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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. [ABSTRACT FROM AUTHOR]

In sampling of pests with low densities, it is common to obtain a large number of zeros, which is difficult to manage since the Poisson and negative binomial probability distributions are not suitable for modeling and equations to estimate the optimal sample size are not available. In this study model the excess of zeros by estimating parameters through the methods of moments and maximum likelihood of the zero-inflated Poisson and zero-inflated negative binomial distributions, and to derive equations to calculate the optimal sample size. Systematic sampling was used to select 100 trees per grove of Río Red grapefruit (Citrus paradisi Macfad) at Finca Sayula, Veracruz, Mexico (latitude 19.20722, longitude -96.35194), from June to July 2021 and January 2022. The number of leafminers (Phyllocnistis citrella Stainton) and aphids (Toxoptera citricida Kirkaldy) present in three leaves per shoot per tree, considered as a sample unit, was counted. Simulations were performed in RStudio with different proportions of zero (0.1, 0.4, and 0.6) to compare the parameters obtained in the field using the methods of moments and maximum likelihood. Equations were derived to estimate the optimal sample size in studies of pests with low densities, based on the zero-inflated Poisson and zero-inflated negative binomial probability distributions. The method of moments yields optimal sample sizes smaller than those obtained by maximum likelihood, because they distinguish the origin from zero, so its use is recommended. [ABSTRACT FROM AUTHOR]

Abstract Background Choosing appropriate models for count health outcomes remains a challenge to public health researchers and the validity of the findings thereof. For count data, the mean–variance relationship and proportion of zeros is a major determinant of model choice. This study aims to compare and identify the best Bayesian count modelling technique for the number of childhood vaccine uptake in Nigeria. Methods We explored the performances of Poisson, negative binomial and their zero-inflated forms in the Bayesian framework using cross-sectional data pooled from the Nigeria Demographic and Health Survey conducted between 2003 and 2018. In multivariable analysis, these Bayesian models were used to identify factors associated with the number of vaccine uptake among children. Model selection was based on the -2 Log-Likelihood (-2 Log LL), Leave-One-Out Cross-Validation Information Criterion (LOOIC) and Watanabe-Akaike/Widely Applicable Information Criterion (WAIC). Results Exploratory analysis showed the presence of excess zeros and overdispersion with a mean of 4.36 and a variance of 12.86. Observably, there was a significant increase in vaccine uptake over time. Significant factors included the mother’s age, level of education, religion, occupation, desire for last-child, place of delivery, exposure to media, birth order of the child, wealth status, number of antenatal care visits, postnatal attendance, healthcare decision maker, community poverty, community illiteracy, community unemployment, rural proportion and number of health facilities per 100,000. The zero-inflated negative binomial model was best fit with -2Log LL of -27171.47, LOOIC of 54464.2, and WAIC of 54588.0. Conclusion The Bayesian zero-inflated negative binomial model was most appropriate to identify factors associated with the number of childhood vaccines received in Nigeria due to the presence of excess zeros and overdispersion. Improving vaccine uptake by addressing the associated risk factors should be promptly embraced.

Single cell RNA-sequencing (scRNA-seq) technology has significantly advanced the understanding of transcriptomic signatures. Although various statistical models have been used to describe the distribution of gene expression across cells, a comprehensive assessment of the different models is missing. Moreover, the growing number of features associated with scRNA-seq datasets creates new challenges for analytical accuracy and computing speed. Here, we developed a Python-based package (TensorZINB) to solve the zero-inflated negative binomial (ZINB) model using the TensorFlow deep learning framework. We used a sequential initialization method to solve the numerical stability issues associated with hurdle and zero-inflated models. A recursive feature selection protocol was used to optimize feature selections for data processing and downstream differentially expressed gene (DEG) analysis. We proposed a class of hybrid models combining nested models to further improve the model's performance. Additionally, we developed a new method to convert a continuous distribution to its equivalent discrete form, so that statistical models can be fairly compared. Finally, we showed that the proposed TensorFlow algorithm (TensorZINB) was numerically stable and that its computing speed and performance were superior to those of existing ZINB solvers. Moreover, we implemented seven hurdle and zero-inflated statistical models in Python and systematically assessed their performance using a real scRNA-seq dataset. We demonstrated that the ZINB model achieved the lowest Akaike information criterion compared with other models tested. Taken together, TensorZINB was accurate, efficient and scalable for the implementation of ZINB and for large-scale scRNA-seq data analysis with DEG identification. [ABSTRACT FROM AUTHOR]

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.

In response to controlling the COVID-19 pandemic, the Indian government implemented a nationwide lockdown on 24 March 2020. We study the effect of lockdown on crimes against women. Using district-level panel data from 457 districts in India for five months (before, during and post-lockdown), we examine the interaction effect of COVID-19 containment zones and lockdown on crimes against women. Results suggest a differential impact of the lockdown on crime across different containment zones. Compared to the most COVID-19 affected zone, the less affected zones show a larger fall in crimes against women due to the imposition of a lockdown. JEL Codes: C33, J16, J78 [ABSTRACT FROM AUTHOR]

