About the Author(s)


Leriska Haupt Email symbol
Department of Haematology and Cell Biology, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Department of Haematology and Cell Biology, National Health Laboratory Service, Bloemfontein, South Africa

Wilhelm Burger symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Nosipho Dimba symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Salomina Joubert symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Karina Kemp symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Relebohile Makhalima symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Jean Mudima symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Kayla Swanepoel symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Janco Viljoen symbol
School of Clinical Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Anne-Cecilia van Marle symbol
Department of Haematology and Cell Biology, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa

Citation


Haupt L, Burger W, Dimba N, et al. The relationship between seasonality and the diagnosis of acute leukaemia in central South Africa. S. Afr. j. oncol. 2024; 8(0), a307. https://doi.org/10.4102/sajo.v8i0.307

Original Research

The relationship between seasonality and the diagnosis of acute leukaemia in central South Africa

Leriska Haupt, Wilhelm Burger, Nosipho Dimba, Salomina Joubert, Karina Kemp, Relebohile Makhalima, Jean Mudima, Kayla Swanepoel, Janco Viljoen, Anne-Cecilia van Marle

Received: 24 May 2024; Accepted: 27 Aug. 2024; Published: 08 Nov. 2024

Copyright: © 2024. The Author(s). Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: Leukaemias are haematological malignancies resulting from the abnormal clonal proliferation of haematopoietic precursors. Their incidence is influenced by various environmental and genetic factors.

Aim: With this retrospective descriptive study, the authors aimed to explore the possible influence of seasonality on the type and number of acute leukaemia (AL) diagnoses made at the Universitas Academic Hospital (UAH) National Health Laboratory Service (NHLS) from 01 January 2018 to 31 December 2021.

Setting: Universitas Academic Hospital, Bloemfontein, South Africa.

Methods: Archived laboratory reports of all patients diagnosed with lymphoid and myeloid during the study period were included. Patients’ age, sex, ethnicity, final diagnosis and date of diagnosis were recorded. Information was pseudonymised to maintain confidentiality. A descriptive statistical analysis was performed to explore the possible influence of seasonality on the number of cases and type of leukaemia diagnosed.

Results: In all, 249 patients were included. Acute myeloid leukaemia (AML) was the most common AL subtype (n = 117; 47.0%). Over the 4-year study period, all AL subtypes were more frequently diagnosed in summer (n = 131; 52.6%). However, the monthly number of AL diagnoses was relatively consistent over the 4 years for all subtypes (p = 0.7603), with consistent peaks of AML cases during January and February (summer) and May (autumn).

Conclusion: No statistically significant association between the different seasons and AL diagnosis was noted.

Contribution: Further studies using a larger study population and a wider geographical area, conducted over a more extended period, might affect the observations made in this study.

Keywords: acute leukaemia; pathogenesis; oncogenesis; seasonality; epidemiology; leukaemogenesis; geography; flow cytometry.

Introduction

Acute leukaemias (ALs) are a group of potentially fatal haematological disorders presenting with the abnormal clonal proliferation of myeloid or lymphoid haematopoietic precursors in the peripheral blood, bone marrow and extramedullary sites.1 The pathogenesis of AL involves a complex series of genetic events that alter normal cell growth and differentiation.2,3,4 These poorly differentiated, immature leukaemic cells accumulate in the bone marrow, subsequently displacing the normal haematopoietic precursors and resulting in the failure of normal bone marrow function.1,2,3

The presence of more than 20% blast cells in either the peripheral blood or bone marrow usually confirms a diagnosis of AL.5 In 2017, acute lymphoblastic leukaemia (ALL) accounted for 12.4% and acute myeloid leukaemia (AML) accounted for 23.1% of all leukaemia cases worldwide.1

The incidence of AL differs demographically, varies according to the AL subtype and is influenced by various environmental and genetic factors. Acute myeloid leukaemia is the most common type of AL among adults, and the prevalence increases with age.1,2,5 Conversely, only 20% of ALL cases occur in adults, whereas 85% of all paediatric AL cases are ALL.6,7

Macro- and micro-environmental risk factors of leukaemogenesis include socioeconomic and lifestyle influences, occupational exposure to carcinogens such as agricultural pesticides and benzene, pollution, infections and impaired regulation of the immune system.8,9,10 Global cancer statistics reported the incidence of leukaemia to be two to three times higher in transitioned countries than transitioning countries.11

