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Do you Recommend an Interdisciplinary Field to Your Graduate Student? 2 2 1 - 1 2 - - Ali-Akbar Haghdoost Research Center for Modeling in Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Research Center for Modeling in Health, Institute Iran ahaghdoost@gmail.com - - Arash Shahravan Kerman Oral and Dental Diseases Research Center, Department of Endodontics, School of Dentistry, Kerman University of Medical Sciences, Kerman, Iran Kerman Oral and Dental Diseases Research Iran arashahravan@gmail.com 1. Grigg L. Cross-disciplinary research. Australian Research Council 1999. ##2. Sharp PA, Cooney CL, Kastner MA, Lees J, Sasisekharan R, Yaffe MB, et al. The third revolution: the convergence of the life sciences, physical sciences, and engineering. Massachusetts Institute of Technology 2011. ##3. Sharp PA, Langer R. Promoting convergence in biomedical science. Science 2011; 333: 527. doi: 10.1126/science.1205008 ##4. Hindle T, Checkland P, Mumford M, Worthington D. Developing a methodology for multidisciplinary action research: a case study. Journal of the Operational Research Society 1995; 46: 453-64. doi: 10.2307/2584593 ##5. Nelson B. Interdisciplinary studies: seeking the right toolkit. Nature 2011; 476: 115-7. doi: 10.1038/nj7358-115a ##6. Porter AL, Chubin DE. An indicator of cross-disciplinary research. Scientometrics 1985; 8: 161-76.##

The Need for Balanced Health Policies to Avoid Path Dependent Medicine 2 2 1 - 3 6 - - Amirhossein Takian Division of Health Studies, School of Health Sciences & Social Care, Brunel University London, Uxbridge, UK Division of Health Studies, School of Health Iran amir.takian@brunel.ac.uk 1. Gregor M. Bird flu: a virus of our own hatching. Lantern Books; New York: 2006. ##2. Smith R, Coast J. The true cost of antimicrobial resistance. BMJ 2013; 346: f1493. doi: http://dx.doi.org/10.1136/bmj.f1493 ##3. Cars O, Hogberg LD, Murray M, Jasper W, Nordberg O, Sivaraman S, et al. Meeting the challenge of antibiotic resistance. BMJ 2008; 337:726-8. doi: http://dx.doi.org/10.1136/bmj.a1438 ##4. Bratzer DW, Houck PM. Antimicrobial prophylaxis for surgery: an advisory statement from the National Surgical Infection Prevention Project. Clin Infect Dis 2004; 38: 1706-15. ##5. Wild SM. Antibiotic prophylaxis at caesarean section. Lancet 2002; 360: 724. doi: 10.1016/S0140-6736(02)09862-8 ##6. So AD, Ruiz-Esparza Q, Gupta N, Cars O. 3Rs for innovating novel antibiotics: sharing resources, risks, and rewards. BMJ 2012: 344: e1782. doi: http://dx.doi.org/10.1136/bmj.e1782 ##7. Cormican M, Vellinga A. Existing classes of antibiotics are probably the best we will ever have. BMJ 2012; 344: e3369. doi: http://dx.doi.org/10.1136/bmj.e3369 ##8. Coast J, Smith RD, Millar MR. Disentangling value: assessing the benefits of containing antimicrobial resistance. In: Roberts J, ed. The economics of infectious disease. Oxford University Press; 2006: 201-14. ##9. Walshea K, McKeeb M, McCarthyc M, Groenewegend P, Hansene J, Figueras J, et al. Health systems and policy research in Europe: Horizon 2020. Lancet 2013 Mar 15. [In press] ##10. World Bank, World Bank annual report, 2012. ##11. Heath I. Words are all we have. BMJ 2011; 343: d7166. doi: http://dx.doi.org/10.1136/bmj.d7166 ##12. Global Forum for Health Research .Monitoring financial flows for health research. Geneva: Global Forum for Health Research. 2004. (cited 2013 May 17) Available at: http://www.globalforumhealth.org ##13. Takian A, Petrakaki D, Cornford T, Sheikh A, Barber N. Building a house on shifting sand: Methodological considerations when evaluating the implementation and adoption of national electronic health record systems. BMC Health Serv Res 2012, 12:105. doi: 10.1186/1472-6963-12-105 ##14. Greenhalgh T, Russell J. Why do evaluation of ehealth programs fail? An alternative set of guiding principles. PLoS Med 2010; 7: e1000360. doi: 10.1371/journal.pmed.1000360 ##15. European Commission, COM 211(811). Proposal for a council decision establishing the specific programme implementing horizon 2020 —the framework programme for research and innovation (2014–2020). Brussels: European Commission, 2012. ##16. Howitt P, Darzi A, Yang G-Z, Ashrafian H, Atun R, Barlow J, et al. Technologies for global health.Lancet 2012; 380: 507-35. doi: 10.1016/S0140-6736(12)61127-1 ##17. HSR Europe. Health services research into European policy and practice. Utrecht: Netherlands Institute for Health Services Research; 2011. ##18. McCarthy M. Public health research—Europe’s future (STEPS report). London: University College London, 2011. (accessed 2013 May 21). Available at: http://discovery.ucl.ac.uk/1329165 ##19. Takian A, Cornford T. NHS information: Revolution or evolution? Health Policy and Technology 2012; 1: 193-198. doi: 10.1016/j.hlpt.2012.10.005##

A Network Based Theory of Health Systems and Cycles of Well-being 2 2 There are two dominant approaches to describe and understand the anatomy of complete health and well-being systems internationally. Yet, neither approach has been able to either predict or explain occasional but dramatic crises in health and well-being systems around the world and in developed emerging market or developing country contexts. As the impacts of such events can be measured not simply in terms of their social and economic consequences but also public health crises, there is a clear need to look for and formulate an alternative approach. This paper examines multi-disciplinary theoretical evidence to suggest that health systems exhibit natural and observable systemic and long cycle characteristics that can be modelled. A health and well-being system model of two slowly evolving anthropological network sub-systems is defined. The first network sub-system consists of organised professional networks of exclusive suppliers of health and well-being services. The second network sub-system consists of communities organising themselves to resource those exclusive services. Together these two network sub-systems interact to form the specific (sovereign) health and well-being systems we know today. But the core of a truly ‘complex adaptive system’ can also be identified and a simplified two sub-system model of recurring Lotka-Volterra predator-prey cycles is specified. The implications of such an adaptive and evolving model of system anatomy for effective public health, social security insurance and well-being systems governance could be considerable. 