Health: Neurodevelopmental Disorders
Methods
Indicator
H6. Percentage of children ages 5 to 17 years reported to have attention deficit/hyperactivity
disorder, by sex, 1997-2019.
H7. Percentage of children ages 5 to 17 years reported to have a learning disability, by sex, 1997-
2019.
H8. Percentage of children ages 5 to 17 years reported to have autism, 1997-2019.
H9. Percentage of children ages 5 to 17 years reported to have intellectual disability (mental
retardation), 1997-2019.
Summary
Since 1957, the National Center for Health Statistics, a division of the Centers for Disease
Control and Prevention, has conducted the National Health Interview Survey (NHIS), a series of
annual U.S. national surveys of the health status of the non-institutionalized civilian population.
These indicators use responses to questions on neurodevelopmental disorders for children ages 5
to 17 from the NHIS 1997-2019 surveys. Indicator H6 gives the trends in the percentages of
children reported to have attention deficit/hyperactivity disorder, stratified by sex. Indicator H7
gives the trends in the percentages of children reported to have a learning disability, stratified by
sex. Indicator H8 gives the trends in the percentages of children reported to have autism.
Indicator H9 gives the trends in the percentages of children reported to have intellectual
disability (mental retardation), stratified by sex.
For each indicator, the corresponding supplemental tables H6a, H7a, H8a, and H9a give the
percentage of children reported to have the specified neurodevelopmental disorder over the
period 2016 -2019, stratified both by age and sex. For each indicator, the corresponding tables
H6b, H7b, H8b, and H9b give the percentage of children reported to have the specified
neurodevelopmental disorder over the period 2016-2019, stratified both by race/ethnicity (using
NHIS information on race and Hispanic origin) and family income (using reported or imputed
NHIS poverty-income ratio data for each respondent). Percentages are calculated by combining
positive responses to the relevant questions with the survey weights for each respondent. The
survey weights are the annual numbers of children in the noninstitutionalized civilian population
represented by each respondent.
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Data Summary
The survey response data in the table below was used to support the following indicators:
Indicator H6. Percentage of children ages 5 to 17 years reported to have attention deficit/hyperactivity disorder (ADHD), by sex,
1997-2019.
H7. Percentage of children ages 5 to 17 years reported to have a learning disability, 1997-2019.
H8. Percentage of children ages 5 to 17 years reported to have autism, 1997-2019.
H9. Percentage of children ages 5 to 17 years reported to have intellectual disability (mental retardation), 1997-2019.
Years
1997
1998
1999
2000
2001
2002
2003
2004
Children
10,006
9,564
9,169
9,506
9,638
8,876
8,738
8,830
ADHD non-missing
responses (%)
9,971
(99.6%)
9,536
(99.7%)
9,155
(99.8%)
9,481
(99.7%)
9,617
(99.8%)
8,845
(99.7%)
8,722
(99.8%)
8,813
(99.8%)
ADHD missing responses
(%)
35 (0.4%)
28 (0.3%)
14 (0.2%)
25 (0.3%)
21 (0.2%)
31 (0.3%)
16 (0.2%)
17(0.2%)
Learning disability non-
missing responses (%)
9,974
(99.7%)
9,552
(99.9%)
9,155
(99.8%)
9,490
(99.8%)
9,624
(99.9%)
8,862
(99.8%)
8,724
(99.8%)
8,823
(99.9%)
Learning disability missing
responses (%)
32 (0.3%)
12(0.1%)
14 (0.2%)
16 (0.2%)
14(0.1%)
14 (0.2%)
14 (0.2%)
7(0.1%)
Autism non-missing
responses (%)
9,996
(99.9%)
9,557
(99.9%)
9,165
(100.0%)
9,501
(99.9%)
9,633
(99.9%)
8,873
(100.0%)
8,730
(99.9%)
8,825
(99.9%)
Autism missing responses
(%)
10(0.1%)
7(0.1%)
4 (0.0%)
5 (0.1%)
5 (0.1%)
3 (0.0%)
8(0.1%)
5 (0.1%)
Intellectual disability non-
missing responses (%)
9,991
(99.8%)
9,549
(99.8%)
9,165
(100.0%)
9,494
(99.9%)
9,628
(99.9%)
8,856
(99.8%)
8,728
(99.9%)
8,828
(100.0%)
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Intellectual disability
missing responses (%)
15 (0.2%)
15 (0.2%)
4 (0.0%)
12(0.1%)
10(0.1%)
20 (0.2%)
10(0.1%)
2 (0.0%)
Years
2005
2006
2007
2008
2009
2010
2011
2012
Children
8,974
7,019
6,604
6,328
8,009
7,995
9,040
9,481
ADHD non-missing
responses (%)
8,952
(99.8%)
7,003
(99.8%)
6,595
(99.9%)
6,311
(99.7%)
7,994
(99.8%)
7,980
(99.8%)
9,029
(99.9%)
9,464
(99.8%)
ADHD missing responses
(%)
22 (0.2%)
16 (0.2%)
9(0.1%)
17(0.3%)
15 (0.2%)
15 (0.2%)
11 (0.1%)
17(0.2%)
Learning disability non-
missing responses (%)
8,959
(99.8%)
7,004
(99.8%)
6,583
(99.7%)
6,319
(99.9%)
8,001
(99.9%)
7,986
(99.9%)
9,031
(99.9%)
9,469
(99.9%)
Learning disability missing
responses (%)
15 (0.2%)
15 (0.2%)
21 (0.3%)
9(0.1%)
8 (0.1%)
9(0.1%)
9(0.1%)
12(0.1%)
Autism non-missing
responses (%)
8,971
(100.0%)
7,012
(99.9%)
6,600
(99.9%)
6,328
(100.0%)
8,004
(99.9%)
7,987
(99.9%)
9, 036
(100.0%)
9,474
(99.9%)
Autism missing responses
(%)
3 (0.0%)
7(0.1%)
4(0.1%)
0 (0.0%)
5 (0.1%)
8 (0.1%)
4 (0.0%)
7(0.1%)
Intellectual disability non-
missing responses (%)
8,968
(99.9%)
7,015
(99.9%)
6,603
(100.0%)
6,322
(99.9%)
8,005
(99.9%)
7,990
(99.9%)
9, 037
(100.0%)
9,477
(100.0%)
Intellectual disability
missing responses (%)
6(0.1%)
4(0.1%)
1 (0.0%)
6(0.1%)
4(0.1%)
5 (0.1%)
3 (0.0%)
4 (0.0%)
Years
2013
2014
2015
2016
2017
2018
2019
Children
9,252
9,689
8,881
8,065
6,439
6,096
6,776
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ADHD non-missing
responses (%)
9,243
(99.9%)
9,668
(99.8%)
8,866
(99.8%)
8,049
(99.8%)
6,431
(99.8%)
6,089
(99.9%)
6,752
(99.8%)
ADHD missing responses
(%)
9(0.1%)
21 (0.2%)
15 (0.2%)
16 (0.2%)
8 (0.1%)
7(0.1%)
24 (0.2%)
Learning disability non-
missing responses (%)
9.244
(99.9%)
9,672
(99.8%)
8,872
(99.9%)
8,051
(99.8%)
6,428
(99.8%)
6,085
(99.8%)
6,760
(99.8%)
Learning disability missing
responses (%)
8(0.1%)
17(0.2%)
9(0.1%)
14 (0.2%)
11 (0.2%)
11 (0.2%)
16 (0.2%)
Autism non-missing
responses (%)
9,246
(99.9%)
9,682
(99.9%)
8,875
(99.9%)
8,055
(99.9%)
6,433
(99.9%)
6,092
(99.9%)
6,762
(99.8%)
Autism missing responses
(%)
6(0.1%)
7(0.1%)
6(0.1%)
10(0.1%)
6(0.1%)
4(0.1%)
14 (0.2%)
Intellectual disability non-
missing responses (%)
9.246
(99.9%)
9,681
(99.9%)
8,872
(99.9%)
8,060
(99.9%)
6,438
(100.0%
6,091
(99.9%)
6,766
(99.8%)
Intellectual disability
missing responses (%)
6(0.1%)
8 (0.1%)
9(0.1%)
5 (0.1%)
1 (0.0%)
5 (0.1%)
10 (0.2%)
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National Health Interview Survey (NHIS)
Since 1957, the National Center for Health Statistics, a division of the Centers for Disease
Control and Prevention, has conducted the National Health Interview Survey (NHIS), a series of
annual U.S. national surveys of the health status of the noninstitutionalized civilian population.
