Poisson Distribution Research Articles

Caries trajectories from childhood to adolescence: Analysis of data from a nationwide school dental service.

The aim of the study was to assess patterns of longitudinal changes in caries status among school-going children in Singapore. Dental records for a single cohort of students who received dental examinations in six standard examination years between 2009 and 2017 were analysed (n = 24 699). Group-based trajectory modelling with a zero-inflated Poisson distribution was carried out to determine dental caries trajectories in the permanent dentition. Associations between sociodemographic factors and trajectory group membership were assessed using multinomial logistic regression. The predicted population distribution across the four caries trajectory groups identified was 65.0% ('none'), 16.8% ('low'), 14.8% ('medium') and 3.4% ('high'). The 'none' trajectory group had a decayed, missing and filled teeth (DMFT) score of 0 throughout the 8 years. Higher baseline DMFT counts and nonlinear increases in DMFT scores were noted for the 'low', 'medium' and 'high' trajectory groups. The correlation coefficient between DMFT counts in years 6 and 8 was 0.91, as compared to 0.77 between baseline and year 1. Factors associated with the 'high' caries trajectory include lower socio-economic status, female gender, Chinese race (compared to the Indian race), enrolment in primary schools in the Eastern and Western regions of Singapore, and enrolment in public secondary schools. Under a nationwide school dental service, four trajectory patterns of caries counts in the permanent dentition were identified over 8 years. Among students in the 'low', 'medium' and 'high' trajectory groups, greater caries increment was noted during the transition from primary to secondary school. The correlation between DMFT counts in successive examinations was stronger in older than younger ages.

Spatio-temporal analysis of pulmonary tuberculosis in Astambul District, South Kalimantan, Indonesia 2020-2021

Respiratory tuberculosis (TB) remains a significant global health concern and ranks among the top 10 leading causes of death worldwide, following human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Globally, it is estimated that 1.2 billion people are at risk of being infected with tuberculosis, and Indonesia is the country that contributes the third-highest number of TB cases worldwide, behind China and India. Tuberculosis is still a significant issue in South Kalimantan, notably in Banjar Regency. This study is a descriptive study that intends to identify respiratory TB clusters spatially and temporally in Astambul District during 2020–2021 with the SatScan application, which is visualized as a map of respiratory TB clusters using the Quantum GIS 3.28 application. Data on respiratory TB cases collected from the Banjar District Health Office were examined using retrospective space-time scan statistics, employing a Poisson probability model for analysis. This study found 4 respiratory TB clusters, and 2 of them had significant results, namely in 2020 (RR=6.90, p-value=0.000030) and 2021 (RR=5.27, p-value=0.00003). Factors that affect these clusters are population density, the physical condition of houses, humidity levels, and the availability of health facilities.

Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information

The time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state-specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, eventually advancing our understanding of the dynamic transcription regulation during development and disease.

HIV incidence and recreational drug use among men who have sex with men aged 18-24 years in Tianjin, China: a retrospective cohort study.

Recreational drug use has been identified as a significant risk factor for the transmission of human immunodeficiency virus (HIV). This behavior is particularly prevalent among young men who have sex with men (YMSM). However, limited research has been conducted to investigate the correlation between recreational drug use and HIV incidence within this population. To examine HIV incidence and explore the association between recreational drug use and HIV incidence among YMSM. A retrospective cohort study was conducted by a local non-governmental organization (NGO) among MSM aged 18-24 years from October 2017 to December 2023 in Tianjin, China. Participants were included if they had at least two HIV test records during this period. HIV incidence density and its 95% confidence interval (CI) were calculated using person-years (PYs) with a Poisson distribution. Risk factors for HIV incidence were identified using univariable and multivariable Cox proportional hazards regression models. A total of 56 HIV seroconversions were reported with 2901.9 PYs, resulting in an overall incidence density of 1.9/100 PYs (95% CI: 1.4-2.4). Among YMSM who do not use recreational drugs (YMSM-URD), the incidence density was 1.1/100 PYs (95% CI: 0.5-1.6), while it was 2.8/100 PYs (95% CI: 1.9-3.7) among those who do use recreational drugs (YMSM-RD). Adjusted for confounders, factors associated with HIV infection included recreational drug use (adjusted hazard ratio = 2.6, 95% CI = 1.4-4.7) and unprotected anal intercourse in the past six months (adjusted hazard ratio = 2.3, 95% CI = 1.2-4.4). Recreational drug use and unprotected anal intercourse significantly increase the risk of HIV infection. To effectively mitigate the spread of HIV, interventions should focus on these risk factors by employing HIV counseling and testing clinics, collaborating with NGOs focused on MSM, and establishing peer education programs aimed at YMSM.

