The document assesses the quality of mortality information and causes of death, focusing on definitions and frameworks for evaluating data quality. It outlines attributes for measuring data quality under the ICD framework, identifies improper coding for underlying causes of death, and highlights the prevalence of improper codes in various countries. The findings reveal that about 25% of deaths in the region are attributed to improper codes, indicating significant issues in mortality reporting.

1. Quality Assessment of Mortality
Information
Causes of death
March 26, 2012
Rafael Lozano
Professor, IHME

2. Outline
• Quality and data quality
o definitions and assessment framework
• Measuring quality in Causes of Death under the ICD
framework
• Adding value to the CoD quality
o Identification of improper codes for UCD
o Defining the cause list
• Results
• Final remarks
2

3. 3

4. What is quality?
• Quality (from Latin qualitas) is an attribute or a property
o Attributes are given, by a subject, whereas properties are owned
• For Locke, a quality is an idea of a sensation or a perception
o primary qualities are intrinsic to an object
o secondary qualities are dependent on the interpretation of the subjective
mode and the context of appearance
• From the neutral point of view, the quality of something is
the sum of its essential attributes or properties
• Something might be good because it is
o Useful Quality means the
o Beautiful understanding of
o Exists
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5. What is data quality?
• It is difficult to determine the exact definition, but in our daily lives we
have a pretty good sense of what is poor data quality
• Sometimes it is easier to identify inaccurate data – data that are not
relevant, data that are not timely, data that are misleading, etc.
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6. What do you mean by “data quality?”
The majority of people consider accuracy the most relevant
dimension of data quality. Completeness, currency, and
consistency come next on the list. However, we need to
understand better the multidimensional concept of data
quality.
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7. Approaches used in the literature to
study data quality
• An intuitive is based on the researchers´ experience trying to
understand which attributes of data are important.
• A theoretical focuses on how data may become deficient
during the data manufacturing process. Highly recommended
but with few examples. Through this approach we can assess
the intrinsic attributes to a data product.
• An empirical captures the attributes of data quality that are
important for consumers. How data fit for use in their task.
Capture the voice of customers and reveal characteristics that
researchers have not considered.
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8. Selected attributes to measure data quality
Dimension Definition (The extent to which)
Objectivity data is unbiased, unprejudiced , and impartial
Believability data is regarded as true and credible
Accuracy Data is correct, free of error
Reputation data is highly regarded in terms of its sources or context
Completeness data is not missing and is of sufficient breadth and depth for
the task at hand
Value added data is beneficial to provide advantages from its use
Relevancy data is applicable and helpful for the task at hard
Timeliness data is sufficient up to date for the task at hard
Appropriate volume of data is appropriate for the task at hand
amount of data
Concise data is compactly represented
representation
Consistent data is presented in the same format
representation
Ease manipulation data is easy to manipulate and apply to different task
Understandability data is easily comprehended
Interpretability data is in appropriate language, symbols, units, and the
definitions are clear
Accessibility data is available, or easily and quickly retrieved
Security access to data is restricted appropriately to maintain its
security
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9. Assessment framework for CoD statistics
Attribute Indicator
Accuracy
Coverage % of population covered by medical certification of cause of death
Completeness % of deaths with medically-certified cause of death
Missing data % of cause-of-death reports for which age/sex data are missing
Use of ill-defined categories % of deaths classified under various miscellaneous and ill-defined categories
Improbable classifications Number of deaths assigned to improbable age or sex categories per 100,000 coded deaths
Consistency between CoD and % of cause-of-death data points deviating more than 2 (or 3) SDs from general mortality
general mortality based predictions
Relevance
Routine tabulations By sex, and at least by eight broad age groups—namely, 0, 1–4,5–14, 15–29, 30–44, 45–59,
60–69, and 70+ years
Small area statistics Number of cause-of-death tabulation areas per million population
Comparability
Over time Consistency of cause specific mortality proportions over consecutive years
Across space ICD to certify and code deaths; revision used and code level to which tabulations are
published
Timeliness
Production time Mean time from end of reference period to publication
Regularity SD of production time
Accessibility
Media Number of formats in which data are released
Metadata Availability and quality of documentation
User service Availability and responsiveness of user service
Mahapatra P. et al Lancet 2007
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10. Outline
• Quality and data quality
• Measuring quality in Causes of Death under the ICD
framework
• Adding value to the CoD quality
o Identification of improper codes for UCD
o Defining the cause list
• Results
• Final remarks
10

