factors that influence personal perceptions of the risk of an acute myocardial infarction

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Factors That Influence Personal Perceptions of the Risk ofan Acute Myocardial InfarctionHendrika Meischke PhD, MPH , Deborah E. Sellers PhD , Deborah E. Sellers PhD , David C. Goff MD,PhD , Mohamud R. Daya MD , Angela Meshack DrPH , Judy Taylor PhD , Jane Zapka ScD & MaryMcDonald Hand MSPH, RNPublished online: 25 Mar 2010.

To cite this article: Hendrika Meischke PhD, MPH , Deborah E. Sellers PhD , Deborah E. Sellers PhD , David C. Goff MD, PhD , MohamudR. Daya MD , Angela Meshack DrPH , Judy Taylor PhD , Jane Zapka ScD & Mary McDonald Hand MSPH, RN (2000) Factors That InfluencePersonal Perceptions of the Risk of an Acute Myocardial Infarction, Behavioral Medicine, 26:1, 4-13, DOI: 10.1080/08964280009595748

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Factors That Influence Personal Perceptions of the Risk of an Acute Myocardial Infarction

Hendrika Meischke, PhD, MPH; Deborah E. Sellers, PhD; Mark L. Robbins, PhD; David C. Goff, MD, PhD; Mohamud R. Daya, MD; Angela Meshack, DrPH; Judy Taylor, PhD;

Jane Zapka, ScD; Mary McDonald Hand; MSPH, RN

Personal risk perceptions of acute myocardial infarction (AMI) affect people's preventive health behaviors as well as their beliefs during a heart attack episode. The authors investigated factors that are associated with personal risk perceptions of having an AMI. A random-digit-dial survey was conducted among 1294 respondents, aged 18 years or older; in 20 communities across the nation as part of the Rapid Early Action for Coronary Treatment (REACT) trial. Results of two mixed-model linear regression analyses suggested that worse perceived general health, more risk factors, and greater knowledge were associated with greater perception of AMI risk. The results also showed that women who answered, incorrectly, that heart disease is not the most common cause of death for women in the United States reported signiJicantly lower risk perceptions than women who answered this question correctly. The findings in this study suggest that interventions need to target speciJic misconceptions regarding AM1 risk. Index Terms: acute myocardial infarction, misconceptions, risk perceptions

Although a great deal of information is available about pri- mary prevention of heart disease and great strides have been made in treatment efficacy, acute myocardial infarction

Dr Meischke is an associate professor in the Department of Health Services, University of Washington, Seattle; Dr Sellers is a senior scientist, Education Development Center, Newton, Massa- chusetts: Dr Robbins is an assistant research professor, Cancer Prevention Research Center, University of Rhode Island, Kingston: Dr Goff is an associate professo< Public Health Sci- ences and Internal Medicine, Bowman Gray School of Medicine, Winston-Salem, North Carolina: Dr Daya is an associate professor of emergency medicine, Oregon Health Sciences University, Portland; Dr Meshack is a project director, University of Texas Health Sciences Center, School of Public Health, Houston; Dr Taylor is the Director of Health Education Graduate Studies, Mis- sissippi University for Women, Columbus; Dr Zapka is a professor with the University of Massachusetts, Department of Medicine, Division of Preventive and Behavioral Medicine, Worcester; and Ms Hand is a coordinator, National Heart Attack Alert Program of the National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland.

(AMI) remains the leading cause of death in the United States, accounting for more than 500 OOO deaths annually.' Interventions aimed at lifestyle changes, such as smoking cessation, blood cholesterol-lowering dietary changes, and regular exercise are recommended to reduce the chance of having a first or recurrent heart However, about one quarter of the US population currently smokes, an estimated 61 million American adults are 20% or more above their desirable weight, and about one fourth of the American adult population report no leisure time physical activity at all.z

Even after the onset of a heart attack, patients can affect the outcome of the event by responding in a prompt and appropriate manner. Reperfusion therapy can reduce AM1 morbidity and mortality, but its efficacy decreases as the time between symptom onset and treatment increase^!^^ Regrettably, many patients wait hours or even days before seeking care, thereby reducing their chances for recovery.6

