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Body Mass as a Determinant of Seat Belt Use
Body Mass as a Determinant of Seat Belt Use BY MICHAEL
J. LICHTENSTEIN, MD,* ALLEN BOLTON, BSc,t GEORGE WADE, MA*
ABSTRACT: Prevention of death and injury from motor vehicle accidents depends in part on seat belt use. Understanding the determinants of seat belt use is important for developing strategies to increase seat belt use. The crosssectional association between body mass index (BMI) and self-reported seat belt use was analyzed using data from 3,140 Health Risk Appraisals completed by Tennessee residents during 1986. Persons in the lowest quintile BMI (:::;;21.8 kg/m2 ) stated they use seat belts 63% (SD = 38%) of the time compared to persons in the highest BMI quintile (~29.0 kg/m 2 ) who reported using seat belts 50% (SD = 38%) of the time. BMI remained associated with seat belt use after adjustment for age, sex, race, education, cigarette use, alcohol use, drug use, urbani rural residence, state area of residence, miles driven per year, self-reported physical activity, and satisfaction with life. For a 1 kg/m 2 increase in BMI, seat belt use declined -0.73% (95% CI = -1.01, -0.46), and the relative odds of not being a frequent seat belt user increased 3% (odds ratio 1.03,95% CI = 1.01, 1.05). BMI was the third variable selected in a step-wise multiple linear regression after education and race. The BMI/seat belt association, if causal, has implications for (1) targeting of education programs to likely nonusers by traffic safety agencies; (2) targeting health promotion messages to likely nonusers by primary care providers; and (3) design of automobile seats and restraint devices. KEY INDEXING TERMS: Injury Prevention; Body Mass; Seat Belts. [Am J Med Sci 1989; 297(4):233-237.]
From the *Division of General Internal Medicine, Vanderbilt University Medical Center, the tHealth Promotion Section and the tCenter for Health Statistics, Tennessee Department of Health and Environment, Nashville, Tennessee. Linda Sadler and Drs. Michael Decker and William Schaffner provided thoughtful comments on the manuscript. Reprint requests: Dr. Michael J. Lichtenstein, Room 2553, The Vanderbilt Clinic, Vanderbilt University Medical Center, Nashville, TN 37232. THE AMERICAN JOURNAL OF THE MEDICAL SCIENCES
I
njury is the leading cause of death in persons under 45 years old in the United States. l The largest proportion of serious injuries arises from motor vehicle accidents, resulting in costs of more than $36 billion annually; direct costs of motor vehicle accidents are twice those attributable to heart disease. l Prevention of motor vehicle injury depends in part on the use of seat belts by drivers and passengers. Previous investigations have shown that education, race, age, alcohol use, urban/rural differences, socio-economic status, physical activity and driving patterns (miles/year, accident history, weather conditions, vehicle type) are all determinants of seat belt use. 2 ,3 A few reports have suggested an association between measures of body physique (obesity, height, relative weight) and seat belt use, with larger persons using seat belts less than thinner persons. 2- 4 We used the cross-sectional data obtained from Health Risk Appraisals to examine the association of body mass with self-reported seat belt use and to assess its importance relative to other determinants. Methods Data Collection. The Health Risk Appraisal (HRA)
is a health promotion tool developed to estimate a person's risk of mortality. First, risk factors are assessed by self report and measurement. Second, the risk factor profile is compared with mortality statistics to arrive at an estimate of risk-age. 5 ,6 Developed by the Centers for Disease Control, the version used by the Tennessee Department of Health and Environment is a 37-item multiple choice questionnaire marketed by Plantree Medical Systems. We used the information from the HRA to conduct a cross-sectional analysis of the associations between the health information and self-reported seat belt use. From January 1, 1986, to December 31, 1986,3,140 HRAs were administered to individuals in Tennessee by the Tennessee Department of Health and Environment. More than half of the participants (58.3%) completed the HRA as an optional component of a multiphasic screening clinic or a voluntary module of work site wellness programs located in Nashville and offered only to state employees. The remaining participants (41.7%) completed HRAs through health promotion programs throughout the state; these respondents consisted of state, public, and private employees. A small percentage (3.2%) of respondents
233
Body Mass and Seat Bell Use
Table 1. Health Risk Appraisal Questions and Scoring Used in Analyses Question
Response/Scoring
