期刊论文详细信息
BMC Medical Research Methodology
Linked versus unlinked hospital discharge data on hip fractures for estimating incidence and comorbidity profiles
Caroline F Finch1  Lesley Day1  Trang Vu1 
[1] Injury Research Institute, Monash University, Melbourne, Victoria, 3800, Australia
关键词: Sensitivity and specificity;    Hospital discharge data;    Incidence;    Hip fracture;    Comorbidity;   
Others  :  1127093
DOI  :  10.1186/1471-2288-12-113
 received in 2011-10-20, accepted in 2012-07-23,  发布年份 2012
PDF
【 摘 要 】

Background

Studies comparing internally linked (person–identifying) and unlinked (episodes of care) hospital discharge data (HDD) on hip fractures have mainly focused on incidence overestimation by unlinked HDD, but little is known about the impact of overestimation on patient profiles such as comorbidity estimates. In view of the continuing use of unlinked HDD in hip fracture research and the desire to apply research results to hip fracture prevention, we concurrently assessed the accuracy of both incidence and comorbidity estimates derived from unlinked HDD compared to those estimated from internally linked HDD.

Methods

We analysed unlinked and internally linked HDD between 01 July 2005 and 30 June 2008, inclusive, from Victoria, Australia to estimate the incidence of hospital admission for fall-related hip fracture in community-dwelling older people aged 65+ years and determine the prevalence of comorbidity in patients. Community-dwelling status was defined as living in private residence, supported residential facilities or special accommodation but not in nursing homes. We defined internally linked HDD as the reference standard and calculated measures of accuracy of fall-related hip fracture incidence by unlinked HDD using standard definitions. The extent to which comorbidity prevalence estimates by unlinked HDD differed from those by the reference standard was assessed in absolute terms.

Results

The sensitivity and specificity of a standard approach for estimating fall-related hip fracture incidence using unlinked HDD (i.e. omitting records of in-hospital deaths, inter-hospital transfers and readmissions within 30 days of discharge) were 94.4% and 97.5%, respectively. The standard approach and its variants underestimated the prevalence of some comorbidities and altered their ranking. The use of more stringent selection criteria led to major improvements in all measures of accuracy as well as overall and specific comorbidity estimates.

Conclusions

This study strongly supports the use of linked rather than unlinked HDD in injury research. In health systems where linked HDD are unavailable, current approaches for identifying incident hip fractures may be enhanced by incorporating additional evidence-based criteria.

【 授权许可】

   
2012 Vu et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150219050111100.pdf 356KB PDF download
Figure 1. 70KB Image download
【 图 表 】

Figure 1.

