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      Discriminative ability of the generic and condition specific Oral Impact on Daily Performance (OIDP) among adolescents with and without hypodontia

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          The aims of this study were to (1) investigate to what extent the generic and condition specific (CS) forms of the oral impact of daily performance (OIDP) inventory discriminate between a group of patients with hypodontia and a group of patients having malocclusion, (2) assess the association of the generic and CS OIDP with severity and localisation of hypodontia, whilst adjusting for patients’ age and sex.


          A total of 163 patients aged 10–17 years were included in a cross-sectional study. Two groups were investigated: 62 patients with non-syndromic hypodontia and 101 non-hypodontia patients. Both groups had a malocclusion of similar treatment need. All patients underwent a clinical and radiographic examination and completed a Norwegian version of the generic and the CS OIDP inventory. CS scores were established for impacts attributed to hypodontia.


          The mean number of missing teeth in the hypodontia group was 6.2. The prevalence of generic and CS oral impacts in the hypodontia group were 64% and 30%, and the corresponding rates in the non-hypodontia group were 62% and 10%. The generic OIDP did not discriminate between the two groups with respect to overall scores. The CS OIDP discriminated strongly between patients with and without hypodontia regarding problems with emotional status, showing teeth, social contact, speaking and carrying out work. Compared to the non-hypodontia group, patients with hypodontia, with severe hypodontia (≥ 6 missing teeth) and upper anterior hypodontia were respectively 3.4, 2.5 and 7.0 times more likely to report any oral impact attributed to small teeth, gaps between teeth and missing teeth.


          Hypodontia and malocclusion patients report a considerable burden of oral impacts. The CS-OIDP measure discriminated most effectively between patients with and without hypodontia and was related to severity and upper anterior localisation of hypodontia.

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          Most cited references 36

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          Measuring health-related quality of life.

           D. Patrick,  G Guyatt,  D Feeny (1993)
          Clinicians and policymakers are recognizing the importance of measuring health-related quality of life (HRQL) to inform patient management and policy decisions. Self- or interviewer-administered questionnaires can be used to measure cross-sectional differences in quality of life between patients at a point in time (discriminative instruments) or longitudinal changes in HRQL within patients during a period of time (evaluative instruments). Both discriminative and evaluative instruments must be valid (really measuring what they are supposed to measure) and have a high ratio of signal to noise (reliability and responsiveness, respectively). Reliable discriminative instruments are able to reproducibly differentiate between persons. Responsive evaluative measures are able to detect important changes in HRQL during a period of time, even if those changes are small. Health-related quality of life measures should also be interpretable--that is, clinicians and policymakers must be able to identify differences in scores that correspond to trivial, small, moderate, and large differences. Two basic approaches to quality-of-life measurement are available: generic instruments that provide a summary of HRQL; and specific instruments that focus on problems associated with single disease states, patient groups, or areas of function. Generic instruments include health profiles and instruments that generate health utilities. The approaches are not mutually exclusive. Each approach has its strengths and weaknesses and may be suitable for different circumstances. Investigations in HRQL have led to instruments suitable for detecting minimally important effects in clinical trials, for measuring the health of populations, and for providing information for policy decisions.
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            The simplest approach to dealing with missing data is to restrict the analysis to complete cases, i.e. individuals with no missing values. This can induce bias, however. Inverse probability weighting (IPW) is a commonly used method to correct this bias. It is also used to adjust for unequal sampling fractions in sample surveys. This article is a review of the use of IPW in epidemiological research. We describe how the bias in the complete-case analysis arises and how IPW can remove it. IPW is compared with multiple imputation (MI) and we explain why, despite MI generally being more efficient, IPW may sometimes be preferred. We discuss the choice of missingness model and methods such as weight truncation, weight stabilisation and augmented IPW. The use of IPW is illustrated on data from the 1958 British Birth Cohort.
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                Author and article information

                [1 ]Department of Orthodontics, Faculty of Dentistry, University of Oslo, Oslo, Norway
                [2 ]Department of Clinical Dentistry, Community Dentistry, University of Bergen, Bergen, Norway
                BMC Oral Health
                BMC Oral Health
                BMC Oral Health
                BioMed Central
                22 May 2014
                : 14
                : 57
                Copyright © 2014 Hvaring et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

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