Social factors matter in cancer risk and survivorship

Social factors matter in cancer risk and survivorship Greater attention to social factors, such as race/ethnicity, socioeconomic position, and others, are needed across the cancer continuum, including breast cancer, given differences in tumor biology and genetic variants have not completely explained the persistent Black/White breast cancer mortality disparity. In this commentary, we use examples in breast cancer risk assessment and survivorship to demonstrate how the failure to appropriately incorporate social factors into the design, recruitment, and analysis of research studies has resulted in missed opportunities to reduce persistent cancer disparities. The conclusion offers recommendations for how to better document and use information on social factors in cancer research and care by (1) increasing education and awareness about the importance of inclusion of social factors in clinical research; (2) improving testing and documentation of social factors by incorporating them into journal guidelines and reporting stratified results; and (3) including social factors to ren fi e extant tools that assess cancer risk and assign cancer care. Implementing the recommended changes would enable more effective design and implementation of interventions and work toward eliminat- ing cancer disparities by accounting for the social and environmental contexts in which cancer patients live and are treated. Keywords United States · Breast cancer · Social determinants · Race/ethnicity · Disparities * Lorraine T. Dean Department of Epidemiology, Johns Hopkins Bloomberg lori.dean@jhu.edu School of Public Health and Department of Oncology, Johns Hopkins School of Medicine, 615 N Wolfe St, E6650, Sarah Gehlert Baltimore, MD 21205, USA sgehlert@mailbox.sc.edu College of Social Work, University of South Carolina, Marian L. Neuhouser Columbia, SC, USA mneuhous@fredhutch.org Cancer Prevention Program, Division of Public Health April Oh Sciences, Fred Hutchinson Cancer Research Center, Seattle, april.oh@nih.gov WA, USA Krista Zanetti Behavioral Research Program Division of Cancer Control zanettik@mail.nih.gov and Population Sciences, National Cancer Institute, Bethesda, Melody Goodman MD, United States melody.goodman@nyu.edu Epidemiology and Genomics Research Program, Division Beti Thompson of Cancer Control and Population Sciences, National Cancer bthompso@fredhutch.org Institute, Bethesda, MD, USA Kala Visvanathan Department of Biostatistics, College of Global Public Health, kvisvan1@jhu.edu New York University, New York, NY, USA Kathryn H. Schmitz Department of Public Health Sciences, College of Medicine, kschmitz@phs.psu.edu Pennsylvania State University, Hershey, PA, USA Vol.:(0123456789) 1 3 612 Cancer Causes & Control (2018) 29:611–618 the persistent Black/White breast cancer mortality disparity Introduction [11]. First, using an example of breast cancer risk assess- ment, we identify how excluding and then including the In 2015, a ground-breaking study highlighted how atten- social factor of race [1, 2] has changed conclusions about tion to two social factors, race/ethnicity [1, 2] and socio- eligibility for a breast cancer risk reduction strategy. Then, economic position (SEP), led to a key discovery in mortal- in two additional examples from breast cancer survivor- ity trends in the United States (US) that had been masked ship studies, we point to how the treatment and reporting for years [3]: All-cause mortality rates had been steadily of social factors have led to missed opportunities to address declining in the US between 1999 and 2013; however when disparities. Finally, we offer recommendations for how to mortality rates were stratified by race and SEP, it became better document and address the cumulative effect of social apparent that midlife mortality was actually increasing factors and experiences that patients face. among one particular social group, Non-Hispanic Whites with low-education [3]. This example of contrasting find- What are social factors? ings by race/ethnicity, which in the US includes categories of Hispanic/Latinx or non-Hispanic Latinx White, Black/ Social factors include non-biological individual-level fac- African-American, Native American, Asian and Pacific tors that influence health, such as race/ethnicity and SEP, Islanders and others, and SEP demonstrates that attention as well as “upstream” community and societal-level factors to social factors in research points us to conclusions that [10, 12]. SEP is an aggregate latent construct that includes might have been missed had those social factors not been both resource-based (income, wealth, consumer credit) and explored. Breast cancer risk and survivorship research also prestige-based (education, social status) measures that rep- offers several examples of disparities by race/ethnicity and resent both individual social position and access to material other social factors that might otherwise have remained goods [13]. While race/ethnicity and SEP are the focus of masked by the overall lower breast cancer incidence rates this commentary as the most studied social factors in dispar- for most racial/ethnic minority groups compared to Non- ities research [14], we acknowledge that many other factors Hispanic White women [4, 5]. Contrasting findings by race/ are significant across the cancer continuum: sexual identity ethnicity, SEP, and other social factors are present across the and orientation [15–18], poverty and discrimination [6, 19], cancer continuum. healthcare system distrust [20–22], health insurance quality For example, recent findings suggest an increased risk of [23], geography [19], nativity or immigration status [24–28], ER- breast cancer incidence among women born in states and housing [29–31], among others. Social factors of clini- with Jim Crow laws (post-slavery laws in place in the US cal relevance have been documented at the individual-level, from the 1870s to 1964 that limited Black advancements interpersonal-level (e.g., patient and provider communi- and freedoms) [6]. Despite higher overall breast cancer cation), and systems-level, extending to the societal-level mortality rates for Black women [4], both Black and Non- and beyond [32, 33]. Further, some of these factors may