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An Analysis of the Demand for a Hemodialysis Facility in the Seiling, Oklahoma, Medical Service Area Photo: www.renalmanagement.com Oklahoma Cooperative Extension Service Rural Development Oklahoma State University Oklahoma Office of Rural Health Center for Rural Health OSU Center for Health Sciences September 2010 AE-10038 An Analysis of the Demand for a Hemodialysis Facility in the Seiling, Oklahoma, Medical Service Area Lara Brooks- Assistant State Extension Specialist, OSU, Stillwater 405-744-4857; FAX 405-744-9835 Fred C. Eilrich - Assistant State Extension Specialist, OSU, Stillwater 405-744-6083 Brian Whitacre - Extension Economist, OSU, Stillwater 405-744-9825 Stan Ralstin - District Rural Development Specialist, OSU, Enid 580-237-7677 Michael Weber - Dewey County Extension Director, OSU, Taloga 580-328-5351 Val Schott - Director, Oklahoma Office of Rural Health, Oklahoma City 405-842-3101 Oklahoma Cooperative Extension Service Rural Development Oklahoma State University Oklahoma Office of Rural Health Center for Rural Health OSU Center for Health Sciences September 20101 An Analysis of the Demand for a Hemodialysis Facility in the Seiling, Oklahoma, Medical Service Area This report will examine the need for a hemodialysis facility in the Seiling, Oklahoma medical service area. This report briefly describes the process decision makers can utilize to help determine the demand for a hemodialysis facility. Specifically, the study will: 1. Determine the medical service area and population; 2. Estimate the number of potential patients in the medical service area; and 3. Estimate the number of dialysis stations for a hemodialysis facility in the medical service area. No recommendations will be made. The information included in this report is designed to assist local decision-makers in assessing the need and potential for a hemodialysis facility. Introduction The need for hemodialysis (commonly referred to as kidney dialysis) treatment continues to increase. One of the most common causes of kidney failure is diabetes. The latest report by the American Diabetes Association (2008) shows that among adults, diabetes increased across men, women, and in all age groups, but still disproportionately affects the elderly. Over 23 percent of the population 60 years and older had diabetes in 2007. The aging “Baby Boomer” population continues to impact the need for hemodialysis treatments. Furthermore, race and ethnicity remain influentials determinant in diabetes prevalence. After adjusting data from a 2004 -2006 national survey for population age differences, the rate of diagnosed diabetes was highest among American Indians and Alaska Natives (16.5 percent). This was followed by African Americans (11.8 percent) and Hispanics (10.4 percent), which includes rates for Puerto 2 Ricans (12.6 percent), Mexican Americans (11.9 percent), and Cubans (8.2 percent). By comparison, the rate for Asian Americans was 7.5 percent, with Whites at 6.6 percent. With this increased need for treatment facilities, hospital administrators could be considering the option of adding a kidney dialysis treatment unit to their current facilities. Alternatively, community leaders might be exploring the possibilities of a center in their area. The Center for Medicare and Medicaid (CMS) identify four types of dialysis facilities or units: 1) Renal Transplantation Center; 2) Renal Dialysis Center; 3) Renal Dialysis facility (free-standing); and 4) Self Dialysis Unit. In the short term, a kidney dialysis unit will require a significant financial investment. Over the longer term (3 years or more), a dialysis unit could provide a much needed service to the residents and could prove to be a cost effective service for the hospital or the community. Rural hospitals, in particular, are looking for cost effective or “profit generating” medical services that will fill the need in the community as well as assist with the financial stability of the hospital. Rural leaders, including hospital administrators, will need to take a serious look at the potential market for dialysis patients; the most critical criteria for success of a center being patient participation. Hemodialysis units provide medical treatment for end-stage renal disease (ESRD) caused primarily by the chronic diseases of diabetes and/or hypertension (high blood pressure). The need for hemodialysis units is increasing as people live longer and more people develop the diseases that lead to kidney (renal) failure. Also, improvements in dialysis technologies, care, and related drugs enable dialysis patients to live longer on dialysis. The increased number of patients requiring hemodialysis has placed an increased demand on urban and rural communities to provide hemodialysis units that are within a one-hour drive to the patient’s home. According 3 to the 2009 U.S. Renal Data System Annual Data Report, the number of dialysis units nationwide grew by 18 percent between 2002 and 2007. Nearly 60 percent of patients were treated in units owned by one of the four largest dialysis organizations. In 2007, hospital-based and independently owned units accounted for 15.4 percent and 17.6 percent of all units, respectively. Rural hemodialysis units provide the patient with needed services that are easily accessible with minimal travel time. Preferably a family member or a friend drives the hemodialysis patient to and from the treatment facility, especially if the facility is a significant distance from the patient’s residence. However, it is not uncommon for the patient to transport him/herself because treatments are so frequent (typically 2-3 times per week). If the patient is driven, the driver waits at the facility while the patient receives treatment (which takes approximately 4-5 hours) then drives the patient home. In instances of bad weather, the travel to and from the treatment unit may take more time and be more stressful to both the patient and/or the driver. For the patient who needs hemodialysis yet does not live within easy commuting distance of a treatment unit, the only option may be to move to a community that has a unit. This means the patient may incur additional expense in relocating and may no longer have a social support system available to him/her in the local community. It also adds to current problem of decreasing population numbers experienced by numerous rural communities. The information provided in this study is a starting point for the hospital administrator, board members, or potential investor to use in determining whether their community can support this medical service. It should be combined with information on the costs of installing and running a hemodialysis facility to determine whether implementation is feasible. 