title: Combining estimates from complementary surveys: a case study using prevalence estimates from national health surveys of households and nursing homes.
abstract: OBJECTIVE
  When a single survey does not cover a domain of interest, estimates from two or more complementary surveys can be combined to extend coverage. The purposes of this article are to discuss and demonstrate the benefits of combining estimates from complementary surveys and to provide a catalog of the analytic issues involved.
  
  
  METHODS
  The authors present a case study in which data from the National Health Interview Survey and the National Nursing Home Survey were combined to obtain prevalence estimates for several chronic health conditions for the years 1985, 1995, and 1997. The combined prevalences were estimated by ratio estimation, and the associated variances were estimated by Taylor linearization. The survey weights, stratification, and clustering were reflected in the estimation procedures.
  
  
  RESULTS
  In the case study, for the age group of 65 and older, the combined prevalence estimates for households and nursing homes are close to those for households alone. For the age group of 85 and older, however, the combined estimates are sometimes substantially different from the household estimates. Such differences are seen both for estimates within a single year and for estimates of trends across years.
  
  
  CONCLUSIONS
  Several general issues regarding comparability arise when there is a goal of combining complementary survey data. As illustrated by this case study, combining estimates can be very useful for improving coverage and avoiding misleading conclusions.
authors.affiliation: Office of Research and Methodology, National Center for Health Statistics, Hyattsville, MD 20782, USA. nschenker@cdc.gov
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Schenker
authors.forename: Jane F
authors.initials: JF
authors.lastname: Gentleman
authors.forename: Deborah
authors.initials: D
authors.lastname: Rose
authors.forename: Esther
authors.initials: E
authors.lastname: Hing
authors.forename: Iris M
authors.initials: IM
authors.lastname: Shimizu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001168:Arthritis / Q000453:epidemiology; D001943:Breast Neoplasms / Q000453:epidemiology; D002561:Cerebrovascular Disorders / Q000453:epidemiology; D002908:Chronic Disease / Q000145:classification / Q000453:epidemiology*; D003430:Cross-Sectional Studies; D003920:Diabetes Mellitus / Q000453:epidemiology; D005191:Family Characteristics; D005260:Female; D006306:Health Surveys; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D008297:Male; D017202:Myocardial Ischemia / Q000453:epidemiology; D009309:National Center for Health Statistics, U.S.; D009735:Nursing Homes / Q000706:statistics & numerical data*; D019982:Organizational Case Studies; D015995:Prevalence; D040442:Public Health Informatics; D018511:Systems Integration; D014481:United States / Q000453:epidemiology