Do People Care What s Done with Their Biobanked Samples?

by Tom Tomlinson, Stan A. Kaplowitz, and Meghan Faulkner Do People Care What s Done with Their Biobanked Samples? Table 1. Positive Projects Researchers have found a chemical in blood that can predict the start of rheumatoid arthritis three years before symptoms begin. This finding could lead to new treatments or even prevention of rheumatoid arthritis. Researchers have found a substance within melanoma cancer cells that makes them grow and spread. Melanoma is the deadliest form of skin cancer. Researchers can now work toward targeting this specific substance in order to kill the cancer cells. Researchers have found a substance present in the spinal fluid of individuals with Alzheimer s disease. This discovery may lead to earlier diagnoses and a treatment for this disease. continued IRB: Ethics & Human Research July-August 2014 1

Table 2. Descriptions of Problematic Projects Brief Description Expanded Description Adds Cystic Fibrosis Researchers have developed tests for genetic But some fear that the results of such factors causing cystic fibrosis so that tests might lead to abortion of some affected fetuses can be detected early in fetuses with cystic fibrosis. pregnancy. Cystic fibrosis is one of the most widespread genetic life-shortening diseases. Even with current treatments, most Americans with this disease are expected to die before reaching 40 years of age. In addition, the disease typically causes considerable discomfort and pain throughout a patient s life. Information about these genetic factors might lead to new methods of treatment. Diabetes Researchers funded in part by health But some fear that insurance companies insurance companies have developed tests might use this test to determine for predicting diabetes. whether to offer or refuse insurance to people at higher risk of diabetes This information could help focus prevention or to require them to pay a higher on those at greatest risk. premium. Mental Illness Researchers have extracted information But some fear that screening for such from tissue samples to determine genetic factors might become widespread and that factors associated with mental illness. there will be many people who would have legal access to these medical records. If The results of such research might lead to so, many people might be labeled at risk new methods of treatment. of mental illness or have increased fear of becoming mentally ill even though they would never become mentally ill. Table 3. True or False Comprehension Questions before Presentation of the Projects (with Correct Answers) A. Biobank research may help improve ability to prevent disease. (T) B. A biobank stores human tissue for use in medical treatment. (F) C. Tissues or medical information stored in a biobank are not directly linked to a person s name or other identifying information. (T) D. When someone donates his or her tissue to a biobank, it is for use only in a single research project. (F) E. When researchers want to use tissue someone previously donated to a biobank, they will not usually ask the donor for consent. (T) 2 July-August 2014 IRB: Ethics & Human Research

Table 4. Mean Willingness to Donate by Project, by Description Type, and by Measurement of Dependent Variable 1 Dichotomous Measure 3 Nondichotomous Measure 4 Brief (N = 323) Expanded (N = 330) Brief (N = 323) Expanded (N = 330) Positive Projects 2.941a.930a.564a, b.516a Cystic Fibrosis.938a.858c.571a,b.440c Diabetes.907b.864c.531b.374d Mental Illness.935a.864c.599a.406c, d 1. Means in the same column that have no common letters and are one letter apart in alphabetical order are significantly different at p <.05. Means in the same column that are two letters apart are significantly different at p <.01 and sometimes at p <.001. 2. Since the positive projects were always presented the same way and before the problematic projects, any differences between their mean responses in Brief versus Expanded conditions is a result of chance sampling. However, to compare the positive projects with the problematic ones properly, it was necessary that we report the results for the positive projects separately by description. 3. For the Dichotomous measure, the entry in each cell is the proportion willing to donate after hearing that their sample might be used for a project like that. 4. The nondichotomous measure was originally on a scale from -7 to + 7. It was first subject to a linear transformation that divided the initial value by 7, thereby restricting its range from -1 to + 1. Taking the 8th power and then multiplying initially negative values by -1 to preserve the sign further transformed this value. This transformation greatly magnifies departures from being completely certain about donating (which would equal 1.0). Table 5. Mean Certainty of Donation (Standard Error of the Mean) (N) among Those Willing to Donate, by Problematic Project and Version of Project Descriptions 1 Description of Project Brief Expanded Cystic Fibrosis 6.35a 6.25a (.05) (.06) (304) (283) Diabetes 6.29a 5.97b (.06) (.07) (302) (285) Mental Illness 6.39a 6.04b (.05) (.07) (302) (285) 1. Means with different subscripts are significant at p <.001. This is true both when using the raw certainty variables, as shown in the table, and when using the nonlinear transformation. IRB: Ethics & Human Research July-August 2014 3

