scholarly journals Surgery as a Teachable Moment for Smoking Cessation

2010 ◽  
Vol 112 (1) ◽  
pp. 102-107 ◽  
Author(s):  
Yu Shi ◽  
David O. Warner
Keyword(s):  
Smoking Cessation ◽  
Surgical Procedures ◽  
Negative Binomial ◽  
Outpatient Surgery ◽  
Negative Binomial Regression ◽  
The United States ◽  
Major Surgery ◽  
Negative Binomial Regression Model ◽  
Teachable Moment ◽  
Nationally Representative

Background A "teachable moment" is an event that motivates spontaneous behavior change. Some evidence suggests that major surgery for a smoking-related illness can serve as a teachable moment for smoking cessation. This study tested the hypotheses that surgery increases the likelihood of smoking cessation and that cessation is more likely after major surgical procedures compared with outpatient surgery. Methods Secondary analyses were performed of longitudinal biennial survey data (1992-2004) from the nationally representative Health and Retirement Study of U.S. adults older than 50 yr, determining the relationship between the incidence of smoking cessation and the occurrence of surgery. Results Five thousand four hundred ninety-eight individuals reported current smoking at enrollment, and 2,444 of them (44.5%) quit smoking during the period of examination. The incidence of quitting in smokers undergoing major surgery was 20.6/100 person-years of follow-up and 10.2/100 person-years in those undergoing outpatient surgery. In a multivariate negative binomial regression model, the incidence rate ratio of quitting associated with major surgery was 2.02 (95% CI: 1.67-2.44) and that of those associated with outpatient surgery was 1.28 (95% CI: 1.09-1.50). Estimates derived from national surgical utilization data show that approximately 8% of all quit events in the United States annually can be attributed to the surgical procedures analyzed. Conclusions Undergoing surgery is associated with an increased likelihood of smoking cessation in the older U.S. population. Cessation is more likely in association with major procedures compared with outpatient surgery. These data support the concept that surgery is a teachable moment for smoking cessation.

scholarly journals Vacant Properties and Violence in Neighborhoods

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Charles C. Branas ◽  
David Rubin ◽  
Wensheng Guo
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Public Health Issue ◽  
Negative Binomial Regression Model ◽  
Alcohol Outlets ◽  
Aggravated Assault ◽  
Increased Risk ◽  
Significant Public Health ◽  
Block Groups

Objectives. Violence remains a significant public health issue in the United States. To determine if urban vacant properties were associated with an increased risk of assaultive violence and if this association was modified by important neighborhood institutions (e.g., schools, parks/playgrounds, police stations, and alcohol outlets). Methods. Longitudinal ecologic study of all 1816 block groups in Philadelphia. Aggravated assault and vacant property data were compiled yearly from 2002 to 2006 and linked to block groups. A mixed effects negative binomial regression model examined the association of vacant properties and assaults between and within block groups. Results. Among all block groups, 84% experienced at least one vacant property, 89% at least one aggravated assault, and 64% at least one gun assault. Between block groups, the risk of aggravated assault increased 18% for every category shift of vacant properties (IRR 1.18, 95% CI: 1.12, 1.25, ). Parks/playgrounds and alcohol outlets potentially modified the association between vacant properties and aggravated assaults but only at low levels of vacancy. Conclusions. Increasing levels of vacancy were associated with increased risk of assaultive violence in urban block groups.

scholarly journals The association between opioids, environmental, demographic, and socioeconomic indicators and COVID-19 mortality rates in the United States: an ecological study at the county level

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Fares Qeadan ◽  
Nana Akofua Mensah ◽  
Benjamin Tingey ◽  
Rona Bern ◽  
Tracy Rees ◽  
...  
Keyword(s):  
Public Health ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Mortality Rates ◽  
Negative Binomial Regression Model ◽  
Socioeconomic Indicators ◽  
Opioid Use ◽  
Average Maximum ◽  
Related Mortality

