Abstract
Background:
Helping Her Live (HHL) is a community health worker-led outreach model that navigates women from vulnerable communities to mammography screening and diagnostic follow up. The objective of this study was to evaluate HHL implementation on the southwest side of Chicago.
Methods:
HHL has been implemented on the west side of Chicago since 2008, where it has increased mammogram completion and diagnostic follow-up rates among Black and Hispanic women from resource poor communities. In 2014, HHL was translated to the southwest side of Chicago; implementation success was evaluated by comparing outreach, navigation request, and mammogram completion metrics with the west side.
Results:
During January 2014-December 2015, outreach was less extensive in the southwest setting (SW) compared to the benchmark west setting (W), however, the proportion of women who completed mammograms in SW was 50%, which compared favorably to the proportion observed in the benchmark setting W (43%). The distribution of insurance status and the racial and ethnic makeup of individuals met on outreach in the W and SW were significantly different (p<.0005).
Conclusions:
This successful expansion of HHL in terms of both geographic and demographic reach justifies further studies leveraging these results and tailoring HHL to additional underserved communities.
Keywords: breast cancer, mammography, lay navigators, navigation, underserved, vulnerable
INTRODUCTION
Mammography remains indispensable in breast cancer screening as the sole screening modality demonstrated to reduce breast cancer mortality [21]. Attributing the increased risk for breast cancer mortality among African American women and other underserved populations to inequitable healthcare is supported by studies demonstrating that racial and ethnic minorities in the US, including African American, Hispanic, Asian, and Native American women, are less likely than White women to receive adequate mammography screening with respect to both its frequency and its quality [15, 22]. Further, the inadequate screening of underserved populations likely contributes to observations that racial/ethnic minorities are less likely than their White counterparts to be diagnosed at an early stage of breast cancer [4, 13, 17]. Late-stage diagnosis is associated with higher breast cancer mortality rates [16]. In addition to the disparities in mammography screening, racial/ethnic minority women often delay follow up of abnormal results as well as treatment initiation [23].
In addition to race/ethnicity, individual-level variables that contribute to the disparity in utilizing cancer care include age, income, education (including literacy and numeracy issues), insurance, language, transportation, and misconceptions stemming from culture and beliefs [11, 13]. Further community-level barriers associated with breast cancer diagnosis at a late stage include low income, low education, large immigrant population, limited availability of primary care physicians and radiologists, low percentage with insurance, and low percentage ever having had a mammogram [4].
Chicago has the 10th highest Black:White breast cancer mortality disparity in the US [10]. Helping Her Live (HHL) was conceived in 2008 in response to substantial racial/ethnic disparities in breast cancer mortality in Chicago, with a focus on African American and Hispanic women living on the west side. HHL is a mammography screening and diagnostic follow-up model that utilizes a community outreach platform with community health workers (CHWs) as navigators. Implemented for several years on the west side of Chicago, it has been demonstrated effective in improving screening and diagnostic follow up of racial/ethnic minority and other vulnerable women in that setting [8, 9]. In addition to HHL, a growing body of research has concluded that community health worker (CHW) and patient navigation programs are efficacious in improving mammography screening utilization in underserved settings [14, 24], while fewer studies have examined scaling CHW/ patient navigation programming for breast cancer screening of underserved and vulnerable communities [20].
Because the southwest side of Chicago is proximal to and demographically related to the west side, it is well suited to evaluate the feasibility and adaptability of expansion of the HHL intervention to new settings. This is the first report of HHL implementation in a new setting. We evaluate the implementation of the HHL model in the southwest setting (SW), using the west setting (W) as a benchmark comparator. The findings reported here have implications to the long term goal of scaling HHL implementation throughout Chicago and other urban centers in the US.
METHODS
Setting and Project.
Chicago is the third largest city in the US, and the majority of its population of 3 million is African American or Hispanic. The project, Helping Her Live (HHL), has been described previously [8, 9]. Briefly, it is a breast cancer awareness and outreach program that is designed to help minimize breast cancer disparities by addressing the barriers to mammography screening, timely follow-up to diagnostic services, and timely cancer treatment that are frequently encountered by women living in vulnerable and underserved communities. This is done through provision of CHW-led community-based outreach and navigation services.
