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. Author manuscript; available in PMC: 2011 Feb 17.
Published in final edited form as: Ethn Dis. 2010 Summer;20(3):267–275.

Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS): Overcoming barriers to implementing a longitudinal, epidemiologic, urban study of health, race, and socioeconomic status

Michele K Evans 1, James M Lepkowski 1, Neil R Powe 1, Thomas LaVeist 1, Marie Fanelli Kuczmarski 1, Alan B Zonderman 1
PMCID: PMC3040595  NIHMSID: NIHMS270428  PMID: 20828101

Abstract

Objective

Examine the influences of race, socioeconomic status, sex, and age on barriers to participation in a study of cross-sectional differences and longitudinal changes in health-related outcomes.

Methods

We designed a multidisciplinary, community-based, prospective longitudinal epidemiologic study among socioeconomically diverse African Americans and whites. The study protocol facilitated our ability to recruit 3722 participants from Baltimore, MD with mean age 47.7 (range 30–64) years, % males/female, 2200 African Americans (59%) and 1522 whites (41%); 41% reported household incomes below the 125% poverty delimiter.

Results

There were no significant age differences associated with sex or race. Participants below the 125% poverty delimiter were slightly younger than those above the delimiter. Age, race, and sex, but not poverty status, were associated with the likelihood of an examination. Older participants, women, and whites were more likely to complete their examinations. Among those who completed their examinations, there were no age differences associated with sex and poverty status, but African Americans were negligibly younger than whites.

Conclusions

Although some literature suggests that minorities and low-income people are less willing to participate in clinical research, these baseline data suggest that African Americans individuals and individuals from households with incomes below 125% of the poverty level are at least as willing to participate in observational clinical studies as whites and higher income individuals of similar age and sex.

Keywords: Healthcare disparities, socioeconomic status, Population groups, Epidemiologic research design, Health surveys, Longitudinal studies

As long ago as 1985, the Task Force on Black and Minority Health1 reported that racial and ethnic minorities were underrepresented in health research. The report noted that the consequence of this underrepresentation was significant gaps in knowledge about the health of racial and ethnic minority populations and their responses to interventions.

More recent studies show that in some instances minorities enroll and participate in observational clinical research at rates comparable to non-minorities2. However, it is also evident that significant barriers to participation exist for minorities and other population subgroups3, 4. The challenges of recruiting both minority participants and those from diverse socioeconomic status (SES) backgrounds, regardless of race have not been thoroughly examined. Unfortunately the failure to consistently evaluate the inclusion of minority and socioeconomically diverse research participants has hampered efforts in clinical research to address disparate health outcomes.

Health disparities are marked differences or inequalities in health measures such as morbidity or mortality between two or more population groups based on race or ethnicity, gender, education, socioeconomic status (SES), or other criteria.5. There are disparities for overall life expectancy as well as for specific chronic and acute diseases. Recent work by Murray and colleagues examined mortality disparities across races and counties in the United States.6 This work defined eight subgroups of the US population based on a number of sociodemographic and geographic variables and showed there were significant disparities in mortality among these subgroups. These disparities were most pronounced for urban dwelling African American men who experienced a 20.7-year life expectancy gap compared to Asian women who had the best overall survival.

The challenge in investigating health disparities is to design and implement studies that recruit and retain racially and socioeconomically diverse cohorts.7, 8 However, systematic reviews of the threats to the validity of clinical trials could not identify clearly all of the barriers to participation in clinical research.9 In comparison with ethnicity, there is little literature about participation in clinical research associated with SES. Anecdotal reports, suggest that it is more difficult to recruit participants among low socioeconomic strata, particularly among minorities. There is little empirical evidence on this issue, but the evidence available indicates that this widely held belief is incorrect. In fact, lower SES minorities may be the most willing, but lower socioeconomic whites may be the least willing to participate in clinical research.10

With this background, we developed the Healthy Aging in Neighborhoods of Disparities across the Life Span (HANDLS) study to investigate whether race and SES influence health status and age-related health disparities separately or synergistically as co-factors of behavioral, psychosocial, and environmental conditions. The scientific objectives of HANDLS are to establish a single-site, prospective longitudinal epidemiologic study of health disparities in socioeconomically diverse African Americans and whites residing in the city of Baltimore. Specifically, we designed HANDLS to disentangle the effects of race and SES on risk factors for morbidity and mortality, to examine the incidence and progression of pre-clinical disease, and to assess the development and persistence of health disparities, longitudinal health status, and health risks. This report summarizes the recruitment strategies developed specifically for this study that met our baseline accrual goals.

