Association of Dietary Fluoride Intake and Diet Variables with Dental Caries in Adolescents from the ELEMENT Cohort Study

2021 ◽  
Vol 55 (2) ◽  
pp. 88-98
Author(s):  
Alejandra Cantoral ◽  
Teresa V. Muñoz-Rocha ◽  
Lynda Luna-Villa ◽  
Andres Mantilla-Rodriguez ◽  
José L. Ureña-Cirett ◽  
...  
Keyword(s):  
Dental Caries ◽  
Dietary Intake ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Carbohydrate Intake ◽  
Caries Experience ◽  
Total Carbohydrate ◽  
Fluoride Intake ◽  
Binomial Regression

To examine the association of dietary fluoride intake, total carbohydrate consumption and other key dietary variables with dental caries experience among adolescents, a cross-sectional analysis was conducted in a sample of 402 participants from the Early Life Exposures in Mexico to Environmental Toxicants cohort. The presence and severity of dental caries were assessed using the International Caries Detection and Assessment System (ICDAS) to calculate the number of decayed, missing, and filled teeth or surfaces (D1MFT/D4MFT). The dietary intake of fluoride, energy, carbohydrates, and food groups was estimated using a validated food frequency questionnaire (FFQ). Multivariate zero-inflated negative binomial regression models and negative binomial regression models were run to estimate the association of fluoride intake (mg/day) and total carbohydrate intake (g/day) with the D1MFT/D4MFT index. We found that 80% of the adolescents experienced dental caries (D1MFT &#x3e;0), with 30% presenting cavitated lesions (D4MFT &#x3e;0). The mean scores for D1MFT and D4MFT were 6.2 (SD 5.3) and 0.67 (SD 1.3), respectively. The median intake of fluoride estimated by the FFQ was 0.015 mg/kg/day. This intake was statistically higher in participants with a D4MFT = 0 compared to those with a D4MFT &#x3e;0 (0.90 vs. 0.82 mg/day; 0.016 vs. 0.014 mg/kg/day; <i>p</i> &#x3c; 0.05). For D1MFT, D1MFS, D4MFT, and D4MFS scores, a higher fluoride consumption (mg/day) from foods and beverages was associated with a statistically significant reduction in the number of lesions. The reported frequency of consumption of sugary foods in a whole day was statistically higher in those with D1MFT &#x3e;0 than in those with D1MFT = 0 (<i>p</i> &#x3c; 0.05). The total carbohydrate intake (g/day) was positively associated with dental caries experience. We conclude that a higher fluoride intake through foods and beverages is associated with a lower dental caries experience among adolescents; this effect was seen even when the dietary intake of fluoride was 0.015 mg/kg/day, which is lower than the average intake recommendation. In contrast, a higher total carbohydrate intake and the frequency of intake of sugary foods were associated with a higher dental caries experience, with no apparent threshold for the effects.

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.

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.

Intuition and mood of investors in financial markets

2017 ◽  
Vol 30 (2) ◽  
pp. 231-248
Author(s):  
Robson Braga ◽  
Luiz Paulo Lopes Fávero ◽  
Renata Turola Takamatsu
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Real Data ◽  
Financial Assets ◽  
Risky Assets ◽  
Content Type ◽  
Binomial Regression ◽  
Investor Behaviour ◽  
Investment Portfolios

Purpose The purpose of this paper is to evaluate investor behaviour related to the timing of selling financial assets based on an intuitive evaluation of the current market trend and growth expectation. Design/methodology/approach The experiment involved 1,052 volunteer participants who made decisions about stock sales in an environment that simulated a home broker platform to negotiate stocks. Zero-inflated regression models were used. Findings The results show that investors’ attitudes, or beliefs, determine whether they will buy or keep risky assets in their investment portfolios; they may decide to sell such assets, even though market shows an upward trend. Such results make a new contribution to behavioural finance within the context of prospect theory and the disposition effect. Originality/value The originality of this paper lies in the use of new and innovative techniques (zero-inflated Poisson and negative binomial regression models) applied to real data obtained experimentally.

Sign in / Sign up

Export Citation Format

Share Document