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Risk Factors for Asthma and Asthma Severity in Nonurban Children in Connecticut^*

^* From the Department of Pediatrics, University of Connecticut Health Center (Ms. Higgins and Ms. Wakefield), and Asthma Center, Connecticut Children’s Medical Center (Dr. Cloutier), Hartford, CT.

Correspondence to: Pamela Sangeloty Higgins, MS, MPH, Asthma Center, Connecticut Children’s Medical Center, 282 Washington St, Hartford, CT 06106; e-mail: Higgins{at}nso1.uchc.edu

	Abstract

TOP Abstract Introduction Materials and Methods Results Discussion References

 
Study objective: To examine asthma diagnosis, asthma severity, and the presence of established asthma risk factors in children who reside in nonurban communities.

Design: A cross-sectional study was conducted of 19,076 children (6 months to 18 years of age) who lived in 146 nonurban communities in the greater Hartford, CT, region and who were enrolled in a disease-management program (Easy Breathing II; Michelle Cloutier, MD; Hartford, CT) designed to improve asthma diagnosis and treatment.

Results: The overall frequency of physician-confirmed asthma in children seeking health care was 18%. Asthma frequency was related to low socioeconomic status (SES), non-Caucasian ethnicity, male gender, age 5 years, and exposure to tobacco smoke, dust, or cockroaches in the multivariate analysis. When controlling for SES, African-American children were 1.33 times more likely (95% confidence interval [CI], 1.15 to 1.53) and Hispanic children were 1.60 times as likely (95% CI, 1.38 to 1.85) as Caucasian children to have asthma. In contrast, asthma severity was related to dust exposure, a family history of asthma, non-Caucasian ethnicity, and age 4 years in the multivariate analysis. African-American children (odds ratio, 1.31; 95% CI, 1.03 to 1.67) had more severe asthma diagnosed as compared to Caucasian children. Hispanic ethnicity was not associated with an increase in asthma severity.

Conclusion: Risk factors for asthma in nonurban children are similar to risk factors in urban children. Ethnicity is a risk factor for asthma regardless of SES. Even in nonurban environments, African-American and Hispanic children have more asthma, and African-American children have more severe disease than their Caucasian counterparts.

Key Words: asthma risk factors • asthma severity • pediatric asthma

	Introduction

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Asthma is a common, chronic disease in children. Despite effective treatment, asthma is associated with high morbidity and significant mortality.¹²³⁴ Asthma prevalence, asthma severity, and health-care utilization disproportionately affect urban minority communities.⁵⁶⁷⁸ The demographics of asthma within low-income urban children have been extensively characterized⁹¹⁰¹¹; however, little is known about the impact of asthma on children living in nonurban communities.¹²¹³ According to the 2000 US Census, 81 million children live in the United States; of these, approximately 50% live in nonurban communities defined as areas with a population density < 1,000 people per square mile.¹⁴¹⁵

The goal of this study was to examine the demographics of children with asthma who live in nonurban settings. Using a program called Easy Breathing (Michelle Cloutier, MD; Hartford, CT), we examined physician-determined asthma diagnosis, asthma severity, and the presence of established asthma risk factors in children seeking health care who live in 146 nonurban communities in the greater Hartford, CT, region.

	Materials and Methods

TOP Abstract Introduction Materials and Methods Results Discussion References

 
The Easy Breathing II Program
Easy Breathing is an asthma management program that was originally designed for use by primary care clinicians in urban settings.¹⁶ In 2000, the program was expanded into the private sector (Easy Breathing II). The goals of Easy Breathing II are to assist clinicians in private practice to improve recognition of asthma and classification of asthma severity, and to develop a systematic, standardized approach to asthma management including the creation of a written, asthma treatment plan. Program entry begins with the Easy Breathing Survey, which includes four previously validated questions that assist the clinician in diagnosing asthma.¹⁷ Clinicians were encouraged to administer the survey to all children 6 months to 18 years of age who presented for care regardless of chief complaint. Many clinicians, however, preferentially surveyed children with known asthma and children with a history of respiratory symptoms.

A clinical diagnosis of asthma was made by the clinician using information from the survey, from the child’s medical record, and from additional questioning as needed. A diagnosis of asthma was considered "new" if the parent recorded on the survey that the asthma had never been diagnosed in the child and that no asthma medications had been used. A positive response to either of these questions was considered a "previous diagnosis of asthma." The clinician then determined asthma severity using a scripted set of questions that recorded frequency of daytime and nocturnal symptoms, exercise impairment, and school absenteeism for asthma. Asthma treatment was determined from the asthma severity using a treatment selection guide, and a standardized asthma treatment plan was developed for each child. The program stresses a systematic approach with the creation of a written asthma treatment plan.