Numerous studies have investigated the aetiological role of seasonal variation on AL.8,12,13,14 Eshan et al. reported an increase in AML diagnoses when they experienced a long, dry and hot summer followed by a hot and wet monsoon season in Pakistan.15 This seasonal pattern also correlated with an increase in various viral diseases, suggesting a viral link between seasonality and AL pathogenesis.12 Viruses have long been associated with the development of haematological malignancies, such as Epstein-Barr virus (EBV) in Burkitt lymphoma or human T-lymphotropic virus 1 (HTLV-1) in adult T-cell leukaemia and/or lymphoma.3,16 Chronic antigen stimulation followed by immune dysregulation may explain the causal relationship between infection and development of haematological malignancies.17 In addition to increased infections, high rainfall seasons are also associated with increased drainage of pollutants into water sources, which have also been postulated to contribute indirectly to disease seasonality.8,15

Seasonal variation also affects the immune system.14 Significant seasonal patterns can be demonstrated for lymphocyte and neutrophil counts and inflammatory markers such as C-reactive protein (CRP), independent of confounding lifestyle and environmental factors.18 Acute leukaemia may, therefore, develop when vulnerable populations are exposed to pathogens during periods of seasonal and immunological changes.

According to an Iraqi study, seasonal differences in diet, mobility and health-seeking behaviour may explain the higher incidence of AL during the winter when people are more likely to visit healthcare centres for common colds and flu.8 Conversely, a Swedish study found a decrease in the rate of cancer diagnoses during the summer vacation when patients tend to delay their visits to healthcare facilities.19

The Universitas Academic Hospital (UAH) National Health Laboratory Service (NHLS) serves as the referral laboratory for the Free State, North West and Northern Cape provinces in South Africa. The UAH NHLS flow cytometry bench has anecdotally noted clusters of ALs diagnosed by immunophenotyping during specific seasons of the year. Subjective impression identified seasonal surges, particularly in acute promyelocytic leukaemias and acute T-cell lymphoblastic leukaemias.

The aim of this study was to retrospectively explore the possible influence of seasonality on the type and number of AL cases diagnosed at UAH NHLS between 01 January 2018 and 31 December 2021.

Methods

This retrospective descriptive study included all newly diagnosed AL cases (T-cell and B-cell acute lymphoblastic leukaemia and AML) for which flow cytometric immunophenotyping was performed by the flow cytometry bench at UAH NHLS from 01 January 2018 to 31 December 2021.

Consolidated flow reports, generated for all patients on whom immunophenotyping had been performed, were used to collect the data. Study numbers were assigned in ascending order of collection, thereby pseudonymising study participants, and these study numbers were used to collect and analyse data. Patients’ age, sex, place of residence, final diagnosis made by flow cytometric immunophenotyping and date of diagnosis were collected.

The seasons were defined according to a South African study that used the minimum and maximum temperature data from 35 selected South African Weather Service meteorological stations over a 35-year period (1980–2015). Summer stretches from 01 October to 31 March, autumn from 01 April to 31 May, winter from 01 June to 31 August and spring includes only the month of September.20

Data collection and analysis

Data were captured in a Microsoft Excel spreadsheet (version 2016; Microsoft Corporation; Redmond, WA, United States). Descriptive statistical analysis was done by the Department of Biostatistics at the University of the Free State. Results were summarised by frequencies and percentages (categorical variables) and mean and standard deviations (numerical variables). The relationship between the frequency of AL subtypes for both the seasons and the months of presentation was calculated by Chi square and Fischer’s exact tests. A p-value of less than 0.05 was considered statistically significant.

Ethical considerations

Ethical approval to conduct the research was obtained from the Health Sciences Research Ethics Committee (HSREC) of the University of the Free State (ref. no. UFS-HSD2022/0376), the Free State province Department of Health and the NHLS. Because of the retrospective nature of the study and using only archived patient records for data collection, informed consent was not applicable to this research.

Results

The study included 249 patients’ records, of which 144 (57.8%) were male patients. The median age was 23 years, ranging from 6 months to 89 years.

The majority of patients were from the Free State province (n = 183; 73.5%), followed by the Northern Cape (n = 51; 20.5%) and North West provinces (n = 14; 5.6%). One patient came from the Western Cape province.

