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Household Characteristics, Smoking Bans, and Passive Smoke Exposure in Young Children
Original Article
Household Characteristics, Smoking Bans, and Passive Smoke Exposure in Young Children Yvonne K. Yousey, RN, CPNP, PhD
ABSTRACT Introduction: Young children are vulnerable to the health effects of environmental tobacco smoke (ETS) exposure in their own homes. Characteristics of households and the use of smoking bans (i.e., no smoking allowed) as an indicator of smoke exposure need to be understood before interventions can be developed to eliminate ETS exposure in homes where young children live. Methods: This cross-sectional, descriptive study investigated demographic characteristics, knowledge, attitudes/beliefs, health of children, smoking practices, and the presence of smoking bans in households. A survey questionnaire was administered to a convenience sample of 226 English- and Spanish-speaking subjects, 18 to 50 years of age, including both smokers and nonsmokers. Cotinine levels of urine samples from children measured actual smoke exposure to confirm reports of home smoking policies. Results: Ethnicity of households (P ⬍ .001) and negative attitudes toward smoke exposure (P ⬍ .001) predicted the presence of smoking bans. The number of households with no or partial smoking bans correlated significantly with urine cotinine levels (r ⫽ .486); the presence of no or partial smoking bans predicted smoke exposure in households.
Yvonne K. Yousey is Assistant Professor, University of North Carolina, Charlotte, Department of Family and Community Nursing. Funded by the Colorado Tobacco Research Program, Boulder, Colo. Reprint requests: Yvonne K. Yousey, RN, CPNP, PhD, University of North Carolina, Charlotte, Department of Family and Community Nursing, 9201 University City Blvd, Charlotte, NC 28223; e-mail: [email protected]. 0891-5245/$32.00 Copyright © 2006 by the National Association of Pediatric Nurse Practitioners. doi:10.1016/j.pedhc.2005.08.006
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Volume 20 • Number 2
www.jpedhc.org
Discussion: Because the use of smoking bans in predicting household smoke exposure has not been previously demonstrated, further study is needed to determine how smoking bans can be utilized to eliminate or reduce smoke exposure in homes where children live. J Pediatr Health Care. (2006) 20, 98-105.
Exposure to environmental tobacco smoke (ETS) is an important preventable cause of child morbidity (Aligne & Stoddard, 1997), yet little is known how to effectively reduce or eliminate passive smoke in households with children. Whereas other risky behaviors generally only harm the individual performing them, tobacco smoking is a health risk for both the smoker and for those not directly involved in the behavior (Sockrider, 1996). For example, in the United States, 31.2% of children have daily ETS exposure in their own homes (Mannino, Carabello, Benowitz, & Repace, 2001). Health effects associated with ETS exposure in children, including decreased lung function, increased prevalence and exacerbations of asthma, middle ear disease, Sudden Infant Death Syndrome, and lung cancer, have been documented in meta-analyses by Cook and Strachan (1999) and Li, Peat, Xuan, and Berry (1999). For example, child illness is directly related to parent smoking (Cook & Strachan), with children of smokers twice as likely to have a serious respiratory infection requiring hospitalization than children of nonsmokers (Li et al.). Regarding sociodemographic characteristics that affect ETS exposure, low-income, minimally educated, younger parents report more smoke exposure than do older, higher income, educated parents (Arborelius, Hallberg, & Hakansson, 2000; Eriksen & Bruusgaard, 1995). Whitlock, McMahon, Vander Home, Davis, Jackson, and Norton (1998) Journal of Pediatric Health Care
reported an inverse relationship between socioeconomic status, educational levels, and reported smoke exposure. In other words, lower socioeconomic status and lower educational levels were associated with higher smoke exposure in the household. Consequently, children living in homes associated with poverty are at greater risk for exposure and for associated health problems. Smoke exposure is also affected by ethnicity, with non-Hispanic White families reporting more smoke exposure than Hispanic families (Mannino, Moorman, Kingsley, Rose, & Repace, 2001). The smoking behaviors of individuals and households are best understood in the context of cultural norms, environmental cues, and infrastructure constraints including costs and restrictive polices (Corbett, 2001). Based on the household production of health framework (Berman, Kendall, & Bhattacharya, 1994), families use a combination of knowledge, attitudes, and behavioral norms interfacing with social factors such as age, income level, and education to make decisions about smoking in their households. Even though the awareness of dangers of ETS is increasing (up to 43% among smokers and 78% among nonsmokers) and awareness is positively correlated with higher levels of education, only 34% of homes containing nonsmokers and 20% of homes containing smokers are smoke free (Ashley et al., 1998). As suggested by Berman, Kendall, and Bhattacharya, the reduction or elimination of ETS exposure occurs by considering multiple household, cultural, and demographic characteristics. While policies that restrict smoking in public places have effectively reduced exposure for nonsmokers, no such policies exist for homes. Yet recent research findings suggest that home smoking restrictions are one method of reducing smoke exposure for children (Gilpin, White, Farkas, & Journal of Pediatric Health Care
Pierce, 1999; Pizacani, Martin, Stark, Koepsell, Thompson, & Diehr, 2003). For example, home smoking bans were found to increase cessation and reduce smoking (Farkas, Gilpin, Distefan, & Pierce, 1999), prevent smoking initiation (Biener, Cullen, Zhu, & Hammond, 1997), and influence an antismoking community climate (Farkas et al., 1999). Home smoking bans are relatively new approaches used to address ETS exposure, yet they have not achieved a high level of support (Ashley & Ferrence, 1998). One of the reasons for this lack of support is related to the commonly held belief that external agents should not interfere with behavior in private settings, particularly when it pertains to child rearing. The purpose of this study was to investigate household characteristics, household smoking behaviors including bans, and the relationships between household smoking behaviors and exposure of children to smoke. The first hypothesis investigated whether there were differences in demographic characteristics in households that did and did not report complete smoking bans. The second hypothesis tested whether households with complete smoking bans (i.e., no smoking allowed) would demonstrate greater knowledge of smoke exposure, more negative attitudes toward smoke exposure, and greater child health status than households without smoking bans (i.e., smoking allowed). The third hypothesis tested whether children in households with complete smoking bans would have less smoke exposure as measured by cotinine levels than children in households without smoking bans. METHODS This descriptive, cross-sectional study examined the relationships between household characteristics, smoking practices, and level of exposure of children to smoke.
