Medication Adherence In African Americans

2. Methodology:

Critically examine possible quantitative and qualitative approaches to examining medication adherence/compliance in African Americans and choose the ‘best’ research method (support your choice).

Denote the strengths and weaknesses of using multiple regression and logistic regression when analyzing data. When you have a dependent variable that can be continuous or dichotomous, choose which regression you would use and support your answer.

What are the ethical implications of examining perceived injustice, coping, and relationship with health care provider in African Americans?


Quantitative Approaches to Medication Adherence/Compliance in African Americans

The purpose of the research study conducted by Thomas (2007) was to examine the relationship of components of self-concept (body sensation, body image, self-consistency, self-idea, and moral-ethical-spiritual self) and cognitive perceptions with adherence to prescribed recommended health regimes (low sodium diet, regular aerobic activity, and prescribed medications) in individual with heart failure (HF).

This study used a descriptive correlational design was used to determine if relationships existed between predictor variables and medication regimes was a threat or challenge to self-concept and the outcome variable of adherence to prescribe health regime. Because of the some of the wording when describing the variables, clarity was problematic and re-reading was done several times to get an understanding of what the study was trying to measure. The research questions were written clearer than the description of the statistical analysis with the independent and dependent variables. A power analysis was conducted. The effect size of 0.13 was considered small and may not adequately measure the strength of the relationship between variables.

Institutional Review Board (IRB) approval was obtained. The convenience sample was identified by clinic staff from two different heart failure clinics that screened for inclusion and exclusion criteria, 97 out of 134 subjects met the criteria. There was no mention of how the principal investigator trained the data collector to ensure consistency.

No instruments were found that used Roy’s Self-concept Mode Theory to investigate adherence in individual with HF, so a demographic questionnaire and three research instruments were developed using expert assistance. One of the instruments, the HF screening test (10 items) was not pilot tested prior to use. The cognitive perception of cardiovascular healthy lifestyles (57 items) was pilot tested and considered reliable. The adherence questionnaire (6 items) had low reliability scores. Validity indexes were high for all three instruments. Descriptive correlational and predictive correlation analysis using Pearson product moment correlation and stepwise multiple regression was appropriate statistical test to answer the research questions. Of the two research questions, one addressed relationships and the other addressed explaining variances. SPSS was used for data analysis.

All data was collected during a regularly scheduled office visit by clinic staff. Verbal consent was obtained and implied consent was evidenced by completion of the questionnaires. Verbal and written instructions were given and confidentiality was explained. Subjects were given the questionnaires, pen, clipboard, and return envelope. Questionnaires were collected by the principal investigator, and kept in a locked, secure file until entered in the database. Returned questionnaires were 70 to 90 percent incomplete. No mechanism was developed to ensure that data collection was complete, such as taking the subjects to a quiet comfortable setting, thoroughly explaining the need that all blanks be filled, and visually scanning the questionnaires after completion. It seems as if subjects completed the questionnaire in an office waiting room that may have had frequent interruptions. No incentive was offered to subjects and this may have affected their commitment to the research study.

The most obvious challenges with this study included the instrumentation and collection of data. The reliability of the adherence questionnaire was low, so more testing and refinement may be needed. In addition, the procedure for data collection could have been improved to engage subjects more into the study, such as private area for testing and small incentives.

Next, is a 12-week intervention study (Resnick, et al., 2009) was conducted to test the People Reducing Risk and Improving Strength through Exercise, Diet, and Drug Adherence (PRAISEDD) in a group of elderly African Americans who were low income.

The sample resided in a senior housing site and were 65 years of age and older. IRB approval and inclusion and exclusion criteria was listed. Recruitment was completed in one meet-and-greet session at the housing site. The sample size was 22 out of 40 who had expressed interest in the study. The social ecological model was used to guide the study.