These indicators use responses from a knowledgeable adult family member residing in the
household to neurodevelopmental disorder prevalence questions for children ages 5 to 17 years
for the surveys 1997-2019. The NHIS data were obtained from the NHIS website:
http://www.cdc.gov/nchs/nhis.htm.
For these indicators we used the responses to the following questions: For Attention
Deficit/Hyperactivity Disorder for 1997-2018: "Has a doctor or health professional ever told you
that had Attention Deficit/Hyperactivity Disorder (ADHD) or Attention Deficit
Disorder (ADD)?" Attention Deficit/Hyperactivity Disorder for 2019: "Has a doctor or health
professional ever told you that had Attention Deficit/Hyperactivity Disorder or
ADHD or Attention Deficit Disorder or ADD?"
For Learning disability: "Has a representative from a school or a health professional ever told
you that had a learning disability?"
For Autism for 1997-2010: "Has a doctor or health professional ever told you that had Autism?" Autism for 2011-2013: "Has a doctor or health professional ever told you
that had Autism/Autism Spectrum Disorder?" Autism for 2014 2019: "Has a
doctor or health professional ever told you that had Autism, Asperger's disorder,
pervasive developmental disorder, or autism spectrum disorder?" (In previous years, the autism
question was included in a set of questions about 10 health conditions, asked after the question
about any other developmental delay. For 2014- 2019, the autism question was a separate
question asked before the question about any other developmental delay).
For Intellectual disability (mental retardation) for 1997-2010: "Has a doctor or health
professional ever told you that had Mental Retardation?" Intellectual disability
(mental retardation) for 2011-2019: "Has a doctor or health professional ever told you that
had an intellectual disability, also known as mental retardation?"
The NHIS uses a complex multi-stage, stratified, clustered sampling design. Certain
demographic groups have been deliberately over-sampled. Oversampling is performed to
increase the reliability and precision of estimates of health status indicators for these population
subgroups. For 1997-2005, Blacks and Hispanics were over-sampled. For 2006- 2015, Blacks,
Hispanics, and Asians were over-sampled. No over-sampling was implemented for 2016- 2019.
The publicly released data includes survey weights to adjust for the over-sampling, non-
response, and non-coverage. The statistical analyses used the sample child survey weights
(WTFA SC for 1997-2018, WTFA C for 2019) to re-adjust the responses to represent the
national population.
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The sample design was changed in 2006 and changed again in 2016. New strata were defined,
and primary sampling units (PSUs) were selected from these new strata. For example, pseudo-
stratum 100 for 1997-2005 is unrelated to pseudo-stratum 100 for 2006-2015 and pseudo-stratum
100 for 2016. To properly treat the 2006-2015 data as independent from the 1997- 2005 data,
1,000 was added to each of the 2006- 2015 pseudo-stratum numbers for these statistical analyses.
To properly treat the 2016-2019 data as independent from the 1997-2015 data, 2,000 was added
to each of the year 2016- 2019 pseudo-stratum numbers for these statistical analyses.1
Race/Ethnicity and Family Income
For Supplementary Tables H6b, H7b, H8b, and H9b, the prevalence percentages were calculated
for demographic strata defined by the combination of race/ethnicity and family income.
The family income was characterized based on the poverty income ratio variable (POVRATI3
for 2010-2018, and POVRATTCC for 2019), which gives the level of the ratio of the family
income to the poverty level. The National Center for Health Statistics obtained the family
income for the respondent's family during the family interview. The U.S. Census Bureau defines
annual poverty level money thresholds varying by family size and composition. The poverty
income ratio (PIR) is the family income divided by the poverty level for that family. For 2019,
the public release variable POVRATTC C gives the numerical value of PIR in hundredths. For
2010-2018, the public release variable POVRATI3 gives the numerical value of PIR in
thousandths. For prior years, the numerical values of PIR can be obtained from the Supplemental
Imputed Income files available from the NHIS website: http://www.cdc.gov/nchs/nhis.htm.
Family income was stratified into the following groups:
Below Poverty Level: PIR < 1.
Between 100% and 200% of Poverty Level: 1 < PIR < 2.
Above 200% of Poverty level: PIR > 2.
Above Poverty Level: PIR > 1 (combines the previous two groups).
