Assessing and Improving Civil Registration and Vital ...

Assessing and Improving Civil Registration and Vital ...

U.S. Centers for Disease Control and Prevention National Center for Health Statistics International Statistics Program Assessing Vital Statistics These materials have been developed by the National Center for Health Statistics, International Statistics Program, Hyattsville, Md., as part of the CDC Global Program for Civil Registration and Vital Statistics Improvement. Outline Adequacy of vital statistics Quality of vital statistics Accuracy Timeliness Comparability Relevance Accessibility ANACoD: a tool for Analyzing Mortality Levels & Cause of Death Data Assessing Vital Statistics SOURCES: NCHS, Unit 18. 2 Adequacy of Vital Statistics Good statistical systems are: efficient, credible, objective Adequacy of statistics: Data content Tabulations Availability of population data for rate computation Register of births. Panos/Jenny Matthews. Quality of vital statistics data Assessing Vital Statistics SOURCES: NCHS, Unit 18; Mahapatra. 3 Quality of Vital Statistics Quality of vital statistics data

Accuracy Timeliness Comparability Relevance Accessibility Data manager. WHO/Evelyn Hockstein. 4 Assessing Vital Statistics SOURCES: NCHS, Unit 18; Mahapatra. Mahapatra et al. (2007) Assessment Framework Timeliness Production time Regularity Accuracy Completeness / coverage Missing data* Use of ill-defined categories Improbable classifications * other models also include erroneous data SOURCES: Mahapatra, Table 1 (slightly modified for better understanding and consistency with other sources). Assessing Vital Statistics Comparability Over time Across space Relevance Routine tabulations Small area statistics Accessibility Media Metadata User service 5 Accuracy of Vital Statistics Coverage Error 1. Completeness / coverage Content Error 2.

Missing & erroneous data 3. Use of ill-defined categories 4. Improbable classifications Evaluated by : analysis of trends & frequency distributions Anomalies caused by reporting practices, i.e. digit preference Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; PRVSS2, Ch. V. 6 Accuracy of Vital Statistics 1. C ompleteness / coverage Civil registration systems: every vital event that has occurred is registered in system Complete : 90% of events registered Incomplete: < 90% of

events registered Vital statistics: all registered events are forwarded to agency to compile & produce vital statistics Coverage error (various measures) Explore reasons for undercoverage Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; Mahapatra; PRVSS2, Ch. V, Glossary; WHO/HMN, Box 1. 7 Completeness / Coverage Coverage errors in civil registration systems Geographic coverage % access level = # in districts with registration points X 100 total population of the country Civil registration system coverage of deaths (WHO) % coverage = total deaths reported from system in year X 100 total deaths estimated for year (by WHO) Coverage of medical certification of cause of death (COD) % covered by COD certification = # in districts with certification X 100 total population of country 8 Assessing Vital Statistics SOURCES: Mahapatra; PRVSS2, Ch. V, Glossary; WHO/UQ, Box 3.3; WHO/IMR; Freedman, p 24. Completeness / Coverage Coverage errors in civil reg. systems (contd): Approximations of completeness by comparison with corresponding statistics Estimated birth registration completeness (%) = _ Actual # registered births X 100

(Crude birth rate per 1,000* x total population size/1,000) Estimated death registration completeness (%) = _ Actual # registered deaths X 100 (Crude death rate per 1,000* x total population size/1,000) * As estimated by the United Nations or other sources Assessing Vital Statistics SOURCES: PRVSS2, p 88; WHO/HMN, Box 1. 9 Completeness / Coverage Coverage errors in civil reg. systems (contd): Checking entries against independent sources Using death register to verify birth registration Administrative & social records Matching to census & survey records Dual record system 1 0 SOURCES: NCHS, Unit 18. Assessing Vital Statistics Panos/Heldur Netocny. Medical workers register women and babies. Completeness / Coverage Dual record system Retrospective survey of vital events (quarterly/annually) Census enumeration Classify matched events: 1) Events recorded in both register and other system 2) Events recorded in register but not other system 3) Events reported in other system but not register 4) Estimate unknown number of events omitted from 1 1 both systems* * Chandra Sekar,Vital C. andStatistics

Deming, W. Edwards. On a Method of Estimating Birth and Death Rates93-44. and the SOURCES: NCHS, 18; PRVSS2, p 86-87, Assessing Extent of Registration. Journal of the American Statistical Association. 44(245):101-115, March, 1949. Completeness / Coverage Example: birth registration coverage Births registered during 3 month period Census enumeration of infants 1 SOURCES: Elis S. Marks, William Seltzer and Karol J. Krotki. Population Growth Estimation: A Handbook of Vital 2 Statistics Measurement. New York: The Population Control, 1974 ; NCHS 18. Assessing Vital Statistics Completeness / Coverage Example: birth registration coverage Births registered during 3 month period Census enumeration of infants Recorded in register & census 1 SOURCES: Elis S. Marks, William Seltzer and Karol J. Krotki. Population Growth Estimation: A Handbook of Vital 3 Statistics Measurement. New York: The Population Control, 1974 ; NCHS, 18.

Assessing Vital Statistics Completeness / Coverage Example: birth registration coverage Recorded in register only Births registered during 3 month period Census enumeration of infants Recorded in register & census 1 SOURCES: Elis S. Marks, William Seltzer and Karol J. Krotki. Population Growth Estimation: A Handbook of Vital 4 Statistics Measurement. New York: The Population Control, 1974; NCHS 18 . Assessing Vital Statistics Completeness / Coverage Example: birth registration coverage Recorded in register only Recorded in census only Births registered during 3 month period Census enumeration of infants Recorded in register & census 1 SOURCES: Elis S. Marks, William Seltzer and Karol J. Krotki. Population Growth Estimation: A Handbook of Vital 5 Statistics Measurement. New York: The Population Control, 1974; NCHS 18 . Assessing Vital Statistics

Completeness / Coverage Example: birth registration coverage Recorded in register only Recorded in census only Births registered during 3 month period Census enumeration of infants Recorded in register & census Events not recorded in either system 1 SOURCES: Elis S. Marks, William Seltzer and Karol J. Krotki. Population Growth Estimation: A Handbook of Vital 6 Statistics Measurement. New York: The Population Control, 1974; NCHS, 18 . Assessing Vital Statistics Completeness / Coverage Coverage errors in vital statistics Direct Assessment Monitoring registrar returns Reports received on time Every registration area has reported Health Metrics Network Frequencies of events reported similar to expected values % of deaths with medically-certified cause of death 1 7 Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; Mahapatra; PRVSS2, Ch. V.