The PRAISEDD method was designed to motivate, educate, and exercise subjects toward the outcome of improved self-efficacy and adherence to cardiovascular disease preventive measures such as exercise, diet, and medication. The first of the 12 weeks was an education class on preventive measures and goal setting, with advanced practice nurses and a pharmacist teaching the material. Weeks 2-12 were focused on a one hour exercise class three days per week with blood pressure and weights, motivational activities, health promotion education, and review of daily goal activities. These activities were lead by an experienced lay exercise trainer and the research nurse. Treatment fidelity was monitored to ensure PRAISEDD interventions were delivered as planned.

A number of efficacy measurement tools were completed by the subjects: medication adherence self-efficacy scale (26 items), cardiac medication adherence outcome expectation scale (5 items), self-efficacy for health related diet (20 items), diet outcome expectations (1 item), self-efficacy for exercise (9 items), and outcome expectations for exercise (13 items). All instruments had established reliability and validity for older adults except the cardiac medication adherence outcome expectation scale, that was adapted from the osteoporosis scale and revised to address antihypertensives and lipid-lowering agents. Test-retest reliability and validity was established for this study. Instruments and baseline data was completed prior to the PRAISEDD intervention. This seemed to be a lot of instruments for this elderly population. There was no mention of the time involved to administer the instruments nor if frequent breaks were given.

Outcome behaviors included the Yale physical activity survey (questionnaire on physical activity), block brief food questionnaire (for recall of sodium and cholesterol intake), compliance questionnaire (self-report of medication taking), and blood pressure. All outcome measures except blood pressure were subjective and based on recall. Better objective outcome measures such as actigraphy (to monitor physical activity), pill counts (medication adherence), food diary (recall of food intake), and serum cholesterol and urine sodium (monitor food intake).

Data was analyzed using descriptive statistics and a repeated-measures analysis of variance to detect changes from baseline to 4-month follow-up. The statistical software package was not mentioned.

In summary, the study was well designed, but the outcome measures were a major weakness. Recruitment efforts should have been more aggressive than just one day. Objective outcome data would have made this a solid study. In addition, a control group may strengthened the study as well by providing comparison data. Although follow-up was provided a four months to post test the instruments, a periodic follow-up at weekly intervals may have helped to maintain the sustainability of health behavioral activities and goals.

Qualitative Approaches to Medication Adherence/Compliance in African Americans

In a study conducted by Ogedegbe, Harrison, Robbins, Mancuso, and Allegrante (2004), the perceived barriers and facilitators to medication adherence in African Americans with hypertension was explored. An open-ended individual interview was used to gather data on subjects in two primary care practices during the course of a year. Purposeful sampling was used to recruit the best participants who could provide the useful information about medication adherence for this study. IRB approval and inclusion and exclusion criteria were listed. No incentives were provided and confidentiality was ensured.

Subjects were identified from computerized medical records and appointment logs and approached during office visits or via telephone. Those who consented verbally were interviewed immediately, lasting about 20-45 minutes. Interviewing continued until saturation occurred. Four open-ended question guided the interview: (1) What difficulties so you have in taking your blood pressure medications as prescribed by your doctor; (2) What situations make it hard for you to take your blood pressure mediations as prescribed by your doctor; (3) What situations make it easy for you to take your blood pressure medications as prescribed by your doctor; and (4) What are the skills that make it necessary for you to take your blood pressure medications as prescribed? All interviews were tape recorded and transcribed. Medical records were used to determine if blood pressure was controlled at less than 140/90 mmHg and retrieve antihypertension medications and comorbidities.

Data analysis and collection occurred at the same time. Grounded theory methodology was used to analyze data whereby data of previous subjects was compared to that of new subjects. Transcripts were read over and over until recurring concepts to coding, to categories to themes to final coding to final categories. A qualitative software package, Ethnograph, was used to organize date and help with analysis. No mention was made of moving to the theoretical level in the research study.