did not graduate from high school = 1 completed high school = 2 some college = 3 college or professional degree = 4 white (non-hispanic) = 1 Race/origin black = 2 continuous variable Body mass index (kg/m2 ) continuous variable Age at last birthday (years) male = 1 Sex female = 2 almost every day = 1 How often do you use drugs or medication which affect your mood sometimes = 2 rarely or never = 3 or help you to relax? 0-10,000 = 1 Miles per year as a driver of a motor vehicle and/or passenger of an 10,001-20,000 = 2 20,001= 3 automobile (10,000 = average). none = 0 Tobacco: enter average number of 1-10 = 1 cigarettes smoked per day in the 11-20 = 2 last five years (exsmokers should 21- = 3 use the last five years before quitting) Urban/rural residence urban = 1 rural = 2 west = 1 Region of state of Tennessee middle = 2 east = 3 In general how satisfied are you with mostly satisfied = 1 partly satisfied = 2 your life? mostly disappointed = 3 not sure = 4 little or none = 1 Physical activity level occasional = 2 regular (3X/wk) = 3 Considering your age, how would rate excellent = 1 your overall physical health? good = 2 fair = 3 poor = 4 drinks per week Alcohol use Marital status single (never married) = 1 married = 2 separated = 3 widowed = 4 divorced = 5
Education - schooling completed (one choice only)
were unemployed. The HRAs were completed before the implementation of fines for violation of a mandatory seat belt use law in Tennessee. The self-reported data from the HRA used in these analyses included age, race, sex, tobacco use, alcohol use, body mass index (kg/m 2 ), use of drugs and medications that affect mood, physical activity level, overall physical health, satisfaction with life, marital status, schooling completed, and miles driven per year. The site of HRA completion was used to determine urban-rural and within state regional differences in self-reported seat belt use. The state was divided into the three administrative regions used by the state government; east, middle, and west Tennessee. Urban 234
areas were defined as the standard metropolitan statistical areas of Memphis, Nashville, and Chattanooga/Knoxville. The coding and scaling for the responses to the HRA questions are given in Table 1. The dependent variable, self-reported seat belt use, was recorded as the percent of time seat belts were used. Data Analysis. Body mass index (BMl) was calculated as kg/m 2 from the height (in feet and inches) and weight (in pounds) reports. 7 Quintiles of BMI were determined from the frequency distribution ofBMI. Self-reported seat belt use was stratified by BMI quintile and the other variables to detect confounding associations. Step-wise multiple linear regression was performed to adjust for confounding and to assess the effect of the independent variables on the dependent variable, self-reported seat belt use.s Seat belt use, body mass index, and age were treated as continuous variables. The regression coefficients (slopes) represent the percent change in self-reported seat belt use per unit or level change in each independent variable. Ninty-five percent confidence intervals (95% CI) were calculated for each regression coefficient; an interval that excludes the ,value zero was considered statistically significant. As persons tend to overestimate their seat belt use, logistic regression was next carried out with the dependent variable self-reported seat belt use dichotomized into two groups.9 The groups were those who stated almost always using seat belts (91-100% usage, recoded as 0) compared with those with less usage (090%, recoded as 1). The natural base exponential of the logistic regression coefficients were determined to calculate the odds ratio of being a 0-90% user vs. a 91100% user per unit or level change in each independel1t variable.9 Ninty-five percent confidence intervals (95% Cl) were calculated for each odds ratio; an interval that excludes the value one was considered statistically significant. Results
The univariate association between BMI and seat belt use is given in Table 2. In quintiles 1 and 2 (the thinnest individuals) the mean self-reported seat belt Table 2. Average Seat Belt Use (percent) Stratified by Quintile of Body Mass Index (kg/m2 )
Quintile BMI
kg/m 2
Number
Average Seat Belt Use Mean (SD)
1 2 3 4 5
-21.8 21.9-23.8 23.9-26.0 26.1-28.9 29.0-
624 628 633 622 633
63 (38) 64 (38) 57 (39) 53 (38) 50 (38)
3140
57 (39)
Total
April 1989 Volume 297 Number 4
Lichtenstein, Bolton, and Wade
Table 3. Average Seat Belt Use (percent) Stratified by Race, Body Mass Index, and Education* Education Level Less than High School
Graduated High School
College or Professional Oegree
Some College
BMI Quintiles
N
Mean
(SO)
N
Mean
(SD)
N
Mean
(SO)
N
Race: White 1 2 3 4 5
16 18 23 15 18
35 41 26 24 22
(29) (41) (38) (31) (29)
113 115 120 125 117
51 51 44 38 45
(38) (40) (38) (35) (35)
118 108 117 123 150
59 64 58 47 49
(39) (37) (40) (36) (36)
90
30
(34)
590
46
(37)
616
55
2 2 2 3 5
25 45 0 0 16
(35) (8) (0) (0) (23)
8 7 13 15 23
59 30 53 38 18
(32) (36) (39) (36) (28)
16 15 13 10 23
14
16
(22)
66
36
(36)
77
Total Race: black 1 2 3 4 5 Total
Mean
SO
315 331 280 294 240
72 72 68 67 62
(35) (35) (36) (37) (39)
(38)
1460
69
(37)
24 54 35 30 41
(29) (40) (36) (33) (37)
26 27 58 31 53
57 54 47 43 37
(38) (45) (39) (35) (35)
38
(36)
195
46
(38)
*Excludes 32 persons of other races (7 Hispanic, 18 Asian, 4 Native American, and 3 not sure).