1 - 7 15 - - Michael Grant Rhodes Orrery Consulting, Prins Hendrikkade 160, Amsterdam, The Netherlands Orrery Consulting, Prins Hendrikkade 160, Iran orreryconsulting@gmail.com Health Systems Network Theory Social Insurance Public Health Governance 1. McKee M. 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Washington DC: World Bank; 1997. ##7. Jackson M. Social  and  economic  networks. Princeton University Press; 2008. p. 20-51. ##8. Christakis N, Fowler J. The Collective Dynamics of Smoking in a Large Social Network. N Engl J Med 2008; 358: 2249-58. doi: 10.1056/NEJMsa0706154 ##9. Demange G, Wooders M. (ed.s). Group formation in economics; Networks, clubs and coalitions. Cambridge: Cambridge University Press; 2005. doi: http://dx.doi.org/10.1017/CBO9780511614385.001 ##10. Rothenberg R. Social network dynamics and HIV transmission. AIDS 1998: 12; 1529-36. ##11. Liou Y, Daly A. Closer to learning social networks trust and professional communities. Journal of School Leadership. [In press] ##12. Jones W, Avant R, Davis N. Task Force Report 3. Report of the Task Force on Continuous Personal, Professional, and Practice Development in Family Medicine. Ann Fam Med 2004; 2: s65–74. doi: 10.1370/afm.136 ##13. WHO. 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Ann Intern Med 2008; 149: 135-6. doi: 10.7326/0003-4819-149-2-200807150-00009 ##21. Barr N. The economics of the welfare state. 2nd ed. Oxford University Press; 1993. ##22. Gross K. Framing Persuasive Appeals: Episodic and Thematic Framing, Emotional Response, and Policy Opinion. Polit Psychol 2008; 29: 169–92. doi: 10.1111/j.1467-9221.2008.00622.x ##23. Lewis J. The British Empire and world history: welfare imperialism and ‘soft power’ in the rise and fall of British rule. In: Midgeley, James and Piachaud, David, eds. Colonialism and welfare, social policy and the British imperial legacy. Cheltnam: Edward Elgar Publishing; 2011. ##24. Leon D. Cities, urbanization and health. Int J Epidemiol2008; 37: 4-8. ##25. Porter D. Health,  Civilization,  and  the  State:  A  History  of  Public Health from Ancient to Modern Times. New York: Routledge; 1999. p. 79-97. doi: 10.1056/NEJM199912023412322 ##26. Vienonen M, Wlodarczyk W. Health care reforms on the European scene: evolution, revolution or seesaw? World Health Stat Q 1993; 46: 166-9. ##27. Neubourg C. de. The welfare pentagon and the social management of risks. In: Sigg R, Behrendt C. eds. Social security in the global village. New Brunswick: Transaction publishers; 2002. Vol. 8, p. 313-31. ##28. Newhouse J, Phelps C. Price  and  Income  elasticities  for  medical care services. Santa Monica: RAND (NBER); 1974. ##29. Preker AS, Carrin G, Dror D, Jakab M, Hsiao W, Arhin-Tenkorang D. Effectiveness of community health financing in meeting the cost of illness. Bull World Health Organ2002: 80; 143-50. ##30. Lotka AJ. Elements of physical biology. Baltimore: Williams & Wilkins Co; 1925. ##31. Volterra V. [Variations and fluctuations of the number of individuals in animal species living together]. Mem. R. Accad. Naz. dei Lincei 1928: 3; 3-51. ##32. Ooms G, Hammonds R. 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Evidence for Policy Making: Clinical Appropriateness Study of Lumbar Spine MRI Prescriptions Using RAND Appropriateness Method 2 2 Background MRI is a new and expensive diagnostic technology, which has been used increasingly all over the world. Low back pain is a worldwide prevalent disorder and MRI technique is one of the several ways to diagnose it. This paper aims to identify the appropriateness of lumbar spine MRI prescriptions in Shiraz teaching hospitals using standardized RAND Appropriateness Method (RAM) criteria in 2012.   Methods This study consisted of two phases. The first phase involved a qualitative enquiry and the second phase had a quantitative cross-sectional nature. In the first phase RAM was used for developing lumbar spine MRI indications and scenarios. In the second phase, the finalized scenarios were compared with the history and physical examination of 300 patients with low back pain. The rate of appropriateness of lumbar spine MRI prescription was then calculated.   Results Of 300 cases of lumbar spine MRI prescriptions, approximately 167 (56%) were considered inappropriate, 72 (24%) were uncertain, and 61 (20%) were deemed to be appropriate. The economic burden of inappropriate prescriptions was calculated at 88,009,000 Rials. In addition, the types of expertise and physical examination were considered as related factors to appropriateness of prescriptions.   Conclusion In conclusion, a large proportion of lumbar spine MRI prescriptions, which result in financial burden on the insurance companies and the patients alike is unnecessary. This study suggests that policy makers consider this evidence while decision-making. Our findings highlight the imperative role of Health Technology Assessment (HTA) and Clinical Practice Guidelines (CPGs). As a result, developing local clinical guidelines may create the commitment needed in physicians in prescribing appropriate prescriptions within the health sector. The study further recommends that appropriate scenarios should be considered as a criterion for payment and reimbursement. 1 - 17 21 - - Hedayat Salari Department of Health Economics and Management, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran | Research Center for Health Services Management, Kerman University of Medical Sciences, Kerman, Iran Department of Health Economics and Management, Iran salarihedayat@gmail.com - - Rahim Ostovar Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran Social Determinants of Health Research Center, Iran rahimostovar@yahoo.com - - Atefeh Esfandiari Department of Health Economics and Management, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran | Research Center for Health Services Management, Kerman University of Medical Sciences, Kerman, Iran Department of Health Economics and Management, Iran atefehesfandiari88@gmail.com - - Ali Keshtkaran Department of Medical Informatics and Management, Shiraz University of Medical Sciences, Shiraz, Iran Department of Medical Informatics and Management, Iran keshtkarana@sums.ac.ir - - Ali Akbari Sari Department of Health Economics and Management, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Department of Health Economics and Management, Iran akbarisari@tums.ac.ir - - Hossein Yousefi Manesh Department of medicine, Shiraz Medical Students Research Committee, Shiraz University of Medical Science, Shiraz, Iran. Department of medicine, Shiraz Medical Students Iran hossein_sums@yahoo.com - - Amir Rakhshan Department of Foreign Languages, Tehran University of Medical Sciences, Tehran, Iran Department of Foreign Languages, Tehran University Iran amirrakhshan@gmail.com MRI Prescription Lumbar Spine RAND Appropriateness Method Evidence Clinical Practice Guideline Shiraz 1.  