Prospective longitudinal study of men who have sex with men and transgender women to determine HIV incidence in two provinces in Thailand.

In Thailand, HIV transmission is well characterized in large urban centers such as Bangkok and Chiang Mai but less so outside of these areas. The main purpose of this study was to assess HIV incidence and associated risk factors in Nakhon Ratchasima and Ratchaburi. Participants assigned male sex at birth were enrolled in this prospective observational cohort study between November 2017 and July 2018. HIV and syphilis testing and sociobehavioral questionnaires were administered over 18 months. HIV incidence rates and 95% confidence intervals (CIs) were estimated using a Poisson distribution. Cox proportional hazards models were used to estimate unadjusted and adjusted hazard ratios (aHRs) and 95% CIs for associations between potential risk factors and HIV seroconversion. A total of 1003 participants were enrolled. Overall HIV incidence was 1.56 per 100 person-years (95% CI:1.02-2.44) and similar at both sites. In the fully adjusted model, sex with a sex worker in the past six months was associated with reduced risk of seroconversion (aHR:0.10, 95% CI:0.01-0.77). In the reduced adjusted model, receptive anal sex (aHR:3.40, 95% CI:1.32-8.74) and STI diagnosis in the past six months (aHR:3.58, 95% CI:1.19-10.76) were associated with seroconversion, while sex with a sex worker in the past six months was associated with reduced risk of seroconversion (aHR:0.11, 95% CI:0.02-0.67). Additionally, 56% reported interest in taking PrEP and 82% reported willingness to participate in a hypothetical future vaccine trial. Recent receptive anal sex practices were associated with HIV acquisition in these populations, highlighting the continued need for interventions encouraging safer anal sex practices to reduce HIV incidence.

Cervical cancer screening rates in females living with HIV at three healthcare settings in the United States, 2010-2019.

Females living with human immunodeficiency virus (FLWHIV) are at increased risk of cervical cancer and U.S. guidelines, first published in 2009 and updated since then, recommend more frequent screening in this population. We examined screening rates among FLWHIV in the U.S. during 2010-2019. This cohort study included 18-89-year-old FLWHIV during 2010-2019 at three U.S. healthcare settings. Sociodemographics, comorbidities, and cervical cancer screening tests were ascertained from administrative and clinical databases. We reported cervical cancer screening rates overall and by modality. Generalized estimating equations with Poisson distribution were used to estimate screening rate ratios (SRRs) and 95% confidence intervals (CIs) for the associations between screening rates and calendar year, age, race and ethnicity, and comorbidity. Among 3,556 FLWHIV, a total of 7,704 cervical cancer screening tests were received over 18,605 person-years during 2010-2019 (screening rate = 41.4 per 100 person-years). Relatively lower screening rates were associated with later calendar years (SRR = 0.71 [95% CI 0.68-0.75] for 2017-2019 versus 2010-2013), older age (SRR = 0.82 [95% CI 0.74-0.89] for 50-65-year-olds versus 18-29-year-olds), non-Hispanic white race versus non-Hispanic Black race (SRR = 0.89 [95% CI 0.81-0.98]) and greater comorbidity burden (SRR = 0.89 [95% CI 0.82-0.98] for ≥ 9 versus 0-6 comorbidity score). The decrease in cervical cancer screening rates during 2010-2019 in this large cohort of FLWHIV may be explained at least partly by guideline changes during the study period recommending longer screening intervals. Our findings of relatively lower screening rates in FLWHIV who were non-Hispanic white, older, and with greater comorbidity burden should be confirmed in other U.S.

