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Background: Data on disability are regularly collected by different institutions or ministries using specific tools for different purposes, for instance to estimate the prevalence of disability or eligibility of specific populations for social benefits. The interoperability of disability data collected in countries is essential for policy making and to monitor the implementation of the Convention on the Rights of Persons with Disabilities. The first objective of this paper is to map and compare tools that collect data on disability for different purposes, more specifically the Brazilian National Health Survey and the Brazilian Functioning Index to the World Health Organization (WHO) and the World Bank Model Disability Survey (MDS), currently recommended as a standard tool for disability measurement. The second objective is to demonstrate the usefulness and value of the International Classification of Functioning, Disability and Health Linking Rules to map and compare population-based surveys and other content-related tools collecting data on disability, even when these have already been developed based on the International Classification of Functioning, Disability and Health. Methods: Disability information collected with the three different tools was mapped and compared using the International Classification of Functioning, Disability and Health Linking Rules. Results: Although the disability module in the Brazilian National Health Survey is fundamentally different from the MDS, the mapping disclosed that several modules of the Brazilian National Health Survey already cover many aspects necessary to estimate prevalence and understand disability as currently recommended by the WHO and the World Bank. The Brazilian Functioning Index and the MDS are both based on the International Classification of Functioning, Disability and Health and are very similar in the approach and content of their questions on functioning. Specific information on environmental factors is essential to identify needs and barriers, as well as to devise procedures to reduce injustice and inequalities. This information is still not targeted broadly enough in both the Brazilian National Health Survey and the Brazilian Functioning Index. Conclusions: Overall, this mapping exercise showed that applying the International Classification of Functioning, Disability and Health linking rules to population-based data coming from different sources provides researchers and stakeholders involved in decision-making with standardized and straightforward information about overlaps and gaps. Implications for Rehabilitation: Data on functioning and disability regularly collected with different purposes and by different institutions or ministries within a country can be compared using the International Classification of Functioning, Disability and Health as a reference framework and the International Classification of Functioning, Disability and Health linking rules. The recently published refinements of the International Classification of Functioning, Disability and Health Linking Rules go beyond the sole linking to International Classification of Functioning, Disability and Health categories and provide standardized procedures to document the perspective of linked questions or the categorization of response options. They are therefore useful to compared tools that have been developed based on the International Classification of Functioning, Disability and Health. The current disability module of the Brazilian Health Survey needs a revision to be suitable to collect data on disability that is Convention on the Rights of Persons with Disabilities conform and guarantees interoperability with disability data from other sources in Brazil, especially from disability assessment for social benefits and implementation of policies. [ABSTRACT FROM AUTHOR]

Imputation becomes common practice through availability of easy-to-use algorithms and software. This study aims to determine if different imputation strategies are robust to the extent and type of missingness, local item dependencies (LID), differential item functioning (DIF), and misfit when doing a Rasch analysis. Four samples were simulated and represented a sample with good metric properties, a sample with LID, a sample with DIF, and a sample with LID and DIF. Missing values were generated with increasing proportion and were either missing at random or completely at random. Four imputation techniques were applied before Rasch analysis and deviation of the results and the quality of fit compared. Imputation strategies showed good performance with <15% of missingness. The analysis with missing values performed best in recovering statistical estimates. The best strategy, when doing a Rasch analysis, is the analysis with missing values. If for some reason imputation is necessary, we recommend using the expectation-maximization algorithm. [ABSTRACT FROM AUTHOR]