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Keywords:

  • cancer;
  • navigator;
  • cost-effectiveness;
  • modeling

Abstract

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

Patient navigators—individuals who assist patients through the healthcare system to improve access to and understanding of their health and healthcare—are increasingly used for underserved individuals at risk for or with cancer. Navigation programs can improve access, but it is unclear whether they improve the efficiency and efficacy of cancer diagnostic and therapeutic services at a reasonable cost, such that they would be considered cost-effective. In the current study, the authors outline a conceptual model for evaluating the cost-effectiveness of cancer navigation programs. They describe how this model is being applied to the Patient Navigation Research Program, a multicenter study supported by the National Cancer Institute's Center to Reduce Cancer Health Disparities. The Patient Navigation Research Program is testing navigation interventions that aim to reduce time to delivery of quality cancer care (noncancer resolution or cancer diagnosis and treatment) after identification of a screening abnormality. Examples of challenges to evaluating cost-effectiveness of navigation programs include the heterogeneity of navigation programs, the sometimes distant relation between navigation programs and outcome of interest (eg, improving access to prompt diagnostic resolution and life-years gained), and accounting for factors in underserved populations that may influence both access to services and outcomes. In this article, the authors discuss several strategies for addressing these barriers. Evaluating the costs and impact of navigation will require some novel methods, but will be critical in recommendations concerning dissemination of navigation programs. Cancer 2009. © 2009 American Cancer Society.

Populations with limited access to or knowledge of the healthcare system often have difficulty using the system effectively for cancer services, and this may result in delays in cancer diagnosis,1, 2 added costs,3 and less efficient and effective use of recommended therapies. Patient navigation programs provide support and guidance to persons with the goal of improving access to the cancer care system and overcoming barriers to timely, quality care.4-14 In this article, we present a conceptual model for evaluating the cost-effectiveness of cancer patient navigation programs, discuss methodologic challenges, and suggest approaches for addressing these challenges.

Rationale for and History of Patient Navigation Programs

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

The origins of patient navigator programs are widely attributed to Harold Freeman, who, as president of the American Cancer Society (ACS), commissioned a study of barriers to cancer care among the poor in the United States. The report documented substantial disparities in both cancer care and outcomes between poor and nonpoor Americans, identifying, among other issues, significant barriers to care and a sense of fatalism regarding cancer that prevented many from seeking care in the first place.15 As a result of this report, the ACS supported the first Patient Navigation program in 1990 at the Harlem Hospital Center. A pre-post comparison of women diagnosed with breast cancer at this facility demonstrated that 41% of breast cancer patients diagnosed between 1995 and 2000 were diagnosed with early disease, compared with 6% of patients diagnosed between 1964 and 1986.16, 17 Five-year survival rates increased from 39% to 70% over the same period.

Because of the success of this pioneer program, and in recognition that significant barriers to effective cancer screening, diagnosis, and care continue to exist among minority and underserved populations, patient navigation programs are becoming more common, particularly among health systems that serve these populations. The Centers for Medicare and Medicaid Services is funding demonstration projects to reduce barriers to care at all levels.18 Despite their growing popularity and the publication of promising observational studies,19-22 to our knowledge very few prospective, controlled trials have evaluated the efficacy of navigator programs. Controlled trials, most of which are small, have shown significant improvements in time to diagnosis, reductions in anxiety, and greater levels of satisfaction with the care process.23-25 The impact of navigation programs on cancer-related morbidity and survival, and the cost-effectiveness of these programs, are not yet known.

The Patient Navigation Research Program

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

The National Cancer Institute and the ACS are sponsoring a 9-site Patient Navigation Research Program (Table 1).10 The primary aim of the Patient Navigation Research Program is to evaluate navigation programs' impact on the time from an abnormal finding (from a screening test or clinical examination for case finding) to definitive diagnosis and treatment initiation. Secondary aims include evaluating the impact of navigation on patient satisfaction and the cost-effectiveness of navigation.

Table 1. Patient Navigation Research Program Study Populations, Setting, and Programs
PN SitesCancersPopulationsNavigatorStudy DesignSettingPN InterventionControl
  • PN indicates Patient Navigation; B, Black, H, Hispanic; U, underserved navigator; O, other; A/PI, Asian and Pacific Islander; AI/AN, American Indian/Alaska Native; Lay, lay or community worker; NP, nurse practitioner, nurse clinicians, physician assistant; SW, social worker; RN, registered nurse; PRO, Promotoras.

