Journal of Pediatric Psychology Advance Access originally published online on May 11, 2006
Journal of Pediatric Psychology 2007 32(2):154-166; doi:10.1093/jpepsy/jsj123
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Prevalence and Correlates of Early Onset Asthma and Wheezing in a Healthy Birth Cohort of 2- to 3-Year Olds
1 Department of Psychology, University of Massachusetts Boston, 2 Department of Sociology, State University of New York, 3 Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, 4 Department of Psychology, Washington and Lee University, and, 5 Department of Psychiatry, University of Connecticut Health Center
All correspondence concerning this article should be addressed to Alice S. Carter, Department of Psychology, University of Massachusetts Boston, Boston, Massachusetts 02125-3393. E-mail: alice.carter{at}umb.edu
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Abstract |
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Objective The combined contribution of neonatal, perinatal, and maternal health, demographic, environmental, and family psychosocial factors to early onset asthma and wheezing in a healthy birth cohort was examined. Methods Participants included 1,158 ethnically and socioeconomically diverse parents of 2- and 3-year olds who completed mailed questionnaires. Results Asthma and wheezing prevalence was 8.4 and 8.1%, respectively. Asthma during pregnancy, smoking in the home, and being male increased risk for asthma diagnosis and wheezing whereas social support minimized risk for both. Shorter gestational age, exposure to violence, and maternal anxiety increased risk for wheezing. The negative impact of smoking in the home was greatest for children with shorter gestational ages and mothers with asthma during pregnancy. Conclusion Findings confirm and extend previous work documenting demographic risks and highlight smoking, violent events, and social support in early onset asthma and wheezing. Findings illustrate the need for ecologically based interventions to treat asthma and wheezing in young children.
Key words: asthma; early childhood; health; stress.
Asthma is the most pervasive chronic illness among children in the United States, affecting nearly 13% of children under the age of 18 (Asthma Prevalence, Health Care Use and Mortality, 2000–2001
). The prevalence of childhood asthma has steadily increased between 1980 and 1999 with greatest increases among young children (Mannino et al., 2002). During the period from 1980 to 1995, asthma prevalence in 0- to 4-year olds increased from 2.3 to 6.1% (Mannino et al., 2002). In addition to diagnosed asthma, there exists a substantial group of preschool-aged children with "possible asthma," who wheeze but who have not received a diagnosis of asthma (Wolf, Berry, O’Connor, & Coover, 1999). Although there has been little research on this subgroup, many young children with persistent wheeze will probably be subsequently diagnosed with asthma (Kurukulaaratchy, Matthews, & Arshad, 2004). Despite the growing rates of early childhood asthma, a defined set of correlates of early asthma onset has yet to be determined (Salam, Li, Langholz, & Gilliland, 2004). Moreover, many studies have focused on health and demographic characteristics; less attention has been paid to family and community contextual factors.
Utilizing a biopsychosocial framework informed by the older child extant literature may advance understanding of risks for asthma and wheezing in young children (see Engel, 1977). Within the biopsychosocial model, the following broad categories of risk for asthma and wheezing have been identified: (a) health factors in the child and family (e.g., gestational age and maternal asthma), (b) demographic factors (e.g., child sex and poverty), (c) environmental factors (e.g., violent events and smoking in the home), and (d) family psychosocial factors (e.g., parenting stress and social supports) (See Fig. 1).
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Health Factors |
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Birth status variables such as preterm delivery (<36 weeks gestation), mode of delivery (Caesarian section), low birth weight (<2,500 g), and lower Apgar scores are also reported to be associated with childhood asthma (Anand, Stevenson, West, & Pharoah, 2003
; Hakansson & Kallen, 2003; Siltanen, Savilahti, Pohjavuori, & Kajosaari, 2004).
Childhood asthma and wheezing have been associated with maternal health factors such as asthma status as well as general physical and mental health (e.g., depression and anxiety). For example, nearly half of all inner-city mothers of children with asthma reported clinically significant depression levels (Kaugars, Klinnert, & Bender, 2004).