HHL is located in a community-based research institute within a safety-net health care system. HHL serves primarily African American and Hispanic communities on the west and southwest sides of Chicago. In HHL, W is defined as including zip codes 60608, 60612, 60622, 60623, 60624, 60639, 60644, 60647, and 60651, and the SW includes zip codes 60629, 60632, and 60636 (Figure 1). Both the W and SW settings are catchment areas of the safety-net hospital system that reflect a primarily African American and Hispanic population, with surrounding communities that are resource-deprived and lack access to preventive services such as mammography [18].
Figure 1.
Map of Helping Her Live program areas in the City of Chicago
Founded in 2008, HHL was originally limited to W, as defined above by targeted zip codes. Outcomes of HHL outreach and navigation related to W have been reported for various periods of implementation [8, 9]. Implementation of the project in the new setting SW (as defined by targeted zip codes above) was initiated in the autumn of 2013. This is the first report pertaining to HHL implementation in SW, and it compares outreach and navigation outcomes in SW for the period January 2014-December 2015 with those in W, which serves as a benchmark comparator.
Outreach and Navigation.
Previously published outreach and navigation methodology was similarly applied to SW and W. The methodology has been described in detail elsewhere [8, 9] and is summarized here. Outreach aimed to reach women who were African American or Hispanic, un- or under-insured; 40 years old or older; and had never had a mammogram or had not had a mammogram in the past two years. To be eligible for the expanded navigation services, participants had to be recruited from HHL outreach activities in one of the targeted zip code areas on the southwest side of Chicago.
CHWs were required to reside in or be from the target setting (SW or W), and have a GED or high school diploma. CHWs from both settings underwent a rigorous training program that included culturally appropriate breast health education, outreach techniques, and case management training [8]. Each setting had both African American and bilingual Spanish-speaking Hispanic CHWs, with a total of 2 CHWs serving SW and 3 serving the benchmark W.
CHWs served as full time community navigators who perform outreach in the communities they serve in order to recruit women into mammography navigation. The CHWs hosted workshops, attended health fairs, and conducted woman-to-woman outreach activities, which could include one-on-one interactions at food pantries or churches, for example [9]. During outreach, the CHW’s use a form called a “pink sheet.” During this process, basic demographic information is collected from the client and the CHW determines if the client would like help obtaining a mammogram or would like to receive a mammogram reminder in the mail. The CHW also obtains consent from the client by having the client sign the HIPAA authorization if the client agrees. This allows the CHW to obtain the client’s mammogram results if the client is interested in navigation services. A signature on the HIPAA form is not a requirement for HHL assistance.
Clients who requested assistance with scheduling a mammogram were assigned to a CHW for breast imaging navigation. The CHWs served as lay navigators bridging the community and the hospital by assisting the patient with linkage to breast imaging services. A navigation protocol was utilized, with constant input and feedback from CHWs to adapt to real world barriers to mammograms, and to ensure that all navigation would be performed systematically. HHL instituted regular chart review by the program supervisor to help correct any gaps in data, errors in navigation, or identifying areas for retraining. The CHWs would then complete the three steps of navigation: (1) an Intake, where CHWs collected pertinent information, including basic demographics, insurance information, and clinic preferences; (2) scheduling of an appointment with primary care physicians for the client to obtain a mammogram referral (if necessary); and, (3) scheduling of the mammogram, ensuring that the clients attend their mammogram, receive their imaging results and navigation to any needed follow-up imaging or tests. Clients were navigated to the hospital of their choice dependent on factors such as insurance requirements and available safety net programs for uninsured women needing breast imaging services.
Outreach and Navigation Metrics – Evaluation of HHL Implementation in SW using the W as a Benchmark.
Using methods from the HHL model previously applied to W [8, 9, 20], we evaluated outreach and navigation activities in SW by tracking the volume of activities (the number of women spoken to (Number of Attendees), the number of pink sheets completed (Number of Contacts), the demographics of the women completing the sheets (e.g., race/ethnicity, age, insurance status, etc.), and the number of requests for services made (Number of Valid Requests). Outreach Productivity (OP) is the percentage of attendees at each type of outreach event/activity who complete a pink sheet. Service Demand (SD) is the percentage of pink sheets that contain a valid request (i.e. a pink sheet that has a request for navigation or a mammogram reminder, not a pink sheet where the client has indicated that she does not need any help at this time). A successful outreach activity would have high percentages of both Outreach Productivity and Service Demand. The Product of Outreach Productivity and Service Demand (Product) is a summary measure of both of these metrics. All outreach and navigation outcomes in SW were compared to outcomes in W, which served as a benchmark comparator. All outreach and navigation data in this report were captured during the period January 2014 - December 2015; some mammograms occurred after 12/31/2015.
