Zero-inflated Poisson Mixed Model Research Articles

774-P: Impact of Transition to Tandem Control-IQ on Sleep Awakenings in Youth with Type 1 Diabetes (T1D) and Their Parents

Youth with T1D and their parents are susceptible to diabetes-related nocturnal awakenings, which have been associated with reduced sleep duration and quality. We conducted an observational study of youth with T1D starting on the Tandem Control-IQ (CIQ) hybrid closed-loop (HCL) system to evaluate the impact of system use on sleep. Thirty-nine youth (age 11.1±3.6 yrs, T1D duration 2.5±3.0 yrs, 54% male) and a parent (age 42.5±6.7 yrs, 18% male) completed sleep diaries capturing the number of and reasons for nocturnal awakenings over a 7-day period. Analyses compared diaries from before starting CIQ (baseline) to 3- and 6-months after starting CIQ using a zero-inflated Poisson mixed model with random intercept for participants. Youth and parents reported significantly fewer total awakenings per night at 3- and 6-months compared to baseline (Table) . A significant decrease in diabetes-related awakenings was observed after 6 months in youth but not in parents. Following CIQ initiation, fewer nocturnal awakenings were reported by parents and youth, with youth additionally reporting significantly fewer diabetes-related awakenings at 6 months. Due to the subjective nature of diaries, the number of true awakenings may be different. The results suggest that HCL technology may be beneficial in reducing sleep disruptions in youth and parents. Disclosure A.J.Karami: None. L.Pyle: None. T.B.Vigers: None. E.Jost: Other Relationship; Tandem Diabetes Care, Inc. R.Wadwa: Advisory Panel; Dompé, Consultant; Beta Bionics, Inc., Other Relationship; Tandem Diabetes Care, Inc., Research Support; Dexcom, Inc., Eli Lilly and Company, Tandem Diabetes Care, Inc. E.C.Cobry: None.

Fate of Mycobacterium avium subsp. paratuberculosis and changes in bacterial diversity populations in dairy slurry after chemical treatments.

A major drawback of using dairy slurry as fertilizer is that it may contains pathogens such as Mycobacterium avium subsp. paratuberculosis (MAP), and it could represent a risk to animal and public health. Thus, the aim of this study was to evaluate the fate of MAP and bacterial communities in dairy slurry after chemical treatments. Cattle slurry, naturally contaminated with MAP, was collected from a dairy herd and divided into 32 glass bottles which were assigned to eight different treatments (control, 3·0% CaO, 0·5% NaOH; 0·087%, 0·11% and 0·14% H2 SO4 ; and 1·0 and 2·5% KMnO4 ). Treated dairy slurry samples were evaluated at 0, 1, 3, 7, 15, 30 and 60-days following treatment application for viable MAP and dairy slurry pH, and in addition temperature in this material was monitored continuously. Bacterial counts were estimated at each sampling time. A Bayesian zero-inflated Poisson mixed model was fitted to assess the effect of each treatment on the count of MAP cells. Model results indicated that only the 3·0% CaO treatment had a statistically important negative effect on MAP counts during the study period. For most treatments, MAP was undetectable immediately after chemical treatment but re-appeared over time, in some replicates at low concentrations. However, in those cases MAP counts were not statistically different than the control treatment. Regarding the fate of the other bacterial populations, the Firmicutes phylum was the dominant population in the un-treated slurry while Clostridia class members were among the most prevalent bacteria after the application of most chemical treatments. Only 3% CaO treatment had a statistically important negative effect on MAP viability in cattle slurry. This study provides evidence of MAP partial control in dairy slurry. This information should be considered as a best management practice to reduce MAP and other pathogens for slurry management on dairy farms.

Weighted zero-inflated Poisson mixed model with an application to Medicaid utilization data

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

The Effects of Text Message Content on the Use of an Internet-Based Physical Activity Intervention in Hong Kong Chinese Adolescents

This study examined the effects of text message content (generic vs. culturally tailored) on the login rate of an Internet physical activity program in Hong Kong Chinese adolescent school children. A convenience sample of 252 Hong Kong secondary school adolescents (51% female, 49% male; M age = 13.17 years, SD = 1.28 years) were assigned to one of 3 treatments for 8 weeks. The control group consisted of an Internet physical activity program. The Internet plus generic text message group consisted of the same Internet physical activity program and included daily generic text messages. The Internet plus culturally tailored text message group consisted of the Internet physical activity program and included daily culturally tailored text messages. Zero-inflated Poisson mixed models showed that the overall effect of the treatment group on the login rates varied significantly across individuals. The login rates over time were significantly higher in the Internet plus culturally tailored text message group than the control group (β = 46.06, 95% CI 13.60, 156.02; p = .002) and the Internet plus generic text message group (β = 15.80, 95% CI 4.81, 51.9; p = .021) after adjusting for covariates. These findings suggest that culturally tailored text messages may be more advantageous than generic text messages on improving adolescents’ website login rate, but effects varied significantly across individuals. Our results support the inclusion of culturally tailored messaging in future online physical activity interventions.

