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International Journal for Quality in Health Care 15:189-196 (2003)
© 2003 International Society for Quality in Health Care

Paper

Survival of hemodialysis patients: modeling differences in risk of dialysis centers

MARILIA SÁ CARVALHO1,2, ROBIN HENDERSON1, SILVIA SHIMAKURA1,3 and INÊS PEREIRA SILVA CUNHA SOUSA1

1Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
2National School of Public Health, FIOCRUZ, Rio de Janeiro
3Departamento de Estatística da Universidade Federal do Paraná, Curitiba, Brazil

Objective. Dialysis is the most common renal replacement therapy for patients with end stage renal disease. This paper considers survival of dialysis patients, aiming to assess quality of renal replacement therapy at dialysis centers in Rio de Janeiro, Brazil, and to investigate differences in survival between health facilities.

Methods. A Cox proportional hazards model, allowing for time-varying covariates and prevalent data, was the basic method used to analyze the survival of 11 579 patients on hemodialysis in 67 health facilities in Rio de Janeiro State from January 1998 until August 2001, using data obtained from routine information systems. A frailty random effects model was applied to investigate differences in mortality between health centers not explained by measured characteristics.

Results. The individual variables associated with the outcome were age and underlying disease, with diabetes being the main isolated risk factor. Considering covariates of the health unit, two factors were associated with performance: bigger units had on average better survival times than smaller ones and units which offered cyclic peritoneal dialysis performed less well than those that did not. There were significant frailty effects among centers, with relative risks varying between 0.24 and 3.15, and an estimated variance of 0.43.

Conclusions. Routine assessment based on health registries of the outcome of any high technology medical treatment is extremely important in maintaining quality of care and in estimating the impact of changes in therapies, units, and patient profiles. The frailty model allowed estimation of variation in risk between centers not attributable to any measured covariates. This can be used to guide more specific investigation and changes in health policies related to renal transplant therapies.

Keywords: health services assessment, renal replacement therapies, survival frailty models


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