Background: Choosing appropriate models for count health outcomes remains a challenge to public health researchers and the validity of the findings thereof. For count data, the mean–variance relationship and proportion of zeros is a major determinant of model choice. This study aims to compare and identify the best Bayesian count modelling technique for the number of childhood vaccine uptake in Nigeria. Methods: We explored the performances of Poisson, negative binomial and their zero-inflated forms in the Bayesian framework using cross-sectional data pooled from the Nigeria Demographic and Health Survey conducted between 2003 and 2018. In multivariable analysis, these Bayesian models were used to identify factors associated with the number of vaccine uptake among children. Model selection was based on the -2 Log-Likelihood (-2 Log LL), Leave-One-Out Cross-Validation Information Criterion (LOOIC) and Watanabe-Akaike/Widely Applicable Information Criterion (WAIC). Results: Exploratory analysis showed the presence of excess zeros and overdispersion with a mean of 4.36 and a variance of 12.86. Observably, there was a significant increase in vaccine uptake over time. Significant factors included the mother's age, level of education, religion, occupation, desire for last-child, place of delivery, exposure to media, birth order of the child, wealth status, number of antenatal care visits, postnatal attendance, healthcare decision maker, community poverty, community illiteracy, community unemployment, rural proportion and number of health facilities per 100,000. The zero-inflated negative binomial model was best fit with -2Log LL of -27171.47, LOOIC of 54464.2, and WAIC of 54588.0. Conclusion: The Bayesian zero-inflated negative binomial model was most appropriate to identify factors associated with the number of childhood vaccines received in Nigeria due to the presence of excess zeros and overdispersion. Improving vaccine uptake by addressing the associated risk factors should be promptly embraced. [ABSTRACT FROM AUTHOR]

Mediation analyses play important roles in making causal inference in biomedical research to examine causal pathways that may be mediated by one or more intermediate variables (ie, mediators). Although mediation frameworks have been well established such as counterfactual‐outcomes (ie, potential‐outcomes) models and traditional linear mediation models, little effort has been devoted to dealing with mediators with zero‐inflated structures due to challenges associated with excessive zeros. We develop a novel mediation modeling approach to address zero‐inflated mediators containing true zeros and false zeros. The new approach can decompose the total mediation effect into two components induced by zero‐inflated structures: the first component is attributable to the change in the mediator on its numerical scale which is a sum of two causal pathways and the second component is attributable only to its binary change from zero to a non‐zero status. An extensive simulation study is conducted to assess the performance and it shows that the proposed approach outperforms existing standard causal mediation analysis approaches. We also showcase the application of the proposed approach to a real study in comparison with a standard causal mediation analysis approach. [ABSTRACT FROM AUTHOR]

Research has shown that crime is concentrated at a small number of micro-places. This research has found that these spatial patterns are generalisable across different urban settings and are relatively stable over time. Despite this, little is known about the explanatory factors of crime at the micro-spatial scale. Using police incident data and land-use information obtained from the Vancouver Open-Data catalogue, zero-inflated negative binomial models are used to explain the spatial patterns of various types of property crimes at the street segment level. The results demonstrate that aspects of micro-places (multi-unit housing, restaurants (with and without liquor), and retail outlets) have a significant positive impact on these crime types at the micro-spatial level. Depending on the crime type, the strength of the relationship varies in magnitude and level of significance. [ABSTRACT FROM AUTHOR]

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

Single-cell RNA sequencing (scRNA-seq) data often contain doublets, where a doublet manifests as 1 cell barcode that corresponds to combined gene expression of two or more cells. Existence of doublets can lead to spurious biological interpretations. Here, we present single-cell MOdel-driven Doublet Detection (scMODD), a model-driven algorithm to detect doublets in scRNA-seq data. ScMODD achieved similar performance compared to existing doublet detection algorithms which are primarily data-driven, showing the promise of model-driven approach for doublet detection. When implementing scMODD in simulated and real scRNA-seq data, we tested both the negative binomial (NB) model and the zero-inflated negative binomial (ZINB) model to serve as the underlying statistical model for scRNA-seq count data, and observed that incorporating zero inflation did not improve detection performance, suggesting that consideration of zero inflation is not necessary in the context of doublet detection in scRNA-seq. [ABSTRACT FROM AUTHOR]

Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Mosquito biting or exposure is a risk factor for malaria transmission. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in vector abundance hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots of vector abundance, and spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at abundance hotspots which may increase malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda.  Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown reduction of household biting propensity. Improvements in house quality should be recommended as a supplementary measure for malaria control reducing risk of infection.

Background: Social media has become an essential tool to implement risk communication, giving health information could gain more exposure by retweeting during the COVID-19 pandemic. Methods: Content analysis was conducted to scrutinize the official (national and provincial) public health agencies' Weibo posts (n = 4396) to identify features of information sources and message features (structure, style content). The Zero-Inflated Negative Binomial (ZINB) model was adopted to analyze the association between these features and the frequency of the retweeted messages. Results: Results indicated that features of source and health information, such as structure, style, and content, were correlated to retweeting. The results of IRR further suggested that compared to provincial accounts, messages from national health authorities' accounts gained more retweeting. Regarding the information features, messages with hashtags#, picture, video have been retweeted more often than messages without any of these features respectively, while messages with hyperlinks received fewer retweets than messages without hyperlinks. In terms of the information structure, messages with the sentiment (!) have been retweeted more frequently than messages without sentiment. Concerning content, messages containing severity, reassurance, efficacy, and action frame have been retweeted with higher frequency, while messages with uncertainty frames have been retweeted less often. Conclusions: Health organizations and medical professionals should pay close attention to the features of health information sources, structures, style, and content to satisfy the public's information needs and preferences to promote the public's health engagement. Designing suitable information systems and promoting health communication strategies during different pandemic stages may improve public awareness of the COVID-19, alleviate negative emotions, and promote preventive measures to curb the spread of the virus. [ABSTRACT FROM AUTHOR]