Completeness / Coverage Coverage errors in vital statistics (contd) Indirect Assessment Comparison of trends Delayed registration Incomplete data methods Comparison w/ rates observed in similar populations or previous periods Assessing Vital Statistics 1 8 SOURCES: NCHS, Unit 15, 18; Mahapatra; PRVSS2, Ch. V. Completeness / Coverage Coverage errors in vital statistics (contd) Comparisons VS (# of events registered) in a given period with corresponding VS in previous years VS in a given period with population census or other estimates Proportion of delayed registrations as estimate of under-reporting in previous years Portions of VS with corresponding data collected through other means (i.e. fertility surveys) Vital rates with corresponding rates for similar countries Sex ratio at birth (under certain circumstances) Assessing Vital Statistics SOURCES: NCHS, 18; PRVSS2, p 84; Freedman, p 27-28. 1 9 Number of registered deaths by source of data and year of death, 1997-2008* 2 0 SOURCES: Statistics South Africa, Assessing Vital Statistics 2009. Completeness/Coverage Reasons for under-coverage Geographic: lack of access to the system Late registration

Health infrastructure Under-registration: most crucial aspect of evaluation Poor legislation Failure of informant to comply with law Lack of proficiency of registrars 2 1 Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; Mahapatra; PRVSS2, Ch. V; HMN/UQ, Subcomponent B3; Freedman p 25. Improving Completeness/Coverage Reduce barriers to registration Hire part-time & adjunct registration officials Track pregnant women Educational campaign to improve registration of infant deaths & stillbirths Improve relationships between local registrars and coroners & police Review classification of maternal deaths Statistical adjustment for under-coverage Assessing Vital Statistics SOURCES: Freedman, p 28-31. 2 2 Improving Completeness/Coverage 1) Completeness & coverage Coverage as a measure of completeness Comparisons with corresponding statistics Checking with independent sources (dual record system) Coverage errors in vital statistics Reasons for under-coverage Improving completeness/coverage 2 3 Assessing Vital Statistics Methods for Completeness/ Coverage in [COUNTRY] List the methods that are used for measuring completeness in [country]. Consider methods for measuring completeness in the civil registration system and methods for measuring completeness in the countrys vital statistics.

2 4 Assessing Vital Statistics Discuss Discuss what geographic areas and population groups exist in your country. Do any of these groups present reporting or data-collection problems for civil registration? What is the best way to make it easy for the public to participate but still collect complete information for either births or deaths? 2 5 Assessing Vital Statistics Accuracy of Vital Statistics 2) Missing & Erroneous Data Missing data % of key variables with no response % of COD reports with missing age/sex Erroneous data Response error: Matching sample of reports with independent records % responses classified as unknown Internal consistency of data Coding error: double coding Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; Mahapatra; PRVSS2, Ch. V; Freedman, p 32. 2 6 Accuracy of Vital Statistics 3) Use of ill-defined categories % of deaths classified as miscellaneous/ill-defined Should be < 25% unknown 4) Improbable classifications # deaths assigned improbable age/sex per 100,000 coded deaths 2 7

Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; Mahapatra; PRVSS2, Ch. V. Proportion of natural deaths due to ill defined natural causes by age group South Africa, 2007 Source: Statistics, South Africa Assessing Vital Statistics 2 8 SOURCES: Bradshaw D, et al. Cause of death statistics for South Africa: Challenges and possibilities for improvement. Medical Research Council, South Africa. November 2010. What to do with Imperfect Data When youre not confident on the certification of cause of death: combine causes into broader groups When you have ill-defined causes of death: you can allocate deaths across other causes using advanced techniques (see National Burden of Edition 2.0) Disease Studies: A Practical Guide. 2 9 Assessing Vital Statistics Accuracy of Vital Statistics in [COUNTRY] List the following for the country, if known (use the most recent data year available): 2) Missing data % of key variables with no response % of COD reports with missing age/sex Erroneous data Response error: Methods used for matching sample of reports with independent records? % responses classified as unknown Coding error: is double coding conducted? Assessing Vital Statistics 3

0 Accuracy of Vital Statistics in [COUNTRY] (cont.) List the following for the country, if known (use the most recent data year available): 3) Use of ill-defined categories % of deaths classified as miscellaneous/ill-defined Is this < 25% unknown (ideal)? 4) Improbable classifications # deaths assigned improbable age/sex per 100,000 coded deaths Assessing Vital Statistics 3 1 Review: Accuracy of Vital Statistics 1) Completeness & coverage 2) Missing & erroneous data 3) Use of ill-defined categories 4) Improbable classifications 3 2 Assessing Vital Statistics Timeliness of Vital Statistics Factors influencing timeliness: (1) Promptness of event registration (2) Transmission of data (3) Promptness of data production & dissemination Enforcing laws can be challenging Health Metrics Network Know magnitude & effect of delayed registration 3 3 Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; PRVSS2, p 82; Freedman, p 40-44. Timeliness of Vital Statistics Indices of timeliness:

% of events that occurred in previous years Production time: mean time from end of reference period to publication Regularity: SD of production time 3 4 Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; Mahapatra. Timeliness of Vital Statistics Delayed reporting of certain types of events 1. Delay release of national file 2. Publish without delayed records 3. Use surrogate statistics (e.g. 9/11 World Trade Center attacks in United States) 3 5 Assessing Vital Statistics SOURCES: Freedman, p 43. Timeliness of Vital Statistics 3 6 Assessing Vital Statistics SOURCES: Statistics South Africa, 2009 Timeliness of Vital Statistics Data in [COUNTRY] List the following for COUNTRY, if known (use the most recent year for which data are avialable): % of events that occurred in previous years Production time: mean time from end of reference period to publication Regularity: SD of production time Was there a delay in the release of the national file? If yes, for how long? Were reports published without delayed records? 3 7 Assessing Vital Statistics Discuss

What factors can affect the timeliness of vital statistics? 3 8 Assessing Vital Statistics Comparability Need to accommodate necessary changes Proper procedures Implemented so users can employ new statistics Comparability Across space: within country & between countries Uniformity of definitions across areas ICD to certify & code deaths; version used; code level used Over time: Stability of key definitions for VS Consistency of cause specific mortality proportions Assessing Vital Statistics SOURCES: NCHS, Unit 15, 18; Mahapatra. 3 9 Comparability: Differences in Reporting Requirements: Live Birth 4 0 Assessing Vital Statistics SOURCES: MacDorman, MF and Mathews TJ. Behind International Rankings of Infant Mortality: How the United States Compares with Europe. NCHS Data Brief No. 23. Nov. 2009 (see references). Comparability: International Comparisons Presentation & Interpretation Data Quality Consistency Methodology Presentation Coverage

Explanation Time period Underlying differentials Choice of Countries Context Comparability Assessing Vital Statistics SOURCES: Australian Institute of Health and Welfare 2012. A working guide to international comparisons of health. Cat. No. PHE 159. Canberra: AIHW. 4 1 Relevance Routine tabulations: by sex & specified age groups Small area statistics: # of tabulation areas per million population 4 2 Assessing Vital Statistics SOURCES: Mahapatra; WHOSIS. Accessibility Media: # of formats in which data are released Metadata: availability & quality of documentation User service: availability & responsiveness of user service 4 3 Assessing Vital Statistics SOURCES: Mahapatra.

