scholarly journals The Effect of Various Police Enforcement Actions on Violent Crime: Evidence From a Saturation Foot-Patrol Intervention

2018 ◽  
Vol 29 (6-7) ◽  
pp. 611-629 ◽  
Author(s):  
Eric L. Piza
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Crime Prevention ◽  
Regression Models ◽  
Policy Implications ◽  
Target Area ◽  
Spatial Displacement ◽  
Foot Patrol ◽  
Logistic Regression Models

The current study tests the crime prevention effect of different police actions conducted during a foot-patrol saturation initiative in Newark, New Jersey. Police actions were categorized into two typologies: enforcement actions (i.e., arrests, quality of life summonses and field interrogations) and guardian actions (i.e., business checks, citizen contacts, bus checks, and taxi inspections). Logistic regression models tested the effect of enforcement and guardian actions on crime during daily (i.e., 24-hr) periods as well as the intervention’s operational (6:00 p.m.-2:00 a.m.) and nonoperational (2:00 a.m.-5:00 p.m.) periods. Analyses were conducted twice, once for the Operation Impact target area and once for a surrounding catchment zone (to measure spatial displacement). Findings suggest that guardian actions had a greater crime prevention effect than enforcement actions on crime occurrence. Policy implications of the findings are discussed.

Physician Perceptions on Quality of Life and Resuscitation Preferences for Extremely Early Newborns

2021 ◽  
Author(s):  
Danielle LoRe ◽  
Christopher Mattson ◽  
Dalia M. Feltman ◽  
Jessica T. Fry ◽  
Kathleen G. Brennan ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Mixed Effects ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Comfort Care ◽  
Logistic Regression Models ◽  
Extremely Preterm

Objective The study aimed to explore physician views on whether extremely early newborns will have an acceptable quality of life (QOL), and if these views are associated with physician resuscitation preferences. Study Design We performed a cross-sectional survey of neonatologists and maternal fetal medicine (MFM) attendings, fellows, and residents at four U.S. medical centers exploring physician views on future QOL of extremely early newborns and physician resuscitation preferences. Mixed-effects logistic regression models examined association of perceived QOL and resuscitation preferences when adjusting for specialty, level of training, gender, and experience with ex-premature infants. Results A total of 254 of 544 (47%) physicians were responded. A minority of physicians had interacted with surviving extremely early newborns when they were ≥3 years old (23% of physicians in pediatrics/neonatology and 6% in obstetrics/MFM). The majority of physicians did not believe an extremely early newborn would have an acceptable QOL at the earliest gestational ages (11% at 22 and 23% at 23 weeks). The majority of physicians (73%) believed that having an extremely preterm infant would have negative effects on the family's QOL. Mixed-effects logistic regression models (odds ratio [OR], 95% confidence interval [CI]) revealed that physicians who believed infants would have an acceptable QOL were less likely to offer comfort care only at 22 (OR: 0.19, 95% CI: 0.05–0.65, p < 0.01) and 23 weeks (OR: 0.24, 95% CI: 0.07–0.78, p < 0.02). They were also more likely to offer active treatment only at 24 weeks (OR: 9.66, 95% CI: 2.56–38.87, p < 0.01) and 25 weeks (OR: 19.51, 95% CI: 3.33–126.72, p < 0.01). Conclusion Physician views of extremely early newborns' future QOL correlated with self-reported resuscitation preferences. Residents and obstetric physicians reported more pessimistic views on QOL. Key Points

scholarly journals Ordinal logistic regression models: application in quality of life studies

2008 ◽  
Vol 24 (suppl 4) ◽  
pp. s581-s591 ◽  
Author(s):  
Mery Natali Silva Abreu ◽  
Arminda Lucia Siqueira ◽  
Clareci Silva Cardoso ◽  
Waleska Teixeira Caiaffa
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Ordinal Logistic Regression ◽  
Proportional Odds Model ◽  
Outcome Variable ◽  
Proportional Odds ◽  
Logistic Regression Models

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.

Logistic regression models for predicting physical and mental health-related quality of life in rheumatoid arthritis patients

2008 ◽  
Vol 18 (6) ◽  
pp. 601-608 ◽  
Author(s):  
Gholam Hossein Alishiri ◽  
Noushin Bayat ◽  
Ali Fathi Ashtiani ◽  
Seyed Abbas Tavallaii ◽  
Shervin Assari ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Rheumatoid Arthritis ◽  
Mental Health ◽  
Regression Models ◽  
Health Related Quality ◽  
Physical And Mental Health ◽  
Logistic Regression Models ◽  
Related Quality ◽  
Health Related

scholarly journals Discrepancy Between Perceived Diet Quality and Actual Diet Quality Among US Adult Cancer Survivors (OR14-05-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Hong Xue ◽  
Jin Liu
Keyword(s):  
Logistic Regression ◽  
Cancer Survivors ◽  
Diet Quality ◽  
Regression Models ◽  
Vegetable Intake ◽  
Multinomial Logistic Regression ◽  
Calorie Intake ◽  
Fruit Intake ◽  
Logistic Regression Models

