An analysis of the demand for a hemodialysis facility in the Seiling, Oklahoma, medical service area

              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).