Leukemia mortality in children from Latin America: Trends and predictions to 2030

doi:10.21203/rs.3.rs-64774/v1

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Leukemia mortality in children from Latin America: Trends and predictions to 2030

https://doi.org/10.21203/rs.3.rs-64774/v1

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published 07 Nov, 2020

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Background

Reports suggest that Latin American and Caribbean (LAC) countries have not achieved the downward mortality trends in leukemia seen in other countries. However, updated trends remain largely unknown in the region. Given that leukemia is the leading cause of cancer-related death in LAC children, we aimed to evaluate mortality trends in children (0-14y) from 15 LAC countries for the period 2000–2017 and to predict mortality until 2030.

Methods

We retrieved cancer mortality data through the World Health Organization Mortality Database. Age-standardized (world standard population) rates were computed for LAC countries. Joinpoint regression analysis was used to examine trends in the mortality rates of leukemia and provide an estimated annual percent change (EAPC). Nordpred was utilized for the calculation of predictions until 2030.

Results

Between 2013 and 2017, the highest mortality rates were reported in Venezuela, Ecuador, Nicaragua, Mexico, and Peru. Upward mortality trends were reported in Nicaragua (EAPC by 1.2% in boys, and EAPC by 2.0% in girls), Panama (EAPC by 1.8% in boys, and by 2.7% in girls), and Peru (EAPC by 1.4% in both sexes). Puerto Rico experienced large declines in mortality among both boys (APC by − 9.7%), and girls (EAPC by − 6.0%). Forecasting models predicted that leukemia mortality between 2015 and 2030, will increase in Argentina, Ecuador, Guatemala, Panama, Peru, and Uruguay.

Conclusion

Leukemia mortality is predicted to increase unless efforts are made to intervene. Interventions include addressing the inequities in health care diagnosing cases earlier,, avoiding treatment abandonment, and proper supportive care such as infection control programs will reduce the mortality in a great proportion.

Pediatrics

Child

Leukemia

Mortality rate

Trends

Epidemiology

Latin America

Leukemia is a heterogeneous group of hematologic malignancies. In 2018, GLOBOCAN reported nearly 437,000 new cases and 309,000 deaths worldwide (1). Both incidence and mortality are commonly higher in males than females, with an overall male to female ratio of 1.4 to 1.0 (2). GLOBOCAN estimates that the incidence rate among boys was 6.1 per 100,000 and the mortality rate was 4.2, while the incidence rate among girls was 4.3 and the mortality rate was 2.8 (3). Among children, most have acute lymphoblastic leukemia (80%), followed by acute myeloid leukemia (15%), and other types of leukemia (5%) (2, 4).

Over the last several years, many countries have experienced declining mortality due to childhood leukemia, mostly due to an improvement of treatment regimens and supportive care (5–7). For example, a study in the European children reported a decrease in age-standardized mortality rates (ASMRs) from 1.35 in 1997 to 0.85 in 2007 per 100,000 in boys (37% reduction) and from 1.07 in 1997 to 0.70 in 2007 per 100,000 in girls (35% reduction) (5).

However, leukemia remains the leading cause of cancer mortality in Latin American and the Caribbean (LAC) children and a major public health challenge (8, 9). For example, Mexico reported a rise of mortality rates (19.19% in boys and 20.98% in girls) between 1980 and 2014 (9). The barriers to healthcare access, social disparities, and the lack of economic resources in this region hamper the survival improvements seen in high-income countries (10, 11). Other factors related to increased mortality include delayed diagnosis, treatment abandonment, lack of a proper supportive care, lack o national pediatric cancer registries, and a shortage of pediatric oncologists as well as nurses dedicated to pediatric oncology care (12, 13).

To our knowledge, few studies have attempted to provide a comprehensive population-based analysis of trends in leukemia mortality in the LAC region (9). Given the high mortality burden among LAC countries, it is important to perform updated trend analysis to identify and evaluate the current status of leukemia mortality for the future development of evidence-based health policies. In this vein, the use of cancer predictions provides further insight toward optimal decision making in public health and the correct planning and allocation of resources for health improvement (14). Therefore, we aimed to identify leukemia mortality trends in children from LAC countries between 2000 and 2017 and to predict mortality until 2030.