Perceptions of personal risk for a disease appear to be an important factor in many preventive health behavior^.'.^

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Perceived risk has been studied in light of many theoretical frameworks, most notably the health belief model (HBM). The HBM suggests that an individual is likely to take a recommended health action if (a) the person perceives himself or herself vulnerable to getting the disease, (b) the person perceives that getting the disease as serious, and (c) the benefits of a recommended health action outweigh the bar- riers to this action.x In a comprehensive review of HBM research, Janz and Becker’ noted that perceived vulnerability to a disease was a strong predictor of preventive health behaviors, such as getting flu shots, preventive screening, and preventive care visits. In an additional review on HBM and cardiovascular risk-factor-reduction behaviors, Janz9 found that perceived vulnerability was a significant con- tributor in slightly half of the studies that assessed these behaviors. Janz attributed the relatively low power for perceived risk as attributable to the inherent difficulty in measuring this dimension after diagnosis. In addition, Janz found that perceived vulnerability was not very predictive of smoking cessation. Because smoking is an addictive and habitual behavior, perceived risk may not be quite as strong a predictor as for other risk-reducing behaviors, such as screening for hypertension or acting quickly in response to AM1 symptoms.

Perceived risk of cardiovascular disease (CVD) has been positively related to the desire to make risk-reducing behavior changes as well as to actual behavior change.’*.” Per- ceived risk has also been related to people’s assessment of a situation and their subsequent response to a cardiac emergency. A low perceived personal risk for having a heart attack was one of the reasons why AM1 patients reported disbelief that the symptoms they experienced during their first heart attack were cardiac in origin.I2 The belief that symptoms are cardiac in origin has been related to quicker care seeking after symptom onset.I3 In general, perceived risk has been related to utilization of health services.l4-I6 Thus, personal risk perceptions of an AM1 may influence both preventive health behaviors to reduce the risk of ever having a heart attack as well as beliefs about the nature of the situation and subsequent responses during a cardiac emergency (ie, heart attack episode). The literature suggests several factors, including perceptions of general health,17 awareness of risk factor^,^'.^^ awareness of AM1 in one’s social environment, knowledge of general disease risk, and demographic variables, such as age, education, and gender,I9qZ0 that have been related to increased perceived risk for certain diseases, including heart attack.

Our purpose in making this study was to investigate the factors that are associated with personal risk perceptions of having an AMI. Specifically, we analyzed how perceived

AM1 risk was related to people’s perceptions regarding their (a) general health, (b) risk-factor status, (c) awareness of AM1 in their social environment, (d) knowledge about general AM1 risk, and (e) demographic variables. We hypothe- sized that worse general health, presence of a history of an AMI, greater awareness of other self-reported risk factors, greater awareness of AM1 in one’s social environment, misconceptions regarding general AM1 risk, male gender, lower education level, and older age were positively related to perceived risk of AMI.

METHOD Sample

Data for this study come from participants in a random- digit-dial survey conducted as part of the REACT (Rapid Early Action for Coronary Treatment) trial, a multicenter community trial designed to test the effects of a community education program to reduce prehospital delay among persons experiencing heart attack symptoms. The design and rationale of this randomized trial have been described in detail elsewhere.21.22 In brief, 20 communities were included in a matched design yielding 10 pairs of communities in 5 regions throughout the United States. Communi- ties were chosen, in part, to provide geographic diversity and to reflect the racial and ethnic composition of the US population. The REACT communities are in Alabama, Louisiana, Massachusetts, Minnesota, North Dakota, Ore- gon, South Dakota, Texas, Washington, and Wisconsin.

