Health: Respiratory Diseases
Methods for Respiratory Diseases
HI. Percentage of children ages 0 to 17 years with asthma, 1997-2019.
H2. Percentage of children ages 0 to 17 years with current asthma, by
race/ethnicity and family income, 2016-2019.
For Indicator H3 Children's emergency room visits and hospitalizations for asthma and other
respiratory causes, children ages 0 to 17 years, 1996-2018. See below.
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 noninstitutionalized civilian population.
These indicators use responses to questions on asthma for children ages 0 to 17 years from the
NHIS 1997-2019 surveys; these questions have changed overtime. Indicator HI gives the
percentages of children ever diagnosed with asthma that also had an asthma attack in the
previous 12 months (1997-2019), and of children that currently have asthma (2001-2019).
Indicator H2 uses responses to questions on asthma for children ages 0 to 17 years from the
NHIS 2016 to 2019 surveys. Indicator H2 gives the percentages of children that currently have
asthma, 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).
Supplementary Tables: Table Hla gives the percentages of children with asthma in the previous
12 months for 1997-2019, by sex. Table Hlb gives the percentages of children with asthma in
the previous 12 months for 1980-1996. Table Hlc gives the percentages of children with current
asthma that had an asthma attack in the past 12 months for 2001 to 2019. Table H2a gives the
percentages of children that currently have asthma for 2016-2019, stratified both by age group
and sex. 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
Survey response data used to Support Indicators HI and H2:
HI. Percentage of children ages 0 to 17 years with asthma, 1997-2019.
H2. Percentage of children ages 0 to 17 years with current asthma, by race/ethnicity and family income, 2016-2019.
Data
Asthma prevalence in c
hildren ases 0 to 17 vears
Years
l'W7
IWS
I '¦)'¦)'¦)
2000
2oo I
2oo2
2003
2oo4
Children
14,290
13,645
12,910
13,376
13,579
12,524
12,249
12,424
Asthma attack
non-missing
responses (%)
14,242
(99.7%)
13,608
(99.8%)
12,685
(99.8%)
13,350
(99.8%)
13,556
(99.9%)
12,492
(99.8%)
12,224
(99.8%)
12,395
(99.8%)
Asthma attack
missing
responses (%)
48 (0.3%)
37 (0.2%)
25 0.2%)
26 (0.2%)
23 (0.1%)
32 (0.2%)
25 (0.2%)
29 (0.2%)
Current asthma
non-missing
responses (%)*
13,534
(99.7%)
12,475
(99.6%)
12,207
(99.7%)
12,386
(99.7%)
Current asthma
missing
responses (%)
45 (0.3%)
49 (0.4%)
42 (0.3%)
38 (0.3%)
Years
2005
2OO0
2oo7
Zoos
2009
201 o
20| I
2012
Children
12,523
9.837
9,417
8,815
11,156
11,277
12,850
13,275
Asthma attack
non-missing
responses (%)
12,500
(99.8%)
9,810
(99.8%)
9,401
(99.9%)
8,798
(99.8%)
11,141
(99.9%)
11,256
(99.8%)
12,842
(99.9%)
13,261
(99.9%)
Asthma attack
missing
responses (%)
23 (0.2%)
27 (0.2%)
16
(0.1%)
17 (0.2%)
15 (0.1%)
21 (0.2%)
8(0.1%)
14(0.1%)
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Current asthma
non-missing
responses (%)
12,496
(99.8%)
9,797
(99.6%)
9,394
(99.8%)
8,793
(99.7%)
11,129
99.8%)
11,253
(99.8%)
12,831
(99.8%)
13,248
(99.8%)
Current asthma
missing
responses (%)
27 (0.2%)
40 (0.4%)
23
(0.2%)
22 (0.3%)
27 (0.2%)
24 (0.2%)
19 (0.2%)
27 (0.2%)
Yours
2n i;,
Zn|4
2d 15
2d 10
2i)l 7
2<) 1S
2<) 1
Children
12,860
13,380
12,291
11,107
8,845
8,269
9,193
Asthma attack
non-missing
responses (%)
12,848
(99.9%)
13,365
(99.9%)
12,279
(99.9%)
11,095
(99.9%)
8,830
(99.9%)
8,257
(99.9%)
9,176
(99.8%)
Asthma attack
missing
responses (%)
12(0.1%)
15 (0.1%)
12
(0.1%)
12(0.1%)
15 (0.1%)
12(0.1%)
17 (0.2%)
Current asthma
non-missing
responses (%)
12,844
(99.9%)
13,359
(99.8%)
12,269
(99.8%)
11,087
(99.8%)
8,823
(99.8%)
8,257
(99.8%)
9,171
(99.8%)
Current asthma
missing
responses (%)
16(0.1%)
21 (0.2%)
22
(0.2%)
20 (0.2%)
22 (0.2%)
12 (0.2%)
22 (0.2%)
* This survey question was first asked in 2001.
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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 non-institutionalized civilian population.
These indicators use responses from a knowledgeable adult family member residing in the
household to asthma prevalence questions for children ages 0 to 17 years for the surveys for
1997-2019. The NHIS data were obtained from the NHIS website:
http://www.cdc.gov/nchs/nhis.htm.
For 1997-2019, the first asthma question was: "Has a doctor or other health professional ever
told you that had asthma?" (CASHMEV for 1997-2018, ASEVC for 2019). If
the response was Yes, then the second question "During the past 12 months, has
had an episode of asthma or an asthma attack?" was asked (CASHYR for 1997-2018,
ASAT12M C for 2019). For 2001-2019, Yes responders to the CASHMEV or ASEV_C
question were also asked "Does still have asthma?" (CASSTILL for 1997-2018,
ASTILLC for 2019). For all three questions, responses other than Yes or No were treated as
missing data. For the CASHYR and CASSTILL questions, responders who said No to the
CASHMEV question were, for these analyses, treated as also responding No to the CASHYR
and CASSTILL questions, even though they were not asked those questions. For the
ASAT12M C and ASTILL C questions, responders who said No to the ASEV C question were,
for these analyses, treated as also responding No to the ASAT12M C and ASTILL C questions,
even though they were not asked those questions. For 1980-1996, the asthma survey question
was "Did have asthma in the past 12 months?"
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 include 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.
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 2016- 2019 pseudo-stratum numbers for these statistical analyses.1
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 2018 was chosen to make the stratum
numbers for 2016 to 2018 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
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Race/Ethnicity and Family Income
For Indicator H2, 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 the 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
categories above or below $50,000 and were then asked appropriate additional questions to
refine the income range. For 2007-2010, 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 2011-2018, 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
Data Release Survey Description," CDC, June, 2019,
http://www.cdc.gov/nchs/nhis/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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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 was 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
0 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:
• 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
III "Multiple imputation of family income and personal earnings in the National Health Interview Survey: Methods
and Examples," htto://www.cdc.gov/nchs/data/nhis/tecdoc 18.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
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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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)
• 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 HISDETP C variable indicates the specific Hispanic origin or ancestry:
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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• 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 to 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: HISDETPC = 1
o Puerto Rican: Not available
• All Other Races: RACEALLP C = 4, 5, 6, 7, 8, or 9, HISP C = 2
o American Indian, Alaska Native, Non-Hispanic: RACEALLP C = 4, HISP C =
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 HISDETP C variable.
