1 Department of Health Evaluation Sciences, Pennsylvania State University College of Medicine, 600 Centerview Drive, PO Box 855, Hershey, PA 17033, USA
2 Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry of New Jersey and Rutgers University, 170 Frelinghuysen Road, Piscataway, NJ 08854, USA
3 New Jersey Department of Environmental Protection, Division of Science, Research and Technology and the Division of Biometrics, UMDNJ-SPH, 401 East State Street, Trenton, NJ 086250409, USA
4 China National Environmental Monitoring Center, Beisihuandonglu, Chaoyang District, Beijing 100029, China
5 Formerly with National Center for Environmental Assessment, US Environmental Protection Agency, Mail Drop B24301, Research Triangle Park, NC 27711, USA
Correspondence: Dr Z Qian, 600 Centerview Drive, PO Box 855, Hershey, PA 17033, USA. E-mail: zqian{at}psu.edu
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Abstract |
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Methods We analysed data collected in the Four Chinese Cities Study (FCCS) to examine health effects on prevalence rates of respiratory symptoms and illnesses in 7058 school children living in the four Chinese cities: Lanzhou, Chongqing, Wuhan, and Guangzhou. We used factor analysis approaches to reduce the number of the children's lifestyle/household variables and to develop new uncorrelated factor variables. We used unconditional logistic regression models to examine associations between the factor variables and the respiratory health outcomes, while controlling for other covariates.
Results Five factor variables were derived from 21 original variables: heating coal smoke, cooking coal smoke, socioeconomic status, ventilation, and environmental tobacco smoke (ETS) and parental asthma. We found that higher exposure to heating coal smoke was associated with higher reporting of cough with phlegm, wheeze, and asthma. Cooking coal smoke was not associated with any of the outcomes. Lower socioeconomic status was associated with lower reporting of persistent cough and bronchitis. Higher household ventilation was associated with lower reporting of persistent cough, persistent phlegm, cough with phlegm, bronchitis, and wheeze. Higher exposure to ETS and the presence of parental asthma were associated with higher reporting of persistent cough, persistent phlegm, cough with phlegm, bronchitis, wheeze, and asthma.
Conclusions Our study suggests that independent respiratory effects of exposure to indoor air pollution, heating coal smoke, and ETS may exist for the studied children.
Accepted 26 June 2003
There is growing evidence that indoor air pollution is a risk factor for the development of respiratory symptoms and illnesses in children.14 In the current literature of air pollution epidemiology, household gas combustion has often been considered a major source of indoor air pollution.58 The reason is that coal and other dirty solid fuels are rarely used in Western countries where most available studies have been conducted. In many parts of the world, including China, another major source of indoor air pollution is household coal combustion, used for heating and cooking.9,10 For example, in the four Chinese cities of Chongqing, Guangzhou, Lanzhou, and Wuhan, approximately 5171% of households used coal for heating or cooking.11,12 Burning coal generally produces more air pollutants, in greater amounts, than burning liquid fuels or gas.13 However, few studies have systematically examined the health effects of household coal combustion. The results of the health effects from a limited number of published studies are somewhat inconsistent.1416 This inconsistency may partly result from uncertainties associated with indoor exposure characterization and classification.
Most analyses of health effects of indoor air pollution exposure have relied on the data collected through questionnaire surveys. In epidemiological settings, a questionnaire is usually designed to ask a large set of questions on the subjects' attributes with the purpose of obtaining enough information for the subsequent exposure assessment. However, sometimes the questions may not be the direct indicators of true exposure variables. The true exposure variables may be difficult or impossible to define, or may not be directly measurable.1719 Sorting out useful information from the large amount of data collected is always a challenging task but is critical to obtain objective and unbiased exposure assessment in an epidemiological study. In the Four Chinese Cities Study (FCCS), we established a large dataset using a standardized questionnaire survey approach.16 This dataset contains a large amount of information on household air pollution sources (e.g. types of fuels and tobacco smoking), house characteristics that affect indoor air pollution levels (e.g. house type and ventilation conditions), and activities affecting pollution exposure (e.g. cooking location and cooking frequencies). It is of public health importance to ascertain the degree to which the known indoor air pollution sources (coal smoke targeted specifically) affect health and to identify potential risk factors from the large set of household variables collected from the questionnaire survey.
