Hazard Rate Research Articles

Initiation of Alcohol Consumption Among the Tribal Male Adolescents in Dooars Region of West Bengal: A Failure Event Analysis Approach.

Alcohol consumption is a significant social and health problem among tribal male adolescents. The Dooars region of West Bengal has the highest concentration of the tribal population. This study aimed to examine the role of various factors in the early initiation of alcohol consumption among tribal male adolescents. This study relied on primary data and included 600 tribal adolescents. To fulfil the objective, the Cox regression hazard model was employed. Additionally, hazard rates were calculated to identify the changing hazard rates over a specific age. The Kaplan-Meier failure graph was also used to illustrate the occurrence of alcohol consumption events among different groups of tribal adolescents. The risk of alcohol consumption was higher among tribal adolescents who dropped out of the educational system (HR: 2.94), did not participate in outdoor games (HR: 3.10), worked for pay (HR: 3.23), smoked (HR: 12.55), drank traditional liquor (HR: 3.61), and had siblings (HR: 4.27) or peers (HR: 3.60) who used substances or fathers who consumed alcohol (HR: 3.80). Unsupervised time with peers (HR: 3.61) and stress (HR: 3.61) also increased the risk of alcohol consumption. In contrast, self-esteem was found to be protective (HR: 0.93). The government has introduced various interventions and schemes to reduce alcohol consumption, but more targeted actions are needed at the household and individual levels. In the tea gardens of the Dooars region, NGOs and the government should promote education, organize extracurricular activities for adolescents' holistic development, and raise awareness in tribal communities about the harmful effects of alcohol consumption.

A target trial emulation comparing the antidepressant effectiveness of selective serotonin reuptake inhibitors (SSRIs) highlighting the importance of patent-related confounding by indication.

The comparative effectiveness of selective serotonin reuptake inhibitors (SSRIs) has been subjected to relatively little research. However, a recent study based on target trial emulation suggested that sertraline may be more effective than escitalopram. To investigate whether sertraline, citalopram, and escitalopram differ in their effectiveness-assessed via the risk of psychiatric hospital admission and suicide following treatment initiation. The choice to focus on sertraline, citalopram, and escitalopram was made to limit confounding by indication, as the Danish depression treatment guideline from 2007 specifically listed these three SSRIs as first choice. We conducted a target trial emulation based on data from Danish registers. We identified all individuals that initiated treatment for depression with sertraline, citalopram, or escitalopram in the period from January 1, 2007, to March 1, 2019. These individuals were followed until psychiatric hospital admission or suicide (separate analyses), death, 1 year after treatment initiation or end of data. Cox proportional hazards regression adjusted for relevant baseline covariates was performed to emulate randomized treatment allocation, comparing the rate of psychiatric hospital admission and suicide for individuals treated with sertraline (used as reference), citalopram or escitalopram, respectively. For escitalopram, we conducted a sensitivity analysis excluding data from the period during which the drug was sold under patent, as the price of the drug during that time likely entailed a different prescription pattern, increasing the risk of ("patent-related") confounding by indication. We identified 56,865, 118,145, and 31,083 individuals initiating treatment with sertraline, citalopram, and escitalopram, respectively. Using sertraline as reference, the adjusted hazard rate ratio (aHRR) for psychiatric admission was 0.98 (95% CI = 0.91-1.05) for citalopram and 1.21 (95% CI = 1.10-1.32) for escitalopram. Notably, in the sensitivity analysis only including patients initiating treatment after the escitalopram patent had expired, the increased risk of psychiatric hospital admission associated with escitalopram treatment was no longer present (aHRR = 0.98, 95% CI = 0.82-1.18). The results of the analyses of suicide were inconclusive, due to few outcome events. Sertraline, citalopram, and escitalopram do not seem to have differential effectiveness in the treatment of depression. Taking potential patent-related, time varying, confounding by indication (via severity) into account is critical for pharmacoepidemiological studies, including those employing target trial emulation.

