Literature DB >> 28411119

Worldwide comparison of survival from childhood leukaemia for 1995-2009, by subtype, age, and sex (CONCORD-2): a population-based study of individual data for 89 828 children from 198 registries in 53 countries.

Audrey Bonaventure1, Rhea Harewood2, Charles A Stiller3, Gemma Gatta4, Jacqueline Clavel5, Daniela C Stefan6, Helena Carreira2, Devon Spika2, Rafael Marcos-Gragera7, Rafael Peris-Bonet8, Marion Piñeros9, Milena Sant10, Claudia E Kuehni11, Michael F G Murphy12, Michel P Coleman2, Claudia Allemani2.   

Abstract

BACKGROUND: Global inequalities in access to health care are reflected in differences in cancer survival. The CONCORD programme was designed to assess worldwide differences and trends in population-based cancer survival. In this population-based study, we aimed to estimate survival inequalities globally for several subtypes of childhood leukaemia.
METHODS: Cancer registries participating in CONCORD were asked to submit tumour registrations for all children aged 0-14 years who were diagnosed with leukaemia between Jan 1, 1995, and Dec 31, 2009, and followed up until Dec 31, 2009. Haematological malignancies were defined by morphology codes in the International Classification of Diseases for Oncology, third revision. We excluded data from registries from which the data were judged to be less reliable, or included only lymphomas, and data from countries in which data for fewer than ten children were available for analysis. We also excluded records because of a missing date of birth, diagnosis, or last known vital status. We estimated 5-year net survival (ie, the probability of surviving at least 5 years after diagnosis, after controlling for deaths from other causes [background mortality]) for children by calendar period of diagnosis (1995-99, 2000-04, and 2005-09), sex, and age at diagnosis (<1, 1-4, 5-9, and 10-14 years, inclusive) using appropriate life tables. We estimated age-standardised net survival for international comparison of survival trends for precursor-cell acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML).
FINDINGS: We analysed data from 89 828 children from 198 registries in 53 countries. During 1995-99, 5-year age-standardised net survival for all lymphoid leukaemias combined ranged from 10·6% (95% CI 3·1-18·2) in the Chinese registries to 86·8% (81·6-92·0) in Austria. International differences in 5-year survival for childhood leukaemia were still large as recently as 2005-09, when age-standardised survival for lymphoid leukaemias ranged from 52·4% (95% CI 42·8-61·9) in Cali, Colombia, to 91·6% (89·5-93·6) in the German registries, and for AML ranged from 33·3% (18·9-47·7) in Bulgaria to 78·2% (72·0-84·3) in German registries. Survival from precursor-cell ALL was very close to that of all lymphoid leukaemias combined, with similar variation. In most countries, survival from AML improved more than survival from ALL between 2000-04 and 2005-09. Survival for each type of leukaemia varied markedly with age: survival was highest for children aged 1-4 and 5-9 years, and lowest for infants (younger than 1 year). There was no systematic difference in survival between boys and girls.
INTERPRETATION: Global inequalities in survival from childhood leukaemia have narrowed with time but remain very wide for both ALL and AML. These results provide useful information for health policy makers on the effectiveness of health-care systems and for cancer policy makers to reduce inequalities in childhood cancer survival. FUNDING: Canadian Partnership Against Cancer, Cancer Focus Northern Ireland, Cancer Institute New South Wales, Cancer Research UK, US Centers for Disease Control and Prevention, Swiss Re, Swiss Cancer Research foundation, Swiss Cancer League, and the University of Kentucky.
Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.

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Year:  2017        PMID: 28411119      PMCID: PMC5418564          DOI: 10.1016/S2352-3026(17)30052-2

Source DB:  PubMed          Journal:  Lancet Haematol        ISSN: 2352-3026            Impact factor:   18.959


Introduction

Worldwide inequalities in health and health care are reflected in global differences in life expectancy and overall mortality in both adults and children, and the findings from several studies have highlighted global differences in cancer incidence and survival.3, 4 Diagnostic techniques and treatment for childhood leukaemia have improved since the 1990s. Access to these techniques and treatment has, however, been limited in some countries, partly by a shortage of resources. Leukaemias, a heterogeneous group of diseases of mostly unknown origin, are globally the most common malignancies in children (aged 0–14 years), except in Africa. Unlike in adults, acute lymphoid leukaemias are the commonest subtype in children (accounting for approximately 80% of cases), and acute myeloid leukaemia (AML) represents about 15% of cases. For both types, incidence varies widely with age; in lymphoid leukaemias, incidence is slightly higher in boys than girls, and in industrialised high-income countries (HIC). In low-income and middle-income countries (LMIC), where the population is young, the incidence of childhood leukaemias is lower than in HIC, but these diseases are still responsible for many deaths.2, 5 Evidence before this study In 2015, the CONCORD-2 study initiated surveillance of survival trends for childhood leukaemia at a worldwide scale. Results from CONCORD-2 identified huge worldwide variation in 5-year net survival for children diagnosed with precursor-cell acute lymphoblastic leukaemia or lymphoma (ALL) during 1995-2009. Our analysis extends those results to cover survival trends for several subtypes of childhood leukaemia, grouped according to the third edition of the International Classification of Childhood Cancer. Added value of this study We included 89 828 children diagnosed with leukaemia during 1995–2009 in 53 countries. Despite substantial improvements in survival from childhood acute myeloid leukaemia (AML) in most countries during 1995–2009, huge international disparities in 5-year survival persisted up to 2009, matching those previously reported in the 2015 CONCORD-2 paper for childhood ALL. 5-year age-standardised net survival from AML (ie, the probability of surviving at least 5 years after diagnosis) was consistently lower than 5-year age-standardised net survival from ALL, but the difference narrowed in most countries since the early 2000s. Children aged 1–9 years at diagnosis had higher 5-year net survival than older or younger children, both for ALL and AML. Survival for older children (10–14 years) improved by 2009, but infants (aged <1 year) diagnosed with either ALL or AML still had the lowest 5-year net survival. Implications of all the available evidence Data obtained in the CONCORD programme provide a unique opportunity to explore disparities in survival from childhood leukaemia at an unprecedented scale. The results suggest that good access to health care and appropriate treatment have a clear population effect on survival for children with leukaemia. The findings support the need for continuing international efforts to improve worldwide access to appropriate cancer care for children. They can also be used to assess the effect of cancer strategies targeting childhood cancers. Although cancer mortality trends provide a useful measure of the societal cancer burden, they depend on trends in both incidence and survival. Cancer survival is the probability that cancer patients survive up to a certain point after diagnosis. Observed survival and event-free survival are clinically important, but population-based net survival is the appropriate indicator for comparisons between populations. Net survival is the probability of surviving after controlling for mortality from other causes. The CONCORD programme was designed to address the shortage of globally comparable data on population-based cancer survival.3, 4 Population-based cancer survival reflects several aspects of health care, from screening and early diagnosis, to access to effective treatment. This metric is increasingly used as a measure of the effectiveness of health-care systems in the management of cancer, and to assess the effectiveness of national cancer plans.8, 9 The second cycle of CONCORD (CONCORD-2) established global surveillance of population-based cancer survival for patients diagnosed with one of ten common cancers, or childhood leukaemia, during the 15-year period 1995–2009, using data from 279 population-based cancer registries in 67 countries. In this analysis, we examine worldwide trends in survival from precursor-cell acute lymphoblastic leukaemia (ALL) in children, by age and sex, alongside trends in survival from acute myeloid leukaemia (AML) and other types of childhood leukaemia.

Methods

Search strategy and selection criteria

Cancer registries participating in CONCORD-2 were asked to submit tumour registrations for all children (aged 0–14 years) diagnosed with a haematological malignancy between Jan 1, 1995, and Dec 31, 2009, including information about their vital status at Dec 31, 2009. Depending on the registry, patients were followed up actively, via direct investigation, or passively, using linkage to national or regional databases of death. Haematological malignancies were defined by morphology codes in the range 9590–9989 in the International Classification of Diseases for Oncology, third revision (ICD-O-3). 215 registries in 60 countries submitted data on 126 830 children with a haematological malignancy. We excluded data from 13 registries from which the data were judged to be less reliable, or included only lymphomas (two), and data from countries for which fewer than 10 children were available for analysis (two). This left 124 015 records for children with a haematological malignancy. We did standardised data cleaning in three phases, as detailed previously. Records that were ineligible (eg, for patients aged 15 years or older), inaccurate or inappropriate for survival analysis (eg, incoherent date sequence, or registration only from a death certificate or autopsy report) were excluded.4, 11 For patients with more than one record of a haematological malignancy diagnosed during 1995–2009, we kept only the record of the first malignancy. A few registries submitted records coded to earlier revisions of ICD-O or to the first revision of ICD-O-3. In agreement with these registries, we recoded those morphology codes to be compliant with ICD-O-3. Data from Sétif (Algeria), Arkhangelsk (Russia), Wrocław (Poland), and Northern Ireland (UK) were included after their data were recoded to ICD-O-3.

