title: Put the Family Back in Family Health History: A Multiple-Informant Approach.
abstract: BACKGROUND
  An accurate family health history is essential for individual risk assessment. This study uses a multiple-informant approach to examine whether family members have consistent perceptions of shared familial risk for four common chronic conditions (heart disease, Type 2 diabetes, high cholesterol, and hypertension) and whether accounting for inconsistency in family health history reports leads to more accurate risk assessment.
  
  
  METHODS
  In 2012-2013, individual and family health histories were collected from 127 adult informants of 45 families in the Greater Cincinnati Area. Pedigrees were linked within each family to assess inter-informant (in)consistency regarding common biological family member's health history. An adjusted risk assessment based on pooled pedigrees of multiple informants was evaluated to determine whether it could more accurately identify individuals affected by common chronic conditions, using self-reported disease diagnoses as a validation criterion. Analysis was completed in 2015-2016.
  
  
  RESULTS
  Inter-informant consistency in family health history reports was 54% for heart disease, 61% for Type 2 diabetes, 43% for high cholesterol, and 41% for hypertension. Compared with the unadjusted risk assessment, the adjusted risk assessment correctly identified an additional 7%-13% of the individuals who had been diagnosed, with a ≤2% increase in cases that were predicted to be at risk but had not been diagnosed.
  
  
  CONCLUSIONS
  Considerable inconsistency exists in individual knowledge of their family health history. Accounting for such inconsistency can, nevertheless, lead to a more accurate genetic risk assessment tool. A multiple-informant approach is potentially powerful when coupled with technology to support clinical decisions.
authors.affiliation: Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland. Electronic address: lin.jielu@nih.gov.
authors.forename: Jielu
authors.initials: J
authors.lastname: Lin
authors.affiliation: Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Marcum
authors.affiliation: Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
authors.forename: Melanie F
authors.initials: MF
authors.lastname: Myers
authors.affiliation: Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland.
authors.forename: Laura M
authors.initials: LM
authors.lastname: Koehly
mesh_terms: D000328:Adult; D000367:Age Factors; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000235:genetics; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000235:genetics*; D005260:Female; D030342:Genetic Diseases, Inborn / Q000453:epidemiology*; D006801:Humans; D006949:Hyperlipidemias / Q000453:epidemiology / Q000235:genetics; D006973:Hypertension / Q000453:epidemiology / Q000235:genetics; D015994:Incidence; D008297:Male; D008487:Medical History Taking / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D010375:Pedigree; D018570:Risk Assessment; D012737:Sex Factors; D011795:Surveys and Questionnaires; D014481:United States / Q000453:epidemiology