2.1 Design and study setting

A study of mortality trends was conducted based on data from the World Health Organization Mortality Database (15). Countries that had data available for analysis between 2000 and 2017 included: Argentina, Brazil, Chile, Costa Rica, Cuba, Ecuador, Guatemala, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Uruguay, and Venezuela (2000–2014). Analysis included deaths due to leukemia (C91-C95) according to the International Statistical Classification of Diseases and Related Health Problems – 10^th revision (ICD10) (16). Population data by country, sex, and age were obtained from the Pan American Health Organization (17) similar to previous studies in Latin America (18, 19).

2.2 Study procedures

Age-standardized mortality rates (ASMRs) were calculated based on information on the adjusted number of deaths, using the direct method and the world standard SEGI population per 100,000 persons-years (20). Leukemia mortality trends were analyzed for the age group of 0 to 14 years (0-4, 5-9, and 10-14) between 2000 and 2017. We also provided an estimate of overall leukemia mortality rates in the LAC for the year 2030.

2.3 Statistical analysis

Mortality trend analysis was carried out by Joinpoint regression, utilizing the Joinpoint Regression Program, version 4.6.0.0 (National Cancer Institute, Bethesda, Maryland, USA). (21). The objective of the analysis was to identify the years where a significant change in the linear slope of the trend (on a log‐scale) was detected over the study period. The method identified joinpoints with a maximum of three change points. The final selected model estimated annual percentage change (EAPC) based on the trend of each segment, estimating whether these values were statistically significant at a 0.05 level. The significance levels utilized were based on the Monte Carlo permutation model and on the calculation of the annual percentage change of ratio, utilizing the logarithm of the ratio (22, 23).

Predictions were made for each period utilizing the age-period-cohort model from the Nordpred program (Cancer Registry of Norway, Oslo, Norway), using the R software program. Data were compiled in blocks of five years and the limit age group considered for analysis was the first with more than 10 cases for the combined period (24, 25). This model allows the comparison of birth cohorts, calculated by the decrease in the age range by calendar period \ calendar, which results in intervals for the years of birth of each 5-year cohort. The calculation can be represented by the following formula (25):

R_ap = exp (A_a + D · p + P_p + C_c),

In this formula, R_ap is the incidence ratio for the age group "a" in the "p" period; D is the common drift parameter (which is the linear average of increase in the observed period); A_a is the age-related component for group "a"; P_p represents the nonlinear component for the period "p"; and C_c corresponds to the nonlinear component of cohort "c".

The results of the predictions are presented for the total number of deaths observed and expected for each period by country (except for Venezuela due to information available only until 2014). For each period, adjusted mortality rates were calculated based on the world standard SEGI population for global comparisons, expressed per 100,000 persons-years. The predictions of the most recent linear trend for the last ten years was attenuated in the drift parameter of 25% in the second and 50% in third 5-year period prediction periods, and 75% from the fourth period (26). The objective of this mathematical operation is to reduce the influence of the current trend on predictions. The proposed model is based on empirical comparisons from different methods of predictions (26).

Annual changes were calculated for the number of predicted deaths in 2030 compared to the observed deaths in 2015, where the proportion of the change could occur in terms of changes in risks or demographics (size or structure of the population). These two components can be different from zero and can be either positive or negative in direction. The calculation can be expressed as (26)

Δtot = Δrisk + Δ pop= (Nfff – Noff) + (Noff – Nooo)

Where Δtot is the total change, Δrisk is the change in function of risk, Δpop is the change in function of the population, Nooo is the number of observed cases, Nfff is the number of projected cases, and Noff is the number of expected cases when the mortality rates increase during the observed period.

2.4 Ethical considerations

This manuscript is based on administrative databases and does not use any personal identifiable information.

Figure 1 shows the ASMRs per 100,000 person-years in children from 15 LAC countries between 2013 and 2017. Venezuela, Ecuador, Nicaragua, Mexico, and Peru reported the highest mortality rates in boys (above 2 deaths per 100,000 persons-years), while Nicaragua, Ecuador, and Mexico reported the highest mortality rates in girls (above 2 deaths per 100,000 persons-years). The lowest mortality rates were reported in Uruguay and Puerto Rico for both sexes (below 1 death per 100,000 persons-years).