Methods

Study design

HANDLS is a prospective population-based longitudinal study. Our baseline is representative of working-age African Americans and whites between 30–64 years old recruited as a fixed cohort of participants by household screenings from an area probability sample of twelve neighborhoods (contiguous census tracts) in Baltimore City and one dress rehearsal neighborhood. Power analyses for longitudinal analyses after twenty years of follow-up with repeated assessments every three years specify at least 80% power for a minimum sample size of 30 participants per cell defined by race (African American, white), socioeconomic status (self-reported household income based on 125% of the 2004 Health and Human Services Poverty Guidelines), age (seven 5-year age groups 30–64 years old), and sex. We identified neighborhoods that were likely to yield representative distributions of Baltimore City with sufficient individuals to fill the sampling design based on 2000 census data. Neighborhoods in Baltimore City are generally well-defined combinations of contiguous census tracts.

Study planning and execution

A central objective of HANDLS is to examine the effect of SES on health in urban-dwelling African Americans and whites. Consequently, it was crucial to develop a community-based presence in neighborhoods that historically do not participate in clinical research. In doing so, we hoped to eliminate participation barriers related to traveling to a central examination site at a major medical center or related to a mistrust of physicians or health care institutions. We also believed that establishing a community-based presence would increase overall recruitment because participation in clinical research has declined even in higher SES and majority population groups.11

Community-based research platform: Medical Research Vehicles

The most effective way to establish a community-based research platform in different neighborhoods of the city was to follow the National Health and Nutrition Examination Survey (NHANES) model of mobile examination centers. We designed two trucks to serve as mobile examination centers after consulting with NHANES staff.

We designed a flexible space in which participants would feel safe and comfortable that we could use to administer tests and examinations central to ongoing research in aging. MRV 1 is a 53-foot customized semi-trailer with an examination room and blood donor station, a cardiovascular fitness and physical performance testing area, and a bone density and vascular studies testing area. MRV 2 is a 40-foot customized self-propelled truck with three interview rooms for cognitive and neuropsychological testing, psychosocial and other questionnaires and inventories, and psychophysiological.

Cultural competency in clinical research

The changing demographics of the United States population makes it imperative for all health-related professionals to provide culturally competent care. Failure to understand the principles of cultural competence and the failure to modify care patterns accordingly have had substantial detrimental effects on health outcomes for individuals and for communities.12 Many believe that the development of cultural competence or cultural proficiency is a crucial strategy for the amelioration or elimination of racial and ethnic health disparities health outcomes as well as health care.13, 14 The majority of the literature on cultural proficiency and cultural competency is focused on health care systems, health care access, health care professionals, and on the quality of care delivered.14, 15 However, understanding the cultural context of an individual and a community is equally important or perhaps even more important for the ethical and successful conduct of clinical research. Therefore, the HANDLS study principal investigators developed a cultural competence-training course that is mandatory for all HANDLS-related research staff. Cultural competency is not a static technical skill or proficiency. We regard cultural competence as a key element in researchers’ toolkits for understanding experiences and values that differ from their own. An anthropological interpretation of this core competency is in keeping with work that emphasizes that culture is not homogeneous or static even among individuals with a similar ethnic background.16

The introductory sessions of the curriculum consist of three thematic units taught in three 4-hour sessions. The goals of this portion curriculum are to: (1) Explain the scientific rationale for including underserved populations and minority groups in clinical research and the changing diversity dynamics nationwide; (2) Define, describe, and explain the need for cultural competence and sensitivity among community-based health care professionals and clinical researchers; (3) Introduce the concepts of ethnic and social class diversity, and specific facets of African American culture, white culture, and poverty and its effects and diversity within the U.S. African American population; (4) Provide the historical contexts in which minorities and low SES medically underserved individuals view healthcare services and biomedical research; (5) Help researchers avoid cultural generalization and introduce researchers to cross-cultural communication techniques; (6) Discuss how ethnocentrism, prejudice, anxiety, assumptions, and stereotyping influence interpersonal relationships with persons from a culture other than one’s own; and (7) Explain the dynamics of healthcare delivery in medically underserved, minority, or socioeconomically disadvantaged communities, and how they may influence rates of research participation in those communities.