Unknown Income: PIR is missing ("undefinable").11
Approximately 30% of families did not report their exact family income. From 1997 to 2006, the
majority of these families either reported their income by selecting from two categories (above or
below $20,000) or from 44 categories. For 2007 and later, the income questions were revised, so
that families not reporting an exact income were first asked to report their income as the two
1 The addition of 1,000 for 2006 to 2015 was chosen to make the stratum numbers for 2005 and earlier distinct from
the stratum numbers for 2006 to 2015. The addition of 2,000 for 2016 to 2017 was chosen to make the stratum
numbers for 2016 to 2017 distinct from the stratum numbers for 2015 and earlier. This follows the recommendations
in Appendix IV of the survey description document "2018 National Health Interview Survey (NHIS) Public Use
Data Release Survey Description," CDC, June, 2019,
http://www.cdc.gov/nchs/nliis/auest data related 1997 forward.htm.
"Although missing values of family income were statistically imputed for the vast majority of respondents, there
were a few respondents that still had an unknown income after the income imputation.
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categories above or below $50,000, and were then asked appropriate additional questions to
refine the income range. From 2007 to 2010, the income ranges were either 0-$34,999, $35,000-
$49,999, $50,000-74,999, $75,000-$99,999, or $100,000 and above. For 2011-2017, the
additional questions included questions about the size of the family and whether the income was
above or below 100%, 138%, 200%, 250%, or 400% of the poverty threshold, and the income
ranges were either 0-$34,999, $35,000-$49,999, $50,000-74,999, $75,000-$99,999, $100,000-
$149,999, or $150,000 and above. For 2019, the families were asked a series of questions using
an unfolding bracket method to establish a small range for the family income relative to specific
dollar values and relative to the federal poverty threshold taking into account the family size. For
2007-2018, between 91% and 95% of families either gave the exact income or a categorical
response. For 2019, the weighted percentage of families with unknown exact family income was
23% for the exact value and 8% for any of the family income bracketing questions.
NCHS reports111 evidence that the non-response to the income question is related to person-level
or family-level characteristics, including items pertaining to health. Therefore, treating the
missing responses as being randomly missing would lead to biased estimates. To address this
problem, NCHS applied a statistical method called "multiple imputation" to estimate or "impute"
the family income based on the available family income and personal earnings information and
on responses to other survey equations. A series of regression models were used to predict the
exact family income from the available responses. For 1997-2018, five sets of simulated family
income values were generated for each family that did not report their exact family income. In
this manner, NCHS generated five data sets, each containing a complete set of family income
values (either the reported or the imputed values). The poverty income ratio categories or values
were calculated from the income values and the family size and composition variables. An
estimated prevalence percentage was computed for each of the five data sets. For 2019, ten sets
of simulated family income values were generated for each family that did not report their exact
family income. As suggested in the 2019 technical support document"7, in order to combine the
1997-2018 data with 5 imputations with the more recent 2019 data with 10 imputations, only the
first five imputations from the 2019 imputed income data were used for these analyses. The
overall estimated prevalence percentage is the arithmetic mean of the five estimates.
The poverty income ratios were calculated by NCHS using the exact family income, if available,
or otherwise were calculated from the imputed family income. Among the sampled children ages
5 to 17 years for 2016-2019, the weighted percentage of children with imputed poverty income
ratios was 16%.
For 1997-2018, race was characterized using the race variable for the 1997 OMB standards/
RACERPI2. The possible values of this variable are:
III "Multiple imputation of family income and personal earnings in the National Health Interview Survey: Methods
and Examples." http://www.cdc.gov/nchs/data/nliis/tecdocl8.pdf. August, 2019.
IV "Multiple imputation of family income in 2019 National Health Interview Survey: Methods," September, 2020.
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2019/NHIS2019-imputation-techdoc-
508.pdf
v Revised race standards were issued by the Office of Management and Budget in 1997 and were to be fully
implemented across the federal statistical system by January 2003. Under the new standards, the minimum available
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1. White only
2. Black / African American only
3. American Indian Alaska Native (AIAN) only
4. Asian only
5. Race group not releasable
6. Multiple race
The Native Hawaiian or Other Pacific Islander (NHOPI) race group is not specified in the public
release version due to confidentiality concerns. Respondents with the single race NHOPI have
RACERPI2 = 5 and respondents of multiple races including NHOPI have RACERPI2 = 6.
For 2019, race was characterized using the race variable for the 1997 OMB standards,"
RACEALLPC. The possible values of this variable are:
1. White only
2. Black / African American only
3. Asian only
4. American Indian Alaska Native (AIAN) only
5. AIAN and another race.
6. Other race or other multiple races.
7. Refused.
8. Not ascertained.
9. Don't know.
For 1997-2018, the ORIGINI variable indicates whether or not the ethnicity is Hispanic or
Latino. ORIGIN I = 1 if the respondent is Hispanic or Latino. ORIGIN I = 2 if the respondent is
not Hispanic or Latino. For 2019, the HISPC variable indicates whether or not the ethnicity is
Hispanic or Latino. HISP C = 1 if the respondent is Hispanic or Latino. HISP C = 2 if the
respondent is not Hispanic or Latino.
For 1997-2018, the HISPAN I variable indicates the specific Hispanic origin or ancestry.
00 Multiple Hispanic
01 Puerto Rico
02 Mexican
03 Mexican-American
04 Cuban/Cuban American
05 Dominican (Republic)
race categories include: White, Black, AIAN, Asian, and Native Hawaiian or Other Pacific Islander (NHOPI). A
very important change was that under the new standards, respondents may select more than one race category.
Vl Revised race standards were issued by the Office of Management and Budget in 1997 and were to be fully
implemented across the federal statistical system by January 2003. Under the new standards, the minimum available
race categories include: White, Black, AIAN, Asian, and Native Hawaiian or Other Pacific Islander (NHOPI). A
very important change was that under the new standards, respondents may select more than one race category.
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06 Central or South American
07 Other Latin American, type not specified
08 Other Spanish
09 Hispanic/Latino/Spanish, non-specific type
10 Hispanic/Latino/Spanish, type refused
11 Hispanic/Latino/Spanish, type not ascertained
12 Not Hispanic/Spanish origin
For 2019, the HISDETPC variable indicates the specific Hispanic origin or ancestry:
1. Hispanic/Mexican/Mexican American
2. Hispanic (all other groups)
3. Not Hispanic
4. Refused
5. Not Ascertained
6. Don't know
For 1997-2018, the race/ethnicity was defined based on RACERPI2, ORIGINI, and HISPANI:
Race/ethnicity for 1997-2018:
White Non-Hispanic: RACERPI2 = 1, ORIGIN I = 2
Black or African-American, Non-Hispanic: RACERPI2 = 2, ORIGIN I = 2
Asian Non-Hispanic: RACERPI2 = 4, ORIGIN I = 2
Hispanic: ORIGIN I = 1
o Mexican: ORIGIN I = 1 and HISPAN I = 02, 03
o Puerto Rican: ORIGIN I = 1 and HISPAN I = 01
All Other Races: RACERPI2 = 3, 5 or 6, ORIGIN I = 2
o American Indian, Alaska Native, Non-Hispanic: RACERPI2 = 3, ORIGIN I = 2
The "All Other Races" category includes non-Hispanics and all other races not specified,
together with those individuals who report more than one race.