  • For more information, visit http://crchd.cancer.gov/pnp/pnrp-index.html Accessed August 4, 2009.

  • *

    Totals.

Boston UniversityBreast CervixB H U6 OGroup randomized, controlledCommunity health center12001200
Denver Health and Hospital AuthorityBreast Colorectal ProstateB H U A/PI AI/AN4.5 LayRandomizedCommunity health center, hospital870870
George Washington University, Washington, DCBreastB H U1 NP 1 SW 7 ONonrandomized, controlledClinic800800
H. Lee Moffitt Cancer CenterBreast ColorectalB H U3 LayGroup randomized, controlledClinic and hospital600600
Northwest Portland Area Indian Health BoardBreast Cervix Colorectal ProstateAI/AN3 RN 1 LayNonrandomized, controlledClinic650650
University of Illinois at Chicago/Northwestern University, ChicagoBreast Cervix Colorectal ProstateB H U2 SW, 5 LayRandomized, controlledCommunity health centers, clinics, hospital25002500
University of Rochester, NYBreast ColorectalB U3 LAYRandomized, controlled (patient)Hospital400400
University of Texas Health Science Center at San AntonioBreast CervixB H U4 PRO, 2 RN, 2 SWNonrandomized, controlledClinic700700
Ohio State University, ColumbusBreast Cervix ColorectalB H U3 LAYGroup randomized, controlledClinic42584258
      11,978*11,978*

Patient Navigation Research Program sites serve diverse patient populations. Navigation programs focus on follow-up of abnormal breast, cervical, prostate, and colorectal cancer screening tests, among minority populations including African Americans, American Indians, Asians, Hispanics, and the rural underserved. Navigation models vary across sites, using different professionals and healthcare systems (Table 1) to follow patients through the completion of initial treatment.

Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

Patient navigator programs can be time and resource intensive. Similar to other interventions that may improve the health of poor and underserved populations, navigation programs must be viewed in the context of allocating resources such that health outcomes are maximized under limited budgets. It is particularly important to evaluate the cost-effectiveness of publicly funded navigator programs, because funding for these programs typically come from global health budgets that are fixed in the short run with many competing needs. Cost-effectiveness analysis can assist decision makers by demonstrating the health benefit for expenditure of navigator programs relative to other interventions, particularly those that are targeted to the same disease or condition of interest. The desirability of navigator programs can also be assessed in terms of commonly accepted thresholds (eg, $100,000 per quality-adjusted life-year [QALY] gained) in the health system or country.26

Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

For the Patient Navigation Research Program, we are using cost-effectiveness analysis to compare the added (incremental) costs of navigation interventions versus those of the status quo for the given target populations.27 Cost-effectiveness analysis is a comparison of alternatives, typically a new intervention such as navigation versus usual care, which is patients and their family members seeking care without formal assistance. Costs and consequences flowing from each alternative (navigated vs usual care) are summarized over the time period that is relevant to the episode of care (Fig. 1). The incremental cost-effectiveness of navigation is derived using the following formula:

  • equation image(1)

in which CNav and CUC refer to the incremental difference in total costs of the navigation program compared with usual care, and ENav and EUC refer to the difference in total effectiveness between navigation and usual care (Fig. 1). Although the comparator is typically usual care; that is, care as it occurs in usual practice in the absence of navigators, one could also compare ≥2 navigation programs versus usual care, or 1 program with another. Both the navigator program and usual care have costs that flow from the point of entry (eg, abnormal finding on mammogram) to short-term and long-term downstream costs and consequences. Generally the time horizon is the individuals' remaining years of life. Because the Patient Navigation Research Program will only observe individuals over a maximum of the 5 years of the program, examining impact on survival and costs per QALYs saved will require estimation using mathematical models.

thumbnail image

Figure 1. A conceptual model of patient navigator intervention versus usual care is shown. *Examples may include persons eligible for cancer screening procedures or those with cancer who are eligible for treatment.

Download figure to PowerPoint

Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

Evaluating the cost-effectiveness of patient navigation programs poses several unique challenges (Table 2). In this section, we describe particular challenges for evaluating the cost-effectiveness of the Patient Navigation Research Program and how we plan to address those issues.

Table 2. Unique Challenges to Evaluating the Cost-Effectiveness of Navigation Programs
  1. QALY indicates quality-adjusted life-year.