Data analysis

We estimated 5-year net survival with the Pohar-Perme estimator using the STNS command implemented in Stata 13. We used the life tables of background mortality rates by sex, single year of age, calendar year and—for USA, Israel, Malaysia, and New Zealand—by race or ethnic group, produced for CONCORD-2. For each country, we estimated net survival by calendar period of diagnosis (1995–99, 2000–04, and 2005–09). We used the classic cohort approach for children diagnosed during 1995–99 and 2000–04, because 5 years of follow-up data were available for all children. We used the period approach to predict 5-year survival for leukaemias diagnosed more recently (2005–09), as this approach allows for the prediction of survival where 5 years of follow-up are not yet available. Survival estimates for each country were based on data from a national registry or from one or several subnational registries. We excluded data from some regional registries from the pooled estimate for a given country if data quality or information about vital status were deemed unsatisfactory. Country estimates were flagged if data quality was considered less reliable. We estimated 5-year survival by sex and age at diagnosis (<1, 1–4, 5–9, 10–14 years, inclusive). Exact age at diagnosis was calculated from the dates of birth and diagnosis. The rules adopted to impute missing components of dates have been described previously.4, 11 Age-standardised survival was calculated from three equally weighted age-specific estimates (0–4, 5–9, and 10–14 years). Data for age groups with fewer than ten patients were pooled with data for the adjacent age group; we then re-estimated survival for both age groups combined, and the pooled estimate was attributed to each age group. Leukaemias were grouped according to the International Classification of Childhood Cancer (ICCC-3). We estimated survival for all lymphoid leukaemias combined (ICCC-3 group Ia), for acute myeloid leukaemias (AML; Ib), and for unspecified and other specified leukaemias (Ie) (appendix p 5). We also estimated survival separately for two subgroups of lymphoid leukaemia: precursor-cell lymphoid leukaemias (ALL; Ia1) and mature B-cell leukaemias (Ia2). We did not analyse survival for chronic myeloproliferative diseases (group Ic) or myelodysplastic syndrome and other myeloproliferative diseases (group Id). Ethical approval for access to the data was obtained from the Ethics and Confidentiality Committee of the UK's statutory National Information Governance Board (now the Health Research Authority; ECC 3-04(i)/2011) and the UK National Health Service (NHS) Research Ethics Service (South-East; 11/LO/0331), and from other jurisdictions as required.

Role of the funding source

The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all data in the study and had final responsibility for the decision to submit for publication.

Results

Of the 124 015 children who were considered for analysis, we excluded 1623 (1%) as ineligible, usually because of missing information on date of birth, diagnosis, or last known vital status (table 1). More than 75% of records from the Tunisia Central Registry were ineligible because of incomplete data. Overall, only 0·5% of records were excluded because the registration was based solely on a death certificate or an autopsy report. We excluded five patients with synchronous leukaemia and lymphoma, and 106 children whose leukaemia followed a lymphoma, also diagnosed during 1995–2009. We also excluded 2222 children with chronic myeloproliferative disease (ICCC-3 group Ic) and 2002 children with myelodysplastic syndrome or other myeloproliferative disease (Id). Lymphomas (27 609) were not included.
Table 1

Data quality indicators for children aged 0–14 years diagnosed with leukaemia between 1995–2009: by continent and country