Two methods of data collection were used. First, individuals with children were asked to complete a self-administered survey that describes their smoking behaviors. Second, a urine sample was obtained from a child in the household and tested for cotinine to confirm reports of smoking practices in the household. Setting and Sample A convenience sample of 226 households containing an adult between 18 and 50 years of age and a child younger than 6 years of age was selected from 4718 families whose children were enrolled in preschools and Head Start facilities (1021 families) or in schoolbased health centers (3717 families). Households were selected from three suburban counties of a metropolitan area in the midwestern United States. Sixty-two percent of the study sample described themselves as Hispanic, 24% as non-Hispanic White, and 15% as “other” (African American, Asian, or Native American). No significant differences in sociodemographic characteristics were found between families recruited from Head Start and preschools and those from school-based health centers. The average age of respondents completing the surveys was 28.7 years; 81% were married or cohabiting; and 75% were women. Forty-three percent of mothers and 45% of fathers had less than a high school education, and 66% of households earned less than $30,000 per year. Eighty-four percent of the sample had incomes at or below the poverty level, based on federal poverty guidelines (US Department of Health and Human Services, 2002). Urine samples for cotinine measurement were collected from children younger than 6 years of age in 211 households. Each of the 15 families who did not provide a urine sample was contacted on three occasions without success to obtain the urine specimen; no data March/April 2006
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were available that described their reasons for not participating. Instrument Development The 67-item survey instrument, developed by the principal investigator, examined individual and household demographics, smoking behaviors in households, presence of home smoking restrictions, knowledge of harms and health effects of smoke exposure, health status of children in the household, and attitudes/beliefs related to smoke exposure. The survey was designed for a 5th-grade reading level using the SMOG Readability Tool (US Department of Health and Human Services, 1989). Items on household smoking behaviors and presence of home smoking restrictions were adapted from National Health Interview Survey (NHIS). Sociodemographic characteristics previously identified as influencing ETS exposure were incorporated into the survey. These characteristics, measured as continuous and categorical variables, included the number of persons living in household, race, ethnicity, age, sex, marital status, educational level of respondent and spouse/partner, type of dwelling (apartment or single family), number of rooms in the dwelling, and income of the household. A 12-item true/false subscale measured knowledge of health effects associated with smoke exposure. Six items measured knowledge of actual health problems including otitis media and respiratory problems, and six items focused on dangers of smoke exposure at various ages and the perceived effects on children’s general health status. The answer to each item was rated as “1” if awareness/knowledge of harmful health effects associated with ETS was indicated and “0” if it was not; therefore, a higher total score indicated greater knowledge of ETS exposure. 100 Volume 20 • Number 2
Negative attitudes and beliefs about smoke exposure were measured using a 13-item Likert scale that includes questions related to parent (a) attitudes toward protecting children against smoke exposure, (b) beliefs about smoke exposure to self or to child, (c) beliefs about the effects of smoke exposure on children, and (d) beliefs about legal protection of children from smoke exposure. Respondents were asked to indicate whether they strongly agreed, agreed, were neutral, disagreed, or strongly disagreed with the statements provided. The respondent’s answer to each item was assigned a value from 1 to 5 (positive to negative) regarding smoke exposure; a higher overall score indicated a more negative attitude toward ETS exposure. The health status of children in the household was assessed by asking parents questions related to the following four measures: (a) the perceived health of each child (healthy or not healthy); (b) the number of minor acute illnesses each child experienced during the past year; (c) the number of children with chronic illnesses; and (d) the number of children with asthma. Reported smoking practices and behaviors in homes included the following questions: (a) the last time someone smoked in home, (b) the number of cigarettes smoked in the home in the past week, (c) the smoking status of the respondent (e.g., current smoker, ex-smoker, never smoked); (d) the number of smokers living in the home, and (e) the presence of household smoking bans. Households were categorized as having complete smoking bans (no smoking allowed) if they reported that no one is allowed to smoke anywhere anytime in the home. Households with no or partial bans (smoking allowed) allowed unlimited smoking or smoking with restrictions in the home.
The survey was pilot tested with 10 English-speaking and 10 Spanish-speaking adults. Questions were revised based on the feedback from pilot testing, and the final draft of the questionnaire was reviewed by two expert researchers for clarity and face validity. The survey was translated into Spanish by a certified translator and reviewed by two bilingual expert researchers and two Spanish-speaking clinic assistants for clarity. Care was taken to ensure that each survey item had comparable responses in Spanish and English in order to minimize bias and increase accuracy. Measurement of Smoke Exposure Evaluating smoke exposure in households by measuring cotinine reduces potential bias and errors inherent in self-reporting, sometimes associated with social desirability (Fried, Perkins, Watkinson, & McCartney, 1995; Matt, Hovell, Zakarian, Bernert, & Pirkle, 2000). Cotinine, a sensitive and specific biomarker for nicotine, is a surrogate marker for smoke exposure and is considered the “gold standard” for assessing questionnaire validity. Cotinine provides an integrated measure of the total amount of nicotine absorbed from all sources of smoke exposure during the previous 2 to 3 days, with a half-life of 32 to 82 hours (Jarvis, 1999; Rickert, 1999). Although cotinine is present in all body fluids, urine was selected for this study because obtaining a sample is less invasive than obtaining other body fluids. NicAlert (Nymox Corp., Maywood, NJ), used in this study, is a colorimetric screening test using immunoassay with urine that detects cotinine at seven different levels. This tool has a cut-off for smoke exposure consistent with previous screening tests (Jarvis; Rickert). For example, ⱕ30 ng/dL indicates no smoke exposure and ⱖ 31ng/dL indicates smoke exposure. As previously demonstrated Journal of Pediatric Health Care
by Peterson, Johnson, and Ownby (1997), a single measurement of cotinine adequately demonstrated exposure in this study. Validity and Reliability The use of predetermined valid and reliable questions on smoking practices from the NHIS survey increased the internal validity of the survey instrument. Evaluation of the knowledge subscale revealed a reliability estimate of .60 (K-R 20). Factor analysis evaluated construct validity of the attitude scale, confirming 67% of variance through four factors identified in the measure. The results of the factor analysis and the reliability estimate (Cronbach’s ␣ ⫽ .80) of the attitude scale were adequate to confirm the reliability of the scale as measurement of negative attitudes toward smoke exposure. The Nicalert screening test had a sensitivity of 87% and specificity of 100%. Data Collection Procedures The study was approved by the Colorado Multidisciplinary Institutional Review Board and was administered in Colorado. Data were collected during a 3-month period in autumn of 2002. Individuals aged 18 to 50 years who spoke and wrote English or Spanish fluently and who had children from newborn to age 6 years were invited to participate in the study. Permission for data collection was obtained from administrators of preschools, Head Start, and school-based health centers. Flyers in Spanish and English, distributed prior to the study, informed families when data collection would occur at each site. After the researcher or assistant explained the study, eligible parents provided written consent, completed the self-administered survey, and assisted in obtaining a urine sample from their child. Specimen cups were numbered to correspond with completed surveys, and results of cotinine testing were recorded on the survey and Journal of Pediatric Health Care
shared with parents if they requested. Families who completed the survey and provided a urine sample received a $20 grocery gift certificate. A research assistant who was fluent in Spanish assisted with Spanish-speaking families. Data were entered using the software package Statistical Program for Social Sciences (SPSS) 11.0. Outliers were identified and removed from the data set or transformed into categorical variables. Cases with missing data were excluded on a case-by-case basis, resulting in 225 completed surveys and 203 urine samples eligible for data analysis. Statistical Tests Chi-square tests were used to compare categorical sociodemographic variables and the health status of children in households with and without smoking bans. Continuous sociodemographic variables, knowledge, and attitude scores were evaluated by t tests (two-tailed, ␣ ⫽ .05). The ␣ on sociodemographic variables was reduced to .001 (Bonferroni adjustment) to reduce the possibility of a Type I error. Spearman and Pearson correlations or Kendall’s Tau_b (when assumptions for homoscedasticity and normal distribution were not met) evaluated correlations between these variables and the presence of home smoking bans. Nine sociodemographic variables and knowledge and attitude scores were used to predict the presence of smoking bans. Thus, logistic regression analysis tested the relationship between these variables and the reported presence of smoking bans in the first and second hypotheses. Bivariate analyses indicated that the health status of children is not significantly correlated with complete smoking bans. Therefore, the child health status variable was entered into the model after the previous variables to determine if it changed the predictability of the model and to test the second hypothesis.
