The association between smoking and the diet and health attitudes, awareness, and knowledge of low-income parents

The association between smoking and the diet and health attitudes, awareness, and knowledge of low-income parents

Melissa J. Smith

Smokers are known to have fewer healthy behaviors than nonsmokers. For example, smokers are more likely to drink alcohol and less likely to get sufficient sleep and exercise than nonsmokers (17, 23). Research shows that, on average, the diet of smokers is of poorer quality than nonsmokers. For example, the consumption of fruits, vegetables, dietary fiber, and dairy products has been negatively associated with smoking (1, 10, 13-15, 18, 24). In addition, smokers have poorer scores when tested on their knowledge of diseases or illnesses in comparison with nonsmokers (12). Hence, being a current smoker seems to negatively influence dietary intake and other health behaviors as well as knowledge concerning health and diet.

Income is negatively related to smoking (16). Thus, children residing in low-income households may be most apt to experience dietary differences associated with their parents’ smoking status. We studied the association between parental smoking and the diet quality of children in low-income households and found that parental smoking was positively associated with saturated fat, sodium, and total energy intake and negatively associated with vitamin A and fiber intake (11). Hence, children of heavy smokers in low-income households had diets that were farther away from contemporary dietary recommendations than did children of light or nonsmokers in low-income households.

Because of these findings, there is a need to examine why, in low-income households, children of heavy smokers have poorer diets than children of light or nonsmokers. The objective of this study was to examine the association between smoking and attitudes, awareness, and knowledge regarding diet among low-income parents in an attempt to explain how these factors might affect the diets of their children.

Methods

The Research Instrument

For almost 60 years, the U.S. Department of Agriculture (USDA) has been a monumental force in the monitoring of nutritional status and food intakes for the United States (20). The 1989 and 1990 USDA Continuing Survey of Food Intakes of Individuals (CSFII) examined the food intakes and dietary patterns of 7,677 persons in 2,947 U.S. households (26). USDA added the Diet and Health Knowledge Survey (DHKS) to the CSFII in 1989 to gather additional information concerning diet and health attitudes, awareness, and knowledge in an effort to examine how these factors may influence people’s food choices (26).

The CSFII was designed to obtain a nationally representative sample of households in the 48 conterminous United States and consists of an all-income and a low-income sample. In the all-income sample, all households, including low-income households, were eligible to be interviewed. For the low-income sample, participation was limited to individuals in households with a gross income for the previous month at or below 130 percent of the Federal poverty thresholds.

For the CSFII, trained interviewers visited each household and obtained socioeconomic, demographic, health-related, and dietary intake data on households and their members. For the DHKS, one member of each CSFII household was contacted about 6 weeks after dietary data were collected. Ideally, the individual contacted was the person who had identified himself or herself as the household’s main meal planner/ preparer. In about 6 percent of the households, this person could not be contacted so the DHKS respondents were not the main meal planner/ preparer.

Selection of Sample

The sampling frame for this study consisted of parents of children age 17 and younger who were included in an earlier study using CSFII data that examined the association between parental smoking and children’s diet quality in low-income households (n=515) (11). Low-income was defined in the study sample as at or below 185 percent of the Federal poverty thresholds. This percentage was used because it is the cutoff for some government-sponsored food assistance programs, such as WIC–the Special Supplemental Nutrition Program for Women, Infants, and Children and the reduced-price National School Lunch and School Breakfast Programs.

Eighty-nine percent of the sample (n=458) from the earlier study also participated in the DHKS. The final sample (n=302) consisted of only those parents who answered each of the attitude, awareness, and knowledge questions used in this study (see box, pp. 18-19). Sampling weights were not applied because the sample was a subsample derived from an earlier study (11). Thus, it cannot be assumed that the study results are generalizable to the entire U.S. population.

Description of the Sample

The final sample of parents (n=302) was 72 percent White, 23 percent Black, and 5 percent Other (Asian, Pacific Islander, Eskimo, American Indian). The mean annual household income was $11,544 (+$6,282), and the mean percent of Federal poverty level was 94 percent ([+ or -] 44 percent). Thirteen percent of the sample was from the Northeast; 25 percent, the Midwest; 43 percent, the South; and 19 percent, the West. Thirty-seven percent of the sample resided in central cities; 37 percent, in suburban areas; and 26 percent, in nonmetropolitan areas. Ninety-two percent of the sample was female. The parents’ ages ranged from 13 to 69 years old with a mean of 32.2 years ([+ or -] 8.5 years). The mean year for highest level of education attained by the respondents was 11.5 years ([+ or -] 2.6 years) with a range from second grade to more than 4 years of college. Thirty-five percent of the respondents received food stamps and 6 percent were WIC participants. The majority of the participants were service or clerical workers.

