Impact of Patient Navigation on Timely Cancer Care: The Patient Navigation Research Program

Abstract

Background Patient navigation is a promising intervention to address cancer disparities but requires a multisite controlled trial to assess its effectiveness. Methods The Patient Navigation Research Program compared patient navigation with usual care on time to diagnosis or treatment for participants with breast, cervical, colorectal, or prostate screening abnormalities and/or cancers between 2007 and 2010. Patient navigators developed individualized strategies to address barriers to care, with the focus on preventing delays in care. To assess timeliness of diagnostic resolution, we conducted a meta-analysis of center-and cancer-specific adjusted hazard ratios (aHRs) comparing patient navigation vs usual care. To assess initiation of cancer therapy, we calculated a single aHR, pooling data across all centers and cancer types. We conducted a metaregression to evaluate variability across centers. All statistical tests were two-sided. Results The 10521 participants with abnormal screening tests and 2105 with a cancer or precancer diagnosis were predominantly from racial/ethnic minority groups (73%) and publically insured (40%) or uninsured (31%). There was no benefit during the first 90 days of care, but a benefit of navigation was seen from 91 to 365 days for both diagnostic resolution (aHR = 1.51; 95% confidence interval [CI] = 1.23 to 1.84; P <. 001)) and treatment initiation (aHR = 1.43; 95% CI = 1.10 to 1.86; P <. 007). Metaregression revealed that navigation had its greatest benefits within centers with the greatest delays in follow-up under usual care. Conclusions Patient navigation demonstrated a moderate benefit in improving timely cancer care. These results support adoption of patient navigation in settings that serve populations at risk of being lost to follow-up. © 2014 The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected]
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DOI:10.1093/jnci/dju115 © The Author 2014. Published by Oxford University Press. All rights reserved.
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ARTICLE
Impact of Patient Navigation on Timely Cancer Care: The Patient
Navigation Research Program
Karen M.Freund, Tracy A.Battaglia, ElizabethCalhoun, Julie S.Darnell, Donald J.Dudley, KevinFiscella, Martha L.Hare,
NancyLaVerda, Ji-HyunLee, PaulLevine, David M.Murray, Steven R.Patierno, Peter C.Raich, Richard G.Roetzheim,
MelissaSimon, Frederick R.Snyder, VictoriaWarren-Mears, Elizabeth M.Whitley, PaulWinters, Gregory S.Young,
Electra D.Paskett; for the Writing Group of the Patient Navigation Research Program
Manuscript received August 21, 2013; revised March 17, 2014; accepted March 27,2014.
Correspondence to: Karen M. Freund, MD, MPH, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center/ Tufts University School of
Medicine, 800 Washington St, #63, Boston, MA 02111 (e-mail: kfreund@tuftsmedicalcenter.org).
Background Patient navigation is a promising intervention to address cancer disparities but requires a multisite controlled trial
to assess its effectiveness.
Methods The Patient Navigation Research Program compared patient navigation with usual care on time to diagnosis or
treatment for participants with breast, cervical, colorectal, or prostate screening abnormalities and/or cancers
between 2007 and 2010. Patient navigators developed individualized strategies to address barriers to care, with
the focus on preventing delays in care. To assess timeliness of diagnostic resolution, we conducted a meta-anal-
ysis of center- and cancer-specific adjusted hazard ratios (aHRs) comparing patient navigation vs usual care. To
assess initiation of cancer therapy, we calculated a single aHR, pooling data across all centers and cancer types.
We conducted a metaregression to evaluate variability across centers. All statistical tests were two-sided.
Results The 10 521 participants with abnormal screening tests and 2105 with a cancer or precancer diagnosis were pre-
dominantly from racial/ethnic minority groups (73%) and publically insured (40%) or uninsured (31%). There was
no benefit during the first 90days of care, but a benefit of navigation was seen from 91 to 365days for both
diagnostic resolution (aHR= 1.51; 95% confidence interval [CI] =1.23 to 1.84; P < .001)) and treatment initiation
(aHR=1.43; 95% CI= 1.10 to 1.86; P < .007). Metaregression revealed that navigation had its greatest benefits
within centers with the greatest delays in follow-up under usual care.
Conclusions Patient navigation demonstrated a moderate benefit in improving timely cancer care. These results support adop-
tion of patient navigation in settings that serve populations at risk of being lost to follow-up.
