scholarly journals Factors related to baseline CD4 cell counts in HIV/AIDS patients: comparison of poisson, generalized poisson and negative binomial regression models

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Maryam Farhadian ◽  
Younes Mohammadi ◽  
Mohammad Mirzaei ◽  
Nasrin Shirmohammadi-Khorram
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Clinical Stage ◽  
Negative Binomial Regression ◽  
Cd4 Count ◽  
Generalized Poisson ◽  
Cell Counts ◽  
Cd4 Cell Counts ◽  
Binomial Regression ◽  
Cd4 Cell

Abstract Objective CD4 Lymphocyte Count (CD4) is a major predictor of HIV progression to AIDS. Exploring the factors affecting CD4 levels may assist healthcare staff and patients in management and monitoring of health cares. This retrospective cohort study aimed to explore factors associated with CD4 cell counts at the time of diagnosis in HIV patients using Poisson, Generalized Poisson, and Negative Binomial regression models. Results Out of 4402 HIV patients diagnosis in Iran from 1987 to 2016, 3030 (68.8%) were males, and the mean age was 34.8 ± 10.4 years. The results indicate that the Negative Binomial model outperformed the other models in terms of AIC, log-likelihood and RMSE criteria. In this model, factors include sex, age, clinical stage and Tuberculosis (TB) co-infection were significantly associated with CD4 count (P < 0.05). Conclusion Given the effect of age, sex, clinical stage and stage of HIV on CD4 count of the patients, adopting policies and strategies to increase awareness and encourage people to seek early HIV testing and care is advantageous.

scholarly journals Modeling COVID-19 Cases in Nigeria Using Some Selected Count Data Regression Models

2020 ◽  
pp. 64-73
Author(s):  
Samuel Olorunfemi Adams ◽  
Muhammad Ardo Bamanga ◽  
Samuel Olayemi Olanrewaju ◽  
Haruna Umar Yahaya ◽  
Rafiu Olayinka Akano
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Significance Level ◽  
Generalized Poisson ◽  
Binomial Regression ◽  
Selection Test ◽  
Generalized Poisson Regression

COVID-19 is currently threatening countries in the world. Presently in Nigeria, there are about 29,286 confirmed cases, 11,828 discharged and 654 deaths as of 6th July 2020. It is against this background that this study was targeted at modeling daily cases of COVID-19’s deaths in Nigeria using count regression models like; Poisson Regression (PR), Negative Binomial Regression (NBR) and Generalized Poisson Regression (GPR) model. The study aim at fitting an appropriate count Regression model to the confirmed, active and critical cases of COVID-19 in Nigeria after 118 days. The data for the study was extracted from the daily COVID-19 cases update released by the Nigeria Centre for Disease Control (NCDC) online database from February 28th, 2020 – 6th, July 2020. The extracted data were used in the simulation of Poisson, Negative Binomial, and Generalized Poisson Regression model with a program written in STATA version 14 and fitted to the data at a 5% significance level. The best model was selected based on the values of -2logL, AIC, and BIC selection test/criteria. The results obtained from the analysis revealed that the Poisson regression could not capture over-dispersion, so other forms of Poisson Regression models such as the Negative Binomial Regression and Generalized Poisson Regression were used in the estimation. Of the three count Regression models, Generalized Poisson Regression was the best model for fitting daily cumulative confirmed, active and critical COVID-19 cases in Nigeria when overdispersion is present in the predictors because it had the least -2log-Likelihood, AIC, and BIC. It was also discovered that active and critical cases have a positive and significant effect on the number of COVID-19 related deaths in Nigeria.

Causes and outcomes of hospitalizations among people living with HIV in Georgia’s referral institution, 2012–2017

2021 ◽  
Vol 32 (7) ◽  
pp. 662-670
Author(s):  
Nino Rukhadze ◽  
Ole Kirk ◽  
Nikoloz Chkhartishvili ◽  
Natalia Bolokadze ◽  
Lali Sharvadze ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Clinical Immunology ◽  
Fatal Outcome ◽  
Cd4 Count ◽  
People Living With Hiv ◽  
Lethal Outcome ◽  
Cell Counts ◽  
Cd4 Cell Counts ◽  
Living With Hiv ◽  
Cd4 Cell

