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© 2006 American Public Health Association
DOI: 10.2105/AJPH.2004.059253
RESEARCH AND PRACTICE |
Irene H. Yen, Edward H. Yelin, Patricia Katz, Mark D. Eisner, and Paul D. Blanc are all with the Department of Medicine, University of California, San Francisco.
Correspondence: Requests for reprints should be sent to Irene H. Yen, PhD, Department of Medicine, University of California, San Francisco, 3333 California St, Suite 335, San Francisco, CA 94143-0856(e-mail: irene.yen{at}ucsf.edu).
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ABSTRACT |
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Objectives. We investigated associations between perceived neighborhood problems and quality of life (QOL), physical functioning, and depressive symptoms among adults with asthma.
Methods. Using cross-sectional data from adults with asthma in northern California (n=435), we examined associations between 5 types of perceived neighborhood problems (traffic, noise, trash, smells, and fires) and asthma-specific QOL (Marks instrument), physical functioning (Short Form-12 physical component summary), and depressive symptoms (Center for Epidemiological Studies–Depression). We used multivariate regression analysis.
Results. When asthma severity and sociodemographics were taken into account, people reporting a score of 8 or higher on a scale of 0 to 25 for serious problems (the top quartile of seriousness) in their neighborhoods had significantly poorer QOL scores (mean difference=5.91; standard error [SE]=1.63), poorer physical functioning (mean difference=–3.04; SE=1.27), and almost a fivefold increase in depressive symptoms (odds ratio=4.79; 95% confidence interval=2.41, 9.52).
Conclusions. A high level of perceived neighborhood problems was associated with poorer QOL, poorer physical functioning, and increased depressive symptoms among people with asthma when disease severity and sociodemographic factors were taken into account.
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INTRODUCTION |
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Increasingly, public health researchers and practitioners are turning to the neighborhood as a potential target for population-based interventions to influence individual health. In 1993, Macintyre and colleagues1 reviewed public health scientific studies on the role of neighborhoods, concluding that there was a need for direct study of local social and physical environments with regard to how they might influence health. Since that review appeared, several studies have documented links between neighborhoods and health. Neighborhood physical environment factors tied to health or health behaviors include air pollution,2,3 types of stores and the presence of services,4–6 general physical condition (e.g., crowding, cleanliness, level of noise),7,8 and advertising.9–11 Neighborhood social environment factors, such as the percentage of people with incomes below the poverty level in a census tract or census block group, percentage of adults with less than a high-school education, and percentage of adults who are unemployed, alone or in combination, have been linked to all-cause mortality,12–18 cause-specific mortality,19–21 coronary heart disease,11,22 low birthweight,7,23–26 perceived poor health,27 and cardiovascular risk behaviors.28,29
Environmental psychologists have identified several characteristics of the "built environment" that are potential sources of stress, such as noise, crowding, pollution, monotonous physical settings, extreme light or temperature, and crime and safety problems.30 The physiological effects of stress stemming from the neighborhood experience (i.e., the concept of "urban press," defined as the "stimuli related to the physical, social, visual, and aural aspects of environments"30) may lead to adverse health outcomes, for example, an altered immune response. Few studies, however, have linked neighborhoods to the stress process. Elliot31 suggested that the social environment is a source of both stressors and resources to cope with stressors. Steptoe and Feldman32 reported that high levels of neighborhood stress (measured with perceptions of problems such as trash, noise, traffic, smells) were associated with poorer self-rated health; psychological distress; and a reduced ability to carry out activities of daily living. And the level of perceived problems was associated with social capital33 and physical functioning in the elderly.34
Asthma is a common chronic condition that affects millions of adults and children around the world. Asthma prevalence and associated morbidity are increasing.35,36 Management of asthma symptoms and attacks, whether by regular use of medications, environmental interventions, stress reduction methods, or some combination of these is believed to be a key to maintaining a good quality of life (QOL).37,38 The neighborhood environment has been linked with asthma, primarily through the physical features of the area studied, specifically ambient air quality. Most relevant studies have focused on children. Ambient air pollution was associated with asthma severity and asthma-associated medical care utilization, such as emergency department visits and hospital admissions.39–42 In addition, studies have reported links between high traffic flows and increased medical attention for asthma43 or an increased likelihood of wheezing in children with asthma.44,45 In terms of neighborhood social environment and asthma, studies found associations between neighborhood deprivation and asthma prevalence in New Zealand46 and childhood asthma hospitalizations in England.47
We sought to investigate potential links between the neighborhood environment and asthma-related health by analyzing the association between the perceived neighborhood environment and health outcomes among adults with asthma. We focused our study on asthma-specific health-related QOL, depressive symptoms, and general physical functioning, 3 different but key health measures in asthma patients. If the perceived neighborhood environment is associated with QOL and mental and physical functioning among people with asthma, this link would provide more evidence supporting the hypothesized connections between the neighborhood and health status, further supporting the need for targeted interventions to promote health at this level.
