Dataset Information

Improving Caries Risk Prediction Modeling: A Call for Action.

ABSTRACT: Dentistry has entered an era of personalized/precision care in which targeting care to groups, individuals, or even tooth surfaces based on their caries risk has become a reality to address the skewed distribution of the disease. The best approach to determine a patient's prognosis relies on the development of caries risk prediction models (CRPMs). A desirable model should be derived and validated to appropriately discriminate between patients who will develop disease from those who will not, and it should provide an accurate estimation of the patient's absolute risk (i.e., calibration). However, evidence suggests there is a need to improve the methodological standards and increase consistency in the way CRPMs are developed and evaluated. In fact, although numerous caries risk assessment tools are available, most are not routinely used in practice or used to influence treatment decisions, and choice is not commonly based on high-quality evidence. Research will propose models that will become more complex, incorporating new factors with high prognostic value (e.g., human genetic markers, microbial biomarkers). Big data and predictive analytic methods will be part of the new approaches for the identification of promising predictors with the ability to monitor patients' risk in real time. Eventually, the implementation of validated, accurate CRPMs will have to follow a user-centered design respecting the patient-clinician dynamic, with no disruption to the clinical workflow, and needs to operate at low cost. The resulting predictive risk estimate needs to be presented to the patient in an understandable way so that it triggers behavior change and effectively informs health care decision making, to ultimately improve caries outcomes. However, research on these later aspects is largely missing and increasingly needed in dentistry.

SUBMITTER: Fontana M

PROVIDER: S-EPMC7649255 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Worldwide, lifestyle and resource disparities among adolescents contribute to unmet health needs, which have crucial present and future public health implications for both adolescents and broader communities. Risk of infection among adolescents is amplified by biological, behavioral, and environmental factors; however, infectious diseases to which adolescents are susceptible are often preventable with vaccines. Beyond these concerns, there is a lack of knowledge regarding adolescent vaccination and disease risk among parents and adolescents, which can contribute to low vaccine uptake. Promising efforts have been made to improve adolescent vaccination by programs with motivational drivers and comprehensive communication with the public. In May 2017, a multidisciplinary group of experts met in Amsterdam, Netherlands, to discuss adolescent vaccine uptake, as part of an educational initiative called the Advancing Adolescent Health Spring Forum. This article presents consensus opinions resulting from the meeting, which pertain to the burden of vaccine-preventable diseases among adolescents, reasons for low vaccine uptake, and common characteristics of successful strategies for improving adolescent vaccination.Conclusion: There is an urgent "call to action," particularly targeting healthcare providers and public health authorities, for the prioritization of adolescent vaccination as a necessary element of preventive healthcare in this age group.What is Known:• Despite increased risk of certain infectious diseases, adolescent vaccination uptake remains low.What is New:• Barriers to adolescent vaccine uptake include lack of information regarding vaccines and disease risk, health system inadequacies, and insufficient healthcare follow-up.• Successful efforts to improve adolescent vaccine uptake need cohesive leadership and involvement of multiple stakeholders, as well as youth-friendly messaging; healthcare providers and policymakers should prioritize adolescent vaccination and implement proven program strategies to improve adolescent health worldwide.

Project description:Despite development of new technologies for caries control, tooth decay in primary teeth remains a major global health problem. Caries risk assessment (CRA) models for toddlers and preschoolers are rare. Among them, almost all models use dental factors (e.g., past caries experience) to predict future caries risk, with limited clinical/community applicability owing to relatively uncommon dental visits compared to frequent medical visits during the first year of life. The objective of this study was to construct and evaluate risk prediction models using information easily accessible to medical practitioners to forecast caries at 2 and 3 y of age. Data were obtained from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) mother-offspring cohort. Caries was diagnosed using modified International Caries Detection and Assessment System criteria. Risk prediction models were constructed using multivariable logistic regression coupled with receiver operating characteristic analyses. Imputation was performed using multiple imputation by chained equations to assess effect of missing data. Caries rates at ages 2 y (n = 535) and 3 y (n = 721) were 17.8% and 42.9%, respectively. Risk prediction models predicting overall caries risk at 2 and 3 y demonstrated area under the curve (AUC) (95% confidence interval) of 0.81 (0.75-0.87) and 0.79 (0.74-0.84), respectively, while those predicting moderate to extensive lesions showed 0.91 (0.85-0.97) and 0.79 (0.73-0.85), respectively. Postimputation results showed reduced AUC of 0.75 (0.74-0.81) and 0.71 (0.67-0.75) at years 2 and 3, respectively, for overall caries risk, while AUC was 0.84 (0.76-0.92) and 0.75 (0.70-0.80), respectively, for moderate to extensive caries. Addition of anterior caries significantly increased AUC in all year 3 models with or without imputation (all P < 0.05). Significant predictors/protectors were identified, including ethnicity, prenatal tobacco smoke exposure, history of allergies before 12 mo, history of chronic maternal illness, maternal brushing frequency, childbearing age, and so on. Integrating oral-general health care using medical CRA models may be promising in screening caries-susceptible infants/toddlers, especially when medical professionals are trained to "lift the lip" to identify anterior caries lesions.

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Improving Caries Risk Prediction Modeling: A Call for Action.

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