The grounded theory approach was a good choice, considering the limited research in 2004 when this study was completed. One area of concern was the telephone interview, since the non-verbal responses were not captured. From the methodological perspective, the visualization of an emerging theory was not evident. The study seems more like phenomenology, the lived experience rather than grounded research.

In another study (Lukoschek, 2003), focus groups were used to explore different beliefs held by adherent and nonadherent subjects that affect treatment. Uninsured, Medicaid-insured, and lower socioeconomic class African Americans with hypertension who attended an outpatient medical clinic in a large urban setting were invited to participate in this study. Contacts were made during visits, via telephone, or mail. IRB approval and inclusion and exclusion criteria was listed.

A qualitative, comparative case design was used to categorized subjects as nonadherent, noncontrolled hypertensive, or as adherent, controlled hypertensive. Groups were in session for 90 minutes. All groups were audiotaped and tapes were transcribed. Sociodemographic variables were obtained and subjects received a $15 incentive for participation. Eight focus groups were obtained from 42 subjects.

African American research assistants who were trained in focus group methodology served as moderators along with an assistant who took care of logistics such as refreshments, tape recording, and took field notes. The moderator began each session with a story to foster nonjudgemental atmosphere. Groups were asked eight identical, open ended questions that evolved from the health belief model. Examples of questions included: What do you think is hypertension; Why do you think some people get hypertension; Is there anything a person could do to treat hypertension; and What do you think patients about the medication that doctors give to patients for their hypertension?

SPSS was used to compute statistical analysis sociodemographic data. A comparison of the three groups was completed with chi-square for categorical data and f-test for continuous data.

The principal investigator along with moderators and assistants analyzed all transcripts, independently for themes, and group discussion and agreement of categories and themes. Group work continued until themes were identified and agreed on by all and theoretical saturation occurred. The QRS NUD*IST software program was used for data analysis.

The ‘Best’ Research Method

For the purposed research study on medication adherence issues in African American women with hypertension, a quantitative research design will serve as the best methodological approach. From the review of the literature, two qualitative studies were found on the proposed topic. According to Munhall (2007), quantitative research has its origins in qualitative research. The descriptions, interpretations, and understandings from qualitative data, if appropriate, may become the focus of a quantitative study, and unexplained grey areas of statistical data may lead back to another qualitative study. Munhall (2007)describes this process as the qualitative-quantitative cyclical continuum.

A number of qualitative studies have been written on African Americans with hypertension (L. M. Lewis, Askie, Randleman, & Shelton-Dunston, 2010; Lukoschek, 2003; Ogedegbe, et al., 2004; Ogedegbe, Schoenthaler, & Fernandez, 2007; Peters, Aroian, & Flack, 2006; Wexler, Elton, Pleister, & Feldman, 2009). All of these studies mention antihypertension medication adherence in some form. As a result, the data has begun to repeat itself or saturate. Many of the themes are similar. In other words, we have a good idea of the problem, now it is time to determine relationships among variables and determine if certain combinations of variables predict adherence to the prescribed treatment regimen. Quantitative research will allow more sophisticated manipulation of data. When unexplained grey areas arise, we can resort back to qualitative research to gain more insight and the cyclical continuum may continue to evolve.

Strengths and Weaknesses of Using Multiple Regression

Multiple regression is concerned with relationships and prediction among variables. Multiple regression involves a single dependent variable (DV) and two or more independent variables (IV). The IV can be dichotomous or continuous and the DV is continuous (Huck, 2008; Tabachnick & Fidell, 2007). For example, a study might want to learn, what are the best predictors of an elevated blood pressure (greater than 140/90)? The dichotomous IV can have two values such as reactance/nonreactance or coping/not coping. A continuous IV such as age, or a continuous DV such as blood pressure can have infinite measures.

