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Analytical Example Using NHIS Data Files
Analytical Example Using NHIS Data Files
John R. Pleis John R. Pleis
Research QuestionResearch Question
Is the type of health insurance coverage held by adults > 65 years of age associated with
flu shot use?
Is the type of health insurance coverage held by adults > 65 years of age associated with
flu shot use?
Additional CovariatesAdditional Covariates
• Race/ethnicity
• Region of residence
• Education, marital status, sex
• Smoking
• Number of physician office visits
• Race/ethnicity
• Region of residence
• Education, marital status, sex
• Smoking
• Number of physician office visits
Additional CovariatesAdditional Covariates
• Regular place of health care
• Selected chronic conditions
• diabetes, respiratory difficulties, or heart disease
• Low-income program participation
• Regular place of health care
• Selected chronic conditions
• diabetes, respiratory difficulties, or heart disease
• Low-income program participation
Data Files Data Files
• Determine which data files are needed for the analysis
• A good source for determining the file content is the Survey
• Description document:http://www.cdc.gov/nchs/nhis.htm
• Determine which data files are needed for the analysis
• A good source for determining the file content is the Survey
• Description document:http://www.cdc.gov/nchs/nhis.htm
Data FilesData Files• This analysis will utilize data
from several files, which include:
• Person
• Sample adult
• Family
• This analysis will utilize data from several files, which include:
• Person
• Sample adult
• Family
Person FilePerson File• Each person record also has a
sampling weight
• Used to inflate each observation
• Adjusted for non-response as well as U.S. Census population totals by age, sex, and race/ethnicity
• Each person record also has a sampling weight
• Used to inflate each observation
• Adjusted for non-response as well as U.S. Census population totals by age, sex, and race/ethnicity
Person FilePerson File
• Sum of the weights = Size of the Civilian Non-Institutionalized Population
• For more information regarding weights and other design issues, please attend:Practical Applications in Design and Analysis of Complex Sample
Surveys (Session # 30)
• Sum of the weights = Size of the Civilian Non-Institutionalized Population
• For more information regarding weights and other design issues, please attend:Practical Applications in Design and Analysis of Complex Sample
Surveys (Session # 30)
Sample Adult FileSample Adult File• Each sample adult record
has a sampling weight • Different from the person
sampling weight• Sum of the weights = Size of
the Civilian Non-Institutionalized Population of adults > 18 years of age
• Each sample adult record has a sampling weight
• Different from the person sampling weight
• Sum of the weights = Size of the Civilian Non-Institutionalized Population of adults > 18 years of age
Sampling WeightsSampling Weights
• Each data file has its own sampling weights
• Weights should be used, if not:
• Totals, means, and proportions are affected
• Estimates such as regression coefficients are biased
• Each data file has its own sampling weights
• Weights should be used, if not:
• Totals, means, and proportions are affected
• Estimates such as regression coefficients are biased
2000: Race/ethnicity (%)Sample Adults (aged >
65)
2000: Race/ethnicity (%)Sample Adults (aged >
65)
Source: 2000 NHISSource: 2000 NHIS
RACE/ETHNICITY UNWEIGHTED WEIGHTED
Hispanic 9.3 5.9
NH White 77.8 83.9
NH Black 11.3 8.2
Sample DesignSample Design• The NHIS has a complex
sample design• The sample design affects
the computation of variance of estimates
• A complex sample will produce larger variances than a Simple Random Sample (SRS)
• The NHIS has a complex sample design
• The sample design affects the computation of variance of estimates
• A complex sample will produce larger variances than a Simple Random Sample (SRS)
Sample DesignSample Design• Compared to a SRS,
confidence intervals are wider, and statistical significance is harder to achieve for complex survey data
• If variance estimates are needed, the complex sample design should be accounted for in the analysis
• Compared to a SRS, confidence intervals are wider, and statistical significance is harder to achieve for complex survey data
• If variance estimates are needed, the complex sample design should be accounted for in the analysis
NHW vs. NHB Men Aged < 65: Bed Days
NHW vs. NHB Men Aged < 65: Bed Days
(1) (2) (3)
SRSUnweighted
SRSWeighted
ComplexWeighted
NHW NHB NHW NHB NHW NHB
Mean 3.42 5.19 3.52 5.36 3.52 5.36
S.E. ofMean .23 .78 .28 .89 .28 1.01
t-stat 2.18 1.96 1.79
Sig. Level p = .0293 p = .0497 p = .0738
Source: 2000 NHIS
Research QuestionResearch QuestionIs the type of health
insurance coverage held by adults > 65 years of age
associated with flu shot use?
Is the type of health insurance coverage held by
adults > 65 years of age associated with flu shot use?
