Zero-inflated Negative Binomial Model Research Articles

Reconsidering screening thresholds in health assessments for obstructive sleep apnea using operational and safety incident data

The rail industry in Australia screens workers for probable obstructive sleep apnea (OSA) due to known safety risks. However, existing criteria to trigger screening only identify a small proportion of workers with OSA. The current study sought to examine the relationship between OSA risk and rail incidents in real-world data from Australian train drivers, and conducted a proof of concept analysis to determine whether more conservative screening criteria are justified. Health assessment (2016–2018) and subsequent rail incident data (2016–2020) were collected from two passenger rail service providers. Predictors included OSA status (confirmed no OSA with a sleep study, controlled OSA, unknown OSA [no recorded sleep assessment data] and confirmed OSA with no indication of treatment); OSA risk according to the current Standard, and OSA risk according to more conservative clinical markers (BMI threshold and cardiometabolic burden). Coded rail safety incidents involving the train driver were included. Data were analysed using zero-inflated negative binomial models to account for over-dispersion with high 0 counts, and rail safety incidents are reported using Incidence Risk Ratios (IRRs). A total of 751 train drivers, typically middle-aged, overweight to obese and mostly men, were included in analyses. There were 43 (5.7%) drivers with confirmed OSA, 62 (8.2%) with controlled OSA, 13 (1.7%) with confirmed no OSA and 633 (84.4%) drivers with unknown OSA. Of the 633 train drivers with unknown OSA status, 21 (3.3%) met ‘at risk’ criteria for OSA according to the Standard, and incidents were 61% greater (IRR: 1.61, 95% Confidence Interval (CI) 1.02–2.56) in the years following their health assessment compared to drivers who did not meet ‘at risk’ criteria. A more conservative OSA risk status using lower BMI threshold and cardiometabolic burden identified an additional 30 ‘at risk’ train drivers who had 46% greater incidents compared to drivers who did not meet risk criteria (IRR (95% CI) 1.46 (1.00–2.13)). Our more conservative OSA risk criteria identified more workers, with greater prospective incidents. These findings suggest that existing validated tools could be considered in future iterations of the Standard in order to more sensitively screen for OSA.

Assessing economic performance and aviation accidents using zero-inflated and over-dispersed panel data models

This paper explores the link between air transport safety and profitability. Traditionally, the empirical literature has used Poisson regression models to estimate the expected number of accidents given the profitability of the airline. However, there are two major deficiencies in this analysis related to the statistical properties of the data. First, the equi-dispersion assumed in Poisson models hardly holds in the airline data. Second, accidents are rare cases, so the data has an excess of zeros. In this paper, we propose the use of a zero-inflated negative binomial model to deal with these shortcomings. Our results show several interesting facts. On the one hand, they show that airlines with higher levels of profitability are less likely to have an accident. On the other hand, when an accident occurs, there is a higher expected number of accidents in airlines with higher profitability. Finally, the severity of an accident has an inverse relationship with profitability.

Green innovation: the role of government subsidies under the system of digital finance -based on a zero-inflated negative binomial model

Green innovation: the role of government subsidies under the system of digital finance -based on a zero-inflated negative binomial model

Increased Postoperative Opioid Consumption in the Presence of Co-administration of 5-HT3 Antagonists with Acetaminophen: A Hospital Registry Study.

