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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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Analytical example using nhis data files l.jpg

Analytical Example Using NHIS Data Files

John R. Pleis


Research question l.jpg
Research Question

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


Additional covariates l.jpg
Additional Covariates

  • Race/ethnicity

  • Region of residence

  • Education, marital status, sex

  • Smoking

  • Number of physician office visits


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Additional Covariates

  • Regular place of health care

  • Selected chronic conditions

    • diabetes, respiratory difficulties, or heart disease

  • Low-income program participation


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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


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Data Files

  • This analysis will utilize data from several files, which include:

    • Person

    • Sample adult

    • Family


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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


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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)


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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


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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


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2000: Race/ethnicity (%)Sample Adults (aged > 65)

Source: 2000 NHIS


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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)


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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



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Research Question

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


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Additional Covariates

  • Race/ethnicity

  • Region of residence

  • Education, marital status, sex

  • Smoking

  • Number of physician office visits


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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)


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Additional Covariates

  • Respiratory difficulties

    • Asthma (EVER)

    • Chronic Obstructive Pulmonary Disease (COPD)


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Additional Covariates

  • Heart disease (EVER)

  • Coronary heart disease

  • Angina pectoris

  • Heart attack

  • Any other heart condition


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Additional Covariates

  • Low-income programs

    • Supplemental Security Income

    • Temporary Assistance for Needy Families (TANF)

    • Food stamps

    • Governmental rental assistance


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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


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Creating the File

  • Person file

    • Health insurance

    • Race/ethnicity (all)

    • Governmental rental assistance (last 12 months)


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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)


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Creating the File

  • Sample Adult file

    • Sample Adult weight


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Creating the File

  • Family file

    • Any family member received any of the following in the past 12 months:

      • Supplemental Security Income

      • TANF

      • Food stamps


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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)


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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


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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


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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


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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)


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Creating the File

Person

file

Sample

Adult file

=

New file

Family

file

= Adults aged < 65, non-Sample Adults

aged > 65, and all children


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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


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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


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Analysis

  • Crosstabs of flu shot propensity among adults > 65 years of age

  • Multiple logistic regression

  • Data from the NHIS 2000 public use files


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Subpopulation Analyzed

  • 6,180 Sample Adults > 65 years of age

  • Representing a population of 32.7 million


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Analysis

  • 89 adults > 65 years of age (1%) did not provide their flu shot status and were excluded from the analysis


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Flu Shot Rates By Health Insurance (aged > 65)

Medicaid and Medicare 54%

Medicare 58%

Medicare and Private 69%

Medicare and other 72%


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Flu Shot Rates By Race/ethnicity (aged > 65)

Non-Hispanic black 48%

Hispanic 56%

Non-Hispanic other 62%

Non-Hispanic white 67%


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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%


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Flu Shot RatesBy Regular Place of Health Care (aged > 65)

Yes 65%

No 25%


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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)


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Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


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Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


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Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression45 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression46 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression47 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression48 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression49 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression50 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression51 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression52 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression53 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


Odds ratio or from logistic regression54 l.jpg
Odds Ratio (OR) From Logistic Regression

dependent variable = flu shot in last 12 months

p<0.05


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