Binomial Distribution Research Articles

Sex-specific analysis of acute alcohol use in suicides and reporting of alcohol as a contributor to suicide deaths in New Zealand 2007-2020: a cross-sectional study of coronial data.

Acute alcohol use (AAU) can increase suicide risk. It is unknown if this effect differs by population sub-group in New Zealand, and what characteristics are associated with alcohol being coded as contributory to death, when AAU is identified. This study aimed to answer: 1) are the characteristics associated with suicide involving AAU different between females and males, and 2) among suicides that involved AAU, what factors are associated with alcohol being coded as a contributory factor? Secondary analysis was conducted of suicide data from 2007-2020, from the National Coronial Information System. Binomial regression models for females and males were used to estimate sex-specific differences in risk of suicide involving AAU. Poisson regression modelling was used to estimate the relative risk of alcohol being coded as contributory where AAU was identified. Suicide was more likely to involve AAU among Māori females (adjusted risk ratio [ARR] 1.35, 95% confidence interval [CI] 1.08-1.68) and Pacific females (ARR 1.75, 95% CI 1.22-2.51), compared to European females. Compared to males who were employed, all other employment statuses had significantly lower risk of suicide that involved AAU. Those who died by hanging (ARR 0.75, 95% CI 0.62-0.92) or firearms (ARR 0.55, 95% CI 0.38-0.90) were less likely to have alcohol coded as contributory, compared to those who died by poisoning. Targeted public health interventions designed by and for specific demographic groups (particularly Māori and Pacific females) are needed, alongside universal interventions that address social and structural determinants. Data systems and coding must accurately reflect the association between AAU and suicide in New Zealand.

Design of Bayesian evaluation test for missile damage effectiveness based on multiple damage level standards

The qualified missile damage effectiveness is an important guarantee for giving full play to combat effectiveness. In order to test whether the missile damage effectiveness meets the standard, it is particularly necessary to carry out damage effectiveness identification test research. In view of the limitation that the existing "single-shot damage probability" damage effectiveness quantitative characterization index is difficult to fully describe the missile damage effectiveness based on a single damage standard, considering the different damage level results formed by a single missile hitting the target, the damage effectiveness is characterized by the probability of occurrence of different damage level results. Missile identification from the perspective of multiple damage level standards is conducive to comprehensive inspection of missile damage effectiveness. In addition, in order to overcome the problem of insufficient information in small sample missile assembly tests, the Bayesian method is combined with the system contribution degree to fuse multi-source prior information in the identification test design, and a test scheme with full information utilization and controllable risks for both parties is designed. The example shows that compared with the quantitative characterization index of a single damage standard, this study can fully describe the missile damage effectiveness, and compared with the improved binomial distribution hypothesis test method, the superiority of this method is verified.

The Vasicek distribution autoregressive time-series model for aggregated data of default and delinquency rates

Abstract Is it possible to analyse time series of aggregated data of default and delinquency rates with a simple model that includes both cross-sectional and serial dependence? To address this question, this paper proposes an autoregressive time-series model for Vasicek-distributed random variables. A direct result from the proposed model is that it leads to a novel out-of-time validation test for aggregated default and delinquency rates. The motivation for the model comes from an analysis of a time series of aggregated US credit card delinquencies, for which the Poisson or binomial distribution cannot be used. The proposed model can also be employed to analyse a time series of the average default probability of a portfolio.

Identifying measures for understanding and addressing county-level disparities in adolescent HPV vaccination coverage in North Carolina.

HPV vaccination coverage is characterized by geographic disparities in the US, with national studies finding lower coverage in rural versus nonrural areas. To direct quality improvement efforts in North Carolina, we sought to understand how different rurality measures characterize these disparities. We used separate negative binomial regression models to test associations between 5 dichotomized county-level rurality measures and HPV vaccination coverage (≥1 dose) among North Carolina adolescents, ages 11-12 (n=326,345). Rurality measures were derived from: Office of Management and Budget's Metropolitan Statistical Areas, Rural-Urban Continuum Codes, Index of Relative Rurality, US Census Bureau classifications, and North Carolina Rural Center classifications. Models controlled for Social Vulnerability Index (SVI) percentile and rate of pediatricians per county. Vaccination data came from the North Carolina Immunization Registry. HPV vaccination coverage was 29% across North Carolina's 100 counties (range: 13%, 46%). Agreement between rurality measures ranged from 54% to 93% of counties. In adjusted analyses, none of the 5 rurality measures correlated with HPV vaccination coverage, but higher SVI and higher rate of pediatricians were positively associated with coverage (p< 0.01). Exploratory moderation analyses suggested regional variation in the relationship between rurality and coverage, with a positive association in one region, a negative association in one region, and no association in four regions. County-level rurality measures did not identify disparities in HPV vaccination coverage in North Carolina. Measures related to social vulnerability and access to pediatricians may be better suited for understanding and addressing the state's substantial county-level vaccination disparities.

