Prediction of the Number of Tuberculosis Cases and Estimation of Its Treatment Cost in Saudi Arabia Using Proxy Information
Affiliation(s)
1 National Biotechnology Centre, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia. 2 Mycobacteriology Research Section, Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia. 3 Department of Cell Biology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
1 National Biotechnology Centre, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia. 2 Mycobacteriology Research Section, Department of Infection and Immunity, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia. 3 Department of Cell Biology, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
ABSTRACT
It is well known that the number of people with Tuberculosis (TB) and those who develop multidrug resistance (MDR) are the fundamental components that affect the total cost of treatment of TB. This paper has two-fold objectives. Firstly, we use the Generalized Linear Regression Models (GLM) to predict the future count of persons with TB and MDR. Due to the fact that assessment of TB cost is methodologically difficult, and compounded with the lack of concrete information about the treatment cost in Saudi Arabia, our second objective is to use cost information from the EU countries as proxy to estimate the cost of treating TB. The cost predictions provide essential information that is part of the evidence needed for budgeting and financing the health care facilities of TB services, especially with respect to avoiding under-estimation of the cost of TB-MDR treatment.
It is well known that the number of people with Tuberculosis (TB) and those who develop multidrug resistance (MDR) are the fundamental components that affect the total cost of treatment of TB. This paper has two-fold objectives. Firstly, we use the Generalized Linear Regression Models (GLM) to predict the future count of persons with TB and MDR. Due to the fact that assessment of TB cost is methodologically difficult, and compounded with the lack of concrete information about the treatment cost in Saudi Arabia, our second objective is to use cost information from the EU countries as proxy to estimate the cost of treating TB. The cost predictions provide essential information that is part of the evidence needed for budgeting and financing the health care facilities of TB services, especially with respect to avoiding under-estimation of the cost of TB-MDR treatment.
Cite this paper
Shoukri, M. , Varghese, B. , Al-Hajoj, S. and Al-Mohanna, F. (2014) Prediction of the Number of Tuberculosis Cases and Estimation of Its Treatment Cost in Saudi Arabia Using Proxy Information. Open Journal of Statistics, 4, 726-735. doi: 10.4236/ojs.2014.49067.
Shoukri, M. , Varghese, B. , Al-Hajoj, S. and Al-Mohanna, F. (2014) Prediction of the Number of Tuberculosis Cases and Estimation of Its Treatment Cost in Saudi Arabia Using Proxy Information. Open Journal of Statistics, 4, 726-735. doi: 10.4236/ojs.2014.49067.
References
[1] (2014) WHO TB Report 2013.
http://www.who.int/tb/publications/global_report/en/ [2] Donald, P.R. and Van Helden, P.D. (2009) Global Burden of TB-Combating Drug Resistance in Difficult Times. NEJM, 360, 23.
http://dx.doi.org/10.1056/NEJMp0903806 [3] Abouzaid, M.S., Zumla, A.I., Felemban, S., Alotaibi, B., O’Grady, J. and Memish, Z.A. (2012) Tuberculosis Trends in Saudi and Non-Saudi in the Kingdom of Saudi Arabia—10 Year Retrospective Study (2000-2009). PLoS ONE, 7, e39478.
http://dx.doi.org/10.1371/journal.pone.0039478 [4] Al-Hajoj, S.A. (2010) Tuberculosis in Saudi Arabia: Can We Change the Way We Deal with the Disease? Journal of Infection and Public Health, 3, 17-24.
http://dx.doi.org/10.1016/j.jiph.2009.12.001 [5] Diel, R., Vandeputte, J., de Vries, G., Stillo, J., Wanlim, M. and Nienhaus, A. (2014) Costs of Tuberculosis Disease in the EU—A Systematic Analysis and Cost Calculation. European Respiratory Journal, 43, 554-565.
http://dx.doi.org/10.1183/09031936.00079413 [6] Fitzmaurice, G.M., Laird, N.M. and Ware, J. (2011) Applied Longitudinal Analysis. 2nd Edition, Wiley, New York. [7] Palmgren, J. (1981) The Fisher Information Matrix for Log-Linear Models Arguing Conditionally in the Observed Explanatory Variables. Biometrika, 68, 563-566. [8] Allison, P.D. (1996) Fixed Effects Partial Likelihood for Repeated Events. Sociological Methods & Research, 25, 207-222.
http://dx.doi.org/10.1177/0049124196025002003 [9] Cameron, A.C. and Trivedi, P.K. (1998) Regression Analysis of Count Data. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511814365 [10] Lin, L.I. (1989) A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometric, 45, 255-268.
http://dx.doi.org/10.2307/2532051 [11] World Health Organization (2012) Global Tuberculosis Report 2012. WHO, Geneva. [12] The CIA World Fact-Book (2014) https://www.cia.gov/library/publications [13] Landis, J.R. and Koch, G.G. (1977) The Measurement of Observer Agreement for Categorical Data. Biometrics, 33, 159-174.
http://dx.doi.org/10.2307/2529310 [14] Russell, S. (2004) The Economic Burden for Household in Developing Countries: A Review of Studies Focusing on Malaria, Tuberculosis, and Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome. American Journal of Tropical Medicine and Hygiene, 71, 147-155. [15] Stahlecker, P. and Trenkler, G. (1993) Some Further Results on the Use of Proxy Variables in Prediction. The Review of Economics Statistics, 75, 707-711.
