nhanesA was developed to enable fully customizable retrieval of data from the National Health and Nutrition Examination Survey (NHANES). The survey is conducted by the National Center for Health Statistics (NCHS), and data are publicly available at: https://www.cdc.gov/nchs/nhanes.htm. NHANES data are reported in well over one thousand peer-reviewed journal publications every year.
Since 1999, the NHANES survey has been conducted continuously, and the surveys during that period are referred to as “continuous NHANES” to distinguish from several prior surveys. Continuous NHANES surveys are grouped in two-year intervals, with the first interval being 1999-2000.
Most NHANES data are in the form of tables in SAS ‘XPT’ format. The survey is grouped into five data categories that are publicly available, as well as an additional category (Limited access data) that requires written justification and prior approval before access. Package nhanesA is intended mostly for use with the publicly available data, but some information pertaining to the limited access data can also be retrieved.
The five publicly available data categories are: - Demographics (DEMO) - Dietary (DIET) - Examination (EXAM) - Laboratory (LAB) - Questionnaire (Q). The abbreviated forms in parentheses may be substituted for the long form in nhanesA commands.
For limited access data, the available tables and variable names can be listed, but the data cannot be downloaded directly. To indicate limited access data in nhanesA functions, use: - Limited (LTD)
To quickly get familiar with NHANES data, it is helpful to display a listing of tables. Use nhanesTables to get information on tables that are available for a given category for a given year.
library(nhanesA)
nhanesTables('EXAM', 2005)## Data.File.Name Data.File.Description
## 1 BPX_D Blood Pressure
## 2 BMX_D Body Measures
## 3 AUX_D Audiometry
## 4 AUXTYM_D Audiometry - Tympanometry
## 5 DXXFEM_D Dual Energy X-ray Absorptiometry - Femur
## 6 OPXFDT_D Ophthalmology - Frequency Doubling Technology
## 7 OHX_D Oral Health
## 8 PAXRAW_D Physical Activity Monitor
## 9 VIX_D Vision
## 10 DXXAG_D Dual Energy X-ray Absorptiometry - Android/Gynoid
## 11 AUXAR_D Audiometry - Acoustic Reflex
## 12 OPXRET_D Ophthalmology - Retinal Imaging
## 13 DXXSPN_D Dual Energy X-ray Absorptiometry - SpineNote that the two-year survey intervals begin with the odd year. For
convenience, only a single 4-digit year is entered such that
nhanesTables('EXAM', 2005) and
nhanesTables('EXAM', 2006) yield identical output.
After viewing the output, we decide we are interested in table ‘BMX_D’ that contains body measures data. To better determine if that table is of interest, we can display detailed information on the table contents using nhanesTableVars.
nhanesTableVars('EXAM', 'BMX_D')## Variable.Name Variable.Description
## 1 BMDSTATS Body Measures Component status Code
## 2 BMIARMC Arm Circumference Comment
## 3 BMIARML Upper Arm Length Comment
## 4 BMICALF Maximal Calf Comment
## 5 BMIHEAD Head Circumference Comment
## 6 BMIHT Standing Height Comment
## 7 BMILEG Upper Leg Length Comment
## 8 BMIRECUM Recumbent Length Comment
## 9 BMISUB Subscapular Skinfold Comment
## 10 BMITHICR Thigh Circumference Comment
## 11 BMITRI Triceps Skinfold Comment
## 12 BMIWAIST Waist Circumference Comment
## 13 BMIWT Weight Comment
## 14 BMXARMC Arm Circumference (cm)
## 15 BMXARML Upper Arm Length (cm)
## 16 BMXBMI Body Mass Index (kg/m**2)
## 17 BMXCALF Maximal Calf Circumference (cm)
## 18 BMXHEAD Head Circumference (cm)
## 19 BMXHT Standing Height (cm)
## 20 BMXLEG Upper Leg Length (cm)
## 21 BMXRECUM Recumbent Length (cm)
## 22 BMXSUB Subscapular Skinfold (mm)
## 23 BMXTHICR Thigh Circumference (cm)
## 24 BMXTRI Triceps Skinfold (mm)
## 25 BMXWAIST Waist Circumference (cm)
## 26 BMXWT Weight (kg)
## 27 SEQN Respondent sequence number.We see that there are 27 columns in table BMX_D. SEQN is a subject identifier that is used to join information across tables.