Statistical Analysis.
The primary focus of this report is on analyzing the outreach and navigation metrics in SW, a new setting of HHL implementation, and comparing them with the metrics in W, where HHL program implementation originated. We present descriptive comparisons of outreach, navigation, and screening completion metrics for SW versus the benchmark W. Statistical analysis of demographic differences between the proportion of women in SW versus W who were met on outreach, accepted navigation, and completed mammograms was done using Pearson’s Chi-square test in Stata 14.1. Race/ethnicity was categorized as Hispanic-Puerto Rican, Hispanic-Mexican, Hispanic-Other, African American or Other Race. Insurance status was grouped as publicly insured, privately insured or uninsured.
RESULTS
This is the first report of HHL program implementation in SW. SW is a new setting where the theory and framework that underpin HHL program implementation in W are being utilized both to expand the reach of the HHL program and to allow a comparison of HHL program implementation in two distinct settings that are proximal and closely related demographically. Table 1 shows descriptive characteristics of women who 1) were met through outreach and 2) made at least one navigation request during January 2014-December 2015, and 3) completed at least one mammogram while being served by the HHL program through March 2016 in SW or the benchmark W. Within SW, a total of 723 women were recruited at outreach events; the majority of these women were Mexican (69%), 70% were uninsured, 21% were African American and 21% were publicly insured. The distributions of race/ethnicity and insurance status were significantly different between participants met on W and SW outreach (p<0.0005).
Table 1.
Descriptive characteristics of women served by the program on the southwest and west sides of Chicago: 2014–2015
| Individuals who were met through outreach | Individuals who made at least one navigation request | Individuals who completed at least one mammogram | |||||
|---|---|---|---|---|---|---|---|
| West | Southwest | West | Southwest | West | Southwest | ||
| N (Percent) | N (Percent) | N (Percent) | N (Percent) | N (Percent) | N (Percent) | ||
| Age | 40 years and older | 724 (86%) | 610 (86%) | 615 (86%) | 529 (87%) | 321 (91%) | 334 (93%) |
| Under 40 years | 119 (14%) | 103 (14%) | 103 (14%) | 78 (13%) | 33 (9%) | 26 (7%) | |
| N | 843(100%) | 713(100%) | 718(100%) | 607(100%) | 354(100%) | 360(100%) | |
| Race/ethnicity*,**, ^ | African American | 398 (51%) | 144 (21%) | 318 (47%) | 94 (16%) | 110 (32%) | 26 (8%) |
| Puerto Rican | 30 (4%) | 6 (1%) | 25 (4%) | 5 (1%) | 10 (3%) | 3 (1%) | |
| Mexican | 315 (40%) | 465 (69%) | 294 (43%) | 426 (74%) | 202 (59%) | 295 (86%) | |
| Other Hispanic | 28 (4%) | 36 (5%) | 26 (4%) | 30 (5%) | 15 (4%) | 13 (4%) | |
| Other Race | 15 (2%) | 22 (3%) | 14 (2%) | 20 (3%) | 5 (1%) | 7 (2%) | |
| N | 786(100%) | 673(100%) | 677(100%) | 575(100%) | 342(100%) | 344(100%) | |
| Insurance*,**, ^ | Uninsured | 383 (49%) | 469 (70%) | 364 (54%) | 446 (78%) | 233 (70%) | 302 (87%) |
| Public Insurance | 323 (41%) | 140 (21%) | 268 (40%) | 91 (16%) | 92 (27%) | 33 (10%) | |
| Private Insurance | 84 (11%) | 60 (9%) | 44 (7%) | 36 (6%) | 10 (3%) | 12 (3%) | |
| N | 790(100%) | 669(100%) | 676(100%) | 573(100%) | 335(100%) | 347(100%) | |
| Mammogram history of women aged 40 years and older | In past 2 years | 255 (38%) | 191 (33%) | 176 (30%) | 138 (28%) | 105 (33%) | 100 (31%) |
| More than 2 years ago | 246 (36%) | 220 (38%) | 239 (41%) | 209 (42%) | 140 (45%) | 129 (40%) | |
| Never | 175 (26%) | 161 (28%) | 165 (28%) | 154 (31%) | 69 (22%) | 96 (30%) | |
