IDMOD: An Australian microsimulation model of lifetime economic and social factors in familial intellectual disability

Intellectual disability (ID) is associated with far reaching economic and psychosocial outcomes. People with ID require a wide range of supports from both governments, and families. No study has thoroughly assessed the economic and social costs of care for individuals with familial ID. Understanding the comprehensive costs of ID is important for policy makers to decide on resources required to support the families affected by the condition. In the Australian Economic and Psychosocial Impacts of Caring for Families Affected by Intellectual Disability (EPIC-ID Study), we developed a microsimulation model, IDMOD, to provide a holistic perspective of the economic costs of ID for use in cost-effectiveness studies related to genomic testing and precision medicine for familial ID. This paper describes the construction of IDMOD. The model base population are individuals who were referred to the Genetics of Learning Disability (GoLD) clinics. Through a detailed questionnaire, we collected information including health expenditure, income, education, welfare payments, savings, housing and residential care, and support received for purchase of aids and equipment, employment for both people with ID and their carers. Both government and patient costs were included. Data on quality of life, psychological wellbeing, relationship strength and social inclusion are also collected before and after genomic diagnostic testing. Patients’ use of medical services, prescription medicines, and hospital services were captured via linked datasets. Lost income, assets, and tax were imputed via synthetic matching to records from the Static Incomes Model (STINMOD). Each unit record in the model was weighted using input from the Survey of Disability, Ageing, and Carers (SDAC) to reflect the total familial ID population in Australia. The model will provide data on the economic costs of familial ID in Australia, and the associated effects of implementing genomic testing and precision medicine for this population group.