SOURCES: Statistics South Africa. Mortality and causes of death in South Africa, 2009: Findings from death notification. Statistical release P0309.3. (p16) Example of Mahapatra Framework: South Africa Death Notification Data, 2009 4 4 Assessing Vital Statistics Review: Quality of Vital Statistics Data Accuracy Vital Statistics Civil Registration Timeliness Comparability Relevance Accessibility 4 5 Assessing Vital Statistics Assessing the Quality of Mortality Data: 10 step process 1) Prepare basic tabulations of deaths by age, sex and 1) Review the distribution of cause of death major causes of death 2) Review crude death rates 2) Review age patterns of 3) Review age and sexspecific death rates 4) Review the age distribution of deaths major causes of death 3) Review leading causes of death 4) Review ratio of 5) Review child mortality rates noncommunicable to communicable disease deaths

SOURCES: World Health Organization (2011). Analysing mortality levels and causes of death (ANACoD) Electronic Tool. Department of Health4 6 Statistics and Information Systems. Geneva, World Health Organization. Available from [email protected] (ANACoD) AbouZahr C, Mikkelsen L, Rampatige R, and Lopez A. Mortality statistics: a tool to improve understanding and quality. Health Information Systems Knowledge Hub, University of Queensland. Working Paper Series 13. November 2010. Assessing Vital Statistics http://www.uq.edu.au/hishub/wp13 (UQ Working Paper 13) 5) Review ill-defined causes of death WHO Traumatic shock recommends the use of the Internal injuries International Form of Medical Pedestrian hit by car Certification of Cause of Death to document the underlying cause of death AIDS 4 7 Assessing Vital Statistics International Statistical Classification of Diseases and Related Health Problems: 10th Revision (ICD-10) Chapter Blocks Title includes natural causes & external causes of death I A00-B99 II C00-D48 III D50-D89 IV E00-E90 V

F00-F99 VI G00-G99 VII H00-H59 VIII H60-H95 IX I00-I99 X J00-J99 XI K00-K93 XII L00-L99 XIII M00-M99 XIV N00-N99 XV O00-O99 XVI P00-P96 XVII Q00-Q99 XVIII R00-R99 XIX S00-T98 XX V01-Y98 XXI Z00-Z99 IU00-U99 Certain infectious and parasitic diseases Neoplasms Diseases of the blood and blood-forming organs Endocrine, nutritional and metabolic diseases Mental and behavioral disorders Diseases of the nervous system Diseases of the eye and adnexa Diseases of the ear and mastoid process Diseases of the circulatory system Diseases of the respiratory system Diseases of the digestive system Diseases of the skin and subcutaneous tissue Diseases of the musculoskeletal system and connective tissue Diseases of the genitourinary system Pregnancy, childbirth and the puerperium Certain conditions originating in the perinatal period Congenital malformations, deformations and chromosomal abnormalities Symptoms, signs and abnormal clinical and laboratory findings Injury, poisoning and certain other consequences of external causes External causes of morbidity and mortality Factors influencing health status and contact with health services Codes for special purposes Assessing Vital Statistics 4 8 ANACoD: Analysing mortality levels & cause-of-death data An electronic tool to automate the 10 step process

Step-by-step tool for analysis of data on mortality levels and cause of death Developed by: WHO The University of Queensland Health Info. Systems Knowledge Hub Health Metrics Network (financial support) 4 SOURCES FOR ANACoD SLIDES: 9 (ANACoD) World Health Organization (2011). Analysing mortality levels and causes of death (ANACoD) Electronic Tool. Department of Health Statistics and Information Systems. Geneva, World Health Organization. Available from [email protected]; (UQWP13) AbouZahr C, Mikkelsen L, Rampatige R, and Lopez A. Mortality statistics: a tool to improve understanding and quality. Assessing Vital Statistics Health Information Systems Knowledge Hub, University of Queensland. Working Paper Series 13. November 2010. ( ANACoD version 1.1 Analysing mortality level and cause-of-death data About this version Click on the buttons to select analysis Step by step core analyses Step0 Input data: raw mortality data by age, sex and ICD10 3 or 4 character codes; population by age and sex Step 6 Distribution of deaths according to the Global Burden of Disease list Step1 Basic check of input data Step 7 Age pattern of broad groups of causes of deaths Step 2 Crude death rates Step 8

Leading causes of death Step 3 Age- and sex-specific death rates Step 9 Ratio of non-communicable to communicable causes of death Step 4 Age distribution of deaths Step 5 Child mortality rates Step 10 Summary Ill-defined causes of death Summary of analyses Supplementary analyses S1 Age pattern of individual cause of death S2 Age-specific death rates of individual cause of death ICD10 List of ICD-10 codes valid for underlying causes of0 death Background information About About the tool Assessing Vital Statistics Global Burden of Disease cause categories and ICD-10 codes

GBD list 5 ANACoD version 1.1 Analysing mortality level and cause-of-death data About this version Click on the buttons to select analysis Step by step core analyses Step0 Input data: raw mortality data by age, sex and ICD10 3 or 4 character codes; population by age and sex Step1 Basic check of input data Step 2 Crude death rates Step 3 Age- and sex-specific death rates Step 4 Age distribution of deaths Step 5 Child mortality rates INPUT DATA MORTALITY LEVELS ANALYSIS CAUSES OF DEATH ANALYSIS Step 6 Distribution of deaths according to the Global Burden of Disease list Step 7 Age pattern of broad groups of causes of deaths

Step 8 Leading causes of death Step 9 Ratio of non-communicable to communicable causes of death Step 10 Summary Ill-defined causes of death Summary of analyses Supplementary analyses S1 Age pattern of individual cause of death S2 Age-specific death rates of individual cause of death ICD10 List of ICD-10 codes valid for underlying causes of1 death Background information About About the tool Assessing Vital Statistics Global Burden of Disease cause categories and ICD-10 codes GBD list 5 Getting Started Open Excel file: ANACoD version 1.1 2013Feb_blank.xls Enable macros Go to sheet step0-Input data

Enter information at top of page: Country: Colombia Year: 2009 Source of data: Civil registration ICD level used: ICD-10, 4-character codes Input data from Excel file: Country Data_Anacod.xlsx Copy Population data; paste into ANACoD tool, starting in E14 Copy Deaths: data; paste into ANACoD tool, starting in C20 Assessing Vital Statistics 5 2 ANACoD - PART I: INPUT DATA Step 0 - Input data: raw mortality data by age and sex and ICD 3 or 4 character codes; population data by sex and age Population Sex All ages 1=male 2=female 22464882 23189162 0 year 1-4 year 466526 446815 1828674 1753044 5-9 years 10-14 years 15-19 years 20-24 years 2250657 2160252 2240827 2155587

2201572 2130962 25-29 years 2050933 2019554 30-34 years 35-39 years 1894170 1912832 1707701 1774594 1510151 1612906 Number of deaths Cause in ICD Sex A010 A020 A020 A021 A021 A039 A042 A046 A047 A047 A049 A049 A059 A060