11. 11

12. Critical concepts
• One cause - one death (UCD)
o General principle and selection rules
o Modification of the selected cause
o The modification rules
– Underlying cause of death (UCD)
– Intervening cause
– Highly improbable, unlikely to cause death
– Ill- defined (symptoms, signs and abnormal clinical
and laboratory findings, not elsewhere classified)
12

13. 4.1.11 Notes for use in underlying cause of
death mortality
• E86 Volume depletion with mention of A00-A09 (intestinal infectious
diseases) code A00-A09
• What happen when E86 or I10 appear alone or
the sequence turn into I10 as UCD…
Source: ICD 10th Vol II, Second Edition 2010, pages 37 and 39
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14. Quality Assessment of Causes of Death
National Systems
• Mahapatra P. et al India,
2001
• Rao C. and Lopez A
China, 2005
• Mathers C. et al. Bull of
WHO, 2005
• França E. et al. Brazil,
2008
14

15. Outline
• Quality and data quality
• Measuring quality in Causes of Death under the ICD
framework
• Adding value to the CoD quality
o Identification of improper codes for UCD
o Defining the cause list
• Results
• Final remarks
15

16. What is an improper code for UCD?
• Ill-Defined causes (Chapter XVIII,
ICD 10th )
• Unlikely cause of death (page 175
Vol II, 2010)
• Intermediate or immediate cause
of death
• CoD that may be considered as
risk factor
o Hypertension or Atherosclerosis
• And depending of the granularity
of the cause list, other and/or
unspecified CoD within ICD
chapters
16