Advancing Forest-Fire Management: Exploring Sensor Networks, Data Mining Techniques, and SVM Algorithm for Prediction

Forest-fire is a pressing global problem that has far-reaching effects on human life and the environment, with climate change exacerbating their frequency and intensity. There is an urgent need for advanced predictive systems to mitigate these impacts. To address this issue, this study introduces a forest-fire prediction framework integrating wireless sensor networks (WSNs), data analysis, and machine learning. Sensor nodes deployed in a forest area collected real-time meteorological data, which was transmitted using LoRaWAN technology. Data mining techniques prepared the data for analysis using the SVM algorithm, revealing relationships between meteorological parameters and wildfire risk. The SVM model demonstrated an accuracy of 86% in classifying forest-fire risk levels based on temperature, humidity, wind speed, and rainfall data. The integrated framework of WSNs and the SVM algorithm provides a high-accuracy model for forest-fire risk prediction. The model is compared to the Canadian Forest Fire Hazard Rating System to validate its accuracy, demonstrating strong agreement with historical records and reports. The model's practical implications include efficient management, early detection, and prevention strategies. However, the model's limitations suggest avenues for future research, we should consider broader geographic applications and using advanced machine-learning methods to enhance the model's predictive capabilities.

Hazard Identification and Risk Assessment for Sustainable Shipyard Floating Dock Operations: An Integrated Spherical Fuzzy Analytical Hierarchy Process and Fuzzy CoCoSo Approach

Background: This study investigated the process of selecting sustainable safety protocols for floating dock operations in shipyards by identifying potential workplace risks in emergency situations. Thirteen occupational hazards for shipyard floating dock operations were identified through a literature review and expert discussions. Methods: We incorporated four risk elements (consequence: C, frequency: F, probability: P, and number of people at risk: NP) from the Fine–Kinney and Hazard Rating Number System (HRNS) approaches as the risk assessment criteria. We obtained the importance weights of the risk assessment criteria via the Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and extended the Combined Compromise Solution (CoCoSo) method within the fuzzy framework to prioritize occupational hazards. This study demonstrated the practicality and efficiency of the proposed emergency risk assessment model for shipyard floating dock operations through a case example of occupational risk assessment. Results: The analysis results show that H4 is the occupational hazard with the highest priority, with a score of 3.553. H4 represents the hazard associated with insufficient access to the entire pool area. The second and third most important hazards are the inability of cranes to move freely in and out of the berthing dock and the inability to dispatch emergency teams. These hazards, denoted H1 and H12, follow closely behind with scores of 3.391 and 3.344, respectively. H10 is deemed the least concerning hazard, with a score of 1.802. Conclusions: Professionals can handle complex and uncertain risk assessment data more flexibly using the proposed system, which excels in accurately organizing occupational hazards.

Different Statistical Inference Algorithms for the New Pareto Distribution Based on Type-II Progressively Censored Competing Risk Data with Applications

In this research, the statistical inference of unknown lifetime parameters is proposed in the presence of independent competing risks using a progressive Type-II censored dataset. The lifetime distribution associated with a failure mode is assumed to follow the new Pareto distribution, with consideration given to two distinct competing failure reasons. Maximum likelihood estimators (MLEs) for the unknown model parameters, as well as reliability and hazard functions, are derived, noting that they are not expressible in closed form. The Newton–Raphson, expectation maximization (EM), and stochastic expectation maximization (SEM) methods are employed to generate maximum likelihood (ML) estimations. Approximate confidence intervals for the unknown parameters, reliability, and hazard rate functions are constructed using the normal approximation of the MLEs and the normal approximation of the log-transformed MLEs. Additionally, the missing information principle is utilized to derive the closed form of the Fisher information matrix, which, in turn, is used with the delta approach to calculate confidence intervals for reliability and hazards. Bayes estimators are derived under both symmetric and asymmetric loss functions, with informative and non-informative priors considered, including independent gamma distributions for informative priors. The Monte Carlo Markov Chain sampling approach is employed to obtain the highest posterior density credible intervals and Bayesian point estimates for unknown parameters and reliability characteristics. A Monte Carlo simulation is conducted to assess the effectiveness of the proposed techniques, with the performances of the Bayes and maximum likelihood estimations examined using average values and mean squared errors as benchmarks. Interval estimations are compared in terms of average lengths and coverage probabilities. Real datasets are considered and examined for each topic to provide illustrative examples.