A060 A061 Assessing All ages 0 year 1-4 years 2 1 0 1 3 1 2 2 0 1 1 1 2 2 1 2 1 0 1 1 0 2 1 0 1 12 6 2 7 2 1 12 1 2 8 1 1 2 0

1 1 0 2Vital Statistics 2 0 2 1 0 0 0 0 0 0 0 0 0 2 2 3 0 0 0 0 0 5-9 years 10-14 years 15-19 years 20-24 years 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 25-29 years 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 30-34 years 35-39 years 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 3 0 0 0 ANACoD - PART I: INPUT DATA Step 1 - Basic check of input data

Population: The entered data automatically generate a table and population pyramid (discussed further in Step 2). 1. Population All ages 0 1-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ Population male female 22 464 882 23 189 162 466 526 446 815 1 828 674 1 753 044 2 250 657 2 160 252 2 240 827 2 155 587 2 201 572 2 130 962 2 050 933 2 019 554 1 894 170 1 912 832 1 707 701 1 774 594 1 510 151 1 612 906 1 479 874 1 603 908 1 275 551 1 399 558 1 040 753

1 158 799 833 936 945 156 600 560 697 959 408 106 492 649 289 037 366 559 193 494 261 311 192 360 296 717 Assessing Vital Statistics Population pyramid 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 femal e male Age Age-group (yrs) 15 10 5 0 5

% of population 10 15 5 4 ANACoD - PART I: INPUT DATA Any non-zero numbers indicate age groups for which country data are not consistent. sex No deaths in "AAA": all causes m f Sum of deaths in all other codes m f Difference: should be zero m f Step 1 - Basic check of input data all ages 0 1-4 5-9 10-14 15-19 20-24 113327 83354 5333 4225 1121

931 629 469 848 523 3604 1042 5622 1255 113327 83354 5333 4225 1121 931 629 469 848 523 3604 1042 5622 1255 0 0 0 0 0 0 0 0

0 0 0 0 0 0 2.2 Distribution of total death SeeAssessing cite slide 54. Vital Statistics Percentage of total deaths male female 50 45 40 35 male 30 female 25 20 15 10 Age 75-79 65-69 55-59 45-49 35-39

0 25-29 5 15-19 5.1 1.1 0.6 0.6 1.3 1.5 1.5 1.6 1.9 2.5 3.3 4.2 5.0 5.8 7.6 10.1 12.2 46.2 5-9 4.7 1.0 0.6 0.8 3.2 5.0 5.4 4.3 3.7 3.7 4.1 4.5 5.3 6.0 7.4 8.8 10.1 31.5 Age distribution of reported deaths 0 An attempt

should be made to query and correct the specific death certificate. All ages 0 1-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ No of deaths male female 113 327 83 354 5 368 4 234 1 128 933 633 470 854 524 3 628 1 044 5 659 1 258 6 112 1 289 4 863 1 361 4 197 1 582 4 187 2 117

4 646 2 791 5 129 3 525 6 046 4 132 6 808 4 863 8 366 6 323 9 990 8 396 11 431 10 206 24 281 28 307 % of deaths Age-group (yrs) 5 5 ANACoD - PART I: INPUT DATA Step 1 - Basic check of input Look for expected patterns: Deviations may indicate errors in age or sex information. data 2.2 Distribution of total death Percentage of total deaths male female 50 45 40 35 male 30 female 25 MALES > Females 20

15 10 75-79 65-69 55-59 45-49 35-39 0 25-29 5 15-19 5.1 1.1 0.6 0.6 1.3 1.5 1.5 1.6 1.9 2.5 3.3 4.2 5.0 5.8 7.6 10.1 12.2 46.2 5-9 4.7 1.0 0.6 0.8 3.2 5.0 5.4 4.3 3.7 3.7 4.1

4.5 5.3 6.0 7.4 8.8 10.1 31.5 Age distribution of reported deaths 0 All ages 0 1-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ No of deaths male female 113 327 83 354 5 368 4 234 1 128 933 633 470 854 524 3 628 1 044 5 659 1 258 6 112 1 289 4 863

1 361 4 197 1 582 4 187 2 117 4 646 2 791 5 129 3 525 6 046 4 132 6 808 4 863 8 366 6 323 9 990 8 396 11 431 10 206 24 281 28 307 % of deaths Age-group (yrs) Age Males < FEMALES Higher percentages in the 0 and 65+ age groups Higher percentages for males compared to females in the 15-64 age groups, due to a higher number of deaths from external causes Higher percentages for females compared to males in the oldest age groups Assessing Vital Statistics 5 6 ANACoD - PART I: INPUT DATA Step 1 - Basic check of input data Check for standard patterns: - Generally higher rates of male versus female mortality. - Smooth, increasing lines after age 35 years. 2.3 Age-specific mortality rate Age-specific mortality rate per 100 000 (m x ) Assessing Vital Statistics 100000 male

10000 female 1000 100 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 1 15-19 10 5-9 948 53 22 24 49 62 67 77 98 132

199 304 437 697 1 283 2 290 3 906 9 540 10-14 1 151 62 28 38 165 276 323 285 278 283 364 493 725 1 134 2 050 3 456 5 908 12 623 0 1-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80+ log of age-specific mortality rates

female 1-4 male 0 log of mx per 100,000 population Age-group (yrs) Age 5 7 ANACoD - PART I: INPUT DATA Step 1 - Basic check of input data Checking for invalid ICD codes -- All cells should contain a 0 or 0%. 2.4 Deaths (in years) labelled with codes not valid for underlying cause of death according to ICD10 2 cases: Deaths with ICD10 codes that should not be used for causes of deaths. Codes not existing in ICD10. No As % of total ICD10 Go sex m f m f all ages 0 0 0% 0% 0 0 0 0% 0% 1-4 0 0 0%

0% 5-9 0 0 0% 0% Go to the list of valid ICD10 codes for underlying causes of deaths 10-14 0 0 0% 0% 15-19 0 0 0% 0% 20-24 0 0 0% 0% Go to step1-Input data sheet, column AB flags non valid codes Click to see a list of valid ICD codes for underlying cause of death or to see where non valid codes are flagged. Assessing Vital Statistics 5 8 ANACoD - PART I: INPUT DATA Step 1 - Basic check of input data 2.5 Cause, age, sex specific check Includes invalid codes Sex specific codes. Pink: female only, blue: male only ICD Disease

No of deaths O00-O99 Pregnancy, child birth and the puerperium - male 0 C53 C54-C55 C56 Cervix uteri cancer - male Corpus uteri cancer - male Ovary cancer - male 0 0 0 C61 Prostate cancer - female N40 Benign prostatic hypertrophy - female Pls check if sum is not equal to zero ---> 0 0 0 Diseases unlikely to cause death ICD Disease F32-F33 Unipolar major depression F43 Post-traumatic stress disorder F42 Obsessive-compulsive disorders NA in ICD103 Panic disorder F51 Sleep disorders G43 Migraine F70-F79 Mental Retardation NA in ICD103 Presbyopia H90-H91 Deafness K02

