Structural Equation Modeling to Explore Patient to Staff Ratios as an Explanatory Factor for Variation in Dialysis Facility Outcomes

Abstract

Objective: Patient to staff ratios vary based on facility characteristics, and therefore have been proposed as an explanatory factor for the variation in dialysis facility outcomes. This analysis tested that hypothesis.

Design and methods: Observational study using Dialysis Facility Report data. Reported staff numbers from the Annual Facility Survey were converted to full time equivalents (FTE). Subsequently, ratios were created for patients per FTE registered dietitian (RD), social worker, nurse, and patient care technician. Bivariate associations and structural equation modeling (SEM) were used to explore relationships between these ratios and patient outcomes: standardized mortality ratio and standardized hospitalization rate, when also considering the impact of non-modifiable facility characteristics (region, chain, profit status). Our focus was on RD staffing; therefore we also included serum phosphorus and normalized protein catabolic ratio in the model, and also conducted a sub-analysis of the 198 facilities that exceeded the KDOQI maximum of 150 patients:FTE RD.

Subjects: Dialysis centers in the US with at least 30 adult patients and no pediatric patients. 4035 facilities had complete data for the proposed variables.

Main outcome measure: Standardized mortality ratio and standardized hospitalization rate were the primary outcomes.

Results: The mean and standard deviation for patients per FTE staff were 90.0 ± 34.0, 88.7 ± 32.8, 17.1 ± 20.5 and 11.9 ± 7.0 for RDs, social workers, nurses, and technicians, respectively. Facility characteristics impacted staffing in bivariate analyses and SEM. The only significant paths from staffing ratio to outcomes were for patient:FTE social worker to SMR (standardized beta = -0.09, 95% CI -0.13, -0.04) and Patients:FTE RD to SHR Days (standardized beta = 0.04, 95% CI 0.001, 0.09). In the sub-analysis, there were no significant paths from staffing to outcomes.

Conclusions: This study did not provide evidence that patient per staff ratios explain variation in dialysis facility outcomes. While there are some important bivariate relationships, these disappear in more complex models. Future research should investigate the impacts of staffing ratios on individual patients, to overcome the possible ecological fallacy.