There are many strengths of multiple regression such as more than two IVs. These IVs can be combined to predict a value of the DV. For example, the relationship between a set of IVs such as coping, perceived racism, and trust in health care provider can be correlated with the DV blood pressure (Tabachnick & Fidell, 2007). When each of the IVs are strongly correlated with the DV, the regression is better (Polit, 1996; Tabachnick & Fidell, 2007). With multiple regression, IVs can be analyzed in different units of measure by converting the IVs to z scores, thus making standardized scores for all IVs. This allows all IVs to be measured on the same scale with a mean of zero and a standard deviation of one (Polit, 1996).

Multiple regression can be set up to accommodate covariates that researchers wish to control while looking at the impact of other IVs on the DV (Huck, 2008). In addition, regression modeling can be done whereby variables can be placed in or taken out of the model using methods such as simultaneous, hierarchical, or stepwise regression strategies (Polit, 1996).

Another strength is the ability to ensure accurate and reliable statistical results by calculating a power analysis. The sample size is dependent on the number of IVs, the desired power, alpha level, and expected effect size(Tabachnick & Fidell, 2007). In addition, when data is missing, it can be estimated from other variables and instead of the grand mean by using computer statistical packages such as SPSS (Tabachnick & Fidell, 2007).

There are many weaknesses of multiple regression, one of which is assumptions. The results of multiple regression are not trustworthy when assumptions are violated, resulting in Type I or Type II error. Knowledge of assumption violations and not reporting them leads to serious bias and the validity of data is questioned and difficult to interpret (Tabachnick & Fidell, 2007). Assumptions include independence, normality, linearity, and homoscedasticy. Of these assumptions, independence is robust to violation, if the violations are not too bad. Normality violations indicate skewed distributions representing increased variance in the form of outliers. Outliers are problematic, can affect the multiple regression analysis, and cause bias results, but they can also a means for further investigation to determine the reason they occurred (Huck, 2008). Nonlinearity is problematic because the residuals are not concentrated in the center along a straight line (Polit, 1996). The homoscedasticity assumption is violated when the variance is not constant (Polit, 1996). If assumptions are violated, transformations can be done to stabilize linearity and normality (Polit, 1996).

Another potential weakness is multicollinearity that occurs when two or more IVs are highly correlated to each other, and essentially measuring the same thing(Tabachnick & Fidell, 2007). An example would be two predictors of high blood pressure: (1) not taking blood pressure medications and (2) not taking blood pressure medications on most days. The medication taking behavior is redundant since the blood pressure medication is rarely to never taken.

In addition, if the sample size is too small, taking the time, energy, and resources to complete the research study is futile. If the sample is too large, results are vague and not useful (Tabachnick & Fidell, 2007). Also, missing data could be problematic. For example, if study participants refuse to answer sensitive demographic data such as income, which may be related to another variable such as reactance, then if the participant’s data is deleted, it could distort the sample’s values on the reactance variable (Tabachnick & Fidell, 2007).

Misinterpretations of data analysis are seen as a weakness, not of the statistic, but with the researcher. With multiple regression, only relationships can be determined and there is never an underlying cause and effect mechanism (Huck, 2008). For example, there may be a strong relationship between lower income and blood pressure measurements > 140/90, but we cannot conclude that lower income causes an elevated blood pressure. The most likely explanation of the correlation may be medication nonadherence related to an inability to afford the costs of medications (these variables need to be included in the study to determine their relationship to high blood pressure). The higher the blood pressure, two or more drug categories may be needed to manage the blood pressure. Lastly, statistical data may have statistical significance but have little to no clinical significance (Huck, 2008). With an increased emphasis on evidence-based research, studies that yield practical clinical significance are held in high regard.

Strengths and Weaknesses of Using Logistic regression

Like multiple regression, logistic regression is also concerned prediction, trying to predict whether something will or will not happen. Logistic regression involves a single DV and one or more IVs. The DV is dichotomous, for example, a study might want to learn, whether or not a person will adhere to their blood pressure medication regime. The IVs can be categorical or continuous. A categorical IV has no numerical meaning such as gender, neighborhood, or types of comorbidities and an example of continuous IVs may be blood pressure, height, or weight.