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Demographic Factors |
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There is agreement in the literature that child sex (higher in males until puberty), race (minorities are at increased risk), and maternal age (children of younger mothers are at increased risk) are asthma correlates (Aligne, Auinger, Byrd, & Weitzman, 2000
; Bjornson & Mitchell, 2000; Infante-Rivard, 1995; Jenkins et al., 2003). However, there is less conclusive research on maternal education, single-parent status, household size, child age, and poverty (Burr, Verrall, & Kaur, 1997; Gottlieb, Beiser, & O’Connor, 1995; Persky et al., 1998; Weiss & Gold, 1995). Furthermore, Bisgaard and colleagues found that wheezing was more prevalent in infants from families burdened by low socioeconomic status (Bisgaard, Dalgaard, & Nyboe, 1987). For asthma, black race appears to be an important risk factor; however, this association may be due to "race-associated" factors including poverty, poor health care, and urban environments (Cunningham, Dockery, & Speizer, 1996; Schwartz, Gold, Dockery, Weiss, & Speizer, 1990; Smith, Hatcher-Ross, Wertheimer, & Kahn, 2005). Some research suggests that such race-associated factors are more highly correlated with asthma than race alone (Aligne et al., 2000), but work by Schwartz and colleagues (1996) and Cunningham and colleagues (1990) suggest that the prevalence of asthma and frequent wheeze remains elevated among black children even after adjusting for other demographic characteristics. |
Environmental Factors |
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Stressful life events and chronic stress have been found to have an impact on asthma (Kaugars et al., 2004
; Sandberg et al., 2000; Wright et al., 2004). In a study of school-aged children with asthma, Sandberg and colleagues found that exacerbations in asthma symptoms were present for up to 1 month following a significant life event (e.g., death of a loved one) (Sandberg et al., 2000). In addition, a child’s risk of having an asthma attack was heightened if the child was under chronic stress. Moreover, new research shows that children exposed to violence are at risk for greater physical complications of asthma (Wright et al., 2004). Though there is no research on the link between violence exposure and asthma or wheezing in very young children, Wright and colleagues’ (2004) study of school-aged children found a significant association between increased exposure to violence and increased asthma symptoms (Wright et al., 2004).
Infants exposed to tobacco smoke in utero are at an increased risk of developing wheeze and/or asthma, and children exposed to environmental tobacco smoke may be at an increased risk for symptoms ranging from wheezy bronchitis to severe asthma attacks (Lux, Henderson, & Pocock, 2000; Mannino et al., 2002; McQuaid, Walders, & Borrelli, 2003; Strachan & Cook, 1998).
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Family Psychosocial Factors |
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Factors including family conflict, parenting difficulties, and parental social support may predict the onset and course of asthma in children (Kaugars et al., 2004
). For example, marital conflict and parenting difficulty predicted earlier age of asthma onset (Klinnert, 2000). In addition, lack of parental social support has been found to be related to early onset asthma (Chernoff, Ireys, DeVet, & Kim, 2002; Gustafsson, Kjellman, & Bjorksten, 2002). Specifically, Gustafsson and colleagues found that social support contributed to the course of early childhood illness, minimizing maladaptive outcomes in children (Gustafsson et al., 2002). Given the likelihood that stress can lead to exacerbated asthma symptoms, these findings suggest that social support may play a buffering role among families with children with asthma and/or may buffer the onset of asthma and wheeze. To our knowledge, no study has addressed the combined contribution of child and parent health, demographic, environmental, and family psychosocial factors as correlates of asthma and wheezing in a healthy birth cohort. The current study fills this gap by examining a range of possible correlates of childhood asthma and wheezing in young children born at full term and at healthy birth weight. We hypothesized that (1) parent and child health factors including birth/neonatal risks, parents’ report of poorer physical health [i.e., mother’s asthma status during pregnancy, the respondent’s (typically mother’s) self-reported health status, and maternal anxiety and depression] would be associated with reported asthma and wheezing; (2) child and family demographic risk factors including minority status (i.e., black vs. non-black race), older child age, young maternal age, low maternal education, single-parent status, and poverty would be correlated with asthma and wheezing; (3) parents of children in the asthma and wheezing groups would report poorer environmental conditions including greater negative life events, violent events, smoking in the home, and number of people in the household; (4) parents of children in the asthma and wheezing groups would report higher family contextual risk factors (i.e., higher family conflict, parenting laxness, parenting over reactivity, parental distress, and parent–child dysfunction and lower family expressiveness and social support); and (5) environmental and family contextual risk factors would moderate the relationship between health/demographic factors and asthma status.
