Towards valid ‘serious non-fatal injury’ indicators for international comparisons based on probability of admission estimates

Cryer, Colin; Miller, Ted R.; Lyons, Ronan A; Macpherson, Alison; Pérez, Katherine; Petridou, Eleni; Dessypris, Nick; Davie, Gabrielle; Gulliver, Pauline; Lauritsen, Jens; Boufous, Soufiane; Lawrence, Bruce B.; Graaf, Brandon de; Steiner, Claudia

doi:10.1136/injuryprev-2016-042020

Cited by 5 publications

(10 citation statements)

References 11 publications

(5 reference statements)

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“…In synopsis, data quality, specificity of coding and completeness of coding are likely explanations for many of the observed variations in diagnosis-specific estimates between regions. It was suggested that the true PrAs may be higher than shown in that paper’s Tables, and hence those shown in this paper’s table 1 6…”

Section: Discussionmentioning

confidence: 60%

“…PrA estimates were only available for ICD-10 injury diagnosis codes at the 4-character level 6. As a result, there was some loss of specificity compared with the use of ICD-10-AM at the 5-character level 9.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, there was some loss of specificity compared with the use of ICD-10-AM at the 5-character level 9. This restricts the ability to distinguish, for example, between open fractures, which tend to be admitted, and closed fractures 6. It appears, however, that ICISS is less sensitive to this degradation than one would expect, as illustrated by similar abilities of ICD-10 and ICD-10-AM ICISS to predict mortality, and their abilities to discriminate between serious and other injury 7…”

Section: Discussionmentioning

confidence: 99%

“…The previous paper describes limitations of the PrA estimates for the regions of Canada, Denmark and Greece 6. In synopsis, data quality, specificity of coding and completeness of coding are likely explanations for many of the observed variations in diagnosis-specific estimates between regions.…”

Section: Discussionmentioning

confidence: 99%

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Empirical validation of the New Zealand serious non-fatal injury outcome indicator for ‘all injury’

et al. 2017

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Our purpose was to empirically validate the official New Zealand (NZ) serious non-fatal 'all injury' indicator. To that end, we aimed to investigate the assumption that cases selected by the indicator have a high probability of admission. Using NZ hospital in-patient records, we identified serious injury diagnoses, captured by the indicator, if their diagnosis-specific survival probability was ≤0.941 based on at least 100 admissions. Corresponding diagnosis-specific admission probabilities from regions in Canada, Denmark and Greece were estimated. Aggregate admission probabilities across those injury diagnoses were calculated and inference made to New Zealand. The admission probabilities were 0.82, 0.89 and 0.90 for the regions of Canada, Denmark and Greece, respectively. This work provides evidence that the threshold set for the official New Zealand serious non-fatal injury indicator for 'all injury' captures injuries with high aggregate admission probability. If so, it is valid for monitoring the incidence of serious injuries.

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Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Empirical validation of the New Zealand serious non-fatal injury outcome indicator for ‘all injury’

et al. 2017

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“…We used admission to an inpatient ward as an indicator of relative seriousness within this cohort. While the validity of length of stay and likelihood of admission has been questioned as indicators of seriousness over time (Cryer et al, ; Cryer, Gulliver, Langley, & Davie, ), inpatient admission implies the need for ongoing medical support. Furthermore, the current analysis is concerned with a single year cohort, rather than trends over time, indicating the suitability of this measure for the current analysis (Cryer et al, , ).…”

Section: Methodsmentioning

confidence: 99%

Unintentional non‐fatal hospitalised falls and motor vehicle injuries in the Auckland and Northland regions: Geographical patterns and associations with rurality

et al. 2019

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This paper examined the relationship between rurality and unintentional non-fatal hospitalisations for falls and motorised vehicle transport injuries and their geographic patterns in New Zealand. Data were constructed from Statistics New Zealand's Integrated Data Infrastructure. Logistic regression revealed that the odds of hospitalisations from falls were lower in more rural areas, but motor vehicle injuries were higher. The odds of injured people being discharged from a ward increased with rurality for both injury types. This study discovered geographical disparities in the two injury types.The findings of this study direct attention to underlying contextual factors that require targeted prevention strategies. K E Y W O R D Sfalls, geographic patterns, motorised vehicle transport injuries, Rurality Index, New Zealand

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Using a severity threshold to improve occupational injury surveillance: Assessment of a severe traumatic injury‐based occupational health indicator across the International Classification of Diseases lexicon transition

Sears,

Victoroff,

Bowman

et al. 2023

American J Industrial Med

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BackgroundTraumatic injury is a leading cause of death and disability among US workers. Severe injuries are less subject to systematic ascertainment bias related to factors such as reporting barriers, inpatient admission criteria, and workers' compensation coverage. A state‐based occupational health indicator (OHI #22) was initiated in 2012 to track work‐related severe traumatic injury hospitalizations. After 2015, OHI #22 was reformulated to account for the transition from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9‐CM) to ICD‐10‐CM. This study describes rates and trends in OHI #22, alongside corresponding metrics for all work‐related hospitalizations.MethodsSeventeen states used hospital discharge data to calculate estimates for calendar years 2012–2019. State‐panel fixed‐effects regression was used to model linear trends in annual work‐related hospitalization rates, OHI #22 rates, and the proportion of work‐related hospitalizations resulting from severe injuries. Models included calendar year and pre‐ to post‐ICD‐10‐CM transition.ResultsWork‐related hospitalization rates showed a decreasing monotonic trend, with no significant change associated with the ICD‐10‐CM transition. In contrast, OHI #22 rates showed a monotonic increasing trend from 2012 to 2014, then a significant 50% drop, returning to a near‐monotonic increasing trend from 2016 to 2019. On average, OHI #22 accounted for 12.9% of work‐related hospitalizations before the ICD‐10‐CM transition, versus 9.1% post‐transition.ConclusionsAlthough hospital discharge data suggest decreasing work‐related hospitalizations over time, work‐related severe traumatic injury hospitalizations are apparently increasing. OHI #22 contributes meaningfully to state occupational health surveillance efforts by reducing the impact of factors that differentially obscure minor injuries; however, OHI #22 trend estimates must account for the ICD‐10‐CM transition‐associated structural break in 2015.

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Towards valid ‘serious non-fatal injury’ indicators for international comparisons based on probability of admission estimates

Cited by 5 publications

References 11 publications

Empirical validation of the New Zealand serious non-fatal injury outcome indicator for ‘all injury’

Empirical validation of the New Zealand serious non-fatal injury outcome indicator for ‘all injury’

Unintentional non‐fatal hospitalised falls and motor vehicle injuries in the Auckland and Northland regions: Geographical patterns and associations with rurality

Using a severity threshold to improve occupational injury surveillance: Assessment of a severe traumatic injury‐based occupational health indicator across the International Classification of Diseases lexicon transition

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