Relation between navigation and endpoints (costs, survival, QALY) is nonlinear
Content (and costs) of navigation interventions are variable due to site-specific program needs
Confounding between need for navigation and stage, mortality endpoints
Difficulty in allocating costs and effects over multiple cancers
Short-term intervention outcomes (eg, distress) do not map easily to QALYs
Difficulty collecting uniform data across sites and at relevant time points (eg, time costs)
Difficulty detecting the impact of modest reductions in diagnostic or treatment delays on mortality
Personal characteristics of navigators (difficult to measure) may influence program effectiveness

Defining the navigation intervention

The first issue in conducting the cost-effectiveness analysis of navigation is that the navigator intervention itself is not uniform for all patients, because part of the principle of navigation is to identify patient-specific issues and tailor the program to those needs. Moreover, navigation interventions (including the Patient Navigation Research Program) are quite heterogeneous, and are typically tailored to the needs and available resources of a particular region and the cancers of interest. Even within a single program site, the navigator will tailor the intervention to the needs of the particular patient-client, with wide variation in services provided between individuals. A related issue is that programs differ in expectations, qualifications, training, and supervision of navigators. In many settings, navigators are trained to assist patients with abnormal screening tests for several cancers (eg, cervical and colorectal, or breast and prostate). Although there are economies of scale in these situations, it is more difficult to segregate the time costs for each cancer and model each separately. One could capture the economies of scale by modeling all screening, but this requires extension of the time horizon in a model capturing the natural history of multiple cancers at once.

At present, we are not aware of models that are designed to incorporate the natural history of multiple cancers simultaneously. However, this is an important research priority, because the majority of providers recommend screening for multiple cancers to their patients, and navigators assist individuals in navigating through to diagnostic resolution for >1 cancer type.

Therefore, we address the issue of the heterogeneity of interventions by defining the navigation programs broadly, as specified by the study protocols.28 This approach emphasizes the type of navigator (eg, nurse, layperson) and the general scope of services that that individual is able to provide. We will then have to model the cost-effectiveness of navigation for each individual cancer separately, allocating navigator time and other efforts in proportion for each cancer site.

Measuring effectiveness of navigation programs

The recommended measure of effectiveness of navigation programs for cost-effectiveness analyses is the QALY,29 which requires data on survival with and without the program and evaluation of health state preferences (utilities). However, outcome measures being directly tracked by the Patient Navigation Research Program research sites are intermediate outcomes: time to definitive diagnosis/resolution and time to initiation/completion of recommended cancer therapy for those with a cancer diagnosis.28 Moreover, the period of observation under the 5-year Patient Navigation Research Program will be too short to observe any mortality endpoints.

Estimating QALYs will require simulation modeling. To address the need to extrapolate from the observation period to estimate the impact of navigation over a lifetime, we will use simulation models to extend the time frame of observation and look at stage distribution of patients diagnosed under navigation and usual care, using local cancer registries, hospitals, and patient charts. Age-specific, race-specific, and stage-specific survival from cancer registries (local or national) can then be used to project the life expectancy, or mortality experience of each group of patients.

Even using this approach, modeling the effects of mortality based on delays in diagnosis or treatment is challenging and requires modeling assumptions. For example, most models portray screening benefits in terms of decreases in tumor size (and number of lymph nodes involved) or stage shifts. In this situation, for navigation to demonstrate a benefit, the intervention would have to lead to an early stage diagnosis in a patient who would otherwise have been lost to follow-up and only presented clinically at more advanced stages. Less dramatic within-stage shifts (eg, early in the course of local disease vs later in local disease, but before transition to regional spread) are also likely to improve survival, but to our knowledge there are only limited primary data on which to model these effects. It is also possible that small within-stage shifts do not affect cancer-specific mortality. We will use sensitivity analysis to evaluate how different assumptions regarding stage shift or cure affect results. If navigation is not cost-effective under the most favorable assumptions concerning small effects, then one could conclude that the investment does not yield a return on investments in QALYs. However, if programs would be considered cost-effective under assumptions that are clinically reasonable, then programs with small effects could be considered to have the potential to be cost-effective.

The relation between the intervention (navigation) and the endpoints (survival, QALYs) may not be straightforward, because the intermediate outcome of navigation—adherence to timely diagnostic services (in which the majority does not have cancer) and to recommended therapy—will not necessarily be uniform and linear in its relation to endpoints. We address this issue with simulation modeling and sensitivity analysis, the latter evaluating how changes in the association between specific input parameters (eg, expenditures on navigation services and adherence to screening recommendations over time) influence long-term outcomes.