Dental caries Pls check if sum is not equal to zero ---> No of deaths 3 0 0 0 0 0 5 0 0 0 8 Assessing Vital Statistics An attempt should be made to query and correct the death certificate for any deaths listed in these columns that indicate unlikely disease/sex combinations or unlikely causes of death. 5 9 ANACoD - PART I: INPUT DATA Step 1 - Basic check of input data 2.5 Cause, age, sex specific check Includes invalid codes Disease-Age-specific check: for some diseases, ages unlikely to have deaths, ICD Disease Ages No of deaths O00-O99 Maternal conditions <10&> 54yr 0 P00-P96 Conditions arising during the perinatal period > 4yr 28 P05-P07 Prematurity and low birth weight > 4yr 0

P03, P10-P15, P20-P29 Birth asphyxia and birth trauma > 4yr 23 P00-P02, P04, P08, P35-P96 Other conditions arising during the perinatal period > 4yr 5 C00-C97 Malignant neoplasms C00-C20 Mouth and oropharynx cancers 0-4yr 0 C15 Oesophagus cancer 0-4yr 0 C16 Stomach cancer 0-4yr 0 C18-C21 Colon and rectum cancers 0-4yr 0 C22 Liver cancer 0-4yr 3 C25 Pancreas cancer 0-4yr 0 C33-C34 Trachea, bronchus and lung cancers 0-4yr 3 C43-C44 Melanoma and other skin cancers 0-4yr 0 C50 Breast cancer 0-4yr 0 C53 Cervix uteri cancer 0-9yr 1 C54-C55 Corpus uteri cancer 0-9yr 1

C56 Ovary cancer 0-9yr 0 C61 Prostate cancer 0-9yr 0 C67 Bladder cancer 0-4yr 1 C81-C90, C96 Lymphomas and multiple myeloma 0-4yr 13 C91-C95 Leukaemia 0-4yr 0 I00-I99 Cardiovascular diseases I01-I09 Rheumatic heart disease 0-4yr 3 I10-I13 Hypertensive disease 0-4yr 1 I20-I25 Ischaemic heart disease 0-4yr 23 I60-I69 Cerebrovascular disease 0-4yr 59 I30-I33, I38, I40, I42 Inflammatory heart diseases 0-4yr 74 N40 Benign prostatic hypertrophy 0-34yr 0 X60-X84 Self-inflicted injuries 0-4yr 0 Assessing Vital Statistics

An attempt should be made to query and correct the death certificate for any deaths listed in this column that indicates an unlikely disease/age combination. 6 0 ANACoD - PART II: MORTALITY LEVELS ANALYSIS Steps 2-5 Focus on simple steps to assess the plausibility of the mortality levels. The tool compiles and formats the raw data to enable the calculation of: crude death rates age-specific mortality rates life expectancy at birth child mortality 6 1 Assessing Vital Statistics ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 2: Crude death rates (CDR) Enables users to: Calculate the CDR and use the countrys population pyramid to helps in the interpretation of the CDR Crude death rate = Number of deaths in resident population in given year X 1000 Size of the midyear resident population in that year Use the CDR as an approximate indicator of completeness of death registration Compare the CDR to the expected CRD based on life expectancy and population growth rates 6 2 Assessing Vital Statistics ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 2: Crude death rates

Population data to aid in interpretation of crude death rates: All ages No of deaths male female 113 327 83 354 0 5 368 4 234 1-4 1 128 933 5-9 633 470 10-14 854 524 15-19 3 628 1 044 20-24 5 659 1 258 25-29 6 112 1 289

30-34 4 863 1 361 35-39 4 197 1 582 40-44 4 187 2 117 45-49 4 646 2 791 50-54 5 129 3 525 55-59 6 046 4 132 Proportion of Population male female 22 464 882 80+ 23 189 162 466 526 1 828 674 2 250 657 2 240 827 2 201 572 2 050 933 1 894 170 1 707 701

1 510 151 1 479 874 1 275 551 1 040 753 833 936 600 560 Assessing6 808 Vital Statistics 4 863 60-64 446 815 1 753 044 2 160 252 2 155 587 2 130 962 2 019 554 1 912 832 1 774 594 1 612 906 1 603 908 1 399 558 1 158 799 945 156 697 959 Population pyramid 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 femal e male Age

Age-group (yrs) 15 10 5 0 5 % of population 10 15 CDR as approximate indicator of completeness of death registration: 90% is defined as good by UN standards. 6 3 ANACoD - PART II: MORTALITY LEVELS ANALYSIS CDRs < 5.0 are suspiciously low and indicate under-reporting. Step 2: Crude deaths rates Observed Crude death rate per 1000 population 4.3 5.0 3.6 Both sexes Males Females % Annual rate of population growth Both sexes (UN*) Males Females Life expectancy at birth (years) 1.46

1.43 1.48 *UN source: United Nations, World Population Prospects the 2010 revision Expected crude death rates Life expectancy at birth (years) 77.2 Males 73.6 Females 80.8 Compare the observed CDR to the expected CRD based on life expectancy and population growth rates at different levels of life expectancy and population growth (based on Coale-Demeny West model) Male 10 40 45 50 55 60 65 70 5 26.7 20.8 16.0 12.0 8.7 5.9 3.8 3 23.6 19.0 15.2 12.1 9.5 7.3

5.6 2.5 23.2 18.9 15.4 12.5 10.1 8.0 6.4 75 2.3 4.2 5.1 5 27.4 21.6 16.8 12.7 9.4 6.6 4.3 3 24.1 19.5 15.7 12.5 9.9 7.7 5.8 2.5 23.6 19.3 15.8 12.9 10.4 8.4 6.6 Annual rate of population growth (percent) 2 1.5 1 0.5 23.1

23.1 23.4 24.1 19.1 19.4 20.1 21.0 15.8 16.4 17.3 18.5 13.1 14.0 15.1 16.5 10.9 11.9 13.2 14.8 9.0 10.2 11.6 13.3 7.4 8.7 12.1 10.2 0 25.0 22.2 20.0 18.2 16.7 15.4 14.3 -0.5 26.3 23.8 21.8 20.2 18.9 17.7 16.8 -1 27.9 25.7 24.0 22.6 21.4

20.4 19.6 11.1 13.3 15.9 18.8 Annual rate of population growth (percent) 2 1.5 1 0.5 23.4 23.6 24.1 24.1 19.4 19.6 20.2 21.1 16.1 16.7 17.5 18.6 13.4 14.2 15.2 16.5 11.1 12.1 13.3 14.8 9.2 10.3 11.7 13.4 7.6 8.8 10.4 12.2 0 25.0 22.2 20.0 18.2 16.7 14.8