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Introduction: Recently the Federal Railroad Administration (FRA) released a new model for accident prediction at railroad grade crossings using a Zero Inflated Negative Binomial (ZINB) model with Empirical Bayes (EB) adjustments for accident history (2). This new model is adopted from the work that was conducted by the authors (3–6). The unique feature of the new FRA model is that it has a single equation for all three warning devices (crossbuck, flashing light, and gates) and uses the same variables regardless of the warning devices at the crossing. Since the New FRA model incorporates the warning device category as one of the variables in its model equation, the predicted accident frequency is higher when a crossing has crossbucks than flashing lights, and higher when it has flashing lights than gates. While this model is significantly better than the old USDOT model (7), its shortcoming is that the single equation does not accurately represent the field condition. Method: This paper presents the ZINEBS model (Zero Inflated Negative binomial with Empirical Bayes adjustment System). The ZINEBS model gives three different equations depending on the type of warning device used at the crossings (gates, flashing lights, and crossbucks). The three equations use variables, some of which are common across all warning devices, while other variables are specific to a warning device. The predicted values for the ZINEBS model show a closer agreement with the field data than the new FRA model. This observation was true for all three warning device types analyzed. Practical Applications: Based on the results of this study, the ZINEBS compliments the new FRA model and should be used when the single equation is not adequately representing the role of traffic control device types and relevant variables associated with that device type. [ABSTRACT FROM AUTHOR]

Abstract Background In India, around 20,000 women die every year due to abortion-related complications. In count data modeling, there is sometimes a prevalence of zero counts. This article is concerned with the estimation of various count regression models to predict the average number of spontaneous abortions among women in Punjab and few northern states in India. The study also assesses the factors associated with the number of spontaneous abortions. Methods This study includes 27,173 married women of Punjab obtained from the DLHS-4 survey (2012–13) to train the count models. The study predicts the average number of spontaneous abortions using various count regression models, and also identifies the determinants affecting the spontaneous abortions. Further, the best model is validated with other northern states of India using the latest data (NFHS-4, 2015–16). Results Statistical comparisons among four estimation methods reveals that the ZINB model provides the best prediction for the number of spontaneous abortions. The study suggests total children born to a woman, antenatal care (ANC) place, place of residence, woman’s education, and economic status are the most significant factors affecting the instance of spontaneous abortion. Conclusions This article offers a practical demonstration of techniques designed to handle count outcome variables. The statistical comparisons among four estimation models revealed that the ZINB model provides the best prediction for the number of spontaneous abortions, and it suggests policymakers to use this model to predict the number of spontaneous abortions. The study recommends promoting higher education among women in Punjab and other northern states of India. It also suggests that women must receive institutional antenatal care and have a limited number of children.

Swine erysipelas (SE) is one of the best-known and most serious diseases that affect domestic pigs, which is caused by Erysipelothrix rhusiopathiae. It is endemic in Nanning and has been circulating for decades, causing considerable economic losses. The aim of this study was to investigate the effect of meteorological-related variations on the epidemiology of swine erysipelas in Nanning City, a subtropical city of China. Data on monthly counts of reported swine erysipelas and climate data in Nanning are provided by the authorities over the period from 2006 to 2015. Cross-correlation analysis was applied to identify the lag effects of meteorological variables. A zero-inflated negative binomial (ZINB) regression model was used to evaluate the independent contribution of meteorological factors to SE transmission. After controlling seasonality, autocorrelation and lag effects, the results of the model indicated that Southern Oscillation Index (SOI) has a positive effect on SE transmission. Moreover, there is a positive correlation between monthly mean maximum temperature and relative humidity at 0-1 month lag and the number of cases. Furthermore, there is a positive association between the number of SE incidences and precipitation, with a lagged effect of 2 months. In contrast, monthly mean wind velocity negatively correlated with SE of the current month. These findings indicate that meteorological variables may play a significant role in SE transmission in southern China. Finally, more public health actions should be taken to prevent and control the increase of SE disease with consideration of local weather variations.

Background Malaria parasites reproduce asexually, leading to the production of large numbers of genetically identical parasites, here termed a clonal line or clone. Infected hosts may harbor one or more clones, and the number of clones in a host is termed multiplicity of infection (MOI). Understanding the distribution of parasite clones among hosts can shed light on the processes shaping this distribution and is important for modeling MOI. Here, I determine whether the distribution of clones of the lizard malaria parasite Plasmodium mexicanum differ significantly from statistical distributions commonly used to model MOI and logical extensions of these models. Methods The number of clones per infection was assessed using four microsatellite loci with the maximum number of alleles at any one locus used as a simple estimate of MOI for each infection. I fit statistical models (Poisson, negative binomial, zero-inflated models) to data from four individual sites to determine a best fit model. I also simulated the number of alleles per locus using an unbiased estimate of MOI to determine whether the simple (but potentially biased) method I used to estimate MOI influenced model fit. Results The distribution of clones among hosts at individual sites differed significantly from traditional Poisson and negative binomial distributions, but not from zero-inflated modifications of these distributions. A consistent excess of two-clone infections and shortage of one-clone infections relative to all fit distributions was also observed. Any bias introduced by the simple method for estimating of MOI did not appear to qualitatively alter the results. Conclusions The statistical distributions used to model MOI are typically zero-truncated; truncating the Poisson or zero-inflated Poisson yield the same distribution, so the reasonable fit of the zero-inflated Poisson to the data suggests that the use of the zero-truncated Poisson in modeling is adequate. The improved fit of zero-inflated distributions relative to standard distributions may suggest that only a portion of the host population is located in areas suitable for transmission even at small sites (

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities. [ABSTRACT FROM AUTHOR]

BackgroundAtopy varies by ethnicity, even within Latino groups. This variation might be due to environmental, sociocultural, or genetic factors.ObjectiveWe sought to examine risk factors for atopy within a nationwide study of US Latino children with and without asthma.MethodsAeroallergen skin test responses were analyzed in 1830 US Latino subjects. Key determinants of atopy included country/region of origin, generation in the United States, acculturation, genetic ancestry, and site to which subjects migrated. Serial multivariate zero-inflated negative binomial regressions stratified by asthma status examined the association of each key determinant variable with the number of positive skin test responses. In addition, the independent effect of each key variable was determined by including all key variables in the final models.ResultsIn baseline analyses African ancestry was associated with 3 times (95% CI, 1.62-5.57) as many positive skin test responses in asthmatic participants and 3.26 times (95% CI, 1.02-10.39) as many positive skin test responses in control participants. Generation and recruitment site were also associated with atopy in crude models. In final models adjusted for key variables, asthmatic patients of Puerto Rican (exp[β] [95% CI], 1.31 [1.02-1.69]) and mixed (exp[β] [95% CI], 1.27 [1.03-1.56]) ethnicity had a greater probability of positive skin test responses compared with Mexican asthmatic patients. Ancestry associations were abrogated by recruitment site but not region of origin.ConclusionsPuerto Rican ethnicity and mixed origin were associated with degree of atopy within US Latino children with asthma. African ancestry was not associated with degree of atopy after adjusting for recruitment site. Local environment variation, represented by site, was associated with degree of sensitization.