Hispanic White women with high education and who live change over the life course, with periods of exposure playing in low SEP neighborhoods have similar mortality [7]. Early an important role for later cancer-related health risk [34]. childhood abuse and neglect, which is more common among Despite the body of evidence supporting the importance of Black children than White [8], have been associated with social factors to cancer, incorporating social factors such elevated markers of inflammation among breast cancer sur - as patient race/ethnicity and socioeconomic position into vivors completing primary breast cancer treatment [9, 10]. cancer research and clinical practice continues to be a chal- These findings reinforce how recognizing social risk factors lenge in both risk assessment and survivorship research [35]. in cancer etiology research points us to inferences or novel For example, focusing on race/ethnicity has faced criticism hypotheses that might have been missed otherwise. because it is not a “modifiable factor,” while poverty and In this commentary, we describe social factors, and socioeconomic indicators face criticism because they are then use examples in breast cancer to demonstrate how the beyond the scope of health practitioners to treat. Nonethe- failure to appropriately incorporate social factors into the less, race/ethnicity and SEP are associated with differential design, recruitment, and analysis of research studies has social, political, cultural, and economic experiences which led to missed opportunities to reduce persistent cancer dis- can be modified and can influence cancer risk and care [10, parities. Greater attention to social factors across the breast 36]. cancer continuum is needed, given that differences in tumor biology and genetic variants have not completely explained Latinx is a gender-neutral term for Latinos. 1 3 Cancer Causes & Control (2018) 29:611–618 613 49]. This finding has been missed in regression models that Opportunities to focus on social factors: example from breast cancer risk assessment evaluate risk factors for breast cancer-related lymphedema, such as obesity and hypertension, which are also more The history of the Breast Cancer Risk Assessment Tool, prevalent among Black women than White women [50–53]. Consequently, these differences by race and other social fac- previously known as the Gail model [37], exemplifies the potential for information on social factors to reduce cancer tors remain unaddressed in statistical models from which inferences for clinical practice are made. Until this issue disparities. The Gail model, which emphasizes non-genetic risk factors [38], is one of the foundational breast cancer is addressed, we fail to fully acknowledge that the obese Black cancer survivor simultaneously lives the experience risk assessment models and remains in use. It has been vali- dated in many populations internationally [39], and played of being Black, obese, and at higher breast cancer-related lymphedema risk, when in fact Black women (or any other a substantial role in determining high-risk breast cancer populations to target for chemoprevention trials and addi- women, for that matter) do not live their lives “adjusted” for race and obesity. In the words of Galea and Link: “What tional screening in the United States [40]. The original Gail model was validated only in White women over the age of does it mean to enter a parallel universe wherein everything is the same except for one’s race?” [54, 55] The same ration- 35 [41, 42] and best estimated breast cancer risk for women who participated in regular mammographic screening [43]. ale can be applied for social factors other than race. While adjusting for social factors is appropriate if one is Meanwhile, the model greatly underestimated breast can- cer risk for Black women [44]: whose social grouping and trying to isolate the contribution of a specific mechanism or exposure, this approach overlooks variations by social fac- experiences make them more likely than White women to develop breast cancer at younger ages; who have histori- tors as a critical piece of information. Reporting analyses of results within social groupings can point to how social cally had low access to routine screening [45]; and who were under-represented in clinical trials preceding the develop- factors influence patient outcomes, which can lead to the development of effective interventions to help reduce exist- ment of the Gail model [46]. The original model’s underesti- mation of breast cancer risk among Black women precluded ing disparities. Of course, that requires that data on social factors be collected in the first place, which is not always discussions that clinicians would have with Black women about eligibility assessments for tamoxifen chemopreven- the case. A 2014 review of over 20 years of National Cancer Institute (NCI) clinical trials found that as few as 1.5–58% of tion trials [40]. In 2015, over 25 years after the initial Gail model was studies reported results on race/ethnicity and that only 20% of randomized controlled studies reported results stratified published, it was modified to explicitly account for Black race. The revised model more accurately estimated the by race/ethnicity [56]. More recently, an as-yet unpublished analysis by this paper’s authors examined the 57 breast expected number of breast cancer cases for Black women over age 30—narrowing the prediction for expected cases to cancer observational and randomized controlled trials that were published in a major cancer clinical journal in 2016. be within 4% of the number of actual observed cases [46]. These revised models now estimate Black female eligibility Fewer than half, 44% (n = 26), reported descriptive informa- tion on race/ethnicity and fewer than 25% (n = 13) reported for chemoprevention at nearly three times the rate of the original Gail model, increasing the estimated percentage of the social or economic composition of their study samples (unpublished data compiled by study authors solely for the Black women eligible for chemoprevention trials to 17.1%, up from an earlier value of 5.7% [47]. Future risk assessment purpose of this commentary). Excluding those in which the primary focus was disparities, fewer than 5% reported models