Even if navigation interventions do not improve survival, they still may improve an individual's quality of life. In cost-effectiveness analyses, these effects are recorded as health state utilities to be used in computing QALYs. Utilities are measures of health state preference, measured on a scale from 0 (death) to 1 (ideal health). QALYs are a summary measure of survival weighted by utilities over the period after the intervention.29 Utility weights for navigator program participants and a comparator group can be measured using a generic multiattribute utility instrument such as the EQ-5D.30 Multiattribute utility instruments are questionnaires filled out by respondents assessing their quality of life across several domains. The individual responses are weighted using data derived from large population surveys on the utility of the different quality of life states. Scores are summed and converted to a 0 to 1 scale, with 0 representing the worst health imaginable (or death) and 1 representing perfect health. This approach provides societal rather than individual patient ratings of the potential quality of life improvements that might occur with navigation, so that results are generalizable.

Because of budget constraints, not all Patient Navigation Research Program sites will administer multiattribute utility instrument surveys to their participants. Utility weights for the comparison (no navigator) group will be based on the literature and, when available, surveys of low-income populations with cancer but no navigation services.31 We will compare patient populations where utilities are being collected and those where they are not. In cases in which health and socioeconomic status are similar, we use data from the populations in which utilities are collected as proxies for those where utilities were not collected. We also explore the use of regression models based on navigator study populations with utility data to impute utilities for those without utility data.

It should be noted that problems that are highly prevalent in underserved populations that are being targeted by navigation (such as low literacy rates and frequently changing residences) pose challenges to measuring outcomes after navigation using existing utility surveys. For example, populations with very low literacy or special groups such as the homeless or persons with mental illnesses may have great difficulty completing written questionnaires. The Patient Navigation Research Program address this issue28 by allowing telephone and face-to-face interviews with patients and, if necessary, patient representatives.

Another issue that is embedded in the navigation program that poses a challenge to cost-effectiveness analysts is that patients with significant barriers to access to health systems often have complex social and health issues, such as poor educational attainment or non–cancer-related comorbidity, that themselves may influence long-term outcomes, such as life expectancy and/or cancer- specific survival rates after treatment.32, 33 Education, health status, and comorbidity are measured in the parent Patient Navigation Research Program study. In our projections of effects from the trial horizon to a lifetime horizon, we construct multivariate models with covariates to account for these characteristics to allow us to vary projected outcomes based on the characteristics of the cohort of interest; we can also use national data on the distribution of these factors to conduct sensitivity analyses to estimate the impact of navigation in broader settings and populations.

Navigator programs also aim to improve patient satisfaction and self-efficacy, and reduce the short-term distress associated with evaluation of an abnormal screening result. However, self-efficacy and satisfaction with care are generally not incorporated in surveys that measure utilities. In such situations, one could calculate a cost per unit decrease in distress.34 However, to our knowledge, there are no established benchmarks for comparison to determine whether particular reductions in stress are cost-efficient compared with other ways to accomplish the same goal.

Navigation programs aimed at cancer patients may also have goals such as informed use of procedures based on patient preference (eg, lumpectomy vs mastectomy) or completion rates of planned therapy. These measures of outcome, as well as distress and other outcomes (eg, stage at diagnosis, time to diagnostic resolution, and satisfaction), can be summarized using cost-consequence analysis.35 Cost-consequence analyses summarize program costs and effects in tabular fashion (Table 3). For example, one can evaluate the costs per patient of timely diagnostic resolution for the navigator program versus usual care. Cost-consequence analysis can be useful to decision makers who use components of cost-effectiveness analysis rather than the cost per QALY ratio.36

Table 3. Cost Consequence Analysis Sample Table, With Specific Elements of Interest in Navigator Interventions
  • QALY indicates quality-adjusted life-year.

  • *

    Modeled.

Costs
 Training costs (Ctraining[program])
  Initial training
  Training replacements and additional navigators
 Navigation program (Cnavigator[program])
  Fixed costs: navigator program
   Costs associated with developing navigator-related materials (eg, pamphlets, telephone scripts)
   Allocated fixed operation costs (office space leasing, telephone, furniture, etc)
  Variable costs: navigator program
   Time spent in navigation (travel, meeting with patients, documentation)
   Travel-associated costs
 Variable direct nonmedical costs: all patients (Cnonmed[program] and Cnonmed[usual care])
  Patient time costs seeking treatment
  Travel-associated costs
 Variable direct medical costs: patients (Cmedical[program] and Cmedical[usual care])
Outcomes
 Time from abnormal screening test or suspicious finding to diagnosis
 Time from diagnosis to initial therapy
 Time from initial therapy to resolution (end of initial therapy including therapeutic combinations such as surgery plus chemotherapy)
 Percentage of patients receiving initial therapy (surgery, chemotherapy, radiotherapy)
 Percentage completing therapy
 Satisfaction with care
 Quality of life during care
 Quality of life after care
 Survival (years of life)*
 Quality-adjusted survival (QALY)*

