Zero-inflated Poisson Model Research Articles

Ecological change increases malaria risk in the Brazilian Amazon

Ecological change in the Brazilian Amazon is closely linked to human mobility and health. Mining, agriculture, logging, and other activities alter highly diverse ecological and demographic contexts and subsequent exposure to diseases such as malaria. Studies that have attempted to quantify the impact of deforestation on malaria in the Brazilian Amazon have produced conflicting results. However, they varied in methodology and data sources. Most importantly, all studies used annual data, neglecting the subannual seasonal dynamics of malaria. Here, we fill the knowledge gap on the subannual relationship between ecological change in the Brazilian Amazon and malaria transmission. Using the highest spatiotemporal resolution available, we estimated the effect of deforestation on malaria cases between 2003 and 2022 using a stratified Bayesian spatiotemporal hierarchical zero-inflated Poisson model fitted with the Integrated Nested Laplace Approximation. The model was also stratified by state. We found that a 1% increase in 1-mo lagged deforestation increased malaria cases in a given month and municipality by 6.3% [95% credible interval (Crl): 6.2, 6.5%]. Based on an interaction term included in the model, the effect of deforestation on malaria was even larger in areas with higher forest cover. We found that the coefficients for deforestation and mobility were highly variable when stratified by state. Our results provide detailed evidence that, on average, deforestation increases malaria transmission, but that the relationship is not spatiotemporally uniform. These results have implications for stratifying malaria control interventions based on ecological dynamics to help Brazil achieve its goal of malaria elimination by 2035.

Interpreting the Epidemiological Characteristics of HIV-1 in Heterosexually Transmitted Population Based on Molecular Transmission Network in Kunming, Yunnan: A Retrospective Cohort Study.

Heterosexuals have become the most prevalent group of HIV-1 in Kunming, Yunnan Province. Utilizing the principle of genetic similarity between their gene sequences, we built a molecular transmission network by gathering data from earlier molecular epidemiological studies. This allowed us to analyze the epidemiological features of this group and offer fresh concepts and approaches for the prevention and management of HIV-1 epidemics. Cytoscope was used to visualize and characterize the network following the processing of the sample gene sequences by BioEdit and HyPhy. The number of possible links and the size of the clusters were investigated as influencing factors using a zero-inflated Poisson model and a logistic regression model, respectively. A scikit-learn-based prediction model was developed to account for the dynamic changes in the HIV-1 molecular network. Six noteworthy modular clusters with network scores ranging from 4 to 9 were found from 150 clusters using Molecular Complex Detection analysis at a standard genetic distance threshold of 0.01. The size of the number of possible links and the network's clustering rate were significantly impacted by sampling time, marital status, and CD4+ T lymphocytes (all p < 0.05). The gradient boosting machine (GBM) model had the highest area under the curve value, 0.884 ± 0.051, according to scikit-learn. Though not all cluster subtypes grew equally, the network clusters were relatively specific and aggregated. The largest local transmission-risk group for HIV-1CRF08_BC is now the heterosexual transmission population. The most suitable model for constructing the HIV-1 molecular network dynamics prediction model was found to be the GBM model.

Joint effects of ill-health, health shocks and social protection on the intensive margin of labour supply: evidence from Malawi

BackgroundThere is sparse evidence on the joint effects of ill-health, health shocks and social protection on the intensive margin of labour supply, particularly in developing countries. We interact ill-health and health shocks with access to social protection and estimate their joint effects on weekly hours of work.MethodsWe employ a zero-inflated Poisson model to assess joint effects of ill-health, health shocks and social protection on weekly hours of work exploiting pooled repeated cross-sectional data from Malawi.ResultsWe find that overall, individuals who suffered from ill-health or a health shock, including an illness/injury, a hospital admission or a chronic illness and benefited from social protection, reduced their weekly hours of work.ConclusionsThe study provides novel empirical evidence on the potential joint effects of ill-health, health shocks and social protection on the intensive margin of labour supply, shedding light on the role social protection can play in developing countries.

Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.