may be in danger of repeating the Gail model’s his- tory if they are not tested in other race and social risk groups findings stratified by race or other socioeconomic factors. If health disparities are to be addressed, they must first be and if they fail to include social factors that contribute to differential breast cancer risk assessments. identified. Collecting information on social factors, and then exploring stratified results by those social factors, can help Missed opportunities in analysis and reporting: identify disparities and directly inform strategies for their elimination. example of breast cancer‑related lymphedema Stratic fi ation may lead to die ff rent conclusions than those that would be drawn looking at trends across the full sample. Overlooking differences by social factors also exacerbates disparities on the survivorship end of the breast cancer con- Simpson’s paradox occurs when the trend seen in the aggre- gated data does not hold in the stratified homogenous groups tinuum. Descriptive analyses unadjusted for race suggest that Black breast cancer survivors when compared to White [57]. For example, although Asians in general have histori- cally had lower breast cancer mortality compared to Non- breast cancer survivors have higher incidence of breast can- cer-related lymphedema, a persistent adverse effect of treat - Hispanic Whites [4], stratifying by country of origin shows that Filipina women have worse 5-year survival rates for ment that affects up to 35% of breast cancer survivors [48, 1 3 614 Cancer Causes & Control (2018) 29:611–618 late-stage disease than Whites, while Japanese women have Recommendation: increase education more favorable rates [28]. Having this information might and awareness among researchers and health suggest that Asian subpopulations need special attention, providers of the importance of inclusion of social which might have been missed without examining within factors race strata. Attention to social factors in clinical care and at the com- Missed opportunities in intervention due to lack munity level begins with clinical research that meaningfully of attention to social factors: example of physical accounts for a diverse sample of patients. Higher recruit- activity for breast cancer survivors ment and accrual of racial/ethnic minorities from low SEP backgrounds who are underrepresented in clinical trials Social factors also need to be addressed when assigning [35, 65, 66] will allow researchers to focus on how race follow-up care to ensure that it is appropriate, reasonable, and modifiable social factors may be primary exposures that and accessible for a patient. Physical activity interventions drive disease patterns. This work may necessitate additional are increasingly recommended as part of effective cancer resources for demographic data collection with real-time survivorship care plans [58] to reduce cancer-related fatigue, tracking of accruals [67] and targeted recruitment for the and to promote cardiopulmonary function and weight con- larger sample sizes this will require [66], as well as commit- trol [59–62] among women with a history of cancer. Physical ment to diversity from individuals, teams, and institutions activity interventions have been less successful among Black [67]. Having diverse samples that allow for documentation women when compared with other racial/ethnic groups, of race-based disparities is critical to continue to emphasize, largely due to lack of attention to the social factors that especially given recent calls by members of the U.S. Con- frame their health behaviors, including where they live and gress to exclude data collection on racial disparities . the demands on their time and resources [63]. For example, When there is scientific evidence or a theory-driven a physician’s recommendation of increased physical activity hypothesis to do so, investigators should consider expanding after cancer treatment will fail unless the patient’s barri- their research questions to examine social factors explicitly ers to physical activity are addressed, such as ensuring safe and consider including investigators with expertise in social places to exercise or access to a gym facility [64]. Failure to epidemiology, community-based participatory research, and address these factors likely contributes to disparities in qual- health disparities. It is especially important to use quantita- ity of life, and thus risk factors for breast cancer recurrence tive and qualitative approaches and statistical tools [68–71] persist for Black women, and may contribute to their shorter that are designed to explore differences by race, or other survival times after cancer treatment [11]. social factors (e.g., ethnicity, immigrant status, SEP) [72, 73]. For example, analyses might include hierarchical lin- Social factors can be meaningfully incorporated ear modeling and/or geospatial analyses to disentangle the into cancer research and practice role of environmental social factors (e.g., neighborhood access to health services) that influence health outcomes Although the examples in this commentary were from breast for individual cancer survivors embedded within these con- cancer, the implications are not specific to breast cancer, texts. Propensity scores could be used in regression analysis and the recommendations can be applied to cancer research in situations in which accounting for a high number of clini- more broadly. In light of these examples, we make three cal and social factors is required. The Peters-Belson method recommendations for how cancer research and practice can (called the Blinder–Oaxaca approach in economics) is most better incorporate social factors into research and practice: commonly used to determine wage discrimination but has (1) increase education and awareness about the importance many applications to the study of health disparities [74–76]. of inclusion of social factors in clinical research; (2) improve The Peters-Belson method estimates the proportion of an testing and documentation of social factors by incorporating overall disparity that is not explained by the covariates in them into journal guidelines and reporting stratified results; the regression. This method r fi st t fi s a regression model with and (3) include social