We now import BMX_D along with the demographics table DEMO_D.
bmx_d <- nhanes('BMX_D')
demo_d <- nhanes('DEMO_D')We merge the tables and display several variables:
bmx_demo <- merge(demo_d, bmx_d)
options(digits=4)
select_cols <- c('RIAGENDR', 'BMXHT', 'BMXWT', 'BMXLEG', 'BMXCALF', 'BMXTHICR')
print(bmx_demo[5:8,select_cols], row.names=FALSE)## RIAGENDR BMXHT BMXWT BMXLEG BMXCALF BMXTHICR
## 2 156.0 75.2 38.0 36.6 53.7
## 1 167.6 69.5 40.4 35.6 48.0
## 2 163.7 45.0 39.2 31.7 41.3
## 1 182.4 101.9 41.5 42.6 50.5NHANES uses coded values for many fields. In the preceding example, gender is coded as 1 or 2. To determine what the values mean, we can list the code translations for the gender field RIAGENDR in table DEMO_D
nhanesTranslate('DEMO_D', 'RIAGENDR')## $RIAGENDR
## Code.or.Value Value.Description
## 1 1 Male
## 2 2 Female
## 3 . MissingIf desired, we can use nhanesTranslate to apply the code translation to demo_d directly by assigning data=demo_d.
demo_d <- nhanesTranslate('DEMO_D', 'RIAGENDR', data=demo_d)## Translated columns: RIAGENDRbmx_demo <- merge(demo_d, bmx_d)The RIAGENDR field is now recoded as Male, Female instead of 1,2.
print(bmx_demo[5:8,select_cols], row.names=FALSE)## RIAGENDR BMXHT BMXWT BMXLEG BMXCALF BMXTHICR
## Female 156.0 75.2 38.0 36.6 53.7
## Male 167.6 69.5 40.4 35.6 48.0
## Female 163.7 45.0 39.2 31.7 41.3
## Male 182.4 101.9 41.5 42.6 50.5An NHANES table may have dozens of columns with coded values. Translating all possible columns is a three step process. 1: Download the table 2: Save a list of table variables 3: Pass the table and variable list to nhanesTranslate
bpx_d <- nhanes('BPX_D')
head(bpx_d[,6:11])## BPQ150A BPQ150B BPQ150C BPQ150D BPAARM BPACSZ
## 1 NA NA NA NA NA NA
## 2 2 2 2 2 1 3
## 3 1 2 2 2 1 4
## 4 2 2 2 2 1 3
## 5 2 2 2 2 1 4
## 6 2 2 2 2 1 4bpx_d_vars <- nhanesTableVars('EXAM', 'BPX_D', namesonly=TRUE)
#Alternatively may use bpx_d_vars = names(bpx_d)
bpx_d <- suppressMessages(nhanesTranslate('BPX_D', bpx_d_vars, data=bpx_d))## Translated columns: BPAARM BPACSZ BPAEN2 BPAEN3 BPAEN4 BPQ150A BPQ150B BPQ150C BPQ150D BPXPTY BPXPULS PEASCCT1 PEASCST1head(bpx_d[,6:11])## BPQ150A BPQ150B BPQ150C BPQ150D BPAARM BPACSZ
## 1 <NA> <NA> <NA> <NA> <NA> <NA>
## 2 No No No No Right Adult (12X22)
## 3 Yes No No No Right Large (15X32)
## 4 No No No No Right Adult (12X22)
## 5 No No No No Right Large (15X32)
## 6 No No No No Right Large (15X32)Some discretion should be applied when translating coded columns as code translations can be quite long. To improve readability the translation string is restricted to a default length of 32 but can be set as high as 128. Also, columns that have at least two categories (e.g. Male, Female) will be translated, but mincategories can be set to 1 to perform the translation even if only a single category is present.
The primary goal of nhanesA is to enable fully customizable processing of select NHANES tables. However, it is quite easy to download entire surveys using nhanesA functions. Say we want to download every questionnaire in the 2007-2008 survey. We first get a list of the table names by using nhanesTables with namesonly = TRUE. The tables can then be downloaded using nhanes with lapply.
q2007names <- nhanesTables('Q', 2007, namesonly=TRUE)
q2007tables <- lapply(q2007names, nhanes)
names(q2007tables) <- q2007namesSome NHANES measurements require special handling, e.g. due to statistical considerations. Furthermore, there are surveys conducted outside the scope of the continuous survey, but that are provided in a similar format such that nhanesA can be readily adapted to retrieve their data. Note that nhanesA cannot be used to handle accelerometer data from 2003-2006. For those data, please see packages nhanesaccel https://r-forge.r-project.org/R/?group_id=1733 and accelerometry https://cran.r-project.org/package=accelerometry.