Abstract Background The spread of the COVID-19 pandemic throughout the world presents an unprecedented challenge to public health inequities. People who use opioids may be a vulnerable group disproportionately impacted by the current pandemic, however, the limited prior research in this area makes it unclear whether COVID-19 and opioid use outcomes may be related, and whether other environmental and socioeconomic factors might play a role in explaining COVID-19 mortality. The objective of this study is to evaluate the association between opioid-related mortality and COVID-19 mortality across U.S. counties. Methods Data from 3142 counties across the U.S. were used to model the cumulative count of deaths due to COVID-19 up to June 2, 2020. A multivariable negative-binomial regression model was employed to evaluate the adjusted COVID-19 mortality rate ratios (aMRR). Results After controlling for covariates, counties with higher rates of opioid-related mortality per 100,000 persons were found to be significantly associated with higher rates of COVID-19 mortality (aMRR: 1.0134; 95% CI [1.0054, 1.0214]; P = 0.001). Counties with higher average daily Particulate Matter (PM2.5) exposure also saw significantly higher rates of COVID-19 mortality. Analyses revealed rural counties, counties with higher percentages of non-Hispanic whites, and counties with increased average maximum temperatures are significantly associated with lower mortality rates from COVID-19. Conclusions This study indicates need for public health efforts in hard hit COVID-19 regions to also focus prevention efforts on overdose risk among people who use opioids. Future studies using individual-level data are needed to allow for detailed inferences.

scholarly journals COVID-19 in Youth Soccer During Summer 2020

2021 ◽  
Author(s):  
Andrew M. Watson ◽  
Kristin Haraldsdottir ◽  
Kevin Biese ◽  
Leslie Goodavish ◽  
Bethany Stevens ◽  
...  
Keyword(s):  
United States ◽  
Risk Reduction ◽  
Incidence Rate ◽  
Negative Binomial ◽  
Risk Mitigation ◽  
Negative Binomial Regression ◽  
The United States ◽  
Incidence Rates ◽  
Negative Binomial Regression Model ◽  
Youth Soccer

ABSTRACT Context: As sports reinitiate around the country, the incidence of COVID-19 among youth soccer athletes remains unknown. Objective: To determine the incidence of COVID-19 among youth soccer athletes and the risk mitigation practices utilized by youth soccer organizations. Design: Retrospective cohort. Participants: Youth soccer club directors throughout the United States. Main Outcome Measures: Surveys were completed in late August 2020 regarding phase of return to soccer (individual only, group non-contact, group contact), date of reinitiation, number of players, cases of COVID-19, and risk reduction procedures being implemented. Case and incidence rates were compared to national pediatric data and county data from the prior 10 weeks. A negative binomial regression model was developed to predict club COVID-19 cases with local incidence rate and phase of return as covariates and the log of club player-days as an offset. Results: 124 respondents had reinitiated soccer, representing 91,007 players with a median duration of 73 days (IQR: 53-83 days) since restarting. Of the 119 that had progressed to group activities, 218 cases of COVID-19 were reported among 85,861 players. Youth soccer players had a lower case rate and incidence rate than children in the US (254 v. 477 cases per 100,000; incidence rate ratio [IRR]=0.511, 95% CI = [0.40-0.57], p<0.001) and the general population from the counties where data was available (268 v. 864 cases per 100,000; IRR=0.202 [0.19–0.21], p<0.001). After adjusting for local COVID-19 incidence, there was no relationship between club COVID-19 incidence and phase of return (non-contact: b=0.35±0.67, p=0.61; contact: b=0.18±0.67, p=0.79). Soccer clubs reported utilizing a median of 8 (IQR: 6-10) risk reduction procedures. Conclusions: The incidence of COVID-19 among youth soccer athletes is relatively low when compared to the background incidence among children in the United States in summer of 2020. No relationship was identified between club COVID-19 incidence and phase of return to soccer.

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.

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