Appendix 1. Sample Description
Survey Administration and Sample Construction
Survey participants in the YouGov/Polimetrix panel were drawn from a pool of individuals who had chosen to complete recruitment surveys on the internet about a variety of popular topics 1. The respondents who had completed a recruitment survey were then matched on select
demographics (age, race, sex, etc.) to a random sample drawn from the 2005-2007 US Census Bureau’s American Community Study (ACS) 2.
Because the sample of individuals who had completed a recruitment survey did not include all combinations of demographic characteristics found in the ACS, a set of sampling weights was constructed. Use of the sampling weights ensures that summary statistics and inferences are representative of the U.S. population.
The baseline wave was fielded on December 17th, 2007 and five subsequent waves were fielded in 2008. Each wave was in the field for approximately 2 weeks.
The four questions relating to medical errors were only asked in the March 21, 2008, wave and only put to panelists from Illinois, yielding 1,484 respondents.
Response Rates
Covariates With the exception of the respondent’s ZIP code, which was not used in the matching process, information on respondents’ demographic variables listed in Table 2 is complete because these variables were collected at baseline and used for matching respondents
to the ACS random sample. Item response rates were less than 100% for several of the respondents’ attitudinal markers (Table A.2).
Dependent Variables Item response rates for the four questions on medical errors fielded in
the March 21, 2008, wave were slightly larger than 70 percent (Table A.1.).
As we are interested in the associations among responses to the medical-error questions, we limited the sample to respondents who answered all four questions on medical error. As item response rates were very highly correlated, the final weighted sample represents a response rate of 69 percent, which is only three percentage points smaller than if we had retained all
respondents who answered at least one of the four questions about medical errors (72 percent).
Appendix 2. Robustness Checks
Respondents’ demographic characteristics (Table 2) and attitudinal markers (Table A.2) allow us to control for a number of potential confounders that might render spurious the associations among the medical-error questions. As we describe in detail below, to net out the influence of these variables on the response patterns among the medical-error questions, we fitted simple and ordered logistic regressions and used the estimates to compute predicted proportions for the modal respondent and to test for statistical significance of the associations among responses.
Literature Review
An extensive literature documents systematic correlates of patients to file medical malpractice claims. The patient’s sex 3-6, age 3, 7-11, level of educational attainment 3, 5, income and
employment status 3, 5, 8-12, populations density of residential location 3, 7-8, 12
, as well as availability of legal services 3-4, 6-8, 12
may all play influential roles in a patient’s propensity to sue. Additionally, there is some evidence that cultural shifts are reshaping patients’ acceptance of medical risk (accepting less) and faith in their provider’s agency ability 4 while simultaneously altering their perception of the contemporary legal climate 13. To account for this, we used attitudinal specific correlates (party affiliation, trust in government and scientists, views on malpractice caps, and scope of health care affiliates one is allowed to sue).
A survey involving a hypothetical decision-making scenario similar to this study found personal attitudes, perceptions of the contemporary legal climate, and demographic characteristics to all correlate with respondents’ preferred verdict and their propensity to favor punitive damages 13.
Description of the Modal Respondent
The largest group of respondents who shared all eight demographic characteristics in Table 2.
consisted of white married men between 50 and 55 years of age who had earned a postgraduate degree, were employed full-time, reported an annual household income over $100,000, and were living in a ZIP code classified as metropolitan. 14 respondents (seven weighted respondents) matched this profile.
Of these 14 respondents who shared identical demographic characteristics, two respondents (one weighted respondent) also gave identical answers to the seven questions measuring party
identification, trust, and attitudes towards medico-legal policies in Table A.2. 1 These two modal respondents identified themselves as Democrats, sometimes trusted the federal and Illinois state governments, completely or mostly trusted scientists, and were against damage caps in medical malpractice lawsuits but for the right of patients to sue their HMOs. They also professed to interact often with lawyers and accountants.
Estimating Equations
We fitted an ordered logistic regression to the following stylized equation:
disclosure_likelihoodi= α + β1 own_experiencei + β2 friends_or_familyi + β3 mediai + δ1 female
+ δ2age_19_39 + δ3age_40_49 + δ4age_56_64 + δ5 age_65plus + ∑δd demographic_characteristicd i+ εi, (1)
1 Two other groups also included two respondents each who were identical on all eight demographic characteristics and all seven attitudinal markers. For ease of comparison with Tables A.3.a and A.4.a, we report fitted values for the
where disclosure_likelihoodi is the ordered categorical dependent variable taking on integer values from one to four, which reflect the four possible levels of likelihood that respondent i's doctor would disclose medical errors. All variables on the right-hand side of equation (1) are binary. Specifically, for each possible answer to each question, including the respondent’s refusal to provide an answer, we constructed a binary variable indicating that answer. Thus, own_experiencei is one if respondent i stated that his or her knowledge of medical errors was based on “own experience” and zero otherwise. The other variables are coded analogously.
demographic_characteristicd i represents the binary variables that describe the remaining demographic characteristics in Table 2. (race, education, marital status, household income, employment status, and RUCA classification of ZIP code).