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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, HISPANI, and RACERPI2 use imputed responses if the original answer
was missing or incomplete. Among the sampled children ages 0 tol7 years for 2015-2016, 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 2015-2016, 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.
Calculation of Indicator
Indicator HI is the percentage of children ages 0 to 17 years for whom the response was Yes to
the asthma attack in the last 12 months or current asthma questions, as detailed in the section
"National Health Interview Survey (NHIS)." Indicator H2 is the percentage of children ages 0 to
17 years for whom the response was Yes to the current asthma question, stratified by
race/ethnicity and family income. Table H2a is the percentage of children ages 0 to 17 years for
whom the response was Yes to the current asthma question, stratified by age and sex.
To simply demonstrate the calculations, we will describe the calculations for indicator H2, and
will use the NHIS 2007-2010 responses to the CASSTILL question asking if the child still had
asthma for White non-Hispanic children of all incomes. This question was only asked if the
response was Yes to the CASHMEV question about whether the child was ever diagnosed with
asthma. As described above, the question of interest is whether the child was ever diagnosed
with asthma and still had asthma. We shall call this combined question the current asthma
question. This question is answered Yes if CASHMEV = 1 (Yes) and CASSTILL = 1 (Yes).
This question is answered No if either CASHMEV = 1 (Yes) and CASSTILL = 2 (No), or if
CASHMEV = 2 (No). Otherwise, the response is missing. We have rounded all the numbers to
make the calculations easier:
We begin with all the non-missing responses to the current asthma question in the NHIS 2007-
2010 surveys for White non-Hispanic children ages 0 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. White non-Hispanic
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children in 2007-2010 represented by that sampled child. For example, the first response for a
White non-Hispanic child aged 0 to 17 years was No with a survey weight of 10,000, and so
represents 10,000 White non-Hispanic children ages 0 to 17 years. A second White non-Hispanic
child aged 0 to 17 years responded No with a survey weight of 7,000, and so represents 7,000
White non-Hispanic children ages 0 to 17 years o. A third White non-Hispanic child aged 0 to 17
years responded Yes with a survey weight of 21,000, and so represents 21,000 White non-
Hispanic children ages 0 to 17 years. The total of the survey weights for the sampled White non-
Hispanic children equals 160 million, the total U.S. population of White non-Hispanic children
ages 0 to 17 years summed over all four years; thus, the annual population is about 40 million.
To calculate the proportion of White non-Hispanic children with current asthma, we can use the
survey weights to expand the data to the total four-year U.S. White non-Hispanic population of
160 million White non-Hispanic children ages 0 to 17 years. We have 10,000 No responses from
the first child, 7,000 No responses from the second child, 21,000 Yes responses from the third
child, and so on. Of these 160 million responses, a total of 13 million responses are Yes and the
remaining 147 million are No. Thus 13 million of the 160 million White non-Hispanic children
have current asthma, giving a proportion of about 8%.
In reality, the calculations need to consider 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
17,692 of the 17,738 sampled White non-Hispanic children ages 0 to 17 years over the four-year
period. ("Don't know" responses or refusals to answer are treated as missing). The survey
weights for all 17,738 sampled children add up to 164.8 million, the total four-year U.S.
population of White non-Hispanic children ages 0 to 17 years. The survey weights for the 17,692
sampled White non-Hispanic children with non-missing responses add up to 164.4 million. Thus,
the available data represent 164.4 million children, which is more than 99%, but not all, of the
four-year U.S. population of White non-Hispanic children ages 0 to 17 years. The survey weights
for the Yes responses add up to 13.5 million, which is 8.2% of the population with responses
(13.5 million/164.4 million = 8.2%). 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, we use the five sets of imputed income values,
which each give different results. Suppose we wish to estimate the proportion of White non-
Hispanic children below the poverty level with current asthma. Using the above calculation
method applied for White non-Hispanic children below the poverty level, the proportions for the
five sets of imputed values are: 10.6%, 10.7%, 10.5%, 10.6%, and 10.7%. The estimated
proportion of White non-Hispanic children below the poverty level with current asthma is given
by the average of the five estimates, (10.6 + 10.7 + 10.5 + 10.6 + 10.7) /5 = 10.6%.
Equations
The following equations give the mathematical calculations for the example of White non-
Hispanic children below the poverty level. Let w(i) denote the survey weight for the i'th
surveyed White non-Hispanic child of ages 0 to 17 years. Exclude any surveyed children with a
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response other than Yes or No. For the current asthma 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) = £ w(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:
D(j) = £w(i) x c(i) x d(i, j)
3. Divide D(j) by W(j) to get the percentage of children with asthma 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
current asthma:
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.
Table Hlc gives the percentages of children with current asthma that had an asthma attack in the
past 12 months. For each year, these percentages were calculated by dividing the percentage of
children that both have current asthma and had an asthma attack in the past 12 months by the
percentage of children with current asthma:
Percentage of children with current asthma who had an asthma attack in the past 12
months =
Percentage of children with Yes responses to both the current asthma and asthma attack
questions
/ Percentage of children with Yes responses to the current asthma question
x 100%
Relative Standard Error
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The uncertainties of the percentages were calculated using SUDAAN® (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
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demographic groups were statistically significant. Using a logistic regression model, the
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 are 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 SUDAAN® (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;
All Other Races. 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 three age groups: 0-5, 6-10, 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 disease 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 current asthma in children ages 0 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
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comparisons, the statistical test compares the pair of race/ethnicity groups 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 is used in Table 2 for the unadjusted comparisons between a race/ethni city 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 for
each race/ethni city group, the two 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 0 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 0 to 17 years. For the Asthma attack variable, the "Against" =
"year" trend analysis for the Subset "Current asthma" evaluates whether there is a statistically
significant trend in the percentage with an asthma attack in the past 12 months among children
with current asthma.
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 0 to 17 with
current asthma, between pairs of race/ethni city groups, for 2016-2019.
v" 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
Level
Below
Poverty
Level
(adjusted
for age,
sex)
At or
Above
Poverty
Level
At or
Above
Poverty
Level
(adjusted
for age, sex)
Current
asthma
White non-
Hispanic
Black non-
Hispanic
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Current
asthma
White non-
Hispanic
Asian non-
Hispanic
<0.001
<0.001
0.074
0.061
<0.001
<0.001
Current
asthma
White non-
Hispanic
Hispanic
0.100
0.361
0.867
0.855
0.450
0.354
Current
asthma
White non-
Hispanic
Other
<0.001
<0.001
0.009
0.012
<0.001
<0.001
Current
asthma
Black non-
Hispanic
Asian non-
Hispanic
<0.001
<0.001
0.003
0.002
<0.001
<0.001
Current
asthma
Black non-
Hispanic
Hispanic
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Current
asthma
Black non-
Hispanic
Other
0.011
0.047
0.708
0.818
0.002
0.006
Current
asthma
Asian non-
Hispanic
Hispanic
<0.001
<0.001
0.087
0.073
<0.001
<0.001
Current
asthma
Asian non-
Hispanic
Other
<0.001
<0.001
0.003
0.003
<0.001
<0.001
Current
asthma
Hispanic
Other
<0.001
<0.001
0.006
0.007
<0.001
<0.001
Current
asthma
Mexican
Puerto Rican
<0.001
<0.001
0.029
0.035
<0.001
<0.001
* "Other" represents the "All Other Races" category, which includes all other races not specified, together with those individuals who report
more than one race.