JNCI J Natl Cancer Inst (2014) 106(6): dju115 doi:10.1093/jnci/dju115
Patient navigation refers to support and guidance offered to per-
sons with abnormal cancer screening results or cancer, with the
goal of improving access and coordination of timely care (1). The
primary purposes of navigation are to identify and remove barriers
to care. Patient navigation was conceived to address health dispari-
ties and assist those at risk for delays in care among racial and ethnic
minority and lower-income populations. Although patient naviga-
tion is rapidly becoming a standard of care (2,3), and literature
reviews (4,5) suggest that patient navigation improves timeliness
of care, many of the studies have been small, conducted at a single
institution, lacked concurrent control arms, or had dissimilar out-
come metrics. Strategies and operational definitions of navigation
that currently exist in the literature vary considerably, with little
consensus on the roles and scope of patient navigation, which also
limits the ability to assess the impact of this intervention (1,6,7).
The Patient Navigation Research Program (PNRP) is the
first multicenter clinical trial to examine the benefits of patient
navigation. Using community-based participatory research meth-
ods and addressing care to a diverse group of communities, PNRP
targeted four common cancers (breast, cervical, colorectal, and
prostate) with available screening tests and evidence of disparate
outcomes in underserved populations. We present the findings
of the two primary outcomes of the trial: 1)time from abnormal
screening to diagnostic resolution and 2)time to initiation of treat-
ment after a diagnosis of cancer or precancerous lesion.
Methods
Overall StudyDesign
We report here on the combined analyses of nine of the 10 PNRP
centers. Each center designed and implemented the intervention
within the context of the community setting in which it operated,
most using community-based participatory research principles.
Data sharing agreements with local communities at the 10th center
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precluded inclusion into the combined dataset (8). Two centers
conducted an individually randomized clinical trial (9–11), two
centers conducted a group-randomized trial (12–14), and five cent-
ers used quasi-experimental designs with nonrandom assignment
into the intervention and controls arms at the group level (15–20).
This individualization of study design based on community input
required modifications from methods traditionally used for analy-
ses of multicenter trials. For the abnormal cancer screening resolu-
tion analysis, eight of the nine centers had sufficient sample size
and power to conduct a center-specific analysis (11,13,14,16–18);
therefore we developed an a priori plan for a prospective meta-
analysis (21). For analysis of the initiation of cancer treatment,
none of the individual centers was powered to conduct separate
analyses; therefore, we conducted a pooled analysis combining
data across the nine centers and all cancer types. The institutional
review board of each respective institution approved the research
(ClinicalTrials.gov identifiers: NCT00613275, NCT00496678,
NCT00375024, NCT01569672).
Participant Eligibility
Participants aged 18years and older were included if they had an
abnormal breast, cervical, colorectal, or prostate cancer screening
result (22). Participants with invasive or preinvasive lesions with
guidelines recommending treatment (23–26) were eligible for the
cancer treatment initiation analysis. In addition to invasive can-
cers, we included breast ductal carcinoma in situ, cervical carci-
noma in situ, cervical intraepithelial lesions grade II and III, and
colorectal carcinoma in situ. Participants for the cancer treatment
initiation analysis included both those with the abnormal screening
and those recruited after their cancer diagnosis. Exclusion criteria
included prior history of cancer, prior patient navigation support,
cognitive conditions that would exclude participation in naviga-
tion, and pregnancy.
Study Centers
The study was conducted at nine centers recruiting participants
from between one and 21 care sites. The majority of the sites were
community health centers, in addition to several outpatient prac-
tice settings within and outside of safety-net hospitals. Most sites
cared for primarily patients who were low income, uninsured or
publically insured, and from racial and ethnic minority populations.
Intervention
Navigators used the care management model (27) to identify barriers
to recommended care, develop strategies to address these barriers,
and track participants through the steps in their medical evaluation.
Their focus was on timely diagnostic resolution and therapy initia-
tion. Navigation was initiated after a clinician informed the par-
ticipant of the abnormal test result. Most programs were imbedded
within clinical care systems with close interface with the clinical
practice, and most included opportunities for face-to-face interac-
tion between participants and navigators, as well as telephone and
mail contact. In addition to patient contact, navigators worked with
families, health-care providers, and social service agencies to iden-
tify resources to address barriers to care. Navigators documented
their activities in a standardized, structured template that captured
the barriers identified and the activities performed to address the
barriers for each encounter with the patient. Examples of naviga-
tion services included arranging financial support, scheduling and
arranging for transportation to scheduled appointments, coordi-
nating care among providers, arranging for interpreter services,
and linking to community resources.