Behavioral Design Strategies Improve Healthy Food Sales in a Military Cafeteria.

This study examined the use of behavioral design strategies to improve healthier food sales. A quasi-experimental, one-group, repeated measures design examined changes in food sales following behavioral design adjustments. United States military base hospital dining facility. U.S. military service members, retirees, and civilian employees. Behavioral design changes included placement, layout, messaging, default healthy bundling, a stoplight rating system, strategic positioning of healthy items on menu boards, and an increase in healthier snacks. Food sales were assessed by point-of-sales data. T-tests examined total sales of each food adjusted weekly between baseline and intervention and intervention and post-intervention. 16 food items targeted by the intervention were examined. Weekly food sales were calculated for the 18-week baseline, 18-week intervention, and 9-week post-intervention. Further, analysis estimated negative binomial models for food item sales. The hospital dining facility served 600 to 900 meals per day. Weekly foods sales decreased during the intervention for desserts, cooked starches, hummus, and yogurt (P < 0.01). Sales increased during the intervention for fruit cups, cooked vegetables, vegetable and turkey burgers, grilled chicken, packaged salads, French fries, hamburgers, and hot dogs (P < 0.02). This study demonstrates that a mixture of behavioral design strategies can be operationalized with reasonable fidelity and can lead to increases in the sales of some healthy foods in military worksites.

Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models

The surge in e-commerce during the past decade has led to dramatic changes in consumer shopping behaviour. The study applies two Generalized Extreme Value (GEV) family models to investigate households' e-shopping demands. The study proposes a model structure to jointly model ordinal-based choice behaviour with choice-makers' latent class membership. Introducing latent class structure with the OGEV formulation accounts for the relationship between choice-makers heterogeneous preferential groups and their ordinal choice outcomes. Furthermore, the study also applies the Ordered General Extreme Value (OGEV)-Negative Binomial (NB) model, capturing the interplay between consumers' in-store shopping demands and online shopping behaviour. The RUM principle inherited within the OGEV-NB model allows econometric valuation of in-store shopping activity explicitly considering households' e-shopping demands. Both models are empirically estimated using a dataset collected in the Greater Toronto Area (GTA), Canada. The empirical findings and behavioural implications are also discussed.

Diversity of ancestral brainstem noradrenergic neurons across species and multiple biological factors.

The brainstem region, locus coeruleus (LC), has been remarkably conserved across vertebrates. Evolution has woven the LC into wide-ranging neural circuits that influence functions as broad as autonomic systems, the stress response, nociception, sleep, and high-level cognition among others. Given this conservation, there is a strong possibility that LC activity is inherently similar across species, and furthermore that age, sex, and brain state influence LC activity similarly across species. The degree to which LC activity is homogenous across these factors, however, has never been assessed due to the small sample size of individual studies. Here, we pool data from 20 laboratories (1,855 neurons) and show diversity across both intrinsic and extrinsic factors such as species, age, sex and brain state. We use a negative binomial regression model to compare activity from male monkeys, and rats and mice of both sexes that were recorded across brain states from brain slices ex vivo or under different anesthetics or during wakefulness in vivo . LC activity differed due to complex interactions of species, sex, and brain state. The LC became more active during aging, independent of sex. Finally, in contrast to the foundational principle that all species express two distinct LC firing modes ("tonic" or "phasic"), we discovered great diversity within spontaneous LC firing patterns. Different factors were associated with higher incidence of some firing modes. We conclude that the activity of the evolutionarily-ancient LC is not conserved. Inherent differences due to age and species-sex-brain state interactions have implications for understanding the role of LC in species-specific naturalistic behavior, as well as in psychiatric disorders, cardiovascular disease, immunology, and metabolic disorders.