【 参考文献 】
  • [1]Kreisfeld R, Newson R: Hip fracture injuries. AIHW National Injury Surveillance Unit, In. Adelaide; 2006.
  • [2]Leslie WD, O’Donnell S, Jean S, Lagace C, Walsh P, Bancej C, Morin S, Hanley DA, Papaioannou A: Trends in hip fracture rates in Canada. JAMA 2009, 302(8):883-889.
  • [3]Australian Institute of Health and Welfare (AIHW): The problem of osteoporotic hip fracture in Australia. Bulletin no. 76. Cat. no. AUS 121. AIHW, Canberra; 2010.
  • [4]Lawrence TM, White CT, Wenn R, Moran CG: The current hospital costs of treating hip fractures. Injury 2005, 36(1):88-91.
  • [5]Gehlbach SH, Avrunin JS, Puleo E: Trends in hospital care for hip fractures. OsteoporosInt 2007, 18(5):585-591.
  • [6]Haentjens P, Autier P, Barette M, Boonen S: The economic cost of hip fractures among elderly women. A one-year, prospective, observational cohort study with matched-pair analysis. Belgian Hip Fracture Study Group. J Bone Joint Surg Am 2001, 83-A(4):493-500.
  • [7]Tiedemann AC, Murray SM, Munro B, Lord SR: Hospital and non-hospital costs for fall-related injury in community-dwelling older people. NSW Public Health Bull 2008, 19(10):161-165.
  • [8]Kannegaard PN, van der Mark S, Eiken P, Abrahamsen B: Excess mortality in men compared with women following a hip fracture. National analysis of comedications, comorbidity and survival. Age Ageing 2010, 39(2):203-209.
  • [9]Vestergaard P, Rejnmark L, Mosekilde L: Increased mortality in patients with a hip fracture-Effect of pre-morbid conditions and post-fracture complications. OsteoporosInt 2007, 18(12):1583-1593.
  • [10]Brophy S, John G, Evans E, Lyons R: Methodological issues in the identification of hip fractures using routine hospital data: A database study. OsteoporosInt 2006, 17(3):405-409.
  • [11]Boufous S, Finch C, Close J, Day L, Lord S: Hospital admissions following presentations to emergency departments for a fracture in older people. InjPrev 2007, 13(3):211-214.
  • [12]Boufous S, Finch C: Estimating the incidence of hospitalized injurious falls: Impact of varying case definitions. InjPrev 2005, 11(6):334-336.
  • [13]Clark DE, DeLorenzo MA, Lucas FL, Wennberg DE: Epidemiology and short-term outcomes of injured medicare patients. J Am GeriatrSoc 2004, 52(12):2023-2030.
  • [14]Cassell E, Clapperton A: A decreasing trend in fall-related hip fracture incidence in Victoria, Australia. Osteoporos Int 2012.
  • [15]Dodds MK, Codd MB, Looney A, Mulhall KJ: Incidence of hip fracture in the Republic of Ireland and future projections: A population-based study. OsteoporosInt 2009, 20(12):2105-2110.
  • [16]Department of Human Services (DHS): VAED Manual. 18th edition. DHS, In. Melbourne; 2008.
  • [17]Henderson T, Shepheard J, Sundararajan V: Quality of diagnosis and procedure coding in ICD-10 administrative data. Med Care 2006, 44(11):1011-1019.
  • [18]Hayen AD, Boufous S, Harrison JE: A discussion of the potential benefits to injury surveillance through inclusion of date of injury in hospitalisation data in New South Wales and Australia. NSW Public Health Bull 2007, 18(7–8):130-132.
  • [19]National Centre for Classification in Health (NCCH): The International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification (ICD-10-AM) Fourth Edition. NCCH, In. Sydney; 2004.
  • [20]National Centre for Classification in Health (NCCH): The International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification (ICD-10-AM) Fifth Edition. NCCH, In. Sydney; 2006.
  • [21]Bradley C: Hospitalisations due to falls by older people, Australia 2008–09. Injury research and statistics series no. 62. Cat. no. INJCAT 138. Australian Institute of Health and Welfare, In. Canberra; 2012.
  • [22]Sundararajan V, Henderson T, Ackland MJ, Marshall R: Linkage of the Victorian admitted episodes dataset. In Symposium on health data linkage: its value for Australian health policy development and policy relevant research. Sydney, Australia; 2002. Available at http://www.publichealth.gov.au/publications/symposium-on-health-data-linkage:-its-value-for-australian-health-policy-development-and-policy-relevant-research:-proceedings webcite (accessed 15 September 2011)
  • [23]Ryg J, Rejnmark L, Overgaard S, Brixen K, Vestergaard P: Hip fracture patients at risk of second hip fracture: A nationwide population-based cohort study of 169,145 cases during 1977–2001. J Bone Miner Res 2009, 24(7):1299-1307.
