title: Trends in acute myocardial infarction hospitalizations: Are we seeing the whole picture?
abstract: BACKGROUND
  Payers and policy makers rely on studies of trends in acute myocardial infarction (AMI) hospitalizations and spending that count only hospitalizations where the AMI is the principal discharge diagnosis. Hospitalizations with AMI coded as a secondary diagnosis are ignored. The effects of excluding these hospitalizations on estimates of trends are unknown.
  
  
  METHODS
  Observational study of all AMI hospitalizations in Fee-for-Service Medicare beneficiaries 65 years and older, from 2002 through 2011.
  
  
  RESULTS
  We studied 3,663,137 hospitalizations with any AMI discharge diagnosis over 288,873,509 beneficiary-years. Of these, 66% had AMI coded as principal (versus secondary). From 2002 to 2011, AMI hospitalization rates declined 24.5% (from 1,485 per 100,000 beneficiary-years in 2002 to 1,122 in 2011). Meanwhile, the proportion of these hospitalizations with a secondary AMI diagnosis increased from 28% to 40%; by 2011 these secondary AMI hospitalizations accounted for 43% of all expenditures for hospitalizations with AMI, or $2.8 billion. Major changes in comorbidities, principal diagnoses and mean costs for hospitalizations with a non-principal AMI diagnosis occurred in the 2006-2008 timeframe.
  
  
  CONCLUSIONS
  Current estimates of the burden of AMI ignore an increasingly large proportion of overall AMI hospitalizations and spending. Changes in the characteristics of hospitalizations that coincided with major payment and policy changes suggest that non-clinical factors affect AMI coding. Failing to consider all AMIs could inflate estimates of population health improvements, overestimate the value of AMI prevention and treatment and underestimate current and future AMI burden and expenditures.
authors.affiliation: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA. Electronic address: naomi.sacks@umassmed.edu.
authors.forename: Naomi C
authors.initials: NC
authors.lastname: Sacks
authors.affiliation: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA.
authors.forename: Arlene S
authors.initials: AS
authors.lastname: Ash
authors.affiliation: National Bureau of Economic Research, Cambridge, MA.
authors.forename: Kaushik
authors.initials: K
authors.lastname: Ghosh
authors.affiliation: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA; Center for Healthcare Organization and Implementation Research (CHOIR), VA Boston Healthcare System, Boston, MA.
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
authors.affiliation: Division of Clinical Decision Making, Department of Medicine, Tufts Medical Center, Boston, MA.
authors.forename: John B
authors.initials: JB
authors.lastname: Wong
authors.affiliation: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA; National Bureau of Economic Research, Cambridge, MA.
authors.forename: Allison B
authors.initials: AB
authors.lastname: Rosen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D017281:Cost of Illness; D005260:Female; D005102:Health Expenditures / Q000706:statistics & numerical data / Q000639:trends; D006293:Health Promotion; D006760:Hospitalization / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D009203:Myocardial Infarction / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology / Q000517:prevention & control / Q000628:therapy; D014481:United States / Q000453:epidemiology