factors to refine extant tools that assess cancer risk and assign cancer care. In January 2017, bills were introduced in the US Senate and House of Representatives to restrict the use of Federal funds “to design, build, maintain, utilize, or provide access to a Federal database of geospatial information on community racial disparities or disparities in access to affordable housing.” [http://www.congr ess.gov/bill/115th -congr ess/house -bill/482; http://www.congr ess.gov/bill/115th -congr ess/senat e-bill/103]. Bills with nearly identical language had been introduced into Congress in 2015 as well. 1 3 Cancer Causes & Control (2018) 29:611–618 615 individual-level covariates to the majority/advantaged group medical journals (http://www.conso rt-state ment.org/about and then uses the fitted model to estimate the expected val--consor t/endors ers), and STROBE and RECORD checklists ues for minority-group members had they been members of for observational studies [66, 80–82], call for reporting of the majority group [69, 71]. Using qualitative interview data demographic data. Yet, none is prescriptive about reporting or focus group data can often reveal why research results are race/ethnicity or other social factors specifically, or about what they are, beyond what the effect size of an odds ratio conducting sub-analysis by groups that are historically or hazard ratio can ever reveal. underrepresented in medical research. Assessing interac- Efforts are likewise needed to expand the collection of tions among social factors and conducting stratified analy - data on social factors beyond race to other factors of sig- sis when interactions between variables that are found to be nificance across the cancer continuum, such as sexual iden- significant are important for ensuring that study conclusions tity and orientation [15–18], poverty and discrimination [6, hold for the entire sample. Specifying regression models 19], healthcare system distrust [20–22], and geography [19]. for specific groups, and comparing parameter estimates for Some health systems have already started incorporating data the same variables in each of the models are recommended on social factors into electronic health records [77], which [83]. Conducting stratified analysis in this way allows for could have the dual purpose of expanding research opportu- inclusion of different variables in the models for different nities to link social factors to individual health outcomes and groups based on what is most relevant, in contrast to inter- biomarkers, and informing clinical practice. It might later action models which assume the same predictors and con- allow providers to develop treatment plans that recognize founders across all groups. More specific reporting on how and address patients that may be facing adverse social condi- social factors influence patient outcomes can help translate tions that undermine treatment success (e.g., food insecurity, research to clinical care, by guiding how study results might poor geographic accessibility). be translated into practice. Recommendation: improve testing Recommendation: use social factors to refine tools and documentation of social factors that assess cancer risk and assign cancer care by incorporating them into journal guidelines and reporting stratified results As the body of data on social factors grows, and reporting of these factors increase, this information may be used to refine Both measurement and reporting of social factors remain tools for cancer screening, care, and survivorship. Similar important because the sociocultural classification of indi- to the way in which the Gail model was adapted to include viduals in our society has resulted in differential health race information, treatment risk profiles might be expanded practices and outcomes. Exposure to depressed and segre- to include other elements of social context. In survivor- gated economic and residential environments, poor access ship care, the recent Commission on Cancer and National to health care, poor access to quality education, and greater Accreditation Program for Breast Centers’ accreditation exposure to psychosocial stressors, can all impact can- requirement of Survivorship Care Plans offers yet another cer risk, treatment receipt and whether there is partial or opportunity to incorporate the role of social factors. The complete pathologic response [36, 78]. Despite calls for success of the care plans will depend on the ability of health increased accountability in reporting social factors [79], providers and health systems to ensure that each patient’s suboptimal measurement and reporting of social factors care plan goal is achievable, and will confront the reality persist in published studies in cancer [79]. This suboptimal of designing a care plan that allows patients to successfully reporting and measurement may mask social differences or navigate their social lives and experiences to achieve healthy inequalities that, if reported, could be addressed to optimize outcomes. patient care and survivorship. When reporting data on social factors, reviewing results to examine the distribution of pre- existing social factors is a simple first step in understanding Conclusion and addressing social context. When possible, findings should stratify these data by race, Incorporating social factors such as patient race/ethnicity SEP, country of origin, sexual orientation, health insurance, and SEP into cancer research and clinical practice continues or other social factors that theory or previous research sug- to be a challenge in both cancer risk assessment and survi- gest are important to the exposures or outcomes being stud- vorship research [35]. For example, trying to resolve dispari- ied. This examination is essential for the identification of ties by “race/ethnicity” has fallen under criticism because those social factors that are the most salient. Current journal race is not a “modifiable factor” and the social experiences publication reporting checklists, including the CONSORT associated with race, such as poverty and SEP, are often checklist for clinical trials that is used in over 50% of core 1 3 616 Cancer Causes & Control (2018) 29:611–618 9. Crosswell AD, Bower JE, Ganz PA. 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Abstract