Logistic regression has several advantages, one of which includes less restrictive assumptions than multiple regression. For instance, logistic regression does not assume multivariate normality (Polit, 1996). In addition, IVs do not have to be linearly related or have equal variances within each group. Another strength, like multiple regression is that logistic regression can be done using methods such as simultaneous, hierarchical, or stepwise regression strategies (Tabachnick & Fidell, 2007). In addition, the DV is calculated into the probability that an event will happen using an odds ratio, that transforms the probability of an event occurring into two probabilities, occurring or not occurring. An example is the probability of developing cardiovascular disease or the probability of adhering to hypertension medications. Overall, logistic regression is a more flexible analysis than multiple regression.

Logistical regression has several disadvantages, one of which is that too few cases to the number of IVs predicts outcomes poorly. Therefore, a power analysis will needed to determine sample size. In addition, logistic regression can be costly especially when the number of IVs is large. Also, with multiple regression, there is a sensitivity to high correlations among IVs and this could result in multicollinearity whereby redundant variables need to be deleted. Lastly, sensitivity to outliers is problematic as with multiple regression.

Which Regression for Continuous or Dichotomous Dependent Variable

In summary, when a DV is continuous, multiple regression is used and when a DV is dichotomous, logistic regression is used. Multiple regression allows the measurement of variables that are continuous or interval, representing a numerical value such as age, income, and blood pressure. On the other hand, logistical regression allows the measurement of variables that are categorical or nominal, representing two possible levels such as gender (male or female), religious (yes or no), and neighborhood (urban or rural). It is possible for continuous variable such as age or income to be transformed into dichotomous variables, for example age can be displayed as < 65 years of age or > 65 years of age, and income can be displayed as < $30,000 or >$30,000.

For the proposed research study on “Issues Influencing Mediation Adherence among African American Women with Hypertension?, logistic regression will be chosen to analyze data resulting from the proposed aims and associated research questions listed:

Examine the relationships of demographic characteristics, perceived injustice, relationship with health care provider, medication knowledge, and coping to medication adherence in African American women with hypertension.

Q1. Do the demographic characteristics (age, education, income, etc.), perceived injustice, relationship with health care provider, medication knowledge, and coping predict medication adherence in African American women with hypertension?

Q2. When controlling for demographic characteristics, do perceived injustice, relationship with health care provider, medication knowledge, and coping predict medication adherence in African American women with hypertension?

Explore the association between antihypertensive medication adherence and reactant behaviors in African American women.

Q3. Is there a relationship between medication adherence and reactant behaviors in African American women with hypertension?

The research question drives the method and in this instance, the IVs are categorical (education, perceived injustice, relationship with health care provider, medication knowledge, and coping) or continuous (age, income) and the DV is dichotomous (medication adherence; adhere or not adhere). The intent of the research questions are to determine which IVs are predictors of whether or not medication adherence will occur and to determine if the odds ratio for these IVs indicate the likelihood of whether or not medication adherence will occur.

Ethical Implications of Perceived Injustice in African Americans

Justice implies equality, being just, treating people fairly and equally (Hall, 1996). Many African Americans in the U. S. have experienced perceived injustices in the health care system and been victims of disparate health care.

Research findings confirmed the perceptions of injustices and discrimination experienced by many African Americans in various health care situations. An extensive study ("Unequal treatment: What healthcare providers need to know about racial and ethnic disparities in healthcare," 2002) revealed that minorities are less likely than Caucasians to receive needed services, procedures, and routine treatments for common health problems and diseases such as cancer, cardiovascular disease, and diabetes.

