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The ICD-10 injury diagnosis matrix: Grouping S and T codes by body
The ICD-10 injury diagnosis matrix: Grouping S and T codes by body
Background
Background
Background
Background
Coding and Validating the Algorithm
Coding and Validating the Algorithm
In order to validate the algorithm, we first tested it against the
In order to validate the algorithm, we first tested it against the
Data & Methods
Data & Methods
For every injury death, we applied the algorithm to each injury
For every injury death, we applied the algorithm to each injury
Results
Results
Top 5 Nature of Injury Categories
Top 5 Nature of Injury Categories
Top 5 Body Region Categories
Top 5 Body Region Categories
Top 10 Injury Diagnosis Categories as a function of Nature of Injury
Top 10 Injury Diagnosis Categories as a function of Nature of Injury
Incidence of Most Common Fatal Injury Diagnoses by Age and Sex
Incidence of Most Common Fatal Injury Diagnoses by Age and Sex
Some injury categories were concentrated in people over 50: Fractures
Some injury categories were concentrated in people over 50: Fractures
Other fatal injury categories were more common among people 50 and
Other fatal injury categories were more common among people 50 and
Other fatal injury categories were more common among people 50 and
Other fatal injury categories were more common among people 50 and
5.4% of injury death certificates lacked any injury diagnoses
5.4% of injury death certificates lacked any injury diagnoses
Discussion
Discussion
Conclusion
Conclusion
The algorithm proved robust against a large mortality dataset that
The algorithm proved robust against a large mortality dataset that

Презентация: «The ICD-10 injury diagnosis matrix: Grouping S and T codes by body region and nature of injury». Автор: PIRE Employee. Файл: «The ICD-10 injury diagnosis matrix: Grouping S and T codes by body region and nature of injury.ppt». Размер zip-архива: 261 КБ.

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

содержание презентации «The ICD-10 injury diagnosis matrix: Grouping S and T codes by body region and nature of injury.ppt»
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1 The ICD-10 injury diagnosis matrix: Grouping S and T codes by body

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

2 Background

Background

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.

3 Background

Background

Body Sites

Body Sites

Body Sites

Body Sites

Body Sites

Body Sites

Body Sites

Nature of Injury

Nature of Injury

1

Amputation

2

Burn

3

Blood Vessel

4

Crush

5

Dislocation

6

Foreign Body

7

Fracture

8

Internal Injury

9

Multiple

10

Muscle/Tendon

11

Nerve

12

Open Wound

13

Other Ext Eff

14

Other Spec

15

Poisoning

16

Sprain/strain

17

Super/cont

18

Toxic effect

19

Unspecified

1

1

Head

20

Abd+ low bk + pelv

39

Lower ext other

Lower ext other

2

2

Face

21

Trunk other

40

Unspecified

Unspecified

3

3

Eye

22

Shouldr/upp arm

41

Multi regions

Multi regions

4

4

Neck

23

Elbow

42

Systemic

Systemic

5

5

Head+Neck

24

Forearm

Forearm

Forearm

Forearm

6

6

SC-neck

25

Wrist/hand

Wrist/hand

Wrist/hand

Wrist/hand

7

7

SC-upper back

26

Fingers

Fingers

Fingers

Fingers

8

8

SC-lower back

27

Upper ext mult

Upper ext mult

Upper ext mult

Upper ext mult

9

9

SC-other

28

Upper ext other

Upper ext other

Upper ext other

Upper ext other

10

10

SC-Multiple

29

Hip

Hip

Hip

11

11

VC-neck

30

Thigh

Thigh

Thigh

12

12

VC-upper back

31

Hip + thigh

Hip + thigh

Hip + thigh

Hip + thigh

13

13

VC-lower back

32

Upp leg + thigh

Upp leg + thigh

Upp leg + thigh

Upp leg + thigh

14

14

VC-lower back + pelv

33

Knee

Knee

Knee

15

15

VC-other

34

Lower leg

Lower leg

Lower leg

Lower leg

16

16

Thorax

35

Foot

Foot

Foot

17

17

Abdomen

36

Ankle

Ankle

Ankle

18

18

Pelvis

37

Ankle + foot

Ankle + foot

Ankle + foot

Ankle + foot

19

19

Lower back + pelv

38

Toes

Toes

Toes

4 Coding and Validating the Algorithm

Coding and Validating the Algorithm

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.