Different Statistical Inference Algorithms for the New Pareto Distribution Based on Type-II Progressively Censored Competing Risk Data with Applications

In this research, the statistical inference of unknown lifetime parameters is proposed in the presence of independent competing risks using a progressive Type-II censored dataset. The lifetime distribution associated with a failure mode is assumed to follow the new Pareto distribution, with consideration given to two distinct competing failure reasons. Maximum likelihood estimators (MLEs) for the unknown model parameters, as well as reliability and hazard functions, are derived, noting that they are not expressible in closed form. The Newton–Raphson, expectation maximization (EM), and stochastic expectation maximization (SEM) methods are employed to generate maximum likelihood (ML) estimations. Approximate confidence intervals for the unknown parameters, reliability, and hazard rate functions are constructed using the normal approximation of the MLEs and the normal approximation of the log-transformed MLEs. Additionally, the missing information principle is utilized to derive the closed form of the Fisher information matrix, which, in turn, is used with the delta approach to calculate confidence intervals for reliability and hazards. Bayes estimators are derived under both symmetric and asymmetric loss functions, with informative and non-informative priors considered, including independent gamma distributions for informative priors. The Monte Carlo Markov Chain sampling approach is employed to obtain the highest posterior density credible intervals and Bayesian point estimates for unknown parameters and reliability characteristics. A Monte Carlo simulation is conducted to assess the effectiveness of the proposed techniques, with the performances of the Bayes and maximum likelihood estimations examined using average values and mean squared errors as benchmarks. Interval estimations are compared in terms of average lengths and coverage probabilities. Real datasets are considered and examined for each topic to provide illustrative examples.

Hazard Identification and Risk Assessment for Sustainable Shipyard Floating Dock Operations: An Integrated Spherical Fuzzy Analytical Hierarchy Process and Fuzzy CoCoSo Approach

Background: This study investigated the process of selecting sustainable safety protocols for floating dock operations in shipyards by identifying potential workplace risks in emergency situations. Thirteen occupational hazards for shipyard floating dock operations were identified through a literature review and expert discussions. Methods: We incorporated four risk elements (consequence: C, frequency: F, probability: P, and number of people at risk: NP) from the Fine–Kinney and Hazard Rating Number System (HRNS) approaches as the risk assessment criteria. We obtained the importance weights of the risk assessment criteria via the Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and extended the Combined Compromise Solution (CoCoSo) method within the fuzzy framework to prioritize occupational hazards. This study demonstrated the practicality and efficiency of the proposed emergency risk assessment model for shipyard floating dock operations through a case example of occupational risk assessment. Results: The analysis results show that H4 is the occupational hazard with the highest priority, with a score of 3.553. H4 represents the hazard associated with insufficient access to the entire pool area. The second and third most important hazards are the inability of cranes to move freely in and out of the berthing dock and the inability to dispatch emergency teams. These hazards, denoted H1 and H12, follow closely behind with scores of 3.391 and 3.344, respectively. H10 is deemed the least concerning hazard, with a score of 1.802. Conclusions: Professionals can handle complex and uncertain risk assessment data more flexibly using the proposed system, which excels in accurately organizing occupational hazards.