Ostovar  R,  Rashidian A,  Pourreza A,  Rashidi  BH,  Hantooshzadeh S, Ardebili HE, et al. Developing criteria for cesarean section using the RAND appropriateness method. BMC Pregnancy Childbirth 2010; 10: 52. doi:10.1186/1471-2393-10-52 ##2.  Borowitz  M,  Sheldon  T.  Controlling  health  care:  from  economic incentives to micro-clinical regulation. Health Econ 1993; 2: 201-4. doi: 10.1002/hec.4730020302 ##3.  Phelps  CE.  The  methodologic  foundations  of  studies  of  the appropriateness of medical care. N Engl J Med 1993; 329: 1241-5. doi: 10.1056/NEJM199310213291707 ##4. Fitch K, Bernstein SJ, Aguilar MD, Burnand B, LaCalle JR, Lazaro P, et al. The RAND/UCLA Appropriateness Method: Users Manual. RAND Europe; 2001. ##5. KanouseDE, Brook RH, Winkler JD, Kosecoff J, Berry SH, Carter GM, et al. Changing medical practice through technology assessment. RAND Corporation; 1989. ##6. Shekelle PG, Woolf SH, Eccles M, Grimshaw J. Clinical guidelines: developing guidelines. BMJ 1999; 318: 593-6. doi: http://dx.doi.org/10.1136/bmj.318.7183.593 ##7. PennsylvaniaHealth Care Cost Containment Council. The growth in Additional file 1: Contains the appendix 1. Additional file 2: Contains the appendix 2. diagnostic imaging utilization. Accessed 2007 July. Available from: http://www.phc4.org/ reports/FYI/fyi27.htm ##8.  Palesh  M,  Fredrikson  S,  Jamshidi  H,  Jonsson  PM,  Tomson  G. Diffusion of magnetic resonance imaging in Iran. Int J Technol Assess Health Care 2007; 23: 278-85. ##9. Gilbert FJ, Grant AM, Gillan MG, Vale LD, Campbell MK, Scott NW, et al. Low back pain: influence of early MR imaging or CT on treatment and outcome--multicenter randomized trial. Radiology 2004; 231: 343-51. doi: http://dx.doi.org/10.1148/radiol.2312030886 ##10. Hart LG, Deyo RA, Cherkin DC. Physician office visits for low back pain. Frequency, clinical evaluation, and treatment patterns from a U.S. national survey. Spine (Phila Pa 1976) 1995; 20: 11-9. ##11. Lower  Back  Pain  Fact  Sheet.  National  Institute  of  Neurological Disorders  and  Stroke.  Accessed  2011  December  5. Available  from: http://www.ninds.nih.gov/disorders/backpain/detail_backpain.htm. ##12. Deyo RA, Mirza SK, Martin BI. Back pain prevalence and visit rates: estimates from U.S. national surveys, 2002. Spine (Phila Pa 1976)2006; 31: 2724-7. ##13.  Keshtkaran  A,  Bagheri  MH,  Ostovar  R,  Salari  H,  Farokhi  MR, Esfandiari  A,  et  al.  Developing  criteria  for  lumbar  spine  magnetic resonance imaging (MRI) using RAND appropriateness method (RAM). Iran J Radiol 2012; 9: 130-8. doi: 10.5812/iranjradiol.4063 ##14.  Lehnert  BE,  Bree  RL.  Analysis  of  appropriateness  of  outpatient CT and MRI referred from primary care clinics at an academic medical center: how critical is the need for improved decision support? J Am Coll Radiol 2010; 7: 192-7. doi: http://dx.doi.org/10.1016/j.jacr.2009.11.010 ##15.  Emery  DJ,  Shojania  KG,  Forster  AJ,  Mojaverian  N,  Feasby  TE. Overuse of magnetic resonance imaging. JAMA Intern Med2013; 25: 1-3. doi: 10.1001/jamainternmed.2013.3804 ##16.  Thom  DH,  Kravitz  RL,  Kelly-Reif  S,  Sprinkle  RV,  Hopkins  JR, Rubenstein  LV.  A  new  instrument  to  measure  appropriateness  of services in primary care. Int J Qual Health Care2004; 16: 133-40. ##17. Palesh M, Tishelman C, Fredrikson S, Jamshidi H, Tomson G, Emami A. “We noticed that suddenly the country has become full of MRI”. Policy makers’ views on diffusion and use of health technologies in Iran. 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Women in Healthcare: Barriers and Enablers from a Developing Country Perspective 2 2 Background As the under-representation of women in management positions continues to persist globally, little is known about the experiences of women in the healthcare sector in the context of the developing Middle Eastern nations. In an attempt to address this knowledge gap, the current study explores some of the barriers that hinder and the enablers that foster women’s career advancement in the healthcare sector. To meet its objectives, the current study uses a relational approach that integrates the macro socio-cultural, meso-organisational, and micro-individual levels of analysis.   Methods Guided by institutional theory as a theoretical framework and social constructionism as a philosophical stance, the current study adopts a qualitative research methodology. It capitalizes on in-depth, semi-structured, face-to-face interviews with women managers in different occupational fields, across the managerial hierarchy in the healthcare sector in Lebanon. Snowballing and purposeful sampling procedures were used, and the interviews were analysed using thematic analysis, focusing on identifying new, emerging themes.   Results The results of the study confirm the salience of discriminatory cultural values, gendered social roles and expectations in Middle Eastern societies, and illustrate their role as barriers hindering women’s career advancement. The results also portray the spillover effect of societal expectations and cultural gender stereotypes into the organisational realm, resulting in widely experienced attitudinal and structural organisational barriers. This study also illustrates how the enablers that facilitate and promote women’s career progression unfold amidst the interplay between the macro and meso factors, lending credence to the role of women’s agency at the individual micro level. Amongst the toll of barriers, Middle Eastern women navigate the patriarchy of their cultures and the discrimination inherent in their organisations by using their agency and persistence as they construct and negotiate their careers in management.   