For 2019, the race/ethnicity was defined based on RACEALLPC, HISPC, and HISPDETP C:
Race/ethnicity for 2019:
White Non-Hispanic: RACEALLP C = 1, HISP C = 2
Black or African-American, Non-Hispanic: RACEALLP C = 2, HISP C = 2
Asian Non-Hispanic: RACEALLP C = 3, HISP C = 2
Hispanic: HISP C = 1
o Mexican: HISDETP C = 1
o Puerto Rican: Not available
All Other Races: RACEALLP C = 4, 5, 6, 7, 8, or 9, HISP C = 2
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o American Indian, Alaska Native, Non-Hispanic: RACEALLPC = 4, HISPC =
2
The "All Other Races" category includes non-Hispanics and all other races not specified,
together with those individuals who report more than one race.
Note that for 2019, the Puerto Rican specific Hispanic origin is not identifiable since all
Hispanics that are not Mexican-American have the same value for the HISDETPC variable.
Some respondents gave missing or incomplete answers to the race/ethnicity questions. For 1997-
2018, in those cases NCHS applied a statistical method called "hot-deck imputation" to estimate
or "impute" the race or ethnicity based on the race/ethnicity responses for other household
members, if available, or otherwise based on information from other households. The NHIS
variables ORIGINI, HISPAN I, and RACERPI2 use imputed responses if the original answer
was missing or incomplete. Among the sampled children ages 0 to 17 years for2015-2016, the
weighted percentage of children with an imputed race or ethnicity was 9%. Among the sampled
Hispanic (defined by ORIGIN I) children ages 0 to 17 years for the years 2015 to -2016, the
weighted percentage of children with an imputed specific Hispanic origin was 3%. To protect
subject confidentiality, the public release version of NHIS for 2017-2018 did not include
information of which subjects had their race, ethnicity, or specific Hispanic origin imputed.
For 2019 the publicly available documentation does not provide information about whether race
or ethnicity was imputed in the public release version.
New for the 2019 survey, possible responses for the age and sex questions included Refused, Not
Ascertained, and Don't Know. For age, none of the sampled children had those responses. For
sex, there was one sample child with a refusal response and three sample children with a don't
know response. Rather than excluding the data for these four children, we included them in the
tabulations for the All gender group and in the gender subgroup Unknown, but we treated their
sex as a missing value in the statistical analyses. This did not impact the analysis for
neurodevelopmental disorders because all four of those children were ages 4 years or under.
Calculation of Indicator
Indicator H6 is the percentage of children reported to have attention deficit/hyperactivity
disorder. Indicator H7 is the percentage of children reported to have a learning disability.
Indicator H8 is the percentage of children reported to have autism. Indicator H9 is the percentage
of children reported to have intellectual disability (mental retardation). For each indicator, the
corresponding table H6a, H7a, H8a, and H9a gives the percentage of children reported to have
the given neurodevelopmental disorder for 2016-2019, stratified both by age and sex. For each
indicator, the corresponding table H6b, H7b, H8b, and H9b gives the percentage of children
reported to have the given neurodevelopmental disorder for 2016 -2019, stratified both by
race/ethnicity and family income.
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To simply demonstrate the calculations, we will describe the calculations for indicator H6 for
2010, using the NHIS 2010 responses to the question: "Has a doctor or health professional ever
told you that had Attention Deficit/Hyperactivity Disorder (ADHD) or Attention
Deficit Disorder (ADD)?" We shall refer to this question as the ADHD question. The
calculations for the other indicators and supplementary tables use exactly the same method,
except for the stratification by family income, which uses the five sets of imputed income values
as demonstrated below. We have rounded all the numbers to make the calculations easier.
We begin with all the non-missing responses to the ADHD question in the NHIS 2010 survey for
children ages 5 to 17 years. Assume for the sake of simplicity that Yes or No responses were
available for every sampled child. Each sampled child has an associated survey weight that
estimates the total number of U.S. children in 2010 represented by that sampled child. For
example, the first response for a child aged 5 to 17 years was No with a survey weight of 3,000,
and so represents 3,000 children ages 5 to 17 years. A second child aged 5 to 17 years responded
No with a survey weight of 9,000, and so represents 9,000 children ages 5 to 17 years. A third
child aged 5 to 17 years responded Yes with a survey weight of 16,000, and so represents 16,000
children ages 5 to 17 years. The total of the survey weights for the sampled children equals 50
million, the total U.S. population of children ages 5 to 17 years for 2010.
To calculate the proportion of children ages 5 to 17 years with ADHD/ADD, we can use the
survey weights to expand the data to the 2010 U.S. population of 50 million children ages 5 to 17
years. We have 3,000 No responses from the first child, 9,000 No responses from the second
child, 16,000 Yes responses from the third child, and so on. Of these 50 million responses, a total
of 5 million responses are Yes and the remaining 45 million are No. Thus 5 million of the 50
million children have ADHD/ADD, giving a proportion of about 10%.
In reality, the calculations need to take into account that Yes or No responses were not reported
for every respondent, and to use exact rather than rounded numbers. There were non-missing
responses for 7,980 of the 7,995 sampled children ages 5 to 17 years. (Don't know responses or
refusals to answer are treated as missing). The survey weights for all 7,995 sampled children add
up to 53.2 million, the total U.S. population of children ages 5 to 17 years. The survey weights
for the 7,980 sampled children with non-missing responses add up to 53.1 million. Thus the
available data represent 53.1 million children, which is more than 99 %, but not all, of the 2010
U.S. population of children ages 5 to 17 years. The survey weights for the Yes responses add up
to 5.0 million, which is 9.5 % of the population with responses (5.0 million/53.1 million = 9.5
%). Thus we divide the sum of the weights for participants with Yes responses by the sum of the
weights for participants with non-missing responses. These calculations assume that the sampled
children with non-missing responses are representative of the children with missing responses.
For calculation of prevalence by income group in Tables H6b, H7b, H8b, and H9b, we use the
five sets of imputed income values, which each give different results. For example, suppose we
wish to estimate the proportion of White non-Hispanic children below the poverty level with
ADHD/ADD in 2009-2012. Using the above calculation method applied for White non-Hispanic
children below the poverty level for the combined set of years 2009-2012, the proportions for the
five sets of imputed values are:18.8%, 19.1%, 19.0%, 19.0% and 19.1%. The estimated
proportion of White non-Hispanic children below the poverty level with ADHD/ADD in 2009-
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2012 is given by the average of the five estimates, (18.8 + 19.1 + 19.0 + 19.0 + 19.1) /5 = 19.0
%.