http://dx.doi.org/10.2307/2110026 [16] Sobel, M.E. (2006) What Do Randomized Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference. Journal of the American Statistical Association, 101, 1398-1407.
http://dx.doi.org/10.1198/016214506000000636 [17] Sobel, M.E. (2008) Identification of Causal Parameters in Randomized Studies with Mediating Variables. Journal of Educational and Behavioral Statistics, 33, 230-251. [18] Wright, A., Zignol, M., Van Deun, A., Falzon, D., Gerdes, S.R., Feldman, K., et al. (2009) Epidemiology of Antituberculosis Drug Resistance 2002-07: An Updated Analysis of the Global Project on Anti-Tuberculosis Drug Resistance Surveillance. The Lancet, 373, 1861-1873. [19] Broekman, J.F., Migliori, G.B., Rieder, H.L., Lees, J., Ruutu, P., Loddenkemper, R., et al. (2002) European Framework for Tuberculosis Control and Elimination in Countries with Low Incidence. European Respiratory Journal, 19, 765-775.
[1] (2014) WHO TB Report 2013.
http://www.who.int/tb/publications/global_report/en/ [2] Donald, P.R. and Van Helden, P.D. (2009) Global Burden of TB-Combating Drug Resistance in Difficult Times. NEJM, 360, 23.
http://dx.doi.org/10.1056/NEJMp0903806 [3] Abouzaid, M.S., Zumla, A.I., Felemban, S., Alotaibi, B., O’Grady, J. and Memish, Z.A. (2012) Tuberculosis Trends in Saudi and Non-Saudi in the Kingdom of Saudi Arabia—10 Year Retrospective Study (2000-2009). PLoS ONE, 7, e39478.
http://dx.doi.org/10.1371/journal.pone.0039478 [4] Al-Hajoj, S.A. (2010) Tuberculosis in Saudi Arabia: Can We Change the Way We Deal with the Disease? Journal of Infection and Public Health, 3, 17-24.
http://dx.doi.org/10.1016/j.jiph.2009.12.001 [5] Diel, R., Vandeputte, J., de Vries, G., Stillo, J., Wanlim, M. and Nienhaus, A. (2014) Costs of Tuberculosis Disease in the EU—A Systematic Analysis and Cost Calculation. European Respiratory Journal, 43, 554-565.
http://dx.doi.org/10.1183/09031936.00079413 [6] Fitzmaurice, G.M., Laird, N.M. and Ware, J. (2011) Applied Longitudinal Analysis. 2nd Edition, Wiley, New York. [7] Palmgren, J. (1981) The Fisher Information Matrix for Log-Linear Models Arguing Conditionally in the Observed Explanatory Variables. Biometrika, 68, 563-566. [8] Allison, P.D. (1996) Fixed Effects Partial Likelihood for Repeated Events. Sociological Methods & Research, 25, 207-222.
http://dx.doi.org/10.1177/0049124196025002003 [9] Cameron, A.C. and Trivedi, P.K. (1998) Regression Analysis of Count Data. Cambridge University Press, Cambridge.
http://dx.doi.org/10.1017/CBO9780511814365 [10] Lin, L.I. (1989) A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometric, 45, 255-268.
http://dx.doi.org/10.2307/2532051 [11] World Health Organization (2012) Global Tuberculosis Report 2012. WHO, Geneva. [12] The CIA World Fact-Book (2014) https://www.cia.gov/library/publications [13] Landis, J.R. and Koch, G.G. (1977) The Measurement of Observer Agreement for Categorical Data. Biometrics, 33, 159-174.
http://dx.doi.org/10.2307/2529310 [14] Russell, S. (2004) The Economic Burden for Household in Developing Countries: A Review of Studies Focusing on Malaria, Tuberculosis, and Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome. American Journal of Tropical Medicine and Hygiene, 71, 147-155. [15] Stahlecker, P. and Trenkler, G. (1993) Some Further Results on the Use of Proxy Variables in Prediction. The Review of Economics Statistics, 75, 707-711.
http://dx.doi.org/10.2307/2110026 [16] Sobel, M.E. (2006) What Do Randomized Studies of Housing Mobility Demonstrate? Causal Inference in the Face of Interference. Journal of the American Statistical Association, 101, 1398-1407.
http://dx.doi.org/10.1198/016214506000000636 [17] Sobel, M.E. (2008) Identification of Causal Parameters in Randomized Studies with Mediating Variables. Journal of Educational and Behavioral Statistics, 33, 230-251. [18] Wright, A., Zignol, M., Van Deun, A., Falzon, D., Gerdes, S.R., Feldman, K., et al. (2009) Epidemiology of Antituberculosis Drug Resistance 2002-07: An Updated Analysis of the Global Project on Anti-Tuberculosis Drug Resistance Surveillance. The Lancet, 373, 1861-1873. [19] Broekman, J.F., Migliori, G.B., Rieder, H.L., Lees, J., Ruutu, P., Loddenkemper, R., et al. (2002) European Framework for Tuberculosis Control and Elimination in Countries with Low Incidence. European Respiratory Journal, 19, 765-775.