  • [24]Nymark T, Lauritsen JM, Ovesen O, Rock ND, Jeune B: Short time-frame from first to second hip fracture in the Funen County Hip Fracture Study. OsteoporosInt 2006, 17(9):1353-1357.
  • [25]National Health Service (NHS) Quality Improvement Scotland: Surgical profiles for Scottish NHS Boards. Criteria 2007. In.: NHS; 2007. Available at: http://www.indicators.scot.nhs.uk/Surg_Docs/Criteria_2007.doc webcite. Accessed 03 December 2009
  • [26]Sackett DL, Haynes RB, Guatt GH, Tugwell P: Clinical epidemiology. A basic science for clinical medicine. 2nd edition. Little, Brown and Company, Boston; 1991.
  • [27]Australian Institute of Health and Welfare (AIHW): Residential aged care in Australia 2006–07: A statistical overview. Aged care statistics series 26. Cat. no. AGE 56. AIHW, Canberra; 2008.
  • [28]Australian Institute of Health and Welfare (AIHW): Residential aged care in Australia 2007–08: A statistical overview. Aged care statistic series 28. Cat. no. AGE 58. AIHW, Canberra; 2009.
  • [29]Australian Bureau of Statistics (ABS): Population by age and sex, Australian States and Territories. ABS, Canberra; 2009.
  • [30]Australian Institute of Health and Welfare (AIHW): Residential aged care in Australia 2005–2006: A statistical overview. Aged care statistics series 24. Cat. no. AGE 54. AIHW, Canberra; 2007.
  • [31]Australian Institute of Health and Welfare (AIHW): Australia’s health 2008. AIHW, Canberra; 2008.
  • [32]Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA: New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J ClinEpidemiol 2004, 57(12):1288-1294.
  • [33]Brand CA, Sundararajan V: A 10-year cohort study of the burden and risk of in-hospital falls and fractures using routinely collected hospital data. QualSaf Health Care 2010.
  • [34]Preen DB, Holman CDAJ, Spilsbury K, Semmens JB, Brameld KJ: Length of comorbidity lookback period affected regression model performance of administrative health data. J ClinEpidemiol 2006, 59(9):940-946.
  • [35]StataCorp: Stata Statistical Software: Release 11. StataCorp LP, In. College Station, TX; 2009.
  • [36]Australian Institute of Health and Welfare (AIHW): Improving the quality of Indigenous identification in hospital separations data. AIHW, In. Canberra; 2005.
  • [37]Vidal EI, Moreira-Filho DC, Coeli CM, Camargo KR, Fukushima FB, Blais R: Hip fracture in the elderly: Does counting time from fracture to surgery or from hospital admission to surgery matter when studying in-hospital mortality? Osteoporos Int 2009, 20(5):723-729.
  • [38]Humphries KH, Rankin JM, Carere RG, Buller CE, Kiely FM, Spinelli JJ: Co-morbidity data in outcomes research: Are clinical data derived from administrative databases a reliable alternative to chart review? J ClinEpidemiol 2000, 53(4):343-349.
  • [39]Australian Institute of Health and Welfare (AIHW): Arthritis and osteoporosis in Australia 2008. Arthritis series no. 8. Cat. no. PHE 106. AIHW, Canberra; 2008.
  • [40]Access Economics: The economic cost of not adhering to bisphosphonate treatment for osteoporosis. Access Economics, Canberra; 2006. Available at http://www.accesseconomics.com.au/publicationsreports/showreport.php?id=109&searchfor=2006&searchby=year webcite. Accessed 20 November 2009
  • [41]Australian Institute of Health and Welfare (AIHW): Vision problems among older Australians. AIHW bulletin no. 27. AIHW, Canberra; 2005. Available from http://www.aihw.gov.au/publications/index.cfm/title/10141 webcite. Accessed August 12, 2010
  • [42]Dowling AM, Finch CF: Baseline indicators for measuring progress in preventing falls injury in older people. Aust N Z J Public Health 2009, 33:413-417.
  • [43]Boockvar KS, Halm EA, Litke A, Silberzweig SB, McLaughlin M, Penrod JD, Magaziner J, Koval K, Strauss E, Siu AL: Hospital readmissions after hospital discharge for hip fracture: Surgical and nonsurgical causes and effect on outcomes. J Am GeriatrSoc 2003, 51(3):399-403.
  • [44]Teixeira A, Trinquart L, Raphael M, Bastianic T, Chatellier G, Holstein J: Outcomes in older patients after surgical treatment for hip fracture: A new approach to characterise the link between readmissions and the surgical stay. Age Ageing 2009, 38(5):584-589.
  • [45]Weatherall M: Contralateral fracture of the proximal femur. Implications for planning trials. J Bone Joint Surg Br 1999, 81(1):77-79.
  文献评价指标  
  下载次数:9次 浏览次数:11次