Socio–Economic and Behavioural Risk Factors for Tooth Loss from Age 18 to 26 among Participants in the Dunedin Multidisciplinary Health and Development Study

2000 ◽  
Vol 34 (5) ◽  
pp. 361-366 ◽  
Author(s):  
W.M. Thomson ◽  
R. Poulton ◽  
E. Kruger ◽  
D. Boyd
Keyword(s):  
Risk Factors ◽  
Tooth Loss ◽  
Development Study ◽  
Behavioural Risk Factors ◽  
Health And Development

Behavioural risk factors for HIV infection amongst blood donors in London

1996 ◽  
Vol 6 (1) ◽  
pp. 31-36 ◽  
Author(s):  
F. M. Cowan ◽  
A. M. Johnson ◽  
J. Wadsworth ◽  
M. Brennan
Keyword(s):  
Risk Factors ◽  
Hiv Infection ◽  
Blood Donors ◽  
Behavioural Risk Factors

scholarly journals Development of a prognostic tool: based on risk factors for tooth loss after active periodontal therapy

2021 ◽  
Author(s):  
Sonja Rahim-Wöstefeld ◽  
Dorothea Kronsteiner ◽  
Shirin ElSayed ◽  
Nihad ElSayed ◽  
Peter Eickholz ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Tooth Loss ◽  
Treatment Plan ◽  
Periodontal Therapy ◽  
Outcome Variable ◽  
Individual Treatment ◽  
Risk Indicator ◽  
Related Factors ◽  
Prognostic Tool

Abstract Objectives The aim of this study was to develop a prognostic tool to estimate long-term tooth retention in periodontitis patients at the beginning of active periodontal therapy (APT). Material and methods Tooth-related factors (type, location, bone loss (BL), infrabony defects, furcation involvement (FI), abutment status), and patient-related factors (age, gender, smoking, diabetes, plaque control record) were investigated in patients who had completed APT 10 years before. Descriptive analysis was performed, and a generalized linear-mixed model-tree was used to identify predictors for the main outcome variable tooth loss. To evaluate goodness-of-fit, the area under the curve (AUC) was calculated using cross-validation. A bootstrap approach was used to robustly identify risk factors while avoiding overfitting. Results Only a small percentage of teeth was lost during 10 years of supportive periodontal therapy (SPT; 0.15/year/patient). The risk factors abutment function, diabetes, and the risk indicator BL, FI, and age (≤ 61 vs. > 61) were identified to predict tooth loss. The prediction model reached an AUC of 0.77. Conclusion This quantitative prognostic model supports data-driven decision-making while establishing a treatment plan in periodontitis patients. In light of this, the presented prognostic tool may be of supporting value. Clinical relevance In daily clinical practice, a quantitative prognostic tool may support dentists with data-based decision-making. However, it should be stressed that treatment planning is strongly associated with the patient’s wishes and adherence. The tool described here may support establishment of an individual treatment plan for periodontally compromised patients.

The Dunedin Multidisciplinary Health and Development Study

2021 ◽  
pp. 33-42
Author(s):  
Amber L. Beckley ◽  
Terrie E. Moffitt ◽  
Richie Poulton
Keyword(s):  
Development Study ◽  
Health And Development

scholarly journals Risk factors for tooth loss with a mean follow-up period of 13.9 years in supportive periodontal therapy patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tomotaka Kato ◽  
Natsuki Fujiwara ◽  
Tomohisa Ogawa ◽  
Yukihiro Numabe
Keyword(s):  
Risk Factors ◽  
Tooth Loss ◽  
Periodontal Therapy ◽  
Dental Hygienists ◽  
Multiple Classification Analysis ◽  
Hazard Ratios ◽  
Dental Office ◽  
Multiple Classification ◽  
Supportive Periodontal Therapy

Abstract Background Clinical evidence indicates that there are various risk factors of tooth loss. However, the degree of this risk among other risk factors remains unclear. In this retrospective cohort study, the authors evaluated the hazard ratios of several risk factors for tooth loss. Methods Included patients had all been treated for dental disorders, were in the supportive phase of periodontal therapy by dental hygienists, and visited a Japanese dental office continually during a 10-year period. Periodontal parameters, tooth condition, and general status of all teeth (excluding third molars) at the initial visit and at least 10 years later were evaluated by using multiple classification analysis. Results The authors evaluated a total of 7584 teeth in 297 patients (average age: 45.3, mean follow-up time: 13.9 years) Non-vital pulp was the most significant predictor of tooth loss according to Cox hazards regression analysis (hazard ratio: 3.31). The 10-year survival rate was approximately 90% for teeth with non-vital pulp and 99% for teeth with vital pulp. Fracture was the most common reason for tooth loss. Conclusions Non-vital pulp had the most significant association with tooth loss among the parameters. Therefore, it is very important to minimize dental pulp extirpation.

Multiple behavioural risk factors of non-communicable diseases among adolescents in four Caribbean countries: prevalence and correlates

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Supa Pengpid ◽  
Karl Peltzer
Keyword(s):  
Risk Factors ◽  
Parental Support ◽  
Fast Food ◽  
Vegetable Intake ◽  
Communicable Diseases ◽  
Cross Sectional ◽  
Non Communicable Diseases ◽  
Behavioural Risk Factors ◽  
Fast Food Consumption ◽  
High Prevalence

Abstract Objectives The study assessed the prevalence and associated factors of behavioural risk factors of non-communicable diseases (NCDs) among adolescents in four Caribbean countries. Content In all 9,143 adolescents (15 years = median age) participated in the cross-sectional “2016 Dominican Republic, 2016 Suriname, 2017 Jamaica, and 2017 Trinidad and Tobago Global School-Based Student Health Survey (GSHS)”. Eight behavioural risk factors of NCDs were assessed by a self-administered questionnaire. Summary Prevalence of each behavioural NCD risk factor was physical inactivity (84.2%), inadequate fruit and vegetable intake (82.2%), leisure-time sedentary behaviour (49.6%), daily ≥2 soft drinks intake (46.8%), ever drunk (28.6%), twice or more days a week fast food consumption (27.6%), having overweight/obesity (27.4%), and current tobacco use (13.8%). Students had on average 3.6 (SD=1.4), and 79.0% had 3–8 behavioural NCD risk factors. In multivariable linear regression, psychological distress and older age increased the odds, and attending school and parental support decreased the odds of multiple behavioural NCD risk factors. Outlook A high prevalence and co-occurrence of behavioural risk factors of NCDs was discovered and several factors independently contributing to multiple behavioural NCD risk factors were identified.

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