4 Several national and local providers of dialysis services are available to partner with local communities, hospitals, physicians, and investors to develop and operate a dialysis facility. A hemodialysis facility can enter into a management contract or joint venture arrangement with many of the regional or national corporations involved in the business of providing hemodialysis services. Looking further into these contracts and the associated costs is a logical next step for communities/hospitals who feel they have sufficient demand to support a hemodialysis center. The management contract could provide the facility with 1) consultation services from a clinical nutritionist and a social worker; 2) in-service training programs for staff; 3) computer programs for clinical documentation of services, billing and collections, and laboratory work; 4) purchasing or leasing capacity for equipment; 5) purchasing capacity for expendable supplies; and 6) quality assurance procedures for documentation to CMS. Purchasing equipment and supplies as part of a corporate group would enable the center to obtain these items at less cost. Corporate groups also have the capacity of doing their own market feasibility study. Under a joint venture arrangement, the corporate partner also shares in development expenses, capital expenditures, start-up and ongoing working capital requirements, and operating expenses. The End Stage Renal Disease (ESRD) Program was established in 1972 by federal legislation that extended Medicare coverage to almost all individuals with ESRD who require either dialysis or transplantation to sustain life. There are currently eighteen ESRD Networks who provide information on the Medicare-approved hemodialysis and transplant centers functioning in their region (ESRD Networks, 2009). The United States Renal Data System (USRDS) is a national data system that collects, analyzes, and distributes information about ESRD from the ESRD Networks. The USRDS defines dialysis patients as either prevalent or incident. A prevalent patient is a one who is currently receiving renal replacement therapy or 5 having a functioning kidney transplant (regardless of when the transplant was performed), or the number of people on hemodialysis at a given time. An incident patient is one who is starting renal replacement therapy for end stage renal disease during the calendar year, or the new patients starting hemodialysis during a calendar year. Both definitions (prevalent or incident) exclude persons with acute renal failure, persons with chronic renal failure who die before receiving treatment for ESRD, and persons whose ESRD treatments are not reported through CMS. Data on prevalent and incident patients is available at the national, state, and county level from the USRDS website (www.usrds.org) from the Renal Data and Extraction Reference (RenDER) database. The monetary proportion of Medicare devoted to ESRD treatments has remained fairly constant around 6-6.5 percent since 2000; this is due to both expenditures and Medicare funding increasing at a similar rate (USRDS 2009 Annual Data Report). Total expenditures reached $24 billion in 2007 or 5.8 percent of the Medicare budget. While it appears that ESRD expenditures experienced a significant decrease in 2007, Part D prescription costs are included in the Medicare budget, but not for ESRD patients. Therefore, once these costs are included, it is most likely the impact will be much larger on the Medicare budget. Medicare begins to pay for hemodialysis after the patient has been receiving treatments for 90 days. Hemodialysis treatments covered by Medicare totaled nearly $17.6 billion in 2007 (USRDS 2009 Annual Data Report). If the patient has health care coverage, it will pay for treatments for the period of time identified in the policy, and then the patient will apply for Medicare. If the patient does not have any other coverage and is unable to pay for treatment, the hemodialysis facility absorbs the cost for three months until the patient qualifies for Medicare. Most new patients on hemodialysis do not have any other health care coverage. 6 The total number of reported patients receiving ESRD therapy on December 31, 2007 was 527,283; a 2.3 percent increase over the previous year. Among the prevalent patients, 368,544 or nearly 70 percent were undergoing dialysis. The number of new patients totaled around 110,000, nearly the same as 2006. The racial and ethnic disparities in ESRD persist, with 2007 rates in African American and Native American populations being 3.7 and 1.8 times greater than the rate among Whites; and the rate in the Hispanic population being 1.5 times greater than rates for non-Hispanics. Medical Service Area Estimating potential patient participation in a hemodialysis unit requires defining the service area for the unit, identifying the population of the service area and calculating the prevalence and incidence rates for different age and racial groups in that service area. Figure 1 shows the proposed medical service area with the surrounding hemodialysis facilities according to the latest Oklahoma Medical Facilities Directory (August, 2010) obtained from the Oklahoma State Department of Health website (www.health.ok.us). The proposed service area for the Seiling hemodialysis facility is derived by considering the relative travel distances to the alternative centers. The proposed medical service area includes all of the zip codes shown in Table 1. Table 1 presents the 2000 census estimates and 2000 estimates from ESRI (a different data source) for comparison purposes. Zip code delineations are arbitrary and change frequently resulting in slight differences between the two estimates. Zip Code data is not available from the U.S. Census for 2009. Therefore, population from the 2009 ESRI estimates will be utilized in estimating number of patients and stations. The 2009 ESRI estimated population of the medical service area is 20,126. The service area has had a relatively constant population between 2000 and 2009. The largest population in the medical service area is Watonga with a combined 7 population of 7,078 people (zip code 73772). The zip code for Fairview, 73737 is the next largest zip code area with 3,641. The total population by race and age for the proposed service area is given in Table 2 and Table 3, respectively. As is typical in much of western Oklahoma, the vast majority of the population of the proposed service area is Caucasian. Further, a significant proportion (40.4%) is over the age of 45. Tables 4 and 5 present the total prevalent and incident data for the state of Oklahoma and the three Oklahoma counties (Blaine, Dewey, and Major) included in the service area. Data is also presented by race and age for Oklahoma. Note that at the county level, data is not reported by USRDS if fewer than 11 patients exist. In Table 4, all three counties reported fewer than 11 patients for all three years displayed; therefore, there is no data present for the county level. The same is true in Table 5, where incident patients are displayed. 