Comparative assessment of the nature of the combined action of insecticides: different methodical approaches

Introduction. Pesticide mixtures containing insecticide methomyl are commonly used in agriculture. However, the interaction of their active ingredients in a mixture has been insufficiently studied. The purpose of the study was to evaluate the nature of the combined action of a mixture of insecticides methomyl (carbamate) and bifenthrin (pyrethroid) using various methods: a classical model (orthogonal planning of the experiment using nonlinear variables); a generalized linear regression model for the binomial distribution (probit regression); and an isobologram. The study was conducted at the Federal Scientific Center of Hygiene named after F.F. Erisman of the Federal Service for Supervision in Protection of the Rights of Consumer and Man Wellbeing, according to the protocol approved by the local bioethical commission. Materials and methods. Ninety male outbred rats were administered 9 combined doses of the insecticides (combinations of LD16, LD33 and LD50 for the individual compounds, as devised from a preliminary study). Results. Impaired coordination, tremor of the limbs and head, turning into convulsions, rapid (shallow) breathing were observed in all groups. The data on mortality as used to construct 2 mathematical models, as well as to construct an isobologram. Limitations. The study was limited to studying the indicators of acute oral toxicity, without taking into account the possible repeated exposure. Conclusion. The models showed that the additive effect for the combination of methomyl and bifenthrin was only observed for combinations when the individual dose of bifenthrin was equal to or greater than 45 mg/kg b.w. (LD33) and the dose of methomyl was less than 30 mg/kg b.w., as all animals receiving a dose of 30 mg/kg b.w. of methomyl (LD50) in the mixture died. Therefore, the dose of methomyl dictated the toxic effect of the mixture, which should be accounted for in hygienic regulation of methomyl-containing formulations.

Analyzing Rear-End Crash Counts on Ohio Interstate Freeways Using Advanced Multilevel Modeling

This study presents a new modeling approach for rear-end crash counts on Ohio’s interstate freeways based on a dataset for 2021 that contains 2745 rear-end crashes. The analysis encompasses 20 interstate freeways, comprising 1833 homogeneous segments and extending over approximately 1313 miles. These interstate freeways exhibit varying safety performances, indicating a significant degree of heterogeneity. A unique rear-end crash risk rate was devised for each interstate, capturing diverse risk profiles. Three distinct models were developed: a standard negative binomial model, an uncorrelated two-level negative binomial model, and a correlated two-level negative binomial model. The correlated two-level negative binomial model demonstrated superior fit, as evidenced by the likelihood ratio test, Akaike information criterion, and Bayesian information criterion. The correlated two-level negative binomial model exhibited enhanced forecasting precision, as measured by the Root Mean Square Error. A significant finding is that the rear-end crash risk rate significantly improves the fit of the models. The study also reveals that rear-end crashes are expected to occur more frequently in urban segments of interstate freeways with high rear-end risk rates. However, rural segments experience no such significant variations in the rear-end crash risk rate. However, an increase in the inner shoulder width is associated with a decrease in expected rear-end crashes. This research offers a valuable methodology for modeling rear-end crashes on interstate freeways, providing insights into the contributing variables that could inform targeted safety improvements.

Effects of the COVID-19 pandemic on hospital admissions and inpatient mortality in Kenya: a retrospective cohort study

Background The impact of COVID-19 in Africa remains poorly described. We examined hospitalisation trends for all medical causes, clinician-diagnosed pneumonia admissions, and inpatient mortality in Kenya two years before and across the first six waves of the pandemic. Methods We conducted a hospital-based observational study of patients admitted to 13 public referral facilities in Kenya from January 2018 to December 2022. The pre-COVID-19 population included admissions before 1st March 2020. Time series models, adjusted for seasonality and hospital, compared observed and predicted trends. To estimate the impact of the COVID-19 pandemic, we calculated incidence rate ratios (IRR) from negative binomial mixed-effects models. Results 357,631 patients were admitted across the 13 sites (range 15,354 to 67,241 per hospital). 45,349 patients (42.1%) were admitted to the adult medical wards. On the paediatric ward, 163,608 individuals (47.4%) were aged under five years and 36,227 individuals (10.5%) were aged five years and older. In comparison to the pre-pandemic period, hospitalisations reduced for adults (IRR 0.75, 95% CI 0.69–0.82) and paediatric cases (IRR 0.69, 95% CI 0.64–0.75). In-hospital deaths also declined for adults (IRR 0.83, 95% CI 0.77–0.89) and children (IRR 0.85, 95% CI 0.77–0.94). Adult pneumonia admissions increased (IRR 1.59, 95% CI 1.36–1.85), while paediatric cases decreased overall, (IRR 0.78, 95% CI 0.51–1.20), but became elevated in late 2021 compared to the pre-pandemic period. Conclusions The COVID-19 pandemic did not cause a surge in hospitalisations in Kenya. However, pneumonia admissions among adults (but not children) increased significantly, with peaks aligning with the pandemic waves. These findings underscore the importance of syndromic inpatient surveillance in detecting and monitoring outbreaks.

