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.

Use of the National Health Interview Survey Linked to Medicaid Analytic eXtract Data to Identify Children With Medicaid-covered Births.

Objective-This report illustrates the use of National Health Interview Survey (NHIS) data linked to Medicaid Analytic eXtract (MAX) data to identify children whose births were covered by Medicaid, as indicated in MAX data, among those participating in NHIS in early childhood, and briefly describes their selected health characteristics.

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.

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.

Characteristics of Medicare Advantage and Fee-for-Service Beneficiaries Upon Enrollment in Medicare at Age 65.

Previous research has found differences in characteristics of beneficiaries enrolled in Medicare fee-for-service versus Medicare Advantage (MA), but there has been limited research using more recent MA enrollment data. We used 1997-2005 National Health Interview Survey data linked to 2000-2009 Medicare enrollment data to compare characteristics of Medicare beneficiaries before their initial enrollment into Medicare fee-for-service or MA at age 65 and whether the characteristics of beneficiaries changed from 2006 to 2009 compared with 2000 to 2005. During this period of MA growth, the greatest increase in enrollment appears to have come from those with no chronic conditions and men.

The relationship between linkage refusal and selected health conditions of survey respondents.

To maximize limited resources and reduce respondent burden, there is an increased interest in linking population health surveys with other sources of data, such as administrative records. Health differences between adults who consent to and refuse linkage could bias study results with linked data. National Health Interview Survey (NHIS) data are routinely linked to administrative records from the Social Security Administration and the Centers for Medicare and Medicaid Services. Using the NHIS 2010-2013, we examined the association between selected health conditions and respondents' linkage refusal. Linkage refusal was significantly lower for adults with serious psychological distress, chronic obstructive pulmonary disease, diabetes, heart disease, stroke, hypertension, and cancer compared to those without these conditions. Linkage refusal decreased as the number of conditions increased and health status decreased. Our finding that linkage consent was associated with respondents' health characteristics suggests that researchers should try to address potential linkage bias in their analyses.

Linkage of NCHS Population Health Surveys to Administrative Records From Social Security Administration and Centers for Medicare Medicaid Services.

Federally sponsored health surveys are a critical source of information on public health in the United States. The National Center for Health Statistics (NCHS) is the nation's principal health statistics agency and is responsible for collecting accurate, relevant, and timely data. NCHS conducts several population-based national surveys as well as collecting vital statistics data, which are used by a broad range of users (researchers and policy makers, among others) to evaluate and profile the health of the American people. These national health surveys provide rich cross-sectional information on risk factors such as smoking, height and weight, health status, and socioeconomic circumstances, but information on longitudinal outcomes is often missing. Demand is increasing to incorporate information from additional sources in order to enhance the availability and quality of information on exposures and outcomes.

Serious Psychological Distress Among Adults: United States, 2009-2013.

In every age group, women were more likely to have serious psychological distress than men. Among all adults, as income increased, the percentage with serious psychological distress decreased. Adults aged 18-64 with serious psychological distress were more likely to be uninsured (30.4%) than adults without serious psychological distress (20.5%). More than one-quarter of adults aged 65 and over with serious psychological distress (27.3%) had limitations in activities of daily living. Adults with serious psychological distress were more likely to have chronic obstructive pulmonary disease, heart disease, and diabetes than adults without serious psychological distress.

Characteristics of Children in Medicaid Managed Care and Medicaid Fee-for-service, 2003-2005.

OBJECTIVES: Medicaid claims have been used to characterize utilization patterns of child Medicaid beneficiaries. However, because states are increasingly adopting Medicaid managed care plans, analyses of children enrolled in Medicaid based only on claims for fee-for-service (FFS) programs may not apply to the general Medicaid population. METHODS: The 2003-2005 National Health Interview Survey and 2003-2005 Medicaid Analytic eXtract linked files were used to examine associations between sociodemographic, health, and geographic characteristics of children aged 0-17 years and enrollment in Medicaid FFS compared with a comprehensive managed care (CMC) program. Additional analyses of age-specific health outcomes were performed on a subset of children aged 6-17 years. Chi-square tests were used to assess associations, and 95% confidence intervals are provided for point prevalence estimates. RESULTS: Higher percentages of children in CMC compared with FFS were non-Hispanic white, lived in families with income less than 100% of the federal poverty level, had excellent or very good health, lived in the Northeast and West, and lived in large central metro areas. No significant differences were observed by sex, age, and asthma diagnoses between children enrolled in CMC and FFS. Among children aged 6-17 years, higher percentages of children enrolled in FFS compared with children in CMC were diagnosed with learning disabilities or developmental delays and attention deficit hyperactivity disorder. Researchers using data from children enrolled only in Medicaid FFS programs to describe children enrolled in Medicaid should understand differences between children in CMC and FFS. Generalization of study results from FFS claims may depend on the outcomes examined.

