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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Cancer. Author manuscript; available in PMC Dec 1, 2010.
Published in final edited form as:
PMCID: PMC2790004
NIHMSID: NIHMS140673
Evaluating the Cost Effectiveness of Cancer Patient Navigation Programs: Conceptual and Practical Issues
Scott Ramsey, MD, PhD,1 Elizabeth Whitley, RN, PhD,2 Victoria Warren Mears, RD, PhD,3 June M. McKoy, MD, MPH, JD,4 Rachel M. Everhart, MS,2 Robert J. Caswell, PhD,5 Kevin Fiscella, MD, MPH,6 Thelma C. Hurd, MD,7 Tracy Battalgia, MD, MPH,8 and Jeanne Mandelblatt, MD, MPH9, For the Patient Navigation Research Program Group
1 Fred Hutchinson Cancer Research Center, Seattle, Washington
2 Denver Health & Hospital Authority, Community Voices, Denver, Colorado
3 Northwest Portland Area Indian Health Board, Northwest Tribal Epidemiology Center, Portland, Oregon
4 Northwestern University Feinberg School of Medicine, Departments of Medicine and Preventive Medicine, Chicago, Illinois
5 Ohio State University, College of Public Health, Columbus, Ohio
6 University of Rochester School of Medicine, Department of Family Medicine, Rochester, New York
7 University of Texas Health Science Center, San Antonio, Texas
8 Women’s Health Research Unit, Boston, Massachusetts
9 Georgetown University Medical Center, Cancer Control Program, Lombardi Comprehensive Cancer Center, Washington, DC
Address for correspondence: Scott D. Ramsey, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North M3-B232, PO Box 19024, Seattle, WA 98109. Phone (206) 667-7846, Fax (206) 667-5977, sramsey/at/fhcrc.org
Background
Patient navigators--individuals who assist patients through the healthcare system to improve access to and understanding of their health and health care—are increasingly utilized 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.
Methods
We outline a conceptual model for evaluating the cost effectiveness of cancer navigation programs. We describe how this model is being applied to the Patient Navigation Research Program (PNRP), a multi-center study supported by the National Cancer Institute’s Center to Reduce Cancer Health Disparities.
Results
The PNRP is testing navigation interventions which aim to reduce time to delivery of quality cancer care (non-cancer 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 relationship between navigation programs and outcome of interest (e.g., 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, we discuss several strategies for addressing these barriers.
Conclusions
Evaluating the costs and impact of navigation will require some novel methods, but will be critical in recommendations about dissemination of navigation programs.
Keywords: cancer, navigator, cost effectiveness, modeling
Populations with limited access to or knowledge of the health care system often have difficulty utilizing 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.414 In this paper, we present a conceptual model for evaluating the cost effectiveness of cancer patient navigation programs, discuss methodological challenges, and suggest approaches for addressing these challenges.
Rationale for and History of Patient Navigation Programs
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 both in cancer care and outcomes between poor and non-poor Americans, identifying, among other issues, significant barriers to care and a sense of fatalism about 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 showed that 41% of breast cancer patients diagnosed between 1995 and 2000 were diagnosed with early disease compared to 6% of patients between 1964 and 1986.16, 17 Five-year survival rates increased from 39% to 70% over the same period.
Due to 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 studies1922 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.2325 The impact of navigation programs on cancer-related morbidity and survival, and the cost-effectiveness of these programs is not yet known.
The Patient Navigation Research Program
The National Cancer Institute (NCI) and the ACS are sponsoring a 9-site Patient Navigation Research Program (PNRP) [Table 1]10 The primary aim of the PNRP is to evaluate navigation programs’ impact on the time between 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
Table 1
Patient Navigation Research Program study populations, setting, and programs
PNRP 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, utilizing different professionals and health care systems [Table 1] to follow patients through the completion of initial treatment.