| N | 676(100%) | 572(100%) | 580(100%) | 501(100%) | 314(100%) | 325(100%) | |
| Residence in project area | Yes | 660 (78%) | 612 (85%) | 579 (80%) | 531 (87%) | 298 (84%) | 346 (96%) |
| No | 187 (22%) | 111 (15%) | 142 (20%) | 81 (13%) | 57 (16%) | 14 (4%) | |
| N | 847(100%) | 723(100%) | 721(100%) | 612(100%) | 355(100%) | 360(100%) | |
| Total | 852 | 723 | 726 | 612 | 355 | 360 | |
Distributions are significantly different between W and SW individuals met on outreach, P<.0005
Distributions are significantly different between W and SW individuals that made at least one navigation request, p<.0005
Distributions are significantly different between W and SW individuals that completed at least one mammogram, p<.0005
Demographic Factors for Navigation Seeking and Screening Completion.
Within SW, requests for navigation were made by the vast majority (85%; n=612) of the 723 women met through outreach. Similar to outreach demographics, the majority of women in SW seeking navigation were Mexican (74%) and/or uninsured (78%); 16% were African American and 16% were publicly insured. These distributions of race/ethnicity and insurance status were significantly different between the women that requested navigation met in W and SW (p<0.0005).
At least one mammogram was completed by the majority (59%; n=360) of the 612 women who sought navigation within SW. Of the 360 individuals who completed a mammogram, the majority were Mexican (86%) and/or uninsured (87%); 8% were African American and 10% were on public insurance. The racial/ethnic makeup and insurance status of women met on the W and SW sides that completed mammograms were significantly different (p<0.0005). Thus, the majority of the population served in the SW was Mexican and uninsured at the levels of outreach, navigation request, and mammogram completion.
Comparisons of outreach, navigation, mammogram completion.
Of the 723 SW women met through outreach events, the proportion who sought navigation was 85% (n=612), and the proportion who both sought navigation and completed a mammogram was 50% (n=360). These outcomes compared favorably to W, where the proportion who sought navigation was 85% (n=726), and the proportion who both sought navigation and completed a mammogram was 42% (n=355). These data demonstrate that outreach was not as extensive in SW as W, but that the proportion of women met on outreach who requested navigation and completed a mammogram in SW compared favorably to the proportion who did so in W.
Comparisons of outreach productivity.
Table 2 delineates outreach and navigation metrics for the outreach activities deployed for the period January 2014-December 2015. Results from SW and W are shown for each metric. In SW, the number of workshops held was 4, and there were 23 events and 12 woman-to-woman activities. Outreach was less extensive in SW compared to W, with the exception of the similar number of workshops held in these settings. Outreach productivity in SW was highest for workshops (81%) and much lower for events (30%) and woman-to-woman activities (22%). Outreach productivity was somewhat higher in SW compared to W for workshops and events, but somewhat lower for woman-to-woman activities. Service demand in SW was 81% for workshops, 89% for events, and 96% for woman-to-woman activities. Service demand was higher in SW compared to W for workshops, and similar for events and woman-to-woman activities. The summary measure (product of outreach productivity and service demand) in SW was highest for workshops (66%) and much lower for events (27%) and woman-to-woman activities (21%). The summary measure was substantially higher in SW compared to W for workshops and events, and similar for woman-to-woman activities.
Table 2.
