An application of triangles to safety research. Steve Gallivan Clinical Operational Research Unit UCL

Transcription

1 An applcaton of trangles to safety research Steve Gallvan Clncal Operatonal Research Unt UCL

2 Collaborators Nck Barber, Katja Taxs, Bryony Dean Frankln, Chrstos Paschaldes Martn Utley School of Pharmacy,London Unversty of Gronngen Academc Pharmacy Unt, Hammersmth Hosptal CORU CORU

3 The Henrch rato H.W. Henrch, Industral accdent preventon (1931)

4 Project funded by Natonal Patent Safety Agency (NPSA) Objectves 1. Is there a stable rato of mnor:moderate:severe medcaton errors? 2. What are the values of the rato?

5 Methodologcal approach Logc thought experments Mathematcal modelng Revew of the lterature Examnaton of emprcal evdence

6 Logcal thought experments and mathematcal modellng

7 Vgnette 1: Irsh roulette Lve cartrdge Blank cartrdge Empty Rato Death:powder burn:no njury Orgnal loadng pattern :2:3 Reduce mnor njures :1:4 Henrch s concept would not be true

8 Vgnette 2 - Categorsaton Normal Underweght Grossly underweght v v 1 2 Brthweght Rato s exqustely senstve to the thresholds used, n prncple any rato could exst If you want rato A:B:C what values for V and V gve ths? 1 2

9 Emprcal evdence: Constructng Henrch ratos from exstng data

10 Studes of medcaton errors Included studes Severe Moderate Mnor Adverse drug events and medcaton errors (all types) Bates et al Kaushal et al Prescrbng errors Dean et al Dean et al Lesar Admnstraton errors Dean and Barber Taxs and Barber Van den Bemt et al Hartley and Dhllon Tssot et al

11 Trangles comparng studes Suppose -th study fnds nstances of: x no-harm events y mnor events z major events 1 ˆ ˆ ˆ = + + z y x Note Form vector of relatve ratos: ( ) = = z y x z z y x y z y x x z y x,, ˆ, ˆ, ˆ r So,,, r r r all le n a plane wth equaton 1 = + + z y x

12 z Major ncdents (0,0,1) 1 3( z y ) 2x y z + +, x y z 2 2 Pont representng outcome of -th study (1,0,0) x No harm ncdents (0,1,0) y Mnor ncdents

13 Approxmate 95% confdence regon for relatve ratos Major No harm Mnor 95% C.I. for proporton of No harm ncdents

14 Approxmate confdence regon compared to exact verson

15 Example of a trangle plot Data source: Wadman et al.2003 ER outcomes Ratos of outcome by type of accdent from patents vstng emergency unts are not stable

16 Studes of medcaton errors Included studes Severe Moderate Mnor Adverse drug events and medcaton errors (all types) Bates et al Kaushal et al Prescrbng errors Dean et al Dean et al Lesar Admnstraton errors Dean and Barber Taxs and Barber Van den Bemt et al Hartley and Dhllon Tssot et al

17 Ratos from medcaton error studes are wdely dfferent

18 Lesar 1997

19 Dean et al 2002

20 Some of the varablty n ratos may be due to Lack of generally agreed defntons for basc terms n medcaton error research Lack of assessng relablty and valdty of research methods Poor standard of reportng detals of methods and defntons n publshed studes So we studed examples wth establshed defntons/methods.

21 Road Traffc Accdents for UK Death njury Data source: Transport Statstcs Data ponts Mnor njury Serous njury Ratos of outcomes of road traffc accdents are not stable, but change systematcally over tme

22 Conclusons We developed a novel graphcal llustraton of Henrch ratos whch allows statstcal comparsons There s no Henrch rato for harm resultng from medcaton errors Orgnal concept may not be vald Logc shows other stuatons occur Mathematcs shows that dependng on the defnton of boundares any rato could be acheved No emprcal evdence for stable rato analysng medcaton error and other data Defntons of medcaton errors and the ensung harm are wdely dfferent

23 A smple lnear form - halve the errors and you halve the harm - s for most cases not vald

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