Interpersonal styles and commitments of navigators may influence the outcomes of particular programs. Although this factor is very difficult to measure and account for across sites, we will evaluate variations in sensitivity analysis, using proxy measures such as volume-outcome correlations (eg, volume of patients seen and adherence to follow-up of abnormal mammograms) and sociodemographics of the navigators themselves (age, sex, education).

Cost Impact of Navigation Programs

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

Navigation program costs include allocated fixed (eg, office space, proportional allocation of supervisory personnel, new equipment or contracts initiated for the program) and variable (eg, navigator time and transportation costs, direct medical care) components (Table 3). We denote the sum of allocated fixed and variable costs as Cnavigator(program). There are also costs associated with training navigators, including replacements or additional navigators as needed (Ctraining[program]). We denote the total direct medical care cost of diagnostic services and treatments received for persons using navigation programs as Cmedical(program). Patients who receive care without using navigator services have a cost, denoted Cmedical(usual care).

Patients and their caregivers incur nonmedical costs when seeking care, such as transportation costs, time costs related to testing and treatment, and time lost from work. We denote related nonmedical patient costs for those receiving and not receiving navigator services as Cnonmed(program) and Cnonmed(usual care). Note that in the short run, medical and related nonmedical costs are likely to be higher for the navigation program because of improvements in patient access to care and adherence to protocols for care. Longer-term costs for the navigation program may be lower if a program results in diagnostic resolution at an earlier stage based on an abnormal screening test, because patients lost to follow-up are likely to present again with more advanced, more time-consuming (and costly) stages of disease. Navigation may also lower costs if patients use care more appropriately and efficiently or better adhere to planned therapy such that cancer recurrence rates fall. Thus, in the long run, the net cost of navigation programs can be more or less than those under usual care.

One of the potential cost offsets of a navigator program is decreasing the time required by the medical staff and office support staff in trying to support patients who need help through the complex medical system. Because of the heterogeneity of care settings involved, it is not possible to track these offsets directly. We will explore the impact of offsets, based on time navigators spend with patients, in sensitivity analyses.

Direct medical care related to navigation (eg, screening tests and care related to follow-up of abnormal tests) will be assessed based on the routine core data elements collected by the Patient Navigation Research Program and valued using representative reimbursement rates, such as regionally adjusted Medicare payments. Longer-term costs, such as lifetime costs related to cancer treatment, will be estimated based on the stage at diagnosis, using published sources.37 Navigators' time costs are likely to be the most significant program cost. Time costs will vary substantially depending on training (eg, professionals vs laypersons), the complexity of the care system, and the needs of the target population. Time spent by volunteer navigators is not free and should be valued as the opportunity cost of those persons, given other options for spending their time. Time costs for professionals can be valued based on their wages. Valuing time costs for volunteers can be more difficult. For persons who are employed, time is typically valued based on their wages or the prevailing national wage rates for those of the individual”s age and sex. For those who do not work for pay (eg, homemakers or retired persons), there is no generally agreed on method, but most base costs on national wage surveys.27 By using navigator logs, the Patient Navigation Research Program will collect self-reported information on the time spent by navigators in direct contact with patients and in activities required for coordination of care.

In the process of seeking care, patients incur costs that may be significant barriers to accessing care in the first place.38 Patient costs can be evaluated using patient logs or, if this is infeasible, by estimating time and associated expenses when traveling to specific services. Although the Patient Navigation Research Program will not collect patient log data, navigator logs will include information on the provision of these patient services, including transportation and child care costs. Patient time costs will be valued using census region-specific wage rates for individuals that match the age and sex of the patient population.

It is important to separate research-related costs from intervention costs. For the Patient Navigation Research Program evaluations, research costs will be identified from audits of research budgets during site visits with investigators (eg, navigator time filling out study-related paperwork and complying with institutional review board documentation). In practice, it can be difficult to separate research from intervention costs, thus necessitating the documentation and reporting of assumptions made when there is uncertainty.

