Screening for Diabetes Risk Using Integrated Dental and Medical Electronic Health Record Data

Development and External Validation of a Multivariable Prediction Model to Identify Nondiabetic Hyperglycemia and Undiagnosed Type 2 Diabetes: Diabetes Risk Assessment in Dentistry Score (DDS)

The aim of this study was to develop and externally validate a score for use in dental settings to identify those at risk of undiagnosed nondiabetic hyperglycemia (NDH) or type 2 diabetes (T2D). The Studies of Health in Pomerania (SHIP) project comprises 2 representative population-based cohort studies conducted in northeast Germany. SHIP-TREND-0, 2008 to 2012 (the development data set) had 3,339 eligible participants, with 329 having undiagnosed NDH or T2D. Missing data were replaced using multiple imputation. Potential covariates were selected for inclusion in the model using backward elimination. Heuristic shrinkage was used to reduce overfitting, and the final model was adjusted for optimism. We report the full model and a simplified paper-based point-score system. External validation of the model and score employed an independent data set comprising 2,359 participants with 357 events. Predictive performance, discrimination, calibration, and clinical utility were assessed. The final model included age, sex, body mass index, smoking status, first-degree relative with diabetes, presence of a dental prosthesis, presence of mobile teeth, history of periodontal treatment, and probing pocket depths ≥5 mm as well as prespecified interaction terms. In SHIP-TREND-0, the model area under the curve (AUC) was 0.72 (95% confidence interval [CI] 0.69, 0.75), calibration in the large was -0.025. The point score AUC was 0.69 (95% CI 0.65, 0.72), with sensitivity of 77.0 (95% CI 76.8, 77.2), specificity of 51.5 (95% CI 51.4, 51.7), negative predictive value of 94.5 (95% CI 94.5, 94.6), and positive predictive value of 17.0 (95% CI 17.0, 17.1). External validation of the point score gave an AUC of 0.69 (95% CI 0.66, 0.71), sensitivity of 79.2 (95% CI 79.0, 79.4), specificity of 49.9 (95% CI 49.8, 50.00), negative predictive value 91.5 (95% CI 91.5, 91.6), and positive predictive value of 25.9 (95% CI 25.8, 26.0). A validated prediction model involving dental variables can identify NDH or undiagnosed T2DM. Further studies are required to validate the model for different European populations.

Factors Associated with Self-Report of Type 2 Diabetes Mellitus in Adults Seeking Dental Care in a Developing Country

The aims of the present study were to identify the prevalence and risk indicators of type 2 diabetes mellitus (T2DM) in urban-based Mexican adults seeking care in a university-based triage/intake dental clinic, and to develop a predictive model. A cross-sectional study was conducted on 3354 medical/dental records of adults who sought care at the triage/intake dental clinics of a public university. The dependent variable was self-report of a previous diagnosis of T2DM made by a physician. Several socio-demographic and socioeconomic covariates were included, as well as others related to oral and general health. A multivariate binary logistic regression model was generated. We subsequently calculated well-known statistical measures employed to evaluate discrimination (classification) using an (adjusted) multivariate logistic regression model (goodness-of-fit test). The average age of patients was 42.5 ± 16.1 years old and the majority were female (64.1%). The prevalence of T2DM was 10.7% (95%CI = 9.7−11.8). In the final multivariate model, the variables associated (p < 0.05) with the presence of T2DM were older age (40 to 59 years old, OR = 2.00; 60 to 95 years old, OR = 2.78), having any type of health insurance (OR = 2.33), having high blood pressure (OR = 1.70), being obese (OR = 1.41), and having a functional dentition (OR = 0.68). Although the global fit of the model and the calibration tests were adequate, the sensitivity (0.0%) and positive predictive (0.0%) values were not. The specificity (100%) and negative predictive (89.3%) values, as well as the correctly classified (89.3%) value, were adequate. The area under the ROC curve, close to 0.70, was modest. In conclusion, a prevalence of T2DM of 10.7% in this sample of Mexican adults seeking dental care was similar to national figures. Clinical (blood pressure, BMI and functional dentition), demographic (age), and socioeconomic (health insurance) variables were found to be associated with T2DM. The dental setting could be appropriate for implementing preventive actions focused on identifying and helping to reduce the burden of T2DM in the population.

Can periodontal measures assist in the identification of adults with undiagnosed hyperglycaemia? A systematic review

AIM

The aim of this review was to answer the following question: Can periodontal measures be used to identify dental patients with undiagnosed hyperglycaemia?