Abstract Background Controlling drinking behaviour requires the ability to block out distracting alcohol cues in situations in which drinking is inappropriate or harmful. However, at present few studies have investigated whether distraction and response inhibition to contextual alcohol cues are related to alcohol use in adolescents and young adults. We aimed to investigate whether tendencies towards distraction and failures of response inhibition in the presence of contextual alcohol cues, and alcohol craving were associated with higher levels of alcohol consumption, beyond what could be explained by demographic variables. Methods To test this, 108 participants (Mean age = 21.7, range = 16–27), whom were both drinkers and non-drinkers performed a modified Go/NoGo task tailored to measure distraction and response inhibition in the presence of alcohol cues relative to neutral stimuli. Alcohol craving was assessed using a visual analogue scale of craving for different types of alcohol cues. Levels of alcohol use and problematic alcohol use were assessed using a self-report measure of number of drinking days in the previous month and the Alcohol Use Disorders Identification Test. Data were analysed using sequential multiple regression using a zero-inflated negative binomial distribution model. Results Drinking days correlated with distraction but not response inhibition to contextual alcohol cues. Sequential regression analyses revealed that the inclusion of distraction bias accounted for 11% additional variance (significant) in alcohol use, in addition to that explained by demographics alone (17%). Craving for alcohol explained an additional 30% variance (significant) in alcohol use. Conclusions The results reported here support the idea that both biased distraction towards alcohol cues and alcohol craving are associated with preceding drinking days, but not necessarily drinking status. Further studies are warranted that address whether cognitive distraction to alcohol-related cues cause or is an effect of alcohol use among youth.

Abstract Objective About three-fourth of adults with new-onset epilepsy become seizure-free with current anti-epileptic drugs, but around one-fourth of the patients continue to experience seizure which increases the risk of accident, disability, death and treatment side effects. Therefore, this study aimed to address the gap in determining the magnitude of the number of seizure attacks and identify the factors that provoke a repeated seizure in a patient with epilepsy. Results A total of 166(40.68%) study participants were experienced seizure attacks with a minimum of one and a maximum of seventeen times attacks. Perceived exposure to noise (adjusted incidence risk ratio (AIRR) = 1.91, 95% confidence interval (CI) [1.46, 2.49]), light (AIRR = 1.48, 95% CI [1.09, 2.00]), head injury (AIRR = 1.71, 95% CI [1.14, 2.57]) and sleep deprivations (AIRR = 1.41, 95% CI [1.02, 1.94]) were associated with increased incidence of seizure, while adherence adjusted odds ratio (AOR) = 18.18, 95% CI [3.49, 94.63]), being in middle wealth index (AOR = 3.52, 95% CI [1.14, 11.02]) and being in rich wealth index (AOR = 4.05, 95% CI [1.54, 10.69]) were associated with inflation of zero count.

Abstract Bifidobacterium and Lactobacillus are beneficial for human health, and many strains of these two genera are widely used as probiotics. We used two large datasets published by the American Gut Project (AGP) and a gut metagenomic dataset (NBT) to analyze the relationship between these two genera and the community structure of the gut microbiota. The meta‐analysis showed that Bifidobacterium, but not Lactobacillus, is among the dominant genera in the human gut microbiota. The relative abundance of Bifidobacterium was elevated when Lactobacillus was present. Moreover, these two genera showed a positive correlation with some butyrate producers among the dominant genera, and both were associated with alpha diversity, beta diversity, and the robustness of the gut microbiota. Additionally, samples harboring Bifidobacterium present but no Lactobacillus showed higher alpha diversity and were more robust than those only carrying Lactobacillus. Further comparisons with other genera validated the important role of Bifidobacterium in the gut microbiota robustness. Multivariate analysis of 11,744 samples from the AGP dataset suggested Bifidobacterium to be associated with demographic features, lifestyle, and disease. In summary, Bifidobacterium members, which are promoted by dairy and whole‐grain consumption, are more important than Lactobacillus in maintaining the diversity and robustness of the gut microbiota.

Abstract Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

Background: In India, around 20,000 women die every year due to abortion-related complications. In count data modeling, there is sometimes a prevalence of zero counts. This article is concerned with the estimation of various count regression models to predict the average number of spontaneous abortions among women in Punjab and few northern states in India. The study also assesses the factors associated with the number of spontaneous abortions. Methods: This study includes 27,173 married women of Punjab obtained from the DLHS-4 survey (2012–13) to train the count models. The study predicts the average number of spontaneous abortions using various count regression models, and also identifies the determinants affecting the spontaneous abortions. Further, the best model is validated with other northern states of India using the latest data (NFHS-4, 2015–16). Results: Statistical comparisons among four estimation methods reveals that the ZINB model provides the best prediction for the number of spontaneous abortions. The study suggests total children born to a woman, antenatal care (ANC) place, place of residence, woman's education, and economic status are the most significant factors affecting the instance of spontaneous abortion. Conclusions: This article offers a practical demonstration of techniques designed to handle count outcome variables. The statistical comparisons among four estimation models revealed that the ZINB model provides the best prediction for the number of spontaneous abortions, and it suggests policymakers to use this model to predict the number of spontaneous abortions. The study recommends promoting higher education among women in Punjab and other northern states of India. It also suggests that women must receive institutional antenatal care and have a limited number of children. [ABSTRACT FROM AUTHOR]

