National Center for Health Statistics Guidelines for Analysis of Trends.

Many reports present analyses of trends over time based on multiple years of data from National Center for Health Statistics (NCHS) surveys and the National Vital Statistics System (NVSS). Trend analyses of NCHS data involve analytic choices that can lead to different conclusions about the trends. This report discusses issues that should be considered when conducting a time trend analysis using NCHS data and presents guidelines for making trend analysis choices. Trend analysis issues discussed include: choosing the observed time points to include in the analysis, considerations for survey data and vital records data (record level and aggregated), a general approach for conducting trend analyses, assorted other analytic issues, and joinpoint regression. This report provides 12 guidelines for trend analyses, examples of analyses using NCHS survey and vital records data, statistical details for some analysis issues, and SAS and SUDAAN code for specification of joinpoint regression models. Several an lytic choices must be made during the course of a trend analysis, and the choices made can affect the results. This report highlights the strengths and limitations of different choices and presents guidelines for making some of these choices. While this report focuses on time trend analyses, the issues discussed and guidelines presented are applicable to trend analyses involving other ordinal and interval variables.

Leading Causes of Death in Nonmetropolitan and Metropolitan Areas- United States, 1999-2014.

PROBLEM/CONDITION: Higher rates of death in nonmetropolitan areas (often referred to as rural areas) compared with metropolitan areas have been described but not systematically assessed. PERIOD COVERED: 1999-2014 DESCRIPTION OF SYSTEM: Mortality data for U.S. residents from the National Vital Statistics System were used to calculate age-adjusted death rates and potentially excess deaths for nonmetropolitan and metropolitan areas for the five leading causes of death. Age-adjusted death rates included all ages and were adjusted to the 2000 U.S. standard population by the direct method. Potentially excess deaths are defined as deaths among persons aged <80 years that exceed the numbers that would be expected if the death rates of states with the lowest rates (i.e., benchmark states) occurred across all states. (Benchmark states were the three states with the lowest rates for each cause during 2008-2010.) Potentially excess deaths were calculated separately for nonmetropolitan and metropolitan areas. Data are presented for the United States and the 10 U.S. Department of Health and Human Services public health regions. RESULTS: Across the United States, nonmetropolitan areas experienced higher age-adjusted death rates than metropolitan areas. The percentages of potentially excess deaths among persons aged <80 years from the five leading causes were higher in nonmetropolitan areas than in metropolitan areas. For example, approximately half of deaths from unintentional injury and chronic lower respiratory disease in nonmetropolitan areas were potentially excess deaths, compared with 39.2% and 30.9%, respectively, in metropolitan areas. Potentially excess deaths also differed among and within public health regions; within regions, nonmetropolitan areas tended to have higher percentages of potentially excess deaths than metropolitan areas. INTERPRETATION: Compared with metropolitan areas, nonmetropolitan areas have higher age-adjusted death rates and greater percentages of potentially excess deaths from the five leading causes of death, nationally and across public health regions. PUBLIC HEALTH ACTION: Routine tracking of potentially excess deaths in nonmetropolitan areas might help public health departments identify emerging health problems, monitor known problems, and focus interventions to reduce preventable deaths in these areas.

National Center for Health Statistics Data Presentation Standards for Proportions.

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

Differences in Stroke Mortality Among Adults Aged 45 and Over: United States, 2010-2013.

KEY FINDINGS: •During 2010-2013, the age-adjusted stroke death rate for non-Hispanic black men aged 45 and over (154.8 deaths per 100,000 population) was 54% to 68% higher than the rates for men of the same age in other race-ethnicity groups. The rate for non-Hispanic black women aged 45 and over was 30% to 61% higher than the rates for women of the same age in other race-ethnicity groups. •The age distribution of stroke deaths differed by race and ethnicity. •Stroke death rates were 32% higher in counties in the lowest median household income quartile than in counties in the highest income quartile. •Nonmetropolitan counties had higher stroke death rates than counties at other urbanization levels. •Stroke mortality inside and outside the Stroke Belt differed by race and ethnicity.

Deaths attributed to heat, cold, and other weather events in the United States, 2006-2010.

