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Caries Risk Prediction Models in a Medical Health Care Setting.

ABSTRACT: 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.

SUBMITTER: Kalhan TA

PROVIDER: S-EPMC7343522 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Caries Risk Prediction Models in a Medical Health Care Setting.

Kalhan T A TA Un Lam C C Karunakaran B B Chay P L PL Chng C K CK Nair R R Lee Y S YS Fong M C F MCF Chong Y S YS Kwek K K Saw S M SM Shek L L Yap F F Tan K H KH Godfrey K M KM Huang J J Hsu C-Y S CS

Journal of dental research 20200420 7

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 w ...[more]

PMID: 32311276

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Project description:Expanded partnership with the medical community is an important strategy for reducing dental caries disparities. The purpose of this study was to assess the relationship between fluoride (F) "in office" (drops/tablets and/or varnish), as prescribed or applied by a health care professional by age 1 y, and 1) caries development and 2) presence of other caries risk factors or mediators (e.g., socioeconomic status). Child-primary caregiver (PCG) pairs ( N = 1,325) were recruited in Indiana, Iowa, and North Carolina as part of a longitudinal cohort study to validate a caries risk tool for primary health care settings. PCGs completed a caries risk questionnaire, while children received caries examinations per the criteria of the International Caries Detection and Assessment System at ages 1, 2.5, and 4 y. Baseline responses regarding children's history of F in office were tested for association with other caries risk variables and caries experience at ages 2.5 and 4 y via generalized estimating equation models applied to logistic regression. The sample was 48% female, and many children (61%) were Medicaid enrolled. The prevalence of cavitated caries lesions increased from 7% at age 2.5 y to 25% by age 4 y. Children who received F in office were likely deemed at higher caries risk and indeed were significantly ( P < 0.01) more likely to develop cavitated caries lesions by ages 2.5 and 4 y, even after F application (odds ratios: 3.5 and 2.3, respectively). Factors significantly associated with receiving F included the following: child being Medicaid enrolled, not having an employed adult in the household, child and PCG often consuming sugary drinks and snacks, and PCG having recent caries experience. Increased F in office from a health care provider by age 1 y was associated with known caries risk factors. Most (69%) children had never been to the dentist, suggesting that risk factors could be alerting medical providers and/or parents, thereby affecting in-office F recommendations. Differences among states could also be related to state-specific F-varnish reimbursement policies (ClinicalTrials.gov NCT01707797).

Project description:OBJECTIVE:To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings. DESIGN:Systematic review. DATA SOURCES:MEDLINE and Embase databases combined from inception and up to the end of January 2019. ELIGIBILITY:Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). DATA EXTRACTION AND SYNTHESIS:Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance. RESULTS:An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21. CONCLUSION:Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Project description:BackgroundContinuing medical education (CME), as a systematic attempt to facilitate change in General Practitioners' (GPs) practices, is considered crucial, assuming that if physicians are up-to-date, they will change and improve their practice, resulting in better performance and ultimately better patient care. However, studies continue to demonstrate considerable gaps between the real and ideal performance and patient-related outcomes. The objective of this study was to explore GP's perception of the factors affecting the implementation of a CME digital platform in a primary health care setting in Portugal.MethodsOur work is framed in a larger effectiveness-implementation hybrid type 1 study, where a Digital Behaviour Change Intervention (DBCI), called ePrimaPrescribe, was developed and implemented with the aim of changing benzodiazepines (BZD) prescribing patterns. Our design used mixed methodologies to obtain an enriched knowledge on GPs' perspectives on the facilitators and barriers to implementing a Digital Behaviour Change Intervention (DBCI) applied to CME. To do so, we used data coming from an onsite questionnaire, an adapted version of the Barriers and Facilitators Assessment Instrument (BaFAI) and in-depth interviews.ResultsFrom the 47 GPs successfully included in the intervention arm of our cluster-randomized effectiveness study, we collected 37 onsite questionnaires, 24 BaFAIs, and performed 12 in-depth interviews. GPs reported as the main barriers to CME a lack of time, a perception of work overload, a lack of digital competence, a lack of digital infrastructure, and motivational and emotional factors. They reported as facilitators to CME delivered through a DBCI the convenience of the delivery method, the practical and pragmatic characteristics of the content, and the possibility for CME to be mandatory.ConclusionsThe perceptions of the barriers and facilitators reported by GPs represent an important contribution to improving knowledge regarding the factors influencing the implementation of CME in primary health care settings. We consider that our study might bring useful insights to other countries where primary health care plays a central role in the provision of care.Trial registrationClinicalTrials.gov number NCT04925596 .

Project description:BackgroundAssessing caries risk is an essential element in the planning of preventive and therapeutic strategies. Different caries risk assessment (CRA) models have been proposed for the identification of individuals running a risk of future caries. This systematic review was designed to evaluate whether standardized caries risk assessment (CRA) models are able to evaluate the risk according to the actual caries status and/or the future caries increment.MethodsRandomized clinical trials, cross-sectional studies, cohort studies, comparative studies, validation studies and evaluation studies, reporting caries risk assessment using standardized models (Cariogram, CAMBRA, PreViser, NUS-CRA and CAT) in patients of any age related to caries data recorded by DMFT/S or ICDAS indices, were included. PubMed, Scopus and Embase were searched from 2000 to 2016. A search string was developed. All the papers meeting the inclusion criteria were subjected to a quality assessment.ResultsOne thousand three-undred ninety-two papers were identified and 32 were included. In all but one, the Cariogram was used both as sole model or in conjunction with other models. All the papers on children (n?=?16) and adults (n?=?12) found a statistically significant association between the risk levels and the actual caries status and/or the future caries increment. Nineteen papers, all using the Cariogram except one, were classified as being of good quality. Three of four papers comprising children and adults found a positive association. For seven of the included papers, Cariogram sensibility and specificity were calculated; sensibility ranged from low (41.0) to fairly low (75.0), while specificity was higher, ranging from 65.8 to 88.0. Wide 95% confidence intervals for both parameters were found, indicating that the reliability of the model differed in different caries risk levels.ConclusionsThe scientific evidence relating to standardized CRA models is still limited; even if Cariogram was tested in children and adults in few studies of good quality, no sufficient evidence is available to affirm the method is effective in caries assessment and prediction. New options of diagnosis, prognosis and therapy are now available to dentists but the validity of standardized CRA models still remains limited.

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Caries Risk Prediction Models in a Medical Health Care Setting.

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Caries Risk Prediction Models in a Medical Health Care Setting.

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