In cases in which navigation influences the use of multiple cancer screening programs, we will disaggregate costs to particular services (eg, mammography) based on the patient and navigator diaries. If feasible, we will also estimate the cost-effectiveness of a bundle of services (eg, mammography + Papanicolaou smear + colorectal cancer screening) compared with usual care.

Perspective and Time Horizon

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

In cost-effectiveness analysis, perspective refers to the point of view taken for evaluating the impacts and costs of the study. The societal perspective is favored for cost-effectiveness analysis in which public health issues are under evaluation,27 and is particularly important for navigation programs, because the resources for navigators may come from 1 source (eg, foundations, government programs, hospitals), whereas payment for medical care may come from another (eg, Medicaid). As discussed above, navigation programs have short-term and long-term impacts. Thus, the cost-effectiveness of navigation programs is best estimated over the entire period that the program is expected to influence costs and outcomes. The relevant time horizon for navigation programs that assist patients with evaluation of abnormal findings is the time from the initial point of detection of abnormal findings to their resolution. For navigation programs that change care such that longer-term endpoints are affected (eg, survival), this implies using a lifetime time horizon. Because the Patient Navigation Research Program will only observe participants over a 4-year to 5-year horizon, evaluating cost-effectiveness will require simulation modeling to estimate the lifetime impact of navigation on populations.

Uncertainty Analysis

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

One-way sensitivity and multiway uncertainty analyses can identify factors that most substantially influence the cost-effectiveness of the programs.39 One-way sensitivity analysis is a process of varying individual parameters across a range, then recalculating the cost-effectiveness ratio. This gives a sense of the relative influence of individual factors (eg, the hourly wage of navigators) on the overall cost-effectiveness of the program. Multiway analysis is a process of varying all parameters simultaneously such that a distribution or confidence interval can be derived around the point estimate of cost-effectiveness.

Particular attention should be paid to the impact of various assumptions regarding costs, quality of life, and survival for the usual care (non-navigator) group. The comparison or usual care group in some Patient Navigation Research Program studies uses historical data from the period before navigation or convenience samples from comparable communities that are not involved in the Patient Navigation Research Program; to the best of our knowledge, few use randomized controlled trials (Table 1). Navigator program-specific factors that should be considered for sensitivity analyses include patient time, type of navigator used, ranges of time to navigate different subgroups of patients, and the basis for time costs (eg, local vs national, average or race-specific wages).

Conclusions

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

It is rare for an economic evaluation to be free of conceptual and/or practical challenges, and cost-effectiveness analysis of cancer patient navigation is no exception. In this report, we outline several special conceptual challenges to evaluating navigation interventions, as well as many practical issues of data collection, instrument choice, and cost measurement. We have outlined several issues related to assessing costs and effectiveness in navigation programs, as well as methods Patient Navigation Research Program investigators will take to identify them. Although it is possible to derive nationally representative estimates of cost-effectiveness for particular programs, many navigation programs are tailored to specific local situations, and thus also merit evaluation of economic value in a local context. However, we do not know if navigation will translate into improved cancer survival, and if it will improve the effectiveness of cancer care at a reasonable cost (ie, be cost-effective).40-43 Thus, the process of defining processes, costs, and outcomes that is part and parcel of cost-effectiveness analysis can also provide valuable information for local decision makers allocating limited health resources to navigation programs.

Conflict of Interest Disclosures

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References

Funding was provided by the National Cancer Institute, via the Center to Reduce Cancer Health Disparities, through Contract 263-FQ-612391; by National Institutes of Health Grants U01 CA116892, U01 CA117281, U01 CA116903, U01 CA116937, U01 CA116924, U01 CA116885, U01 CA116875, and U01 CA116925; and by American Cancer Society Grant SIRSG-05-253-01.

Dr. Mandelblatt is also supported in part by National Cancer Institute grants U01 CA88283 and KO5 CA96940.

References

  1. Top of page
  2. Abstract
  3. Rationale for and History of Patient Navigation Programs
  4. The Patient Navigation Research Program
  5. Rationale for Evaluating the Cost-Effectiveness of Patient Navigation Programs
  6. Conceptual Model for Cost-Effectiveness Analysis of Patient Navigation Interventions
  7. Navigation Cost-Effectiveness Analysis and Approaches for Addressing Challenges
  8. Cost Impact of Navigation Programs
  9. Perspective and Time Horizon
  10. Uncertainty Analysis
  11. Conclusions
  12. Conflict of Interest Disclosures
  13. References
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