Smoking practices that significantly correlated with household smoking policies (complete smoking ban versus no ban or a partial ban) were entered simultaneously into a second logistic regression model to predict ETS exposure and to test the third hypothesis. The reference group in this regression model was “no ETS exposure” defined as less than 30 ng/dL of cotinine and given a value of “0”; ETS exposure as measured by cotinine levels of 30 ng/dL or greater was given a value of “1.” Hosmer and Lemeshow Goodness-of-fit and Model 2 tests assessed adequacy of the logistic regression models, and odds ratios with confidence intervals were calculated. RESULTS Fifty-five non-Hispanic White families (24%) and 140 Hispanic families (62%) completed the survey; 86 Hispanic families (61%) completed the survey in Spanish. Hispanic families reported significantly lower educational levels of fathers (P ⬍ .000) and mothers (P ⬍ .000) and lower household income (P ⬍ .000) than did nonHispanic White families. Of 225 households reporting, 61 (27%) had no smoking bans (i.e., smoking allowed) and 164 (73%) had complete smoking bans. Of 203 urine samples, (48%) tested negative and 106 (52%) tested positive for exposure to smoke. The majority of the sociodemographic characteristics were similar between those who had no smoking bans and those who had complete smoking bans. However, significant differences were found between the two groups in language spoken in the home (P ⬍ .001), reported ethnicity (P ⬍ .001), racial categories (P ⫽ .004), and language in which the survey was completed (P ⬍ .001). The first hypothesis was only partially supported because there were no significant differences in the education of mothers (P ⫽ .182) or fathers (P ⫽ .104), marital status (P ⫽ .202), household income (P ⫽ .226), or the March/April 2006 101
TABLE 1. Summary of logistic regression analysis for variables predicting smoking bans in households
Variable
Estimated coefficient ()
Standard error
Significance
.235 ⫺.048 ⫺.186 .178 2.985 ⫺.507 ⫺.073 ⫺.181 7.042
.215 .067 1.162 .824 .967 .882 .190 .052 4.068
.275 .477 .873 .829 .0022* .565 .701 .000* .083
Residents in household Age of respondent Marital status Household income Spanish or English Educational level of father Score of knowledge items (12) Score of attitude items (13) Constant
Exp () (odds ratio)
1.264 .953 .830 1.195 19.987 .602 .930 .834 1144.209
95% Confidence interval EXP () Lower
Upper
.830 .836 .085 .238 2.974 .107 .640 .754
1.927 1.087 8.102 6.014 131.649 3.391 1.350 .923
*Significance ⬍ .01.
type of dwelling in which they lived (P ⫽ .747). Although households with complete home smoking bans had significantly higher levels of knowl-
smoke exposure than did households with partial or no smoking bans (M ⫽ 49.18) (t ⫽ 6.85, P ⬍ .001). Attitude scores correlated negatively and significantly with no ban
Of all the household characteristics that were investigated, language in which the survey was completed and attitudes toward smoke exposure were predictive of smoke exposure. edge about the negative effects of smoking on child health than did households without smoking bans (t ⫽ 2.642, P ⫽ .009), knowledge was not a predictor of smoking bans in multivariate analysis (OR ⫽ .93, P ⫽ .701). The mean score on the knowledge subscale was 10.58 (range 3-12), and a low but statistically significant negative correlation (r ⫽ –.179) existed between the level of knowledge and a no ban or partial home smoking ban. While scores differed significantly between households with bans and those without bans, all scores were high (M ⫽ 10.58), making differences between groups less evident and more difficult to interpret. Based on the attitude subscale, households with complete smoking bans (M ⫽ 58.12) had significantly more negative attitudes toward 102 Volume 20 • Number 2
or partial smoking bans in the household (r ⫽ –.365, P ⫽ .01) and predicted the presence of smoking bans in homes (P ⬍ .001, OR ⫽ .83 {95% CI ⫽ .76 –.91}) , confirming the relationship between negative attitudes toward smoke exposure and presence of smoking bans. No significant differences existed between households with and without smoking bans in reported health of children (P ⫽ .377), report of minor acute illnesses (P ⫽ .110), or presence of asthma (P ⫽ .120). The health status of children was not predictive of the presence of smoking bans in the multivariate analysis, and the hypothesized relationship between health status and smoking bans was not confirmed. Of all the household characteristics that were investigated, lan-
guage in which the survey was completed and attitudes toward smoke exposure were predictive of smoke exposure (see Table 1). The Hosmer and Lemeshow Goodness-of-fit test (2 ⫽ 7.62, df ⫽ 8, P ⫽ .472) and the Model 2 test (2 ⫽ 51.78, df ⫽ 16, P ⬍ .000) confirmed the use of these variables in the model. The predictive ability of the model (86.9%) did not improve when health variables were added, confirming their lack of predictability in this study. All smoking practices in the home, except the number of smokers living in the home, correlated with no smoking bans (i.e., smoking allowed) (P ⬍ .01) and with cotinine levels (P ⬍ .01) (see Table 2). One hundred forty-eight households (73%) reported complete smoking bans, but only 97 households (48%) had children with negative tests for ETS exposure. As expected, significantly fewer households with complete smoking bans tested positive for ETS exposure than did households without smoking bans (P ⬍ .001). The number of homes without smoking bans correlated positively and significantly with cotinine levels indicative of ETS exposure (r ⫽ .472, P ⫽ .01). Of all smoking practices, no bans or partial smoking bans predicted smoke exposure as measured by cotinine levels (P ⫽ .04. OR ⫽ .22), supporting the third hypothesis (see Table 3). Journal of Pediatric Health Care
TABLE 2. Summary of correlation of smoking practices and smoking bans 1 N ⴝ 206
1. Last time someone smoked in home 2. No. smokers living in home 3. No. cigarettes smoked in home in past week 4. Smoking status of respondent 5. Smoking situations score of households 6. Complete smoking ban
.474* .565* .473* .552* .625*
2 N ⴝ 222
3 N ⴝ 213
4 N ⴝ 215
5 N ⴝ 218
6 N ⴝ 222
.474*
.565* .765*
.473* .598* .542*
.552* .269* .352* .387*
.625* .339* .492* .440* .694*
.765* .598* .269* .339†
542* .352* .492*
.387* .440*
.694*
*P ⬍ .01. †P ⬍ .05.
TABLE 3. Summary of logistic regression analysis for smoking practices that predict cotinine levels as indicators of smoke exposure
Variable
Last time someone smoked in home No. smokers in home No. cigarettes smoked in past week Smoking status of respondent Complete ban No/partial ban Constant
Estimated coefficient 
Standard error
Significance
Exp () Odds ratio
.861 ⫺.363 ⫺.383 .028 ⫺1.497 1.240
.509 .795 .792 .530 .711 .777
.090 .649 .629 .958 .0352* .111
2.366 .696 .682 1.028 .224 3.456
95.0% Confidence interval EXP () Lower
Upper
.873 .146 .144 .364 .056
6.413 3.308 3.222 2.907 .902
*Significance ⬍ .05.