MATERIALS AND METHODS

Systematic searches of electronic databases and the grey literature were carried out to identify studies developing and/or validating prediction models, based on any periodontal measure, to screen adults for undiagnosed hyperglycaemia (pre-diabetes and diabetes). Risk of bias was evaluated using the PRediction mOdel risk-of-Bias ASsessment Tool (PROBAST).

RESULTS

Ten studies were identified, of which eight were model development studies. The remaining two studies reported the external validation of one existing prediction model. The periodontal prediction model with some evidence of external validation showed moderate diagnostic performance in the development sample but lower performance in the external validation samples. According to PROBAST, all studies had high risk of bias mainly due to methodological limitations in data analysis, but also in the recruitment of participants, choice and measurement of periodontal predictors and diabetes.

CONCLUSIONS

There is a need for more robust external validation studies of existing prediction models adhering to current recommendations. Dental professionals who see patients at risk of diabetes and routinely collect periodontal measures have an important role to play in their identification and referral.

Implementation and analysis of an enhanced screening and testing protocol for prediabetes in a dental school patient population

OBJECTIVES

This project aimed to enhance the identification of patients at-risk for prediabetes or diabetes within a dental school patient population by introduction of a modified screening tool and related training of dental residents and students.

METHODS

The American Diabetes Association Risk Tool (ADART) was modified by addition of three diabetes-linked oral health questions. Of the 1477 dental patients screened, 551 (37.3%) indicated an at-risk status using the modified tool. A subset of 138 patients received follow-up chairside HbA1c blood testing conducted by dental residents and students. Data was analysed to determine a) the influence of the modifications on the tool's discrimination strength and b) change in the tool's predictive value.

RESULTS

The addition of the 3 oral health questions to the 7-item ADART resulted in a 9.4% increase in identification of patients at-risk for pre-diabetes/diabetes. The predictive value of the tool remained stable. Residents and students successfully incorporated the new screening activities within their assigned clinics.

CONCLUSIONS

This project demonstrates that screening for risk for prediabetes/diabetes is both prudent and practical in the dental setting. Dental personnel, including trainees, can successfully incorporate enhanced screening methods within their traditional activities. Further, screening tools used in the dental setting might be enhanced by inclusion of certain oral health variables associated with diabetes. These findings add to emerging knowledge on the importance of screening for prediabetes/diabetes in dental settings and have particular relevance and application to institutional practice.

Systematic review of studies examining contribution of oral health variables to risk prediction models for undiagnosed Type 2 diabetes and prediabetes

Objective

To conduct systematic review applying “preferred reporting items for systematic reviews and meta‐analyses statement” and “prediction model risk of assessment bias tool” to studies examining the performance of predictive models incorporating oral health‐related variables as candidate predictors for projecting undiagnosed diabetes mellitus (Type 2)/prediabetes risk.

Materials and Methods

Literature searches undertaken in PubMed, Web of Science, and Gray literature identified eligible studies published between January 1, 1980 and July 31, 2018. Systematically reviewed studies met inclusion criteria if studies applied multivariable regression modeling or informatics approaches to risk prediction for undiagnosed diabetes/prediabetes, and included dental/oral health‐related variables modeled either independently, or in combination with other risk variables.

Results

Eligibility for systematic review was determined for seven of the 71 studies screened. Nineteen dental/oral health‐related variables were examined across studies. “Periodontal pocket depth” and/or “missing teeth” were oral health variables consistently retained as predictive variables in models across all systematically reviewed studies. Strong performance metrics were reported for derived models by all systematically reviewed studies. The predictive power of independently modeled oral health variables was marginally amplified when modeled with point‐of‐care biological glycemic measures in dental settings. Meta‐analysis was precluded due to high inter‐study variability in study design and population diversity.

Conclusions

Predictive modeling consistently supported “periodontal measures” and “missing teeth” as candidate variables for predicting undiagnosed diabetes/prediabetes. Validation of predictive risk modeling for undiagnosed diabetes/prediabetes across diverse populations will test the feasibility of translating such models into clinical practice settings as noninvasive screening tools for identifying at‐risk individuals following demonstration of model validity within the defined population.