Definition of Study Variables Independent variable

The independent variable was the average number of cigarettes smoked per day as self-reported by the sample parents. From this a five-category scale (never smoked, ax-smoker, 1-10, 11-20, or 21+ cigarettes per day) was devised.

Dependent variables

The dependent variables were scales that were developed to measure diet and health attitudes, awareness, and knowledge using designated questions from the DHKS (8, 9). An Attitude 1, Attitude 2, Awareness, and Knowledge scale made up the four dependent variables. These scales, developed by Haines and colleagues, were derived to reflect the diet and health attitudes, awareness, and knowledge of the main meal planner in the household.

Ultimately, these scales were designed to help explain food choices among American households (8, 9). From these scales, attitudes regarding dietary guidance, diet-health perceptions or awareness, and nutrient composition knowledge were evaluated (8, 9). The Chronbach’s alpha test was used as an index of reliability with a value of [is greater than] 0.60 as a marker for acceptable values. Haines et al. tested the reliability of the scales using both the all-income and the low-income sample from the 1989 CSFII-DHKS and found all the scales to be acceptable (8).

The attitude scales were developed to measure attitudes toward dietary guidance (8, 9). The Attitude 1 scale included six questions that measured the importance of avoiding certain nutrients. A sample question was, “How important is it to you personally to avoid too much salt or sodium?” The score for each question ranged from a “one-not at all important” to a “six=very important.” A mean Attitude 1 score was calculated using the responses from the six Attitude 1 questions.

The Attitude 2 scale included five questions that measured the importance of including certain nutrients and dietary components in the respondents’ diet (8, 9). A sample question was, “How important is it to you personally to eat foods with adequate fiber?” A score of one was given to the response “not at all important,” and six points were given to the response “very important.” A mean Attitude 2 score was calculated using the responses from the five Attitude 2 questions.

An Awareness scale was developed to measure diet and health awareness (8, 9). Questions included awareness of any diseases relating to certain nutrients. A sample question was, “Have you heard about any health problems that might be related to how much fat a person eats?” One point was given for a “yes” response and zero points for a “no” response. A mean Awareness score was calculated using the responses from nine questions. There was no correct or incorrect answer for these questions due to the fact that the scale was developed to determine the respondents’ perceived awareness of whether there was a relationship (8, 9).

The Knowledge scale was developed to measure knowledge of nutrients and nutrient content in certain foods (8, 9). These questions focused on specific nutrients: Calories, fat, fiber, and cholesterol. A pair of foods was presented and the respondent had to choose which food had more of the particular nutrient in question. A sample question was, “Based on your knowledge, which has more fiber–fruit or meat?” Respondents were given one point for the correct answer and zero points for an incorrect answer. A mean Knowledge score was calculated using the responses from 23 knowledge questions.

Control variables

The potential control variables were selected based on their known association with smoking in other samples. These included the participants’ attained education level, occupation, total household income, percent of Federal poverty level, race, age, and gender (16, 25, 27, 28).

Statistical Analysis

The statistical relationships between smoking status and diet and health attitudes, awareness, and knowledge scores were assessed using the Statistical Analysis System (SAS) (22). Statistical significance was set at p [is greater than or equal to] 0.05.

The relationships among the variables were assessed in three steps. First, the bivariate relationships between the independent variable (smoking status) and the dependent variables (diet and health attitudes, awareness, and knowledge scores) were analyzed using analysis of variance (ANOVA) and the Duncan’s multiple range test for variables. This was to establish any significant bivariate relationships among the variables before adjusting for the control variables.

Second, bivariate analyses were used (ANOVA and chi-square) to determine any relationships between the independent variable and the potential control variables (male and female education level, male and female occupation, total household income, percent of Federal poverty level, race, age, and gender) in order to determine potential confounding variables.