Acute myeloid leukaemia was the most common type of AL, diagnosed in 117 (47.0%) patients, followed by B-cell acute lymphoid leukaemia (B-ALL) (n = 65; 26.1%). Acute promyelocytic leukaemia (APL), a subtype of AML, comprised less than 10% of the AL cases. One patient had a mixed phenotype AL (B/myeloid, not otherwise specified) and was included in the AML category.

Overall, all cases of AL over the 4 years were most frequently diagnosed in summer (n = 131; 52.6%), followed by winter (n = 53; 21.3%), autumn (n = 43; 17.3%) and spring (n = 22; 8.8%). For each subtype of AL, summer was also the season with the highest number of cases, although not statistically significant (p = 0.6838). Table 1 summarises the number of cases for each AL subtype during the various seasons.

TABLE 1: The seasonal occurrence rate of the different subtypes of acute leukaemia over the 4-year study period.

The summer peak in AL diagnoses was not unexpected, considering the South African summer (1 October to 31 March) constitutes 6 months of the year. Regarding the individual calendar months (Figure 1), the monthly AL occurrence rate was relatively consistent over the 4 years, with no statistically significant differences among the AL subtypes (p = 0.7603). The highest number of cases diagnosed with AL (n = 9 per month) was observed during October 2018 and November 2018 and 2020. Table 2 summarises the incidence of each AL subtype diagnosed per month over the 4-year period.

FIGURE 1: The monthly acute leukaemia occurrence rate over the 4-year study period.

TABLE 2: Monthly occurrence of acute leukaemia cases over the 4-year study period.

Discussion

The relationship between AL and seasonality has been widely speculated, and several studies have attempted to elucidate this supposition.8,12,13,14,15,21,22,23,24 Confirmation of seasonal variation in AL incidence could provide supportive evidence of seasonal aetiological factors and improve our understanding of the underlying pathogenesis.22 Anecdotal evidence from our centre suggested seasonal surges in specific AL subtypes, which our research set out to investigate. Similar to another study,13 we found AML to be the most prevalent subtype of AL, with peaks recorded in both summer and autumn, although not statistically significant.

The geographical regions sampled during our study fall within two classifications of the Kӧppen-Geiger classification system.25 The Free State and Northern Cape provinces are classified as arid-steppe or semi-desert climates, with warm and hot summers, respectively. Although the majority of AL diagnoses were made in summer, partially explained by the very long summer, there was also a clear peak of AML diagnoses in January and February. When dividing the year into 3-monthly intervals (summer: 01 December to 28 February; autumn: 01 March to 31 May, winter: 01 June to 31 August; Spring: 01 September to 30 November), no prominent seasonal peaks were evident for any AL subtype, except AML in summer. This AML peak might be explained by the timing of the rainfall season and increased drainage of pollutants into water sources, a theory proposed by Mohammed et al.8 Additionally, rainfall is also associated with increased waterborne infections, adding another possible risk factor for developing leukaemia.15

Rainfall is seasonal in the Free State province, with onset in early September and cessation by March.26 The Northern Cape province has the lowest annual rainfall in South Africa, with the rainy season extending later, from December to April.27 A 3-year clinicopathological review of AML at a tertiary academic hospital in the Western Cape province, South Africa, found the highest incidence of AML in winter.28 Rainfall in the Western Cape province predominates in the austral winter (June to August),29 suggesting that rainfall, rather than climate temperature, may influence leukaemogenesis.

With an established causal link between infections and some haematological malignancies, it is not unreasonable to consider seasonal infections as role players in leukaemogenesis.3,16 Seasonal infectious and environmental factors, including springtime allergens, have been debated to contribute to AL pathogenesis.12

Two large studies on AML incidence rates from the northern hemisphere (Europe and the United States, respectively) found seasonal peaks in December and January, two winter months characterised by dry, cold temperatures.14,22 Dry and cold conditions experienced during winter in temperate regions increase virus stability and nasopharyngeal transmission while weakening the host’s airway immune system.30 Respiratory viral infections such as influenza, respiratory syncytial virus (RSV) and more recently, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are known to culminate in the winter.31 The respiratory disease season in South Africa peaks in the 23rd to 25th week of the year (May and June), which falls in autumn and winter, respectively, and also in the dry season in central South Africa.30 These conditions might contribute to a pathogenesis theory for the second AML peak we observed in May.

