Abstract
Background: Ohio, the catchment area of The Ohio State University Comprehensive Cancer Center (OSUCCC), includes diverse populations with different cancer profiles. As part of the National Cancer Institute (NCI)-funded initiative to conduct population health assessments in cancer center catchment areas, the OSUCCC surveyed residents, focusing on factors contributing to cancer disparities in Ohio populations.
Methods: Two sampling strategies were used: (i) probability sampling of mailing lists and (ii) convenience sampling at community events, coupled with phone/in-person/web surveys. Survey items were chosen along multilevel framework constructs, used in concert with other funded NCI-Designated Cancer Centers. Multivariable logistic regression models investigated predictors associated with health behaviors, cancer beliefs, knowledge, and screening.
Results: The sample of 1,005 respondents were white (46.6%), African American (24.7%), Hispanic (13.7%), Somali (7.6%), and Asian (7.5%). A total of 216 respondents were Appalachian. Variations in cancer attitudes, knowledge, and behaviors were noted by racial/ethnic and geographic group. Multivariable models identified individuals with less financial security as less likely to exercise or be within guidelines for screening, but more likely to smoke and have a poor diet. At the community-level, measures of poverty were highest in Appalachia, whereas children in female-headed households were greater in urban minority areas.
Conclusions: This population health assessment reinforced the diversity of the OSUCCC catchment area. These populations are ripe for implementation science strategies, focusing in communities and clinics that serve vulnerable populations.
Impact: Understanding attitudes, knowledge, and behaviors of this population can assist tailoring outreach and research strategies to lessen the cancer burden.
Introduction
The Ohio State University Comprehensive Cancer Center (OSUCCC) is a matrix cancer center in Columbus, Ohio, with a catchment area that covers all of Ohio's 88 counties and nearly 11.7 million people. The majority (82%) of Ohio's population are white, whereas 12.8% identify as black, 2.2% Asian, and 3% some other race or two or more races (1), with nearly 7% of Ohioans speaking a non-English language at home (2). Immigrants make up approximately 4.3% of Ohio's population, and the Columbus metropolitan area contains one of the highest concentrations of Somali refugees (estimated 45,000) in the United States (3), primarily because of existing ties to extended family members in the region as well as availability of low-cost housing and jobs (4). The median household income in Ohio in 2016 was $50,674, about $5,000 less than the U.S. median (1).
Ohio is geographically heterogeneous. A total of 32 of Ohio's 88 counties are classified as “Appalachian,” and 42% of the Appalachian region is also considered rural (5). The 2013 National Center for Health Statistics urban–rural classifications define 50 of Ohio's counties as rural (6). These 50 rural counties are home to just over 2.3 million people, approximately 20% of Ohio's population. Regional disparities in poverty are evident; approximately 21% of those in Ohio's rural counties are living in poverty, as are 20% of those in Appalachian counties (7).
According to the National Cancer Institute (NCI), low socioeconomic status (SES) and a lack of healthcare coverage are the most obvious factors associated with cancer disparities (8). NCI defines cancer disparities “as adverse differences in cancer incidence, prevalence, mortality, survivorship, and burden of cancer or related health conditions that exist among specific population groups (e.g., racial/ethnic, age, geography, language, poverty)” (8). Considering the higher prevalence of poverty in many Ohio regions, it is expected that Ohio would experience increased cancer incidence and mortality burdens. From 2009 to 2013, the age-adjusted incidence rate for invasive cancer in Ohio was 459.9 cases per 100,000, higher than the 448.7 cases per 100,000 in the United States (9). In 2015, the age-adjusted cancer mortality rate in Ohio was 10% higher than the U.S. cancer mortality rate (10). Cancer disparities by race are also evident in Ohio: from 2009 to 2013, the average annual cancer incidence rate was 6.5% higher for blacks compared with whites, and the highest mortality rates for all cancers combined in Ohio was among blacks (9). Further, the Appalachian and rural areas of Ohio experience higher cancer mortality rates than non-Appalachian and urban areas of Ohio (11).
This paper presents the Community Initiative Towards Improving Equity and Health Status (CITIES) project, a population health assessment in the Ohio catchment area with a focus on multilevel factors contributing to cancer disparities in specific Ohio populations. This study was supported through a supplement to the OSUCCC Cancer Center Support Grant (CCSG), and was part of an NCI-funded initiative, Population Health Assessments, in which 15 NCI-Designated Cancer Centers were funded to work collectively to develop core survey items and implement population surveys in their respective catchment areas (12). Each site had its own theoretical framework and survey methods. Our site conducted all aspects of this study, including survey development, sampling, administration, and statistical analysis.