OBJECTIVES: This report examines heat-related mortality, cold-related mortality, and other weather-related mortality during 2006-2010 among subgroups of U.S. residents. METHODS: Weather-related death rates for demographic and area-based subgroups were computed using death certificate information. Adjusted odds ratios for weather-related deaths among subgroups were estimated using logistic regression. RESULTS AND CONCLUSIONS: During 2006-2010, about 2,000 U.S. residents died each year from weather-related causes of death. About 31% of these deaths were attributed to exposure to excessive natural heat, heat stroke, sun stroke, or all; 63% were attributed to exposure to excessive natural cold, hypothermia, or both; and the remaining 6% were attributed to floods, storms, or lightning. Weather-related death rates varied by age, race and ethnicity, sex, and characteristics of decedent's county of residence (median income, region, and urbanization level). Adjustment for region and urbanization decreased the risk of heat-related mortality among Hispanic persons and increased the risk of cold-related mortality among non-Hispanic black persons, compared with non-Hispanic white persons. Adjustment also increased the risk of heat-related mortality and attenuated the risk of cold-related mortality for counties in the lower three income quartiles. The differentials in weather-related mortality observed among demographic subgroups during 2006-2010 in the United States were consistent with those observed in previous national studies. This study demonstrated that a better understanding of subpopulations at risk from weather-related mortality can be obtained by considering area-based variables (county median household income, region, and urbanization level) when examining weather-related mortality patterns.

2013 NCHS Urban-Rural Classification Scheme for Counties.

OBJECTIVES: This report details development of the 2013 National Center for Health Statistics' (NCHS) Urban-Rural Classification Scheme for Counties (update of the 2006 NCHS scheme) and applies it to health measures to demonstrate urban-rural health differences. METHODS: The methodology used to construct the 2013 NCHS scheme was the same as that used for the 2006 NCHS scheme, but 2010 census-based data were used rather than 2000 census-based data. All U.S. counties and county-equivalent entities are assigned to one of six levels (four metropolitan and two nonmetropolitan) based on: 1) their February 2013 Office of Management and Budget designation as metropolitan, micropolitan, or noncore; 2) for metropolitan counties, the population size of the metropolitan statistical area (MSA) to which they belong; and 3) for counties in MSAs of 1 million or more, the location of principal city populations within the MSA. The 2013 and 2006 NCHS schemes were applied to data from the National Vital Statistics System (NVSS) and National Health Interview Survey (NHIS) to illustrate differences in selected health measures by urbanization level and to assess the magnitude of differences between estimates from the two schemes. RESULTS AND CONCLUSIONS: County urban-rural assignments under the 2013 NCHS scheme are very similar to those under the 2006 NCHS scheme. Application of the updated scheme to NVSS and NHIS data demonstrated the continued usefulness of the six categories for assessing and monitoring health differences among communities across the full urbanization spectrum. Residents of large central and large fringe metro counties differed substantially on many health measures, illustrating the importance of continuing to separate these counties. Residents of large fringe metro counties generally fared better than residents of less urban counties. Estimates obtained from the 2013 and 2006 schemes were similar.

NCHS urban-rural classification scheme for counties.

OBJECTIVES: This report details the National Center for Health Statistics' (NCHS) development of the 2006 NCHS Urban-Rural Classification Scheme for Counties and provides some examples of how the scheme can be used to describe differences in health measures by urbanization level. METHODS: The 2006 NCHS urban-rural classification scheme classifies all U.S. counties and county-equivalents into six levels--four for metropolitan counties and two for nonmetropolitan counties. The Office of Management and Budget's delineation of metropolitan and nonmetropolitan counties forms the foundation of the scheme. The NCHS scheme also uses the cut points of the U.S. Department of Agriculture Rural-Urban Continuum Codes to subdivide the metropolitan counties based on the population of their metropolitan statistical area (MSA): large, for MSA population of 1 million or more; medium, for MSA population of 250,000-999,999; and small, for MSA population below 250,000. Large metro counties were further separated into large central and large fringe metro categories using classification rules developed by NCHS. Nonmetropolitan counties were assigned to two levels based on the Office of Management and Budget's designated micropolitan or noncore status. The 2006 scheme was applied to data from the National Vital Statistics System (NVSS) and the National Health Interview Survey (NHIS) to illustrate its ability to capture health differences by urbanization level. RESULTS AND CONCLUSIONS: Application of the 2006 NCHS scheme to NVSS and NHIS data shows that it identifies important health disparities among communities, most notably those for inner city and suburban communities. The design of the NCHS Urban-Rural Classification Scheme for Counties makes it particularly well-suited for assessing and monitoring health differences across the full urbanization continuum.