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METHODS |
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Overview
Our results are based on cross-sectional analyses of 1 wave (wave 5 of 6 completed waves) of data from an ongoing cohort study of adults with asthma and rhinitis. Beginning in 1992, the University of California, San Francisco, Asthma and Rhinitis Panel sampled persons with asthma recorded on visit logs maintained by a random sample of northern California adult pulmonologists, allergists/immunologists, and family practice physicians. The participation rate of physicians was 57 of 92 (62%) pulmonologists, 17 of 19 (89%) allergists, and 34 of 74 (46%) family practice physicians. Each participating physician was asked to maintain a registry of persons aged 18 through 50 years with outpatient visits for asthma over a prospectively defined 4-week period (increased to 8 weeks in cases of low visit frequency). Of 869 eligible patients, 751 (86%) were recruited successfully. In 1999, an additional group of subjects, some with asthma and others with rhinitis alone, was recruited by random-digit dialing. Study eligibility was based on the report of the physician’s diagnosis of the condition. For the random-digit-dialing sample, 300 of 455 (66%) eligible patients participated. Details of recruitment and initial follow-up have been previously reported.48–53 At the time of original enrollment, all subjects ranged in age from 18 to 50 years.
The respondents were interviewed at the time of enrollment (wave 1), with 5 follow-up interviews conducted at 18- to 24-month intervals thereafter to date. The follow-up interviews (wave 5) on which this article is based occurred between February 2000 and May 2001. Of these, 439 had asthma with or without concomitant rhinitis. An additional 109 interview subjects who reported a physician’s diagnosis of rhinitis alone but not asthma were excluded from this analysis in order to more specifically investigate the associations between neighborhood environment and asthma. Of the 439 persons remaining for analysis, 4 did not have information about race/ethnicity, leaving a final analytic sample of 435. The subjects responded to a 45-minute computer-assisted telephone interview.
Key Interview Variables Analyzed
Neighborhood problems.
Neighborhood problems were ascertained through the respondent’s reply to the question "Thinking about your neighborhood as a whole, how much of a problem do you feel each of the following is in your neighborhood?" Respondents were then asked about 5 specific problems: too much traffic, excessive noise, trash and litter, smells or odors from factories or farms, and smoke from fires or burning. They were asked to indicate the seriousness of each on a scale of 0 to 5, where 0 is not a problem and 5 is a serious problem. This 5-item battery was adapted from the neighborhood problem question used in the Alameda County Study.34
We analyzed neighborhood problems both individually and as a group. We did not have an a priori assumption abut the relative weighting of each type of problem among the 5 types, but anticipated that some problems would be less frequent or might be perceived as less serious than others, for example, smoke from fires compared with trash or litter. Moreover, in initial examination of the response frequencies to the individual questions, we noted a skewed distribution. To adjust for this, we reduced each 5-point scaled response to 3 categories for the purposes of regression modeling: 0 (no problem at all; reference category), levels 1 through 4, and level 5 (maximal) seriousness.
We also created an integrated measure of perceived neighborhood problems by summing the responses to all the individual neighborhood problems, with a potential range of 0 to 25. In this summary score, a value of 0 corresponds to a person’s reporting no serious problem with any of the 5 types of issues. We also created 4 categories approximating quartiles of the summary score: quartile 1 (total score= 0), quartile 2 (total score=1 to 3), quartile 3 (total score=4 to 7), and quartile 4 (score 
8).
Health variables. Health variables were as follows:
Marks asthma QOL. To assess QOL, we used the 20-item asthma-specific QOL measure developed and validated by Marks and colleagues54 and later adapted by our study for modified scoring.55 The maximal summary score is 60; higher scores reflect a greater negative effect of asthma on QOL.
