Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties

Joseph Kuria Waitara; Gregory Kerich; John Kihoro; Anne Korir

doi:doi:10.11648/j.ajtas.20211004.11

| Peer-Reviewed

Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir

Published in American Journal of Theoretical and Applied Statistics (Volume 10, Issue 4)

Received: 1 June 2021 Accepted: 18 June 2021 Published: 30 June 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Oesophageal cancer is the cancer that forms in tissues lining the oesophagus (the muscular tube through which food passes from the throat to the stomach) while Lung cancer is the cancer that forms in tissues of the lung, usually in the cells lining air passages. In this study, Data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of oesophageal cancer cases for counties in Kenya. The study revealed, counties where data was available Bomet had highest relative risk of oesophageal cancer, followed by Meru, Nyeri, Embu, Nakuru, Kakamega Nairobi, Mombasa, Kiambu and Machakos counties respectively. The study revealed that smoking and alcohol use were significant risk factors of oesophageal cancer in Kenya. Generation of spatio-temporal maps and identification of the risk factors from various counties with notified oesophageal cancer cases is a major milestone since previous studies focused on specific regions. The multiplicative effect of smoking was observed to be 1.012, indicating that oesophageal cancer is 1.2% higher to those who smoke compared to non-smokers. The multiplicative effect of alcohol use was observed to be 1.0346, indicating that oesophageal cancer was 3.5% higher to alcohol users as compared to non-alcohol users. The study findings revealed that, the multiplicative effect of smoking was 1.4021, indicating that lung cancer was 40.21% higher to smokers as compared to non-smokers from the available data. The multiplicative effect of alcohol use was 1.3689 indicating that the risk of lung cancer was 36.89% higher to alcohol users compared to non-alcohol users. Clearly, counties where the data was not available the relative risks were relatively low, therefore even though the data was not available in these counties application of spatial-temporal accounting for covariates revealed that there is risk of oesophageal and lung cancer in the counties. To enhance research on oesophageal, lung and other types of cancer in Kenya the National Cancer Registry in collaboration with Counties health departments should work very closely to enhance cancer data collection to facilitate research and to inform the appropriate measures to be implemented to mitigate the increase of cancer cases.

Published in	American Journal of Theoretical and Applied Statistics (Volume 10, Issue 4)
DOI	10.11648/j.ajtas.20211004.11
Page(s)	175-183
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Spatial-temporal, Integrated Nested Laplace Approximation, Generalized Linear Mixed Models