Knowledgeability, Attitude and Practice Behaviors of Dental Providers Toward Provisions of Integrated Care Delivery for Patients With Prediabetes/Diabetes: Wisconsin Statewide Survey

Introduction: To conduct a statewide survey among Wisconsin-based dental providers evaluating current knowledgeability, attitudes and practice behaviors surrounding management of patients with diabetes/prediabetes in the dental setting. The study explored perceptions on feasibility, value, barriers, and current status of integrated care model (ICM) adoption by dental practices

Materials and Methods: A 32-question paper-based survey was mailed to all licensed dentists and dental hygienists practicing in Wisconsin. The study was conducted over a 4 week period in 2019. The survey instrument was adapted from a previous validated survey and was expanded to include questions on ICM adoption. Content and validity analyses and beta testing were conducted prior to dissemination of the survey. Descriptive statistics and chi-square tests were applied for data analyses. Thematic analyses was performed on open-ended questions.

Results: Survey response rate was 12% (N = 854/7,356) representing 41% dentists and 59% dental hygienists. While 68% reported educating patients on oral health-diabetes association, only 18% reported medical consultations to inform dental treatment, and “frequent” (22%) or “occasional” (40%), medical triage. Knowledge-based questions were correctly answered by &gt;70% of participants. While 50% valued chair-side glycemic screening and 85% supported non-invasive chair-side screening to identify at-risk patients,&gt;88% relied on patient-reported diabetic status. Barriers to ICM adoption included time investment (70%), patient activation/cooperation (62%), cost (50%), insurance coverage (50%), infrequent interdisciplinary communication (46%), lack of equipment (33%) and provider (31%).

Conclusion: Low rates of ICM adoption, chair-side testing, medical consultation and triage, and need for educational curricula reform were identified.

Dental Treatment Effect on Blood Glucose Level Fluctuation in Type 1 Balanced Diabetic Children

Introduction

Pediatric dentists, while treating diabetic patients, have a major concern about the occurrence of hypo- or hyperglycemia complications. So far, no dental care protocol is elaborated to help practitioners in creating a safe environment for their patients. This study aims to evaluate the blood glucose level (BGL) fluctuation in type 1 diabetic children according to the type of the delivered treatment and its duration, to outline guidance for predicting the occurrence of a hypo- or hyperglycemia complication.

Materials and methods

A cross-sectional approach was conducted on 70 balanced diabetic children aged between 7 years and 12 years old (32 females and 38 males) in the Department of Pediatric Dentistry at the Lebanese University in Beirut. Only the patients having a BGL between 70 mg/dL and 300 mg/dL could undergo dental treatment. The gender, the duration of the dental session (≤30 or >30 minutes), the BGLs at the baseline and the end of the session were noted. The types of the performed dental treatment were classified as simple, and unpleasant acts.

Results

For the female group, only eight sessions lasted ≤30 minutes. The results of simple and unpleasant interventions >30 minutes showed that BGLs before and after treatment were almost identical (p > 0.05). In the male group, for the simple acts >30 minutes, the BGL showed a significant fluctuation (p = 0.02).

Conclusion

Dentists must be aware of signs of discomfort in balanced type 1 diabetic children to avoid severe complications. This study highlights a new range of BGLs (70–300 mg/dL) that allows safe dental treatment.

How to cite this article

Nassif N, Noueiri B. Dental Treatment Effect on Blood Glucose Level Fluctuation in Type 1 Balanced Diabetic Children. Int J Clin Pediatr Dent 2021;14(2):212–216.

Diabetes and Oral Health: Summary of Current Scientific Evidence for Why Transdisciplinary Collaboration Is Needed

This Perspective provides a brief summary of the scientific evidence for the often two-way links between hyperglycemia, including manifest diabetes mellitus (DM), and oral health. It delivers in a nutshell examples of current scientific evidence for the following oral manifestations of hyperglycemia, along with any available evidence for effect in the opposite direction: periodontal diseases, caries/periapical periodontitis, tooth loss, peri-implantitis, dry mouth (xerostomia/hyposalivation), dysbiosis in the oral microbiome, candidiasis, taste disturbances, burning mouth syndrome, cancer, traumatic ulcers, infections of oral wounds, delayed wound healing, melanin pigmentation, fissured tongue, benign migratory glossitis (geographic tongue), temporomandibular disorders, and osteonecrosis of the jaw. Evidence for effects on quality of life will also be reported. This condensed overview delivers the rationale and sets the stage for the urgent need for delivery of oral and general health care in patient-centered transdisciplinary collaboration for early detection and management of both hyperglycemia and oral diseases to improve quality of life.

