Dialysis Transportation: The Intersection of Transportation and Healthcare (2019)

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Introduction As communities struggle to meet increasing demand for publicly supported transportation to dialysis facilities, two key pieces of information they may want to examine are the number of trips the public sector needs to provide to ensure patients can access dialysis treatments and the costs associated with those trips. This chapter presents a data tool that can be used to make these estimates. The data tool responds to the �irst of the research project’s two stated objectives—to quantify current and projected costs associated with dialysis transportation in the United States. The Community Data Tool The data tool provides estimates of: • Current and projected demand for public sector trips to kidney dialysis facilities; • Current and projected cost of this transportation; and • Potential decreases in demand for, and cost of, public sector trips if use of home dialysis increases beyond current rates (“what if” scenarios). The tool can be used to predict unconstrained public sector trips currently needed to dialysis centers and to forecast future demand for such trips. Public sector trips are those trips that are not provided by patients driving themselves or being driven by family or friends—with the patient’s or family member or friend’s personal car. Public sector trips include all other modes, including public transit and its specialized services, Medicaid NEMT, other van services, and taxis (as described in Chapter 3). Where the Data Come From—Inputs The community data tool was built using data from a number of sources: States Renal Data System (USRDS), a national data system that 1. USRDS—Much of the data used in the tool is from the United Chapter 5 Demand and Costs for Dialysis Transportation: A Data Tool Transportation to Dialysis Chapter 5—Demand and Costs for Dialysis Transportation: A Data Tool 5.1

Patient Survey for the Research Study Administered through the American Association of Kidney Patients (AAKP) collects, analyzes, and distributes information about chronic kidney disease (CKD) and ESRD in the United States. 2. Public Transportation Agency Survey—The research project’s survey of more than 500 public transportation agencies provided data for use in creating a default value for the cost per trip to dialysis treatments. 3. Dialysis Facility Survey—The research project’s survey of dialysis facilities coupled with survey data from several other studies of dialysis transportation (as explained in Chapter 3) are the sources of information for the percentage of dialysis patients that rely on public sector transportation to travel to/from dialysis treatments. Information from the research project’s survey of dialysis patients added supporting information on dialysis transportation. More on the USRDS Data The USRDS is a national data system that collects, analyzes, and distributes comprehensive information about CKD and ESRD in the country. The USRDS includes data on current patients with ESRD who are receiving dialysis treatments, their treatment modality, Medicare expenditures, hospitalizations, and mortality rates. The data are longitudinal with trends provided for projections into the future. The USRDS is funded directly by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Data are reported annually. All ESRD patients, regardless of insurance coverage and age, are included in the USRDS database (63). The research project’s data tool uses data from the most recent annual USRDS report—issued in 2017—that reports on 2015 data, which is used as the base year for the data tool. The USRDS data are available from the USRDS online system on national, state, and county levels. The USRDS also summarizes data by using what are known as Health Service Areas or HSAs and ESRD Networks. HSAs are de�ined by the National Center for Health Statistics to be a single county or cluster of contiguous counties that are relatively self-contained with respect to hospital care. The country is categorized into 824 HSAs. HSAs that 5.2 Dialysis Transportation: The Intersection of Transportation and Healthcare

crossed state boundaries were split so that all counties from one HSA were in one state. The names of the HSAs are derived from the names of the two counties with the highest populations in 2000 within the HSA. Additionally, if there is a major city in one of the counties that name is added in parentheses. ESRD Networks are essentially regions of the country, with 18 Networks used by the USRDS. Given the availability of some of the USRDS data, the data tool uses certain data at either the HSA or Network level, as explained below. While the community data tool makes an effort to use data speci�ic to individual counties as much as possible, the USRDS does not report detailed data if a cell contains fewer than 11 records. For this reason, some of the variables—annual compound growth rates and percentage of patients on home dialysis—are used on the ESRD Network and HSA levels. The USRDS county, HSA, and ESRD Network data are used in the tool as follows: 1. Number of Dialysis Patients by County—The number of dialysis patients for each of the 3,141 counties in the United States is based on the prevalence rates for hemodialysis (HD) and peritoneal (PD) patients as documented in the USRDS. 2. Number of Dialysis Patients Traveling to Dialysis Centers by County—The number of dialysis patients who need to travel to a facility for treatment is based on the treatment modalities of ESRD patients (HD, PD, and transplants) as identi�ied in the USRDS. The number of patients using HD (hemodialysis) is used as the estimate of persons in each county who are receiving dialysis in centers. The number of patients using PD (peritoneal dialysis) is used as a proxy for persons who dialyze at home. 3. Average Annual Compound Growth Rate in Patients Traveling to Dialysis Centers—The community data tool estimates the number of patients who will travel to dialysis centers in the future by using the change in the number of patients receiving HD from 2010 to 2015. Speci�ically, the change was calculated—and then used in the data tool—based on the annual compound growth rate in HD patients in the 18 ESRD Networks over the 5-year period. 4. Percentage of Patients on Home Dialysis—The percentage of patients currently using home dialysis is based on data on dialysis patients using PD (peritoneal dialysis) as a proxy. The percentage of persons who currently dialyze at home is estimated—and used in the data tool—based on the actual percentage of PD patients in each of the 824 HSAs. Chapter 5—Demand and Costs for Dialysis Transportation: A Data Tool 5.3