use is 63% and 64% respectively. Self-reported seat belt use steadily decreases thereafter, such that the average level of usage in quintile 5 (the largest individuals) is 50%. The effect of stratification by education level, race, and BMI is shown in Table 3. Race was associated with seat belt use, with blacks reporting a mean use of 41 % (SD = 37%) compared to 59% (SD = 39%) for whites. Education was strongly associated with seat belt use. Blacks with less than a high school education reported using seat belts 16 % of the time compared to 46% use reported by college educated blacks. Whites with less than a high school education reported using seat belts 30% of the time compared to 69% for college educated whites. Within each race/education category, the effect of BMI remained present, with larger persons reporting less seat belt use than thinner persons. The results of the multiple linear and logistic regressions are presented in Table 4. For the multiple linear regression coefficients, a negative value implies a decrease and a positive value an increase in self-reported seat belt use for a unit increase in the independent variables as coded. For the logistic regression, an odds ratio greater than one increases whereas an odds ratio less than one decreases the probability of not being a frequent seat belt user for a unit change in the independent variables as coded. In the step-wise multiple regression BMI was the third variable selected (after education and race) as a determinant of self-reported seat belt use. According to the linear regression, for a 1 kg/m 2 increase in BMI, self-reported seat belt use would decrease -0.73% (95% CI = -1.01, -0.46). As the 90% range of reTHE AMERICAN JOURNAL OF THE MEDICAL SCIENCES
ported BMI was 17.8 to 33.7 kg/m2, this association would account for a 10%-12% change in seat belt use. In the logistic regression the odds ratio for not being a seat belt user per 1 kg/m 2 increase in BMI was 1.03 (95% C.1. = 1.01, 1.05). Across the 90% range ofBMI (a 16 kg/m2 change in BMI) the odds ratio is 1.60, implying that a person at the 95th percentile of BMI is 60% more likely not to be a frequent seat belt user than a person at the 5th BMI percentile. Table 4. Results of Multiple Linear and Logistic Regressions Multiple Linear Regre8sion
Logistic Regression
Independent Variable
Slope" (%)
(95% CIl
Odds Ratiot
(95% CIl
Education level Race Body mass index Cigarette use Urban/rural residence Region of state of Tennessee Life satisfact ion Miles driven per year Drug use Physical activity Sex Age Physical Health
8.95 -16.65 -0.73 -3.27
(7.46,10.44) (-20.75, -12.25) (-1.01, -0.46) (-4.53, -2.02)
0.57 2.53 1.03 1.18
(0.52,0.64) (1.87,3.43) (1.01, 1.05) (1.09, 1.28)
-8.17
(-10.47, -5.88)
1.57
0 .35, 1.83)
4.21 -3.20
(2.69, 5.72) (-5.4 7, -0.92)
0.77 1.24
(0.70,0.85) (1.06, 1.44)
4.29 4.29 2.56 3.41 0.14 -2.19
(2.26,6.31) (1.58,7.01) (0.57,5.54) (0.49,6.33) (0.02,0.27) (-4.53,0.19)
0.78 0.92 0.99 0.70 0.99 1.29
(0.69,0.88) (0.76,1.10) (0.87, 1.12) (0.58,0.84) (0.98, 1.00) (1.11,1.49)
"Change in percent self-reported seat belt use per unit change in independent variable.
t Natural base exponential of logi.•tic regression coefficient. The relative odds of not being a frequent seat belt user per unit change in independent variable.
235
For a one level increase in education completed there would be an anticipated 8.95% (95% CI = 7.46, 10.44) increase in seat belt use, with an odds ratio of 0.57 (95% CI = 0.52, 0.64). That is, with more education, the probability of an individual not being a seat belt user decreases. After adjustment for the other variables, race accounts for a 16.65% (95% CI = 20.55, 12.55) difference in self-reported seat belt use. Blacks report using seat belts less frequently than whites and are 2.53 (95% CI = 1.87,3.43) times more likely not to report frequent seat belt use. Other variables that were associated with seat belt use but entering the multiple linear regression after BMI included cigarette use, urban/rural differences, within state regional differences, satisfaction with life, miles driven per year, use of mood affecting drugs, physical activity, sex, and age (Table 4). In all, the linear model accounted for 15% of the variance in self-reported seat belt use. The linear and logistic regressions produced similar results. The exceptions were that drug use and physical activity entered the linear regression but not the logistic model. Physical health entered the logistic model but not the multiple linear regression. Alcohol use and marital status were not associated with self-reported seat belt use in either regression after adjustment for the other variables. Discussion
This study provides further evidence for a causal association between body mass and seat belt use. We have shown a graded relationship between body mass and self-reported seat belt use that persists after adjustment for a number of confounding variables. This finding is consistent with prior reports. 2,4 Although larger persons may have a lower regard for healthconcious activities, compared to thinner persons, an alternative hypothesis suggests that the association makes biologic sense; larger persons may well find restraint devices more uncomfortable than thinner persons and therefore may be less likely to use seat belts. The association between body mass and seat belt use reported here must be interpreted within the limitations of the study design. First, the survey sample consisted of volunteers who may not be representative of the general Tennessee population. For example, 52.7% of the sample had a college or professional degree, an educational level higher than the average for Tennessee. Also, the weakness of the association between age and seat belt use in this study was attributable to only 0.2% subjects being less than 20 years old whereas 83.7% were 30 years or older. Second, the data were self reported. Self-reported seat belt use figures tend to be twice as high as estimates obtained by direct observation. 2 Self-reported heights and weights have been shown to be reliable estimates of measured heights and weightsY Using 236