A one-chart scheme for joint monitoring of the two parameters of zero-inflated Poisson processes

The high-quality processes usually have more count of zeros than are expected under chance variation and are commonly modeled by zero-inflated Poisson (ZIP) distribution. A ZIP model has two parameters— ϕ ( ϕ ∈ [ 0 , 1 ] ) and λ ( λ >0). Often separate control charts are used for monitoring the two parameters. But a one-chart scheme for joint monitoring of the two parameters offers significant operational advantages. A few one-chart schemes for joint monitoring of the two parameters are reported in literature. However, the monitoring statistic of none of these schemes is defined directly on the observed quality characteristics. This leads to difficulty in understanding of these schemes by the practitioners. Any control charting scheme developed directly on the observed quality characteristic is intuitively appealing to the practitioners and can be easy to interpretations by the practitioners. In this article, a one-chart scheme (called Gamma chart) is developed considering average number of nonconformities in the samples as the monitoring statistic. The performance of the Gamma chart is studied via simulation. The results reveal that it efficiently detect the out-of-control process conditions resulting from moderate shifts in ϕ and/or λ . Finally, a case study from an Indian automobile industry is presented.

Including the Household: Individual, Community and Household Factors Affecting Antiretroviral Therapy Adherence After ART Initiation in Cape Town, South Africa

Antiretroviral therapy (ART) adherence is crucial for health outcomes of people living with HIV (PLHIV), influenced by a complex interplay of individual, community, and household factors. This article focuses on the influence of household factors, as well as individual and community factors, on ART adherence among PLHIV in Cape Town who have recently initiated ART. Baseline data for a cluster-randomized controlled trial were collected from 316 PLHIV in 12 districts in Cape Town between 6th May 2021 and 22nd May 2022. Zero-inflated Poisson models, with cluster-adjusted standard errors, were used to analyse the association between individual, household, and community factors and ART adherence measures. At the household-level, household support was associated with both better self-rated adherence (exp(β) = 0.81, z = − 4.68, p < 0.001) and fewer days when pills were missed (exp(β) = 0.65, z = − 2.92, p = 0.003). Psychological violence (exp(β) = 1.37, z = 1.97, p = 0.05) and higher household asset scores (exp(β) = 1.29, z = − 2.83, p = 0.05) were weakly associated with poorer ART adherence. At the individual-level, male gender (exp(β) = 1.37, z = 3.95, p < 0.001) and reinitiating ART (exp(β) = 1.35, z = 3.64, p < 0.001) were associated with worse self-rated ART adherence; higher education levels (exp(β) = 0.30 times, z = − 3.75, p < 0.001) and better HIV knowledge (exp(β) = 0.28, z = − 2.83, p = 0.005) were associated with fewer days where pills were missed. At the community-level, community stigma was associated with worse self-rated ART adherence (exp(β) = 1.24, z = 3.01, p = 0.003). When designing interventions to improve ART adherence, household, individual and community factors should all be considered, particularly in addressing gender-based disparities, reducing stigma, tackling violence, and enhancing household support.Clinical Trial Number: Pan African Clinical Trial Registry, PACTR201906476052236. Registered on 24 June 2019.

The relative effectiveness of a high-dose quadrivalent influenza vaccine versus standard-dose quadrivalent influenza vaccines in older adults in France: a retrospective cohort study during the 2021–2022 influenza season

ObjectivesHigh-dose quadrivalent influenza vaccine (HD-QIV) was introduced during the 2021/2022 influenza season in France for adults aged ≥65 years as an alternative to standard-dose quadrivalent influenza vaccine (SD-QIV). The aim of this study is to estimate the relative vaccine effectiveness of HD-QIV vs. SD-QIV against influenza-related hospitalizations in France. MethodsCommunity-dwelling individuals aged ≥65 years with reimbursed influenza vaccine claims during the 2021/2022 influenza season were included in the French national health insurance database. Individuals were followed up from vaccination day to 30 June 2022, nursing home admission or death date. Baseline socio-demographic and health characteristics were identified from medical records over the five previous years. Hospitalizations for influenza and other causes were recorded from 14 days after vaccination until the end of follow-up. HD-QIV and SD-QIV vaccinees were matched using 1:4 propensity score matching with an exact constraint on age group, sex, week of vaccination, and region. Incidence rate ratios were estimated using zero-inflated Poisson or zero-inflated negative binomial regression models. ResultsWe matched 405 385 HD-QIV to 1 621 540 SD-QIV vaccinees. HD-QIV was associated with a 23.3% (95% CI, 8.4–35.8) lower rate of influenza hospitalizations compared with SD-QIV (69.5/100 000 person years vs. 90.5/100 000 person years). Post-matching, we observed higher rates in the HD-QIV group for hospitalizations non-specific to influenza and negative control outcomes, suggesting residual confounding by indication. DiscussionHD-QIV was associated with lower influenza-related hospitalization rates vs. SD-QIV, consistent with existing evidence, in the context of high SARS-CoV-2 circulation in France and likely prioritization of HD-QIV for older/more comorbid individuals.

