Evaluation of four gamma-based methods for calculating confidence intervals for age-adjusted mortality rates when data are sparse.

BACKGROUND: Equal-tailed confidence intervals that maintain nominal coverage (0.95 or greater probability that a 95% confidence interval covers the true value) are useful in interval-based statistical reliability standards, because they remain conservative. For age-adjusted death rates, while the Fay-Feuer gamma method remains the gold standard, modifications have been proposed to streamline implementation and/or obtain more efficient intervals (shorter intervals that retain nominal coverage). METHODS: This paper evaluates three such modifications for use in interval-based statistical reliability standards, the Anderson-Rosenberg, Tiwari, and Fay-Kim intervals, when data are sparse and sample size-based standards alone are overly coarse. Initial simulations were anchored around small populations (P = 2400 or 1200), the median crude all-cause US mortality rate in 2010-2019 (833.8 per 100,000), and the corresponding age-specific probabilities of death. To allow for greater variation in the age-adjustment weights and age-specific probabilities, a second set of simulations draws those at random, while holding the mean number of deaths at 20 or 10. Finally, county-level mortality data by race/ethnicity from four causes are selected to capture even greater variation: all causes, external causes, congenital malformations, and Alzheimer disease. RESULTS: The three modifications had comparable performance when the number of deaths was large relative to the denominator and the age distribution was as in the standard population. However, for sparse county-level data by race/ethnicity for rarer causes of death, and for which the age distribution differed sharply from the standard population, coverage probability in all but the Fay-Feuer method sometimes fell below 0.95. More efficient intervals than the Fay-Feuer interval were identified under specific circumstances. When the coefficient of variation of the age-adjustment weights was below 0.5, the Anderson-Rosenberg and Tiwari intervals appeared to be more efficient, whereas when it was above 0.5, the Fay-Kim interval appeared to be more efficient. CONCLUSIONS: As national and international agencies reassess prevailing data presentation standards to release age-adjusted estimates for smaller areas or population subgroups than previously presented, the Fay-Feuer interval can be used to develop interval-based statistical reliability standards with appropriate thresholds that are generally applicable. For data that meet certain statistical conditions, more efficient intervals could be considered.

National Health Interview Survey, COVID-19, and Online Data Collection Platforms: Adaptations, Tradeoffs, and New Directions.

High-quality data are accurate, relevant, and timely. Large national health surveys have always balanced the implementation of these quality dimensions to meet the needs of diverse users. The COVID-19 pandemic shifted these balances, with both disrupted survey operations and a critical need for relevant and timely health data for decision-making. The National Health Interview Survey (NHIS) responded to these challenges with several operational changes to continue production in 2020. However, data files from the 2020 NHIS were not expected to be publicly available until fall 2021. To fill the gap, the National Center for Health Statistics (NCHS) turned to 2 online data collection platforms-the Census Bureau's Household Pulse Survey (HPS) and the NCHS Research and Development Survey (RANDS)-to collect COVID-19‒related data more quickly. This article describes the adaptations of NHIS and the use of HPS and RANDS during the pandemic in the context of the recently released Framework for Data Quality from the Federal Committee on Statistical Methodology. (Am J Public Health. 2021;111(12):2167-2175. https://doi.org/10.2105/AJPH.2021.306516).

Particulate Matter Air Pollution Exposure and Heart Disease Mortality Risks by Race and Ethnicity in the United States: 1997 to 2009 National Health Interview Survey With Mortality Follow-Up Through 2011.

BACKGROUND: Most US studies of mortality and air pollution have been conducted on largely non-Hispanic white study populations. However, many health and mortality outcomes differ by race and ethnicity, and non-Hispanic white persons experience lower air pollution exposure than those who are non-Hispanic black or Hispanic. This study examines whether associations between air pollution and heart disease mortality differ by race/ethnicity. METHODS: We used data from the 1997 to 2009 National Health Interview Survey linked to mortality records through December 2011 and annual estimates of fine particulate matter (PM2.5) by census tract. Proportional hazards models were used to estimate hazard ratios and 95% confidence intervals between PM2.5 (per 10 µg/m3) and heart disease mortality using the full sample and the sample adults, which have information on additional health variables. Interaction terms were used to examine differences in the PM2.5-mortality association by race/ethnicity. RESULTS: Overall, 65 936 of the full sample died during follow-up, and 22 152 died from heart disease. After adjustment for several factors, we found a significant positive association between PM2.5 and heart disease mortality (hazard ratio, 1.16; 95% confidence interval, 1.08-1.25). This association was similar in sample adults with adjustment for smoking and body mass index (hazard ratio, 1.18; 95% confidence interval, 1.06-1.31). Interaction terms for non-Hispanic black and Hispanic groups compared with the non-Hispanic white group were not statistically significant. CONCLUSIONS: Using a nationally representative sample, the association between PM2.5 and heart disease mortality was elevated and similar to previous estimates. Associations for non-Hispanic black and Hispanic adults were not statistically significantly different from those for non-Hispanic white adults.