For example, cardiac care is one area of health care that consistently demonstrates disparity in health care access and delivery. When compared to Caucasians with similar clinical manifestations for cardiovascular disease, African Americans are less likely to receive pharmacological therapy (e.g. thrombolytic therapy), diagnostic angiography, heart transplantation, cardiac catheterization and invasive surgical treatments (e.g. coronary bypass surgery) even when treatments and procedures are judged to be appropriate.

Although disparate health care is evident, another factor is the strong link between finances and access to cardiac care (Mayberry, Mili, & Ofili, 2002). A sad but true revelation in healthcare as well as other facets of life is that injustice is pervasive among the poor and needy. African Americans have the highest poverty rate in the U.S. at 24.7% as compared to 8.6% of Caucasians (Income, poverty and health insurance coverage in the United States: 2008, 2009, September). However, this does not explain the injustices to people of color who have the ability to pay for health care (Kennedy, Mathis, & Woods, 2007).

Coping with perceived injustices may contribute to illness. A cross-sectional comparative study on discrimination and hypertension in older African Americans and Caucasian adults provided support that perceived discrimination was associated with higher diastolic BP. Authors concluded that discrimination may cause adverse effects on BP levels in people of African American descent (T. T. Lewis, et al., 2009). In another cross-sectional study of diverse middle aged women (African-American, Hispanic, White, Japanese, and Chinese women) examined the association between perceived unfair treatment and hypertension. African American women reported the highest levels of perceived unfair treatment followed by Chinese women. However, results did not indicate a positive correlation between perceived unfair treatment and elevated blood pressure (Brown, Matthews, Bromberger, & Chang, 2006).

Although the results of these studies show conflicting results, the realities of prolonged exposure to perceived injustices may contribute to illness. The issues surrounding hypertension are complex with multiple causes for this complex health disparity that results in disproportionate mortality rates. According to Fiscella and Holt (2008) the elimination of racial disparities in African Americans with hypertension will substantially decrease the number of deaths from cardiovascular disease. Better control of hypertension among African Americans can be obtained with adequate resources to discover and address treatment adherence (Fiscella & Holt, 2008; Fongwa, Evangelista, & Doering, 2006). However, it is imperative that current resources are equitable, and clients are treated fairly and given quality health care without regard to racial or ethnic status (Clark, 2009).

Ethical Implications of Coping in African Americans

Non-maleficence is the duty to do no harm or duty not to harm others (Hall, 1996). In the health care arena, harm seems inherent for those with a lower socioeconomic status, especially when healthcare services are not readily available when illnesses occur. Being unable to get proper medical care may create undue harm by overtaxing an individual’s coping skills with multiple fears and anxiety because of increased healthcare costs and decreased access to care.

Historical evidence has shown that socioeconomic status is a strong predictor of health outcomes with poverty as the leading cause of avoidable morbidity and mortality (Bierman & Dunn, 2006). James (1996) noted an inverse correlation between socioeconomic status and health; those with lower socioeconomic status are more likely to experience illness and premature death than those with higher socioeconomic status, thus adversely affecting African Americans, and other minority/ethnic groups.

One hypothesis according to James (1996) that may be a possible explanation for how socioeconomic status increases the susceptibility of African Americans and other minority/ethnic groups to increased morbidity and mortality is derived from the legend of John Henry. Based on this folktale, there was a contest between John Henry and a machine; he defeated the machine and suffered mental and physical exhaustion resulting in death. According to James (1996), John Henry symbolizes the relentless struggles of unskilled laborers in their effort to cope with psychosocial, economic, and environmental stressors that eventually erodes their health over time contributing to increased morbidity and mortality.

Thus, harm is evident when differences in access to treatment play a role in why morbidity and mortality rates for some diseases are higher among African Americans than among Caucasians (Stover, 2002). African Americans and other minority/ethnic groups as compared to Caucasians have poorer access to health care services as evidenced by less annual visits to a health care provider, lower use of preventive services, an increased likelihood of not having a primary health care provider, and the likelihood of being uninsured (Mayberry, et al., 2002).

