Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties

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

doi:doi:10.11648/j.ajtas.20211003.14

| Peer-Reviewed

Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer 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 3)

Received: 1 June 2021 Accepted: 18 June 2021 Published: 26 June 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

In Africa, Cancer is an emerging health problem where in 2012 new cancer cases were about 847,00 and around 519,00 deaths, three quarters of those deaths occurred in sub-Saharan region. In 2018, cancer was ranked as the third leading cause of deaths in Kenya after infectious and cardiovascular diseases. In 2018 cancer incidences were estimated to be 47,887 new cancer cases and 32,987 deaths. According to data from World Health Organization in 2020, cervical cancer is the second most prevalent cancer among women while breast cancer is the first. In this study, data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of the cervical cancer in counties in Kenya. The results showed that counties where data was available among them Embu, Meru, Machakos, Mombasa, Nyeri, Kiambu, Kakamega, Nairobi and Bomet respectively had high risk of cervical cancer. Availability of county-based estimates and spatial-temporal distribution of cervical cancer cases will aide development of targeted county strategies, enhance early detection, promote awareness and implementation of universal coverage of major control interventions which will be crucial in reducing and halting the rising burden of the cancer cases in Kenya. In counties where data was not available the model showed relative risks for cervical cancer disease was minute but it was present, therefore spatial temporal models are very appropriate to estimate relative risks of diseases even when there is a small sample (and possibly without a sample) in a given area by borrowing information from other neighboring regions.

Published in	American Journal of Theoretical and Applied Statistics (Volume 10, Issue 3)
DOI	10.11648/j.ajtas.20211003.14
Page(s)	158-166
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

Small Area Estimation, Spatial Temporal, Integrated Nested Laplace Approximation, Generalized Linear Mixed Models

References

[1]	The Global Cancer Observatory, (2021, March 2021): https://gco.iarc.fr/today/data/factsheets/populations/404-kenya-fact-sheets.pdf.
[2]	Roberto Benavent and Domingo Morales. Multivariate fay–herriot models for small area estimation. Computational Statistics & Data Analysis, 94: 372–390, 2016.
[3]	L Bernardinelli, D Clayton, C Pascutto, C Montomoli, M Ghislandi, and M Songini. Bayesian analysis of spaceâ€”time variation in disease risk. Statistics in medicine, 14 (21-22): 2433–2443, 1995.
[4]	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.
[5]	Roger Bivand. Creating neighbours. 2019.
[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]	Hukum Chandra. Overview of small area estimation techniques. Indian Agricultural Statistics Research Institute, pages 75–88, 2003.
[8]	Avital Cnaan, Nan M Laird, and Peter Slasor. Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data. Statistics in medicine, 16 (20): 2349–2380, 1997.
[9]	Elizabeth TH Fontham, Andrew MD Wolf, Timothy R Church, Ruth Etzioni, Christopher R Flowers, Abbe Herzig, Carmen E Guerra, Kevin C Oeffinger, Ya-Chen Tina Shih, Louise C Walter, et al. Cervical cancer screening for individuals at average risk: 2020 guideline update from the american cancer society. CA: A Cancer Journal for Clinicians, 70 (5): 321–346, 2020.
[10]	Alan E Gelfand, Peter Diggle, Peter Guttorp, and Montserrat Fuentes. Handbook of spatial statistics. CRC press, 2010.
[11]	Virgilio Gómez-Rubio, Nicky Best, Sylvia Richardson, Guangquan Li, and Philip Clarke. Bayesian statistics small area estimation. 2010.
[12]	Marie-Josèphe Horner, Sean F Altekruse, Zhaohui Zou, Louise Wideroff, Hormuzd A Katki, and David G Stinchcomb. Us geographic distribution of prevaccine era cervical cancer screening, incidence, stage, and mortality. Cancer Epidemiology and Prevention Biomarkers, 20 (4): 591–599, 2011.
[13]	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.
[14]	Leonhard Knorr-Held and G Rasser. Bayesian detection of clusters and discontinuities in disease maps: Simulations. (revised, june 1999). 1999.
[15]	Andrew B Lawson. Bayesian disease mapping: hierarchical modeling in spatial epidemiology. Chapman and Hall/CRC, 2013.
[16]	Andrew B Lawson, AB Biggeri, Dankmar Böhning, Emmanuel Lesaffre, Jean-François Viel, ALLAN Clark, PETER Schlattmann, and Fabio Divino. Disease mapping models: an empirical evaluation. disease mapping collaborative group. Statistics in medicine, 19 (17): 2217–41, 2000.
[17]	Sara Martino and Håvard Rue. Implementing approximate bayesian inference using integrated nested laplace approximation: A manual for the inla program. Department of Mathematical Sciences, NTNU, Norway, 2009.
[18]	Thiago G Martins, Daniel Simpson, Finn Lindgren, and Håvard Rue. Bayesian computing with inla: new features. Computational Statistics & Data Analysis, 67: 68–83, 2013.
[19]	Mathtype. https://www.wiris.com/en/mathtype/office-tools/. April 2021.
[20]	Kirumba John Mwangi. Use of GIS in mapping of cancer prevalence a case study of Uasin Gishu County. PhD thesis, UNIVERSITY OF NAIROBI, 2014.
[21]	Thomas Neyens, Christel Faes, and Geert Molenberghs. A generalized poisson-gamma model for spatially overdispersed data. Spatial and spatio-temporal epidemiology, 3 (3): 185–194, 2012.
[22]	JNK Rao. Some new developments in small area estimation. Journal of the Iranian Statistical Society, 2 (2): 145–169, 2003.
[23]	Håvard Rue, Sara Martino, and Nicolas Chopin. Approximate bayesian inference for latent gaussian models by using integrated nested laplace approximations. Journal of the royal statistical society: Series b (statistical methodology), 71 (2): 319–392, 2009.
[24]	Hâ «vard Rue and Sara Martino. Approximate bayesian inference for hierarchical gaussian markov random field models. Journal of statistical planning and inference, 137 (10): 3177–3192, 2007.
[25]	M Schiffman, J Doorbar, N Wentzensen, S de Sanjose, C Fakhry, BJ Monk, MA Stanley, and S Franceschi. vol. 2. 2016. carcinogenic human papillomavirus infection. Nature Reviews. Disease Primers).[Abstract][Google Scholar], page 16086, 2016.
[26]	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.
[27]	Chiara Vidoni, Letizia Vallino, Alessandra Ferraresi, Eleonora Secomandi, Amreen Salwa, Menaka Chinthakindi, Alessandra Galetto, Danny N Dhanasekaran, and Ciro Isidoro. Epigenetic control of autophagy in womenâ€™s tumors: role of non-coding rnas. Journal of Cancer Metastasis and Treatment, 7, 2021.
[28]	WHO (2020, March2020), Human papillomavirus (HPV) and cervical cancer: https://www.who.int/en/news-room/fact-sheets/detail/human-papillomavirus-(hpv)-and-cervical-cancer.