Worker exit and suicide and overdose mortality in a cohort of male non-metal mine workers

BackgroundSuicides, overdoses, and drug-related liver diseases (deaths of despair) are greatly elevated in the mining and extraction industries compared to other industries. To better understand the drivers of these deaths, we examined suicide or overdose mortality in relation to age at worker exit among a retrospective cohort of 11,009 male mine workers from the Diesel Exhaust in Miners Study II. MethodsWe calculated age-adjusted mortality rates (1947–2015) and estimated hazard ratios (HRs) and 95% Confidence Intervals (CI) for suicide or overdose death in relation to age at worker exit (leaving work). The mining industry curtailed its workforce in the early 1980s due to economic downturn; therefore, separate models were fit for worker exit pre- and post-1980. ResultsSuicide deaths peaked between 1980 and 1989 at 67.2 per 100,000, more than double the rate in the general population. The hazard rate for suicide or overdose was 2.56 times higher for inactive versus actively employed workers (95% CI: 1.88, 3.50). Based on 187 suicides or overdoses, associations between younger age at worker exit and suicide or overdose death were null or protective before 1980. However post-1980, those who exited between ages 30–39 years had an elevated risk of death HR = 1.33, 95% CI (0.70, 2.53) and those who exited before age 30 had a HR for suicide or overdose of 1.57 (0.83, 2.96) compared to those who exited work after age 55. ConclusionWorker exit may contribute to the elevated mortality from suicide or overdose among male mine workers, particularly among younger age groups.

Joining late, leaving early? Immigrant-native disparities in labor market exit

The retirement behavior of immigrants has received limited attention, despite many countries seeing rising immigrant shares in their aging populations. Population-wide data for Sweden reveals that the retirement hazard rate is higher among immigrants as early as age 50. Approaching age 65, marginal migrant groups are instead more likely to remain in the labor force rather than conform to retirement age norms. While education and family circumstances explain little of these retirement gaps, labor market history, health, and occupation are important determinants. Immigrant-native differences are more pronounced among men than women. Findings suggest that economic necessity or opportunity, rather than varying preferences, are the driving factors behind the observed retirement disparities.

Effective comparison of two potentially crossing hazard rate curves

In survival data analysis, comparison of two hazard rate curves is critically important for evaluating a treatment effect. In many applications, the two hazard curves could potentially cross each other, violating the proportional hazards assumption in the Cox's model. In such cases, the traditional tests like the log-rank test and the Peto-Peto test that were developed based on that assumption would be ineffective. There have been some discussions in the literature on comparison of two potentially crossing hazard curves, based on either parametric modeling or nonparametric testing approaches. However, the assumed models of the existing parametric methods are often difficult to justify in practice. On the other hand, the existing nonparametric tests are usually based on the maximization with respect to an unknown crossing point, leading to complex null distributions for the corresponding test statistics. We suggest a novel method in this paper for comparing two hazard curves based on a nonparametric testing procedure. Its test statistic avoids the maximization mentioned above and consequently has the desirable asymptotic normality property under some regularity conditions. We show that the new method is effective for comparing two potentially crossing hazard curves.

A comment on the use of visually assessed fuel hazard ratings and scores for Australian fire management and research

Background Assessment of fuel hazard has become the dominant method of describing Australian forest fuel complexes, despite a lack of evidence supporting the veracity of its underpinning assumptions. Aims To analyse and discuss the merits of fuel hazard ratings and scores in representing measurable fuel characteristics, such as fuel load and fire behaviour potential. Methods Published findings were reviewed, and available data analysed to investigate the validity of the Australian fuel hazard assessment concepts. Key results Multiple published studies showed the Australian fuel hazard assessment methods to be subjective and non-replicable. All available evidence shows no relationship between fuel hazard ratings and fuel quantity. No relationship between the ratings and fire behaviour potential was found. Conclusions The principles underpinning the use of fuel hazard ratings for fuel assessment were shown to be unfounded. The ratings cannot be converted into physical fuel characteristics or fire behaviour potential, and its application in Australian fire management is unwarranted.

Dynamic evolution characteristics and hazard assessment of compound drought/waterlogging and low temperature events for maize

Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001–2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011–2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security.