A baseline random-digit-dial telephone survey was conducted prior to educational activities in all 20 communities to assess the knowledge, attitudes, and beliefs of community members as well as their level of exposure to programs promoting a message similar to REACT. The survey was designed to collect data from 60 adults, 18 years of age or older, in each of the 20 communities, for a total sample of 1200. Each community’s designated geographical target area was defined by a specified set of zip codes. Telephone exchanges and a count of the households with listed telephone numbers in each zip code were obtained from a com- mercial vendor. Counts of households with listed telephone numbers were supplemented with estimates of the number of households with unlisted phone numbers. Eligible telephone exchanges were divided into five strata based on the proportion of households (listed and unlisted). The overall household rate was increased by sampling disproportionate- ly from the strata with larger proportions of households. Within the household, the adult 18 years of age or older who had most recently celebrated a birthday was selected as the r e~ponden t .~~ To adjust for the complex sampling design, survey responses were weighted by the reciprocal of

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MYOCARDIAL INFARCTION

the probability of selection. Sampling and interviewing were both completed by the REACT coordinating center. All telephone interviews for this study were conducted by REACT’S coordinating center, according to a set study pro- tocol. We obtained human subject approval from each of the 5 participating academic centers.

A total of 5603 random telephone numbers was generat- ed for this survey. The dispositions of these numbers were as follows: 1584 were nonworking numbers; 635 resulted in no contact after 5 calls (their household status was not deter- mined); 150 were not screened for eligibility as a result of refusal or language other than Spanish or English; 944 were not households; 55 were households with no one contacted after at least 15 calls; 520 resulted in refusals; 285 were ineligible because of zip code or illness; 136 interviews were not completed; and 1294 resulted in completed interviews. Overall, 36.9% of the numbers were for zip code-eligible households. The overall interview rate (completed interviews divided by potentially eligible households) was 64.5%. The range across the 20 study communities was 48% to 77%. Participation rates were generally higher in the northern communities, intermediate in Alabama, and lowest in Texas and Louisiana.

Measures Perceived risk of AM1 was measured by the question:

“Compared to other [insert Women or Men depending on their gender] your age, how likely do you think it is that you could have a heart attack in the next five years? Would that be much less likely, somewhat less likely, about the same, somewhat more likely, or much more likely than other [insert Women or Men depending on their gender] your age?’ This variable was coded from 1 to 5 with 1 indicating much less likely to have a heart attack in the nextjve years than other men or women my age and 5 indicating much more likely to have a heart attack in the next five years than other men or women my age.

General health was measured by the question: “In general, would you say your health is: Excellent, Very good, Good, Fair, Poor?’ This variable was coded from 1 to 5 with 1 indicating excellent and 5 indicating poor health.

Personal history of AM1 was measured by asking participants two questions: “Have you ever had a heart attack?’ and “Have you ever been told by a doctor that you have a heart condition?’ An affirmative response to one or both of these questions was coded as a “yes.”

Family history of AM1 was measured by asking participants: “Have your spouse, your parents, or a brother or sis- ter ever had a heart attack?’

Medical risk factors were measured by asking participants

several (yesho) questions: “Have you ever been told by a doctor that you have diabetes?’ and “Have you ever been told by a doctor that you have high blood pressure?’ “Have you ever been told by a doctor that you have high blood cholesterol?’ Persons who reported smoking a cigarette in the past week were considered to be current smokers.

Awareness of AM1 in social environment consisted of two questions. “Have any of your other relatives or close friends ever had a heart attack?’ Exposure to media messages or other information sources around AM1 was measured by responses to the question “Thinking back now over the past month, what kinds of messages about health do you recall in the media or from other sources such as people you talk with?’ Respondents could cite as many as five health messages. Any respondent who cited a message pertaining to heart disease, heart problems, heart attacks, heart attack symptoms, or getting rapid medical care for heart attacks was considered to have received a message pertaining to AMI. If responses to this question did not include any “heart-related” information, the respondent was considered not to have received such information.

Knowledge of general AM1 risk was measured by asking participants two truelfalse questions: “Heart disease is the most common cause of death in women in the United States (“true”) and “Almost all heart attacks occur in people over the age of 65 (“false”).

Sociodemographic characteristics included age, gender, race/ethnicity, and educational attainment.