National Health and Nutrition Examination Survey: Estimation Procedures, 2011-2014.

This report describes the methods used to create NHANES 2011-2014 sample weights and variance units for the public-use data files, including sample weights for selected subsamples, such as the fasting subsample. The impacts of sample design changes on estimation for NHANES 2011-2014 and the addition of the NHANES National Youth Fitness Survey (NNYFS) 2012 are described. Approaches that data users can employ to modify sample weights when combining survey cycles or when combining subsamples are also included.

National Health and Nutrition Examination Survey: California and Los Angeles County, Estimation Methods and Analytic Considerations, 1999-2006 and 2007-2014.

Background California is the most populated state and Los Angeles County is the most populated county in the United States. National Health and Nutrition Examination Survey (NHANES) sample weights and variance units were developed for these places to obtain subnational estimates. Objective This report describes the California and Los Angeles County NHANES 1999-2006 and 2007-2014 samples, including the creation of the sample weights and variance units and descriptions of the resulting data files. Some analytic guidelines are provided. Results Eight years of NHANES data were combined for each data file to provide an adequate sample size and reduce disclosure risks. Because Los Angeles County has been a self-representing primary sampling unit, sample weights for Los Angeles County were relatively straightforward. However, a modelbased approach was used to create sample weights for California. The relatively large proportion of Mexican- American and other Hispanic persons in California, coupled with the different NHANES 1999-2014 sample design requirements for oversampling these groups within the small number of NHANES locations selected each cycle, led to a relatively large size of these groups in the California and Los Angeles County NHANES files. For example, 1,137 and 374 of the 3,353 Mexican-Americans persons in NHANES 2007-2014 were in the California and Los Angeles County samples, respectively. Conclusion The California and Los Angeles County NHANES 1999-2006 and 2007-2014 samples are available in the National Center for Health Statistics Research Data Center.

National Center for Health Statistics Data Presentation Standards for Proportions.

The National Center for Health Statistics (NCHS) disseminates information on a broad range of health topics through diverse publications. These publications must rely on clear and transparent presentation standards that can be broadly and efficiently applied. Standards are particularly important for large, cross-cutting reports where estimates cannot be individually evaluated and indicators of precision cannot be included alongside the estimates. This report describes the NCHS Data Presentation Standards for Proportions. The multistep NCHS Data Presentation Standards for Proportions are based on a minimum denominator sample size and on the absolute and relative widths of a confidence interval calculated using the Clopper-Pearson method. Proportions (usually multiplied by 100 and expressed as percentages) are the most commonly reported estimates in NCHS reports.

Racial and Ethnic Differences in a Linkage with the National Death Index.

OBJECTIVES: Differences in the availability of a Social Security Number (SSN) by race/ethnicity could affect the ability to link with death certificate data in passive follow-up studies and possibly bias mortality disparities reported with linked data. Using 1989-2009 National Health Interview Survey (NHIS) data linked with the National Death Index (NDI) through 2011, we compared the availability of a SSN by race/ethnicity, estimated the percent of links likely missed due to lack of SSNs, and assessed if these estimated missed links affect race/ethnicity disparities reported in the NHIS-linked mortality data. METHODS: We used preventive fraction methods based on race/ethnicity-specific Cox proportional hazards models of the relationship between availability of SSN and mortality based on observed links, adjusted for survey year, sex, age, respondent-rated health, education, and US nativity. RESULTS: Availability of a SSN and observed percent linked were significantly lower for Hispanic and Asian/Pacific Islander (PI) participants compared with White non-Hispanic participants. We estimated that more than 18% of expected links were missed due to lack of SSNs among Hispanic and Asian/PI participants compared with about 10% among White non-Hispanic participants. However, correcting the observed links for expected missed links appeared to only have a modest impact on mortality disparities by race/ethnicity. CONCLUSIONS: Researchers conducting analyses of mortality disparities using the NDI or other linked death records, need to be cognizant of the potential for differential linkage to contribute to their results.