A Finish study32 found an increase in AL diagnoses during the cold and dark months of the year compared to warmer and lighter periods. These authors hypothesised that sunlight deprivation, causing vitamin D deficiency, contributed to dysregulated cell growth and differentiation.32 Considering the substantial sunlight exposure in South Africa, even during winter,33 might explain why we did not find statistically significant associations between AL diagnoses and seasonality.

Despite the AML peaks observed in summer and autumn, and similar to previous research,13,15,34 we did not find any statistically significant association between AL incidence and seasonality. Yet, an absence of statistically significant seasonal association does not exclude the clustering of cases unrelated to seasons or months. These perceived clusters might be explained by health-seeking behaviour, which is indirectly influenced by the climate.19

Subgroup analysis investigating the seasonality of specific leukaemia subtypes or AL incidence in specific age-groups could possibly reveal significant findings. Calip et al. observed seasonality in AML diagnosis, particularly among men and older individuals (65 years and older).14 In turn, Sánchez-Vizcaíno et al. demonstrated seasonal effects for specific age-groups except in the elderly population, and attributed the loss of seasonal influence in older patients to changes in the molecular background of AML with advancing age.22 Such stratified analyses were beyond the scope of our study but lend itself to future research.

A limitation of our study was that the time of presentation to a healthcare facility and the subsequent diagnosis of AL did not necessarily match the date of onset of patients’ symptoms. It is possible that some patients only sought medical care once their symptoms became more severe, which, depending on the AL subtype, could span several weeks. We only included AL cases with flow cytometric immunophenotyping reports. Patients diagnosed on cellular morphology who subsequently demised or were referred elsewhere for follow-up and treatment, would therefore not have been included. Similarly, for patients referred from distant peripheral clinics, significant delays between initial morphological diagnosis and subsequent flow cytometric confirmation could have occurred. South Africa was also in different levels of national lockdown because of the coronavirus disease 2019 (COVID-19) pandemic during part of the study period, which may have affected health-seeking behaviour. Nevertheless, the annual number of patients diagnosed with AL during the pandemic years (2020 and 2021) was comparable to the pre-pandemic years (2018 and 2019). Finally, with the limited study period of 4 years, no definitive conclusion regarding the relationship between seasonality and AL diagnosis can be drawn, therefore future studies extending over longer periods are recommended.

Conclusion

Although we did not find definitive evidence of seasonality in AL diagnosis, we observed AML peaks in summer and again in autumn. South Africa has a very diverse climate and includes large temperate areas as well as a small tropical savannah area. As this is the first investigation of seasonality in AL diagnosis in South Africa, it would be informative to extend this study to the other climate categories in the country and include stratified analysis of various subgroups according to AL subtype, age and sex.

Acknowledgements

The authors would like to acknowledge Mrs. Marianne van der Westhuizen, head technologist at the flow cytometry bench, Universitas Academic Hospital National Health Laboratory Service, for her excellent record-keeping; Mr. Cornel van Rooyen, Department of Biostatistics, University of the Free State, for the statistical analysis of the data; and Dr. Daleen Struwig, medical writer/editor, Faculty of Health Sciences, University of the Free State, for technical and editorial preparation of the article. This article is partially based on the research conducted by the medical student authors’ (W.B., N.D., S.J., K.K., R.M., J.M., K.S. and J.V.) research report titled ‘Relationship between seasonality and acute leukaemia diagnosed in central South Africa’ towards the degree MBChB in the School of Clinical Medicine, University of the Free State, South Africa, on 31 March 2023, with supervisors Leriska Haupt and Anne-Cecilia van Marle. The research report is not electronically available in an open access repository, but can be obtained from the corresponding author, L.H., upon request.

Competing interests

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

Authors’ contributions

L.H. and A.-C.v.M. contributed to the conceptualisation and design of the study. W.B., N.D., S.J., K.K., R.M., J.M., K.S. and J.V. wrote the protocol, collected the data and prepared the research report. A.-C.v.M. and L.H. assisted with the interpretation of data and acted as study supervisors. A.-C.v.M. wrote the first draft of the article. L.H. reviewed and edited the first draft of the article. L.H., W.B., N.D., S.J., K.K., R.M., J.M., K.S., J.V. and A.-C.v.M. approved the final version of the article.

Funding information

This research received no specific grant from any funding agency in the public commercial or not-for-profit sectors.

Data availability

Data will be available from the corresponding author, L.H., upon reasonable request and subject to ethical clearance if indicated.