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. However, interpretations of those models focus on the at-risk subpopulation of a two-component population mixture and fail to provide direct inference about marginal effects for the overall population. Recently, new approaches have been proposed to facilitate such marginal inferences for count responses with excess zeros. However, they are likelihood based and impose strong assumptions on data distributions. In this paper, we propose a new distribution-free, or semiparametric, alternative to provide robust inference for marginal effects when population mixtures are defined by zero-inflated count outcomes. The proposed method also applies to longitudinal studies with missing data following the general missing at random mechanism. The proposed approach is illustrated with both simulated and real study data. [ABSTRACT FROM AUTHOR]

Fecal egg count (FEC) is used as an indicator of parasite infection level in sheep. The distribution of FEC is non-Gaussian and typically overdispersed, often with an excess in zero counts. Quantifying the extent of inflation of zero counts can be difficult. Our objective was to assess the potential zero-inflation problem in FEC resulting from variation in infection with gastrointestinal nematodes by using a generalized linear model approach. The zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models are useful techniques to analyze count data with excess zeros; ZINB also handles overdispersion. The ZINB model has the potential to delineate 'true' zeros, in this case, animals resistant to parasitism and thereby with zero FEC, from 'false' zeros, animals never or minimally exposed to a parasite challenge. By distinguishing false zeros, those animals expressing parasite resistance may be more clearly identified. Two datasets on Katahdin sheep, a hair breed known to express resistance to gastrointestinal nematodes, were investigated; a smaller set (n = 3,048) with FEC and FAMACHA (FAM) scores, a subjective measure of anemia indicative of parasitism by Haemonchus contortus, a blood sucking helminth; and a larger set (n = 14,405) with FEC and a contemporary group (CG) designation, assigned based on the flock, birth year, management group, and FEC recording date of the animal. Among animals with FAM recorded, 14% had scores indicative of at least border line anemia. Amongst the 410 CG, 22% had mean FEC more than 500 egg/g, a threshold value routinely used to indicate a substantial infection level with H. contortus. For each dataset, the Poisson, Negative Binomial, ZIP, and ZINB models were fit and compared using R (pscl package) and SAS software, with the ZINB providing the best fit. In the models considered, FEC was the response variable and either FAM or CG was the explanatory variable, depending on the dataset. Despite evidence of parasite challenge, the true and false zeros could not be delineated in both data sets using these models. The estimated proportion of false zeros due to inflation did not differ from the proportion of zeros observed in the data set. Either all zeros coincided with no infection, which seems unlikely in Katahdins, or neither FAM nor CG provided sufficient information to distinguish resistant from uninfected individuals. Alternative or additional explanatory variables, such as packed cell volume or immunoglobulin concentrations indicative of parasitic infection, may be necessary to separate true from false zero FEC in sheep challenged with gastrointestinal nematodes using the ZINB model. [ABSTRACT FROM AUTHOR]

Compliance to standard has been the main doctrine in highway design, but its relationship with accident count has not been widely scrutinized. One of the key programs in road safety in Indonesia is road-worthiness test which assesses the compliance of a road to national design specifications and criteria. In light of current improvements in the crash data system in Indonesia, this study is carried out to develop a model to predict the accident count per type of crashes and to identify significant road features based on their compliance to a national standard. 272,200 km of arterial road in East Java North Corridor (EJNC) is selected as case study and 2012–2014 crash data is analyzed. Zero-Inflated Negative Binomial (ZINB) Model is preferred to develop crash prediction model with significant variables. This study has several findings. First, the constant values of the prediction model are relatively close to the average number of accident which implies that the mere compliance to current standard cannot warrant the safeness of Indoensian highways. Second, the number of median opening per unit length and disturbance level to pedestrian and road reserve area are the features that having positive relationships with total accident count. Meanwhile, the ROW disturbance, conformance of intersection and of road marking also show significant value but negative relationship with total accident count. Third, significant variables for each type of crash may have different sign. For example, in right angle crash, median width has positive relation with the number of accident, while in run off and rear end crash, median width compliance is shown to have negative relation.

Abstract Background Childhood mortality has remained a major challenge to public health amongst families in Nigeria and other developing countries. The menace of incessant childhood mortality has been a major concern and this calls for studies to generate new scientific evidence to determine its prevalence and explore predisposing factors associated with it in Nigeria. Method Data was obtained from Nigeria DHS, 2013. The study outcome variable was the total number of children lost by male partners and female partners respectively who were married. The difference between the numbers of child births and the number of living children was used to determine the number of children lost. Study variables were obtained for 8658 couples captured in the data set. Descriptive statistics were computed to examine the presence of over-dispersion and zero occurrences. Data were analysed using STATA Software version 12.0. Zero-inflated negative binomial (ZINB) regression analysis was carried out to determine the factors associated with childhood mortality. Results of ZINB were reported in terms of IRR and 95% confidence interval (CI). Results The age (mean ± std.) of male and female participants were 36.88 ± 7.37 and 28.59 ± 7.30 respectively. The data showed that 30.8% women reported loss of children and 37.3% men reported the same problem. The study revealed age (years), region, residence, education, wealth index, age at first birth and religion of father and mother as factors associated with childhood mortality. In terms of education, secondary and tertiary educated fathers exhibited 3.8% and 12.1% lower risk of childhood mortality respectively than non-educated fathers. The results showed that the risk of childhood mortality are 26.7%, 39.7 and 45.9% lower among the mothers having primary, secondary and tertiary education respectively than those with no formal education. The mothers living in rural areas experienced 28.3% increase in childhood mortality than those in urban areas, while the fathers in rural areas experienced 33.5% increase in childhood mortality than the urban areas. The risk of childhood mortality was significantly lower in middle, richer and richest (11.1%, 37.5 and 49%) economic quintiles respectively when compared to the risk of childhood mortality with female spouse who are poorest. Similar results were obtained for the fathers, with reduction in the incidence-rate ratio of 3.3%, 20.2 and 28.7% for middle, richer and richest economic quintiles respectively, compared to the poorest status. Furthermore, region and religion were found to be significant factors associated with childhood mortality in Nigeria. Conclusion The findings suggested that age, region, residence, education, wealth index, age at first birth and religion of fathers and mothers are key determinants associated with childhood mortality. The correlation between childhood mortality and fathers’ and mothers’ ages were found to increase the incidence of the outcome for every unit increase in age. The converse was however, true for age at first birth which was also statistically significant. The implication of this study is that policy makers and stakeholders in health care should provide for improved living standards to achieve good life expectancy meeting SDG3.