8 Figure 1. Proposed Service Area for the Seiling Hemodialysis Facility SOURCE: Oklahoma State Department of Health Proposed Hemodialysis Service Area Location of Existing Hemodialysis Units 1. Anadarko Dialysis Center- Anadarko 2. Chickasha Dialysis- Chickasha 3. Clinton Dialysis Center- Clinton 4. El Reno Regional Dialysis Center- El Reno 5. Elk City Dialysis Center- Elk City 6. Renal Care Group- Enid 7. Fresenius Medical Care of Woodward 9 Table 1 Population of Proposed Service Area Zip Code Area 2000 Census 2000 ESRI 2009 ESRI 73663 Seiling 1,332 1,613 1,562 73838 Chester 486 462 442 73737 Fairview 3,587 3,785 3,641 73763 Okeene 1,609 1,644 1,526 73744 Hitchcock 378 242 232 73772 Watonga 5,992 6,139 7,078 73669 Thomas 1,596 1,708 1,654 73659 Putnam 182 105 101 73667 Taloga 639 681 654 73835 Camargo 204 139 133 73646 Fay 448 110 106 73859 Vici 1,295 1,211 1,169 73755 Longdale 901 686 662 73658 Oakwood 275 262 252 73654 Leedey 887 769 738 73043 Greenfield 165 185 176 TOTAL 19,976 19,741 20,126 SOURCE: U.S. Census Bureau, 2000 Census Data, ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions10 Table 2 Population by Race for Proposed Service Area Age Census 2000 ESRI 2000 % of Total ESRI 2009 % of Total White 17,076 16,805 85.1 16,906 84.0 A. American 753 768 3.9 880 4.4 N. American 1,048 1,063 5.4 1,126 5.6 Other 1,099 1,105 5.6 1,214 6.0 Total 19,976 19,741 20,126 SOURCE: U.S. Census Bureau, 2000 Census Data, ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions Table 3 Population by Age for Proposed Service Area Age Census 2000 ESRI 2000 % of Total ESRI 2009 % of Total 0-19 5,340 5,276 26.7 5,357 26.6 20-44 6,433 6,393 32.4 5,525 33.0 45-64 4,579 4,496 22.8 4,546 22.6 65-74 1,638 1,606 8.1 1,616 8.0 75+ 1,986 1,971 10.0 1,971 9.8 Total 19,976 19,741 20,126 SOURCE: U.S. Census Bureau, 2000 Census Data, ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions 11 Table 4 Prevalent1 Hemodialysis Patients for the State of Oklahoma with Blaine, Dewey, and Major Counties 2005 2006 2007 Total State of Oklahoma 3,195 3,317 3,513 Blaine County * * * Dewey County * * * Major County * * * Race (Oklahoma) White 1,764 1,872 1,980 African American 775 794 839 Native American 583 586 630 Other 73 65 64 Age (Oklahoma) 0-19 15 15 14 20-44 488 491 519 45-64 1,373 1,424 1,525 65-74 724 759 804 75+ 595 628 651 SOURCE: The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government. 1Prevalent patient - A patient receiving renal replacement therapy or having a functioning kidney transplant (regardless of when the transplant was performed.) *If less than 11 patients, data is not reported; number included in total count for all years. The numbers have been supplied based upon secondary data from RenDER. N/A- Data Not Available 12 Table 5 Incident1 Hemodialysis Patients for the State of Oklahoma with Blaine, Dewey, and Major Counties 2005 2006 2007 Total State of Oklahoma 1,035 1,079 1,133 Blaine County * * * Dewey County * * N/A Major County * * * Race (Oklahoma) White 690 717 735 African American 168 192 194 Native American 166 158 188 Other 11* 12* 16* Age (Oklahoma) 0-19 5* 4* 6* 20-44 129 118 144 45-64 399 405 459 65-74 255 289 264 75+ 247 263 260 SOURCE: The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government. 1Incident patient - A patient starting renal replacement therapy for end-stage renal disease during the calendar year. *If less than 11 patients, data is not reported; number included in total count for all years. The numbers have been supplied based upon secondary data from RenDER. N/A- Data Not Available 13 Estimating Patient Participation The number of patients receiving hemodialysis changes during the year due to deaths of existing patients and the addition of new patients. Estimating potential patient participation in a hemodialysis facility requires calculating the prevalence and incidence rates for different age and racial groups. Coefficients have been calculated for Oklahoma that indicate the number of hemodialysis patients per 100,000 population for both prevalence and incidence. The coefficients are the latest available based on the 2007 data from the RenDER database. Note that these coefficients are higher for categories that are commonly associated with higher diabetes rates (Native American, African American, ages 65 +). The number of projected hemodialysis patients is estimated by multiplying these coefficients with a service area’s population. The coefficients allow for prediction of hemodialysis patients by three methods: (1) population by race; (2) population by age; and (3) total population. The prevalence coefficients calculate the number of current hemodialysis patients. Table 6 shows the coefficient for each of the three methods, along with prevalent predictions by race, age, and total population for the Seiling proposed medical service area. Similarily, incidence coefficients for age, race, or total population are used to calculate the number of new patients (rounded to the nearest person) that will receive treatment without Medicare reimbursement for the first three months. Table 7 shows these coefficients along with the incidence predictions by race, age, and total population for the Seiling proposed medical service area. Again, these coefficients are based on the latest available information. Populations are taken from 2009 ESRI zip code data. 14 Table 6 Estimated Number of Current (Prevalent) Hemodialysis Patients for the Proposed Service Area 2009 ESRI Population Coefficients1 Estimated Current Patients Race White 16,906 72.9 12.3 African American 880 318.4 2.8 Native American 1,126 260.4 2.9 Other 1,214 16.7 0.2 Total 20,126 18.3 Age 0-19 5,357 1.4 0.1 20-44 6,636 42.6 2.8 45-64 4,546 168.4 7.7 65-74 1,616 317.5 5.1 75+ 1,971 286.2 5.6 Total 20,126 21.3 Total Population Service Area 20,126 97.4 19.6 SOURCE: ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions, United States Renal System Renal Data Extraction and Reference (RenDER) 1Coefficients based on 2009 ESRI estimated population and 2007 RenDER data represent the number of current (prevalent) hemodialysis patients per 100,000 population. The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government.15 Table 7 Estimated Number of New (Incident) Hemodialysis Patients for the Proposed Service Area 2009 ESRI Population Coefficients1 Estimated New Patients Race White 16,906 27.0 4.6 African American 880 73.6 0.6 Native American 1,126 77.7 0.9 Other 1,214 4.2 0.1 Total 20,126 6.1 Age 0-19 5,357 0.6 0.0 20-44 6,636 11.8 0.8 45-64 4,546 50.7 2.3 65-74 1,616 104.3 1.7 75+ 1,971 114.3 2.3 Total 20,126 7.1 Total Population Service Area 20,126 31.4 6.3 SOURCE: ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions, United States Renal System Renal Data Extraction and Reference (RenDER) 1Coefficients based on 2009 ESRI estimated population and 2007 RenDER data represent the number of current (prevalent) hemodialysis patients per 100,000 population. The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government.16 Estimating Number of Dialysis Stations Table 6 suggests that between 18 and 21 prevalent patients exist in the proposed service area, while Table 7 indicates that an additional 6-7 incident patients are in the area. This implies a range of 24-28 patients in total. In the analysis that follows, 27 patients (20 prevalent, 7 incident) is used to determine the number of dialysis stations needed. Table 8 estimates the number of stations, annual treatments, and potential maximum expansion for the proposed service area. Each of the options presents variations in the number of stations and staffing levels. The total cost per station decreases as the number of stations