Physical functioning. We used the Short Form-12 physical component summary (SF-12 PCS) score to measure physical functioning. The SF-12 PCS is a short form of the well-known SF-36.56 Scores range from 0 to 100; higher scores reflect better functioning. Test-retest reliability for the SF-12 PCS scale was 0.89 in the United States.57
Depressive symptoms. We used the Center for Epidemiological Studies Depression Scale (CES-D), a 20-item, self-report scale developed for the general population, to measure depressive symptoms.58 Overall scores range from 0 to 60, with higher scores indicating more symptoms and a higher level of depressive symptomatology. We analyzed the scale as a continuous variable and as a categorical variable using the cutoff of 16 or greater. Scores of 16 or more are commonly taken as indicative of high depressive symptomatology.59
Severity-of-asthma score. The severity-of-asthma score was previously developed and validated by Blanc and colleagues51 and includes items on symptoms, systemic corticosteroid and other medication use in the prior 2 weeks, and hospitalizations and intubations. The score incorporates a widely accepted stepwise approach to asthma pharmacotherapy in which higher levels of treatment are indicative of an inability to control asthma with medications used at prior steps.38 The score is calculated on the basis of the subjects’ responses at the time of each interview. Three variables in the score represent cumulative lifetime experience: ever hospitalized for asthma, ever received mechanical ventilatory support for asthma, and ever received oral or parenteral (intravenous or intramuscular) corticosteroids for asthma. For these variables, responses from previous interview waves can contribute to the score calculated at the time of a subsequent interview. The maximal score is 28; higher scores reflect greater asthma severity.
Demographic variables.
Demographic variables have been shown to be associated with QOL,60 psychological status,61,62 and physical functioning,63,64 and may affect the perception and reporting of neighborhood problems.65 Therefore, analyses controlled for the following demographic variables: age, gender, income, education, and race/ethnicity. The demographic characteristics of the sample are shown in Table 1
. For regression models, age was entered as a continuous variable. Household income was classified into 4 categories: less than $40 000 (24%), $40 000 to $80000 (34%), greater than $80000 (37%—reference group), and refused (5%). Education was classified into 4 categories: high-school graduate or less (18%), some college or an associate degree (38%), college graduate (27%), and graduate degree (17%—reference group). Although it was not included in the multivariate analysis, we did categorize addresses with US Census definitions of urban and rural and found that 375 (86%) of the respondents lived in urban areas.
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Analysis
In order to determine how neighborhood problems might be associated with each other and with demographic characteristics, we carried out bivariate analyses. We conducted
2 analyses of the association between the responses to individual problems in pairwise comparisons. We also conducted 2 analyses of key demographic variables with individual neighborhood problems to determine whether demographic characteristics were associated with reporting certain types of problems. Finally, we used the t test to compare the mean total perceived neighborhood problem score by demographic category. We used linear regression models to analyze the association between neighborhood problems (individual and summed) and the 3 outcome variables (QOL, SF-12 PCS, and CES-D). We also used logistic regression to analyze the association between neighborhood problems and CES-D, dichotomized, to assess high levels of depressive symptomatology (score of 16 or higher). We tested simple (unadjusted) models and then added demographic covariates (age, gender, race/ ethnicity, income, and education). In a final model, we also included asthma severity score as a covariate. (In preliminary modeling we did find that the neighborhood environment was not statistically associated with asthma severity. Nevertheless, because asthma severity is a key covariate in studies of asthma-related health outcomes, we included it in the analysis of the other health outcome variables.) Because results for models including asthma severity did not differ substantially for those with just demographic covariates, we present the results for the fully adjusted models only.
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RESULTS |
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Frequency of Serious Neighborhood Problems
For the individual perceived neighborhood issues, respondents perceived the most serious problems with traffic (19% rated it
4); and the least problems with smells (5% rated 4) and trash (4% 4). In analyses between neighborhood problems, traffic was associated with noise and with trash, and noise was associated with trash (all P< .0001).