Each center hired navigators with a minimum of a high
school diploma and used the same protocol for the intervention.
Navigators participated in annual national trainings and webinars
in order to standardize the intervention (28) and were assessed
for national core competencies twice annually using a standard-
ized checklist. Centers determined the specific job description and
supplemented the national training with ongoing, local training to
provide navigators with local context and resource information.
Data Collection
Definitions of all variables were developed by the PNRP investiga-
tors and adhered to by all centers. Coding questions were reviewed
weekly by investigators to maintain rigorous data entry standards.
Clinical variables were abstracted from the participants’ medical
records, including type of screening abnormality, type and stage of
cancer, dates and types of clinical services, and clinical outcomes.
Each center coded race/ethnicity uniformly for all participants
either from self-report or medical records. Race/ethnicity was col-
lapsed into a single categorical variable: white, black, Hispanic, and
other (Asian, Native American, unknown). Primary language was
coded as English or other. Health insurance coverage at the time
of study entry was hierarchically categorized into private, public, or
no insurance coverage.
Statistical AnalysisPlan
Time to Diagnostic Resolution. The first outcome of inter-
est was whether and when diagnostic resolution of the abnormal
cancer screening result was achieved, defined as time from date
of initial abnormal cancer screening test result to date when the
definitive diagnostic test or evaluation was completed. We ana-
lyzed this outcome using two methods determined a priori (21):
First, for each study center and cancer screening type, we calcu-
lated an unadjusted rate of achieving diagnostic resolution within
1year, comparing the intervention with the control arm. The sec-
ond method used a prospective meta-analysis and provided a sin-
gle adjusted effect estimate (the adjusted hazard rate ratio [aHR])
across all centers. An adjusted hazard rate ratio was calculated for
time to diagnostic resolution for each cancer type within each
center. Models were adjusted for participant race/ethnicity, insur-
ance status, language, marital status, and age, except for centers C,
F, G, and H, which did not include marital status, and centers C
and H, which did not include language, because of missing data.
To account for potential intraclass correlations within the sites of
care for centers with group-randomized trial or quasi-experimental
designs, we used either a clustering variable in a proportional haz-
ards regression (29) (centers A, B, C, F, G, and H) or a shared frailty
model (30) (centerE).
In developing the proportional hazards models, almost half of
the models from centers had a violation of the proportional hazards
assumptions, indicating that the effect of navigation varied across
time. Because most of these changes occurred at approximately
90 days within the 365 days of observation, we addressed these
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violations by calculating a separate adjusted hazard rate ratio for 0
to 90days and for 91 to 365days, per methods previously described
(31,32). Effect size (aHR) estimates were calculated for each time
period for each center–cancer combination. Separate meta-anal-
yses were conducted for each time period with a random effects
model using the method described by DerSimonion and Laird (33)
and a test for heterogeneity across the centers (34,35). Because of
the heterogeneity noted, we conducted a meta-regression with two
center-level variables, baseline rates of diagnostic resolution in the
usual care arm at each center for each cancer type, and method of
subject assignment to intervention at the center (random vs non-
random designs). An influence analysis was conducted by compar-
ing the recalculated effect estimate to the confidence interval to the
initial estimate after removing one center at a time to determine
whether the results were unduly influenced by a single center. All
analyses were conducted using Stata version 10.0 (36).
Time to Treatment Initiation. The second outcome was time
to initiation of treatment for participants with invasive cancer or
precancerous lesions. We pooled data across all centers for a sin-
gle analysis. This method does not account for intraclasscorrela-
tion at the site within center level. However, 27% of subjects were
recruited from the sites conducting individual randomized clinical
trial designs, and 50% of subjects were recruited from a single site
within a center or sites with five or fewer cases, thereby reducing
the potential impact of intraclasscorrelation. Guidelines developed
by the Centers for Disease Control recommend that more than
90% of women initiate therapy within 90days (37), and a number of
analyses have suggested reduced cancer survival with delays beyond
60days (37–41). Therefore, we compared the unadjusted propor-
tion of participants who initiated treatment within 60, 90, and
365days between the intervention and control arms. We calculated
an adjusted hazard rate ratio adjusting for race/ethnicity, insurance
status, age, language, marital status, and cancer type. Because of
violations in proportional hazards, we calculated separate adjusted
hazard rate ratios for 0 to 90days and 91 to 365days. Influence
analyses recalculated the adjusted hazard rate ratio, removing the
data from each center to assess whether the effect size was unduly
influenced by one center. All statistical tests were two-sided.