Appendix A Causal Model of Donation Decisions (Table 6 and Figure 1) We used structural equation modeling (SEM) to test and estimate a causal model predicting attitudes towards donating tissue samples. To build a parsimonious model, we used the analyses described above to eliminate variables whose effects on Trust or Willingness to Donate were nonsignificant or very small. Exploratory analysis showed that the Willingness to Donate variables associated with of each the Problematic Projects were highly correlated with each other and with Willingness to Donate after hearing of the Positive Projects. Hence, all of these variables were viewed as indicators of a latent variable that we call General Disposition to Donate. Each of these Willingness variables also had a substantial correlation with Trust in Medical Research. In our SEM, we used the transformed indicators of Trust in Medical Research and Willingness to Donate described in the main text. Specific Assumptions in The Model The exogenous variables (those predicted by no other variables in the model) are the sociodemographic variables and the experimental variable, Description Type (Brief or Expanded). These variables are assumed to have direct effects on a set of intervening variables. These intervening variables are attitudes and beliefs that we regard as causally prior to a General Disposition to Donate tissue samples. We assume that the intervening variable Trust in Medical Research influences the General Disposition to Donate. This General Disposition, in turn, is assumed to influence the responses to the Positive Projects and to each of the Problematic Projects. We assume responses to each of the Problematic Projects are also influenced by Description Type (Expanded = 1; Brief = 0). In addition, the variables 1) Accepting Abortion and 2) the Accepting Abortion x Expanded interaction are assumed to influence the Willingness to Donate after hearing of the Cystic Fibrosis project. We also assume that Description Type affects Trust in Medical Research. This is because (1) those who received the Expanded descriptions may become less trusting as a result and (2) Trust was measured at the end of the survey. But we assume that Description Type has no effect on Willingness to Donate after hearing of the Positive Projects, as these projects had no separate Brief versus Expanded description and were presented before the Problematic Projects. SEM permits estimation of models in which a set of observed variables are regarded as indicators of other latent (i.e., unobserved) variables. Thus, Trust in Medical Research is indicated by two observed variables: (1) How beneficial or harmful do you think that medical research is to human health and well-being? and (2) How much do you trust medical researchers to protect the rights and well-being of their research subjects? The latent variable, General Disposition to Donate, has four indicators. These are the Willingness to Donate after learning of the Positive Projects and the analogous variables after learning of each of the three Problematic Projects. Responses to the Problematic Projects were also influenced by other variables. (See Figure 1 for the model and standardized coefficients.) Model Fit The model presented has chi-sq (35) = 76.2, p <.001. However, the appropriate measures of whether the model has a good fit are independent of sample size. The CFI =.988 and RMSEA =.042, both of which are considered indicators of very good fit. 1 Coefficients The model and standardized coefficient estimates are presented in Figure1. 4 July-August 2014 IRB: Ethics & Human Research

Table 6. Variance Explained in Trust in Medical Research and Willingness to Donate Variables Estimate Trust in Medical Research.033 General Disposition to Donate.409 Positive Project Willingness.567 Mental Illness Willingness.732 Diabetes Willingness.647 Cystic Fibrosis Willingness.634 Figure 1. IRB: Ethics & Human Research July-August 2014 5

The model explains 40.9% of the variance in General Disposition to Donate. All of this is explained by Trust in Medical Research. For the specific projects, the explained variance in Willingness to Donate ranges from 56.4% for the Positive Projects to 75.0% for Mental Illness. An Expanded description modestly but significantly (ps range from <.001 to.008, one tailed) reduced respondents Willingness to Donate after learning of each of the Problematic Projects. In addition, the highly significant (p <.001) coefficients of Accept Abortion and of its interaction with Expanded Description confirm our earlier findings a) that those who were more accepting of abortion were more willing to donate when they knew that their tissue sample might be used for the Cystic Fibrosis project and b) that this effect was stronger with the Expanded description. While Trust in Medical Research is a major predictor of General Disposition to Donate, we were able to explain only 3.4% of its variance. Seeing the Expanded version caused respondents to be modestly but significantly (p <.001) less trustful. Blacks were slightly less trustful, and this result was significant at p <.05 (one tailed). Education past high school has no direct effect on Willingness to Donate. It has, however, a small indirect effect since it is associated with less Acceptance of Abortion, which, in turn, is associated with less Willingness to Donate after learning of the Cystic Fibrosis project. Being black has no direct effect on Willingness to Donate. But as a result of its effect on Trust in Medical Research, it has a small (.04 standardized), indirect effect on Willingness to Donate after the individual hears about each of the three projects. An Expanded description has both a direct effect on the Willingness to Donate associated with each Problematic Project and at least one indirect path affecting each of those variables. For each Problematic Project, the indirect path has the following segments: from (1) Expanded description to (2) Trust in Medical Research to (3) General Disposition to Donate to (4) Willingness to Donate for that project. For each project, this path contributes a standardized indirect effect of approximately -.08, (= [-.156][.639][.8]), where.8 is the approximate value of each path from General Disposition to Donate to Willingness to Donate after hearing of the specific project. This is added to the negative direct effects shown in Figure 1 when calculating the standardized total effect of an Expanded description. Legends Figure 1. Final Causal Model (without Error Terms) and Standardized Coefficients. Correlations between Expanded and the other exogenous variables (black and education past high school) both have absolute values less than 0.05. All path coefficients are significant at p <.05, one tailed. All paths exceeding 0.1 are significant at p <.01, one tailed, and most paths have p <.001. References 1. Browne MW, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long SJ. Testing Structural Equation Models. Newbury Park, CA: Sage; 1993. p. 136-162; Hu LT, Bentler PM. Cut-off criteria in fit indices in covariance structure analysis: Conventional criteria vs. new alternatives. Structural Equation Modeling1999;6:1-55. 6 July-August 2014 IRB: Ethics & Human Research