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Methods |
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Participants
Participants included 1,158 parents, 96.1% of whom were biological mothers, of 2- and 3-year-old children who participated in the second year of an on-going longitudinal study of young children’s social-emotional development. The initial sample was an age-and-sex–stratified sample (n = 8,404) of families living in the 15 urban and suburban towns that comprise a small Northeastern Metropolitan Statistical Area (MSA). Excluded from the sample were 675 children who were born prematurely (<36 weeks) or with birth complications [e.g., low birth weight (<2,200 g), low Apgar scores (5 min scores <6), known chromosomal anomalies (e.g., Down syndrome), and significant birth complications (e.g., severe anoxia or need for resuscitation)]. Also excluded were children who were deceased or adopted (n = 18), the child of an investigator, and siblings within eligible families (n = 277). A post-exclusion random sample of 1,788 was drawn. Subsequently, non-English-speaking families (n = 50), parents who lost custody of the child (n = 17), and families who had moved out of state (n = 116) were excluded, resulting in a final eligible sample of 1,605. Excluded families (n = 183) did not differ from eligible families (n = 1,605) on most demographic characteristics. Parental age and 5-minute Apgar scores were slightly higher in the final eligible sample (t = 2.04–4.03, p < .05) (See Briggs-Gowan, Carter, Skuban, & Horwitz, 2001
). The 1,158 families who participated in this study represented 72% of the 1,605 eligible families who were initially randomly selected from birth records for children born healthy at a university teaching hospital between July 1995 and September 1997.
Procedures
Birth record data were collected for all participants. In addition, from June to September of 1999, parents were mailed a questionnaire booklet and a children’s book. Informed consent procedures, approved by an Institutional Review Board, were followed. Parents received $25 for participation.
Measures
Data for this study include that collected from birth records and from the second time point of a longitudinal study when children were between 2 and 4 years of age. Measures were collected when children were between 2 and 4 years of age unless otherwise noted.
Child and Parental Health Factors
Birth Status Variables
Birth status variables were obtained from birth records and included infant birth weight, gestational age, and 1- and 5-minute Apgar scores.
Parental Health Variables
Asthma During Pregnancy and Parental Self-Rating of Health.
Respondents were asked to indicate whether the mother had asthma during pregnancy. In addition, respondents themselves rated their own and maternal health status on a 5-point Likert scale, in which five represented excellent health and one represented poor health.
Beck Anxiety Inventory.
The Beck Anxiety Inventory (BAI) is a self-report measure that consists of statements that describe common symptoms of anxiety and has adequate psychometric properties (Beck, Epstein, Brown, & Steer, 1988). The individual indicates how much he/she has been bothered by each symptom on a 4-point scale from "not at all" to "severely bothered." In the current sample, the BAI achieved alpha coefficient of .88.
The Center for Epidemiological Studies—Depression Scale.
The Center for Epidemiological Studies—Depression Scale (CES-D) (Radloff, 1977) is one of the most widely used measures to assess depressive symptoms of adults (Gatz & Hurwicz, 1990). It has demonstrated high internal consistency (coefficient alpha from .84 to .90) and modest test–retest reliability for 2- to 4-week intervals (r from .51 to .67) (Radloff, 1977). It consists of 20 symptoms, each rated on a 4-point scale based on the frequency with which the item has been experienced in the previous week. In the current sample, the alpha coefficient was .90.
Child and Family Demographic Factors
Demographic information was gathered both from birth records (e.g., parental age and education) and survey questionnaires. Respondents were asked to provide information about child sex, age, ethnicity, and birth order, as well as maternal age and education, family type (i.e., married, cohabiting, single), number of adults and children living in the home, receipt of governmental services, and before-tax household income. Birth record data were used to assess the comparability of participants to non-participants. In all subsequent analyses, parent-reported demographic information was used.
Environmental Factors
Life Events Inventory
Respondents were asked to indicate whether any of 43 life events had occurred to a family member in the past year (divorce, moving to a new home, etc.) (Cochrane & Robertson, 1973). This measure was derived in part from the Schedule of Recent Life Experiences (Holmes & Rahe, 1967).
Exposure to Violence
Respondents rated whether 14 stressful events have ever happened to their child in his/her whole life (Carter & Briggs-Gowan, 2000, 1993). Four of these events related to violence exposure: (a) seen violence in your neighborhood; (b) seen someone use a weapon to threaten or hurt a family member; (c) seen someone hit, push, or kick a family member; and (d) seen or heard adult family members arguing very loudly or fighting. One item from the 43 family-based life events was included in the violence exposure composite: (Parent or family member) victim of violence or theft. These five items were summed to create the exposure to violence variable.