increase. However, due to the significant capital investment, decision makers will have to investigate the best alternative mix of stations and staffing. This report presents several alternatives that can be considered to provide the necessary treatments for the proposed medical service area. The alternatives range from a three day per week treatment option with two treatments per day to six days per week with three daily treatments. The first column (of Table 8) presented allows for 2 daily treatments three days a week. This would require a total of 14 stations to meet the demand of the 27 estimated patients, resulting in total annual treatments of 4,212. This scenario would allow for expansion to 28 patients with current staffing for the 14 stations. The numbers at the bottom of the table represent the maximum capacity for 14 stations if staffing allowed for three daily treatments, six days a week. The maximum expansion capacity would be 81 patients or 12,636 annual treatments. Three other options for staffing versus number of stations are provided. As the analysis shows, anywhere from 5 to 14 actual stations could be used to service the needs of the proposed service area. Since Watonga is about the same distance from Seiling that it is from an alternative facility in El Reno, Watonga residents may choose to use the El Reno facility. Watonga 17 comprises a significant portion of the current proposed service area, so this sensitivity should be explored. Table 9 estimates the number of stations, annual treatments, and potential maximum for the service area without the Watonga zip code population included. When Watonga is removed, the number of prevalent patients decreases to13, and the number of incident patients decline to 4. This leaves a total of 17 possible patients for the new service area. Therefore, with the decline in patients, the number of stations needed per option decreases as well. The previous 3-day week 2 times per day option that used 14 stations needs 9 stations to fulfill the smaller service area. At maximum expansion with staffing for a 6 day week 3 times per day, the service area could accommodate 52 patients or 8,081 treatments annually. 18 Table 8 Estimating Number of Stations and Annual Treatments for the Proposed Service Area 3-day week 2 x/day 3-day week 3 x/day 6-day week 3&1 x/day 6-day week 3 x/day Number of Stations A. Number of current patients estimated from coefficients 20 20 20 20 B. Expected number of new patients estimated from coefficients 7 7 7 7 C. Total estimated number of patients (A + B) 27 27 27 27 D. Number of daily treatments per M.W.F. rotation per station 2 3 3 3 E. Number of daily treatments per T.Th.Sat. rotation per station 0 0 1 3 F. Total Number of daily treatments for all rotations (D + E) 2 3 4 6 G. Number of stations required (C/F) 13.5 9 6.8 4.5 H. Actual number of stations (round up to whole number) 14 9 7 5 Number of annual treatments I. Number of annual treatments from prevalent patients (A x 3 x 52) 3,120 3,120 3,120 3,120 J. Number of annual treatments from new patients (B x 3 x 52) 1,092 1,092 1,092 1,092 K. Total number of annual treatments (I + J) 4,212 4,212 4,212 4,212 L. Maximum number of patients based on current staffing (H x F) 28 27 28 30 M. Maximum number of annual treatments based on current staffing (L x 3 x 52) 4,368 4,212 4,368 4,680 Maximum Capacity based on number of Stations w/ 6-day week, 3X/day Actual Number of Stations 14 9 7 5 Total Patients 81 54 41 27 Total Annual Treatments 12,636 8,424 6,318 4,680 19 Table 9 Estimating Number of Stations and Annual Treatments for the Proposed Service Area without Watonga 3-day week 2 x/day 3-day week 3 x/day 6-day week 3&1 x/day 6-day week 3 x/day Number of Stations A. Number of current patients estimated from coefficients 13 13 13 13 B. Expected number of new patients estimated from coefficients 4 4 4 4 C. Total estimated number of patients (A + B) 17 17 17 17 D. Number of daily treatments per M.W.F. rotation per station 2 3 3 3 E. Number of daily treatments per T.Th.Sat. rotation per station 0 0 1 3 F. Total Number of daily treatments for all rotations (D + E) 2 3 4 6 G. Number of stations required (C/F) 8.6 5.8 4.3 2.9 H. Actual number of stations (round up to whole number) 9 6 4 3 Number of annual treatments I. Number of annual treatments from prevalent patients (A x 3 x 52) 2,028 2,028 2,028 2,028 J. Number of annual treatments from new patients (B x 3 x 52) 666 666 666 666 K. Total number of annual treatments (I + J) 2,694 2,694 2,694 2,694 L. Maximum number of patients based on current staffing (H x F) 18 18 16 18 M. Maximum number of annual treatments based on current staffing (L x 3 x 52) 2,808 2,808 2,496 2,808 Maximum Capacity based on number of Stations w/ 6-day week, 3X/day Actual Number of Stations 9 6 4 3 Total Patients 52 35 26 18 Total Annual Treatments 8,081 5,387 4,041 2,808 20 Summary Many assumptions have been made in the preceding analysis. These include items that may change such as the population of the service area or service area delineation. For example, the service area depicted here may change due to the exit or entry of dialysis facilities. Should this occur, revised estimates of hemodialysis patients and stations should be made. This analysis identifies the potential demand for hemodialysis in the Seiling service area. The largest number of patients is prevalent patients. These patients are already receiving treatment at a hemodialysis facility, possibly one displayed in the previous map. The likelihood of the prevalent patients switching to Seiling to receive treatment is unknown, and should be evaluated in lieu of simply assuming the patients will use a Seiling-based facility. In order to fully investigate the feasibility of a dialysis center, the costs allocated with opening and operating must be compared to the anticipated revenue it will bring in. This report has focused on the potential number of users and stations for a dialysis center in Seiling, OK. The next step in this process would be to determine how costly setting up such a center would be; not only for equipment and space, but in terms of personnel as well. Contacting a provider in this industry should provide answers to many of the associated questions. Hemodialysis stations can be very costly to start up and staff. Therefore, all assumptions should be closely examined by local decision-makers to verify that they reflect local conditions. Local data should be included when available. If further analysis is needed, please contact the authors on the cover page or your county extension office listed on the cover page of this document.21 References American Diabetes Association, Diabetes 4-1-1 Facts, Figures and Statistics at a Glance, www.diabetes.org. ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions. Lawler, MK, & Doeksen, GA. (2002). Guidebook Estimating the Economic Viability of a Hemodialysis Center. Stillwater, OK: Oklahoma State University. United States Renal Data System. USRDS 2009 Annual Data Report: Atlas of End Stage Renal Disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. 2009. United States Renal Data System. www.usrds.org (accessed November 2009).
