Quality Assessment of Mortality Information


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IHME professor Rafael Lozano chaired the 2012 meeting of the Regional Advisory Committee on Health Statistics, Comité Regional Asesor sobre Estadísticas de Salud (CRAES), in Havana, Cuba. Dr. Lozano spoke on quality assessment of mortality information, explaining IHME’s work in the identification and redistribution of cause of death codes. This research supports the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study.

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  • Some philosophers assert that a quality cannot be defined.In contemporary philosophy, the idea of qualities and especially how to distinguish certain kinds of qualities from one another remains controversial
  • Quality Assessment of Mortality Information

    1. 1. Quality Assessment of MortalityInformationCauses of deathMarch 26, 2012Rafael LozanoProfessor, IHME
    2. 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. 3
    4. 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 4
    5. 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. 5
    6. 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. 6
    7. 7. Approaches used in the literature tostudy 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. 7
    8. 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 8
    9. 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 9
    10. 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. 11
    12. 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. 13. 4.1.11 Notes for use in underlying cause ofdeath 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 13
    14. 14. Quality Assessment of Causes of DeathNational 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. 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. 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. 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 17
    18. 18. o Causes that cannot or should • Unlikely to cause death not be considered as o ICD underlying causes of death. o IHME • Ill- definedo 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 chapterso Unspecified causes within a • Hypertension and larger cause grouping Atherosclerosis 18
    19. 19. Distribution of improper codes for UCD ICD 10th Type 3 digit 4 digitUnlikely CoD ICD 181 1,175Unlikely CoD IHME 85 429Ill-Defined Specified 10 51Ill-Defined Unspecified 76 249Intermediate 30 137Inmediate 3 6Other and Unspecifiedwithin chapters 76 155Hypertension andAtherosclerosis 3 9All 464 2,211 40 million of deaths (ICD 10th) 26.7% of total deaths 19
    20. 20. Leading improper codes for UCD in the AmericasNo. 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 group10 Chronic renal failure, unspecified N189 2.3 Intermediate11 Unspecified renal failure N19 2.1 Intermediate12 Sequelae of stroke, not specified as haemorrhage or infarction I694 2.0 Other within group13 Exposure to unspecified factor causing other and unspecified injury X599 1.9 Other within group14 Pneumonitis due to food and vomit J690 1.6 Intermediate15 Generalized and unspecified atherosclerosis I709 1.5 H&A16 Senility R54 1.5 Ill-def Spe17 Gastrointestinal haemorrhage, unspecified K922 1.4 Other within group18 Cardiac arrest, unspecified I469 1.4 Inmediate19 Pulmonary embolism without mention of acute cor pulmonale I269 1.4 Intermediate20 Respiratory arrest R092 1.4 Ill-def Unsp Rest 27.7 All causes 13,646,225 20
    21. 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. 21
    22. 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 22
    23. 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 23
    24. 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) + εWhere:• %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 24
    25. 25. New ways to group causes Hypertensive Heart Diseases Chronic Diabetes Kidney Diseases NephropathiesCKD due to Diabetes: E10.2, E11.2, E12.2, E13.2, E14.2CKD due to Hypertension: I12.0, I12.9, I13.0, I13.1, I13.2, I13.9Other CKD: N02‐N07, N15.0 25
    26. 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. 27. Improper codes for UCD, selected countries, last yearavailable 27
    28. 28. Types of improper codes, last year available 28
    29. 29. Ill- Defined 29
    30. 30. Intermediate 30
    31. 31. Others and unspecifiedwithin chapters 31
    32. 32. Annual change of inappropriate codesfraction, 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 32
    33. 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 33
    34. 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. 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 35
    36. 36. What do we need to do on the data qualityfront?• 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? 36
    37. 37. To improve the quality … is not only raising the barThe 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) 37
    38. 38. THANKSAcknowledgments to the Causes of Death Research Team, IHME.Data Analysts: David Philips, Charles Atkinson, Diego Gonzalez-MedinaResearchers: Kyle Foreman MPH, Prof. Mohsen Naghavi, Prof. Christopher Murray 38