Household Exposures (Smoking in the Home and Number in Household)
Respondents were asked whether someone smokes in the home in which the child lives and the number of people living in the household.
Family Psychosocial Factors
Family Environment Scale
Two scales were included, expressiveness (e.g., There are lots of spontaneous discussions in our family) and conflict (e.g., Family members often criticize each other), which are comprised of a total of 18 items (Moos & Moos, 1983). These scales have demonstrated adequate reliability and validity and have been shown to discriminate between distressed and non-distressed families. Alphas in the current study for the family expressiveness and family conflict subscales were .50 and .64, respectively.
Parenting Practices
The Parenting Scale (PS) (Arnold, O’Leary, Wolff, & Acker, 1993; O’Leary, Arnold, Wolff, & Acker, 1991) was designed to assess dysfunctional discipline techniques among parents of 2- to 4-year-old children. Items consist of discipline "mistakes" and their effective alternatives, which anchor 7-point Likert-type scales. Items assess discipline delivery independent of the nature and rate of children’s misbehavior and of the particular types of punishment used. The full scale includes three subscales: laxness, over-reactivity, and verbosity. To minimize respondent burden, only the laxness (e.g., When my child does not do what I ask I often let it go or end up doing it myself) and over-reactivity (e.g., When my child misbehaves I raise my voice or yell) scales were included. The measure and its subscales have demonstrated adequate reliability and validity (Arnold et al., 1993; O’Leary, Smith Slep, & Reid, 1999). In the current study, the alphas for parenting laxness and parenting over-reactivity were .77 and .65, respectively.
Parental Distress and Parent–Child Dysfunction
Parenting Stress Index Short Form [(PSI/SF), 1990] is a 36-item measure that assesses perceived child difficulty, parent distress, and parent–child dysfunctional interactions. The measure has shown high internal consistency and good test–retest reliability (Abidin, 1990). For the purposes of this study, only the parental distress (e.g., I feel trapped by my responsibilities as a parent) and parent–child dysfunction (e.g., My child makes more demands on me than most children) subscales were used. The alpha coefficient in this sample was .87 for parental distress and .91 for parent–child dysfunction.
The Medical Outcomes Study Survey (MOS) was used to assess social support, including tangible support (e.g., Someone to take you to the doctor if you needed it) and emotional informational support (e.g., Someone you can count on to listen to you when you need to talk) (Sherbourne & Stewart, 1991). These scales are comprised of 12 items and have demonstrated adequate psychometric properties, including modest 1-year stability. In the current sample, the tangible support and emotional informational support subscales achieved alpha coefficients of .97 and .91, respectively.
Asthma Status
Asthma status was divided into three groups: asthma, wheezing, and no asthma. Children were placed in the "asthma" group (n = 98) if the parent reported that the child had been diagnosed with asthma by a physician (n = 91) or if the parent reported that the child visited the emergency room at least one time in the past year for an asthma attack (n = 7). "Wheezing" was included in analyses because it is a symptom of asthma that may be present in undiagnosed children and is understudied. Children were placed in the "wheezing" group if the parent reported the child wheezing on more than one occasion during the past year but no asthma diagnosis or emergency room visits for asthma (n = 93). Children were placed in the "no asthma" group if the parent reported no asthma diagnosis, no emergency room visits for asthma, and no wheezing during the past year (n = 967).
Analytic Strategy
A multinomial logit model (MNL)1 was used to investigate the correlates of asthma and wheezing because the three-category response variable—no asthma or wheezing, wheezing, and asthma—is naturally ordered (Greene, 1997). The MNL model permits the simultaneous comparison of asthma and wheezing statuses to a single reference group, the baseline category of no asthma or wheezing. Because MNL models like logistic regression models are non-linear in terms of probability but are linear in terms of odds ratios, in most cases, we present RRR (odds ratios). In a few cases, we present estimated probabilities so that the reader can assess the prevalence of these symptoms in this sample.