self report introduces errors that vary 1 %-2% from the measured values. 12 Shorter persons overestimate their height to a slightly greater extent than taller persons; overweight persons tend to underestimate their weight to a slightly greater extent than thinner persons,13 We did not have any independently validated measures of height, weight, or seat belt use to assess the effect of these potential biases. There also is the potential recall bias that persons with different BMI may report their seat belt use differently. If larger persons overestimated their seat belt use to a greater extent than thinner persons, this would tend to mask rather than exaggerate the association between seat belt use and body mass index. Third, as in any cross-sectional study, it is impossible to know if other unmeasured factors that determine both seat belt use and body mass may account for the observed association. In this regard it is pertinent that our model accounted for only 15% of the variance in self-reported seat belt use. Despite these potential limitations, the present findings generally are consistent with other work on determinants of seat belt use. Our analyses were able to confirm prior reports of associations between seat belt use and education, race, miles driven per year, cigarette use, urban/rural status, and regional differences. 2-4 In addition, we were able to detect associations between seat belt use and self-reported use of mood altering drugs, physical activity, and satisfaction with life. Just how important is body mass in determining seat belt use? Body mass was the third variable to enter the step-wise regression, following education and race. Although the expected change in seat belt use per 1 kg/m2 change in body mass is modest (0.73%), across the range of body mass this would account for a 10%-12% change in seat belt use. To put this in context, a 1 % increase in national seat belt use rates would result in a $100 million savings in death and injury costs yearly,2 so while the effect on the individual is small, the benefit to society is potentially large. Similarly, the change in odds for not being a frequent seat belt user is modest for a unit change in BMI, about 3%. This difference is multiplicative such that for a 10 unit and 16 unit change in BMI, the odds of not being a frequent seat belt user increase 34% and 60% respectively. Would decreasing body mass in individuals increase their seat belt use? This is unknown, but the effect of such an intervention is likely to be small compared to the effects of mandating seat belt use by law. Seat belt use nearly doubles after such a law is passed, but its effect may wear off after 6-12 months. 3 Past experience has shown that persons less likely to use seat belts also are less likely to obey the law. 10 This may be partly attributable to relatively minor penalties for not using a seat belt. In Tennessee a driver may not be stopped solely for nonuse of seat April 1989 Volume 297 Number 4
lichtenstein. Bolton, and Wade
belts. If found not using a seat belt when stopped for a primary traffic offense, the driver's seat belt penalty consists of a warning on the first violation and a $50 fine on the second. If seat belt laws do not successfully reach the persons .least likely to use restraint devices, how might the association between seat belt use and body mass be used to increase use or further protect drivers and passengers? First, agencies involved in driver education and traffic safety may use the information to target education programs to groups who may be less likely to use seat belts. Second, primary care physicians interested in health promotion could use the information to encourage seat belt use in persons they recognize as potential nonseat belt users. Just as physicians are expected to give clear smoking cessation messages,13 so should they be giving advice about traffic injury prevention. Third, within model classes of cars, seat and restraint design might be varied such that heavier persons would be more comfortable, and perhaps more likely to buckle up. References 1. National Research Council Committee on Trauma Research and the Institute of Medicine. Injury: Magnitude and Characteristics of the Problem, in Injury in America: A Continuing Public Health Problem. Washington DC, National Academy Press, 1985, pp 18-24. 2. Mayas JM, Boyd NK, Collins MA, Harris Bl: A Study of Demographic, Situational, and Motivational Factors Affecting Re-
THE AMERICAN JOURNAL OF THE MEDICAL SCIENCES
straint Usage in Automobiles. US Dept of Transportation publication HS·806-402. Washington, DC, National Highway Traffic Safety Administration, 1983. 3. Goldbaum GM, Remington PL, Powell KE, Hogelin GC, Gentry EM: Failure to use seat belts in the United States: the 1981-83 behavioral risk factor surveys. JAMA 255:2459-2462, 1986. 4. Merrill BE, Sleet DA: Safety belt use and related health variables in a worksite health promotion program. Health Educ Q 11:171-179,1984. 5. Wagner EH, Beery WL, Schoenbach VJ, Graham RM: An assessment of health hazard/health risk appraisal. Am J Public Health 72:347-352, 1982. 6. Foxman B, Edington DW: The accuracy of health risk appraisal in predicting mortality. Am J Public Health 77:971974,1987. 7. Revicki DA,Israel RG: Relationship between body mass indices and measures of body adiposity. Am J Public Health 76: 992-994, 1986. 8. Armitage P: Multiple regression and multivariate analysis, in Statistical Methods in Medical Research. Oxford, Blackwell Scientific Publications, 1971, pp 302-348. 9. Breslow NE, Day NE: Unconditional logistic regression for large strata, in The Analysis of Case-Control Studies. IARC Scientific Publication No. 32. Lyon, France, 1980, pp 192-247. 10. Hakkert AS, Zaidel DM, Sarelle E: Patterns of safety belt usage following introduction of a ssfety belt wearing law. Accident Analysis and Prevention 13:65-82, 1981. 11. Stewart AL: The reliability and validity of self-reported weight and height. J Chron Dis 35:295-309, 1982. 12. Palta M, Prineas RJ, Berman R, Hannan P: Comparison of self-reported and measured height and weight. Am J Epidemiol 115:223-230, 1982. 13. Health and Public Policy Committee, American College of Physicians. Methods for Stopping Smoking. Ann Intern Med 105:281-291, 1986.
