PowerPoint Presentation from May 2011 Personal Validation and Entity Resolution Conference. Presenters: T. Lamagni, N. Potz, D. Powell, N. Hinton, A. Grant, E. Sheridan, R. Pebody. Presentation Title: Application of probabilistic linkage methods to join infectious disease surveillance records to death registrations
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Application of Probabilistic Linkage Methods_Join Infectious Disease Surveillance Records-Death Registrations_PVERConf_May2011
1. Application of probabilistic linkage methods to join infectious disease surveillance records to death registrations T Lamagni, N Potz, D Powell, N Hinton, A Grant, E Sheridan, R Pebody Healthcare-Associated Infection & Antimicrobial Resistance Department
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8. blocking and weighting variables Block A1 A2 1941 1942 1941 1942 … … … … Match 1941 1941 Weight blocked by SOUNDEX* blocked by year of birth blocked by SOUNDEX* blocked by year of birth Weight of matched SOUNDEX* Weight of matched year of birth A1 A1 A1 A2 weights are based on the likelihood of each value representing a true match matching variables (e.g. patient identifiers) compared within each matched pair of records * code based on surname Infection data Mortality data Format A112 A112 +17.2 A112 A420 -8.0 1941 1941 +6.8 + … …
9. post-matching stages merge and de-duplicate set threshold for auto accept/reject manually check pairs in ‘grey zone’ final matched dataset matched record pairs from SOUNDEX blocking matched record pairs from year of birth blocking
10. evaluation of probabilistic matching vs NHS Central Register Tracing Potz N et al. Probabilistic record linkage of infection records and death registrations: a tool to strengthen surveillance. Stat Commun Infect Dis 2010; 2(1):article 6. manual checking zone
11. probability of true match according to distribution of total weight scores Potz N et al. Probabilistic record linkage of infection records and death registrations: a tool to strengthen surveillance. Stat Commun Infect Dis 2010; 2(1):article 6.
12. evaluation of probabilistic matching vs NHS Central Register Tracing +ve predictive value 97.7% (465/476) to 99.8% (465/466) -ve predictive value 90.2% (692/767) to 97.9% (692/707) Potz N et al. Probabilistic record linkage of infection records and death registrations: a tool to strengthen surveillance. Stat Commun Infect Dis 2010; 2(1):article 6. NHS CR Tracing Traced Dead Traced Not dead Not traced Probabilistic record linkage Matched to a death record 465 1 10 476 Not matched to a death record 15 692 60 767 480 693 70 1243
13. interval between diagnosis of MRSA bacteraemia and death England 2004-5 30 day case fatality rate = 38% 7 day case fatality rate = 20% Lamagni TL, et al. Mortality in patients with MRSA bacteraemia, England 2004-05. J Hosp Infect 2011;77:16-20.
14. Kaplan-Meier time to death following invasive S. pyogenes infection England & Wales 2003-04 Lamagni TL et al. Predictors of death after severe Streptococcus pyogenes infection. Emerg Infect Dis 2009;15(8):1304-7.
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Editor's Notes
Stages of matching Pre-match preparation = formatting, blocking, weighting etc. Explanation of graph: Distribution of total weights of record pairs is roughly bimodal. i.e. two overlapping populations. Where they overlap is a grey area that requires manual checking. Above = good matches, below = non-matches. Gives us matched and unmatched pairs, and a set of records for manual checking.