Abstract
In this paper, we focus on the problem of locating preventive health care (PHC) facilities. The most important factors that promote participation rates in PHC programs include the establishment of an appropriate infrastructure and the provision of a satisfactory quality of care. For this purpose, we develop a strategic level multi-objective mixed integer linear programming model for locating PHC facilities to ensure maximum participation and provide timely service to potential clients. We, then, apply the model to a case study of locating Cancer Early Diagnosis, Screening and Training Centers in Istanbul, Turkey and solve it considering the forecasted population of each district in Istanbul for the next 15 years. We also perform a sensitivity analysis to quantify the effect of different weighting strategies on the value of each term in the objective function.
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References
Aboolian R, Berman O, Drezner Z (2008) Location and allocation of service units on a congested network. IIE Trans 40(4):422–433
Aboolian R, Berman O, Verter V (2015) Maximal accessibility network design in the public sector. Transp Sci 50(1):336–347
Afshari H, Peng Q (2014) Challenges and solutions for location of healthcare facilities. Ind Eng Manag 3(2):1–12
Ahmadi-Javid A, Seyedi P, Syam SS (2017) A survey of healthcare facility location. Comput Oper Res 79:223–263
Anttila A, Ronco G, Ponti A, Senore C, Basu P, Segnan N, Tomatis N, Zakelj MP, Dillner J, Fernan M, Elfström KM, Lönnberg S, Soerjomataram R, Vale D (2017) Cancer screening in the European Union. Report on the implementation of the Council Recommendation on cancer screening
Baron RC, Rimer BK, Breslow RA, Coates RJ, Kerner J, Melillo S, Habarta N, Kalra GP, Chattopadhyay S, Wilson KM, Lee NC, Mullen PD, Coughlin SS, Briss PA, Task Force on Community Preventive Services (2008a) Client-directed interventions to increase community demand for breast, cervical, and colorectal cancer screening: a systematic review. Am J Prev Med 35(1):S34–S55
Baron O, Berman O, Krass D (2008b) Facility location with stochastic demand and constraints on waiting time. Manuf Serv Oper Manag 10(3):484–505
Berman O, Drezner Z (2006) Location of congested capacitated facilities with distance-sensitive demand. IIE Trans 38(3):213–221
Berman O, Krass D (2002) The generalized maximal covering location problem. Comput Oper Res 29(6):563–581
Berman O, Krass D (2015) Stochastic location models with congestion. In: Laporte G, Nickel S, Saldanha da Gama F (eds) Location science. Springer, New York, pp 443–486
Berman O, Krass D, Drezner Z (2003) The gradual covering decay location problem on a network. Eur J Oper Res 151(3):474–480
Berman O, Krass D, Wang J (2006) Locating service facilities to reduce lost demand. IIE Trans 38(11):933–946
Castillo I, Ingolfsson A, Sim T (2009) Socially optimal location of facilities with fixed servers, stochastic demand and congestion. Prod Oper Manag 18(6):721–736
Charnes A, Cooper WW (1961) Management models and industrial applications of linear programming. Wiley, New York
Charnes A, Cooper WW (1977) Goal programming and multiple objective optimizations—part 1. Eur J Oper Res 1(1):39-j4
Daskin MS, Dean LK (2004) Location of health care facilities. In: Brandeau ML, Sainfort F, Pierskalla WP (eds) Operations research and health care. A handbook of methods and applications, Kluwer’s International Series. Kluwer Academic Publishers Group, London, pp 43–76
Davari S, Kilic K, Ertek G (2015) Fuzzy bi-objective preventive health care network design. Health Care Manag Sci 18(3):303–317
Davari S, Kilic K, Naderi S (2016) A heuristic approach to solve the preventive health care problem with budget and congestion constraints. Appl Math Comput 276:442–453
Elhedhli S (2006) Service system design with immobile servers, stochastic demand, and congestion. Manuf Serv Oper Manag 8(1):92–97
