title: Predicting patterns of mammography use: a geographic perspective on national needs for intervention research.
abstract: OBJECTIVE
  To introduce a methodology for planning preventive health service research that takes into account geographic context.
  
  
  METHODS
  National Health Interview Survey (NHIS) self-reports of mammography within the past two years, 1987, and 1993-94. Area Resource File (ARF), 1990. Database of mammography intervention research studies conducted from 1984 to 1994.
  
  
  METHODS
  Bayesian hierarchical modeling describes mammography as a function of county-level socioeconomic data and explicitly estimates the geographic variation unexplained by the county-level data. This model produces county use estimates (both NHIS-sampled and unsampled), which are aggregated for entire states. The locations of intervention research studies are examined in light of the statewide mammography utilization estimates.
  
  
  METHODS
  Individual level NHIS data were merged with county-level data from the ARF.
  
  
  RESULTS
  State maps reveal the estimated distribution of mammography utilization and intervention research. Eighteen states with low mammography use reported no intervention research activity. County-level occupation and education were important predictors for younger women in 1993-94. In 1987, they were not predictive for any demographic group.
  
  
  CONCLUSIONS
  Opportunities exist to improve the planning of future intervention research by considering geographic context. Modeling results suggest that the choice of predictors be tailored to both the population and the time period under study when planning interventions.
authors.affiliation: Statistical Research and Applications Branch, Surveillance Research Program, National Cancer Institute, Bethesda, MD 20892, USA.
authors.forename: Julie
authors.initials: J
authors.lastname: Legler
authors.forename: Nancy
authors.initials: N
authors.lastname: Breen
authors.forename: Helen
authors.initials: H
authors.lastname: Meissner
authors.forename: Don
authors.initials: D
authors.lastname: Malec
authors.forename: Cathy
authors.initials: C
authors.lastname: Coyne
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data; D000367:Age Factors; D000368:Aged; D001499:Bayes Theorem; D003625:Data Collection / Q000706:statistics & numerical data / Q000639:trends; D005260:Female; D005843:Geography; D019538:Health Care Surveys; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D008875:Middle Aged; D010342:Patient Acceptance of Health Care / Q000208:ethnology / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D013997:Time Factors; D014481:United States