Greater ethnic/racial disparities are found among the uninsured and Medicaid populations than those who are privately insured with indications that financial factors outweigh race/ethnicity when considering access to medical care (Mayberry, et al., 2002). Williams (2009, November) purports that a lower socioeconomic status predicts everything in society from the cradle to the grave, including SAT scores, income, jobs, housing, health, and health insurance coverage. Williams (2009, November) states that socioeconomic status is stronger than genetics and a lower socioeconomic status is greatly impacted when race and racism becomes part of the equation. However, the difference in health disparity between lower versus higher socioeconomic status is bigger than the health disparity between African Americans and Caucasians (Williams, 2009, November). According to Williams (2009, November), the issue is not one of availability of health care, but one of care that is accessible (office hours, distance, transportation, affordability, risk of job loss verses doctor appointment, etc.).

In a research study conducted by Kumar, Schlundt and Wallston et al, (2009), a telephone survey of 2001 community based participants revealed that race concordance was not a significant predictor of health care quality. Findings did show that other factors such as higher income, higher education, and health insurance were predictors of better health care quality. In addition, socioeconomic status and access to quality health care were more important factors in achieving health status and health satisfaction. This study provides evidence that access to health care is primarily determined by socioeconomic status and daunting assumption that inaccessible health care may result in undue harm.

Ethical Implications of Relationship with Health Care Provider in African Americans

Respect for persons has two meanings: the (1) client should be treated as an autonomous agent, able to make their own choices, and (2) client who is unable to be autonomous has the right to be protected. It is important that the client and health care provider establish a working relationship built on mutual trust (Clark, 2009). In relationships, trust is vital, especially since the primary matter is adherence to the treatment regime. For many African Americans, health care disparities have hindered the establishment of a trusting client-provider relationship (L. M. Lewis & Ogedegbe, 2008).

African Americans differ from other racial/ethnic groups, such as Caucasians and Hispanics, because of their history of slavery, oppression, and discrimination. Because of skin color and other distinctive features such as hair texture, thick lips, body shape, the lives of African Americans was not valued. They were frequently used in medical experiments by Caucasian doctors to perfect their technique before attempting procedures on the Caucasian race. These types of exploitations by Caucasian physicians endured a long history (Gamble, 1997). As a result, perceived stereotypes and prejudices experienced by African Americans in the health care arena have resulted in mistrust, refusal of treatment, and/or poor adherence with treatment regimes by African Americans ("Unequal treatment: What healthcare providers need to know about racial and ethnic disparities in healthcare," 2002).

A study conducted by Benkert, Peters, Clark, and Keves-Foster (2006) found that the majority of low-income, urban dwelling African Americans were fairly trusting of their healthcare providers and satisfied with the health care given, although negative effects of perceived racism on trust and satisfaction were evident. This study confirms that African Americans do experience an element of trust is in their health care providers, but skepticism continues.

According to Benkert, Hollie, Nordstrom, Wickson and Bins-Emerick (2009), nurse practitioners have better trusting relationships with African American clients. Study participants with higher trust and satisfaction were in concordant client-provider relationships. Further study findings revealed that African American men reported less satisfaction with care provided by nurse practitioners and were more suspicious of the health care system. Reasons for lack of trust in this study was not investigated. However, if the men in the study perceived that their treatment was different from Caucasian clients, then that may contribute to mistrust (Clark, 2009).

Disparate health care among African Americans and other racial/ethnic groups is well documented ("Unequal treatment: What healthcare providers need to know about racial and ethnic disparities in healthcare," 2002). Because remnants of African American history continue to exist in subtle configurations, many approach health care with fear, skepticism, and caution (Gamble, 1997). Therefore, it is important that health care providers, along with African American clients, devise mechanisms to transcend the effects of history, restore trust in the health system, and overcome barriers to forming relationships to foster optimal health care.