Determinants of acute psychiatric inpatient length of stay in Ireland.

Ireland has had an historic over-reliance on inpatient mental health care accompanied by poorly resourced community provision. There has been an increasing policy focus on provision of mental health care in the community to facilitate diversion from, or shorten stays in, inpatient care. However, little is known about the determinants of psychiatric inpatient length of stay (LOS) to allow for the targeting of community services. To use a large cross-sectional national dataset to examine the determinants of psychiatric inpatient LOS in the Irish context. Using information on 60,607 discharges from adult acute psychiatric units in Ireland between 2015 and 2019, the study employs both descriptive and regression analysis. Using negative binomial regression models, we control for patient demographic, socioeconomic, and clinical characteristics. In addition, as LOS may be related to provision of non-acute services in the patient area of residence, the analysis also controls for aspects of community service provision. The study finds that longer LOS is associated with older age, being female, and having an involuntary admission. Clinical diagnosis significantly impacts LOS, with diagnoses such as schizophrenia being associated with longer LOS. There is also significant variation in LOS across region of residence, with discharges from some regions staying up to 5days longer. The regional variation observed in inpatient LOS supports the assumption that increased provision of community services diverts or reduces the quantum of care required in inpatient settings. However, without substantive improvement in the community data collected and reported, future planning of mental health services, both community and inpatient, will be substantially curtailed.

Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.

High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the P-value with DOTNB. Extensive validation using simulated and real scRNA-seq data sets demonstrates that DEGage outperforms five popular DEG analysis tools: DEGseq2, DEsingle, edgeR, Monocle3, and scDD. DEGage is robust against high dropout levels and exhibits superior sensitivity when applied to balanced and imbalanced data sets, even with small sample sizes. We utilize DEGage to analyze prostate cancer scRNA-seq data sets and identify marker genes for 17 cell types. Furthermore, we apply DEGage to scRNA-seq data sets of mouse neurons with and without fear memory and reveal eight potential memory-related genes overlooked in previous analyses. The theoretical results and supporting software for DOTNB can be widely applied to comparative analyses of dispersed count data in HTS and broad research questions.

Traffic Congestion and Safety: Mixed Effects on Total and Fatal Crashes

This paper examines the effects of traffic congestion on total crashes, fatal or serious injury (FSI) crashes, and fatal-only crashes in peak periods using a zone-level safety analysis in Greater Melbourne. Bayesian mixed-effect negative binomial models are employed to investigate the relationship between a congestion index and the frequency of total and FSI crashes. In addition, Bayesian mixed-effect binary logistic models are adopted to explore the association between the congestion index and the likelihood of having fatal crashes in Statistical Area Level 2 (SA2) zones. Modelling results indicate that traffic congestion tends to increase total crashes in both the AM and PM peak periods and FSI crashes in the AM peak period. In contrast, traffic congestion tends to decrease the likelihood of having fatal crashes at both the AM and PM peaks. These findings suggest that many policies to reduce traffic congestion may also enhance road safety by lowering the overall number of crashes. However, it is crucial to incorporate careful speed management within these policies to reduce the risk of fatal crashes effectively.

Event‐Triggered Secure Control of Positive Networked Control Systems Under Multi‐Channels Attacks

ABSTRACTThis paper focuses on the secure control of positive networked control systems under multi‐channel attacks characterized by a Bernoulli distribution. Specifically, the sensor‐to‐controller channel suffers from false data injection (FDI) attacks, whereas the controller‐to‐actuator channel suffers from denial of service (DoS) attacks. The objective is to design a static output feedback controller that guarantees the positivity and stochastic stability of the closed‐loop system in the presence of DoS and FDI attacks. To conserve communication resources, we employ an event‐triggered scheme to reduce the frequency of information transmission. Due to the positivity of the system, the proposed event‐triggering mechanism employs the 1‐norm form instead of the 2‐norm form, which allows that the controller parameter is determined via linear programming rather than LMI technique. Finally, two simulation examples are provided to verify the effectiveness of our methods.