Materials and Methods
Theoretical framework
The overall goal of the CITIES project was to collect local data to better define and describe the OSUCCC catchment area, using a multilevel, socioecological population health framework (Fig. 1) developed by Warnecke and colleagues for the Centers for Population Health and Health Disparities (CPHHD; ref. 13) We utilized the framework to identify the following relevant constructs needed to understand disparities among various Ohio populations: biological, individual risk factors, interpersonal relationships, organizational factors, community influences, social conditions, and policy. We identified appropriate survey items including core constructs (see Supplementary Table S1) that were used in concert with NCI and other funded NCI-Designated Cancer Centers (14) through the use of items from national surveys such as Health Information National Trends Survey (HINTS; ref. 15), National Health Interview Survey (NHIS; ref. 16), and the Behavioral Risk Factor Surveillance System (BRFSS; ref. 17). In addition, we utilized constructs from prior surveys developed and conducted by the OSU CPHHD (18). Depending on the method of survey administration, participants provided verbal or written informed consent. This project was approved by the OSU Institutional Review Board in February 2017.
Model for analysis of population health and health disparities. Figure depicts various upstream and downstream factors that contribute to health disparities. This model is based on the model published by Warnecke and colleagues in the American Journal of Public Health in 2008. Each panel describes information collected by our study as it relates to each of the upstream and downstream factors. In panels 1 and 2, we outline the fundamental causes of health disparities, including social conditions (e.g., perceived discrimination) and policies as well as institutions (e.g., political affiliation and religion) that were measured in our surveys to participants. Panels 3 and 4 outline the social/physical context (e.g., source of care, health insurance) as well as social relationships (e.g., social support and coping strategies) that contribute to health disparities. Finally, panels 5 to 7 outline the downstream factors that contribute to health disparities. These downstream factors include individual risk factors (e.g., diet and smoking status), demographics (e.g., race and gender) as well as biological risk factors (e.g., family history of cancer) which contribute to disparities in health outcomes.
Sample selection
Eligible participants were Ohio residents aged 21 to 74 years. To ensure that the sample included underrepresented populations, recruitment targeted substantive percentages of racial/ethnic minorities and rural and Appalachian Ohio residents, who were further categorized by age group (21–40; 41–50; 51–65; 66–74).
We had two tailored simultaneous recruitment strategies. First, to recruit mainly white urban, rural, and Appalachian participants, Ohio residents were randomly selected from a customized, randomly ordered list provided by Marketing Systems Group (white pages, commercial and United States Postal Service lists; ref. 19). Second, to reach goals of minority participation, especially non-English speakers, we worked with community partners to identify community events and venues to recruit participants. As a result, participants represented various population groups in Ohio—white (urban and rural), Appalachian, African American, Hispanic, Somali, and Asian.
Interview/data collection
We used several data collection techniques (phone-based, in-person interviews, web surveys), with translation as needed, to accommodate needs of catchment area populations. For phone interviews, respondents received a letter introducing the study, followed by a telephone call from a trained interviewer 1 week later. For in-person interviews, potential participants were approached individually or in a group setting where the study was explained and individual consent was obtained. For participants who preferred to complete the survey on their own, a web link to the survey was sent via email. Study data were collected and managed using REDCap (Research Electronic Data Capture), a secure, web application designed to support electronic data capture, hosted at OSU (20).
Analysis
Descriptive statistics were generated for each subgroup. For outcomes associated with health behaviors, cancer beliefs, knowledge, and screening, we were interested in relationships within two subsets: whites (Appalachian, rural non-Appalachian, and urban) and urban (Somali, Hispanic, African American, Asian, and white urban) subgroups. For these outcomes, we fit multivariable logistic regression models within these subgroups adjusting for age, degree of financial security (in lieu of income, which had greater missingness), educational attainment, and gender. Analyses were conducted in SAS v9.4 (SAS Institute).
