Cost-Effectiveness of a Patient Navigation Program to Improve Cervical Cancer Screening

July 25, 2017
Yan Li, PhD
Yan Li, PhD

,
Erin Carlson, DrPH
Erin Carlson, DrPH

,
Roberto Villarreal, MD
Roberto Villarreal, MD

,
Leah Meraz, MA
Leah Meraz, MA

,
Jos&eacute
Jos&eacute

,
A. Pag&aacute
A. Pag&aacute

,
n, PhD
n, PhD

Volume 23, Issue 7

This study investigates the cost-effectiveness of a community-based patient navigation program to improve cervical cancer screening.

ABSTRACT

Objectives: To assess the cost-effectiveness of a community-based patient navigation program to improve cervical cancer screening among Hispanic women 18 or older in San Antonio, Texas.

Study Design: We used a microsimulation model of cervical cancer to project the long-term cost-effectiveness of a community-based patient navigation program compared with current practice.

Methods: We used program data from 2012 to 2015 and published data from the existing literature as model input. Taking a societal perspective, we estimated the lifetime costs, life expectancy, and quality-adjusted life-years and conducted 2-way sensitivity analyses to account for parameter uncertainty.

Results: The patient navigation program resulted in a per-capita gain of 0.2 years of life expectancy. The program was highly cost-effective relative to no intervention (incremental cost-effectiveness ratio of $748). The program costs would have to increase up to 10 times from $311 for it not to be cost-effective.

Conclusions: The 3-year community-based patient navigation program effectively increased cervical cancer screening uptake and adherence and improved the cost-effectiveness of the screening program for Hispanic women 18 years or older in San Antonio, Texas. Future research is needed to translate and disseminate the patient navigation program to other socioeconomic and demographic groups to test its robustness and design.

Takeaway Points

Community-based patient navigation programs may improve cervical cancer screening uptake, especially among Hispanic women. This study provides healthcare managers with knowledge about patient navigation programs that are multilevel and include some elements and principles from behavioral economics to improve cancer screening.

  • This study adds to the existing literature by assessing the long-term cost-effectiveness of a community-based patient navigation program for Hispanic women.
  • This study promotes the implementation of patient-centered cancer screening services.

Although advances in screening and vaccination technologies have substantially lowered the risk of cervical cancer among women, it still accounts for more than 4000 deaths per year in the United States.1 There are also persistent disparities in incidence and mortality rates, not only by socioeconomic status and geography, but also by ethnicity and race.2,3 Hispanic women have a higher risk of cervical cancer than other major ethnic/racial groups and are more likely to be diagnosed at a later stage.4 In urban areas, reducing cervical cancer morbidity and mortality is particularly difficult as the success of cancer prevention programs requires knowledge and self-control that few patients possess.5

Patient navigation refers to “the support and guidance offered to persons with abnormal cancer screening or a new cancer diagnosis in accessing the cancer care system, overcoming barriers, and facilitating timely, quality care provided in a culturally sensitive manner.”6 In public health practice, patient navigation may include a variety of specific services and interventions, such as scheduling appointments with culturally sensitive caregivers, providing transportation or interpretation services, and assisting participants with childcare during scheduled appointments.6 The results of several randomized controlled trials have shown that patient navigation is effective in increasing patient satisfaction, decreasing the anxiety associated with screening processes and procedures, and improving cancer screening uptake and adherence.7,8 However, there is still limited evidence supporting the efficacy of patient navigation in improving patient outcomes over the long term or assessing the cost-effectiveness (CE) of specific patient navigation programs.9

In this study, we explored the implementation and results from a community-based patient navigation program (designed to increase the cervical cancer screening [Pap test] rate) in San Antonio, Texas, for an underserved Hispanic female population 18 years or older. The program was multilevel and included some elements and principles from behavioral economics.10 Because the benefits of cervical cancer screening are hardly observed in the short term, we also used an evidence-based microsimulation model to assess improvements in long-term patient outcomes, and to evaluate the CE of the program versus the status quo. Finally, to ensure the robustness of the CE analysis, we conducted sensitivity analyses to assess several key cost and effectiveness parameters.