Acetaminophen and 5-hydroxytryptamine-type-3 (5-HT3) receptor antagonists are administered as standard prophylaxes for postoperative pain, nausea, and vomiting. Preclinical studies however suggest that 5-HT3 antagonists may compromise acetaminophen's analgesic effect. This hospital registry study investigates whether 5-HT3 antagonists mitigate the analgesic effect of prophylactic acetaminophen in a perioperative setting. This study included 55,016 adult patients undergoing general anesthesia for ambulatory procedures at a tertiary healthcare center in Massachusetts, United States of America, from 2015 to 2022. Using binary exposure variables and a comprehensive selection of pre-planned patient- and procedure-related covariates for confounder control, we investigated whether intraoperative 5-HT3 antagonists affected the association between pre- or intraoperative acetaminophen and postoperative opioid consumption, gauged by opioid dose in mg oral morphine equivalents (OME) administered in the post-anesthesia care unit (PACU). A multivariable, zero-inflated negative binomial regression model was applied. 3,166 (5.8%) patients received only acetaminophen, 15,438 (28.1%) only 5-HT3 antagonists, 31,850 (57.9%) both drugs, and 4,562 (8.3%) neither drug. The median PACU opioid dose was 7.5 mg OME (interquartile range 7.5 to 14.3 mg OME) among 16,640/55,016 (30.3%) patients who received opioids and the average opioid dose was 3.2 mg OME across all patients (maximum cumulative dose: 20.4 mg OME). Acetaminophen administration was associated with a 5.5% (95%CI -9.6% to -1.4%;p=0.009; adjusted absolute difference -0.19 mg OME;95%CI -0.33 to -0.05;p=0.009) reduction in opioid consumption among patients who did not receive a 5-HT3 antagonist, while there was no effect in patients who received a 5-HT3 antagonist (adjusted absolute difference 0.00 mg OME; 95%CI -0.06 to 0.05;p=0.93,p-for-interaction=0.012). A dose-dependent association of pre- or intraoperative acetaminophen with decreased postoperative opioid consumption was not observed when 5-HT3 antagonists were co-administered, suggesting that physicians might consider reserving 5-HT3 antagonists as rescue medication for postoperative nausea or vomiting when acetaminophen is administered for pain prophylaxis.

The impact of the COVID-19 pandemic on HIV treatment gap lengths and viremia among people living with HIV British Columbia, Canada, during the COVID-19 pandemic: Are we ready for the next pandemic?

The SARS-CoV-2 (COVID-19) pandemic has impacted the care of people living with HIV (PLWH). This study aims to characterize the impact of the pandemic on the length of HIV treatment gap lengths and viral loads among people living with HIV (PLWH) in British Columbia (BC), Canada, with a focus on Downtown Eastside (DTES), which is one of the most impoverished neighbourhoods in Canada. We analyzed data from the HIV/AIDS Drug Treatment Program from January 2019 to February 2022. The study had three phases: Pre-COVID, Early-COVID, and Late-COVID. We compared results for individuals residing in DTES, those not residing in DTES, and those with no fixed address. Treatment gap lengths and viral loads were analyzed using a zero-inflated negative binomial model and a two-part model, respectively, adjusting for demographic factors. Among the 8982 individuals, 93% were non-DTES residents, 6% were DTES residents, and 1% had no fixed address during each phase. DTES residents were more likely to be female, with Indigenous Ancestry, and have a history of injection drug use. Initially, the mean number of viral load measurements decreased for all PLWH during the Early-COVID, then remained constant. Treatment gap lengths increased for all three groups during Early-COVID. However, by Late-COVID, those with no fixed address approached pre-COVID levels, while the other two groups did not reach Early-COVID levels. Viral loads improved across each phase from Pre- to Early- to Late-COVID among people residing and not residing in DTES, while those with no fixed address experienced consistently worsening levels. Despite pandemic disruptions, both DTES and non-DTES areas enhanced HIV control, whereas individuals with no fixed address encountered challenges. This study offers insights into healthcare system preparedness for delivering HIV care during future pandemics, emphasizing community-driven interventions with a particular consideration of housing stability.

Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data.

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

Health Care Utilization and Direct Costs Prior to Subspecialty Care in Children with Chronic Pain Compared with Other Chronic Childhood Diseases: A Cohort Study