Additional CovariatesAdditional Covariates
• Race/ethnicity• Region of residence• Education, marital status,
sex• Smoking• Number of physician office
visits
• Race/ethnicity• Region of residence• Education, marital status,
sex• Smoking• Number of physician office
visits
Additional CovariatesAdditional Covariates
• Regular place of health care
• One place that the adult usually went to when either sick care or preventive health care was needed
• Does not include emergency rooms (< 0.5% of the sample)
• Regular place of health care
• One place that the adult usually went to when either sick care or preventive health care was needed
• Does not include emergency rooms (< 0.5% of the sample)
Additional CovariatesAdditional Covariates
• Respiratory difficulties
• Asthma (EVER)
• Chronic Obstructive Pulmonary Disease (COPD)
• Respiratory difficulties
• Asthma (EVER)
• Chronic Obstructive Pulmonary Disease (COPD)
Additional CovariatesAdditional Covariates
• Heart disease (EVER)
• Coronary heart disease
• Angina pectoris
• Heart attack
• Any other heart condition
• Heart disease (EVER)
• Coronary heart disease
• Angina pectoris
• Heart attack
• Any other heart condition
Additional CovariatesAdditional Covariates
• Low-income programs
• Supplemental Security Income
• Temporary Assistance for Needy Families (TANF)
• Food stamps
• Governmental rental assistance
• Low-income programs
• Supplemental Security Income
• Temporary Assistance for Needy Families (TANF)
• Food stamps
• Governmental rental assistance
Creating the FileCreating the File
• Not all the variables of interest for this analysis are contained in one file
• The Person, Sample Adult, and Family files can be merged to create one data file
• Not all the variables of interest for this analysis are contained in one file
• The Person, Sample Adult, and Family files can be merged to create one data file
Creating the FileCreating the File
• Person file
• Health insurance
• Race/ethnicity (all)
• Governmental rental assistance (last 12 months)
• Person file
• Health insurance
• Race/ethnicity (all)
• Governmental rental assistance (last 12 months)
Creating the FileCreating the File• Sample Adult file
• Flu shot use (last 12 months)
• Race/ethnicity (partial)
• Smoking, chronic conditions
• Number of physician office visits (last 12 months)
• Sample Adult file
• Flu shot use (last 12 months)
• Race/ethnicity (partial)
• Smoking, chronic conditions
• Number of physician office visits (last 12 months)
Creating the FileCreating the File• Sample Adult file
• Sample Adult weight
• Sample Adult file
• Sample Adult weight
Creating the FileCreating the File• Family file
• Any family member received any of the following in the past 12 months:• Supplemental Security Income• TANF• Food stamps
• Family file
• Any family member received any of the following in the past 12 months:• Supplemental Security Income• TANF• Food stamps
Creating the FileCreating the File• Person and Sample Adult files
• Education, marital status, sex
• All files
• Region of residence
• STRATUM/PSU (design info for correct variance estimates)
• Person and Sample Adult files
• Education, marital status, sex
• All files
• Region of residence
• STRATUM/PSU (design info for correct variance estimates)
Creating the FileCreating the File
• Data available at the NHIS URL:
http://www.cdc.gov/nchs/nhis.htm
• Data available at the NHIS URL:
http://www.cdc.gov/nchs/nhis.htm
• SAS and SPSS programs are
also available to create datasets
from the provided data
• SAS and SPSS programs are
also available to create datasets
from the provided data
Creating the FileCreating the File• Merge the Person, Sample
Adult, and Family files together to create one data file
• Needed to merge files to analyze the association between health insurance coverage and flu shot use
• Merge the Person, Sample Adult, and Family files together to create one data file
• Needed to merge files to analyze the association between health insurance coverage and flu shot use
Creating the FileCreating the File
• Each person and each family has a unique identifier (ID) in the NHIS
• These IDs are used to merge the data sets together
• Each person and each family has a unique identifier (ID) in the NHIS
• These IDs are used to merge the data sets together
Creating the FileCreating the File• Person-level ID
• Created from household number
• (HHX) and person number (PX)
• Family-level ID
• Created from household number
• (HHX) and family number (FMX)
• Person-level ID
• Created from household number
• (HHX) and person number (PX)
• Family-level ID
• Created from household number
• (HHX) and family number (FMX)
Creating the FileCreating the File
SampleAdult file
Person file
= Adults aged < 65, non-Sample Adults aged > 65, and all children
Familyfile
= New file
Creating the FileCreating the File
• Why not drop the records for all children, all Adults aged < 65, and all adults aged > 65 who were non-Sample Adults?
• Depending on the situation, this could alter the variance estimates
• Why not drop the records for all children, all Adults aged < 65, and all adults aged > 65 who were non-Sample Adults?