Table 2. Statistical significance tests comparing the percentages of children ages 0 to 17 years
with current asthma, between those below poverty level and those at or above poverty level,
2016-2019.
P-VALUES
Variable
Population*
Unadjusted
Adjusted**
Current asthma
All
<0.001
<0.001
Current asthma
White non-Hispanic
0.001
<0.001
Current asthma
Black non-Hispanic
0.567
0.350
Current asthma
Asian non-Hispanic
0.862
0.862
Current asthma
Hispanic
0.010
0.007
Current asthma
Other
0.024
0.033
Current asthma
Mexican
0.173
0.156
Current asthma
Puerto Rican
0.775
0.831
* "Other" represents the "All Other Races" category, which includes all other races not specified, together with those individuals who report
more than one race.
** Comparison for "All" is adjusted for age, sex, and race/ethnicity; comparisons for race/ethnicity categories are adjusted for age and sex.
Table 3. Other statistical significance tests comparing the percentages of children ages 0 to 17
years with asthma, for 2016-2019 (trends for 2001-2019, 1997-2019, and sub-periods).
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P-VALUES
Variable
From
To
Against
Subset
Unadjusted
Adjusted*
Current asthma
2016
2019
age
<0.001
<0.001
Current asthma
2016
2019
income
<0.001
<0.001
Current asthma
2016
2019
sex
<0.001
<0.001
Current asthma
2001
2010
year
0.001
0.001
Current asthma
2001
2019
year
<0.001
<0.001
Current asthma
2010
2019
year
<0.001
<0.001
Current asthma
2001
2019
year
Boys
0.002
0.001
Current asthma
2001
2019
year
Girls
0.025
0.028
Asthma attack
1997
2011
year
0.730
0.659
Asthma attack
1997
2019
year
<0.001
<0.001
Asthma attack
2011
2019
year
<0.001
<0.001
Asthma attack
2001
2019
year
Current asthma
<0.001
<0.001
*For Against = "age," the comparison is between the age groups 0-5, 6-10, 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 or "Current asthma," 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 "Boys" or "Girls," 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 SAS® 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
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
2000-2018), 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. For 1997-2008 imputed income files we need the imputed poverty
income ratio (RAT CATI) which gives the ratio in categories. For 2009 imputed income
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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). The questionnaire variables needed
for these analyses are the responses to the following questions: "Has a doctor or other
health professional ever told you that had asthma?" (CASHMEV) and if
yes, "During the past 12 months, has had an episode of asthma or an
asthma attack?" (CASHYR). For 2001-2018 another needed variable is the response to
the question: "Does still have asthma?" (CASSTILL).
• 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
variables needed for this indicator are the sample child survey weight (WTFA C), age
(AGEP C), sex (SEX C), the pseudo-stratum (PSTRAT), the pseudo-PSU (PPSU), the
race (RACEALLP C, using the 1997 OMB definitions), the Hispanic origin (HISP C),
and the detailed Hispanic origin (HISDETP C). 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 other health professional ever told you that
had asthma?" (ASEV C) and if yes, "During the past 12 months, has had
an episode of asthma or an asthma attack?" (ASAT12M C). Another needed variable is
the response to the question: "Does still have asthma?" (ASTILL C).
• NHIS 1980-1996. Condition file conditon.dat. This file is an ASCII file that contains the
age (AGE), condition number (CNUM), survey weight (WTFA), and the parent's
response to "Did have this condition in the past 12 months?"
(CPAST12). Data for children ages 0 to 17 years and for the asthma condition were
extracted. Used only for Table Hlb.
Methods for H3
Indicator H3 Children's emergency room visits and hospitalizations for
asthma and other respiratory causes, children ages 0 to 17 years, 1996-2018.
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See above for Indicators HI. Percentage of children ages 0 to 17 years with asthma, 1997-2019.
and H2. Percentage of children ages 0 to 17 years with current asthma, by race/ethnicity and
family income, 2016-2019.
Summary
Emergency Room Visits
Since 1992, the National Center for Health Statistics, a division of the Centers for Disease
Control and Prevention, has conducted the National Hospital Ambulatory Medical Care Survey
(NHAMCS), a series of annual U.S. national surveys of visits to the emergency departments and
outpatient departments of noninstitutional general and short-stay hospitals, exclusive of federal,
military, and Veteran's Administration hospitals. For emergency room visits, this indicator uses
the first diagnosis International Classification of Diseases 9th edition (ICD-9) or 10th edition
(ICD-10) code to count emergency room visits for asthma and all other respiratory causes,
asthma, and all respiratory causes other than asthma (composed of the following subcategories:
acute respiratory infections, pneumonia or influenza, and other lower respiratory conditions
besides asthma).
The ICD-9 codes were used from 1992 to 2015, and the ICD-10 codes were used starting in
2016. The national numbers of emergency room visits by children ages 0 to 17 years are
calculated by combining visits for each respiratory disease diagnosis with the survey weights for
each child patient. The survey weights are the numbers of hospital emergency room visits by
children ages 0 to 17 years in the noninstitutionalized civilian population represented by each
patient visit in the survey database. This indicator shows the rate of emergency room visits per
10,000 children, calculated by dividing the national number of emergency room visits by the
total U.S. population of noninstitutionalized civilian children ages 0 to 17 years.
Supplementary Tables: Table H3a provides the rate of emergency room visits by children 0 to 17
years, stratified by race/ethnicity, for 2015-2018. Table H3b provides the rate of emergency
room visits by children 0 to 17 years, stratified by age group, for 2015-2018.
Hospitalizations
From 1965 to 2010, the National Center for Health Statistics, a division of the Centers for
Disease Control and Prevention, conducted the National Hospital Discharge Survey (NHDS), a
series of annual U.S. national surveys of hospital discharges from non-federal short-stay
hospitals. This indicator uses the first diagnosis International Classification of Diseases 9th
edition (ICD-9) code to count hospital discharges for asthma and all other respiratory causes,
asthma, and all respiratory causes other than asthma (composed of the following subcategories:
acute respiratory infections, pneumonia or influenza, and other lower respiratory conditions
besides asthma). The national numbers of hospital discharges by children ages 0 to 17 years are
calculated by combining hospital discharges for each respiratory disease diagnosis with the
survey weights for each child patient. The survey weights are the numbers of hospital discharges
by children ages 0 to 17 years in the noninstitutionalized civilian population represented by each
hospital discharge in the survey database. This indicator shows the rate of hospitalizations per
10,000 children, calculated by dividing the national number of hospital discharges by the total
U.S. population of noninstitutionalized civilian children ages 0 to 17 years. Table H3c provides
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the rate of hospitalizations by children ages 0 to 17 years, stratified by race, for the years 2007-
2010. Table H3d provides the rate of hospitalizations by children ages 0 to 17 years, stratified by
age group, for the years 2007-2010.
Data Summary
Indicator H3. Children's emergency room visits and hospitalizations for asthma and other
respiratory causes, children ages 0 to 17 years, 1996-2018.
Data
Emergency room visits and hospitalizations by children ages 0 to 17 years.