Third, a multivariate analysis, a combination of analysis of covariance (ANCOVA) and least-squares means comparisons, was used to determine the effect of smoking status on each dependent variable (Attitudes 1 and 2, Awareness, and Knowledge scores) while adjusting for those control variables that were significantly associated with smoking status in the sample (race and female occupation). Thus, race and female occupation were entered as control variables in the ANCOVA analysis to adjust for variations in the dependent variables that otherwise might be attributable to them. Additionally, simple regression, ANOVA and chi-square analysis were used to explore the relationships between all potential control variables and the dependent scale scores.

Results

Scale Results

The mean scores for the Attitude 1 and Attitude 2 scales were 4.6 ([+ or -] 1.0) and 4.5 ([+ or -] 1.0) respectively. A perfect score for either of these two scales was 6.0. These two scores show that the sample placed some importance on including or avoiding certain nutrients and food components in their diet. The mean score for the Awareness scale was 0.65 ([+ or minus] 0.29) with a perfect score being 1.0. There was no correct or incorrect answer for the awareness questions. The score of 0.65 can be interpreted as the sample being somewhat aware of some nutrients related to various diseases. The mean Knowledge score was 15.0 ([+ or -] 2.7) with a perfect score being 23. This is equivalent to 65 on a 100-point scale and can be interpreted as the sample being somewhat knowledgeable about nutrients and nutrient content in certain foods.

Bivariate Analyses

The bivariate relationships between the smoking status variables and the four dependent variables (Attitudes 1 and 2, Awareness, and Knowledge scales) are shown in table 1. Overall, there were no significant relationships between the independent variable and dependent variables. However, in the preplanned multiple comparison using Duncan’s multiple range test for variables, significance was found within the smoking categories and the Attitude 2 scale (importance of including certain nutrients and dietary components in the diet) score. Those who smoked 1-10 cigarettes per day had a significantly higher Attitude 2 score than those who smoked 21 or more cigarettes per day.

Table 1. Bivariate relationships between smoking status and

four scale scores using ANOVA (n = 302)(*)

1-10

Scale Nonsmoker Ex-smoker cigarettes/day

scores (n = 134) (n = 31) (n = 44)

Attitude 1 4.72(a) 4.44(a) 4.47(a)

Attitude 2 4.57(a,b) 4.41(a,b) 4.70(a)

Awareness 0.67(a) 0.71(a) 0.59(a)

Knowledge 14.96(a) 15.71(a) 14.52(a)

11-20 21+

cigarettes/ cigarettes/

Scale day day Overall

scores (n = 65) (n = 28) F value p value

Attitude 1 4.62(a) 4.36(a) 1.30 0.271

Attitude 2 4.46(a,b) 4.16(b) 1.49 0.206

Awareness 0.60(a) 0.67(a) 1.38 0.240

Knowledge 15.11(a) 15.32(a) 1.02 0.397

(*) Results represent mean values. Row means with the same superscript letter (a,b) are not significantly different using the Duncan’s Multiple Range test.

Bivariate analysis was also used to test for any relationships between the independent variable (smoking category) and potential control variables. Only race (p=0.001) and female occupation (p=0.012) were found to be significantly related to smoking status. Whites had the highest percentage of current smokers, and Blacks had the highest percentage of nonsmokers. Among female occupations, the farmer/operators had the highest percentage of current smokers, and the professional/managers had the highest percentage of nonsmokers. Because of these associations, race and female occupation were controlled for in all further multivariate analyses.

Multivariate Analysis

As shown in table 2, p. 22, when the effect of race and female occupation were held constant, the parents who smoked 1-10 cigarettes per day continued to have significantly higher Attitude 2 scale (importance of including certain nutrients and dietary components in the diet) scores than those who smoked 21 or more cigarettes per day.

Table 2. Least square means comparison of scale scores

by smoking status, controlling by race and female

occupation using ANOVA (n = 302)(*)

1-10

Scale Nonsmoker Ex-smoker cigarettes/day

scores (n = 134) (n = 31) (n = 44)

Attitude 1 4.69(a) 4.44(a) 4.47(a)

Attitude 2 4.54(a,b) 4.41(a,b) 4.74(a)

Awareness 0.67(a) 0.70(a) 0.62(a)

Knowledge 15.01(a) 15.62(a) 14.72(a)

11-20 21+

cigarettes/ cigarettes/

Scale day day Overall

scores (n = 65) (n = 28) F value p value

Attitude 1 4.64(a) 4.34(a) 1.04 0.398

Attitude 2 4.43(a,b) 4.15(b) 1.93 0.076

Awareness 0.59(a) 0.70(a) 4.60 0.0002

Knowledge 14.95(a) 15.31(a) 3.14 0.005

(*) Results represent mean values. Row means with the same superscript letter (a,b) are not significantly different using the Duncan’s Multiple Range test.