Data analysis with applications in physics and engineering using XLindley model with improved adaptive Type-II progressively censored samples

Recently, a novel improved adaptive Type-II progressive censoring strategy has been suggested in order to obtain adequate data from trials that require a lengthy amount of time. Considering this scheme, this paper focuses on various classical and Bayesian estimation challenges for parameter and some reliability metrics for the XLindley distribution. Two classical estimation methods are considered from the classical perspective to get the point and interval estimations of the model parameter as well as reliability and hazard rate functions. In addition to the usual approaches, the Bayesian methodology is looked at by leveraging the squared error loss function and the Markov chain Monte Carlo technique. The Bayes point and credible intervals are obtained based on two forms of the posterior distribution. A simulation examination is implemented adopting multiple circumstances to distinguish between the conventional and Bayesian estimations. The simulation results demonstrate that the Bayesian approach using the likelihood function is superior for estimating the model parameter when compared with the other methods. In contrast, when estimating reliability metrics, it is advisable to utilize the Bayesian method with the spacings function. Two real-world data sets are analyzed to integrate the proposed approaches into practice, and the ideal progressive censoring strategy is chosen using some optimality criteria.

A NEW INVERSE TWO-PARAMETER LINDLEY DISTRIBUTION AND ITS APPLICATION

This paper proposes an inverse two-parameter Lindley distribution by utilizing the one and two parameter Lindley distributions. The key properties of the novel distribution like, its survival function, shape characteristics of the density, entropy measure, hazard rate function, stochastic ordering and stress-strength reliability were examined. Two data sets were employed in the empirical studies. Method of maximum likelihood was used to estimate the parameters. The goodness- of -fit was accessed using the HQIC, BIC, CAIC, and AIC. The proposed distribution was compared with the inverse Lindley and the inverse Akash distributions in order to access its superiority over the two distributions. Ultimately, the new inverse two parameter Lindley distribution was found to be superior by providing a better fit.

Parameter estimation of Chen distribution under improved adaptive type-II progressive censoring

Parameter estimation of Chen distribution under improved adaptive type-II progressive censoring

Participation in Special Olympics reduces the rate for developing diabetes in adults with intellectual and developmental disabilities.

Adults with intellectual and developmental disabilities (IDD) have a significantly higher prevalence of Type 2 diabetes than the general population. Evidence that lifestyle and/or behavioural interventions, such as participation in Special Olympics, decreases the risk of developing diabetes in adults with IDD could help minimize health disparities and promote overall health in this population. This was a 20-year retrospective cohort study of adults with IDD (30-39 years) in the province of Ontario, Canada, that compared hazard rates of diabetes among Special Olympics participants (n = 4145) to non-participants (n = 31,009) using administrative health databases housed at ICES. Using cox proportional hazard models, crude and adjusted hazard ratios were calculated for the association between the primary independent variable (Special Olympics participation status) and the dependent variable (incident diabetes cases). After controlling for other variables, the hazard ratio comparing rates for developing diabetes between Special Olympics participants and non-participants was 0.85. This represents a 15% reduction in the hazard among Special Olympics participants when followed for up to 20 years. This result was statistically significant and represents a small effect size. Special Olympics could be considered a complex intervention that promotes physical activity engagement through sport participation, health screenings, and the promotion of healthy eating habits through educational initiatives. This study provides evidence that Special Olympics participation decreases the rate for developing diabetes.

Socioeconomic status and risk of cardiovascular diseases in patients with retinal vein occlusion: A Danish nationwide cohort study

