Identifying and Characterizing Children of Parents with a Serious Illness Using Survey and Register Data

PURPOSE: To estimate the prevalence and characterize children and adolescents aged 0–21 years with a physically or mentally ill parent based on registers. Further, to explore the use of register and survey data to identify parental serious illness. METHODS: The study is based on: 1) a 20% register sample of children and adolescents aged 0–21 in 2014; and 2) survey data from the Danish Youth Profile 2014 including 63,437 youth education students linked to registers. In registers, parental physical illness comprised hospital diagnoses included in the Charlson Comorbidity Index, and parental mental illness encompassed all mental diagnosis in the registers. Information about socioeconomic and demographic characteristics and use of health care services was retrieved from national registers. In the survey, students were asked if they had experienced serious illness of a parent. RESULTS: In the register sample of 0–21-year-olds, 25.3% had a parent with a physical or mental diagnosis, the prevalence increasing with age of the child. Compared to children without parental illness, children with an ill parent more frequently had unemployed parents, lower parental educational level, and a chronic medical condition. Analyses of the discrepancies between register and survey data revealed that 9% of the adolescents were identified as having an ill parent in both data sources and 64.1% had no identified ill parents. Moreover, a higher frequency of parental primary health care service use was seen for adolescents with an ill parent, across identification method, indicating that both methods identify adolescents with an ill parent. CONCLUSION: The social inequality and elevated frequency of health problems among children and adolescents with an ill parent, underline the vulnerability of this population. Parental illness can be identified from both parental hospital diagnoses as well as self-reported by adolescents, however the two methods detect different populations. Both methods have several limitations and would benefit from further refinement and validation.