237
J. LICHTENSTEIN, MD,* ALLEN BOLTON, BSc,t GEORGE WADE, MA*
ABSTRACT: Prevention of death and injury from motor vehicle accidents depends in part on seat belt use. Understanding the determinants of seat belt use is important for developing strategies to increase seat belt use. The crosssectional association between body mass index (BMI) and self-reported seat belt use was analyzed using data from 3,140 Health Risk Appraisals completed by Tennessee residents during 1986. Persons in the lowest quintile BMI (:::;;21.8 kg/m2 ) stated they use seat belts 63% (SD = 38%) of the time compared to persons in the highest BMI quintile (~29.0 kg/m 2 ) who reported using seat belts 50% (SD = 38%) of the time. BMI remained associated with seat belt use after adjustment for age, sex, race, education, cigarette use, alcohol use, drug use, urbani rural residence, state area of residence, miles driven per year, self-reported physical activity, and satisfaction with life. For a 1 kg/m 2 increase in BMI, seat belt use declined -0.73% (95% CI = -1.01, -0.46), and the relative odds of not being a frequent seat belt user increased 3% (odds ratio 1.03,95% CI = 1.01, 1.05). BMI was the third variable selected in a step-wise multiple linear regression after education and race. The BMI/seat belt association, if causal, has implications for (1) targeting of education programs to likely nonusers by traffic safety agencies; (2) targeting health promotion messages to likely nonusers by primary care providers; and (3) design of automobile seats and restraint devices. KEY INDEXING TERMS: Injury Prevention; Body Mass; Seat Belts. [Am J Med Sci 1989; 297(4):233-237.]
From the *Division of General Internal Medicine, Vanderbilt University Medical Center, the tHealth Promotion Section and the tCenter for Health Statistics, Tennessee Department of Health and Environment, Nashville, Tennessee. Linda Sadler and Drs. Michael Decker and William Schaffner provided thoughtful comments on the manuscript. Reprint requests: Dr. Michael J. Lichtenstein, Room 2553, The Vanderbilt Clinic, Vanderbilt University Medical Center, Nashville, TN 37232. THE AMERICAN JOURNAL OF THE MEDICAL SCIENCES
I
njury is the leading cause of death in persons under 45 years old in the United States. l The largest proportion of serious injuries arises from motor vehicle accidents, resulting in costs of more than $36 billion annually; direct costs of motor vehicle accidents are twice those attributable to heart disease. l Prevention of motor vehicle injury depends in part on the use of seat belts by drivers and passengers. Previous investigations have shown that education, race, age, alcohol use, urban/rural differences, socio-economic status, physical activity and driving patterns (miles/year, accident history, weather conditions, vehicle type) are all determinants of seat belt use. 2 ,3 A few reports have suggested an association between measures of body physique (obesity, height, relative weight) and seat belt use, with larger persons using seat belts less than thinner persons. 2- 4 We used the cross-sectional data obtained from Health Risk Appraisals to examine the association of body mass with self-reported seat belt use and to assess its importance relative to other determinants. Methods Data Collection. The Health Risk Appraisal (HRA)
is a health promotion tool developed to estimate a person's risk of mortality. First, risk factors are assessed by self report and measurement. Second, the risk factor profile is compared with mortality statistics to arrive at an estimate of risk-age. 5 ,6 Developed by the Centers for Disease Control, the version used by the Tennessee Department of Health and Environment is a 37-item multiple choice questionnaire marketed by Plantree Medical Systems. We used the information from the HRA to conduct a cross-sectional analysis of the associations between the health information and self-reported seat belt use. From January 1, 1986, to December 31, 1986,3,140 HRAs were administered to individuals in Tennessee by the Tennessee Department of Health and Environment. More than half of the participants (58.3%) completed the HRA as an optional component of a multiphasic screening clinic or a voluntary module of work site wellness programs located in Nashville and offered only to state employees. The remaining participants (41.7%) completed HRAs through health promotion programs throughout the state; these respondents consisted of state, public, and private employees. A small percentage (3.2%) of respondents
233
Body Mass and Seat Bell Use
Table 1. Health Risk Appraisal Questions and Scoring Used in Analyses Question
Response/Scoring