Due to COVID, data collection for the 2019-2020 cycle was not completed. In order to make better use of data that were collected, data from the 2017-2018 survey were included to form a representative sample of 2017-March 2020 pre-pandemic data. The table structure and variable format of pre-pandemic tables is essentially identical to standard continuous NHANES. The table names include the prefix “P_”. To list pre-pandemic tables, use ‘P’ or ‘p’ as the year.
#List all pre-pandemic tables
nhanesSearchTableNames('^P_')
#List pre-pandemic EXAM tables
nhanesTables('EXAM', 'P')
#Table import and variable translation operate as usual
p_dxxfem <- nhanes('P_DXXFEM')
nhanesTranslate('P_BMX', 'BMDSTATS')Dual Energy X-Ray Absorptiometry (DXA) data were acquired from 1999-2006. These data were found to contain a higher amount of missing values than usual, and a multiple imputation process was applied to fill in missing and invalid values. More information may be found at https://wwwn.cdc.gov/nchs/nhanes/dxa/dxa.aspx. By default the DXA data are imported into the R environment, however, because the tables are quite large it may be desirable to save the data to a local file then import to R as needed. When nhanesTranslate is applied to DXA data, only the 2005-2006 translation tables are used as those are the only DXA codes that are currently available in html format.
#Import into R
dxx_b <- nhanesDXA(2001)
#Save to file
nhanesDXA(2001, destfile="dxx_b.xpt")
#Import supplemental data
dxx_c_s <- nhanesDXA(2003, suppl=TRUE)
#Apply code translations
dxalist <- c('DXAEXSTS', 'DXIHE')
dxx_b <- nhanesTranslate(colnames=dxalist, data=dxx_b, dxa=TRUE)NNYFS is a 2012 ancillary study conducted to assess physical activity and fitness levels of children and teens from ages 3-15. This study consists of questionnaires and basic measurements. There are no LAB tables. NNYFS table names include the prefix “Y_”. To list tables, use ‘Y’ or ‘y’ as the year.
#List NNYFS EXAM tables
nhanesTables('EXAM', 'Y')
#Table import and variable translation operate as usual
y_cvx <- nhanes('Y_CVX')
nhanesTranslate('Y_CVX','CVXPARC')The NHANES repository is extensive, thus it is helpful to perform a targeted search to identify relevant tables and variables. There are several nhanesA functions that allow the user to search using different criteria including the variable description, variable name, and table name pattern.
Comprehensive lists of NHANES variables are maintained for each data group. For example, the demographics variables are available at https://wwwn.cdc.gov/nchs/nhanes/search/variablelist.aspx?Component=Demographics. The nhanesSearch function allows the investigator to input search terms, match against the comprehensive variable descriptions, and retrieve the list of matching variables. Matching search terms (variable descriptions must contain one of the terms) and exclusive search terms (variable descriptions must NOT contain any exclusive terms) may be provided. The search can be restricted to a specific survey range as well as specific data groups.
# nhanesSearch use examples
#
# Search on the word bladder, restrict to the 2001-2008 surveys,
# print out 50 characters of the variable description
nhanesSearch("bladder", ystart=2001, ystop=2008, nchar=50)
#
# Search on "urin" (will match urine, urinary, etc), from 1999-2010, return table names only
nhanesSearch("urin", ignore.case=TRUE, ystop=2010, namesonly=TRUE)
#
# Search on "urin", exclude "During", search surveys from 1999-2010, return table names only
nhanesSearch("urin", exclude_terms="during", ignore.case=TRUE, ystop=2010, namesonly=TRUE)
#
# Restrict search to 'EXAM' and 'LAB' data groups. Explicitly list matching and exclude terms, leave ignore.case set to default value of FALSE. Search surveys from 2009 to present.
nhanesSearch(c("urin", "Urin"), exclude_terms=c("During", "eaten during", "do during"), data_group=c('EXAM', 'LAB'), ystart=2009)
#
# Search on "tooth" or "teeth", all years
nhanesSearch(c("tooth", "teeth"), ignore.case=TRUE)
#
# Search for variables where the variable description begins with "Tooth"
nhanesSearch("^Tooth")nhanesSearch is a versatile search function as it imports the comprehensive variable lists to a data frame. That allows for detailed conditional extraction of the variables. However, each call to nhanesSearch takes up to a minute or more to process. Faster processing can be achieved when we know the name of a specific variable of interest and we look only for exact matches to the variable name. Function nhanesSearchVarName matches a given variable name in the html directly, then only the matching elements are converted to a data frame. Consequently, a call to nhanesSearchVarName executes much faster than nhanesSearch; typically under 30s. nhanesSearchVarName is useful for finding all data tables that contain a given variable.