Prior to implementation of the program, < 5% of the children seen in the participating practices received a written asthma treatment plan. All of the elements of the Easy Breathing II program (asthma diagnosis, severity, and treatment) are directly based on National Asthma Education and Prevention Program guidelines.¹⁸ Demographic information on each child including age, gender, ethnicity, town of residence, self-reported exposure to known asthma triggers, and medical services utilization data including self-reported hospitalizations, intensive care admissions, and emergency department and acute office visits for asthma in the previous year were also collected. The program was approved by the Institutional Review Boards of Connecticut Children’s Medical Center and the University of Connecticut Health Center.

Clinicians and Patients
Twenty-seven practices with 76 clinicians who were enrolled in two independent practice associations with high penetration in a managed care organization were invited to participate in the Easy Breathing II program. The two independent practice associations were located in the greater Hartford, CT, region and were chosen because the communities they serve demonstrate cultural and socioeconomic diversity. Sixty-five clinicians (86%) from 20 practices (74%) agreed to participate.

Clinicians, nurses, and office support staff in participating practices were trained in the use of the Easy Breathing II program and were provided with all program materials. The program coordinator supported the practices in implementing the program through regular visits. The study sample included children between 6 months and 18 years of age who presented for care for any reason at any of the 20 private primary care practices between January 2001 and July 2004 and were enrolled in the Easy Breathing II program.

Data Sources
The Connecticut Education Reference Grouping (ERG) was used as a surrogate marker for socioeconomic status (SES). The ERG classification system groups together public school students with a similar SES.¹⁹ School districts were assessed on seven variables (median income, education, occupation, poverty, family structure, home language, and district enrollment) [Table 1 ]. Based on these variables, each town is assigned to one of nine distinct ERG classifications by the Connecticut Department of Education. For data analysis purposes, the nine categories were divided into high SES (median income, $44,197 to $98,000; percentage receiving welfare assistance, 0.06 to 3.7%) and low SES (median income, $24,000 to $47,036; percentage receiving welfare assistance, 7.0 to 43%). Using the ERG category of the town of residence, each child was classified as high or low SES.

Statistical Analysis
Data from the Easy Breathing II Survey were analyzed using statistical software (Version 8.2; SAS Institute; Cary, NC) for descriptive statistics, contingency tables, unadjusted odds ratios (ORs), Poisson regression, and logistic regression. Pearson ² tests were conducted to compare proportions. Associations between asthma and asthma risk factors and exposures were expressed as ORs with 95% confidence intervals (CIs). Children with "unknown" for answers to risk factors and exposures were excluded from the individual analysis, and ranged from 1 and 5% depending on the question. Poisson regression models were used to compare the number of self-reported hospitalizations, intensive care admissions, and emergency department and acute office visits for "new" asthma diagnosis to previous asthma diagnosis while controlling for gender, ethnicity, age, and asthma severity. Logistic regression was used to develop the multivariate models of the relation between asthma and asthma severity to each of the risk factors.

	Results

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Study Population
Overall, in the town-by-town comparison, the gender distribution and ethnic distribution of surveyed children were similar to reports from the 2000 US Census in 92% and 71% of towns, respectively. Of the 29% of the towns that had a different ethnic distribution, there was no consistent pattern in sample differences. For example, ethnic groups were undersampled in some towns and oversampled in others. These differences could reflect nationwide trends in the use of health-care services due to inconsistent health insurance coverage, as well as the constant demographic changes within the US population.²¹²² Differences in gender were small and did not favor one group over another (three towns had more boys and two towns had more girls). Thus, the children surveyed in Easy Breathing II were representative by ethnicity and gender of the communities in which they resided.

The 19,076 children enrolled in the Easy Breathing II program (Table 2 ) were younger than those represented in the 2000 US Census. This oversampling is not unexpected since children < 4 years of age have more provider encounters and therefore have greater opportunity to be enrolled.

Asthma
Eighteen percent of the children surveyed had a physician-confirmed diagnosis of asthma. Asthma diagnosis in children enrolled in the Easy Breathing II program varied by ethnicity; African-American children were 1.72 times as likely (95% CI, 1.51 to 1.96) and Hispanic children were 1.91 times as likely (95% CI, 1.66 to 2.20) as Caucasian children to have physician-confirmed asthma. The children with asthma were more likely to be male and 5 years of age (Table 3 ).