Linkage of 1986-2009 National Health Interview Survey with 1981-2010 Florida Cancer Data System.

BACKGROUND: National survey data linked with state cancer registry data has the potential to create a valuable tool for cancer prevention and control research. A pilot project-developed in a collaboration of the Centers for Disease Control and Prevention's National Center for Health Statistics (NCHS) and the Florida Cancer Data System (FCDS) at the University of Miami -links the records of the 1986-2009 National Health Interview Survey (NHIS) and the 1981-2010 FCDS. The project assesses the feasibility of performing a record linkage between NCHS survey data and a state-based cancer registry, as well as the value of the data produced. The linked NHIS-FCDS data allow researchers to follow NHIS survey participants longitudinally to examine factors associated with future cancer diagnosis, and to assess the characteristics and quality of life among cancer survivors. METHODS: This report provides a preliminary evaluation of the linked national and state cancer data and examines both analytic issues and complications presented by the linkage. CONCLUSIONS: Residential mobility and the number of years of data linked in this project create some analytic challenges and limitations for the types of analyses that can be conducted. However, the linked data set offers the ability to conduct analyses not possible with either data set alone.

Diabetes and colorectal cancer screening among men and women in the USA: National Health Interview Survey: 2008, 2010.

PURPOSE: Adults with diabetes are at increased risk of being diagnosed with and dying from colorectal cancer, but it is unclear whether colorectal cancer screening (CRCS) use is lower in this population. Using the 2008 and 2010 National Health Interview Survey data, we examined whether guideline-concordant CRCS is lower among men and women with self-reported diabetes. METHODS: We calculated the weighted percentage of guideline-concordant CRCS and unadjusted and adjusted prevalence ratios (PR) comparing adults aged 51-75 years with diabetes (n = 6,514) to those without (n = 8,371). We also examined effect modification by age (51-64 and 65-75), race/ethnicity, and number of medical office visits (0-3, ≥ 4). RESULTS: The unadjusted prevalence of CRCS among men with diabetes was significantly higher than men without (63.3 vs. 58.0 %; PR = 1.09 95 % CI 1.03-1.16). In adjusted models, this relationship was evident among older [adjusted PR (aPR) = 1.13 95 % CI 1.06-1.21] but not younger men (aPR = 0.99 95 % CI 0.91-1.08; p for interaction term ≤ 0.01). There was no significant association between diabetes and CRCS among women overall (56.6 vs. 57.9 %; PR = 0.98 95 % CI 0.92-1.04) or by age group. Race/ethnicity and the number of medical visits did not significantly modify the association between diabetes and CRCS for men or women. CONCLUSIONS: Men and women with self-reported diabetes were not less likely to be up to date with CRCS than those without diabetes. Older men with diabetes were more likely to be up to date with CRCS than those without diabetes.

Evaluating data quality for blended data using a data quality framework.

In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released "A Framework for Data Quality", organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data. The FCSM Framework applies to all types of data, however best practices for implementation have not been documented. We applied the FCSM Framework for three health-research related case studies. For each case study, assessments of data quality dimensions were performed to identify threats to quality, possible mitigations of those threats, and trade-offs among them. From these assessments the authors concluded: 1) data quality assessments are more complex in practice than anticipated and expert guidance and documentation are important; 2) each dimension may not be equally important for different data uses; 3) data quality assessments can be subjective and having a quantitative tool could help explain the results, however, quantitative assessments may be closely tied to the intended use of the dataset; 4) there are common trade-offs and mitigations for some threats to quality among dimensions. This paper is one of the first to apply the FCSM Framework to specific use-cases and illustrates a process for similar data uses.

Identifying implausible gestational ages in preterm babies with Bayesian mixture models.

