Determinants of survival in children with cancer in

Johannesburg

Nadia Beringer1,3

Kate Gwynneth Bennett1,2,3

Janet Elizabeth Poole1,2,3

Jennifer Ann Geel1,2,3

1Division of Paediatric Haematology and Oncology, Charlotte Maxeke Johannesburg Academic Hospital2Division of Paediatric Haematology and Oncology, Wits Donald Gordon Medical Centre

3Department of Paediatric and Child Health, Faculty of Health Sciences, University of the Witwatersrand

Why Paediatrics?

Introduction

• Rare1

• 80% of cases low- and middle income countries (LMIC)2

• High income countries (HIC): Second commonest cause of

childhood mortality3

1. Childhood and Cancer: Children’s Health and Environment. Available at: www. who.int/ceh/capacity/cancer.pdf. Accessed January 25, 2017.

2. Stefan DC. Epidemiology of childhood cancer and the SACCSG tumour registry. CME. 2010 Jul; 28(7):317-319.

3. Kaatsch P. Epidemiology of childhood cancer. Cancer Treat Rev. 2010 Jun;36(4):277-28

Top 5 causes of death in Africa (WHO):3

4. World Health Organization. Global Health Observatory (GHO) data. Causes of child mortality. Available at:

https://www.who.int/gho/child_health/mortality/causes/en/. Accessed November 16, 2018.

Epidemiology

INCIDENCE (0-15 years):

• High-income countries (HIC): +- 140 per million3

• South Africa: 33.4 - 47.2 per million5

Marked discrepancy in reporting6

3. Kaatsch P. Epidemiology of childhood cancer. Cancer Treat Rev. 2010 Jun;36(4):277-285.

5. Stefan DC, Stones, DK. The South African Paediatric Tumour Registry – 25 years of activity. SAMJ. 2012;102(7):605-606. DOI:10.7196/SAMJ.5719

6. Howard SC, Metzger ML, Wilmas JA, Quintana Y, Pui CH, Robison LL, Ribeiro RC. Childhood cancer epidemiology in low-income countries. Cancer. 2008 Feb;

112(3):461-472

7. The Cancer Atlas. Children in low-income countries continue to have worse cancer-related outcomes than those in high-income countries Available at:

http://canceratlas.cancer.org/the-burden/cancer-in-children//. Accessed November 13, 2018

Overall Survival (OS)

• HIC - dramatic increase in OS over

past 40 years3,8

• Earlier detection, procedures,

multimodal treatment and better

supportive care

• Improved disease understanding (eg

minimal residual disease)

• Targeted therapies

• Meticulous OS documentation HIC:

• Monitoring of treatment advances

• Means to gauge improvement

3. Kaatsch P. Epidemiology of childhood cancer. Cancer Treat Rev. 2010 Jun;36(4):277-285.

8. Stefan DC, Kruger M, Poole J, Steliarova-Foucher E. Childhood cancer incidence in South Africa, 1987-2007. SAMJ. 2015 Nov;105(11):939-947.

9. Ferman S, de Oliveira Santos M, De Oliveria Ferreira JM, de Souza Reis R, Oliveira JFP, Pombo-de-Oliveira MS, et al. Childhood cancer mortality trends in Brazil,

1979-2008. Clinics 2013;68(2):219-224. DOI:10.6061/clinic/2013(02)OA16

Overall Survival (OS)

10. SlideShare. Late effects of childhood cancer treatment. Kerry Moss, MD Connecticut Children’s Medical Center Alex’s Lemonade Stand Foundation November

16, 2014. https://www.slideshare.net/alexslemonade/late-effects-of-childhood-cancer. Accessed: November 16, 2018.

Overall Survival (OS)

Favourable trends less pronounced in less-developed

regions9

Not routinely calculated in LMIC

• Therefore cannot be used as a monitoring tool3,5

• Under-registration multifactorial6

➢ Inaccurate death certificates

➢ Misdiagnosis

➢ Underreporting9

3. Kaatsch P. Epidemiology of childhood cancer. Cancer Treat Rev. 2010 Jun;36(4):277-285.

4. Stefan DC, Stones, DK. The South African Paediatric Tumour Registry – 25 years of activity. SAMJ. 2012;102(7):605-606. DOI:10.7196/SAMJ.571

6. Howard SC, Metzger ML, Wilmas JA, Quintana Y, Pui CH, Robison LL, Ribeiro RC. Childhood cancer epidemiology in low-income countries. Cancer. 2008

Feb; 112(3):461-472

9. Ferman S, de Oliveira Santos M, De Oliveria Ferreira JM, de Souza Reis R, Oliveira JFP, Pombo-de-Oliveira MS, et al. Childhood cancer mortality trends in Brazil,

1979-2008. Clinics 2013;68(2):219-224. DOI:10.6061/clinic/2013(02)OA16

What is a Kaplan-Meier

curve?

