This study investigated the direct relationship between nursing home price and QOL of nursing home residents, as knowledge about this topic is currently lacking. Literature suggested that indicators such as resident characteristics (e.g.: age, education, chronical conditions) and facility characteristics (e.g.: facility size, nursing home staffing rates) are significantly associated with QOL [7, 9, 11]. The study of Shippee et al. (2015a) is one of the first studies that subdivides QOL in different domains, in order to have a more nuanced understanding of how indicators are associated with QOL. Therefore, they use a survey consisting of 52 items measuring 6 QOL domains (environment, personal attention, food, personal engagement, negative mood and positive mood). In this study, we chose the internationally validated and widely accepted InterRAI instrument as QOL measure because of two reasons. First, the InterRAI instrument supports multiple clinical and management applications for different audiences (ex.: care planning, quality measurement, patient safety assessment) . Second, it has been developed in an international context and has shown to be reliable and valid in multiple languages (also in Dutch) and countries [24, 25].
The descriptive results suggested that average price levels are statistically different between public and private nursing homes. Therefore, ownership type was used as a control variable in the regression analyses. The multiple regression analyses suggested that a 10 euro increase in prices is associated with a significant decrease of 0.1 in QOL in access to services, comfort and environment, food and meals, respect, and safety and security. When splitting up the regression analyses in private and public nursing homes, results indicate a weak association between price and the QOL domains of access to services, comfort and environment, food and meals, respect, and safety and security, but only in private nursing homes. Nevertheless, based on this data we do not find evidence of an association between price and the varying domains of QOL. This result suggests that a price increase does not always lead to higher QOL, even though it is the popular belief. This result adds evidence to the study of Shippee et al. (2015a) who only investigated the association between QOL and some financial indicators. They suggest that nursing homes with a higher percentage of Medicaid-only residents (US health insurance program only for people with a limited income); compared to Medicare (US health insurance program for people older than 65 years old and younger for people with disabilities), self-pay, and privately insured residents; are associated with lower QOL in the domains of personal attention and engagement. Besides the research question, our findings also suggest that nursing homes that have more highly dependent residents are associated with higher QOL in food and meals, respect, safety and security, and staff-resident bonding (while holding the other variables constant). This result contradicts the results of other studies. One study finds that the dependency rate is negatively associated with QOL for patients with dementia . Another study finds no statistically significant association between the two variables in nursing home residents . The reason why dependency rate and QOL may be associated is because residents needing a high degree of care are mostly residents that have long-term chronic and multimorbid conditions, causing physical and/or mental disabilities that may affect their QOL . However, for Flemish nursing homes there is a reason why dependency rate and QOL might be positively related. In Flanders, nursing homes have two sources of income: the daily price that residents pay, and a reimbursement of the Flemish government. The latter is based on the dependency rate of their residents . Possibly, nursing homes spend this higher reimbursement in better patient care, which might translate in higher QOL of their residents. Another possible explanation is that this study does not include the QOL of residents with cognitive problems, and as they may be more dependent, their QOL is not reflected in our results. This might have positively influenced our results. Furthermore, our results suggest that there is no significant association between ownership type and QOL. Meanwhile, the study of Shippee et al. (2015a) indicate the opposite, more specifically that public ownership type was positively associated with the QOL dimension of environment. The different results between our study and the study of Shippee et al. (2015a) might be explained by the varying country-specific variations in ownership type. Another variable that might influence QOL, through the dependency rate, is the socioeconomic status of the residents. It is possible that residents with a higher socioeconomic status substitute nursing home care for home care. These residents might therefore wait longer, until they are more frail and care dependent, to enter a nursing home. To test the latter, we included the average income level per inhabitant of each municipality where the nursing home is situated . We then calculated the correlation between the average income level and the dependency rate, but this was statistically insignificant (r = 0.01, P = 0.86). Thus, we found no correlation between nursing homes in areas with higher socioeconomic status and more highly dependent residents.
The findings of this study should be interpreted carefully. First of all, the means of the QOL domains in the descriptive statistics are the means of ordinal variables. It only indicates if residents are on average more or less satisfied on a QOL domain or not. The number itself does not have a meaning. Second, because of the cross-sectional design of this study we could only assess associations but no causal relationships. Third, all the nursing homes are situated in Flanders. National data on the QOL domains were not available. Therefore, the results can only be generalized on a Flemish level since the sample includes 82% of all Flemish nursing homes. Fourth, the InterRAI survey was only taken from people without cognitive problems. Due to a low response level per nursing home, residents with cognitive problems were not included in the study. Therefore, the findings cannot be generalized for residents with cognitive problems. Lastly, the sample consists of aggregated information of residents in nursing homes, but not of individual data per resident. Therefore, we lose a lot of information that could make the data more sensitive and that could uncover more relationships. Additionally, we could not control for individual differences in QOL in the same nursing home such as: health status, socio-demographic characteristics, educational level, whether residents have Alzheimer’s disease or chronic conditions, which are predictive for a number of QOL domains . However, we tried to compensate this loss by including the dependency rate. Still, a considerable part of the variation in the QOL dimensions cannot be explained simply with price, ownership type and dependency rate.
The result of this study does not correspond with our hypothesis that higher price levels are associated with higher quality of care and therefore also with higher QOL. There might be several reasons for this finding. Some questions or domains in the InterRAI instrument might not translate price changes into QOL changes. For instance, the statement “I like to go outside” might be more suitable than the current statement “I can go outside when I want”, as more expensive nursing homes might be situated in more beautiful surroundings. Other questions or domains might be more suited for our study to capture the relationship between price and QOL. Furthermore, other researchers already found that facility characteristics (in comparison to resident characteristics) explain only the minority of the variability of QOL: 9% in the research of Degenholtz et al. (2006) and only 3% in the study of Shippee et al. (2015a). Therefore, it is not a surprise that our results suggest a weak association between price and QOL. Though other researchers suggested that resident characteristics have a stronger association with QOL than facility characteristics, we strongly suggest future researchers to focus on other facility characteristics that might be associated with QOL, while controlling for resident characteristics such as Alzheimer’s disease, the number of chronic conditions, etc. The reason is that implementing policy recommendations based on resident characteristics such as age or marital status is unrealistic, as these characteristics are non-modifiable through policy . Instead, focusing on facility characteristics such as perceived quality of care or work environment characteristics (e.g.: team climate, multidisciplinary collaboration) might be more easily modifiable through policy. The study of Backhaus et al. (2017) suggests that previous variables are associated with quality of care. Therefore, they might also be associated with QOL. Other variables that may influence QOL are demand and supply variables. Demand and supply variables may influence quality of care in nursing homes. For instance, research showed that competition in the nursing home market corresponds to a decrease in quality of care and a decrease in nursing home price . As quality of care and QOL are related , competition and other variables related to quality of care might also influence QOL. Investigating these and other variables may help to improve the QOL of nursing home residents and enable managers and policy makers to select better targeted improvement strategies.