Factors that influence data quality in caries experience detection: A multilevel modeling approach

Caries experience detection is prone to misclassification. For this reason, calibration exercises which aim at assessing and improving the scoring behavior of dental raters are organized. During a calibration exercise, a sample of children is examined by the benchmark scorer and the dental examiners. This produces a 2 × 2 contingency table with the true and possibly misclassified responses. The entries in this misclassification table allow to estimate the sensitivity and the specificity of the raters. However, in many dental studies, the uncertainty with which sensitivity and specificity are estimated is not expressed. Further, caries experience data have a hierarchical structure since the data are recorded for the surfaces nested in the teeth within the mouth. Therefore, it is important to report the uncertainty using confidence intervals and to take the clustering into account. Here we apply a Bayesian logistic multilevel model for estimating the sensitivity and specificity. The main goal of this research is to find the factors that influence the true scoring of caries experience accounting for the hierarchical structure in the data. In our analysis, we show that the dentition type and tooth or surface type affect the quality of caries experience detection. Copyrigh

Examiner performance in calibration exercises compared with field conditions when scoring caries experience

The objective of this study was to verify how valid misclassification measurements obtained from a 'pre-survey' calibration exercise are by comparing them to validation scores obtained in 'field' conditions. Validation data were collected from the 'Smile for Life' project, an oral health intervention study in Flemish children. A calibration exercise was organized under 'pre-survey' conditions (32 age-matched children examined by eight examiners and the benchmark scorer). In addition, using a pre-determined sampling scheme blinded to the examiners, the benchmark scorer re-examined between six and 11 children screened by each of the dentists during the survey. Factors influencing sensitivity and specificity for scoring caries experience (CE) were investigated, including examiner, tooth type, surface type, tooth position (upper/lower jaw, right/left side) and validation setting (pre-survey versus field). In order to account for the clustering effect in the data, a generalized estimating equations approach was applied. Sensitivity scores were influenced not only by the calibration setting (lower sensitivity in field conditions, p < 0.01), but also by examiner, tooth type (lower sensitivity in molar teeth, p < 0.01) and tooth position (lower sensitivity in the lower jaw, p < 0.01). Factors influencing specificity were examiner, tooth type (lower specificity in molar teeth, p < 0.01) and surface type (the occlusal surface with a lower specificity than other surfaces) but not the validation setting. Misclassification measurements for scoring CE are influenced by several factors. In this study, the validation setting influenced sensitivity, with lower scores obtained when measuring data validity in 'field' conditions. Results obtained in a pre-survey calibration setting need to be interpreted with caution and do not (always) reflect the actual performance of examiners during the field work

Dealing with misclassification and missing data when estimating prevalence and incidence of caries experience

Objectives: The aim of this research was to estimate the prevalence and incidence of caries experience (CE) in first permanent molars while dealing with misclassification and missing of data. Methods: CE was modeled as a Hidden Markov Model in which the response variable is subject to misclassification and missingness. The proposed analysis extends that of Garcia-Zattera et al. (Stat Med 2010; 29: 3103) by allowing for various patterns of missing data. Findings were illustrated using data from the Signal Tandmobiel (R) study that is a longitudinal oral health intervention study. Results: Differences in the parameter estimates were noted between models that take into account misclassification and missing data and those that do not. Unbiased parameter estimates of prevalence and incidence were obtained without the use of validation data. Models that include subjects with missing data have smaller standard deviations than models that do not. Conclusions: It is important to account for misclassification to obtain less biased estimates of prevalence and incidence. For a proper estimation of prevalence and incidence in a longitudinal study subject to misclassification, validation data are not needed but when internal they can increase the efficiency in estimating the model. Also, including subjects with missing data increases the efficiency of estimating the parameters

Measurement, analysis and interpretation of examiner reliability in caries experience surveys: some methodological thoughts