Cigarette smoking in women after BRCA1/2 genetic test disclosure: a 5-year follow-up study of the GENEPSO PS cohort.

This study aimed to measure patients' smoking patterns for 5 years after BRCA1/2 test result disclosure. A national cohort consisting of 621 French cancer-free women from families with BRCA1/2 mutations (mean age (SD): 40.5 years (11.5 years)) were included from December 1999 to January 2006, before disclosure of genetic test results, and followed for 5 years. They completed self-administered questionnaires about their cigarette smoking behaviors before receiving their test results (baseline) and 6, 12, 24, and 60 months after disclosure. Multivariate statistical analyses of the changes in participants' smoking behaviors were performed using a zero-inflated Poisson mixed model. Baseline smoking was found to depend on age, educational level, marital status, alcohol consumption, body mass index, and cancer risk perception. The zero-inflated part of the model showed the occurrence of no significant changes in the percentage of smokers during the 5 years after disclosure of the BRCA1/2 test results; however, daily smoking among BRCA1/2 carriers decreased significantly compared with that of noncarriers (adjusted hazard ratio = 0.83; (95% confidence interval: 0.69-0.99); P = 0.04) after adjusting for baseline smoking behavior. It would be worth investigating the possibility of counseling women during the genetic testing process about the multiple risk factors involved in cancer, such as genetic and lifestyle factors.

Application of Zero-Inflated Poisson Mixed Models in Prognostic Factors of Hepatitis C

Background and Objectives. In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. Methods. The data was collected from a longitudinal study during 2005–2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. Results. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. Conclusions. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

Semiparametric analysis of longitudinal zero-inflated count data

In this article, we consider a semiparametric zero-inflated Poisson mixed model that postulates a possible nonlinear relationship between the natural logarithm of the mean of the counts and a particular covariate in the longitudinal studies. A penalized log-likelihood function is proposed and Monte Carlo expectation–maximization algorithm is used to derive the estimates. Under some mild conditions, we establish the consistency and asymptotic normality of the resulting estimators. Simulation studies are carried out to investigate the finite sample performance of the proposed method. For illustration purposes, the method is applied to a data set from a pharmaceutical company where the variable of interest is the number of episodes of side effects after the patient has taken the treatments.

Score tests for overdispersion in zero-inflated Poisson mixed models

This note is motivated by recent works of Xie et al. (2009) and Xiang et al. (2007). Herein, we simplify the score statistic presented by Xie et al. (2009) to test overdispersion in the zero-inflated generalized Poisson (ZIGP) mixed model, and discuss an extension to test overdispersion in zero-inflated Poisson (ZIP) mixed models. Examples highlight the application of the extended results. The extensive simulation study for testing overdispersion in the Poisson mixed model indicates that the proposed score statistics maintain the nominal level reasonably well. In practice, the appropriate model is chosen based on the approximate mean–variance relationship in the data, and a formal score test based on asymptotic standard normal distribution can be employed for testing overdispersion. A case study is provided to illustrate procedures for data analysis.

A zero-inflated Poisson mixed model to analyze diagnosis related groups with majority of same-day hospital stays

With increasing trend of same-day procedures and operations performed for hospital admissions, it is important to analyze those Diagnosis Related Groups (DRGs) consisting of mainly same-day separations. A zero-inflated Poisson (ZIP) mixed model is presented to identify health- and patient-related characteristics associated with length of stay (LOS) and to model variations in LOS within such DRGs. Random effects are introduced to account for inter-hospital variations and the dependence of clustered LOS observations via the generalized linear mixed models (GLMM) approach. Parameter estimation is achieved by maximizing an appropriate log-likelihood function using the EM algorithm to obtain approximate residual maximum likelihood (REML) estimates. An S-Plus macro is developed to provide a unified ZIP modeling approach. The determination of pertinent factors would benefit hospital administrators and clinicians to manage LOS and expenditures efficiently.