Figure 2 illustrates the percent changes between the periods 2000–2005 and 2012–2017 in LAC countries. Most countries reported decreases, mainly in boys. The greatest decreases were in Venezuela (− 60% in boys and − 55% girls), Puerto Rico (− 69% in boys and 43% in girls), and Uruguay (− 63% in boys and − 22% in girls). Some differences by sexes were reported in Panama, Nicaragua, Peru, Costa Rica, Ecuador, and Guatemala. For instance, Panama declined from by 12.6% in boys, but increased by 34.9% in girls, Nicaragua declined by 7.8% in boys, but increased by 26.6% in girls, and Peru declined by 21.4% in boys, but increased 19.3% in girls (See Supplementary 1).

In boys, Nicaragua and Peru were the countries with significant upward trends, more pronounced in Nicaragua (EAPC = 2.9, 95% CI: 0.5, 5.3 versus EAPC = 1.4, 95% CI: 0.3, 2.5); while Puerto Rico (EAPC = − 14.8, 95% CI:−25.4, − 2.8) and Uruguay (EAPC = − 4.4, 95% CI: −7.3, − 1.3) experienced downward trends. In girls, Peru was the only country with an upward trend (EAPC = 1.4, 95% CI: 0.1, 2.8), while, among the three countries with downward trends, Puerto Rico (EAPC = − 8.8, 95% CI: −16.4, − 0.5) experienced the greatest reduction, followed by Uruguay (EAPC = − 7.4, 95% CI: −14.7, − 0.3) and Mexico (EAPC = − 0.795% CI: −1.3, − 0.1) (Fig. 3, Fig. 4, and Supplementary 2).

Table 1 shows the number of projected leukemia deaths in boys, age-standardized mortality rates and percentage change in deaths due to population and risk between 2015 and 2030. The forecast indicates that mortality rates will increase in Argentina, Brazil, Chile, Ecuador, Guatemala, Mexico, Peru, Puerto Rico, and Uruguay; and decline in Costa Rica, Cuba, Nicaragua, Panama, and Paraguay. The evaluation of change due to risk between 2015 and 2030, among boys, found a pronounced increase in Argentina (+ 21.8%), Uruguay (+ 29.8%), and Ecuador (+ 31.8%). The predicted reduction in rates in Puerto Rico would be predominantly due to population change, despite an increase in the risk of leukemia mortality (− 56.7%). Ecuador is forecast to have an increase in population and risk of death, resulting in an overall increase in leukemia mortality (+ 37.6%).

Table 1

Number of leukemia death in boys, age-standardized mortality rates and percentage change in cases due to population and risk, 2015 and 2030.
Countries	Population ( per million)		Number of deaths		Age-standardized mortality rates		Total change (%)	Change due to population (%)	Change due to risk (%)
Countries	2015	2030	2015	2030	2015	2030	Total change (%)	Change due to population (%)	Change due to risk (%)
Argentina	5.5	5.6	450	554	1.61	1.91	23.0	1.2	21.8
Brazil	24.4	20.9	1813	1645	1.48	1.57	−9.3	−14.3	5.0
Chile	1.8	1.6	153	146	1.64	1.69	−4.6	−9.0	4.4
Costa Rica	0.5	0.5	55	43	1.97	1.70	−21.1	−8.0	−13.1
Cuba	1.0	0.8	87	65	1.91	1.70	−25.9	−15.3	−10.6
Ecuador	2.3	2.5	326	449	2.68	3.44	37.6	5.8	31.8
Guatemala	3.0	3.1	288	313	1.87	1.93	8.8	5.2	3.5
Mexico	17.8	16.2	2164	2031	2.38	2.45	−6.2	−9.0	2.8
Nicaragua	0.8	0.9	114	115	2.42	2.32	0.5	2.3	−1.8
Panama	0.5	0.6	53	56	1.96	1.80	5.1	13.4	−8.3
Paraguay	1.0	1.1	82	74	1.52	1.40	−9.4	0.9	−10.3
Peru	4.4	4.2	465	490	2.07	2.27	5.4	−4.9	10.3
Puerto Rico	0.4	0.2	6	3	0.32	0.34	−56.7	−58.7	2.1
Uruguay	0.4	0.4	13	16	0.71	0.93	23.1	−6.7	29.8

Among girls, mortality rates will potentially increase in most LAC countries. In addition, there will be an increase in the risk of leukemia mortality in Argentina, Chile, Cuba, Ecuador, Nicaragua, Peru, Puerto Rico, and Uruguay which was the basis for the predicted increase among all the countries (except for a decline in Chile, − 4.9%). For Cuba, and Puerto Rico, there will a reduction in moartality resulting from changes in population structure and size, while in Brazil, Costa Rica, and Mexico there will be a reduction in mortality due to both population changes and a decrease in the risk of leukemia mortality (Table 2).