Disclaimer

The views and opinions expressed in this article are those of the authors and are the product of professional research. The article does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.

References

  1. Dong Y, Shi O, Zeng Q, et al. Leukemia incidence trends at the global, regional, and national level between 1990 and 2017. Exp Hematol Oncol. 2020;9(1):14. https://doi.org/10.1186/s40164-020-00170-6
  2. Okikiolu J, Dillon R, Raj K. Acute leukaemia. Medicine. 2021;49(5):274–281. https://doi.org/10.1016/j.mpmed.2021.02.004
  3. Tebbi CK. Etiology of acute leukemia: A review. Cancers (Basel). 2021;13(9):2256. https://doi.org/10.3390/cancers13092256
  4. Shahab F, Raziq F. Clinical presentations of acute leukemia. J Coll Physicians Surg Pak [serial online]. 2014 [cited 2024 May 13];24(7):472–476. Available from: https://www.jcpsp.pk/archive/2014/Jul2014/06.pdf
  5. Hoffman R, Benz E, Silberstein LE, et al. Hematology: Basic principles and practice. 7th ed. St. Louis, MO: Elsevier; 2017.
  6. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021;71(3):209–249. https://doi.org/10.3322/caac.21660
  7. Terwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: A comprehensive review and 2017 update. Blood Cancer J. 2017;7(6):e577. https://doi.org/10.1038/bcj.2017.53
  8. Mohammed A, Ali T, Alwan A. Seasonality in acute promyelocytic leukemia: Fact or myth? Iraqi J Hematol. 2020;9(2):113–117. https://doi.org/10.4103/ijh.ijh_27_20
  9. Filippini T, Heck JE, Malagoli C, Del Giovane C, Vinceti M. A review and meta-analysis of outdoor air pollution and risk of childhood leukemia. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev. 2015;33(1):36–66. https://doi.org/10.1080/10590501.2015.1002999
  10. Puett RC, Poulsen AH, Taj T, et al. Relationship of leukaemias with long-term ambient air pollution exposures in the adult Danish population. Br J Cancer. 2020;123(12):1818–1824. https://doi.org/10.1038/s41416-020-01058-2
  11. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2024;74(3):229–263. https://doi.org/10.3322/caac.21834
  12. Mutlu M, Erduran E. The relationship between seasonal variation in the diagnosis of acute lymphoblastic leukemia and its prognosis in children. Turk J Haematol. 2012;29(2):188–190. https://doi.org/10.5505/tjh.2012.12244
  13. Yiğenoğlu TN, Şahin DA, Başcı S, et al. Evaluation of seasonal changes in the diagnosis of acute leukemia in Turkey. Med Sci Discov. 2020;7(5):501–504. https://doi.org/10.36472/msd.v7i5.381
  14. Calip GS, McDougall JA, Wheldon MC, Li CI, De Roos AJ. Evaluation of seasonality in the diagnosis of acute myeloid leukaemia among adults in the United States, 1992–2008. Br J Haematol. 2013;160(3):343–350. https://doi.org/10.1111/bjh.12137
  15. Ehsan A, Khan MA, Lone A, Arif M, Asif MJ, Riaz S. Acute myeloid leukemia, epidemiology and seasonality, a single center experience. Biomedica [serial online]. 2015 [cited 2024 May 14];31(3):219–222. Available from: https://www.researchgate.net/publication/304244901_acute_myeloid_leukemia_epidemiology_and_seasonality_a_single_center_experience
  16. Ishitsuka K, Tamura K. Human T-cell leukaemia virus type I and adult T-cell leukaemia-lymphoma. Lancet Oncol. 2014;15(11):e517–e526. https://doi.org/10.1016/S1470-2045(14)70202-5
  17. Greaves M. A causal mechanism for childhood acute lymphoblastic leukaemia. Nat Rev Cancer. 2018;18(8):471–484. https://doi.org/10.1038/s41568-018-0015-6
  18. Wyse C, O’Malley G, Coogan AN, McConkey S, Smith DJ. Seasonal and daytime variation in multiple immune parameters in humans: Evidence from 329,261 participants of the UK Biobank cohort. iScience. 2021;24(4):102255. https://doi.org/10.1016/j.isci.2021.102255
  19. Lambe M, Blomqvist P, Bellocco R. Seasonal variation in the diagnosis of cancer: A study based on national cancer registration in Sweden. Br J Cancer. 2003;88(9): 1358–1360. https://doi.org/10.1038/sj.bjc.6600901