DISCUSSION Implications In contrast to previous studies (Mannino, Carabello, Benowitz, & Repace, 2001), educational level, income, age of parents, and marital status were not significantly different between groups with or without smoking bans in this study. The lack of differences may have been influenced by the sample, 84% of whom had a household income at or below the poverty level, and the fact that the groups being compared were not equal in number. In the findings related to race and ethnicity, Hispanic families (a) had lower incomes (P ⬍
.001), (b) completed less education (P ⬍ .001), (c) reported a greater number of smoking bans (P ⬍ .001), and (d) had more negative cotinine measurements (P ⫽ .05) than did non-Hispanic White families, suggesting that race and ethnicity may influence behaviors more than education or income. Gilpin et al. (1999) found similar differences in prevalence of smoking practices in Hispanic and nonHispanic White groups. No known studies to date have confirmed these reported differences with cotinine measurements. Even though knowledge levels of smoke exposure were high in
Educational level, income, age of parents, and marital status were not significantly different between groups with or without smoking bans in this study. Journal of Pediatric Health Care
this study, higher levels of knowledge did not predict presence of smoking bans in households. This is consistent with findings by Goldstein (1994), where 90% of the sample acknowledged awareness of the dangers of smoke exposure but only 24% implemented activities to reduce exposure. Greenberg, Strecher, and Bauman (1994) demonstrated limited success in changing behaviors when information-based interventions were used to reduce smoke exposure. While knowledge about smoke exposure may be useful, consideration of strategies based on more than just information may be necessary to change behavior. The importance of negative attitudes as a predictor of smoking bans in this study is consistent with the findings of Fearnow, Chassin, and Presson (1998), who found that health dangers of smoking were highly correlated with parental activism related to smoking. Parents with negative attitudes toMarch/April 2006 103
ward smoking demonstrated stronger relationships between values and actions than did those with less negative attitudes. Gilpin et al. (1999) found that beliefs in the harmfulness of smoke exposure of babies and children were associated with increased reports of smoking restrictions in the home. They also reported that families with smoking bans expressed a preference for no smoking within the household. Arborelius et al. (2000) investigated interventions useful in preventing smoke exposure of young children and concluded that interventions need to focus on smoke-free environments for children, not on the smoking cessation of parents. No effects for interventions that focused on providing factual information about
as cotinine, and all of these used smoking measures other than smoking bans. Knowing which factors predict presence of smoking bans in households will assist in developing targeted intervention strategies that facilitate adoption by households that otherwise would not have smoking bans (Okah, Choi, Okuyemi, & Ahluwalia, 2000). Limitations The cross-sectional nature of the study allowed assessment of associations between variables and examined differences between households with and without smoking bans. The convenience sample was small, limiting power of analysis and generalizability of the findings. No data
Even though knowledge levels of smoke exposure were high in this study, higher levels of knowledge did not predict presence of smoking bans in households. the dangers of ETS exposure were found in their study; however, demonstrable effects for interventions geared to behavioral strategies that considered parental beliefs and effects on children were found. Demographic findings such as income level, educational level, and age of parents associated with smoking behaviors in previous studies were not demonstrated in this study. Differences in ethnicity and smoking behaviors were evident. Relationships between knowledge and attitudes and smoking policies were similar to previously reported studies. Only a few studies have validated results of reported smoking behaviors with actual measurement of smoke exposure through a measure such 104 Volume 20 • Number 2
were available on families who did not participate in the study but attended Head Start, preschool, or the school-based health centers. Based on total enrollments at these facilities, only a small percentage of families participated in this study. Given that this study consists of a convenience sample, it is not possible to investigate differences that may have influenced findings, nor is there a well-defined population to which findings can be generalized. Indicators of child health measured in this study did not predict no bans or partial household smoking bans in contrast to previous meta-analyses demonstrating relationships between minor acute illness such as otitis media, respiratory illnesses including asthma,
and smoke exposure in households (Cook & Strachan, 1999; Li et al, 1999). The lack of significance in health indicators of children between families with and without smoking bans in this study may have been related to the level of measurement, the use of recall in collecting these data, or the relatively small number of subjects. The questions related to health were only retrievable in categorical variables providing less precise measurement, with decreased specificity and variability. The cotinine screening test had a sensitivity of 87% percent, indicating the possibility of false-negative results. In addition, no back-up method of confirmation was used. Given the results that slightly more than half of the children had positive tests for exposure and only 23% of families reported smoke exposure at home, it is not likely that children tested from homes with smoking bans in place were missed because of limitations of testing. Sources of exposure outside of the home other than day care were not identified and could also have affected the cotinine results. Finally, this screening test could not adjust for variations in dilution of the urine, making it slightly less accurate than other available methods of analysis. CONCLUSIONS The findings in this descriptive study support the use of smoking bans as a necessary and integral part of effective interventions to reduce and/or eliminate smoke exposure in homes where children live. They further contribute to an increased understanding of racial, ethnic, and behavioral factors that affect how families establish polices regarding smoke exposure. Finally, this study confirms the reliability of reported smoking bans in homes where children live. Primary care providers are consistently challenged to assist families in maintaining smoke-free enJournal of Pediatric Health Care
vironments for their children. Screening for exposure using an inexpensive, user-friendly technique such as NicAlert can be the basis for teaching about the positive effects of reducing smoke exposure indoors. Families can be encouraged to implement smoking bans in primary care settings where smoke exposure in families is identified. The focus on smoking bans as a method for predicting smoke exposure was central to this study. The next step will be to evaluate the use of household smoking bans in reducing or eliminating smoke exposure in households where young children reside. Health care providers who have frequent and regular contact with parents of young children can implement these interventions. These efforts can keep children’s environments smoke free and reduce morbidity associated with smoke exposure. Additional research is needed to determine the effectiveness of interventions and to implement them into the standard of care. REFERENCES Aligne, C. A., & Stoddard, J. (1997). Tobacco and children: A economic evaluation of the medical effects of parental smoking. Archives of Pediatric Adolescent Medicine, 151, 648-653. Arborelius, E., Hallberg, A. C., & Hakansson, A. (2000). How to prevent exposure to tobacco smoking among small children: A literature review. Acta Paediatrica, 89, 65-70. Ashley, M..J., Cohen, J., Ferrence, R., Bull, S., Bondy, S., Poland, B., et al. (1998). Smoking in the home: Changing attitudes and current practices. American Journal of Public Health, 88, 797-799. Ashley, M. J., & Ferrence, R. (1998). Reducing children’s exposure to environmental tobacco smoke in homes: Issues and strategies. Tobacco Control, 7, 6165. Berman, P., Kendall, C., & Bhattacharya, K. (1994). The household production of health: Integrating social science perspectives on micro-level health determinants. Social Sciences and Medicine, 38, 205-215.
Journal of Pediatric Health Care
Biener, L., Cullen, D., Zhu, X. D., & Hammond, S. K. (1997). Household smoking restrictions and adolescents’ exposure to environmental tobacco smoke. Preventive Medicine, 26, 358-363. Cook, D. G., & Strachan, D. P. (1999). Health effects of passive smoke. 10. Summary of effects of parental smoking on the respiratory health of children and implications for research. Thorax, 53, 884-893. Corbett, K. (2001). Susceptibility of youth to tobacco: A social ecological framework for prevention. Respiration Physiology, 128, 103-118. Eriksen, W., & Bruusgaard, D. (1995). Smoking behavior in young families. Do parents take practical measures to prevent passive smoking by the children? Scandinavian Journal of Social Medicine, 25, 93-99. Farkas, A., Gilpin, E., Distefan, J., & Pierce, J. P. (1999). The effects of household and workplace smoking restrictions on quitting behaviors. Tobacco Control, 8, 261-265. Fearnow, M., Chassin, L., & Presson, C. (1998). Determinants of parental attempts to deter their children’s cigarette smoking. Journal of Applied Developmental Psychology, 19, 453-468. Fried, P., Perkins, S., Watkinson, B., & McCartney, J. (1995). Association between creatinine-adjusted and unadjusted urine cotinine values in children and the mother’s report of exposure to environmental tobacco smoke. Clinical Biochemistry, 28, 415-420. Gilpin, E., White, M., Farkas, A., & Pierce J. (1999). Home smoking restrictions: Which smokers have them and how they are associated with smoking behavior. Nicotine and Tobacco Research, 1, 153-162. Goldstein, J. (1994). Informal smoking controls in Winnipeg households. Canadian Journal of Public Health, 85, 106-109. Greenberg, R., Strecher, V., & Bauman, K. (1994). Evaluation of a home-based intervention to reduce infant passive smoking and lower respiratory illness. Journal of Behavioral Medicine, 17, 273-290. Jarvis, M. (1999). Children’s exposure to passive smoking: Survey methodology and monitoring trends. WHO International Consultation of Environmental Tobacco Smoke and Child Health. Li, J., Peat, J., Xuan, W., & Berry, G. (1999). Meta-analysis on the association between environmental tobacco smoke (ETS) exposure and the prevalence of lower respiratory tract infection in early childhood. Pediatric Pulmonology, 27, 5-13.