ObjectivesTo understand the burden associated with pediatric chronic pain (CP) on the healthcare system compared with other costly chronic diseases prior to subspecialty care. Study designIn this retrospective cohort study, we assessed all-cause healthcare utilization and direct healthcare costs associated with pediatric CP (n=91) compared with juvenile-onset arthritis (JA, n=135), inflammatory bowel disease (IBD, n=90), type 1 or type 2 diabetes (T1D, n=475, T2D, n=289), anxiety (n=7,193) and controls (n=273) two and five years prior to patients entering subspecialty care in Manitoba, Canada. Linked data from physician encounters, emergency department visits, hospitalizations, and prescriptions were extracted from administrative databases. Differences in healthcare utilization and direct healthcare costs associated with CP versus the other conditions were tested using negative binomial and zero-inflated negative binomial regression models, respectively. ResultsAfter adjustment for age at diagnosis, sex, location of residence, and socioeconomic status, CP continued to be associated with the highest number of consulted physicians and subspecialists and the highest number of physician billings compared with all other conditions (p<0.01, respectively). CP was significantly associated with higher physician costs than JA, IBD, T1D, T2D, or controls (p<0.01, respectively); anxiety was associated with the highest physician and prescription costs among all cohorts (p<0.01, respectively). ConclusionCompared with chronic inflammatory and endocrinologic conditions, pediatric CP and anxiety were associated with substantial burden on the healthcare system prior to subspecialty care, suggesting a need to assess gaps and resources in the management of CP and mental health conditions in the primary care setting.

Association of Acute Respiratory Disease Events with Quantitative Interstitial Abnormality Progression at CT in Individuals with a History of Smoking.

Background Acute respiratory disease (ARD) events are often thought to be airway-disease related, but some may be related to quantitative interstitial abnormalities (QIAs), which are subtle parenchymal abnormalities on CT scans associated with morbidity and mortality in individuals with a smoking history. Purpose To determine whether QIA progression at CT is associated with ARD and severe ARD events in individuals with a history of smoking. Materials and Methods This secondary analysis of a prospective study included individuals with a 10 pack-years or greater smoking history recruited from multiple centers between November 2007 and July 2017. QIA progression was assessed between baseline (visit 1) and 5-year follow-up (visit 2) chest CT scans. Episodes of ARD were defined as increased cough or dyspnea lasting 48 hours and requiring antibiotics or corticosteroids, whereas severe ARD episodes were those requiring an emergency room visit or hospitalization. Episodes were recorded via questionnaires completed every 3 to 6 months. Multivariable logistic regression and zero-inflated negative binomial regression models adjusted for comorbidities (eg, emphysema, small airway disease) were used to assess the association between QIA progression and episodes between visits 1 and 2 (intercurrent) and after visit 2 (subsequent). Results A total of 3972 participants (mean age at baseline, 60.7 years ± 8.6 [SD]; 2120 [53.4%] women) were included. Annual percentage QIA progression was associated with increased odds of one or more intercurrent (odds ratio [OR] = 1.29 [95% CI: 1.06, 1.56]; P = .01) and subsequent (OR = 1.26 [95% CI: 1.05, 1.52]; P = .02) severe ARD events. Participants in the highest quartile of QIA progression (≥1.2%) had more frequent intercurrent ARD (incidence rate ratio [IRR] = 1.46 [95% CI: 1.14, 1.86]; P = .003) and severe ARD (IRR = 1.79 [95% CI: 1.18, 2.73]; P = .006) events than those in the lowest quartile (≤-1.7%). Conclusion QIA progression was independently associated with higher odds of severe ARD events during and after radiographic progression, with higher frequency of intercurrent severe events in those with faster progression. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Little in this issue.

The Moderating Role of Collectivism on Physical Activity and Family Support: A Cross-Sectional Design.

An important value among Korean Americans is collectivism or giving a group priority over the individual. This value, family support, and demographic characteristics have been reported to influence health behaviors among this group. Yet, no study has examined how collectivism, family support, and demographic characteristics interact with each other and then influence physical activity among Korean American adults. The purpose of the study was to examine the association between family support and physical activity, and the moderating role of demographic characteristics and collectivism on this association among Korean American adults. A descriptive, cross-sectional study was conducted with a sample of 110 Korean American adults 18 years and older, residing in a Southwestern city in the United States. Self-administered questionnaires were used to measure demographics, collectivism, family support, and physical activity. Data analysis using a zero-inflated negative binomial regression model revealed a positive relationship between family support and physical activity, while age and gender were moderators of the relationship between family support and physical activity. Collectivism was positively related to physical activity and moderated the relationship between family support and physical activity. These findings indicate the significance of collectivism and family support that influence Korean American adults' physical activity.

