The icd 10 injury diagnosis matrix grouping s and t codes by body region and nature of injury
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The ICD-10 injury diagnosis matrix: Grouping S and T codes by body region and nature of injury. Paul R. Jones and Bruce A. Lawrence Pacific Institute for Research and Evaluation Lois A. Fingerhut National Center for Health Statistics November, 2004. Background.

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The icd 10 injury diagnosis matrix grouping s and t codes by body region and nature of injury

The ICD-10 injury diagnosis matrix: Grouping S and T codes by body region and nature of injury

Paul R. Jones and Bruce A. Lawrence

Pacific Institute for Research and Evaluation

Lois A. Fingerhut

National Center for Health Statistics

November, 2004


Background
Background by body region and nature of injury

  • ICD-10, like its predecessor ICD-9, contains so many detailed codes that it is often difficult to see the forest for the trees. Researchers, epidemiologists, and public health administrators, therefore, often rely on various methods for grouping codes into more manageable categories.

  • For injury research, one of the most useful tools has been the Barell Matrix (Barell et al., 2001), which categorizes ICD-9-CM injury morbidity codes by body region and nature of injury.

  • Since 1999, mortality data have been coded in ICD-10. A successor to the Barell Matrix for use with ICD-10 injury mortality diagnosis codes would be a new tool to aid researchers and policymakers.


Background1
Background by body region and nature of injury


Coding and validating the algorithm
Coding and Validating the Algorithm by body region and nature of injury

  • A draft of the ICD-10 injury diagnosis matrix was first provided by Lois A. Fingerhut (NCHS). That draft was based on earlier work by Richard Hockey in Australia.

  • The matrix classifies all injury ‘S’ and ‘T’ codes by body region and nature of injury. With 19 nature-of-injury categories and 42 body-region categories, it is somewhat more detailed than the original Barell Matrix. Like the original, it also provides for collapsing the body regions into broader categories.

  • PIRE translated the matrix into a SAS algorithm, which can operate on any valid ICD-10 S or T code.


In order to validate the algorithm, we first tested it against the ICD-10 coded Multiple Cause of Death (MCOD) data for 2000.

For records containing an injury diagnosis (i.e., an S or T code), we selected the injury diagnosis from the entity axis assigned by the death certificate as the earliest injury diagnosisin the chain of causes leading to death.

We ran this classifying diagnosis (Dx0) through our algorithm. The algorithm successfully assigned nature-of-injury and body-region codes to each case.


Data methods
Data & Methods against the ICD-10 coded Multiple Cause of Death (MCOD) data for 2000.

  • We next applied the algorithm to the 1999-2001 MCOD data. We selected all cases with at least one injury diagnosis on the record axis.

  • This gave us 540,748 cases, which broke down by age and sex as follows:


For every injury death, we applied the algorithm to each injury diagnosis on the record axis (except superficial injuries, which were judged to be unlikely to cause death).

In order to avoid double counting deaths with multiple injury diagnoses, we gave each diagnosis a weight equal to the reciprocal of the number of injury diagnoses on the record.

Example: a death that involved a head fracture and a crushed thorax would be counted as half a death from head fracture and half a death from crushed thorax.

By diagnosis matrix cell, we then computed the weighted numbers of cases across all injury deaths.


Results
Results injury diagnosis on the record axis (except superficial injuries, which were judged to be unlikely to cause death).

  • Top 5 Nature of Injury Categories

  • Top 5 Body Region Categories

  • Top 10 Injury Diagnosis Categories as a function of Nature of Injury and Body Region


Top 5 nature of injury categories
Top 5 Nature of Injury Categories injury diagnosis on the record axis (except superficial injuries, which were judged to be unlikely to cause death).

Remaining Categories

24.3%

Unspecified

Injury

26.1%

Other External Effects19.1%

Open

Wound

15.8%

Poisoning10.9%

Fracture

13.8%

Note. 1 = E.g., asphyxiation, drowning.


