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Predictability of hematopoietic stem cell transplantation rates

From Hematology, Department of Medicine, University Hospital Basel, Switzerland (AG, HB, MG); Institute for Operations Research and Computational Finance, University of St. Gallen, Switzerland (AS, KF); Hematology, Hammersmith Hospital, London, United Kingdom (JA); Hematology and Oncology, University of Leipzig, Germany (DN)

Correspondence: Alois Gratwohl, Haematology, University Hospital Basel, CH-4031 Basel, Switzerland. E-mail: hematology{at}uhbs.ch

Background and Objectives: Hematopoietic stem cell transplantation (HSCT) is a complex and expensive procedure. Trends in the use of this procedure have appeared erratic in the past. Information on future needs is essential for health care administrators.

Design and Methods: We analyzed the evolution of transplant rates, e.g. numbers of transplants per 10 million inhabitants, in Europe from 1990 to 2004 for all major disease categories and used Gross National Income (GNI) per capita, team density (numbers of teams per 10 million inhabitants) and team distribution (numbers of teams per 10,000 km²) to measure the impact of economic factors in participating countries. Trends were compared by regression analyses, and countries were grouped by World Bank definitions into high, middle and low income categories.

Results: Transplant rates increased over time with nearly linear trends, in clear association with GNI per capita (R²=0.72), and distinct by World Bank category within a narrow window of variation for both autologous HSCT (R²=0.95, 0.98 and 0.94 for high, middle and low income categories, respectively) and allogeneic HSCT (R²=0.99, 0.96 and 0.95 for high, middle and low income categories, respectively) when breast cancer (autologous) and chronic myeloid leukemia (allogeneic) were excluded. Team density (R²=0.72) and team distribution (R²=0.51) were also associated with transplant rates.

Interpretation and Conclusions: Transplant rates for HSCT in Europe are highly predictable. They are primarily influenced by GNI per capita. The absence of saturation and a nearly linear trend indicate that infrastructure lags behind medical needs. Isolated changes in single disease entities can easily be recognized.

Key words: predictability, HSCT rates.