Years (1 iwr>-
:<)
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National Hospital Ambulatory Medical Care Survey (Emergency Room
Visits)
The National Hospital Ambulatory Medical Care Survey (NHAMCS) is conducted by the
National Center for Health Statistics, a division of the Centers for Disease Control and
Prevention. The complex multi-stage survey is designed to collect data on ambulatory care
services in hospital emergency and outpatient departments; these analyses only used the
emergency department visits. Sampled hospitals are noninstitutional general and short-stay
hospitals located in all states and Washington DC, but exclude federal, military, and Veteran's
Administration hospitals. Data from sampled visits are obtained on the demographic
characteristics, expected source(s) of payments, patients' complaints, physician's diagnoses,
diagnostic and screening services, procedures, types of health care professionals seen, and causes
of injury.
These analyses focused on visits to emergency rooms by children ages 0 to 17 years for
respiratory diseases. Emergency room data was selected by using the ED files only. The age
variable was used to select visits by children ages 0 to 17 years.
For 1996 -2015, the respiratory disease categories were selected based on the first physician's
diagnosis code (DIAG1) using the International Classification of Diseases 9th Revision (ICD-9),
first three characters:
• Asthma and all other respiratory causes: codes 460-466, 480-488, 490-496
• All respiratory causes other than asthma: codes 460-466, 480-488, 490-492, 494-496
• Acute respiratory infections: codes 460-466
• Pneumonia or influenza: codes 480-488
• Other lower respiratory: codes 490-492, 494-496
• Asthma: code 493
Note that, starting with the 2019 update of this indicator, the indicator's label for the Acute
respiratory infections category (codes 460-466) was changed from the previously used "Upper
respiratory" to the more accurate label "Acute respiratory infections." The ICD-9 codes are
unchanged.
For 2016 -2018, the respiratory disease categories were selected based on the first physician's
diagnosis code (DIAG1) using the International Classification of Diseases 10th Revision (ICD-
10), first three characters:
• Asthma and all other respiratory causes: codes J00-J06, J20-J22, J09-J18, J40-J47, J67
• All respiratory causes other than asthma: codes J00-J06, J20-J22, J09-J18, J40-J44, J46-
J47, J67
• Acute respiratory infections: codes J00-J06, J20-J22
• Pneumonia or influenza: codes J09-J18
• Other lower respiratory: codes J40-J44, J46-J47, J67
• Asthma: code J45
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The NHAMCS uses a complex multi-stage, stratified, clustered sampling design. The statistical
analyses used the patient visit survey weights (PATWT) to re-adjust the sample of visits to
represent the total national population of emergency room visits in each calendar year.
National Hospital Discharge Survey (Hospitalizations)
The National Hospital Discharge Survey (NHDS) was conducted by the National Center for
Health Statistics, a division of the Centers for Disease Control and Prevention, fori 965-2010.
The complex multi-stage survey is designed to collect data on inpatients discharged from non-
federal short-stay hospitals. Sampled hospitals are short-stay general or children's general
hospitals located in all states and Washington DC, with an average length of stay of fewer than
30 days and six or more beds staffed for patients use. Federal, military, and Veteran's
Administration hospitals are excluded, as are hospital units of institutions. Data from sampled
visits are obtained on the demographic characteristics and physician's diagnoses.
These analyses focused on hospital discharges by children ages 0 to 17 years for respiratory
diseases. The age variable was used to select visits by children ages 0 to 17 years. The
respiratory disease categories were selected based on the first physician's diagnosis code
(DIAG1) using the International Classification of Diseases (ICD-9), first three characters:
• Asthma and all other respiratory causes: codes 460-466, 480-488, 490-496
• All respiratory causes other than asthma: codes 460-466, 480-488, 490-492, 494-496
• Acute respiratory infections: codes 460-466
• Pneumonia or influenza: codes 480-488
• Other lower respiratory: codes 490-492, 494-496
• Asthma: code 493
The NHDS uses a complex multi-stage, stratified, clustered sampling design. The statistical
analyses used the survey analysis weights to re-adjust the sample of discharges to represent the
total national population of hospital discharges in each calendar year.
Although the available data were collected for hospital discharges, we assume for these analyses
that admission and discharge rates are equal.
Calculation of Indicator
Emergency Room Visits
Indicator H3 shows the rate of emergency room visits by noninstitutionalized civilian children
ages 0 to 17 years that were for a given respiratory disease.
For each year and respiratory disease, we carried out the following calculations:
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1. We extracted the NHAMCS survey data for all the emergency room visits by children ages 0
to 17 years for the given respiratory disease. We selected all visits where the age was between 0
and 17 and the first three characters of the first diagnosis code were:
Years 1996 -2015 (ICD-9-CM)
• Asthma and all other respiratory causes: codes 460-466, 480-488, 490-496
• All respiratory causes other than asthma: codes 460-466, 480-488, 490-492, 494-496
• Acute respiratory infections: codes 460-466
• Pneumonia or influenza: codes 480-488
• Other lower respiratory: codes 490-492, 494-496
• Asthma: code 493
Years 2016 -2018 (ICD-10-CM)
• Asthma and all other respiratory causes: codes J00-J06, J20-J22, J09-J18, J40-J47, J67
• All respiratory causes other than asthma: codes J00-J06, J20-J22, J09-J18, J40-J44, J46-
J47, J67
• Acute respiratory infections: codes J00-J06, J20-J22
• Pneumonia or influenza: codes J09-J18
• Other lower respiratory: codes J40-J44, J46-J47, J67
• Asthma: code J45
For each visit, the patient weight (PATWT) denotes the national number of patient visits
represented by that visit.
2. We summed the NHAMCS patient weights for all the selected visits to estimate the total
number of emergency room visits by children ages 0 to 17 years for the given respiratory
disease:
Total Number of Visits = E PATWT, summed over all selected visits
3. Using the census data, we calculated the total population of children ages 0 to 17 years by
summing the populations for the ages 0, 1,2, ... 17:
Population = E Population (age A), summed over ages 0, 1,2, ... 17
4. We divided the total number of visits (NHAMCS data) by the total population (census data) to
get the rate per 10,000 of children's visits for the respiratory disease:
Rate per 10,000 = [Total Number of Visits / Population] x 10000
For Table H3b, rates stratified by age group were tabulated for 2015, 2016, 2017, and 2018, and
for 12015-2018. These rates were calculated using the same procedure as above, except that the
visits and populations were summed across the children in each age group.
Race/Ethnicity
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For Table H3a, rates stratified by race/ethnicityvm group were tabulated for 2015, 2016, 2017,
2018, and for 2015-2018. These rates were calculated using the same procedure as above, except
that the visits and populations were summed across the children in each race/ethnicity group and
year.
The race/ethnicity groups were defined using the variables RACEIM (RACE for 1996 to 2006,
RACEUN for 2009 and later) and ETHIM (ETHNIC for 1996 to 2006) in the NHAMCS files.
These are the patient's race and ethnicity, and are given statistically imputed values in the
database if they are not reported. For 2009 and later, the combined race and ethnicity variable
RACEETH was also used; this variable is given statistically imputed values in the database if not
reported. For children ages 0 to 17 years in 2015 - 2018, a weighted percentage of 23% of the
values for race, 22% of the values for ethnicity, and 33% of the values for race and ethnicity
combined were imputed.