Understanding the impact of pandemics on long-term medication adherence: directly observed therapy in a tuberculosis treatment cohort pre- and post-COVID-19 lockdowns

BackgroundThe COVID-19 pandemic negatively impacted tuberculosis (TB) treatment services, including directly observed therapy (DOT) programs used to promote medication adherence. We compared DOT adherence embedded in a research study before and after COVID-19 lockdowns in South Africa.MethodsWe analyzed data from 263 observational study participants undergoing drug susceptible (DS)-TB DOT between May 2017 to March 2022. Participants enrolled before October 2019 were considered ‘pre-COVID-19’ and those enrolled after September 2020 were considered ‘post-COVID-19 lockdown groups. Negative binomial regression models were used to compare DOT non-adherence rates between the two lockdown groups. We then conducted a sensitivity analysis which only included participants enrolled in the immediate period following the first COVID-19 lockdown.ResultsDOT non-adherence rate was higher in the post-COVID-19 lockdown group (aIRR = 1.42, 95% CI = 1.04–1.96; p = 0.028) compared to pre-COVID-19 lockdown period, adjusting for age, sex, employment status, household hunger, depression risk, and smoked substance use. DOT non-adherence was highest immediately following the initial lockdown (aIRR = 1.74, 95% CI = 1.17–2.67; p = 0.006).ConclusionThe COVID-19 lockdowns adversely effected adherence to TB DOT in the period after lockdowns were lifted. The change in DOT adherence persisted even after adjusting for socioeconomic and behavioral variables. We need a better understanding of what treatment adherence barriers were exacerbated by COVID-19 lockdowns to improve outcomes in post-pandemic times.Trial registrationClinicalTrials.gov Registration Number: NCT02840877. Registered on 19 July 2016.

On the Conflation of Negative Binomial and Logarithmic Distributions

In recent decades, the study of discrete distributions has received increasing attention in the field of statistics, mainly because discrete distributions can model a wide range of count data. One common distribution used for modeling count data, for instance, is the negative binomial distribution (NBD), which performs well with over-dispersed data. In this paper, a new count distribution is introduced, called the conflation of negative binomial and logarithmic distributions, which is formed by conflating the negative binomial and logarithmic distributions, resulting in a distribution that possesses some of the properties of negative binomial and logarithmic distributions. The distribution has two parameters and is verified by a positive integer. Two modifications are proposed to the distribution, which includes zero as a support point. The new distribution is valuable from a theoretical perspective since it is a member of the weighted negative binomial distribution family. In addition, the distribution differs from the NBD in the sense that the probability of lower counts is inflated. This study discusses the characteristics of the proposed distribution and its modified versions, such as moments, probability generating functions, likelihood stochastic ordering, log-concavity, and unimodality properties. Real-world data are used to evaluate the performance of the proposed models against other models. All computations shown in this paper were produced using the R programming language.

Factors Associated with Sexual Intercourse Frequency Among South African Women: Evidence from a National Cross-sectional Survey

Background: Engaging in regular healthy sexual intercourse activities is good for human health and well-being. Greater sexual intercourse frequency (SIF) for people in relationships is often associated with well-being, greater relationship satisfaction, reduced mortality, increased life expectancy, positive emotions and happiness, while lack of sexual activity is usually associated with depression and marital disharmony. Aim: The primary purpose of this study is to identify the factors associated with SIF among women in South Africa. Data and Methods: The South African Demographic and Health Survey 2016 data were used, and the negative binomial regression model (NBRM) was employed for analysis. Results: Age was significantly associated with high SIF (IRR = 1.02; p &lt; .001). African women had significantly higher SIF than Indian/Asian women (IRR = 1.28; p = .049). Married women had a significantly higher SIF than women who were never married (IRR = 1.42; p &lt; .001). Women who resided in urban areas were significantly associated with higher SIF than women who resided in rural areas (IRR = 1.14; p &lt; .001). Conclusion: Being young, being Indian/Asian, being unmarried, and residing in rural areas is associated with low SIF, whereas being older, being African, being married, and staying in urban areas is associated with high SIF. Generally, without violating cultural, religious and societal beliefs, married women should be taught about the benefits of sexual intercourse activity (SIA) and be encouraged not to be ashamed of entering into sexual intercourse activities.

