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Analytical Example Using NHIS Data Files John R. Pleis Research Question Is the type of health insurance coverage held by adults > 65 years of age associated with flu shot use? Additional Covariates Race/ethnicity Region of residence Education, marital status, sex Smoking

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research question
Research Question

Is the type of health insurance coverage held by adults > 65 years of age associated with flu shot use?

additional covariates
Additional Covariates
  • Race/ethnicity
  • Region of residence
  • Education, marital status, sex
  • Smoking
  • Number of physician office visits
additional covariates4
Additional Covariates
  • 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
data files6
Data Files
  • This analysis will utilize data from several files, which include:
    • Person
    • Sample adult
    • Family
person file
Person 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
person file8
Person 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)

sample adult file
Sample 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
sampling weights
Sampling 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
sample design
Sample 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)
sample design13
Sample 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
research question15
Research Question

Is the type of health insurance coverage held by adults > 65 years of age associated with flu shot use?

additional covariates16
Additional Covariates
  • Race/ethnicity
  • Region of residence
  • Education, marital status, sex
  • Smoking
  • Number of physician office visits
additional covariates17
Additional 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)
additional covariates18
Additional Covariates
  • Respiratory difficulties
    • Asthma (EVER)
    • Chronic Obstructive Pulmonary Disease (COPD)
additional covariates19
Additional Covariates
  • Heart disease (EVER)
  • Coronary heart disease
  • Angina pectoris
  • Heart attack
  • Any other heart condition
additional covariates20
Additional Covariates
  • Low-income programs
    • Supplemental Security Income
    • Temporary Assistance for Needy Families (TANF)
    • Food stamps
    • Governmental rental assistance
creating the file
Creating 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
creating the file22
Creating the File
  • Person file
    • Health insurance
    • Race/ethnicity (all)
    • Governmental rental assistance (last 12 months)
creating the file23
Creating 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)
creating the file24
Creating the File
  • Sample Adult file
    • Sample Adult weight
creating the file25
Creating the File
  • Family file
    • Any family member received any of the following in the past 12 months:
      • Supplemental Security Income
      • TANF
      • Food stamps
creating the file26
Creating the File
  • Person and Sample Adult files
    • Education, marital status, sex
  • All files
    • Region of residence
    • STRATUM/PSU (design info for correct variance estimates)
creating the file27
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

Creating the File
creating the file28
Creating 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
creating the file29
Creating 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
creating the file30
Creating 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)
creating the file31
Creating the File

Person

file

Sample

Adult file

=

New file

Family

file

= Adults aged < 65, non-Sample Adults

aged > 65, and all children

creating the file32
Creating 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
creating the file33
Creating 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
analysis
Analysis
  • 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
analysis36
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%

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%

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%

flu shot rates by 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%

8-9 visits 72%

10-12 visits 73%

13-15 visits 74%

16+ visits 75%

Flu Shot RatesBy No. of Physician Office Visits, Last Year (aged > 65)
odds ratio or from logistic regression
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression43
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression44
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression45
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression46
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression47
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression48
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression49
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression50
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression51
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression52
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression53
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05

odds ratio or from logistic regression54
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05