European journal of haematology · 2017
The impact of socio-economic factors on treatment choice and mortality in chronic myeloid leukaemia.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
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Larfors Gunnar, Sandin Fredrik, Richter Johan, Själander Anders, Stenke Leif, Lambe Mats, Höglund Martin
Аннотация
PURPOSE: To evaluate the influence of socio-economic variables on treatment selection and survival of patients with chronic myeloid leukaemia (CML).
METHODS: Using information available in population-based Swedish registries, we evaluated indices of health, education and economy from the 980 patients in the Swedish CML register diagnosed between 2002 and 2012. Apart from internal comparisons, five age-, gender- and region-matched control subjects per patient served as control cohort. Median follow-up time from CML diagnosis was 4.8 years.
RESULTS: Among patients with CML, low personal or household income, short education, living alone, poor performance status and high age (>60 years) were significantly associated with an inferior survival (in univariate analyses). However, similar findings were noted also in the matched control group, and in comparisons adjusted for calendar year, age and performance status, socio-economic variables were not significantly associated with CML survival. Meanwhile, both education and income were independently linked to TKI treatment overall and to upfront treatment with second-generation TKIs.
CONCLUSIONS: In conclusion, socio-economic conditions were associated with survival in the studied CML cohort but these associations could be explained by differences at baseline. Meanwhile, socio-economic conditions appeared to influence treatment choice.
METHODS: Using information available in population-based Swedish registries, we evaluated indices of health, education and economy from the 980 patients in the Swedish CML register diagnosed between 2002 and 2012. Apart from internal comparisons, five age-, gender- and region-matched control subjects per patient served as control cohort. Median follow-up time from CML diagnosis was 4.8 years.
RESULTS: Among patients with CML, low personal or household income, short education, living alone, poor performance status and high age (>60 years) were significantly associated with an inferior survival (in univariate analyses). However, similar findings were noted also in the matched control group, and in comparisons adjusted for calendar year, age and performance status, socio-economic variables were not significantly associated with CML survival. Meanwhile, both education and income were independently linked to TKI treatment overall and to upfront treatment with second-generation TKIs.
CONCLUSIONS: In conclusion, socio-economic conditions were associated with survival in the studied CML cohort but these associations could be explained by differences at baseline. Meanwhile, socio-economic conditions appeared to influence treatment choice.