Conclusion This study provides new knowledge on the status of Middle Eastern women in the healthcare sector, a sub-category of female employees that to date, is under-researched. It primarily highlights the role of agency in building women’s careers. 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How do Students Conceptualize Health and its Risk Factors? A Study among Iranian Schoolchildren 2 2 Background To assess the concept of children concerning their health and its risk factors, a group of primary and middle school students were asked to draw a few relevant pictures in order to deeply explore the comprehension of this key group.   Methods In this cross-sectional study 1165 students, aged 7-15 years old, selected through random stratified sampling, were asked to draw a number of eight paintings, four paintings on health concepts, and the other four on health risk factors. The paintings were then assessed by two independent observers, and their themes and contents were abstracted and analysed.   Results The students drew a total of 2330 paintings, 1165 paintings on the concept of health, and 1165 paintings on health risk factors. The most and least expressed health concepts concerned “mental health” and “healthy diet” (73.3% and 4.8%, respectively). Considering health risk factors, “unhealthy diet” and the two concepts of “environmental hazards” and “neglected personal hygiene” had the most (95%) and least (1.4% each) frequencies. Students in public schools, primary level and girls drew more pictures about health concept or/and its risk factors (P<0.05). The association between parents’ education level and the numbers of pictures were not statistically significant.   Conclusion Although students had a broad view about health and its risk factors, generally little attention had been paid to some of the main aspects such as physical activity, healthy diet, mental and oral health, and environmental hazards. In addition, it seems that parents’ educational level, as one of the main socio-economic factors, did not have any significant impact on their concepts. 1 - 35 42 - - Ali-Akbar Haghdoost Research Center for Modeling in Health, Institute of Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Research Center for Modeling in Health, Institute Iran ahaghdoost@gmail.com - - Ahad Ashrafi Asgar-Abad Kerman Medical Student Research Center, Kerman University of Medical Sciences, Kerman, Iran Kerman Medical Student Research Center, Kerman Iran ahadashrafi@gmail.com - - Mostafa Shokoohi Physiology Research Center, Kerman University of Medical Sciences Physiology Research Center, Kerman University Iran shokouhi.mostafa@gmail.com - - Mahin Alam Health Deputy, Kerman University of Medical Sciences, Kerman, Iran Health Deputy, Kerman University of Medical Iran mahin.alam@gmail.com - - Maryam Esmaeili Research Center for Modeling in Health, Institute of Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Research Center for Modeling in Health, Institute Iran esmaili.kha@gmail.com - - Neda Hojabri Research Center for Modeling in Health, Institute of Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Research Center for Modeling in Health, Institute Iran neda.hozhabri@gmail.com Concepts of Health Health Risk Factors Painting Schoolchildren 1. 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Quality of Working Life: An Antecedent to Employee Turnover Intention 2 2 Background The purpose of this study was to measure the level of quality of work life (QWL) among hospital employees in Iran. Additionally, it aimed to identify the factors that are critical to employees’ QWL. It also aimed to test a theoretical model of the relationship between employees’ QWL and their intention to leave the organisation. Methods A survey study was conducted based on a sample of 608 hospital employees using a validated questionnaire. Face, content and construct validity were conducted on the survey instrument. Results Hospital employees reported low QWL. Employees were least satisfied with pay, benefits, job promotion, and management support. The most important predictor of QWL was management support, followed by job proud, job security and job stress. An inverse relationship was found between employees QWL and their turnover intention. Conclusion This study empirically examined the relationships between employees’ QWL and their turnover intention. Managers can take appropriate actions to improve employees’ QWL and subsequently reduce employees’ turnover. 1 - 43 50 - - Ali Mohammad Mosadeghrad Health Management and Economics Research Centre, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran Health Management and Economics Research Iran mosadeghrad@gmail.com Quality of Working Life Turnover Intention Human Resource Management Hospital 1. Raduan CR, LooSee B, Jegak U, Khairuddin I. Quality of Work Life: Implications of Career Dimensions. Journal of Social Sciences 2006; 2: 61-7. doi: 10.3844/jssp.2006.61.67 ##2. Adhikari DR, Gautam DK. Labour legislations for improving quality of work life in Nepal. International Journal of Law and Management 2010; 52: 40-53. doi: http://dx.doi.org/10.1108/17542431011018534 ##3. Connell J. 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Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study 2 2 Background Road traffic accidents and their related deaths have become a major concern, particularly in developing countries. Iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. In this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying ARIMA models on data from Taybad city.   