Calendar periodRegistries (n)Patients submitted (n)Eligible patients (n [%])DCO (n [%])Any haematological malignancy (n [% of eligible])Leukaemia
Included in analyses (n [% of any haematological malignancy])Microscopically verified (n [%])Follow-up time (years) of alive patients* (median [IQR])Lymphoid leukaemia (n [%])Acute myeloid leukaemia (n [%])Unspecified and other specified leukaemia (n [%])
Africa····373223 (60%)1 (<1%)193 (87%)153 (79%)152 (99%)3·5 (0·5–4·7)111 (73%)33 (22%)9 (6%)
Algerian registries2000–092131128 (98%)099 (77%)62 (63%)61 (98%)0·7 (0·2–3·0)37 (60%)20 (32%)5 (8%)
Lesotho1995–200912727 (100%)027 (100%)27 (100%)27 (100%)6·6 (0·9–8·4)25 (93%)02 (7%)
Libya (Benghazi)2003–0412323 (100%)1 (4%)22 (96%)21 (95%)21 (100%)4·9 (0·0–5·2)14 (67%)5 (24%)2 (10%)
Tunisia (Central)1996–2007119245 (23%)045 (100%)43 (96%)43 (100%)1·7 (1·0–2·2)35 (81%)8 (19%)0
America (Central and South)····57315722 (>99%)175 (3%)5488 (96%)5086 (93%)5063 (>99%)9·2 (7·3–11·5)4788 (94%)195 (4%)103 (2%)
Argentina2000–09136713671 (100%)128 (3%)3496 (95%)3496 (100%)3496 (100%)6·5 (4·9–8·3)3496 (100%)00
Brazilian registries1996–20095687678 (99%)41 (6%)626 (92%)497 (79%)475 (96%)11·2 (9·8–13·1)387 (78%)79 (16%)31 (6%)
Chilean registries1998–20082116116 (100%)0116 (100%)96 (83%)96 (100%)9·2 (7·7–12·4)66 (69%)14 (15%)16 (17%)
Colombia (Cali)1995–20091383383 (100%)0382 (>99%)381 (>99%)381 (100%)6·8 (3·6–9·2)381 (100%)00
Ecuador (Quito)1995–20091507507 (100%)4 (1%)503 (99%)375 (75%)375 (100%)13·8 (11·5–17·3)295 (79%)67 (18%)13 (3%)
Puerto Rico2000–091367367 (100%)2 (1%)365 (99%)241 (66%)240 (>99%)7·4 (6·2–8·6)163 (68%)35 (15%)43 (18%)
America (North)····51 19551 110 (>99%)144 (<1%)50 920 (>99%)36 970 (73%)36 156 (98%)10·7 (8·3–13·3)30 088 (81%)5894 (16%)988 (3%)
Canada1995–20091352595200 (99%)13 (<1%)5186 (>99%)4152 (80%)3983 (96%)11·0 (8·6–13·5)3363 (81%)623 (15%)166 (4%)
US registries1995–20093845 93645 910 (>99%)131 (<1%)45 734 (>99%)32 818 (72%)32 173 (98%)10·3 (8·0–13·1)26 725 (81%)5271 (16%)822 (3%)
Asia····17 37117 176 (99%)74 (<1%)17 094 (>99%)12 552 (73%)12 309 (98%)9·6 (7·4–11·8)8553 (68%)3051 (24%)948 (8%)
Chinese registries1995–200917741728 (98%)0728 (100%)632 (87%)619 (98%)7·1 (6·3–8·2)309 (49%)149 (24%)174 (28%)
Cyprus2004–0914949 (100%)2 (4%)47 (96%)46 (98%)46 (100%)5·4 (5·1–5·8)35 (76%)10 (22%)1 (2%)
India (Karunagappally)1995–200915454 (100%)054 (100%)53 (98%)53 (100%)11·2 (7·4–14·8)43 (81%)8 (15%)2 (4%)
Indonesia (Jakarta)2005–0712929 (100%)028 (97%)28 (100%)26 (93%)··14 (50%)6 (21%)8 (29%)
Israel1995–2009118801775 (94%)13 (1%)1759 (99%)1002 (57%)997 (>99%)10·3 (8·1–12·8)731 (73%)185 (18%)86 (9%)
Japanese registries1995–2009917291727 (>99%)46 (3%)1681 (97%)1441 (86%)1407 (98%)10·1 (6·2–11·3)969 (67%)414 (29%)58 (4%)
Korea1995–2009175697507 (99%)07507 (100%)5422 (72%)5279 (97%)11·5 (9·2–14·1)3583 (66%)1419 (26%)420 (8%)
Malaysia (Penang)1995–20091274269 (98%)3 (1%)263 (98%)261 (99%)260 (>99%)9·4 (7·6–11·9)163 (62%)67 (26%)31 (12%)
Mongolia2005–0914742 (89%)041 (98%)41 (100%)31 (76%)··25 (61%)8 (20%)8 (20%)
Taiwan1995–2009136423642 (100%)03642 (100%)2641 (73%)2612 (99%)11·5 (9·2–14·0)1935 (73%)614 (23%)92 (3%)
Thai registries1995–20093537537 (100%)6 (1%)531 (99%)469 (88%)463 (99%)10·8 (7·8–12·8)327 (70%)85 (18%)57 (12%)
Turkey (Izmir)1995–20091820817 (>99%)4 (<1%)813 (>99%)516 (63%)516 (100%)9·2 (7·3–12·3)419 (81%)86 (17%)11 (2%)
Europe····45 12744 003 (98%)165 (<1%)43 815 (>99%)31 797 (73%)31 256 (98%)11·0 (8·9–13·2)26 262 (83%)4856 (15%)679 (2%)
Austria1995–2009212481209 (97%)01203 (>99%)826 (69%)823 (>99%)12·2 (9·5–14·4)681 (82%)122 (15%)23 (3%)
Belarus1995–20091745745 (100%)0745 (100%)745 (100%)744 (>99%)12·6 (10·0–15·0)745 (100%)00
Belgium2004–091747747 (100%)0747 (100%)466 (62%)465 (>99%)8·0 (7·8–8·3)396 (85%)68 (15%)2 (<1%)
Bulgaria1995–2009110791079 (100%)01079 (100%)714 (66%)678 (95%)13·0 (9·9–15·5)558 (78%)96 (13%)60 (8%)
Croatia1998–20091651651 (100%)0651 (100%)446 (69%)446 (100%)9·9 (8·8–11·8)375 (84%)61 (14%)10 (2%)
Denmark1995–20091695695 (100%)0695 (100%)680 (98%)660 (97%)10·4 (8·3–12·9)551 (81%)112 (16%)17 (3%)
Estonia1995–20081160160 (100%)1 (1%)159 (99%)99 (62%)99 (100%)11·9 (9·1–14·4)73 (74%)24 (24%)2 (2%)
Finland1995–2009110071007 (100%)6 (1%)1001 (99%)728 (73%)726 (>99%)11·0 (8·5–13·5)593 (81%)101 (14%)34 (5%)
France1995–2009110 61910 032 (95%)010 030 (>99%)6875 (69%)6873 (>99%)9·4 (7·2–12·5)5611 (82%)1109 (16%)155 (2%)
German registries1995–20091338013781 (99%)96 (3%)3684 (97%)2661 (72%)2460 (92%)10·7 (8·4–13·4)2162 (81%)436 (16%)63 (2%)
Iceland1995–200913636 (100%)036 (100%)36 (100%)36 (100%)10·9 (10·1–13·0)29 (81%)6 (17%)1 (3%)
Ireland1995–20091817797 (98%)2 (<1%)794 (>99%)571 (72%)570 (>99%)10·6 (8·2–13·2)458 (80%)99 (17%)14 (2%)
Italian registries1995–20093239553602 (91%)10 (<1%)3584 (>99%)2313 (65%)2247 (97%)9·7 (7·3–12·6)1930 (83%)336 (15%)47 (2%)
Latvia1995–20091205205 (100%)0204 (>99%)196 (96%)194 (99%)13·4 (11·5–16·6)97 (49%)35 (18%)64 (33%)
Lithuania1995–20091532529 (99%)2 (<1%)527 (>99%)358 (68%)358 (100%)10·9 (9·1–13·6)301 (84%)53 (15%)4 (1%)
Malta1995–200919292 (100%)092 (100%)61 (66%)60 (98%)10·8 (7·9–13·2)53 (87%)8 (13%)0
Netherlands1995–2009128602858 (>99%)3 (<1%)2855 (>99%)1988 (70%)1988 (100%)12·4 (10·1–15·0)1652 (83%)307 (15%)29 (1%)
Norway1995–20091665665 (100%)1 (<1%)664 (>99%)646 (97%)646 (100·0)10·8 (7·9–13·3)531 (82%)108 (17%)7 (1%)
Poland (Wroclaw)1995–20091205205 (100%)0203 (99%)120 (59%)120 (100%)10·2 (9·2–11·4)96 (80%)24 (20%)0
Portugal1998–20094872855 (98%)0854 (>99%)665 (78%)565 (85%)9·3 (8·2–10·9)511 (77%)134 (20%)20 (3%)
Russia (Arkhangelsk) 2000–0916060 (100%)1 (2%)59 (98%)59 (100%)59 (100%)10·7 (9·0–11·8)51 (86%)08 (14%)
Slovakia2000–071426423 (99%)6 (1%)417 (99%)284 (68%)284 (100%)9·7 (8·6–10·9)223 (79%)59 (21%)2 (1%)
Slovenia1995–20091280280 (100%)2 (1%)278 (99%)181 (65%)181 (100%)13·1 (10·9–15·2)147 (81%)33 (18%)1 (1%)
Spanish registries1995–20091114871479 (99%)9 (1%)1470 (99%)1152 (78%)1136 (99%)10·9 (8·6–13·3)997 (87%)123 (11%)32 (3%)
Sweden1995–2009111621162 (100%)01162 (100%)1145 (99%)1145 (100%)11·1 (8·2–13·6)984 (86%)154 (13%)7 (1%)
Switzerland1995–20091853852 (>99%)1 (<1%)851 (>99%)813 (96%)812 (>99%)11·2 (9·2–13·2)684 (84%)119 (15%)10 (1%)
UK1995–2009398689797 (99%)25 (<1%)9771 (>99%)6969 (71%)6881 (99%)12·5 (10·0–14·9)5773 (83%)1129 (16%)67 (1%)
Oceania····42184158 (99%)6 (<1%)4151 (>99%)3270 (79%)3143 (96%)11·2 (9·1–13·9)2672 (82%)544 (17%)54 (2%)
Australian registries1995–2009635373477 (98%)2 (<1%)3474 (>99%)2607 (75%)2480 (95%)9·7 (7·5–12·4)2145 (82%)424 (16%)38 (1%)
New Zealand1995–20091681681 (100%)4 (1%)677 (99%)663 (98%)663 (100%)12·8 (10·7–15·4)527 (79%)120 (18%)16 (2%)
Total··198124 015122 392 (99%)565 (<1%)121 661 (99%)89 828 (74%)88 079 (98%)··72 474 (81%)14 573 (16%)2781 (3%)

Calendar period shows the maximum time span covered by the data. DCO=patients diagnosed from autopsy or Death Certificate Only (% of eligible patients). Microscopic verification=cytology or histology, or morphological verification unspecified (% of patients included in analyses).

Median follow-up of patients diagnosed during 1995–2004, and IQR.

National coverage—the data are derived from a population-based cancer registry (registries) covering the entire country.

Korea: Republic of Korea; Russia: Russian Federation.

Data quality was generally very high, and a high proportion of diagnoses were reported with a specific morphology. Improvements in diagnosis and registration are illustrated in Latvia by the drop in the number of unspecified and other specified leukaemias (group Ie) between 1995–99 and 2005–09. We focused our analyses on 89 828 children (73·8% of all haematological malignancies) from 198 registries in 53 countries who were diagnosed with lymphoid leukaemia (ICCC-3 group Ia), acute myeloid leukaemia (Ib), or unspecified or other specified leukaemia (Ie). For children diagnosed during 1995–2004 who were not known to have died, the median follow-up was at least 5 years in all participating countries in North, Central, and South America, Europe, Asia, and Oceania. In Africa, the median follow-up of surviving children diagnosed up to 2004 was 0·7 years (IQR 0·2–3·0) in the two Algerian registries and 1·7 years (1·0–2·2) in the Tunisia Central Registry, although the maximum follow-up was 8·9 and 13·3 years, respectively. All lymphoid leukaemias combined represented 81% of leukaemias, AML 16%, and unspecified and other specified leukaemias the remaining 3% (table 1). In Lesotho, no AML was registered during 1995–2009. Information from the cancer registries in Belarus, Argentina, and Colombia (Cali) was only available for lymphoid leukaemias. Precursor-cell ALL was by far the most common type of lymphoid leukaemia, but some registries submitted data on rarer types, such as mature B-cell (mostly Burkitt leukaemia), mature T and Natural-Killer (NK) cell leukaemias (figure 1). Due to the rarity of mature T-cell and NK-cell leukaemia (n=94, ICCC-3 group Ia3) and unspecified lymphoid leukaemias (n=446, Ia4), we did not estimate survival separately for these morphological groups. In the data from Indonesia (Jakarta), 17 Chinese registries, and Latvia, more than 25% of the childhood leukaemias were coded as unspecified or other specified leukaemia (ICCC-3 group Ie).
Figure 1

Distribution (%) of leukaemia subtypes in children diagnosed during 1995–2009 and included in survival analyses, by continent

Leukaemias were classified according to the third edition of the International Classification of Childhood Cancer.