17. What is the right name for these codes?
• Murray and Lopez,
1996, “Garbage Codes” • Unwanted
• Mathers C. et al, 2005, • Inaccurate
“Ill-defined codes” • Misclassified
• Mahapatra P. et al
2007, “Ill-defined
categories” • Improper codes
• Naghavi M. et al, 2010, for Underlying
“Garbage Codes”
Cause of Death
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18. o Causes that cannot or should • Unlikely to cause death
not be considered as o ICD
underlying causes of death. o IHME
• Ill- defined
o Intermediate causes of death
o Specified
such as heart failure,
o Unspecified
septicemia, peritonitis,
osteomyelitis, or pulmonary • Intermediate
embolism.
o Immediate causes of death • Immediate
that are the final steps in a
disease pathway leading to • Other and unspecified
death causes within chapters
o Unspecified causes within a • Hypertension and
larger cause grouping Atherosclerosis
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19. Distribution of improper codes for UCD
ICD 10th
Type 3 digit 4 digit
Unlikely CoD ICD 181 1,175
Unlikely CoD IHME 85 429
Ill-Defined Specified 10 51
Ill-Defined Unspecified 76 249
Intermediate 30 137
Inmediate 3 6
Other and Unspecified
within chapters 76 155
Hypertension and
Atherosclerosis 3 9
All 464 2,211
40 million of deaths (ICD 10th)
26.7% of total deaths
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20. Leading improper codes for UCD in the Americas
No. Cause ICD % Type
1 Stroke, not specified as haemorrhage or infarction I64 13.1 Other within group
2 Other ill-defined and unspecified causes of mortality R99 7.5 Ill-def Unsp
3 Unattended death R98 7.3 Ill-def Unsp
4 Congestive heart failure I500 6.4 Intermediate
5 Septicaemia, unspecified A419 5.2 Intermediate
6 Heart failure, unspecified I509 4.9 Intermediate
7 Essential (primary) hypertension I10 3.5 H&A
8 Malignant neoplasm without specification of site C80 3.3 Other within group
9 Person injured in unspecified motor-vehicle accident, traffic V892 2.5 Other within group
10 Chronic renal failure, unspecified N189 2.3 Intermediate
11 Unspecified renal failure N19 2.1 Intermediate
12 Sequelae of stroke, not specified as haemorrhage or infarction I694 2.0 Other within group
13 Exposure to unspecified factor causing other and unspecified injury X599 1.9 Other within group
14 Pneumonitis due to food and vomit J690 1.6 Intermediate
15 Generalized and unspecified atherosclerosis I709 1.5 H&A
16 Senility R54 1.5 Ill-def Spe
17 Gastrointestinal haemorrhage, unspecified K922 1.4 Other within group
18 Cardiac arrest, unspecified I469 1.4 Inmediate
19 Pulmonary embolism without mention of acute cor pulmonale I269 1.4 Intermediate
20 Respiratory arrest R092 1.4 Ill-def Unsp
Rest 27.7
All causes 13,646,225
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21. Cause list for reports
• The list of Cause of Death selected must be confined to a
limited number of mutually exclusive categories able to
encompass the whole range of Public Health conditions.
o The categories have to be chosen to facilitate the statistical study
of CoD phenomena in the Public Health Framework.
o There will be residual categories for other miscellaneous conditions
that cannot be allocated to the more specific categories. As few
conditions as possible should be classified to residual categories.
o The list should has different levels of detail using a hierarchical
structure with subdivisions. The list should retain the ability both to
identify specific entities and to allow statistical presentation of data
for broader groups, to enable useful and understandable
information to be obtained.
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22. Examples of short cause list for reports
GBD 2010 Cause list
Level Group Group Group Total
• Taucher E., 1978 I II III
• Avoidable Mortality, 1990 First 1 1 1 3
Second 7 8 4 19
• BTL (ICD 9th), 1979
Third 50 83 14 147
• Tab 1 (ICD 10th), 1994 Fourth 75 128 22 225
• PAHO 6/67, 2002
A Communicable, maternal, perinatal and nutritional Conditions
• Becker R. et al 2006 A.1 HIV and tuberculosis
A.1.1 Tuberculosis
A.1.2 HIV/AIDS
• GBD 1990 A.1.2.1 HIV disease resulting in mycobacterial infection
A.1.2.2 HIV disease resulting in other specified or unspecified diseases
A.2 Infectious diseases predominantly in children
• GBD 2010 A.2.1 Diarrheal diseases
A.2.1.1 Cholera
A.2.1.2 Other salmonella infections
A.2.1.3 Shigellosis
A.2.1.4 Enteropathogenic Escherichia coli infection
A.2.1.5 Enterotoxigenic Escherichia coli infection
• New one ?? A.2.1.6 Campylobacter enteritis
A.2.1.7 Amoebiasis
A.2.1.8 Cryptosporidiosis
A.2.1.9 Rotaviral enteritis
A.2.1.10 Other diarrheal disease
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23. I60-I69 Cerebrovascular diseases
• Ischemic stroke
o I63 Cerebral infarction
o I65 Occlusion and stenosis of pre-cerebral arteries, not resulting in cerebral infarction
o I66 Occlusion and stenosis of cerebral arteries, not resulting in cerebral infarction
o I67(except I67.4) Other cerebrovascular diseases (Hypertensive encephalopathy)
o I69.3 Sequelae of cerebral infarction
• Hemorrhagic and other non-ischemic stroke
o I60 Subarachnoid hemorrhage
o I61 Intra-cerebral hemorrhage
o I62 Other no traumatic intracranial hemorrhage
o I69.0-I69.2 Sequelae of: subarachnoid hemorrhage, intra-cerebral hemorrhage, and other no
traumatic intracranial hemorrhage
o I67.4 Hypertensive encephalopathy
• Stroke not specified as hemorrhagic or Ischemic
o I64 Stroke, not specified as hemorrhage or infarction
o I69.4 Sequelae of stroke, not specified as hemorrhage or infarction
o I69.8 Sequelae of other and unspecified cerebrovascular diseases
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24. Assumptions
• The proportion of deaths assigned to unspecified stroke is negatively
associated with the proportion of deaths assigned to individual target
codes.
• The epidemiological distribution of causes, as well as the nosological
paradigms within the stroke universe, tend to be similar within given
country.
%Target = α+ β(% Unspecified Stroke) + Ζ(μ) + ε
Where:
• %Target = proportion of deaths attributable to a given target code (either
ischemic or hemorrhagic) within the stroke universe
• % Unspecified Stroke = proportion of deaths attributable to a unspecified
stroke within the Stroke universe
• μ = a vector of normally-distributed random effects with mean Ε(μ)=0
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25. New ways to group causes
Hypertensive
Heart
Diseases Chronic Diabetes
Kidney
Diseases
Nephropathies
CKD due to Diabetes: E10.2, E11.2, E12.2, E13.2, E14.2
CKD due to Hypertension: I12.0, I12.9, I13.0, I13.1, I13.2, I13.9
Other CKD: N02‐N07, N15.0
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26. Outline
• Quality and data quality
• Measuring quality in Causes of Death under the ICD
framework
• Adding value to the CoD quality
o Identification of improper codes for UCD
o Defining the cause list
• Results
• Final remarks
26