All models were estimated using sampling weights that adjust for unequal selection probabilities and differential non-response. An examination of design effects (DEFF) was conducted to ensure that the weights did not introduce significant bias. Examination of DEFF for the variables included in the analysis indicates minimal losses (or slight gains) in statistical efficiency due to the stratified sampling design and weighting (with DEFF values ranging from 0.88 to 1.15). Variance estimators based on the Taylor Series linearization approach to complex survey sample variance estimation were used to estimate all standard errors (Cochran, 1977; Wolter, 1985). Unadjusted weights sum to the estimated eligible population total. Weights normalized to the sample size were used in these analyses. Models were estimated using the multinomial logit estimation procedures for complex sample survey data in the Stata software package.
Because statistical power is limited by the modest sizes of the asthma and wheezing groups, we employed a multi-stage analytic strategy. First, a baseline model was estimated that included no predictor variables. This simple model makes it possible to estimate overall prevalence rates and to assess the precision of these estimates. Second, a series of bivariate models was estimated to determine what health, demographic, environmental, and family psychosocial characteristics are associated with asthma and wheezing status (Table II). Finally, a series of analytic models was estimated. A domain-level multivariate model that included all variables that were significant in predicting either wheezing or asthma status at the 
= 0.05 level in the bivariate models was estimated. Backward selection was then employed to determine the predictor set for the final model. Only those variables significant at the = 0.05 level are included in this final model.
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Results |
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The mean, standard deviation, and range for participant characteristic variables for the entire sample as well as the asthma and wheezing subgroups are presented using sampling weights that adjust for unequal selection probabilities and differential non-response (See Table I). In addition, it is important to note that the observed rates of non-Hispanic Caucasian families (71.6%), poverty (16%), and single parenthood (21%) were similar to rates reported for parents of young children in the 1990 census for the targeted MSA (67, 16, and 19% respectively), indicating that the study sample is demographically comparable to the region from which it was drawn.
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The baseline model reveals that 8.1% of children in this population exhibited wheezing with no asthma diagnosis, and 8.4% are estimated to have been diagnosed with asthma. The standard errors estimated for these coefficients suggest a moderate degree of precision in estimating the prevalence of wheezing and asthma, with a 95% confidence interval ranging from 6.6 to 10.0% for wheezing and from 6.9 to 10.7% for asthma diagnosis.
Bivariate RRR for all biopsychosocial risk factors, including birth status, parental health, child demographics, family demographics, life events, environmental factors, family functioning, and social support variables are presented in Table II. Although bivariate models provide an informative picture of the individual child, parent, and family characteristics associated with asthma diagnosis and wheezing, they tell us little about the relative unique contributions of these different characteristics, which often co-occur in children’s lives. Thus, a series of multivariate models was estimated to examine unique and shared effects within domains. Results of domain-level (e.g., birth status variables) multivariate analyses are also presented in Table II.
An RRR of 1 indicates that a characteristic does not affect asthma or wheezing status. A ratio greater than 1 indicates increased risk of asthma or wheezing. For example, an RRR of 1.50 for asthma diagnosis would reflect a 50% greater chance of asthma diagnosis. A ratio of less than 1, by contrast, indicates reduced risk of asthma or wheezing. For example, an RRR of 0.67 for asthma diagnosis would indicate a 50% lower risk of asthma diagnosis.
Hypothesis 1 was partially supported. When examined with bivariate methods, asthma status and wheezing status are associated with selected birth status and parental physical health characteristics. Greater gestational age at birth is associated with a 25% lower risk of wheezing. The effect of asthma during pregnancy is particularly strong, with children whose mothers had asthma during pregnancy being nearly seven times more likely than children whose mothers did not have asthma during pregnancy to be diagnosed with asthma and two and a half times more likely to wheeze. Children with healthier primary caretakers have reduced risk of wheezing. Children in families reporting greater anxiety, depressive symptoms, parental distress, and parent–child dysfunction are at increased risk of both asthma diagnosis and reported wheezing. When examined with multivariate methods by domain, the presence of early childhood asthma was significantly associated with parental depression as well as maternal asthma during pregnancy. The presence of wheezing was associated with decreased gestational age at birth.
Partial support was found for hypothesis 2. Bivariate results indicate that certain child and family demographic characteristics are significantly associated with asthma and wheezing in young children. The risk of a black child being diagnosed with asthma is two times greater than the risk for a non-black child. Boys are at significantly greater risk of both asthma diagnosis and wheezing than girls. Similarly, children in single-parent households are more than twice as likely as children in two-parent families to be diagnosed with asthma. These findings remain significant in the domain-level multivariate model.