These variables were coded as follows:
For 2008 and earlier:
ETHIM: Patient ethnicity (Hispanic/Non-Hispanic)
1 = Hispanic or Latino
2 = Not Hispanic or Latino
RACEIM: Patient race
1 = White only
2 = Black/African American only
3 = Asian only
4 = Native Hawaiian/Other Pacific Islander only
5 = American Indian/Alaska Native only
6 = More than one race reported
Using these variables, for 2008 and earlier, the following race/ethnicity groups were defined for
the NHAMCS emergency room visits data:
• All: RACEIM = any, ETHIM = any
• White non-Hispanic: RACEIM = 1, ETHIM = 2
• Black non-Hispanic: RACEIM = 2, ETHIM = 2
• American Indian/Alaska Native, Non-Hispanic: RACEIM = 5, ETHIM = 2
• Asian and Pacific Islander, Non-Hispanic: RACEIM = 3 or 4, ETHIM = 2
• Hispanic: ETHIM = 1
For 2009 and later:
ETHIM: Patient ethnicity (Hispanic/Non-Hispanic)
Vl" These data are not stratified by income because the NHAMCS data do not give the patient's income. Since 2006,
NHAMCS reports the median family income for the patient's zip code, which would poorly match the available
census income data (the patient's zip code is not available in the publicly released NHAMCS data).
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1 = Hispanic or Latino
2 = Not Hispanic or Latino
RACEUN: Patient race (unimputed)
1 = White only
2 = Black/African American only
3 = Asian only
4 = Native Hawaiian/Other Pacific Islander only
5 = American Indian/Alaska Native only
6 = More than one race reported
-9 = Not reported
RACERETH: Patient race/ethnicity group (imputed if not reported)
1 = White only, non-Hispanic
2 = Black only, non-Hispanic
3 = Hispanic
4 = Other, non-Hispanic
Using these variables, for 2009 and later, the following race/ethnicity groups were defined for
the NHAMCS emergency room visits data:
• All: RACEUN = any, ETHIM = any, RACEETH = any
• White non-Hispanic: RACERETH = 1
• Black non-Hispanic: RACERETH = 2
• American Indian/Alaska Native, Non-Hispanic: RACEUN = 5, ETHIM = 2
• Asian and Pacific Islander, Non-Hispanic: RACEUN = 3 or 4, ETHIM = 2
• Hispanic: ETHIM = 1
The associated populations for 2015 to 2018 were computed from the post-censal 2010
noninstitutionalized civilian population files for 2020 at:
https://www2.census.gov/programs-survevs/popest/datasets/2010-202Q/national/asrh/
For each month, year, and age, the file provides the total population as well as the populations by
age, sex, race and ethnicity. Populations are provided for male Hispanics, female Hispanics, male
Non-Hispanics, and female Non-Hispanics of the following race combinations:
RACENUM (Census data)
1. White alone
2. Black alone
3. American Indian/Alaska Native alone
4. Asian alone
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5. Hawaiian or Pacific Islander alone
6. Two or more races
(Other specific multiple race combinations are also provided in the dataset, such as "White alone,
or in combination with another race").
Thus the total census populations corresponding to the selected NHAMCS race/ethnicity groups
are obtained by summing the populations as follows:
• All: Total population
• White non-Hispanic: RACENUM = 1, Non-Hispanic, Age <= 17, Gender = male or
female
• Black non-Hispanic: RACENUM = 2, Non-Hispanic, Age <= 17, Gender = male or
female
• American Indian/Alaska Native non-Hispanic: RACENUM = 3, Non-Hispanic, Age <=
17, Gender = male or female
• Asian and Pacific Islander non-Hispanic: RACENUM = 4 or 5, Non-Hispanic, Age <=
17, Gender = male or female
• Hispanic: RACENUM = 1 to 6, Hispanic, Age <= 17, Gender = male or female
Using the same four steps described above under "Calculation of Indicator," the total number of
visits and total population for each race/ethnicity group are used to get the rate per 10,000 of
children's visits for the respiratory disease:
Rate per 10,000 = [Total Number of Visits / Population] x 10000
Relative Standard Error
The uncertainties of the rates were calculated using SUDAAN® (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. For this purpose, the public release version of NHAMCS includes the following
variables:
• Masked Stratum (CSTRATM)
• Masked Primary Sampling Unit (CPSUM)
These variables are "Masked" so that the sample design represented by these variables is an
approximation to the true sample design, which was not made publicly available in order to
protect confidentiality. Note that starting in 2003, the public release version does not include
masked sampling design variables beyond the first stage of sampling. For approximate variance
estimation, the survey design can be approximated as being a multi-stage random sample where
the first stage samples with replacement the masked primary sampling units from the masked
strata.
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The survey software was used to estimate the standard deviation of the total number of visits by
children ages 0 to 17 years for the given respiratory disease, SD (Total Visits).
The rate of visits is calculated as:
Rate per 10000 = [Total Number of Visits / Population] x 10000
Treating the census population estimates as having negligible uncertainty, we get the standard
error of the rate by dividing the standard deviation of the total by the population:
Standard Error (Rate) = [SD (Total Visits) / Population] x 10000
The relative standard error is the standard error divided by the estimated rate:
Relative Error (%) = [Standard Error (Rate) / Rate] x 100%
Rates with a relative error less than 30% and with at least 30 sampled visits (for the given
disease) sampled were treated as being reliable and were tabulated. Rates with a relative error
greater than or equal to 30% but less than 40% and with at least 30 sampled visits were treated as
being unstable; these values were tabulated but were flagged to be interpreted with caution.
Rates with a relative error greater than or equal to 40% or missing or with at most 29 sampled
visits were treated as being unreliable; these values were not tabulated and were flagged as
having a large uncertainty.
Hospitalizations
Indicator H3 also shows the rate of hospitalizations by noninstitutionalized civilian children ages
0 to 17 years that were for a given respiratory disease.
For each year and respiratory disease, we carried out the following calculations:
1. We extracted the NHDS survey data for all the hospital discharges by children ages 0 to 17
years for the given respiratory disease. We selected all hospital discharges where the age was
between 0 and 17 years and the first three characters of the first diagnosis code were:
• Asthma and all other respiratory causes: codes 460-466, 480-488, 490-496
• All respiratory causes other than asthma: codes 460-466, 480-488, 490-492, 494-496
• Acute respiratory infections: codes 460-466
• Pneumonia or influenza: codes 480-488
• Other lower respiratory: codes 490-492, 494-496
• Asthma: code 493
For each hospital discharge, the survey analysis weight (WEIGHT) denotes the national number
of hospital discharges represented by that discharge.
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2. We summed the NHDS analysis weights for all the selected hospital discharges to estimate the
total number of hospital discharges by children ages 0 to 17 years for the given respiratory
disease:
Total Number of Hospital Discharges = E WEIGHT,
summed over all selected discharges
3. Using the census data, we calculated the total population of children ages 0 to 17 years by
summing the populations for the ages 0, 1,2, ... 17:
Population = E Population (age A), summed over ages 0, 1,2, ... 17
4. We divided the total number of hospital discharges (NHDS data) by the total population
(census data) to get the estimated rate of children's hospitalizations for the respiratory disease:
Rate per 10000 = [Total Number of Hospital Discharges / Population] x 10000.
For Table H3d, rates stratified by age group were tabulated for the years 2007, 2008, 2009, 2010,
and for the four-year period 2007-2010. These rates were calculated using the same procedure as
above, except that the hospital discharges and populations were summed across the children in
each age group.