Table 2

Number of leukemia deaths in girls, age-standardised mortality rates and percentage change in cases due to population and risk, 2015 and 2030.
Countries	Population (per million)		Number of new deaths		Age-standardised mortality rates		Total change (%)	Change due to population (%)	Change due to risk (%)
Countries	2015	2030	2015	2030	2015	2030	Total change (%)	Change due to population (%)	Change due to risk (%)
Argentina	5.3	5.4	369	419	1.37	1.51	13.5	1.4	12.1
Brazil	23.4	19.9	1370	1109	1.18	1.14	−19.0	−15.0	−4.0
Chile	1.8	1.6	103	98	1.16	1.22	−4.9	−9.4	4.6
Costa Rica	0.5	0.4	41	32	1.51	1.29	−22.2	−7.6	−14.5
Cuba	0.9	0.8	55	49	1.23	1.28	−11.3	−14.5	3.3
Ecuador	2.3	2.4	241	295	2.09	2.40	22.3	5.2	17.1
Guatemala	2.9	3.1	211	206	1.43	1.35	−2.6	4.3	−6.9
Mexico	17.2	15.5	1784	1545	2.05	1.99	−13.4	−9.5	−3.8
Nicaragua	0.8	0.9	96	141	2.14	2.91	47.1	3.1	43.9
Panama	0.5	0.6	52	51	1.94	1.71	−2.5	10.7	−13.2
Paraguay	0.9	1.0	62	51	1.29	1.05	−17.3	6.6	−23.8
Peru	4.3	4.1	378	507	1.74	2.40	34.1	−4.8	38.9
Puerto Rico	0.3	0.1	5	3	0.34	0.42	−40.0	−53.1	13.1
Uruguay	0.4	0.3	13	14	0.73	0.83	6.2	−7.0	13.2

This study provides a comprehensive population-based analysis of leukemia mortality trends among children in 15 LAC countries. We found that Peru and Nicaragua had significant upward trends in the period 2000–2017, while most of the remaining countries had unremarkable variation in mortality trends. Moreover, the analysis identified a wide range of mortality rates among LAC countries in the last period of observation (2013–2017), from 0.32 to 2.88 in boys and from 0.33 to 2.14 in girls.

Puerto Rico and Uruguay were the only LAC countries that had significant downward trends among both sexes, along with the lowest mortality rates in the region. These outcomes are in line with worldwide trends. A study of southern and eastern European countries found a significant decrease in mortality rates of childhood leukemia (27). Similarly, Bertuccio et al. found decreasing mortality rates in a study that compared population-based data from the United States, Japan, and Western and Central European countries (28), which could be explained by the improvement of healthcare delivery and the development and implementation of novel treatment regimens (5, 29, 30).

However, for the rest of the LAC countries, mortality rates of childhood leukemia remain higher than those in high-income countries and did not display downward trends (10, 31). Our study shows mortality rates up to 2.88 in boys and up to 2.14 in girls, while a study in European countries, reported rates up to 1.63 in boys and up to 1.35 in girls (28). Another study in the United States estimated a mortality rate of 0.71 for both sexes during the period 2007–2010 (6). Few countries in our analysis have mortality rates as low as those of the United States and European nations for both sexes.. Between 2013 and 2017, Venezuela, Ecuador, Nicaragua, reported the highest mortality rates for boys and Nicaragua, Ecuador, and Mexico for girls. Previous studies also reported higher mortality rates in Mexico, Ecuador, and Venezuela than other LAC countries (8, 9). The consistency of our results highlights the need to identify risk factors for mortality in further research in these countries.