Infant birth weight and gestational age are two important variables in obstetric research. The primary measure of gestational age used in US birth data is based on a mother's recall of her last menstrual period, which has been shown to introduce random or systematic errors. To mitigate some of those errors, Oja et al., Platt et al., and Tentoni et al. estimated the probabilities of gestational ages being misreported under the assumption that the distribution of infant birth weights for a true gestational age is approximately Gaussian. From this assumption, Oja et al. fitted a three-component mixture model, and Tentoni et al. and Platt et al. fitted two-component mixture models. We build on their methods and develop a Bayesian mixture model. We then extend our methods using reversible jump Markov chain Monte Carlo to incorporate the uncertainty in the number of components in the model. We conduct simulation studies and apply our methods to singleton births with reported gestational ages of 23-32 weeks using 2001-2008 US birth data. Results show that a three-component mixture model fits the birth data better for gestational ages reported as 25 weeks or less; and a two-component mixture model fits better for the higher gestational ages. Under the assumption that our Bayesian mixture models are appropriate for US birth data, our research provides useful statistical tools to identify records with implausible gestational ages, and the techniques can be used in part of a multiple-imputation procedure for missing and implausible gestational ages.

Comparison of Mental Health Estimates by Sociodemographic Characteristics in the Research and Development Survey 3 and the 2019 National Health Interview Survey.

Objectives-This report compares national and subgroup estimates of any (mild, moderate, or severe) level of major depressive disorder (depression) and generalized anxiety disorder (GAD) symptoms among the U.S. adult population from two data sources, the 2019 National Health Interview Survey (NHIS) and the third round of the Research and Development Survey (RANDS 3). Methods-Data from the 2019 NHIS (n = 31,997) and RANDS 3 (n = 2,646) were used. The eight-item Patient Health Questionnaire (PHQ-8), scores ranging from 0 to 24, and the seven-item GAD scale (GAD-7), scores ranging from 0 to 21, were used to measure the severity of depression and GAD symptoms, respectively. Binary indicators of exhibiting symptoms were based on scores of 5 to 24 for depression and 5 to 21 for GAD. The estimates were compared by the following sociodemographic characteristics: age, sex, race and Hispanic origin, education, and region. Results-Nearly all of the national and subgroup estimates of adults with depression and GAD symptoms were significantly higher based on RANDS 3 compared with the 2019 NHIS. The only exception was the depression symptoms estimate among adults aged 65 and over, where the estimates were comparable across the two data sources. Both data sources found that depression symptoms were associated with sex, age, race and Hispanic origin, and education, and GAD symptoms were associated with age, race and Hispanic origin, and education. However, NHIS identified a few associations that RANDS did not, including associations between depression symptoms and region and GAD symptoms and sex. Conclusions-Mental health estimates from RANDS, a web-based survey, may be overestimated when compared with a traditional in-person household survey. These results may inform potential strategies to improve the comparability of mental health estimates from RANDS and other surveys like NHIS, such as calibration weights or other model-based methods.

Evaluation of the National Center for Health Statistics Data Presentation Standards for Rates From Vital Statistics and Sample Surveys.

For the CIs used in the Standards for rates from vital statistics and complex health surveys, this report evaluates coverage probability, relative width, and the resulting percentage of rates flagged as statistically unreliable when compared with previously used standards. Additionally, the report assesses the impact of design effects and the denominator's sampling variability, when applicable.

Exposures to fine particulate air pollution and respiratory outcomes in adults using two national datasets: a cross-sectional study.

BACKGROUND: Relationships between chronic exposures to air pollution and respiratory health outcomes have yet to be clearly articulated for adults. Recent data from nationally representative surveys suggest increasing disparity by race/ethnicity regarding asthma-related morbidity and mortality. The objectives of this study are to evaluate the relationship between annual average ambient fine particulate matter (PM2.5) concentrations and respiratory outcomes for adults using modeled air pollution and health outcome data and to examine PM2.5 sensitivity across race/ethnicity. METHODS: Respondents from the 2002-2005 National Health Interview Survey (NHIS) were linked to annual kriged PM2.5 data from the USEPA AirData system. Logistic regression was employed to investigate increases in ambient PM2.5 concentrations and self-reported prevalence of respiratory outcomes including asthma, sinusitis and chronic bronchitis. Models included health, behavioral, demographic and resource-related covariates. Stratified analyses were conducted by race/ethnicity. RESULTS: Of nearly 110,000 adult respondents, approximately 8,000 and 4,000 reported current asthma and recent attacks, respectively. Overall, odds ratios (OR) for current asthma (0.97 (95% Confidence Interval: 0.87-1.07)) and recent attacks (0.90 (0.78-1.03)) did not suggest an association with a 10 µg/m3 increase in PM2.5. Stratified analyses revealed significant associations for non-Hispanic blacks [OR = 1.73 (1.17-2.56) for current asthma and OR = 1.76 (1.07-2.91) for recent attacks] but not for Hispanics and non-Hispanic whites. Significant associations were observed overall (1.18 (1.08-1.30)) and in non-Hispanic whites (1.31 (1.18-1.46)) for sinusitis, but not for chronic bronchitis. CONCLUSIONS: Non-Hispanic blacks may be at increased sensitivity of asthma outcomes from PM2.5 exposure. Increased chronic PM2.5 exposures in adults may contribute to population sinusitis burdens.

