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Statistical Methodology of the National Immunization Survey: 1994-2002 Philip J. Smith, Ph.D., Centers for Disease Control and Prevention; David C. Hoaglin,

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Presentation on theme: "Statistical Methodology of the National Immunization Survey: 1994-2002 Philip J. Smith, Ph.D., Centers for Disease Control and Prevention; David C. Hoaglin,"— Presentation transcript:

1 Statistical Methodology of the National Immunization Survey: 1994-2002 Philip J. Smith, Ph.D., Centers for Disease Control and Prevention; David C. Hoaglin, Ph.D. Abt Associates Inc.; Michael P. Battaglia, M.S., Abt Associates Inc.; Meena Khare, M.S.., Centers for Disease Control and Prevention; Lawrence E. Barker, Ph.D., Centers for Disease Control and Prevention

2 NIS Sampling Design Independent samples in each of 78 Immunization Action Plan (IAP) areas. These areas will begin to “rotate” soon AtlantaSeattle San Diego Phoenix El Paso San Antonio Dallas Memphis Nashville Indianapolis Detroit Cleveland Columbus Miami Jacksonville Los Angeles Santa Clara New Orleans Birmingham Houston Milwaukee Philadelphia Newark NYC Boston Baltimore DC Chicago

3 Target Pop: 5.8M children 19-35 months of age 3.4 million telephone numbers sampled 31,693 kids 19-35 m.o.a completing the RDD survey 21,410 kids with sufficient provider-reported data to know their UTD status NIS Sampling Plan RDDPRC

4 3 Important Advances: NIS Survey Weights National AdjustmentPercentage Children who Received No Vacc Doses 0.3% Nontelephone Households 4.5% Vaccination Provider Nonresponse 32.7%

5 Rates per 100,000: Zero Dose Children 19-35 moa

6 Characteristics of Zero Dose Children Child Characteristics Non-Hispanic White 2-5x more likely than Hisp. or Non-Hisp Black Maternal Characteristics College education 2-3x more likely than ≤ 12 years > 30 years of age 2-3x more likely than moms 20-29 years Households Characteristics Income >$75k/yr 2-6x more likely than ≤ $75k/yr 4+ children in HH 2-10x more likely than 1 child

7 Variation in Telephone Coverage

8 How the “Nontelephone” Adjustment Works Research by Frankel et al. has shown that HHs that experienced an interruption in telephone service are very much like nontelephone households. Determine which HHs have had an interruption Increase the survey weights of children living in HHs with a recent interruption to represent both interruption and nontelephone HHs.

9 Characteristics of HHs with an Interruption Child Characteristics Hispanics 2x more likely than Non-Hisp. Whites Blacks 2x more likely than Non-Hisp. Whites Native Americans 3x more likely than Non-Hisp. Whites Foreign Born 2x more likely than US born Maternal Characteristics Divorced or Never Married> 2x more likely than married 2x more likely than w/ some college Preferred language: Spanish 2x more likely than English Maternal age ≤ 29 years > 2x more likely than ≥ 30 years Households Characteristics Income below poverty 10x more likely than >$75k/yr 4+ children in HH~ 2x more likely than 1 child

10 Provider Nonresponse Rates

11 How the Provider Nonresponse Adjustment Works Identify characteristics associated with provider nonresponse W/in each IAP area, group children into adjustment classes: with similar propensities of having provider data Within each adjustment class: Increase the survey weights of children with adequate provider reported vacc histories to represent all of the children in the class

12 Children w/out Adequate Provider Report Child Characteristics Non-Hipanaic Black and Hispanics ~1.2x more likely than Non-Hisp. Whites Foreign Born 1.4x more likely than US born Maternal Characteristics No Shot Card 1.1 x more likely than shot card moms Households Characteristics Unknown HH income 1.4x more likely than married

13 Summary Progress Adjusting NIS vacc coverage estimates for nonsampling errors


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