Food and Drug Administration (2001) Compliance guidance: the mammography quality standards act final regulations: Preparing for MQSA Inspections; final guidance for industry and FDA. US Department of Health and Human Services, Washington
Grodzevich O, Romanko O (2006) Normalization and other topics in multi-objective optimization. In: Proceedings of the fields—MITACS industrial problems workshop, pp 89–101
Gu W, Wang X, McGregor SE (2010) Optimization of preventive health care facility locations. Int J Health Geogr 9(1):17
Güneş ED, Nickel S (2015) Location problems in healthcare. In: Laporte G, Nickel S, Saldanha da Gama F (eds) Location science. Springer, Cham, pp 555–579
Güneş ED, Yaman H, Çekyay B, Verter V (2014) Matching patient and physician preferences in designing a primary care facility network. J Oper Res Soc 65(4):483–496
Hakimi SL (1964) Optimum locations of switching centers and the absolute centers and medians of a graph. Oper Res 12(3):450–459
Holt CC (1957) Forecasting seasonals and trends by exponentially weighted moving averages. Office of Naval Research Memorandum 52, Carnegie Institute of Technology, Pittsburgh
Hosking M, Roberts S, Uzsoy R, Joseph TM (2013) Investigating interventions for increasing colorectal cancer screening: insights from a simulation model. Socio-Econ Plan Sci 47(2):142–155
IARC (2017) International agency for research on cancer, cancer today. http://gco.iarc.fr/. Accessed 17 May 2004
Jones D, Tamiz M (2010) Practical goal programming, vol 141. Springer, New York
Kan L, Olivotto IA, Warren Burhenne LJ, Sickles EA, Coldman AJ (2000) Standardized abnormal interpretation and cancer detection ratios to assess reading volume and reader performance in a breast screening program. Radiology 215(2):563–567
Karasakal O, Karasakal EK (2004) A maximal covering location model in the presence of partial coverage. Comput Oper Res 31(9):1515–1526
Karatas M (2017) A multi-objective facility location problem in the presence of variable gradual coverage performance and cooperative cover. Eur J Oper Res 262(3):1040–1051
Karatas M, Sulukan E, Karacan I (2018) Assessment of Turkey’s energy management performance via a hybrid multi-criteria decision-making methodology. Energy 153:890–912
Keskinkılıc B, Gültekin M, Akarca AS, Ozturk C, Boztas B, Karaca MZ, Utku E, Hacikamiloglu E, Turan H, Dede I, Dündar S (2016) Turkey cancer control programme. The Ministry of Health of Turkey, Ankara
KETEM (2017) Kanser Erken Teşhis, Tarama ve Eğitim Merkezi İletişim Adresleri. http://kanser.gov.tr/kanser/kanser-taramalari/887-ketem-iletişim-adresleri.html/. Accessed 17 May 2004
Khan-Gates JA, Ersek JL, Eberth JM, Adams SA, Pruitt SL (2015) Geographic access to mammography and its relationship to breast cancer screening and stage at diagnosis: a systematic review. Women’s Health Issues 25(5):482–493
Marianov V, Serra D (2002) Location–allocation of multiple-server service centers with constrained queues or waiting times. Ann Oper Res 111(1–4):35–50
Miles A, Cockburn J, Smith RA, Wardle J (2004) A perspective from countries using organized screening programs. Cancer 101(S5):1201–1213
Nazim A, Afthanorhan A (2014) A comparison between single exponential smoothing (SES), double exponential smoothing (DES), Holt’s (Brown) and adaptive response rate exponential smoothing (ARRES) techniques in forecasting Malaysia population. Glob J Math Anal 2(4):276–280
Or Z, Renaud T (2012) Impact du volume d’activité sur les résultats de soins à l’hôpital en France. Public Econ 24–25:187–219
Ozmen V, Anderson BO (2008) The challenge of breast cancer in low-and middle-income countries—implementing the breast health global initiative guidelines. US oncology, touch briefing, pp 76–79
Özmen Tolga, Yüce Salih, Güler Tekin, Ulun Canan, Özaydın Nilufer, Pruthi Sandhya, Akkapulu Nezih, Karabulut Koray, Soran Atilla, Özmen Vahit (2016) Barriers against mammographic screening in a socioeconomically underdeveloped population: a population-based, cross-sectional study. Literacy 865:44
Razı N, Karatas M (2016) A multi-objective model for locating search and rescue boats. Eur J Oper Res 254(1):279–293. https://doi.org/10.1016/j.ejor.2016.03.026