27. Improper codes for UCD, selected countries, last year
available
27

28. Types of improper codes, last year available
28

29. Ill- Defined
29

30. Intermediate
30

31. Others and unspecified
within chapters
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32. Annual change of inappropriate codes
fraction, in selected countries
Country All ill def intermediate other & Unsp H&A First Last
Colombia -3.5% -7.2% -1.1% -4.9% -1.7% 1997 2008
Chile -3.0% -5.0% 0.4% -4.6% 0.9% 1997 2007
Brazil -2.4% -5.3% -1.0% -1.5% 4.4% 1996 2009
Cuba -2.1% 2.2% -1.4% 2.6% -10.3% 2001 2008
Ecuador -2.0% -2.9% -2.6% 0.8% -3.6% 1997 2009
Peru -1.4% -9.0% 4.3% 1.6% 1.6% 1999 2004
Nicaragua -1.2% 0.4% 1.4% -4.4% 2.9% 1997 2008
Paraguay -1.2% 3.0% -2.9% -4.3% 1.6% 1996 2008
Canada -1.2% -1.1% 0.6% -2.6% 3.4% 2000 2004
Panama -0.7% -13.7% 5.3% -0.1% 8.0% 1998 2008
Venezuela -0.5% -10.9% -3.6% 2.5% -3.3% 1996 2007
United States -0.4% 3.1% 0.7% -1.7% -1.2% 1999 2007
Costa Rica -0.1% 1.9% -1.6% 0.7% -1.1% 1997 2009
Mexico 0.1% 2.1% -0.3% -1.1% 8.6% 1998 2009
Argentina 0.1% 2.6% 0.0% -2.3% -5.1% 1997 2009
Guatemala 0.3% -17.4% 3.9% 1.2% 10.3% 2005 2008
El Salvador 0.6% -1.3% 2.6% -0.1% 13.5% 1997 2008
Uruguay 1.0% 3.0% 1.5% -0.5% -1.4% 1997 2004
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33. Age ICD Chapter
Chapter
IX Diseases of the circulatory system
XVII Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified
XX Injuries
XIV Diseases of the genitourinary system
II Neoplasms
I Certain infectious and parasitic diseases
X Diseases of the respiratory system
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34. Outline
• Quality and data quality
• Measuring quality in Causes of Death under the ICD
framework
• Adding value to the CoD quality
o Defining the cause list
o Identification of improper codes for UCD
• Results
• Final remarks
34

35. Conclusions
• The amount of Improper Codes for UCD (based in ICD
10th) is 25% of all deaths in the region and it varies
across countries, ages and years
• The amount of improper codes depends on:
o the quality of COD registries (70-80%) and
o the cause list for report selected (20-30%)
• Twenty ICD 10th codes accumulate 73% of all deaths
associate to improper codes, e.g., heart failure
(13%), stroke unspecified (13%), ill-defined (> 20%), etc.
• There are many good experiences in the region from
which to learn and also important lags to fix
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36. What do we need to do on the data quality
front?
• Are we “ok” with the current
indicators or do we need to
expand the scope?
• Do we need a different cause
list for reports?
• Shall we set up a common
framework as users of data
and producers of information?
(new studies of validation of
the accuracy of death
certificates)
• Do we have to explore and
learn more from our
customers?
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37. To improve the quality … is not only raising the bar
The most achieved was 1.97 m In 1968 (Mex), Richard Fosbury (USA)
revolutionized the technic and jumped 2.18 m
The current global record is 2.45m
and belongs to Javier Sotomayor
(Cuba)
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38. THANKS
Acknowledgments to the Causes of Death Research Team, IHME.
Data Analysts: David Philips, Charles Atkinson, Diego Gonzalez-Medina
Researchers: Kyle Foreman MPH, Prof. Mohsen Naghavi, Prof. Christopher Murray
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