Mannino, D. M., Carabello, R., Benowitz, N., & Repace, J. (2001). Predictors of cotinine levels in US children: Data from the third National Health and Nutrition Examination Survey. Chest, 120, 1-13. Mannino, D.M., Moorman, J., Kingsley, B., Rose, D., & Repace, J. (2001). Health effects related to environmental tobacco smoke exposure in children in the United States. Archives of Pediatric and Adolescent Medicine, 115, 36-41. Matt, G., Hovell, M., Zakarian, J., Bernert, J., & Pirkle, J. (2000). Measuring secondhand smoke exposure in babies: The reliability and validity of mother reports in a sample of low-income families. Health Psychology, 19, 232-241. Okah, F., Choi, W., Okuyemi, K., & Ahluwalia, J. (2000). Passive cigarette smoke exposure of infants: Importance of non-parental sources. Archives of Pediatric and Adolescent Medicine, 154, 1237-1241. Peterson, E., Johnson, C., & Ownby, D. (1997). Use of urinary cotinine and questionnaires in the evaluation of infant exposure to tobacco smoke in epidemiologic studies. Journal of Clinical Epidemiology, 50, 917-923. Pizacani, B., Martin, D., Stark, M., Koepsell, T., Thompson, B., & Diehr, P. (2003). Household smoking bans: Which households have them and do they work? Preventive Medicine, 36, 99-107. Rickert, W. S. (1999). Environmental tobacco smoke: Properties, measurement techniques, and applications. Presented at International Consultation on Environmental Smoke (ETS) and Child Health, January 11-14, 1999. Geneva, Switzerland. Retrieved April 14, 2003, from http://www.smoke-free/ca/ pdf_1/whoetsreport.pdf. Sockrider, M. (1996). The respiratory effects of passive tobacco smoking. Current Opinion in Pulmonary Medicine, 2, 129133. U. S. Department of Health and Human Services. (1989). Making health communication programs work: A planner’s guide. Bethesda, MD: National Cancer Institute, Office of Cancer Communications. U. S. Department of Health and Human Services. (2002). Prior HHS poverty guidelines and Federal Register references. Retrieved August 12, 2005, from http://www.aspe.hhs.gov/poverty/figuresfed-reg.shtml. Whitlock, G., McMahon, S., Vander Home, S., Davis, P., Jackson, R., & Norton, R. (1998). Association of environmental tobacco smoke exposure with socioeconomic status in a population of 7725 New Zealanders. Tobacco Control, 7, 276-280.
March/April 2006 105
Household Characteristics, Smoking Bans, and Passive Smoke Exposure in Young Children Yvonne K. Yousey, RN, CPNP, PhD
ABSTRACT Introduction: Young children are vulnerable to the health effects of environmental tobacco smoke (ETS) exposure in their own homes. Characteristics of households and the use of smoking bans (i.e., no smoking allowed) as an indicator of smoke exposure need to be understood before interventions can be developed to eliminate ETS exposure in homes where young children live. Methods: This cross-sectional, descriptive study investigated demographic characteristics, knowledge, attitudes/beliefs, health of children, smoking practices, and the presence of smoking bans in households. A survey questionnaire was administered to a convenience sample of 226 English- and Spanish-speaking subjects, 18 to 50 years of age, including both smokers and nonsmokers. Cotinine levels of urine samples from children measured actual smoke exposure to confirm reports of home smoking policies. Results: Ethnicity of households (P ⬍ .001) and negative attitudes toward smoke exposure (P ⬍ .001) predicted the presence of smoking bans. The number of households with no or partial smoking bans correlated significantly with urine cotinine levels (r ⫽ .486); the presence of no or partial smoking bans predicted smoke exposure in households.
Yvonne K. Yousey is Assistant Professor, University of North Carolina, Charlotte, Department of Family and Community Nursing. Funded by the Colorado Tobacco Research Program, Boulder, Colo. Reprint requests: Yvonne K. Yousey, RN, CPNP, PhD, University of North Carolina, Charlotte, Department of Family and Community Nursing, 9201 University City Blvd, Charlotte, NC 28223; e-mail: [email protected]. 0891-5245/$32.00 Copyright © 2006 by the National Association of Pediatric Nurse Practitioners. doi:10.1016/j.pedhc.2005.08.006
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Discussion: Because the use of smoking bans in predicting household smoke exposure has not been previously demonstrated, further study is needed to determine how smoking bans can be utilized to eliminate or reduce smoke exposure in homes where children live. J Pediatr Health Care. (2006) 20, 98-105.
Exposure to environmental tobacco smoke (ETS) is an important preventable cause of child morbidity (Aligne & Stoddard, 1997), yet little is known how to effectively reduce or eliminate passive smoke in households with children. Whereas other risky behaviors generally only harm the individual performing them, tobacco smoking is a health risk for both the smoker and for those not directly involved in the behavior (Sockrider, 1996). For example, in the United States, 31.2% of children have daily ETS exposure in their own homes (Mannino, Carabello, Benowitz, & Repace, 2001). Health effects associated with ETS exposure in children, including decreased lung function, increased prevalence and exacerbations of asthma, middle ear disease, Sudden Infant Death Syndrome, and lung cancer, have been documented in meta-analyses by Cook and Strachan (1999) and Li, Peat, Xuan, and Berry (1999). For example, child illness is directly related to parent smoking (Cook & Strachan), with children of smokers twice as likely to have a serious respiratory infection requiring hospitalization than children of nonsmokers (Li et al.). Regarding sociodemographic characteristics that affect ETS exposure, low-income, minimally educated, younger parents report more smoke exposure than do older, higher income, educated parents (Arborelius, Hallberg, & Hakansson, 2000; Eriksen & Bruusgaard, 1995). Whitlock, McMahon, Vander Home, Davis, Jackson, and Norton (1998) Journal of Pediatric Health Care
reported an inverse relationship between socioeconomic status, educational levels, and reported smoke exposure. In other words, lower socioeconomic status and lower educational levels were associated with higher smoke exposure in the household. Consequently, children living in homes associated with poverty are at greater risk for exposure and for associated health problems. Smoke exposure is also affected by ethnicity, with non-Hispanic White families reporting more smoke exposure than Hispanic families (Mannino, Moorman, Kingsley, Rose, & Repace, 2001). The smoking behaviors of individuals and households are best understood in the context of cultural norms, environmental cues, and infrastructure constraints including costs and restrictive polices (Corbett, 2001). Based on the household production of health framework (Berman, Kendall, & Bhattacharya, 1994), families use a combination of knowledge, attitudes, and behavioral norms interfacing with social factors such as age, income level, and education to make decisions about smoking in their households. Even though the awareness of dangers of ETS is increasing (up to 43% among smokers and 78% among nonsmokers) and awareness is positively correlated with higher levels of education, only 34% of homes containing nonsmokers and 20% of homes containing smokers are smoke free (Ashley et al., 1998). As suggested by Berman, Kendall, and Bhattacharya, the reduction or elimination of ETS exposure occurs by considering multiple household, cultural, and demographic characteristics. While policies that restrict smoking in public places have effectively reduced exposure for nonsmokers, no such policies exist for homes. Yet recent research findings suggest that home smoking restrictions are one method of reducing smoke exposure for children (Gilpin, White, Farkas, & Journal of Pediatric Health Care
Pierce, 1999; Pizacani, Martin, Stark, Koepsell, Thompson, & Diehr, 2003). For example, home smoking bans were found to increase cessation and reduce smoking (Farkas, Gilpin, Distefan, & Pierce, 1999), prevent smoking initiation (Biener, Cullen, Zhu, & Hammond, 1997), and influence an antismoking community climate (Farkas et al., 1999). Home smoking bans are relatively new approaches used to address ETS exposure, yet they have not achieved a high level of support (Ashley & Ferrence, 1998). One of the reasons for this lack of support is related to the commonly held belief that external agents should not interfere with behavior in private settings, particularly when it pertains to child rearing. The purpose of this study was to investigate household characteristics, household smoking behaviors including bans, and the relationships between household smoking behaviors and exposure of children to smoke. The first hypothesis investigated whether there were differences in demographic characteristics in households that did and did not report complete smoking bans. The second hypothesis tested whether households with complete smoking bans (i.e., no smoking allowed) would demonstrate greater knowledge of smoke exposure, more negative attitudes toward smoke exposure, and greater child health status than households without smoking bans (i.e., smoking allowed). The third hypothesis tested whether children in households with complete smoking bans would have less smoke exposure as measured by cotinine levels than children in households without smoking bans. METHODS This descriptive, cross-sectional study examined the relationships between household characteristics, smoking practices, and level of exposure of children to smoke.