Screening of undiagnosed hyperglycaemia in the dental setting: The DiabetRisk study. A field trial

AIM

To evaluate the efficacy of different screening protocols for undiagnosed hyperglycaemia in a Research Network of Dental Clinics coordinated by the Spanish Society of Periodontology (SEPA).

MATERIAL AND METHODS

A total of 1143 patients were included in the study. Participants filled a questionnaire considering diabetes risk factors (FINDRISC) and received a periodontal screening examination. Patients with a slightly elevated score according to the Findrisc (≥7), received a point-of-care HbA1c and were eventually referred to their physician for confirmatory diagnosis. Receiver Operating Characteristic (ROC) curves were used to assess the performance of various predictive models with confirmed hyperglycaemia as outcome.

RESULTS

From this population, 97 (8.5%) were finally diagnosed of diabetes (n = 28; 2.5%) or prediabetes (n = 69; 6.0%). When only including the results from the FINDRISC questionnaire, the model reported an area under the curve (AUC) of 0.866 (95% confidence interval - CI [0.833; 0.900]). This model significantly improved when a basic periodontal examination (EPB Code; AUC = 0.876; 95% CI [0.845: 0.906]; p = .042) or a point-of-care HbA1c were added (AUC = 0.961; 95% CI [0.941; 0.980]; p < .001).

CONCLUSIONS

The tested protocol, combining the FINDRISC questionnaire and a point-of-care HbA1c, showed to be feasible when carried out in a dental clinic setting and was efficient to identify subjects with undiagnosed diabetes or prediabetes.

Periodontal Disease as a Predictor of Undiagnosed Diabetes or Prediabetes in Dental Patients

OBJECTIVES

 The study investigates whether periodontal parameters can identify subjects with undiagnosed diabetes mellitus (DM) or pre-DM in patients seeking dental treatment at a university dental hospital.

MATERIALS AND METHODS

 Adults older than 35 years, not being diagnosed with DM before and have at least one of the risk factors of DM were included in the study. All subjects received a complete periodontal examination, filled a medical history survey, and a fasting blood glucose measurement was obtained. A multiple logistic regression test using a backward elimination method to assess factors that predict if the participant is healthy, prediabetic or diabetic was done. A p-value of <0.05 was considered significant.

RESULTS

 A total of 61 subjects were enrolled with an average age of 42.9 ± 9.4 years. Having a family member diagnosed with DM was reported by 64.5% of the subjects; 59% were diagnosed with advanced periodontal disease. The final logistic regression model included smoking, hypertension, family history of DM, and percentage of clinical attachment loss >3 mm was statistically significant (P < 0.001). The model explained 47.7% of the diabetes condition and correctly classified 69.4% of cases. Participants with a family history of diabetes are 4.98 times more likely to exhibit prediabetic or diabetic status. Each unit increase in the percentage of clinical attachment loss increases the likelihood of participant to be prediabetic or diabetic by 1.104 times.

CONCLUSION

 Dental patients presenting with severe clinical attachment loss and family history of DM have increased likelihood of undiagnosed DM or pre-DM and would benefit from screening at the dental office.

Evaluation of a questionnaire-based diabetes screening concept in German patients with Stage III or IV periodontitis: A practice-based study

BACKGROUND

This practice-based retrospective study evaluated a screening method for diabetes mellitus (DM) in patients with Stage III or IV periodontitis using the FINDRISC questionnaire.

METHODS

Patients with Stage III or IV periodontitis who received FINDRISC screening in a German private dental practice were recruited. Individuals with positive FINDRISC scores (≥12, FINDRISC+) were referred for diabetological examination. Several general and periodontal findings from the patients' documentation were recorded and analyzed.

RESULTS

A total of 179 patients (52.65 ± 11.49 years) were included. In DM screening, 24.6% (n = 44) patients were FINDRISC+, including all patients with currently known DM (21 of 21). Of the remaining FINDRISC+ patients, 82.6% (19 of 23) visited their general practitioner, and 63.2% (12 of 19, 7.6% of total cohort) had an HbA1c ≥5.7%. Accordingly, 75% of the FINDRISC+ patients were diabetologically conspicuous (HbA1c ≥ 5.7%), including those with already known DM. Patients with previously unknown DM showed higher mean age, more missing teeth, a higher periodontitis stage (more Stage IV) and more frequently teeth with suppuration compared with the diabetologically inconspicuous individuals (P <0.01).

CONCLUSION

The FINDRISC questionnaire is appropriate for patients with Stage III or IV periodontitis and can be recommended in dental practice setting.