Race
For Table H3c, rates stratified by race group were tabulated for the years 2007, 2008, 2009,
2010, and for the four-year period 2007-2010. These rates were calculated using the same
procedure as above, except that the hospital discharges and populations were summed across the
children in each race group and year.
The race groups were defined using the variable RACE in the NHDS files. There is no variable
for Hispanic ethnicity in NHDS.
For 2007-2010, this variable was coded as follows:
RACE: Patient race
1 = White
2 = Black/African American
3 = American Indian/Alaskan Native
4 = Asian
5 = Native Hawaiian or other Pacific Islander
6 = Other
8 = Multiple race indicated
9 = Not stated
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Using this variable, the following race groups were defined for the NHDS hospital discharge
data:
• All: RACE = any
• White: RACE = 1
• Black: RACE = 2
• American Indian/Alaska Native: RACE = 3
• Asian and Pacific Islander: RACE = 4 or 5
• All Other Races: RACE = 6, 8 or 9
Note that following NCHS recommendations, due to concerns about high uncertainty, detailed
results are not presented for the American Indian/Alaskan Native, Asian and Pacific Islander,
and All Other Races categories. However those three categories were used to define the race
groups for the statistical comparisons presented in the "Statistical Comparisons" section below.
The associated populations for 2007 -2009 were computed from the post-censal 2000
noninstitutionalized civilian population files for 2009 originally at:
http://www.census.gov/popest/data/national/asrh/2009/20Q9-nat-ni.html
These data have since been archived to the url:
https://www2.census.gov/programs-survevs/popest/datasets/2000-20Q9/national/asrh/
The associated populations for 2010 were computed from the post-censal 2010
noninstitutionalized civilian population files for 2013 originally at:
http://www.census.gov/popest/data/national/asrh/2013/2013-nat-ni.html
These data have since been archived to the url:
https ://www2. census, gov/programs-survevs/popest/datasets/2010-2013/national/asrh/
For each month, year, and age, the file provides the total population as well as the populations by
age, sex, race, and ethnicity. Populations are provided for various combinations including males
and females of the following race combinations:
RACENUM (Census data)
1. White alone
2. Black alone
3. American Indian/Alaska Native alone
4. Asian alone
5. Hawaiian or Pacific Islander alone
6. Two or more races
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(Other specific multiple race combinations are also provided in the dataset, such as "White alone,
or in combination with another race").
Thus the total census populations corresponding to the selected NHDS race groups are obtained
by summing the populations as follows:
• All: Total population
• White: RACENUM = 1, Age <= 17, Gender = male or female
• Black: RACENUM = 2, Age <= 17, Gender = male or female
• American Indian/Alaska Native: RACENUM = 3, Age <= 17, Gender = male or female
• Asian and Pacific Islander: RACENUM = 4 or 5, Age <= 17, Gender = male or female
• All Other Races: RACENUM = 6, Age <= 17, Gender = male or female
Note that following NCHS recommendations, due to concerns about high uncertainty, detailed
results are not presented for the American Indian/Alaskan Native, Asian and Pacific Islander,
and All Other Races categories. However those three categories were used to define the race
groups for the statistical comparisons presented in the "Statistical Comparisons" section below.
Using the same four steps described above under "Calculation of Indicator," the total number of
discharges and total population for each race group are used to get the estimated rate per 10,000
of children's hospitalizations for the respiratory disease:
Rate per 10,000 = [Total Number of Hospital Discharges / Population] x 10000
Relative Standard Error
The uncertainties of the rates were computed for 1996 -2010 using approximate relative standard
error equations provided in the file documentation for each year. The documentation is provided
at the ftp site:
ftp://ftp.cdc.gov/pub/Health Statistics/NCHS/Dataset Documentation/NHDS/
The equation provided in the documentation is of the form:
Relative Standard Error (Total Discharges) =
V(a + b / Total Discharges) x 100%
The relative standard error is defined as the standard deviation divided by the estimated value:
Relative Standard Error (Total Discharges) =
[Standard Deviation (Total Discharges) / Total Discharges] x 100%
To derive error estimates for public release that would be applicable to a wide variety of
statistics, NCHS produced numerous estimates and their variances. NCHS then used a regression
model to produce best-fit curves, based on the empirically determined relationship between the
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size of an estimate X and its relative variance. The regression intercepts a and slopes b were
tabulated by NCHS for various population subgroups and selected statistics.
The NCHS tabulated parameters a and b for the first-listed diagnosis for the Under 15 age group
are listed in the following table.
Year
a
b
1996
0.017
229.443
1997
0.0147
181.262
1998
0.013772
221.956
1999
0.016494
223.072
2000
0.021332
284.1142
2001
0.019559
255.6805
2002
0.0211
241.964
2003
0.02189
278.306
2004
0.02165
252.708
2005
0.02222
211.185
2006
0.02734
220.637
2007
0.036972
167.01187
2008
0.05044
516.705
2009
0.0502
670.537
2010
0.0830
143.722
The rate equals the total discharges divided by the total population:
Rate per 10000 = [Total Number of Hospital Discharges / Population] x 10000
The relative standard error of each rate is the estimated standard deviation of the rate divided by
the estimated rate. Assuming that the uncertainty of the census populations is negligible, the
relative standard error of the rate is equal to the relative standard error of the total discharges:
Relative Standard Error (Rate) = V(a + b / Total Discharges) x 100%
For the rates for 2007-2010 combined, the calculation is more complicated.
1. Use the above equations for each year, 2007, 2008, 2009 and 2010 to obtain the standard
deviation for the total discharges in that year:
SD (Total Discharges, Year Y) =
[Relative Standard Error (Total Discharges) x Total Discharges] /100 =
V(a + b / Total Discharges) x Total Discharges
2. Calculate the variance, Var, for each year:
Var (Total Discharges, Year Y) = [SD (Total Discharges, Year Y)]2
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3. Estimate the total discharges for 2005 -2008 by summing the four annual estimates:
Total Discharges (2007-2010) =
Total Discharges (2007) + Total Discharges (2008) + Total Discharges (2009)
+ Total Discharges (2010)
4. Estimate the total population for 2007 -2010 by summing the four annual populations:
Total Population (2007-2010) =
Population (2007) + Population (2008) + Population (2009) + Population (2010)
5. Estimate the rate for 2007-2010 by dividing the total discharges by the total population:
Rate per 10000 (2007-2010) =
[Total Discharges (2007-2010) / Total Population (2007-2010)] x 10000
6. Estimate the variance of the total discharges for 2007-2010. Assuming that the annual
estimates are (approximately) independent, the variance of the sum equals the sum of the
variances, which gives:
Var (Total Discharges (2007-2010)) = Var (Total Discharges, 2007) +
Var (Total Discharges, 2008) + Var (Total Discharges, 2009)
+ Var (Total Discharges, 2010)
(This uses the results of the second step).