Two methods of data collection were used. First, individuals with children were asked to complete a self-administered survey that describes their smoking behaviors. Second, a urine sample was obtained from a child in the household and tested for cotinine to confirm reports of smoking practices in the household. Setting and Sample A convenience sample of 226 households containing an adult between 18 and 50 years of age and a child younger than 6 years of age was selected from 4718 families whose children were enrolled in preschools and Head Start facilities (1021 families) or in schoolbased health centers (3717 families). Households were selected from three suburban counties of a metropolitan area in the midwestern United States. Sixty-two percent of the study sample described themselves as Hispanic, 24% as non-Hispanic White, and 15% as “other” (African American, Asian, or Native American). No significant differences in sociodemographic characteristics were found between families recruited from Head Start and preschools and those from school-based health centers. The average age of respondents completing the surveys was 28.7 years; 81% were married or cohabiting; and 75% were women. Forty-three percent of mothers and 45% of fathers had less than a high school education, and 66% of households earned less than $30,000 per year. Eighty-four percent of the sample had incomes at or below the poverty level, based on federal poverty guidelines (US Department of Health and Human Services, 2002). Urine samples for cotinine measurement were collected from children younger than 6 years of age in 211 households. Each of the 15 families who did not provide a urine sample was contacted on three occasions without success to obtain the urine specimen; no data March/April 2006
99
were available that described their reasons for not participating. Instrument Development The 67-item survey instrument, developed by the principal investigator, examined individual and household demographics, smoking behaviors in households, presence of home smoking restrictions, knowledge of harms and health effects of smoke exposure, health status of children in the household, and attitudes/beliefs related to smoke exposure. The survey was designed for a 5th-grade reading level using the SMOG Readability Tool (US Department of Health and Human Services, 1989). Items on household smoking behaviors and presence of home smoking restrictions were adapted from National Health Interview Survey (NHIS). Sociodemographic characteristics previously identified as influencing ETS exposure were incorporated into the survey. These characteristics, measured as continuous and categorical variables, included the number of persons living in household, race, ethnicity, age, sex, marital status, educational level of respondent and spouse/partner, type of dwelling (apartment or single family), number of rooms in the dwelling, and income of the household. A 12-item true/false subscale measured knowledge of health effects associated with smoke exposure. Six items measured knowledge of actual health problems including otitis media and respiratory problems, and six items focused on dangers of smoke exposure at various ages and the perceived effects on children’s general health status. The answer to each item was rated as “1” if awareness/knowledge of harmful health effects associated with ETS was indicated and “0” if it was not; therefore, a higher total score indicated greater knowledge of ETS exposure. 100 Volume 20 • Number 2
Negative attitudes and beliefs about smoke exposure were measured using a 13-item Likert scale that includes questions related to parent (a) attitudes toward protecting children against smoke exposure, (b) beliefs about smoke exposure to self or to child, (c) beliefs about the effects of smoke exposure on children, and (d) beliefs about legal protection of children from smoke exposure. Respondents were asked to indicate whether they strongly agreed, agreed, were neutral, disagreed, or strongly disagreed with the statements provided. The respondent’s answer to each item was assigned a value from 1 to 5 (positive to negative) regarding smoke exposure; a higher overall score indicated a more negative attitude toward ETS exposure. The health status of children in the household was assessed by asking parents questions related to the following four measures: (a) the perceived health of each child (healthy or not healthy); (b) the number of minor acute illnesses each child experienced during the past year; (c) the number of children with chronic illnesses; and (d) the number of children with asthma. Reported smoking practices and behaviors in homes included the following questions: (a) the last time someone smoked in home, (b) the number of cigarettes smoked in the home in the past week, (c) the smoking status of the respondent (e.g., current smoker, ex-smoker, never smoked); (d) the number of smokers living in the home, and (e) the presence of household smoking bans. Households were categorized as having complete smoking bans (no smoking allowed) if they reported that no one is allowed to smoke anywhere anytime in the home. Households with no or partial bans (smoking allowed) allowed unlimited smoking or smoking with restrictions in the home.
The survey was pilot tested with 10 English-speaking and 10 Spanish-speaking adults. Questions were revised based on the feedback from pilot testing, and the final draft of the questionnaire was reviewed by two expert researchers for clarity and face validity. The survey was translated into Spanish by a certified translator and reviewed by two bilingual expert researchers and two Spanish-speaking clinic assistants for clarity. Care was taken to ensure that each survey item had comparable responses in Spanish and English in order to minimize bias and increase accuracy. Measurement of Smoke Exposure Evaluating smoke exposure in households by measuring cotinine reduces potential bias and errors inherent in self-reporting, sometimes associated with social desirability (Fried, Perkins, Watkinson, & McCartney, 1995; Matt, Hovell, Zakarian, Bernert, & Pirkle, 2000). Cotinine, a sensitive and specific biomarker for nicotine, is a surrogate marker for smoke exposure and is considered the “gold standard” for assessing questionnaire validity. Cotinine provides an integrated measure of the total amount of nicotine absorbed from all sources of smoke exposure during the previous 2 to 3 days, with a half-life of 32 to 82 hours (Jarvis, 1999; Rickert, 1999). Although cotinine is present in all body fluids, urine was selected for this study because obtaining a sample is less invasive than obtaining other body fluids. NicAlert (Nymox Corp., Maywood, NJ), used in this study, is a colorimetric screening test using immunoassay with urine that detects cotinine at seven different levels. This tool has a cut-off for smoke exposure consistent with previous screening tests (Jarvis; Rickert). For example, ⱕ30 ng/dL indicates no smoke exposure and ⱖ 31ng/dL indicates smoke exposure. As previously demonstrated Journal of Pediatric Health Care
by Peterson, Johnson, and Ownby (1997), a single measurement of cotinine adequately demonstrated exposure in this study. Validity and Reliability The use of predetermined valid and reliable questions on smoking practices from the NHIS survey increased the internal validity of the survey instrument. Evaluation of the knowledge subscale revealed a reliability estimate of .60 (K-R 20). Factor analysis evaluated construct validity of the attitude scale, confirming 67% of variance through four factors identified in the measure. The results of the factor analysis and the reliability estimate (Cronbach’s ␣ ⫽ .80) of the attitude scale were adequate to confirm the reliability of the scale as measurement of negative attitudes toward smoke exposure. The Nicalert screening test had a sensitivity of 87% and specificity of 100%. Data Collection Procedures The study was approved by the Colorado Multidisciplinary Institutional Review Board and was administered in Colorado. Data were collected during a 3-month period in autumn of 2002. Individuals aged 18 to 50 years who spoke and wrote English or Spanish fluently and who had children from newborn to age 6 years were invited to participate in the study. Permission for data collection was obtained from administrators of preschools, Head Start, and school-based health centers. Flyers in Spanish and English, distributed prior to the study, informed families when data collection would occur at each site. After the researcher or assistant explained the study, eligible parents provided written consent, completed the self-administered survey, and assisted in obtaining a urine sample from their child. Specimen cups were numbered to correspond with completed surveys, and results of cotinine testing were recorded on the survey and Journal of Pediatric Health Care
shared with parents if they requested. Families who completed the survey and provided a urine sample received a $20 grocery gift certificate. A research assistant who was fluent in Spanish assisted with Spanish-speaking families. Data were entered using the software package Statistical Program for Social Sciences (SPSS) 11.0. Outliers were identified and removed from the data set or transformed into categorical variables. Cases with missing data were excluded on a case-by-case basis, resulting in 225 completed surveys and 203 urine samples eligible for data analysis. Statistical Tests Chi-square tests were used to compare categorical sociodemographic variables and the health status of children in households with and without smoking bans. Continuous sociodemographic variables, knowledge, and attitude scores were evaluated by t tests (two-tailed, ␣ ⫽ .05). The ␣ on sociodemographic variables was reduced to .001 (Bonferroni adjustment) to reduce the possibility of a Type I error. Spearman and Pearson correlations or Kendall’s Tau_b (when assumptions for homoscedasticity and normal distribution were not met) evaluated correlations between these variables and the presence of home smoking bans. Nine sociodemographic variables and knowledge and attitude scores were used to predict the presence of smoking bans. Thus, logistic regression analysis tested the relationship between these variables and the reported presence of smoking bans in the first and second hypotheses. Bivariate analyses indicated that the health status of children is not significantly correlated with complete smoking bans. Therefore, the child health status variable was entered into the model after the previous variables to determine if it changed the predictability of the model and to test the second hypothesis.