Important considerations remain for countries with rising mortality trends. Possible explanations for upward trends are socioeconomic inequalities, low access to high-quality healthcare delivery, delayed diagnosis, and limited access to novel treatment (31–34). Studies have shown a relationship between leukemia and factors related to economic status, mainly in low-income African countries (31, 35) In comparison, leukemia and economic status do not appear to be significantly related in high-income countries such as Switzerland or the US (33, 36). Venezuela is undergoing special socioeconomic and political instability (37), which may affect the current and future outcomes of childhood leukemia. Other reports have found that abandonment of treatment is a major barrier to successful treatment and remission of the disease in low- and middle-income countries in Central America (13, 38, 39). Moreover, economic disparities in certain communities within countries likely affect access to health services (34, 40). These outcomes and inequities are also plausible explanations for the unremarkable trends seen in our analysis. Since 2018, the World Health Organization chose Peru as the Index Country to accomplish a 60% increase in survival of Pediatric Cancer in 2030 (41), the Ministry of Health and stakeholders are committed to working toward that goal. .

The comparison between changes resulting from the increase in leukemia mortality and changes resulting from demographic population structure are notable. Ecuador and Argentina showed a higher risk of death for boys, and Nicaragua and Peru for girls. Possible problems related to the increased risk of leukemia mortality may be related to social inequalities and access to health systems as mentioned above (31–34), In addition, countries such as Puerto Rico had a decline in the mortality due to changes in population size and structure, even with an increased risk of leukemia mortality. The changes associated with the population structure may be related to factors that differ by country, such as a reduction in the birthrate. Studies that assess trends over time allow the creation and improvement of public policies aimed at a better structuring of the health system, based on predictions for the future.. Moreover, they allow health planning, especially for the most vulnerable groups (42, 43).

A possible limitation of this study is the variability of the data records of each country and the lack of availability of data for some countries such as Honduras, Belize, and Bolivia. Secondly, we could not analyze incidence data given the lack of population-based cancer registries in most countries.. Nevertheless, our study gives the most recent comprehensive epidemiological analysis of leukemia mortality patterns of LAC children and provides a forecast of trends to 2030, the target year by which WHO aims to improve childhood cancer survival to least 60%. The results of this study should prompt further research in areas such as the relationship between socioeconomic status mortality and incidence of leukemia.

Overall, we found stable mortality trends in most LAC countries over 18 years (2000–2017), which may be related to a lack of access to quality healthcare treatment. Hence, the assessment of health care inequities and strengthening of country-wide diagnosis programs to identify cases in the earliest stage should improve mortality outcomes in these countries.

ASMRs: age-standardized mortality rates

LAC: Latin American and the Caribbean

ICD10: Classification of Diseases and Related Health Problems – 10th revision

Ethics approval and consent to participate

Ethical approval and consent of the participant were not necessary since this study involved the use of a previously published secondary database.

Consent for publication

Not applicable

Availability of data and materials

The datasets generated and/or analysed during the current study are available in

the following link: https://www.who.int/healthinfo/statistics/mortality_rawdata/en/

Conflict of Interest Statement

The author(s) declare that they have no competing interests.

Funding

Self-funded.

Authors' contributions

Conceived and designed the idea: JSTR, BV, PGC

Had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis: JSTR, PGC, CAV,DLBS

Contributed to the writing of the manuscript: All authors.

Contributed to the statistical analysis: JSTR, BV, PGC, CAV, DLBS

Critical revision of the manuscript: KAM, CL.

Approval of the submitted and final version: All authors.

Acknowledgements

We would like to thank Dra. Rosdali Diaz and Dra. Liliana Vasquez for his willingness to discuss and review the study.