Exponentially weighted moving average chart using zero-inflated negative binomial distribution

Zero-inflated models are frequently used to deal with data having many zeros. A commonly used model for over-dispersed data containing zeros is known as the zero-inflated Poisson model. However, to account for the heterogeneity of counts that leads to excess variance besides inflation of zeros in the data using a more flexible model than the zero-inflated Poisson model, a zero-inflated negative binomial (ZINB) is suggested. In the present study, Shewhart and exponentially weighted moving average (EWMA) control charts are suggested to monitor the ZINB data. The charts are compared using the average run length and standard deviation of run length by using extensive Monte Carlo simulations. Besides a comprehensive simulation study assuming different settings of parameters of ZINB, a real data set is used to show the practicality of the proposed charts. The results indicate that the EWMA chart is better than the Shewhart chart.

Comparative analysis of the yields of dicentrics and chromosomal translocations

Purpose Chromosomal dicentrics and translocations are commonly employed as biomarkers to estimate radiation doses. The main goal of this article is to perform a comparative analysis of yields of both types of aberrations. The objective is to determine if there are relevant distinctions between both yields, allowing for a comprehensive assessment of their respective suitability and accuracy in the estimation of radiation doses. Materials and methods The analysis involved data from a partial-radiation simulation study with the calibration data obtained through two scoring methods: conventional and PAINT modified. Subsequently, a Bayesian bivariate zero-inflated Poisson model was employed to compare the posterior marginal density of the mean of dicentrics and translocations and assess the differences between them. Results When employing the conventional method of scoring, the findings indicate that there is no notable disparity between the yield of observed translocations and dicentrics. However, when utilizing the PAINT modified method, a notable discrepancy is observed for higher doses, indicating a relevant difference in the mean number of the two types of aberrations. Conclusions The choice of scoring method significantly influences the analysis of radiation-induced aberrations, especially when distinguishing between complex and simple chromosomal formations. Further research and analysis are necessary to gain a deeper understanding of the factors and mechanisms impacting the formation of dicentrics and translocations.

The association between county ordinances allowing off-road vehicles on public roads and crash rates

BackgroundLegislative bodies across the country have increasingly allowed off-road vehicles (ORVs) including all-terrain vehicles (ATVs) and utility task vehicles (UTVs) on public roads, an environment for which they are not designed. In 2004, Iowa gave individual counties the discretion to pass ordinances allowing ORVs on public roadways. The objective of this study was to evaluate the relationship between the passage of ORV ordinances and ORV crash rates, especially on public roads.MethodsAn Iowa ORV roadway ordinance database and an Iowa ORV crash database (2002–2018) for all 99 counties were compiled. Crashes for which county location could not be determined were excluded. Utilizing a zero-inflated Poisson model, correcting for background crash frequency trends and population, investigators compared the relative rates of crashes after ordinance passage to time points before ordinance implementation and to counties without such ordinances. Sub-analyses, including that focused on more recent years (2008–2018), were also performed.ResultsForty-five county ORV roadway ordinances went into effect between 2011 and 2018 and 2,347 crashes (69%) met inclusion criteria. Adjusted for year, there was a 58% greater ORV crash rate in counties after passing an ORV roadway ordinance (incidence rate ratio (IRR) 1.58, 95% CI 1.32–1.90). Roadway crashes (n = 834) increased 48% after ordinance passage (IRR 1.48, 95% CI 1.14–1.94). This roadway crash association remained statistically significant when analysis was limited to the years 2008–2018 (IRR 1.39, CI 1.06–1.83, n = 544); to ATV crashes only (IRR 1.70, CI 1.20–2.40, n = 683); and to ATV crashes excluding counties with UTV-only ordinances (IRR 1.74, CI 1.40–2.15, n = 2,011).ConclusionsORV roadway and total crashes increased significantly after implementation of county ordinances allowing ORVs on public roadways and when compared to counties without such ordinances. It is likely that these increased crashes have resulted in more injuries and possibly deaths. Results from this study may help inform policymakers as they consider legislation regarding ORV usage on public roads.