Cite This Article

Plain Text BibTeX RIS

APA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. (2021). Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties. American Journal of Theoretical and Applied Statistics, 10(3), 158-166. https://doi.org/10.11648/j.ajtas.20211003.14

Copy | Download

ACS Style

Joseph Kuria Waitara; Gregory Kerich; John Kihoro; Anne Korir. Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties. Am. J. Theor. Appl. Stat. 2021, 10(3), 158-166. doi: 10.11648/j.ajtas.20211003.14

Copy | Download

AMA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties. Am J Theor Appl Stat. 2021;10(3):158-166. doi: 10.11648/j.ajtas.20211003.14

Copy | Download

@article{10.11648/j.ajtas.20211003.14,
  author = {Joseph Kuria Waitara and Gregory Kerich and John Kihoro and Anne Korir},
  title = {Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {3},
  pages = {158-166},
  doi = {10.11648/j.ajtas.20211003.14},
  url = {https://doi.org/10.11648/j.ajtas.20211003.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211003.14},
  abstract = {In Africa, Cancer is an emerging health problem where in 2012 new cancer cases were about 847,00 and around 519,00 deaths, three quarters of those deaths occurred in sub-Saharan region. In 2018, cancer was ranked as the third leading cause of deaths in Kenya after infectious and cardiovascular diseases. In 2018 cancer incidences were estimated to be 47,887 new cancer cases and 32,987 deaths. According to data from World Health Organization in 2020, cervical cancer is the second most prevalent cancer among women while breast cancer is the first. In this study, data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of the cervical cancer in counties in Kenya. The results showed that counties where data was available among them Embu, Meru, Machakos, Mombasa, Nyeri, Kiambu, Kakamega, Nairobi and Bomet respectively had high risk of cervical cancer. Availability of county-based estimates and spatial-temporal distribution of cervical cancer cases will aide development of targeted county strategies, enhance early detection, promote awareness and implementation of universal coverage of major control interventions which will be crucial in reducing and halting the rising burden of the cancer cases in Kenya. In counties where data was not available the model showed relative risks for cervical cancer disease was minute but it was present, therefore spatial temporal models are very appropriate to estimate relative risks of diseases even when there is a small sample (and possibly without a sample) in a given area by borrowing information from other neighboring regions.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties
AU  - Joseph Kuria Waitara
AU  - Gregory Kerich
AU  - John Kihoro
AU  - Anne Korir
Y1  - 2021/06/26
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211003.14
DO  - 10.11648/j.ajtas.20211003.14
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  - 158
EP  - 166
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211003.14
AB  - In Africa, Cancer is an emerging health problem where in 2012 new cancer cases were about 847,00 and around 519,00 deaths, three quarters of those deaths occurred in sub-Saharan region. In 2018, cancer was ranked as the third leading cause of deaths in Kenya after infectious and cardiovascular diseases. In 2018 cancer incidences were estimated to be 47,887 new cancer cases and 32,987 deaths. According to data from World Health Organization in 2020, cervical cancer is the second most prevalent cancer among women while breast cancer is the first. In this study, data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of the cervical cancer in counties in Kenya. The results showed that counties where data was available among them Embu, Meru, Machakos, Mombasa, Nyeri, Kiambu, Kakamega, Nairobi and Bomet respectively had high risk of cervical cancer. Availability of county-based estimates and spatial-temporal distribution of cervical cancer cases will aide development of targeted county strategies, enhance early detection, promote awareness and implementation of universal coverage of major control interventions which will be crucial in reducing and halting the rising burden of the cancer cases in Kenya. In counties where data was not available the model showed relative risks for cervical cancer disease was minute but it was present, therefore spatial temporal models are very appropriate to estimate relative risks of diseases even when there is a small sample (and possibly without a sample) in a given area by borrowing information from other neighboring regions.
VL  - 10
IS  - 3
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, Nairobi, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. (2021). Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties. American Journal of Theoretical and Applied Statistics, 10(3), 158-166. https://doi.org/10.11648/j.ajtas.20211003.14