Whenever values were missing for a variable, we created an additional binary variable that is one if the value is missing and zero otherwise. For instance, we include the binary variable
party_identification_missingi, which is one when respondent i declined to identify as Democrat, Republican, or Independent and zero otherwise. This approach allowed us to include respondents with incomplete information on select variables. We could thus exploit the correlations provided by these respondents on variables for which their information was complete and yet still take proper account of the fact that they differ from other respondents in that they failed to answer select questions.
We chose “no knowledge about medical error” and the modal respondent’s demographic
characteristics (“male”, “age_50_55”, “white”, etc.) as the omitted categories. ε is an error term.
For each knowledge source, we used the estimate of equation (1) to predict the modal
respondent’s stated disclosure likelihood. We tested whether the coefficients of the three binary variables representing the respondent’s knowledge source, β1, β2, and β3, were jointly all zero.
Similarly, we fitted a simple logistic regression to the following stylized equation:
lawsuiti= α + β1 disclosure_very_likelyi + β2 somewhat_likely i + β3 not_very_likely i + δ1 female
+ δ2 age_19_39 + δ3 age_40_49 + δ4 age_56_64 + δ5 age_65plus
+ ∑δd demographic_characteristicd i+ εi, (2)
where lawsuiti is one if respondent i affirmed that he or she would file a medical malpractice lawsuit after a medical error disclosure and zero otherwise. The right-hand side variable
disclosure_very_likelyi is one if respondent i stated that his or her doctor would be very likely to disclose a medical error and zero otherwise. The other two binary variables somewhat_likely i
and not_very_likely i are defined analogously, and the other variables are defined as in equation (1). As before, whenever values were missing for a variable, we created an additional binary variable that is one if the value is missing and zero otherwise. We chose “disclosure not at all likely” and again the modal respondent’s demographic characteristics as the omitted categories.
We also estimated (2) by replacing the dependent variable lawsuiti with a binary variable indicating whether the respondent would continue to recommend the hospital to friends and family after error disclosure.
For each likelihood level of medical error disclosure, we used the estimate of equation (2) to predict the modal respondent’s propensity to file a lawsuit or to continue recommending the hospital. We tested whether each coefficient of the three binary variables representing the respondent’s stated disclosure likelihood, β1, β2, and β3, was different from zero and whether these three coefficients were jointly all zero.
In a second specification, we fitted an ordered or simple logistic regression to equation (1) and (2) respectively that in addition to respondents’ demographic characteristics also includes the sets of binary variables representing the respondents’ seven attitudinal markers.
Results
The covariate-adjusted proportions of respondents stating that their doctor would be “very likely”, “somewhat likely”, “not very likely”, or “not at all likely” to disclose a medical error to them were very similar to the unadjusted proportions (Tables A.3.a and A.3.b), both when the set of covariates was restricted to the demographic characteristics and when it was expanded to include the attitudinal markers. For neither set of covariates were we able to reject the hypothesis at the ten percent level that the predicted proportions varied by the respondents’ stated source of knowledge about medical errors.
The adjusted proportions of all respondents who said they would file a lawsuit are very similar to the unadjusted proportions, although the evidence of a gradient by respondents’ assessments of their doctor’s likelihood to disclose medical errors is weaker (Tables A.4.a and A.4.b).
When the sample was restricted to respondents with own experience of medical error, the average proportion of respondents who would file a lawsuit decreased, particularly among respondents who said their doctor was “not very likely”, “somewhat likely”, or “very likely” to disclose errors. While none of these respondents were significantly less likely to sue than respondents who said their doctor was “not at all likely” to disclose errors, we can reject at the five-percent level that these coefficients are jointly all zero.
The predicted proportions of respondents who would continue to recommend the hospital are very similar to the unadjusted proportions, regardless of the set of covariates included in the adjustment (Tables A.4.a and A.4.b). Respondents who estimated that their doctor would be
“very likely” to disclose medical errors were significantly more likely to recommend the hospital than respondents who had no faith in their provider’s commitment to disclose medical errors and we can reject at the one-percent level that these coefficients are jointly all zero.
When the sample was restricted to respondents with own experience of medical error, the predicted proportion who would recommend the provider was larger than the unadjusted proportion at all levels of disclosure likelihood. Although none of the respondent groups were significantly more likely to recommend their provider than respondents who said their doctor was “not at all likely” to disclose medical errors, we can reject that the coefficients of the binary variables measuring the four levels of disclosure likelihood are jointly equal to zero.
Additional Robustness Checks
To assess how sensitive the associations between respondents’ answers to the questions about medical errors are to our assumption that the distribution of the error term is logistic and to our
decision to compute the predictions for the modal respondent, we also estimated equation (2) by fitting a linear model using ordinary least squares (OLS) and computed the marginal effects at the mean values of the covariates (Tables A.5.a – A.5.d). For the full sample (all respondents), the estimated marginal effects of the various likelihood levels of disclosure are nearly identical when they are computed at the mean or via OLS. Although the estimates and their levels of statistical significance fluctuate more when the sample is restricted to respondents with own experience of medical error, the conclusions in the main text remain robust. Given the small size of the restricted sample, the estimates are least stable when the full set of covariates is included in the estimation.