Hypothesis 3, examining the association between the domains that make up environmental factors (i.e., life events, violent events, and household exposures) and asthma status, was partially supported. Bivariate results indicate that children residing in a household in which someone smokes are two times more likely to be diagnosed with asthma. Children whose parents report a higher number of stressful parental life events or who experience more violent events themselves are at significantly greater risk of asthma diagnosis. Children with more violent life events are also at significantly greater risk for wheezing. Multivariate analyses of these domains suggest that children who experience more violent events are at increased risk of wheezing, and children who live with a smoker are at increased risk of asthma.
Partial support was also found for hypothesis 4. Specifically, early childhood asthma and wheezing are associated with multiple indicators of family psychosocial factors at the bivariate level. Children in more expressive families have reduced risk of asthma diagnosis as do children in families with greater social support, who also experience reduced risk of reported wheezing. Children in families reporting greater parental distress and parent–child dysfunction are at increased risk of both asthma diagnosis and reported wheezing. Children in families with greater parenting over-reactivity are at greater risk of wheezing.
Table III presents the estimated RRR and standard errors for the final MNL model predicting asthma and wheezing status. This final model includes only those variables that are statistically significant at the < 0.05 level after stepwise elimination of non-significant variables. In the final model, a child’s sex, family social support, smoking in the home, and maternal asthma during pregnancy remain strong correlates of both wheezing and asthma diagnosis. Boys are 70% more likely than girls to wheeze and more than twice as likely to be diagnosed with asthma. Children in families with strong social support are 50% less likely to wheeze or be diagnosed with asthma. Though also a significant risk factor for wheezing, asthma during pregnancy remains the strongest predictor of asthma diagnosis in the multivariate models. Children whose mothers had asthma during pregnancy are greater than six times more likely to be diagnosed with asthma. Gestational age, smoking in the home, violent events, and parental anxiety are also significantly related to wheezing. Increased gestational age decreases the risk of wheezing by 17%. Parental report of smoking in the home increased the odds of children having asthma by nearly 68% when compared with children with no wheezing or asthma. Each of five additional violent events raises the odds of wheezing by approximately 35%. Each one point increase in maternal anxiety raises the odds of a child wheezing by 4%.
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To test hypothesis 5 that environmental and family psychosocial factors moderate the relationship between health/demographic factors and asthma status, an additional set of MNL models examined the interaction between each of the significant health-related and demographic correlates of wheezing and asthma status and each of the significant environmental and family psychosocial factors. Three significant interactions were observed. For both wheezing and asthma, smoking in the home significantly interacted with asthma during pregnancy (wheezing: RRR = 9.90e-17, p < .001; asthma: RRR = 7.1, p = .05) and gestational age (wheezing: RRR = .55, p < .05; asthma: RRR = .74, p < .05). For the asthma group, the presence of smoking in the home magnified the negative impact of both asthma during pregnancy and lower gestational age.
To explore the nature of the significant interactions in this model, MNL models were conducted separately by group (i.e., no smoking in the home vs. smoking in the home, and low and high levels of exposure to violence). For the model including asthma during pregnancy, the RRR for homes with smoking was 28.9 (p < .001) compared with 4.2 (p < .001) for homes without smoking. For the model testing gestational age, the RRR in homes with smoking was 1.4 for asthma (p < .05) and 1.9 (p < .01) for wheezing compared with the non-significant RRRs for asthma and wheezing when there was no smoking in the home. Of interest, for the wheezing group, the combination of asthma during pregnancy and smoking in the home reduced risk (RRR = 0) relative to the combination of asthma during pregnancy and no smoking in the home (RRR = 3.0, p < .05). Of note, 75% (n = 8) of children in this sample whose mothers had asthma during pregnancy and who were exposed to smoking in the home developed asthma, whereas none of the children with this combination of risk factors showed signs of wheezing without an asthma diagnosis. Exposure to violence significantly interacted with asthma during pregnancy for the wheezing group only. Risk for wheezing from asthma during pregnancy was smaller in conditions of high-violence exposure (RRR = 2.5, p < .05) vs. low-violence exposure (RRR = 6.0, p < .001). None of the children with this combination of risk factors were found to wheeze without an asthma diagnosis, whereas 35% (n = 6) were found to be diagnosed with asthma.