14.3 -0.5 26.2 23.7 21.8 20.2 18.8 16.7 16.7 -1 27.8 25.6 23.9 22.5 21.3 19.5 19.5 6.2 7.6 9.2 85 Female pectancy at birth (years) Both sexes 10 40 45 50 55 60 65 70 Assessing Vital Statistics 6 4 ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 3. Age and sex-specific death rates

Enables users to: Calculate the mortality rate specific to a population age group (usually a five-year grouping), known as the age-specific mortality rate (ASMR) deaths in a specific age group in a ASMR = population during a specified time period 100 000 total mid-year population in the same age group, population and time period Compare relative age patterns in ASMR for country to expected global patterns to identify potential under registration at certain ages Compare patterns in male:female ASMR ratio to countries with various infant mortality rates to identify issues with completeness of registration Look for deviations in expected patterns of the log ASMR to indicate under-reporting at certain ages or mis-reporting of correct age of death Assessing Vital Statistics 6 5 Compare relative age patterns to expected patterns in ASMR: Deviations may indicate under-registration in certain age groups and/or missing age or sex information. M o rt a lit y ra t e p e r 1 0 0 0 0 0 p o p u la t io n ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 3. Age and sex-specific death rates Age-specific mortality rates 14000 12000 10000 ma le s 8000 6000 4000 80+ 7 5 -7 9 70-74 65-69

60-64 55-59 50-54 45-49 4 0 -4 4 3 5 -3 9 3 0 -3 4 2 5 -2 9 20-24 15-19 5-9 0 10-14 2000 0-4 Figure 3: ASMR for Australia, Russia and South Africa, males and females, 2000 (ANACoD) Age 6 6 Assessing Vital Statistics ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 3. Age and sex-specific death rates Compare patterns in ratio of male:female ASMR: Deviations may indicate country abnormalities or under-registration. 6.0 Ratio of male to female age-specific mortality rates

5.0 4.0 IMR* = 16.0 per 1 000 live births 3.0 2.0 Country World 80+ 7 5-7 9 7 0 -7 4 6 5 -6 9 Age 6 0 -6 4 Assessing Vital Statistics 5 5 -5 9 5 0-5 4 4 5 -4 9 4 0 -4 4 3 5 -3 9 3 0-3 4 2 5 -2 9 2 0 -2 4 1 5 -1 9 1 0-1 4 0.0 5 -9 1.0

0 -4 R a tio o f m a le to fe m a le A S M R Figure 5: Ratio of male to female age-specific mortality rates at different levels of infant mortality (expected patterns) (WHO Global Health Observatory; 2010) * From a source independent of the value from the 6 data being assessed. 7 ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 3. Age and sex-specific deaths rates M o r ta lity r a te p e r 1 0 0 0 0 0 p o p u la tio n Look for deviations in the expected patterns of the log ASMR: Deviations may indicate systematic underreporting at a given age. Log of age-specific mortality rates 100000 Males Females 10000 1000 Age Assessing Vital Statistics Country World High income Upper middle Lower middle Low income 80+ 7 5 -7 9 7 0 -7 4

6 5 -6 9 6 0 -6 4 5 5 -5 9 5 0 -5 4 4 5 -4 9 4 0 -4 4 3 5 -3 9 3 0 -3 4 2 5 -2 9 2 0 -2 4 1 5 -1 9 1 0 -1 4 5 -9 0 -4 80+ 7 5 -7 9 7 0 -7 4 6 5 -6 9 6 0 -6 4 5 5 -5 9 5 0 -5 4 4 5 -4 9 4 0 -4 4 3 5 -3 9 3 0 -3 4 2 5 -2 9

2 0 -2 4 1 5 -1 9 5 -9 0 -4 10 1 0 -1 4 100 6 8 ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 4: Review the age distribution of deaths Enables users to: Examine the age distribution of reported deaths Compare the calculated distribution of deaths to expected distributions corresponding to: Country income group (ANACoD guidance) Country infant mortality rate (UQ Working Paper 13) 6 9 Assessing Vital Statistics Step 4: Review the age distribution of deaths Look for expected patterns in age-specific mortality: Deviations may indicate selective bias in age-specific death reporting. MALE > female mortality, except in oldest age groups In countries with low income/high infant mortality, female rates may be comparable to male rates. Lower middle income countries 40 40 35 35 male

20 Age 80+ 75-79 70-74 65-69 60-64 55-59 50-54 High income countries 40 40 35 35 male 30 female 20 15 10 25 female 20 15 7 0 10 Age Age

80+* 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34

25-29 20-24 0-4 0 15-19 5-9 0-4 male 5 5 0 30 5-9 Peak in male mortality between 15-44 years due to Assessing Vital Statistics external causes % of deaths 25 10-14 % of deaths 45-49 Age Upper middle income countries (less so in countries with low income/high infant mortality) 40-44

35-39 30-34 0-4 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 Oldest age groups 15-19 0 5-9 5 0 10-14 10 5 25-29

15 10 20-24 15 15-19 20 female 25 5-9 % of deaths 25 male 30 female 10-14 30 0-4 (less so in countries with high income/low infant mortality) Low income countries % of deaths Peak in overall mortality in: 0-4 years * Female 80+ value is ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 4: Review the age distribution of deaths

Compare the calculated distribution of deaths to expected distributions corresponding to: country income group % of deaths Age distribution of reported deaths 40 35 male 30 female 25 20 Upper middle income countries male female 10 5 Age 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24

15-19 10-14 5-9 0-4 0 % of deaths 15 40 35 30 25 20 15 10 7 1 5 Age 80+ 75-79 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34

25-29 20-24 15-19 10-14 5-9 Assessing Vital Statistics 0-4 0 ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 4: Review the age distribution of deaths Compare the calculated distribution of deaths to expected distributions corresponding to: infant mortality group % of deaths Age distribution of reported deaths 40 35 male 30 female 25 20 15 10 5 80+ 75-79 70-74 65-69 60-64 55-59 50-54

45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 0 Age 7 2 Assessing Vital Statistics ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 5: Child mortality rates Enables users to: Calculate & interpret indicators of under-five mortality Infant mortality rate (ANACoD, UQWP13) Probability (per 1,000 live births) of a child born in a specified year dying before reaching the age of 1 if subject to current ASMRs Under 5 mortality rate (ANACoD, UQWP13) Probability (1,000 live births) of a child born in a specified year dying before reaching the age of 5 if subject to current ASMRs Neonatal mortality rate (UQWP13) Post neonatal mortality rate (UQWP13) Use under-five mortality indicators from various sources to analyze the quality of mortality data Assessing Vital Statistics 7 3

ANACoD - PART II: MORTALITY LEVELS ANALYSIS Calculate indicators of under-five mortality: Step 5: Child mortality rates 1. Child deaths by age and calculation of mortality indicators: Data from Civil registration, 2009 x n Population Deaths nmx 0 1 1 4 913341 3581718 Infant mortality rate per 1000 live births Under-5 mortality rate per 1000 live births 9601.941 0.0105 2061.323 0.0006 n qx 0.0104 0.0023 = 1000* 1q0 ==>10.4 = 1000*[1-(1-1q0)(1-4q1)] ==>12.7 x = beginning of the age interval n = number of years in the interval Population = from entered data; sum of male and female population in Step 2. Deaths = from entered data; sum of male and femal deaths in Step 2. n mx = mortality rate (ASMR) for age x to age n; Deaths/Population. q = probability of a child dying between age x and age n; automatically n x calculated (see ANACoD guidance for calculation details).