Data obtained from calibration exercises are used to assess the level of agreement between examiners (and the benchmark examiner) and/or between repeated examinations by the same examiner in epidemiological surveys or large-scale clinical studies. Agreement can be measured using different techniques: kappa statistic, percentage agreement, dice coefficient, sensitivity and specificity. Each of these methods shows specific characteristics and has its own shortcomings. The aim of this contribution is to critically review techniques for the measurement and analysis of examiner agreement and to illustrate this using data from a recent survey in young children, the Smile for Life project. The above-mentioned agreement measures are influenced (in differing ways and extents) by the unit of analysis (subject, tooth, surface level) and the disease level in the validation sample. These effects are more pronounced for percentage agreement and kappa than for sensitivity and specificity. It is, therefore, important to include information on unit of analysis and disease level (in validation sample) when reporting agreement measures. Also, confidence intervals need to be included since they indicate the reliability of the estimate. When dependency among observations is present [as is the case in caries experience data sets with typical hierarchical structure (surface-tooth-subject)], this will influence the width of the confidence interval and should therefore not be ignored. In this situation, the use of multilevel modelling is necessary. This review clearly shows that there is a need for the development of guidelines for the measurement, interpretation and reporting of examiner reliability in caries experience surveys

A multilevel model for spatially correlated binary data in the presence of misclassification: an application in oral health research

Dental caries is a highly prevalent disease affecting the tooth's hard tissues by acid-forming bacteria. The past and present caries status of a tooth is characterized by a response called caries experience (CE). Several epidemiological studies have explored risk factors for CE. However, the detection of CE is prone to misclassification because some cases are neither clearly carious nor noncarious, and this needs to be incorporated into the epidemiological models for CE data. From a dentist's point of view, it is most appealing to analyze CE on the tooth's surface, implying that the multilevel structure of the data (surface-tooth-mouth) needs to be taken into account. In addition, CE data are spatially referenced, that is, an active lesion on one surface may impact the decay process of the neighboring surfaces, and that might also influence the process of scoring CE. In this paper, we investigate two hypotheses: that is, (i) CE outcomes recorded at surface level are spatially associated; and (ii) the dental examiners exhibit some spatial behavior while scoring CE at surface level, by using a spatially referenced multilevel autologistic model, corrected for misclassification. These hypotheses were tested on the well-known Signal Tandmobiel (R) study on dental caries, and simulation studies were conducted to assess the effect of misclassification and strength of spatial dependence on the autologistic model parameters. Our results indicate a substantial spatial dependency in the examiners' scoring behavior and also in the prevalence of CE at surface level. Copyright (c) 2013 John Wiley & Sons, Ltd

Better interprofessional teamwork, higher level of organized care and lower risk of burnout in acute healthcare teams using care pathways: A cluster randomized controlled trial.

BACKGROUND: Effective interprofessional teamwork is an essential component for the delivery of high-quality patient care in an increasingly complex medical environment. The objective is to evaluate whether the implementation of care pathways (CPs) improves teamwork in an acute hospital setting. DESIGN AND MEASURES: A posttest-only cluster randomized controlled trial was performed in Belgian acute hospitals. Teams caring for patients hospitalized with a proximal femur fracture and those hospitalized with an exacerbation of chronic obstructive pulmonary disease, were randomized into intervention and control groups. The intervention group implemented a CP. The control group provided usual care. A set of team input, process, and output indicators were used as effect measures. To analyze the results, we performed multilevel statistical analysis. RESULTS: Thirty teams and a total of 581 individual team members participated. The intervention teams scored significantly better in conflict management [β=0.30 (0.11); 95% confidence interval (CI), 0.08 to 0.53]; team climate for innovation [β=0.29 (0.10); 95% CI, 0.09 to 0.49]; and level of organized care [β=5.56 (2.05); 95% CI, 1.35 to 9.76]. They also showed lower risk of burnout as they scored significantly lower in emotional exhaustion [β=-0.57 (0.21); 95% CI, -1.00 to -0.14] and higher in the level of competence (β=0.39; 95% CI, 0.15 to 0.64). No significant effect was found on relational coordination. CONCLUSIONS: CPs are effective interventions for improving teamwork, increasing the organizational level of care processes, and decreasing risk of burnout for health care teams in an acute hospital setting. Through this, high-performance teams can be built