Health insurance coverage during a 24-month period: a comparison of estimates from two national health surveys

National estimates of the uninsured are available from multiple surveys and differ across surveys. Previous efforts to better understand reasons for differences among these estimates have primarily focused on annual estimates. This study compares national estimates of health insurance coverage over generally comparable 24-month time periods using two integrated Federal health-related surveys, the Medical Expenditure Panel Survey (MEPS) and the National Health Interview Survey (NHIS) for the years 2002–2003 and replicated analyses for 2001–2002. We examine survey participants insurance status in year 1 and year 2 based on the NHIS linked with the MEPS and also for MEPS year 1 and year 2 participants. We also examine characteristics associated with 24-month coverage status. National estimates of the percents continuously insured did not differ significantly between the two data sources. In contrast, the MEPS longitudinal estimate of the percent continuously uninsured was higher than the NHIS-MEPS linked estimate whereas the MEPS longitudinal estimate of the discontinuously insured was lower than that derived from the NHIS-MEPS linked data. Factors that help explain these differences include the non-equivalence of the time periods covered by the data sources, modest differences in the length of time covered by the MEPS and NHIS survey instruments, and length of recall. Regression analyses yielded highly consistent correlates of being continuously uninsured versus continuously insured for both data sources. Regression results for discontinuous versus continuous coverage were also generally similar for both data sources. Gaining a better understanding of the alignment in findings based on alternative data sources that support comparable analyses of health insurance coverage helps policymakers to make the most appropriate use of resultant estimates. The views expressed in this paper are those of the authors and no official endorsement by the Department of Health and Human Services or the Agency for Healthcare Research and Quality or the Centers for Disease Control and Prevention is intended or should be inferred.     

The impact of inter-survey differences in the definition of current smokeless tobacco use on comparability of US national and state-specific prevalence estimates, 2009–2011

BackgroundWe assessed how varying definitions of adult current smokeless tobacco (SLT) use affected overall prevalence estimates.MethodsNational prevalence estimates were from five surveys: 2009–2010 National Health and Nutrition Examination Survey (NHANES), 2009–2010 National Adult Tobacco Survey (NATS), 2010–2011 Tobacco Use Supplement of the Current Population Survey (TUS-CPS), 2010 National Survey on Drug Use and Health (NSDUH), and 2010 National Health Information Survey (NHIS). State-specific prevalence estimates were from three surveys: 2009–2010 NATS, 2010–2011 TUS-CPS, and 2010 Behavioral Risk Factor Surveillance System (BRFSS). Current SLT use definitions were as follows: past 5-day use (NHANES), past 30-day use (NATS and NSDUH), and “every day” or “some days” use (TUS-CPS, NHIS, and BRFSS). Inter-survey variations further existed in number and types of SLT products assessed.ResultsNational prevalence estimates of current SLT use were as follows: NATS (3.9%), NSDUH (3.6%), NHIS (2.8%), NHANES (2.3%), and TUS-CPS (1.6%). State-specific prevalence estimates of SLT use were generally lower for TUS-CPS (median = 2.1%, range: 0.5% in California and New York, to 7.2% in Wyoming) compared to either BRFSS (median = 4.0%: range: 0.9% in Washington D.C., to 8.2% in Wyoming) or NATS (median = 4.7%; range: 1.3% in New Jersey, to 9.8% in Wyoming).ConclusionConcerted efforts are needed among interagency groups to harmonize SLT definition within different surveys.     

Peer Reviewed: Tracking Chronic Disease and Risk Behavior Prevalence as Survey Participation Declines: Statistics From the Behavioral Risk Factor Surveillance System and Other National Surveys

Introduction

Response rates for the Behavioral Risk Factor Surveillance System (BRFSS) have declined in recent years. The response rate in 1993 was approximately 72%; in 2006, the response rate was approximately 51%. To assess the impact of this decline on the quality of BRFSS estimates, we compared selected health and risk factor estimates from BRFSS with similar estimates from the National Health Interview Survey (NHIS) and the National Health and Nutrition Examination Survey (NHANES).