For the summed neighborhood problem score, 112 (26%) of respondents scored 0, reporting no problems with any of the 5 types of problems; 45 (11%) reported only 1 problem and only at a minimal level of seriousness (total score of 1), and another 42 (10%) reported either 1 problem at the seriousness level of 2 or 2 problems at the level of 1 (total summary score= 2). The maximal score observed was 25. The quartiles of the score are included in Table 1
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Bivariate Analyses of the Demographic Variables and Neighborhood Problems
Bivariate 2 analyses of the association between the demographic variables and the individual neighborhood issues identified 3 statistically significant associations. People with low educational attainment were more likely to report serious problems with traffic and noise in their neighborhoods: 29% of people who reported traffic as 5 on the seriousness scale of 0 to 5 had not completed high school, compared with 18% of the overall sample (2 = 12.92; P= .01). For noise, 39% of the people who reported a seriousness level of 5 had not completed high school (2 = 16.22; P= .003). People with lower income were more likely to report serious problems with smells in their neighborhoods. People who reported a serious problem with smells were more than twice as likely to have lower incomes than the overall sample (53% vs 25%; 2 = 10.72; P= .03). As income level increased, the total perceived neighborhood problem score decreased (F = 6.70; P= .001). There was no statistically significant association between total perceived neighborhood problem score and age, gender, race/ethnicity, or education.
Association of Neighborhood Problems With Health Outcomes
The results from linear and logistic regression models of the associations between individual neighborhood problems and asthma-specific QOL, SF-12 PCS, and depressive symptoms suggest effects linked to individual neighborhood issues among a range of health outcomes (Table 2). For most problems, the associations were present after adjustment for age, gender, race/ethnicity, income, education, and asthma severity (model 1 in Table 2). People who reported problems in any of the 5 issues reported a lower QOL. People who reported serious problems with noise had lower physical functioning as measured by SF-12 PCS than people who did not.
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People who reported the most serious problems (5 on a scale of 0 to 5) with traffic (mean difference = 4.74), noise (mean difference = 7.32), trash (mean difference = 8.11 points), and smells (mean difference = 10.41) had higher CES-D scores. When using the depressive symptom cutoff of 16 and analyzing by logistic rather than linear regression (data not shown in Table 2
), there was an approximate 2- to 3-fold increased risk of depression among people who reported traffic (odds ratio [OR] = 2.43; 95% confidence interval [CI] = 1.21, 4.91) or noise (OR = 2.99; 95% CI = 1.35, 6.65) as a serious problem in their neighborhoods, respectively, compared with those who did not perceive these neighborhood problems.
Table 3 provides the linear regression results for the associations between the total perceived neighborhood problem score and asthma-specific QOL, physical functioning, and depressive symptoms. People who had scores in quartile 2 (1 to 3 on a scale of 0 to 25) did not have a more adverse asthma-specific QOL compared with people who reported no neighborhood problems (quartile 1). People who had scores in quartile 3 (mean difference = 5.58) and quartile 4 (mean difference = 5.91) had a decreased asthma-specific QOL, compared with people who reported no serious neighborhood problems and taking into consideration demographic characteristics and asthma severity. The pattern of association was less distinct for the SF-12 PCS. People who had scores in quartile 3 or 4 had lower physical functioning compared with people who reported no serious neighborhood problems (mean difference = –3.92 and –3.04, respectively).
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For the CES-D score, there were strong associations with perceived neighborhood problems, whether assessed as a continuous (Table 3
) or dichotomous (Table 4) measure. When the CES-D was analyzed as a continuous score, people who had scores in quartiles 3 and 4 had scores approximately 6 points higher than people who had scores in quartile 1 (6.05 and 6.04, respectively). When we analyzed CES-D as a dichotomous measure and used logistic regression analysis, people who had scores in quartile 2 were more than 2 times as likely to be also classified as having high depressive symptomatology (OR = 2.38; 95% CI = 1.18, 4.79) (Table 4). People who had scores in quartile 3 were 3.5 times as likely to be classified as having high depressive symptomatology (OR = 3.50; 95% CI = 1.73, 7.11). People who had scores in quartile 4 were almost 5 times as likely to be classified as having high depressive symptomatology (OR = 4.79; 95% CI = 2.41, 9.52). In each case, these results take into consideration demographic characteristics and asthma severity.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Asthma is a multifaceted condition whose status is subject to a wide range of factors. The extent to which one’s environment can be a source of physical and psychosocial stress and that this stress is associated with asthma-related outcomes has yet to be studied extensively. Stress, which could well be mediated through neighborhood factors, has been linked to asthma.66,67 Wright and colleagues66 present a biopsychosocial model linking environmental demands (stressors or life events) leading to negative emotional responses, then physiological responses, and finally an increased risk of physical disease. Clinical reports have suggested connections between an individual’s experience of stress, upset, and anxiety and asthma attacks.68,69 Urban environments can produce stress in the form of nuisances as excessive noise or in a more extreme form, violence. Indeed, exposure to violence has been linked to asthma hospitalizations and a higher number of days with symptoms among children.70,71
These analyses suggest that perceived neighborhood environments are cross-sectionally associated with a poorer disease-specific QOL, physical functioning, and depressive symptoms among adults with asthma. This is consistent with prior neighborhood research in general population samples that has reported associations between neighborhood disadvantage or poverty and depression.72 The association between the perceived neighborhood environment and asthma-related health outcomes was not substantially attenuated after we took into consideration the severity of asthma, suggesting that the association between neighborhood environmental stress and health and functioning is not occurring by way of an increase in clinical disease severity.
