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. © 2024 - IOS Press. All rights reserved.

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.

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.

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.

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).

Data from the National Health and Nutrition Examination Survey (NHANES) have been linked to the Center for Medicare and Medicaid Services' Medicaid Enrollment and Claims Files. As not all survey participants provide sufficient information to be eligible for record linkage, linked data often includes fewer records than the original survey data. This project presents an application of multiple imputation (MI) for handling missing Medicaid/CHIP status due to linkage refusals in linked NHANES-Medicaid data using the linked 1999-2004 NHANES data. By examining multiple outcomes and subgroups among children, the analyses compare the results from a multi-purpose dataset produced from a single MI model to that of individualized MI models. Outcomes examined here include obesity, untreated dental caries, attention deficit hyperactivity disorder (ADHD), and exposure to second hand smoke.

Data from the National Health and Nutrition Examination Survey have been linked to the Center for Medicare and Medicaid Services' Medicaid Enrollment and Claims Files for the survey years 1999-2004. The linked data are produced by the National Center for Health Statistics' (NCHS) Data Linkage Program and are available in the NCHS Research Data Center. This project compares the usefulness of multiple imputation to account for data linkage ineligibility and other survey nonresponse with currently recommended weight adjustment procedures. Estimated differences in environmental smoke exposure across Medicaid/Children's Health Insurance Program (CHIP) enrollment status among children ages 3-15 years are examined as a motivating example. Comparisons are drawn across the three different estimates: one that uses MI to impute the administrative Medicaid/CHIP status of those who are ineligible for linkage, a second that uses the linked data restricted to linkage eligible participants with a basic weight adjustment, and a third that uses self-reported Medicaid/CHIP status from the survey data. The results indicate that estimates from the multiple imputation analysis were comparable to those found when using weight adjustment procedures and had the added benefit of incorporating all survey participants (linkage eligible and linkage ineligible) into the analysis. We conclude that both multiple imputation and weight adjustment procedures can effectively account for survey participants who are ineligible for linkage.

Background -Most U.S. 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 exposures 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 1997-2009 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 (HR) and 95% confidence intervals (95% CI) between PM2.5 (per 10 mug/m(3)) 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 (HR 1.16 95% CI 1.08-1.25). This association was similar in sample adults with adjustment for smoking and body mass index (HR 1.18 95% CI 1.06-1.31). Interaction terms for non-Hispanic black and Hispanic groups compared to 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 that for non-Hispanic white adults.

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.

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.

BACKGROUND: Warmer temperature can alter seasonality of pollen as well as pollen concentration, and may impact allergic diseases such as hay fever. Recent studies suggest that extreme heat events will likely increase in frequency, intensity, and duration in coming decades in response to changing climate. OBJECTIVE: The overall objective of this study was to investigate if extreme heat events are associated with hay fever. METHODS: We linked National Health Interview Survey (NHIS) data from 1997 to 2013 (n = 505,386 respondents) with extreme heat event data, defined as days when daily maximum temperature (TMAX) exceeded the 95th percentile values of TMAX for a 30-year reference period (1960-1989). We used logistic regression to investigate the associations between exposure to annual and seasonal extreme heat events and adult hay fever prevalence among the NHIS respondents. RESULTS: During 1997-2013, hay fever prevalence among adults 18 years and older was 8.43%. Age, race/ethnicity, poverty status, education, and sex were significantly associated with hay fever status. We observed that adults in the highest quartile of exposure to extreme heat events had a 7% increased odds of hay fever compared with those in the lowest quartile of exposure (odds ratios: 1.07, 95% confidence interval: 1.02-1.11). This relationship was more pronounced for extreme heat events that occurred during spring season, with evidence of an exposure-response relationship (Ptrend < .01). CONCLUSIONS: Our data suggest that exposure to extreme heat events is associated with increased prevalence of hay fever among US adults.

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.

