Analysis of count data in the setting of cervical cancer detection

2020 ◽  
Vol 68 (6) ◽  
pp. 1196-1198
Author(s):  
Christina G Bracamontes ◽  
Thelma Carrillo ◽  
Jane Montealegre ◽  
Leonid Fradkin ◽  
Michele Follen ◽  
...  
Keyword(s):  
Sexual Abuse ◽  
Count Data ◽  
Pap Smear ◽  
Negative Binomial ◽  
El Paso ◽  
Language Preference ◽  
A Value ◽  
History Of ◽  
Zip Model ◽  
Endocervical Canal

Women with an abnormal Pap smear are often referred to colposcopy, a procedure during which endocervical curettage (ECC) may be performed. ECC is a scraping of the endocervical canal lining. Our goal was to compare the performance of a naïve Poisson (NP) regression model with that of a zero-inflated Poisson (ZIP) model when identifying predictors of the number of distress/pain vocalizations made by women undergoing ECC. Data on women seen in the colposcopy clinic at a medical school in El Paso, Texas, were analyzed. The outcome was the number of pain vocalizations made by the patient during ECC. Six dichotomous predictors were evaluated. Initially, NP regression was used to model the data. A high proportion of patients did not make any vocalizations, and hence a ZIP model was also fit and relative rates (RRs) and 95% CIs were calculated. AIC was used to identify the best model (NP or ZIP). Of the 210 women, 154 (73.3%) had a value of 0 for the number of ECC vocalizations. NP identified three statistically significant predictors (language preference of the subject, sexual abuse history and length of the colposcopy), while ZIP identified one: history of sexual abuse (yes vs no; adjusted RR=2.70, 95% CI 1.47 to 4.97). ZIP was preferred over NP. ZIP performed better than NP regression. Clinicians and epidemiologists should consider using the ZIP model (or the zero-inflated negative binomial model) for zero-inflated count data.

Added Cost and Time Spent by Patients With History of Abuse in Florida Emergency Departments

2020 ◽  
Vol 35 (5) ◽  
pp. 741-750
Author(s):  
Natasha Kurji ◽  
Etienne E. Pracht ◽  
Barbara Langland-Orban ◽  
Kathleen Pracht
Keyword(s):  
Sexual Abuse ◽  
Interpersonal Violence ◽  
Healthcare Costs ◽  
Negative Binomial ◽  
Linear Regression Analysis ◽  
Physical And Mental Health ◽  
For Profit ◽  
Visit Duration ◽  
History Of ◽  
Log Linear

Interpersonal violence is known to lead to both short- and long-term health effects. Victims of sexual abuse tend to have higher healthcare costs and higher rates of physical and mental health issues than nonvictims. In this study, we investigate whether the comorbidity of mental illness and a personal history of adult physical and sexual abuse (HAPSA) results in higher healthcare costs and length of emergency department (ED) stay among Florida residents. A Negative Binomial and Log-Linear Regression Analysis suggest increased ED visit duration for those with a history of abuse, Hispanics, the uninsured, and those with multiple comorbidities. In addition, increased costs were found to be associated with White race, the uninsured, those with multiple comorbidities, and the facility type (for-profit hospitals).

scholarly journals Modeling the Rainfall Count data Using Some Zero Type models with application

2019 ◽  
Vol 11 (2) ◽  
pp. 14-27
Author(s):  
Luay Habeeb Hashim ◽  
Ahmad Naeem Flaih
Keyword(s):  
Quality Control ◽  
Alcohol Use ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Model ◽  
Rainfall Data ◽  
Binomial Model ◽  
Health Marketing ◽  
Zip Model ◽  
Zero Counts

Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc.  Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.