Equations
The following equations give the mathematical calculations for the example of White non-
Hispanic children below the poverty level using the ADHD question. Let w(i) denote the survey
weight for the i'th surveyed White non-Hispanic child of ages 5 to 17 years. Exclude any
surveyed children with a response other than Yes or No. For the ADHD question, let the
response indicator c(i) = 1 if the i'th surveyed White non-Hispanic child had a Yes response and
let c(i) = 0 if the i'th surveyed White non-Hispanic child had a No response. Let the income
indicator d(i, j) = 1 if the i'th surveyed White non-Hispanic child was below the poverty level
according to the j 'th set of imputed values and let d(i, j) = 0 if the i'th surveyed White non-
Hispanic child was not below the poverty level according to the j 'th set of imputed values.
1. Fix j = 1, 2, 3, 4 or 5. Sum (over i) all the survey weights multiplied by the income indicators
to get the total weight W(j) for set j:
W(j) = ฃw(i) x d(i, j)
2. Fix j = 1, 2, 3, 4 or 5. Sum (over i) all the survey weights multiplied by the response indicators
and multiplied by the income indicators to get the total weight D(j) for set j for White non-
Hispanic children below the poverty level with a Yes response:
DG) = Zw(i) x c(i) x d(i, j)
3. Divide D(j) by W(j) to get the percentage of children with ADHD/ADD in set j:
Percentage (j) = (D(j) / W(j)) x 100 %
4. Average the percentages across the 5 sets to get the estimated percentage of children with
ADHD/ADD:
Percentage = [Percentage (1) + Percentage (2) + Percentage (3)
+ Percentage (4) + Percentage (5)] / 5
If the demographic group of interest includes all incomes, then the percentages will be equal for
all five sets of imputed values, so the calculation in steps 1 to 3 need only be done for j =1, and
step 4 is not required.
Relative Standard Error
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The uncertainties of the percentages were calculated using SUDAANฎ (Research Triangle
Institute, Research Triangle Park, NC 27709) statistical survey software. SUDAAN was used to
calculate the estimated percentages and the standard errors of the estimated percentages. The
standard error is the estimated standard deviation of the percentage, and this depends upon the
survey design. The standard error calculation also incorporates the extra uncertainty due to the
multiple imputations of the income variables (based on the variation between the estimated
percentages from each of the five sets of imputations). For this purpose, the public release
version of NHIS includes the variables STRATUM and PSU, which are the Masked Variance
Unit pseudo-stratum and pseudo-primary sampling unit (pseudo-PSU). For approximate variance
estimation, the survey design can be approximated as being a stratified random sample with
replacement of the pseudo-PSUs from each pseudo-stratum; the true stratum and PSU variables
are not provided in the public release version to protect confidentiality.
The sample design was changed in 2006 and changed again in 2016. New strata were defined
and PSUs were selected from these new strata. For example, pseudo-stratum 100 for 1997-2005
is unrelated to pseudo-stratum 100 for 2006-2015 and pseudo-stratum 100 for 2016. To properly
treat the 2006-2015 data as independent from the 1997-2005 data, 1,000 was added to each of the
2006-2015 pseudo-stratum numbers for these statistical analyses. To properly treat the 2016-
2019 data as independent from the 1997-2015 data, 2,000 was added to each of the 2016-2019
pseudo-stratum numbers for these statistical analyses.
The relative standard error is the standard error divided by the estimated percentage:
Relative Standard Error (%) = [Standard Error (Percentage) / Percentage] x 100%
Percentages with a relative standard error less than 30% were treated as being reliable and were
tabulated. Percentages with a relative standard error greater than or equal to 30% but less than
40% were treated as being unstable; these values were tabulated but were flagged to be
interpreted with caution. Percentages with a relative standard error greater than or equal to 40%,
or without an estimated relative standard error, were treated as being unreliable; these values
were not tabulated and were flagged as having a large uncertainty.
Questions and Comments
Questions regarding these methods, and suggestions to improve the description of the methods,
are welcome. Please use the "Contact Us" link at the bottom of any page in the America's
Children and the Environment website.
Statistical Comparisons
Statistical analyses of the percentages of children with a positive response to the question of
interest were used to determine whether the differences between percentages for different
demographic groups were statistically significant. Using a logistic regression model, the
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logarithm of the odds that a given child has a positive response is assumed to be the sum of
explanatory terms for the child's age group, sex, income group, and/or race/ethnicity. The odds
of a positive response is the probability of a positive response divided by the probability of a
negative response. Thus if two demographic groups have similar (or equal) probabilities of a
positive response, then they will also have similar (or equal) values for the logarithm of the odds.
Using this model, the difference in the percentage between different demographic groups is
statistically significant if the difference between the corresponding sums of explanatory terms is
statistically significantly different from zero. The uncertainties of the regression coefficients
were calculated using SUDAANฎ (Research Triangle Institute, Research Triangle Park, NC
27709) statistical survey software to account for the survey weighting and design. A p-value at or
below 0.05 implies that the difference is statistically significant at the 5% significance level. No
adjustment is made for multiple comparisons.
For these statistical analyses we used two income groups: below poverty level, and at or above
poverty level. The small number of children with unknown (and unimputed) incomes were
included in the at or above poverty level group. For the main analyses we also used five
race/ethnicity groups: White non-Hispanic, Black non-Hispanic, Asian non-Hispanic, Hispanic,
Other. In addition, for specific comparisons between the Mexican and Puerto Rican subgroups,
we applied a similar statistical analysis using three ethnicity groups: Mexican, Puerto Rican,
Other Hispanic or Non-Hispanic. For those statistical analyses comparing the Mexican and
Puerto Rican subgroups we excluded the 2019 data because the Puerto Rican subgroup was not
identifiable for that year and therefore, only including the Mexican subgroup could bias the
comparison. We also used two age groups: 5-10 and 11-17.
For each type of comparison, we present unadjusted and adjusted analyses. The unadjusted
analyses directly compare a percentage between different demographic groups. The adjusted
analyses add other demographic explanatory variables to the statistical model and use the
statistical model to account for the possible confounding effects of these other demographic
variables. For example, the unadjusted race/ethnicity comparisons use and compare the
percentages between different race/ethnicity pairs. The adjusted analyses add age, sex, and
income terms to the statistical model and compare the percentages between different
race/ethnicity pairs after accounting for the effects of the other demographic variables. For
example, if White non-Hispanics tend to have higher family incomes than Black non-Hispanics,
and if the prevalence of a neurodevelopmental disorder strongly depends on family income only,
then the unadjusted differences between these two race/ethnicity groups would be significant but
the adjusted difference (taking into account income) would not be significant.