Results
Sample description
Survey administration began May 30, 2017, and ended February 16, 2018. A total of 1,013 participants completed a survey, eight of which did not respond to one or more questions regarding gender, race, or county of residence and were excluded from analyses. Of 1,005, 359 (35.7%) were sampled from the commercial list and 646 (64.3%) were obtained from community events. For the mailed sample, of 3,160 letters mailed, 53 were ineligible and 30 were deceased, leaving 3,077 eligible. Of these, 2,371 could not be contacted, 346 refused and 360 completed the survey; therefore, 51% of those contacted agreed to complete the survey. Most minority respondents were recruited through community events, whereas the majority of white (urban and rural) and Appalachian participants were recruited from the commercial list. Most surveys were conducted in English (84.7%), whereas 121 (12%) were conducted in Spanish and 33 (3.3%) were conducted in Chinese. The final race/ethnicity distribution was 468 white (46.6%), 248 (24.7%) African American, 138 (13.7%) Hispanic, 76 (7.6%) Somali, and 75 (7.5%) Asian. A total of 216 respondents were Appalachian, 142 were white, rural, and 118 were white, urban.
Demographics.
As shown in Table 1, more women participated 637 (63.4%) than men, and the distribution was similar in each race/location group. The greatest proportion of respondents were in the 51- to 65-year age group (32.2%). Most of the participants were born in the United States (72.9%), whereas more of the Hispanic, Somali, and Asian participants were not born in the United States. Although a minority were never married (17.6%), there were significant differences by race—more white, rural respondents were ever married (94.4%) and more African Americans were never married (30.4%). The majority reported a religious affiliation (85.7%), with some variation. Household income was equally divided into thirds; however, there was variation, with Somalis reporting lowest incomes and Asians and all white groups reporting highest incomes. About 20% reported finding it difficult/very difficult to live on their income, with almost half of Hispanics reporting difficulty. Most participants reported Democratic political affiliation (45.5%), with variation—Somali and African Americans were predominately Democratic and rural and Appalachian predominately Republican. The average Perceived Discrimination Score was 7.1 with scores highest among African American (9.2) and white rural (8.9) samples.
Characteristics of Ohio CITIES participants, stratified by race and location (N = 1,005)
Community Identity was moderate (26.7 of 36 possible), with little variation. Neighborhood cohesion was high (18.9 of 25), also with little variation. Among rural respondents, rural identity was moderate (23.2 of 36). Neighborhood-level variables indicated most respondents were metropolitan based on both rural–urban continuum code (RUCC; 74.1%) and rural–urban commuting area (RUCA; 73.6%) designations. Lower SES was indicated for Appalachian respondents by county-level indicators (education, per capita income in past year, and percent of families in poverty).
Health characteristics and behaviors.
Overall, most respondents had health insurance (84.7%); however, 70.8% of Hispanics did not. A quarter of respondents did not seek health information anywhere, whereas the most popular source of health information was the internet (39.5%), with variation by group. Most of those aged 50 and older were within guidelines for colorectal cancer screening (70%) or breast cancer screening (76.2%). Unmet medical needs were few (average of 1.2 for total sample). The majority were never smokers (68.6%), with more current and former smokers in white subgroups. Most were in the overweight/obese body mass index (BMI) categories (74%), with some variation. Few respondents were in the ideal diet group (14%), with Somali (21.1%) and Asian (24.4%) groups reporting the highest percentages. Binge drinking was reported by 12.8%, with higher percentages among Appalachians (20.5%). Only 36.5% reported receiving three shots of the Hepatitis B vaccine (HBV), with variation from 15.2% (Somali) to 53.2% (Asian). Almost 70% had visited a dentist in the last year, with Hispanic (57.4%) and Somali (49.2%) groups reporting lower rates. Approximately 25% reported no weekly physical activity, with over 40% of Hispanics reporting no weekly physical activity. Only 11.7% reported any cardiac or vascular condition, with variation from 1.4% (Asian) to 17.6% (white rural).
Approximately 10% reported a personal history of cancer, with a range from 1.4% (Somali) to 14.4% (white urban). In contrast, 45.7% reported at least one first-degree relative (FDR) with a history of cancer, with a range from 0% (Somali) to 63.1% (white urban). There was little variation in perceived loneliness (4.4 mean, range 3–9). The three subscales of the COPE scale showed some variation; Asian respondents had the highest emotional support; Somali respondents had the highest behavioral disengagement; and Asian and African American respondents had the highest active coping. Medical Outcomes Study (MOS) quality of life scores were moderate (24.4 mean, range 6–30), with little variation. Finally, one-item scores (ranging from 1 to 10), which collected self-reported quality of life, overall health, anxiety, fatigue, and distress, only showed variation in anxiety and distress, both of which were more deleterious for Hispanics.
Cancer knowledge, attitudes, and beliefs.