Methods This study is a cross-sectional study. We used data from accidents occurring in Taybad between 2007 and 2011. We obtained the data from the Ministry of Health (MOH) and used the time series method with a time lag of one month. After plotting the trend, non stationary data in mean and variance were removed using Box-Cox transformation and a differencing method respectively. The ACF and PACF plots were used to control the stationary situation. Results The traffic accidents in our study had an increasing trend over the five years of study. Based on ACF and PACF plots gained after applying Box-Cox transformation and differencing, data did not fit to a time series model. Therefore, neither ARIMA model nor seasonality were observed. Conclusion Traffic accidents in Taybad have an upward trend. In addition, we expected either the AR model, MA model or ARIMA model to have a seasonal trend, yet this was not observed in this analysis. Several reasons may have contributed to this situation, such as uncertainty of the quality of data, weather changes, and behavioural factors that are not taken into account by time series analysis. 1 - 51 55 - - Alireza Razzaghi Kerman Medical Students Research Center, Kerman University of Medical Sciences, Kerman, Iran Kerman Medical Students Research Center, Iran alirezarazzaghi_21@yahoo.com - - Abbas Bahrampour Research Center for Social Determinants of Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Research Center for Social Determinants of Iran abahrampour@yahoo.com - - Mohammad Reza Baneshi Research Center for Modeling in Health, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Research Center for Modeling in Health, Institute Iran rbaneshi2@gmail.com - - Farzaneh Zolala Regional Knowledge for HIV/AIDS Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Regional Knowledge for HIV/AIDS Surveillance, Iran farzanehzolala@yahoo.com Road Accident Time Series Trend Seasonality Assessment 1. 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Determinants of Life Expectancy in Eastern Mediterranean Region: A Health Production Function 2 2 Background Determinants of health or health production function in health economics literature constitute noticeable issues in health promotion. This study aimed at estimating a health production function for East Mediterranean Region (EMR) based on the Grossman theoretical model.   Methods This ecological study was performed using the econometric methods. The panel data model was used in order to determine the relationship between life expectancy and socioeconomic factors. The data for 21 EMR countries between 1995 and 2007 were used. Fixed-effect-model was employed to estimate the parameters based on Hausman test.   Results In estimating the health production function, factors such as income per capita (β=0.05, P<0.001), education index (β=0.07, P<0.001), food availability (β=0.01, P<0.001), level of urbanisation (β=0.10, P<0.001), and employment ratio (β=0.11, P<0.001) were specified as determinants of health status, proxied by life expectancy at birth. A notable result was the elasticity of life expectancy with respect to the employment rate and its significance level was different between males (β=0.13, P<0.001) and females (β=0.08, P>0.001).   Conclusion In order to improve the health status in EMR countries, health policymakers should focus on the factors which lie outside the healthcare system. These factors are mainly associated with economic growth and development level. Thus, the economic stabilisation policies with the aim of increasing the productivity, economic growth, and reducing unemployment play significant roles in the health status of the people of the region. 1 - 57 61 - - Mohsen Bayati Department of Health Economics, School of Management and Medical Information, Shiraz University of Medical Sciences, Shiraz, Iran Department of Health Economics, School of Iran bayatim@sums.ac.ir - - Reza Akbarian Department of Economics, School of Economy, Management, and Social Science, Shiraz University, Shiraz, Iran Department of Economics, School of Economy, Iran rakbarian@rose.shirazu.ac.ir - - Zahra Kavosi Department of Health Economics, School of Management and Medical Information, Shiraz University of Medical Sciences, Shiraz, Iran Department of Health Economics, School of Iran zhr.kavosi@gmail.com Health Production Function Healthcare System Life Expectancy Eastern Mediterranean Region Panel Data Model 1. Von Schirnding Y. 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Determinants of Healthcare Expenditure in Economic Cooperation Organization (ECO) Countries: Evidence from Panel Cointegration Tests 2 2 Background Over the last decade there has been an increase in healthcare expenditures while at the same time the inequity in distribution of resources has grown. These two issues have urged the researchers to review the determinants of healthcare expenditures. In this study, we surveyed the determinants of health expenditures in Economic Cooperation Organization (ECO) countries.   Methods We used Panel data econometrics methods for the purpose of this research. For long term analysis, we used Pesaran cross sectional dependency test followed by panel unit root tests to show first whether the variables were stationary or not. Upon confirmation of no stationary variables, we used Westerlund panel cointegration test in order to show whether long term relationships exist between the variables. At the end, we estimated the model with Continuous-Updated Fully Modified (CUP-FM) estimator. For short term analysis also, we used Fixed Effects (FE) estimator to estimate the model.   Results A long term relationship was found between the health expenditures per capita and GDP per capita, the proportion of population below 15 and above 65 years old, number of physicians, and urbanisation. Besides, all the variables had short term relationships with health expenditures, except for the proportion of population above 65 years old.   Conclusion The coefficient of GDP was below 1 in the model. Therefore, health is counted as a necessary good in ECO countries and governments must pay due attention to the equal distribution of health services in all regions of the country. 1 - 63 68 - - Alihussein Samadi Department of Economics, School of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran Department of Economics, School of Economics, Iran asamadi@rose.shirazu.ac.ir - - Enayatollah Homaie Rad Department of Health Economics, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran Department of Health Economics, School of Iran ehomaie@yahoo.com Health Expenditures Determinants Panel Cointegration Economic Cooperation Organization (ECO) Countries Continuously-Updated and Fully-Modified (CUP-FM) 1. Weil DN. Accounting for the Effect of Health on Economic Growth. Q J Econ 2006: 122: 41-5. ##2. Bolin K, Lindgren B, Lindström M, Nystedt P. 