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018.
Miranda-Filho A, Piñeros M, Ferlay J, Soerjomataram I, Monnereau A, Bray F. Epidemiological patterns of leukaemia in 184 countries: a population-based study. Lancet Haematol. 2018;5(1):e14–24.
International Agency for Research on Cancer. World Cancer Report: Cancer research for cancer prevention Lyon: WHO; 2020.
Bhatnagar N, Qureshi A, Hall G. Leukaemias: a review. Paediatr Child Health. 2017;27(11):489–94.
Bertuccio P, Bosetti C, Malvezzi M, Levi F, Chatenoud L, Negri E, et al. Trends in mortality from leukemia in Europe: an update to 2009 and a projection to 2012. Int J Cancer. 2013;132(2):427–36.
Smith MA, Altekruse SF, Adamson PC, Reaman GH, Seibel NL. Declining childhood and adolescent cancer mortality. Cancer. 2014;120(16):2497–506.
Petridou ET, Dimitrova N, Eser S, Kachanov D, Karakilinc H, Varfolomeeva S, et al. Childhood leukemia and lymphoma: time trends and factors affecting survival in five Southern and Eastern European Cancer Registries. Cancer Causes Control. 2013;24(6):1111–8.
Chatenoud L, Bertuccio P, Bosetti C, Levi F, Negri E, La Vecchia C. Childhood cancer mortality in America, Asia, and Oceania, 1970 through 2007. Cancer. 2010;116(21):5063–74.
Curado MP, Pontes T, Guerra-Yi ME, Cancela MdC. Leukemia mortality trends among children, adolescents, and young adults in Latin America. Rev Panam Salud Publica. 2011;29:96–102.
Kehm RD, Spector LG, Poynter JN, Vock DM, Altekruse SF, Osypuk TL. Does socioeconomic status account for racial and ethnic disparities in childhood cancer survival? Cancer. 2018;124(20):4090–7.
Silva-Junior AL, Alves FS, Kerr MWA, Xabregas LA, Gama FM, Rodrigues MGA, et al. Acute lymphoid and myeloid leukemia in a Brazilian Amazon population: Epidemiology and predictors of comorbidity and deaths. PloS one. 2019;14(8):e0221518.
Vasquez L, Oscanoa M, Tello M, Tapia E, Maza I, Geronimo J. Factors associated with the latency to diagnosis of childhood cancer in Peru. Pediatric Blood Cancer. 2016;63(11):1959–65.
Vasquez L, Diaz R, Chavez S, Tarrillo F, Maza I, Hernandez E, et al. Factors associated with abandonment of therapy by children diagnosed with solid tumors in Peru. Pediatric blood cancer. 2018;65(6):e27007.
Malvezzi M, Carioli G, Bertuccio P, Rosso T, Boffetta P, Levi F, et al. European cancer mortality predictions for the year 2016 with focus on leukaemias. Ann Oncol. 2016;27(4):725–31.
Team WER. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. N Engl J Med. 2014;2014(371):1481–95.
World Health Organization. International Classification of Disease and Related Health Problems: 10th Revision. Geneva: World Health Organization; 1992.
Pan American Health Organization. Population National Level. [ Cited March 20, 2020]. Available in: https://www.paho.org/data/index.php/en/indicators/demographics-core/308-poblacion-nac-en.html.
Carioli G, La Vecchia C, Bertuccio P, Rodriguez T, Levi F, Boffetta P, et al. Cancer mortality predictions for 2017 in Latin America. Ann Oncol. 2017;28(9):2286–97.
Carioli G, Bertuccio P, Malvezzi M, Rodriguez T, Levi F, Boffetta P, et al. Cancer mortality predictions for 2019 in Latin America. Int J Cancer. 2020;147(3):619–32.
World Health Organization. Age Standardization of Rates: A new Who Standard. 2011. Available from: http://www.who.int/healthinfo/paper31.pdf?ua=1.
National Cancer Institute. Joinpoint regression program. [Accesed 4 April, 2020].Available in: https://surveillance.cancer.gov/help/joinpoint.
Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19(3):335–51.
Kim HJ, Fay MP, Yu B, Barrett MJ, Feuer EJ. Comparability of segmented line regression models. Biometrics. 2004;60(4):1005–14.
Barbosa IR, de Souza DL, Bernal MM, do Costa CC Í. Cancer mortality in Brazil: temporal trends and predictions for the year 2030. Medicine. 2015;94(16).
Moller B, Fekjær H, Hakulinen T, Tryggvadóttir L, Storm HH, Talback M, et al. Prediction of cancer incidence in the Nordic countries up to the year 2020. Eur J Cancer. 2002;11.
Møller B, Fekjær H, Hakulinen T, Sigvaldason H, Storm HH, Talbäck M, et al. Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches. Stat Med. 2003;22(17):2751–66.
Petridou ET, Dimitrova N, Eser S, Kachanov D, Karakilinc H, Varfolomeeva S, et al. Childhood leukemia and lymphoma: time trends and factors affecting survival in five Southern and Eastern European Cancer Registries. Cancer causes control: CCC. 2013;24(6):1111–8.
Bertuccio P, Bosetti C, Malvezzi M, Levi F, Chatenoud L, Negri E, et al. Trends in mortality from leukemia in Europe: an update to 2009 and a projection to 2012. International journal of cancer. 2013;132(2):427–36.
Jakab Z, Juhasz A, Nagy C, Schuler D, Garami M, Hungarian Paediatric Haemato-Oncology N. Trends and territorial inequalities of incidence and survival of childhood leukaemia and their relations to socioeconomic status in Hungary, 1971–2015. European journal of cancer prevention: the official journal of the European Cancer Prevention Organisation. 2017;26 Joining forces for better cancer registration in Europe:S183-S90.
Incidence and survival time trends for Spanish children and adolescents with leukaemia from 1983 to 2007