METHODSProgram Description

This study focused on a 3-year patient navigation program for cervical cancer screening implemented by the Bexar County Hospital District (University Health System) in San Antonio, Texas, from 2012 to 2015. The program targeted an urban female Hispanic population 18 years or older enrolled in CareLink, a financial assistance program for the uninsured population in San Antonio. This population has a particularly high risk for cervical cancer: in 2009, approximately 67% of women aged at least 18 years who were enrolled in CareLink had not had the recommended Pap test within the past 3 years.11 In addition, these women faced a range of cultural and socioeconomic barriers to undergoing cancer screening (eg, lack of financial resources to access screening services, fear of embarrassment, and concerns about provider sensitivity to patient comfort).

The patient navigation program is recognized as a major component of a community-based, culturally competent, secondary cancer prevention program described in a previous study.11 It was designed to provide personalized social communication by encouraging participants to call “Claudia,” a bilingual female contact person who would act as a program navigator in disseminating health information. This is consistent with the behavioral economics principle of relying on social and cultural norms because using the same Hispanic name as a contact person helps participants recall similar events in memory in a culturally competent way.10 The program also included participant reminders to call Claudia within newsletters, public service announcements, and automated messages. Afterward, these patient navigators provided assessments of the cervical cancer and screening knowledge of patients they had spoken with, as well as personalized education about the potential benefits of screening.

In addition to the services provided by patient navigators, the program also implemented multilevel strategies designed to increase the uptake and adherence of cancer screening within the target population. For example, the program relied on a mass media health promotion campaign, which allowed women to align their subjective assessment of cervical cancer risk with their actual risk by receiving health education and information messages provided by patient navigators who are similar to, or representative of, the target population. The program also provided patients with accurate information related to cervical cancer risk to address unrealistic expectations (ie, individuals may have unreasonably low or high estimates of their cervical cancer risk).10 Lastly, as an incentive to each program participant, all screening tests were free. The patient navigation program was designed to be multilevel and to integrate general principles of behavioral economics by taking into account key factors that patients consider when making screening decisions.

The patient navigation program demonstrated its effectiveness at improving cervical cancer screening through interviews and focus groups that took place between program staff members and participants. In particular, 94% of program staff (including patient navigators, care providers, and others who participated in the implementation of the program) agreed that it had addressed the needs of cervical cancer screening among Hispanic women and participants were either very satisfied or satisfied working with the program. In addition, patients reported a positive experience using the program services provided, including increased knowledge about cervical cancer and HPV and stronger motivation to participate in cancer screenings. Overall, the program has navigated 4500 women in the target population and increased the 3-year screening rate from 65% to 80% during the 3-year study period.

Model Structure

Although empirical studies based on actual data may produce important insights into the CE of a given patient navigation program, they are costly or may not be able to assess the long-term impact of cervical cancer screening strategies.12 Simulation modeling—particularly microsimulation models—offers a more flexible, cost-effective approach to conducting economic evaluations and making informed decisions compared with studies based on actual behavioral observations.12 By incorporating the best available biological, clinical, and epidemiological evidence, a microsimulation model of cervical cancer enables researchers to simulate a population of interest, capture the disease progression of each individual, predict the long-term consequences of different interventions within a virtual environment, and provide insights into the CE of different strategies designed for cervical cancer prevention.

Our model structure is as follows (a detailed model description can be found in the eAppendix [available at ajmc.com]): our microsimulation model incorporates state-of-the-art knowledge from previous cervical cancer decision models, and estimated parameters based on our specific program.10,13,14 The natural history of cervical cancer was modeled using 16 states, including: well (healthy) HPV infection, low- and high-grade squamous intraepithelial lesions (SILs), hysterectomy for benign disease, undetected and detected cervical cancer stages I to IV, survival from cancer, and death due to cervical cancer or other causes (Figure 1). Transitions between health states were governed by transition probabilities that were dependent on age, SILs level, cancer stage, and screening or vaccination strategies. The basic cycle length was 1 year.

Each year, women in the simulation model could be infected with HPV or stay uninfected. We assumed all cases of cervical cancer start with HPV infection, which is consistent with the epidemiologic finding that HPV causes a majority of cervical cancer cases.15,16 HPV infection, clearance, and progression to low- or high-grade SILs is a complex process that varies depending on HPV virus type and patient characteristics, such as age and immune status. We used average transition probabilities for all virus types (ie, we did not distinguish between different types of HPV), which simplified our model without losing important information. The incidence of HPV infection was modeled to be a function of age, and the parameters of the incidence function did not change throughout the simulation.