During 1995–99, 5-year age-standardised net survival for all lymphoid leukaemias combined ranged from 10·6% (95% CI 3·1–18·2) in the Chinese registries to 86·8% (81·6–92·0) in Austria (table 2). This wide range in survival narrowed over time, with survival in 2005–09 ranging from 52·4% (42·8–61·9) in Cali, Colombia, to 91·6% (89·5–93·6) in the German registries. Survival from precursor-cell ALL was very close to that of all lymphoid leukaemias combined, with similar variation (figure 2). The greatest absolute difference in survival between all lymphoid leukaemias and ALL was noted in Iceland (80·9% and 90·1%, respectively) but these estimates were not age-standardised because of small numbers. Survival from precursor-cell ALL increased between 1995–99 and 2005–09 in most countries (figure 3).
Table 2

5-year age-standardised net survival in children aged 0–14 years diagnosed with leukaemia

Lymphoid leukaemia
Acute myeloid leukaemia (ICCC-3 group Ib)
Unspecified & other leukaemias (ICCC-3 group Ie)
All lymphoid (Ia)
Precursor cell (Ia1)
Mature B cell (Ia2)
nNet survival (%), 95% CInNet survival (%), 95% CInNet survival (%), 95% CInNet survival (%), 95% CInNet survival (%), 95% CI
Africa
Algerian registries
2000–041921·6%*(0·0–45·6)1921·6%*(0·0–45·6)....1323·9%*(0·0–48·5)3..
2005–0918..17......7..2..
Lesotho
1995–20092543·0%(20·9–65·1)2239·5%(16·3–62·7)3......2..
Libya (Benghazi)
2003–041470·2% (43·4–96·9)1470·2% (43·4–96·9)....5..2..
Tunisia (Central)
1996–992046·1%*(13·2–79·1)2046·1%*(13·2–79·1)....3......
2000–046..6......2......
2005–079..9......3......
America (Central and South)
Argentina
2000–04178564·6% (62·3–67·0)178564·6% (62·3–67·0)............
2005–09171166·9% (64·4–69·3)171166·9% (64·4–69·3)............
Brazilian registries
1996–996772·9% (61·8–84·0)6471·8% (60·0–83·6)....1338·6% (13·8–63·5)4..
2000–0417267·9% (60·5–75·3)16867·1% (59·6–74·7)....4146·1% (32·1–60·1)1675·5% (55·0–96·0)
2005–0914866·2% (58·5–73·9)13266·4% (58·4–74·3)1080·3%(48·7–100·0)2553·1% (36·9–69·4)1155·8% (31·7–79·9)
Chilean registries
1998–991741·2% (19·1–63·3)1741·2% (19·1–63·3)....3......
2000–042171·5% (52·7–90·3)2171·5% (52·7–90·3)....2..12100·0%
2005–082883·9%§(73·3–94·5)2377·5% (60·5–94·6)....9..4..
Colombia (Cali)
1995–9912540·7% (31·6–49·9)12440·4% (31·2–49·6)............
2000–0413748·4% (39·4–57·4)13648·8% (39·7–57·8)............
2005–0911952·4% (42·8–61·9)11752·4% (42·8–61·9)............
Ecuador (Quito)
1995–998564·3% (54·2–74·4)8163·7% (53·3–74·1)....3349·0%§(35·9–62·1)5..
2000–0411263·5% (54·2–72·8)11064·1% (54·7–73·6)....1850·2% (28·2–72·1)6..
2005–099862·5% (53·5–71·5)9563·1% (54·0–72·1)....1659·3% (36·8–81·7)2..
Puerto Rico
2000–047979·4% (70·5–88·2)7379·0% (69·7–88·3)3..1753·0% (30·3–75·8)3472·4%§(60·8–83·9)
2005–098478·7% (69·5–87·9)8178·9% (69·4–88·4)3..1848·1% (26·4–69·8)9..
America (North)
Canada
1995–99115686·0% (83·6–88·4)113485·9% (83·4–88·3)1181·9% (60·2–100·0)23556·1% (49·5–62·7)5368·0% (55·2–80·8)
2000–04109291·0% (89·0–93·0)107491·0% (89·0–93·0)1090·0% (72·4–100·0)18864·0% (57·0–70·9)5172·7% (60·6–84·9)
2005–09111590·5% (88·4–92·6)109790·7% (88·6–92·9)1480·0% (60·5–99·6)20071·8% (65·1–78·5)6283·1% (72·9–93·3)
US registries
1995–99780182·9% (81·9–83·9)767082·9% (81·9–83·9)7377·0% (67·7–86·4)157451·5% (48·9–54·1)24168·8% (62·4–75·2)
2000–04902586·5% (85·7–87·4)884286·6% (85·8–87·5)12886·5% (80·1–92·9)179959·7% (57·3–62·1)28563·8% (57·7–69·8)
2005–09989987·7% (86·9–88·5)973587·7% (86·9–88·5)13288·7% (83·0–94·5)189863·3% (60·9–65·6)29669·8% (64·2–75·4)
Asia
Chinese registries
1995–992910·6%§(3·1–18·2)2811·1%§(3·3–19·0)....274·2%§(0·0–8·6)238·7% (0·0–18·9)
2000–049845·6% (36·3–54·9)8448·8% (38·8–58·8)....6120·9% (10·1–31·7)6915·0% (6·7–23·2)
2005–0918269·2% (61·6–76·8)15169·4% (61·1–77·8)1068·7%(40·8–96·6)6141·1% (27·8–54·4)8226·0% (15·9–36·0)
Cyprus
2004–093583·0% (70·9–95·0)3480·8% (68·9–92·6)1..1060·1%(32·2–88·0)1..
India (Karunagappally)
1995–991759·3% (36·7–81·9)1759·3% (36·7–81·9)....1......
2000–041457·4% (32·6–82·1)1457·4% (32·6–82·1)....4......
2005–091280·2% (60·3–100·0)1280·2% (60·3–100·0)....3..2..
Indonesia (Jakarta)
2005–071444·3% (13·4–75·3)1444·3% (13·4–75·3)....6..8..
Israel
1995–9919281·4% (74·9–87·8)18881·0% (74·5–87·6)2..4063·2%§(51·6–74·9)2774·1% (57·9–90·3)
2000–0426486·3% (81·6–91·1)25886·3% (81·5–91·1)4..6767·5% (56·6–78·4)2777·8% (62·5–93·2)
2005–0927584·4% (79·6–89·1)27184·5% (79·7–89·3)1..7866·2% (56·2–76·3)3289·2%§(80·7–97·7)
Japanese registries
1995–9929478·0% (72·9–83·1)29478·0% (72·9–83·1)....12262·4% (53·7–71·0)2560·1% (41·4–78·8)
2000–0440978·0% (73·2–82·9)40677·9% (73·1–82·8)2..18161·2% (53·5–68·9)2487·5% (74·6–100·0)
2005–0926682·5% (78·0–86·9)25982·2% (77·6–86·8)4..11169·4% (62·0–76·9)9..
Korea†;
1995–99119163·1% (60·2–66·0)117163·2% (60·3–66·1)....48240·1% (35·6–44·5)16744·2% (36·8–51·6)
2000–04122173·0% (70·3–75·6)119773·2% (70·5–75·8)....50449·7% (45·4–54·1)12855·4% (46·9–63·9)
2005–09117176·4% (73·9–78·9)113777·1% (74·5–79·6)4365·2% (52·9–77·5)43353·9% (49·3–58·6)12563·9% (55·7–72·1)
Malaysia (Penang)
1995–995177·3% (65·5–89·1)5177·3% (65·5–89·1)....2268·3% (49·3–87·2)1..
2000–045382·8%§(74·2–91·3)5181·3%§(72·1–90·4)....2374·0% (56·5–91·5)5..
2005–095970·1% (58·4–81·9)5769·5% (57·4–81·5)1..2249·1% (27·8–70·5)2576·4% (58·5–94·4)
Mongolia
2005–092518·7% (0·0–40·8)2419·5% (0·0–42·5)....8..8..
Taiwan
1995–9963062·8% (58·8–66·9)60162·5% (58·4–66·6)1872·3% (52·3–92·4)19540·8% (33·8–47·8)5250·8% (38·2–63·3)
2000–0468272·2% (68·5–76·0)66572·0% (68·2–75·8)1580·0% (60·5–99·6)21448·6% (41·8–55·5)2277·3% (60·3–94·4)
2005–0962377·6% (74·1–81·2)61078·1% (74·6–81·7)1162·1% (39·2–85·0)20555·6% (48·8–62·3)1863·9% (43·2–84·7)
Thai registries
1995–9910251·1% (39·4–62·8)10251·1% (39·4–62·8)....1821·8% (2·1–41·5)2052·0% (28·7–75·4)
2000–0412058·9%(49·2–68·5)12058·9% (49·2–68·5)....3130·5%§(18·0–43·0)2037·3% (16·6–58·1)
2005–0910555·0% (45·4–64·7)10255·6% (46·0–65·3)....3644·7%§(31·8–57·5)1757·0% (31·5–82·5)
Turkey (Izmir)
1995–9911862·8% (53·0–72·7)11663·5% (53·7–73·4)2..3459·2%§(46·1–72·4)2..
2000–0413571·0% (62·6–79·3)13170·9% (62·5–79·3)3..2431·2% (13·1–49·4)1..
2005–0916673·6% (66·3–81·0)16174·2% (66·8–81·7)4..2850·6%§(35·7–65·4)8..
Europe
Austria
1995–9924286·8% (81·6–92·0)24086·6% (81·3–91·9)....4760·1%§(49·1–71·1)7..
2000–0421390·1% (85·7–94·5)20889·7% (85·2–94·3)1..4065·0%§(53·4–76·5)1266·7% (41·4–92·1)
2005–0922691·1% (86·9–95·3)22191·4% (87·2–95·7)3..3572·6%§(61·2–84·1)4..
Belarus