Migrants and boomtowns: evidence from the US shale boom

ABSTRACT This paper analyses if and how oil and gas developments foster in-migration of workers into boomtowns. In particular, we focus on the workers’ human capital, as a way to help local growth. Using a zero-inflated negative binomial model, we find that oil and gas shocks, on average, take three years to significantly impact migration flows into boomtowns. The migration response is heterogeneous with a disproportionately higher positive effect for medium-high human capital workers. The types of human capital gained by rural and sparsely populated boomtowns can have important policy implications for their long-run growth and economic resilience.

MbDecoda: a debiased approach to compositional data analysis for microbiome surveys.

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Age-Specific Association of Co-Morbidity With Home-Time After Acute Stroke.

To examine the association of co-morbidity with home-time after acute stroke and whether the association is influenced by age. We conducted a province-wide study using linked administrative databases to identify all admissions for first acute ischemic stroke or intracerebral hemorrhage between 2007 and 2018 in Alberta, Canada. We used ischemic stroke-weighted Charlson Co-morbidity Index of 3 or more to identify those with severe co-morbidity. We used zero-inflated negative binomial models to determine the association of severe co-morbidity with 90-day and 1-year home-time, and logistic models for achieving ≥ 80 out of 90 days of home-time, assessing for effect modification by age and adjusting for sex, stroke type, comprehensive stroke center care, hypertension, atrial fibrillation, year of study, and separately adjusting for estimated stroke severity. We also evaluated individual co-morbidities. Among 28,672 patients in our final cohort, severe co-morbidity was present in 27.7% and was associated with lower home-time, with a greater number of days lost at younger age (-13 days at age < 60 compared to -7 days at age 80+ years for 90-day home-time; -69 days at age < 60 compared to -51 days at age 80+ years for 1-year home-time). The reduction in probability of achieving ≥ 80 days of home-time was also greater at younger age (-22.7% at age < 60 years compared to -9.0% at age 80+ years). Results were attenuated but remained significant after adjusting for estimated stroke severity and excluding those who died. Myocardial infarction, diabetes, and cancer/metastases had a greater association with lower home-time at younger age, and those with dementia had the greatest reduction in home time. Severe co-morbidity in acute stroke is associated with lower home-time, more strongly at younger age.

Unsupervised Gene-Cell Collective Representation Learning with Optimal Transport

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

Healthcare utilization and its association with socioeconomic status in China: Evidence from the 2011-2018 China Health and Retirement Longitudinal Study.

Healthcare utilization often favors the higher-socioeconomic status (SES) and having chronic diseases may exacerbate this inequality. This study aims to examine the trends in health service use over time, the effect of SES on healthcare utilization, and the role of chronic diseases in this association. Data used in this study were from the China Health and Retirement Longitudinal Study (CHARLS) in 2011, 2013, 2015, and 2018, which is the first nationally representative survey of the middle-aged and older. The sample included people aged 45 years and older who responded to all the waves. A total of 10,922 adults were included in this study. Healthcare utilization was categorized into outpatient and inpatient service use and SES was measured by per-capita household expenditure. A multilevel zero-inflated negative binomial regression model was performed to analyze outpatient and inpatient service use, separately. The rates of outpatient service use in 2011, 2013, 2015, and 2018 were 19.11%, 21.45%, 20.12%, and 16.32%, respectively, while the rates of inpatient service use were 8.40%, 13.04%, 14.17%, and 18.79%, respectively. Compared to individuals in the lowest quintile of per-capita household expenditure, those in higher quintiles had higher odds of outpatient service use (Q2: odds ratio = 1.233, p < 0.0001; Q3: 1.416, p < 0.0001; Q4: 1.408, p < 0.0001; or Q5: 1.439, p < 0.0001) and higher rates of inpatient service use (Q2: incidence rate ratio = 1.273, p < 0.0001; Q3: 1.773, p < 0.0001; Q4: 2.071, p < 0.0001; or Q5: 1.992, p < 0.0001). Additionally, having morbidity generally increased healthcare utilization, but did not play a significant role in moderating the relationship between SES and healthcare utilization. Healthcare utilization rates were overall low in China, but relatively high for people in higher quintiles of per-capita household expenditure or those with morbidity, compared to their counterparts. Policy actions are required to provide more health education to the public, to further optimize health insurance schemes targeting outpatient services, especially for the low-SES, and to establish new health delivery models for NCD management in the primary health care setting.

