Child and Parent Demographic Characteristics and Oral Health Perceptions Associated with Clinically Measured Oral Health

Marcus, M; Maida, Carl A.; Wang, Y; Xiong, Dayuan; Coulter, Ian D.; Lee, S Y; Spolsky, Vladimir W.; Shen, Jie; Crall, James J.; Liu, H

doi:10.1177/2380084418774549

Cited by 6 publications

(12 citation statements)

References 27 publications

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“…In this study, we used RF to identify questionnaire items that were predictors of active caries and caries experiences using the DMFT index among 2 to 7-year-old children. This is the most recent of multiple papers that develop oral health algorithms or tools which could be used by dentists, oral health researchers and professionals and public policy makers for oral health screening, program assessment, oral health evaluation and oral health management and policy planning [14][15][16][17][18]26]. The studies in this series on children between 8 and 17 years of age (where both parents and children completed the questionnaire) [14][15][16][17][18] showed similar predictors of oral health as we found in our study among children between 2 and 7 years of age (where only the parents completed the questionnaire).…”

Section: Discussionmentioning

confidence: 99%

Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

et al. 2021

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Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using a machine learning algorithm applied to parent perceptions of their child’s oral health assessed by survey. Methods: The sample consisted of 182 parents/caregivers and their children 2–7 years of age living in Los Angeles County. Random forest (a machine learning algorithm) was used to identify survey items that were predictors of active caries and caries experience. We applied a three-fold cross-validation method. A threshold was determined by maximizing the sum of sensitivity and specificity conditional on the sensitivity of at least 70%. The importance of survey items to classifying active caries and caries experience was measured using mean decreased Gini (MDG) and mean decreased accuracy (MDA) coefficients. Results: Survey items that were strong predictors of active caries included parent’s age (MDG = 0.84; MDA = 1.97), unmet needs (MDG = 0.71; MDA = 2.06) and the child being African American (MDG = 0.38; MDA = 1.92). Survey items that were strong predictors of caries experience included parent’s age (MDG = 2.97; MDA = 4.74), child had an oral health problem in the past 12 months (MDG = 2.20; MDA = 4.04) and child had a tooth that hurt (MDG = 1.65; MDA = 3.84). Conclusion: Our findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents.

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Section: Discussionmentioning

confidence: 99%

Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

et al. 2021

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“…Parents from disadvantaged groups were more likely to rate their children's oral health less favorably. Another study found poorer oral health ratings from single and migrant parents (Marcus et al 2018). An association between household income and parental ratings was supported by some studies (Talekar et al 2005;Piovesan et al 2011) but not all (Sohn et al 2008;Gomes et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Parental ratings often also differ from child self-ratings. As a proxy for child self-ratings, parental ratings are more valid for observable factors, such as eating and speaking, and less valid for subjective factors, such as aesthetics and pain (Reissmann et al 2017;Marcus et al 2018). However, despite limitations inherent in proxy assessments, parental ratings provide an insight into parents' understanding of oral health and disease and their priorities and concerns (Locker and Miller 1994)-information complementary to that obtained from clinical examinations.…”

Section: Introductionmentioning

confidence: 99%

A Causative Approach to Demographic and Socioeconomic Factors Affecting Parental Ratings of Child Oral Health

Foley

Spencer

Lalloo

et al. 2020

JDR Clinical & Translational Research

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Introduction: Many studies have investigated associations between demographic, socioeconomic status (SES), behavioral, and clinical factors and parental ratings of child oral health. Caries experience, pain, missing teeth, malocclusions, and conditions and treatments likely to negatively affect the child or family in the future have been consistently associated with poorer parental ratings. In contrast, effect sizes for associations between demographic and SES indicators (race/ethnicity, country of birth, family structure, household income, employment status, and parental education levels) and parental ratings vary greatly. Objectives: The primary objectives of this study were to estimate effect sizes for associations between demographic and SES variables and parental ratings of child oral health and then to consider possible causal implications. Methods: This article uses a nationally representative data set from 24,664 Australian children aged 5 to 14 y, regression analyses guided by a directed acyclic graph causal model, and sensitivity analyses to investigate effects of demographic and SES factors on parental ratings of oral health. Results: One in 8 children had oral health rated as fair or poor by a parent. Indigenous children, older boys, young children with a migrant parent, children from single-parent families, low-income households and families where no parent worked full-time, and children whose parents had lower education levels were much more likely to receive a fair or poor parental oral health rating in crude and adjusted models. Conclusion: This cross-sectional study helps to clarify inconsistent findings from previous research and shows many demographic and SES variables to be strong determinants of parental ratings of child oral health, consistent with the effects of these variables on other health outcomes. Sensitivity analyses and consideration of the potential for chance and bias to have affected these findings suggest that many of these associations may be causal. Knowledge Transfer Statement: Based on regression analyses driven by a directed acyclic graph causal model, this research shows a strong impact of demographic and socioeconomic determinants on parental ratings of child oral health, consistent with associations between these variables and other oral and general health outcomes. Many of these associations may be causal. We demonstrate the value of causal models and causal thinking when analyzing complex multilevel observational data.

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“…While 90% of children reported speaking English at home, 33% of parents responded that English was not their primary language. Almost three‐quarters of parents were female as were those either married or living with a partner; also, three‐quarters of the households had one or more fully employed members, while 11% of households …”

Section: Methodsmentioning

confidence: 99%

Development of toolkits for detecting dental caries and caries experience among children using self‐report and parent report

Marcus

Xiong

Wang

et al. 2019

Comm Dent Oral Epid

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Objectives:To develop child-and parent-reported toolkits for active caries and caries experience in children and adolescents, ages 8-17.Methods: A sample of 398 child/parent dyads recruited from 12 dental practices in Los Angeles County completed a computer-assisted survey that assessed oral health perceptions. In addition, children received a dental examination that identified the presence or absence of active caries and caries experience. A Multiple Adaptive Regression Splines model was used to identify a subset of survey items associated with active caries and caries experience. The splines and coefficients were refined by generalized cross-validation. Sensitivity and specificity for both dependent variables were evaluated.Results: Eleven child self-reported items were identified that had sensitivity of 0.82 and specificity of 0.45 relative to active caries. Twelve parent-reported items had a sensitivity of 0.86 and specificity of 0.50. Seven child self-reported items had a sensitivity of 0.86 and specificity of 0.34, and 11 parent-reported items had a sensitivity of 0.86 and specificity of 0.47 for caries experience. Conclusions:The survey items identified here are useful in distinguishing children with and without active caries and with and without caries experience. This research presents a path towards using children's and their parents' reports about oral health to screen for clinically determined caries and caries exposure. The items identified in this study can be useful when clinical information is unavailable. K E Y W O R D Scaries, caries risk, health services research, oral health, paediatric dentistry | 521 MARCUS et Al. Examination results Examination results No caries experience Caries experience No caries experience Caries experience Model predictions No caries experience 61 (15.3%) 31 (7.8%) No caries experience 83 (20.9%) 30 (7.5%) Caries experience 116 (29.1%) 190 (47.7%) Caries experience 94 (23.6%) 191 (48.0%)

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Child and Parent Demographic Characteristics and Oral Health Perceptions Associated with Clinically Measured Oral Health

Cited by 6 publications

References 27 publications

Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

A Causative Approach to Demographic and Socioeconomic Factors Affecting Parental Ratings of Child Oral Health

Development of toolkits for detecting dental caries and caries experience among children using self‐report and parent report

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