Marcos-Gragera R, Galceran J, Martos C, de Munain AL, Vicente-Raneda M, Navarro C, et al. Incidence and survival time trends for Spanish children and adolescents with leukaemia from 1983 to 2007. Clinical translational oncology: official publication of the Federation of Spanish Oncology Societies of the National Cancer Institute of Mexico. 2017;19(3):301–16.
Force LM, Abdollahpour I, Advani SM, Agius D, Ahmadian E, Alahdab F, et al. The global burden of childhood and adolescent cancer in 2017: an analysis of the Global Burden of Disease Study 2017. Lancet Oncol. 2019;20(9):1211–25.
Escamilla-Santiago RA, Narro-Robles J, Fajardo-Gutiérrez A, Rascón-Pacheco RA, López-Cervantes M. Tendencia de la mortalidad por cáncer en niños y adolescentes según grado de marginación en México (1990–2009). Salud publica de Mexico. 2012;54(6):587–94.
Poole C, Greenland S, Luetters C, Kelsey JL, Mezei G. Socioeconomic status and childhood leukaemia: a review. Int J Epidemiol. 2006;35(2):370–84.
Magrath I, Steliarova-Foucher E, Epelman S, Ribeiro RC, Harif M, Li C-K, et al. Paediatric cancer in low-income and middle-income countries. Lancet Oncol. 2013;14(3):e104-e16.
Hashemizadeh H, Boroumand H, Noori R, Darabian M. Socioeconomic status and other characteristics in childhood leukemia. Iran J Ped Hematol Oncol. 2013;3(1):182.
Adam M, Kuehni CE, Spoerri A, Schmidlin K, Gumy-Pause F, Brazzola P, et al. Socioeconomic status and childhood leukemia incidence in Switzerland. Front Oncol. 2015;5:139.
Page KR, Doocy S, Ganteaume FR, Castro JS, Spiegel P, Beyrer C. Venezuela's public health crisis: a regional emergency. Lancet. 2019;393(10177):1254–60.
Navarrete M, Rossi E, Brivio E, Carrillo JM, Bonilla M, Vasquez R, et al. Treatment of childhood acute lymphoblastic leukemia in central America: a lower-middle income countries experience. Pediatric blood cancer. 2014;61(5):803–9.
Seah T, Zhang C, Halbert J, Prabha S, Gupta S. The magnitude and predictors of therapy abandonment in pediatric central nervous system tumors in low-and middle‐income countries: Systematic review and meta‐analysis. Pediatr Blood Cancer. 2019;66(6):e27692.
Dávila-Cervantes CA, Agudelo-Botero M. Health inequalities in Latin America: persistent gaps in life expectancy. The Lancet Planetary Health. 2019;3(12):e492-e3.
World Health Organization

WHO Global Initiative for Childhood Cancer: progress on all fronts. [Cited 15 June, 2020]

World Health Organization. WHO Global Initiative for Childhood Cancer: progress on all fronts. [Cited 15 June, 2020]. Available in: https://www.who.int/news-room/detail/14-02-2020-who-global-initiative-for-childhood-cancer-progress-on-all-fronts.
Marinho F, de Azeredo Passos VM, Malta DC, França EB, Abreu DM, Araújo VE, et al. Burden of disease in Brazil, 1990–2016: a systematic subnational analysis for the Global Burden of Disease Study 2016. Lancet. 2018;392(10149):760–75.
Souza MdFMd, Malta DC, França EB, Barreto ML. Changes in health and disease in Brazil and its States in the 30 years since the Unified Healthcare System (SUS) was created. Cienc Saude Colet. 2018;23:1737–50.

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Leukemia mortality in children from Latin America: Trends and predictions to 2030

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