Factors Influencing Time to Return to Learn Among NCAA Student-Athletes Enrolled in the Concussion Assessment, Research, and Education (CARE) Study.

The aim was to describe the demographic and post-injury factors that influence time to return to learn (RTL) among student-athletes enrolled in the Concussion Assessment, Research and Education (CARE) Consortium. A total of 47,860 student-athletes enrolled in the National Collegiate Athletic Association-Department of Defense (NCAA-DoD) CARE Consortium study from 2014 to 2020, with 1485 sport-related concussions (SRCs) analyzed in the present dataset. Demographic and post-injury characteristics were calculated using descriptive statistics, followed by Kaplan-Meier estimates to examine median time to return to normal academic performance (i.e., RTL) by sex (male, female), baseline psychiatric conditions (depression, anxiety) and/or learning disorder, NCAA division (I, II, III), SRC history (0, 1, 2, 3+), NCAA sport category (contact, limited contact, non-contact sport), and median difference in baseline/post-injury symptom severity scores (< 21, ≥ 21). Further, a multivariable zero-inflated negative binomial (ZINB) regression model was used to examine their association with RTL. Overall, time to RTL (mean = 4.96 ± 8.24days, median = 3.0 [interquartile range = 0.0, 6.0] days) was found to be influenced by several factors resulting in earlier trajectories. Notably, nearly 25% of the sample demonstrated immediate RTL (i.e., 0days). Among student-athletes who did not immediately RTL, males demonstrated a decreased rate in RTL (rate = 0.79; 95% CI 0.66-0.96) compared to females. Further, student-athletes with a ≥ 21 change in symptom severity score (post-injury baseline) demonstrated a higher rate of RTL (rate = 1.47; 95% CI 1.21-1.79) compared to student-athletes with a symptom severity change score < 21. Lastly, male student-athletes demonstrated two times higher odds (odds ratio = 1.95; 95% CI 1.02-3.73) of immediate RTL compared to female student-athletes. No other covariates were associated with time to RTL. Collectively, the present findings suggest a rapid return to the classroom following concussion. Specifically, males demonstrated higher odds of time to RTL, whereas those with greater differences in symptom severity resulted in a higher rate of time to RTL among those who did not immediately RTL. Ultimately, these findings support prior work emphasizing an individualized approach to SRC management.

Modeling clustered count data using mixed effect discrete Weibull regression model with cubic splines

This paper investigates the use of mixed-effect discrete Weibull (DW) regression model with cubic splines for clustered count data. DW regression model can be used for both over and under-dispersed data thus, it can be an appropriate choice for researchers to use without having the risk to violate assumptions for using a particular model distribution. We used a cubic spline function to model the non-linear relationship between the outcome variable and the covariate. The proposed model is applied to a clustered dataset from the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the amount of alcohol drink in 17 different regions. We compared the results with our proposed model with those of Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models in the presence of spline function. The results show that using DW regression model with cubic spline gives the best fit. We also held a simulation study to investigate the performance and the robustness of DW with a spline function. It turns out that DW regression model with a spline function can flexibly model clustered count data with various dispersion and data with many zeros.