Motivated by a study examining spatiotemporal patterns in inpatient hospitalizations, we propose an efficient Bayesian approach for fitting zero-inflated negative binomial models. To facilitate posterior sampling, we introduce a set of latent variables that are represented as scale mixtures of normals, where the precision terms follow independent P'olya-Gamma distributions. Conditional on the latent variables, inference proceeds via straightforward Gibbs sampling. For fixed-effects models, our approach is comparable to existing methods. However, our model can accommodate more complex data structures, including multivariate and spatiotemporal data, settings in which current approaches often fail due to computational challenges. Using simulation studies, we highlight key features of the method and compare its performance to other estimation procedures. We apply the approach to a spatiotemporal analysis examining the number of annual inpatient admissions among United States veterans with type 2 diabetes. [ABSTRACT FROM AUTHOR]

While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with excess zeros. This work develops a zero-inflated sum-of-Conway-Maxwell-Poissons (ZISCMP) regression as a flexible analysis tool to model count data that express significant data dispersion and contain excess zeros. This class of models contains several special case zero-inflated regressions, including zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), zero-inflated binomial (ZIB), and the zero-inflated Conway-Maxwell-Poisson (ZICMP). Through simulated and real data examples, we demonstrate class flexibility and usefulness. We further utilize it to analyze shark species data from Australia's Great Barrier Reef to assess the environmental impact of human action on the number of various species of sharks. [ABSTRACT FROM AUTHOR]

Due to the technical advancement and popularity of social media in adolescent lives, there is a growing social and policy concern about cyberbullying and victimization. However, research about cyberbullying victimization and perpetration among African American youth is limited. The current study focuses on a nationally representative sample of 2560 African American youths. Data were drawn from the 2009–2010 Health Behavior in School-aged Children, negative binomial and zero-inflated negative binomial analyses were incorporated, and the social-ecological approach was applied to examine the correlates of cyberbullying victimization and perpetration among African Americans. Results revealed that fathers' monitoring and unstructured activities with peers had a significant effect on the probability of being cyberbullied or engaging in cyberbullying in both analyses. Also, youth who talked more about their problems with friends were less likely to be cyberbullied. Further, youth who spent less time using computers had an significanlty lower likelihood of engaging in cyberbullying. The significance and implications of the social-ecological approach for African American youth cyberbullying perpetration and victimization are also discussed. • African American youth's behavioral characteristics affect the odds of cyberbullying. • Unstructured activities with peers increase cyberbullying perpetration and victimization risk. • Face-to-face bullying in school increases the likelihood of cyberbullying. • Spending more time on the Internet increases the likelihood of cyberbullying. [ABSTRACT FROM AUTHOR]

When a count data set has excessive zero counts, nonzero counts are overdispersed, and the effect of a continuous covariate might be nonlinear, for analysis a semiparametric zero-inflated negative binomial (ZINB) regression model is proposed. The unspecified smooth functional form for the continuous covariate effect is approximated by a cubic spline. The semiparametric ZINB regression model is fitted by maximizing the likelihood function. The likelihood ratio procedure is used to evaluate the adequacy of a postulated parametric functional form for the continuous covariate effect. An extensive simulation study is conducted to assess the finite-sample performance of the proposed test. The practicality of the proposed methodology is demonstrated with data of a motorcycle survey of traffic regulations conducted in 2007 in Taiwan by the Ministry of Transportation and Communication. [ABSTRACT FROM AUTHOR]

Epiphytic bryophytes are known to perform essential ecosystem functions, but their sensitivity to environmental quality and change makes their survival and development vulnerable to global changes, especially habitat loss in urban environments. Fortunately, extensive urban tree planting programs worldwide have had a positive effect on the colonization and development of epiphytic bryophytes. However, how epiphytic bryophytes occur and grow on planted trees remain poorly known, especially in urban environments. In the present study, we surveyed the distribution of epiphytic bryophytes on tree trunks in a Schima superba Gardn. et Champ. urban plantation and then developed count data models, including tree characteristics, stand characteristics, human disturbance, terrain factors, and microclimate to predict the drivers on epiphytic bryophyte recruitment. Different counting models (Poisson, Negative binomial, Zero-inflated Poisson, Zero-inflated negative binomial, Hurdle-Poisson, Hurdle-negative binomial) were compared for a data analysis to account for the zero-inflated data structure. Our results show that (i) the shaded side and base of tree trunks were the preferred locations for bryophytes to colonize in urban plantations, (ii) both hurdle models performed well in modeling epiphytic bryophyte recruitment, and (iii) both hurdle models showed that the tree height, diameter at breast height (DBH), leaf area index (LAI), and altitude (ALT) promoted the occurrence of epiphytic bryophytes, but the height under branch and interference intensity of human activities opposed the occurrence of epiphytic bryophytes. Specifically, DBH and LAI had positive effects on the species richness recruitment count; similarly, DBH and ALT had positive effects on the abundance recruitment count, but slope had a negative effect. To promote the occurrence and growth of epiphytic bryophytes in urban tree planting programs, we suggest that managers regulate suitable habitats by cultivating and protecting large trees, promoting canopy closure, and controlling human disturbance.

