Factors related to the use of antenatal care services in Ethiopia: Application of the zero-inflated negative binomial model

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.     

昭通市农村居民两周患病卫生服务需求与利用现状及影响因素分析

[目的]了解农村居民卫生服务需求与利用现状,并找出相关的影响因素,为卫生政策的制定提供参考依据。[方法]采用分层随机整群抽样方法,对昭通市3个县(区)共3645户10,584个居民进行入户调查。通过两周就诊率、两周就诊次数、两周患病就诊率了解农村居民两周患病卫生服务需求与利用现况,运用二分类Logistic回归、零膨胀泊松模型等方法进行影响因素分析。[结果]昭通市农村居民两周就诊率为4.72%,两周患病就诊率为73.49%。卫生服务需求受年龄、性别、职业、婚姻状况、人均收入的影响;卫生服务利用的影响因素有性别、职业、文化程度,人均收入。[结论]昭通市农村居民卫生服务需求和卫生服务利用均低于国家农村平均水平。农民卫生服务需求高,但卫生服务利用不足;低收入人群和男性就诊可能性低,就诊时疾病严重程度高。应更加关注男性、农民和低收入人群的卫生服务需求和利用情况。     

Modeling daily and weekly moderate and vigorous physical activity using zero-inflated mixture Poisson distribution

Recently developed accelerometer devices have been used in large epidemiological studies for continuous and objective monitoring of physical activities. Typically, physical movements are summarized as minutes in light, moderate, and vigorous physical activities in each wearing day. Because of preponderance of zeros, zero-inflated distributions have been used for modeling the daily moderate or higher levels of physical activity. Yet, these models do not fully account for variations in daily physical activity and cannot be extended to model weekly physical activity explicitly, while the weekly physical activity is considered as an indicator for a subject's average level of physical activity. To overcome these limitations, we propose to use a zero-inflated Poisson mixture distribution that can model daily and weekly physical activity in same family of mixture distributions. Under this method, the likelihood of an inactive day and the amount of exercise in an active day are simultaneously modeled by a joint random effects model to incorporate heterogeneity across participants. If needed, the method has the flexibility to include an additional random effect to address extra variations in daily physical activity. Maximum likelihood estimation can be obtained through Gaussian quadrature technique, which is implemented conveniently in an R package GLMMadaptive. Method performances are examined using simulation studies. The method is applied to data from the Hispanic Community Health Study/Study of Latinos to examine the relationship between physical activity and BMI groups and within a participant the difference in physical activity between weekends and weekdays.     

现场调查中零频数过多的统计分析方法

目的:研究零过多计数资料的分布拟合方法并应用于急性上呼吸道感染调查资料.方法:将数据分为两部分,一部分为零部分,另一部分为Poisson分布部分,然后通过转换,形成条件零堆积模型(ZTP),运用logit和log两种连接函数进行拟合.结果:在ZTP部分表示,低年龄和住在低层的发病次数多;logit部分显示年龄小、有锻炼习惯者、健康状况差和有慢性呼吸系统疾病史的人易发病.结论:转换后的零过多模型可以更好地对影响因素进行解释.     

Modelling RNA-Seq data with a zero-inflated mixture Poisson linear model

RNA sequencing (RNA-Seq) has been frequently used in genomic studies and has generated a vast amount of data. The RNA-Seq data are composed of two parts: (a) a sequence of nucleotides of the genome; and (b) a corresponding sequence of counts, standing for the number of short reads whose mapped positions start at each position of the genome. One common feature of these count data is that they are typically nonuniform; recent studies have revealed that the nonuniformity is partially owing to a systematic bias resulted from the sequencing preference. Existing works in the literature model the nonuniformity with a single component Poisson linear model that incorporates the effects of the sequencing preference. However, we observe consistently that the short reads mapped to a gene may have a mixture structure and can be zero-inflated. A single component model may not suffice to model the complexity of such data. In this paper, we propose a zero-inflated mixture Poisson linear model for the RNA-Seq count data and derive a fast expectation–maximisation-based algorithm for estimating the unknown parameters. Numerical studies are conducted to illustrate the effectiveness of our method.     