did not graduate from high school = 1 completed high school = 2 some college = 3 college or professional degree = 4 white (non-hispanic) = 1 Race/origin black = 2 continuous variable Body mass index (kg/m2 ) continuous variable Age at last birthday (years) male = 1 Sex female = 2 almost every day = 1 How often do you use drugs or medication which affect your mood sometimes = 2 rarely or never = 3 or help you to relax? 0-10,000 = 1 Miles per year as a driver of a motor vehicle and/or passenger of an 10,001-20,000 = 2 20,001= 3 automobile (10,000 = average). none = 0 Tobacco: enter average number of 1-10 = 1 cigarettes smoked per day in the 11-20 = 2 last five years (exsmokers should 21- = 3 use the last five years before quitting) Urban/rural residence urban = 1 rural = 2 west = 1 Region of state of Tennessee middle = 2 east = 3 In general how satisfied are you with mostly satisfied = 1 partly satisfied = 2 your life? mostly disappointed = 3 not sure = 4 little or none = 1 Physical activity level occasional = 2 regular (3X/wk) = 3 Considering your age, how would rate excellent = 1 your overall physical health? good = 2 fair = 3 poor = 4 drinks per week Alcohol use Marital status single (never married) = 1 married = 2 separated = 3 widowed = 4 divorced = 5
Education - schooling completed (one choice only)
were unemployed. The HRAs were completed before the implementation of fines for violation of a mandatory seat belt use law in Tennessee. The self-reported data from the HRA used in these analyses included age, race, sex, tobacco use, alcohol use, body mass index (kg/m 2 ), use of drugs and medications that affect mood, physical activity level, overall physical health, satisfaction with life, marital status, schooling completed, and miles driven per year. The site of HRA completion was used to determine urban-rural and within state regional differences in self-reported seat belt use. The state was divided into the three administrative regions used by the state government; east, middle, and west Tennessee. Urban 234
areas were defined as the standard metropolitan statistical areas of Memphis, Nashville, and Chattanooga/Knoxville. The coding and scaling for the responses to the HRA questions are given in Table 1. The dependent variable, self-reported seat belt use, was recorded as the percent of time seat belts were used. Data Analysis. Body mass index (BMl) was calculated as kg/m 2 from the height (in feet and inches) and weight (in pounds) reports. 7 Quintiles of BMI were determined from the frequency distribution ofBMI. Self-reported seat belt use was stratified by BMI quintile and the other variables to detect confounding associations. Step-wise multiple linear regression was performed to adjust for confounding and to assess the effect of the independent variables on the dependent variable, self-reported seat belt use.s Seat belt use, body mass index, and age were treated as continuous variables. The regression coefficients (slopes) represent the percent change in self-reported seat belt use per unit or level change in each independent variable. Ninty-five percent confidence intervals (95% CI) were calculated for each regression coefficient; an interval that excludes the ,value zero was considered statistically significant. As persons tend to overestimate their seat belt use, logistic regression was next carried out with the dependent variable self-reported seat belt use dichotomized into two groups.9 The groups were those who stated almost always using seat belts (91-100% usage, recoded as 0) compared with those with less usage (090%, recoded as 1). The natural base exponential of the logistic regression coefficients were determined to calculate the odds ratio of being a 0-90% user vs. a 91100% user per unit or level change in each independel1t variable.9 Ninty-five percent confidence intervals (95% Cl) were calculated for each odds ratio; an interval that excludes the value one was considered statistically significant. Results
The univariate association between BMI and seat belt use is given in Table 2. In quintiles 1 and 2 (the thinnest individuals) the mean self-reported seat belt Table 2. Average Seat Belt Use (percent) Stratified by Quintile of Body Mass Index (kg/m2 )
Quintile BMI
kg/m 2
Number
Average Seat Belt Use Mean (SD)
1 2 3 4 5
-21.8 21.9-23.8 23.9-26.0 26.1-28.9 29.0-
624 628 633 622 633
63 (38) 64 (38) 57 (39) 53 (38) 50 (38)
3140
57 (39)
Total
April 1989 Volume 297 Number 4
Lichtenstein, Bolton, and Wade
Table 3. Average Seat Belt Use (percent) Stratified by Race, Body Mass Index, and Education* Education Level Less than High School
Graduated High School
College or Professional Oegree
Some College
BMI Quintiles
N
Mean
(SO)
N
Mean
(SD)
N
Mean
(SO)
N
Race: White 1 2 3 4 5
16 18 23 15 18
35 41 26 24 22
(29) (41) (38) (31) (29)
113 115 120 125 117
51 51 44 38 45
(38) (40) (38) (35) (35)
118 108 117 123 150
59 64 58 47 49
(39) (37) (40) (36) (36)
90
30
(34)
590
46
(37)
616
55
2 2 2 3 5
25 45 0 0 16
(35) (8) (0) (0) (23)
8 7 13 15 23
59 30 53 38 18
(32) (36) (39) (36) (28)
16 15 13 10 23
14
16
(22)
66
36
(36)
77
Total Race: black 1 2 3 4 5 Total
Mean
SO
315 331 280 294 240
72 72 68 67 62
(35) (35) (36) (37) (39)
(38)
1460
69
(37)
24 54 35 30 41
(29) (40) (36) (33) (37)
26 27 58 31 53
57 54 47 43 37
(38) (45) (39) (35) (35)
38
(36)
195
46
(38)
*Excludes 32 persons of other races (7 Hispanic, 18 Asian, 4 Native American, and 3 not sure).