Greater attention to social factors, such as race/ethnicity, socioeconomic position, and others, are needed across the cancer continuum, including breast cancer, given differences in tumor biology and genetic variants have not completely explained the persistent Black/White breast cancer mortality disparity. In this commentary, we use examples in breast cancer risk assessment and survivorship to demonstrate how the failure to appropriately incorporate social factors into the design, recruitment, and analysis of research studies has resulted in missed opportunities to reduce persistent cancer disparities. The conclusion offers recommendations for how to better document and use information on social factors in cancer research and care by (1) increasing education and awareness about the importance of inclusion of social factors in clinical research; (2) improving testing and documentation of social factors by incorporating them into journal guidelines and reporting stratified results; and (3) including social factors to ren fi e extant tools that assess cancer risk and assign cancer care. Implementing the recommended changes would enable more effective design and implementation of interventions and work toward eliminat- ing cancer disparities by accounting for the social and environmental contexts in which cancer patients live and are treated. Keywords United States · Breast cancer · Social determinants · Race/ethnicity · Disparities * Lorraine T. Dean Department of Epidemiology, Johns Hopkins Bloomberg lori.dean@jhu.edu School of Public Health and Department of Oncology, Johns Hopkins School of Medicine, 615 N Wolfe St, E6650, Sarah Gehlert Baltimore, MD 21205, USA sgehlert@mailbox.sc.edu College of Social Work, University of South Carolina, Marian L. Neuhouser Columbia, SC, USA mneuhous@fredhutch.org Cancer Prevention Program, Division of Public Health April Oh Sciences, Fred Hutchinson Cancer Research Center, Seattle, april.oh@nih.gov WA, USA Krista Zanetti Behavioral Research Program Division of Cancer Control zanettik@mail.nih.gov and Population Sciences, National Cancer Institute, Bethesda, Melody Goodman MD, United States melody.goodman@nyu.edu Epidemiology and Genomics Research Program, Division Beti Thompson of Cancer Control and Population Sciences, National Cancer bthompso@fredhutch.org Institute, Bethesda, MD, USA Kala Visvanathan Department of Biostatistics, College of Global Public Health, kvisvan1@jhu.edu New York University, New York, NY, USA Kathryn H. Schmitz Department of Public Health Sciences, College of Medicine, kschmitz@phs.psu.edu Pennsylvania State University, Hershey, PA, USA Vol.:(0123456789) 1 3 612 Cancer Causes & Control (2018) 29:611–618 the persistent Black/White breast cancer mortality disparity Introduction [11]. First, using an example of breast cancer risk assess- ment, we identify how excluding and then including the In 2015, a ground-breaking study highlighted how atten- social factor of race [1, 2] has changed conclusions about tion to two social factors, race/ethnicity [1, 2] and socio- eligibility for a breast cancer risk reduction strategy. Then, economic position (SEP), led to a key discovery in mortal- in two additional examples from breast cancer survivor- ity trends in the United States (US) that had been masked ship studies, we point to how the treatment and reporting for years [3]: All-cause mortality rates had been steadily of social factors have led to missed opportunities to address declining in the US between 1999 and 2013; however when disparities. Finally, we offer recommendations for how to mortality rates were stratified by race and SEP, it became better document and address the cumulative effect of social apparent that midlife mortality was actually increasing factors and experiences that patients face. among one particular social group, Non-Hispanic Whites with low-education [3]. This example of contrasting find- What are social factors? ings by race/ethnicity, which in the US includes categories of Hispanic/Latinx or non-Hispanic Latinx White, Black/ Social factors include non-biological individual-level fac- African-American, Native American, Asian and Pacific tors that influence health, such as race/ethnicity and SEP, Islanders and others, and SEP demonstrates that attention as well as “upstream” community and societal-level factors to social factors in research points us to conclusions that [10, 12]. SEP is an aggregate latent construct that includes might have been missed had those social factors not been both resource-based (income, wealth, consumer credit) and explored. Breast cancer risk and survivorship research also prestige-based (education, social status) measures that rep- offers several examples of disparities by race/ethnicity and resent both individual social position and access to material other social factors that might otherwise have remained goods [13]. While race/ethnicity and SEP are the focus of masked by the overall lower breast cancer incidence rates this commentary as the most studied social factors in dispar- for most racial/ethnic minority groups compared to Non- ities research [14], we acknowledge that many other factors Hispanic White women [4, 5]. Contrasting findings by race/ are significant across the cancer continuum: sexual identity ethnicity, SEP, and other social factors are present across the and orientation [15–18], poverty and discrimination [6, 19], cancer continuum. healthcare system distrust [20–22], health insurance quality For example, recent findings suggest an increased risk of [23], geography [19], nativity or immigration status [24–28], ER- breast cancer incidence among women born in states and housing [29–31], among others. Social factors of clini- with Jim Crow laws (post-slavery laws in place in the US cal relevance have been documented at the individual-level, from the 1870s to 1964 that limited Black advancements interpersonal-level (e.g., patient and provider communi- and freedoms) [6]. Despite higher overall breast cancer cation), and systems-level, extending to the societal-level mortality rates for Black women [4], both Black and Non- and beyond [32, 33]. Further, some of these factors may Hispanic White women with high education and who live change over the life course, with periods