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Okla State Agency |
Oklahoma Cooperative Extension Service |
Okla Agency Code | '012' |
Title | An analysis of the demand for a hemodialysis facility in the Seiling, Oklahoma, medical service area |
Authors |
Brooks, Lara. Eilrich, Fred C., 1959- Whitacre, Brian. Ralstin, Stan. Weber, Michael (Lawrence Michael) Schott, Val. Oklahoma. Office of Rural Health. Oklahoma Cooperative Extension Service. Rural Development. |
Publisher | Oklahoma Cooperative Extension Service, Oklahoma State University |
Publication Date | 2010-09 |
Publication type | Research Report/Study |
Subject |
Hemodialysis facilities--Oklahoma--Dewey County. Hemodialysis facilities--Oklahoma--Blaine County. Hemodialysis facilities--Oklahoma--Major County. |
Purpose | This report will examine the need for a hemodialysis facility in the Seiling, Oklahoma medical service area. This report briefly describes the process decision makers can utilize to help determine the demand for a hemodialysis facility. Specifically, the study will: 1. Determine the medical service area and population; 2. Estimate the number of potential patients in the medical service area; and 3. Estimate the number of dialysis stations for a hemodialysis facility in the medical service area. |
Contents | Introduction; Medical Service Area; Estimating Patient Participation; Estimating Number of Dialysis Stations; Summary; References |
Notes | (AE-10038) |
OkDocs Class# | Z2130.8 A532s 2010 |
Digital Format | PDF, Adobe Reader required |
ODL electronic copy | Downloaded from agency website: http://www.okruralhealthworks.org/PDFWEB/AE-10038.pdf |
Rights and Permissions | This Oklahoma state government publication is provided for educational purposes under U.S. copyright law. Other usage requires permission of copyright holders. |
Language | English |
Full text | An Analysis of the Demand for a Hemodialysis Facility in the Seiling, Oklahoma, Medical Service Area Photo: www.renalmanagement.com Oklahoma Cooperative Extension Service Rural Development Oklahoma State University Oklahoma Office of Rural Health Center for Rural Health OSU Center for Health Sciences September 2010 AE-10038 An Analysis of the Demand for a Hemodialysis Facility in the Seiling, Oklahoma, Medical Service Area Lara Brooks- Assistant State Extension Specialist, OSU, Stillwater 405-744-4857; FAX 405-744-9835 Fred C. Eilrich - Assistant State Extension Specialist, OSU, Stillwater 405-744-6083 Brian Whitacre - Extension Economist, OSU, Stillwater 405-744-9825 Stan Ralstin - District Rural Development Specialist, OSU, Enid 580-237-7677 Michael Weber - Dewey County Extension Director, OSU, Taloga 580-328-5351 Val Schott - Director, Oklahoma Office of Rural Health, Oklahoma City 405-842-3101 Oklahoma Cooperative Extension Service Rural Development Oklahoma State University Oklahoma Office of Rural Health Center for Rural Health OSU Center for Health Sciences September 20101 An Analysis of the Demand for a Hemodialysis Facility in the Seiling, Oklahoma, Medical Service Area This report will examine the need for a hemodialysis facility in the Seiling, Oklahoma medical service area. This report briefly describes the process decision makers can utilize to help determine the demand for a hemodialysis facility. Specifically, the study will: 1. Determine the medical service area and population; 2. Estimate the number of potential patients in the medical service area; and 3. Estimate the number of dialysis stations for a hemodialysis facility in the medical service area. No recommendations will be made. The information included in this report is designed to assist local decision-makers in assessing the need and potential for a hemodialysis facility. Introduction The need for hemodialysis (commonly referred to as kidney dialysis) treatment continues to increase. One of the most common causes of kidney failure is diabetes. The latest report by the American Diabetes Association (2008) shows that among adults, diabetes increased across men, women, and in all age groups, but still disproportionately affects the elderly. Over 23 percent of the population 60 years and older had diabetes in 2007. The aging “Baby Boomer” population continues to impact the need for hemodialysis treatments. Furthermore, race and ethnicity remain influentials determinant in diabetes prevalence. After adjusting data from a 2004 -2006 national survey for population age differences, the rate of diagnosed diabetes was highest among American Indians and Alaska Natives (16.5 percent). This was followed by African Americans (11.8 percent) and Hispanics (10.4 percent), which includes rates for Puerto 2 Ricans (12.6 percent), Mexican Americans (11.9 percent), and Cubans (8.2 percent). By comparison, the rate for Asian Americans was 7.5 percent, with Whites at 6.6 percent. With this increased need for treatment facilities, hospital administrators could be considering the option of adding a kidney dialysis treatment unit to their current facilities. Alternatively, community leaders might be exploring the possibilities of a center in their area. The Center for Medicare and Medicaid (CMS) identify four types of dialysis facilities or units: 1) Renal Transplantation Center; 2) Renal Dialysis Center; 3) Renal Dialysis facility (free-standing); and 4) Self Dialysis Unit. In the short term, a kidney dialysis unit will require a significant financial investment. Over the longer term (3 years or more), a dialysis unit could provide a much needed service to the residents and could prove to be a cost effective service for the hospital or the community. Rural hospitals, in particular, are looking for cost effective or “profit generating” medical services that will fill the need in the community as well as assist with the financial stability of the hospital. Rural leaders, including hospital administrators, will need to take a serious look at the potential market for dialysis patients; the most critical criteria for success of a center being patient participation. Hemodialysis units provide medical treatment for end-stage renal disease (ESRD) caused primarily by the chronic diseases of diabetes and/or hypertension (high blood pressure). The need for hemodialysis units is increasing as people live longer and more people develop the diseases that lead to kidney (renal) failure. Also, improvements in dialysis technologies, care, and related drugs enable dialysis patients to live longer on dialysis. The increased number of patients requiring hemodialysis has placed an increased demand on urban and rural communities to provide hemodialysis units that are within a one-hour drive to the patient’s home. According 3 to the 2009 U.S. Renal Data System Annual Data Report, the number of dialysis units nationwide grew by 18 percent between 2002 and 2007. Nearly 60 percent of patients were treated in units owned by one of the four largest dialysis organizations. In 2007, hospital-based and independently owned units accounted for 15.4 percent and 17.6 percent of all units, respectively. Rural hemodialysis units provide the patient with needed services that are easily accessible with minimal travel time. Preferably a family member or a friend drives the hemodialysis patient to and from the treatment facility, especially if the facility is a significant distance from the patient’s residence. However, it is not uncommon for the patient to transport him/herself because treatments are so frequent (typically 2-3 times per week). If the patient is driven, the driver waits at the facility while the patient receives treatment (which takes approximately 4-5 hours) then drives the patient home. In instances of bad weather, the travel to and from the treatment unit may take more time and be more stressful to both the patient and/or the driver. For the patient who needs hemodialysis yet does not live within easy commuting distance of a treatment unit, the only option may be to move to a community that has a unit. This means the patient may incur additional expense in relocating and may no longer have a social support system available to him/her in the local community. It also adds to current problem of decreasing population numbers experienced by numerous rural communities. The information provided in this study is a starting point for the hospital administrator, board members, or potential investor to use in determining whether their community can support this medical service. It should be combined with information on the costs of installing and running a hemodialysis facility to determine whether implementation is feasible. 