Analyses of the Third National Health and Nutrition Examination Survey (NHANES III) in 1988 to 1994 found an association of increasing blood lead levels <10 mug/dL with a higher risk of cardiovascular disease (CVD) mortality. The potential need to correct blood lead for hematocrit/hemoglobin and adjust for biomarkers for other metals, for example, cadmium and iron, had not been addressed in the previous NHANES III-based studies on blood lead-CVD mortality association.We analyzed 1999 to 2010 NHANES data for 18,602 participants who had a blood lead measurement, were ≥40 years of age at the baseline examination and were followed for mortality through 2011. We calculated the relative risk for CVD mortality as a function of hemoglobin- or hematocrit-corrected log-transformed blood lead through Cox proportional hazard regression analysis with adjustment for serum iron, blood cadmium, serum C-reactive protein, serum calcium, smoking, alcohol intake, race/Hispanic origin, and sex.The adjusted relative risk for CVD mortality was 1.44 (95% confidence interval = 1.05, 1.98) per 10-fold increase in hematocrit-corrected blood lead with little evidence of nonlinearity. Similar results were obtained with hemoglobin-corrected blood lead. Not correcting blood lead for hematocrit/hemoglobin resulted in underestimation of the lead-CVD mortality association while not adjusting for iron status and blood cadmium resulted in overestimation of the lead-CVD mortality association.In a nationally representative sample of U.S. adults, log-transformed blood lead was linearly associated with increased CVD mortality. Correcting blood lead for hematocrit/hemoglobin and adjustments for some biomarkers affected the association.

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.

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 chi(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.

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. (Published 2012. This article is a US Government work and is in the public domain in the USA.)

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 (beta = 0.09 units, 95% confidence interval (CI): 0.08, 0.11; and beta = 0.07 units, 95% CI: 0.04, 0.11, respectively. However, fixed effects resulted in null associations for both prepregnancy BMI (beta = 0.03 units, 95% CI: -0.01, 0.07) and gestational weight gain (beta = 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.

KEY FINDINGS: Data from the National Health and Nutrition Examination Survey, 2003-2008 In general, U.S. pregnant women have low levels of lead (less than 5 microg/dL) in their blood. Pregnant women have lower mercury and lead levels than nonpregnant women. Among pregnant women, mercury levels, but not lead levels, increase with age. Pregnant women with a prior pregnancy have lower mercury levels and higher lead levels than those without a prior pregnancy. Mexico-born Mexican-American pregnant women have higher lead levels than their U.S.-born counterparts, but similar mercury levels.

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.

Reviews find a likely adverse effect of air pollution on perinatal outcomes, but variation of findings hinders the ability to incorporate the research into policy. The International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) was formed to better understand relationships between air pollution and adverse birth outcomes through standardized parallel analyses in datasets from different countries. A planning group with 10 members from 6 countries was formed to coordinate the project. Collaboration participants have datasets with air pollution values and birth outcomes. Eighteen research groups with data for approximately 20 locations in Asia, Australia, Europe, North America, and South America are participating, with most participating in an initial pilot study. Datasets generally cover the 1990s. Number of births is generally in the hundreds of thousands, but ranges from around 1,000 to about one million. Almost all participants have some measure of particulate matter, and most have ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Strong enthusiasm for participating and a geographically-diverse range of participants should lead to understanding uncertainties about the role of air pollution in perinatal outcomes and provide decision-makers with better tools to account for pregnancy outcomes in air pollution policies.

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.

BACKGROUND: The sex ratio in the USA has declined over recent decades, resulting in fewer male births. Concurrent changes in the childbearing population may have influenced the sex ratio, including increases in multiple births, improvements in perinatal survival and increased Hispanic births. METHODS: Data from the US natality files (1981-2006) were analyzed to determine the impact of changes in birth characteristics on male birth proportion. Male birth proportion was calculated as the number of male births divided by the total number. In separate analyses, trends in male birth proportion from 1981 to 2006 were adjusted for plurality (singleton, multiple), gestational age (<28, 28-32, 33-36, ≥37 weeks) and, from 1989, maternal Hispanic ethnicity. Separate analyses were conducted for white and black births. Log binomial regression was performed to estimate crude and adjusted trends with year as independent variable. RESULTS: Trends in male birth proportion differed significantly according to plurality among white (P < 0.01), but not black births. Adjustment for gestational age tempered the trends among white singletons (P < 0.0001) and multiples (P < 0.05) but had no effect on trends in black male birth proportion. Adjustment for Hispanic ethnicity had no impact on trends in black male birth proportion and any effect on white births was negated by changes in gestational age trends. CONCLUSIONS: Lack of consistent influences on, or patterns of change in, the proportion of male births between different subpopulations of births suggests that a single mechanism is unlikely to explain the oft-referenced decrease in the overall US sex ratio.