New Statistical Residuals for Regression Models in the Exponential Family: Characterization, Simulation, Computation, and Applications

Residuals are essential in regression analysis for evaluating model adequacy, validating assumptions, and detecting outliers or influential data. While traditional residuals perform well in linear regression, they face limitations in exponential family models, such as those based on the binomial and Poisson distributions, due to heteroscedasticity and dependence among observations. This article introduces a novel standardized combined residual for linear and nonlinear regression models within the exponential family. By integrating information from both the mean and dispersion sub-models, the new residual provides a unified diagnostic tool that enhances computational efficiency and eliminates the need for projection matrices. Simulation studies and real-world applications demonstrate its advantages in efficiency and interpretability over traditional residuals.

MODELING LONGITUDINAL FLOOD DATA IN WEST SUMATRA USING THE GENERALIZED ESTIMATING EQUATION (GEE) APPROACH

Flooding is one of the many natural disasters that often hit Indonesia. In July 2023, three areas in West Sumatra experienced floods and landslides which caused damages and even 2 missing victims. Since November 16th, 2023, 8 hamlets in Meranti Village, Landak District, West Sumatra have been inundated by floods which affected families and many public facilities. This research uses data from West Sumatra Province Central Statistics Agency. The data used is 2014, 2018 and 2021. The response variable used is the number of villages/sub-districts experiencing natural disasters according to district/city ( ). The predictor variables used are regional topography , the number of water channels such as rivers, reservoirs, etc. , the number of fields cleared through burning , the number of villages/sub-districts in C excavation area , and the number of dumpsters . This research uses Negative Binomial Regression with the Generalized Estimating Equation (GEE) approach. In the Poisson regression test, the QIC value based on Independent Working Correlation Structure (WCS) is with deviance value of , degree of freedom of , and dispersion score of 4,6144. Because the dispersion value is greater than 1, it can be concluded that there is overdispersion. Because there is more than one overdispersion, it is overcome by using negative binomial. The results of parameter estimation using negative binomial regression based on Independent WCS showed that only one variable was significant, which is the number of fields cleared through burning with deviance value of , degrees of freedom of and a QIC of . Negative Binomial regression model that was formed is ). From the two regression models used, namely Poisson and negative binomial, it was found that the negative binomial regression model was the best model because it had the lowest QIC value of .

Children’s self-reported exposure to sugary beverage advertisements and association with intake across six countries before and during the COVID-19 pandemic: a repeat cross-sectional study

BackgroundChildren’s exposure to sugary beverage advertising may have changed during the COVID-19 pandemic due to shifts in media habits, which could subsequently have influenced intake. This study aimed to examine: 1) children’s frequency and setting of exposure to advertisements of sugary beverages in six countries before and during the COVID-19 pandemic; 2) the association between exposure to sugary beverage advertisements and intake.MethodsChildren aged 10–17 years (n = 28,908) in Australia, Canada, Chile, Mexico, the United Kingdom (UK) and the United States (US) completed online surveys in 2019, 2020 and 2021 as part of the International Food Policy Study using a repeat cross-sectional study design. Respondents self-reported frequency and setting of exposure to sugary beverage advertisements, location of school classes (in-person/online, 2020–2021 only), screen time, and sugary beverage intake. Adjusted weighted logistic and negative binomial regression models stratified by country examined associations between year and reported sugary beverage advertising exposure, and associations between sugary beverage advertising exposure and intake. Differences in reported advertising exposure between students taking online or in-person school classes were explored.ResultsSelf-reported exposure to advertisements for sugary beverages at least weekly was relatively stable across years within countries, with differences in settings of exposure. Exposure to sugary beverage advertisements increased on digital media independently of screen time from 2019 to 2021 in Australia, Canada, the UK and US, with a concomitant decrease in exposure in retail settings in all countries except the UK. In Australia and the UK, children attending all classes online were more likely to report at least weekly (vs less than once a week) exposure to sugary beverage advertisements, and children attending all classes online were more likely to report exposure to advertisements on digital media and in other settings (e.g., billboard, magazines) compared to children attending in-person classes in Australia, Canada and the UK. Exposure to sugary beverage advertisements at least weekly (IRR = 1.12,99%CI:1.09–1.15) and in each of the settings was associated with sugary beverage intake.ConclusionsExposure to digital advertisements for sugary beverages increased from 2019 to 2021 in most countries, and exposure was associated with sugary beverage intake. Reducing children’s exposure to advertising of less healthy foods, including on digital media, may reduce sugary beverage intake.