Women infected with HPV could regress to well, stay unchanged, or progress to low- or high-grade SILs. Similarly, women with low- or high-grade SILs could undergo regression, no change, or progression to stage I cancer without symptoms. Current knowledge about the natural history of cervical cancer suggests that most HPV infections will regress on their own and some persistent HPV infections may progress to high-grade SILs and cancer.17,18

Women with asymptomatic stage I cancer either become symptomatic or progress to higher stages without detection. Once cancer becomes symptomatic or is detected by screening, the patient will undergo medical treatment. Women without cancer may undergo a hysterectomy due to other causes19 and all women could die from causes outside those identified in the study.

Parameter Estimation

Tables 1 and 2 summarize all input parameters in the microsimulation model. We estimated the incidence of HPV and transition probabilities among different health states from the published literature13,14,19-21 and age-specific female mortality rates from other causes by subtracting the rates due to cervical cancer from age-specific, all-cause mortality rates obtained from 2010 US life tables.22 Quality of life weights were determined by either age (for women without cervical cancer) or cancer stage (women with).23-25

We used a societal perspective in the CEA by incorporating both program and treatment costs; the patient navigation and screening program costs were estimated to be $311 per person. We calculated this figure by adding all costs incurred in the program ($1,399,815), including navigation and screening-related program staff salaries, health promotion media and outreach costs, and Pap test cost, then dividing the total by the number of women (4500) who received patient navigation and screening services. We estimated annual treatment costs for different cancer stages from the published literature.26-29 Women with HPV infection or low-grade SILs do not need treatment, and thus, did not incur additional costs.

Lastly, the sensitivity and specificity of the screening test were estimated to be 80% and 95%, respectively, based on the published literature.25,30,31 Although we expected that the prevalence of HPV infection in the San Antonio metropolitan area would be higher than the national average, we still used the national average (26.8%) in our model due to a lack of population-specific data in our community of interest.17 All input data and parameters can be found in the eAppendix.

Experiments

Our model followed 100,000 simulated women with the same age distribution and prevalence of HPV infection as the population of interest (Hispanic women 18 years or older) throughout their lifetime, with and without the patient navigation program. Simulating a large population provides stable estimates of long-term outcomes for each simulation scenario. In the CEA, we assumed that women who were successfully navigated through the study received Pap tests at suitable intervals, appropriate diagnostic procedures (eg, colposcopy, biopsy), and treatment based on the results of screening. Specifically, women with low-grade SILs were reexamined every 6 to 12 months until they had 3 negative screening test results.32 In addition, women with confirmed high-grade SILs or cancer were treated according to published guidelines.32

We tracked the overall costs, life expectancy, and quality-adjusted life-years (QALYs) of the simulated population for each scenario and discounted them by 3% (a widely accepted discount rate in CEA) annually. We then measured the performance of the program by estimating the incremental cost-effectiveness ratio (ICER) between the patient navigation program and no intervention. We implemented the microsimulation model using the software package AnyLogic 7.1 (AnyLogic North America; Chicago, Illinois).

RESULTSBaseline Cost-Effectiveness Analysis

Table 3 presents the CE estimates of the patient navigation program for cervical cancer screening compared with no intervention. Our results show that the program costs an average of $45 more per person than the no intervention scenario. Also, the screening program showed an increase in the life expectancy of the studied population by 0.2 years and an increase in QALYs by 0.06 years, which results in an ICER of $748 per QALY for the patient navigation program versus no intervention. We used $50,000 per QALY as the CE threshold to determine that the patient navigation program was considered to be cost-effective in the baseline scenario.

Sensitivity Analysis

We studied the robustness of the baseline results by conducting sensitivity analyses that accounted for uncertainties in the cost and effectiveness of the patient navigation program. Figure 2 reports the results of a 2-way sensitivity analysis of the program cost per participant and screening rate. Again, we used $50,000 per QALY as the CE threshold. Specifically, if a combination of program cost per participant and the screening rate falls below the CE frontier shown in Figure 2, the ICER of the patient navigation program relative to the status quo is less than $50,000 per QALY, which means the program is more cost-effective; otherwise, the status quo is more cost-effective. The results show that, because 80% of program participants received cervical cancer screening tests, the cost of the program could increase up to 10 times—from $311 to $3312—before the program becomes less cost-effective. In addition, even if the patient navigation program only resulted in a screening rate of 70% (a 5% increase from the status quo), the program is considered cost-effective as long as its price tag remains below $2353 per person.