Photo courtesy of KFH Group. What the Tool Reports—Outputs Trips—Demand for Dialysis Transportation Current Number of Dialysis Patients Traveling to Centers—The data tool begins with an estimate of the number of dialysis patients in a community (2015) and then narrows this to the number traveling to dialysis facilities for treatment (this excludes those dialysis patients who use home dialysis). As described below, the data used to estimate the number of patients traveling to dialysis centers builds on the USRDS HD prevalence rates, since about 90% of patients requiring dialysis use hemodialysis and the vast majority of these treatments are at dialysis facilities. Projected Number of Dialysis Patients Traveling to Centers—Future projected demand is dependent on increases in the number of patients traveling to dialysis centers. For the data tool, the rate of increase was developed by calculating the annual compound rate of growth in patients receiving center-based dialysis from 2010 to 2015 in the USRDS. These data were not consistent on a county level so a single annualized rate of growth was applied to all counties in each of the 18 Networks. Number of Trips Needed—The number of trips required to meet center-based travel needs is based on the assumption that patients receive treatments three days a week—six one-way trips a week—for a total of 312 one-way trips annually. Number of Public Sector Trips Needed—The number of trips that the public sector would need to provide for in-center dialysis treatment is estimated based on the research project’s survey of dialysis facilities coupled with survey data from several other studies of dialysis transportation. As explained in Chapter 3, these various surveys together indicate that about half of dialysis patients drive themselves or are driven to dialysis centers by family or friends. The remaining half of patients’ trips to dialysis centers relies on other modes that are provided by the public sector (e.g., ADA paratransit, other transit agency specialized services, or Medicaid NEMT, human service agency vans, etc.). It is also noted that the estimate of public trips is unconstrained by funding availability and is conservative, since it does not account for latent demand. If the funding for these travel options is increased, if more trips are available, and if restrictions are fewer (and quality improved), patients may switch from family or friends or even driving themselves to using the publicly provided services. 5.4 Dialysis Transportation: The Intersection of Transportation and Healthcare

Derived from Research Project’s Survey of 500+ Public Transportation Agencies, April 2017. $0.00 $5.00 $10.00 $15.00 $20.00 $25.00 $30.00 $35.00 $40.00 Large Metro Area Large City Smaller City Rural/Small Community $35.01 $32.19 $29.58 $26.22 Cost per One-Way Public Transit Agency Specialized Trip (2016 $) Costs and Funding Current and Projected Cost of Public Sector Trips—The current and projected cost of public sector trips can be estimated either based on local information (if the data tool user knows the current cost per trip for such trips in their community) or the user can use the default value built into the data tool. Default values come from the research project’s survey of transit agencies, which estimated the cost per specialized trip in four different sizes of community, according to 2016 data provided by respondents of the transit agency survey. These default values are $35.01 for large metro area transit agencies, $32.19 for large city agencies, $29.58 for smaller city agencies, and $26.22 for transit agencies in rural areas and small communities. The cost estimates in the data tool are unconstrained by funding availability. Because of the complex and varied ways in which public sector trips to dialysis are currently funded, the data tool does not predict funding available to support this transportation. However, the user’s knowledge of funding available in his or her community, along with the description of potential funding sources in Chapter 3, could allow one to identify potential funding sources. Impact of Increasing Home Dialysis There is a trend toward an increase in home dialysis—albeit small—as discussed in Chapter 4. If one assumes that home dialysis continues to increase, this would mean that a lower percentage of dialysis treatments will be done in-center in the future. While the tool already accounts for the current level of home dialysis in each county, it also allows some exploration of the potential impact if home dialysis were increased. Using the tool, communities can create “what if” scenarios to project the potential impact that increasing the adoption of home dialysis could have on trip demand and public sector transportation. Chapter 5—Demand and Costs for Dialysis Transportation: A Data Tool 5.5