Monitoring of zero-inflated COM-Poisson processes

This paper investigates control charts for count data with a significant number of zeros. The proposed models are generalized to handle data exhibiting different types of dispersion, i.e. equidispersion, overdispersion, and underdispersion. To this effect, the zero-inflated Conway–Maxwell Poisson (ZICMP) distribution is employed. A Shewhart and an exponentially weighted moving average (EWMA) control chart are developed, referred to as ZICMP-Shewhart and ZICMP-EWMA charts, respectively. The ZICMP distribution incorporates various distributions, including the Conway–Maxwell–Poisson (COM-Poisson) and zero-inflated Poisson (ZIP), Geometric, and Bernoulli distributions, as special cases. Consequently, the flexibility and versatility of the ZICMP distribution enhance the applicability of the proposed control charts, thereby providing practitioners with an adaptable tool suitable for various scenarios.The control charts are examined for their ability to detect upward shifts in the process mean level, and their statistical performance is evaluated in terms of the average run-length (ARL). Through a simulation study, we demonstrate that the ZICMP-Shewhart chart is more effective for monitoring over-dispersed data, while the ZICMP-EWMA chart is more suitable for under-dispersed data. Finally, we provide two real-life examples to illustrate the applicability of the proposed charts.

Assessing the impact of ecological, climatic, and socioeconomic factors on age-specific malaria incidence in India: a mixed-model approach using the Global Burden of Disease Study (2010–2019)

BackgroundMalaria continues to be a critical public health concern in India, predominantly driven by complex interplays of ecological, climatic, and socioeconomic factors.MethodsThis study aimed to assess the association between climatic variables (temperature and precipitation) and malaria incidence across India from 2010 to 2019, utilizing data from the Global Historical Weather and Climate Data for climate metrics and the Global Burden of Disease Study for malaria incidence rates. Generalized Linear Mixed Models (GLMMs) with a Poisson distribution were employed to analyze the data, adjusting for socio-economic status, as indexed by the Human Development Index (HDI).ResultsThe results indicated a declining trend in both the number of malaria cases and age-specific incidence rates (ASIR) over the study period. In 2010, India reported approximately 20.7 million cases with an ASIR of 1688.86 per 100,000 population, which significantly reduced to 9.8 million cases and an ASIR of 700.80 by 2019. High malaria incidence was consistently observed in the states of Jharkhand and Odisha, whereas Sikkim reported the lowest numbers. Statistical analysis identified significant associations between malaria incidence and both temperature deviations and precipitation levels, with variations also linked to HDI, suggesting better detection and reporting capabilities in more developed areas.ConclusionThe study underscores the critical interactions between climatic variables and socio-economic factors in shaping the trends of malaria incidence across India. These findings highlight the necessity for adaptive, localized public health strategies that integrate environmental monitoring with socio-economic data to efficiently predict and manage malaria outbreaks.

Maximum-likelihood estimation of the Po-MDDRCINAR(p) model with analysis of a COVID-19 data

Integer-valued data are frequently encountered in time series studies. A pth-order mixed dependence-driven random coefficient integer-valued autoregressive time series model (Po-MDDRCINAR(p)) in view of binomial and negative binomial operators, where the innovation sequence follows a Poisson distribution, is investigated to provide meaningful theoretical explanations. Strict stationary and ergodicity of the model are demonstrated. Furthermore, the conditional least-squares and conditional maximum-likelihood methods are adopted to estimate the parameters, where the asymptotic characterization of the estimators is derived. Finite-sample properties of the conditional maximum-likelihood estimator are examined in relation to the widely used conditional least-squares estimator. The conclusion is that, if the Poisson assumption of the innovation sequence can be justified, conditional maximum-likelihood method performs better in terms of MADE and MSE. Finally, the practical performance of the model is illustrated by a set of COVID-19 data of suspected cases in China with a comparison with relevant models that exist so far in the literature.

