Performance Assessment of Some Count Data Models to Immunization Coverage Data

Usman Aliyu; Umar Usman; Abubakar Umar Bashar; Daha Umar Faruk

doi:doi:10.11648/j.ijsd.20241004.12

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Performance Assessment of Some Count Data Models to Immunization Coverage Data

Usman Aliyu^*

, Umar Usman, Abubakar Umar Bashar, Daha Umar Faruk

Published in International Journal of Statistical Distributions and Applications (Volume 10, Issue 4)

Received: 11 July 2024 Accepted: 21 October 2024 Published: 22 November 2024

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Abstract

This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.

Published in	International Journal of Statistical Distributions and Applications (Volume 10, Issue 4)
DOI	10.11648/j.ijsd.20241004.12
Page(s)	89-100
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

PR, ZIP, TPR, NB, NBR, AIC, BIC, LR

References

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APA Style

Aliyu, U., Usman, U., Bashar, A. U., Faruk, D. U. (2024). Performance Assessment of Some Count Data Models to Immunization Coverage Data. International Journal of Statistical Distributions and Applications, 10(4), 89-100. https://doi.org/10.11648/j.ijsd.20241004.12

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ACS Style

Aliyu, U.; Usman, U.; Bashar, A. U.; Faruk, D. U. Performance Assessment of Some Count Data Models to Immunization Coverage Data. Int. J. Stat. Distrib. Appl. 2024, 10(4), 89-100. doi: 10.11648/j.ijsd.20241004.12

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AMA Style

Aliyu U, Usman U, Bashar AU, Faruk DU. Performance Assessment of Some Count Data Models to Immunization Coverage Data. Int J Stat Distrib Appl. 2024;10(4):89-100. doi: 10.11648/j.ijsd.20241004.12

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@article{10.11648/j.ijsd.20241004.12,
  author = {Usman Aliyu and Umar Usman and Abubakar Umar Bashar and Daha Umar Faruk},
  title = {Performance Assessment of Some Count Data Models to Immunization Coverage Data
},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {10},
  number = {4},
  pages = {89-100},
  doi = {10.11648/j.ijsd.20241004.12},
  url = {https://doi.org/10.11648/j.ijsd.20241004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20241004.12},
  abstract = {This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.
},
 year = {2024}
}

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TY  - JOUR
T1  - Performance Assessment of Some Count Data Models to Immunization Coverage Data

AU  - Usman Aliyu
AU  - Umar Usman
AU  - Abubakar Umar Bashar
AU  - Daha Umar Faruk
Y1  - 2024/11/22
PY  - 2024
N1  - https://doi.org/10.11648/j.ijsd.20241004.12
DO  - 10.11648/j.ijsd.20241004.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 89
EP  - 100
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20241004.12
AB  - This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.

VL  - 10
IS  - 4
ER  -

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Author Information

Usman Aliyu

Department of Statistics, Waziri Umar Federal Polytechnic, Birnin Kebbi, Nigeria

Contact Email

http://orcid.org/0000-0002-0315-0448
Umar Usman

Department of Statistics, Usmanu Danfodiyo University, Sokoto, Nigeria

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Abubakar Umar Bashar

Department of Statistics, Waziri Umar Federal Polytechnic, Birnin Kebbi, Nigeria

Contact Email
Daha Umar Faruk

Department of Mathematics, Federal University, Birnin Kebbi, Nigeria

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Plain Text BibTeX RIS

APA Style

Aliyu, U., Usman, U., Bashar, A. U., Faruk, D. U. (2024). Performance Assessment of Some Count Data Models to Immunization Coverage Data. International Journal of Statistical Distributions and Applications, 10(4), 89-100. https://doi.org/10.11648/j.ijsd.20241004.12

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ACS Style

Aliyu, U.; Usman, U.; Bashar, A. U.; Faruk, D. U. Performance Assessment of Some Count Data Models to Immunization Coverage Data. Int. J. Stat. Distrib. Appl. 2024, 10(4), 89-100. doi: 10.11648/j.ijsd.20241004.12

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AMA Style

Aliyu U, Usman U, Bashar AU, Faruk DU. Performance Assessment of Some Count Data Models to Immunization Coverage Data. Int J Stat Distrib Appl. 2024;10(4):89-100. doi: 10.11648/j.ijsd.20241004.12

Copy | Download

@article{10.11648/j.ijsd.20241004.12,
  author = {Usman Aliyu and Umar Usman and Abubakar Umar Bashar and Daha Umar Faruk},
  title = {Performance Assessment of Some Count Data Models to Immunization Coverage Data
},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {10},
  number = {4},
  pages = {89-100},
  doi = {10.11648/j.ijsd.20241004.12},
  url = {https://doi.org/10.11648/j.ijsd.20241004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20241004.12},
  abstract = {This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Performance Assessment of Some Count Data Models to Immunization Coverage Data

AU  - Usman Aliyu
AU  - Umar Usman
AU  - Abubakar Umar Bashar
AU  - Daha Umar Faruk
Y1  - 2024/11/22
PY  - 2024
N1  - https://doi.org/10.11648/j.ijsd.20241004.12
DO  - 10.11648/j.ijsd.20241004.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 89
EP  - 100
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20241004.12
AB  - This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.

VL  - 10
IS  - 4
ER  -

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