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Influence of Pattern of Missing Data on Performance of Imputation Methods: An Example from National Data on Drug Injection in Prisons 2 2 Background Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern, to be addressed here, is the role of the pattern of missing data.   Methods We used information of 2720 prisoners. Results derived from fitting regression model to whole data were served as gold standard. Missing data were then generated so that 10%, 20% and 50% of data were lost. In scenario 1, we generated missing values, at above rates, in one variable which was significant in gold model (age). In scenario 2, a small proportion of each of independent variable was dropped out. Four imputation methods, under different Event Per Variable (EPV) values, were compared in terms of selection of important variables and parameter estimation.   Results In scenario 2, bias in estimates was low and performances of all methods for handing missing data were similar. All methods at all missing rates were able to detect significance of age. In scenario 1, biases in estimations were increased, in particular at 50% missing rate. Here at EPVs of 10 and 5, imputation methods failed to capture effect of age.   Conclusion In scenario 2, all imputation methods at all missing rates, were able to detect age as being significant. This was not the case in scenario 1. Our results showed that performance of imputation methods depends on the pattern of missing data. 1 - 69 77 - - Saiedeh Haji-Maghsoudi Regional Knowledge Hub for HIV/AIDS Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Regional Knowledge Hub for HIV/AIDS Surveillance, Iran sa.maghsoudi@gmail.com - - Ali-Akbar Haghdoost Regional Knowledge Hub for HIV/AIDS Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Regional Knowledge Hub for HIV/AIDS Surveillance, Iran ahaghdoost@gmail.com - - Azam Rastegari Social Determinant of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Social Determinant of Health Research Center, Iran az.rastegari@gmail.com - - Mohammad Reza Baneshi Research Center for Modeling in Healtth, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Research Center for Modeling in Healtth, Iran rbaneshi2@gmail.com Missing Data Mice Expectation Maximum Algorithm Drug Injection National Data 1. Shokoohi M, Baneshi MR, Haghdoost AA. Estimation of the Active Network Size of Kermanian Males. Addiction and Health 2011; 2(3-4): 81-88. ##2. Prison and AIDS: UNAIDS point of view. [cited 2013 May). Available at: http://www.unaids.org/en/media/unaids/contentassets/dataimport/publications/irc-pub05/prisons-pov_en.pdf ##3. Donders ART, van der Heijden GJMG, Stijnen T, Moons KGM. Review: a gentle introduction to imputation of missing values. J  Clin Epidemiol 2006; 59(10): 1087-91. doi: 10.1016/j.jclinepi.2006.01.014 ##4. Marshall A, Altman DG, Royston P, Holder RL. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study. BMC Med Res Methodol 2010; 10(1): 7. doi: 10.1186/1471-2288-10-7 ##5. Knol MJ, Janssen KJM, Donders ART, Egberts ACG, Heerdink ER, Grobbee DE, et al. Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. J Clin Epidemiol 2010; 63: 728-36. ##6. Barzi F, Woodward M. Imputations of missing values in practice: results from imputations of serum cholesterol in 28 cohort studies. Am J Epidemiol 2004; 160(1): 34-45. doi: 10.1093/aje/kwh175 ##7. Baneshi MR, Talei AR. Impact of imputation of missing data on estimation of survival rates: an example in breast cancer. Iranian Journal of Cancer Prevention 2010; 3(3): 127-31. ##8. Baneshi MR, Talei AR. Prevention of Disease Complications through Diagnostic Models: How to Tackle the Problem of Missing Data? Iran J Public Health 2012; 41(1). ##9. Vargas-Chanes D, Decker PA, Schroeder DR, Offord KP. An Introduction to Multiple Imputation Methods: Handling Missing Data with SAS@ V8. 2. Rochester, MN: Mayo Foundation; 2003. ##10. Farhangfar A, Kurgan L, Dy J. Impact of imputation of missing values on classification error for discrete data. Pattern Recognit 2008; 41(12): 3692-705. doi: http://dx.doi.org/10.1016/j.patcog.2008.05.019 ##11. Horton NJ, Kleinman KP. Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models. Am Stat 2007; 61(1): 79-90. doi: 10.1198/000313007X172556 ##12. Langkamp DL, Lehman A, Lemeshow S. Techniques for handling missing data in secondary analyses of large surveys. Acad Pediatr 2010; 10(3): 205-10. doi: http://dx.doi.org/10.1016/j.acap.2010.01.005 ##13. Marlin BM. Missing  data  problems  in  machine  learning. Toronto: University of Toronto; 2008. ##14. Klebanoff MA, Cole SR. Use of multiple imputation in the epidemiologic literature. Am J Epidemiol 2008; 168(4): 355-7. doi: 10.1093/aje/kwn071 ##15. Harel O, Zhou XH. Multiple imputation: review of theory, implementation and software. Stat Med 2007; 26(16): 3057-77. doi: 10.1002/sim.2787 ##16. Chen Q, Wang S. Variable selection for multiply-imputed data with application to dioxin exposure study. Stat Med 2013. [In Press] doi: 10.1002/sim.5783 ##17. Faris PD, Ghali WA, Brant R, Norris CM, Galbraith PD, Knudtson ML. Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses. J  Clin Epidemiol 2002; 55(2): 184-91. ##18. Van Buuren S, Groothuis-Oudshoorn K. MICE: Multivariate imputation by chained equations in R. Journal  of  Statistical  Software 2011; 45(3): 1-68. ##19. JC W. Multiple Imputation For Missing Data: What Is It And How Can I Use It? Annual Meeting of the American Educational Research Association; Chicago, IL; 2003. ##20. White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 2010; 30(4): 377-99. doi: 10.1002/sim.4067 ##21. Suraphee S, Raksmanee C, Busaba J, Chaisorn C, Nakornthai W. A Comparison of Estimation Methods for Missing Data in Multiple Linear Regression with Two Independent Variables. Thailand Statistician 2006; 4: 13-26. ##22. Lin TH. A Comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data. Qual  Quant 2010; 44: 277-87. doi: 10.1007/s11135-008-9196-5 ##23. N.Otwombe K, Galpin J. Weighting Method for Binary Longitudinal Data With Incomplete Covariates and Outcomes Incorporating Auxiliary Information. Available at: http://www.statssa.gov.za/ycs/SpeakerPresentations/Acropolis5/Day3/Session%20VIIID_Prof.