4 Several national and local providers of dialysis services are available to partner with local communities, hospitals, physicians, and investors to develop and operate a dialysis facility. A hemodialysis facility can enter into a management contract or joint venture arrangement with many of the regional or national corporations involved in the business of providing hemodialysis services. Looking further into these contracts and the associated costs is a logical next step for communities/hospitals who feel they have sufficient demand to support a hemodialysis center. The management contract could provide the facility with 1) consultation services from a clinical nutritionist and a social worker; 2) in-service training programs for staff; 3) computer programs for clinical documentation of services, billing and collections, and laboratory work; 4) purchasing or leasing capacity for equipment; 5) purchasing capacity for expendable supplies; and 6) quality assurance procedures for documentation to CMS. Purchasing equipment and supplies as part of a corporate group would enable the center to obtain these items at less cost. Corporate groups also have the capacity of doing their own market feasibility study. Under a joint venture arrangement, the corporate partner also shares in development expenses, capital expenditures, start-up and ongoing working capital requirements, and operating expenses. The End Stage Renal Disease (ESRD) Program was established in 1972 by federal legislation that extended Medicare coverage to almost all individuals with ESRD who require either dialysis or transplantation to sustain life. There are currently eighteen ESRD Networks who provide information on the Medicare-approved hemodialysis and transplant centers functioning in their region (ESRD Networks, 2009). The United States Renal Data System (USRDS) is a national data system that collects, analyzes, and distributes information about ESRD from the ESRD Networks. The USRDS defines dialysis patients as either prevalent or incident. A prevalent patient is a one who is currently receiving renal replacement therapy or 5 having a functioning kidney transplant (regardless of when the transplant was performed), or the number of people on hemodialysis at a given time. An incident patient is one who is starting renal replacement therapy for end stage renal disease during the calendar year, or the new patients starting hemodialysis during a calendar year. Both definitions (prevalent or incident) exclude persons with acute renal failure, persons with chronic renal failure who die before receiving treatment for ESRD, and persons whose ESRD treatments are not reported through CMS. Data on prevalent and incident patients is available at the national, state, and county level from the USRDS website (www.usrds.org) from the Renal Data and Extraction Reference (RenDER) database. The monetary proportion of Medicare devoted to ESRD treatments has remained fairly constant around 6-6.5 percent since 2000; this is due to both expenditures and Medicare funding increasing at a similar rate (USRDS 2009 Annual Data Report). Total expenditures reached $24 billion in 2007 or 5.8 percent of the Medicare budget. While it appears that ESRD expenditures experienced a significant decrease in 2007, Part D prescription costs are included in the Medicare budget, but not for ESRD patients. Therefore, once these costs are included, it is most likely the impact will be much larger on the Medicare budget. Medicare begins to pay for hemodialysis after the patient has been receiving treatments for 90 days. Hemodialysis treatments covered by Medicare totaled nearly $17.6 billion in 2007 (USRDS 2009 Annual Data Report). If the patient has health care coverage, it will pay for treatments for the period of time identified in the policy, and then the patient will apply for Medicare. If the patient does not have any other coverage and is unable to pay for treatment, the hemodialysis facility absorbs the cost for three months until the patient qualifies for Medicare. Most new patients on hemodialysis do not have any other health care coverage. 6 The total number of reported patients receiving ESRD therapy on December 31, 2007 was 527,283; a 2.3 percent increase over the previous year. Among the prevalent patients, 368,544 or nearly 70 percent were undergoing dialysis. The number of new patients totaled around 110,000, nearly the same as 2006. The racial and ethnic disparities in ESRD persist, with 2007 rates in African American and Native American populations being 3.7 and 1.8 times greater than the rate among Whites; and the rate in the Hispanic population being 1.5 times greater than rates for non-Hispanics. Medical Service Area Estimating potential patient participation in a hemodialysis unit requires defining the service area for the unit, identifying the population of the service area and calculating the prevalence and incidence rates for different age and racial groups in that service area. Figure 1 shows the proposed medical service area with the surrounding hemodialysis facilities according to the latest Oklahoma Medical Facilities Directory (August, 2010) obtained from the Oklahoma State Department of Health website (www.health.ok.us). The proposed service area for the Seiling hemodialysis facility is derived by considering the relative travel distances to the alternative centers. The proposed medical service area includes all of the zip codes shown in Table 1. Table 1 presents the 2000 census estimates and 2000 estimates from ESRI (a different data source) for comparison purposes. Zip code delineations are arbitrary and change frequently resulting in slight differences between the two estimates. Zip Code data is not available from the U.S. Census for 2009. Therefore, population from the 2009 ESRI estimates will be utilized in estimating number of patients and stations. The 2009 ESRI estimated population of the medical service area is 20,126. The service area has had a relatively constant population between 2000 and 2009. The largest population in the medical service area is Watonga with a combined 7 population of 7,078 people (zip code 73772). The zip code for Fairview, 73737 is the next largest zip code area with 3,641. The total population by race and age for the proposed service area is given in Table 2 and Table 3, respectively. As is typical in much of western Oklahoma, the vast majority of the population of the proposed service area is Caucasian. Further, a significant proportion (40.4%) is over the age of 45. Tables 4 and 5 present the total prevalent and incident data for the state of Oklahoma and the three Oklahoma counties (Blaine, Dewey, and Major) included in the service area. Data is also presented by race and age for Oklahoma. Note that at the county level, data is not reported by USRDS if fewer than 11 patients exist. In Table 4, all three counties reported fewer than 11 patients for all three years displayed; therefore, there is no data present for the county level. The same is true in Table 5, where incident patients are displayed. 