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On Statistical Analysis of Eight Sexually Transmitted Diseases Using Categorical Analysis of Variance: A Case Study of the University of Nigeria Teaching Hospital

Received: 6 February 2022    Accepted: 28 February 2022    Published: 13 January 2023
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Abstract

This paper focused on the statistical analysis of eight Sexually Transmitted Infections (STIs) reported in the University of Nigeria Teaching Hospital from 2010-2020. A population of 20,704 patients was recorded to have contracted eight (8) selected STIs. Prevalence analysis was computed to determine the most prevalent STI. Two-way CATANOVA cross-classification was computed to ascertain age group and gender that suffer more of each of these infections. Logistic regression model was fitted to predict reproductive status of patients that suffer the most prevalent STI. The prevalence analysis results showed Gonorrhea infection as the most prevalent STI. Two-way CATANOVA results for Gonorrhea and Chlamydia infections showed that there were significant difference in gender, age and interaction effects, significant difference in age and interaction effect for Trichomoniases infection, significant difference in age for Syphilis and HIV infections but no significant difference in gender, age and interaction effects for Human Papillomavirus (HPV), Hepatitis B Virus and Herpes infections. The results showed that the percentage of male that suffers STIs is more than the percentage of female, the percentage of 30-39 years that suffer STIs is more than the percentage of any other age group and the percentage of people without STIs history is more than the percentage of those that have history of them. Logistic regression results on Gonorrhea infection showed that an increase in age, body mass index, blood pressure, blood sugar, bacteria quantity, and Gonorrhea history were associated with an increased likelihood of the Gonorrhea patient being infertile.

Published in Science Journal of Applied Mathematics and Statistics (Volume 11, Issue 1)
DOI 10.11648/j.sjams.20231101.11
Page(s) 1-16
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), 2024. Published by Science Publishing Group

Keywords

Chi-square Test, Contigency Table, Odds Ratio, Significance Level, Prediction

References
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[2] Azmi M., Muataz A., Ali M. A. and Mohammad S. E (2008). Prevalence of Sexually Transmitted Infections Among Sexually Active Jordanian Females. Sex Transm. Dis., 35 (6): 607-710.
[3] Boateng, E. Y. and Abaye, D. A. (2019). A Review of the Logistic Regression Model with Emphasis on Medical Research. Journal of Data Analysis and Information Processing, 7: 190-207.
[4] Centers for Diseases Control and Prevention (2013). Incidence, prevalence and cost of living in the United States.
[5] Centers for Diseases Control and Prevention (2022). Gonorrhea-CDC Fact Sheet. Accessed: 06 January, 2022. Available: https://www.cdc.gov/std/gonorrhea/stdfact-gonorrhea.htm
[6] Creighton, S. (2014). Gonorrhea. BMJ clinical evidence, 1604. Accessed: 20 August, 2021. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3931440/
[7] Deyheul N., Mohamaddoost T. and Hossini M. (2017). Infertility related risk factors: A systematic review. International Journal of women health and reproduction science, 5 (1): 24-29.
[8] Eze, N. M., Asogwa, O. C. and Eze, C. M. (2021). Principal component factor analysis of some development factors in southern Nigeria and its extension to regression analysis. Journal of Advances in Mathematics and Computer Science, 36 (3): 132-160. DOI: 10.9734/JAMCS/2021/v36i330351
[9] Fienberg, S. E. (1973). Analysis of incomplete multiway contingency tables. Biometrics, 28: 177-202. DOI: https://doi.org/10.2307/2528967
[10] Fisher, L. D (1998). Self-designing clinical trials. Statistics in Medicine; 17: 1551-1562.
[11] Florian, T. J. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. J Mem Lang, 59 (4): 434-446.
[12] Kesah F. N. C., Vincent K. P and Augustine A. (2013). Prevalence and etiology of Sexually Transmitted Infections I gynecologic unit of a developing country. Annals of tropical medicine and public health, 6 (5): 526.
[13] Mayo Foundation for Medical Education and Research (2022). Gonorrhea. Accessed: 04 January, 2022. Available: https://www.mayoclinic.org/diseases-conditions/gonorrhea/symptoms-causes/syc-20351774
[14] Onukogu, I. B. (2014). Analysis of variance of categorical data-nested designs. Journal of Statistics: Advances in Theory and Applications, 12: 109-116.
[15] Onukogu, I. B. (1985). Reasoning by analogy from ANOVA to CATANOVA, Biom J, 27: 839-849.
[16] Otaru O. P. and Ogbonda N. P (2020). CATANOVA analysis of knowledge and control practices of hepatitis B virus infection amongst tertiary university students. Galician Medical Journal, 27 (1).
[17] Piszczek, J., St. Jean, R. and Khaliq, Y. (2015). Gonorrhea: Treatment update for an increasingly resistant organism, 148 (2): 82-89. doi: 10.1177/1715163515570111
[18] Scatterwhite, C. L., Torrone, E., Meites, E., Dunne, E. F., Mahajan, R., Ocfemia, C. Su, J., Xu, F. and Weinstock, H. (2013). Sexually transmitted infections among U.S. women and men: Prevalence and incidence estimates, 2008 Sexually Transmitted Diseases; 40 (3): 187-193.
[19] Scheffe, H. (1959). The Analysis of Variance. Wiley: New York.
[20] Singh, B. (2004). CATANOVA for analysis of nominal data from repeated measures design. J Ind Soc Agril Statist, 58 (3): 257-268.
[21] World Health Organization (2016). WHO Guidelines for the Treatment of Neisseria Gonorrhoeae. Accessed: 12 December, 2021. Available: https://apps.who.int/iris/bitstream/handle/10665/246114/9789241549691-eng.pdf
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    Nnaemeka Martin Eze, Chimeremeze Davidson Sibigem, Oluchukwu Chukwuemeka Asogwa, Chinonso Michael Eze, Samson Offorma Ugwu, et al. (2023). On Statistical Analysis of Eight Sexually Transmitted Diseases Using Categorical Analysis of Variance: A Case Study of the University of Nigeria Teaching Hospital. Science Journal of Applied Mathematics and Statistics, 11(1), 1-16. https://doi.org/10.11648/j.sjams.20231101.11