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Discussion |
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Utilizing a biopsychosocial model, this paper addressed associations between the presence of asthma, wheezing, or neither asthma nor wheezing and factors within four domains of risk, including child and family health, demographic characteristics, environmental characteristics, and family psychosocial characteristics. Findings suggest that as many as 16.5% of young children in the current sample suffer from at least transient difficulty breathing, which is consistent with increasing asthma prevalence in the United States (Asthma Prevalence, Health Care Use and Mortality, 2000–2001
). Given that 50–80% of asthma cases are expected to onset by age 3, the rate of asthma diagnosis in this sample will likely increase over time (Martinez et al., 1995; Mrazek, Schuman, & Klinnert, 1998; Sandberg et al., 2000). This paper highlights the utility of using a biopsychosocial approach to pediatric research, as factors within each of four domains contributed unique variance to the prediction of asthma and/or wheezing in early childhood. Within the child and family health domain, gestational age was found to be significantly associated with wheezing in bivariate, domain-level and final multivariate models, such that greater gestational age decreased the chances that the child would exhibit wheezing by age 2–3 years. Of note, although socioeconomically diverse and representative of the community in other respects, the current sample only included children born full term and healthy at birth. Thus, the current findings demonstrate that even among "full-term" babies, shorter gestational age may increase risk for wheezing. That being said, it is important to note that greater gestational age did not decrease the chances that the child would exhibit wheezing. The restricted gestational age range in the sample may explain the lack of association between gestational age and asthma found in this study, as preterm delivery may be more of a risk factor for asthma.
Indicators of parental physical and mental health were also associated with risk of childhood asthma and/or wheezing. Specifically, the association between maternal asthma in pregnancy and childhood asthma was more than twice as large as that for maternal asthma in pregnancy and childhood wheezing. Both parental self-rating of health and maternal anxiety were significantly associated with childhood wheezing, and maternal depression was significantly associated with childhood asthma. In addition to understanding the distinct impact maternal mental health has on childhood wheezing and asthma, it is also important to consider the ways in which maternal mental health may interact with other family stressors and/or parental characteristics to exacerbate asthma and wheezing in children. For example, to the extent that parents experience elevated anxiety, children may be experiencing more stressful household climates which may increase risk for wheezing (See Kaugars et al., 2004). Alternatively, anxious parents may have a heightened level of awareness of and/or sensitivity to breathing difficulties in their children and, thus, may report more wheezing symptoms.
In the domain of demographic factors, boys were significantly more likely than girls to be reported as having wheezing and asthma in all statistical models, as expected. In addition, that black children were significantly more likely to have an asthma diagnosis than were non-black children was evident in all models, even when controlling statistically for race-associated factors. This finding is consistent with Aligne and colleagues (2000) who reported that black race is a risk factor even after controlling for race-associated factors.
Environmental factors also appear to play an important role in the onset, exacerbation, and/or reporting of asthma and wheezing in young children. Several current findings support a role of potentially stressful life events in early asthma onset and wheezing, including having been exposed to violence and smoking in the home. Exposure to violence may be related to a potential stress mechanism and/or may be a proxy for disadvantaged urban living environments. The implications of this finding are noteworthy given the rising prevalence of asthma in disadvantaged neighborhoods, where violent events are more likely to occur and where environmental asthma triggers are more prevalent (Aligne et al., 2000). Furthermore, that smoking in the home was associated with twice the likelihood of children receiving an asthma diagnosis is important given the fact that smoking behavior can be promoted by stress, and that smoking in the home can exacerbate asthma in children, which, in turn, can increase family stress. Findings suggest that exposure to smoking in the home serves as a trigger for diagnosable asthma in very young children with genetic vulnerability. Results also suggest that asthma diagnosis is significantly more likely when children are faced with multiple risk factors including asthma during pregnancy and smoking in the home or violent events. It should be noted, however, that both of these moderation effects reflect very small sample sizes. Future studies that include greater numbers of children with both genetic vulnerability and exposure to smoking are needed to confirm this result.
In the family psychosocial domain, higher levels of parenting distress and parent–child dysfunction were associated with greater risk of asthma and/or wheezing, whereas higher levels of family expressiveness and social support were associated with reduced risk. Mrazek and Klinnert (1996), who studied the role of stress in early asthma onset in a genetically at-risk population, found that psychosocial stressors including parenting difficulties and poor coping skills predicted early onset asthma in children with a family history of the illness. Family psychosocial findings from the current study extend Mrazek and Klinnert’s (1996) work by documenting that psychosocial stressors (e.g., parenting distress) confer unique risk over and above maternal asthma in pregnancy. Findings regarding the potentially positive role of family expressiveness and social support suggest that prevention and intervention programs targeting intrafamily and/or interfamily harmony and support may be warranted for families with multiple risk factors for children’s development of problems in respiratory functioning.