Other Findings

Since few relationships were found between smoking and the scale scores, the effects of the control variables were analyzed as well. Race and female occupation were examined to test their relationships with the four scale scores. Table 3, p. 23, shows the results from the ANCOVA with female occupation as the independent variable, adjusting for race and smoking categories and then using race as the independent variable, controlling for female occupation and smoking categories. Female occupation was significantly related to Attitude 2 scores, Awareness scores, and Knowledge scores. A least-squares means comparison showed that the female occupation group, other (which included any occupations not included in the other occupation categories), had significantly lower Attitude 2 scores than the clerical/service or farmer/ operator/craftsperson occupations. The professionals/managers had significantly higher Awareness and Knowledge scores than any of the other three occupation groups. Race was significantly related to Awareness scores and Knowledge scores with Whites having significantly higher Awareness scores than those in the Other race category (which included Asians, Pacific Islanders, Aleuts, American Indians, and Eskimos) and significantly higher Knowledge scores than Blacks.

Table 3. Least squares means comparison of scale scores by female

occupation (controlling for race and smoking status) and by race

(controlling for female occupation and smoking status) (n = 302)(*)

Female occupation

Professional Clerical/ Farmer/

Scale managerial service operator

scores (n = 28) (n = 165) (n = 32)

Attitude 1 4.89(a) 4.63(a) 4.71(a)

Attitude 2 4.57(a,b) 4.79(a) 4.79(a)

Awareness 0.88(a) 0.65(b) 0.68(a,b)

Knowledge 17.21(a) 14.92(b) 14.13(b)

Female occupation

Scale Other

scores (n = 68) F value p value

Attitude 1 4.56(a) 0.63 0.680

Attitude 2 4.15(b) 2.39 0.040

Awareness 0.57(b) 6.23 0.001

Knowledge 14.90(b) 4.45 0.001

Race

Scale White Black Other

scores (n = 218) (n = 68) (n = 16) F value p value

Attitude 1 4.59(a) 4.75(a) 4.91(a) 0.60 0.664

Attitude 2 4.59(a) 4.40(a) 4.35(a) 2.10 0.084

Awareness 0.70(a) 0.60(a,b) 0.48(a) 6.10 0.001

Knowledge 15.63(a) 13.71(b) 15.10(a,b) 4.84 0.001

(*) Results represent mean values. Row means with the same superscript letter (a,b) are not significantly different using the Duncan’s Multiple Range test.

Other potential control variables (education level and age) were also associated with the various scale scores. The level of education achieved was positively related to Knowledge (p=0.003) and Awareness scores (p=0.0001) and age was positively related to Knowledge scores (p=0.03).

Discussion

The chief finding of this study was that the low-income parents in this sample who were heavy smokers (21+ cigarettes per day) were significantly less likely to place importance on including nutrients and other dietary components needed for health (i.e., fiber) in their diets than light smokers (1-10 cigarettes per day). Other factors, such as female occupation, race, education level, and age were also found to be associated with the parents’ diet and health attitudes, awareness, and knowledge.

Because the premise of the study was based on findings from an earlier study (11), the present study sample (n=302) was compared with the sample generated for our earlier study (n=5 15) for a number of characteristics including income levels, levels of urbanization, regional distribution, parental age, and years of parental education. The samples were found to be analogous. This was important because the parents in the present study were a subsample–those who answered all the attitude, awareness, and knowledge questions used in this study–of the parents included in the earlier study (11). As no major sociodemographic differences were found between the two samples, it was assumed that the samples were comparable and that there was no reason to presume that the nonrespondents of the DHKS were different from the respondents with regards to the variables measured in this study.

Since this study found differences in diet and health attitudes between heavy and light smokers, it is meaningful to examine other differences that are known to occur between these groups. On average, heavy smokers take on a plethora of risky health behaviors in addition to smoking. Heavy smokers often incorporate a lifestyle consisting of a poor diet and sedentary activity levels (19). Studies have shown that heavy smokers have lower health-consciousness scores than light smokers (2). Heavy smokers are less likely to be employed, have less education, and are less confident of their ability to quit than light smokers (29).

