title: Updated New Zealand health system cost estimates from health events by sex, age and proximity to death: further improvements in the age of 'big data'.
abstract: OBJECTIVE
  We aimed to: (i) update previous health system cost estimates (Blakely et al NZMJ 2014;127(1393)) using updated costing data and more refined methods; and (ii) provide context around current developments in the improved networking of health information systems in New Zealand.
  
  
  METHODS
  As per our previous work, national health event data were linked for hospitalisations, inpatient procedures, outpatient events, pharmaceuticals, laboratory tests, and primary care consultations for the whole country. For each health event a cost was assigned. Health expenditure by sex and age, and proximity to death (last 6 or 12 months of life), was then calculated.
  
  
  RESULTS
  The updated and more accurate method allocated lower amounts of total public health expenditure than the previous work: $6.1, $6.0 and $6.7 billion dollars (inflation-adjusted to 2011 NZ$) in 2007/08, 2008/09 and 2009/10 financial years, respectively. But the latter is still only 52% of total health system costs ($6.7/$12.98 billion). Health system costs for people not within six months of death were similar to the previous work, except for being reduced in the most elderly age groups (range: $495 per person-year in 10-14 year old females; to $5,239 per person-year in 85-89 year old males). Costs in the last six months of life remained highly variable by age group (by a factor of 14 and being maximal at $23,400 or more among 1-4 year olds). The proportion of cumulative health expenditure in the last year of life declined with increasing age of death: eg, 47%, 25%, 13% and 6% for individuals aged 40, 70, 80 and 90 respectively.
  
  
  CONCLUSIONS
  Health system costs vary markedly across the life course, and are skewed to the last year of life. This analysis has benefited from quality improvements in cost data and method refinements, but further improvements in coming years are likely. This is particularly so with access to additional data sources, and with the move towards better integration of "big data" in the New Zealand health sector.
authors.affiliation: Department of Public Health, University of Otago, Wellington, PO Box 7343, Wellington, New Zealand. tony.blakely@otago.ac.nz.
authors.forename: Tony
authors.initials: T
authors.lastname: Blakely
authors.forename: June
authors.initials: J
authors.lastname: Atkinson
authors.forename: Giorgi
authors.initials: G
authors.lastname: Kvizhinadze
authors.forename: Nhung
authors.initials: N
authors.lastname: Nghiem
authors.forename: Heather
authors.initials: H
authors.lastname: McLeod
authors.forename: Anna
authors.initials: A
authors.lastname: Davies
authors.forename: Nick
authors.initials: N
authors.lastname: Wilson
mesh_terms: D000367:Age Factors; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D005102:Health Expenditures / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D009520:New Zealand; D012737:Sex Factors