Productivity of outreach efforts: January 2014-December 2015
| Activities | Number of activities | Number of attendees | Number of contacts | Number of valid requests | Outreach productivity (OP) (%) | Service demand (SD) (%) | Product (%) (OP X SD) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| W | SW | W | SW | W | SW | W | SW | W | SW | W | SW | W | SW | |
| Workshops | 3 | 4 | 25 | 53 | 19 | 43 | 12 | 35 | 76% | 81% | 63% | 81% | 48% | 66% |
| Events | 44 | 23 | 1537 | 758 | 310 | 231 | 281 | 206 | 20% | 30% | 91% | 89% | 18% | 27% |
| Woman-to-woman activities | 40 | 12 | 1425 | 325 | 355 | 72 | 331 | 69 | 25% | 22% | 93% | 96% | 23% | 21% |
Activities = outreach activities; attendees = women spoken to at outreach activities; contacts = pink sheets completed; valid requests = pink sheets with requests for services; outreach productivity = percentage of attendees who completed a pink sheet; service demand = percentage of pink sheets with a valid request.
DISCUSSION
In this report, we evaluate implementation of the HHL breast cancer outreach and navigation model in a new setting, SW. Our results show that the proportion of enrolled individuals who completed mammograms in SW was similar to the proportion of completers in the original setting W during the period January 2014-December 2015. The demographic characteristics of the women who participated in HHL in SW significantly differed from those in W. In the SW setting, the majority of women participating was Mexican and/or uninsured, and there was much less participation by African American women and women on public insurance compared to W. Similar mammogram completion rates in SW vs W settings shows for the first time not only that the HHL outreach-navigation framework can be implemented in a new setting with closely related demographic characteristics, increasing its geographic reach, but also that it is successful in achieving mammogram completion in women who are predominantly uninsured and/or Hispanic.
Breast cancer is a leading cause of cancer death among Hispanic women within the US [3]. National survey data (2008–2013) have shown that Hispanic women have lower proportions of breast cancer screening than non-Hispanic women in the US [19], implicating disparities in accessing healthcare in the rate of breast cancer mortality among Hispanic women. Insurance status is an important factor in mammography utilization [19]. In a study demonstrating a higher likelihood of late stage diagnosis of breast cancer and a higher breast cancer mortality risk among Hispanic women compared to non-Hispanic white women in California (California Cancer Registry), socioeconomic status and health insurance were determined to be major contributing factors to the observed disparity in mortality [12]. Uninsured status alone has been demonstrated to increase the risk of late-stage diagnosis of breast cancer as well as breast cancer mortality in women in the US in a study of a national cancer registry database [7]. Barriers to mammography screening among Hispanic women who are uninsured include lower health literacy, lower patient activation, and more health care system distrust than English-speaking counterparts [20]. In a comparison of 8 Hispanic subgroups, Mexican women had the lowest proportion of mammography use among women with public insurance, and the third highest proportion of mammography use among the uninsured [19]. These observations suggest that lack of insurance may in some cases motivate Mexican women to accept mammography services when available, which aligns with our observation that on the southwest side of Chicago, most women accepting navigation services and completing mammograms were Mexican and uninsured.
In both SW and W, most of the population recruited was Mexican or African American. In SW the proportion of women met on outreach, requesting navigation services and completing a mammogram that were Hispanic-Mexican was higher than in W. We posit that this may have influenced mammogram completion rates, because in both settings there was an attrition of the proportion of participating African Americans but an increment in the proportion of participating Mexicans from outreach to mammogram completion; for example, the proportion of Mexicans was 69% at outreach and increased to 74% (requested navigation) and 86% (completed mammogram) (Table 1). Thus, the higher proportion of Mexicans compared to African Americans met on outreach in SW may have favored mammogram completion in that setting.
Most of the population recruited on outreach in both settings was uninsured or on public insurance. In SW, the percentage of the women met on outreach who were on public insurance was very low compared to W. Conversely, the percentage of the women met on outreach who were uninsured was significantly different in the SW setting compared to W. In view of the high percentages for mammogram completion by the uninsured in both SW and W settings in this study compared to those of the publicly insured, the higher proportion of uninsured women compared to publicly insured women met on outreach in SW may have favored mammogram completion in that setting.