PurposeThis study examines the association between socioeconomic status and the occurrence of both cardiovascular diseases and mortality following retinal vein occlusion (RVO), a condition known to elevate the risk of cardiovascular health issues. Despite recognition of the impact socioeconomic factors might have on a patient's course through the healthcare system, its association with cardiovascular outcomes post-RVO has not previously been thoroughly explored. DesignNationwide cohort study. MethodsWe included all subjects with RVO from the Danish nationwide registries diagnosed between 2000 and 2018. Information was combined using a personal identification number each Danish resident is given at birth or upon immigration. We categorized participants by their socioeconomic levels to evaluate the five-year incidence rate of cardiovascular conditions and overall mortality. The analysis was further refined by sex and age groups to elucidate any differential impacts. Furthermore, the cumulative incidence was estimated using the Aalen-Johansen estimator and the differences between the socioeconomic groups were quantified using a cox proportional hazard model. Key quantitative results were summarized, including hazard rate ratios (HR), confidence intervals (CI), and p-values ResultsThe cohort was comprised of 14,041 individuals with RVO. Our analysis revealed a relationship between educational attainment and cardiovascular health outcomes. Lower education levels were consistently linked with heightened risks of mortality, diabetes, ischemic heart disease, myocardial infarction, and peripheral artery disease following RVO. Individuals with a middle educational level also exhibited a higher prevalence of peripheral artery disease when compared to those with the highest education levels. In terms of income, the lowest earners faced increased risks across several categories, including mortality, diabetes, heart failure, ischemic heart disease, myocardial infarction, and peripheral artery disease, when compared to the highest-income earners. The middle-income group also showed elevated risks for mortality, heart failure, and peripheral artery disease. Being unmarried was additionally associated with higher risks of mortality and heart failure compared to being married. ConclusionSocioeconomic status was significantly associated with clinically relevant differences in post-RVO risk of cardiovascular diseases and death. Our data suggests that socioeconomic factors should be considered in the attempt to reduce the risk of cardiovascular disease and death in patients with RVO.

The effect of dialysis modality on annual mortality: A prospective cohort study

Despite numerous studies on the effect of each dialysis modality on mortality, the issue remains controversial. We investigated the hazard rate of mortality in patients with incident end-stage renal disease (ESRD) concerning initial dialysis modality (hemodialysis vs. peritoneal dialysis). Using a nationwide, multicenter, prospective cohort in South Korea, we studied 2207 patients, of which 1647 (74.6%) underwent hemodialysis. We employed the weighted Fine and Gray model over the follow-up period using inverse probability of treatment and censoring weighting. Landmark analysis was used for identifying the changing effect of dialysis modality on individuals who remained event-free at each landmark point. No significant difference in hazard rate was observed overall. However, the peritoneal dialysis group had a significantly higher hazard rate than the hemodialysis group among patients under 65 years after 4- and 5- year follow-up. A similar pattern was observed among those with diabetes mellitus. Landmark analysis also showed the higher hazard rate for peritoneal dialysis at 2 years for the education-others group and at 3 years for the married group. These findings may inform dialysis modality decisions, suggesting a preference for hemodialysis in young patients with diabetes, especially for follow-ups longer than 3 years.

Familywise error for multiple time-to-event endpoints in a group sequential design.

We investigate the familywise error rate (FWER) for time-to-event endpoints evaluated using a group sequential design with a hierarchical testing procedure for secondary endpoints. We show that, in this setup, the correlation between the log-rank test statistics at interim and at end of study is not congruent with the canonical correlation derived for normal-distributed endpoints. We show, both theoretically and by simulation, that the correlation also depends on the level of censoring, the hazard rates of the endpoints, and the hazard ratio. To optimize operating characteristics in this complex scenario, we propose a simulation-based method to assess the FWER which, better than the alpha-spending approach, can inform the choice of critical values for testing secondary endpoints.

Advances in medical data modeling: A new logarithmic beta generated family of distributions with theory and inference

This paper introduces a novel logarithmic beta generated family of distributions, designed specifically for the nuanced analysis of medical data. Through meticulous development, we have establish a comprehensive suite of distributional properties, including quantiles, moments, and hazard rate functions. Central to our contributions is the exploration of the power function distribution, a pivotal sub-model within this innovative family, demonstrating unparalleled flexibility across a broad spectrum of data types. Our investigation delves into seven prominent parameter estimation techniques, rigorously assessing their efficacy through both simulation studies and real-life medical datasets. This comparative analysis highlights the superior performance of the proposed model against existing standards, particularly through improved goodness-of-fit measures across four distinct medical science lifetime datasets. The findings highlight the new model's dominance in capturing complex data patterns, attributable to its adaptable density and hazard rate functions. These attributes significantly enhance modeling capabilities, paving the way for more precise and reliable medical research outcomes. The study concludes by emphasizing the model's potential in advancing statistical methodologies for medical science, backed by theoretical and empirical validations of its superior performance and versatility.