Factor analysis has been widely used to identify and summarize many inter-relationships that exist among individual variables. In a factor analysis, inter-correlated variables are combined into a smaller number of new variables (factors). This may enable us to simplify the dataset and consequently gain insights about underlying risk factors and true exposures that are linked to adverse health effects.20 In this study, we hypothesize that exposure to household coal combustion and environmental tobacco smoke (ETS) is positively associated with high prevalence of respiratory conditions in children. We extracted a set of uncorrelated (orthogonal) household risk factors from a large set of original variables obtained through the questionnaire survey by using a factor analysis method. We then used the factor scores of the extracted factors as independent variables in subsequent logistic regression models to examine associations between exposure and health outcomes. We expected the use of factor-score based variables to minimize the multicollinearity problem that is present in conventional regression analyses and to limit uncertainties associated with indoor exposure assessment.21,22
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Methods |
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Definition of respiratory health outcomes
Children's lifetime respiratory conditions were determined from questionnaire responses and were defined as follows:
Persistent cough
The answers to several cough-related questions indicate that the child under study had coughed for at least one month per year either along with or apart from colds.
Persistent phlegm
The answers to several phlegm-related questions indicate that the child had brought up phlegm or mucus from the chest for at least one month per year either along with or apart from colds.
Cough with phlegm
A yes answer to both cough and phlegm questions. (Cough: a yes answer to either of the two questions When this child has a cold, does he/she usually have a cough? or When this child does not have a cold, does he/she usually have a cough? Phlegm: a yes answer to either of the two questions When this child has a cold, does he/she usually bring up phlegm or mucus from his/her chest? or When this child does not have a cold, does he/she usually bring up phlegm or mucus from his/her chest?)
Wheeze
A yes answer to any of the following questions Has this child's chest ever sounded wheezy or whistling when he/she has had a cold?; Has this child's chest ever sounded wheezy or whistling when he/she has not had a cold?; or Has this child's chest ever sounded wheezy or whistling on most days or nights?.
Bronchitis
A yes answer to the question Has a doctor ever diagnosed bronchitis in this child?.
Asthma
A yes answer to the question Has a doctor ever diagnosed asthma in this child?.
Statistical analysis
We conducted data analysis at two levels, factor analysis, and unconditional logistic regression analysis. We first screened all original variables in the study database. These variables corresponded to individual questions in the survey questionnaire. These variables consisted of 222 fields in SAS database format. We excluded some variables from the factor analyses based on the following considerations: (1) if a variable only provided information (e.g. children's school grades and other non-household variables) that was not directly related to the purpose of the current analysis, i.e. identifying household factors that were potentially associated with children's respiratory health outcomes. (After this step, 58 fields remained); and (2) if a variable had close to a uniform response across all subjects and it was less likely to have an association with the health outcomes of concern. After the above two screenings, we obtained 51 fields that were recoded and constructed into either ordinal variables or indicator variables. This resulted in the formation of 21 re-organized variables that were used in the factor analysis.25
We used the SAS Factor procedure and report the results from the principal components analysis with varimax rotations here. We chose variables with factor loadings 0.4 for interpretation in the study.20,22 The names of the extracted factors were based on their high loading variables (
0.4). In each unconditional logistic regression model,26 the dependent variable was one of the respiratory health outcomes of concern (persistent cough, persistent phlegm, cough and phlegm, wheeze, bronchitis, or asthma). The independent variables were the factor scores of the factors obtained from the factor analysis, along with age, gender, and the district dummy variables.
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Results |
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Discussion |
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Surprisingly, the results show that lower socioeconomic status was significantly associated with lower reporting of persistent cough and bronchitis in children. There are two possible reasons that may explain the results. In this study, low socioeconomic status means that the parents had low education levels and were manual labourers. There might be a difference in rates of reporting based on the parents' education level. That is, the better-educated parents might be more likely to report symptoms. Another reason is that the current analysis used data collected in China from 1993 to 1996 when society was unstable with many reforms being implemented. Lower socioeconomic status households, as defined in the current analysis, might not represent a group with low incomes as it would in developed countries where the level of socioeconomic status is generally in agreement with household incomes. For example, at that time parents with low education levels might have higher incomes than those with high education, and manual labourers might also obtain higher salaries than non-manual labourers. Households with high incomes tended to use cleaner but more expensive fuel at home, such as gas or electricity. The children living in these households were then exposed to less indoor air pollutants from household heating and cooking activities and had weaker health effects. Due to practical concerns, family income information was not collected in any of the participating households.