Methods

We reviewed questionnaires from the 3 surveys and identified a set of comparable measures related to smoking prevalence, alcohol consumption, medical conditions, vaccination, health status, insurance coverage, cost barriers to medical care, testing for human immunodeficiency virus, and body measurements (height and weight).We compared weighted estimates for up to 15 outcome measures, including overall measures and measures for 12 population subgroups. We produced design-appropriate point estimates and carried out statistical tests of hypotheses on the equality of such estimates. We then calculated P values for comparisons of NHIS and NHANES estimates with their BRFSS counterparts.

Results

Although BRFSS and NHIS estimates were statistically similar for 5 of the 15 measures examined, BRFSS and NHANES estimates were statistically similar for only 1 of 6 measures. The observed differences for some of these comparisons were small, however.

Conclusion

These surveys produced similar estimates for several outcome measures, although we observed significant differences as well. Many of the observed differences may have limited consequences for implementing related public health programs; other differences may require more detailed examination. In general, the range of BRFSS estimates examined here tends to parallel those from NHIS and NHANES, both of which have higher rates of participation.     

An assessment of the effect of data collection setting on the prevalence of health risk behaviors among adolescents.

PURPOSE: To examine the effect of data collection setting on the prevalence of priority health risk behaviors among adolescents. METHODS: Analyses were conducted using data from two national probability surveys of adolescents, the 1993 national school-based Youth Risk Behavior Survey (YRBS) and the 1992 household-based National Health Interview Survey (NHIS/YRBS). Forty-two items were worded identically on both surveys. RESULTS: Thirty-nine of the 42 identically worded items (93%) showed that the YRBS produced estimates indicating higher risk than the NHIS. Twenty-four of these comparisons yielded statistically significant differences. The prevalence estimates affected most were those for behaviors that are either illegal or socially stigmatized. CONCLUSIONS: School-based surveys produce higher prevalence estimates for adolescent health risk behaviors than do household-based surveys. Each has advantages and disadvantages, and both can play a role in assessing these behaviors.     

Are Gatekeeper Requirements Associated with Cancer Screening Utilization?

OBJECTIVE: There is widespread debate over whether health plans should require enrollees to use "gatekeepers," which are primary care providers that coordinate care and control access to specialists. However, little is known about whether health plan gatekeeper requirements improve or reduce quality-of-care. Our objective was to examine whether gatekeeper requirements are associated with the utilization of cancer screening for breast, cervical, and prostate cancer. DATA SOURCES: Three linked sources (N = 13,534): (1) 1996 Medical Expenditure Panel Survey (MEPS) Household Survey, a nationally representative, ongoing survey sponsored by the Agency for Healthcare Research and Quality; (2) 1996 MEPS Health Insurance Plan Abstraction, which codes data from health plan booklets obtained from privately insured respondents, and (3) 1995 National Health Interview Survey. STUDY DESIGN/DATA COLLECTION: Cross-sectional, multivariate logistic regression analysis using secondary data. PRINCIPAL FINDINGS: We found in multivariate analyses that women in gatekeeper plans were significantly more likely to obtain mammography screening (Odds Ratio [OR] = 1.22, 95 percent Confidence Interval [CI] 1.07-1.40), clinical breast examinations (OR = 1.39, 95 percent CI 1.23-1.57), and Pap smears (OR = 1.33, 95 percent CI 1.16-1.52) than women not in gatekeeper plans. In contrast, gatekeeper requirements were not associated with prostate cancer screening (OR = 1.11, 95 percent CI 0.93-1.33). We found no association between screening utilization and aggregate plan types (HMO, POS, PPO, FFS). CONCLUSIONS: Gatekeeper requirements are associated with higher utilization of widely recommended cancer screening procedures, but not with utilization of a less uniformly recommended cancer screening procedure. Researchers should consider the analysis of specific plan characteristics rather than aggregate plan types in conducting future research, and insurers and policymakers should consider the potential benefits of gatekeepers with respect to preventive care when designing health plans and legislation.     