7. Calculate the standard deviation of the total discharges for 2007-2010:
SD (Total Discharges, 2007-2010) = V[Var (Total Discharges, 2007-2010)]
8. Calculate the relative standard error of the total discharges using the results of the third and
seventh steps:
Relative Standard Error (Total Discharges, 2007-2010) =
[SD (Total Discharges, 2007-2010) / (Total Discharges, 2007-2010)] x 100%
9. Calculate the relative standard error of the rate of discharges for 2007-2010. Assuming the
populations have negligible uncertainty, it again follows that the relative standard error of the
rate equals the relative standard error of the total discharges, which is given in the eighth step:
Relative Standard Error (Rate per 10000, 2007-2010) =
Relative Standard Error (Total Discharges, 2007-2010)
Rates with a relative error less than 30% and at least 30 sampled hospital discharges (for the
given disease) were treated as being reliable and were tabulated. Rates with a relative error
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greater than or equal to 30% but less than 40% and with at least 30 sampled hospital discharges
were treated as being unstable; these values were tabulated but were flagged to be interpreted
with caution. Rates with a relative error greater than or equal to 40% or missing or with at most
29 sampled hospital discharges 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.
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Statistical Comparisons
Statistical analyses of the emergency room visit rates or hospitalization rates were used to
determine whether the differences between rates for different demographic groups were
statistically significant. For these analyses, the rates and their standard errors were calculated for
each combination of age group, sex, and race/ethnicity or race group using the method described
in the corresponding "Relative Standard Error" section. For emergency room visits, rates and
their standard errors are calculated for each combination of age group, sex, and race/ethnicity.
For hospitalizations, rates and the relative standard errors of the rates are calculated for each
combination of age group, sex, and race. The standard error of the rate is given by the product of
the rate and its relative standard error. These calculated standard errors account for the survey
weighting and design.
Using a weighted linear regression model, the rate was assumed to be the sum of explanatory
terms for age, sex, and/or race/ethnicity or race and a random error term; the error terms were
assumed to be approximately independent and normally distributed with a mean of zero and a
variance equal to the square of the standard error. Using this model, the difference in the value of
a rate between different demographic groups is statistically significant if the difference between
the corresponding sums of explanatory terms is statistically significantly different from zero. 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 each type of comparison, we present unadjusted and adjusted analyses. The unadjusted
analyses directly compare a rate 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 for emergency room visits use and compare
the visit rates between different race/ethnicity pairs. The adjusted race/ethnicity comparisons use
the rates for each age/sex/race/ethnicity combination. The adjusted analyses add age and sex
terms to the statistical model and compare the rates between different race/ethnicity pairs after
accounting for the effects of the other demographic variables. For example, if Hispanic children
tend to be younger than White non-Hispanics, and if the visit rate strongly depends on age only,
then the unadjusted differences between these two race/ethnicity groups would be significant but
the adjusted difference (taking into account age) would not be significant.
Comparisons of emergency room visit rates for asthma and other respiratory causes between
pairs of race/ethnicity groups are shown in Table 1. Comparisons of hospitalization rates for
asthma and other respiratory causes between Whites and Blacks are also shown in Table 1. In
Table 1, for the "Unadjusted" comparisons, the only explanatory variables are terms for each
race/ethnicity or race group. For these unadjusted comparisons, the statistical tests compare the
percentiles for each pair of race/ethnicity or race groups. For the "Adjusted for age, sex"
comparisons, the explanatory variables are terms for each race/ethnicity or race group together
with terms for each age group and sex. For these adjusted comparisons, the statistical test
compares the pair of race/ethnicity or race groups after accounting for any differences in the age
and sex distributions between the race/ethnicity or race groups.
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Additional comparisons are shown in Table 2 for emergency room visits and in Table 3 for
hospitalizations. The Against = "age" unadjusted p-value compares the rates between all the age
groups. The adjusted p-value includes adjustment terms for sex and race/ethnicity or race in the
model. The Against = "year" unadjusted p-value compares the trends in the rates by regressing
against the calendar year. The adjusted p-value includes adjustment terms for age, sex and
race/ethnicity or race in the model.
For the analyses of emergency room visits, the race/ethnicity groups used were: White non-
Hispanic; Black non-Hispanic; API non-Hispanic; AIAN non-Hispanic; Hispanic; Other. API
denotes either Asian or Native Hawaiian or Pacific Islander. AIAN denotes American Indian or
Alaska Native. For these data the "Other" race/ethnicity category denotes children reporting
multiple races and was not an available category for the years 1996 to 1998. For the analyses of
hospitalizations, the race groups used were: White; Black; API; AIAN; All Other Races. API
denotes either Asian or Native Hawaiian or Pacific Islander. AIAN denotes American Indian or
Alaska Native. For these data the "All Other Races" category includes children of Other races,1X
children of multiple races (for 2000 or later), and children with a race that was not stated. For the
analyses of emergency room visits and hospitalizations, the age groups used were: < 12 months,
1 to < 2 years, 2 to < 3 years, 3 to < 6 years, 6 to < 11 years, 11 to < 16 years, and 16 to < 18
years.
For more details on these statistical analyses, see the memorandum by Cohen (2011). x
Table 1. Statistical significance tests comparing the rates of emergency room visits in 2015-2018
or hospitalizations in 2007-2010 for asthma and other respiratory causes by children ages 0 to 17
years, between pairs of race/ethnicity groups.
P-VALUES
Indicator
Variable
First
race/ethnicity
group
Second race/ethnicity
group
Unadjusted
Adjusted for
age, sex
Emergency
room visits
Asthma and all
other respiratory
causes
White non-
Hispanic
Black non-Hispanic
<0.001
<0.001
Emergency
room visits
Asthma and all
other respiratory
causes
White non-
Hispanic
AIAN non-Hispanic
0.831
<0.001
Emergency
room visits
Asthma and all
other respiratory
causes
White non-
Hispanic
API non-Hispanic
<0.001
<0.001
Emergency
room visits
Asthma and all
other respiratory
causes
White non-
Hispanic
Hispanic
0.047
0.165
IX Although the NHDS hospital discharge data includes Other races as a possible category, the corresponding census
population data only provides estimates for the single race groups: White, Black, Asian, AIAN, Hawaiian and
Pacific Islander; and for multiple races.
x Cohen, J. 2011. Selected statistical methods for testing for trends and comparing years or demographic groups in
other ACE health-based indicators. Memorandum from J. Cohen, ICF to Dan Axelrad, EPA, 16 June, 2011.
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P-VALUES
Indicator
Variable
First
race/ethnicity
group
Second race/ethnicity
group
Unadjusted
Adjusted for
age, sex
Emergency
room visits
Asthma and all
other respiratory
causes
Black non-
Hispanic
AIAN non-Hispanic
<0.001
<0.001
Emergency
room visits
Asthma and all
other respiratory
causes
Black non-
Hispanic
API non-Hispanic
<0.001
<0.001
Emergency
room visits
Asthma and all
other respiratory
causes
Black non-
Hispanic
Hispanic
<0.001
<0.001
Emergency
room visits
Asthma and all
other respiratory
causes
AIAN non-
Hispanic
API non-Hispanic
0.154
0.369
Emergency
room visits
Asthma and all
other respiratory
causes
AIAN non-
Hispanic
Hispanic
0.230
<0.001
Emergency
room visits
Asthma and all
other respiratory
causes
API non-
Hispanic
Hispanic
<0.001
<0.001
Hospitalizatio
ns
Asthma and all
other respiratory
causes
White
Black
0.001
<0.001
Table 2. Other statistical significance tests comparing the rates of emergency room visits for
asthma and other respiratory causes by children ages 0 to 17 years for 2015 to 2018 (trends for
1996-2016).
