**1. Introduction**

According to the World Health Organization (WHO) in 2010, it is estimated that more than one billion people from all over the world, representing about 15% of the world population and in the case of Brazil, according to the Geography Brazilian Institute (IBGE) in 2010, it is estimated that 45.6 million people, equivalent to approximately 23.9% of the Brazilian population, live with some type of disability. In general, disabled people have worse health prospects, educational, economical participation, and a higher rate of poverty compared to non-disabled people.

Disabled people make up a group of excluded people who have always aroused feelings that range from repulsion to extreme pity and have even been considered less human or lacking in humanity. Currently, within the scope of social and educational inclusion policies, they have become the target of affirmative actions, which seek to guarantee their rights in various aspects of life in society [1, 2].

It is believed that the low working conditions of disabled people are due to situations such as: difficulty in accessing education, inadequate infrastructure, prejudice, little knowledge, and better accessibility conditions on the part of schools and companies that make these people have a lower education, which makes it difficult to enter the formal job market [3].

In order for disabled people to achieve better and more lasting prospects, it is necessary to empower them and remove barriers that prevent them from participating in the community, accessing quality education, finding decent work, and having their voices heard [4].

To better assess the needs of disabled people, it is necessary to describe this group of people to know the answers to questions such as: How many are there? Where they live? How do you live? What implications does disability have on these people's access to all the different human services in an autonomous and comprehensive way? In short, how can disability influence the life quality of these people?

In statistical terms, it shows the existence of few formal studies, among which the data obtained through censuses stand out, allowing questions such as: How are disabled people distributed across the country? How to assess the access of disabled people to the different services mentioned earlier? How is the evolution of disabled people when comparing them with those without disabilities? What would be the variables that most contribute to cases of disability? How do disabled people compare to people without disabilities? Answering these and other questions can contribute to better support for these people so that they can be better assisted and resources to be better managed and optimized by public policy actions in this area.

Statistically, a useful alternative to assist in the monitoring of public policies in this area is the risk index, which consists of evaluating which factors are most impacting for this risk, as well as its intensity and direction, generating a score that can be ordered or classified according to the probability of people becoming disabled. In the case of this work, we propose the use of techniques such as binary and ordinal logistic regression to select the most significant factors by applying criteria such as binary and ordinal logistic regression to select the most significant factors by applying criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Deviation Information Criterion (DIC) and calculate the risk probability for the different disabilities (vision, hearing, movement, and intellectual) for the sample dataset composed of 20,800,804 respondents of the Complete Questionnaire of the IBGE 2010 Census by state, region, and country.

In a previous work [1], we considered as response variable, the different disabilities, and the existence of at least one disability as binary variable, that is, whether a given individual is or is not a disability person. In this work, we are considering the different deficiencies, incorporating their different degrees of severity and number of deficiencies as ordinal response variable, which allows better quality in terms of information and fit in the model.

In Section 2, we present an introduction to the problem, we establish and characterize the variables to be used, the stereotyped ordinal logistic model, selection of variables such as the Wald test, and models using the AIC, BIC, and DIC criteria, and we define the risk of disability for different degrees of severity "cannot at all," "can, but with great difficulty" and "can, but with a little difficulty" for visual, hearing and physical disability, and, in the case of intellectual disability, it was proposed the following levels the use of the risk "has" or "does not have" intellectual disability. In Section 3, we present results and discussions; and, in Section 4, we present conclusions and suggestions for future work.