Almost 40% agreed that cancer is most often caused by personal behavior/lifestyle, with Hispanic, Somali, and Asian respondents mostly agreeing. Most respondents perceived risk of developing cancer “about the same as others” (43.2%). Hispanic respondents were more likely to rate their risk as “more likely” than others (42.7%). In terms of knowledge of the correct age to begin mammography screening (age 50), 8.8% responded correctly, with variation, from 0% (Somali) to 16.9% (Appalachian). More (45.4%) respondents knew the correct age to begin colorectal cancer screening (age 50), with variation from 2.7% (Somali) to 71.9% (white rural). Most respondents agreed with the statements “When I think about cancer, I automatically think about death” (53.9%), “It seems like everything causes cancer” (58.6%), and “There are so many different recommendations about preventing cancer, it's hard to know which ones to follow” (68.8%). Only 23.3% agreed with the statement “There's not much you can do to lower your chances of getting cancer.”
Multivariable predictors of health characteristics and behaviors.
White participants
Among the white participants (Table 2), those who reported physical activity at least one day per week were more likely to be younger (P = 0.022), college educated (P = 0.03), living comfortably (P < 0.0001), and male (P = 0.02). Those reporting less education (P = 0.002) and finding it difficult to get by on their present income (P = 0.01) were less likely to have visited a dentist in the last year. Those up-to-date with HBV were younger (P < 0.0001) and had more education (P = 0.001). Males were more likely to be overweight or obese (P < 0.0001). Those reporting a poor diet were more likely to find it difficult to get by on their present income (P = 0.03). Ever smokers were more likely to be male (P = 0.008), less educated (P = 0.01), and report difficulty living on their present income (P = 0.006). No differences were observed in these health behaviors by white subgroup.
Multivariable logistic regression results for health behaviors and characteristics among white CITIES participants (N = 468)
Urban participants
Among the predominantly urban participants (Table 3), those who reported physical activity at least one day per week were more likely to be educated (P = 0.02) and living comfortably (P = 0.007), whereas Hispanic respondents were less likely to report such activity (P = 0.01). Those reporting less education (P = 0.01) and finding it difficult to get by on their present income (P = 0.06) were less likely to have visited a dentist in the last year. Those up to date with HBV were younger (P < 0.0001) and had more education (P < 0.0001). Younger respondents were less likely to be overweight or obese (P = 0.006) whereas Hispanic and African American respondents were more likely to be overweight or obese (P < 0.001). Those reporting a poor diet were more likely to have a high school education (P = 0.008). Ever smokers were more likely to be older (P = 0.01), less educated (P = 0.01), finding it difficult to live on their present income (P = 0.04), male (P = 0.0001), and white (P < 0.001).
Multivariable logistic regression results for health behaviors and characteristics among Somali, Asian, African American, and urban white CITIES participants (N = 647)
Multivariable predictors of health beliefs and attitudes.
White participants
Among white participants (Table 4), males were 1.48 times as likely to agree that cancer is caused by behavior/lifestyle (P = 0.06). Those with a high school education were 1.71 times as likely to agree that cancer is a death sentence compared with college graduates (P = 0.07). Knowledge of the correct age to start colorectal cancer screening was statistically different by age (P = 0.0002) with respondents aged 41 to 65 correctly reporting age 50 compared with other age groups. Those who agreed with the statement “everything causes cancer” were more likely to be under age 66 (P < 0.0001) and have less than a college degree (P = 0.007). Those with less than a college degree were more likely to agree that “there's not much you can do to prevent cancer” (P = 0.0006) and those living comfortably on their income were less likely to agree with the statement (P = 0.0007). Those with high school education or less were more likely to agree that there were too many recommendations for preventing cancer (OR = 1.85; 95% CI, 1.04–3.30). Those who were within guidelines for colorectal cancer screening tended to be older (P = 0.0003) and reported living comfortably (P = 0.04). Women living comfortably and getting by were more likely to be within guidelines for mammography (P = 0.01). No differences in any of these variables were noted by white subgroups.