Analysis The mean level of perceived risk for an AM1 was exam-

ined within groups defined by general health, risk-factor status, awareness of AM1 in the social environment, knowledge of general AM1 risk, and sociodemographic characteristics. The data were analyzed using SAS. Two mixed-model linear regression analyses were performed to examine the independent association between perceived risk of a heart attack, general health, risk-factor status, AM1 in one’s social environment, knowledge about general AM1 risk, and sociodemographic characteristics. In these mixed models, community was a random effect nested within community pair and region. Pair was a random effect nested within region. General health, risk factors, awareness of AM1 in social environment, and knowledge of general AM1 risk were fixed effects. Several interactions were included as fixed effects as well. These interaction included interactions between knowledge of general risk and demographic factors such as age and gender, interactions between risk factors and general health, as well as interactions between risk factors and age. in the first model, each of the six risk factors,

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two measures of awareness of AM1 in the social environment, and two measures of knowledge about general AM1 risk were entered in the model as separate indicators. In the second model, a risk-factor index, awareness of AM1 in social environment index and knowledge of general risk index were included in the model (rather than the individual variables). The extent of risk factors was measured by counting the number of risk factors that respondents reported (the risk-factor index). The extent of AM1 in one’s social environment was captured with two indicator variables.

One indicator variable was coded yes if the respondent had a positive response to just one of the two measures of social influences; the other indicator variable was coded yes if the respondent had a positive response to both of the measures of social environment. (the social-environment index). Similarly, knowledge of general AM1 risk was captured by two indicator variables, one of which was coded as yes if the respondent answered one of the knowledge questions correctly and one of which was coded yes if both knowledge questions were answered correctly (the knowledge index). Using two indicator variables to operationalize these indices facilitated explicit assessment of the difference between having one versus two AM1 events in one’s social environment and between correctly answering one versus both of the knowledge questions. In both models, general health was included as a continuous measure.

RESULTS Demographic characteristics of the survey respondents

were as follows: Age distribution: 22% were 5 5 years and older, 40% were between 35 and 54, 36% were between 18 and 34, and 2% did not respond to this question. Fifty-seven percent of the sample were female, 42% male, and 1 % did not respond. The ethnic breakdown was as follows: White (75%), Hispanic ( 1 1.2%), African American (8.2%), Asian Pacific (2.4%), Native American (.6%), missing information (.4%). The majority of respondents had some college education (46.5%) or a college degree (16%); 37% reported high school or less, and 1.2% refused to respond or questions were unanswered. Responses to the additional questions are shown in Table 1. The majority of respondents (75%) rated their AM1 risk as same or less likely than other people their age, and rated their general health as good or better (83%). Almost half of all respondents reported either a personal or a family history of AMI. More than half reported having a friend who had had a heart attack. Although three quarters of the respondents answered the question regarding age and AM1 risk correctly, only one third of the sample answered the question regarding gender and AM1 risk correctly. The degree to which selected char-

TABLE 1 Frequency Distributions for Survey Questions on

Factors That Influence People’s Perceptions of Person- al Risk of Acute Myocardial Infarction (AMI)

Variable

Perceived risk of AM1 Much less likely Somewhat likely Same Somewhat more likely Much more likely Refuseddon’t know

General health Excellent Very good Good Fair Poor Don‘t know/missing

AM1 in social environment Friend with heart attack AM1 messages in environment

Personal history AM1 or heart

Family history of heart attack Diabetes High blood pressure High cholesterol Current smoker

Womedcorrect answer Over 65korrect answer

Risk factors

condition

Knowledge of general AM1 risk

Frequency %

24.2 21.1 30.2 14.5 7.2 2.8

18.2 33.8 31.1 13.4 2.5 1 .o

59.8 20.5

12.8 33.3 4.7

21.4 20.9 24.1

31.1 74.5

Total N -

312 273 393 188 93 35

236 437 402 I73 33 13

774 265

166 430 61

277 270 31 I

402 964

acteristics of the survey sample approximated characteristics of the general US population was assessed as part of another REACT study by comparing observed distributions of sociodemographic characteristics and medical attributes with published data on US population samples.24 Sociode- rnographic characteristics of the survey respondents and the prevalence of self-reported risk factors for coronary heart disease suggest that the lowest socioeconomic group is underrepresented in this sample relative to the total US population, as would be expected for a random-digit-dialed telephone survey. The prevalence of a personal history of AM1 was also slightly higher in the study sample ( 1 3%) than in the US population (7.3%).