Ries LAG, Eisner MP, Kosary CL, Hankey BF, Miller BA, Clegg L (2008) Surveillance, epidemiology, and end results (SEER) program SEER* stat database: incidence—SEER 9 Regs Public-Use, Nov 2005 Sub (1973–2003). National Cancer Institute, Division of Cancer Control and Population Sciences, Surveillance Research Program, Cancer Statistics Branch. Released April 2006, based on the November 2005 submission
Ryu K, Sanchez A (2003) The evaluation of forecasting methods at an institutional foodservice dining facility. J Hosp Financ Manag 11(1):27–45
Schniederjans M (2012) Goal programming: methodology and applications: methodology and applications. Springer, New York
SEER (2017) Surveillance, epidemiology, and end results program. Cancer statistics. Cancer mortality rates. https://seer.cancer.gov/statistics/types/mortality.html/. Accessed 17 May 2004
Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65(2):87–108
Tsouros C, Satratzemi M (1994) Supply centers allocation under budgeted restrictions minimizing the longest delivery time. Int J Prod Econ 35(1–3):373–377
Tuncer M, Ozgul GM (2011) National cancer control program 2009–2015. Turkish Ministry of Health Publication, Ankara
Verter V, Lapierre SD (2002) Location of preventive health care facilities. Ann Oper Res 110(1–4):123–132
Verter V, Zhang Y (2015) Location models for preventive care. In: Eiselt HA, Marianov V (eds) Applications of location analysis. Springer, New York, pp 223–241
Vidyarthi N, Kuzgunkaya O (2015) The impact of directed choice on the design of preventive healthcare facility network under congestion. Health Care Manag Sci 18(4):459–474
Wang Q, Batta R, Rump CM (2002) Algorithms for a facility location problem with stochastic customer demand and immobile servers. Ann Oper Res 111(1–4):17–34
Weiss JE, Greenlick MR, Jones JF (1971) Determinants of medical care utilization: the impact of spatial factors. Inquiry 8(4):50–57
World Health Organization (2007) Cancer control: knowledge into action. WHO guide for effective programmes. Early detection
World Health Organization (2014) WHO position paper on mammography screening. World Health Organization
Zhang Y, Berman O, Verter V (2009) Incorporating congestion in preventive healthcare facility network design. Eur J Oper Res 198(3):922–935
Zhang Y, Berman O, Marcotte P, Verter V (2010) A bilevel model for preventive healthcare facility network design with congestion. IIE Trans 42(12):865–880
Zhang Y, Berman O, Verter V (2012) The impact of client choice on preventive healthcare facility network design. OR Spectr 34(2):349–370
Zimmerman SM (1997) Factors influencing Hispanic participation in prostate cancer screening. In: Oncology nursing forum, vol 24, no 3, pp 499–504
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Appendix
Appendix
DESUHM (Holt 1957) uses two smoothing constants, α and β, and two smoothing equations that calculate the value of intercept and the slope. The equations and parameters (see Table 8) used in this method are explained below.
In Eq. (14), the most current value of demand, \( D_{t} \), is averaged with the summation of \( S_{t - 1} \) and \( G_{t - 1} \) to calculate the value of intercept at time t, \( S_{t} \). In Eq. (15), the new value of the \( S_{t} \) is used to update the value of slope, \( G_{t} \), by averaging \( S_{t} - S_{t - 1} \) with the previous value of \( G_{t - 1} \). Same values can also be used for the smoothing constants; but in most applications, \( \beta \le \alpha \) equation is preferred for better stability. The forecast of the nth period at time t, is formulated as \( F_{t,t + n} = S_{t} + nG_{t} \).
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Dogan, K., Karatas, M. & Yakici, E. A model for locating preventive health care facilities. Cent Eur J Oper Res 28, 1091–1121 (2020). https://doi.org/10.1007/s10100-019-00621-4
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DOI: https://doi.org/10.1007/s10100-019-00621-4