Results
Each center focused on different cancers, eight of nine centers enrolled
in breast cancer, four enrolled in cervical cancer, four enrolled in colo-
rectal cancer, and two enrolled in prostate cancer. Table 1 presents
the demographic data on the two outcomes, the sample size for each
Table1. Demographic characteristics of participants enrolled in the Patient Navigation Research Program
Variable
Outcome 1: diagnostic evaluation
(n=10 521)
Outcome 2: cancer treatment
(n=2105)
Intervention Control Intervention Control
No. (%) No. (%) No. (%) No. (%)
Race /ethnicity
White 1224 (24) 1370 (25) 285 (28) 376 (35)
Black 1487 (29) 1843 (34) 385 (37) 425 (40)
Hispanic 2142 (42) 1964 (36) 338 (33) 213 (20)
Other 207 (4) 185 (3) 16 (2) 39 (4)
Insurance
Private 1202 (24) 1599 (29) 342 (33) 461 (43)
Public 1969 (39) 2290 (42) 448 (43) 492 (46)
Uninsured 1837 (36) 1548 (28) 236 (23) 119 (11)
Sex
Female 4665 (92) 5006 (92) 874 (85) 920 (86)
Marital status
Married 1772 (35) 1588 (29) 383 (37) 397 (37)
Age, y
Mean ± standard deviation 43.6 ± 14.8 47.2 ± 14.9 51.7 ± 15.0 53.8 ± 15.3
Cancer type
Breast 3083 (61) 3643 (67) 605 (59) 683 (64)
Cervical 1455 (29) 1226 (22) 245 (24) 207 (19)
Colorectal 219 (4) 278 (5) 52 (5) 58 (5)
Prostate 306 (6) 311 (6) 130 (13) 125 (12)
Sites
A 1496 (30) 1543 (28) 126 (12) 74 (7)
B 357 (7) 542 (10) 68 (7) 100 (9)
C 243 (5) 245 (4) 85 (8) 86 (8)
D 490 (10) 508 (9) 121 (12) 114 (11)
E 444 (9) 346 (6) 46 (4) 24 (2)
F 639 (13) 408 (7) 226 (22) 145 (14)
G 586 (12) 683 (13) 25 (2) 47 (4)
H 808 (16) 1183 (22) 167 (16) 328 (31)
I 0 0 168 (16) 155 (14)
Total 5063 (100) 5458 (100) 1032 (100) 1073 (100)
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Figure1. Unadjusted proportion of participants with abnormal cancer screening or symptoms who reach diagnostic resolution within 365days in
navigated and control arms by cancer screening type, study center: Patient Navigation Research Program.
cancer type, and the enrollment at each of the centers. We enrolled
10 521 participants with abnormal cancer screening results and 2105
with a diagnosis of cancer or of a precancerous lesion. Refusal rates
by center ranged from less than 1% to 23%. Enrolled participants
were diverse; 73% were from racial and ethnic minority groups,
40% were publically insured, and 31% were uninsured at the time of
study enrollment. The imbalances in recruitment by race/ethnicity
and insurance reflect the group-allocated designs. More than 85%
of all participants were women because most participants had abnor-
mal breast or cervical cancer screening results. The low enrollment in
colorectal cancer reflected the shift at most institutions to colonos-
copy, where concurrent diagnostic biopsy during the screening test
eliminated the need for follow-up.
Diagnostic ResolutionTrial
Figure1 shows the unadjusted proportions of diagnostic resolution
within 365days, organized by cancer screening type and resolu-
tion rates in the control arms of the centers. For most centers, the
navigated arm had a higher percentage of participants who reached
a diagnostic resolution than the control arm, and this was similar
for all four cancer types. The effect of navigation was greatest at
centers where the control arms had the lowest rates, with differ-
ences of 20% at several centers. We observed a ceiling effect, where
navigation had little or no impact when the control arms had 90%
or greater resolution by 1year.