Inference for the Parameters of a Zero-Inflated Poisson Predictive Model

In the insurance sector, Zero-Inflated models are commonly used due to the unique nature of insurance data, which often contain both genuine zeros (meaning no claims made) and potential claims. Although active developments in modeling excess zero data have occurred, the use of Bayesian techniques for parameter estimation in Zero-Inflated Poisson models has not been widely explored. This research aims to introduce a new Bayesian approach for estimating the parameters of the Zero-Inflated Poisson model. The method involves employing Gamma and Beta prior distributions to derive closed formulas for Bayes estimators and predictive density. Additionally, we propose a data-driven approach for selecting hyper-parameter values that produce highly accurate Bayes estimates. Simulation studies confirm that, for small and moderate sample sizes, the Bayesian method outperforms the maximum likelihood (ML) method in terms of accuracy. To illustrate the ML and Bayesian methods proposed in the article, a real dataset is analyzed.

Biomarkers associated with pulmonary exacerbations in a randomized trial of nintedanib for radiation pneumonitis

Background and purposeRadiation pneumonitis (RP) is a common side effect of thoracic radiotherapy and often has a long course characterized by acute exacerbations and progression to permanent lung fibrosis. There are no validated biomarkers of prognosis in patients diagnosed with RP. Materials and methodsWe analyzed a time course of serum chemokines, cytokines, and other proteins from patients with grade 2+ RP in a randomized clinical trial of a steroid taper plus nintedanib, a multiple tyrosine kinase inhibitor, versus placebo plus a steroid taper for the treatment of RP. Weighted gene correlation network analysis (WGCNA) and univariable zero inflated Poisson models were used to identify groups of correlated analytes and their associations with clinical outcomes. ResultsThirty enrolled patients had biomarker data available, and 17 patients had enough analytes tested for network analysis. WGNCA identified ten analytes, including transforming growth factor beta-1 (TGF-β1), monocyte chemoattractant protein-1 (MCP-1), and platelet-derived growth factor (PDGF), that in aggregate were correlated with the occurrence of pulmonary exacerbations (p = 0.008), the total number of acute pulmonary exacerbations (p = 0.002), and treatment arm (p = 0.036). By univariable analysis, an increase in rate of change of two components of the RP module were associated with an increased incidence rate of pulmonary exacerbations: interleukin 5 (IL-5, incidence rate ratio (IRR) 1.02, 95% CI 1.01–1.04, p = 0.002), and tumor necrosis factor superfamily 12 (TNFSF12, IRR 1.06, CI 1–1.11, p = 0.036). An increased slope of epidermal growth factor (EGF) was associated with a decreased incidence rate of exacerbations (IRR 0.94, CI 0.89–1, p = 0.036). ConclusionWe identified a panel of serum biomarkers that showed association with nintedanib treatment and acute pulmonary exacerbations in patients with RP. A confirmatory study will be needed to validate this panel for use as a prognostic tool in patients with RP.

Control chart for detecting the scale parameter of the zero‐inflated Poisson model

AbstractWhen monitoring risk in public health, count data commonly exhibit an excessive number of zero, and the zero‐inflated Poisson (ZIP) model is often used to fit this type of data. Most previous methods for monitoring of the ZIP model have focused on the changes in the location parameter and the existence of the scale parameter and usually assumed that the scale parameter is zero in the H0 stage. However, in an objective environment, data often have certain fluctuations, meaning that the scale parameter always exists. Therefore, it is more meaningful to monitor the changes in the scale parameter on top of the predefined baseline than to monitor its existence. In this study, we derive a score test statistic based on the generalized Henderson's joint likelihood function, construct a risk‐adjusted exponentially weighted moving average (EWMA) control chart to monitor the variability of the random effects variance component in the ZIP mixed‐effects model. And the convergence property of the score test statistic is proved through derivation, which shows that the new method has theoretical reliability. The simulation results Indicate that when the scale parameter has different predefined baselines and different variation amplitudes, the proposed method is more effective than the existing RA‐ZIP and PR‐ZIP control charts. In addition, the proposed method is applied to real data from a Hong Kong hospital for online influenza surveillance to demonstrate its practicability.