Leveraging big data for outdoor recreation management: A case study from the York river in Virginia

Outdoor recreation is important for improving quality of life, well-being, and local economies, but quantifying its value without direct monetary transactions can be challenging. This study explores combining non-market valuation techniques with emerging big data sources to estimate the value of recreation for the York River and surrounding parks in Virginia. By applying the travel cost method to anonymous human mobility data, we gain deeper insights into the significance of recreational experiences for visitors and the local economy. Results of a zero-inflated Negative Binomial model show a mean consumer surplus value of $26.91 per trip, totaling $15.5 million across nearly 600,000 trips observed in 2022. Further, weekends, holidays, and the summer and fall months are found to be peak visitation times, whereas those with young children and who are Hispanic or over 64 years old are less likely to visit. These findings shed light on various factors influencing visitation patterns and recreation values, including temporal effects and socio-demographics, revealing disparities that warrant targeted efforts for inclusivity and accessibility. Policymakers can use these insights to make informed and sustainable choices in outdoor recreation management, fostering the preservation of natural resources for the benefit of both visitors and the environment.

Ambulance Traffic Crashes in Japan: Characteristics of Casualties and Efforts to Improve Ambulance Safety

Background An ambulance traffic crash not only leads to injuries among emergency medical service (EMS) professionals but also injures patients or their companions during transportation. We aimed to describe the incidence of ambulance crashes, seating location, seatbelt use for casualties (ie, both fatal and nonfatal injuries), ambulance safety efforts, and to identify factors affecting the number of ambulance crashes in Japan. Methods We conducted a nationwide survey of all fire departments in Japan. The survey queried each fire department about the number of ambulance crashes between January 1, 2017, and December 31, 2019, the number of casualties, their locations, and seatbelt usage. Additionally, the survey collected information on fire department characteristics, including the number of ambulance dispatches, and their safety efforts including emergency vehicle operation training and seatbelt policies. We used regression methods including a zero-inflated negative binomial model to identify factors associated with the number of crashes. Results Among the 726 fire departments in Japan, 553 (76.2%) responded to the survey, reporting a total of 11,901,210 ambulance dispatches with 1,659 ambulance crashes (13.9 for every 100,000 ambulance dispatches) that resulted in a total of 130 casualties during the 3-year study period (1.1 in every 100,000 dispatches). Among the rear cabin occupants, seatbelt use was limited for both EMS professionals (n = 3/29, 10.3%) and patients/companions (n = 3/26, 11.5%). Only 46.7% of the fire departments had an internal policy regarding seatbelt use. About three-fourths of fire departments (76.3%) conducted emergency vehicle operation training internally. The output of the regression model revealed that fire departments that conduct internal emergency vehicle operation training had fewer ambulance crashes compared to those that do not (odds of being an excessive zero −2.20, 95% CI: −3.6 to −0.8). Conclusion Two-thirds of fire departments experienced at least one crash during the study period. The majority of rear cabin occupants who were injured in ambulance crashes were not wearing a seatbelt. Although efforts to ascertain seatbelt compliance were limited, Japanese fire departments have attempted a variety of methods to reduce ambulance crashes including internal emergency vehicle operation training, which was associated with fewer ambulance crashes.

Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders

BackgroundGeographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas.MethodsWe constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters.ResultsWe show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p = 5.67E-05). We find nine clusters/hotspots of inpatients.ConclusionsOur results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.

Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents

Railroads play a pivotal role in the Korean national economy, necessitating a thorough understanding of factors influencing accidents for effective mitigation strategies. Unlike prior research focused on accident frequency and severity, this study delves into the often-overlooked aspect of time delays resulting from railroad accidents. Analyzing 15 years of nationwide data (2008–2022), encompassing 3244 human-related and 3350 technical events, this research identifies key factors influencing delay likelihood and duration. Factors considered include event type, season, train type, location, operator size, person type involved, facility type, and causes. Despite an overall decrease in events, variable delay times highlight the need to comprehend specific contributing factors. To address excess zeros, the study employs a two-stage model and a zero-inflated negative binomial (ZINB) model, alongside artificial neural networks (ANNs) for non-linear pattern recognition. Human-related delays are influenced by event types, seasons, and passenger categories, exhibit nuanced impacts. Technical-related delays are influenced by incident types and facility involvement. Regarding model performance, the ANN models outperform regression-based models consistently in all cases. This study emphasizes the importance of considering both human and technical factors in predicting and understanding railroad accident delays, offering valuable insights for formulating strategies to mitigate service disruptions associated with these incidents.