Pilot studies

We conducted two pilot studies. In the first, from October 2000 through December 2001, we assessed the feasibility of a community-based study using a mobile medical research vehicle. The first goal tested the logistics for conducting clinical research using these tools. The second goal was to test whether we could recruit sufficient numbers of volunteers and collect meaningful data in such a setting. The protocol for this pilot included a medical and physical examination, clinical laboratory measures, carotid Doppler, bone densitometry, psychophysiology assessment, and cognitive evaluation. We finished the first pilot after examining 442 volunteers. Participants in this sample of convenience ranged in age from 18–92 (median age 47), and were 99% African American with a median household income of $7,764; 44% were men and 56% women. Although the first pilot was successful in recruiting low SES African Americans, it was clear we needed to develop and test re-contact and participant retention strategies because we were planning a longitudinal study. Therefore, we conducted a second logistic pilot from February 2003 through November 2003 to evaluate re-contact strategies for this convenience sample. Without any particular re-contact strategy, we successfully re-examined approximately 66% of the original cohort. Some notable findings from the pilot were increased frequency of depressive symptoms; premature increases in intimal medial thickness in the carotid artery; altered frequency of genetic polymorphisms implicated in cardiovascular disease;17 decreased muscle strength; altered blood pressure and heart rate variability responses to stress and delays in cardiovascular recovery among African Americans;18, 19 significant association between symptoms of depression and cardiovascular reactivity; differences in emotion recognition between African Americans and whites;20 and, the invariant factor structure of the Center for Epidemiologic Studies Depression Scale (CES-D) using confirmatory factor analysis suggesting the equivalency of the CES-D scale in samples with differential demographic characteristics including race and SES.21

Study protocol

The HANDLS study collects baseline data in two separate phases, household recruitment and interview, followed by examination on our Medical Research Vehicles. Detailed descriptions of the procedures are described in the Appendix.

Results

Recruitment and participant accrual

We accrued 3,722 participants (Table 1): 2,200 African Americans (59%) and 1,522 whites (41%), 1,536 (41%) with household incomes below 125% of the poverty level and 2,186 (59%) above the poverty level. The distribution by race, sex, poverty status, and 5-year age strata shows that we accrued approximately equal numbers of participants in each race by sex group except for whites with household incomes below 125% of the poverty level. Of those with household incomes below the 125% poverty limit, 32% were white and 68% were African American. Of those above the 125% poverty delimiter, 47% were white and 53% were African American. The mean age of participants was 47.7 years. There were no significant age differences associated with sex or race. Participants below the 125% poverty delimiter were slightly younger than those above the delimiter (47.3 v. 48.0 years; F[1,3719]=5.37, p<.05).

Table 1.

Sample accrual and ages (N = 3722), and medical examination accrual (N = 2802) by race, 125% poverty level, and sex.

≤125% Poverty
 >125% Poverty
 Overall
Black
 White
 Black
 White
Sample accrual n (% total)
 Women 588 (16) 295 (8) 614 (16) 539 (15) 2036 (55)
 Men 455 (12) 198 (5) 543 (15) 490 (13) 1686 (45)
 Overall 1043 (28) 493 (13) 1157 (31) 1030 (28) 3722
Age Mean (SD)
 Women 47.2 (9.2) 47.6 (9.3) 48.7 (9.6) 47.7 (9.5) 47.8 (9.4)
 Men 47.2 (9.0) 47.6 (9.4) 47.6 (9.3) 48.0 (9.4) 47.6 (9.2)
 Overall 47.2 (9.1) 47.6 (9.4) 48.2 (9.5) 47.9 (9.4) 47.7 (9.3)
Medical examination accrual n (% total exams)
 Women 446 (16) 236 (8) 466 (17) 432 (15) 1580 (56)
 Men 317 (11) 143 (5) 388 (14) 374 (13) 1122 (44)
 Overall 763 (27) 379 (14) 854 (30) 806 (29) 2802
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Medical examination accrual