References

[1]	Cancer.Net (2021, February 2021), Esophageal Cancer Symptoms and Sings: https://www.cancer.net/cancer-types/esophageal-cancer/symptoms-and-signs.
[2]	Gabriela P. (2020, January 2020), Esophageal Cancer, https://www.webmd.com/cancer/esophageal-cancer.
[3]	American Cancer Society (2021, February 2021), Lung Cancer risk factors: https://www.cancer.org/cancer/lung-cancer/causes-risks-prevention/risk-factors.html.
[4]	Elisa V Bandera, Jo L Freudenheim, and John E Vena. Alcohol consumption and lung cancer: a review of the epidemiologic evidence. Cancer Epidemiology and Prevention Biomarkers, 10 (8): 813–821, 2001.
[5]	Julian Besag, Jeremy York, and Annie Mollié. Bayesian image restoration, with two applications in spatial statistics. Annals of the institute of statistical mathematics, 43 (1): 1–20, 1991.
[6]	Freddie Bray, Jacques Ferlay, Isabelle Soerjomataram, Rebecca L Siegel, Lindsey A Torre, and Ahmedin Jemal. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68 (6): 394–424, 2018.
[7]	Jacques Ferlay, Isabelle Soerjomataram, Rajesh Dikshit, Sultan Eser, Colin Mathers, Marise Rebelo, Donald Maxwell Parkin, David Forman, and Freddie Bray. Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012. International journal of cancer, 136 (5): E359–E386, 2015.
[8]	Dennis Kasper, Anthony Fauci, Stephen Hauser, Dan Longo, J Jameson, and Joseph Loscalzo. Harrison’s principles of internal medicine, 19e, volume 1. Mcgraw-hill, 2015.
[9]	Diba Khana, Lauren M Rossen, Holly Hedegaard, and Margaret Warner. A bayesian spatial and temporal modeling approach to mapping geographic variation in mortality rates for subnational areas with r-inla. Journal of data science: JDS, 16 (1): 147, 2018.
[10]	Anne Korir, Nathan Okerosi, Victor Ronoh, Geoffrey Mutuma, and Max Parkin. Incidence of cancer in n airobi, k enya (2004–2008). International journal of cancer, 137 (9): 2053–2059, 2015.
[11]	Jeffrey E Korte, Paul Brennan, S Jane Henley, and Paolo Boffetta. Dose-specific meta-analysis and sensitivity analysis of the relation between alcohol consumption and lung cancer risk. American journal of epidemiology, 155 (6): 496–506, 2002.
[12]	Andrew B Lawson. Bayesian disease mapping: hierarchical modeling in spatial epidemiology. Chapman and Hall/CRC, 2013.
[13]	Jyoti Malhotra, Matteo Malvezzi, Eva Negri, Carlo La Vecchia, and Paolo Boffetta. Risk factors for lung cancer worldwide. European Respiratory Journal, 48 (3): 889–902, 2016.
[14]	Paula Moraga. Small area disease risk estimation and visualization using r. R J, 10: 495–506, 2018.
[15]	Joab Otieno Odera, Elizabeth Odera, Jessie Githangâ€™a, Edwin Oloo Walong, Fang Li, Zhaohui Xiong, and Xiaoxin Luke Chen. Esophageal cancer in Kenya. American journal of digestive disease, 4 (3): 23, 2017.
[16]	R Pacella-Norman, MI Urban, F Sitas, H Carrara, R Sur, M Hale, P Ruff, M Patel, R Newton, D Bull, et al. Risk factors for oesophageal, lung, oral and laryngeal cancers in black south africans. British journal of cancer, 86 (11): 1751–1756, 2002.
[17]	Kirtika Patel, Johnston Wakhisi, Simeon Mining, Ann Mwangi, and Radheka Patel. Esophageal cancer, the topmost cancer at mtrh in the rift valley, kenya, and its potential risk factors. International Scholarly Research Notices, 2013, 2013.
[18]	Torin Schaafsma, Jon Wakefield, Rachel Hanisch, Freddie Bray, Joachim Schüz, Edward JM Joy, Michael J Watts, and Valerie McCormack. Africaâ€™s oesophageal cancer corridor: geographic variations in incidence correlate with certain micronutrient deficiencies. PloS one, 10 (10): e0140107, 2015.
[19]	David J Spiegelhalter, Nicola G Best, Bradley P Carlin, and Angelika Van Der Linde. Bayesian measures of model complexity and fit. Journal of the royal statistical society: Series b (statistical methodology), 64 (4): 583–639, 2002.
[20]	CN Tenge, RT Kuremu, NG Buziba, K Patel, and PA Were. Burden and pattern of cancer in western kenya. East African medical journal, 86 (1), 2009.

Cite This Article

Plain Text BibTeX RIS

APA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. (2021). Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties. American Journal of Theoretical and Applied Statistics, 10(4), 175-183. https://doi.org/10.11648/j.ajtas.20211004.11

Copy | Download

ACS Style

Joseph Kuria Waitara; Gregory Kerich; John Kihoro; Anne Korir. Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties. Am. J. Theor. Appl. Stat. 2021, 10(4), 175-183. doi: 10.11648/j.ajtas.20211004.11

Copy | Download

AMA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties. Am J Theor Appl Stat. 2021;10(4):175-183. doi: 10.11648/j.ajtas.20211004.11

