DESIRABILITY OPTIMIZATION BASED ON THE POISSON REGRESSION MODEL: ESTIMATION OF THE OPTIMUM DENTAL WORKFORCE PLANNING

Öz

Aim: This study aims to estimate the optimum number of dentists needed by determining the social and economic variables that affect the dental workforce planning in Turkey. Methods: A desirability optimization model based on the Poisson regression model was used to evaluate the importance of the variables of this study and to calculate the optimum values of the variables. The data used in the study cover the years 1960-2018. Population (𝑥𝑝), gross domestic product per capita (𝑥𝑝𝑐), life expectancy (𝑥𝑙𝑒), and literacy rate (𝑥𝑙𝑟) were considered as input variables affecting the dental workforce (𝑦𝑑). Results: The values of deviance R 2 , adjusted R 2 , and Akaike Information Criterion (AIC) were computed as 0.9941, 0.9941, and 960.11, respectively, which confirm the validity of the Poisson statistical test. The dual mechanism reliability was obtained by adhering to the 'what-if' perspective and desirability values of the top-ten optimum values of the dental workforce. Conclusion: The results of the study show that social and economic determinants play an important role in the estimated dental workforce planning assessment required for oral and dental health in Turkey

Anahtar Kelimeler

• dental workforce planning
• factors
• poisson regression
• desirability optimization
• estimation