Comparisons of the prevalence of each neurodevelopmental disorder in children ages 5 to 17
years between pairs of race/ethnicity groups and between the two income groups are shown in
Tables 1 and 2, respectively. For the unadjusted "All incomes" comparisons, the only
explanatory variables are terms for each race/ethnicity group. For these unadjusted comparisons,
the statistical tests compare the percentage for each pair of race/ethnicity groups. For the
adjusted "All incomes (adjusted for age, sex, income)" comparisons, the explanatory variables
are terms for each race/ethnicity group together with terms for each age, sex, and income group.
For these adjusted comparisons, the statistical test compares the pair of race/ethnicity groups
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after accounting for any differences in the age, sex, and income distributions between the
race/ethnicity groups.
In Table 1, for the unadjusted "Below Poverty Level" and "At or Above Poverty Level"
comparisons, the only explanatory variables are terms for each of the 10 race/ethni city/income
combinations (combinations of five race/ethni city groups and two income groups). For example,
in row 1, the p-value for "Below Poverty Level" compares White non-Hispanics below the
poverty level with Black non-Hispanics below the poverty level. The same set of explanatory
variables are used in Table 2 for the unadjusted comparisons between one race/ethnicity group
below the poverty level and the same race/ethni city group at or above the poverty level. The
corresponding adjusted analyses include extra explanatory variables for age and sex, so that
race/ethnicity/income groups are compared after accounting for any differences due to age or
sex. Also in Table 2, the unadjusted p-value for the population "All" compares the percentages
for children ages 5 to 17 years below poverty level with those at or above poverty level, using
the explanatory variables for the two income groups. The adjusted p-value includes adjustment
terms for age, sex, and race/ethni city in the model.
Additional comparisons are shown in Table 3. The Against = "age" unadjusted p-value compares
the percentages for different age groups. The adjusted p-value includes adjustment terms for
income, sex, and race/ethnicity in the model. The Against = "sex" unadjusted p-value compares
the percentages for boys and girls. The adjusted p-value includes adjustment terms for age,
income, and race/ethni city in the model. The Against = "income" unadjusted p-value compares
the percentages for those below poverty level with those at or above poverty level. The adjusted
p-value includes adjustment terms for age, sex, and race/ethni city in the model. The Against =
"year" p-value examines whether the linear trend in the percentages is statistically significant;
the adjusted model for trend adjusts for demographic changes in the populations from year to
year by including terms for age, sex, income, and race/ethni city. The Subset column specifies the
demographic group of interest. For the Against = "age," "sex," and "income" comparisons, the
comparisons are for all children and so no Subset is defined. For the Against= "year" trend
analyses, results are given for the overall trend (Subset = missing) and for the trends in each sex
group, so that, for example, the Subset = "Boys" examines whether there is a statistically
significant trend for boys ages 5 to 17 years.
For more details on these statistical analyses, see the memorandum by Cohen (2010).vu
Table 1. Statistical significance tests comparing the percentages of children ages 5 to 17 years
with neurodevelopmental disorders, between pairs of race/ethni city groups, for 2016-2019.
vu Cohen, J. 2010. Selected statistical methods for testing for trends and comparing years or demographic groups in
ACE NHIS andNHANES indicators. Memorandum submitted to Dan Axelrad, EPA, 21 March, 2010.
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P-VALUES
Variable
First
race/ethnicity
group
Second
race/ethnicity
group
All
incomes
All
incomes
(adjusted
for age,
sex,
income)
Below
Poverty
Level3
Below
Poverty
Level
(adjusted
for age,
sex)a
At or
Above
Poverty
Level3
At or Above
Poverty Level
(adjusted for
age, sex)a
ADHD/ADD
White non-
Hispanic
Black non-
Hispanic
0.436
0.686
NA
NA
NA
NA
ADHD/ADD
White non-
Hispanic
Asian non-
Hispanic
<0.001
<0.001
NA
NA
NA
NA
ADHD/ADD
White non-
Hispanic
Hispanic
<0.001
<0.001
NA
NA
NA
NA
ADHD/ADD
White non-
Hispanic
Other
0.475
0.822
NA
NA
NA
NA
ADHD/ADD
Black non-
Hispanic
Asian non-
Hispanic
<0.001
<0.001
NA
NA
NA
NA
ADHD/ADD
Black non-
Hispanic
Hispanic
<0.001
<0.001
NA
NA
NA
NA
ADHD/ADD
Black non-
Hispanic
Other
0.901
0.649
NA
NA
NA
NA
ADHD/ADD
Asian non-
Hispanic
Hispanic
<0.001
<0.001
NA
NA
NA
NA
ADHD/ADD
Asian non-
Hispanic
Other
<0.001
<0.001
NA
NA
NA
NA
ADHD/ADD
Hispanic
Other
<0.001
<0.001
NA
NA
NA
NA
ADHD/ADD
Mexican
Puerto Rican
0.001
0.001
0.005
0.010
0.030
0.020
Learn
disability
White non-
Hispanic
Black non-
Hispanic
0.002
0.233
0.080
0.086
0.061
0.047
Learn
disability
White non-
Hispanic
Asian non-
Hispanic
<0.001
<0.001
0.001
0.001
<0.001
<0.001
Learn
disability
White non-
Hispanic
Hispanic
0.137
0.516
0.001
0.001
0.249
0.189
Learn
disability
White non-
Hispanic
Other
0.805
0.542
0.762
0.755
0.194
0.231
Learn
disability
Black non-
Hispanic
Asian non-
Hispanic
<0.001
<0.001
0.002
0.002
<0.001
<0.001
Learn
disability
Black non-
Hispanic
Hispanic
0.071
0.100
0.206
0.215
0.275
0.268
Learn
disability
Black non-
Hispanic
Other
0.061
0.162
0.326
0.328
0.019
0.019
Learn
disability
Asian non-
Hispanic
Hispanic
<0.001
<0.001
0.004
0.004
<0.001
<0.001
Learn
disability
Asian non-
Hispanic
Other
<0.001
<0.001
0.001
0.001
0.001
0.001
Learn
disability
Hispanic
Other
0.558
0.850
0.041
0.040
0.073
0.075
Learn