The data in Table 2 summarize the analyses that included the six risk-factor measures, the two measures of AM1 in one’s social environment, the two measures of knowledge

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of general AM1 risk, interactions between knowledge and demographic characteristics (ie, age, gender), interactions between age and risk factors, as well as general health and demographic characteristics. Data in Table 3 report the analyses in which risk factors, AM1 in social environment, and knowledge were summarized as indices. For this analysis, the overall level of knowledge was of greater interest than knowledge regarding specific questions. Interactions between knowledge about specific items and demographic variables were not included. Both the unadjusted as well as the adjusted results are shown in Tables 2 and 3. The unadjusted results report the mean and the 95% confidence interval (CI) for the outcome variable, perceived risk of a heart attack, for the level of the predictor variable represented in each row. Perceived risk of a heart attack ranged from I to 5 with 1 indicating much lower risk and 5 indicating much higher risk. The adjusted results report the effect of the predictor variable on the outcome after adjusting for all of the other predictors in the model. Because of the very small numbers of participants reporting Native American, Asian Pacific Island, or other racial identity, responses for these participants were excluded. Respondents who did not have

valid responses to all other measures were also excluded from the analyses. Consequently, these analyses are based on 1094 respondents.

The results presented in Table 2 show support for many of the hypotheses. As respondents reported worse general health, their personal risk perception of AM1 increased significantly. Personal risk perception of AM1 also increased significantly with age, although this effect depended on some of the risk factors as well. Gender, race/ethnicity, and education were not associated with risk perceptions. There were also no statistically significant relationships between awareness of AM1 in the social environment and risk perceptions. ' b o risk factors, family history of AM1 and self- reported diagnosis of high blood pressure, significantly increased personal risk perceptions of AMI. The magnitude of the effect of family history, however, depends upon the respondent's age: the increase in perceived risk of AM1 as a result of a family history of AM1 declines with age. That is, younger respondents have the largest increase in perceived risk of AM1 because of a family history of AMI. By con- trast, the increase in perceived risk of AM1 associated with high blood pressure does not depend on age. The increase

TABLE 2 Results of Mixed-Model Linear Regression Analysis Examining the Independent Association Between Perceived Risk

of Acute Myocardial Infarction (ranges 1-5) and Eight Personal and Demographic Risk Factors

Unadiusted results

Variable Category 95% CI Adjusted results

M Lower Upper Difference p

General health

Risk factors Personal history of heart attack or heart

Family history of heart attack

Diabetes

condition

High blood pressure

High cholesterol

Current smoker

Poor Fair Good Very good Excellent

Yes No Yes No Yes No Yes No Yes No Yes No

3.94 3.59 3.42 3.22 2.91 2.81 2.25 2.15 1.90 1.74

3.14 2.96 2.51 2.45 2.92 2.80 2.42 2.34 3.30 3.08 2.56 2.48 3.15 3.01 2.44 2.36 2.94 2.80 2.48 2.40 2.78 2.64 2.52 2.44

4.29 .40 .Ooo 3.62 3.01 2.35 2.06

3.32 .09 .763 2.57 3.04 1.09 .Ooo 2.50 3.52 -.68 .228 2.64 3.29 .8 1 .005 2.52 3.08 -.22 .438 2.56 2.92 .05 313 2.60

Table 2 continues on next page


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TABLE 2 continued

Variable

Unadjusted results 95% CI Adjusted results

Category M Lower Upper Difference p

Awareness of AM1 in social environment Friend with heart attack

Media message about heart attack

Knowledge of general AM1 risk Women

Over 65

Demographics Age Gender

Race/ethnicity

Education

Interactions between gender and knowledge

Interactions with age Knowledge Risk factors

Yes No Yes No

Incorrect Correct Incorrect Correct

Women Men Black Hispanic White College+ College HS or less Women incorrect Women correct Men incorrect Men correct 65+ incorrect History of AMI-yes Family history of AMI-yes Diabetes-yes High blood pressure-yes High cholesterol-yes Smoker-yes

