Predictors of Asthma in Young Children: Does Reporting Source Affect Our Conclusions?

Jane E. Miller

From the Institute for Health, Health Care Policy, and Aging Research and the Department of Urban Studies and Community Health, Rutgers University, New Brunswick, NJ.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Both the size and statistical significance of sociodemographic and early health risk factors on childhood asthma vary across studies, in part because some studies rely on parents' retrospective reports of health conditions while others are based on medical records. The authors compare predictors of asthma alternately using maternal reports and medical records for the same set of children. Data are from the 1988 National Maternal and Infant Health Survey and 1991 Longitudinal Follow-up, which collected information from birth certificates, medical records, and mothers of a nationally representative, population-based cohort, allowing comparison across data sources for a consistent sample of young children in the United States. Concordance between maternal reports and medical records on asthma is moderate (kappa = 0.48). The authors find considerable discrepancies in both the estimated prevalence of asthma and the distribution across children with different sociodemographic and health characteristics, depending on the source of asthma data. Black race, male gender, and preterm birth are found to be risk factors for asthma regardless of data source. Poverty, large family size, urban residence, maternal smoking, and breastfeeding are significantly associated with asthma based on maternal reports but not medical records. Lower health care utilization among poor, uninsured, and urban children may account for part of the discrepancy.

asthma; child; prevalence; research design; risk factors

Abbreviations: LF, 1991 Longitudinal Follow-up; NMIHS, National Maternal and Infant Health Survey


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Asthma is the leading chronic illness among children in the United States (1Go, 2Go). The prevalence of asthma has been rising steadily over the past few decades, particularly among children (3Go). A variety of sociodemographic, health, and health behaviors have been implicated as risk factors for asthma, including income, race, family structure, residence, and smoking (4GoGoGoGoGoGoGo–11Go). Estimates of the risk associated with each of those predictors vary in both size and statistical significance, in part because some studies rely on parents' retrospective reports of health conditions (1Go, 5Go, 6Go, 8GoGo–10Go), while others are based on medical records or on clinical examination at the time of the study (7Go, 11Go). Maternal reports may suffer from recall bias as well as the fact that few parents have been trained in how to diagnose asthma or to differentiate it from other respiratory conditions. Medical records data overcome those drawbacks but typically are available only for children who use a particular health care provider. Hence, they cannot be used to generalize about patterns of health or health care utilization in the general population. The design of the studies from which data are drawn also varies, complicating the comparison of asthma findings.

In this paper, we compare predictors of asthma using two different criteria to identify cases: maternal reports and medical records. We make use of a unique data set that collected information from birth certificates, medical records, and mothers of a nationally representative, population-based cohort, which allows us to compare across two different reporting sources for the same sample of young children. We find considerable discrepancies in not only the estimated prevalence of asthma but also its distribution across children with different sociodemographic and health characteristics, depending on the source reporting on asthma.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Data were extracted from the 1988 National Maternal and Infant Health Survey (NMIHS) and its companion 1991 Longitudinal Follow-up (LF). The NMIHS was based on a sample of birth certificates for children born in 1988, and it oversampled Black infants and low birth weight infants. In 1988 and 1991, mothers of sampled children were sent questionnaires that requested information about demographic background, socioeconomic characteristics, and the child's health (12Go, 13Go). Overall, 83 percent of the livebirths from the NMIHS participated in the 1991 Longitudinal Follow-up.

At follow-up, mothers were asked to list the names and addresses of all medical care providers visited by the sampled child since birth, and they were asked to grant consent for the National Center for Health Statistics to request all medical records for that child from those providers. Consent was granted by 94 percent of mothers. Consent rates did not differ significantly according to the sociodemographic characteristics of the mother or child. If consent was given, a questionnaire was sent to each named provider asking for information on the type of provider, the reasons for each visit, and any diagnoses or procedures that occurred at that visit. The Medical Provider Survey data file comprises 99,117 records, one for each reported visit to a medical provider. Seventy-seven percent of the nominated providers supplied medical visit data for 6,159 children, 75 percent of those present at follow-up (14Go). A discussion of selectivity of provider response is described elsewhere (15Go).