P-VALUES
Variable
From
To
Against
Unadjusted
Adjusted*
Asthma and all other respiratory causes
2015
2018
age
<0.001
<0.001
Asthma and all other respiratory causes
1996
2018
year
0.155
0.018
Other respiratory causes
1996
2018
year
0.050
0.009
Asthma
1996
2018
year
0.081
<0.001
*For Against = "age," the p-values are adjusted for sex and race/ethnicity.
For Against = "year," the p-values are adjusted for age, sex, and race/ethnicity.
Table 3. Other statistical significance tests comparing the rates of hospitalizations for asthma
and other respiratory causes by children ages 0 to 17 years for 2007 to 2010 (trends for 1996-
2010).
P-VALUES
Variable
From
To
Against
Unadjusted
Adjusted*
Asthma and all other respiratory causes
2007
2010
age
<0.001
<0.001
Asthma and all other respiratory causes
1996
2010
year
0.003
<0.001
Other respiratory causes
1996
2010
year
0.008
<0.001
Asthma
1996
2010
year
<0.001
<0.001
*For Against = "age," the p-values are adjusted for sex and race.
For Against = "year," the p-values are adjusted for age, sex, and race.
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Data Files
The following files are needed to calculate this indicator.
Emergency Room Visits
• NHAMCS 1996-2010: EDXXXX.exe, where XXXX denotes the four-digit year. Each
file is a compressed executable file that when decompressed gives an ASCII file
containing emergency room visit data for a survey year. These files were obtained from
the ftp site:
ftp://ftp.cdc.gov/pub/Health Statistics/NCHS/Datasets/NHAMCS/
The variables needed for this indicator are the survey year, age, physician's diagnosis #1
(DIAG1), and the following sampling design information: the patient visit weight
(PATWT), masked stratum (STRATM for 1996-2001, CSTRATM for 20022010), and
masked primary sampling unit (PSUM for 1996-2001, CP SUM for 2002 - 2010) The
masked 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. For the statistical analyses, the patient race
and ethnicity variables RACE (RACEIM for 2007 -2008, RACEUN for 2009 -2010),
ETHNIC (ETHIM for 2007 -2010), and the combined race/ethnicity variable
RACERETH (for 2009 -2010) are also needed.
• NHAMCS 2011-2016: EDXXXX.zip, where XXXX denotes the four-digit year. Each
file is a compressed executable file that when decompressed gives an ASCII file
containing emergency room visit data for a survey year. These files were obtained from
the ftp site:
ftp://ftp.cdc.gov/pub/Health Statistics/NCHS/Datasets/NHAMCS/
The variables needed for this indicator are the survey year, age, physician's diagnosis #1
(DIAG1), and the following sampling design information: the patient visit weight
(PATWT), masked stratum (CSTRATM for 2011 -2016), and masked primary sampling
unit (CPSUM for 2011 -2016) The masked 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. For
the supplemental table and the statistical analyses, the patient race and ethnicity variables
RACEUN, ETHIM, and the combined race/ethnicity variable RACERETH are also
needed.
• NHAMCS 2017-2018: edXXXX_sas.zip, where XXXX denotes the four-digit year. Each
file is a compressed file that when decompressed gives a SAS® data set containing
emergency room visit data for a survey year. These files were obtained from the ftp site:
ftp://ftp.cdc.gov/pub/Health Statistics/NCHS/Dataset Documentation/NHAMCS/sas
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Health: Respiratory Diseases
The variables needed for this indicator are the survey year, age, physician's diagnosis #1
(DIAG1), and the following sampling design information: the patient visit weight
(PATWT), masked stratum (CSTRATM for 2017 -2018), and masked primary sampling
unit (CPSUM for 2017 -2018) The masked 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. For
the supplemental table and the statistical analyses, the patient race and ethnicity variables
RACEUN, ETHIM, and the combined race/ethnicity variable RACERETH are also
needed.
• Census data.xl For 1996 -1999, the national noninstitutionalized civilian populations were
originally obtained from the url:
http://www.census.gov/popest/data/national/asrh/1990s/nat monthly noninstitutional.ht
ml
These data have since been archived to the url:
https://www2.census.gov/programs-survevs/popest/datasets/1990-200Q/national/asrh/
For t 2000 -2009, the national noninstitutionalized civilian populations were originally
obtained from the url:
http://www.census.gov/popest/data/national/asrh/2009/20Q9-nat-ni.html
These data have since been archived to the url:
https://www2.census.gov/programs-survevs/popest/datasets/2000-20Q9/national/asrh/
For 2010 -2018, the national noninstitutionalized civilian populations were obtained
from the url:
https://www2.census.gov/programs-survevs/popest/datasets/2010-202Q/national/asrh/
In each case, the file for each six-month period includes the required variables: month,
year, age, total U.S. population. The "month" gives the date for the population estimate.
For these analyses, data for month = 7 were selected, corresponding to the populations as
of July 1.
X1 Calculation of the indicator values for both emergency room visit and hospitalizations use the "civilian
noninstitutionalized population" as the population denominator. An alternative approach that has been used by the
CDC is to use the "civilian population." The choice of population denominator has a small impact on the calculated
rates; thus the results reported in ACE are slightly different from those in CDC publications such as The State of
Childhood Asthma, United States, 1980-2005, http://www.cdc.gov/nchs/data/ad/ad381 .pdf.
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For Table H3a, populations stratified by race and ethnicity were obtained using the
detailed population data in the same census files for 2015 -2018, as detailed below.
Hospitalizations
• NHDS 1996-2010: NHDSXX.PU.TXT, where XX denotes the two-digit year
(NHDS96.ASC and NHDS97,ASC for 1996 and 1997). Each file is downloadable as a
compressed file that decompresses into an ASCII file containing hospital discharge data
for a survey year. These files were obtained from the ftp site:
ftp://ftp.cdc.gov/pub/Health Statistics/NCHS/Datasets/NHDS/
This site only contains the files from 1996 onwards. The variables needed for this
indicator are the survey year, age, physician's diagnosis #1 (DIAG1), and the analysis
weight. For Table H3c, the patient race variable RACE is also needed.
• Census data. For 1996 -1999, the national noninstitutionalized civilian populations were
originally obtained from the url:
http://www.census.gov/popest/data/national/asrh/1990s/nat monthly noninstitutional.ht
ml
These data have since been archived to the url:
https://www2.census.gov/programs-survevs/popest/datasets/1990-200Q/national/asrh/
For 2000 -2009, the national noninstitutionalized civilian populations were originally
obtained from the url:
http://www.census.gov/popest/data/national/asrh/2009/20Q9-nat-ni.html
These data have since been archived to the url:
https://www2.census.gov/programs-survevs/popest/datasets/2000-20Q9/national/asrh/
For 2010, the national noninstitutionalized civilian populations were obtained from the
url:
http://www.census.gov/popest/data/national/asrh/2013/2013-nat-ni.html
These data have since been archived to the url:
https ://www2. census, gov/programs-survevs/popest/datasets/2010-2013/national/asrh/
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In each case, the file for each six-month period includes the required variables: month,
year, age, total U.S. population. The "month" gives the date for the population estimate.
For these analyses, data for month = 7 were selected, corresponding to the populations as
of July 1.
For Table H3c, populations stratified by race were obtained using the detailed population
data in the same census files for 2007-2010, as detailed below.
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