Kaynakça

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21. Atalan, A., & Dönmez, C. C. (2020). Optimizing experimental simulation design for the emergency departments. Brazilian Journal of Operations & Production Management, 17(4), 1–13. https://doi.org/10.14488/BJOPM.2020.026
22. Ayaz Atalan, Y., Tayanç, M., Erkan, K., & Atalan, A. (2020). Development of Nonlinear Optimization Models for Wind Power Plants Using Box-Behnken Design of Experiment: A Case Study for Turkey. Sustainability, 12(15), 6017. https://doi.org/10.3390/su12156017
23. Dement, J. M., Epling, C., Østbye, T., Pompeii, L. A., & Hunt, D. L. (2004). Blood and body fluid exposure risks among health care workers: Results from the Duke Health and Safety Surveillance System. American Journal of Industrial Medicine, 46(6), 637–648. https://doi.org/10.1002/ajim.20106
24. Eaton, K. A. (2020). Oral healthcare workforce planning in post-Brexit Britain. British Dental Journal, 228(10), 750–752. https://doi.org/10.1038/s41415-020-1579-6
25. Gallagher, J. E., Manickam, S., & Wilson, N. H. (2015). Sultanate of Oman: building a dental workforce. Human Resources for Health, 13(1), 50. https://doi.org/10.1186/s12960-015- 0037-z
26. Gayawan, E. (2014). A Poisson Regression Model to Examine Spatial Patterns in Antenatal Care Utilisation in Nigeria. Population, Space and Place, 20(6), 485–497. https://doi.org/10.1002/psp.1775
27. GEBSKI, V., ELLINGSON, K., EDWARDS, J., JERNIGAN, J., & KLEINBAUM, D. (2012). Modelling interrupted time series to evaluate prevention and control of infection in healthcare. Epidemiology and Infection, 140(12), 2131–2141. https://doi.org/10.1017/S0950268812000179
28. Harper, P., Kleinman, E., Gallagher, J., & Knight, V. (2013). Cost‐effective workforce planning: optimising the dental team skill‐mix for England. Journal of Enterprise Information Management, 26(1/2), 91–108. https://doi.org/10.1108/17410391311289569
29. Hung, M., Xu, J., Lauren, E., Voss, M. W., Rosales, M. N., Su, W., Ruiz-Negrón, B., He, Y., Li,
30. W., & Licari, F. W. (2019). Development of a recommender system for dental care using machine learning. SN Applied Sciences, 1(7), 785. https://doi.org/10.1007/s42452-019- 0795-7
31. Islam, M. A., & Chowdhury, R. I. (2017). A generalized right truncated bivariate Poisson regression model with applications to health data. PLOS ONE, 12(6), e0178153. https://doi.org/10.1371/journal.pone.0178153
32. Jenarthanan, M. P., & Jeyapaul, R. (2018). Optimisation of machining parameters on milling of GFRP composites by desirability function analysis using Taguchi method. International Journal of Engineering, Science and Technology, 5(4), 22–36. https://doi.org/10.4314/ijest.v5i4.3
33. Knevel, R., Gussy, M., & Farmer, J. (2017). Exploratory scoping of the literature on factors that influence oral health workforce planning and management in developing countries. International Journal of Dental Hygiene, 15(2), 95–105. https://doi.org/10.1111/idh.12260
34. Mihaylova, B., Briggs, A., O’Hagan, A., & Thompson, S. G. (2011). Review of Statistical Methods Atalan, A., & Donmez, C. C. (2020). DEVELOPING OPTIMIZATION MODELS TO EVLUATE HEALTHCARE SYSTEMS. Sigma Journal of Engineering and Natural Sciences, 38(2), 853–873.
35. Atalan, A., & Dönmez, C. C. (2020). Optimizing experimental simulation design for the emergency departments. Brazilian Journal of Operations & Production Management, 17(4), 1–13. https://doi.org/10.14488/BJOPM.2020.026
36. Ayaz Atalan, Y., Tayanç, M., Erkan, K., & Atalan, A. (2020). Development of Nonlinear Optimization Models for Wind Power Plants Using Box-Behnken Design of Experiment: A Case Study for Turkey. Sustainability, 12(15), 6017. https://doi.org/10.3390/su12156017
37. Dement, J. M., Epling, C., Østbye, T., Pompeii, L. A., & Hunt, D. L. (2004). Blood and body fluid exposure risks among health care workers: Results from the Duke Health and Safety Surveillance System. American Journal of Industrial Medicine, 46(6), 637–648. https://doi.org/10.1002/ajim.20106
38. Eaton, K. A. (2020). Oral healthcare workforce planning in post-Brexit Britain. British Dental Journal, 228(10), 750–752. https://doi.org/10.1038/s41415-020-1579-6
39. Gallagher, J. E., Manickam, S., & Wilson, N. H. (2015). Sultanate of Oman: building a dental workforce. Human Resources for Health, 13(1), 50. https://doi.org/10.1186/s12960-015- 0037-z
40. Gayawan, E. (2014). A Poisson Regression Model to Examine Spatial Patterns in Antenatal Care Utilisation in Nigeria. Population, Space and Place, 20(6), 485–497. https://doi.org/10.1002/psp.1775 GEBSKI, V., ELLINGSON, K., EDWARDS, J., JERNIGAN, J., & KLEINBAUM, D. (2012).
41. Modelling interrupted time series to evaluate prevention and control of infection in healthcare. Epidemiology and Infection, 140(12), 2131–2141. https://doi.org/10.1017/S0950268812000179
42. Harper, P., Kleinman, E., Gallagher, J., & Knight, V. (2013). Cost‐effective workforce planning: optimising the dental team skill‐mix for England. Journal of Enterprise Information Management, 26(1/2), 91–108. https://doi.org/10.1108/17410391311289569 Hung, M., Xu, J., Lauren, E., Voss, M. W., Rosales, M. N., Su, W., Ruiz-Negrón, B., He, Y., Li,
43. W., & Licari, F. W. (2019). Development of a recommender system for dental care using machine learning. SN Applied Sciences, 1(7), 785. https://doi.org/10.1007/s42452-019- 0795-7
44. Islam, M. A., & Chowdhury, R. I. (2017). A generalized right truncated bivariate Poisson regression model with applications to health data. PLOS ONE, 12(6), e0178153. https://doi.org/10.1371/journal.pone.0178153
45. Jenarthanan, M. P., & Jeyapaul, R. (2018). Optimisation of machining parameters on milling of GFRP composites by desirability function analysis using Taguchi method. International Journal of Engineering, Science and Technology, 5(4), 22–36. https://doi.org/10.4314/ijest.v5i4.3
46. Knevel, R., Gussy, M., & Farmer, J. (2017). Exploratory scoping of the literature on factors that influence oral health workforce planning and management in developing countries. International Journal of Dental Hygiene, 15(2), 95–105. https://doi.org/10.1111/idh.12260
47. Mihaylova, B., Briggs, A., O’Hagan, A., & Thompson, S. G. (2011). Review of Statistical Methods