Investigation of nonresponse bias in NHANES II

In the second National Health and Nutrition Examination Survey (NHANES II), there was a 27% nonresponse rate in the examination phase. This report investigates the potential bias in these data due to this large nonresponse rate. Data from a household and medical history interview are used in the investigation of factors related to examination status. In addition, data from the examined group are compared to data from the 1976 National Health Interview Survey (NHIS). Since there was only a 3.7% nonresponse rate for the 1976 NHIS, proportions calculated from these data represent reasonable estimates of the true population values. Several variables have a significant association with the interview and examination status. However, it appears that the nonresponse and poststratification adjustments performed by the National Center for Health Statistics have removed most of these factors as sources of bias. There is excellent agreement in the marginal distribution of variables between NHANES II for examined persons and the 1976 NHIS.     

On modelling response propensity for dwelling unit (DU) level non-response adjustment in the Medical Expenditure Panel Survey (MEPS)

Non-response is a common problem in household sample surveys. The Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality (AHRQ), is a complex national probability sample survey. The survey is designed to produce annual national and regional estimates of health-care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian non-institutionalized population. The MEPS sample is a sub-sample of respondents to the prior year's National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The MEPS, like most sample surveys, experiences unit, or total, non-response despite intensive efforts to maximize response rates. This paper summarizes research on comparing alternative approaches for modelling response propensity to compensate for dwelling unit (DU), i.e. household level non-response in the MEPS.Non-response in sample surveys is usually compensated for by some form of weighting adjustment to reduce the bias in survey estimates. To compensate for potential bias in survey estimates in the MEPS, two separate non-response adjustments are carried out. The first is an adjustment for DU level non-response at the round one interview to account for non-response among those households subsampled from NHIS for the MEPS. The second non-response adjustment is a person level adjustment to compensate for attrition across the five rounds of data collection. This paper deals only with the DU level non-response adjustment. Currently, the categorical search tree algorithm method, the chi-squared automatic interaction detector (CHAID), is used to model the response probability at the DU level and to create the non-response adjustment cells. In this study, we investigate an alternative approach, i.e. logistic regression to model the response probability. Main effects models and models with interaction terms are both evaluated. We further examine inclusion of the base weights as a covariate in the logistic models. We compare variability of weights of the two alternative response propensity approaches as well as direct use of propensity scores.The logistic regression approaches produce results similar to CHAID; however, using propensity scores from logistic models with interaction terms to form five classification groups for weight adjustment appears to perform best in terms of limiting variability and bias. Published in 2007 by John Wiley & Sons, Ltd.     

A comparison of prevalence estimates for selected health indicators and chronic diseases or conditions from the Behavioral Risk Factor Surveillance System, the National Health Interview Survey, and the National Health and Nutrition Examination Survey, 2007-2008

Objective

To compare the prevalence estimates of selected health indicators and chronic diseases or conditions among three national health surveys in the United States.

Methods

Data from adults aged 18 years or older who participated in the Behavioral Risk Factor Surveillance System (BRFSS) in 2007 and 2008 (n = 807,524), the National Health Interview Survey (NHIS) in 2007 and 2008 (n = 44,262), and the National Health and Nutrition Examination Survey (NHANES) during 2007 and 2008 (n = 5871) were analyzed.

Results

The prevalence estimates of current smoking, obesity, hypertension, and no health insurance were similar across the three surveys, with absolute differences ranging from 0.7% to 3.9% (relative differences: 2.3% to 20.2%). The prevalence estimate of poor or fair health from BRFSS was similar to that from NHANES, but higher than that from NHIS. The prevalence estimates of diabetes, coronary heart disease, and stroke were similar across the three surveys, with absolute differences ranging from 0.0% to 0.8% (relative differences: 0.2% to 17.1%).

Conclusion

While the BRFSS continues to provide invaluable health information at state and local level, it is reassuring to observe consistency in the prevalence estimates of key health indicators of similar caliber between BRFSS and other national surveys.     