%20Jacky%20Galpin/Otwombe%20Kennedy.pdf ##24. Allison PD. Missing Data. Available at: http://www.statisticalhorizons.com/wp-content/uploads/2012/01/Milsap-Allison.pdf. ##25. Baneshi MR, Talei AR. Does the Missing Data Imputation Method Affect the Composition and Performance of Prognostic Models? Iran Red Crescent Med J 2012; 14(1): 51-6. ##26. Yuan YC, editor. Multiple imputation for missing data: concepts and new development (version 9.0). 2000. Available at: http://www.math.montana.edu/~jimrc/classes/stat506/notes/multipleimputation-SAS.pdf ##27. 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The Moderating Role of Power Distance on the Relationship between Employee Participation and Outcome Variables 2 2 Background Many organisations have realised the importance of human resource for their competitive advantage. Empowering employees is therefore essential for organisational effectiveness. This study aimed to investigate the relationship between employee participation with outcome variables such as organisational commitment, job satisfaction, perception of justice in an organisation and readiness to accept job responsibilities. It further examined the impact of power distance on the relationship between participation and four outcome variables.   Methods This was a cross sectional study with a descriptive research design conducted among employees and managers of hospitals affiliated with Tehran University of Medical Sciences, Tehran, Iran. A questionnaire as a main procedure to gather data was developed, distributed and collected. Descriptive statistics, Pearson correlation coefficient and moderated multiple regression were used to analyse the study data.   Results Findings of the study showed that the level of power distance perceived by employees had a significant relationship with employee participation, organisational commitment, job satisfaction, perception of justice and readiness to accept job responsibilities. There was also a significant relationship between employee participation and four outcome variables. The moderated multiple regression results supported the hypothesis that power distance had a significant effect on the relationship between employee participation and four outcome variables.   Conclusion Organisations in which employee empowerment is practiced through diverse means such as participating them in decision making related to their field of work, appear to have more committed and satisfied employees with positive perception toward justice in the organisational interactions and readiness to accept job responsibilities. 1 - 79 83 - - Sima Rafiei Department of Health Economics and Management, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Department of Health Economics and Management, Iran sima.rafie@gmail.com - - Abolghasem Pourreza Department of Health Economics and Management, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Department of Health Economics and Management, Iran abolghasemp@yahoo.com Job Satisfaction Organisational Justice Organisational Readiness Organisational Commitment Power Distance Culture 1. Becker BE, Gerhart B. The impact of human resource management on organizational performance: progress and prospects. Acad Manage J 1996; 39: 20-8. doi: 10.2307/256712 ##2. Blanchard K, Carlos JP, Randolph A. Empowerment takes more than a minute. San Francesco: Berrett-Koehler; 1999. ##3. Conger JA, Kanungo RN. The empowerment process integrating theory and practice. Acad Manage Rev 1988; 13: 11-8. ##4. Bialas S. Power distance as a determinant of relations between managers and employees in the enterprises with foreign capital. Journal of Intercultural Management 2009; 1: 105-15. ##5. Hofstede G. Culture’s consequences: international differences in work related values. Thousand Oaks, California: Sage Publications; 1980. ##6. Mead R. International  Management. Oxford: Blackwell publishing; 2003. ##7. Sagie A, Aycan Z. A cross cultural analysis of participative decision making in organizations. Hum Relat 2003; 56: 32-9. doi: 10.1177/0018726703056004003 ##8. Hofstede G. Cultures and organizations: software of the mind. New York: MC Graw-Hill; 2005. ##9. Francesco AM, Chen ZX. Cross cultural differences within a single culture: power distance as a moderator of participation – outcome relationship in the people’s republic of China. Hong Kong: Business Research Centre, School of Business; 2000. ##10. Gomez CB, Kirkman BL, Shapiro DA. The impact of power distance on the relationship between participation and organizational commitment. annual meeting of the academy of management; 1999; Chicago. ##11. Greer L, Gebren AK. Power distance, conflict resolution and status Conflicts in teams: how do the power dynamics impact team conflict resolution. 2008; [cited 2010 July 25] Available from: http://papers.ssrn.com/sol3/papers doi: http://dx.doi.org/10.2139/ssrn.1298593 ##12. Brockner J, Ackerman G, Greenberg J, Gelfandd MJ, Francescoe AN, Chene ZX, et al. Culture and procedural justice: The influence of power distance on reactions to voice. J Exp Soc Psychol 2001: 37: 300-15. ##13. Asgari A, Silong A, Ahmad A, Samah BA. The relationship between organizational characteristics, task characteristics, cultural context and organizational citizenship Behaviors. European  Journal  of  economics, finance and administrative science 2008; 13: 94-107. ##14. Lam SK, Schaubroeck J, Aryee S. Relationship between organizational justice and employee work outcomes: a cross national study. J Organ Behav 2003; 23: 1-18. doi: 10.1002/job.131 ##15. Greenberg J, Brockner J. Culture and procedural justice: the influence of power distance on reactions to voice. J Exp Soc Psychol 2001; 37: 300-15. doi: http://dx.doi.org/10.1006/jesp.2000.1451 ##16. Oloko M, Ogutu M. Influences of power distance on employee empowerment and MNC performance: a study of multinational corporations in Kenya. Educ Res J 2012; 2: 47-61. ##17. Wahab, E. The moderating role of power distance on the relationship between perceived organizational support and organizational commitment. 5th National Human Resource Management Conference; 2010. ##18. Rahmat Seresht H, Fayazi M. Relationship between power perception, commitment and employee performance.  Journal  of Humanity and Social Sciences 2006; 8: 73-96. ##19. Sadeghi MT. Equity: a basic need for organizations. Journal  of Modiran,  Research  center  of  Human  Sciences  and  Cultural  Studies 2007; 5: 63-70. ##20. Seyed Javadin SR, Farahi MM, Taheri GH. Investigating the effect of organizational justice on different aspects of job satisfaction. Journal of Business Management 2006; 1: 12-9.##