Copula deep learning control chart for multivariate zero inflated count response variables

With the increasing popularity of big data analysis, research on zero-inflated count data with the copula method has garnered significant attention because zero-inflated count data do not follow a normal distribution and have high correlation among variables. Within the domain of quality control, there has been limited emphasis on multivariate statistical process control (SPC) techniques that specifically address the challenge of multicollinearity within regression models for multivariate zero-inflated count responses. In this paper, we explain a computational challenge in handling big data with parametric generalized linear models, such as the zero-inflated Poisson model. This challenge motivates us to introduce a copula-based deep learning and neural network model involving multivariate zero-inflated count response variables with highly correlated explanatory variables. This approach builds upon existing deep learning and neural network models designed for univariate count responses, expanding them to encompass multivariate count response models through the integration of a copula regression framework. To evaluate the performance of our proposed methodology, we conduct a comparative analysis of accuracies using the zero-inflated Poisson model, univariate deep learning and neural network models, multivariate count response deep learning and neural network models, and our proposed copula deep learning and neural network models. This assessment involves employing metrics such as root mean square error (RMSE), weighted mean absolute percentage error (WMAPE), and mean absolute deviation (MAD). We include both copula-based asymmetrical zero-inflated simulated data and real-world data. We also propose a temporal dependence control chart for assessing the temporal dependence between bivariate zero-inflated count response variables. Our proposed copula deep learning and neural network temporal control charts can check time-varying dependence and outliers by leveraging the Shewhart statistical process control chart and t-copula ARMA-GARCH dynamic control correlation.

P078 United Kingdom rates and determinants of romosozumab prescriptions, a need for greater action

Abstract Background/Aims Romosozumab rapidly reduces fracture risk and has been recommended as a first-line option for post-menopausal persons with severe osteoporosis and a recent major fracture from November 2020 (Scotland) and May 2022 (England, Northern Ireland and Wales). Aim: to describe hospital-level romosozumab prescriptions and the determinants across the four nations. Methods Hospital-level romosozumab prescription data were analysed by country. Potential predictors included surrogates of local patient need (the population of women aged 50 years and over), local FLS status (none, partial, full) and, in England and Wales, the number of patient records submitted to the mandatory national audits for Fracture Liaison Service (FLSDB) and National Hip Fracture Database (NHFD) in 2021. Spearman correlations and zero-inflated Poisson models identified independent determinants of: a) any prescription and b) the number of prescriptions or romosozumab. Results 152 sites were included (England-126, N. Ireland-5, Scotland-14, and Wales-7) with 60 full and 28 partial FLSs. 63 sites recorded a least one romosozumab prescription in 2022. The proportion of hospitals with any prescription varied from 20% in Northern Ireland to 64.3% in Scotland. An estimated four million women aged 50+ lived in areas with no romosozumab delivery. No determinants of any prescription were identified. With England as the referent group, the number of prescriptions was greater in Scotland (p = 0.001) and lower in Northern Ireland (p = &amp;lt;0.001). After adjusting for the number of women aged 50 years and over (p = 0.008), a full FLS but not partial FLS predicted the number of prescriptions (Figure). In England and Wales both local NHFD (Rs = 0.19, p = 0.03) and FLSDB records (Rs = 0.25, p = 0.056) predicted the number of local prescriptions. Conclusion Even after inclusion in national guidelines, there is a care gap of at least four million women in the UK. In NHS sites that have started prescribing romosozumab, the patient numbers are significantly below expected rates. A national implementation strategy should be considered for decision makers, healthcare providers and patient groups to minimise the number of subsequent fragility fractures in the FLS setting by increasing the number of sites implementing romosozumab focusing on FLS first and matching local delivery to local population needs. Disclosure M.K. Javaid: Consultancies; MKJ has received honoraria, travel and/or subsistence expenses from: Amgen, Kyowa Kirin Hakin, UCB, Abbvie, Besin Healthcare, Sanofi. Grants/research support; MKJ has received unrestricted research grants Amgen, Kyowa Kirin Hakin, UCB. T. Jones: Corporate appointments; Employee of British and Irish BioPharma SPRL. S. Hepworth: Corporate appointments; Employee UCB BioPharma SPRL.

Population-average mediation analysis for zero-inflated count outcomes.

Mediation analysis is an increasingly popular statistical method for explaining causal pathways to inform intervention. While methods have increased, there is still a dearth of robust mediation methods for count outcomes with excess zeroes. Current mediation methods addressing this issue are computationally intensive, biased, or challenging to interpret. To overcome these limitations, we propose a new mediation methodology for zero-inflated count outcomes using the marginalized zero-inflated Poisson (MZIP) model and the counterfactual approach to mediation. This novel work gives population-average mediation effects whose variance can be estimated rapidly via delta method. This methodology is extended to cases with exposure-mediator interactions. We apply this novel methodology to explore if diabetes diagnosis can explain BMI differences in healthcare utilization and test model performance via simulations comparing the proposed MZIP method to existing zero-inflated and Poisson methods. We find that our proposed method minimizes bias and computation time compared to alternative approaches while allowing for straight-forward interpretations.