What is a Kaplan-Meier

curve?

• Graphical representation of survival data or time-to-event

analysis

• Proportion of patients surviving against time

• Usually drawn in a step function

11. O’Leary Maura, Krailo M, Anderson JR, Reaman GH. Progress in Childhood Cancer: 50 years of research collaboration, a report from the Children’s Oncology

Group. Semin Oncol. 2008 Oct;35(5):484-493.

Acute Lymphoblastic Leukaemia: Children’s Oncology Group (COG)

SEER DATASurveillance, Epidemiology, and End Results (SEER)

• Started in 1973

• Program of the National Cancer Institute (NCI)

• Source of information on the incidence and survival

rates of cancer in the United States

• Document trends, outcomes and improvements12

12. Duggan MA, Anderson WF, Altekruse S, Penberthy L Sherman E. The Surveillance, Epidemiology and End Results (SEER) Program and Pathology:

Towards Strengthening the Critical Relationship. Am J Surg Pathol. 2016 Dec; 40912):e94-e102. doi:10.1097/PAS.0000000000000749.

13. National cancer institute. Annual Report to the Nation 2017: Special section: Survival. Available at https://seer.cancer.gov/report_to_nation/survival.html. Accessed

November 14, 2018

The setting

South Africa

• UMIC5,9

• Childhood cancer mortality unknown8,14

• Fragmented two-tiered health care system

• Inconsistent access to specialised medical services (state

vs. private)

• Possible discrepancies

8. Stefan DC, Kruger M, Poole J, Steliarova-Foucher E. Childhood cancer incidence in South Africa, 1987-2007. SAMJ. 2015 Nov;105(11):939-947.

14. Statistics South Africa. 2015. Midyear estimates - 2015. Statistical release P0302. [Online] Available at:

http://www.statssa.gov.za/publications/P0302/P03022015.pdf. Accessed July15, 2017.

Wits Donald Gordon Medical Centre

(WDGMC)

Charlotte Maxeke Johannesburg

Academic Hospital (CMJAH)

Proximity

WDGMCRest of Africa

CMJAH

WDGMC vs. CMJAH

Isolation

Better equipment

Access to more supportive care

Occasional experimental

agents

Registrars always available

Casualty

MDT

Same

doctors

Same

treatment

protocols

Objectives

• Describe and compare patient populations at a state vs.

private hospital

• Analyse survival rates

• Determine prognostic factors

• Compare statistics with other countries (UIC)

Methods• Retrospective review

• Patient files retrieved (filing cabinets/archives)

• Patients 0-15 years diagnosed with a malignancy at CMJAH

and WDGMC

• 1 January 2012 - 31 December 2016

• Parameters: demographics, ethnicity and race, hospital at

presentation, diagnosis, stage at presentation, nutrition, HIV status,

patient outcome, follow-up time

Data Analysis• De-identified

• Descriptive analysis

• Mann Whitney U test

• Kaplan-Meier OS curves

• Cox regression

• Univariate and Multivariate analysis

• OS: 1,2 & 5 year OS probability

• Significance level: p < 0.05

Results

Study Patients

270

416

WDGMC CMJAH

Total number = 686

Study Patients

413

268

WDGMC CMJAH

Total number = 681

Ethnicity

0

125

250

375

500

Black White Indian Coloured

3838

119

486

Ethnicity

0

0,2

0,4

0,6

0,8

Black White Indian Coloured

6%6%

17%

71%

Demographics

54%46%

Sex

Male Female

626

4218

0

175

350

525

700

Negative Positive Unknown

HIV Status

SA HIV prevalence

15. South African National HIV prevalance, Incidence and Behaviour Survey, 2012. [Online] Available at:

www.hsrc.ac.za/uploads/pageContent/4565/SABSSM%20IV%20LEO%20final.pdf Accessed June15, 2018.

