برآورد تقاضای خدمات درمان مطالعه موردی شهرهای شیراز و ارسنجان «روش لوجیت تعمیم یافته و شبکه عصبی مصنوعی»
محورهای موضوعی : -مدارک پزشکی
1 - استادیار، گروه اقتصاد، واحد ارسنجان، دانشگاه آزاد اسلامی، ارسنجان، ایران
کلید واژه: شاخص سلامت, تقاضای خدمات درمان, الگوی لوجیت, شبکه عصبی مصنوعی,
چکیده مقاله :
مقدمه: پزشک تأمین‌کننده سلامت انسان است که به دلایل بیماری، کار، سوانح، آلودگی‌های‌زیست محیطی و گذر عمر، مستهلک شده و باعث افزایش تقاضای‌خدمات درمانی می‌شود؛ عوامل متعددی بر تقاضای خدمات درمانی تأثیر دارند؛ که در این پژوهش سعی بر اندازه‌گیری میزان تأثیر 18 مورد از مهم‌ترین آنها شده است. روش پژوهش: الگوی مورد استفاده، الگوی لوجیت ترتیبی تعمیم یافته و شبکه عصبی مصنوعی است، جامعه آماری نیز شامل کلیه کسانی است که در سال 94 به بیمارستان‌های شیراز و ارسنجان مراجعه کرده‌اند و نمونه شامل 100 نفر بیمار و 100 نفر از همراهان بیمار (افراد سالم) است و داده‌ها از طریق پرسشنامه جمع‌آوری شده است. یافته‌ها: نتایج آزمون‌های اعتبار الگو شامل، نیکویی برازش (شاخص پیرسون و دویانس)، رگرسیون‌های موازی، حداکثر راست‌نمایی و الگوریتم نیوتن-رافسون، حاکی از اعتبار الگو تا 84 درصد اطمینان است. طبق نتایج افزایش یک درصدی در ویزیت، 2/2 % از متقاضیان خدمات درمان را کاهش می‌دهد. ذخیره اولیه سلامت و اعتقادات نیز همان تفسیر حق ویزیت را دارند. افزایش یک درصدی در حق بیمه باعث کاهش 3/11 % و افزایش یک واحدی شاخص سلامت، باعث کاهش 1/3 % و افزایش یک سال در سن فرد باعث افزایش10% و مصرف روزانه یک عدد سیگار، باعث افزایش 0/04% در تقاضای خدمات درمانی می‌شوند. نتیجه‌گیری: طبق نتایج سطح سلامت، سطح پوشش بیمه، تحصیلات و آگاهی انسان از مسیرهای سلامتی و آناتومی بدن، بیشترین و مصرف سیگار و شغل، کمترین تأثیر را بر تقاضای خدمات درمانی دارند و با افزایش سن، تحصیلات، سطح پوشش بیمه، سطح‌آگاهی و درآمد، بار تقاضای خدمات درمان افزایش می‌یابد از طرف دیگر با گذشت زمان، تحصیلات، سطح آگاهی و درآمد سرانه در حال افزایش است، پس تقاضای این خدمات در آینده افزایش خواهد یافت؛ که باید چاره‌ای اندیشیده شود تا عرضه خدمات نیز به همان نسبت افزایش یابد.
Introduction: The doctor is a health care provider which is depreciated due to, work, accidents, environmental pollution and age and increases the demand for health services; many factors have an impact on the demand for health services; this study attempts to measure the impact of 18 of the most important ones. Methodology: The model used is the Generalized Logit and ANN. The population includes those who referred to Shiraz and Arsanjan hospitals in 1994. The sample consists of 100 patients and 100 patient patients (non-patient) and data Collected through questionnaires. Findings: The results of model validity tests including fit of Goodness (Pearson and Deviance index), Parallel Regression, Maximum likelihood, and Newton-Raphson Algorithm indicate that the validity of the model is up to 84% confidence. According to the results, increase of 1% in visit, 2% of demand for services is reduced. The initial health and beliefs has the same interpretation of visit. An increase of one percent in premiums caused a decrease of 3.11% and increases one unit in health index, decrease of 1.3% and a one year increase in age, increase of 10%, and daily consumption of one cigarette, increases 0.04% in demand Health care. Conclusion: According to the results, health, insurance, education and awareness of body anatomy have the greatest impact and smoking and job have the least impact on the demand for health care. With increasing age, education, insurance coverage, awareness and income, the demand for treatment increases. On the other hand, over time, education, awareness and per capita income are rising, so, the demand for these services will increase in the future.