A longitudinal view of child enrollment in Medicaid.

BACKGROUND: Although national cross-sectional estimates of the percentage of children enrolled in Medicaid are available, the percentage of children enrolled in Medicaid over longer periods of time is unknown. Also, the percentage and characteristics of children who rely on Medicaid throughout childhood, rather than transiently, are unknown. METHODS: We performed a longitudinal examination of Medicaid coverage among children across a 5-year period. Children 0 to 13 years of age in the 2004 National Health Interview Survey file were linked to Medicaid Analytic eXtract files from 2004 to 2008. The percentage of children enrolled in Medicaid at any time during the 5-year observation period and the number of years during which children were enrolled in Medicaid were calculated. Duration of Medicaid enrollment was compared across sociodemographic characteristics by using χ(2) tests. RESULTS: Forty-one percent of all US children were enrolled in Medicaid at least some time during the 5-year period, compared with a single-year estimate of 32.8% in 2004 alone. Of enrolled children, 51.5% were enrolled during all 5 years. Children with lower parental education, with lower household income, of minority race or ethnicity, and in suboptimal health were more likely to be enrolled in Medicaid during all 5 years. CONCLUSIONS: Longitudinal data reveal higher percentages of children with Medicaid insurance than shown by cross-sectional data. Half of children enrolled in Medicaid are enrolled during at least 5 consecutive years, and these children have higher risk sociodemographic profiles.

Self-report of diabetes and claims-based identification of diabetes among Medicare beneficiaries.

OBJECTIVE: This report compares self-reported diabetes in the National Health Interview Survey (NHIS) with diabetes identified using the Medicare Chronic Condition (CC) Summary file. BACKGROUND: NHIS records have been linked with Medicare data from the Centers for Medicare & Medicaid Services. The CC Summary file, one of several linked files derived from Medicare claims data, contains indicators for chronic conditions based on an established algorithm. METHODS: This analysis was limited to 2005 NHIS participants aged 65 and over whose records were linked to 2005 Medicare data. Linked NHIS participants had at least 1 month of fee-for-service Medicare coverage in 2005. Concordance between self-reported diabetes and the CC Summary indicator for diabetes is compared and described by demographics, socioeconomic status, health status indicators, and geographic characteristics. RESULTS: Of the Medicare beneficiaries in the 2005 NHIS, 20.0% self-reported diabetes and 27.8% had an indicator for diabetes in the CC Summary file. Of those who self-reported diabetes in NHIS, the percentage with a CC Summary indicator for diabetes was high (93.1%). Of those with a CC Summary indicator for diabetes, the percentage self-reporting diabetes was comparatively lower (67.0%). Statistically significant differences by subgroup existed in the percentage concordance between the two sources. Of those with self-reported diabetes, the percentage with a CC Summary indicator differed by sex and age. Of those with a CC Summary indicator for diabetes, the percentage with self-reported diabetes differed by age, self-rated health, number of self-reported conditions, and geographic location. CONCLUSIONS: Among Medicare beneficiaries who self-reported diabetes in NHIS, a high concordance was observed with identification of diabetes in the CC Summary file. However, among Medicare beneficiaries with an indicator for diabetes in the CC Summary file, concordance with self-reported diabetes in NHIS is comparatively lower. Differences exist by subgroup.

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.

Health characteristics of Medicare traditional fee-for-service and Medicare Advantage enrollees: 1999-2004 National Health and Nutrition Examination Survey linked to 2007 Medicare data.

BACKGROUND: National Health and Nutrition Examination Survey (NHANES) records have been linked to health care use and expenditure information from Medicare records. Claims data are generally available only for traditional fee-for-service (FFS) enrollees and not for Medicare Advantage enrollees. Differences in health characteristics between Medicare Advantage and traditional FFS enrollees could affect conclusions from analyses of the linked files that are restricted to traditional FFS enrollees. METHODS: Data from the 1999-2004 NHANES linked to the 2007 Medicare enrollment data were analyzed. Using examination and interview data collected in NHANES, we compared health characteristics of Medicare beneficiaries aged 65 and over at the NHANES interview by their type of Medicare enrollment in 2007. RESULTS: We found that the overall percentage of Medicare beneficiaries who had hypertension at the time of the NHANES medical examination was lower for Medicare Advantage enrollees compared with traditional FFS enrollees; this was found for the non-Hispanic white population but not for other race and ethnicity groups. We found no statistically significant differences between Medicare Advantage and traditional FFS enrollees overall or within race and ethnicity groups for other health characteristics that were measured or reported at the time of the NHANES interview or medical examination in 1999-2004. CONCLUSIONS: Despite finding no large differences in health characteristics by Medicare enrollment in this analysis, users of the National Center for Health Statistics Medicare linked files should consider potential differences in health characteristics between Medicare Advantage and traditional FFS enrollees that could influence results limited to traditional FFS beneficiaries only.