Some Considerations for Excess Zeroes in Substance Abuse Research

Background: Count data collected in substance abuse research often come with an excess of “zeroes,” which are typically handled using zero-inflated regression models. However, there is a need to consider the design aspects of those studies before using such a statistical model to ascertain the sources of zeroes. Objectives: We sought to illustrate hurdle models as alternatives to zero-inflated models to validate a two-stage decision-making process in situations of “excess zeroes.” Methods: We use data from a study of 45 cocaine-dependent subjects where the primary scientific question was to evaluate whether study participation influences drug-seeking behavior. The outcome, “the frequency (count) of cocaine use days per week,” is bounded (ranging from 0 to 7). We fit and compare binomial, Poisson, negative binomial, and the hurdle version of these models to study the effect of gender, age, time, and study participation on cocaine use. Results: The hurdle binomial model provides the best fit. Gender and time are not predictive of use. Higher odds of use versus no use are associated with age; however once use is experienced, odds of further use decrease with increase in age. Participation was associated with higher odds of no-cocaine use; once there is use, participation reduced the odds of further use. Conclusion: Age and study participation are significantly predictive of cocaine-use behavior. Scientific Significance: The two-stage decision process as modeled by a hurdle binomial model (appropriate for bounded count data with excess zeroes) provides interesting insights into the study of covariate effects on count responses of substance use, when all enrolled subjects are believed to be “at-risk” of use.     

结构性零和零膨胀模型(英文)

在社会心理学和行为学的研究中,记录某些健康或行为结果发生频率的计数中(如在一段时间内无防护措施的性行为的次数)往往含有大量的零,这是因为当某些对象对于某种研究行为没有危险时就会产生"结构性零"。不像随机零(结果可以是大于零,但是也可能由于样本变异性而成为零),结构性零在统计和临床上通常是非常不同的。如果两种类型零的差异被忽略,就可能会导致对结果和研究发现的错误解释。然而在实践中,结构性零经常会没有被观察到而这种潜在性使数据分析复杂化了。在这篇文章中,我们专注于一种模式,即通常用于解决零膨胀数据的零膨胀泊松(Zero-inflated Poisson,ZIP)回归模型。首先,我们对结构性零和ZIP模型做一个简要概述。然后我们以一项青春期少女艾滋病高危性行为的研究数据来阐述ZIP模型。文中还附有SAS和Stata的示例代码,以帮助运行和解释ZIP分析。     

Applying Zero-inflated Mixed Model to School Absenteeism Surveillance in Rural China

Objective

To describe and explore the spatial and temporal variability via ZIMM for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural China.

Introduction

Absenteeism has great advantages in promoting the early detection of epidemics1. Since August 2011, an integrated syndromic surveillance project (ISSC) has been implemented in China2. Distribution of the absenteeism generally are asymmetry, zero inflation, truncation and non-independence3. For handling these encumbrances, we should apply the Zero-inflated Mixed Model (ZIMM).

Methods

Data for this study was obtained from the web-based data of ISSC in 62 primary schools in two counties of Jiangxi province, China from April 1th, 2012 to June 30st, 2012. The ZIMM was used to explore: 1)the temporal and spatial variability regarding occurrence and intensity of absenteeism simultaneously, and 2) the heterogeneity among the reporting primary schools by introducing random effects into the intercepts. The analyse was processed in the SAS procedure NLMIXED4.

Results

The total 4914 absenteeism events were reported in the 62 primary schools in the study period. The rate of zero report was 49.88% (Fig. 1). According to ZIMM, there are fixed and random effect parameters in this model (Open in a separate windowFig. 1Absenteeism from Apr. 1st to Jun. 30th 2012

Table 1

Fixed parameters and variance components estimates for the absenteeism using ZIMM

The gut microbiome,mild cognitive impairment,and probiotics: A randomized clinical trial in middle-aged and older adults

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Extended multi-item gamma Poisson shrinker methods based on the zero-inflated Poisson model for postmarket drug safety surveillance

Bayesian signal detection methods, including the multiitem gamma Poisson shrinker (MGPS), assume a Poisson distribution for the number of reports. However, the database of the adverse event reporting system often has a large number of zero-count cells. A zero-inflated Poisson (ZIP) distribution can be more appropriate in this situation than a Poisson distribution. Few studies have considered ZIP-based models for Bayesian signal detection. In addition, most studies on Bayesian signal detection methods include simulation studies conducted assuming a gamma distribution for the prior. Herein, we extend the MGPS method using the ZIP model and apply various prior distributions. We evaluated the extended methods through an extensive simulation using more varied settings for the model and prior than existing methods. We varied the total number of reports, the number of true signals, the relative reporting rate, and the probability of observing a true zero. The results show that as the probability of observing a zero count increased, methods based on the ZIP model outperformed the Poisson model in most cases. We also found that using the mixture log-normal prior resulted in more conservative detection than other priors when the relative reporting rate is high. Conversely, more signals were found when using the mixture truncated normal distributions. We applied the Bayesian signal detection methods to data from the Korea Adverse Event Reporting System from 2012 to 2016.