Figures 2 and 3 use forest plots by cancer type to report the
meta-analysis of the adjusted hazard rate ratios of time to resolu-
tion of abnormal cancer screening results for each of the two time
periods, where adjusted hazard rate ratios greater than one indicate
a benefit in the navigated arm. The adjusted hazard rate ratio from
the metanalysis was 1.14 (95% confidence interval [CI] of 0.96 to
1.35; P =.14) from 0 to 90 days and 1.51 (95% CI=1.23 to 1.84;
P < .001) from 91 to 365days. Influence analyses always produced an
effect size within the confidence interval of the original calculation.
Because there was substantial heterogeneity among the effect
size estimates (overall I2 for heterogeneity=84.5 %; P < .001), a
metaregression was performed on the 91 to 365day period, the
period demonstrating a benefit of navigation. Two study center–
level variables examined were patient assignment (random vs
nonrandom) to intervention arm and diagnostic resolution rate of
control subjects (a continuous variable). The results indicated that
patient assignment was not related to effect size estimates (P > .79),
whereas resolution rate of control subjects was statistically signifi-
cantly related to the adjusted hazard rate ratio (P < .01), confirm-
ing that navigation had a larger effect when the time to diagnostic
resolution was delayed in the usual carearm.
Although our measure of time to diagnostic resolution began
at the date that the screening test was performed, patient naviga-
tion efforts did not begin immediately. Navigation was subject to
delays in receiving the test report, in the initial contact by a cli-
nician with the participant, and in contacting the participant for
consent to enroll. The range by center of median times to initia-
tion of navigation was longer with cervical (24–64days), colorectal
(19–48days), and prostate (33–34days) cancer screening tests than
breast (7–33days) cancer screening tests. Initiation of navigation
after a diagnosis of cancer or of a precancerous lesion had fewer
delays because 60% of these participants were already consented
and enrolled at the time of their diagnosis.
Treatment InitiationTrial
We calculated the unadjusted proportions of participants who ini-
tiated treatment at specified time points. The navigated arm had
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a smaller proportion of participants who had initiated treatment
at both 60 days (57% vs 62%) and 90 days (73% vs 75%) com-
pared with the control arm; however, at 365days, the findings had
reversed, and navigated participants had a higher proportion (89%)
who had initiated treatment compared with the control participants
(87%). We calculated from the adjusted Cox regression analysis sep-
arate adjusted hazard rate ratios for 0 to 90days and 91 to 365days.
The adjusted hazard rate ratio was 0.85 (95% CI=0.71 to 1.01;
P=.07) from 0 to 90 days and 1.43 (95% CI = 1.10 to 1.86; P <
.007) from 91 to 365days. Influence analyses removing one center
and recalculating the adjusted hazard rate ratio always produced an
effect size within the confidence interval of the original calculation.
Table2 presents the adjusted hazard rate ratio at 0 to 90days
and 91 to 365days for diagnostic resolution and treatment initia-
tion. The findings for the two outcomes parallel one another, with
no impact of patient navigation in the first 90days of observation.
For both outcomes, navigation showed a statistically significant
benefit from 91 to 365days (diagnostic metanalysis: aHR =1.51,
95% CI=1.23 to 1.84; P < .001; treatment: adjusted Cox regres-
sion aHR=1.43, 95% CI=1.10 to 1.86, P <.007).
Discussion
This is the first multisite study of patient navigation as an interven-
tion to reduce disparities in cancer outcomes by addressing barri-
ers to follow-up care and treatment for underserved and minority
populations. The goal of this study was to investigate the efficacy of
patient navigation in reducing delays in resolving abnormal cancer
screening tests and initiating treatment of cancer among diverse
populations and four cancer types. Results of this trial indicate a
statistically significant, although modest, benefit of navigation on
timely cancer care. Both diagnostic evaluation and cancer treat-
ment were initiated earlier in the navigator arm compared with
the control arm from 91 to 365days of observation, but not in the
first 90days.
Our finding of no benefit of patient navigation in the first
90days may reflect the time required to connect navigators with
participants. We note, for example, that 13% of participants with
abnormal breast cancer screening results were not able to be con-
tacted by their navigator within 60days. Our finding of no benefit
in the first 90days may also reflect the fact that some participants
are able to overcome barriers without a navigator. We observed the
Figure2. Meta-analysis of impact of patient navigation on diagnostic resolution after cancer screening abnormality from 0 to 90days: Patient
Navigation Research Program. I2 addresses the heterogeneity of the model and is not the overall effect of the intervention. The solid vertical line
denotes 1, or no effect. The squares denote the adjusted hazard ratio for each center and cancer type, with the horizontal line indicating the 95% con-
fidence interval. The dotted vertical line denotes the adjusted hazard ratio for the meta-analysis. The diamond indicates the 95% confidence interval
of the adjusted hazard ratio. The letters (A–H) in the first column denote the study center. aHR=adjusted hazard ratio; CI=confidence interval.