DISCUSSION

Large disparities in cancer screening uptake and outcomes exist across many socioeconomic and demographic groups in the United States and, despite substantial progress to reduce these differences by developing new cancer screening initiatives, these gaps in cancer screening stubbornly persist.11 Community-based, multilevel patient navigation programs have shown promise in improving adherence to cancer screening processes and protocols. When these programs can further incorporate some principles of behavioral economics—with a focus on understanding the heuristics individuals use to make decisions—they can address patient biases in cancer risk and decision making surrounding cancer screening and optimize the appropriate architecture for individuals who are considering undergoing cancer screening.

Our results showed that a specific community-based patient navigation program for cervical cancer screening was cost-effective in increasing the screening rate and improving the long-term health outcomes of the target population. We estimated that an average program participant would gain an additional life expectancy of 0.2 years and an additional 0.06 QALYs. Under the baseline scenario, the patient navigation program costs $748 for each additional QALY gained with respect to no intervention, indicating that the program is highly cost-effective. Sensitivity analyses showed the robustness of the CE of the patient navigation program: 1) the program costs would have to increase up to 10 times from $311 before the program ceased to be cost-effective, and 2) the program would be cost-effective even if the screening rate only increased from 65% to 70% instead of the observed 80% after program implementation.

Similar community-based patient navigation programs have been shown to improve the cervical cancer screening rate of Hispanic women and the colorectal cancer (CRC) screening rate of Hispanic men. (In a previous study, we showed that a patient navigation program increased the CRC screening rate among target Hispanic men from 16% to 80% in data collected from 2011-2013.)33 The navigation program would reduce the lifetime overall cost to its participants (due to significantly reduced cancer risk) and thus achieve cost-savings compared with no intervention.33 Given these promising results, healthcare providers may consider testing and evaluating similar patient navigation programs to improve screening for other types of cancer (eg, breast cancer and prostate cancer) in other populations of interest.

Limitations

The cervical cancer natural history model was developed based on parameters that reflect the general US population, not specifically the Hispanic population, which was the target population of this study. We also did not have local cost estimates for cervical cancer treatment; thus, we relied on national average costs estimated from the existing literature. We will update the CE results as more local data for model parameterization become available and conduct more comprehensive sensitivity analyses to address these parameter uncertainties.

We did not model the way in which different population characteristics or socioeconomic factors would influence the choice of screening in the model. One of our next steps will be to model a decision-making process for each individual participating in the simulation regarding whether to undergo screening, which would increase the realism of the model by considering individual heterogeneity and potentially improve the validity of the CE results.

Finally, we did not examine the effect of varying screening intervals or HPV vaccination on the projected outcomes. Modeling these additional scenarios is a next step, as our model can be easily adapted to incorporate a different screening interval or include HPV vaccination as part of cervical cancer prevention practices.

CONCLUSIONS

Our study results demonstrate how a health system serving a low-income, urban, and minority (Hispanic) population was able to develop a cost-effective patient navigation program for cervical cancer screening. Although our findings are promising, the patient navigation program results presented here need to be translated and disseminated to other socioeconomic and demographic groups to test the robustness and design of the program, particularly in terms of how to carefully calibrate behavioral change components and understand which program attributes are most promising.

Acknowledgments

The authors thank David Siscovick, MD, MPH, for his constructive comments.Author Affiliations: Center for Health Innovation, The New York Academy of Medicine (YL, JAP), New York, NY; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai (YL, JAP), New York, NY; College of Nursing and Health Innovation, The University of Texas at Arlington (EC), Arlington, TX; Research and Information Management, University Health System (RV, LM), San Antonio, TX; Leonard Davis Institute of Health Economics, University of Pennsylvania (JAP), Philadelphia, PA.

Source of Funding: This study was funded by the Cancer Prevention and Research Institute of Texas (award grant ID PP120111).

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (YL, JAP); acquisition of data (YL, RV, EC, LM); analysis and interpretation of data (YL, JAP); drafting of the manuscript (YL); critical revision of the manuscript for important intellectual content (YL, RV, LM, JAP); statistical analysis provision of patients or study materials (EC); obtaining funding (RV); and administrative, technical, or logistic support (EC, LM).

Address Correspondence to: Yan Li, PhD, Center for Health Innovation, The New York Academy of Medicine, 1216 Fifth Ave, New York, NY 10029. E-mail: yli@nyam.org. REFERENCES

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