In many biomedical applications, covariates are naturally grouped, with variables in the same group being systematically related or statistically correlated. Under such settings, variable selection must be conducted at both group and individual variable levels. Motivated by the widespread availability of zero-inflated count outcomes and grouped covariates in many practical applications, we consider group regularization for zero-inflated negative binomial regression models. Using a least squares approximation of the mixture likelihood and a variety of group-wise penalties on the coefficients, we propose a unified algorithm (Gooogle: Group Regularization for Zero-inflated Count Regression Models) to efficiently compute the entire regularization path of the estimators. We investigate the finite sample performance of these methods through extensive simulation experiments and the analysis of a German health care demand dataset. Finally, we derive theoretical properties of these methods under reasonable assumptions, which further provides deeper insight into the asymptotic behavior of these approaches. The open source software implementation of this method is publicly available at: https://github.com/himelmallick/Gooogle. [ABSTRACT FROM AUTHOR]

This study investigates how the age of industrial parks may affect their tenants’ performance, to find ways to enhance the positive effects of industrial parks and the efficiency of their regeneration. We identify a relationship between the parks’ age and tenants’ performance. Further, we estimate changes in tenant performance against the age of the park. The analysis is based on firm-level data of 1812 small- and medium-sized manufacturing firms with only one factory in any given industrial park in 2016. The important findings that can be drawn from our study are as follows. First, the relationship between the park’s age and patent applications filed by tenants (the proxy of firm performance) follows a U-shape. Firms located in very new or longer established parks have more patents. Second, the relationship between the age of the park and tenants’ sales follows an inverted U-shape. Firms in middle-aged parks show higher sales per worker. Finally, firms that have just moved into the park or young firms moving into the park perform better. We suggest that a government body should create different approaches for improving innovation and management performance to regenerate industrial parks. [ABSTRACT FROM AUTHOR]

Damage and destruction to schools from climate-related disasters can have significant and lasting impacts on curriculum and educational programs, educational attainment, and future income-earning potential of affected students. As such, assessing the potential impact of hazards is crucial to the ability of individuals, households, and communities to respond to natural disasters, extreme events, and economic crises. Yet, few studies have focused on assessing the vulnerability of schools in coastal regions of the USA. Using Hurricane Ike's tropical storm wind swath in the State of Texas as our study area, we: (1) assessed the spatial distribution patterns of school closures and (2) tested the relationship between school closure and vulnerability factors (namely physical exposure and school demographics) using zero-inflated negative binomial regression models. The regression results show that higher probabilities of hurricane strikes, more urbanized school districts, and school districts located in coastal counties on the right side of Ike's path have significant positive associations with an increase in the number of school closure days. Socioeconomic characteristics were not significantly associated with the number of days closed, with the exception of proportion of Hispanic youth in schools, a result which is not supported by the social vulnerability literature. At a practical level, understanding how hurricanes may adversely impact schools is important for developing appropriate preparedness, mitigation, recovery, and adaptation strategies. For example, school districts on the right side of the hurricane track can plan in advance for potential damage and destruction. The ability of a community to respond to future natural disasters, extreme events, and economic crises depends in part on mitigating these adverse effects. [ABSTRACT FROM AUTHOR]

Overdispersion is a common phenomenon in Poisson modelling. The generalized Poisson (GP) distribution accommodates both overdispersion and under dispersion in count data. In this paper, we briefly overview different overdispersed and zero-inflated regression models. To study the impact of fitting inaccurate model to data simulated from some other model, we simulate data from ZIGP distribution and fit Poisson, Generalized Poisson (GP), Zero-inflated Poisson (ZIP), Zero-inflated Generalized Poisson (ZIGP) and Zero-inflated Negative Binomial (ZINB) model. We compare the performance of the estimates of Poisson, GP, ZIP, ZIGP and ZINB through mean square error, bias and standard error when the samples are generated from ZIGP distribution. We propose estimators of parameters of ZIGP distribution based on the first two sample moments and proportion of zeros referred to as MOZE estimator and compare its performance with maximum likelihood estimate (MLE) through a simulation study. It is observed that MOZE are almost equal or even more efficient than that of MLE of the parameters of ZIGP distribution. [ABSTRACT FROM AUTHOR]

The major causes for poor health in developing countries are inadequate access and under-use of modern health care services. The objective of this study was to identify and examine factors related to the use of antenatal care services using the 2011 Ethiopia Demographic and Health Survey data. The number of antenatal care visits during the last pregnancy by mothers aged 15 to 49 years (n =7,737) was analyzed. More than 55% of the mothers did not use antenatal care (ANC) services, while more than 22% of the women used antenatal care services less than four times. More than half of the women (52%) who had access to health services had at least four antenatal care visits. The zero-inflated negative binomial model was found to be more appropriate for analyzing the data. Place of residence, age of mothers, woman’s educational level, employment status, mass media exposure, religion, and access to health services were significantly associated with the use of antenatal care services. Accordingly, there should be progress toward a health-education program that enables more women to utilize ANC services, with the program targeting women in rural areas, uneducated women, and mothers with higher birth orders through appropriate media. [ABSTRACT FROM PUBLISHER]