Poisson regression models were used to compare the children with a new asthma diagnosis to the children with a previous asthma diagnosis and their self-reported medical-care utilization. Controlling for gender, ethnicity, age, and asthma severity, children with an asthma diagnosis prior to enrolling in Easy Breathing II reported more emergency department and acute office visits (p < 0.0001) and more hospital (p < 0.0001) and intensive care admissions (p < 0.0001) than the children with newly diagnosed asthma.

Risk Factors for Asthma
In the univariate analysis, greater asthma frequency was associated with a positive family history of asthma, low SES, non-Caucasian ethnicity, and exposure to tobacco smoke, dust, solvents, cockroach, and rodents (Table 3). Exposure to pets (p = 0.17), gas stove (p = 0.27), and firewood (p = 0.36) was not associated with a diagnosis of asthma. In the multiple logistic regression, the strongest predictor of asthma was a family history of asthma. Other significant risk factors for asthma were low SES, non-Caucasian ethnicity, male gender, age > 4 years, and exposure to tobacco smoke, dust, or cockroaches (Table 3).

Asthma Severity
In the univariate analysis, greater asthma severity was associated with a positive family history of asthma, non-Caucasian ethnicity, age 4 years, and exposure to dust (Table 4 ). Greater asthma severity was not associated with SES (p = 0.72), gender (p = 0.92), exposure to tobacco smoke (p = 0.36), cockroaches (p = 0.72), rodents (p = 0.15), pets, (p = 0.09), solvents, (p = 0.09), and gas stove (p = 0.45). In the multiple logistic regression analysis, the strongest predictors of asthma severity were exposure to dust, a positive family history of asthma, non-Caucasian ethnicity, and age 4 years. Children who reported exposure to pets were more likely to have intermittent disease.

View larger version (24K): [in this window] [in a new window] [Download PPT slide]   Figure 1. Asthma severity by ethnicity. Bars indicate the percentage of children with asthma in each ethnic category separated by asthma severity.

View larger version (13K): [in this window] [in a new window] [Download PPT slide]   Figure 2. Asthma prevalence by SES. The Connecticut ERG was used as a surrogate marker for SES. The ERG system groups together public school students with a similar SES, including median income, percentage receiving welfare, and percentage of children whose families speak a language other than English (Table 1).

	Discussion

TOP Abstract Introduction Materials and Methods Results Discussion References

 
This is the first large-scale study to our knowledge that examines physician-confirmed pediatric asthma, its risk factors, and asthma severity in US children who do not reside in an urban community. Our data confirm that even in nonurban communities, SES and ethnicity are independent risk factors for asthma diagnosis and severity. Lower SES was associated with a higher frequency of asthma for all children, even children who reside in nonurban settings. Being African-American or Hispanic is associated with a greater risk for asthma whether the child is from an urban or nonurban setting, and being in a lower SES is associated with a greater risk for asthma regardless of ethnicity. Furthermore, the combination of Hispanic or African-American ethnicity and lower SES is associated with an even greater risk for asthma in this nonurban setting.

Urban residence, African-American or Hispanic ethnicity, a family history of asthma, and tobacco smoke exposure are well-documented risk factors for pediatric asthma.²³²⁴²⁵ Maternal history of asthma and maternal cigarette smoking are reported risk factors for asthma prevalence among nonurban and urban African-American children.²⁶ Our findings replicate and extend these findings in a nonurban setting, where African-American or Hispanic ethnicity, a family history of asthma, and tobacco smoke exposure remain significant risk factors for asthma frequency and disease severity. In these risk factors, asthma in nonurban settings is similar to that in urban settings.

This study also demonstrates that the ethnic differences in asthma diagnosis and severity reported in urban residing children exist in the diagnosis and severity of asthma in children residing in nonurban areas. Multivariate analysis demonstrated that African-American or Hispanic ethnicity were associated with a higher frequency of asthma than Caucasian ethnicity independent of SES. Previous studies^823272829 have also shown higher asthma frequency in African-American or Hispanic children compared to Caucasian children in national, international, and local samples independent of SES. Yet, the few studies^28303132 that focus on children living in nonurban settings are limited by small sample sizes, narrow age groupings, or unclear ethnic categories. We found that African-American ethnicity was associated with more severe asthma compared to Hispanic, Asian, or Caribbean ethnicity. This is in contrast to recent findings³³ among children aged 4 to 18 years who were enrolled in the Easy Breathing program in Hartford, CT, in which predictors of severe asthma included not only African-American ethnicity but also Hispanic ethnicity. The difference in asthma severity between Hispanic children from urban and nonurban communities may reflect differences in environmental exposures in Hispanic children (ie, poverty in association with poorer housing) or could reflect differences in susceptibility to exposures that may be genetically controlled. These findings confirm that ethnicity itself is a factor in asthma frequency and severity whether a child resides in an urban or nonurban community.