Thus, past research has shown that there are significant differences between light and heavy smokers and that heavy smokers assimilate more risk-taking behaviors than those who smoke less. This may help explain the primary finding in this study that heavy smokers had poorer attitudes toward the inclusion of certain foods and nutrients needed for a healthy diet than did light smokers.

In low-income households, parental smoking is negatively associated with children’s diet quality. Specifically, children of heavy smokers (21+ cigarettes per day) in low-income households were found to consume more energy, saturated fat, and sodium and less vitamin A than the children of parents who smoked 1-10 cigarettes per day (11). This provides a meaningful link with the present study because it demonstrates that low-income parents who are heavy smokers may convey their poorer attitudes about the importance of incorporating healthy foods in the diet to their children, possibly resulting in poorer quality diets.

In addition to smoking status, other notable associations were found between the control variables and the scale scores. Specifically, female occupation, race, educational attainment, and age were related to Attitude 2, Awareness, and Knowledge scores. Other research supports the role some of these variables play in health behaviors and lifestyle.

A study measuring health knowledge found that race was a strong predictor of knowledge concerning the health consequences of smoking, with Blacks demonstrating significantly less health knowledge than Whites (12). Health knowledge also increased as the level of education and income increased (12). Age has been associated with the health beliefs and health behavior of adults age 50 to 89 years, with those adults age 50 to 69 showing more positive health behaviors than adults age 70 to 89 (5).

According to the Health Belief Model, diverse variables contribute to people’s health-related behaviors (6). In this sample, associations between knowledge, attitudes, or awareness towards diet and health were found with various social and demographic factors (smoking status, occupation, race, educational attainment, and age). Researchers have shown that diet and health knowledge, as measured by the 1989 DHKS, was an important predictor of overall nutrient adequacy in a large sample (n=508) of female heads of households (21).

Others have shown that knowledge of serving recommendations was positively associated with food group consumption (7). Thus, interventions designed to improve diet and health knowledge that are targeted to groups with lower knowledge scores (in this sample: All occupations other than professional/managers, Blacks, young adults, and people with low education levels) could lead to more informed decisions and an improvement in diet quality.

It is important to point out, however, that altogether few differences in the diet and health attitudes, awareness, and knowledge among smokers, ax-smokers, and never smokers were found in this study. Although it has been established that, in general, smokers have diets of poorer quality than nonsmokers (1, 10, 13-15, 17 18, 24), it is plausible that those factors measured using the scales developed from the DHKS did not substantially impact the food choices and dietary intake of these smokers. Thus, future research should be targeted towards elucidating other factors that may help explain the variation found in dietary intake among nonsmokers, light and heavy smokers, and their children.

This research has important implications for public policy. Approximately 28 million low-income Americans are receiving food stamps and about 6.3 million low-income American women and children are served by WIC (3). The cost to the USDA for these programs approached $32 billion in 1995 (4). With all the public dollars focused on improving the health and nutritional status of the low-income population, efforts should be made to target low-income smokers, especially heavy smokers.

This study found that heavy smoking was negatively associated with attitudes regarding the importance of including food and nutrients needed for a healthy diet. We previously found that, on average, the children of heavy smokers had poorer diets than the children of parents who only smoked 1-10 cigarettes per day as well as nonsmokers (11). It is plausible that the heavy smokers conveyed their poorer diet and health attitudes to their children. Hence, programs designed to encourage smoking cessation and positive health behavior changes in low-income parents may not only prevent illness in the parents but may lead to positive nutrition benefits for their children as well.

References

[1.] Cade, J.E. and Margetts, B.M. 1991. Relationship between diet and smoking–is the diet of smokers different? Journal of Epidemiology and Community Health 45:270-272.

[2.] Castro, F.G., Newcomb, M.D., McCreary, C., and Baezconde-Garbanati, L. 1989. Cigarette smokers do more than just smoke cigarettes. Health Psychology 8(1):107-129.

[3.] Community Nutrition Institute. 1995, May 5. Nutrition Week Vol. 25, No. 17.

[4.] Community Nutrition Institute. 1995, June 30. Nutrition Week Vol. 25, No. 25.