of exposure playing in low SEP neighborhoods have similar mortality [7]. Early an important role for later cancer-related health risk [34]. childhood abuse and neglect, which is more common among Despite the body of evidence supporting the importance of Black children than White [8], have been associated with social factors to cancer, incorporating social factors such elevated markers of inflammation among breast cancer sur - as patient race/ethnicity and socioeconomic position into vivors completing primary breast cancer treatment [9, 10]. cancer research and clinical practice continues to be a chal- These findings reinforce how recognizing social risk factors lenge in both risk assessment and survivorship research [35]. in cancer etiology research points us to inferences or novel For example, focusing on race/ethnicity has faced criticism hypotheses that might have been missed otherwise. because it is not a “modifiable factor,” while poverty and In this commentary, we describe social factors, and socioeconomic indicators face criticism because they are then use examples in breast cancer to demonstrate how the beyond the scope of health practitioners to treat. Nonethe- failure to appropriately incorporate social factors into the less, race/ethnicity and SEP are associated with differential design, recruitment, and analysis of research studies has social, political, cultural, and economic experiences which led to missed opportunities to reduce persistent cancer dis- can be modified and can influence cancer risk and care [10, parities. Greater attention to social factors across the breast 36]. cancer continuum is needed, given that differences in tumor biology and genetic variants have not completely explained Latinx is a gender-neutral term for Latinos. 1 3 Cancer Causes & Control (2018) 29:611–618 613 49]. This finding has been missed in regression models that Opportunities to focus on social factors: example from breast cancer risk assessment evaluate risk factors for breast cancer-related lymphedema, such as obesity and hypertension, which are also more The history of the Breast Cancer Risk Assessment Tool, prevalent among Black women than White women [50–53]. Consequently, these differences by race and other social fac- previously known as the Gail model [37], exemplifies the potential for information on social factors to reduce cancer tors remain unaddressed in statistical models from which inferences for clinical practice are made. Until this issue disparities. The Gail model, which emphasizes non-genetic risk factors [38], is one of the foundational breast cancer is addressed, we fail to fully acknowledge that the obese Black cancer survivor simultaneously lives the experience risk assessment models and remains in use. It has been vali- dated in many populations internationally [39], and played of being Black, obese, and at higher breast cancer-related lymphedema risk, when in fact Black women (or any other a substantial role in determining high-risk breast cancer populations to target for chemoprevention trials and addi- women, for that matter) do not live their lives “adjusted” for race and obesity. In the words of Galea and Link: “What tional screening in the United States [40]. The original Gail model was validated only in White women over the age of does it mean to enter a parallel universe wherein everything is the same except for one’s race?” [54, 55] The same ration- 35 [41, 42] and best estimated breast cancer risk for women who participated in regular mammographic screening [43]. ale can be applied for social factors other than race. While adjusting for social factors is appropriate if one is Meanwhile, the model greatly underestimated breast can- cer risk for Black women [44]: whose social grouping and trying to isolate the contribution of a specific mechanism or exposure, this approach overlooks variations by social fac- experiences make them more likely than White women to develop breast cancer at younger ages; who have histori- tors as a critical piece of information. Reporting analyses of results within social groupings can point to how social cally had low access to routine screening [45]; and who were under-represented in clinical trials preceding the develop- factors influence patient outcomes, which can lead to the development of effective interventions to help reduce exist- ment of the Gail model [46]. The original model’s underesti- mation of breast cancer risk among Black women precluded ing disparities. Of course, that requires that data on social factors be collected in the first place, which is not always discussions that clinicians would have with Black women about eligibility assessments for tamoxifen chemopreven- the case. A 2014 review of over 20 years of National Cancer Institute (NCI) clinical trials found that as few as 1.5–58% of tion trials [40]. In 2015, over 25 years after the initial Gail model was studies reported results on race/ethnicity and that only 20% of randomized controlled studies reported results stratified published, it was modified to explicitly account for Black race. The revised model more accurately estimated the by race/ethnicity [56]. More recently, an as-yet unpublished analysis by this paper’s authors examined the 57 breast expected number of breast cancer cases for Black women over age 30—narrowing the prediction for expected cases to cancer observational and randomized controlled trials that were published in a major cancer clinical journal in 2016. be within 4% of the number of actual observed cases [46]. These revised models now estimate Black female eligibility Fewer than half, 44% (n = 26), reported descriptive informa- tion on race/ethnicity and fewer than 25% (n = 13) reported for chemoprevention at nearly three times the rate of the original Gail model, increasing the estimated percentage of the social or economic composition of their study samples (unpublished data compiled by study authors solely for the Black women eligible for chemoprevention trials to 17.1%, up from an earlier value of 5.7% [47]. Future risk assessment purpose of this commentary). Excluding those in which the primary focus was disparities, fewer than 5% reported models may be in danger of repeating the Gail model’s his- tory if they are not tested in other race and