Oral contraceptives and MS disease activity in a contemporary real-world cohort

2017 ◽  
Vol 24 (2) ◽  
pp. 227-230 ◽  
Author(s):  
Riley Bove ◽  
Kelsey Rankin ◽  
Alicia S Chua ◽  
Taylor Saraceno ◽  
Neda Sattarnezhad ◽  
...  
Keyword(s):  
Observational Study ◽  
Relapse Rate ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Hormonal Contraceptives ◽  
Negative Binomial Regression Model ◽  
Childbearing Age ◽  
Primary Analysis ◽  
Disease Modifying Therapy ◽  
Annualized Relapse Rate

Background: There is uncertainty regarding the effect of oral hormonal contraceptives (OC) on multiple sclerosis (MS) course. Objective: To evaluate the hypothesis that OC use is associated with decreased risk of relapses in an observational study of women of childbearing age with new-onset MS starting a first-line injectable disease-modifying therapy (DMT). Methods: From our CLIMB longitudinal observational study, we identified 162 women with MS or CIS with known OC use who initiated injectable DMT within two years of symptom onset, and categorized OC use at DMT onset as past, ever or never. Our primary analysis was comparison of annualized relapse rate from baseline DMT start across the three OC use categories using a negative binomial regression model. Results: In this cohort of 162 women, 81 were treated with interferon therapy and 81 with glatiramer acetate. Mean ages for current-, past-, and never-OC users were 31.4 ( n = 46), 40.3 ( n = 66), and 37.9 ( n = 50) years, respectively ( p < 0.05); mean disease duration (1.0 years) and median baseline EDSS (1.0) did not differ between groups. Prior OC users had significantly lower relapse rates than never-users ( p = 0.031); the lower annualized relapse rate in current-users relative to never-users was not significant ( p = 0.91). Annualized relapse rate was not significantly different across the OC groups ( p = 0.057, three-group comparison). Results: These observations provide reassurance for women newly diagnosed that OC use, past or current, does not appear to be associated with greater risk of relapses.

scholarly journals Impact of Switching to Fingolimod Versus Injectable Disease-Modifying Therapy Cycling on Risk of Multiple Sclerosis–Related Relapses: A Retrospective Analysis

2020 ◽  
pp. 0000-0000
Author(s):  
Maria Cecilia Vieira ◽  
Yunfeng Li ◽  
Xiangyi Meng ◽  
Huanxue Zhou ◽  
Olivia Wenxian Piao ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
Relapse Rate ◽  
Real World ◽  
Negative Binomial ◽  
Office Visit ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Corticosteroid Administration ◽  
Disease Modifying Therapy ◽  
Disease Modifying

Abstract Background: Clinical and real-world studies have shown significant reductions in MS relapses in patients receiving fingolimod versus injectable disease-modifying therapies (iDMTs). The objective was to compare MS relapse rate and incidence in patients switching from an iDMT to fingolimod with those cycling from one iDMT to another or those remaining on their original iDMT. Methods: A retrospective analysis was performed using MarketScan® Commercial and Medicare Supplemental claims data (July-1-2010–June-30-2016). Adult MS patients receiving ≥1 iDMT during the study were included. Relapses were identified from an MS-related hospitalization, outpatient ER or office visit, and corticosteroid administration. Annualized relapse rate ratio was estimated by negative binomial regression model with repeat-measures. Results: Of 16,352 patients, 1,110 were switchers to fingolimod, 908 were iDMT cyclers, and 14,334 were non-switchers. At baseline, rate and incidence of MS relapses were higher in switchers and iDMT cyclers versus non-switchers (P &lt; .001); mean (SD) relapse rates declined from 0.4 (0.7), 0.4 (0.7), and 0.2 (0.5) at baseline to 0.2 (0.5), 0.3 (0.6), and 0.1 (0.4) after follow-up in switchers, iDMT cyclers, and non-switchers, respectively. Relapse incidence also declined in each cohort. The highest reductions in relapse rate and incidence were observed in switchers to fingolimod, where relapse risk was significantly reduced versus iDMT cyclers (22%; P = .0433) and non-switchers (47%; P &lt; .001). Conclusions: This real-world study provides evidence that patients switching from an iDMT to fingolimod have the highest reductions in annualized rate and incidence of MS relapses, and significantly reduced risk of relapse, versus iDMT cyclers and non-switchers.