For this analysis, a maternal report of asthma was based on a response of "yes" to the question on the 1991 mother's questionnaire: "Have you ever been told by a doctor, nurse, or other medical provider that the sampled child had asthma?" As shown in very poor concurrence with the doctor's diagnosis data (see below), however, these data appear to reflect the mother's opinion of whether the child has asthma; hence, we refer to them as "maternal reports of asthma." A doctor's diagnosis of asthma was based on mention of asthma on a medical record in either a checklist of possible conditions or a listing of asthma by name or International Classification of Diseases, Ninth Revision, Clinical Modification, code 493.0 as a diagnosis for any visit (16Go). Medical providers had the option of filling the information for each visit into a National Center for Health Statistics form or attaching a copy of the medical records for the sampled child. Information on the gestational age, gender, and race of the infant was drawn from the birth certificate; information on breastfeeding, smoking, child care, household composition, income, and residence from the mother's questionnaires at baseline and follow-up; and information on other diagnoses from the medical records.

To investigate whether the sociodemographic and health predictors of early childhood asthma depend on the source of asthma report, we estimated logistic regression models of 1) a medical provider diagnosis of asthma or 2) a maternal report of asthma. The two models were estimated with the same set of predictor variables, using only the 6,159 children for whom both maternal and medical records data were available at the time of follow-up. Analysis of medical records data from the NMIHS/LF showed that children who received health care in clinics had Medicaid or no insurance and were members of a racial or ethnic minority, and that those who lived in an urban area were more likely than other children to be excluded from our analysis because none of their medical providers responded. Moreover, those same attributes were associated with having some, but not all, pertinent medical providers respond; hence, it is possible that a doctor's diagnosis could be overlooked because some of the child's medical records were not available for analysis (15Go). To test the sensitivity of our results to completeness of medical records, we compare results of models for the sample of children for whom all nominated providers responded (65 percent of the overall sample) with those for the full sample.

Prevalence estimates are weighted to the population level using the sampling weights provided by the National Center for Health Statistics for the 1991 Longitudinal Follow-up. Sampling weights are calibrated to be representative of children born in the United States in 1988 who were alive at the time of the follow-up and to incorporate adjustments for the initial sampling design as well as loss to follow-up (13Go). Logistic regression is used to examine the effects of sociodemographic and health factors on chances of an asthma diagnosis by the time of the 1991 Longitudinal Follow-up. Estimated standard errors were corrected for complex sampling using SUDAAN computer software (17Go).


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Overall, the mother, the medical provider, or both identified 1,094 cases of asthma. Thirty-six percent of the cases were mentioned by both the mother and the medical provider, 36 percent by the mother only (e.g., the mother but not the medical provider), and the remaining 27 percent by the medical provider only. The kappa statistic for concurrence between maternal reports and medical records was 0.48, indicating moderate agreement (18Go, 19Go). When we restricted the sample to those children for whom all medical providers responded, the kappa statistic was 0.47, suggesting that incomplete provider response did not explain the difference between the two sources' reports of asthma.

Mothers were more likely to recall and report serious cases of asthma, such as those that resulted in a hospitalization. However, there is surprisingly poor concurrence between maternal reports and medical records, even for relatively severe cases. For only 79 percent of the 190 children with medical records that indicated hospitalization for asthma did their mother report that the child had ever been diagnosed with asthma. Inclusion of children hospitalized for other serious respiratory conditions in the comparison (n = 241) resulted in only slightly higher agreement (80 percent).

Estimates of asthma prevalence differ substantially depending on which reporting source is used: Weighted to national levels, the prevalence estimates for asthma in the sample were 7.7 percent based on medical records or 10.0 percent based on maternal reports. Restricting the definition of "cases" to those for whom both the mother and medical provider mention asthma yields a prevalence of 4.1 percent; broadening the definition to include any child for whom either the mother or medical provider reported the condition yields a prevalence of 13.6 percent.

Table 1 presents characteristics of children according to whether they had asthma reported by 1) both the doctor and mother (400 children), 2) the mother only (n = 406), 3) the doctor only (n = 299), or 4) neither the doctor nor the mother (e.g., no asthma reported; n = 5,054). Doctor-only cases were more likely to be non-Hispanic White, nonpoor, and from smaller families in which the mother did not smoke but did breastfeed. Asthma cases reported by both the mother and the doctor had a higher average number of medical visits since birth than those for whom either one or the other source reported asthma. Mother-only cases were considerably more common among children for whom some providers did not respond to the survey (47.6 percent compared with 28.6 percent and 26.5 percent of mother-and-doctor or doctor-only cases), raising the possibility that the child had been diagnosed with asthma by a doctor, but that the pertinent medical records were not included in the database.