Inadequate mammography screening and delayed follow up after abnormal findings are recognized as major contributing factors to observations that racial/ethnic minorities and other underserved women are more likely than White counterparts to be diagnosed at a late stage of breast cancer, increasing their risk of breast cancer mortality [2, 15, 22]. Underutilization of mammography screening and follow up care by underserved women is a striking example of the gap between the results of discovery research on efficacious cancer screening interventions and the desired implementation and scaling of screening programs in practice [5]. This has served as an impetus to develop implementation approaches such as HHL to address this gap in practice, with the ultimate goal of minimizing the disparity in breast cancer mortality rates through the implementation of breast cancer screening and treatment programs for underserved women that are proven effective and structured in a way that is ultimately amenable to scale up and sustainability [6]. Thus it is important to take a long view from the ground up, and structure evidence-based interventions with elements that will allow their development and evolution for successful implementation and scaling in alignment with the magnitude of the gap in practice. For example, the study design should be based on a solid theory and framework rather than trial and error, and it should avoid common barriers to scaling such as high cost, highly expert staff requirements (which contribute to high cost), and limited reach [5]. The study reported here is based on the outreach-navigation HHL framework and the theory of scaling out. Scaling out is defined as “the deliberate use of strategies to implement, test, improve, and sustain evidence-based interventions as they are delivered in novel circumstances distinct from, but closely related to, previous implementations [1].” Here, SW, where expanding out of the HHL model took place, is closely related to but distinct from the original setting of W in its location and demographic characteristics.
With regard to high cost and staff requirements, the question of whether a mammography screening demonstration project for the underserved is sufficiently cost-effective to allow eventual development for sustainable implementation and scaling hinges in part on staffing costs. This is because patient navigation plays a pivotal role in these programs by helping the patient overcome barriers to screening completion and follow up, and the qualifications of patient navigators vary widely; some navigators are CHWs without formal education in healthcare whereas others are nurses, social workers, and research professionals [23]. A systematic review of 18 mammography screening studies utilizing CHWs as navigators demonstrated the efficacy of programs that utilized CHWs, indicating use of CHWs as an appropriate means to control staffing costs by having some tasks appropriately performed by CHWs instead of more costly professionals such as RNs [24]. The cost-effectiveness of this model, which utilized CHWs as navigators, is reflected in the success of expanding the reach of HHL by implementation in a new setting with a similar mammogram completion outcome as observed in the original setting.
With regard to the potential reach of mammography screening programs, a ceiling on this can be removed if underserved women who avoid medical settings can be engaged by community outreach. A systematic review established that both programs recruiting women from community settings and those recruiting women based on clinic-based medical records data were effective in improving mammography screening rates [24]. The efficacy of community outreach models is further buttressed by the successful expansion of the community outreach model HHL reported here.
Further, it bears noting that the reach of many breast cancer navigation programs is limited to screening, even though underserved women who complete mammography screening often do not follow up in the event of abnormal diagnostic findings [8]. Therefore, meaningful implementation requires a framework where navigators guide patients at least through diagnosis and optimally through treatment. This is structured into HHL, where patients are navigated through cancer diagnosis and treatment. In the HHL framework, navigators are recruiting women in the community, an outreach approach that is inclusive of those women who seldom, if ever, seek preventive services at clinics. The HHL framework of community outreach extends to women who avoid medical services owing to distrust of the health system, fear or denial [20], or other reasons, and it navigates them through clinical appointments beyond screening as needed, including diagnosis and treatment.
A limitation of this study is that there is no control group to serve as a comparator for rigorously assessing the productivity of the outreach-navigation model. A second limitation is that even though we looked at outreach productivity, this was not a formal cost-effectiveness or cost-efficacy study. A third limitation is that we also didn’t assess implementation constructs at multiple levels (e.g., organizational, community) that are part of “implementation success”. A strength of this study is that the community was the setting, which is not a controlled environment, such as recruitment from a clinic setting. In fact, this study was intentionally designed to be conducted in a real world setting and to benefit all eligible women reached by this intervention, as its purpose is to reduce health disparities for the underserved who are generally not reached by traditionally controlled trial designs such as randomized controlled trials. Adherence to this goal accounts to some extent for the above-described limitations. Rigorous evaluation of the HHL model will require future research including a formal cost-effectiveness analysis of the model and evaluation of multi-level implementation constructs that serve the model.