Predicting patterns of mammography use: a geographic perspective on national needs for intervention research

OBJECTIVE: To introduce a methodology for planning preventive health service research that takes into account geographic context. DATA SOURCES: National Health Interview Survey (NHIS) self-reports of mammography within the past two years, 1987, and 1993-94. Area Resource File (ARF), 1990. Database of mammography intervention research studies conducted from 1984 to 1994. DESIGN: Bayesian hierarchical modeling describes mammography as a function of county-level socioeconomic data and explicitly estimates the geographic variation unexplained by the county-level data. This model produces county use estimates (both NHIS-sampled and unsampled), which are aggregated for entire states. The locations of intervention research studies are examined in light of the statewide mammography utilization estimates. DATA EXTRACTION: Individual level NHIS data were merged with county-level data from the ARF. PRINCIPAL FINDINGS: State maps reveal the estimated distribution of mammography utilization and intervention research. Eighteen states with low mammography use reported no intervention research activity. County-level occupation and education were important predictors for younger women in 1993-94. In 1987, they were not predictive for any demographic group. CONCLUSIONS: Opportunities exist to improve the planning of future intervention research by considering geographic context. Modeling results suggest that the choice of predictors be tailored to both the population and the time period under study when planning interventions.     

Report of nationally representative values for the noninstitutionalized US adult population for 7 health-related quality-of-life scores.

BACKGROUND: Despite widespread use of generic health-related quality-of-life (HRQoL) scores, few have publicly published nationally representative US values. PURPOSE: To create current nationally representative values for 7 of the most common HRQoL scores, stratified by age and sex. METHODS: The authors used data from the 2001 Medical Expenditures Panel Survey (MEPS) and the 2001 National Health Interview Survey (NHIS), nationally representative surveys of the US noninstitutionalized civilian population: The MEPS was used to calculate 6 HRQoL scores: categorical self-rated health, EuroQoL-5D with US scoring, EuroQoL-5D with UK scoring, EuroQol Visual Analog Scale, mental and physical component summaries from the SF-12, and the SF-6D. The authors estimated Quality of Well-being scale scores from the NHIS. RESULTS: They included 22,523 subjects from MEPS 2001 and 32,472 subjects from NHIS 2001. Most age and sex categories had instrument completion rates above 85%. Females reported lower scores than males across all ages and instruments. In general, those in older age groups reported lower scores than younger age groups, with the exception of the mental component summary from the SF-12. CONCLUSION: This is one of the first sets of publicly available, nationally representative US values for any standardized HRQoL measure. These values are important for use in both generalized comparisons of health status and in cost-effectiveness analyses.     

Identifying children with special health care needs in the National Health Interview Survey: a new resource for policy analysis

OBJECTIVE: To test the feasibility of using the National Health Interview Survey (NHIS) to identify children with chronic illness through a noncategorical approach, as exemplified by the Children with Special Health Care Needs (CSHCN) screener. The ability to use the NHIS to identify CSHCN will permit analyses of the effects of welfare reform and public insurance eligibility expansions during the late 1990s on CSHCN. DATA SOURCES: The NHIS from 1997, 1999, and 2000. The NHIS is an ongoing household survey representative of the civilian, noninstitutionalized population of the United States. STUDY DESIGN: Survey items were selected from the NHIS and thresholds designated to replicate the content and logic of the CSHCN screener. The screener asks explicit questions concerning an elevated need for, or use of health care services, and about limitations in activity, both caused by a chronic health condition. The algorithm created was applied to the pooled 1999-2000 NHIS to generate national prevalence estimates. Multivariate logistic regression was estimated to determine the effect of having particular demographic characteristics on the likelihood of being identified as CSHCN. Log odds ratios were compared to those from earlier NHIS-based estimates and from a pretest of the CSHCN screener. PRINCIPAL FINDINGS: An estimated 12 percent of noninstitutionalized children aged 0 through 17 have a chronic condition that results in elevated service use or limitations in normal activity. This estimate is sensitive to inclusion of children with a broader array of less serious or shorter-term conditions. The estimated effects of child characteristics on the likelihood of being identified as having special health needs are similar but not identical to other algorithms that have been used to identify CSHCN. CONCLUSIONS: It is feasible to use existing questions in the NHIS to identify a population of CSHCN that is substantially similar to children identified through other algorithms or through use of a screening instrument imbedded in a household survey. The availability of this algorithm will permit use of the NHIS for important analyses of the effects of welfare reform and public insurance expansions on children with special health care needs.     