Mortality experience of the 1986-2000 National Health Interview Survey Linked Mortality Files participants.

The National Center for Health Statistics (NCHS) has produced the 1986-2000 National Health Interview Survey (NHIS) Linked Mortality Files by linking eligible adults in the 1986-2000 NHIS cohorts through probabilistic record linkage to the National Death Index to obtain mortality follow-up through December 31, 2002. The resulting files contain more than 120,000 deaths and an average of 9 years of survival time. To assess how well mortality was ascertained in the linked mortality files, NCHS has conducted a comparison of the mortality experience of the 1986-2000 NHIS cohorts with that of the U.S. population. This report presents the results of this comparative mortality assessment. Methods The survival of each annual NHIS cohort was compared with that of the U.S. population during the same period. Cumulative survival probabilities for each annual NHIS cohort were derived using the Kaplan-Meier product limit method, and corresponding cumulative survival probabilities were computed for the U.S. population using information from annual U.S. life tables. The survival probabilities were calculated at various lengths of follow-up for each age-race-sex group of each NHIS cohort and for the U.S. population. Results As expected, mortality tended to be underestimated in the NHIS cohorts because the sample includes only civilian, noninstitutionalized persons, but this underestimation generally was not statistically significant. Statistically significant differences increased with length of follow-up, occurred more often for white females than for the other race-sex groups, and occurred more often in the oldest age groups. In general, the survival experience of the age-race-sex groups of each NHIS cohort corresponds quite closely to that of the U.S. population, providing support that the ascertainment of mortality through the probabilistic record linkage accurately reflects the mortality experience of the NHIS cohorts.

Statistical match of the March 1996 Current Population Survey and the 1995 National Health Interview Survey.

OBJECTIVES: Statistical matching is a method used to combine two files when it is unlikely that individuals on one file are also on the other file. The objectives of this report are to document and evaluate statistical matches of the March 1996 Current Population Survey (CPS) and the 1995 National Health interview Survey (NHIS) and give recommendations for improving future matches. The CPS-NHIS match was motivated by the need for a data set with data on health measures and family resources for use in policy analyses. METHODS: Three statistical matches between the March 1996 CPS and the 1995 NHIS are described in this report. All three matches used person-level constrained matching with partitioning and a predictive mean matching algorithm to link records on the two files. For two of the matches, the CPS served as the Host file and the NHIS served as the Donor file; for the third match, the NHIS was the Host file and the CPS was the Donor file. RESULTS: The results suggest that the constrained predictive mean matches of the March 1996 CPS and the 1995 NHIS successfully combined some of the information on the two files, but that relationships among some Host and Donor variables on the matched file may be distorted. The evaluation of the matches suggested that the variables used to partition the Host and Donor files prior to matching and the variables involved in the predictive mean matching play an important role in determining whether relationships among variables on the matched file correctly represent relationships among those variables in the population. The evaluation also indicated that estimates for small subgroups may be especially subject to error. The results reinforce the need to proceed cautiously when exploring relationships among Host and Donor variables on a statistically matched file.

Frequency of attendance at religious services, hypertension, and blood pressure: the Third National Health and Nutrition Examination Survey.