social risk groups findings stratified by race or other socioeconomic factors. If health disparities are to be addressed, they must first be and if they fail to include social factors that contribute to differential breast cancer risk assessments. identified. Collecting information on social factors, and then exploring stratified results by those social factors, can help Missed opportunities in analysis and reporting: identify disparities and directly inform strategies for their elimination. example of breast cancer‑related lymphedema Stratic fi ation may lead to die ff rent conclusions than those that would be drawn looking at trends across the full sample. Overlooking differences by social factors also exacerbates disparities on the survivorship end of the breast cancer con- Simpson’s paradox occurs when the trend seen in the aggre- gated data does not hold in the stratified homogenous groups tinuum. Descriptive analyses unadjusted for race suggest that Black breast cancer survivors when compared to White [57]. For example, although Asians in general have histori- cally had lower breast cancer mortality compared to Non- breast cancer survivors have higher incidence of breast can- cer-related lymphedema, a persistent adverse effect of treat - Hispanic Whites [4], stratifying by country of origin shows that Filipina women have worse 5-year survival rates for ment that affects up to 35% of breast cancer survivors [48, 1 3 614 Cancer Causes & Control (2018) 29:611–618 late-stage disease than Whites, while Japanese women have Recommendation: increase education more favorable rates [28]. Having this information might and awareness among researchers and health suggest that Asian subpopulations need special attention, providers of the importance of inclusion of social which might have been missed without examining within factors race strata. Attention to social factors in clinical care and at the com- Missed opportunities in intervention due to lack munity level begins with clinical research that meaningfully of attention to social factors: example of physical accounts for a diverse sample of patients. Higher recruit- activity for breast cancer survivors ment and accrual of racial/ethnic minorities from low SEP backgrounds who are underrepresented in clinical trials Social factors also need to be addressed when assigning [35, 65, 66] will allow researchers to focus on how race follow-up care to ensure that it is appropriate, reasonable, and modifiable social factors may be primary exposures that and accessible for a patient. Physical activity interventions drive disease patterns. This work may necessitate additional are increasingly recommended as part of effective cancer resources for demographic data collection with real-time survivorship care plans [58] to reduce cancer-related fatigue, tracking of accruals [67] and targeted recruitment for the and to promote cardiopulmonary function and weight con- larger sample sizes this will require [66], as well as commit- trol [59–62] among women with a history of cancer. Physical ment to diversity from individuals, teams, and institutions activity interventions have been less successful among Black [67]. Having diverse samples that allow for documentation women when compared with other racial/ethnic groups, of race-based disparities is critical to continue to emphasize, largely due to lack of attention to the social factors that especially given recent calls by members of the U.S. Con- frame their health behaviors, including where they live and gress to exclude data collection on racial disparities . the demands on their time and resources [63]. For example, When there is scientific evidence or a theory-driven a physician’s recommendation of increased physical activity hypothesis to do so, investigators should consider expanding after cancer treatment will fail unless the patient’s barri- their research questions to examine social factors explicitly ers to physical activity are addressed, such as ensuring safe and consider including investigators with expertise in social places to exercise or access to a gym facility [64]. Failure to epidemiology, community-based participatory research, and address these factors likely contributes to disparities in qual- health disparities. It is especially important to use quantita- ity of life, and thus risk factors for breast cancer recurrence tive and qualitative approaches and statistical tools [68–71] persist for Black women, and may contribute to their shorter that are designed to explore differences by race, or other survival times after cancer treatment [11]. social factors (e.g., ethnicity, immigrant status, SEP) [72, 73]. For example, analyses might include hierarchical lin- Social factors can be meaningfully incorporated ear modeling and/or geospatial analyses to disentangle the into cancer research and practice role of environmental social factors (e.g., neighborhood access to health services) that influence health outcomes Although the examples in this commentary were from breast for individual cancer survivors embedded within these con- cancer, the implications are not specific to breast cancer, texts. Propensity scores could be used in regression analysis and the recommendations can be applied to cancer research in situations in which accounting for a high number of clini- more broadly. In light of these examples, we make three cal and social factors is required. The Peters-Belson method recommendations for how cancer research and practice can (called the Blinder–Oaxaca approach in economics) is most better incorporate social factors into research and practice: commonly used to determine wage discrimination but has (1) increase education and awareness about the importance many applications to the study of health disparities [74–76]. of inclusion of social factors in clinical research; (2) improve The Peters-Belson method estimates the proportion of an testing and documentation of social factors by incorporating overall disparity that is not explained by the covariates in them into journal guidelines and reporting stratified results; the regression. This method r fi st t fi s a regression model with and (3) include social factors to refine extant tools that assess cancer risk and assign cancer care. In January 2017, bills