[5.] Ferrini, R., Edelstein, S., and Barrett-Connor, E. 1994. The association between health beliefs and health behavior change in older adults. Preventive Medicine 23: 1-5.

[6.] Glanz, K., Lewis, F.M., and Rimer, B.K. 1990. Health Behavior and Health Education. Jossey-Bass Publishers, San Francisco, CA.

[7.] Guthrie, J.F. and Fulton, L.H. 1995. Relationship of knowledge of food group servings recommendations to food group consumption. Family Economics and Nutrition Review 8(4):2-17.

[8.] Haines, P.S., DeVellis, R.D., Popkin, B.M., and Guilkey, D.K. 1993. Technical Report, Phase I analysis. Knowledge and Attitudes Related to Dietary Choices Among U.S. Men and Women.

[9.] Haines, P.S., Popkin, B.M., Guilkey, D.K., and DeVellis, R. 1994. Knowledge and Attitudes of U.S. Men and Women: The 1989 Diet and Health Knowledge Survey and the 1989 Continuing Survey of Food Intakes by Individuals.

[10.] Hebert, J.R. and Kabat, G.C. 1990. Differences in dietary intake associated with smoking status. European Journal of Clinical Nutrition 44:185-193.

[11.] Johnson, R.K., Wang, M., Smith, M.J., and Connolly, G. 1996. The association between parental smoking and the diet quality of low-income children. Pediatrics 97(3):312-317.

[12.] Klesges, R.C., Somes, G., Pascale, R.W., Klesges, L.M., Murphy, M., Brown, K., and Williams, E. 1988. Knowledge and beliefs regarding the consequences of cigarette smoking and their relationships to smoking status in a biracial sample. Health Psychology 7(5):387-401.

[13.] Larkin, F.A., Basiotis, P.P., Riddick, H.A., Sykes, K.E., and Pao, E.M. 1990. Dietary patterns of women smokers and non-smokers. Journal of the American Dietetic Association 90(2):230-237.

[14.] Margetts, B.M. and Jackson, A.A. 1993. Interactions between people’s diet and their smoking habits: The dietary and nutritional survey of British adults. British Medical Journal 307:1381-1384.

[15.] McPhillips, J.B., Eaton, C.B., Gans, K.M., Derby, C.A., Lasater, T.M., McKenney, J.L., and Carleton, R.A. 1994. Dietary differences in smokers and nonsmokers from two Southeastern New England communities. Journal of the American Dietetic Association 94(3):287-292.

[16.] Metropolitan Life Insurance Company. 1992. Cigarette smoking among U.S. adults, 1985-1990, and smoking among selected occupational groups, 1990. Statistical Bulletin 73(4):12-19.

[17.] Morabia, A. and Wynder, E.L. 1990. Dietary habits of smokers, people who never smoked, and exsmokers. The American Journal of Clinical Nutrition 52:933-937.

[18.] Nuttens, M.C., Romon, M., Ruidavets, J.B., Arveiler, D., Ducimetiere, P., Lecerf, J.M., Richard, J.L., Cambou, J.P., Simon, C., and Salomez, J.L. 1992. Relationship between smoking and diet: The MONICA-France project. Journal of Internal Medicine 231 :349-356.

[19.] Patterson, R.E., Haines, P.S., and Popkin, B.M. 1994. Health lifestyle patterns of U.S. adults. Preventive Medicine 23:453-460.

[20.] Peterkin, B.B., Rizek, R., and Tippett, K.S. 1988. Nationwide Food Consumption Survey, 1987. Nutrition Today 88:18-24.

[21.] Ramezani, C.A. and Roeder, C. 1995. Health knowledge and nutritional adequacy of female heads of households in the United States. Journal of Consumer Affairs 29:381402.

[22.] SAS Applications Guide. 1990. SAS Institute, Inc., Cary, NC.

[23.] Schoenborn, C.A. and Benson, V. 1988. Relationships Between Smoking and Other Unhealthy Habits: United States, 1985. U.S. Department of Health and Human Services, Public Health Service. (PHS) 88-1250.

[24.] Subar, A.F. and Harlan, L.C. 1994. Nutrient and food group intake by tobacco use status: The 1987 National Health Interview Survey. Annals of the New York Academy of Sciences.