A systematic review on integration mechanisms in human and animal health surveillance systems with a view to addressing global health security threats

BACKGROUND

Health surveillance is an important element of disease prevention, control, and management. During the past two decades, there have been several initiatives to integrate health surveillance systems using various mechanisms ranging from the integration of data sources to changing organizational structures and responses. The need for integration is caused by an increasing demand for joint data collection, use and preparedness for emerging infectious diseases.

OBJECTIVE

To review the integration mechanisms in human and animal health surveillance systems and identify their contributions in strengthening surveillance systems attributes.

METHOD

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 checklist. Peer-reviewed articles were searched from PubMed, HINARI, Web of Science, Science Direct and advanced Google search engines. The review included articles published in English from 1900 to 2018. The study selection considered all articles that used quantitative, qualitative or mixed research methods. Eligible articles were assessed independently for quality by two authors using the QualSyst Tool and relevant information including year of publication, field, continent, addressed attributes and integration mechanism were extracted.

RESULTS

A total of 102 publications were identified and categorized into four pre-set integration mechanisms: interoperability (35), convergent integration (27), semantic consistency (21) and interconnectivity (19). Most integration mechanisms focused on sensitivity (44.1%), timeliness (41.2%), data quality (23.5%) and acceptability (17.6%) of the surveillance systems. Generally, the majority of the surveillance system integrations were centered on addressing infectious diseases and all hazards. The sensitivity of the integrated systems reported in these studies ranged from 63.9 to 100% (median = 79.6%, n = 16) and the rate of data quality improvement ranged from 73 to 95.4% (median = 87%, n = 4). The integrated systems were also shown improve timeliness where the recorded changes were reported to be ranging from 10 to 91% (median = 67.3%, n = 8).

CONCLUSION

Interoperability and semantic consistency are the common integration mechanisms in human and animal health surveillance systems. Surveillance system integration is a relatively new concept but has already been shown to enhance surveillance performance. More studies are needed to gain information on further surveillance attributes.

Development of non-invasive diabetes risk prediction models as decision support tools designed for application in the dental clinical environment

The objective was to develop a predictive model using medical-dental data from an integrated electronic health record (iEHR) to identify individuals with undiagnosed diabetes mellitus (DM) in dental settings. Retrospective data retrieved from Marshfield Clinic Health System's data-warehouse was pre-processed prior to conducting analysis. A subset was extracted from the preprocessed dataset for external evaluation (Nvalidation) of derived predictive models. Further, subsets of 30%-70%, 40%-60% and 50%-50% case-to-control ratios were created for training/testing. Feature selection was performed on all datasets. Four machine learning (ML) classifiers were evaluated: logistic regression (LR), multilayer perceptron (MLP), support vector machines (SVM) and random forests (RF). Model performance was evaluated on Nvalidation. We retrieved a total of 5319 cases and 36,224 controls. From the initial 116 medical and dental features, 107 were used after performing feature selection. RF applied to the 50%-50% case-control ratio outperformed other predictive models over Nvalidation achieving a total accuracy (94.14%), sensitivity (0.941), specificity (0.943), F-measure (0.941), Mathews-correlation-coefficient (0.885) and area under the receiver operating curve (0.972). Future directions include incorporation of this predictive model into iEHR as a clinical decision support tool to screen and detect patients at risk for DM triggering follow-ups and referrals for integrated care delivery between dentists and physicians.

Medical and Dental Electronic Health Record Reporting Discrepancies in Integrated Patient Care

Introduction:

Oral health mirrors systemic health; yet, few clinics worldwide provide dental care as part of primary medical care, nor are dental records commonly integrated with medical records.

Objectives:

To determine the degree to which misreporting of underlying health conditions poses problems for dental clinicians, we assessed misreporting of 2 common medical health conditions—hypertension and diabetes—at the time of dental examination and assessment.

Methods:

Using comparative chart analysis, we analyzed medical records of a diverse group of patients previously seen at the University of Texas Physician outpatient practice and then treated at the University of Texas Health Science Center at Houston School of Dentistry. Electronic health records of patients aged ≥18 y were extracted from 2 databases: Allscripts (University of Texas Physician) and axiUm (University of Texas Health Science Center at Houston). We identified 1,013 patients with the commonly occurring conditions of diabetes, hypertension, or both, with nonintegrated records contained in Allscripts and axiUm. We identified the percentage of those patients previously diagnosed with diabetes and/or hypertension by their physicians who failed to report these conditions to their dental clinicians.