History of Sexual Abuse Negatively Impacts Resilience to Suicide

2008 ◽  
Author(s):  
William J. Meyer ◽  
Meghan Marty ◽  
Andrea June ◽  
Daniel L. Segal
Keyword(s):  
Sexual Abuse ◽  
History Of

A Numerically Coded File of Operative Procedures Derived from a Free Text Data Collection System : A Measure of the Accuracy

1976 ◽  
Vol 15 (01) ◽  
pp. 21-28 ◽  
Author(s):  
Carmen A. Scudiero ◽  
Ruth L. Wong
Keyword(s):  
Data Collection ◽  
Pap Smear ◽  
Operative Procedures ◽  
Free Text ◽  
Collection System ◽  
Process Data ◽  
Text Data ◽  
Data Collection System ◽  
History Of ◽  
Correlation System

A free text data collection system has been developed at the University of Illinois utilizing single word, syntax free dictionary lookup to process data for retrieval. The source document for the system is the Surgical Pathology Request and Report form. To date 12,653 documents have been entered into the system.The free text data was used to create an IRS (Information Retrieval System) database. A program to interrogate this database has been developed to numerically coded operative procedures. A total of 16,519 procedures records were generated. One and nine tenths percent of the procedures could not be fitted into any procedures category; 6.1% could not be specifically coded, while 92% were coded into specific categories. A system of PL/1 programs has been developed to facilitate manual editing of these records, which can be performed in a reasonable length of time (1 week). This manual check reveals that these 92% were coded with precision = 0.931 and recall = 0.924. Correction of the readily correctable errors could improve these figures to precision = 0.977 and recall = 0.987. Syntax errors were relatively unimportant in the overall coding process, but did introduce significant error in some categories, such as when right-left-bilateral distinction was attempted.The coded file that has been constructed will be used as an input file to a gynecological disease/PAP smear correlation system. The outputs of this system will include retrospective information on the natural history of selected diseases and a patient log providing information to the clinician on patient follow-up.Thus a free text data collection system can be utilized to produce numerically coded files of reasonable accuracy. Further, these files can be used as a source of useful information both for the clinician and for the medical researcher.

scholarly journals Maternal History of Child Sexual Abuse and Maladaptive Outcomes in Sexually Abused Children: The Role of Maternal Mental Health

2021 ◽  
pp. 088626052110139
Author(s):  
Rachel Langevin ◽  
Martine Hébert ◽  
Audrey Kern
Keyword(s):  
Mental Health ◽  
Sexual Abuse ◽  
Psychological Distress ◽  
Sexually Abused ◽  
Maternal Mental Health ◽  
Ptsd Symptoms ◽  
Abused Children ◽  
Maternal History ◽  
History Of ◽  
Sexually Abused Children

The effects of child sexual abuse (CSA) have been found to surpass generations as maternal history of CSA is associated with increased difficulties in sexually abused children. However, little is known about the mechanisms underlying this association. The present study aimed to test maternal mental health symptoms including psychological distress, post-traumatic stress disorder (PTSD) symptoms, and dissociation as mediators of the relationship between maternal CSA and children’s internalizing, externalizing, and dissociation symptoms in a large sample of sexually abused children. A total of 997 sexually abused children aged 3-14 years old and their mothers were recruited at five specialized intervention centers offering services to sexually abused children and their families. The children were divided into two groups depending on their mothers’ self-reported history of CSA. Mothers completed a series of questionnaires assessing their mental health and children’s functioning. Maternal history of CSA was associated with increased maternal psychological distress, PTSD symptoms, and dissociation following children’s disclosure of CSA. In turn, maternal psychological distress and maternal dissociation were associated with increased child internalizing, externalizing, and dissociation symptoms. Maternal PTSD symptoms were associated with child internalizing symptoms. Maternal mental health difficulties mediated the association between maternal CSA and sexually abused children’s maladaptive outcomes. Clinicians should assess for possible history of CSA in mothers of sexually abused children and determine how best to support them to cope with the aftermaths of their child’s disclosure and with their own traumatic past.

scholarly journals A comparison of zero-inflated and hurdle models for modeling zero-inflated count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Cindy Xin Feng
Keyword(s):  
Health Services ◽  
Count Data ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Simulation Studies ◽  
Final Choice ◽  
Hurdle Models ◽  
Count Distribution ◽  
Careful Assessment

AbstractCounts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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