SA HIV incidence

15. South African National HIV prevalance, Incidence and Behaviour Survey, 2012. [Online] Available at:

www.hsrc.ac.za/uploads/pageContent/4565/SABSSM%20IV%20LEO%20final.pdf Accessed June15, 2018.

Nutrition

CMJAH

15%3%

7%

22%

54%

Normal Weight Underweight

Overweight Obese

Unknown

WDGMC

6%8%

12%

15%

59%

Normal Weight Underweight

Overweight Obese

Unknown

Classification of malignancies

• Classification based on morphology rather than site of

origin (adults)

• INTERNATIONAL CLASSIFICATION OF

CHILDHOOD CANCER, THIRD EDITION

• Standardize international, epidemiological studies and

cancer registries

16. Steliarova-Foucher E, Stiller C, Lacour B. Kaatsch P. 2005. International Classification of Childhood Cancer, Third Edition. American

Cancer Society 103(7):1457-1466 DOI:10.1002/cncr.20910

ICCC-3rd EditionDIAGNOSTIC GROUP NUMBERS

I Leukemias, myeloproliferative disease and myelodysplastic diseases 168

II Lymphomas and reticuloendothelial neoplasm 92

III CNS and miscellaneous intracranial and intraspinal neoplasm 130

IV Neuroblastoma and other peripheral nervous cell tumours 35

V Retinoblastoma 35

VI Renal tumors 66

VII Hepatic tumors 9

VIII Malignant bone tumors 29

IX Soft tissue and other extraosseous sarcoma 68

X Germ cell tumors, trophoblastic tumors, and neoplasms of gonads 30

XI Other malignant epithelial neoplasms and malignant melanomas 15

XII Other and unspecified malignant neoplasms 4

TOTAL 681

Stage at presentation

Neuroblastoma

I

II

III

IV

0 4 7 11 14

CMJAH WDGMC

Renal tumours

I

II

III

IV

V

0 5 9 14 18

CMJAH WDGMC

Stage at presentationSoft tissue and other extraosseous

sarcoma

I

II

III

IV

0 4 7 11 14

CMJAH WDGMC

Early stage: Stage I and II

WDGMC: 41% Early stage

CMJAH: 48% Early stage

DEATHS (early stage):

CMJAH: 10%

WDGMC: 0%

14.5% overall

Renal Tumours

0

10

20

30

40

50

60

70

80

90

100

OS - Renal tumours

0 1 2 3 4 5

Time (years)

Surv

ival pro

babili

ty (

%)

Stage

1

2

3

4

5

Neuroblastoma

0

10

20

30

40

50

60

70

80

90

100

OS - Neuroblastoma

0 1 2 3 4 5

Time (years)

Surv

ival pro

babili

ty (

%)

Stage

1

2

3

4

Overall Survival - Stage

0

10

20

30

40

50

60

70

80

90

100

Overall Survival - Stage

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

Stage

1

2

3

4

5

p < 0.0001

Late tumour stage at

presentation

???• Difficult to assess retrospectively

• MOSTLY delayed referrals

• Often late medical referrals (peripheral hospitals,

general practioners etc.)

• NOT commonly parents/caregivers

Overall Survival

Median patient follow-up: 2.1 years (IQR 0.7 to 4.55 years)

1 year OS

= 70%5 year OS

= 55%

2 year OS

= 62%

0

10

20

30

40

50

60

70

80

90

100Overall Survival - Hospital

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

CMJAH

WDGMC

CMJAH

5 year OS = 49%

WDGMC

5 year OS = 62%

p = 0.002

Comparing OS…

1 year OS

= 70%

5 year OS

= 55%

2 year OS

= 62%

Our study: SA Japan: UIC

15. Sugiyama H, Nishi N, Kuwabara M, Ninomiya M, Arita K, Yasui W, et al. Incidence and survival of childhood cancer cases diagnosed between 1998 and

2000 in Hiroshima City, Japan. APJCP, 2009 Oct; 10(4):675-680.

United Kingdom Statistics

17. Cancer Research UK. Children’s cancers survival statistics. Available at: https://www.cancerresearchuk.org/. Accessed November 10, 2018.

Why is our OS not higher?

• Is it demographics,

ethnicity, nationality, HIV

status, sex, nutrition,

hospital stage?????