1995–9928674·3% (69·1–79·6)28274·7% (69·4–79·9)............
2000–0424177·5% (72·0–83·0)23578·4% (72·9–83·9)............
2005–0921888·1% (83·4–92·7)20988·3% (83·6–93·0)1774·8%(54·0–95·7)........
Belgium
2004–0939687·2% (82·9–91·4)38686·9% (82·6–91·3)1090·0%(72·4–100·0)6856·6% (43·5–69·6)2..
Bulgaria
1995–9918457·0% (49·8–64·2)16657·6% (50·0–65·2)....2827·8%§(15·7–39·9)3713·5%§(5·6–21·3)
2000–0415961·8% (53·9–69·6)15462·3% (54·3–70·3)....3523·1%§(12·7–33·5)1729·4% (9·6–49·3)
2005–0921572·0% (65·3–78·8)21571·8% (64·9–78·6)....3333·3% (18·9–47·7)6..
Croatia
1998–995168·7% (56·9–80·6)4469·8%§(59·2–80·4)....1346·2% (20·7–71·8)2..
2000–0417784·1% (77·6–90·6)16081·6% (74·2–89·0)....2065·0% (44·8–85·3)4..
2005–0914785·6% (79·8–91·5)14685·7% (79·8–91·7)1794·1%(83·3–100·0)2855·9% (37·6–74·2)4..
Denmark
1995–9916685·9% (79·7–92·0)16385·6% (79·4–91·8)3..3259·4% (42·9–75·9)4..
2000–0421284·2% (78·6–89·8)20984·5% (78·9–90·1)2..4269·1% (55·3–82·9)1070·0% (43·3–96·8)
2005–0917387·4% (81·9–93·0)17187·2% (81·5–92·9)2..3868·9% (54·5–83·3)3..
Estonia
1995–992953·6%§(40·0–67·3)2953·6%§(40·0–67·3)....7..2..
2000–043161·0%§(47·8–74·2)3161·0%§(47·8–74·2)....1136·4% (10·3–62·6)....
2005–081375·4% (57·0–93·7)1375·4% (57·0–93·7)....6......
Finland
1995–9919382·3% (76·3–88·4)19382·3% (76·3–88·4)....3778·4% (65·4–91·5)4..
2000–0419284·8% (78·1–91·4)19184·7% (78·0–91·4)....2965·4%§(52·1–78·8)1258·4% (32·0–84·8)
2005–0920882·0% (75·4–88·5)20581·8% (75·2–88·4)2..3569·2% (54·3–84·2)1866·9% (45·5–88·4)
France
1995–99180682·4% (80·4–84·3)172882·4% (80·4–84·4)7878·7% (69·2–88·2)38060·2% (55·1–65·3)3652·8% (36·8–68·8)
2000–04188388·0% (86·3–89·6)179388·1% (86·4–89·8)8986·0% (78·7–93·3)39262·4% (57·7–67·1)4859·8% (46·1–73·5)
2005–09192289·2% (87·6–90·8)182889·2% (87·6–90·9)9388·8% (82·2–95·4)33769·4% (64·5–74·2)7164·9% (53·0–76·8)
German registries
1995–9948186·3% (83·0–89·6)46886·5% (83·1–89·8)....10761·4% (52·3–70·5)2774·1% (57·9–90·3)
2000–0466187·3% (84·7–90·0)64287·1% (84·4–89·8)....13971·0% (63·2–78·8)1681·3% (62·8–99·8)
2005–09102091·6% (89·5–93·6)98991·6% (89·5–93·6)3993·7% (85·7–100·0)19078·2% (72·0–84·3)2074·3% (53·0–95·5)
Iceland
1995–999..9..........1..
2000–049..9......2......
2005–091180·9% (58·1–100·0)1090·1% (72·4–100·0)1..4......
Ireland
1995–9914978·9% (71·5–86·3)14679·0% (71·6–86·5)1..2965·6% (48·7–82·5)3..
2000–0414682·9% (76·6–89·2)14582·8% (76·5–89·2)1..3560·0% (44·1–76·0)7..
2005–0916384·7% (78·3–91·1)16284·7% (78·3–91·1)1..3565·8% (51·0–80·7)4..
Italian registries
1995–9967783·8% (80·7–86·9)66183·7% (80·6–86·9)....10960·9% (51·7–70·1)1675·1% (54·6–95·5)
2000–0474082·4% (79·1–85·7)72582·5% (79·1–85·8)....12466·7% (58·5–74·9)1877·8% (59·2–96·5)
2005–0951387·9% (85·0–90·9)50788·0% (85·0–90·9)2476·7%(59·0–94·3)10368·9% (60·4–77·4)1377·2% (55·3–99·2)
Latvia
1995–991650·2% (26·7–73·6)1546·8% (22·8–70·9)....1540·1% (16·6–63·5)4369·6%§(58·4–80·9)
2000–043691·8% (82·9–100·0)3591·6% (82·5–100·0)....1145·5% (18·1–72·9)1963·3% (42·2–84·3)
2005–094577·0%§(66·5–87·6)4576·5%§(65·8–87·2)....9..2..
Lithuania
1995–9910359·4% (49·3–69·6)10259·2% (49·0–69·4)....1241·8% (15·9–67·7)3..
2000–0411273·7% (64·9–82·5)10674·3% (65·3–83·4)2..2722·3% (7·3–37·2)....
2005–098668·2% (58·1–78·4)8669·0% (58·4–79·6)....1444·0% (20·1–67·9)1..
Malta
1995–991963·3% (42·3–84·3)1963·3% (42·3–84·3)............
2000–041681·4% (62·9–99·8)1681·4% (62·9–99·8)....4......
2005–091883·1% (66·0–100·0)1782·5% (64·9–100·0)....4......
Netherlands
1995–9952981·1% (77·2–84·9)52781·0% (77·1–84·9)2..9258·6% (48·0–69·2)1275·1% (51·8–98·5)
2000–0458684·0% (80·6–87·4)58284·0% (80·6–87·4)1..10953·7% (43·7–63·8)1181·9% (60·2–100·0)
2005–0953786·2% (82·9–89·6)53386·2% (82·9–89·6)4..10658·6% (48·2–69·0)6..
Norway
1995–9918079·2% (71·6–86·9)17779·1% (71·4–86·8)2..4254·3% (39·6–69·0)3..
2000–0418287·7% (82·5–93·0)17887·7% (82·3–93·1)4..3765·5%§(53·1–77·9)2..
2005–0916989·7% (84·5–94·9)16989·7% (84·5–94·9)....2967·3% (50·1–84·5)2..
Poland (Wroclaw)
2000–043368·9%§(56·6–81·2)3368·9%§(56·6–81·2)....8......
2005–096380·9% (70·8–90·9)6280·9% (70·8–90·9)1..1666·7% (43·6–89·9)....
Portugal
1998–994566·0%§(54·3–77·6)4566·0%§(54·3–77·6)....1753·0% (30·2–75·8)1..
2000–0425779·3% (73·8–84·8)23879·2% (73·5–84·8)....6553·3% (41·7–65·0)7..
2005–0920984·0% (78·8–89·2)19183·7% (78·1–89·2)2194·5%(84·2–100·0)5260·4% (48·6–72·3)1269·7% (42·5–96·8)
Russia (Arkhangelsk)
2000–042755·7% (37·3–74·0)2352·2% (32·4–72·1)1......6..
2005–092474·8% (57·7–92·0)2373·1% (55·0–91·2)........2..
Slovakia
2000–0413279·4% (72·1–86·7)13179·3% (72·0–86·6)....3545·0% (30·0–59·9)2..
2005–079179·1% (70·4–87·7)8978·3% (69·5–87·2)....2446·7% (23·2–70·3)....
Slovenia
1995–995083·4%§(74·4–92·3)5083·4%§(74·4–92·3)....7......
2000–044989·7%§(81·8–97·6)4989·7%§(81·8–97·6)....1163·7% (36·8–90·5)....
2005–094875·8%§(65·4–86·2)4381·3% (69·7–92·9)5..1579·5% (59·5–99·5)1..
Spanish registries
1995–9929674·4% (68·9–79·9)29074·1% (68·5–79·7)....3847·3%§(34·7–59·9)1758·9% (36·4–81·4)
2000–0431981·6% (77·0–86·1)30881·9% (77·3–86·5)....3964·0% (50·0–78·0)1070·0% (43·3–96·8)
2005–0938283·7% (79·5–87·9)36284·2% (80·0–88·4)3080·0% (67·4–92·7)4660·2% (47·2–73·2)5..
Sweden
1995–9936884·2% (79·8–88·7)34485·0% (80·5–89·5)2..5072·2%§(61·6–82·7)....
2000–0433385·9% (81·7–90·0)31386·8% (82·6–90·9)....5656·3%§(45·8–66·8)3..
2005–0928384·5% (80·1–88·9)23085·5% (80·9–90·1)2..4865·9%§(55·5–76·4)4..
Switzerland
1995–9922486·0% (80·9–91·1)22085·6% (80·3–90·8)....4253·4% (38·7–68·1)....
2000–0422987·6% (82·8–92·4)22287·2% (82·3–92·1)....4353·7%§(42·3–65·2)6..
2005–0923187·9% (83·3–92·6)22588·3% (83·7–92·9)14100·0%(100·0–100·0)3475·2%§(64·0–86·3)4..
UK
1995–99189679·1% (77·0–81·2)187179·2% (77·0–81·3)....37158·6% (53·4–63·8)2770·5% (53·6–87·3)
2000–04197685·9% (84·2–87·7)195486·0% (84·3–87·8)....39065·6% (60·7–70·5)1855·6% (33·7–77·5)
2005–09190189·3% (87·7–90·9)189389·2% (87·6–90·8)5177·8% (66·4–89·1)36868·1% (63·2–73·1)2276·3% (58·4–94·1)
Oceania
Australian registries
1995–9968582·8% (79·6–86·0)68282·9% (79·7–86·1)....12553·4% (44·6–62·2)1346·2% (20·7–71·7)
2000–0483386·0% (83·3–88·7)82586·2% (83·4–88·9)....17070·8% (63·8–77·8)1668·9% (47·0–90·8)
2005–0962788·8% (86·0–91·6)61789·0% (86·2–91·8)1782·0%(64·0–99·9)12968·5% (60·3–76·6)9..
New Zealand
1995–9916882·8% (76·4–89·3)16782·6% (76·0–89·1)1..4267·6%§(56·3–78·9)5..
2000–0418385·2% (79·3–91·2)18285·8% (79·9–91·7)1..4468·5%§(57·7–79·4)6..
2005–0917689·3% (83·8–94·8)17689·3% (83·8–94·8)....3474·9%§(63·7–86·1)5..