Abstract Objectives Postdiagnosis diet and food choices are essential for survivorship management. However, diet quality of cancer survivors is generally low. This study aimed to examine he temporal trends of diet quality of cancer survivors, assess the discrepancy between perceived diet quality and actual diet quality among cancer survivors, and its impact on their actual diet quality. Methods The National Health and Nutrition Examination Survey (NHANES) data 2005 -2014 were used in this study. Logistic regression models were fit to examine the influence of misperception of eating healthiness on diet quality measured by healthy eating index (HEI). Multinomial logistic regression models were fit to examine the factors that may influence the likelihood of diet quality misperception. Results Between 2005 and 2014, there was an increasing trend in HEI in cancer survivors, with a small but significant increase of 0.7 per year. Non-Hispanic white survivors had a consistent higher total HEI score compared to other race/ethnic groups during this period. There were 11.66%, 29.67% and 38.02% cancer survivors who rated their overall diet quality as excellent, very good, or good, while 16.96% and 3.69% perceived their overall diet as fair and poor respectively. Kappa statistic indicated a low agreement between the self-perceived diet quality and the actual diet quality measured by HEI among cancer survivors (0.06, 95% CI: 0.02, 0.09). with adjustment of age, gender, race/ethnicity, and SES status, over-rate misperception was associated with a 5.39 lower total HEI score P < 0.0001), 1.00 lower HEI score of empty calorie intake (P = 0.0028), 0.15 lower score of vegetable intake (P = 0.108) and 0.29 lower score of fruit intake. On the other hand, under-rate misperception was associated with a 7.12 higher total HEI score P < 0.0001), 2.57 higher HEI score of empty calorie intake (P < 0.0001), 0.02 higher score of vegetable intake (P = 0.904) and 0.84 higher score of fruit intake (P = 0.001). Multinomial logistic regression suggested that higher income was also associated with higher odds of being an over-rater. Individuals with college education or above were more likely to over-rate their diet quality compared to those with high school or under education (OR: 1.32, 95% CI: 1.005 1.732). Moreover, Hispanics were more likely than Non-Hispanic Whites to over-rate their diet quality ( OR: 1.792, 95% CI: 1.062, 3.024). Conclusions The divergence between self-assessed eating health and the HEI measured diet quality was an important factor that may have influenced cancer survivors’ diet behavior and diet quality. Funding Sources NA.

Financial toxicity, financial well-being, and quality of life among bladder cancer patients.

2020 ◽  
Vol 38 (29_suppl) ◽  
pp. 133-133
Author(s):  
Reginald Tucker-Seeley ◽  
Weizhou Tang ◽  
Leora Steinberg ◽  
Stephanie Banks ◽  
Hilma Bolton ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Bladder Cancer ◽  
Logistic Regression ◽  
Financial Burden ◽  
Well Being ◽  
Demographic Characteristics ◽  
Financial Toxicity ◽  
Logistic Regression Models ◽  
Long Course

133 Background: Due to a long course of surveillance, and for some patients, multimodal therapy with chemotherapy and surgery, bladder cancer (BC) can present a financial burden to the household. Yet, there are several terms used to describe this burden such as financial hardship and financial toxicity (FT); and it is unclear whether different measures of financial circumstances are correlated and whether they are associated with quality of life (QOL). The goal of this study was to determine whether FT and financial well-being (FWB) were correlated and to determine whether FT and FWB were similarly associated with QOL among BC patients. Methods: Patients that had BC treatment in the last 2 years were recruited from two NCI designated cancer centers for our study (N = 100). The Comprehensive Score for Financial Toxicity (COST) was used to assess FT, the Consumer Financial Protection Bureau’s (CFPB) FWB measure was used to assess FWB, and a BC-specific Functional Assessment of Cancer Therapy (FACT) questionnaire was used to assess physical, social, emotional, and functional domains of QOL. Bivariate analyses were conducted to determine the association among FT, FWB, and QOL; and separate logistic regression analyses predicting FT and FWB were used to determine the association between FT and FWB and each domain of QOL. Results: Bivariate results showed that FT and FWB were highly negatively correlated (r = -.76; p < .0001); however, only FWB was correlated with the total QOL score (p < .05). FT was correlated with physical (p < .01), emotional (p < .05), and functional (p < .05) QOL; and FWB was correlated with physical (p < .05) and functional (p < .05) QOL. Logistic regression models adjusted for socioeconomic and demographic characteristics showed that those reporting higher physical QOL (OR = 1.18; CI: 1.04-1.35) and higher functional QOL (OR = 1. 15; CI: 1.03-1.28) had higher odds of reporting high FWB. Conclusions: Given the surveillance and treatment processes for BC patients, it is important to better understand their financial circumstances as they are navigating and managing care. Our results suggest that physical and functional QOL is relevant for predicting FWB, but after considering socioeconomic and demographic characteristics, no domain of QOL was a significant predictor of FT among BC patients.

scholarly journals Marketability Probability Study of Cherry Tomato Cultivars Based on Logistic Regression Models