2.66 2.56 2.41 2.35 2.61 2.53 2.51 2.49

2.49 2.41 2.83 2.71 2.51 2.31 2.62 2.48

2.56 2.49 2.66 2.56 2.50 2.40 2.86 2.57 2.81 2.65 2.52 2.44 2.43 2.23 2.47 2.37 2.80 2.68 2.52 2.40 2.96 2.80 2.44 2.32 2.66 2.44

2.76 2.59 2.8 I 2.65

2.57 2.95 2.65 2.76

2.64 2.76 2.60 3.15 3.09 2.60 2.63 2.57 2.92 2.64 3.12 2.56 2.88

. I 1 . I 19

-.06 .430

-.I3 ,235

-.36 .078

.01 ,003

. I3 .270

.22 (n.7) ,082

.I5 .24 I

.02 (ns) ,844 -.01 .348

-.22 .010

-.I4 ,335

.oo .265

.oo ,554 -.02 (ns) .ooo

.o 1 ,270 -.Ol ( n ~ ) ,043 .o I .256 -.oo ,533

Notes. The unadjusted results report the mean and the standard error of the mean for the outcome variable, perceived risk of a heart attack, for the level of the predictor variable represented in each row. Unadjusted results are not reported for instructions involving continuous variables. The adjusted results report the effect of the predictor variable for the outcome after adjusting for all of the other predictors in the model. For each two category predictor variable, the value presented in the difference column is the adjusted average difference in outcome between the two categories of the predictor variable. The reference category is the category in the row where no difference is reported. Health status was entered into the model as a continuous variable, so the difference reported represents the change in the outcome variable associated with a one-unit increase in the predictor variable. The significance level of each difference is reported in the p value column. The differences between persons who are Black and persons whtr are Hispanic and between persons with a college degree and with more than a college degree are not significant (ns).

in perceived risk associated with age is negated for persons with a self-reported diagnosis of high blood pressure. Knowledge of general AM1 risk did not have a significant effect on perceived risk of AMI, but there was a significant interaction between gender and knowledge about the risk of heart disease in women. Women who answered. incorrectly,

that heart disease is not the most common cause of death for women in the United States reported significantly lower risk perceptions than women who answered this question correctly. There was not, however, a significant interaction between age and the knowledge question about persons more than 65 years of age. The model summarized in Table

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~~~ ~ ~

TABLE 3 Results of Mixed-Model Linear Regression Analysis Examining Independent Association With Risk Factors, Awareness of

Acute Myocardial Infarction in Social Environment, and Knowledge, Summarized as Indices

Unadiusted results

Variable Category 95% CI Adjusted results

M Lower Upper Difference p

Health status

Number of risk factors

AM1 in social environment index

Knowledge of general risk index

Age Gender

Racefethnicity

Education

Interaction of age and number of risk factors

Poor Fair Good Very good Excellent 4 3 2 1 0 2 1 0 2 1 0

Women Men Black Hispanic White College+ College HS or less

3.94 3.42 2.91 2.25 1.90 3.67 311 7 2.87 2.52 2.13 2.66 2.67 2.42 2.75 2.62 227 2.56 2.67 2.50 2.85 2.87 2.52 2.44 2.47 .280

3.59 3.22 2.81 2.15 1.74 3.36 2.95 2.73 2.40 2.01 2.48 2.57 2.30 2.61 2.54 209 2.49 2.57 2.40 2.56 2.65 2.44 2.24 2.37 2.68

4.29 3.62 3.01 2.35 2.06 3.98 3.39 3.01 2.64 2.25 2.84 2.77 2.54 2.89 2.70 245 2.64 2.77 2.60 3.14 3.09 2.60 2.64 2.57 2.92

.4 1

.38

.08

.09

.37

.3 1

.o 1

.07

.25

.I4

.04 -.06

-.005

.Ooo

.Ooo

,421 (ns) ,207

.OOO (ns)