Large-scale data decipher children's scale errors: A meta-analytic approach using the zero-inflated Poisson models.

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the previous research do not adequately capture the structure of the data. By conducting a secondary analysis of aggregated datasets across nine different studies (n=528) and using more appropriate statistical methods, this study provides a more accurate description of the development of scale errors. We implemented the zero-inflated Poisson (ZIP) regression that could directly handle the count data with a stack of zero observations and regarded developmental indices as continuous variables. The results suggested that the developmental trend of scale errors was well documented by an inverted U-shaped curve rather than a simple linear function, although nonlinearity captured different aspects of the scale errors between the laboratory and classroom data. We also found that repeated experiences with scale error tasks reduced the number of scale errors, whereas girls made more scale errors than boys. Furthermore, a model comparison approach revealed that predicate vocabulary size (e.g., adjectives or verbs), predicted developmental changes in scale errors better than noun vocabulary size, particularly in terms of the presence or absence of scale errors. The application of the ZIP model enables researchers to discern how different factors affect scale error production, thereby providing new insights into demystifying the mechanisms underlying these phenomena. A video abstract of this article can be viewed at https://youtu.be/1v1U6CjDZ1Q RESEARCH HIGHLIGHTS: We fit a large dataset by aggregating the existing scale error data to the zero-inflated Poisson (ZIP) model. Scale errors peaked along the different developmental indices, but the underlying statistical structure differed between the in-lab and classroom datasets. Repeated experiences with scale error tasks and the children's gender affected the number of scale errors produced per session. Predicate vocabulary size (e.g., adjectives or verbs) better predicts developmental changes in scale errors than noun vocabulary size.

Influenza epidemiology and treatment in people with a visual disability: A retrospective cohort study

BackgroundVisual disabilities (VD) are expected to rise with an aging population. Persons with VD experience a higher prevalence of chronic and acute diseases. Despite the significance of influenza to this population, there is limited data comparing influenza care disparities between those with VD and those without. ObjectiveThe study aimed to determine the influenza burden and associated healthcare utilization in individuals with VD compared to those without disabilities. MethodsA retrospective cohort study was conducted using the Korean National Health Information Database, encompassing three influenza seasons (2011–2012 to 2013–2014). The influenza incidence and incidence rate ratio (IRR) was calculated. Adjusted IRRs were calculated using a zero-inflated Poisson model. We assessed the risk of admissions and 30-day post-influenza mortality, employing logistic regression or survival analysis. ResultsA total of 504,374 patients (252,964 patients with VD and 251,410 controls) were followed for 1,471,480 person-years. The influenza incidence was higher in the VD cohort than in the control (8.8 vs. 7.8 cases per 1000 person-years). VD cohort had a higher influenza IRR (adjusted IRR 1·13, 95% confidence interval [CI] 1·02–1·25). Severe VD exhibited higher hospitalization risk (adjusted odds ratio [OR] 1·29, 95% CI 1·10–1·20) and increased medical costs. Severe VD was a significant risk factor for mortality (adjusted Hazard Ratio 1·89, 95% CI 1·04–3·45). ConclusionsPeople with VD have a higher influenza incidence, while their outcomes are comparable to those without. Nevertheless, severe VD significantly contributes more to hospitalization, mortality, and medical costs than controls.

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.

Mixed zeros inflated poisson model: an application in grape disease control

Longitudinal data studies describe the behavior of a feature of observations over time through so-called mixed models, which bear this name because they contain fixed effects and random effects. Still, if many of the observations do not show such a characteristic, i.e., the amount of zeros observed is greater than expected, then we say that it is inflated data of zeros. This work aims to describe the applicability of a mixed zero inflated Poisson (ZIP) model to model the temporal progress of mildew (Plasmopara viticultura), which corresponds to fungal disease of the grape, more specifically in the cultivar BRS Vitória, and assess its tolerance for 6 different treatments. This is intended to evaluate the quality of the proposed model fit and to carry out tests comparing the treatment averages to point out which treatments are equally effective in the control of mildew, thus demonstrating the application of the mixed ZIP model in a real and significant problem. A mixed ZIP model was constructed based on methodologies found in the literature and for inference and estimation of the parameters the method of maximum likelihood was used. The results showed that the mixed ZIP model adjusted well to the data, and hypothesis testing for average pointed to equality in efficacy between some treatment groups, thus making it possible to obtain a treatment with fewer sprays within each group.