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.

Linkage of the US National Health Interview Survey to air monitoring data: an evaluation of different strategies.

The goal of this study is to describe linkages between the National Health Interview Survey (NHIS) and Environmental Protection Agency (EPA) air monitoring data, specifically how the linkage method affects characteristics and exposure estimates of study samples and estimated associations between exposure and health. In the USA, nationally representative health data are collected in the NHIS and annual air quality data are collected by the EPA. The linkage of these data for research is not straightforward and the choices made may introduce bias into results. The 2000-2003 NHIS and air quality data for six air pollutants were linked by residential block group and monitor location, which differ by pollutants. For each pollutant, three annual exposure variables were assigned to respondents: (1) average of all monitors in the county, (2) of monitors within a 5-mile radius of the distance between block group and monitor, and (3) within a 20-mile radius. Exposure estimates, study sample characteristics, and association between fine particle exposure and respondent-reported health status were compared for different geographic linkage methods. The results showed that study sample characteristics varied by geographic linkage method and pollutant linked. Generally, the fewer the NHIS respondents linked, the higher is the pollution exposure and lower is the percentage of non-Hispanic whites. After adjustment for sociodemographic and geographic factors, associations between fine particles and health status were generally comparable across study samples. Because exposure information is not available for all potential participants in an epidemiological study, selection effects should be considered when drawing inferences about air quality-health associations. With the current monitoring data system, the study sample is substantially reduced when linkage to multiple pollutants is performed.

Contextual effect of income inequality on birth outcomes.

BACKGROUND: Though associations between income inequality and birth outcome have been suggested, mechanisms underlying this relationship are not known. In this analysis, we examined the relationship between income inequality and preterm birth (PTB) and post-neonatal mortality (PNM) to explore two potential mechanisms-the proposed psychosocial stress and neo-material pathways. METHODS: Data on singleton births from 1998 to 2000 were obtained from the CDC's National Center for Health Statistics' Linked Birth and Infant Death files. The Gini Index was utilized to measure income inequality and was divided into tertiles representing high, medium, and low county-level inequality. To determine the association between the birth outcomes and county income inequality and to account for clustering within counties, we employed generalized estimating equation (GEE) modelling. RESULTS: PTB increased from 8.3% in counties with low income inequality to 10.0% in counties with high inequality. The Gini Index remained modestly associated with PTB after adjusting for individual level variables and mean county-level per capita income within the total population (AOR: 1.06; 95% CI 1.03-1.09) as well as within most of the racial/ethnic groups. PNM increased from 1.15 deaths per 1000 live births in low inequality counties to 1.32 in high-inequality counties. However, after adjustment, income inequality was only associated with PNM within the non-Hispanic black population (AOR: 1.20; 95% CI 1.03-1.39). CONCLUSIONS: These findings may provide some support for the association between income inequality and PTB. Further research is required to elucidate the biological mechanisms of income inequality.

Disparities in exposure to air pollution during pregnancy.

Previous research shows poorer birth outcomes for racial and ethnic minorities and for persons with low socioeconomic status (SES). We evaluated whether mothers in groups at higher risk for poor birth outcomes live in areas of higher air pollution and whether higher exposure to air pollution contributes to poor birth outcomes. An index representing long-term exposure to criteria air pollutants was matched with birth certificate data at the county level for the United States in 1998-1999. We used linear regression to estimate associations between the air pollution index and maternal race and educational attainment, a marker for SES of the mother, controlling for age, parity, marital status, and region of the country. Then we used logistic regression models both to estimate likelihood of living in counties with the highest levels of air pollution for different racial groups and by educational attainment, adjusting for other maternal risk factors, and to estimate the effect of living in counties with higher levels of air pollution on preterm delivery and births small for gestational age (SGA). Hispanic, African-American, and Asian/Pacific Islander mothers experienced higher mean levels of air pollution and were more than twice as likely to live in the most polluted counties compared with white mothers after controlling for maternal risk factors, region, and educational status [Hispanic mothers: adjusted odds ratio (AOR) = 4.66; 95% confidence interval (95% CI), 1.92-11.32; African-American mothers: AOR = 2.58; 95% CI, 1.00-6.62; Asian/Pacific Islander mothers: AOR = 2.82; 95% CI, 1.07-7.39]. Educational attainment was not associated with living in counties with highest levels of the air pollution index (AOR = 0.95; 95% CI, 0.40-2.26) after adjusting for maternal risk factors, region of the country, and race/ethnicity. There was a small increase in the odds of preterm delivery (AOR = 1.05; 95% CI, 0.99-1.12) but not SGA (AOR = 0.96; 95% CI, 0.86-1.07) in a county with high air pollution. Additional risk of residing in areas with poor air quality may exacerbate health problems of infants and children already at increased risk for poor health.