• Depending on the situation, this could alter the variance estimates
Creating the FileCreating the File
• Important to retain the file with all the observations and target the analysis to the particular domain of interest
• Several software packages for analyzing survey data (such as SUDAAN and STATA) have this capability
• Important to retain the file with all the observations and target the analysis to the particular domain of interest
• Several software packages for analyzing survey data (such as SUDAAN and STATA) have this capability
Analysis Analysis • Crosstabs of flu shot
propensity among adults > 65 years of age
• Multiple logistic regression • Data from the NHIS 2000
public use files
• Crosstabs of flu shot propensity among adults > 65 years of age
• Multiple logistic regression • Data from the NHIS 2000
public use files
Subpopulation Analyzed
Subpopulation Analyzed
• 6,180 Sample Adults > 65 years of age
• Representing a population of 32.7 million
• 6,180 Sample Adults > 65 years of age
• Representing a population of 32.7 million
AnalysisAnalysis
• 89 adults > 65 years of age (1%) did not provide their flu shot status and were excluded from the analysis
• 89 adults > 65 years of age (1%) did not provide their flu shot status and were excluded from the analysis
Flu Shot Rates By Health Insurance (aged >
65)
Flu Shot Rates By Health Insurance (aged >
65)
Medicaid and Medicare 54%Medicare 58%Medicare and Private 69%Medicare and other 72%
Medicaid and Medicare 54%Medicare 58%Medicare and Private 69%Medicare and other 72%
Flu Shot Rates By Race/ethnicity (aged
> 65)
Flu Shot Rates By Race/ethnicity (aged
> 65)
Non-Hispanic black 48%Hispanic 56%Non-Hispanic other 62%Non-Hispanic white 67%
Non-Hispanic black 48%Hispanic 56%Non-Hispanic other 62%Non-Hispanic white 67%
Flu Shot Rates By Education (aged >
65)
Flu Shot Rates By Education (aged >
65)< High School 58%High school/GED 65%Some college 66%A.A. degree 66%Bachelor’s degree + 74%
< High School 58%High school/GED 65%Some college 66%A.A. degree 66%Bachelor’s degree + 74%
Flu Shot RatesBy Regular Place of
Health Care (aged > 65)
Flu Shot RatesBy Regular Place of
Health Care (aged > 65)
Yes 65%No 25%
Yes 65%No 25%
Flu Shot RatesBy No. of Physician Office Visits, Last Year (aged >
65)
Flu Shot RatesBy No. of Physician Office Visits, Last Year (aged >
65)None 38%1 visit 60%2-3 visits 61%4-5 visits 67%6-7 visits 69%
None 38%1 visit 60%2-3 visits 61%4-5 visits 67%6-7 visits 69%
8-9 visits 72%
10-12 visits73%
13-15 visits74%
16+ visits 75%
8-9 visits 72%
10-12 visits73%
13-15 visits74%
16+ visits 75%
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05
dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUEHEALTH
INSURANCE
Medicare and
Medicaid 0.96 0.74
Medicare (1.00) -Medicare
andPrivate
1.34 0.00
Medicare and
Other 1.89 0.01
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05
dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
RACE/ETHNICITY NH black (1.00) -
Hispanic 1.40 0.03
NH other 1.20 0.53
NH white 1.65 0.00
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
IND. VAR. LEVEL OR P-VALUE
EDUCATION < HS (1.00) -
HS / GED 1.26 0.00Some
college 1.22 0.06
A.A. degree 1.30 0.05
Bachelors + 1.64 0.00
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
DR. VISITS None (1.00) -
1 1.97 0.00
2-3 1.86 0.00
4-5 2.56 0.00
6-7 2.72 0.00
8-9 3.16 0.00
10-12 3.29 0.00
13-15 3.29 0.00
16+ 3.34 0.00
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
REGULAR PLACE OF
CAREYes 2.87 0.00
No (1.00) -
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05
dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
REGION Northeast (1.00) -
Midwest 1.01 0.92
South 1.05 0.58
West 1.34 0.01
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05
dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
MARITALSTATUS
NeverMarried 1.33 0.28
Married 1.28 0.04
Separated 1.16 0.58
Widowed 1.30 0.03
Divorced (1.00) -
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
SEX Female 0.89 0.09
Male (1.00) -
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
LOWINCOME
PROGRAMSYes (1.00) -
No 1.14 0.25
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
SMOKINGSTATUS Current (1.00) -
Former 1.48 0.00
Never 1.47 0.00
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
HEARTDISEASE Yes 1.29 0.00
No (1.00) -
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
DIABETES Yes 1.01 0.95
No (1.00) -
Odds Ratio (OR) From Logistic Regression
Odds Ratio (OR) From Logistic Regression
dependent variable = flu shot in last 12 monthsp<0.05dependent variable = flu shot in last 12 monthsp<0.05
IND. VAR. LEVEL OR P-VALUE
RESP.PROBLEM
SYes 1.23 0.02
No (1.00) -