were introduced in the US Senate and House of Representatives to restrict the use of Federal funds “to design, build, maintain, utilize, or provide access to a Federal database of geospatial information on community racial disparities or disparities in access to affordable housing.” [http://www.congr ess.gov/bill/115th -congr ess/house -bill/482; http://www.congr ess.gov/bill/115th -congr ess/senat e-bill/103]. Bills with nearly identical language had been introduced into Congress in 2015 as well. 1 3 Cancer Causes & Control (2018) 29:611–618 615 individual-level covariates to the majority/advantaged group medical journals (http://www.conso rt-state ment.org/about and then uses the fitted model to estimate the expected val--consor t/endors ers), and STROBE and RECORD checklists ues for minority-group members had they been members of for observational studies [66, 80–82], call for reporting of the majority group [69, 71]. Using qualitative interview data demographic data. Yet, none is prescriptive about reporting or focus group data can often reveal why research results are race/ethnicity or other social factors specifically, or about what they are, beyond what the effect size of an odds ratio conducting sub-analysis by groups that are historically or hazard ratio can ever reveal. underrepresented in medical research. Assessing interac- Efforts are likewise needed to expand the collection of tions among social factors and conducting stratified analy - data on social factors beyond race to other factors of sig- sis when interactions between variables that are found to be nificance across the cancer continuum, such as sexual iden- significant are important for ensuring that study conclusions tity and orientation [15–18], poverty and discrimination [6, hold for the entire sample. Specifying regression models 19], healthcare system distrust [20–22], and geography [19]. for specific groups, and comparing parameter estimates for Some health systems have already started incorporating data the same variables in each of the models are recommended on social factors into electronic health records [77], which [83]. Conducting stratified analysis in this way allows for could have the dual purpose of expanding research opportu- inclusion of different variables in the models for different nities to link social factors to individual health outcomes and groups based on what is most relevant, in contrast to inter- biomarkers, and informing clinical practice. It might later action models which assume the same predictors and con- allow providers to develop treatment plans that recognize founders across all groups. More specific reporting on how and address patients that may be facing adverse social condi- social factors influence patient outcomes can help translate tions that undermine treatment success (e.g., food insecurity, research to clinical care, by guiding how study results might poor geographic accessibility). be translated into practice. Recommendation: improve testing Recommendation: use social factors to refine tools and documentation of social factors that assess cancer risk and assign cancer care by incorporating them into journal guidelines and reporting stratified results As the body of data on social factors grows, and reporting of these factors increase, this information may be used to refine Both measurement and reporting of social factors remain tools for cancer screening, care, and survivorship. Similar important because the sociocultural classification of indi- to the way in which the Gail model was adapted to include viduals in our society has resulted in differential health race information, treatment risk profiles might be expanded practices and outcomes. Exposure to depressed and segre- to include other elements of social context. In survivor- gated economic and residential environments, poor access ship care, the recent Commission on Cancer and National to health care, poor access to quality education, and greater Accreditation Program for Breast Centers’ accreditation exposure to psychosocial stressors, can all impact can- requirement of Survivorship Care Plans offers yet another cer risk, treatment receipt and whether there is partial or opportunity to incorporate the role of social factors. The complete pathologic response [36, 78]. Despite calls for success of the care plans will depend on the ability of health increased accountability in reporting social factors [79], providers and health systems to ensure that each patient’s suboptimal measurement and reporting of social factors care plan goal is achievable, and will confront the reality persist in published studies in cancer [79]. This suboptimal of designing a care plan that allows patients to successfully reporting and measurement may mask social differences or navigate their social lives and experiences to achieve healthy inequalities that, if reported, could be addressed to optimize outcomes. patient care and survivorship. When reporting data on social factors, reviewing results to examine the distribution of pre- existing social factors is a simple first step in understanding Conclusion and addressing social context. When possible, findings should stratify these data by race, Incorporating social factors such as patient race/ethnicity SEP, country of origin, sexual orientation, health insurance, and SEP into cancer research and clinical practice continues or other social factors that theory or previous research sug- to be a challenge in both cancer risk assessment and survi- gest are important to the exposures or outcomes being stud- vorship research [35]. For example, trying to resolve dispari- ied. This examination is essential for the identification of ties by “race/ethnicity” has fallen under criticism because those social factors that are the most salient. Current journal race is not a “modifiable factor” and the social experiences publication reporting checklists, including the CONSORT associated with race, such as poverty and SEP, are often checklist for clinical trials that is used in over 50% of core 1 3 616 Cancer Causes & Control (2018) 29:611–618 9. Crosswell AD, Bower JE, Ganz PA. 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Cancer Causes & ControlSpringer Journals

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