Multivariable logistic regression results for cancer beliefs and attitudes among white CITIES participants, agree vs. disagree (N = 468)
Urban participants
Among the urban participants (Table 5), there was more variability in responses. Males were 1.5 times as likely to agree that cancer is caused by personal behavior/lifestyle (P = 0.03), and compared with white urban respondents, minority groups were more likely to agree with the statement (P < 0.0001) as were those with less than a high school education (P = 0.04). Hispanics (P = 0.02) and those with better financial security (P = 0.05) were less likely to agree that cancer is a death sentence. Knowledge of the correct age to start colorectal cancer screening was statistically different by age (P < 0.0001) with respondents aged 51 to 65 correctly reporting age 50 compared with other age groups; those with lower education (P = 0.02), reporting financial difficulty (P = 0.03) and all minority groups (P < 0.0001) were less likely to report the correct age. Those who agreed with the statement “everything causes cancer” were more likely to be under age 66 (P = 0.003) and African American (P < 0.0001). Those with less than a college degree were more likely to agree that “there's not much you can do to prevent cancer” (P < 0.0001) and those living comfortably were less likely to agree with the statement (P = 0.009). All minority groups were less likely to agree that “there were too many cancer prevention recommendations” (P = 0.0008). Those who were within guidelines for colorectal cancer screening tended to be older (P = 0.001), reported living comfortably (P = 0.06), and African American (P < 0.0001). Women living comfortably and getting by (P = 0.007) and African American women (P = 0.002) were more likely to be within guidelines for mammography.
Multivariable logistic regression results for cancer beliefs and attitudes among Somali, Asian, African American, and urban white CITIES participants, agree vs. disagree (N = 647)
Discussion
To effectively reduce the cancer burden in a cancer center catchment area, there needs to be a clear understanding of the population in terms of the underlying Social Determinants of Health (SDH; ref. 21) related to cancer prevention, early detection, and treatment which affect residents' ability to engage in cancer prevention behaviors and obtain care. Understanding the attitudes, knowledge, and behaviors of the individuals in the catchment area can assist in tailoring education, outreach, and research strategies to reduce the cancer burden.
This population health assessment reinforced the diversity of the OSUCCC catchment area and the different outreach, education, and research needs of each population group. Factors such as marital status, income, perceived financial difficulties, and barriers related to accessing cancer screening and care (22) varied by race. For example, our findings suggest that community engagement is a valid approach for outreach among Hispanics who need information about receiving cancer care, including access to free screening. Rural areas, including Appalachia, include mainly white residents, similar to rural areas in the Northern part of the United States (23) and have needs related to access, especially since these areas have greater poverty.
Perceived discrimination was high among African American and rural white respondents. The former has been well described (24); however, the high score in white rural populations is surprising. Social factors influence attitudes about prevention practices and about care and provider choice (25, 26). Similarly, how residents perceive their community is integral to social support and obtaining both emotional and tangible assistance (27). All groups reported high neighborhood cohesion, which speaks well to use of community events to promote prevention and screening (28, 29).
Although nationally the rates for reliance on the internet as a primary source of information are at 68.72%, respondents demonstrated a lower level of reliance on the internet and many subgroups demonstrated rates that were far below national averages (30). Given that the most frequent source of health information is the internet, there have been concerns raised in the literature about the quality of the information (31) it provides. Rather, it would seem that many respondents live without any access to health information, which may explain high levels of incorrect information found among respondents.
Our results suggest that although national norms are useful for policy, national statistics do not adequately reflect the diversity found in local service delivery. Fewer Hispanic (29%) respondents in Ohio reported having health insurance than what is found in U.S. measures. With the high prevalence of insurance (84.7%) and few unmet medical needs, most age 50 and older were within guidelines for colorectal cancer (70%) or breast cancer (76%) screening. These results are somewhat similar to those suggested by the Ohio BRFSS where 66.9% and 77.1% were within colorectal cancer and breast cancer screening guidelines, respectively (32). Reasons for any differences may be attributable to different sampling frames, as we purposefully sampled members of minority groups, and the Ohio BRFSS estimates prevalence for Ohio as a whole (32).
Other areas of concern for cancer prevention include smoking, overweight/obesity, physical activity, binge drinking, and HBV. While there were fewer current smokers, ever smokers (31%), who tended to be white, should be educated about lung cancer screening (33). Overweight/obesity was common in all groups, and diet quality and physical activity, were also problematic. Binge drinking was more common in the Appalachian population, which may also be associated with smoking and obesity (22, 34). Completion of all three HBV shots was low; however, one at-risk population, Asians, reported higher completion rates, possibly due to targeted vaccination (35).
Cancer knowledge, attitudes, and beliefs influence intention and behavior (36). Few respondents knew the correct age to start mammography screening, probably due to confusion over guidelines (37), whereas many more knew the correct age to being colorectal cancer screening. Attitudes about cancer were, for the most part, negative (e.g., many agreed that cancer is fatal and everything causes cancer). Few respondents had a history of cancer (10%); however, almost half reported cancer in a FDR. This represents opportunities for education about familial risk and tailored prevention and screening strategies, including education about accurate risk perception, prevention, early detection, and treatment efficacy (38).
