Uptake of community support measures for palliative care
Research shows that the majority of terminally ill patients prefers to die at home instead of in hospitals. Close family members, friends and significant others traditionally fulfil a prominent role in providing multifaceted supportive care for their needing peers. In recent years, governments are increasingly recognising the position of these informal caregivers through supportive policy measures for palliative home care.
This study will aim to: (1) examine the extent to which supportive measures for palliative care in the community are used in the Belgian population: multidisciplinary palliative home care teams, the palliative status, the palliative forfeit for family carers, the palliative forfeit for nursing care, respite care, caregiver allowance and the palliative care leave from work; (2) map the factors that influence use of these measures and determine which groups and households (in terms of socio-economic position, demographic characteristics, clinical characteristics) are typified by underuse; and (3) evaluate the impact of use of these supportive measures on the patterns of care (e.g. aggressiveness of end-of-life care) and the costs of care in the last weeks of life, using a retrospective cohort design through propensity score matching.
Administrative full-population healthcare claims data will be used to address all three objectives. Linkage between databases from the InterMutualist Agency, Cancer Register and Statistics Belgium will provide data on health care claims and socio-demographic characteristics of the whole population of persons who died between 2012 until the end of the project, with healthcare claims records dating up to two years prior to death. Separate data on palliative care leave from work and data on the caregiver allowance will be administered through the RIZIV database in order to analyse patterns in uptake of supportive measures directed at the informal caregiver. Propensity score matching will be used to account for covariates predicting the use of the supportive measures and for case mix confounders.
Arno Maetens: researcher
Dr. Kim Beernaert: supervisor
Prof. dr. Luc Deliens: co-promotor
Prof. dr. Dirk Houttekier: co-promotor
Prof. dr. Joachim Cohen: promotor