use is 63% and 64% respectively. Self-reported seat belt use steadily decreases thereafter, such that the average level of usage in quintile 5 (the largest individuals) is 50%. The effect of stratification by education level, race, and BMI is shown in Table 3. Race was associated with seat belt use, with blacks reporting a mean use of 41 % (SD = 37%) compared to 59% (SD = 39%) for whites. Education was strongly associated with seat belt use. Blacks with less than a high school education reported using seat belts 16 % of the time compared to 46% use reported by college educated blacks. Whites with less than a high school education reported using seat belts 30% of the time compared to 69% for college educated whites. Within each race/education category, the effect of BMI remained present, with larger persons reporting less seat belt use than thinner persons. The results of the multiple linear and logistic regressions are presented in Table 4. For the multiple linear regression coefficients, a negative value implies a decrease and a positive value an increase in self-reported seat belt use for a unit increase in the independent variables as coded. For the logistic regression, an odds ratio greater than one increases whereas an odds ratio less than one decreases the probability of not being a frequent seat belt user for a unit change in the independent variables as coded. In the step-wise multiple regression BMI was the third variable selected (after education and race) as a determinant of self-reported seat belt use. According to the linear regression, for a 1 kg/m 2 increase in BMI, self-reported seat belt use would decrease -0.73% (95% CI = -1.01, -0.46). As the 90% range of reTHE AMERICAN JOURNAL OF THE MEDICAL SCIENCES
ported BMI was 17.8 to 33.7 kg/m2, this association would account for a 10%-12% change in seat belt use. In the logistic regression the odds ratio for not being a seat belt user per 1 kg/m 2 increase in BMI was 1.03 (95% C.1. = 1.01, 1.05). Across the 90% range ofBMI (a 16 kg/m2 change in BMI) the odds ratio is 1.60, implying that a person at the 95th percentile of BMI is 60% more likely not to be a frequent seat belt user than a person at the 5th BMI percentile. Table 4. Results of Multiple Linear and Logistic Regressions Multiple Linear Regre8sion
Logistic Regression
Independent Variable
Slope" (%)
(95% CIl
Odds Ratiot
(95% CIl
Education level Race Body mass index Cigarette use Urban/rural residence Region of state of Tennessee Life satisfact ion Miles driven per year Drug use Physical activity Sex Age Physical Health
8.95 -16.65 -0.73 -3.27
(7.46,10.44) (-20.75, -12.25) (-1.01, -0.46) (-4.53, -2.02)
0.57 2.53 1.03 1.18
(0.52,0.64) (1.87,3.43) (1.01, 1.05) (1.09, 1.28)
-8.17
(-10.47, -5.88)
1.57
0 .35, 1.83)
4.21 -3.20
(2.69, 5.72) (-5.4 7, -0.92)
0.77 1.24
(0.70,0.85) (1.06, 1.44)
4.29 4.29 2.56 3.41 0.14 -2.19
(2.26,6.31) (1.58,7.01) (0.57,5.54) (0.49,6.33) (0.02,0.27) (-4.53,0.19)
0.78 0.92 0.99 0.70 0.99 1.29
(0.69,0.88) (0.76,1.10) (0.87, 1.12) (0.58,0.84) (0.98, 1.00) (1.11,1.49)
"Change in percent self-reported seat belt use per unit change in independent variable.
t Natural base exponential of logi.•tic regression coefficient. The relative odds of not being a frequent seat belt user per unit change in independent variable.
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For a one level increase in education completed there would be an anticipated 8.95% (95% CI = 7.46, 10.44) increase in seat belt use, with an odds ratio of 0.57 (95% CI = 0.52, 0.64). That is, with more education, the probability of an individual not being a seat belt user decreases. After adjustment for the other variables, race accounts for a 16.65% (95% CI = 20.55, 12.55) difference in self-reported seat belt use. Blacks report using seat belts less frequently than whites and are 2.53 (95% CI = 1.87,3.43) times more likely not to report frequent seat belt use. Other variables that were associated with seat belt use but entering the multiple linear regression after BMI included cigarette use, urban/rural differences, within state regional differences, satisfaction with life, miles driven per year, use of mood affecting drugs, physical activity, sex, and age (Table 4). In all, the linear model accounted for 15% of the variance in self-reported seat belt use. The linear and logistic regressions produced similar results. The exceptions were that drug use and physical activity entered the linear regression but not the logistic model. Physical health entered the logistic model but not the multiple linear regression. Alcohol use and marital status were not associated with self-reported seat belt use in either regression after adjustment for the other variables. Discussion
This study provides further evidence for a causal association between body mass and seat belt use. We have shown a graded relationship between body mass and self-reported seat belt use that persists after adjustment for a number of confounding variables. This finding is consistent with prior reports. 2,4 Although larger persons may have a lower regard for healthconcious activities, compared to thinner persons, an alternative hypothesis suggests that the association makes biologic sense; larger persons may well find restraint devices more uncomfortable than thinner persons and therefore may be less likely to use seat belts. The association between body mass and seat belt use reported here must be interpreted within the limitations of the study design. First, the survey sample consisted of volunteers who may not be representative of the general Tennessee population. For example, 52.7% of the sample had a college or professional degree, an educational level higher than the average for Tennessee. Also, the weakness of the association between age and seat belt use in this study was attributable to only 0.2% subjects being less than 20 years old whereas 83.7% were 30 years or older. Second, the data were self reported. Self-reported seat belt use figures tend to be twice as high as estimates obtained by direct observation. 2 Self-reported heights and weights have been shown to be reliable estimates of measured heights and weightsY Using 236