disability
Mexican
Puerto Rican
<0.001
<0.001
0.101
0.144
0.001
<0.001
Autism
White non-
Hispanic
Black non-
Hispanic
0.730
0.660
0.054
0.054
0.843
0.784
Autism
White non-
Hispanic
Asian non-
Hispanic
0.032
0.031
0.072
0.065
0.121
0.125
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P-VALUES
Variable
First
race/ethnicity
group
Second
race/ethnicity
group
All
incomes
All
incomes
(adjusted
for age,
sex,
income)
Below
Poverty
Level3
Below
Poverty
Level
(adjusted
for age,
sex)a
At or
Above
Poverty
Level3
At or Above
Poverty Level
(adjusted for
age, sex)a
Autism
White non-
Hispanic
Hispanic
0.019
0.010
0.006
0.006
0.210
0.211
Autism
White non-
Hispanic
Other
0.935
0.833
0.891
0.853
0.650
0.639
Autism
Black non-
Hispanic
Asian non-
Hispanic
0.122
0.139
0.261
0.246
0.198
0.183
Autism
Black non-
Hispanic
Hispanic
0.362
0.356
0.619
0.643
0.445
0.407
Autism
Black non-
Hispanic
Other
0.855
0.868
0.126
0.139
0.631
0.589
Autism
Asian non-
Hispanic
Hispanic
0.292
0.326
0.344
0.320
0.358
0.366
Autism
Asian non-
Hispanic
Other
0.091
0.103
0.089
0.083
0.444
0.455
Autism
Hispanic
Other
0.281
0.277
0.032
0.038
0.926
0.937
Autism
Mexican
Puerto Rican
0.011
0.010
0.002
0.002
0.280
0.236
Int disability
White non-
Hispanic
Black non-
Hispanic
0.277
0.728
0.138
0.145
0.357
0.333
Int disability
White non-
Hispanic
Asian non-
Hispanic
0.052
0.049
0.177
0.180
0.121
0.126
Int disability
White non-
Hispanic
Hispanic
0.118
0.498
0.158
0.167
0.092
0.079
Int disability
White non-
Hispanic
Other
0.507
0.750
0.635
0.624
0.712
0.671
Int disability
Black non-
Hispanic
Asian non-
Hispanic
0.012
0.031
0.373
0.375
0.055
0.053
Int disability
Black non-
Hispanic
Hispanic
0.963
0.904
1.000
0.992
0.925
0.927
Int disability
Black non-
Hispanic
Other
0.840
0.996
0.586
0.603
0.670
0.674
Int disability
Asian non-
Hispanic
Hispanic
0.011
0.022
0.378
0.378
0.025
0.024
Int disability
Asian non-
Hispanic
Other
0.038
0.053
0.279
0.284
0.122
0.118
Int disability
Hispanic
Other
0.797
0.924
0.596
0.618
0.552
0.560
Int disability
Mexican
Puerto Rican
0.250
0.263
0.580
0.638
0.306
0.276
aFor ADHD/ADD the p-values for incomes below or at or above the poverty level are not available (NA) because the statistical model failed to
converge. This was due to the fact that there were no children in the Asian, non-Hispanic, below poverty group for four of the five imputation
sets.
Table 2. Statistical significance tests comparing the percentages of children ages 5 to 17 years
with neurodevelopmental disorders, between those below poverty level and those at or above
poverty level, for 2016-2019.
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P-VALUES
Variable
Population
Unadjusted3
Adjusted *a
ADHD/ADD
All
<0.001
<0.001
ADD/ADHD
White non-Hispanic
NA
NA
ADD/ADHD
Black non-Hispanic
NA
NA
ADD/ADHD
Asian non-Hispanic
NA
NA
ADD/ADHD
Hispanic
NA
NA
ADD/ADHD
Other
NA
NA
ADD/ADHD
Mexican
0.716
0.619
ADD/ADHD
Puerto Rican
0.125
0.200
Learn
disability
All
<0.001
<0.001
Learn
disability
White non-Hispanic
<0.001
<0.001
Learn
disability
Black non-Hispanic
0.007
0.006
Learn
disability
Asian non-Hispanic
0.250
0.256
Learn
disability
Hispanic
<0.001
<0.001
Learn
disability
Other
<0.001
<0.001
Learn
disability
Mexican
0.030
0.025
Learn
disability
Puerto Rican
0.922
0.957
Autism
All
0.495
0.328
Autism
White non-Hispanic
0.004
0.005
Autism
Black non-Hispanic
0.630
0.591
Autism
Asian non-Hispanic
0.386
0.362
Autism
Hispanic
0.516
0.528
Autism
Other
0.115
0.126
Autism
Mexican
0.023
0.028
Autism
Puerto Rican
0.336
0.385
Int disability
All
<0.001
<0.001
Int disability
White non-Hispanic
<0.001
<0.001
Int disability
Black non-Hispanic
0.353
0.339
Int disability
Asian non-Hispanic
0.745
0.730
Int disability
Hispanic
0.310
0.290
Int disability
Other
0.124
0.125
Int disability
Mexican
0.996
0.972
Int disability
Puerto Rican
0.979
0.900
* Comparison for "All" is adjusted for age, sex, and race/ethnicity; comparisons for race/ethnicity categories are adjusted for age and sex
aFor ADHD/ADD the p-values for five of the race/ethnicity groups are not available (NA) because the statistical model failed to converge. This
was due to the fact that there were no children in the Asian, non-Hispanic, below poverty group for four of the five imputation sets.
Table 3. Other statistical significance tests comparing the percentages of children ages 5 to 17
years with neurodevelopmental disorders, for 2016-2019 (trends for 1997-2019 and autism
trends for 1997-2013 and 2013-2019).
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P-VALUES
Variable
From
To
Against
Subset
Unadjusted
Adjusted*
ADHD/ADD
2016
2019
age
<0.001
<0.001
ADHD/ADD
2016
2019
income
<0.001
<0.001
ADHD/ADD
2016
2019
sex
<0.001
<0.001
ADHD/ADD
1997
2019
year
<0.001
<0.001
ADHD/ADD
1997
2019
year
Boys
<0.001
<0.001
ADHD/ADD
1997
2019
year
Girls
<0.001
<0.001
Learn disability
2016
2019
age
<0.001
<0.001
Learn disability
2016
2019
income
<0.001
<0.001
Learn disability
2016
2019
sex
<0.001
<0.001
Learn disability
1997
2019
year
0.062
0.416
Learn disability
1997
2019
year
Boys
0.019
0.162
Learn disability
1997
2019
year
Girls
0.956
0.622
Autism
2016
2019
age
0.580
0.577
Autism
2016
2019
income
0.495
0.328
Autism
2016
2019
sex
<0.001
<0.001
Autism
1997
2013
year
<0.001
<0.001
Autism
1997
2019
year
<0.001
<0.001
Autism
2013
2019
year
<0.001
<0.001
Autism
1997
2019
year
Boys
<0.001
<0.001
Autism
1997
2019
year
Girls
<0.001
<0.001
Int disability
2016
2019
age
0.004
0.002
Int disability
2016
2019
income
<0.001
<0.001
Int disability
2016
2019
sex
<0.001
<0.001
Int disability
1997
2019
year
<0.001
<0.001
Int disability
1997
2019
year
Boys
<0.001
<0.001
Int disability
1997
2019
year
Girls
0.003
0.004
*For Against = "age," the comparison is between the age groups 5-10 and 11-17, and the p-values are adjusted for sex, race/ethnicity, and
income.