8 Figure 1. Proposed Service Area for the Seiling Hemodialysis Facility SOURCE: Oklahoma State Department of Health Proposed Hemodialysis Service Area Location of Existing Hemodialysis Units 1. Anadarko Dialysis Center- Anadarko 2. Chickasha Dialysis- Chickasha 3. Clinton Dialysis Center- Clinton 4. El Reno Regional Dialysis Center- El Reno 5. Elk City Dialysis Center- Elk City 6. Renal Care Group- Enid 7. Fresenius Medical Care of Woodward 9 Table 1 Population of Proposed Service Area Zip Code Area 2000 Census 2000 ESRI 2009 ESRI 73663 Seiling 1,332 1,613 1,562 73838 Chester 486 462 442 73737 Fairview 3,587 3,785 3,641 73763 Okeene 1,609 1,644 1,526 73744 Hitchcock 378 242 232 73772 Watonga 5,992 6,139 7,078 73669 Thomas 1,596 1,708 1,654 73659 Putnam 182 105 101 73667 Taloga 639 681 654 73835 Camargo 204 139 133 73646 Fay 448 110 106 73859 Vici 1,295 1,211 1,169 73755 Longdale 901 686 662 73658 Oakwood 275 262 252 73654 Leedey 887 769 738 73043 Greenfield 165 185 176 TOTAL 19,976 19,741 20,126 SOURCE: U.S. Census Bureau, 2000 Census Data, ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions10 Table 2 Population by Race for Proposed Service Area Age Census 2000 ESRI 2000 % of Total ESRI 2009 % of Total White 17,076 16,805 85.1 16,906 84.0 A. American 753 768 3.9 880 4.4 N. American 1,048 1,063 5.4 1,126 5.6 Other 1,099 1,105 5.6 1,214 6.0 Total 19,976 19,741 20,126 SOURCE: U.S. Census Bureau, 2000 Census Data, ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions Table 3 Population by Age for Proposed Service Area Age Census 2000 ESRI 2000 % of Total ESRI 2009 % of Total 0-19 5,340 5,276 26.7 5,357 26.6 20-44 6,433 6,393 32.4 5,525 33.0 45-64 4,579 4,496 22.8 4,546 22.6 65-74 1,638 1,606 8.1 1,616 8.0 75+ 1,986 1,971 10.0 1,971 9.8 Total 19,976 19,741 20,126 SOURCE: U.S. Census Bureau, 2000 Census Data, ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions 11 Table 4 Prevalent1 Hemodialysis Patients for the State of Oklahoma with Blaine, Dewey, and Major Counties 2005 2006 2007 Total State of Oklahoma 3,195 3,317 3,513 Blaine County * * * Dewey County * * * Major County * * * Race (Oklahoma) White 1,764 1,872 1,980 African American 775 794 839 Native American 583 586 630 Other 73 65 64 Age (Oklahoma) 0-19 15 15 14 20-44 488 491 519 45-64 1,373 1,424 1,525 65-74 724 759 804 75+ 595 628 651 SOURCE: The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government. 1Prevalent patient - A patient receiving renal replacement therapy or having a functioning kidney transplant (regardless of when the transplant was performed.) *If less than 11 patients, data is not reported; number included in total count for all years. The numbers have been supplied based upon secondary data from RenDER. N/A- Data Not Available 12 Table 5 Incident1 Hemodialysis Patients for the State of Oklahoma with Blaine, Dewey, and Major Counties 2005 2006 2007 Total State of Oklahoma 1,035 1,079 1,133 Blaine County * * * Dewey County * * N/A Major County * * * Race (Oklahoma) White 690 717 735 African American 168 192 194 Native American 166 158 188 Other 11* 12* 16* Age (Oklahoma) 0-19 5* 4* 6* 20-44 129 118 144 45-64 399 405 459 65-74 255 289 264 75+ 247 263 260 SOURCE: The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government. 1Incident patient - A patient starting renal replacement therapy for end-stage renal disease during the calendar year. *If less than 11 patients, data is not reported; number included in total count for all years. The numbers have been supplied based upon secondary data from RenDER. N/A- Data Not Available 13 Estimating Patient Participation The number of patients receiving hemodialysis changes during the year due to deaths of existing patients and the addition of new patients. Estimating potential patient participation in a hemodialysis facility requires calculating the prevalence and incidence rates for different age and racial groups. Coefficients have been calculated for Oklahoma that indicate the number of hemodialysis patients per 100,000 population for both prevalence and incidence. The coefficients are the latest available based on the 2007 data from the RenDER database. Note that these coefficients are higher for categories that are commonly associated with higher diabetes rates (Native American, African American, ages 65 +). The number of projected hemodialysis patients is estimated by multiplying these coefficients with a service area’s population. The coefficients allow for prediction of hemodialysis patients by three methods: (1) population by race; (2) population by age; and (3) total population. The prevalence coefficients calculate the number of current hemodialysis patients. Table 6 shows the coefficient for each of the three methods, along with prevalent predictions by race, age, and total population for the Seiling proposed medical service area. Similarily, incidence coefficients for age, race, or total population are used to calculate the number of new patients (rounded to the nearest person) that will receive treatment without Medicare reimbursement for the first three months. Table 7 shows these coefficients along with the incidence predictions by race, age, and total population for the Seiling proposed medical service area. Again, these coefficients are based on the latest available information. Populations are taken from 2009 ESRI zip code data. 14 Table 6 Estimated Number of Current (Prevalent) Hemodialysis Patients for the Proposed Service Area 2009 ESRI Population Coefficients1 Estimated Current Patients Race White 16,906 72.9 12.3 African American 880 318.4 2.8 Native American 1,126 260.4 2.9 Other 1,214 16.7 0.2 Total 20,126 18.3 Age 0-19 5,357 1.4 0.1 20-44 6,636 42.6 2.8 45-64 4,546 168.4 7.7 65-74 1,616 317.5 5.1 75+ 1,971 286.2 5.6 Total 20,126 21.3 Total Population Service Area 20,126 97.4 19.6 SOURCE: ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions, United States Renal System Renal Data Extraction and Reference (RenDER) 1Coefficients based on 2009 ESRI estimated population and 2007 RenDER data represent the number of current (prevalent) hemodialysis patients per 100,000 population. The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government.15 Table 7 Estimated Number of New (Incident) Hemodialysis Patients for the Proposed Service Area 2009 ESRI Population Coefficients1 Estimated New Patients Race White 16,906 27.0 4.6 African American 880 73.6 0.6 Native American 1,126 77.7 0.9 Other 1,214 4.2 0.1 Total 20,126 6.1 Age 0-19 5,357 0.6 0.0 20-44 6,636 11.8 0.8 45-64 4,546 50.7 2.3 65-74 1,616 104.3 1.7 75+ 1,971 114.3 2.3 Total 20,126 7.1 Total Population Service Area 20,126 31.4 6.3 SOURCE: ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions, United States Renal System Renal Data Extraction and Reference (RenDER) 1Coefficients based on 2009 ESRI estimated population and 2007 RenDER data represent the number of current (prevalent) hemodialysis patients per 100,000 population. The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. Government.16 Estimating Number of Dialysis Stations Table 6 suggests that between 18 and 21 prevalent patients exist in the proposed service area, while Table 7 indicates that an additional 6-7 incident patients are in the area. This implies a range of 24-28 patients in total. In the analysis that follows, 27 patients (20 prevalent, 7 incident) is used to