Results:

Of those patients with diabetes, 15.1% misreported their diabetes condition to their dental clinicians, while 29.0% of patients with hypertension also misreported. There was no relationship between sex and misreporting of hypertension or diabetes, but age significantly affected reporting of hypertension, with misreporting decreasing with age.

Conclusions:

Because these conditions affect treatment planning in the dental clinic, misreporting of underlying medical conditions can have negative outcomes for dental patients. We conclude that policies that support the integration of medical and dental records would meaningfully increase the quality of health care delivered to patients, particularly those dental patients with underlying medical conditions.

Knowledge Transfer Statement:

Our study illustrates an urgent need for policy innovation within a currently fragmented health care delivery system. Dental clinicians rely on the accuracy of health information provided by patients, which we found was misreported in ~15% to 30% of dental patient records. An integrated health care system can close these misreporting gaps. Policies that support the integration of medical and dental records can improve the quality of health care delivered, particularly for dental patients with underlying medical conditions.

Improving Oral-Systemic Healthcare through the Interoperability of Electronic Medical and Dental Records: An Exploratory Study

OBJECTIVES

Electronic health records (EHRs) are rarely shared among medical and dental providers. The purpose of this study was to assess current information sharing and the value of improved electronic information sharing among physicians and dentists in Germany and the United States.

MATERIALS AND METHODS

A survey was validated and distributed electronically to physicians and dentists at four academic medical centers. Respondents were asked anonymously about EHR use and the medical and dental information most valuable to their practice.

RESULTS

There were 118 responses, a response rate of 23.2%. The majority (63.9%) of respondents were dentists and the remainder were physicians. Most respondents (66.3%) rated the importance of sharing information an 8 or above on a 1-to-10 Likert scale. Dentists rated the importance of sharing clinical information significantly higher than physicians (p = 0.0033). Most (68.5%) providers could recall an instance when access to medical or dental information would have improved patient care. Dentists were significantly more likely to report this than physicians (p = 0.008).

CONCLUSION

Physicians would value a standardized measure of "oral health" in their EHR. Dentists were less likely to find specific medical diagnostic test results of value. Both dentists and physicians agreed that oral-systemic health was important; interoperable EHRs could facilitate information transfer between providers and enhance research on oral-systemic health connections. Both dentists and physicians believed that an interoperable EHR would be useful to practice, but desired information was different between these groups. Refinement of the information needed for shared practice is required.

Prediabetes and Diabetes Screening in Dental Care Settings: NHANES 2013 to 2016

Introduction

Early recognition of prediabetes may prevent progression to diabetes, yet not all adults are aware of their prediabetes risk. To reach all adults unaware of their risk, additional risk assessment strategies are warranted.

Objectives

The objective of this study was to evaluate the potential scope of benefit from prediabetes risk assessment in the dental care setting and to identify characteristics of dental patients likely to unknowingly have prediabetes or diabetes.

Methods

Data from 10,472 adults in the National Health and Nutrition Examination Survey from 2013 to 2014 and 2015 to 2016 were analyzed for associations among prediabetes/diabetes risk factors, health care use, and hemoglobin A1C levels according to chi-square tests and multivariate logistic regression.

Results

A total of 7.73% of US adults had seen a dentist but not a medical provider in the past 12 mo. The composition of this subpopulation was significantly different from that who saw a medical provider, in ways that might affect their diabetes risk. In addition, 31.27% of this subpopulation would be identified as being at high risk for prediabetes according to the CDC Prediabetes Screening Test (Centers for Disease Control and Prevention), and 15.83% had hemoglobin A1C levels indicative of undiagnosed prediabetes or diabetes. Screening in a dental setting would have the highest odds of identifying someone unaware of his or her diabetes risk among those who were non-White, obese, or ≥45 y old.

Conclusion

Extrapolation from this analysis indicates that screening for prediabetes at dental visits has the potential to alert an estimated 22.36 million adults of their risk for prediabetes or diabetes. Incorporating prediabetes or diabetes risk assessment into routine dental visits may enable 1) those with prediabetes to take action to decrease their risk of developing diabetes and 2) those with diabetes to engage in treatment to decrease their risk of diabetes-related complications.

Knowledge Transfer Statement

Screening for prediabetes and diabetes during dental visits has the potential to raise patients' awareness of diabetes risk and prevent prediabetes from progressing to diabetes. For some patients, the dental visit may be the only point of contact with the health care system, which heightens the importance of including diabetes risk assessment for patient well-being.