Government and nongovernmental organizations need national and global estimates on the descriptive epidemiology of common oral conditions for policy planning and evaluation. The aim of this component of the Global Burden of Disease study was to produce estimates on prevalence, incidence, and years lived with disability for oral conditions from 1990 to 2017 by sex, age, and countries. In addition, this study reports the global socioeconomic pattern in burden of oral conditions by the standard World Bank classification of economies as well as the Global Burden of Disease Socio-demographic Index. The findings show that oral conditions remain a substantial population health challenge. Globally, there were 3.5 billion cases (95% uncertainty interval [95% UI], 3.2 to 3.7 billion) of oral conditions, of which 2.3 billion (95% UI, 2.1 to 2.5 billion) had untreated caries in permanent teeth, 796 million (95% UI, 671 to 930 million) had severe periodontitis, 532 million (95% UI, 443 to 622 million) had untreated caries in deciduous teeth, 267 million (95% UI, 235 to 300 million) had total tooth loss, and 139 million (95% UI, 133 to 146 million) had other oral conditions in 2017. Several patterns emerged when the World Bank’s classification of economies and the Socio-demographic Index were used as indicators of economic development. In general, more economically developed countries have the lowest burden of untreated dental caries and severe periodontitis and the highest burden of total tooth loss. The findings offer an opportunity for policy makers to identify successful oral health strategies and strengthen them; introduce and monitor different approaches where oral diseases are increasing; plan integration of oral health in the agenda for prevention of noncommunicable diseases; and estimate the cost of providing universal coverage for dental care.
The worldwide incidence trends of the lip, oral cavity, and pharyngeal cancers (LOCPs) need to be updated. This study aims to examine the temporal incidence trends of LOCPs from 1990 to 2017, using the latest Global Burden of Disease (GBD) study data to explore sex, age, and regional differences. GBD incidence data for LOCPs were driven by population cancer registries and were estimated from mortality data. Age-standardized incidence rates (ASIRs) were directly extracted from the 2017 GBD database to calculate the estimated annual percentage change (EAPC) over the study period. Incidence trends are mapped and compared separately by sex (females vs. males), age groups (15–49, 50–69, and 70+ y), regions (21 geographical and 5 sociodemographic regions), and countries. Among 678,900 incident cases of LOCPs notified in 2017, more than half were lip and oral cavity cancers. From 1990 to 2017, the estimated global incidence for nasopharyngeal cancers decreased dramatically (EAPC = −1.52; 95% confidence interval [CI], –1.70 to −1.34), while the incidence for lip and oral cavity cancers (EAPC = 0.26; 95% CI, 0.16–0.37) and other pharyngeal cancers (EAPC = 0.62; 95% CI, 0.54–0.71) increased. Higher ASIRs were observed among males than females across all age groups. However, females had larger EAPC variation when compared to males. Population groups aged 15 to 49 y presented the lowest ASIRs, with larger values of EAPC than those aged 50 to 69 and 70+ y. While high-income countries had higher ASIRs with little EAPC variation, ASIRs varied across low/middle-income regions with larger EAPC variations. South Asia and East Asia had the highest ASIRs and EAPC for lip and oral cavity cancers, respectively. In conclusion, the global incidence of LOCPs has increased among females, those aged 15 to 49 y, and people from low/middle-income countries over the study period, excepting nasopharyngeal cancers, which had a decreasing worldwide trend.
This study aims to demonstrate the use of the tree-based machine learning algorithms to predict the 3- and 5-year disease-specific survival of oral and pharyngeal cancers (OPCs) and compare their performance with the traditional Cox regression. A total of 21,154 individuals diagnosed with OPCs between 2004 and 2009 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Three tree-based machine learning algorithms (survival tree (ST), random forest (RF) and conditional inference forest (CF)), together with a reference technique (Cox proportional hazard models (Cox)), were used to develop the survival prediction models. To handle the missing values in predictors, we applied the substantive model compatible version of the fully conditional specification imputation approach to the Cox model, whereas we used RF to impute missing data for the ST, RF and CF models. For internal validation, we used 10-fold cross-validation with 50 iterations in the model development datasets. Following this, model performance was evaluated using the C-index, integrated Brier score (IBS) and calibration curves in the test datasets. For predicting the 3-year survival of OPCs with the complete cases, the C-index in the development sets were 0.77 (0.77, 0.77), 0.70 (0.70, 0.70), 0.83 (0.83, 0.84) and 0.83 (0.83, 0.86) for Cox, ST, RF and CF, respectively. Similar results were observed in the 5-year survival prediction models, with C-index for Cox, ST, RF and CF being 0.76 (0.76, 0.76), 0.69 (0.69, 0.70), 0.83 (0.83, 0.83) and 0.85 (0.84, 0.86), respectively, in development datasets. The prediction error curves based on IBS showed a similar pattern for these models. The predictive performance remained unchanged in the analyses with imputed data. Additionally, a free web-based calculator was developed for potential clinical use. In conclusion, compared to Cox regression, ST had a lower and RF and CF had a higher predictive accuracy in predicting the 3- and 5-year OPCs survival using SEER data. The RF and CF algorithms provide non-parametric alternatives to Cox regression to be of clinical use for estimating the survival probability of OPCs patients.
Aims To comprehensively review, identify and critically assess the performance of models predicting the incidence and progression of periodontitis. Methods Electronic searches of the MEDLINE via PubMed, EMBASE, DOSS, Web of Science, Scopus and ProQuest databases, and hand searching of reference lists and citations were conducted. No date or language restrictions were used. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist was followed when extracting data and appraising the selected studies. Results Of the 2,560 records, five studies with 12 prediction models and three risk assessment studies were included. The prediction models showed great heterogeneity precluding meta‐analysis. Eight criteria were identified for periodontitis incidence and progression. Four models from one study examined the incidence, while others assessed progression. Age, smoking and diabetes status were common predictors used in modelling. Only two studies reported external validation. Predictive performance of the models (discrimination and calibration) was unable to be fully assessed or compared quantitatively. Nevertheless, most models had “good” ability to discriminate between people at risk for periodontitis. Conclusions Existing predictive modelling approaches were identified. However, no studies followed the recommended methodology, and almost all models were characterized by a generally poor level of reporting.
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