Modeling EV dynamic wireless charging loads and constructing risk constrained operating strategy for associated distribution systems

Dynamic wireless charging (DWC) is an emerging technology that enables the charging of electric vehicles (EVs) while they are in motion. However, previous load modeling methods have not thoroughly explored the detailed analysis of DWC load characteristics. Existing research only considers the single-node supply mode for dynamic wireless charging roads (DWCRs), and the assessment of operational risks arising from the uncertain DWC loads has not been addressed. This paper begins by conducting an equivalent circuit analysis of a typical EV DWC system with multiple segmented coils. We present a more accurate trapezoidal power model for a single EV. Subsequently, we model the aggregated EV DWC load, accounting for traffic flow and headway using Poisson and negative exponential distribution functions, respectively. In the operation process, we consider a multi-node supply mode for DWCRs. To address the inaccuracy of long-term predictions, we propose a rolling optimization model to coordinate DWC and renewables with heterogeneous uncertainties by introducing a risk metric to manage potential uncertain risks. The proposed optimization model is transformed into a mixed-integer second-order cone programming (MISOCP) problem after convex relaxation. Finally, we conduct case studies to validate the proposed methods.

Estimating an adjusted risk difference in a cluster randomized trial with individual-level analyses.

In cluster randomized trials (CRTs) with a binary outcome, intervention effects are usually reported as odds ratios, but the CONSORT statement advocates reporting both a relative and an absolute intervention effect. With a simulation study, we assessed several methods to estimate a risk difference (RD) in the framework of a CRT with adjustment on both individual- and cluster-level covariates. We considered both a conditional approach (with the generalized linear mixed model [GLMM]) and a marginal approach (with the generalized estimating equation [GEE]). For both approaches, we considered the Gaussian, binomial, and Poisson distributions. When considering the binomial or Poisson distribution, we used the g-computation method to estimate the RD. Convergence problems were observed with the GEE approach, especially with low intra-cluster coefficient correlation values, small number of clusters, small mean cluster size, high number of covariates, and prevalences close to 0. All methods reported no bias. The Gaussian distribution with both approaches and binomial and Poisson distributions with the GEE approach had satisfactory results in estimating the standard error. Results for type I error and coverage rates were better with the GEE than GLMM approach. We recommend using the Gaussian distribution because of its ease of use (the RD is estimated in one step only). The GEE approach should be preferred and replaced with the GLMM approach in cases of convergence problems.

Evidence of the Quantum Optical Nature of High-Harmonic Generation

High-harmonic generation is a light up-conversion process occurring in a strong laser field, leading to coherent bursts of extreme ultrashort broadband radiation [Lewenstein , Phys. Rev. A , 2117 (1994)]. As a new perspective, we propose that ultrafast strong-field electronic or photonic processes such as high-harmonic generation can potentially generate nonclassical states of light well before the decoherence of the system occurs [Gorlach , Nat. Commun. , 4598 (2020); Stammer ., Phys. Rev. Lett. , 123603 (2022)]. This could address fundamental challenges in quantum technology such as scalability, decoherence, or the generation of massively entangled states [Lewenstein , Luca Argenti Michael Chini, 27 (2024)]. Here, we report experimental evidence of the nonclassical nature of the harmonic emission in several semiconductors excited by a femtosecond infrared laser. By investigating single- and double-beam intensity cross-correlation [Loudon, Rep. Prog. Phys. , 913 (1980)], we measure characteristic nonclassical features in the single-photon statistics. We observe two-mode squeezing in the generated harmonic radiation, which depends on the laser intensity that governs the transition from super-Poissonian to Poissonian photon statistics. The measured violation of the Cauchy-Schwarz inequality realizes a direct test of multipartite entanglement in high-harmonic generation [Wasak, Phys. Rev. A , 033616 (2014)]. This result is supported by the theory of multimodal detection and the Hamiltonian from which the effective squeezing modes of the harmonics can be derived [Gonoskov , Phys. Rev. B , 125110 (2024); Christ New J. Phys. , 033027 (2011)]. With this work, we show experimentally that high-harmonic generation is a new quantum bosonic platform that intrinsically produces nonclassical states of light with unique features such as multipartite broadband entanglement or multimode squeezing. The source operates at room temperature, using standard semiconductors and a standard commercial fiber laser, opening up new routes for the quantum industry, such as optical quantum computing, communication, and imaging. Published by the American Physical Society 2024

Fetal growth ultrasound in obese patients for the detection of growth abnormalities.