For Against = "sex," the comparison is between boys and girls, and the p-values are adjusted for age, race/ethnicity, and income.
For Against = "income," the comparison is between those below the poverty level and those at or above the poverty level and the p-values are
adjusted for age, sex, and race/ethnicity.
For Against = "year," where Subset is missing, the comparison is the trend over different years, and the p-values are adjusted for age, sex,
race/ethnicity, and income.
For Against = "year," where Subset is not missing, the comparison is the trend over different years, and the p-values are adjusted for age,
race/ethnicity, and income.
Data Files
The following files are needed to calculate these indicators. All these files together with the
survey documentation and SASฎ programs for reading in the data are available at the NHIS
website: http://www.cdc.gov/nchs/nhis.htm.
NHIS 1997-2018: Sample Child file samchild.dat, Person file personsx.dat, Family file
familyxx.dat, Imputed Income files: incmimpl.dat, incmimp2.dat, incmimp3.dat,
incmimp4.dat, and incmimp5.dat. The Sample child file is an ASCII file containing
interview data for children ages 0 to 17 years. For children ages 0 to 17 years, the
responses were obtained from a knowledgeable adult family member residing in the
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Health: Neurodevelopmental Disorders
household. Demographic data is obtained from the Person and Family files. The
demographic variables needed for this indicator are the sample child survey weight
(WTFASC), age (AGEP), sex (SEX), the pseudo-stratum (STRATUM for 1997-2005,
STRATP for 2006-2015, PSTRAT for 2016-2018), the pseudo-PSU (PSU for 1997-
2005, PSU_P for 2006-2015, PPSU for 2016-2018), the race (RACE for 1997-1998,
RACER P for 1999, RACERP I for 2000-2002, RACERPI2 for 2003-2018, using the
1997 OMB definitions), the Hispanic origin (ORIGIN for 1997-1999, ORIGIN I for
2000-2017), and the detailed Hispanic origin (HISPAN P for 1997-1998, HISPANCR
for 1999, HISPAN I for 2000-2018). The pseudo-stratum and pseudo-PSU variables
provide an approximation to the exact sample design variables, and were created by CDC
by combining stratum information in a manner to protect the confidentiality of the
publicly released data. From 1997-2008 imputed income files we need the imputed
poverty income ratio (RAT CATI) which gives the ratio in categories. From 2009
imputed income files we need the imputed poverty income ratio (POVRATI2) which
gives the numerical value of the poverty income ratio in hundredths. For 2010-2018
imputed income files we need the imputed poverty income ratio (POVRATI3) which
gives the numerical value of the poverty income ratio in thousandths. The files are sorted
and merged using the identifiers HHX, FMX, and FPX (PX for 1997-2000), For 1997-
2010, the questionnaire variables needed for these analyses are the responses to the
following questions: "Has a doctor or health professional ever told you that had Attention Deficit Hyperactivity Disorder (ADHD) or Attention Deficit
Disorder (ADD)?" "Has a doctor or health professional ever told you that
had Autism?" "Has a doctor or health professional ever told you that had
Mental Retardation?" and "Has a representative from a school or a health professional
ever told you that had a learning disability?" For 2011-2013, the
questionnaire variables needed for these analyses are the responses to the following
questions: "Has a doctor or health professional ever told you that had
Attention Deficit Hyperactivity Disorder (ADHD) or Attention Deficit Disorder (ADD)?"
"Has a doctor or health professional ever told you that had
Autism/Autism Spectrum Disorder?" "Has a doctor or health professional ever told you
that had an intellectual disability, also known as mental retardation?" and
"Has a representative from a school or a health professional ever told you that had a learning disability?" For 2014-2018, the questionnaire variables needed for
these analyses are the responses to the following questions: "Has a doctor or health
professional ever told you that had Attention Deficit Hyperactivity
Disorder (ADHD) or Attention Deficit Disorder (ADD)?" "Has a doctor or health
professional ever told you that had Autism, Asperger's disorder,
pervasive developmental disorder, or autism spectrum disorder?" "Has a doctor or health
professional ever told you that had an intellectual disability, also known
as mental retardation?" and "Has a representative from a school or a health professional
ever told you that had a learning disability?"
NHIS 2019: Sample Child file childl9.dat, Imputed Income file: childincl9.dat. The
Sample child file is an ASCII file containing interview and demographic data for children
ages 0 to 17 years. For children ages 0 to 17 years, the responses were obtained from a
knowledgeable adult family member residing in the household. The demographic
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Health: Neurodevelopmental Disorders
variables needed for this indicator are the sample child survey weight (WTFAC), age
(AGEPC), sex (SEXC), the pseudo-stratum (PSTRAT), the pseudo-PSU (PPSU), the
race (RACEALLPC, using the 1997 OMB definitions), the Hispanic origin (HISPC),
and the detailed Hispanic origin (HISDETPC). The pseudo-stratum and pseudo-PSU
variables provide an approximation to the exact sample design variables, and were
created by CDC by combining stratum information in a manner to protect the
confidentiality of the publicly released data. From the year imputed income files we need
the imputed poverty income ratio (POVRATTC C) which gives the numerical value of
the poverty income ratio in hundredths. The files are sorted and merged using the
identifier HHX. The questionnaire variables needed for these analyses are the responses
to the following questions: "Has a doctor or health professional ever told you that
had Attention Deficit Hyperactivity Disorder or ADHD or Attention
Deficit Disorder or ADD?" "Has a doctor or health professional ever told you that
had Autism, Asperger's disorder, pervasive developmental disorder, or
autism spectrum disorder?" "Has a doctor or health professional ever told you that
had an intellectual disability, also known as mental retardation?" and
"Has a representative from a school or a health professional ever told you that had a learning disability?"
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