determine the number of dialysis stations needed. Table 8 estimates the number of stations, annual treatments, and potential maximum expansion for the proposed service area. Each of the options presents variations in the number of stations and staffing levels. The total cost per station decreases as the number of stations increase. However, due to the significant capital investment, decision makers will have to investigate the best alternative mix of stations and staffing. This report presents several alternatives that can be considered to provide the necessary treatments for the proposed medical service area. The alternatives range from a three day per week treatment option with two treatments per day to six days per week with three daily treatments. The first column (of Table 8) presented allows for 2 daily treatments three days a week. This would require a total of 14 stations to meet the demand of the 27 estimated patients, resulting in total annual treatments of 4,212. This scenario would allow for expansion to 28 patients with current staffing for the 14 stations. The numbers at the bottom of the table represent the maximum capacity for 14 stations if staffing allowed for three daily treatments, six days a week. The maximum expansion capacity would be 81 patients or 12,636 annual treatments. Three other options for staffing versus number of stations are provided. As the analysis shows, anywhere from 5 to 14 actual stations could be used to service the needs of the proposed service area. Since Watonga is about the same distance from Seiling that it is from an alternative facility in El Reno, Watonga residents may choose to use the El Reno facility. Watonga 17 comprises a significant portion of the current proposed service area, so this sensitivity should be explored. Table 9 estimates the number of stations, annual treatments, and potential maximum for the service area without the Watonga zip code population included. When Watonga is removed, the number of prevalent patients decreases to13, and the number of incident patients decline to 4. This leaves a total of 17 possible patients for the new service area. Therefore, with the decline in patients, the number of stations needed per option decreases as well. The previous 3-day week 2 times per day option that used 14 stations needs 9 stations to fulfill the smaller service area. At maximum expansion with staffing for a 6 day week 3 times per day, the service area could accommodate 52 patients or 8,081 treatments annually. 18 Table 8 Estimating Number of Stations and Annual Treatments for the Proposed Service Area 3-day week 2 x/day 3-day week 3 x/day 6-day week 3&1 x/day 6-day week 3 x/day Number of Stations A. Number of current patients estimated from coefficients 20 20 20 20 B. Expected number of new patients estimated from coefficients 7 7 7 7 C. Total estimated number of patients (A + B) 27 27 27 27 D. Number of daily treatments per M.W.F. rotation per station 2 3 3 3 E. Number of daily treatments per T.Th.Sat. rotation per station 0 0 1 3 F. Total Number of daily treatments for all rotations (D + E) 2 3 4 6 G. Number of stations required (C/F) 13.5 9 6.8 4.5 H. Actual number of stations (round up to whole number) 14 9 7 5 Number of annual treatments I. Number of annual treatments from prevalent patients (A x 3 x 52) 3,120 3,120 3,120 3,120 J. Number of annual treatments from new patients (B x 3 x 52) 1,092 1,092 1,092 1,092 K. Total number of annual treatments (I + J) 4,212 4,212 4,212 4,212 L. Maximum number of patients based on current staffing (H x F) 28 27 28 30 M. Maximum number of annual treatments based on current staffing (L x 3 x 52) 4,368 4,212 4,368 4,680 Maximum Capacity based on number of Stations w/ 6-day week, 3X/day Actual Number of Stations 14 9 7 5 Total Patients 81 54 41 27 Total Annual Treatments 12,636 8,424 6,318 4,680 19 Table 9 Estimating Number of Stations and Annual Treatments for the Proposed Service Area without Watonga 3-day week 2 x/day 3-day week 3 x/day 6-day week 3&1 x/day 6-day week 3 x/day Number of Stations A. Number of current patients estimated from coefficients 13 13 13 13 B. Expected number of new patients estimated from coefficients 4 4 4 4 C. Total estimated number of patients (A + B) 17 17 17 17 D. Number of daily treatments per M.W.F. rotation per station 2 3 3 3 E. Number of daily treatments per T.Th.Sat. rotation per station 0 0 1 3 F. Total Number of daily treatments for all rotations (D + E) 2 3 4 6 G. Number of stations required (C/F) 8.6 5.8 4.3 2.9 H. Actual number of stations (round up to whole number) 9 6 4 3 Number of annual treatments I. Number of annual treatments from prevalent patients (A x 3 x 52) 2,028 2,028 2,028 2,028 J. Number of annual treatments from new patients (B x 3 x 52) 666 666 666 666 K. Total number of annual treatments (I + J) 2,694 2,694 2,694 2,694 L. Maximum number of patients based on current staffing (H x F) 18 18 16 18 M. Maximum number of annual treatments based on current staffing (L x 3 x 52) 2,808 2,808 2,496 2,808 Maximum Capacity based on number of Stations w/ 6-day week, 3X/day Actual Number of Stations 9 6 4 3 Total Patients 52 35 26 18 Total Annual Treatments 8,081 5,387 4,041 2,808 20 Summary Many assumptions have been made in the preceding analysis. These include items that may change such as the population of the service area or service area delineation. For example, the service area depicted here may change due to the exit or entry of dialysis facilities. Should this occur, revised estimates of hemodialysis patients and stations should be made. This analysis identifies the potential demand for hemodialysis in the Seiling service area. The largest number of patients is prevalent patients. These patients are already receiving treatment at a hemodialysis facility, possibly one displayed in the previous map. The likelihood of the prevalent patients switching to Seiling to receive treatment is unknown, and should be evaluated in lieu of simply assuming the patients will use a Seiling-based facility. In order to fully investigate the feasibility of a dialysis center, the costs allocated with opening and operating must be compared to the anticipated revenue it will bring in. This report has focused on the potential number of users and stations for a dialysis center in Seiling, OK. The next step in this process would be to determine how costly setting up such a center would be; not only for equipment and space, but in terms of personnel as well. Contacting a provider in this industry should provide answers to many of the associated questions. Hemodialysis stations can be very costly to start up and staff. Therefore, all assumptions should be closely examined by local decision-makers to verify that they reflect local conditions. Local data should be included when available. If further analysis is needed, please contact the authors on the cover page or your county extension office listed on the cover page of this document.21 References American Diabetes Association, Diabetes 4-1-1 Facts, Figures and Statistics at a Glance, www.diabetes.org. ESRI 2009 Community Sourcebook of Zip Code Demographics, 23rd ed., ESRI Business Solutions. Lawler, MK, & Doeksen, GA. (2002). Guidebook Estimating the Economic Viability of a Hemodialysis Center. Stillwater, OK: Oklahoma State University. United States Renal Data System. USRDS 2009 Annual Data Report: Atlas of End Stage Renal Disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD. 2009. United States Renal Data System. www.usrds.org (accessed November 2009). |
Date created | 2011-09-22 |
Date modified | 2013-03-12 |
OCLC number | 819810509 |
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