The adequacy of household survey data for evaluating the nongroup health insurance market

OBJECTIVE: To evaluate the accuracy of household survey estimates of the size and composition of the nonelderly population covered by nongroup health insurance. DATA SOURCES/STUDY SETTING: Health insurance enrollment statistics reported to New Jersey insurance regulators. Household data from the following sources: the 2002 Current Population Survey (CPS)-March Demographic Supplement, the 1997 and 1999 National Surveys of America's Families (NSAF), the 2001 New Jersey Family Health Survey (NJFHS), a 2002 survey of known nongroup health insurance enrollees, a small 2004 survey testing alternative health insurance question wording. STUDY DESIGN: To assess the extent of bias in estimates of the size of the nongroup health insurance market in New Jersey, enrollment trends are compared between official enrollment statistics reported by insurance carriers to state insurance regulators with estimates from three general population household surveys. Next, to evaluate possible bias in the demographic and socioeconomic composition of the New Jersey nongroup market, distributions of characteristics of the enrolled population are contrasted among general household surveys and a survey of known nongroup subscribers. Finally, based on inferences drawn from these comparisons, alternative health insurance question wording was developed and tested in a local survey to test the potential for misreporting enrollment in nongroup coverage in a low-income population. DATA COLLECTION/EXTRACTION METHODS: Data for nonelderly New Jersey residents from the 2002 CPS (n=5,028) and the 1997 and 1999 NSAF (n=6,467 and 7,272, respectively) were obtained from public sources. The 2001 NJFHS (n=5,580 nonelderly) was conducted for a sample drawn by random digit dialing and employed computer-assisted telephone interviews and trained, professional interviewers. Sampling weights are used to adjust for under-coverage of households without telephones and other factors. In addition, a modified version of the NJFHS was administered to a 2002 sample of known nongroup subscribers (n=1,398) using the same field methods. These lists were provided by four of the five largest New Jersey nongroup insurance carriers, which represented 95 percent of all nongroup enrollees in the state. Finally, a modified version of the NJFHS questionnaire was fielded using similar methods as part of a local health survey in New Brunswick, New Jersey, in 2004 (n=1,460 nonelderly). PRINCIPAL FINDINGS: General household sample surveys, including the widely used CPS, yield substantially higher estimates of nongroup enrollment compared with administrative totals and yield estimates of the characteristics of the nongroup population that vary greatly from a survey of known nongroup subscribers. A small survey testing a question about source of payment for direct-purchased coverage suggests than many public coverage enrollees report nongroup coverage. CONCLUSIONS: Nongroup health insurance has been subject to more than a decade of reform and is of continuing policy interest. Comparisons of unique data from a survey of known nongroup subscribers and administrative sources to household surveys strongly suggest that the latter overstates the number and misrepresent the composition of the nongroup population. Research on the nongroup market using available sources should be interpreted cautiously and survey methods should be reexamined.     

Reconciling medical expenditure estimates from the MEPS and NHEA, 2002

The Medical Expenditure Panel Survey (MEPS) and National Health Expenditure Accounts (NHEA) are often used for health care policy analysis and simulations because they contain comprehensive estimates of national health care expenditures. The NHEA are primarily based on aggregate provider revenue data, while MEPS is based on person-level data on health care expenditures. This article compares MEPS and NHEA expenditure estimates for 2002 and discusses the differences. When MEPS and the NHEA are adjusted to be on a consistent basis, their expenditure estimates differ by 13.8 percent.     