#nhanesSearchVarName use examples
nhanesSearchVarName('BPXPULS')## [1] "BPX_D" "BPX_E" "BPX" "BPX_C" "BPX_B" "BPX_F" "BPX_G" "BPX_H" "BPX_I"
## [10] "BPX_J"nhanesSearchVarName('CSQ260i', includerdc=TRUE, nchar=38, namesonly=FALSE)## Variable.Name Variable.Description Data.File.Name
## 1 CSQ260i Do you now have any of the following p CSX_G_R
## 2 CSQ260i Do you now have any of the following p CSX_H
## Data.File.Description Begin.Year EndYear Component Use.Constraints
## 1 Taste & Smell 2012 2012 Examination RDC Only
## 2 Taste & Smell 2013 2014 Examination NoneIn order to group data across surveys, it is useful to list all available tables that follow a given naming pattern. Function nhanesSearchTableNames is used for such pattern matching. For example, if we want to work with all available body measures data we can retrieve the full list of available tables with nhanesSearchTableNames(‘BMX’). The search is conducted over the comprehensive table list, which is much smaller than the comprehensive variable list, such that a call to nhanesSearchTableNames takes only a few seconds.
# nhanesSearchTableNames use examples
nhanesSearchTableNames('BMX')## [1] "BMX_D" "BMX" "BMX_E" "BMX_C" "BMX_B" "BMX_F" "BMX_H" "BMX_G" "BMX_I"
## [10] "BMX_J" "P_BMX"nhanesSearchTableNames('HPVS', includerdc=TRUE, nchar=42, details=TRUE)## Years Data.File.Name Doc.File
## 1 2009-2010 Human Papillomavirus (HPV) - 6, 11, 16 & 1 HPVSER_F Doc
## 2 2005-2006 Human Papillomavirus (HPV) - 6, 11, 16 & 1 HPVS_D_R Doc
## 3 2007-2008 Human Papillomavirus (HPV) - 6, 11, 16 & 1 HPVSER_E Doc
## 4 2005-2006 Human Papillomavirus (HPV) - 6, 11, 16 & 1 HPVSER_D Doc
## 5 2005-2006 Human Papillomavirus (HPV) - Multiplexed 6 HPVSRM_D Doc
## 6 2005-2006 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWR_D Doc
## 7 2007-2008 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWR_E Doc
## 8 2009-2010 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWR_F Doc
## 9 2011-2012 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWR_G Doc
## 10 2005-2006 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWR_D Doc
## 11 2009-2010 Human Papillomavirus (HPV) DNA - Vaginal S HPVS_F_R Doc
## 12 2011-2012 Human Papillomavirus (HPV) DNA - Vaginal S HPVS_G_R Doc
## 13 2013-2014 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWR_H Doc
## 14 2013-2014 Human Papillomavirus (HPV) DNA - Vaginal S HPVS_H_R Doc
## 15 2015-2016 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWC_I Doc
## 16 2015-2016 Human Papillomavirus (HPV) DNA - Vaginal S HPVSWR_I Doc
## 17 2015-2016 Human Papillomavirus (HPV) DNA - Vaginal S HPVS_I_R Doc
## 18 2017-2018 Human Papillomavirus (HPV) DNA - Vaginal S HPVS_J_R Doc
## Data.File Date.Published
## 1 HPVSER_F Data [XPT - 171.6 KB] November 2013
## 2 RDC Only July 2013
## 3 HPVSER_E Data [XPT - 155.7 KB] November 2013 2007-2008
## 4 HPVSER_D Data [XPT - 151.6 KB] July 2013 2005-2006
## 5 HPVSRM_D Data [XPT - 302.6 KB] January 2015 2005-2006
## 6 HPVSWR_D Data [XPT - 694.4 KB] November 2010 2005-2006
## 7 HPVSWR_E Data [XPT - 677.9 KB] August 2012 2007-2008
## 8 HPVSWR_F Data [XPT - 725.2 KB] August 2012 2009-2010
## 9 HPVSWR_G Data [XPT - 661.1 KB] March 2015 2011-2012
## 10 HPVSWR_D Data [XPT - 694.4 KB] Updated November 2018 2005-2006
## 11 RDC Only August 2012
## 12 RDC Only March 2015
## 13 HPVSWR_H Data [XPT - 716.6 KB] December 2016 2013-2014
## 14 RDC Only December 2016
## 15 HPVSWC_I Data [XPT - 33.3 KB] November 2018 2015-2016
## 16 HPVSWR_I Data [XPT - 667.5 KB] November 2018 2015-2016
## 17 RDC Only November 2018
## 18 RDC Only December 2020Sincerely,
Christopher J. Endres