2,802 (75%) participants completed their baseline examinations (Table 1). Among those who did not complete their examinations, 765 participants (83%) failed to show up for their appointments despite repeated attempts to reschedule their examinations and 156 participants (17%) were unable to complete their examinations due to insufficient time, misunderstood the time commitment, time conflicts, inability to complete the examination protocol, uncooperative attitudes, or the presence of newly diagnosed, acute or uncontrolled chronic medical conditions such as poorly controlled hypertension requiring immediate medical intervention. Age, race, and sex, but not poverty status, were associated with the likelihood of an examination. Older participants (aged 48–64; OR 1.29, 95% CI = 1.11–1.50), women (OR = 1.32, 95% CI = 1.14, 1.53), and whites (OR = 1.26, 95% CI = 1.08–1.47) were more likely to complete their examinations. Among those who completed their examinations (Table 1), there were no age differences associated with sex and poverty status, but African Americans were negligibly younger than whites (48.0 v. 48.1; F[1,2798] = 5.8, p<.05).

Discussion

Using a two-stage procedure for recruiting participants, we accrued a baseline sample for our longitudinal study of the effects of race and SES on health disparities. Although final sampling weights were not available, our area probability sample matched closely the demographics of the neighborhoods from which we recruited participants. Overall, the demographics of Baltimore City from the 2000 U.S. Census identified 32% of the population as white and 64% as African American. The U.S. Census Labor Force and Employment Data (2000) for Baltimore City report that the median household income is $30,654 with 21% of city residents living below the poverty line as determined by the U.S. Census Bureau. In 2003 dollars, the Health and Human Services (HHS) poverty level was $18,400 for a family of four. Our 125% poverty cut-off identifying the lower SES segment of the study is $23,000, $7,654 below the median income level of city residents. Baltimore is similar to other mid-sized U.S. urban areas. It has a population with a low median income and a moderately high percentage of residents at or below the poverty line. Contrary to stereotypes about minority participation in clinical research,22, 23 we were most successful in recruiting low SES African Americans who are highly prevalent in the city. We were less successful in recruiting low SES whites because they are far less prevalent among whites residing within the city limits.

The sample distribution suggests that the stereotype about the difficulty of recruiting African American participants is untrue in this circumstance. In fact, it appears as though it is more difficult to recruit higher SES participants than participants with lower SES. Our experience recruiting a biracial socioeconomically diverse urban sample appears to duplicate the recruitment results of a structured sample of convenience from a suburban and rural area10 as well as another recent study conducted in an urban area.7 Although the imbalance in our study between numbers of African Americans and whites is proportional to their presence in Baltimore City, it appears as though higher SES families are less willing than low socioeconomic families to participate in our research. This means that we have expended more effort in recruiting higher socioeconomic participants. A cluster of factors may explain our accrual. Higher socioeconomic individuals have less need to participate in clinical research because they probably already have health coverage, and remuneration is proportionately a smaller motivation for higher socioeconomic individuals. Individuals who are employed full-time and are likely to be higher socioeconomic status probably have less time available for study participation, even though weekend appointments were available. We await the results of our first follow-up examination to determine whether there is differential attrition by SES.

Our response rates for screening and the interview are somewhat lower than expected but similar to the response rate reported for the Jackson Heart Study (JHS), a study of cardiovascular disease in African Americans in the metropolitan areas of Jackson Mississippi.24 Though their sampling design and sampling frame are quite different from HANDLS, only 53% of those contacted completed the initial interview and 46% of those contacted completed the clinic exam.25 JHS is an apt comparison for HANDLS because both have a sequential three-stage recruitment procedure (enumeration of selected households, at home interview of eligible subjects, and a clinical examination at a different site) and both are multidisciplinary with multiple measures. The HANDLS response rates of 52% for completed household interviews and 72%for completed baseline MRVs examinations are quite similar to those for the JHS. Atherosclerosis Risk in Communities (ARIC), a prospective study of clinical atherosclerotic disease in four U.S. communities, is also comparable to HANDLS.26 The ARIC study response rate for the home interview was 75% and the response rate for the clinic examination was 60%.27 Subset analysis of the African American population of ARIC shows a response rate for the home interview of 71% for men and 72% for women; however, response rates for the clinic visit were significantly lower at 42% and 49% for African American men and women respectively.