Copy | Download

@article{10.11648/j.ajtas.20211004.11,
  author = {Joseph Kuria Waitara and Gregory Kerich and John Kihoro and Anne Korir},
  title = {Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {4},
  pages = {175-183},
  doi = {10.11648/j.ajtas.20211004.11},
  url = {https://doi.org/10.11648/j.ajtas.20211004.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211004.11},
  abstract = {Oesophageal cancer is the cancer that forms in tissues lining the oesophagus (the muscular tube through which food passes from the throat to the stomach) while Lung cancer is the cancer that forms in tissues of the lung, usually in the cells lining air passages. In this study, Data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of oesophageal cancer cases for counties in Kenya. The study revealed, counties where data was available Bomet had highest relative risk of oesophageal cancer, followed by Meru, Nyeri, Embu, Nakuru, Kakamega Nairobi, Mombasa, Kiambu and Machakos counties respectively. The study revealed that smoking and alcohol use were significant risk factors of oesophageal cancer in Kenya. Generation of spatio-temporal maps and identification of the risk factors from various counties with notified oesophageal cancer cases is a major milestone since previous studies focused on specific regions. The multiplicative effect of smoking was observed to be 1.012, indicating that oesophageal cancer is 1.2% higher to those who smoke compared to non-smokers. The multiplicative effect of alcohol use was observed to be 1.0346, indicating that oesophageal cancer was 3.5% higher to alcohol users as compared to non-alcohol users. The study findings revealed that, the multiplicative effect of smoking was 1.4021, indicating that lung cancer was 40.21% higher to smokers as compared to non-smokers from the available data. The multiplicative effect of alcohol use was 1.3689 indicating that the risk of lung cancer was 36.89% higher to alcohol users compared to non-alcohol users. Clearly, counties where the data was not available the relative risks were relatively low, therefore even though the data was not available in these counties application of spatial-temporal accounting for covariates revealed that there is risk of oesophageal and lung cancer in the counties. To enhance research on oesophageal, lung and other types of cancer in Kenya the National Cancer Registry in collaboration with Counties health departments should work very closely to enhance cancer data collection to facilitate research and to inform the appropriate measures to be implemented to mitigate the increase of cancer cases.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties
AU  - Joseph Kuria Waitara
AU  - Gregory Kerich
AU  - John Kihoro
AU  - Anne Korir
Y1  - 2021/06/30
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211004.11
DO  - 10.11648/j.ajtas.20211004.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 175
EP  - 183
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211004.11
AB  - Oesophageal cancer is the cancer that forms in tissues lining the oesophagus (the muscular tube through which food passes from the throat to the stomach) while Lung cancer is the cancer that forms in tissues of the lung, usually in the cells lining air passages. In this study, Data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of oesophageal cancer cases for counties in Kenya. The study revealed, counties where data was available Bomet had highest relative risk of oesophageal cancer, followed by Meru, Nyeri, Embu, Nakuru, Kakamega Nairobi, Mombasa, Kiambu and Machakos counties respectively. The study revealed that smoking and alcohol use were significant risk factors of oesophageal cancer in Kenya. Generation of spatio-temporal maps and identification of the risk factors from various counties with notified oesophageal cancer cases is a major milestone since previous studies focused on specific regions. The multiplicative effect of smoking was observed to be 1.012, indicating that oesophageal cancer is 1.2% higher to those who smoke compared to non-smokers. The multiplicative effect of alcohol use was observed to be 1.0346, indicating that oesophageal cancer was 3.5% higher to alcohol users as compared to non-alcohol users. The study findings revealed that, the multiplicative effect of smoking was 1.4021, indicating that lung cancer was 40.21% higher to smokers as compared to non-smokers from the available data. The multiplicative effect of alcohol use was 1.3689 indicating that the risk of lung cancer was 36.89% higher to alcohol users compared to non-alcohol users. Clearly, counties where the data was not available the relative risks were relatively low, therefore even though the data was not available in these counties application of spatial-temporal accounting for covariates revealed that there is risk of oesophageal and lung cancer in the counties. To enhance research on oesophageal, lung and other types of cancer in Kenya the National Cancer Registry in collaboration with Counties health departments should work very closely to enhance cancer data collection to facilitate research and to inform the appropriate measures to be implemented to mitigate the increase of cancer cases.
VL  - 10
IS  - 4
ER  -

Copy | Download

Author Information

Joseph Kuria Waitara

School of Sciences and Aerospace Studies, Moi University, Eldoret, Kenya
Gregory Kerich

School of Sciences and Aerospace Studies, Moi University, Eldoret, Kenya
John Kihoro

School of Computing and Mathematics, The Co-operative University of Kenya, Nairobi, Kenya
Anne Korir

National Cancer Registry, Kenya, Nairobi, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. (2021). Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties. American Journal of Theoretical and Applied Statistics, 10(4), 175-183. https://doi.org/10.11648/j.ajtas.20211004.11

Copy | Download

ACS Style

Joseph Kuria Waitara; Gregory Kerich; John Kihoro; Anne Korir. Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties. Am. J. Theor. Appl. Stat. 2021, 10(4), 175-183. doi: 10.11648/j.ajtas.20211004.11

Copy | Download

AMA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties. Am J Theor Appl Stat. 2021;10(4):175-183. doi: 10.11648/j.ajtas.20211004.11

