title: US Spending on Personal Health Care and Public Health, 1996-2013.
abstract: US health care spending has continued to increase, and now accounts for more than 17% of the US economy. Despite the size and growth of this spending, little is known about how spending on each condition varies by age and across time.
  
  
  
  To systematically and comprehensively estimate US spending on personal health care and public health, according to condition, age and sex group, and type of care.
  
  
  
  Government budgets, insurance claims, facility surveys, household surveys, and official US records from 1996 through 2013 were collected and combined. In total, 183 sources of data were used to estimate spending for 155 conditions (including cancer, which was disaggregated into 29 conditions). For each record, spending was extracted, along with the age and sex of the patient, and the type of care. Spending was adjusted to reflect the health condition treated, rather than the primary diagnosis.
  
  
  
  Encounter with US health care system.
  
  
  
  National spending estimates stratified by condition, age and sex group, and type of care.
  
  
  
  From 1996 through 2013, $30.1 trillion of personal health care spending was disaggregated by 155 conditions, age and sex group, and type of care. Among these 155 conditions, diabetes had the highest health care spending in 2013, with an estimated $101.4 billion (uncertainty interval [UI], $96.7 billion-$106.5 billion) in spending, including 57.6% (UI, 53.8%-62.1%) spent on pharmaceuticals and 23.5% (UI, 21.7%-25.7%) spent on ambulatory care. Ischemic heart disease accounted for the second-highest amount of health care spending in 2013, with estimated spending of $88.1 billion (UI, $82.7 billion-$92.9 billion), and low back and neck pain accounted for the third-highest amount, with estimated health care spending of $87.6 billion (UI, $67.5 billion-$94.1 billion). The conditions with the highest spending levels varied by age, sex, type of care, and year. Personal health care spending increased for 143 of the 155 conditions from 1996 through 2013. Spending on low back and neck pain and on diabetes increased the most over the 18 years, by an estimated $57.2 billion (UI, $47.4 billion-$64.4 billion) and $64.4 billion (UI, $57.8 billion-$70.7 billion), respectively. From 1996 through 2013, spending on emergency care and retail pharmaceuticals increased at the fastest rates (6.4% [UI, 6.4%-6.4%] and 5.6% [UI, 5.6%-5.6%] annual growth rate, respectively), which were higher than annual rates for spending on inpatient care (2.8% [UI, 2.8%-2.8%] and nursing facility care (2.5% [UI, 2.5%-2.5%]).
  
  
  
  Modeled estimates of US spending on personal health care and public health showed substantial increases from 1996 through 2013; with spending on diabetes, ischemic heart disease, and low back and neck pain accounting for the highest amounts of spending by disease category. The rate of change in annual spending varied considerably among different conditions and types of care. This information may have implications for efforts to control US health care spending.
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Joseph L
authors.initials: JL
authors.lastname: Dieleman
authors.affiliation: Global Health Sciences, University of California, San Francisco, San Francisco.
authors.forename: Ranju
authors.initials: R
authors.lastname: Baral
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Maxwell
authors.initials: M
authors.lastname: Birger
authors.affiliation: David Geffen School of Medicine, University of California, Los Angeles, Los Angeles.
authors.forename: Anthony L
authors.initials: AL
authors.lastname: Bui
authors.affiliation: Global Health Sciences, University of California, San Francisco, San Francisco.
authors.forename: Anne
authors.initials: A
authors.lastname: Bulchis
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Abigail
authors.initials: A
authors.lastname: Chapin
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Hannah
authors.initials: H
authors.lastname: Hamavid
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Cody
authors.initials: C
authors.lastname: Horst
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Elizabeth K
authors.initials: EK
authors.lastname: Johnson
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Joseph
authors.affiliation: World Bank, Washington, DC.
authors.forename: Rouselle
authors.initials: R
authors.lastname: Lavado
authors.affiliation: Northwell Health, New Hyde Park, New York.
authors.forename: Liya
authors.initials: L
authors.lastname: Lomsadze
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Alex
authors.initials: A
authors.lastname: Reynolds
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Ellen
authors.initials: E
authors.lastname: Squires
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Madeline
authors.initials: M
authors.lastname: Campbell
authors.affiliation: University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
authors.forename: Brendan
authors.initials: B
authors.lastname: DeCenso
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Daniel
authors.initials: D
authors.lastname: Dicker
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Abraham D
authors.initials: AD
authors.lastname: Flaxman
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Rose
authors.initials: R
authors.lastname: Gabert
authors.affiliation: US Bureau of Economic Analysis, Washington, DC.
authors.forename: Tina
authors.initials: T
authors.lastname: Highfill
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Naghavi
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Noelle
authors.initials: N
authors.lastname: Nightingale
authors.affiliation: Department of Statistics, Stanford University, Palo Alto, California.
authors.forename: Tara
authors.initials: T
authors.lastname: Templin
authors.affiliation: New Zealand Ministry of Health, Wellington, New Zealand.
authors.forename: Martin I
authors.initials: MI
authors.lastname: Tobias
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Theo
authors.initials: T
authors.lastname: Vos
authors.affiliation: Institute for Health Metrics and Evaluation, Seattle, Washington.
authors.forename: Christopher J L
authors.initials: CJ
authors.lastname: Murray
mesh_terms: D017677:Age Distribution; D000367:Age Factors; D004194:Disease / Q000145:classification / Q000191:economics*; D016527:Drug Costs / Q000706:statistics & numerical data / Q000639:trends; D035082:Federal Government; D017048:Health Care Costs / Q000706:statistics & numerical data / Q000639:trends; D005102:Health Expenditures / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D038801:International Classification of Diseases; D010548:Personal Health Services / Q000191:economics* / Q000706:statistics & numerical data / Q000639:trends; D011634:Public Health / Q000191:economics* / Q000706:statistics & numerical data / Q000639:trends; D017678:Sex Distribution; D012737:Sex Factors; D014481:United States; D014947:Wounds and Injuries / Q000191:economics