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

Misclassification in multilevel models with application to dental caries research

The main aim of this thesis was to understand more the misclassification process in detecting the presence or absence of CE while taking into account the multilevel data structure. We suggested possible ways of correcting for misclassification using validation data sets.In Chapter 1 we gave a general introduction of misclassification errors. We focused more on existing literature for adjusting for misclassification errors in statistical models.The statistical approaches explained in this thesis were applied to dental caries research. Hence in Chapter 2 we introduced general information concerning dental caries research, e.g. tooth decay process and diagnosis of CE. In this chapter we also introduced the Signal Tandmobiel study, which motivated us to carry out this research.In Chapter 3 we reviewed the general concepts of frequentist and Bayesian approaches to estimate the model parameters.In Chapter 4 we have presented the multilevel models for SE and SP. We investigated the factors that influence SE and SP as a means of assessing examiners' scoring behavior. In this chapter we also emphasized on the importance of taking the multilevel structure into account. In the absence of a gold standard, SE and SP cannot be estimated. Instead the kappa statistic, is often used as a reliability measure to assess the agreement of examiners. Hence, in Chapter 5 we proposed a hierarchical kappa statistic which is used to assess examiners'agreement when scoring data that have a multilevel structure.In Chapter 6 we focused on the use of external validation data to correct for misclassification in the main data set. Misclassification errors in external validation are often different from those from main data. Hence in Chapter 6 we proposed an approach for using external validation data, in a proper manner. The analysis was done in a multilevel context.In addition to multilevel structure, CE data are spatially correlated, i.e. a carious surface may influence the decay process of the neighboring surfaces. Theautologistic regression model is a popular choice for modeling spatially dependent binary data under the assumption that data are misclassification free. Hence in Chapter 7 we extended the multilevel model to also account for spatially correlated observations while correcting for misclassification.In Chapter 8 we focused on the estimation of prevalence and incidence from longitudinal CE data. We propose a binary Hidden Markov Model (HMM) for the analysis of longitudinal CE data subject to misclassification. The model expresses the prevalence and incidence as a function of covariates while taking into account missingness.Finally, in Chapter 9 we gave general conclusions. We highlighted the contributions of our research to statistical methodology and dental caries research. We ended the chapter by suggesting areas of needs further research.status: publishe

The Care Process Self-Evaluation Tool: a valid and reliable instrument for measuring care process organization of health care teams

BACKGROUND: Patient safety can be increased by improving the organization of care. A tool that evaluates the actual organization of care, as perceived by multidisciplinary teams, is the Care Process Self-Evaluation Tool (CPSET). CPSET was developed in 2007 and includes 29 items in five subscales: (a) patient-focused organization, (b) coordination of the care process, (c) collaboration with primary care, (d) communication with patients and family, and (e) follow-up of the care process. The goal of the present study was to further evaluate the psychometric properties of the CPSET at the team and hospital levels and to compile a cutoff score table. METHODS: The psychometric properties of the CPSET were assessed in a multicenter study in Belgium and the Netherlands. In total, 3139 team members from 114 hospitals participated. Psychometric properties were evaluated by using confirmatory factor analysis (CFA), Cronbach's alpha, interclass correlation coefficients (ICCs), Kruskall-Wallis test, and Mann-Whitney test. For the cutoff score table, percentiles were used. Demographic variables were also evaluated. RESULTS: CFA showed a good model fit: a normed fit index of 0.93, a comparative fit index of 0.94, an adjusted goodness-of-fit index of 0.87, and a root mean square error of approximation of 0.06. Cronbach's alpha values were between 0.869 and 0.950. The team-level ICCs varied between 0.127 and 0.232 and were higher than those at the hospital level (0.071-0.151). Male team members scored significantly higher than females on 2 of the 5 subscales and on the overall CPSET. There were also significant differences among age groups. Medical doctors scored significantly higher on 4 of the 5 subscales and on the overall CPSET. Coordinators of care processes scored significantly lower on 2 of the 5 subscales and on the overall CPSET. Cutoff scores for all subscales and the overall CPSET were calculated. CONCLUSIONS: The CPSET is a valid and reliable instrument for health care teams to measure the extent care processes are organized. The cutoff table permits teams to compare how they perceive the organization of their care process relative to other teams.status: publishe