Rationale for Evaluating the Cost-effectiveness of Patient Navigation Programs
Patient navigator programs can be time and resource intensive. Like 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, since 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 showing 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 (e.g., $100,000 per quality adjusted life year gained) in the health system or country. 26
Conceptual Model for Cost Effectiveness Analysis of Patient Navigation Interventions
For the PNRP, we are using cost-effectiveness analysis (CEA) 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 vs. usual care, which in patients and their family members seeking care without formal assistance. Costs and consequences flow from each alternative (navigated vs. usual care) are summarized over the time period that is relevant to the episode of care (Figure 1). The incremental cost effectiveness of navigation is derived using the following formula:
equation M1
(1)
where CNav and CUC refer to the incremental difference in total costs of the navigation program compared to usual care, and ENav and EStd refer to the difference in total effectiveness between navigation and usual care [Figure 1]. While the comparator is typically “usual care;” that is, care as it occurs in usual practice in the absence of navigators, one could also compare two or more navigation programs vs. usual care, or one program with another. Both the navigator program and usual care have costs that flow from the point of entry (e.g., abnormal finding on mammogram) to short- and long-term downstream costs and consequences. Generally the time horizon is the individuals’ remaining years of life. Since the PNRP program will only observe individuals over a maximum of the 5 years of the program, examining impact on survival and costs per quality-adjusted life years saved will require estimation using mathematical models.
Figure 1
Figure 1
Conceptual model of patient navigator intervention vs. usual care
Navigation Cost-effectiveness Analysis and Approaches for Addressing Challenges
Evaluating the cost-effectiveness of patient navigation programs poses several unique challenges [Table 3]. In this section, we describe particular challenges for evaluating the cost effectiveness of the PNRP and how we plan to address those issues.
Table 3
Table 3
Unique challenges to evaluating cost effectiveness of navigation programs
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, since part of the principle of navigation is to identify patient-specific issues and tailor the program to those needs. Moreover, navigation interventions (including PNRP) 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 (e.g., 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 since most providers recommend screening for multiple cancers to their patients and navigators assist individuals in navigating through to diagnostic resolution for more than one 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 (e.g., 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 Quality Adjusted Life Year (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 PNRP 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 five-year PNRP 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-, race-, 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 instance, most models portray screening benefits in terms of decreases in tumor size (and number of nodes involved) or stage shifts. In this situation, for navigation to show a benefit, the intervention would have to lead to an early stage diagnosis in a patient who would have otherwise have been lost to follow up and only presented clinically at more advanced stages. Less dramatic within-stage shifts (e.g., 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 there are limited primary data upon 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 about stage shift or cure affect results. If navigation is not cost effective under the most favorable assumptions about 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 relationship between the intervention (navigation) and the endpoints (survival, QALYs) may not be straightforward, since the intermediate outcome of navigation—adherence to timely diagnostic services (where 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 (e.g., expenditures on navigation services and adherence to screening recommendations over time) influences long term outcomes.
Even if navigation interventions do not improve survival, they still may improve individuals’ 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 following the intervention.29 Utility weights for navigator program participants and a comparator group can be measured using a generic multi-attribute utility instrument such as the EQ-5D.30 Multi-attribute 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 zero representing the worst health imaginable (or death) and one 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.
Due to budget constraints, not all PNRP sites will collect multi-attribute utility data for 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 where health and socioeconomic status are similar, we use data from the populations were utilities are collected as proxies for these where utilities were not collected. We will 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 following 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 PNRP address this issue by 28 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 of 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 following treatment.32, 33 Education, health status and comorbidity are measured in parent PNRP study. In our projections of effects from the trial horizon to a lifetime horizon, we will construct multivariate models with covariates to account for these characteristics. This will 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, 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, there are no established benchmarks for comparison to determine whether particular reductions in stress are cost-efficient compared to 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 (e.g. lumpectomy vs. mastectomy) or completion rates of planned therapy. These measures of outcome as well as distress and other outcomes (e.g., stage at diagnosis, time to diagnostic resolution, satisfaction) can be summarized using cost-consequence analysis.35 Cost-consequence analyses summarize program costs and effects in tabular fashion [Table 2]. For instance, one can evaluate the costs per patient of timely diagnostic resolution for the navigator program vs. usual care. Cost-consequence analysis can be useful to a decision makers who use components of cost-effectiveness analysis rather than the cost per QALY ratio. 36
Table 2
Table 2
Cost consequence analysis sample table, with specific elements of interest in navigator interventions
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 relationships (e.g., volume of patients seen and adherence to follow-up of abnormal mammograms) and socio-demographics of the navigators themselves (age, sex, education).