Multivariable analyses revealed that among white populations in urban, rural, and Appalachian settings, efforts should focus on older, less educated, lower income males for education about cancer, and prevention/early detection behaviors such as smoking cessation, healthy diet, physical activity, healthy weight, and HBV vaccination. Historically, males have been more difficult to reach for intervention and education (39). The OSUCCC National Outreach Network Program's Men's Health Education Series has targeted Ohio Appalachian men to improve knowledge about prevention, screening, and clinical trials; however, efforts to recruit many men have not been overly successful, similar to other sites (39).
Certain subgroups of the urban sample reported worse health characteristics and behaviors. Hispanic and African American respondents were more likely to be overweight/obese, whereas whites were more likely to be ever smokers. In terms of cancer knowledge and beliefs, minority groups were more likely to agree that cancer is caused by behaviors, but less likely to know the correct age to start colorectal cancer screening. African American respondents were more likely to agree that everything causes cancer; however, they were more likely to be within screening guidelines. These latter results are possibly due to OSUCCC efforts in the African American community and churches to promote breast cancer screening through a mobile van and colorectal cancer screening through an interactive inflatable colon (40).
This cross-sectional survey provided important information about Ohio's populations, by subgroup, something not previously reported. Moreover, we used multiple survey techniques to boost recruitment of understudied populations. Validated questions and scales will permit collaborative analyses across many catchment area populations served by other Centers in this initiative. The survey followed a well-established theoretical framework, based on the SDH, and included factors known to impact health disparities, thus providing a grounded approach. There were several weaknesses, however. Our use of community recruitment for minority populations, while necessary to obtain sufficient sample sizes, may limit generalizability in target populations. Sample sizes for some groups (e.g., Somalis), were somewhat small, which limited the number of covariates in multivariable analyses. Several questions with more missing data (e.g., income) also limited analyses.
This assessment provides a snapshot of the needs of catchment area populations with special attention to opportunities for research, education, and outreach. This is especially relevant with the new mandate for NCI-Designated Cancer Centers to have a Community Education and Outreach Core (40). The OSUCCC Center for Cancer Health Equity (CCHE) coordinates both outreach and education efforts and network development in Ohio populations to facilitate research (32). The NCI's new focus on rural health and cancer control will also help expand research to further address needs of our catchment area population and complement efforts that occur in the OSU CCHE (32) and the OSU Center of Excellence in Research for Tobacco Science (41). Finally, many of our populations are ripe for implementation science strategies (e.g., to increase use of cancer screening using evidence-based interventions in federally qualified health centers), focusing both in the community and clinics that serve the most vulnerable populations. These efforts must be tested to determine the impact of cancer prevention and early detection strategies on disparate cancer incidence and mortality rates.
Disclosure of Potential Conflicts of Interest
Electra D. Paskett reports receiving commercial research funding from Merck Foundation and Foxconn and has ownership interest in Pfizer. No potential conflicts of interest were disclosed by the other authors.
Authors' Contributions
Conception and design: E.D. Paskett, B.M. Bernardo, C. Washington, C.R. DeGraffinreid, J.L. Fisher, T.R. Huerta
Development of methodology: E.D. Paskett, G.S. Young, B.M. Bernardo, J.L. Fisher, T.R. Huerta
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): E.D. Paskett, C. Washington, C.R. DeGraffinreid
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): E.D. Paskett, G.S. Young, B.M. Bernardo, J.L. Fisher
Writing, review, and/or revision of the manuscript: E.D. Paskett, G.S. Young, B.M. Bernardo, C. Washington, C.R. DeGraffinreid, J.L. Fisher, T.R. Huerta
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): E.D. Paskett, C. Washington, C.R. DeGraffinreid
Study supervision: E.D. Paskett, C. Washington, C.R. DeGraffinreid
Acknowledgments
This study was supported by a supplement to the NCI grant (P30 CA016058). The Behavioral Measurement Shared Resource at The Ohio State University Comprehensive Cancer Center, which also funded this study, is also funded by a grant from the NCI Grant (P30 CA016058) and the Ohio State University Center for Clinical and Translational Science CTSA grant UL1TR002733.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
- Received July 16, 2018.
- Revision received September 6, 2018.
- Accepted October 23, 2018.
- Published first October 30, 2018.
- ©2018 American Association for Cancer Research.
References
Volume 28, Issue 3