CONCLUSION
In this study, we evaluated implementation of the HHL outreach-navigation program in a new setting, SW, and utilized W, where the program is established, as a benchmark comparator. Our results show that outreach productivity was comparable in SW, even though more individuals were met in W. Collectively, the results reported here demonstrate for the first time the successful dissemination of HHL from its original setting to a new setting in Chicago. This is an important step in scaling up the HHL program to reach a larger group of at risk, underserved women across a broader geographic area to address breast cancer screening, diagnosis, treatment and mortality disparities.
Acknowledgments:
This work was supported by a grant from the Lynn Sage Cancer Research Foundation to Melissa Simon, MD, MPH, of Northwestern University Feinberg School of Medicine and Sinai Urban Health Institute of Sinai Health System. In addition, the Avon Foundation for Women funded portions of this program as did the Susan G. Komen for the Cure Chicago Affiliate. This work was also supported in part by the National Cancer Institute, grants U54CA202995, U54CA202997, and U54CA203000. Melissa Simon is a member of the United States Preventive Services Task Force (USPSTF). This article does not necessarily represent the views and policies of the USPSTF.
Footnotes
COMPLIANCE WITH ETHICAL STANDARDS
The authors have no conflicts to disclose.
This study was approved by the Institutional Review Board of Mount Sinai Hospital and has been performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
This study has a Waiver of HIPAA Authorization approved by the Mount Sinai Hospital Institutional Review Board. All participants in the program are given the opportunity to review an informed consent and HIPAA authorization document, however they are not required to sign it to receive services and participate in the program.
REFERENCES
- 1.Aarons GA, Sklar M, Mustanski B, Benbow N, and Brown CH 2017. “Scaling-out” evidence-based interventions to new populations or new health care delivery systems. Implement Sci 12 (1):111. doi: 10.1186/s13012-017-0640-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Allgood KL, Hunt B, Kanoon JM, and Simon MA 2017. Evaluation of Mammogram Parties as an Effective Community Navigation Method. Journal of Cancer Education. doi: 10.1007/s13187-017-1206-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.American Cancer Society. Cancer Facts and Figures 2016. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2016.html Accessed on Oct 17 2017.
- 4.Davidson Pamela L., Bastani Roshan, Nakazono Terry T., and Carreon Daisy C. 2005. Role of community risk factors and resources on breast carcinoma stage at diagnosis. Cancer 103 (5):922–930. doi: 10.1002/cncr.20852. [DOI] [PubMed] [Google Scholar]
- 5.Glasgow RE, Marcus AC, Bull SS, and Wilson KM 2004. Disseminating effective cancer screening interventions. Cancer 101 (5 Suppl):1239–1250. doi: 10.1002/cncr.20509. [DOI] [PubMed] [Google Scholar]
- 6.Glasgow RE, Vinson C, Chambers D, Khoury MJ, Kaplan RM, and Hunter C 2012. National Institutes of Health approaches to dissemination and implementation science: current and future directions. American Journal of Public Health 102 (7):1274–1281. doi: 10.2105/AJPH.2012.300755. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hsu CD, Wang X, Habif DV Jr., Ma CX, and Johnson KJ 2017. Breast cancer stage variation and survival in association with insurance status and sociodemographic factors in US women 18 to 64 years old. Cancer 123 (16):3125–3131. doi: 10.1002/cncr.30722. [DOI] [PubMed] [Google Scholar]
- 8.Hunt BR, Allgood KL, Kanoon JM, and Benjamins MR 2015. Keys to the Successful Implementation of Community-Based Outreach and Navigation: Lessons from a Breast Health Navigation Program. Journal of Cancer Education. doi: 10.1007/s13187-015-0904-2. [DOI] [PubMed] [Google Scholar]
- 9.Hunt BR, Allgood K, Sproles C, and Whitman S 2013. Metrics for the systematic evaluation of community-based outreach. Journal of Cancer Education 28 (4):633–638. doi: 10.1007/s13187-013-0519-4. [DOI] [PubMed] [Google Scholar]