Some possible factors…

0

10

20

30

40

50

60

70

80

90

100

Overall Survival - Nationality

0 1 2 3 4 5

Time (years)

Surv

ival pro

babili

ty (

%)

South African

Foreign

0

10

20

30

40

50

60

70

80

90

100Overall Survival - Ethnicity

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

Black

Coloured

White

Indian

0

10

20

30

40

50

60

70

80

90

100Overall Survival - HIV status

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

HIV negative

HIV positive

Unknown

Possible factors cont…

0

10

20

30

40

50

60

70

80

90

100Overall Survival - Nutrition

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

Healthy weight

Unhealthy weight

Unknown

0

10

20

30

40

50

60

70

80

90

100Overall Survival - Hospital

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

CMJAH

WDGMC

Cox Regression AnalysisPotential prognostic factor Univariate analysis

Ethnicity < 0.0001

Nationality 0.037

HIV status 0.32

Nutritional status < 0.0001

Stage at presentation < 0.0001

Hospital 0.002

Cox Regression AnalysisPotential prognostic factors Univariate analysis Multivariate analysis

Ethnicity < 0.0001 0.043

Nationality 0.037 0.130

HIV status 0.32 0.325

Nutritional status < 0.0001 0.009

Stage at presentation < 0.0001 <0.0001

Hospital 0.002 0.441

OS: Nutrition per hospital

0

10

20

30

40

50

60

70

80

90

100Overall Survival (CMJAH) - Nutrition

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

Normal weight

Underweight

Overweight

Obese

Unknown

P < 0.0001

0

10

20

30

40

50

60

70

80

90

100Overall Survival (WDGMC) - Nutrition

0 1 2 3 4 5

Time (years)

Su

rviv

al p

rob

ab

ility

(%

)

Normal weight

Underweight

Overweight

Obese

Unknown

P < 0.0001

Overall Survival: Tumour type

HL-Hodgkin Lymphoma, NHL-Non-Hodgkin Lymphoma, ALL-Acute Lymphoblastic Leukaemia, AML-Acute Myeloblastic Leukaemia

p = 0.0005

Acute Lymphoblastic

Leukaemia – COG (USA)

0

10

20

30

40

50

60

70

80

90

100

Overall Survival - ALL

0 1 2 3 4 5

Time (years)

Surv

ival pro

babili

ty (

%)

Overall Survival: Tumour type

p < 0.0001

Retinoblastoma – OS

(South Africa)

16. Kruger M, Reynders D, Omar F, Schoeman J, Wedi O, Harvey J. Retinoblastoma outcome at a single institution in South Africa. SAMJ. 2014 Dec;

104(12):859-863

0

10

20

30

40

50

60

70

80

90

100

Overall Survival - Retinoblastoma

0 1 2 3 4 5

Time (years)

Surv

ival pro

bability (

%)

Causes of Death

20%

51%

23%

3% 2% Treatment Related

Mortality

Disease Progression

Relapse

Other Unknown

Causes of Death

1%

3%

24%

52%

20%

Treatment

Related Mortality

Disease Progression

Relapse

Other Unknown

Causes of Death

1%

3%

24%

52%

20%

Treatment

Related Mortality

Disease Progression

Relapse

OtherUnknown

Treatment Related Mortality

• 20% of deaths

• Definition limitations and principal investigator bias

• Chemotherapy toxicities

• Neutropaenic sepsis

ONCOLOGICAL EMERGENCY

• Supportive care necessary: ICU, High care, Isolation

Disease progression

• 52% of deaths

• Late presentation, Late stage

• AREA to be targeted… improve overall survival

• Awareness programs

Limitations• Retrospective audit problems:

• Nutritional data incomplete – definitions were applied

retrospectively

• Tumour staging – some done retrospectively

• Causes of death – TRM definition?

• Race issue – how is it classified?

• Median follow-up time was short - need time for data to

mature

• Only two centres

Recommendations• National study

• Analyse nutrition more closely

• Anabolic vs. Catabolic state

• Evaluate causes of death more closely

• Tease out TRM

• Improve access to supportive care

• Ongoing awareness programmes to increase earlier

detection

Special Thanks

Susan Kriel

Patricia Gomomo

Portia Luaba

Kate Bennett

Jennifer Geel

All medical and nursing colleagues at the two hospitals

Thank you