Estimating abundance or distribution of species that are scarce or difficult to detect is challenging for wildlife biologists. Pygmy rabbits (Brachylagus idahoensis) are secretive, sagebrush (Artemisia spp.) obligates of conservation concern that occupy the Intermountain West, USA. These unique leporids are difficult to monitor; however using remote cameras in conjunction with ranking of burrow activity may help refine sampling techniques for these mammals. We classified and deployed remote cameras at 405 burrows of pygmy rabbits between 2006 and 2010 in six study areas across Utah, USA. We ranked burrows based on the appearance of fecal pellets, as well as the condition of burrow entrances. We also documented the presence of pygmy rabbits and potential competitors and predators of pygmy rabbits at burrows using remote cameras. We used mixed-effects, zero-inflated negative binomial models and AIC model selection to evaluate the relative influences of burrow activity ranking, season, and rate of photographs for potential competitors and predators of pygmy rabbits on photo rates of pygmy rabbits. The top 2 models supported a simplified (active or inactive) burrow classification system and accounted for 45% of AIC weight. Rates of pygmy rabbit photographs were further influenced by meters from habitat edge (β = 0.0008 ± 0.0004, 95% CI = 7.07E-05-0.002), photo rate of cottontail rabbits (β = 0.31 ± 0.11, 95% CI = 0.08-0.53), and were higher during summer than other times of the year (β = 0.38 ± 0.19, 95% CI = 0.01-0.74). Mean number of days to detection of pygmy rabbits at burrow complexes classified as active was four (SE = 0.61), and a two-week sampling period was needed to capture 81% of first detections. Our results refine commonly used ranking criteria of burrow complexes to a 2-level scale (active and inactive), and also emphasize the use of remote cameras as an effective technique for quantifying activity of pygmy rabbits at burrow complexes. Such information can help researchers and land managers more effectively survey this species for conservation and management efforts. [ABSTRACT FROM AUTHOR]

Background: Childhood mortality has remained a major challenge to public health amongst families in Nigeria and other developing countries. The menace of incessant childhood mortality has been a major concern and this calls for studies to generate new scientific evidence to determine its prevalence and explore predisposing factors associated with it in Nigeria.Method: Data was obtained from Nigeria DHS, 2013. The study outcome variable was the total number of children lost by male partners and female partners respectively who were married. The difference between the numbers of child births and the number of living children was used to determine the number of children lost. Study variables were obtained for 8658 couples captured in the data set. Descriptive statistics were computed to examine the presence of over-dispersion and zero occurrences. Data were analysed using STATA Software version 12.0. Zero-inflated negative binomial (ZINB) regression analysis was carried out to determine the factors associated with childhood mortality. Results of ZINB were reported in terms of IRR and 95% confidence interval (CI).Results: The age (mean ± std.) of male and female participants were 36.88 ± 7.37 and 28.59 ± 7.30 respectively. The data showed that 30.8% women reported loss of children and 37.3% men reported the same problem. The study revealed age (years), region, residence, education, wealth index, age at first birth and religion of father and mother as factors associated with childhood mortality. In terms of education, secondary and tertiary educated fathers exhibited 3.8% and 12.1% lower risk of childhood mortality respectively than non-educated fathers. The results showed that the risk of childhood mortality are 26.7%, 39.7 and 45.9% lower among the mothers having primary, secondary and tertiary education respectively than those with no formal education. The mothers living in rural areas experienced 28.3% increase in childhood mortality than those in urban areas, while the fathers in rural areas experienced 33.5% increase in childhood mortality than the urban areas. The risk of childhood mortality was significantly lower in middle, richer and richest (11.1%, 37.5 and 49%) economic quintiles respectively when compared to the risk of childhood mortality with female spouse who are poorest. Similar results were obtained for the fathers, with reduction in the incidence-rate ratio of 3.3%, 20.2 and 28.7% for middle, richer and richest economic quintiles respectively, compared to the poorest status. Furthermore, region and religion were found to be significant factors associated with childhood mortality in Nigeria.Conclusion: The findings suggested that age, region, residence, education, wealth index, age at first birth and religion of fathers and mothers are key determinants associated with childhood mortality. The correlation between childhood mortality and fathers' and mothers' ages were found to increase the incidence of the outcome for every unit increase in age. The converse was however, true for age at first birth which was also statistically significant. The implication of this study is that policy makers and stakeholders in health care should provide for improved living standards to achieve good life expectancy meeting SDG3. [ABSTRACT FROM AUTHOR]

Background: Respiratory infections continue to be a public health threat, particularly to young children in developing countries. Understanding the geographic patterns of diseases and the role of potential risk factors can help improve future mitigation efforts. Toward this goal, this paper applies a spatial scan statistic combined with a zero-inflated negative-binomial regression to re-examine the impacts of a community-based treatment program on the geographic patterns of acute lower respiratory infection (ALRI) mortality in an area of rural Bangladesh. Exposure to arsenic-contaminated drinking water is also a serious threat to the health of children in this area, and the variation in exposure to arsenic must be considered when evaluating the health interventions. Methods: ALRI mortality data were obtained for children under 2 years old from 1989 to 1996 in the Matlab Health and Demographic Surveillance System. This study period covers the years immediately following the implementation of an ALRI control program. A zero-inflated negative binomial (ZINB) regression model was first used to simultaneously estimate mortality rates and the likelihood of no deaths in groups of related households while controlling for socioeconomic status, potential arsenic exposure, and access to care. Next a spatial scan statistic was used to assess the location and magnitude of clusters of ALRI mortality. The ZINB model was used to adjust the scan statistic for multiple social and environmental risk factors. Results: The results of the ZINB models and spatial scan statistic suggest that the ALRI control program was successful in reducing child mortality in the study area. Exposure to arsenic-contaminated drinking water was not associated with increased mortality. Higher socioeconomic status also significantly reduced mortality rates, even among households who were in the treatment program area. Conclusion: Community-based ALRI interventions can be effective at reducing child mortality, though socioeconomic factors may continue to influence mortality patterns. The combination of spatial and non-spatial methods used in this paper has not been applied previously in the literature, and this study demonstrates the importance of such approaches for evaluating and improving public health intervention programs. [ABSTRACT FROM AUTHOR]