Prepregnancy body mass index and gestational weight gain in relation to child body mass index among siblings.

There is increasing evidence that in utero effects of excessive gestational weight gain may result in increased weight in children; however, studies have not controlled for shared genetic or environmental factors between mothers and children. Using 2,758 family groups from the Collaborative Perinatal Project, the authors examined the association of maternal prepregnancy body mass index (BMI) and gestational weight gain on child BMI at age 4 years using both conventional generalized estimating equations and fixed-effects models that account for shared familial factors. With generalized estimating equations, prepregnancy BMI and gestational weight gain had similar associations with the child BMI z score (ß = 0.09 units, 95% confidence interval (CI): 0.08, 0.11; and ß = 0.07 units, 95% CI: 0.04, 0.11, respectively. However, fixed effects resulted in null associations for both prepregnancy BMI (ß = 0.03 units, 95% CI: -0.01, 0.07) and gestational weight gain (ß = 0.03 units, 95% CI: -0.02, 0.08) with child BMI z score at age 4 years. The positive association between gestational weight gain and child BMI at age 4 years may be explained by shared family characteristics (e.g., genetic, behavioral, and environmental factors) rather than in utero programming. Future studies should continue to evaluate the relative roles of important familial and environmental factors that may influence BMI and obesity in children.

The use of covariates to identify records with implausible gestational ages using the birthweight distribution.

The objective of this study was to evaluate the usefulness of covariates in identifying birth records with implausible values of gestational age. Birthweight distributions for births with early reported gestational ages are markedly bimodal, suggesting a mixture of two distributions. Most births form a normal-shaped left-hand (primary) distribution and a smaller number form the right-hand (secondary) distribution. The births in the secondary distribution are thought to have gestational age mistakenly reported. Prior work has found that births in the secondary distribution are at higher risk of poor outcomes than those in the primary distribution. Using 2002 US Natality data for gestational ages 26-35 weeks, we fit normal mixture models to birthweight with and without covariates (maternal race, education, parity, age, region of the country, prenatal care initiation) by reported gestational age. Additional models were stratified by infant sex. This approach allowed for the relationship between the covariates and birthweight to differ between the components. Mixture models fit reasonably well for reported gestational ages <33 weeks, but not for later weeks. Counter to the hypothesis, results were similar for models with and without covariates or stratification or both, although stratified models without covariates predicted slightly more girls and slightly fewer boys in the secondary distribution than did the corresponding unstratified models. For reported gestational ages <33 weeks, predictions from the four sets of models were highly correlated and predictions were similar for subgroups defined by the clinical estimates of gestational age and other covariates. For births with reported gestational ages of 29 or more weeks, the proportion in the secondary distribution exceeded 30%, although this varied by maternal characteristics. The use of covariates and stratification complicated model fitting without materially improving identification of implausible gestational age values, supporting inferences from prior studies using data 'cleaned' without consideration of maternal or infant characteristics.

The association between childhood asthma prevalence and monitored air pollutants in metropolitan areas, United States, 2001-2004.

