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Aims: Outpatient department is one of the first points of contact for patients accessing health care and provide patients with their primary healthcare as they seek services at the facility. With the introduction of community-based health planning and services, there seems that the outpatient departments have witnessed corresponding progressive and significant increase in attendance at the various health facilities in Ghana of which the research seeks to investigate.
Materials and Methods: The data collected were outpatient hospital attendance of patients on a monthly basis from 2012 to 2019 obtained from the Cape Coast Teaching Hospital. Box Jenkins’s methodology of time series analysis was used to analyse the data. The modified Box Pierce (Ljumg-Box) Chi-square statistic criteria of the largest and minimum Chi-square statistic value was in selecting the best fitted model for outpatient department attendance.
Results: The autocorrelation function (ACF) and partial autocorrelation function (PACF) plots suggested an autoregressive (AR) process with order 2 and moving average (MA) process with order 1 which was used in selecting the appropriate model. Candidate models were obtained using the lowest Chi-square value and highest value to select adequate models and the best model. The best non-seasonal model for the data was ARIMA (2, 2, 1) for the outpatient department attendance. Model diagnostics test was performed using Ljung-Box test.
Conclusion: The findings of the forecast showed that OPD visits will increase in the next five years. Specifically, continued use of the outpatient department in accessing health care at all levels will experience an increase in hospital visits across the months from June 2020 to December 2025. Recommendations from this research included among others that, the health authorities should continue to expand the outpatient department services to increase access to healthcare by all as it services goes to the core people in the community.
Aidoo E. Modelling and forecasting inflation rates in Ghana: An application of SARIMA models (Dissertation). Börlange, SWEDEN; 2010. (Accessed 02 September 2020) Available: http://du.diva-portal.org/smash/record.jsf?pid=diva2%3A518895&dswid=1088
Banor F, Gyan F. Modelling Hospital Attendance in Ghana: A case study of Obuasi Government Hospital” Project work, Garden City University; 2012. (Accessed 22 August 2020) Available:https://www.academia.edu/6626411/Modeling_hospital_attendance_in_Ghana_A_case_of_the_Obuasi_government_hospital.
Luo L, Luo L, Zhang X, He X. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Services Research. 2017;17(1):1–13. (Accessed 17 September 2020) Available: https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-017-2407-9
Borbor BS, Bosson-Amedenu S, Daniel Gbormittah D. Statistical Analysis of Health Insurance and Cash and Carry Systems in Cape Coast Teaching Hospital of Ghana, Science Journal of Applied Mathematics and Statistics. 2019;7(3):36-44. DOI: 10.11648/j.sjams.20190703.12
Assessed: 24 Seotember, 2020. Avilable : http://article.sjams.org/pdf/10.116...
George Box PE, Gwilym Jenkins M. Time series Analysis, Forecasting and control. Holden-Day, Oakland, California, USA, 1976: 2nd edition. (Assessed: 25 September 2020) Avilable: http://garfield.library.upenn.edu/classics1989/A1989AV48500001.pdf
Box GEP, Jenkins GM, Reinsel GC. Time series analysis, forecasting and control (3rd ed.). New Jersey: Prentice Hall, Englewood Cliffs; 1994. (Assessed: 23 September 2020) Available:https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/reference/ReferencesPapers.aspx?ReferenceID=1936224
Dickey D, Fuller W. Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 1981:49(4), 1057-1072. DOI: 10.2307/1912517. Assessed: 24 September 2020 Available: https://www.jstor.org/stable/pdf/1912517.pdf
Qmul. Time series.2018b: Accessed 02 September 2020. Available: http://article.sjams.org/pdf/10.11648.j.sjams.20190703.12.pdf
PSU. Applied time series. 2018b: Accessed 02 September 2020. Available: http://article.sjams.org/pdf/10.11648.j.sjams.20190703.12.pdf
PSU. Applied time series. 2018c: Accessed 02 September 2020. Available: http://article.sjams.org/pdf/10.11648.j.sjams.20190703.12.pdf