1- Mawuli G, Alistair M, Peter Q. The Demand for Public Health Care and the Progressivity of Health Care Services in Ghana. J Afr Dev Rev, 2015; 27(2): 79–91.
2- Manning W, Newhouse J, Duan NA, Keeler E, Leibowitz A. Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment. J AER, 1987; 77(3): 251-277.
3- Johannes SK, Rainer W. An Econometric Model of Healthcare Demand with Nonlinear Pricing. J HE, 2017; 26(6): 685–824.
4- Brot-Goldberg ZC, Amitabh C, Benjamin RH, Jonathan TK. What Does a Deductible Do? The Impact of Cost-Sharing on Health Care Prices, Quantities, and Spending Dynamics. Q J Econ, 2017; 132(3): 1261–1318.
5- Mamaru A, Joel N, Jette M, Penny F, Alfred E, Yawson R .et al. Predictors of public and private healthcare utilization and associated health system responsiveness among older adults in Ghana.Glob Health Action, 2017; 10(1): 1-10.
6- Eichner M. The Demand for Medical Care: What People Pay Does Matter? Am Econ Rev, 2013; 88(2):117-121.
7- Asefzadeh S. The Basics of Health Care Economics.Edit 2, Qazvin: Qazvin University of Medical Sciences, Research Deputy, Hadith Today; 2007. [In Persian]
8- Ellis RP, Bruno M, Wenjia Z. Health Care Demand Elasticities by Type of Service. J HE, 2017; 55: 232-243.
9- Amanda K. Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care. J BES, 2011; 34(1): 107-117.
10- Dunn A. Health insurance and the demand for medical care: Instrumental variable estimates using healt insurer Claims data. J HE, 2016; 48: 74-88.
11- Meredith H, Sandra D, Caroline M, Emily S, Louisa D. Estimating service demand for respite care among informal carers of people with psychological disabilities in Australia. Aust N Z J Public Health, 2015; 39(3): 284–292.
12- Marvasti A. An estimation of the demand and supply physician services using a panel datd. J Econ Model, 2014; 43: 279-286.
13- Acemoglu D, Finkelstein A, Notowidigdo M. Income and Health Spend-ing: Income from Oil Price Shocks. Rev. Econ Stat, 2013; 95(4):1079-1095.
14- Dunn A, Shapiro A. Physician Market Power and Medical-Care Expenditures. BEA Working Paper 2012. https://www.bea.gov/papers/pdf/Physician_Market_Power_and_Medical_Care.pdf6
15- Aizcorbe A, Nestoriak N. Changing mix of medical care services: Stylized facts and implications for price indexes. J HE, 2011; 30: 568– 574.
16- Asefzadeh M, Mohammadi N. Estimating the demand for health care for elderly rural women in Qazvin. Qazvin University of Medical Sciences & Health Services; 2010. [In Persian]
17- Yazdi N, Asefzadeh S. Estimating the demand for health care in elderly rural women in Qazvin in 1388. Journal of the Student Research Committee of Edrak University of Qazvin, 2016; 11(43): 4-8. [In Persian]
18- Ather H. Akbari W. R. Adiqa K. K. Demand for Public Health Care in Pakistan. PDR 2009; 48(2): 141-153.
19- Williams R. Generalized ordered logit models. Midwest sociological meetings, Chicago, 2010 https://www3.nd.edu/~rwilliam/gologit2/MSS2010-Handout.pdf.
20- Saeed B. Yawson A. Nguah S. Agyei-Baffour P. Emmanue N. Ayesu E. Effect of socio-economic factors in utilization of different healthcare services among older adult men and women in Ghana. BMC Health Serv Res, 2016, 16(a), doi: 10.1186/s12913-016-1661-6.