Assessing Vital Statistics 7 4 ANACoD - PART II: MORTALITY LEVELS ANALYSIS Step 5: Child mortality rates Use under-five mortality indicators from various sources to analyze the quality of mortality data: Deviations from best fit line indicate over- or under- reporting. Under-Five Mortality Rate, Columbia Child Mortality Estimates www.childmortality.org Best fit Census data Various surveys Vital registration data Assessing Vital Statistics 7 5 ANACoD - PART III: CAUSES OF DEATH ANALYSIS Steps 6-10 Focus on simple steps to assess the plausibility of data on causes of death The objectives of steps 6-10 are to enable users to: Calculate broad patterns of causes of death Critically analyse and interpret cause of death data Assess the plausibility of the cause of death patterns emerging from the data 7 6 Assessing Vital Statistics ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 6: Distribution of death according to the Global Burden of Enables users to:

Disease list Calculate the percentage distribution of deaths by broad disease groups Compare distribution to what would be expected for the population (based on level of life expectancy) Identify potential problems in quality of data based on deviations from expected patterns 7 7 Assessing Vital Statistics ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 6: Distribution of death according to the Global Burden of Disease list Global Burden of Disease cause list: Group I: Communicable diseases, e.g.: TB, pneumonia, diarrhoea, malaria, measles Maternal and perinatal causes (e.g. maternal haemorrhage, birth trauma) Nutritional conditions (e.g. protein-energy malnutrition) Group II: Non-communicable diseases, e.g.: Cancer, diabetes, heart disease, stroke Group III: External causes of mortality , e.g.: Accidents, homicide, suicide Assessing Vital Statistics 7 8 ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 6: Distribution of death according to the Global Burden of Compare distribution to what would be expected for the population (based on life expectancy): Deviations suggest potential problems with the certification and/or coding of causes of deaths.

Disease list Calculating proportions of groups 1, 2 and 3 after redistribution of deaths from unknown sex and illdefined diseases Proportions to total deaths grp1 0.11 grp2 0.71 grp3 0.18 1.00 New totals after all the above adjustments 196681 Colombia life expectancy, 2011: 78 years (WHO Global Health Observatory) Table 2: Expected distribution of causes of death according to life expectancy by broad groups Life Expectancy 55 years 60 years 65 years 70 years 22% 16% 13% 11% Group I causes of death (communicable) 7 65% 70% 74% 78% 9 Group II causes of death (non-communicable) 13% 14% 13% 11% Group III causesVital of death (external) Assessing Statistics ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 7: Age pattern of broad groups of causes of death (Distribution of major causes of death) Enables users to:

Observe age-pattern of deaths from broad causes Check if pattern is consistent with expected patterns of countries from same income level Identify potential problems associated with: Poor medical certification of cause of death Poor coding practices Age-misreporting of deaths Bias in reporting certain infectious diseases 8 0 Assessing Vital Statistics ANACoD - PART III: CAUSES OF DEATH ANALYSIS Proportion of total deaths Step 7: Age pattern of broad groups of causes of Colombia, 2009 --death Observed (Distribution of major causes of death) group 1 group 2 Male 1.0 0.8 0.6 Upper middle income countries -Expected M85 M80 0.6 0.4 8 1 0.2 M85 M80

M75 M70 M65 M60 M55 M50 age M45 M40 M35 M30 M25 M20 M15 0.0 M10 M65 M75 0.8 M05 Assessing Vital Statistics 1.0 M00 Group 1: Communicable Group 2: Noncommunicable Group 3: External M60

M55 M50 M45 age M40 M35 M30 M25 M20 M15 M10 M05 M1-4 M00 0.0 group 1 group 2 Male M70 0.2 Proportion of total deaths 0.4 ANACoD - PART III: CAUSES OF DEATH ANALYSIS Proportion of total deaths Step 7: Age pattern of broad groups of causes of Colombia, 2009 -- Observed

death (Distribution of major causes of death) gro up 1 Female 1.0 0.8 0.6 0.4 Upper middle income countries -Expected age F85 F 80 F75 F 70 F65 F60 F 55 F50 F 45 F40 F35 F30 F25 F 20 F15 F 10 F05

F1-4 F 00 0.0 Proportion of total deaths 0.2 1.0 gro up 1 Female 0.8 0.6 0.4 0.2 Assessing Vital Statistics age 8 2 F85 F80 F75 F70 F65 F60 F55 F50 F45

F40 F35 F30 F25 F20 F15 F10 F05 0.0 F00 Group 1: Communicable Group 2: Noncommunicable Group 3: External ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 8: Leading causes of death Enables users to: Determine the distribution of leading causes of death for the country Compare observed distribution to distributions expected in other countries of similar income level Identify deviations that would be indicative of potential biases in certification and coding practices 8 3 Assessing Vital Statistics ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 8: Leading causes of death Compare distribution of leading causes: Deviations may indicate biases in certification or coding practices 20 leading causes of death, all ages 1 2 3 4 5 6

7 8 9 10 11 12 13 14 15 16 17 18 19 20 Both sexes Nos %total Ischaemic heart disease Homicide Cerebrovascular disease Chronic obstructive pulmonary dis. Other cardiovascular diseases Other digestive diseases Diabetes mellitus Lower respiratory infections Other malignant neoplasms Road traffic accidents Hypertensive disease Stomach cancer Ill-defined diseases (ICD10 R00-99) Trachea, bronchus and lung cancers Nephritis and nephrosis Other respiratory diseases Colon and rectum cancers Prostate cancer HIV Self-inflicted injuries 27,597 19,680 13,870 10,265 8,674 7,111 6,469 6,442 6,441 6,377

5,664 4,450 4,289 3,898 3,199 2,732 2,575 2,419 2,340 2,259 14.0 10.0 7.1 5.2 4.4 3.6 3.3 3.3 3.3 3.2 2.9 2.3 2.2 2.0 1.6 1.4 1.3 1.2 1.2 1.1 Assessing Vital Statistics Upper middle income countries Both sexes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17 18 19 20 Ischaemic heart disease Cerebrovascular disease Other cardiovascular diseases HIV Lower respiratory infections Diabetes mellitus Hypertensive disease Road traffic accidents Chronic obstructive pulm. dis Other malignant neoplasms Other digestive diseases Other unintentional injuries Trachea, bronchus ,lung can. Homicide Cirrhosis of the liver Stomach cancer Other respiratory diseases Colon and rectum cancers Other infectious diseases Inflammatory heart diseases Nos (000) 1,508 1,035 419 377 295 248 224 196 189 189 183 178 175 171 146 122 117 113 108 104 %total 19.1 13.1 5.3