Copy | Download

ACS Style

Joseph Kuria Waitara; Gregory Kerich; John Kihoro; Anne Korir. Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties. Am. J. Theor. Appl. Stat. 2021, 10(3), 158-166. doi: 10.11648/j.ajtas.20211003.14

Copy | Download

AMA Style

Joseph Kuria Waitara, Gregory Kerich, John Kihoro, Anne Korir. Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties. Am J Theor Appl Stat. 2021;10(3):158-166. doi: 10.11648/j.ajtas.20211003.14

Copy | Download

@article{10.11648/j.ajtas.20211003.14,
  author = {Joseph Kuria Waitara and Gregory Kerich and John Kihoro and Anne Korir},
  title = {Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {10},
  number = {3},
  pages = {158-166},
  doi = {10.11648/j.ajtas.20211003.14},
  url = {https://doi.org/10.11648/j.ajtas.20211003.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211003.14},
  abstract = {In Africa, Cancer is an emerging health problem where in 2012 new cancer cases were about 847,00 and around 519,00 deaths, three quarters of those deaths occurred in sub-Saharan region. In 2018, cancer was ranked as the third leading cause of deaths in Kenya after infectious and cardiovascular diseases. In 2018 cancer incidences were estimated to be 47,887 new cancer cases and 32,987 deaths. According to data from World Health Organization in 2020, cervical cancer is the second most prevalent cancer among women while breast cancer is the first. In this study, data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of the cervical cancer in counties in Kenya. The results showed that counties where data was available among them Embu, Meru, Machakos, Mombasa, Nyeri, Kiambu, Kakamega, Nairobi and Bomet respectively had high risk of cervical cancer. Availability of county-based estimates and spatial-temporal distribution of cervical cancer cases will aide development of targeted county strategies, enhance early detection, promote awareness and implementation of universal coverage of major control interventions which will be crucial in reducing and halting the rising burden of the cancer cases in Kenya. In counties where data was not available the model showed relative risks for cervical cancer disease was minute but it was present, therefore spatial temporal models are very appropriate to estimate relative risks of diseases even when there is a small sample (and possibly without a sample) in a given area by borrowing information from other neighboring regions.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Poisson-Gamma and Spatial-Temporal Models: with Application to Cervical Cancer in Kenya’s Counties
AU  - Joseph Kuria Waitara
AU  - Gregory Kerich
AU  - John Kihoro
AU  - Anne Korir
Y1  - 2021/06/26
PY  - 2021
N1  - https://doi.org/10.11648/j.ajtas.20211003.14
DO  - 10.11648/j.ajtas.20211003.14
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  - 158
EP  - 166
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20211003.14
AB  - In Africa, Cancer is an emerging health problem where in 2012 new cancer cases were about 847,00 and around 519,00 deaths, three quarters of those deaths occurred in sub-Saharan region. In 2018, cancer was ranked as the third leading cause of deaths in Kenya after infectious and cardiovascular diseases. In 2018 cancer incidences were estimated to be 47,887 new cancer cases and 32,987 deaths. According to data from World Health Organization in 2020, cervical cancer is the second most prevalent cancer among women while breast cancer is the first. In this study, data collected by the Nairobi Cancer Registry (NCR) was used to produce spatial-temporal distribution of the cervical cancer in counties in Kenya. The results showed that counties where data was available among them Embu, Meru, Machakos, Mombasa, Nyeri, Kiambu, Kakamega, Nairobi and Bomet respectively had high risk of cervical cancer. Availability of county-based estimates and spatial-temporal distribution of cervical cancer cases will aide development of targeted county strategies, enhance early detection, promote awareness and implementation of universal coverage of major control interventions which will be crucial in reducing and halting the rising burden of the cancer cases in Kenya. In counties where data was not available the model showed relative risks for cervical cancer disease was minute but it was present, therefore spatial temporal models are very appropriate to estimate relative risks of diseases even when there is a small sample (and possibly without a sample) in a given area by borrowing information from other neighboring regions.
VL  - 10
IS  - 3
ER  -

Copy | Download