Asthma prevalence among pregnant and childbearing-aged women in the United States: estimates from national health surveys

PURPOSE: Asthma is a major complication of pregnancy, but there are currently no reliable national estimates for the United States of asthma prevalence in pregnancy or in the childbearing years. METHODS: The prevalence of asthma among pregnant women and all childbearing-aged women was estimated and examined by age group using the National Health Interview Survey (NHIS), 1997-2000, the Behavioral Risk Factor Surveillance System (BRFSS), 2000-2001, and the Third National Health and Nutrition Examination Survey (NHANES III), 1988-1994. Time trends were explored using NHANES II (1976-1980) and NHANES III (1988-1994). RESULTS: Asthma was estimated to affect from 88,573 to 190,650 pregnant women between 1997 and 2001, or approximately 3.7% to 8.4% of pregnant women in the United States. A slightly lower estimate of 3.2% was obtained for the period between 1988 and 1994. Among adult women of childbearing age, a two-fold increase in asthma from 2.9% to 5.8% occurred between 1976-1980 and 1988-1994. Among women aged 18 to 24, the increase was three-fold, from 1.8% to 6.0%. CONCLUSION: The prevalence of asthma during pregnancy may be higher than previously estimated and appears to be continuing to increase.     

A Nonresponse Bias Analysis of the Health Information National Trends Survey (HINTS)

We conducted a nonresponse bias analysis of the Health Information National Trends Survey (HINTS) 4, Cycles 1 and 3, collected in 2011 and 2013, respectively, using three analysis methods: comparison of response rates for subgroups, comparison of estimates with weighting adjustments and external benchmarks, and level-of-effort analysis. Areas with higher concentrations of low socioeconomic status, higher concentrations of young households, and higher concentrations of minority and Hispanic populations had lower response rates. Estimates of health information seeking behavior were higher in HINTS compared to the National Health Interview Survey (NHIS). The HINTS estimate of doctors always explaining things in a way that the patient understands was not significantly different from the same estimate from the Medical Expenditure Panel Survey (MEPS); however, the HINTS estimate of health professionals always spending enough time with the patient was significantly lower than the same estimate from MEPS. A level-of-effort analysis found that those who respond later in the survey field period were less likely to have looked for information about health in the past 12 months, but found only small differences between early and late respondents for the majority of estimates examined. There is some evidence that estimates from HINTS could be biased toward finding higher levels of health information seeking.     

Eating out in America, 1987-2000: trends and nutritional correlates

BACKGROUND: Despite widely held beliefs about increasing popularity of eating away-from-home and its possible contribution to increasing adiposity of the US population, there are little published data on this topic. To address this issue, we examined trends in frequency of consumption of commercially prepared (CP) meals reported by Americans aged > or =18 years, and its nutritional correlates. METHODS: The data were from the National Health Interview Survey (NHIS) 1987 (n = 21,731), NHIS 1992 (n = 11,718), and the National Health and Nutrition Examination Survey (NHANES) 1999-2000 (n = 5,330). The information on CP meal consumption was obtained from questions included in the three surveys. The independent association of reported CP meal frequency with body mass index (BMI), and intakes of energy and macronutrients was examined using multiple linear regression methods. RESULTS: The mean reported number of CP meals per week was 2.5 in 1987 and 1992, and 2.8 in 1999-2000. In 1987, approximately 28% of the population reported 0 or <1 commercially prepared meal per week, decreasing to 24% in 1999-2000 (P for trend <0.0001). However, the proportion of the population reporting three or more weekly CP meals increased from 36% in 1987 to 41% in 1999-2000 (P for trend < or =0.0005). The odds of eating out at least one or more and three or more meals per week were 40% higher (95% CI 1.20-1.70) in 1999-2000 relative to 1987. The reported number of CP meals per week was positively associated with estimates of energy intake (P < or = 0.0001) in each survey. Self-reported and measured BMI were modestly associated with the reported number of weekly CP meals in women in 1999-2000 (P < or = 0.05). CONCLUSION: Our results confirm that in 1999-2000, more Americans ate out, and ate out more frequently than in 1987 and 1992. Higher eating-out frequency was associated with adverse nutritional consequences.     