To examine the impact of maternal obesity on fetal growth abnormalities including fetal growth restriction (FGR) and large for gestational age (LGA) fetuses. Secondary analysis from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b). The study excluded individuals with pregestational or gestational diabetes, chronic hypertension, and other major maternal medical conditions. Ultrasound assessments were performed at 16 - 21 and 22 - 29 weeks of gestation. Our exposure was the presence of pre-pregnancy obesity. Our primary outcome was rates of fetal growth abnormalities identified by ultrasound, defined as FGR (estimated fetal weight [EFW] or abdominal circumference < 10th percentile) or LGA (EFW > 90 th percentile) among obese compared to non-obese women. A secondary analysis was performed after limiting ultrasound performed from 28-29 weeks. To estimate adjusted relative risks (aRR) with 95% confidence intervals (95%CIs), we used generalized linear models with Poisson distribution and log link using robust error variance, adjusting for the predefined covariates. Of 7,354 participants, 1,443 (19.6%) had pre-pregnancy obesity while 5,911 (80.4%) did not. Pre-pregnancy obesity compared with normal weight was associated with an increased risk of fetal growth abnormalities both at 16-21 weeks (16.0% vs. 13.2%; aRR 1.23; 95%CI 1.06-1.42) and 22-29 weeks (16.0% vs. 12.1%; aRR 1.33; 95%CI 1.14-1.54). Furthermore, pre-pregnancy obesity compared with normal weight was associated with an increased risk of LGA both at 16-21 weeks (12.5% vs. 10.3%; aRR 1.24; 95%CI 1.05-1.47) and 22-29 weeks (10.6% vs. 6.9%; aRR 1.66; 95%CI 1.38-2.01). In a secondary analysis limited to the ultrasound 28-29 weeks, both fetal growth abnormalities and LGA were associated with the presence of obesity. In any of the analyses, pre-pregnancy obesity was not associated with FGR compared to normal weight. Maternal obesity is associated with an increased risk of fetal growth abnormalities and LGA fetuses.

Microcavity Array-Based Digital SERS Chip for Rapid and Accurate Label-free Quantitative Detection of Live Bacteria.

In this study, we developed a novel digital surface-enhanced Raman spectroscopy (SERS) chip that integrates an inverted pyramid microcavity array, a microchannel cover plate, and a multilayer gold nanoparticle (AuNP) SERS substrate. This innovative design exploits the synergistic effects of the microcavity array and the microchannel to enable rapid and large-scale digital discretization of bacterial suspensions. The concentration effect of the picoliter cavities, combined with the superior Raman enhancement effect of the multilayer AuNP SERS substrate, allows for the precise identification of live bacteria within the microcavities through in situ and label-free SERS testing after a short incubation period. By counting the resulting positive or negative signals, the concentration of the target analyte can be directly determined via Poisson statistics. Experimental results demonstrate that this method enables the accurate quantification of Escherichia coli (E. coli) BL21 within a 4-h incubation period. Compared with traditional analog SERS detection methods, our proposed digital SERS detection strategy reduces the impact of signal intensity fluctuations, thereby significantly improving detection efficiency and accuracy. We believe that this digital SERS chip has great application prospects in the fields of bacterial detection, antibiotic resistance analysis, and cellular dynamics monitoring.

Modeling of Car Insurance Premiums Using the Bayes Method with Poisson and Exponential Distributions

Car insurance is included in the general insurance category. Determination of general insurance premiums can be done through various approaches, one of which is the Bayes method used in this study. The Poisson distribution is chosen to model the frequency of claims, while the Exponential distribution is used to model the amount of claims. Insurance premiums are calculated by multiplying the expected frequency of claims by the expected amount of claims. Based on the results of the analysis of car insurance data using the Bayes method, it was found that the highest premium rates were for Mercedes brand vehicles, while the lowest rates were for Saab brand vehicles. The results of this calculation can be used by insurance companies as a reference in managing car insurance reserve funds.