Copy | Download

@article{10.11648/j.ajtas.20211004.11,
  author = {Joseph Kuria Waitara and Gregory Kerich and John Kihoro and Anne Korir},
  title = {Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {4},
  pages = {175-183},
  doi = {10.11648/j.ajtas.20211004.11},
  url = {https://doi.org/10.11648/j.ajtas.20211004.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211004.11},
  abstract = {Oesophageal cancer is the cancer that forms in tissues lining the oesophagus (the muscular tube through which food passes from the throat to the stomach) while Lung cancer is the cancer that forms in tissues of the lung, usually in the cells lining air passages. In this study, Data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of oesophageal cancer cases for counties in Kenya. The study revealed, counties where data was available Bomet had highest relative risk of oesophageal cancer, followed by Meru, Nyeri, Embu, Nakuru, Kakamega Nairobi, Mombasa, Kiambu and Machakos counties respectively. The study revealed that smoking and alcohol use were significant risk factors of oesophageal cancer in Kenya. Generation of spatio-temporal maps and identification of the risk factors from various counties with notified oesophageal cancer cases is a major milestone since previous studies focused on specific regions. The multiplicative effect of smoking was observed to be 1.012, indicating that oesophageal cancer is 1.2% higher to those who smoke compared to non-smokers. The multiplicative effect of alcohol use was observed to be 1.0346, indicating that oesophageal cancer was 3.5% higher to alcohol users as compared to non-alcohol users. The study findings revealed that, the multiplicative effect of smoking was 1.4021, indicating that lung cancer was 40.21% higher to smokers as compared to non-smokers from the available data. The multiplicative effect of alcohol use was 1.3689 indicating that the risk of lung cancer was 36.89% higher to alcohol users compared to non-alcohol users. Clearly, counties where the data was not available the relative risks were relatively low, therefore even though the data was not available in these counties application of spatial-temporal accounting for covariates revealed that there is risk of oesophageal and lung cancer in the counties. To enhance research on oesophageal, lung and other types of cancer in Kenya the National Cancer Registry in collaboration with Counties health departments should work very closely to enhance cancer data collection to facilitate research and to inform the appropriate measures to be implemented to mitigate the increase of cancer cases.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Spatial-temporal Modelling of Oesophageal and Lung Cancers in Kenya’s Counties
AU  - Joseph Kuria Waitara
AU  - Gregory Kerich
AU  - John Kihoro
AU  - Anne Korir
Y1  - 2021/06/30
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211004.11
DO  - 10.11648/j.ajtas.20211004.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 175
EP  - 183
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211004.11
AB  - Oesophageal cancer is the cancer that forms in tissues lining the oesophagus (the muscular tube through which food passes from the throat to the stomach) while Lung cancer is the cancer that forms in tissues of the lung, usually in the cells lining air passages. In this study, Data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of oesophageal cancer cases for counties in Kenya. The study revealed, counties where data was available Bomet had highest relative risk of oesophageal cancer, followed by Meru, Nyeri, Embu, Nakuru, Kakamega Nairobi, Mombasa, Kiambu and Machakos counties respectively. The study revealed that smoking and alcohol use were significant risk factors of oesophageal cancer in Kenya. Generation of spatio-temporal maps and identification of the risk factors from various counties with notified oesophageal cancer cases is a major milestone since previous studies focused on specific regions. The multiplicative effect of smoking was observed to be 1.012, indicating that oesophageal cancer is 1.2% higher to those who smoke compared to non-smokers. The multiplicative effect of alcohol use was observed to be 1.0346, indicating that oesophageal cancer was 3.5% higher to alcohol users as compared to non-alcohol users. The study findings revealed that, the multiplicative effect of smoking was 1.4021, indicating that lung cancer was 40.21% higher to smokers as compared to non-smokers from the available data. The multiplicative effect of alcohol use was 1.3689 indicating that the risk of lung cancer was 36.89% higher to alcohol users compared to non-alcohol users. Clearly, counties where the data was not available the relative risks were relatively low, therefore even though the data was not available in these counties application of spatial-temporal accounting for covariates revealed that there is risk of oesophageal and lung cancer in the counties. To enhance research on oesophageal, lung and other types of cancer in Kenya the National Cancer Registry in collaboration with Counties health departments should work very closely to enhance cancer data collection to facilitate research and to inform the appropriate measures to be implemented to mitigate the increase of cancer cases.
VL  - 10
IS  - 4
ER  -

Copy | Download