In this data article, records on demographic data, family problem issues, as well as results of medical tests from five major classes of psychotic disorder namely: bipolar; vascular dementia, minimal brain dysfunction; insomnia; and schizophrenia, were collected on 500 psychotic patients carefully selected from the pool of medical records of Yaba Psychiatric Hospital, Lagos, Nigeria, for the period of 5 years, between January 2010 and December 2014, were examined. X-squared Statistic was used to examine each of psychotic disorders to identify demographic (age, gender, religion, marital status, and occupation) and family issues (loss of parent, history of such ailment in the family (family status), divorce, head injury, and heredity of such ailment (genetic) factors that influence them. A clear description on each of these psychotic disorders (bipolar; vascular dementia, minimal brain dysfunction (MBD), insomnia and Schizophrenia) was considered separately using tables and bar diagrams. Data analysis results are as follows: firstly, 40.2%, of the 500 psychotic patients tested positive to bipolar, 40.6% to insomnia, 75.0% to schizophrenia, 43.6% to MBD and 69.2% to vascular dementia. Secondly, female patients were more prone to all the psychotic indicators than their male counterpart except in MBD. Thirdly, the oldest age group (> 60 years) is more prone to bipolar and insomnia ailments, while the mid age group (30 – 60 years) is prone to schizophrenia and vascular dementia, and the youngest group (< 30 years) is prone to MBD. Lastly, the factors that influence the ailments are listed: bipolar (age, occupation, marital status, divorce, and spiritual consultation); insomnia (age, occupation, marital status, divorce, and spiritual consultation); schizophrenia (age, occupation, religion, marital status, hereditary, and divorce); MBD (gender, age, occupation, and marital status); and vascular dementia (history of the ailment and spiritual consultation). Bipolar and insomnia are influenced by the same set of factors, which implies that any patient having one is most likely to be at risk of having the other.

The study presents a stochastic epidemic model applied to the model of indigenous language extinction. The Susceptible-Infectious-Removed (SIR) categorization of an endemic disease has been reformulated to capture the dynamics of indigenous language decline, based on the assumption of nonhomogeneous mixing. The time in which an indigenous language is expected to be extinct was derived using a modified SIR model with the population segmented into several sub-communities of small sizes representing family units. The data obtained from the 2016 indigenous language survey conducted in several parts of Nigeria and from the 2013 Nigeria Demographic Health Survey (NDHS) were used to estimate the key parameters of the model for Nigeria's several indigenous languages. The parameters of interest included the basic reproduction number, the threshold of endemicity, and the time in which a language is expected to be extinct, starting from the endemic level. On the basis of the time in which a language is expected to be extinct, several of the surveyed languages appeared to be in a precarious condition, while others seemed virile, thanks to a high language transfer quotient within families.