Data stratified by continent, country, and calendar period of diagnosis (1995–99, 2000–04, and 2005–09). Underlined estimates are not age-standardised. ICCC-3= International Classification of Childhood Cancer, 3rd edition.

Estimate judged as less reliable.

National coverage—the data are derived from a population-based cancer registry (registries) covering the entire country.

Estimate based on merging data for 2 (or all 3) calendar periods.

Age-standardised estimate computed by pooling two age-specific estimates and re-estimation.

Korea: Republic of Korea; Russia: Russian Federation.

Figure 2

Age-standardised 5-year net survival (%) for children diagnosed with acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) during 1995–2009

The number of countries for which survival estimates are shown in each box-plot is given in parentheses. Box-plots in light blue are for ALL (group Ia1 according to the third edition of the International Classification of Childhood Cancer [ICCC-3]), and dark blue for AML (ICCC-3 group Ib). The vertical line inside each box denotes the median survival value, and the box shows the IQR between the lower and upper quartiles. The extreme limits of the box-plot are 1·5 times the IQR below the lower quartile and above the upper quartile. Open circles indicate outlier values, outside this range Survival estimates for African countries are not shown because they were either not standardised or less reliable.

Figure 3

Trends in age-standardised 5-year net survival (%) for children diagnosed with acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML), during 1995–1999, 2000–2004, and 2005–2009

Countries have been grouped into ten geographical regions. Survival estimates for African countries are not shown because they were either not standardised or less reliable. ALL: group Ia1 according to the third edition of the International Classification of Childhood Cancer (ICCC-3). AML: ICCC-3 group Ib. ARG=Argentina. AUS=Australia. AUT=Austria. BEL=Belgium. BGR=Bulgaria. BLR=Belarus. BRA=Brazil. CAN=Canada. CHE=Switzerland. CHN=China. COL=Colombia. CYP=Cyprus. DEU=Germany. DNK=Denmark. ECU=Ecuador. ESP=Spain. EST=Estonia. FIN=Finland. FRA=France. GBR=United Kingdom. HRV=Croatia. IRL=Ireland. ISR=Israel. ITA=Italy. JPN=Japan. KOR=Republic of Korea. LTU=Lithuania. LVA=Latvia. MYS=Malaysia. NLD=Netherlands. NOR=Norway. NZL=New Zealand. POL=Poland. PRI=Puerto Rico. PRT=Portugal. SVK=Slovakia. SVN=Slovenia. SWE=Sweden. TWN=Taiwan. THA=Thailand. TUR=Turkey. USA=United States of America.

We estimated survival for mature B-cell leukaemia for 17 countries. Many of these estimates were based on a pooled analysis for children diagnosed throughout 1995–2009, because the number of patients diagnosed in each 5-year period was small. In France and the US registries, 5-year age-standardised net survival from mature B-cell leukaemia was lower than that of precursor-cell ALL in 1995–99, but the difference had disappeared by 2005–09. 5-year age-standardised net survival for AML was consistently lower than that for ALL (table 2, figure 2). Age-standardised survival for AML in 1995–99 ranged from 4·2% (0·0–8·6) in the Chinese registries to 72·2% (61·6–82·7) in Sweden, and from 33·3% (18·9–47·7) in Bulgaria to 78·2% (72·0–84·3) in German registries for 2005–09. In most countries, survival from childhood AML increased quite remarkably over time (figure 3). Age-standardised 5-year survival for unspecified and other specified leukaemia (group Ie) showed wide variation, ranging from 13·5% (Bulgaria) to 69·6% (Latvia) in 1995–99, and from 26·0% (Chinese registries) to 89·2% (Israel) in 2005–09. Survival from ALL in infants (aged <1 year) was much lower than for older children, including those aged 10–14 years (appendix pp 6–14). By contrast, survival for infants with AML in many countries was close to that for children aged 10–14 years (appendix pp 15–22). Children aged 1–4 and 5–9 years had the highest survival for both ALL and AML. The difference in survival between children with ALL aged 10–14 and those aged 1–4 and 5–9 years fell progressively between 1995–99 and 2005–09 in most countries. The pattern was not so clear for AML. Survival from AML and ALL was often slightly higher for girls than for boys, but this pattern was not consistent across all countries (appendix pp 6–22). Survival trends for precursor-cell ALL and AML were not markedly different between 1995–99 and 2000–04 (figure 4); however, between 2000–04 and 2005–09, survival from AML increased more than survival from ALL in most countries (figure 4B), particularly in Thailand and Switzerland.
Figure 4

Change (absolute difference, %) in age-standardised 5-year net survival for acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML), between (A) 1995–99 and 2000–04 and (B) between 2000–04 and 2005–09

Each datapoint represents one of the participating countries. Datapoints above the diagonal indicate that survival from AML increased more than survival from ALL between the two calendar periods. Countries are represented only if 5-year age-standardised estimates were available for ALL and for AML in successive calendar periods. ALL: group Ia1 according to the third edition of the International Classification of Childhood Cancer (ICCC-3). AML: ICCC-3 group Ib. AUS=Australia. AUT=Austria. BGR=Bulgaria. CAN=Canada. CHE=Switzerland. CHN=China. DEU=Germany. ESP=Spain. FRA=France. GBR=United Kingdom. ISR=Israel. ITA=Italy. JPN=Japan. KOR=Republic of Korea. NLD=Netherlands. NOR=Norway. NZL=New Zealand. PRT=Portugal. SWE=Sweden. THA=Thailand. TWN=Taiwan. USA=United States of America.

