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The relationship of 60 disease diagnoses and 15 conditions to preference-based health-related quality of life in Ontario hospital-based long-term care residents

Lam JM, Wodchis WP. Med Care. 2010; 48(4):380-7.

Background — Population-based diagnosis- and condition-specific health-related quality of life (HRQoL) scores are required for decision-making and research purposes. These HRQoL scores do not exist for hospital-based long-term care (LTC) residents.

Objective — To estimate the impact of 60 diseases and 15 conditions on caregiver-assessed preference-based HRQoL.

Methods — Residents in hospital-based LTC facilities in Ontario, Canada were identified from administrative databases containing resident minimum data set (MDS) assessments completed between August 1st, 2003 and March 31st, 2008. A preference-based HRQoL measure, the MDS Health-Status Index (MDS-HSI) score, was calculated for 66,193 residents. Average MDS-HSI scores and multivariate linear regression models were used to estimate the impact of the diagnoses and conditions, respectively.

Results — After adjusting for age, sex, and other diagnoses, aphasia exhibited the largest negative relationship to the MDS-HSI (-0.085), followed by cancer (-0.072) and Alzheimer disease (-0.062). Cancer was also the second most prevalent diagnosis (27.6%). Lack of balance was a common condition (87.3%) and it had the greatest negative relationship to MDS-HSI scores among the 15 conditions (-0.099). The diagnoses and conditions regression models had R values of 0.12 and 0.34, respectively, suggesting that clinical conditions provided better explanatory variables for the MDS-HSI than diagnoses.

Conclusions — The findings suggest that diseases affect preference-based HRQoL differently in a hospital-based LTC population compared with previous studies in the general population. The population-based MDS-HSI scores from this study can be used as reference values in cost-effectiveness analyses for hospital-based LTC populations.

Keywords: Long-term care Research and statistical methods Data collection