The study was designed as a cross-sectional study based on secondary data. Data sources, operationalisation of variables and analytical procedures are described in the following.
The study was based on the most up-to-date assessments of all German nursing homes in the years 2011 and 2012 respectively from the statutory quality audits performed by the health insurers’ medical service departments. Data were provided by Germany’s largest nursing care insurer, the national sickness fund association AOK (Allgemeine OrtsKrankenkasse), and cover a total of N = 10,471 nursing homes offering full inpatient care. Excluded from these data were homes specializing in vigil coma patients (N = 36), those with no identifiable type of ownership (N = 43), homes with incomplete quality data by more than 50 % (N = 2), or with no data on monthly charges for nursing care (N = 222). The number of nursing homes remaining for the analysis was N = 10,168.
Data on the type of ownership were available for each nursing home under consideration. A distinction was made between private for-profit institutions and public not-for profit and charitable facilities. Because only 5 % of all German nursing homes have public ownership, they were combined with charitable homes in the not-for-profit category for the analysis.
Available cost details for each facility cover daily costs for nursing care, room and board. Investment costs which nursing homes charge to each home resident proportionally and on a small scale are not included in the data and therefore do not enter price calculations for each care facility. Moreover, prices per resident depend on the extent of care required. Residents are assigned to one of three care levels; the national distribution of care levels in nursing homes is as follows: care level 1 (lowest) - 38.12 %, care level 2 (medium) – 40.29 %, care level 3 (highest) – 20.45 %. The percentage per nursing home of care recipients assigned to care levels 1 to 3 was not given, so that an equal distribution was assumed, and the daily price per nursing home was calculated as weighted mean of the national distribution.
Average prices charged by nursing homes differ greatly between the 16 federal states in Germany. This is why 5 price categories per federal state were set up with approximately equal numbers of nursing homes for the calculation of correlations between prices and quality of service. As a next step all nursing homes in each of the five price categories were summarized across the nation, and the price was used for analysis as a categorical variable.
Quality of Care
To assess the quality of care provided in nursing homes the authors used evaluations from the health insurers’ medical service departments collected in unannounced audits in 2011/2012. Audits are based on a checklist of 82 criteria (GKV-Spitzenverband 2008). Eighteen criteria relate to satisfaction surveys among home residents; 38 criteria are determined from a sample of 5–15 residents, depending on the size of the facility; the remaining criteria are assessed per facility. In case of resident-related criteria, the values for individual residents are summed up to form a final value per criterion on a scale from 1 to 5. Facility-related criteria are dichotomous, i.e., registered as either existent or non-existent.
For the purposes of this study, criteria were assigned in terms of content to the following categories: facility structure (4 criteria, e.g., “secured recreational areas outside”, “food and drinks provided in a pleasant environment”), nursing processes (23 criteria, e.g., “required pressure sore prophylaxis is implemented”, “systematic pain assessments conducted”, “biography of residents suffering dementia taken into account and being considered when planning daily activities”), support procedures (16 criteria, e.g., “contact with relatives ensured”, “assistance or information provided to familiarize new residents with the nursing facility”), documentation of nursing services (7 criteria, e.g., “individual risk of falling registered”, “individual risks and resources of residents with incontinence or a bladder catheter assessed”), patient outcomes (2 criteria, “nutritional status appropriate”, “supply of fluids appropriate”) and quality management (5 criteria, e.g., “written instructions available on how to proceed in emergencies”, “nursing facility has a system for managing complaints”). Five criteria were not included in the analysis since over 50 % of pertinent data were not available. Inclusion of two further criteria was not possible since the underlying scale did not permit assignment to suitable content categories. Criteria related to surveyed resident satisfaction were not used either, since these surveys were performed on the basis of a non-validated interview tool and with an insufficient case number and sample size in most cases (Hasseler et al. 2010).
The relationships between profit orientation and prices charged by nursing homes on the one hand, and quality of care on the other were first determined bivariately via Mann–Whitney U-Test and Kruskal-Wallis Test respectively, followed by a multivariate variance analysis which also covered the interaction effect between price and type of ownership. Additionally, post hoc tests with Bonferroni-Holm correction for multiple testing were performed to discover the trend of correlations between variables in detail. The statistics software SPSS version 22 was used for all descriptive and analytical evaluations.