Discussion

This study provides the largest population-based comparison of survival from childhood leukaemia. It covers trends over 15 years between 1995–2009 in 53 countries, of which 17 were classified by the World Bank as low-income or middle-income countries (LMIC) in 2011. The results highlight the very wide international differences in 5-year net survival for children with acute lymphoid leukaemia and for children with myeloid leukaemia. 5-year net survival has been increasing for both precursor-cell ALL and AML, but it remains consistently higher for precursor-cell ALL than for AML; this worldwide pattern is consistent with previous studies in various regions of the world.7, 17, 18, 19, 20 For many LMICs, analyses and interpretation are limited by sparse data. Several survival estimates could not be age-standardised. However, our results show that, overall, survival has been increasing for ALL and for AML in most countries during 1995–2009, including both high-income and LMICs. 5-year survival for both ALL and AML was very high in Germany and Austria. This might be attributable to the very tight adherence of paediatric haematologists and oncologists in those countries to the protocols and trials of the BFM Group (Berlin, Frankfurt, Muenster), within the framework of a national paediatric cancer registry and reference laboratories, imaging review and tumour boards. In most countries, the difference in survival between ALL and AML tended to narrow over the period 1995–2009. This happened both in countries where survival from ALL was very high throughout 1995–2009, and in countries where survival from ALL was low in 1995–99, but increased up to 2005–09. The greater improvement in 5-year survival from AML than from ALL might be related to recent improvements in the care of childhood AML. Our results might reflect the effect of better diagnostic characterisation, risk stratification and subsequent adaptation of treatment, and restriction of indications to cranial radiotherapy and haemopoietic stem-cell transplantation. Net survival from AML seemed to approach the level of survival from precursor-cell ALL earlier in some countries than in others. This suggests that improvements in clinical practice have not been implemented in all countries at the same time. In Switzerland, there was a large increase in survival from AML between 1995–2004 and 2005–09, by which time it approached the level of survival from ALL. In Thailand, national protocols were introduced for childhood leukaemia in 2006. This may have contributed to the marked improvement in survival from AML, but did not lead to substantial improvements in survival for ALL. In China, despite the impressive increases in survival from AML and ALL throughout 1995–2009, there was no reduction in the difference in survival between ALL and AML. This large study offered a unique opportunity to examine age-standardised trends in population-based survival from some of the rarer childhood leukaemias, such as mature B-cell leukaemias, mostly Burkitt's leukaemia. In France and the USA, where survival from mature B-cell leukaemia could be age-standardised, the most recent estimates showed that survival from mature B-cell leukaemia was close to that of precursor-cell ALL. A degree of misclassification between precursor-cell ALL and Burkitt's leukaemia is likely because of their common historical classification and the remaining ambiguity in coding, but this is unlikely to explain the increasing survival trends for mature B-cell leukaemia in France and the USA. Leukaemia survival has been reported as higher in girls than in boys, both in Europe from 1970 up to the 1990s, and still recently in North America, but this is not a consistent feature worldwide. As expected, age at diagnosis was an important determinant of 5-year survival: children diagnosed with precursor-cell ALL aged 1–4 years consistently had the best prognosis. Survival from leukaemia in infants (under 1 year) was usually lower than at older ages. Survival was lower for infants with precursor-cell ALL than for infants with AML. This might be because unfavourable prognostic factors such as MLL gene rearrangements are more frequent in infants with ALL than AML. The new coding rules to individualise ALLs and AMLs with specific genetic rearrangements should enable analysis of more specific subgroups in the future. Interpretation might be restricted by changes in the clinical definition of leukaemias and lymphomas over time, and differences in coding between registries. The classifications for grouping the types of leukaemia and lymphoma have also changed over decades. ICCC-3 was proposed in 2005, and, in 2010, the HAEMACARE Working Group proposed a classification for haematological malignancies in both adults and children. Results shown here were based on ICCC-3, which is commonly used for childhood cancer surveillance.7, 17 Using the HAEMACARE classification, we noted similar results for precursor-cell ALL and AML, but some differences for Burkitt's leukaemia, probably due to the broader grouping defined by HAEMACARE (data not shown). We did not have data to show the effect on survival estimates of including endemic Burkitt's lymphoma in Africa in that group. Children are more likely to be diagnosed and registered with a poorly specified type of leukaemia in settings where access to a pathologist is less than optimal. This could have clinical implications, with children not gaining access to the most appropriate treatment for their particular leukaemia. Where the estimates for this poorly specified group of leukaemias could be age-standardised, they indicate that in high-income countries, survival was between that of precursor-cell ALL and AML. For the Chinese registries, where the proportion of unspecified and other specified leukaemias remained higher than 25% throughout 1995–2009, survival for those leukaemias was lower than that for AML. This suggests uncertain diagnosis and insufficient or inappropriate treatment. Implementation of the recent resource-stratified guidelines for the management of childhood ALL in Asia should lead to better diagnostic characterisation, more appropriate treatment and higher survival. In Colombia, the low survival estimate for lymphoid leukaemia (52%) suggests the need for further investigation, since childhood cancer became a national priority in 2010. Leukaemia is the most common malignancy in children in most countries, and we used population-based data, but comparisons of survival by age and type of leukaemia were sometimes limited by low numbers, especially in small populations. Some age-standardised survival estimates have wide CIs. This was particularly true for AML and mature B-cell leukaemia, and for all types of leukaemia in infants. Survival estimates for Tunisian and Algerian registries were less reliable than for other countries. We used the age range 0–14 years for our analysis because this range has been the standard in childhood cancer studies for many years. Adolescents and young adults are increasingly being treated under paediatric protocols, but our choice was agreed with 100 collaborators during a 2-day meeting of the CONCORD Working Group (Cork, Ireland, 2012), at which the protocol was finalised. Detailed analyses of survival for adolescents, young adults, and older adults with leukaemia are under preparation for publication elsewhere. Other studies of childhood cancer survival have not used net survival. The US SEER programme presents relative survival. The European programmes, EUROCARE and ACCIS, present observed survival, because background mortality in children does not vary greatly between European populations. The CONCORD-2 study has worldwide coverage, so we estimated net survival, because infant mortality varied particularly widely between participating countries. For example, infant mortality rates in males covered a 25-fold range in 2007, from 3·0 per 1000 livebirths in Finland to 82·5 per 1000 livebirths in Lesotho. Population-based cancer registry data include all or nearly all cases of a given malignancy in each registry's jurisdiction. By contrast with the best achievable survival estimates obtained from patients included in clinical trials, population-based survival estimates reflect the survival of all cancer patients in the population, irrespective of socioeconomic status and disease features. They reflect the overall effectiveness of the health system, from parents' perception of how to respond to symptoms suggestive of malignancy in their child, as well as the efficiency of referral, the quality of investigation and treatment, and the resourcing and organisation of the health service. In many high-income countries, the incidence of ALL is rising by an average of about 1% every year.7, 18 Whether this is due to a true increase, improved registration, or both, is still debated. It is unlikely that improved diagnosis of the less aggressive forms of leukaemia can explain the very widespread rises in survival that we report. In many countries, childhood cancer treatment is provided in specialised centres, which makes cancer registration for children easier than for adults, although active follow-up can be more challenging in children and adolescents. Population-based cancer registry data might still be restricted by underdiagnosis and under-registration of cancer patients, both of which are difficult to quantify. This might be a more important issue for participating registries in the 17 LMICs, but it is nevertheless important to capture the available information as a guide to policy. There is still room for improvement in the management of childhood leukaemia in many countries. In some LMICs, even the most basic treatment for leukaemia,28, 30 or pain relief, was still not consistently available until recently. Abandonment of treatment is also a major issue in some settings.32, 33 5-year survival for children with precursor-cell lymphoblastic leukaemia can be as high as 90%, and up to 80% for children with AML, but in some countries, survival remains below 60% for both types of leukaemia. Interventions that have been proven to improve outcomes in childhood malignancy include enrolment in clinical trials, international collaboration, and treatment guidelines.5, 34, 35 Wider implementation of these initiatives, together with mobilisation of additional resources, especially in poorer countries, would be likely to improve the delivery of effective treatments, and to reduce worldwide inequalities in survival.8, 9, 28, 36, 37
  27 in total

1.  Childhood cancer survival in Europe, 1978-1992: the EUROCARE study.

Authors:  J W Coebergh; R Capocaccia; G Gatta; C Magnani; C A Stiller
Journal:  Eur J Cancer       Date:  2001-04       Impact factor: 9.162

2.  Outcome of Childhood Acute Lymphoblastic Leukemia Treated Using the Thai National Protocols.

Authors:  Panya Seksarn; Surapon Wiangnon; Gavivann Veerakul; Thirachit Chotsampancharoen; Somjai Kanjanapongkul; Su-On Chainansamit
Journal:  Asian Pac J Cancer Prev       Date:  2015

3.  Childhood cancer survival in Europe 1999-2007: results of EUROCARE-5--a population-based study.

Authors:  Gemma Gatta; Laura Botta; Silvia Rossi; Tiiu Aareleid; Magdalena Bielska-Lasota; Jacqueline Clavel; Nadya Dimitrova; Zsuzsanna Jakab; Peter Kaatsch; Brigitte Lacour; Sandra Mallone; Rafael Marcos-Gragera; Pamela Minicozzi; Maria-José Sánchez-Pérez; Milena Sant; Mariano Santaquilani; Charles Stiller; Andrea Tavilla; Annalisa Trama; Otto Visser; Rafael Peris-Bonet
Journal:  Lancet Oncol       Date:  2013-12-05       Impact factor: 41.316

4.  Global surveillance of cancer survival 1995-2009: analysis of individual data for 25,676,887 patients from 279 population-based registries in 67 countries (CONCORD-2).

