Volume 4, Issue 2, June 2019, Page: 15-21
Statistical Analysis of Age Reporting Errors by Insured and Uninsured Patients in Cape Coast Teaching Hospital of Ghana
Eyiah-Bediako Francis, Department of Statistics, University of Cape Coast, Cape Coast, Ghana
Bosson-Amedenu Senyefia, Department of Mathematics and ICT, Holy Child College of Education, Takoradi, Ghana
Borbor Bridget Sena, Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Received: Jul. 12, 2019;       Accepted: Aug. 4, 2019;       Published: Aug. 15, 2019
DOI: 10.11648/j.bsi.20190402.11      View  118      Downloads  40
Abstract
Age is a very important variable that guides clinicians to carryout diagnosis, treatment, as well as administering medical procedures to patients. Misreporting of age by patients to clinicians can have dire consequences on the patients’ health. This retrospective study used a 10 year demographic data involving the ages reported by 906,383 patients. Demographic indexes such as Whipples, Myers Blended and Joint Score were employed to analyse reported ages among insured and uninsured patients at the Cape Coast Teaching Hospital. The computed joint score values of 76.88 and 85.60 respectively for uninsured and insured patients qualified the data as highly inaccurate by the standards of interpretation of UN index. The summary of the digit preference of the uninsured and insured patients by Myers blended index approach were 29.34 and 29.87 respectively. The blended sum at the digits 0, 1, 2 and 5 exceeded 10% of the total blended population, an indication of over selection of ages ending in those digits by the insured and uninsured patients. Whipple’s index for uninsured and insured patients was 149.3 and 287.1 respectively. These values respectively show that the reliability of the ages reported were rough and very rough, by the Whipple’s index interpretation standards. The insured were found to have higher tendency of concentrating on ages ending in 0 and 5 than the uninsured. The study concluded that age data in Cape Coast Teaching Hospital is misreported and inaccurate and if not adjusted may result in wrong age-dependent medical procedures undertaken by clinicians. It was recommended among others for hospitals to institute innovative ways of recording ages such as using calendar of historical events technique where the patients could not recall their correct age.
Keywords
Myers Index, Whipples Index, UN Index, Hospital, Insured, Uninsured, Demographic Indexes
To cite this article
Eyiah-Bediako Francis, Bosson-Amedenu Senyefia, Borbor Bridget Sena, Statistical Analysis of Age Reporting Errors by Insured and Uninsured Patients in Cape Coast Teaching Hospital of Ghana, Biomedical Statistics and Informatics. Vol. 4, No. 2, 2019, pp. 15-21. doi: 10.11648/j.bsi.20190402.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Pardeshi, G. S. (2010), Age heaping and accuracy of age data collected during a community survey in the Yavatmal district, Maharashtra, Indian J Community Med, 35 (3) 391-395.
[2]
Shirley. L., V. Ravi and M. Margaret, (2004). An evaluation of the age and sex data from the census population of Canada, provinces and territories, 1971 to 2001, Canadian Population Society Annual Meeting, Winnipeg, Manitoba, (2004). http//web.uvic.ca/~canpop/2004/Loh-Verma-Michalowski-CPS04.ppt.
[3]
S. Denic, F. Khatib and H. Saadi, Quality of age data in patients from developing countries, Journal of Public Health, 26 (2) (2003), 168–171.
[4]
Bello Y., (2012). Error Detection in Outpatients’ Age Data Using Demographic Techniques. International Journal of Pure and Applied Sciences and Technology. Int. J. Pure Appl. Sci. Technol., 10 (1) (2012), 27-36. ISSN 2229-6107. Available online at www.ijopaasat.in.
[5]
Barua, R. K (2015). Detection of Digit Preference and Age Misreporting by using Demographic Techniques. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Population, Reproductive Health, Gender and Development (MPRHGD) at East West University, Dhaka, Bangladesh.
[6]
Kpedekpo G. M. K. (1982). Essentials of Demographic Analysis for Africa. Hernerman Educational Books Inc., ew Hemisphere.
[7]
Bwalya B. B, Phiri M. and Mwansa C. (2015). International Journal of Current Advanced Research vol 4, Issue 5, pp 92-97, May 2015.
[8]
Bekele S. (2006). Analysis on the quality of age and sex data collected in two population and housing censuses of Ethiopia. EthiopJournal of Science, 29 (2): 123-132, 2006. ISSN: 0379-2897.
[9]
Bosson-Amedenu S., Oduro-Okyireh T., Osei-Asibey E. Detection of errors in age data of National Health Insurance Scheme registrants in Ghana: demographic indexes approach. International Journal of Mathematical Archive-10 (3), 2019, 6-13 Available online through www.ijma.info ISSN 2229-5046.
[10]
Pardeshi G. S (2010). Age Heaping and Accuracy of Age Data Collected During a Community Survey in the Yavatmal District, Maharashtra. Indian Journal of Community Medicine 35 (3): 391-5.
[11]
Bello Y. (2012). Error Detection in Outpatients’ Age Data Using Demographic Techniques. Int. J. Pure Appl. Sci. Technol., 10 (1) (2012), pp. 27-36.
[12]
G. M. K. Kpedekpo, Essentials of Demographic Analysis for Africa, Hernerman Educational Books Inc., New Hemisphere, 1982.
[13]
B. Kestenbaum, A description of the extreme age population based on improved Medicare enrollment data, Demography, 29 (1992), 565–580.
[14]
H. S. Shryock, J. S. Siegel and Associates. The Methods and Materials of Demography, Condensed edition by Edward G. Stockewell, Academic Press, New York, 1976.
[15]
I. Johannes and F. Polly, Bancroft’s Introduction to Biostatistics, (2nd Edition), Harper and Row publishers, New-York, 1970.
Browse journals by subject