Despite the widespread dissemination of the National Asthma Education and Prevention Program guidelines during the past 13 years, underdiagnosis of asthma remains widespread in the pediatric population.³⁴³⁵³⁶ The increased morbidity, economic burden, and quality-of-life consequences of unrecognized asthma are well documented. Maier et al³¹ demonstrated that children living with unrecognized asthma-like symptoms had a similar or greater risk of school absenteeism, sleep disruption, and reduced participation in physical education classes when compared to children with a physician diagnosis of asthma. To date, most of the studies^31343536 of unrecognized asthma have focused on limited age groups or did not include clinician-confirmed current diagnosis. In contrast, 18% of the children surveyed in the present study had a physician-confirmed diagnosis of asthma. Of these children, 24% had newly diagnosed asthma by a clinician, and one third of these children had a diagnosis of persistent asthma. We believe that many families do not recognize the symptoms of asthma and that the Easy Breathing Survey opens up a dialogue between the clinician and the family.¹⁷ Consequently, the 800 children in the present study with newly diagnosed asthma confirm that asthma remains underdiagnosed even in nonurban settings.

The study is limited by several factors. First, the study sample is representative of children who seek health care; in addition, many clinicians who participated in the Easy Breathing II program targeted children with asthma and respiratory symptoms. As a result, the sample does not represent asthma prevalence in these private practices.⁵ Second, our socioeconomic data are limited to Connecticut ERGs on a group level. Thus, each child is not directly linked to his or her individual family income or parental level of education. Nevertheless, it is beneficial to group children with a similar SES together to make pertinent comparisons between children living in different communities, as the ERG reflects the richness and resources available in a community.¹⁹

Third, because rural communities can vary markedly from suburban and urban periphery communities, and ERG does not distinguish between them, we classified the 38 towns using The Five Connecticuts,³⁷ a description of town-level Connecticut demographics in which each town is assigned to one of five groups reflecting discrete demographic and economic areas. Overall, 13.5% of the towns were rural, and all fell into the high SES group.

Despite these limitations, the results provide important information regarding the risk factors for asthma in children who reside outside of urban communities. Similar to urban settings, African-American or Hispanic ethnicity and low SES regardless of ethnicity are associated with a greater risk for asthma in this nonurban setting. Furthermore, the combination of African-American or Hispanic ethnicity and lower SES is associated with an even greater risk for asthma. The high frequency of asthma in this sample and the similarity of the results to findings in urban settings suggest that asthma is a concern for nonurban as well as urban communities.

	Acknowledgements

 
We thank the clinicians and office staff of the 20 primary care practices for their willingness to participate in the Easy Breathing II Program: Central Pediatrics and Adolescent Medicine; Children’s Medical Group, Hamden; Connecticut Valley Pediatrics; Leonard Forner, MD; John J. Fote, MD, John H. Lavalette, MD, Jennifer L. Schwab, MD, and Jennifer L. Nichols, APRN; Kids Station Pediatrics; Sobhy Ghabrial, MD; Healthwise Medical Associates, LLP: Naseem Deen, MD, Wanda Merced, MD, Barbara Renshaw, APRN, Vernon Pediatrics; Pedicorp, P.C.; Pediatric Associates of Marlborough; Prohealth Physicians MSO, Inc.: Children’s Medical Group; Sherry Banack, MD and Candra Smith-Slatas, MD, PhD; Vandana Sacheti, MD; Shoreline Pediatrics and Adolescent Medicine, P.C.; Michael S. Levine, MD and Leah Galjan Post, MD; Robert R. Toscano, MD, and UCONN Health Partners. We also thank Steven Delaronde, MPH, MSW, for his contributions to this article, Melissa Cowen, RN, BSN, MPH, and Alicia Ignatowicz, MPH for data entry and program support, and Ms. Krissy Larrow, Ms. Glenda Ramirez, and Ms. Jennifer Adkins for their administrative assistance.

	Footnotes

 
Abbreviations: CI = confidence interval; ERG = Education Reference Grouping; OR = odds ratio; SES = socioeconomic status

Supported by a grant from the Agency for Healthcare Research and Quality UH18 HS11147.

Received for publication March 9, 2005. Accepted for publication June 1, 2005.

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This Article Abstract Full Text (PDF) Free Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Article Archive Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (8) Citing Articles via Google Scholar Google Scholar Articles by Higgins, P. S. Articles by Cloutier, M. M. Search for Related Content PubMed PubMed Citation Articles by Higgins, P. S. Articles by Cloutier, M. M.