Data Tool Assumptions (and Caveats) There are a number of assumptions built into the model: 1. Current growth trends for patients needing dialysis will continue into the future—While the researchers recognize that this �ield of medicine is changing, the tool assumes the prevalence rates will continue to grow in the future as the rates have in the past 5 years (2010–2015). As noted in the USRDS 2017, that rate of growth has been consistently in the area of 3% annually (2). 2. Public funding is unconstrained—The tool predicts public sector funding needed without any constraints on funding availability. 3. The percentage of patients using home dialysis is held constant within each of the 824 HSAs—Some counties already exceed the average percentage of patients using home dialysis within their HSA, but this often is not consistent year to year. For this reason, the average home dialysis rate for the HSA was applied to each of the counties in that HSA. This may result in over-reporting or under-reporting the impacts of increasing home dialysis in some individual counties. For example, in the event that a particular county already exceeds the average percentage of home dialysis within their HSA, the tool will project a proportional decrease in trips/public sector costs, but these may be lower than could be achieved in that county. Conversely, if the percentage of home dialysis in a county is less than the average percentage of home dialysis within their HSA, the tool will project decreases in trips/public sector costs that are higher than may be achieved in that county. How to Use the Community Data Tool The tool has been built with Microsoft Excel because this software program is readily available in all communities. Tables of data and relational proportions among the variables are in the background of the data tool. The tool consists of two screens: the input screen allows the user to input data or override defaults for speci�ic variables, and the output screen reports the results of the data calculations. Exhibit 5-1 provides an example of the input screen. Exhibit 5-2 provides an example of the output screen. 5.6 Dialysis Transportation: The Intersection of Transportation and Healthcare

Exhibit 5-1: Sample Input Screen Chapter 5—Demand and Costs for Dialysis Transportation: A Data Tool 5.7

5 Dialysis Transportation: The Intersection of Transportation and Healthcare.8 Exhibit 5-2: Sample Output Screen

In some cases, not enough data were available for the USRDS to report dialysis modality (HD and/or PD) at the county level. In these cases, the data tool will report “Data Not Available” for the estimated total ESRD patients on dialysis and/or the patients traveling to dialysis centers. Should a user of the data tool �ind such a result, the user can refer to Exhibit 5-3 at the end of the chapter, which provides the percentage of the (2015) population in each state that is on hemodialysis (HD). HD can be used as a proxy to estimate the number of persons being provided center-based dialysis and requiring trips to and from the center three times a week. As noted above, approximately half of these patients will rely on the public sector for these trips. User Inputs • Community Location/Service Area—County and State • Percentage of County Population in Service Area. Note: this is an estimate of the percentage of the population served and not the percentage of the geographical area served. • Community Size (large metro area, large city, small city, or rural) • Cost per One-Way Trip for Public Sector Dialysis Trips (if available—if not, use default) • Prediction Year (any year after 2015, which is the current year given in the USRDS data) • Assumptions for a “What If” Scenario Regarding a Possible Increase in the Percentage of Patients on Home Dialysis Outputs—Estimates • Current and Projected Number of Dialysis Patients in Community • Current and Projected Number of Dialysis Patients Traveling to Dialysis Facilities—In-Center Treatments Only • Current and Projected One-Way Dialysis Trips on Public Sector Transportation—Unconstrained • Current and Projected Cost of Public Sector Trips Needed— Unconstrained • Current and Projected Percentage of Patients Using Home Dialysis • Potential Decreases in Trips and Costs with “What If” Scenario Projections for Increases in Home Dialysis Chapter 5—Demand and Costs for Dialysis Transportation: A Data Tool 5.9