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greatest benefits among centers where control participants experi-
enced longer delays; conversely, we observed little benefit among
centers where those in usual care achieved 90% resolution at
1year. These findings suggest that navigation is likely to show the
greatest effects in centers and populations with the greatest delays
in follow-up under usualcare.
Previous studies have reported mixed results in regard to
the benefit of navigation. Whereas some studies reported more
timely care (42–47), others have not (46,48). In the PNRP, the
impact of patient navigation was greatest among centers with
low baseline resolution or treatment initiation rates in the con-
trol arm. This speaks to a need for patient navigation services in
settings that possibly have few resources to assist underserved
participants to complete timely diagnostic resolution and initi-
ate cancer treatment. Other studies have found stronger effects
of patient navigation interventions among populations with low
adherence rates (49).
Some of this variation in prior studies is because of wide vari-
ation in what is considered patient navigation. Alimitation of our
study is the ability to assess the fidelity of implementation of the
intervention across the sites and navigators. Although we were una-
ble to assess all of the variability of navigator activities across the
centers, we addressed this variability by having a standardized defi-
nition of navigation, navigator training and protocols, templates
for assessing and recording barriers to care and navigator actions
to address barriers, and a standardized competency assessment of
Figure3. Meta-analysis of impact of patient navigation on diagnostic resolution after cancer screening abnormality from 91 to 365days: Patient
Navigation Research Program. I2 addresses the heterogeneity of the model and is not the overall effect of the intervention. The solid vertical line
denotes 1, or no effect. The squares denote the adjusted hazard ratio for each center and cancer type, with the horizontal line indicating the 95%
confidence interval. The dotted vertical line denotes the adjusted hazard ratio for the meta-analysis. The diamond indicates the 95% confidence
interval of the adjusted hazard ratio. The letters (A–H) in the first column denote the study center.
Table2. Adjusted hazards ratios for diagnostic care and cancer care trials of the Patient Navigation Research Program*
Outcome Days 0–90 adjusted HR (95% CI) Days 91–365 adjusted HR (95% CI)
Diagnostic phase 1.14 (0.96 to 1.35) 1.51 (1.23 to 1.84)
Treatment phase 0.85 (0.7 to 1.01) 1.43 (1.10 to 1.86)
* CI=confidence interval; HR=hazard ratio.
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navigation standards. Variation in implementation of the interven-
tion may account for some of the heterogeneity of the effect sizes
seen. Even when navigators were imbedded within clinical prac-
tices, we observed delays in initiating navigation, which likely lim-
ited their impact during the first 90days of care. New data on the
efficacy of prostate cancer screening has emerged since the design
of the PNRP (50,51), with new guidelines advising against pros-
tate cancer screening or for informed decision making, as opposed
to population based screening (52–56). We did not have a priori
rules for removing centers from the analysis; thus we kept the two
prostate screening centers in our analysis. Our sensitivity analysis
indicated that no one center affected the overall findings of our
analysis. The generalizability of the findings for treatment initia-
tion is limited by the loss of data for 11% of the participants diag-
nosed with cancer or with a precancerous lesion. We compared the
participants with and without a known date for treatment initiation.
This former group was more likely to be black and less likely to
be Hispanic/Latino or white. No differences were found between
these two groups on insurance coverage, age, primary language,
or type of cancer. For many of the participants without informa-
tion on start date of cancer treatment, chart review indicated that
participants had received care at another institution, but without
specific dates of care available.
This study had several strengths, including a large and diverse
population of participants geographically, demographically, and by
cancer type. The study benefited from the use of community-based
participatory methods to reach populations often not included
in clinical trials. The resulting different research methodologies
required a prospective meta-analysis for diagnostic resolution and
a pooled analysis for treatment initiation. This heterogeneity in
research design and treatment implementation highlights the real-
ity of community-based participatory research. There is a need for
further research to examine which activities of navigation are of
greatest benefit and the role of lay vs clinically trained navigators.