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TABLE 1. Demographic characteristics of children, expressed as percentage,* according to source of asthma diagnosis or report, 1991 Longitudinal Follow-up to 1988 National Maternal and Infant Health Survey{dagger}

 
Logistic regression models of asthma were estimated using either a doctor's diagnosis of asthma (whether or not the mother also reported asthma) or a maternal report of asthma (whether or not a doctor also reported asthma) (table 2). Relations between several of the sociodemographic or health variables and asthma were consistent whether a doctor's diagnosis or a mother's report of asthma is used as the indicator of an asthma case. A gestational age of less than 28 weeks and a gestational age of 28–33 weeks, male gender, and Black race were associated with elevated risks of asthma in either model (p < 0.05 for all variables). However, for each of those variables except gender, the estimated odds ratios were larger based on the maternal report than on the medical records indicator of asthma.


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TABLE 2. Estimated odds ratios and 95% confidence intervals for predictors of asthma by age 3 years, 1991 Longitudinal Follow-up to 1988 National Maternal and Infant Health Survey

 
Moreover, several additional variables (poverty, having three or more siblings, urban residence (p < 0.06), maternal smoking, and lack of breastfeeding) were significant predictors of asthma when maternal reports were used as the asthma indicator but not when medical records were the source of asthma diagnosis. For each of those predictors, the estimated odds ratio was larger in the maternal report model.

When we reestimated the model using only the subset of children for whom all medical providers had responded ("complete provider response"), only preterm birth and male gender were statistically significant risk factors for asthma according to a doctor's diagnosis. The maternal report model for the complete provider response sample again yielded a broader set of risk factors than the doctor's diagnosis model, including non-Hispanic Black race, maternal smoking, and no breastfeeding. The effects of poverty and a large number of siblings were no longer significant. The odds ratios for the effect of poverty on asthma were much more similar for the two reporting sources in the complete provider response models (odds ratios = 1.24 and 1.37 for doctor's diagnosis and maternal reports, respectively) than in the models using the full sample (odds ratios = 1.05 and 1.41, respectively), as the odds ratio increased substantially for the doctor's diagnosis model with complete provider response. In both the mother's and doctor's models with the complete provider sample, poverty did not attain statistical significance, in part because of the much smaller sample size than in the full sample model (n = 3,883 and 6,159, respectively).


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
An analysis of data for a nationally representative cohort reveals substantial discrepancy in maternal reports and medical records of asthma among young children. Maternal reports yield not only a higher estimate of asthma prevalence but also a different distribution, with wider variation across a larger number of sociodemographic and health behaviors. When maternal reports of asthma are used to identify asthma cases, poverty, large family size, maternal smoking, not having been breastfed, and urban residence are all statistically significantly associated with a greater risk of asthma in multivariate models. When medical records are used to identify asthma cases in the same sample of children, none of those factors is significant. Although previous research has examined how characteristics of the mother or child affect the concordance of child health data sources (15Go, 20Go), those studies have not taken the additional step of looking at how discordance affects conclusions about which groups are at higher risk of poor health.

Our findings offer insight that is not available from comparing results of studies of asthma based on medical records against results of different studies based on maternal reports (21Go). Because the NMIHS/LF draws data from medical records and maternal reports for the same set of children, it is possible to rule out differences in study design or sample as reasons for the observed discrepancies between those sources in estimated asthma prevalence and distribution. A handful of other studies with data from both types of sources have also shown substantial discordance between maternal reports and physical examination results for children. Using data from the Health Examination Survey, Jessop and Stein (21Go) found that maternally reported asthma was often not mentioned in the clinical findings. Similar patterns across conditions have been observed for adult health (22Go).

Some of the inconsistency in the published literature regarding the prevalence and distribution of asthma among children stems from differences in definition or measurement of asthma. For example, patterns of point prevalence of asthma at the time of screening via questionnaire or clinical examination differ from patterns of annual prevalence (in the 12 months prior to the study), which in turn will differ from those for cumulative or lifetime prevalence because of variations in symptoms across a child's lifetime (9Go). Both of the reporting sources used in this analysis provide estimates of cumulative prevalence. Hence, differences across sources cannot be attributed to that factor.