Smoking practices that significantly correlated with household smoking policies (complete smoking ban versus no ban or a partial ban) were entered simultaneously into a second logistic regression model to predict ETS exposure and to test the third hypothesis. The reference group in this regression model was “no ETS exposure” defined as less than 30 ng/dL of cotinine and given a value of “0”; ETS exposure as measured by cotinine levels of 30 ng/dL or greater was given a value of “1.” Hosmer and Lemeshow Goodness-of-fit and Model 2 tests assessed adequacy of the logistic regression models, and odds ratios with confidence intervals were calculated. RESULTS Fifty-five non-Hispanic White families (24%) and 140 Hispanic families (62%) completed the survey; 86 Hispanic families (61%) completed the survey in Spanish. Hispanic families reported significantly lower educational levels of fathers (P ⬍ .000) and mothers (P ⬍ .000) and lower household income (P ⬍ .000) than did nonHispanic White families. Of 225 households reporting, 61 (27%) had no smoking bans (i.e., smoking allowed) and 164 (73%) had complete smoking bans. Of 203 urine samples, (48%) tested negative and 106 (52%) tested positive for exposure to smoke. The majority of the sociodemographic characteristics were similar between those who had no smoking bans and those who had complete smoking bans. However, significant differences were found between the two groups in language spoken in the home (P ⬍ .001), reported ethnicity (P ⬍ .001), racial categories (P ⫽ .004), and language in which the survey was completed (P ⬍ .001). The first hypothesis was only partially supported because there were no significant differences in the education of mothers (P ⫽ .182) or fathers (P ⫽ .104), marital status (P ⫽ .202), household income (P ⫽ .226), or the March/April 2006 101
TABLE 1. Summary of logistic regression analysis for variables predicting smoking bans in households
Variable
Estimated coefficient ()
Standard error
Significance
.235 ⫺.048 ⫺.186 .178 2.985 ⫺.507 ⫺.073 ⫺.181 7.042
.215 .067 1.162 .824 .967 .882 .190 .052 4.068
.275 .477 .873 .829 .0022* .565 .701 .000* .083
Residents in household Age of respondent Marital status Household income Spanish or English Educational level of father Score of knowledge items (12) Score of attitude items (13) Constant
Exp () (odds ratio)
1.264 .953 .830 1.195 19.987 .602 .930 .834 1144.209
95% Confidence interval EXP () Lower
Upper
.830 .836 .085 .238 2.974 .107 .640 .754
1.927 1.087 8.102 6.014 131.649 3.391 1.350 .923
*Significance ⬍ .01.
type of dwelling in which they lived (P ⫽ .747). Although households with complete home smoking bans had significantly higher levels of knowl-
smoke exposure than did households with partial or no smoking bans (M ⫽ 49.18) (t ⫽ 6.85, P ⬍ .001). Attitude scores correlated negatively and significantly with no ban
Of all the household characteristics that were investigated, language in which the survey was completed and attitudes toward smoke exposure were predictive of smoke exposure. edge about the negative effects of smoking on child health than did households without smoking bans (t ⫽ 2.642, P ⫽ .009), knowledge was not a predictor of smoking bans in multivariate analysis (OR ⫽ .93, P ⫽ .701). The mean score on the knowledge subscale was 10.58 (range 3-12), and a low but statistically significant negative correlation (r ⫽ –.179) existed between the level of knowledge and a no ban or partial home smoking ban. While scores differed significantly between households with bans and those without bans, all scores were high (M ⫽ 10.58), making differences between groups less evident and more difficult to interpret. Based on the attitude subscale, households with complete smoking bans (M ⫽ 58.12) had significantly more negative attitudes toward 102 Volume 20 • Number 2
or partial smoking bans in the household (r ⫽ –.365, P ⫽ .01) and predicted the presence of smoking bans in homes (P ⬍ .001, OR ⫽ .83 {95% CI ⫽ .76 –.91}) , confirming the relationship between negative attitudes toward smoke exposure and presence of smoking bans. No significant differences existed between households with and without smoking bans in reported health of children (P ⫽ .377), report of minor acute illnesses (P ⫽ .110), or presence of asthma (P ⫽ .120). The health status of children was not predictive of the presence of smoking bans in the multivariate analysis, and the hypothesized relationship between health status and smoking bans was not confirmed. Of all the household characteristics that were investigated, lan-
guage in which the survey was completed and attitudes toward smoke exposure were predictive of smoke exposure (see Table 1). The Hosmer and Lemeshow Goodness-of-fit test (2 ⫽ 7.62, df ⫽ 8, P ⫽ .472) and the Model 2 test (2 ⫽ 51.78, df ⫽ 16, P ⬍ .000) confirmed the use of these variables in the model. The predictive ability of the model (86.9%) did not improve when health variables were added, confirming their lack of predictability in this study. All smoking practices in the home, except the number of smokers living in the home, correlated with no smoking bans (i.e., smoking allowed) (P ⬍ .01) and with cotinine levels (P ⬍ .01) (see Table 2). One hundred forty-eight households (73%) reported complete smoking bans, but only 97 households (48%) had children with negative tests for ETS exposure. As expected, significantly fewer households with complete smoking bans tested positive for ETS exposure than did households without smoking bans (P ⬍ .001). The number of homes without smoking bans correlated positively and significantly with cotinine levels indicative of ETS exposure (r ⫽ .472, P ⫽ .01). Of all smoking practices, no bans or partial smoking bans predicted smoke exposure as measured by cotinine levels (P ⫽ .04. OR ⫽ .22), supporting the third hypothesis (see Table 3). Journal of Pediatric Health Care
TABLE 2. Summary of correlation of smoking practices and smoking bans 1 N ⴝ 206
1. Last time someone smoked in home 2. No. smokers living in home 3. No. cigarettes smoked in home in past week 4. Smoking status of respondent 5. Smoking situations score of households 6. Complete smoking ban
.474* .565* .473* .552* .625*
2 N ⴝ 222
3 N ⴝ 213
4 N ⴝ 215
5 N ⴝ 218
6 N ⴝ 222
.474*
.565* .765*
.473* .598* .542*
.552* .269* .352* .387*
.625* .339* .492* .440* .694*
.765* .598* .269* .339†
542* .352* .492*
.387* .440*
.694*
*P ⬍ .01. †P ⬍ .05.