self report introduces errors that vary 1 %-2% from the measured values. 12 Shorter persons overestimate their height to a slightly greater extent than taller persons; overweight persons tend to underestimate their weight to a slightly greater extent than thinner persons,13 We did not have any independently validated measures of height, weight, or seat belt use to assess the effect of these potential biases. There also is the potential recall bias that persons with different BMI may report their seat belt use differently. If larger persons overestimated their seat belt use to a greater extent than thinner persons, this would tend to mask rather than exaggerate the association between seat belt use and body mass index. Third, as in any cross-sectional study, it is impossible to know if other unmeasured factors that determine both seat belt use and body mass may account for the observed association. In this regard it is pertinent that our model accounted for only 15% of the variance in self-reported seat belt use. Despite these potential limitations, the present findings generally are consistent with other work on determinants of seat belt use. Our analyses were able to confirm prior reports of associations between seat belt use and education, race, miles driven per year, cigarette use, urban/rural status, and regional differences. 2-4 In addition, we were able to detect associations between seat belt use and self-reported use of mood altering drugs, physical activity, and satisfaction with life. Just how important is body mass in determining seat belt use? Body mass was the third variable to enter the step-wise regression, following education and race. Although the expected change in seat belt use per 1 kg/m2 change in body mass is modest (0.73%), across the range of body mass this would account for a 10%-12% change in seat belt use. To put this in context, a 1 % increase in national seat belt use rates would result in a $100 million savings in death and injury costs yearly,2 so while the effect on the individual is small, the benefit to society is potentially large. Similarly, the change in odds for not being a frequent seat belt user is modest for a unit change in BMI, about 3%. This difference is multiplicative such that for a 10 unit and 16 unit change in BMI, the odds of not being a frequent seat belt user increase 34% and 60% respectively. Would decreasing body mass in individuals increase their seat belt use? This is unknown, but the effect of such an intervention is likely to be small compared to the effects of mandating seat belt use by law. Seat belt use nearly doubles after such a law is passed, but its effect may wear off after 6-12 months. 3 Past experience has shown that persons less likely to use seat belts also are less likely to obey the law. 10 This may be partly attributable to relatively minor penalties for not using a seat belt. In Tennessee a driver may not be stopped solely for nonuse of seat April 1989 Volume 297 Number 4
lichtenstein. Bolton, and Wade
belts. If found not using a seat belt when stopped for a primary traffic offense, the driver's seat belt penalty consists of a warning on the first violation and a $50 fine on the second. If seat belt laws do not successfully reach the persons .least likely to use restraint devices, how might the association between seat belt use and body mass be used to increase use or further protect drivers and passengers? First, agencies involved in driver education and traffic safety may use the information to target education programs to groups who may be less likely to use seat belts. Second, primary care physicians interested in health promotion could use the information to encourage seat belt use in persons they recognize as potential nonseat belt users. Just as physicians are expected to give clear smoking cessation messages,13 so should they be giving advice about traffic injury prevention. Third, within model classes of cars, seat and restraint design might be varied such that heavier persons would be more comfortable, and perhaps more likely to buckle up. References 1. National Research Council Committee on Trauma Research and the Institute of Medicine. Injury: Magnitude and Characteristics of the Problem, in Injury in America: A Continuing Public Health Problem. Washington DC, National Academy Press, 1985, pp 18-24. 2. Mayas JM, Boyd NK, Collins MA, Harris Bl: A Study of Demographic, Situational, and Motivational Factors Affecting Re-
THE AMERICAN JOURNAL OF THE MEDICAL SCIENCES
straint Usage in Automobiles. US Dept of Transportation publication HS·806-402. Washington, DC, National Highway Traffic Safety Administration, 1983. 3. Goldbaum GM, Remington PL, Powell KE, Hogelin GC, Gentry EM: Failure to use seat belts in the United States: the 1981-83 behavioral risk factor surveys. JAMA 255:2459-2462, 1986. 4. Merrill BE, Sleet DA: Safety belt use and related health variables in a worksite health promotion program. Health Educ Q 11:171-179,1984. 5. Wagner EH, Beery WL, Schoenbach VJ, Graham RM: An assessment of health hazard/health risk appraisal. Am J Public Health 72:347-352, 1982. 6. Foxman B, Edington DW: The accuracy of health risk appraisal in predicting mortality. Am J Public Health 77:971974,1987. 7. Revicki DA,Israel RG: Relationship between body mass indices and measures of body adiposity. Am J Public Health 76: 992-994, 1986. 8. Armitage P: Multiple regression and multivariate analysis, in Statistical Methods in Medical Research. Oxford, Blackwell Scientific Publications, 1971, pp 302-348. 9. Breslow NE, Day NE: Unconditional logistic regression for large strata, in The Analysis of Case-Control Studies. IARC Scientific Publication No. 32. Lyon, France, 1980, pp 192-247. 10. Hakkert AS, Zaidel DM, Sarelle E: Patterns of safety belt usage following introduction of a ssfety belt wearing law. Accident Analysis and Prevention 13:65-82, 1981. 11. Stewart AL: The reliability and validity of self-reported weight and height. J Chron Dis 35:295-309, 1982. 12. Palta M, Prineas RJ, Berman R, Hannan P: Comparison of self-reported and measured height and weight. Am J Epidemiol 115:223-230, 1982. 13. Health and Public Policy Committee, American College of Physicians. Methods for Stopping Smoking. Ann Intern Med 105:281-291, 1986.
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