OBJECTIVE: To test the hypothesis that frequency of attendance at religious services is inversely related to prevalence of hypertension and blood pressure level. METHODS: In the Third National Health and Nutrition Examination Survey (NHANES III), 14,475 American women and men aged 20 years and over reported frequency of attendance at religious services, history of hypertension treatment, and had blood pressure (BP) measured. RESULTS: The percentage reporting attending religious services weekly (52 times/yr) was 29 and more than weekly (>52 times/yr) was 10. Prevalence of hypertension (systolic BP > or = 140 or diastolic BP > or = 90 mm Hg or current use of blood pressure medication) was 21% in never at attenders, 19% in those attending less than weekly (1-51 times/yr), 26% in those attending weekly, and 26% in those attending more than weekly (p < .01). After controlling for sociodemographic and health variables, religious attendance was associated with reduced prevalence compared with nonattendance, significantly so for weekly (beta = -0.24; 95% confidence interval [CL], -0.37 to -0.11; p < .01) and more than weekly (beta = -0.33; 95% CL, -0.60 to -0.07; p < .05). No significant effect modification by gender or age was observed. Compared with never attenders, persons attending weekly had a systolic BP 1.46 mm Hg (95% CL 2.33, 0.58 mm Hg, p < .01) lower and persons attending >52 times/yr had systolic BP 3.03 mm Hg (95% CL 4.34, 1.72 mm Hg, p < .01) lower. No significant effect modification by gender was observed; these estimates are adjusted for a significant interaction between age and less than weekly attendance (1-51 times) (p < .05). CONCLUSIONS: Compared with never attending, attendance at religious services weekly or more than weekly was associated with somewhat lower adjusted hypertension prevalence and blood pressure in a large national survey.

Bridging between two standards for collecting information on race and ethnicity: an application to Census 2000 and vital rates.

OBJECTIVES: The 2000 Census, which provides denominators used in calculating vital statistics and other rates, allowed multiple-race responses. Many other data systems that provide numerators used in calculating rates collect only single-race data. Bridging is needed to make the numerators and denominators comparable. This report describes and evaluates the method used by the National Center for Health Statistics to bridge multiple-race responses obtained from Census 2000 to single-race categories, creating single-race population estimates that are available to the public. METHODS: The authors fitted logistic regression models to multiple-race data from the National Health Interview Survey (NHIS) for 1997-2000. These fitted models, and two bridging methods previously suggested by the Office of Management and Budget, were applied to the public-use Census Modified Race Data Summary file to create single-race population estimates for the U.S. The authors also compared death rates for single-race groups calculated using these three approaches. RESULTS: Parameter estimates differed between the NHIS models for the multiple-race groups. For example, as the percentage of multiple-race respondents in a county increased, the likelihood of stating black as a primary race increased among black/white respondents but decreased among American Indian or Alaska Native/black respondents. The inclusion of county-level contextual variables in the regression models as well as the underlying demographic differences across states led to variation in allocation percentages; for example, the allocation of black/white respondents to single-race white ranged from nearly zero to more than 50% across states. Death rates calculated using bridging via the NHIS models were similar to those calculated using other methods, except for the American Indian/Alaska Native group, which included a large proportion of multiple-race reporters. CONCLUSION: Many data systems do not currently allow multiple-race reporting. When such data systems are used with Census counts to produce race-specific rates, bridging methods that incorporate geographic and demographic factors may lead to better rates than methods that do not consider such factors.

United States Census 2000 population with bridged race categories.

OBJECTIVES: The objectives of this report are to document the methods developed at the National Center for Health Statistics (NCHS) to bridge the Census 2000 multiple-race resident population to single-race categories and to describe the resulting bridged race resident population estimates. METHOD: Data from the pooled 1997-2000 National Health Interview Surveys (NHIS) were used to develop models for bridging the Census 2000 multiple-race population to single-race categories. The bridging models included demographic and contextual covariates, some at the person-level and some at the county-level. Allocation probabilities were obtained from the regression models and applied to the Census Bureau's April 1, 2000, Modified Race Data Summary File population counts to assign multiple-race persons to single-race categories. RESULTS: Bridging has the most impact on the American Indian and Alaska Native (AIAN) and Asian or Pacific Islander (API) populations, a small impact on the Black population and a negligible impact on the White population. For the United States as a whole, the AIAN, API, Black, and White bridged population counts are 12.0, 5.0, 2.5, and 0.5 percent higher than the corresponding Census 2000 single-race counts. At the sub-national level, there is considerably more variation than observed at the national level. The bridged single-race population counts have been used to calculate birth and death rates produced by NCHS for 2000 and 2001 and to revise previously published rates for the 1990s, 2000, and 2001. The bridging methodology will be used to bridge postcensal population estimates for later years. The bridged population counts presented here and in subsequent years may be updated as additional data become available for use in the bridging process.