The correspondence between interracial births and multiple-race reporting.

OBJECTIVES: Race-specific health statistics are routinely reported in scientific publications; most describe health disparities across groups. Census 2000 showed that 2.4% of the US population identifies with more than 1 race group. We examined the hypothesis that multiple-race reporting is associated with interracial births by comparing parental race reported on birth certificates with reported race in a national health survey. METHODS: US natality data from 1968 through 1998 and National Health Interview Survey data from 1990 through 1998 were compared, by year of birth. RESULTS: Overall multiple-race survey responses correspond to expectations from interracial births. However, there are discrepancies for specific multiple-race combinations. CONCLUSIONS: Projected estimates of the multiple-race population can be only partially informed by vital records.

Family structure, socioeconomic status, and access to health care for children.

OBJECTIVE: To test the hypothesis that among children of lower socioeconomic status (SES), children of single mothers would have relatively worse access to care than children in two-parent families, but there would be no access difference by family structure among children in higher SES families. DATA SOURCES: The National Health Interview Surveys of 1993-95, including 63,054 children. STUDY DESIGN: Logistic regression was used to examine the relationship between the child's family structure (single-mother or two-parent family) and three measures of health care access and utilization: having no physician visits in the past year, having no usual source of health care, and having unmet health care needs. To examine how these relationships varied at different levels of SES, the models were stratified on maternal education level as the SES variable. The stratified models adjusted for maternal employment, child's health status, race and ethnicity, and child's age. Models were fit to examine the additional effects of health insurance coverage on the relationships between family structure, access to care, and SES. PRINCIPAL FINDINGS: Children of single mothers, compared with children living with two parents, were as likely to have had no physician visit in the past year; were slightly more likely to have no usual source of health care; and were more likely to have an unmet health care need. These relationships differed by mother's education. As expected, children of single mothers had similar access to care as children in two-parent families at high levels of maternal education, for the access measures of no physician visits in the past year and no usual source of care. However, at low levels of maternal education, children of single mothers appeared to have better access to care than children in two-parent families. Once health insurance was added to adjusted models, there was no significant socioeconomic variation in the relationships between family structure and physician visits or usual source of care, and there were no significant disparities by family structure at the highest levels of maternal education. There were no family structure differences in unmet needs at low maternal education, whereas children of single mothers had more unmet needs at high levels of maternal education, even after adjustment for insurance coverage. CONCLUSIONS: At high levels of maternal education, family structure did not influence physician visits or having a usual source of care, as expected. However, at low levels of maternal education, single mothers appeared to be better at accessing care for their children. Health insurance coverage explained some of the access differences by family structure. Medicaid is important for children of single mothers, but children in two-parent families whose mothers are less educated do not always have access to that resource. Public health insurance coverage is critical to ensure adequate health care access and utilization among children of less educated mothers, regardless of family structure.

Methodologic implications of allocating multiple-race data to single-race categories.

OBJECTIVE: To illustrate methods for comparing race data collected under the 1977 Federal Office of Management and Budget (OMB) directive, known as OMB-15, with race data collected under the revised 1997 OMB standard. DATA SOURCES/STUDY SETTING: Secondary data from the 1993-95 National Health Interview Surveys. Multiple-race responses, available on in-house files, were analyzed. STUDY DESIGN: Race-specific estimates of employer-sponsored health insurance were calculated using proposed allocation methods from the OMB. Estimates were calculated overall and for three population subgroups: children, those in households below poverty, and Hispanics. PRINCIPAL FINDINGS: Although race distributions varied between the different methods, estimates of employer-sponsored health insurance were similar. Health insurance estimates for the American Indian/Alaska Native group varied the most. CONCLUSIONS: Employer-sponsored health insurance estimates for American Indian/Alaska Natives from data collected under the 1977 OMB directive will not be comparable with estimates from data collected under the 1997 standard. The selection of a method to distribute to the race categories used prior to the 1997 revision will likely have little impact on estimates of employer-sponsored health insurance for other groups. Additional research is needed to determine the effects of these methods for other health service measures.