A possible drawback to the comparison of data from the two reporting sources in the NMIHS/LF is that the length of the recall period is far shorter for the medical records than for the mother's reports. Data are recorded in the medical chart immediately after a patient's visit, but the mothers were asked to report on their child's health over a 3-year period. Nonetheless, the patterns seen in the maternal report data from the NMIHS/LF are likely to be typical of those for health surveys that retrospectively collect information on cumulative or lifetime prevalence (22Go), although they probably overstate recall bias for estimates for shorter time periods such as 3 months or 12 months, intervals often used in health surveys such as the National Health Interview Survey or the National Health and Nutrition Examination Survey.

Another weakness of these data is that roughly one third of children were missing medical records for one or more of their health care providers, raising the possibility that they had in fact been diagnosed with asthma by a doctor but that information was missing from the data set. However, incomplete provider response does not appear to explain differences between maternal reports and medical records in the set of risk factors identified for asthma. When we reestimated the model for doctor's report of asthma focusing only on children for whom all providers responded to the survey, the same set of risk factors was statistically significant as in the model with both complete and incomplete medical records, although the poverty effect became larger and closer to statistical significance.

An issue that affects the study of asthma risk factors among young children is the difficulty in diagnosing asthma in this age group, including such issues as differentiating between bronchitis and asthma and the different criteria for the frequency and severity of wheezing and other respiratory symptoms. However, although these factors introduce variability across doctors in the diagnosis of asthma, they are unlikely to explain the observed discrepancies between doctors' diagnoses and maternal reports of asthma.

Another possible reason for the discrepancy between maternal reports and medical records in these data is that reactive airway disease (a term used by many physicians who suspect but are not yet ready to name asthma) will be coded as 493.0 (asthma) unless the physician specifically notes "reactive airway disease, not asthma" on the patient's chart (23Go). For those visits, the parent would not be told the child had asthma, yet the medical record data would be coded as mentioning asthma. This type of error could affect other health surveys that rely on parents' reports of asthma. Similar errors could occur with other health conditions in instances where the physician uses a synonym for a health condition that doesn't match the precise terminology or coding that would be written on medical charts and, hence, might not match the wording on a health survey. For example, the 1991 Longitudinal Follow-up asks about "chronic orthopedic problems," which a parent might not recognize as synonymous with "bone or joint disease."

When considering the implications of the observed discordance between maternal reports and medical records on asthma or other health conditions, one should note that neither reporting source can be considered to be the definitive "gold standard" against which the other should be judged (21Go). Medical records are completed at the time of each visit by trained medical providers who are familiar with diagnostic criteria. Hence, medical records are less likely to suffer from either recall bias or misdiagnosis than are the retrospective maternal reports. However, if children are not taken to a physician for a particular health problem, that problem will be overlooked in the medical records.

Evidence from other studies suggests that differences in health care utilization may be an important factor in accounting for the higher prevalence estimates based on the maternal reports than on the medical records. Children from urban areas, low income families, and those families lacking health insurance are less likely to receive medical care for asthma (10Go, 24Go), reducing the chances that they will be diagnosed. Joseph et al. (25Go) showed that a substantial share of school-aged children had clinical evidence of asthma despite a lack of a physician's diagnosis of the condition. In a study of discrepant reporting of asthma, Miller et al. (15Go) showed that mother-only reports were nearly twice as common and that physician-only reports were only 60 percent as common among children from poor families. Children who did not have health insurance were also more likely to have asthma reported by their mother but not by a medical provider. These findings suggest that analyses based on medical charts or discharge records are likely to understate the deleterious effects of socioeconomic disadvantage on the risk of asthma or other health conditions.


    ACKNOWLEDGMENTS
 
Funding for this project was provided by the Faculty Scholars Program of the William T. Grant Foundation.

The author thanks Diane Davis and Dr. Dorothy Gaboda for assistance with the data preparation, as well as Dr. Michael Kogan and other colleagues for advice.


    NOTES
 
Correspondence to Dr. Jane E. Miller, Institute for Health, Health Care Policy, and Aging Research, Rutgers University, 30 College Ave., New Brunswick, NJ 08901-5070 (e-mail: jem{at}rci.rutgers.edu).


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 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication August 7, 2000. Accepted for publication December 22, 2000.