study introduces employee-generated business outlook ratings as a low-cost prospective soft-information signal that complements traditional supply chain risk assessment. By showing that employee disclosures inform customer-side supplier evaluation and relationship management decisions, it extends the literature on the broader value of employee disclosures in supply chain contexts. Furthermore, by identifying underlying mechanisms and boundary conditions through which employee-generated business outlook ratings become decision-relevant, this study enhances the understanding of non-traditional information flows in supplier–customer relationships.

Although researchers investigating randomized controlled trials (RCTs) may collect fidelity of implementation data, this information may not be optimally used in analyzing data to yield causal effect estimates. An approach to obtaining causal effects, though underutilized by educational researchers, uses instrumental variable (IV) estimation. This tutorial explains why and how instrumental variables work; differentiates compliance types; illustrates how IVs can be used to properly deal with issues related to noncompliance and dosage effects; provides R syntax to estimate models using two-stage least squares regression, structural equation modeling, and Bayesian regression; and includes a sample writeup. To support more widespread understanding and use in RCTs with issues of noncompliance/nonadherence, this tutorial does not use equations or specialized notation.

The rapid expansion of healthcare infrastructure may exert increasing pressure on the sustainability of social health insurance. China's concurrent pursuit of universal coverage, coupled with extensive hospital construction, offers a valuable context for examining whether the patterns observed in China align with Roemer's Law (1961), which is often summarized as 'a hospital bed built is a bed filled' within an insured population. This study investigates the relationship between hospital bed density and insurance expenditure to deepen our understanding of the factors associated with the growth of healthcare costs. We compiled a panel dataset encompassing 31 Chinese provinces covering the period from 2011 to 2024. To strengthen identification and address potential endogeneity concerns, we employed two-way fixed effects models alongside instrumental variable (IV-2SLS) estimation. Mediation analysis was employed to investigate potential pathways, while panel threshold regression was utilized to examine nonlinear patterns in the relationship between supply and expenditure. The baseline estimates indicated a positive association, suggesting that higher provincial bed density correlates with increased province-level insurance spending. Mediation analysis revealed that the Average Length of Stay may serve as a potential aggregate pathway, accounting for 17.8% of the estimated relationship. Furthermore, threshold regression analysis indicated a possible nonlinear pattern, with an estimated threshold of 7.271 beds per 1,000 population. Below this threshold, hospital bed density is positively related to insurance spending (β = 0.217); however, above this threshold, the association loses statistical significance. Regional analysis demonstrated that the positive association was most pronounced in the western region, while no statistically significant association was observed in the northeastern region. Our findings support a conditional, province-level interpretation of Roemer's Law within the Chinese context, indicating that the association between bed density and insurance expenditure varies across institutional and capacity settings. Specifically, higher provincial bed density is more strongly associated with a longer average length of stay at the provincial level than with increased admission volumes. Beyond the exploratory threshold estimate, the marginal association between additional bed supply and insurance expenditure appears to weaken. These results suggest that payment reform, length-of-stay management, and regulatory oversight should be prioritized alongside careful capacity planning.

Big data fundamentally differs from traditional data, characterized by large volumes, high rates of missing values, and a lack of conformity to the normal distribution assumption for independent variables in traditional regression models. Consequently, there is a pressing need for new methodologies to enhance estimation accuracy and computational efficiency. Considering the hierarchical characteristics of data structure, we extend composite quantile regression (CQR) to accommodate hierarchical data assumptions and propose a hierarchical composite quantile regression (HCQR) model. In case of missing response variables, the regression coefficients are decomposed into individual and common components, where the inverse probability weighting is utilized for imputation. To enhance computational efficiency and achieve more accurate parameter estimates, we optimize the constructed function using the majorization-minimization algorithm. Numerical simulations of the proposed model under various missing rates reveal that our method not only improves parameter estimation accuracy but also effectively addresses non-normally distributed error terms. Finally, we apply the model to predict pancreatic cancer incidence, which provides valuable reference for the prediction and prevention of pancreatic cancer in clinical practice.