Cost Impact of Navigation Programs
Navigation program costs include allocated “fixed” (office space, proportional allocation of supervisory personnel, new equipment or contracts initiated for the program, etc) and “variable” (navigator time and transportation costs, direct medical care, etc) components [Table 2]. 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 utilizing navigation programs as Cmedical(program). Patients who receive care without utilizing 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, since 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 relapse 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. Due to 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 (e.g., screening tests and care related to follow-up of abnormal tests) will be assessed based on the routine core data elements collected by the PNRP and valued use 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 (e.g. 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 individuals’ age and gender. For those who do not work for pay (e.g., homemakers or retired persons), there is no generally agreed upon method, but most base costs on national wage surveys.27 Using navigator logs, the PNRP 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 which 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 PNRP 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 PNRP evaluations, research costs will be identified from audits of research budgets during site visits with investigators (e.g., 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 where navigation influences the use of multiple cancer screening programs, we will disaggregate costs to particular services (e.g., mammography) based on the patient and navigator diaries. If feasible, we will also estimate the cost-effectiveness of a bundle of services (e.g., mammography + pap smear + colorectal cancer screening) compared to usual care.
Perspective and Time Horizon
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 where public health issues are under evaluation, 27 and is particularly important for navigation programs, since the resources for navigators may come from one source (e.g., foundations, government programs, hospitals), while payment for medical care may come from another (e.g., Medicaid). As discussed above, navigation programs have short 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 (e.g., survival), this implies using a lifetime time horizon. Because the PNRP program will only observe participants over a 4 to 5 year horizon, evaluating cost effectiveness will require simulation modeling to estimate the lifetime impact of navigation on populations.
Uncertainty Analysis
One-way sensitivity and multi-way 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 re-calculating the cost-effectiveness ratio. This gives a sense of the relative influence of individual factors (e.g., the hourly wage of navigators) on overall cost-effectiveness of the program. Multi-way 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 varying assumptions regarding costs, quality of life, and survival for the usual care (non-navigator) group. The comparison or “usual care” group in some PNRP studies utilizes historical data from the period prior to navigation or convenience samples from comparable communities that are not involved in the PNRP; 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 sub-groups of patients, and the basis for time costs (local vs. national, average or race-specific wages, etc).
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 PNRP 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 (i.e., be cost-effective).4043 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.
Acknowledgments
Funding provided by the National Cancer Institute, via the Center to Reduce Cancer Health Disparities, through Contract 263-FQ-612391. Also supported by NIH grants: U01 CA116892; U01 CA117281; U01 CA116903; U01 CA116937; U01 CA116924; U01 CA116885; U01 CA116875; U01 CA116925; and American Cancer Society grant SIRSG-05-253-01. Dr. Mandelblatt’s work is also supported in part by NCI grants U01 CA88283 and KO5 CA96940.
PNRP PIs and Affiliation
Charles L. Bennett, M.D., Ph.D., M.P.P., The Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL
Elizabeth Calhoun, Ph.D., Health Policy and Administration, University of Illinois at Chicago, Chicago, IL
Donald J. Dudley, M.D., University of Texas Health Science Center, San Antonio, San Antonio, TX
Kevin Fiscella, M.D., M.P.H., University of Rochester Medical Center, Rochester, NY
Karen M. Freund, M.D., M.P.H., Tracy Battalgia, M.D., M.P.H. (Co-PI), Boston University Medical Center, Women’s Health Research Unit, Boston, MA
Victoria Warren Mears, Ph.D., Northwest Portland Area Indian Health Board, Portland, OR
Electra D. Paskett, Ph.D., Marion N. Rowley Professor of Cancer Research, Division of Epidemiology, College of Public Health, Associate Director for Population Sciences, Comprehensive Cancer Center, Ohio State University, Columbus, OH
Steven R. Patierno, Ph.D., The George Washington University Cancer Institute, Washington, DC
Peter C. Raich, M.D., F.A.C.P., Denver Health & Hospital Authority, Denver, CO
Richard G. Roetzheim, M.D., M.S.P.H. H., Lee Moffitt Cancer Center & Research Institute, Tampa, FL
CRCHD PNRP Directors, Roland Garcia, Ph.D., Mary Ann Van Duyn, Ph.D., Emmanuel Taylor, Dr.PH
NOVA, Amanda Greene, Ph.D., M.P.H., R.N. (Project Manager), Paul Young, M.P.H., M.B.A. (Project Manager)
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