- 10.Hunt BR, and Hurlbert MS 2016. Black:white disparities in breast cancer mortality in the 50 largest cities in the United States, 2005–2014. Cancer Epidemiology 45:169–173. doi: 10.1016/j.canep.2016.07.018. [DOI] [PubMed] [Google Scholar]
- 11.Mandelblatt JS, Yabroff KR, and Kerner JF 1999. Equitable access to cancer services: A review of barriers to quality care. Cancer 86 (11):2378–2390. [PubMed] [Google Scholar]
- 12.Martinez ME, Gomez SL, Tao L, Cress R, Rodriguez D, Unkart J, Schwab R et al. 2017. Contribution of clinical and socioeconomic factors to differences in breast cancer subtype and mortality between Hispanic and non-Hispanic white women. Breast Cancer Research and Treatment 166 (1):185–193. doi: 10.1007/s10549-017-4389-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Onitilo Adedayo A., Engel Jessica M., Liang Hong, Stankowski Rachel V., Miskowiak Douglas A., Broton Michael, and Doi Suhail A. 2013. Mammography Utilization: Patient Characteristics and Breast Cancer Stage at Diagnosis. American Journal of Roentgenology 201 (5):1057–1063. doi: 10.2214/AJR.13.10733. [DOI] [PubMed] [Google Scholar]
- 14.Phillips Christine E., Rothstein Jessica D., Beaver Kristine, Sherman Bonnie J., Freund Karen M., and Battaglia Tracy A. 2011. Patient Navigation to Increase Mammography Screening Among Inner City Women. Journal of General Internal Medicine 26 (2):123–129. doi: 10.1007/s11606-010-1527-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Rauscher GH, Allgood KL, Whitman S, and Conant E 2012. Disparities in screening mammography services by race/ethnicity and health insurance. J Womens Health (Larchmt) 21 (2):154–160. doi: 10.1089/jwh.2010.2415. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Richards MA, Westcombe AM, Love SB, Littlejohns P, and Ramirez AJ 1999. Influence of delay on survival in patients with breast cancer: a systematic review. Lancet 353 (9159):1119–1126. [DOI] [PubMed] [Google Scholar]
- 17.Sassi F, Luft HS, and Guadagnoli E 2006. Reducing racial/ethnic disparities in female breast cancer: screening rates and stage at diagnosis. American Journal of Public Health 96 (12):2165–2172. doi: 10.2105/AJPH.2005.071761. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Shah AM, Whitman S, and Silva A 2006. Variations in the health conditions of 6 Chicago community areas: case for local-level data. American Journal of Public Health 96 (8):1485–1491. doi: 10.2105/AJPH.2004.052076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Shoemaker ML, and White MC 2016. Breast and cervical cancer screening among Hispanic subgroups in the USA: estimates from the National Health Interview Survey 2008, 2010, and 2013. Cancer Causes and Control 27 (3):453–457. doi: 10.1007/s10552-016-0718-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Simon MA, Tom LS, Nonzee NJ, Murphy KR, Endress R, Dong X, and Feinglass J 2015. Evaluating bilingual patient navigation program for uninsured women with abnormal screening tests for breast and cervical cancer: implications for future navigator research. American Journal of Public Health 105 (5):e87–94. doi: 10.2105/AJPH.2014.302341. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Siu AL, and U. S. Preventive Services Task Force. 2016. Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Annals of Internal Medicine 164 (4):279–296. doi: 10.7326/M15-2886. [DOI] [PubMed] [Google Scholar]
- 22.Smith-Bindman R, Miglioretti DL, Lurie N, Abraham L, Barbash RB, Strzelczyk J, Dignan M, Barlow WE, Beasley CM, and Kerlikowske K 2006. Does utilization of screening mammography explain racial and ethnic differences in breast cancer? Annals of Internal Medicine 144 (8):541–553. [DOI] [PubMed] [Google Scholar]
- 23.Wells KJ, Battaglia TA, Dudley DJ, Garcia R, Greene A, Calhoun E, Mandelblatt JS, Paskett ED, Raich PC, and Program Patient Navigation Research. 2008. Patient navigation: state of the art or is it science? Cancer 113 (8):1999–2010. doi: 10.1002/cncr.23815. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wells KJ, Luque JS, Miladinovic B, Vargas N, Asvat Y, Roetzheim RG, and Kumar A 2011. Do community health worker interventions improve rates of screening mammography in the United States? A systematic review. Cancer Epidemiology, Biomarkers and Prevention 20 (8):1580–1598. doi: 10.1158/10559965.EPI-11-0276. [DOI] [PMC free article] [PubMed] [Google Scholar]