Modelling the Number of Household Visit to Health Care Centres in Some Nigeria Communities Using Count Data Regression Models
Introduction: The need to model the impact of some demographic indicators on the frequency of household visits to healthcare centres in Nigeria's community is very important for preventing and spreading community diseases. This study aimed to investigate the effect of the patents' age, gender, marital status, type of illness and amount spent on the frequency of visits to community health care centres in Nigeria and to compared Negative Binomial Regression (NBR) and Generalized Poisson Regression GPR) models to determine the preferred count regression model for the number of household visits to health centres in some communities in Nigeria.
Methods: Survey of 132640 households in some Nigeria communities obtained from the 2018/2019 Nigeria Living Standard Survey (NLSS) were extracted from the National Bureau of Statistics (NBS) in collaboration with the World Bank. The Negative Binomial and Generalised Poisson regression models were used to investigate the five demographic variables on the frequency of visit to the community health centres. The performance of the count regression model was assessed using the Chi-square -2log Likelihood Statistic (2logL), Akaike Information Criterion (AIC) and Bayesian Information Criterion BIC) selection criteria.
Results: Findings from the study showed that the type of illness and amount spent has a significantly positive effect on the number of household members' visits to the community health care centres in Nigeria while age, gender, and marital status was discovered to have a negative effect on the number of household members' visits to the community health care centres in Nigeria.
Conclusion: The Nigeria Government, health centre management and community healthcare service providers' need to be aware that the amount spent and the nature of illness determines the level of health care services utilisation in the Nigeria community, hence the need for the drastic reduction in charges to encourage a household visit to the community health centres whenever the need arises.
2. Singh, A.R., Singh, S.A. The goal: Health for all. The commitment: All for health. Mens Sana Monogr. (2004); 2(1):97–110.
3. Mojekwu JN, Ibekwe U. Maternal mortality in Nigeria: Examination of intervention methods. International Journal of
Humanities and Social Science. (2012); 2(20):135–149.
4. Tumba, S. Addressing Health Challenges in Nigeria. https://www.inigerian.com/addressing-thepublic-health- challenges-nigeria-faces/ (accessed 20 May 2016)
5. Welcome, M.O. The Nigerian Health Care System: Need for Integrating Adequate Medical Intelligence and Surveillance Systems. Journal of Pharmacy & Bioallied Sciences. (2011); 3(4): 470-478. https://doi.org/10.4103/0975-7406.90100.
6. The World Health Statistics of 2011. World Health Organization Annual Compilation of Health-Related Data for its
193 Member States, (2011).
7. Ngugi, A.K., Agoi, F., Mahoney, M.R., Lakhani, A., Mang'ong'o, D., Nderitu. The utilisation of health services in a resourcelimited rural area in Kenya: Prevalence and associated household-level factors. (2017);PLoS ONE 12(2): e0172728. https://doi.org/10.1371/journal.pone.0172728.
8. Tey NP, Lai, S-I. Correlates of and Barriers to the Utilisation of Health Services for Delivery in South Asia and Sub-Saharan Africa. The Scientific World Journal, Hindawi, (2013); 1–11. https://doi.org/10.1155/2013/423403.
9. Yitayal, M., Berhane, Y., Worku, A., Kebede, Y. BMC Health Services Research. (2014); 14(156): 1-9. http://www.biomedcentral.com/1472-6963/14/156.
10. Lubbock, L.A., Stephenson, R.B. Utilisation of maternal health care services in the department of Matagalpa, Nicaragua. Rev Panam Salud Publica (2008); 24(2):75–84.
11. Bakeera, S.K., Wamala, S.P., Galea, S., State, A., Peterson, S., Pariyo, G.W. Community Perceptions and Factors Influencing Utilisation of Health Services in Uganda. International Journal for Equity in Health. (2009); 8(25):1–12.
12. Birmeta K, Dibaba Y, Woldeyohannes D. Determinants of maternal health care utilisation in Holeta town, central Ethiopia. BMC Health Service Research. (2013); 13(256):1–10.
13. Ferdous, F.B., Azam, A.T.M.Z. The utilisation of Child Health Care Services in Thana Health Complex of Bangladesh: A Study of Keraniganj. Asian Journal of Epidemiology. (2009); 2(2):20-32. https://doi.org/10.3923/aje.2009.20.32.
14. Masaki, M., Bina, G. Women's Status. Household Structure and the Utilisation of Maternal Health Services in Nepal. Asia-Pacific Population Journal. (2001); 16(1):23–44. https://doi.org/10.18356/e8a4c9ed-en.
15. Wang, J., Zuo, H., Chen, X., Hou, L., and Ma, J. Analysis of factors influencing the frequency of primary care visits among diabetic patients in two provinces in China. BMC Public Health (2019); 19:1267. https://doi.org/10.1186/s12889-019-7591-6
16. Kevany, S., Murima, O., Singh, B., Hlubinka, D., Kulich M, et al. Socioeconomic status and health care utilisation in rural Zimbabwe: findings from Project Accept (HPTN 043). Journal of Public Health in Africa. (2012); 3(1): e13.
17. Living Standard Survey (NLSS) data collected by for the year 2018/2019. Nigeria Bureau of Statistics (NBS) and the World Bank and Department for International Development (DFID), (2019).
18. Hardin, J. W. and dan Hilbe, J. M. Generalized linear models and extensions. Texas: A Strata Press Publication. (2007).
19. Adams, S.O., Bamanga, M.O., Olanrewaju, S.O., Yahaya, HU and Akano, R.O. Modelling COVID-19 Cases in Nigeria
Using Some Selected Count Data. International Journal of Healthcare and Medical Sciences. (2020); 6(4): 64-73.
20. Famoye, F. Restricted Generalized Poisson Regression Model. Communications in statistics. Theory and Methods. (1993); 22:1335-1354.
21. Wang, W. R. and Famoye, F. Modeling Household Fertility Decisions with Generalized Poisson Regression. Journal of
Population Economics. (1997); 10: 273-283.
22. Nwankwo, C. H. and Nwaigwe, G. I. A statistical model of road traffic crashes data in Anambra State, Nigeria: A Poisson regression approach. European Journal of Statistics and Probability. (2016); 1: 41-60.
23. Shaw-Pin, M. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Center for Transportation Analysis, Energy Division Oak Ridge National Laboratory. (1993).
24. Akaike, H. Information theory and extension of the maximum likelihood principle, 2 nd International Symposium on
Information Theory. (1973); 267-281.
25. Schwarz, G. Estimating the dimension of a model." Annals of Statistic. (1978); 6: 461-464.
26. Girma F, Jira C, Girma B. Health Services Utilisation and Associated Factors in Jimma Zone, South-west Ethiopia. Ethiopian Journal of Health Sciences. (2011); 21(1): 85-94.
27. Develay, A. Sauerborn, R., Diesfeld, H.J. Utilization of Health Care in an African Urban Area: Results from Household Survey in Ouagadougou, Burkina Faso. Social Science & Medicine (1996). 43(11); 1611-1619. https://doi.org/10.1016/S0277-9536(96)00061-5
|Issue||Vol 7 No 1 (2021)|
|Community health Negative binomial regression (NBR) Generalized poisson regression (GPR) Poisson regression (PR) Over-dispersion|
|Rights and permissions|
|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|