Application of the Neutrosophic Poisson Distribution Series on the Harmonic Subclass of Analytic Functions using the Salagean Derivative Operator

The authors explore the innovative application of the Neutrosophic series, particularly the Neutrosophic Poisson Distribution Series (NPDS), to investigate various indeterminacy or uncertainties inherent in the classical univalent harmonic function class. The Neutrosophic Poisson Distribution Series is equipped with a Salangean derivative operator and convoluted with analytic univalent harmonic function class to derive new properties, such as inclusion relation, and coefficient inequalities for star-likeness. The results obtained demonstrate the effectiveness of this approach in capturing the inherent uncertainties and complexities associated with harmonic functions. There are several other areas of importance of our results that can be unlocked by computer engineers, scientists, and other experts. In this investigation, some of these indeterminacy and complexities are revealed using graphs by employing Python software tools. This novel integration enhances the analytical techniques available and opens a new stairway for future research in neutrosophic series and geometric function theory.

Modeling of Long-term Afterglow Counterparts to Gravitational Wave Events: The Full View of GRB 170817A

The arrival of gravitational wave astronomy and a growing number of time-domain-focused observatories are set to lead to an increasing number of detections of short gamma-ray bursts (GRBs) launched with a moderate inclination to Earth. Being nearby events, these are also prime candidates for very long-term follow-up campaigns and very long-baseline interferometry, which has implications for multi-messenger modeling, data analysis, and statistical inference methods applied to these sources. Here, we present a comprehensive modeling update that directly incorporates into afterglowpy astrometric observations of the GRB position, Poissonian statistics for faint sources, and modeling of a trans-relativistic population of electrons. We use the revolutionary event GW170817 to demonstrate the impact of these extensions both for the best-fit physics parameters and model selection methods that assess the statistical significance of additional late-time emission components. By including in our analysis the latest Chandra X-ray observations of GRB 170817A, we find only weak evidence (≲2σ) for a new emission component at late times, which makes for a slightly more natural fit to the centroid evolution and prediction for the external medium density.

New Robust Estimation in Compound Exponential Weibull-Poisson Distribution for both contaminated and non-contaminated Data

تناول البحث مقارنة طريقتين لتقدير معلمات توزيع بواسون–ويبل الاسي المركب ذو الاربع معلمات هي طريقة الامكان الاعظم الاعتيادية وخوارزمية Downhill Simplex وذلك بالاعتماد على حالتين للبيانات الاولى تفترض حالة البيانات الطبيعية (غير الملوثة) والثانية تفترض حالة تلوث البيانات. ومن خلال تنفيذ تجربة المحاكاة لحجوم عينات مختلفة وقيم اولية للمعلمات وتحت مستويات مختلفة للتلويث تبين ان خوارزمية Downhill Simplexكانت هي الافضل في تقدير المعلمات والدالة الاحتمالية ودالة المعولية للتوزيع المركب في حالتي البيانات الطبيعية والملوثة.

Assessment of healthy worker survivor bias among middle-aged manufacturing employees in Korea