5.10 Dialysis Transportation: The Intersection of Transportation and Healthcare Stepping Back—What Does This Mean at the National Level? While the purpose of the tool is to allow local communities to estimate the need for public sector trips to dialysis, one could ask: What would be the results if the relationships in the tool are applied to the country as a whole? What might this suggest regarding policy implications at the national level? Answers to these questions are explored next. Dialysis Transportation Costs for the Public Sector—per Trip and Annually With data from the research project’s public transportation agency survey, the average cost for a one-way trip for dialysis treatment is about $28.56 (using survey information from the four sizes of community). Assuming that a patient traveling to a dialysis facility for treatment needs 312 one-way trips a year, the annual cost for these trips—for just one dialysis patient—can be estimated at around $8,900. Annual Demand for Dialysis Transportation USRDS reports that, at the end of 2015, there were 703,243 patients with ESRD receiving treatment (dialysis or transplant). Of these 703,243 patients, more than 70% were receiving dialysis treatment (about 494,000 patients) while 30% had a functioning kidney transplant. In 2015, nearly 90% of all dialysis patients received in-center hemodialysis—which is almost 445,000 patients receiving in-center dialysis treatments. Assuming that all of these patients are traveling to centers (i.e., none of these patients are receiving dialysis at their care facility or, if they are, the USRDS considers this treatment as “in-home” dialysis), this translates into an upper-bound estimate of almost 139 million one-way trips needed to dialysis centers annually. Of course, not all of these trips need to be provided by the public sector. Annual Cost of Public Sector Trips Needed to Dialysis National transportation costs for the public sector can be estimated by determining how many of the upper-bound dialysis trips needed—139 million annually—would be required of the public sector. Results from the research project’s survey of dialysis facilities and other studies of dialysis transportation provide an estimate that approximately half of dialysis patients rely on public sector modes. Thus, an estimate of almost 70 million trips

annually can be considered trips that may need to be provided by the public sector. Taking this calculation further, this would mean that the public sector trips to dialysis facilities require almost $2 billion annually. This is a large and startling number—de�initely a rough estimate—but it is important to recognize that public transportation agencies have a role in providing and funding millions of those public sector trips. Public Transportation Agencies’ Role As the demand for dialysis transportation continues to increase and with constrained funding for public transit, public transportation agencies may fairly question their role in providing dialysis trips. Should healthcare funding contribute to the cost of public transit agencies’ dialysis trips given that many of the trips resemble medical trips more than public transit trips? Should some of those dialysis trips be served by providers more specialized than public transportation agencies? What about the role now allowed for dialysis providers to participate in providing and funding local transportation for their patients? These are important questions. As the healthcare industry increasingly embraces its responsibility for the social determinants of health—of which transportation is a key determinant— it may be that the healthcare sector recognizes its role and responsibilities related to dialysis transportation. At the least, the fact that public transportation agencies serve Medicaid trips in many communities and some of these trips are dialysis trips, communities should work toward improved coordination and cost-sharing with their state’s Medicaid agency for the dialysis trips. Chapter 5—Demand and Costs for Dialysis Transportation: A Data Tool 5.11

5.12 Dialysis Transportation: The Intersection of Transportation and Healthcare Exhibit 5-3: Percentage of Population on Hemodialysis by State STATE Percentage of Population on Hemodialysis STATE Percentage of Population on Hemodialysis Alabama 0.17% Montana 0.07% Alaska 0.06% Nebraska 0.08% Arizona 0.13% Nevada 0.13% Arkansas 0.12% New Hampshire 0.06% California 0.15% New Jersey 0.15% Colorado 0.07% New Mexico 0.14% Connecticut 0.10% New York 0.15% Delaware 0.16% North Carolina 0.15% District of Columbia 0.31% North Dakota 0.08% Florida 0.13% Ohio 0.14% Georgia 0.18% Oklahoma 0.12% Hawaii 0.23% Oregon 0.09% Idaho 0.07% Pennsylvania 0.13% Illinois 0.15% Rhode Island 0.10% Indiana 0.12% South Carolina 0.18% Iowa 0.07% South Dakota 0.10% Kansas 0.10% Tennessee 0.14% Kentucky 0.12% Texas 0.16% Louisiana 0.19% Utah 0.06% Maine 0.08% Vermont 0.06% Maryland 0.16% Virginia 0.14% Massachusetts 0.09% Washington 0.09% Michigan 0.14% West Virginia 0.12% Minnesota 0.08% Wisconsin 0.10% Mississippi 0.22% Wyoming 0.06% Missouri 0.12%