BACKGROUND: Air pollution exposure has been linked to adverse respiratory health outcomes among children, primarily in studies of acute exposures that are often in limited geographic areas. We sought to assess the association between chronic outdoor air pollution exposure, as measured by 12-month averages by county, and asthma among children in metropolitan areas across the nation. METHODS: Eligible children included those aged 3-17 years residing in US metropolitan areas who were sampled in the 2001-2004 National Health Interview Survey (N=34,073). 12-month average air pollutant levels for sulfur dioxide, nitrogen dioxide, ozone and particulate matter were compiled by county for 2000-2004. Eligible children were linked to pollutant levels for the previous 12 months for their county of residence. Adjusted odds ratios of having current asthma or an asthma attack in the past 12 months were estimated in single pollutant logistic regression models. RESULTS: Children in counties with ozone and, to a less consistent degree, particulate matter levels in the highest quartile were more likely to have current asthma and/or a recent asthma attack than children residing in counties with the lowest pollution levels; the adjusted odds for current asthma for the highest quartile of estimated ozone exposure was 1.56 (95% confidence interval [CI]: 1.15, 2.10) and for recent asthma attack 1.38 (95% CI: 0.99, 1.91). No associations were found with sulfur dioxide or nitrogen dioxide levels. CONCLUSION: Although the current US standard for ozone is based on short-term exposure, this cross-sectional study suggests that chronic (12-month) exposure to ozone and particles is related to asthma outcomes among children in metropolitan areas throughout the US.

Comparison of Quarterly and Yearly Calibration Data for Propensity Score Adjusted Web Survey Estimates.

While web surveys have become increasingly popular as a method of data collection, there is concern that estimates obtained from web surveys may not reflect the target population of interest. Web survey estimates can be calibrated to existing national surveys using a propensity score adjustment, although requirements for the size and collection timeline of the reference data set have not been investigated. We evaluate health outcomes estimates from the National Center for Health Statistics' Research and Development web survey. In our study, the 2016 National Health Interview Survey as well as its quarterly subsets are considered as reference datasets for the web data. It is demonstrated that the calibrated health estimates overall vary little when using the quarterly or yearly data, suggesting that there is flexibility in selecting the reference dataset. This finding has many practical implications for constructing reference data, including the reduced cost and burden of a smaller sample size and a more flexible timeline.

Invited commentary: Crossing curves--it's time to focus on gestational age-specific mortality.

For decades, epidemiologists have observed that, among lower birth weight infants, higher risk infants have lower mortality rates than do lower risk infants. However, among higher birth weight infants, the pattern reverses, leading to a riddle of crossing birth weight-specific mortality curves. The riddle has been considered from different perspectives, including relative z scores, directed acyclic graphs, and, most recently, simulated mathematical models of underlying causal factors that produce the observed curves; similarly paradoxical gestational age-specific mortality curves uncross when calculations include all fetuses-at-risk rather than just infants delivered at a particular gestational age. However, researchers have generally focused on birth weight rather than gestational age, likely because birth weight is accurately measured and, if one assumes that birth weight is an intermediate variable between the underlying causal factors and mortality, is easier to model. Within the framework of existing analytical approaches, adding the complexity of a direct relation between gestational age and mortality, and possibly more complex relations among the casual factors, may be difficult. Nevertheless, duration of pregnancy seems a better proxy for the true construct of interest, whether the baby is mature enough to survive, so shifting attention to understanding the riddle of gestational age-specific mortality is encouraged.

Air pollution and childhood respiratory allergies in the United States.

BACKGROUND: Childhood respiratory allergies, which contribute to missed school days and other activity limitations, have increased in recent years, possibly due to environmental factors. OBJECTIVE: In this study we examined whether air pollutants are associated with childhood respiratory allergies in the United States. METHODS: For the approximately 70,000 children from the 1999-2005 National Health Interview Survey eligible for this study, we assigned between 40,000 and 60,000 ambient pollution monitoring data from the U.S. Environmental Protection Agency, depending on the pollutant. We used monitors within 20 miles of the child's residential block group. We used logistic regression models, fit with methods for complex surveys, to examine the associations between the reporting of respiratory allergy or hay fever and annual average exposure to particulate matter < or = 2.5 microm in diameter (PM2.5), PM < or = 10 microm in diameter, sulfur dioxide, and nitrogen dioxide and summer exposure to ozone, controlling for demographic and geographic factors. RESULTS: Increased respiratory allergy/hay fever was associated with increased summer O3 levels [adjusted odds ratio (AOR) per 10 ppb = 1.20; 95% confidence interval (CI), 1.15-1.26] and increased PM2.5 (AOR per 10 microg/m3 = 1.23; 95% CI, 1.10-1.38). These associations persisted after stratification by urban-rural status, inclusion of multiple pollutants, and definition of exposures by differing exposure radii. No associations between the other pollutants and the reporting respiratory allergy/hay fever were apparent. CONCLUSIONS: These results provide evidence of adverse health for children living in areas with chronic exposure to higher levels of O3 and PM2.5 compared with children with lower exposures.