Because this analysis is cross-sectional, we cannot confirm a direction of effect between neighborhood problems and the asthma-specific QOL, depressive symptoms, and physical functioning. It is possible that people who have a poor QOL or who have depressive symptoms would be more likely to perceive and report more serious problems in their residential environment. On the other hand, if this were the case, taking into account asthma severity might alter the association between neighborhood problems and the QOL. We did not find such alteration. Of course, the measure of severity may not be sensitive enough to detect effects of the current environment, especially if some of the clinical history (e.g., hospitalization and intubation) that is taken into consideration occurred in the more recent past, potentially under different neighborhood conditions.
We did not have objective measures of the neighborhoods, only respondents’ perceptions of their environments. It would be a stronger analysis with both objective and subjective measures. The one neighborhood problem area with available objective measures might be traffic, as there are traffic flow measures available that could potentially be linked to individual data. Although this is a study limitation, subjective measures have been commonly used to assess the neighborhood environment and may be even more appropriate than objective measures within the stress framework31,32,34 underlying this investigation, which proposes that perceived neighborhood problems act as a stressor for people with asthma and contribute to poor physical health, mental health, and QOL.
Although much of the previous literature in this field has described associations between neighborhood social environment characteristics and health, our data rely on measures of perceived physical environment. Nonetheless, these are not likely to be entirely independent of one another. The physical environmental problems we assessed could very well correlate with social environmental issues. For example, public services may be less adequate in areas with more poor people because local governments can be less responsive to marginal populations.73 Furthermore, noise and traffic, problems that are more common in denser environments, disproportionately affect people with lower incomes who tend to live and work in these settings.74
Finally, we should note that our measures of socioeconomic status were limited to income and educational attainment. Employment status, access to health care, and other unmeasured socioeconomic status confounders could be responsible for the observed associations.
Previous studies have linked urban deprivation to asthma.70,75 Despite some limitations, the results of this study suggest that attention to the neighborhood environment is an important potential approach to boosting the QOL of people with asthma. Attention to municipal services such as trash removal, traffic calming measures in residential areas, and regulation of emissions could have beneficial effects on people with asthma in terms of their QOL, physical functioning, and level of depressive symptoms. Neighborhood design, city planning policies, and attention to environmental quality may be important tools for addressing asthma in the community.
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Acknowledgments |
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This study was funded by the National Institute for Environmental Health Sciences, National Institutes of Health (RO1 ES010906).
We are grateful to Marissa San Pedro and Karen van der Meulen for study interviews, Gillian Earnest for data management, and Connie Archea for study coordination. We thank Catherine Cubbin and Teresa Scherzer for comments on prior drafts and the suggestions of 3 anonymous reviewers.
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Footnotes |
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Peer Reviewed
Contributors
I. H. Yen originated the study, analyzed the data, and drafted the article. E. H. Yelin assisted in the interpretation of results and reviewed and commented on all drafts of the article. P. Katz assisted in the analysis and interpretation of the depressive symptoms and QOL variables. M. D. Eisner assisted in the interpretation of results and reviewed drafts of the article. P. D. Blanc is the principal investigator of the asthma cohort study, contributed to the analytic strategy, assisted in the interpretation of results, and reviewed all drafts of the article.
Human Participant Protection
Data were collected under a protocol approved by the University of California, San Francisco, Committee for Human Subjects. Subjects were contacted by letter and if they did not want to participate were asked to mail back a refusal postcard. Those who did not refuse were contacted, and a telephone survey time was set.
Accepted for publication April 7, 2005.
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