5 In order to validate the algorithm, we first tested it against the

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 diagnosis in 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.

6 Data & Methods

Data & Methods

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:

AGE

AGE

SEX

SEX

SEX

Female

Male

Total

50 or younger

71,959 (13.3%)

218,909 (40.5%)

290,868 (53.8%)

Over 50

113,097 (20.9%)

136,783 (25.3%)

249,880 (46.2%)

Total

185,056 (34.2%)

355,692 (65.8%)

540,748 (100.0%)

7 For every injury death, we applied the algorithm to each injury

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.

8 Results

Results

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

9 Top 5 Nature of Injury Categories

Top 5 Nature of Injury Categories

Remaining Categories 24.3%

Unspecified Injury 26.1%

Other External Effects1 9.1%

Open Wound 15.8%

Poisoning 10.9%

Fracture 13.8%

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

10 Top 5 Body Region Categories

Top 5 Body Region Categories

Multiple Regions 10.2%

Remaining Categories 24.9%

Systemic1 23.5%

Unspecified Region 8.3%

Head 23.7%

Trunk, Other 9.4%

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

11 Top 10 Injury Diagnosis Categories as a function of Nature of Injury

Top 10 Injury Diagnosis Categories as a function of Nature of Injury

and Body Region

9.1

12 Incidence of Most Common Fatal Injury Diagnoses by Age and Sex

Incidence of Most Common Fatal Injury Diagnoses by Age and Sex

1

2

3

4

5

6

7

8

9

10

11

12

50 or younger

50 or younger

50 or younger

Older than 50

Older than 50

Older than 50

All ages

All ages

All ages

Female

Male

Total

Female

Male

Total

Female

Male

Total

Poisoning

20.4%

14.8%

16.2%

4.9%

4.6%

4.8%

11.0%

10.9%

10.9%

Other external effects @

10.4%

12.8%

12.2%

4.1%

6.8%

5.6%

6.5%

10.5%

9.1%

Unspecified injury of head

10.8%

9.9%

10.1%

5.3%

7.7%

6.6%

7.4%

9.0%

8.5%

Unspecified injury of multiple regions

12.2%

10.2%

10.7%

5.1%

6.5%

5.9%

7.8%

8.8%

8.5%

Foreign body in trunk, other #

2.5%

1.4%

1.7%

17.5%

14.1%

15.7%

11.7%

6.3%

8.1%

Open wound of head

6.2%

10.7%

9.6%

1.6%

9.0%

5.7%

3.4%

10.0%

7.8%

Hip fracture

0.1%

0.0%

0.0%

22.1%

10.1%

15.6%

13.6%

3.9%

7.2%

Internal injury of head &

3.6%

3.7%

3.7%

7.6%

8.4%

8.1%

6.1%

5.5%

5.7%

Unspecified injury of unspecified region

6.4%

5.4%

5.6%

3.6%

4.6%

4.2%

4.7%

5.1%

5.0%

Toxic effects

4.6%

4.0%

4.2%

2.1%

3.0%

2.6%

3.1%

3.6%

3.4%

Open wound of thorax

2.4%

4.4%

3.9%

0.5%

2.0%

1.3%

1.2%

3.5%

2.7%

Unspecified injury of thorax

2.4%

2.5%

2.4%

1.7%

2.4%

2.1%

1.9%

2.4%

2.3%

Other

18.1%

20.3%

19.8%

23.8%

20.6%

22.1%

21.6%

20.4%

20.8%

Total

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

100.0%

@

Mostly asphyxiation and drowning

#

Mostly associated with choking

&

Brain injury

13 Some injury categories were concentrated in people over 50: Fractures

Some injury categories were concentrated in people over 50: 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.

14 Other fatal injury categories were more common among people 50 and

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.

15 Other fatal injury categories were more common among people 50 and

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.

16 5.4% of injury death certificates lacked any injury diagnoses

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).

17 Discussion

Discussion

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-coded data, 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.

18 Conclusion

Conclusion

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).

19 The algorithm proved robust against a large mortality dataset that

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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