We assessed trends in causes and outcomes of hospitalization among people living with HIV (PLWH) admitted to the Infectious Diseases, AIDS and Clinical Immunology Research Center (IDACIRC) in Tbilisi, Georgia. Retrospective analysis included adult PLWH admitted to IDACIRC for at least 24 h. Internationally validated categorization was used to split AIDS admissions into mild, moderate, and severe AIDS. A total of 2085 hospitalizations among 1123 PLWH were registered over 2012–2017 with 65.1% (731/1123) of patients presenting with CD4 count <200. Of 2085 hospitalizations, 931 (44.7%) were due to AIDS-defining illnesses. In 2012, AIDS conditions accounted for 50.3% of admissions compared to 41.6% in 2017 ( p = 0.16). Overall, 167 hospitalizations (8.0%) resulted in lethal outcome. AIDS admissions had higher mortality than non-AIDS admissions (11.5% vs 5.2%, p < 0.0001). Among 167 deceased patients, 137 (82.0%) had CD4 count <200 at admission. In multivariate analysis, factors significantly associated with mortality included severe AIDS versus non-AIDS admission (OR 2.81, 95% CI: 1.10–7.15), CD4 cell counts <50 (OR 4.34, 95% CI: 2.52–7.47), and 50–100 (OR 2.37, 95% CI: 1.27–4.42) versus >200. Active AIDS disease remains a significant cause of hospitalization and fatal outcome in Georgia. Earlier diagnosis of HIV is critical for decreasing AIDS hospitalizations and mortality.

scholarly journals CD4 Cell Counts at HIV Diagnosis among HIV Outpatient Study Participants, 2000–2009

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Kate Buchacz ◽  
Carl Armon ◽  
Frank J. Palella ◽  
Rose K. Baker ◽  
Ellen Tedaldi ◽  
...  
Keyword(s):  
Hiv Testing ◽  
Population Level ◽  
Cd4 Count ◽  
Hiv Diagnosis ◽  
Cell Counts ◽  
Late Hiv Diagnosis ◽  
Cd4 Cell Counts ◽  
The Usa ◽  
Study Participants ◽  
Cd4 Cell

Background. It is unclear if CD4 cell counts at HIV diagnosis have improved over a 10-year period of expanded HIV testing in the USA.Methods. We studied HOPS participants diagnosed with HIV infection ≤6 months prior to entry into care during 2000–2009. We assessed the correlates of CD4 count <200 cells/mm3at HIV diagnosis (late HIV diagnosis) by logistic regression.Results. Of 1,203 eligible patients, 936 (78%) had a CD4 count within 3 months after HIV diagnosis. Median CD4 count at HIV diagnosis was 299 cells/mm3and did not significantly improve over time (P=0.13). Comparing periods 2000-2001 versus 2008-2009, respectively, 39% and 35% of patients had a late HIV diagnosis (P=0.34). Independent correlates of late HIV diagnosis were having an HIV risk other than being MSM, age ≥35 years at diagnosis, and being of nonwhite race/ethnicity.Conclusions. There is need for routine universal HIV testing to reduce the frequency of late HIV diagnosis and increase opportunity for patient- and potentially population-level benefits associated with early antiretroviral treatment.

Managing Inflation

2016 ◽  
Vol 63 (1) ◽  
pp. 77-87 ◽  
Author(s):  
William H. Fisher ◽  
Stephanie W. Hartwell ◽  
Xiaogang Deng
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Excess Zeros ◽  
Binomial Regression ◽  
Negative Binomial Models ◽  
Using Data ◽  
Over Dispersion ◽  
Binomial Models

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the “excess” zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

scholarly journals SCID genotype and 6-month posttransplant CD4 count predict survival and immune recovery

Blood ◽  
2018 ◽  
Vol 132 (17) ◽  
pp. 1737-1749 ◽  
Author(s):  
Elie Haddad ◽  
Brent R. Logan ◽  
Linda M. Griffith ◽  
Rebecca H. Buckley ◽  
Roberta E. Parrott ◽  
...  
Keyword(s):  
Immune Reconstitution ◽  
Cd4 Count ◽  
Immune Recovery ◽  
Term Survival ◽  
Long Term Survival ◽  
Cell Counts ◽  
Cd4 Cell Counts ◽  
Key Points ◽  
Cd4 Cell

Key Points The genetic cause of SCID impacts on survival and immune reconstitution and should be considered in tailoring HCT for individual patients. Total and naive CD4+ cell counts in SCID patients 6 and 12 months post-HCT predict long-term survival and sustained immune reconstitution.

Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

2018 ◽  
Vol 37 (20) ◽  
pp. 3012-3026 ◽  
Author(s):  
Saptarshi Chatterjee ◽  
Shrabanti Chowdhury ◽  
Himel Mallick ◽  
Prithish Banerjee ◽  
Broti Garai
Keyword(s):  
Health Care ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Care Demand ◽  
Binomial Regression

What Do We Know About Changing Economic Activity of Firms?

2019 ◽  
pp. 232102221886979
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

Sign in / Sign up

Export Citation Format

Share Document