4.8 3.7 3.2 2.8 2.5 2.4 2.4 2.3 2.3 2.2 2.2 1.8 1.5 1.5 8 1.4 4 1.4 1.3 ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 9: Ratio of non-communicable to communicable causes of death Enables users to: Calculate the ratio of deaths from non-communicable diseases to communicable diseases for the country Compare the country ratio to the world and 4 income groupings Identify deviations that are suggestive of errors in cause of death data 8 5 Assessing Vital Statistics R a tio o f n o n c o m m u n ic a b le to c o m m u n ic a b le ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 9: Ratio of non-communicable to communicable causes of death Compare ratio for country to similar income group: Deviations indicate potential errors in cause of death data Ratio of non-communicable to communicable diseases by country income groupings 14

12 10 8 6 4 8 6 2 0 Assessing Vital Statistics Colombia 2009 World High income Upper middle Lower middle Low income income groupings ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 10: Ill-defined causes of death Enables users to: Calculate the proportion of deaths attributed to ill-defined causes of death Evaluate the proportion of ill-defined causes of death against recommended levels Identify target areas for remedial action to reduce usage of ill-defined causes of death 8 7 Assessing Vital Statistics Ill-defined causes are: symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified. They arise from: Deaths classified as ill-defined (Chapter XVIII of ICD-10) Deaths classified to any one of the following vague or unspecific Dx : A40-A41 Streptococcal and other septicaemia C76, C80, C97 Ill-defined cancer sites D65 Disseminated intravascular coagulation [defibrination syndrome] E86

Volume depletion I10 Essential (primary) hypertension I269 Pulmonary embolism without mention of acute cor pulmonale I46 Cardiac arrest I472 Ventricular tachycardia I490 Ventricular fibrillation and flutter I50 Heart failure I514 Myocarditis, unspecified I515 Myocardial degeneration Assessing Vital Statistics I516 Cardiovascular disease, unspecified I519 Heart disease, unspecified I709 Generalized and unspecified atherosclerosis I99 Other and unspecified disorders of circulatory system J81 Pulmonary oedema J96 Respiratory failure, not elsewhere classified K72 Hepatic failure, not elsewhere classified N17 Acute renal failure N18 Chronic renal failure N19 Unspecified renal failure P285 Respiratory failure of newborn 8 Y10-Y34, Y872 External cause of death not8 specified as accidentally or purposely inflicted ANACoD - PART III: CAUSES OF DEATH ANALYSIS

Step 10: Ill-defined causes of death % ill-defined should ideally be: 10% for deaths at ages 65 years and over < 5% for deaths at ages below 65 years Both Male Female All ages All causes Male 0 1-4 196681 113327 83354 5333 1121 5-9 629 Ill-defined causes by ICD-10 chapter: I. II. III. Infectious and parasitic diseases Neoplasms Total of ill-defined as % of All causes Assessing Vital Statistics 1024 1773 74 18989 9.7% 502 843 37

522 930 37 56 2 13 16 7 4 5 5 1 5 4 1 10395 8594 415 145 80 69 9.2% 10.3% 7.8%12.9% 12.7% 8 9 ANACoD - PART III: CAUSES OF DEATH ANALYSIS Step 10: Ill-defined causes of death Specific causes among ill-defined causes can be used to target improvement efforts. 9 0 Assessing Vital Statistics ANACoD Wrap up The Summary sheet provides a summary report of findings With ANACoD, the user is able to:

Derive the mortality profile of the country/area analysed Develop a critical view on the quality of mortality data Understand further cause-of-death statistics Limitations of ANACoD include: Partial data are not adjusted for incompleteness by the tool The tool cannot improve the quality of poor data, but it can provide insights on medical certification or coding problems Currently only data coded to ICD-10 three or four characters can be analysed Assessing Vital Statistics 9 1 References (Freedman) Freedman MA. Improving Civil Registration and Vital Statistics. The World Bank. 2003. (Mahapatra) Mahapatra P, Shibuya K, Lopez AD, et al. Civil registration systems and vital statistics: successes and missed opportunities. Who Counts? 2. Lancet. 370:1653-63. 2007. (NCHS) National Center for Health Statistics. Methods of Civil Registration: Modular Course of Instruction. (PRVSS2) UN. Principles and Recommendations for a Vital Statistics System, Revision 2. New York. 2001. Statistics South Africa. Mortality and causes of death in South Africa, 2009: Findings from death notification. Statistical release P0309.3. (p16) Bradshaw D, et al. Cause of death statistics for South Africa: Challenges and possibilities for improvement. Medical Research Council, South Africa. November 2010. (UQWP13) AbouZahr C, Mikkelsen L, Rampatige R, Lopez A. Mortality statistics: a tool to improve understanding and quality. Health Information Systems Knowledge Hub, University of Queensland. Working Paper 13. Nov 2010. (ANACoD) World Health Organization (2013). Analysing mortality levels and causes-of-death (ANACoD) Electronic Tool, Version 1.1. Department of Health Statistics and Information Systems, WHO, Geneva, Switzerland.

(WHO/HMN) WHO, University of Queensland. Rapid assessment of national civil registration and vital statistics systems. WHO: Geneva. 2010. (WHO/UQ) WHO, University of Queensland. Improving the quality and use of birth, death and cause-of-death information: guidance for a standards-based review of country practices. WHO: Geneva. May 2010. (WHO/IMR) WHO. Indicator and Measurement Registry. Version 1.6.0. Civil registration coverage of deaths (%). (WHOSIS) WHO Statistical Information Systems. Indicator definitions and metadata, 2008. Age-standardized mortality rates by cause. 9 2 Assessing Vital Statistics Activity Comparison of Vital Event Definitions: In small groups, discuss the degree to which the vital event definitions used in your country match those used by WHO. If differences exist, discuss: Philosophies behind them Whether or not those differences affect the registration system or interpretation of vital statistics Share with the class. 9 3 Assessing Vital Statistics Activity Data Quality Review: In small groups, review and compare various reports for the aspects of data quality: Accuracy Timeliness Comparability Relevance Accessibility Discuss observations with class. Assessing Vital Statistics

9 4 Overall Review 1.Good statistical systems are efficient, credible, and (subjective / objective). 2.The quality of vital statistics data is judged based on (reliability / accuracy), timeliness, comparability, relevance, & accessibility. 3. (Direct / Indirect) assessment of coverage error includes comparing the total number of vital events registered and reported to the statistical agency for a given period with the number registered and reported in a previous, similar period. 4. (Direct / Indirect) assessment of coverage error includes regularly querying and monitoring statistical returns from local registrars. 5.Production time is the mean time from (beginning / end) of reference period to publication. Assessing Vital Statistics 9 5

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