[25.] U.S. Department of Agriculture, Agricultural Research Service. 1993. Poverty Thresholds. Family Economics Review 6(3):38.

[26.] U.S. Department of Agriculture, Human Nutrition Information Service. 1989. CSFII-DHKS Data Set: 1989 Continuing Survey of Food Intakes by Individuals and 1989 Diet and Health Knowledge Survey.

[27.] U.S. Department of Health and Human Services, Public Health Service. 1989. Reducing the Health Consequences of Smoking: 25 Years of Progress. A Report of the Surgeon General. DHHS Publication No. (PHS) 89-8411.

[28.] Wagenknecht, L.E., Perkins, L.L., Cutter, G.R., Sidney, S., Burke, G.L., Manolio, T.A., Jacobs, D.R., Liu, K., Friedman, G.D., Hughes, G.H., and Hulley, S.B. 1990. Cigarette smoking behavior is strongly related to educational status: The CARDIA study. Preventive Medicine 19:158-169.

[29.] Wilson, D., Wakefield, M., Owen, N., and Roberts, L. 1992. Characteristics of heavy smokers. Preventive Medicine 21:311-319.

RELATED ARTICLE: U.S. Department of Agriculture (USDA) 1989-1990 Diet and Health Knowledge Survey (DHKS)

DHKS questions for the Attitude 1, Attitude 2, Awareness, and Knowledge scales

Attitude 1

On a scale from 1 to 6, please tell me how important it is to you personally to:

Avoid too much salt or sodium.

Avoid too much saturated fat.

Avoid too much sugar.

Drink alcoholic beverages in moderation if at all.

Avoid too much fat.

Avoid too much cholesterol.

Answers ranged from 1 (“not important at all”) to 6 (“very important”). “Don’t know” and “no answer” were also coded.

Attitude 2

On a scale from 1 to C, please tell me how important it is to you personally to:

Eat at least five servings a day of fruits and vegetables.

Eat foods with adequate fiber.

Eat foods with adequate starch.

Eat a variety of foods.

Eat at least six servings a day of breads, cereals, and other grain products.

Answers ranged from 1 (“not important at all”) to 6 (“very important”). “Don’t know” and “no answer” were also coded.

Awareness

Have you heard about any health problems that might be related to:

How much fat a person eats?

How much saturated fat a person eats?

How much fiber a person eats?

How much salt or sodium a person eats?

How much calcium a person eats?

How much cholesterol a person eats?

How much sugar a person eats?

How much iron a person eats?

Being overweight?

Answers were 1 (“yes”), 2 (“no”), 8 (“don’t know”), and 9 (“no answer”).

Knowledge–The correct answer is noted in ( ) after each question.

Based on your knowledge, which has more fiber?

Fruit or meat? (Fruit)

Cornflakes or oatmeal? (Oatmeal)

Whole wheat bread or white bread? (Whole wheat)

Orange juice or an apple? (Apple)

Kidney beans or lettuce? (Kidney beans)

Popcorn or pretzels? (Popcorn)

Ounce for ounce, which is highest in calories?

Would you say butter, sugar, potatoes,

or straight alcohol? (Straight alcohol)

Which is next highest in calories? (Butter)

Based on your knowledge which has more cholesterol?

Liver or t-bone steak? (Liver)

Butter or margarine? (Butter)

Egg whites or egg yolks? (Egg yolks)

Skim milk or whole milk? (Whole milk)

Which has more fat?

Regular hamburger or ground round? (Regular hamburger)

Loin pork chops or pork spare ribs? (Pork spare ribs)

Hot dogs or ham? (Hot dogs)

Peanuts or popcorn? (Peanuts)

Yogurt or sour cream? (Sour cream)

Porterhouse steak or round steak? (Porterhouse)

Ice cream or sherbet? (Ice cream)

Roast chicken leg or fried chicken leg? (Fried chicken leg)

What kind of fat is more likely to be a liquid rather than a solid?

Saturated fats

Polyunsaturated fats (Correct)

Equally likely to be liquids

Don’t know

No answer

If a food is labeled cholesterol free, is it also?

Low in saturated fat

High in saturated fat

Could be either high or low in

saturated fat (Correct)

Don’t know

No answer

Is cholesterol found in?

Vegetables and vegetable oil

Animal products like meat and

dairy products (Correct)

All foods containing fat

Don’t know

No answer

Total possible correct answers was 23.

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