To our knowledge, this study is the first of its kind to study multiple correlates of asthma and wheezing in a cohort of young children that excludes unhealthy, pre-term, and low-birthweight children. However, several limitations exist. First, prevalence estimates are likely lower than would be expected in a total population study because of the exclusion of preterm and low-birthweight infants. Second, this study was cross-sectional in nature, and, as such, the results do not convey directionality. For example, while the risk factors of caregiver and family stress may serve as correlates of asthma and/or wheezing, it is equally plausible that the presence of asthma and/or wheezing could increase the level of caregiver and family stress in this sample of young children. Thus, this study lacks a clear temporal relationship between stress exposure and disease outcomes, and asthma and wheezing categories may be fluid over time with some children in the wheezing group receiving diagnoses of asthma later in life, and some children in the asthma group outgrowing the disease.
Another limitation is that all data for this study were reported by one parent, including asthma status. Thus, parents may have inaccurately reported the child’s asthma status due to diagnostic error among pediatricians, difficulty recalling symptoms, and symptom prevalence. In addition, the construct of "wheezing" was not well defined in questionnaires. Because what constitutes asthmatic wheezing as opposed to other bronchial or pulmonary problems (e.g., vocal cord dysfunction and croup) in very young children is often unclear, it is possible that parental perception, which is likely influenced by asthma status and knowledge as well as parental affective symptoms and demographic characteristics may play a role in who is included in this group (Hume, 2002; Wamboldt, 1998). Specifically, parents may have had differing and/or incorrect interpretations of what constituted wheezing based on their own experience (or lack thereof) with asthma symptoms. Despite limitations in the diagnosis, the results of this study reveal that pattern of risk varied across the diagnosed asthma and wheezing groups (e.g., smoking in the home conferred higher risk of asthma vs. wheezing). However, there is insufficient information to fully understand the differences between these groups. It is also possible that children with diagnosed asthma had better access to healthcare and/or better healthcare practices (e.g., regular office visits), whereas children in the wheezing group may have escaped diagnosis because of poor access to healthcare and/or less capable healthcare practices.
Another limitation is that symptom severity and the contexts in which wheezing occurs (e.g., allergy induced and associated with illness) were not assessed. In addition, longitudinal follow-up of this sample may reveal which children in the wheezing group subsequently attain an asthma diagnosis and which children will outgrow an asthma diagnosis. It is important to note that findings in samples of very young children, such as this one, may yield different results than studies of older children because of early developmental changes in asthma status that impact group composition.
Results from this study indicate the need for interventions to treat asthma and wheezing in children under the age of 3. In addition, findings suggest that interventions should take an ecological approach, targeting the child, parent, family, and community. Interventions that respond to prenatal and neonatal concerns, maternal, child and family health concerns, as well as community concerns including antismoking and antiviolence measures as well as programs that foster social support are likely to have a profound impact on asthma prevention in the United States. Specifically, the current study illustrates a need for interventions that increase levels of social support among families of children with asthma and wheezing. Perhaps most importantly, in addition to highlighting the need for such interventions, results from this study suggest a need, before intervening, for accurate identification of asthma and wheezing in children under 3 years of age.
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1 The MNL model is: ln[Pij/Pi0] = ßjxi, where ln[Pij/Pi0] is log-odds ratio of a given status in comparison to the no asthma or wheezing status category, xi is a matrix of predictor variables, and ßj is a vector of estimated effects coefficients. Log-odds ratios are difficult to interpret, because they represent the relative effect of each independent variable on the log-odds ratio of a given asthma or wheezing status in relation to the reference category. Consequently, relative risk ratios (RRR), which are calculated by the exponentiation of the log-odds coefficient, exp (ßj), are typically reported. RRR are displayed in the tables presenting the estimated coefficients for the bivariate and multivariate models. In a few cases, we present estimated probabilities, which can be derived from the estimated log-odds ratios according to the following equation: Prob(YI = j) = eßjxj/
eßjxi 
Received February 1, 2005; revision received June 15, 2005; revision received August 25, 2005; revision received February 28, 2006; accepted April 9, 2006
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