Improving on analyses of self‐reported data in a large‐scale health survey by using information from an examination‐based survey

Common data sources for assessing the health of a population of interest include large‐scale surveys based on interviews that often pose questions requiring a self‐report, such as, ‘Has a doctor or other health professional ever told you that you have 〈 health condition of interest〉 ?’ or ‘What is your 〈 height/weight〉 ?’ Answers to such questions might not always reflect the true prevalences of health conditions (for example, if a respondent misreports height/weight or does not have access to a doctor or other health professional). Such ‘measurement error’ in health data could affect inferences about measures of health and health disparities. Drawing on two surveys conducted by the National Center for Health Statistics, this paper describes an imputation‐based strategy for using clinical information from an examination‐based health survey to improve on analyses of self‐reported data in a larger interview‐based health survey. Models predicting clinical values from self‐reported values and covariates are fitted to data from the National Health and Nutrition Examination Survey (NHANES), which asks self‐report questions during an interview component and also obtains clinical measurements during a physical examination component. The fitted models are used to multiply impute clinical values for the National Health Interview Survey (NHIS), a larger survey that obtains data solely via interviews. Illustrations involving hypertension, diabetes, and obesity suggest that estimates of health measures based on the multiply imputed clinical values are different from those based on the NHIS self‐reported data alone and have smaller estimated standard errors than those based solely on the NHANES clinical data. The paper discusses the relationship of the methods used in the study to two‐phase/two‐stage/validation sampling and estimation, along with limitations, practical considerations, and areas for future research. Published in 2009 by John Wiley & Sons, Ltd.     

Prevalence of diabetes and impaired fasting glucose in adults--United States, 1999-2000

Diabetes and its complications are major causes of morbidity and mortality in the United States and contribute substantially to health-care costs. Data from the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS) have documented steady increases in the prevalence of diabetes. However, these surveys rely only on self-reports of previously diagnosed diabetes and cannot measure the prevalence of undiagnosed diabetes. The change in prevalence demonstrated by these data might reflect other factors such as enhanced detection rather than true increases. The National Health and Nutrition Examination surveys (NHANES) are the only nationally representative surveys that examine both diagnosed and undiagnosed diabetes. During 1976-1980 (NHANES II) and 1988-1994 (NHANES III), the overall combined prevalence of diabetes (diagnosed and undiagnosed on the basis of fasting glucose) increased. This report presents data on prevalence of diagnosed and undiagnosed diabetes and impaired fasting glucose from NHANES 1999-2000 and NHANES III (1988-1994). The findings indicate that diabetes and impaired fasting glucose continue to affect a major proportion of the U.S. population. An estimated 29 million (14.4%) persons aged >/=20 years had either diagnosed diabetes, undiagnosed diabetes, or impaired fasting glucose; 29% of diabetes cases were undiagnosed. Persons can reduce their risk for diabetes through weight management and physical activity.     

A comparison of national estimates from the National Health Interview Survey and the Behavioral Risk Factor Surveillance System

OBJECTIVES: The purpose of this study was to compare national estimates from the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS). METHODS: The authors compared data from the 2 surveys on smoking, height, weight, body mass index, diabetes, hypertension, immunization, lack of insurance coverage, cost as a barrier to medical care, and health status. RESULTS: Overall national estimates were similar for 13 of the 14 measures examined. Small differences according to demographic characteristics were found for height and body mass index, with larger differences for health status. CONCLUSIONS: Although estimates differed within subgroups, the BRFSS provided national estimates comparable to those of the NHIS. BRFSS national data could provide rapidly available information to guide national policy and program decisions.     

Drug use and sexual behaviors reported by adults: United States, 1999-2002

This report presents prevalence estimates for self-reported adult drug use and sexual behaviors in the United States. Data are from the National Health and Nutrition Examination Survey (NHANES) collected from 1999 to 2002. NHANES surveys a stratified multistage probability sample of the civilian noninstitutionalized population of the United States. Tables included in this report present estimates for use of cocaine, including crack or freebase, or other street drugs, and sexual behavior by selected sociodemographic characteristics among adults 20-59 years of age.