.Ooo

.Ooo ,275

.044 (ns)

.255

.695 (ns)

.385

.014

Notes. The unadjusted results report the mean and the standard error of the mean for the outcome variable, perceived risk of a heart attack, for the level of the predictor variable represented in each row. Unadjusted results are not reported for instructions involving continuous variables. The adjusted results report the effect of the predictor variable for the outcome after adjusting for all of the other predictors in the model. For each two category predictor variable, the value presented in the difference column is the adjusted average difference in outcome between the two categories of the predictor variable. The reference category is the category in the row where no difference is reported. Health status was entered into the model as a continuous variable so the difference reported represents the change in the outcome variable associated with a one-unit increase in the predictor variable. The significance level of each difference is reported in thep value column. The differences between persons reporting 2 rather than 1 social message, between those correctly answering 2 rather than 1 knowledge question, between Blacks and Hispanics, and between those with a college degree and those with more than a college degree are all not significant (ns).

2 explained about 26% of the variation in perceived risk of AMI. The variability associated with the community and pair terms was too small to estimate.

Data in Table 3 show a similar analysis using indices instead of separate variables for the social environment, knowledge, and risk-factor questions. This analysis explained about 25% of the variation in perceived risk of AMI. In this analysis, general health, knowledge of general AM1 risk index, and risk-factor index were significantly related to

risk perceptions. This suggests that as knowledge of general AM1 risk and awareness of the number of personal risk factors increase, personal risk perceptions for AM1 also increase. Likewise, as general health decreases, personal risk perceptions for AM1 increase. Awareness of AM1 in one’s social environment and most of the demographic variables were not significantly related to risk perceptions. There was a significant difference by race/ethnicity. African Americans had significantly higher risk perceptions than


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Whites, but the difference between African Americans and Hispanics was not significant. There was a significant interaction between age and number of risk factors. That is, the increase in perceived risk of an AM1 associated with an increase of one risk factor depends on the person’s age. For instance, for someone who is 20 years of age, the increase in perceived risk associated with an increase of one risk factor is .28; for someone who is 40, the increase is .18, and for someone who is 60 years of age, the increase is only .08 (not shown in table).

COMMENT The purpose of this study was to investigate what factors

influence personal risk perceptions of AMI. The results suggest that perceived general health, risk-factor status, and knowledge of general AM1 risk are important factors in determining personal AM1 risk perceptions.

It is not surprising that people who believe themselves to be in generally poor health feel more vulnerable to getting any illness, including AMI. However, it is important to emphasize to people who feel generally in good health that they are still at risk for AMI. AM1 patients have reported that their low-perceived risk of AM1 made it more difficult for them to believe they were experiencing heart attack symptoms during their heart attack episode. l 2 1 n additional analyses, we found an inverse relationship between general health and number of self-reported risk factor$, indicating that as risk factors increase, perceived health status decreases. However, more than 50% of the people who reported excellent or good health in this study reported one or more risk factors. Consistent with other s tud ie~ , ’~ . ’~-?~ participants in this study were over optimistic about their risks of getting a heart attack, particularly in light of the many risk factors that they reported. Healthcare providers should emphasize the “objective” risk factors for AM1 so people will be better informed about their personal risk.

Only two risk factors, family history of AM1 and self- reported diagnosis of high blood pressure, appeared to be significant predictors of AM1 risk perceptions. A family history of AM1 increased personal risk perceptions, but the magnitude of this increase declined with age. Although BeckerIR found that siblings of heart attack patients did not perceive themselves to be at higher risk than other people, other investigators have found that the presence of a family history of AM1 increases people’s perceived vulnerability to having a heart attack themselves.28 With respect to the decline in the magnitude of this increased risk perception with age, it is plausible that as people become aware of a family history of AM1 they may feel very vulnerable to getting a heart attack themselves. However, because a family

history is largely out of the control of the person, the increased vulnerability that comes with such a risk factor may not increase with age, but may actually decrease: that one is still alive might attest to the fact that one’s physical constitution is better than expected. A personal history of AM1 was not related to increased risk perceptions. Al- though this may seem surprising, there is some evidence to suggest that people who have experienced an AM1 and sur- vived it feel that their situation is being managed and is under control. As such, they may perceive themselves to be at equal or even less risk than the general population.’*