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    ACS Style

    Nnaemeka Martin Eze; Chimeremeze Davidson Sibigem; Oluchukwu Chukwuemeka Asogwa; Chinonso Michael Eze; Samson Offorma Ugwu, et al. On Statistical Analysis of Eight Sexually Transmitted Diseases Using Categorical Analysis of Variance: A Case Study of the University of Nigeria Teaching Hospital. Sci. J. Appl. Math. Stat. 2023, 11(1), 1-16. doi: 10.11648/j.sjams.20231101.11

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    AMA Style

    Nnaemeka Martin Eze, Chimeremeze Davidson Sibigem, Oluchukwu Chukwuemeka Asogwa, Chinonso Michael Eze, Samson Offorma Ugwu, et al. On Statistical Analysis of Eight Sexually Transmitted Diseases Using Categorical Analysis of Variance: A Case Study of the University of Nigeria Teaching Hospital. Sci J Appl Math Stat. 2023;11(1):1-16. doi: 10.11648/j.sjams.20231101.11

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  • @article{10.11648/j.sjams.20231101.11,
      author = {Nnaemeka Martin Eze and Chimeremeze Davidson Sibigem and Oluchukwu Chukwuemeka Asogwa and Chinonso Michael Eze and Samson Offorma Ugwu and Felix Obi Ohanuba},
      title = {On Statistical Analysis of Eight Sexually Transmitted Diseases Using Categorical Analysis of Variance: A Case Study of the University of Nigeria Teaching Hospital},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {11},
      number = {1},
      pages = {1-16},
      doi = {10.11648/j.sjams.20231101.11},
      url = {https://doi.org/10.11648/j.sjams.20231101.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20231101.11},
      abstract = {This paper focused on the statistical analysis of eight Sexually Transmitted Infections (STIs) reported in the University of Nigeria Teaching Hospital from 2010-2020. A population of 20,704 patients was recorded to have contracted eight (8) selected STIs. Prevalence analysis was computed to determine the most prevalent STI. Two-way CATANOVA cross-classification was computed to ascertain age group and gender that suffer more of each of these infections. Logistic regression model was fitted to predict reproductive status of patients that suffer the most prevalent STI. The prevalence analysis results showed Gonorrhea infection as the most prevalent STI. Two-way CATANOVA results for Gonorrhea and Chlamydia infections showed that there were significant difference in gender, age and interaction effects, significant difference in age and interaction effect for Trichomoniases infection, significant difference in age for Syphilis and HIV infections but no significant difference in gender, age and interaction effects for Human Papillomavirus (HPV), Hepatitis B Virus and Herpes infections. The results showed that the percentage of male that suffers STIs is more than the percentage of female, the percentage of 30-39 years that suffer STIs is more than the percentage of any other age group and the percentage of people without STIs history is more than the percentage of those that have history of them. Logistic regression results on Gonorrhea infection showed that an increase in age, body mass index, blood pressure, blood sugar, bacteria quantity, and Gonorrhea history were associated with an increased likelihood of the Gonorrhea patient being infertile.},
     year = {2023}
    }
    

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    AU  - Chimeremeze Davidson Sibigem
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    AU  - Chinonso Michael Eze
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    AU  - Felix Obi Ohanuba
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    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
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    PB  - Science Publishing Group
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    AB  - This paper focused on the statistical analysis of eight Sexually Transmitted Infections (STIs) reported in the University of Nigeria Teaching Hospital from 2010-2020. A population of 20,704 patients was recorded to have contracted eight (8) selected STIs. Prevalence analysis was computed to determine the most prevalent STI. Two-way CATANOVA cross-classification was computed to ascertain age group and gender that suffer more of each of these infections. Logistic regression model was fitted to predict reproductive status of patients that suffer the most prevalent STI. The prevalence analysis results showed Gonorrhea infection as the most prevalent STI. Two-way CATANOVA results for Gonorrhea and Chlamydia infections showed that there were significant difference in gender, age and interaction effects, significant difference in age and interaction effect for Trichomoniases infection, significant difference in age for Syphilis and HIV infections but no significant difference in gender, age and interaction effects for Human Papillomavirus (HPV), Hepatitis B Virus and Herpes infections. The results showed that the percentage of male that suffers STIs is more than the percentage of female, the percentage of 30-39 years that suffer STIs is more than the percentage of any other age group and the percentage of people without STIs history is more than the percentage of those that have history of them. Logistic regression results on Gonorrhea infection showed that an increase in age, body mass index, blood pressure, blood sugar, bacteria quantity, and Gonorrhea history were associated with an increased likelihood of the Gonorrhea patient being infertile.
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Author Information
  • Department of Statistics, University of Nigeria, Nsukka, Nigeria

  • Department of Statistics, University of Nigeria, Nsukka, Nigeria

  • Department of Mathematics and Statistics, Alex Ekwueme Federal University Ndufu-Alike Ikwo, Nigeria

  • Department of Statistics, University of Nigeria, Nsukka, Nigeria

  • Department of Statistics, University of Nigeria, Nsukka, Nigeria

  • Department of Statistics, University of Nigeria, Nsukka, Nigeria

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