Health System Responsiveness: A Case Study of General Hospitals in Iran 2 2 Background Considering patients’ needs and expectations in the process of healthcare delivery improves the quality of services. This study aimed to investigate the responsiveness of general public and private hospitals in Mashhad, Iran.   Methods In this cross-sectional and explanatory study, hospitalized patients (with at least 2 days of stay) in general private and public hospitals in Mashhad were investigated. In total 425 patients (259 from private and 166 from public hospitals) were selected using a stratified and simple random sampling. Standard responsiveness questionnaire was used as the data collection tool. Data were analysed using descriptive statistics, independent t-tests and ANOVA by SPSS 16 at a significance level of 0.05.   Results Access to the social support during hospitalization as well as confidentiality of the patient’s information achieved the highest score (3.21±0.73) while the patient participation in decision-making process of treatment received the least score (2.34±1.24). Among the research population 1.6%, 4.1%, 17.6%, 63.3% and 13.2% commented on the responsiveness level as very low, low, moderate, good, and excellent, respectively. There was no significant difference between the overall responsiveness scores of public and private hospitals ( P ≥0.05).   Conclusion The hospitals have enough potential to improve various aspects of their responsiveness. We suggest a number of measures can help improve the non-clinical aspects of care. These include: using educational courses to improve the knowledge and attitudes of medical and nonmedical staff, changing the resource allocation method, and using quality tools such as reengineering to modify the healthcare delivery processes. 1 - 85 90 - - Hossein Ebrahimipour Health Sciences Research Center, Department of Health and Management, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Health Sciences Research Center, Department Iran hebrahimip@gmail.com - - Ali Vafaei Najjar Health Sciences Research Center, Department of Health and Management, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Health Sciences Research Center, Department Iran vafaeea@mums.ac.ir - - Ahmad Khanijahani School of Health and Paramedical, Zanjan University of Medical Sciences, Zanjan, Iran School of Health and Paramedical, Zanjan Iran khanjahani@gmail.com - - Arefeh Pourtaleb Student Research Committee, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Student Research Committee, School of Health, Iran arefehpourtaleb@yahoo.com - - Marzieh Javadi Student Research Committee, Health Management and Economic Research Center, Isfahan University of Medical Sciences, Isfahan, Iran Student Research Committee, Health Management Iran m_javadi@mng.mui.ac.ir - - Alireza Rezazadeh Department of Health Services Management, Tehran University of Medical Sciences, Tehran, Iran Department of Health Services Management, Iran alireza.rezazadeh1990@gmail.com - - Marjan Vejdani Student Research Committee, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Student Research Committee, School of Health, Iran - - Arash Shirdel Student Research Committee, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran Student Research Committee, School of Health, Iran arashshirdel67@yahoo.com Responsiveness General Hospital Non-Clinical Aspect of Care Mashhad Health System 1. 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Socio-economic disparities in health system responsiveness in India.Health policy plan 2013; 28: 197-205. doi: 10.1093/heapol/czs051 ##13. Letkovicova H, Prasad A, Valentine N. The health systems analytical guidelines for survey in the multi-country survey study. Switzerland: World heath organization; 2005. ##14. Gostin L, Hodge JVN, Nygren-Krug H. The Domains of Health Responsiveness -A Human Rights Analysis. Geneva: World Health Organization; 2002. ##15. Third and fourth development plans [Online]. [database on the Internet]. 2010. Available from: http://hamahangi.behdasht.gov.ir/index.aspx?siteid=126&pageid=955/. ##16. Javadi M, Karimi S, Raiesi A. Comparison of patients’ and nurses’ viewpoints about responsiveness among a sample from public and private hospitals of Isfahan.Iran Journal of Nurse and Midwifery Research 2011; 16: 273-7. ##17. Peltzer K. Patient experiences and health system responsiveness in South Africa. BMC Health Serv Res 2009; 9: 117. doi: 10.1186/1472-6963-9-117 ##18. Liabsuetrakul T, Petmanee P, Sanguanchua S, Oumudee N. Health system responsiveness for delivery care in Southern Thailand. Int J Qual Health Care 2012; 24: 169-75. doi: 10.1093/intqhc/mzr085 ##19. Ugurluoglu O, Celik Y. How responsive Turkish health care system is to its citizens: the views of hospital managers. J Med Syst 2006; 30: 421-8. doi: 10.1007/s10916-005-9006-8 ##20. Whitehead J, Wheeler H. Patients’ experiences of privacy and dignity. Part 1: a literature review. Br J Nurs 2008; 17: 381-5. ##21. Lin YP, Tsai YF, Chen HF. Dignity in care in the hospital setting from patients’ perspectives in Taiwan: a descriptive qualitative study. J Clin Nurs 2011; 20: 794-801. doi: 10.1111/j.1365-2702.2010.03499.x ##22. Dickert NW, Kass NE. Understanding respect: learning from patients. J Med Ethics 2009; 35: 419-23. doi: 10.1136/jme.2008.027235 ##23. Habibullah S. Responsiveness of the Federal Health System to the Needs of 18-45 Year Old Adults with Physical Disabilities in Islamabad, Pakistan. Graduate School Theses and Dissertations. University of South Florida; 2012. Available from: http://scholarcommons.usf.edu/etd/4059. ##24. Ristea A-L, Stegaroiu I, Dinu V. Responsiveness of Health Systems: a Barometer of the Quality of Health Services. Quality Management in Services 2009; 6: 277-88.##