Top 5 body region categories
Top 5 Body Region Categories injury diagnosis on the record axis (except superficial injuries, which were judged to be unlikely to cause death).

Multiple Regions

10.2%

Remaining

Categories

24.9%

Systemic123.5%

Unspecified

Region

8.3%

Head23.7%

Trunk, Other

9.4%

Note. 1 = E.g., foreign body, poisoning, external effects.




Some injury categories were concentrated in people over 50: Sex

Fractures of the hip were especially prevalent among women over 50, accounting for 22.1% of all injury-related deaths - the highest ranking category for this demographic group. For men over 50, hip fractures accounted for 10.2% of injury deaths. For people under 50, however, hip fracture deaths were almost nonexistent.

Foreign body in the trunk accounted for 15.7% of all injury deaths of people over 50, but only 1.7% for those 50 or under. These are mostly choking deaths.

Internal injuries of the head (brain injuries) accounted for 8.1% of injury deaths of people over 50, but only 3.7% for ages 50 and under.


Other fatal injury categories were more common among people 50 and under:

Among people age 50 or less, the biggest fatal injury category was poisoning, which, together with toxic effects, accounted for 20.3% of all injury deaths. Poisoning and toxic effects were more prevalent among women (25.0%) than among men (18.8%). They were less common among people over 50 (7.4%).

Other external effects (mostly drowning and asphyxiation) accounted for 12.2% of injury deaths among those 50 or under, but only 5.6% among those over 50.

Unspecified injuries of multiple regions accounted for 10.7% of injury deaths among those 50 or less, but only 5.9% among those over 50.


Other fatal injury categories were more common among people 50 and under (continued):

Unspecified head injuries accounted for 10.1% of injury deaths among those 50 or less, but only 6.6% among those over 50.

Open wounds were more common among males than females. Open wounds of the head accounted for 10.0% of injury deaths among men and 3.4% among women. Open wounds of the thorax accounted for 3.5% of injury deaths among men and 1.2% among women.


5.4% of injury death certificates lacked any injury diagnoses. Some coroners and MEs follow the convention (which is permitted by coding rules) of letting a cause code represent the injury without any accompanying injury diagnosis code.

Of the cases with at least one injury diagnosis code (the sub-sample used elsewhere in this study),

70.3% had a single injury diagnosis

19.6% had two injury diagnoses

6.4% had three injury diagnoses, and

3.6% had four or more injury diagnoses.

Internal organ injuries of the head (i.e., brain injuries) and unspecified injuries of the thorax were especially likely to be accompanied by at least one other injury diagnosis (51.1% and 56.5%, respectively).


Discussion
Discussion diagnoses. Some coroners and MEs follow the convention (which is permitted by coding rules) of letting a cause code represent the injury without any accompanying injury diagnosis code.

This exercise gave a clearer picture of a known weakness of ICD-10 coded data - the heavy reliance on “multiple” and “unspecified” categories that are of little use to researchers.

In our injury-codeddata, 31.5% of deaths with injury diagnoses have a multiple or unspecified code for either the nature of injury or the body region, and 13.6% have both.


Conclusion
Conclusion diagnoses. Some coroners and MEs follow the convention (which is permitted by coding rules) of letting a cause code represent the injury without any accompanying injury diagnosis code.

  • The SAS algorithm successfully assigned body region and nature of injury classifications to a multi-year ICD-10 coded mortality dataset.

  • This new injury diagnosis matrix and the SAS algorithm that embodies it will constitute a useful tool for the description and analysis of fatal injury data. The matrix will serve as an initial injury classification benchmark for ICD-10 (and, later, during the transition to ICD-10-CM coding for medical data).


The algorithm proved robust against a large mortality dataset that could reasonably be expected to provide a sufficient test, but it should be validated against other datasets before being widely circulated.

The heavy use of “multiple” and “unspecified” diagnoses will be a challenge to those using these ICD-10 coded data for injury research.


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