A self-reported diagnosis of high blood pressure was the only other risk factor that significantly increased risk perceptions. It is possible that healthcare providers put greater emphasis on the increased AM1 risk that comes with high blood pressure than the AM1 risk that comes with some other chronic conditions such as diabetes (which has so many other health consequences). That risk perceptions did not increase with age may indicate that, as for patients with a prior AMI, patients with such chronic conditions may feel their condition is “under control” and does not put them at higher risk for AM1 than other people.12 It is important that healthcare providers educate their patients with a prior AM1 as well as patients with high blood pressure, diabetes, or high cholesterol, about the increased risk of AM1 because of their condition, regardless of the management status of that condition.

The number of risk factors was significantly and positively related to risk perceptions. although the magnitude of the increased risk associated with each additional risk factor declined with age. This suggests that people do internal- ize the increased risk of having an AM1 that comes with the presence of multiple risk factors.

In terms of knowledge of general risk, the rather preva- lent misconception also reported in other studies2’ that women die more often of illnesses other than heart disease significantly influenced the personal risk perceptions of women. Women who knew that heart disease is the leading cause of death for women had higher personal risk perceptions than women who did not know this fact. This suggests that interventions targeted specifically at women and focus- ing on women’s risk for AM1 are both needed and possibly successful in affecting women’s risk perceptions regarding AMI. With the increased marketing efforts directed at women, smoking rates have dramatically increased among young women in ~articular,~” increasing their chances of developing heart disease. Women also have been found to delay longer before seeking care for AM1 symptoms.6,3’ Women need to be informed about their risk of AM1 so they will not perceive heart attacks solely as a “man’s disease.”

Of the demographic variables, age was significantly and

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positively related to risk perceptions. As people age, they are at higher risk of getting a heart attack. In light of the fact that heart disease is the leading cause of death in the United States, it is not surprising that older people feel more vulnerable to getting AM1 than younger people.

One limitation of this study is the small number of some racial or ethnic minorities, which may limit the generaliz- ability of the findings. However, the surveyed population includes a geographically and sociodemographically di- verse mix. Another limitation is the reliance on self-report for risk factors, which may be less reliable than medical records. However, the objective of this study was to investigate how an individual’s beliefs are related, regardless of the “objective truth” or “gold standard.” Last, all the questions were one-item measures, which may be unreliable. Because this study was exploratory and part of a much larger survey research project, quality of measurement was deemed less critical than measuring the variety of important constructs.

In summary, the results of this study shed light on factors that influence personal risk perceptions of AM1 and suggest that interventions should be designed t o focus on misconceptions about AM1 risk. Correcting these misconceptions may have a positive impact on people’s desire to engage in preventive health behaviors as well as their abil- ities to recognize and label symptoms of a heart attack during a heart emergency.

ACKNOWLEDGMENTS This research was supported by cooperative agreements (UOIs)

from the National Heart Lung and Blood Institute to Dr Hendrika Meischke, Department of Health Services, University of Washing- ton, Seattle. Contributors to the work included many individuals from the following institutions: the University of Alabama at Birmingham, School of Medicine; the University of Massachusetts Medical School, Worcester; the University of Minnesota School of Public Health, Minneapolis; the University of Texas Health Sci- ence Center, School of Public Health, Houston; the King County Health Department, the University of Washington Medical Center, University of Washington, Department of Health Services, Seattle; the Oregon Health Sciences University, Portland; the New England Research Institutes, Watertown; and the National Heart Lung and Blood Institute, Bethesda.

NOTE For further information, please address communications to Dr

Hendrika Meischke, Department of Health Services, Box 357660, University of Washington, Seattle, WA 98 195. (e-mail:hendrika@ u.washington.edu).

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factors that influence personal perceptions of the risk of an acute myocardial infarction

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