National and international trends in overall response rates show significant declines in participation rates over the past 10 years.28 Survey non-response is a particularly important for HANDLS because it threatens the sampling validity when the reasons for non-participation correlate with survey measures. In a population-based cardiovascular study, non-respondents were more likely to be cigarette smokers and have more cardiovascular disease, but did not have different rates of hypertension, dietary habits, or drug therapy for hyperlipidemia.29 Other studies have suggested that non-responders have poorer health, less education, and higher mortality rates than responders. Recently, a Danish cohort study found that non-responders had higher mortality rates as well as higher rates of hospitalization, lower SES, and worse overall health.30 ARIC found that white male non-responders reported poorer general health, had lower SES, higher hospitalization rates, and were more likely to be current smokers.27 A higher percentage of white non-respondents (14%) reported histories of myocardial infarction, diabetes mellitus, and stroke compared to 10% for white responders. ARIC African American respondents and non-respondents were not significantly different in general health status or recent hospitalization rates. Surprisingly, African American male non-responders were less likely to report hypertension than African American male responders, and African American responders (male and female) were slightly more likely to report a history of myocardial infarction, stroke or diabetes mellitus. Overall, in ARIC, the differences between responders and non-responders were significantly different for the whites not for African Americans.

Recognizing the importance of addressing non-response prospectively, we examined potential causes of non-response and devise strategies to ameliorate it. Anecdotal reports from field interviewers suggest that the factors driving non-response include availability of time to participate in the study, the time burden of the study, lack of weekend appointment days, lack of paid time-off, childcare responsibilities, and elder care responsibilities. In response, we revised our procedures to accommodate changes in schedules, decreasing participant time burden, and changing recruitment procedures. We changed the schedule by holding examination sessions on weekends and evenings, and by overbooking to account for no-shows and medically unfit participants. We changed study procedures by shortening the exam, and by dividing the complete examination into two half-day sessions. We also increased compensation after obtaining IRB approval. We changed recruitment procedures by initiating incentives for field interviewers and by adding a refusal conversion specialist to the field interviewer team.

We use two procedures to examine sources of bias introduced by non-response. First, we compare our accrual demographics to the demographics of Baltimore City and the demographics of the neighborhoods in which recruit participants. Second, we ask eligible individuals who refuse participation to complete the SF-12, a brief assessment of health status and an instrument that we also administer to participants as part of the household interview.

The present results have limitations. The present sample may be biased towards poorer health, greater exposure to poor health behaviors, and greater susceptibility to symptoms of depression. Although the participants examined are demographically representative of their neighborhoods, their interest in participating in this study may reflect their concern about their health risks or their poor health. Sampling weights were not available in these analyses.

The HANDLS study and our methodological approaches to barriers to participation is an attempt to address one of the most pressing problems confronting health disparities research. It is well documented that the failure to consistently evaluate the inclusion of minority and socioeconomically diverse research participants has hampered efforts in clinical research to address disparate health outcomes and conduct successful translational research.3, 11, 31 Including minorities and low SES individuals in non-interventional studies is particularly difficult given the lack of immediate tangible benefits to participants whose motivation is reduced as a result.32, 33 Given the crises in our health care system, it is incumbent upon all clinical researches to redevelop their toolkits to include populations at highest risk for disparate health outcomes.

Supplementary Material

appendix

Acknowledgments

We acknowledge the visionary support and guidance from Dr. Dan Longo and we thank Mr. Donald Bortner for his indispensable assistance in designing and procuring our Medical Research vehicles. We recognize support from the NIH Office of the Director, which provided start-up funds. We also recognize the consistent support of the then NIH Office on Minority Health and the current National Center on Minority Health and Health Disparities. The study also was supported through the Office of Behavioral and Social Sciences Research. We thank LifeLine Technologies of Cincinnati who fabricated the medical research vehicles and who have provided conscientious and skilled support since their deployment. We thank all members of the HANDLS staff past and present for their dedication to the goals and objectives of the study and their excellent execution of the protocol.

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Supplementary Materials

appendix
NIHMS270428-supplement-appendix.doc (154KB, doc)

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