Agronomy ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 176 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
Marta Gómez-Galán ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Categorical Variables ◽  
Ripe Fruit ◽  
Storage Test ◽  
Logistic Regression Models ◽  
Solanum Lycopersicum L ◽  
Fruit Cracking ◽  
Tomato Cultivars

The purpose of this study was to demonstrate interest in applying simple and multiple logistic regression analyses to the marketability probability of commercial tomato (Solanum lycopersicum L.) cultivars when the tomatoes are harvested as loose fruit. A fruit’s firmness and commercial quality (softening or over-ripe fruit, cracking, cold damage, and rotting) were determined at 0, 7, 14, and 21 days of storage. The storage test simulated typical conditions from harvest to purchase-consumption by the consumer. The combined simple and multiple analyses of the primary continuous and categorical variables with the greatest influence on the commercial quality of postharvest fruit allowed for a more detailed understanding of the behavior of different tomato cultivars and identified the cultivars with greater marketability probability. The odds ratios allowed us to determine the increase or decrease in the marketability probability when we substituted one cultivar with a reference one. Thus, for example, the marketability probability was approximately 2.59 times greater for ‘Santyplum’ than for ‘Angelle’. Overall, of the studied cultivars, ‘Santyplum’, followed by ‘Dolchettini’, showed greater marketability probability than ‘Angelle’ and ‘Genio’. In conclusion, the logistic regression model is useful for studying and identifying tomato cultivars with good postharvest marketability characteristics.

scholarly journals An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

2016 ◽  
Vol 76 (6) ◽  
pp. 986-1004 ◽  
Author(s):  
Brandi A. Weiss ◽  
William Dardick
Keyword(s):  
Logistic Regression ◽  
Data Model ◽  
Regression Models ◽  
Group Membership ◽  
Mixture Modeling ◽  
Model Fit ◽  
Logistic Regression Models ◽  
Hypothetical Data ◽  
Simulation Results

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories.

Juvenile Criminality: Evidence From Prisoners in Two Brazilian States

2021 ◽  
pp. 073401682110383
Author(s):  
Bárbara Françoise Cardoso Bauermann ◽  
Pery Francisco Assis Shikida ◽  
Alexandre Luiz Schlemper
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Rio Grande ◽  
Young Person ◽  
Policy Implications ◽  
Family Composition ◽  
Rio Grande Do Sul ◽  
Logistic Regression Models ◽  
Juvenile Criminality ◽  
Drug Legalization

The aim of this article is to identify the socioeconomic context that can influence young peoples’ decision to engage in crime. The sample is composed of youngsters (aged between 18 and 23 years old) convicted or detained in an interim regime accused of property crimes, serving a sentence in Brazilian prison units. A survey with 302 respondents was conducted in Paraná and Rio Grande do Sul states. Logistic regression models reveal that a young people’s family composition, use a firearm, be motivated by the idea of easy gain, have a favorable attitude toward drug legalization, and consume alcohol—all factors contribute to an increased probability of the young person engage in crime. This article finalizes with a discussion of the results and policy implications.

scholarly journals PAIN AMONG OLDER MEXICANS: FINDINGS FROM THE MEXICAN HEALTH AND AGING STUDY

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S623-S623
Author(s):  
Sadaf A Milani ◽  
Rafael Samper-Ternent ◽  
Martin Rodriguez ◽  
Rebeca Wong
Keyword(s):  
Quality Of Life ◽  
Regression Models ◽  
Opioid Analgesics ◽  
Major Contributor ◽  
Seguro Popular ◽  
Logistic Regression Models ◽  
Aging Study ◽  
Using Data ◽  
Rural Locality

Abstract In Mexico, palliative care and pain relief was recently added to the essential health services offered through Seguro Popular. Pain is more frequent in older adults, a growing segment of this population, and is a major contributor to decreased quality of life and increased morbidity. However, Mexico only has enough opioid analgesics to treat 36% of those in need. We used logistic regression models to examine correlates of pain using data from the 2012 wave of the Mexican Health and Aging Study, which includes Mexicans aged 50 and older (n=13,727). Overall, 38.2% of individuals reported that they often suffered from pain. Those who reported pain were more likely to be female (OR: 1.56; 95% CI: 1.41, 1.72), insured (OR: 1.17; 95% CI: 1.03, 1.33), live in a semi-rural locality (OR: 1.18; 95% CI: 1.04, 1.34), report their health as fair or poor (OR: 2.99; 95% CI: 2.73, 3.29), be a past smoker (OR: 1.17; 95% CI: 1.06, 1.29), have at least one ADL limitation (OR: 2.58; 95% CI: 2.27, 2.93), report depression (OR: 2.19; 95% CI: 2.02, 2.37), or report arthritis (OR: 2.74; 95% CI: 2.45, 3.07). Those who did not report pain were more likely to be widowed or have higher education. Diabetes, stroke, and cancer were not significantly associated with pain. Given that Mexico does not have the resources to treat over half of individuals living with pain, understanding the high burden of pain in this population is important to inform interventions and improve quality of life.

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