Abstract Background Healthy Worker Survivor Bias (HWSB) describes a selection bias where those continuously employed tend to exhibit superior health outcomes compared to their unemployed counterparts. This bias challenges for adjustments due to its time-dependent nature. This study aims to evaluate the extent of HWSB related to changes in employment status among middle-aged manufacturing workers using comprehensive national data from Korea. Methods The study analyzed data from the National Health Insurance Service, targeting individuals aged 40-49 who maintained consistent insurance coverage-either as manufacturing employees or non-employees (self-employed or unemployed)-from 2008 to 2010. The observation period spanned from January 2011 to December 2022, with all-cause mortality as the primary outcome. We employed landmark analysis to quantify HWSB related to employment status by comparing age-standardized mortality ratios between employees and the general population from the initial assessment to the last landmark period (up to 7 years), calculating 95% confidence intervals (CI) using Poisson distribution. Results The cohort included 4,621,983 participants (726,616 manufacturing employees and 3,895,367 (84.3%) non-employees). Throughout an average follow-up of 10.9 years (43.1% male; median age of 44), there were 116,418 deaths (11,530 (1.6%) among manufacturing employees and 104,888 (2.7%) among non-employees). Analysis revealed a consistent linear increase in HWSB over time. Notably, the bias was more pronounced among females (7-year HWSB-ES [95% CI]: 0.27 [0.17-0.37]) compared to males (7-year HWSB-ES [95% CI]: 0.18 [0.14-0.22]). Conclusions This study quantifies the extent of HWSB among middle-aged manufacturing workers in Korea, highlighting its significant impact, notably in females. These findings are vital for occupational health research, emphasizing the necessity to consider HWSB to prevent the underestimation of health risks associated with hazardous exposures. Key messages • Findings suggest HWSB is more pronounced in female manufacturing workers, indicating the need for targeted occupational health interventions. • The study underscores the importance of accounting for HWSB in occupational health studies to avoid underestimating health risks associated with workplace exposures.

LDL-C target attainment and statin use in patients with ASCVD, familial hypercholesterolaemia, and those otherwise at moderate/high-risk of ASCVD in australian general practice (SCOPE-GP)

Abstract Background The proportion of Australian general practice patients not attaining low-density lipoprotein cholesterol (LDL-C) goals is not well described. The SCOPE-GP study evaluated duration of LDL-C exceeding treatment goals, statin use and discontinuation rates in patients with atherosclerotic cardiovascular disease (ASCVD), without ASCVD events but at moderate/high-risk, and familial hypercholesterolemia (FH). Methods This population-based retrospective cohort study used de-identified medical records of adult patients captured in the IQVIA GP-EMR database. Patients were indexed at the earliest date of ASCVD, FH, or hyperlipidemia diagnosis from January 1, 2010 to June 30, 2022, with a variable follow-up period from index to June 30, 2022. Ascertainment of study cohort was based on the 2021 ESC/EAS guidelines and Framingham Risk Equation. LDL-C test result levels were extracted per patient, summarised and analysed. Patients were stratified based on their test results at latest results/closest to censor date. Average time above target levels is the proportion of number of days above LDL-C target levels over total number of follow-up days for each patient. A Poisson distribution was fitted to estimate 95% confidence interval. Lipid lowering therapies (LLT) were stratified by (ASCVD, FH and moderate/high risk of CVD) at last visit/censor date. Combination of LLT were defined as prescriptions for fixed dose combinations or ≥2 separate scripts of LLT from different classes within 180 days from last visit/censor date (look back period). Discontinuation is defined as no statins were prescribed again after the last script for 270 days or more, limited to the first occurrence. Results The average time with LDL-C levels above target was 1077.5 days in ASCVD patients (n=2,688), 938.6 days in moderate-risk ASCVD (n=37,003), 980.9 days in high-risk ASCVD (n=85,199), and 900.8 days in FH (n=279). The proportion of days above target level for all four cohorts is described in Figure 1. Among 107,552 patients with recorded LDL-C levels ≥1.8mmol/L at their last visit/censor date, 30.9% were on statin monotherapy and 1.0% were on statin+ezetimibe. Statin use was highest in ASCVD patients (65.4%), followed by FH (49.5%), high risk (40.0%) and moderate risk subjects (29.5%) (Table 1). Statin discontinuation rates were 60.3%, 76.3, 66.1%, and 65.8% in these groups, respectively. The predominant reason for statin discontinuation was non-specific adverse drug reactions (12.2% ASCVD, 11.7% moderate-risk, 13.2% high-risk and 17.9% FH). The proportion of subjects contraindicated to statins was highest in moderate risk subjects (0.05%). Conclusions Prolonged periods of elevated LDL-C and high statin discontinuation rates were prevalent among at-risk individuals in Australian primary care. This underscores a significant ASCVD burden that could be prevented with more active lipid management.