Authors:  Claudia Allemani; Hannah K Weir; Helena Carreira; Rhea Harewood; Devon Spika; Xiao-Si Wang; Finian Bannon; Jane V Ahn; Christopher J Johnson; Audrey Bonaventure; Rafael Marcos-Gragera; Charles Stiller; Gulnar Azevedo e Silva; Wan-Qing Chen; Olufemi J Ogunbiyi; Bernard Rachet; Matthew J Soeberg; Hui You; Tomohiro Matsuda; Magdalena Bielska-Lasota; Hans Storm; Thomas C Tucker; Michel P Coleman
Journal:  Lancet       Date:  2014-11-26       Impact factor: 79.321

5.  Population-based survival estimates for childhood cancer in Australia during the period 1997-2006.

Authors:  P D Baade; D R Youlden; P C Valery; T Hassall; L Ward; A C Green; J F Aitken
Journal:  Br J Cancer       Date:  2010-11-09       Impact factor: 7.640

Review 6.  Childhood Acute Lymphoblastic Leukemia: Progress Through Collaboration.

Authors:  Ching-Hon Pui; Jun J Yang; Stephen P Hunger; Rob Pieters; Martin Schrappe; Andrea Biondi; Ajay Vora; André Baruchel; Lewis B Silverman; Kjeld Schmiegelow; Gabriele Escherich; Keizo Horibe; Yves C M Benoit; Shai Izraeli; Allen Eng Juh Yeoh; Der-Cherng Liang; James R Downing; William E Evans; Mary V Relling; Charles G Mullighan
Journal:  J Clin Oncol       Date:  2015-08-24       Impact factor: 44.544

7.  Cancer survival in five continents: a worldwide population-based study (CONCORD).

Authors:  Michel P Coleman; Manuela Quaresma; Franco Berrino; Jean-Michel Lutz; Roberta De Angelis; Riccardo Capocaccia; Paolo Baili; Bernard Rachet; Gemma Gatta; Timo Hakulinen; Andrea Micheli; Milena Sant; Hannah K Weir; J Mark Elwood; Hideaki Tsukuma; Sergio Koifman; Gulnar Azevedo E Silva; Silvia Francisci; Mariano Santaquilani; Arduino Verdecchia; Hans H Storm; John L Young
Journal:  Lancet Oncol       Date:  2008-07-17       Impact factor: 41.316

Review 8.  Effective childhood cancer treatment: the impact of large scale clinical trials in Germany and Austria.

Authors:  C Rossig; H Juergens; M Schrappe; A Moericke; G Henze; A von Stackelberg; D Reinhardt; B Burkhardt; W Woessmann; M Zimmermann; H Gadner; G Mann; G Schellong; C Mauz-Koerholz; U Dirksen; S Bielack; F Berthold; N Graf; S Rutkowski; G Calaminus; P Kaatsch; U Creutzig
Journal:  Pediatr Blood Cancer       Date:  2013-06-05       Impact factor: 3.167

9.  Survival rates of childhood cancer patients in Osaka, Japan.

Authors:  Wakiko Ajiki; Hideaki Tsukuma; Akira Oshima
Journal:  Jpn J Clin Oncol       Date:  2004-01       Impact factor: 3.019

10.  Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

Authors:  Jacques Ferlay; Isabelle Soerjomataram; Rajesh Dikshit; Sultan Eser; Colin Mathers; Marise Rebelo; Donald Maxwell Parkin; David Forman; Freddie Bray
Journal:  Int J Cancer       Date:  2014-10-09       Impact factor: 7.396

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  49 in total

1.  Political priority and pathways to scale-up of childhood cancer care in five nations.

Authors:  Avram E Denburg; Adriana Ramirez; Suresh Pavuluri; Erin McCann; Shivani Shah; Tricia Alcasabas; Federico Antillon; Ramandeep Arora; Soad Fuentes-Alabi; Lorna Renner; Catherine Lam; Paola Friedrich; Brandon Maser; Lisa Force; Carlos Rodriguez Galindo; Rifat Atun
Journal:  PLoS One       Date:  2019-08-19       Impact factor: 3.240

2.  Implementation of a graduated-intensity approach for acute lymphoblastic leukemia in a Rwandan district hospital.

Authors:  Alexis Manirakiza; Grace Dugan; Christina Hanna; Cam Nguyen; Temidayo Fadelu; Esperance Iradukunda; Alexandra E Fehr; Deogratias Ruhangaza; Elizabeth Albert; Tharcisse Mpunga; Cyprien Shyirambere; Leslie Lehmann
Journal:  Blood Adv       Date:  2018-11-30

3.  Survival among children diagnosed with acute lymphoblastic leukemia in the United States, by race and age, 2001 to 2009: Findings from the CONCORD-2 study.

Authors:  Eric W Tai; Kevin C Ward; Audrey Bonaventure; David A Siegel; Michel P Coleman
Journal:  Cancer       Date:  2017-12-15       Impact factor: 6.860

4.  Genomics in childhood acute myeloid leukemia comes of age.

Authors:  Andrew M Brunner; Timothy A Graubert
Journal:  Nat Med       Date:  2018-01-09       Impact factor: 53.440

5.  Validation of the United Kingdom copy-number alteration classifier in 3239 children with B-cell precursor ALL.

Authors:  Lina Hamadeh; Amir Enshaei; Claire Schwab; Cristina N Alonso; Andishe Attarbaschi; Gisela Barbany; Monique L den Boer; Judith M Boer; Marcin Braun; Luciano Dalla Pozza; Sarah Elitzur; Mariana Emerenciano; Larisa Fechina; Maria Sara Felice; Eva Fronkova; Irén Haltrich; Mats M Heyman; Keizo Horibe; Toshihiko Imamura; Marta Jeison; Gábor Kovács; Roland P Kuiper; Wojciech Mlynarski; Karin Nebral; Ingegerd Ivanov Öfverholm; Agata Pastorczak; Rob Pieters; Henriett Piko; Maria S Pombo-de-Oliveira; Patricia Rubio; Sabine Strehl; Jan Stary; Rosemary Sutton; Jan Trka; Grigory Tsaur; Nicola Venn; Ajay Vora; Mio Yano; Christine J Harrison; Anthony V Moorman
Journal:  Blood Adv       Date:  2019-01-22

6.  Long-Term Risk of Hospitalization Among Five-Year Survivors of Childhood Leukemia in the Nordic Countries.

Authors:  Gitte Vrelits Sørensen; Jeanette Falck Winther; Sofie de Fine Licht; Klaus Kaa Andersen; Anna Sällfors Holmqvist; Laura Madanat-Harjuoja; Laufey Tryggvadottir; Andrea Bautz; Timothy L Lash; Henrik Hasle
Journal:  J Natl Cancer Inst       Date:  2019-09-01       Impact factor: 13.506

7.  ECLIM-SEHOP, a new platform to set up and develop international academic clinical trials for childhood cancer and blood disorders in Spain.

Authors:  F Bautista; A Cañete; G L Ramírez-Villar; J M Fernández; J L Fuster; C Diaz de Heredia; I Astigarraga; M García-Ariza; S Rives; J L Dapena; C Márquez; A Molinés; M Del M Bermúdez; S Gallego; M Del M Andrés; J Verdu-Amoros; C Hernández; M López; A Catalá; Á Lassaletta; O Cruz; M Ramírez; F Lendínez; A Carboné; J Gomez Sirvent; M Tallón; T Acha; L Moreno; A Fernández-Teijeiro
Journal:  Clin Transl Oncol       Date:  2019-10-09       Impact factor: 3.405

8.  Birthweight and all-cause mortality after childhood and adolescent leukemia: a cohort of children with leukemia from Denmark, Norway, Sweden, and Washington State.

Authors:  Anne Gulbech Ording; Lotte Brix Christensen; Tone Bjørge; David R Doody; Anders Ekbom; Ingrid Glimelius; Tom Grotmol; Gunnar Larfors; Beth A Mueller; Karin E Smedby; Steinar Tretli; Rebecca Troisi; Henrik Toft Sørensen
Journal:  Acta Oncol       Date:  2020-03-14       Impact factor: 4.089

9.  Childhood cancer incidence and survival in Thailand: A comprehensive population-based registry analysis, 1990-2011.

Authors:  Serena S Bidwell; Catherine C Peterson; Kathryn Demanelis; Katie R Zarins; Rafael Meza; Hutcha Sriplung; Surapon Wiangnon; Thirachit Chotsampancharoen; Imjai Chitapanarux; Donsuk Pongnikorn; Karnchana Daoprasert; Krittika Suwanrungruang; Wasan Chansaard; Laura S Rozek
Journal:  Pediatr Blood Cancer       Date:  2018-08-31       Impact factor: 3.167

10.  A triple-probe FISH screening strategy for risk-stratified therapy of acute lymphoblastic leukaemia in low-resource settings.

Authors:  Mayur Parihar; Manish K Singh; Rubina Islam; Debparna Saha; Deepak Kumar Mishra; Vaskar Saha; Shekhar Krishnan
Journal:  Pediatr Blood Cancer       Date:  2018-08-31       Impact factor: 3.167

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