In conclusion, the PNRP demonstrates the effectiveness of
patient navigation in settings where resources are low or there is
a history of poor follow-up rates and among patients at risk of
failure to comply with follow-up or treatment recommendations
after an abnormal cancer screening test. The impact of naviga-
tion to achieve better cancer care for all populations with the
overall goal of reducing incidence, morbidity, and mortality from
cancer will become more important as the Affordable Care Act is
implemented.
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Funding
This work was supported by the National Cancer Institute, National Institutes
of Health (U01 CA116892, U01 CA117281, U01CA116903, 01CA116937,
U01CA116924, U01CA116885, U01CA116875, and U01CA116925); the
American Cancer Society (#SIRSG-05-253-01 and CRP-12-219-01-CPRB);
and the Avon Foundation.
Notes
Employees of the National Cancer Institute participated in the design of the
study and in review and approval of the manuscript. The funding sources had no
role in the conduct of the study, in the collection, analysis, and interpretation of
the data. The American Cancer Society and the Avon Foundation had no role in
the study design, preparation, review or approval of the manuscripts. The con-
tents are solely the responsibility of the authors and do not necessarily represent
the official views of the Center to Reduce Cancer Health Disparities, National
Cancer Institute. DMM completed his work on this study before assuming his
role at the National Institutes of Health.
We acknowledge the contributions of the following members of the
Patient Navigation Research Program: Patient Navigation Research Program
Investigators: Mollie Howerton, Ken Chu, Emmanuel Taylor, and Mary
Ann Van Dyun (National Cancer Institute, Center to Reduce Cancer Health
Disparities); Paul Young (NOVA Research Company); Heather A. Young,
Heather J.Hoffman (George Washington University Cancer Institute); Cathy
Meade and Kristen J. Wells (H. Lee Moffitt Cancer Center and Research
Institute); Douglas Post and Mira Katz (Ohio State University); Samantha
Hendren (University of Rochester); and Kevin Hall, Anand Karnard, and Amelie
Ramirez (University of Texas Health Science Center at San Antonio Cancer
Therapy and Research Center).
Preliminary findings were presented at the American Association of Cancer
Research, Health Disparities National Meeting, Washington DC, September
2011.
Affiliations of authors: Division of Cancer Prevention and Control,
Department of Internal Medicine, College of Medicine, Comprehensive
Cancer Center (EDP), and Center for Biostatistics (GSY), The Ohio State
University, Columbus, OH; Institute for Clinical Research and Health Policy
Studies, Tufts Medical Center and Tufts University School of Medicine,
Boston, MA (KMF); Women’s Health Unit, Section of General Internal
Medicine, Evans Department of Medicine, Boston Medical Center and
Women’s Health Interdisciplinary Research Center, Boston University
School of Medicine, Boston, MA (TAB); Division of Health Policy and
Administration, School of Public Health, University of Illinois at Chicago,
Chicago, IL (EC, JSD); Department of Obstetrics and Gynecology, University
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of Texas Health Science Center, San Antonio, TX (DLD); Department of
Family Medicine and Public Health Sciences and Wilmot Cancer Center,
University of Rochester Medical Center, Rochester, NY (KF); Center
to Reduce Cancer Health Disparities, National Cancer Institute (MLH),
and Biostatistics and Bioinformatics Branch, Division of Epidemiology,
Statistics, and Prevention Research, Eunice Kennedy Shriver National
Institute of Child Health and Human Development, National Institutes
of Health (DMM), Rockville, MD (MLH); George Washington University
School of Public Health and Health Services, Washington, DC (NL, PL); H.
Lee Moffitt Cancer Center and Research Institute, Tampa, FL (J-HL, RGR);
George Washington Cancer Institute, Washington, DC (PL. SRP); Duke
Cancer Institute, Durham, NC (SRP); Denver Health, Denver, CO (PCR,
EMW); University of Colorado Denver, Aurora, CO (PCR); Department of
Family Medicine, University of South Florida, Tampa, FL (RGR); Department
of Obstetrics and Gynecology and Department of Preventive Medicine,
Northwestern University Feinberg School of Medicine, Chicago, IL (MS);
Robert H.Lurie Comprehensive Cancer Center of Northwestern University,
Chicago, IL (MS); Clinical Research Services, NOVA Research Company,
Bethesda, MD (FRS); Northwest Portland Area Indian Health Board,
Northwest Tribal Epidemiology Center, Portland, OR (VW-M); Department of
Family Medicine, University of Rochester School of Medicine & Dentistry,
Rochester, NY (PW).
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