Another surprising finding of the current analysis is the lack of associations between any health outcomes and cooking coal smoke exposure. This observation, however, is consistent with a finding from our previous analysis using a different statistical method.29 We speculate this is mainly because the children under study usually stayed away from their homes when cooking took place and, hence, avoided exposure to peak concentrations of cooking coal smoke. The cooking coal smoke variable constructed here, therefore, may not represent children's true exposure to cooking coal smoke.
To our knowledge, few epidemiological studies on air pollution have examined associations between respiratory health outcomes and exposure by developing multiple exposure variables using factor scores.30,31 The large sample size in the present study provided us with a good opportunity to explore true exposure for the children under study by comparing the clustering features of the exposure-related variables in terms of factor patterns. By doing so, we reduced 21 lifestyle/household variables to five potential risk factors relevant to the children's exposure in the four study cities. These results of the variable reduction suggest that the children's exposure was multifactorial.32 These multiple risk factors might act as true exposure factors or confounding factors associated with children's respiratory health. First, at the variable level, multiple correlated variables were grouped to form a factor. These variables included not only pollution sources but also exposure-related variables such as house type. The factor cooking coal smoke, for example, was composed of five variables, coal used for cooking, coal stoves used for cooking, outside cooking, apartment, and one-story house. Second, at the factor level, the risk factors could be categorized into pollution sources (heating coal smoke, cooking coal smoke), combination of pollution source and related variables (ETS and parental asthma), socioeconomic status, and a factor that influences indoor pollution levels (household ventilation).
The current analysis used factor scores as new variables in the unconditional logistic regression models to study the associations between children's respiratory health outcomes and factor scores. This strategy has three advantages. First, it minimizes the multicollinearity problem since the five developed factors are orthogonal and the correlated variables are blocked within their factors. Second, potential confounding factors are included in the regression model since the identification of the confounding factors in the analysis is not only based on judgement but also on the results from the factor analysis. Third, the roles of each input variable are included when modelling the health outcome in the logistic regression models because the factor scores represent weighted combinations of the subject's scores on each of the input variables.21,27 This effort prevented or reduced the possible omission of some important variables in the models. Therefore, we consider this approach preferable for revealing the true exposure among a large set of exposure-related variables from an epidemiological questionnaire survey.
Despite the obvious benefits mentioned above for the factor analysis, it is acknowledged that factor analysis is a complicated sequence of procedures, involving a great deal of subjective judgement. For example, determining the number of factors and labelling them were subjective. The selection of the 21 variables to form the factors was also subjective and merely reflected our preconception as to which pollution-related variables were relevant to the health outcomes under study. In performing the factor analysis, we assumed that the variables collected (through the questionnaire survey) were not necessarily the ones that we were interested in. The procedure was asked to find a set of orthogonal factors that presumably reflect exposure better than original variables. Doing this, however, may hamper us from examining some originally collected variables of interest. For example, Factor 5 consisted of three original variables (fathers' smoking status, other household smokers, and parental asthma). Knowing the health effect of this factor, as a whole, is perhaps less interesting and of less practical importance than knowing the effects of the individual variables.
To separate the ETS effect and the parental asthma effect from the overall effect of Factor 5, we performed a new set of logistic regression analyses using the original variables that formed the 5 factors (Table 2), along with age, gender, and the district dummy variables, as independent variables. The results show that parental asthma was significantly associated with all six health outcomes; and that ETS exposure was associated significantly with cough with phlegm and bronchitis (Table 4). The ETS effects and parental asthma effects observed in the current analysis are generally in agreement with findings from a previous analysis using different models.24 These results suggest that the overall effects of Factor 5 were largely driven by parental asthma. Reasons for the strong parental asthma effects on children's respiratory health may include the following. Parental asthma may be linked to increased susceptibility in their children to environmental exposure through geneenvironment interactions. The parents and their children might have been exposed to similar levels of indoor air pollution since they had lived in the same houses. Further studies are needed to better understand this question.
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KEY MESSAGES
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References |
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