21- Schap MG, Leij F, Van Genuchten MT. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil SciSoc Am J, 1998; 62(4): 847–855.
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1- Mawuli G, Alistair M, Peter Q. The Demand for Public Health Care and the Progressivity of Health Care Services in Ghana. J Afr Dev Rev, 2015; 27(2): 79–91.
2- Manning W, Newhouse J, Duan NA, Keeler E, Leibowitz A. Health Insurance and the Demand for Medical Care: Evidence from a Randomized Experiment. J AER, 1987; 77(3): 251-277.
3- Johannes SK, Rainer W. An Econometric Model of Healthcare Demand with Nonlinear Pricing. J HE, 2017; 26(6): 685–824.
4- Brot-Goldberg ZC, Amitabh C, Benjamin RH, Jonathan TK. What Does a Deductible Do? The Impact of Cost-Sharing on Health Care Prices, Quantities, and Spending Dynamics. Q J Econ, 2017; 132(3): 1261–1318.
5- Mamaru A, Joel N, Jette M, Penny F, Alfred E, Yawson R .et al. Predictors of public and private healthcare utilization and associated health system responsiveness among older adults in Ghana.Glob Health Action, 2017; 10(1): 1-10.
6- Eichner M. The Demand for Medical Care: What People Pay Does Matter? Am Econ Rev, 2013; 88(2):117-121.
7- Asefzadeh S. The Basics of Health Care Economics.Edit 2, Qazvin: Qazvin University of Medical Sciences, Research Deputy, Hadith Today; 2007. [In Persian]
8- Ellis RP, Bruno M, Wenjia Z. Health Care Demand Elasticities by Type of Service. J HE, 2017; 55: 232-243.
9- Amanda K. Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care. J BES, 2011; 34(1): 107-117.
10- Dunn A. Health insurance and the demand for medical care: Instrumental variable estimates using healt insurer Claims data. J HE, 2016; 48: 74-88.
11- Meredith H, Sandra D, Caroline M, Emily S, Louisa D. Estimating service demand for respite care among informal carers of people with psychological disabilities in Australia. Aust N Z J Public Health, 2015; 39(3): 284–292.
12- Marvasti A. An estimation of the demand and supply physician services using a panel datd. J Econ Model, 2014; 43: 279-286.
13- Acemoglu D, Finkelstein A, Notowidigdo M. Income and Health Spend-ing: Income from Oil Price Shocks. Rev. Econ Stat, 2013; 95(4):1079-1095.
14- Dunn A, Shapiro A. Physician Market Power and Medical-Care Expenditures. BEA Working Paper 2012. https://www.bea.gov/papers/pdf/Physician_Market_Power_and_Medical_Care.pdf6
15- Aizcorbe A, Nestoriak N. Changing mix of medical care services: Stylized facts and implications for price indexes. J HE, 2011; 30: 568– 574.
16- Asefzadeh M, Mohammadi N. Estimating the demand for health care for elderly rural women in Qazvin. Qazvin University of Medical Sciences & Health Services; 2010. [In Persian]
17- Yazdi N, Asefzadeh S. Estimating the demand for health care in elderly rural women in Qazvin in 1388. Journal of the Student Research Committee of Edrak University of Qazvin, 2016; 11(43): 4-8. [In Persian]
18- Ather H. Akbari W. R. Adiqa K. K. Demand for Public Health Care in Pakistan. PDR 2009; 48(2): 141-153.
19- Williams R. Generalized ordered logit models. Midwest sociological meetings, Chicago, 2010 https://www3.nd.edu/~rwilliam/gologit2/MSS2010-Handout.pdf.
20- Saeed B. Yawson A. Nguah S. Agyei-Baffour P. Emmanue N. Ayesu E. Effect of socio-economic factors in utilization of different healthcare services among older adult men and women in Ghana. BMC Health Serv Res, 2016, 16(a), doi: 10.1186/s12913-016-1661-6.
21- Schap MG, Leij F, Van Genuchten MT. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil SciSoc Am J, 1998; 62(4): 847–855.