  20. Van der Walt AJ, Fitchett JM. Statistical classification of South African seasonal divisions on the basis of daily temperature data. S Afr J Sci. 2020;116(9/10):7614. https://doi.org/10.17159/sajs.2020/7614
  21. Gao F, Chia KS, Machin D. On the evidence for seasonal variation in the onset of acute lymphoblastic leukemia (ALL). Leuk Res. 2007;31(10):1327–1338. https://doi.org/10.1016/j.leukres.2007.03.003
  22. Sánchez-Vizcaíno F, Tamayo C, Ramos F, et al. Identification of seasonal variation in the diagnosis of acute myeloid leukaemia: A population-based study. Br J Haematol. 2022;198(3):545–555. https://doi.org/10.1111/bjh.18279
  23. Eatough JP. Evidence of seasonality in the diagnosis of monocytic leukaemia. Br J Cancer. 2002;87(5):509–510. https://doi.org/10.1038/sj.bjc.6600497
  24. Santoyo-Sánchez A, Ramos-Peñafiel C, Palmeros-Morgado G, et al. [Clinical features of acute leukemia and its relationship to the season of the year] [Article in Spanish]. Rev Med Inst Mex Seguro Soc. 2014 [cited 2024 May 14];52(2):176–781. Available from: https://www.medigraphic.com/cgi-bin/new/resumenI.cgi?IDARTICULO=48734
  25. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data. 2018;5(1):180214. https://doi.org/10.1038/sdata.2018.214
  26. Moeletsi M, Walker S. Rainy season characteristics of the Free State Province of South Africa with reference to rain-fed maize production. Water SA. 2012;38(5):775–782. https://doi.org/10.4314/wsa.v38i5.17
  27. Harmse C, Swanepoel A, Gerber H. Rainfall trends in the Northern Cape Province [homepage on the Internet]. In: 54th Annual Congress of the Grassland Society of Southern Africa, 1–4 July 2019, Upington, South Africa. [cited 2024 May 14]. Available from: https://www.researchgate.net/publication/334784074_Rainfall_trends_in_the_Northern_Cape_Province/link/5d413899299bf1995b594975/download?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19
  28. Moodley K, Nell E, Chapanduka Z. Abstract number PRC23-1310. A 3-year clinicopathological review of acute myeloid leukaemia in a South African tertiary academic centre [homepage on the Internet]. PathReD 2023: Pathology Research and Development Congress, 31 August – 03 September 2023, Johannesburg, South Africa. [cited 2024 May 22]. Available from: https://pathred.nhls.ac.za/files/PathRed_Abstract_Book_20230830_Final.pdf
  29. Lakhraj-Govender R, Grab SW. Rainfall and river flow trends for the Western Cape Province, South Africa. S Afr J Sci. 2019;115(9/10):6028. https://doi.org/10.17159/sajs.2019/6028
  30. Motlogeloa O, Fitchett JM, Sweijd N. Defining the South African acute respiratory infectious disease season. Int J Environ Res Public Health. 2023;20(2):1074. https://doi.org/10.3390/ijerph20021074
  31. Moriyama M, Hugentobler WJ, Iwasaki A. Seasonality of respiratory viral infections. Annu Rev Virol. 2020;7(1):83–101. https://doi.org/10.1146/annurev-virology-012420-022445
  32. Timonen TT. A hypothesis concerning deficiency of sunlight, cold temperature, and influenza epidemics associated with the onset of acute lymphoblastic leukemia in northern Finland. Ann Hematol. 1999;78(9):408–714. https://doi.org/10.1007/s002770050539
  33. Norval M, Coussens AK, Wilkinson RJ, Bornman L, Lucas RM, Wright CY. Vitamin D status and its consequences for health in South Africa. Int J Environ Res Public Health. 2016;13(10):1019. https://doi.org/10.3390/ijerph13101019
  34. Rahimi Pordanjani S, Kavousi A, Mirbagheri B, Shahsavani A, Etemad K. Temporal trend and spatial distribution of acute lymphoblastic leukemia in Iranian children during 2006–2014: A mixed ecological study. Epidemiol Health. 2020;42:e2020057. https://doi.org/10.4178/epih.e2020057


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