TABLE 3. Summary of logistic regression analysis for smoking practices that predict cotinine levels as indicators of smoke exposure
Variable
Last time someone smoked in home No. smokers in home No. cigarettes smoked in past week Smoking status of respondent Complete ban No/partial ban Constant
Estimated coefficient 
Standard error
Significance
Exp () Odds ratio
.861 ⫺.363 ⫺.383 .028 ⫺1.497 1.240
.509 .795 .792 .530 .711 .777
.090 .649 .629 .958 .0352* .111
2.366 .696 .682 1.028 .224 3.456
95.0% Confidence interval EXP () Lower
Upper
.873 .146 .144 .364 .056
6.413 3.308 3.222 2.907 .902
*Significance ⬍ .05.
DISCUSSION Implications In contrast to previous studies (Mannino, Carabello, Benowitz, & Repace, 2001), educational level, income, age of parents, and marital status were not significantly different between groups with or without smoking bans in this study. The lack of differences may have been influenced by the sample, 84% of whom had a household income at or below the poverty level, and the fact that the groups being compared were not equal in number. In the findings related to race and ethnicity, Hispanic families (a) had lower incomes (P ⬍
.001), (b) completed less education (P ⬍ .001), (c) reported a greater number of smoking bans (P ⬍ .001), and (d) had more negative cotinine measurements (P ⫽ .05) than did non-Hispanic White families, suggesting that race and ethnicity may influence behaviors more than education or income. Gilpin et al. (1999) found similar differences in prevalence of smoking practices in Hispanic and nonHispanic White groups. No known studies to date have confirmed these reported differences with cotinine measurements. Even though knowledge levels of smoke exposure were high in
Educational level, income, age of parents, and marital status were not significantly different between groups with or without smoking bans in this study. Journal of Pediatric Health Care
this study, higher levels of knowledge did not predict presence of smoking bans in households. This is consistent with findings by Goldstein (1994), where 90% of the sample acknowledged awareness of the dangers of smoke exposure but only 24% implemented activities to reduce exposure. Greenberg, Strecher, and Bauman (1994) demonstrated limited success in changing behaviors when information-based interventions were used to reduce smoke exposure. While knowledge about smoke exposure may be useful, consideration of strategies based on more than just information may be necessary to change behavior. The importance of negative attitudes as a predictor of smoking bans in this study is consistent with the findings of Fearnow, Chassin, and Presson (1998), who found that health dangers of smoking were highly correlated with parental activism related to smoking. Parents with negative attitudes toMarch/April 2006 103
ward smoking demonstrated stronger relationships between values and actions than did those with less negative attitudes. Gilpin et al. (1999) found that beliefs in the harmfulness of smoke exposure of babies and children were associated with increased reports of smoking restrictions in the home. They also reported that families with smoking bans expressed a preference for no smoking within the household. Arborelius et al. (2000) investigated interventions useful in preventing smoke exposure of young children and concluded that interventions need to focus on smoke-free environments for children, not on the smoking cessation of parents. No effects for interventions that focused on providing factual information about
as cotinine, and all of these used smoking measures other than smoking bans. Knowing which factors predict presence of smoking bans in households will assist in developing targeted intervention strategies that facilitate adoption by households that otherwise would not have smoking bans (Okah, Choi, Okuyemi, & Ahluwalia, 2000). Limitations The cross-sectional nature of the study allowed assessment of associations between variables and examined differences between households with and without smoking bans. The convenience sample was small, limiting power of analysis and generalizability of the findings. No data
Even though knowledge levels of smoke exposure were high in this study, higher levels of knowledge did not predict presence of smoking bans in households. the dangers of ETS exposure were found in their study; however, demonstrable effects for interventions geared to behavioral strategies that considered parental beliefs and effects on children were found. Demographic findings such as income level, educational level, and age of parents associated with smoking behaviors in previous studies were not demonstrated in this study. Differences in ethnicity and smoking behaviors were evident. Relationships between knowledge and attitudes and smoking policies were similar to previously reported studies. Only a few studies have validated results of reported smoking behaviors with actual measurement of smoke exposure through a measure such 104 Volume 20 • Number 2
were available on families who did not participate in the study but attended Head Start, preschool, or the school-based health centers. Based on total enrollments at these facilities, only a small percentage of families participated in this study. Given that this study consists of a convenience sample, it is not possible to investigate differences that may have influenced findings, nor is there a well-defined population to which findings can be generalized. Indicators of child health measured in this study did not predict no bans or partial household smoking bans in contrast to previous meta-analyses demonstrating relationships between minor acute illness such as otitis media, respiratory illnesses including asthma,
and smoke exposure in households (Cook & Strachan, 1999; Li et al, 1999). The lack of significance in health indicators of children between families with and without smoking bans in this study may have been related to the level of measurement, the use of recall in collecting these data, or the relatively small number of subjects. The questions related to health were only retrievable in categorical variables providing less precise measurement, with decreased specificity and variability. The cotinine screening test had a sensitivity of 87% percent, indicating the possibility of false-negative results. In addition, no back-up method of confirmation was used. Given the results that slightly more than half of the children had positive tests for exposure and only 23% of families reported smoke exposure at home, it is not likely that children tested from homes with smoking bans in place were missed because of limitations of testing. Sources of exposure outside of the home other than day care were not identified and could also have affected the cotinine results. Finally, this screening test could not adjust for variations in dilution of the urine, making it slightly less accurate than other available methods of analysis. CONCLUSIONS The findings in this descriptive study support the use of smoking bans as a necessary and integral part of effective interventions to reduce and/or eliminate smoke exposure in homes where children live. They further contribute to an increased understanding of racial, ethnic, and behavioral factors that affect how families establish polices regarding smoke exposure. Finally, this study confirms the reliability of reported smoking bans in homes where children live. Primary care providers are consistently challenged to assist families in maintaining smoke-free enJournal of Pediatric Health Care
vironments for their children. Screening for exposure using an inexpensive, user-friendly technique such as NicAlert can be the basis for teaching about the positive effects of reducing smoke exposure indoors. Families can be encouraged to implement smoking bans in primary care settings where smoke exposure in families is identified. The focus on smoking bans as a method for predicting smoke exposure was central to this study. The next step will be to evaluate the use of household smoking bans in reducing or eliminating smoke exposure in households where young children reside. Health care providers who have frequent and regular contact with parents of young children can implement these interventions. These efforts can keep children’s environments smoke free and reduce morbidity associated with smoke exposure. Additional research is needed to determine the effectiveness of interventions and to implement them into the standard of care. REFERENCES Aligne, C. A., & Stoddard, J. (1997). Tobacco and children: A economic evaluation of the medical effects of parental smoking. Archives of Pediatric Adolescent Medicine, 151, 648-653. Arborelius, E., Hallberg, A. C., & Hakansson, A. (2000). How to prevent exposure to tobacco smoking among small children: A literature review. Acta Paediatrica, 89, 65-70. Ashley, M..J., Cohen, J., Ferrence, R., Bull, S., Bondy, S., Poland, B., et al. (1998). Smoking in the home: Changing attitudes and current practices. American Journal of Public Health, 88, 797-799. Ashley, M. J., & Ferrence, R. (1998). Reducing children’s exposure to environmental tobacco smoke in homes: Issues and strategies. Tobacco Control, 7, 6165. Berman, P., Kendall, C., & Bhattacharya, K. (1994). The household production of health: Integrating social science perspectives on micro-level health determinants. Social Sciences and Medicine, 38, 205-215.
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