scholarly journals Waning COVID-19 Vaccine Effectiveness for BNT162b2 and CoronaVac in Malaysia: An Observational Study

2022 ◽  
Author(s):  
Jing Lian Suah ◽  
Masliyana Husin ◽  
Peter Seah Keng Tok ◽  
Boon Hwa Tng ◽  
Thevesh Thevananthan ◽  
...  
Keyword(s):  
Observational Study ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Vaccine Effectiveness ◽  
Negative Binomial Regression Model ◽  
Early Group ◽  
Icu Admission ◽  
Nationally Representative ◽  
Late Group ◽  
Over Time

Evaluation of vaccine effectiveness over time against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or coronavirus disease 2019 (COVID-19) is important. Evidence on effectiveness over time for the CoronaVac vaccine is lacking despite its widespread use globally. In Malaysia, a diverse set-up of COVID-19 vaccines was rolled out nationwide, and the waning of vaccine protection is a concern. We aimed to investigate and compare waning vaccine effectiveness against COVID-19 infections, COVID-19 related ICU admission and COVID-19 related deaths for BNT162b2 and CoronaVac vaccines. In this observational study, we consolidated nationally representative data on COVID-19 vaccination and patients′ outcomes. Data on all confirmed COVID-19 cases from 1 to 30 September 2021 were used to compare vaccine effectiveness between the ′early′ group (fully vaccinated in April to June 2021) and the ′late′ group (fully vaccinated in Jul to Aug 2021). We used a negative binomial regression model to estimate vaccine effectiveness against COVID-19 infections for both ′early′ and ′late′ groups, by comparing the rates of infection for individuals vaccinated in the two different periods relative to the unvaccinated. Among confirmed COVID-19 cases, we used logistic regression to estimate and compare vaccine effectiveness against ICU admission and deaths between the two different periods. For BNT162b2, vaccine effectiveness against COVID-19 infections declined from 90.8% (95% CI 89.4, 92.0) in the late group to 79.1% (95% CI 75.8, 81.9) in the late group. Vaccine effectiveness for BNT162b2 against ICU admission and deaths were comparable between the two different periods. For CoronaVac, vaccine effectiveness waned against COVID-19 infections from 74.4% in the late group (95% CI 209 70.4, 77.8) to 30.0% (95% CI 18.4, 39.9) in the early group. It also declined significantly against ICU admission, dropping from 56.1% (95% CI 51.4, 60.2) to 29.9% (95% CI 13.9, 43.0). For deaths, however, CoronaVac′s effectiveness did not wane after three to five months of full vaccination. Vaccine effectiveness against COVID-19 infections waned after three to five months of full vaccination for both BNT162b2 and CoronaVac in Malaysia. Additionally, for CoronaVac, protection against ICU admission declined as well. Evidence on vaccine effectiveness over time informs evolving policy decisions on vaccination.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

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.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

Bike Count Forecast Model with Multimodal Network Connectivity Measures

2021 ◽  
pp. 036119812110218
Author(s):  
Bingqing Liu ◽  
Divya Bade ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
Negative Binomial ◽  
Network Connectivity ◽  
Negative Binomial Regression ◽  
Forecast Model ◽  
Negative Binomial Regression Model ◽  
Significance Level ◽  
Commute Time ◽  
Improve Model ◽  
Subway Stations

With the rise of cycling as a mode choice for commuting and short-distance delivery, as well as policy objectives encouraging this trend, bike count models are increasingly critical to transportation planning and investment. Studies have found that network connectivity plays a role in such models, but there remains a lack of measure for the connectivity of a link in a multimodal trip context. This study proposes a connectivity measure that captures the importance of a link in connecting the origins of cyclists and nearby subway stations, and incorporates it in a negative binomial regression model to forecast bike counts at links. Representative bike trips are generated with regard to bike-friendliness using the New York City transit trip planner and used to determine the deviation from the shortest path via the designated link. The measure is shown to improve model fitness with a significance level within 10%. Insights are also drawn for income levels, bike lanes, subway station availability, and average commute time of travelers.

A Negative Binomial Regression Model for Accuracy Tests

2012 ◽  
Vol 36 (2) ◽  
pp. 88-103 ◽  
Author(s):  
Lai-Fa Hung
Keyword(s):  
Negative Binomial ◽  
Social Science Research ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Science Research ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Negative Binomial Regression Model ◽  
Marginal Maximum Likelihood ◽  
The Mean

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.

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