Theme: Strengthening vital statistics and cause-of-death data Mortality statistics: a tool to improve understanding and quality Carla AbouZahr Lene Mikkelsen Rasika Rampatige Alan Lopez Working Paper Series • Number 13 • November 2010 • WORKING PAPER For the PDF version of this paper and other Strengthening health systems related documents, visit in Asia and the Pacific through better evidence and practice www.uq.edu.au/hishub About this series Acknowledgments The Health Information Systems Knowledge Hub’s Working Paper Series is the principal means to We gratefully acknowledge the assistance of Rafael disseminate the knowledge products developed by the Lozano in preparing the table and graphs for this guide. hub as easily accessible resources that collectively form a lasting repository of the research findings and knowledge generated by the hub’s activities. Working papers are intended to stimulate debate and promote the adoption Author details of best practice for health information systems in the region. The series focuses on a range of knowledge gaps, Carla AbouZahr, Lene Mikkelsen, Rasika Rampatige and including new tools, methods and approaches, and raises Alan Lopez, Health Information Systems Knowledge Hub, and debates fundamental issues around the orientation, University of Queensland purpose and functioning of health information systems. Generally, working papers contain more detailed information than a journal article, are written in less- academic language, and are intended to inform health system dialogue in and between countries and a range of development partners. Many working papers have accompanying products such as summaries, key points and action guides. The full range of documents, as well as other resources and tools, is available on the Health Information Systems Knowledge Hub website at www.uq.edu.au/hishub/publication-tools. The opinions or conclusions expressed in the Working Paper Series are those of the authors and do not necessarily reflect the views of institutions or governments. © The University of Queensland 2010 Published by the Health Information Systems Knowledge Hub, School of Population Health The University of Queensland Public Health Building, Herston Rd, Herston Qld 4006, Australia Please contact us for additional copies of this publication, or send us feedback: Email: [email protected] Tel: +61 7 3346 4732 Fax: +61 7 3365 5442 www.uq.edu.au/hishub Design by Biotext, Canberra, Australia Contents Acronyms and abbreviations ������������������������������������������������������������������������������������������������������������2 Objectives of this guide ��������������������������������������������������������������������������������������������������������������������3 Using the electronic mortality data quality assessment tool �������������������������������������������������������������5 Conclusions ������������������������������������������������������������������������������������������������������������������������������������29 References �������������������������������������������������������������������������������������������������������������������������������������30 Health Information Systems Knowledge Hub Knowledge Systems Health Information Working Paper Series • Number 13 • November 2010 1 Acronyms and abbreviations ASMR age-specific mortality rate CDR crude death rate ICD-10 International Classification of Diseases, 10th revision IMR infant mortality rate NNMR neonatal mortality rate PNNMR postneonatal mortality rate U5MR under-five mortality rate WHO World Health Organization Health Information Systems Knowledge Hub Knowledge Systems Health Information 2 Working Paper Series • Number 13 • November 2010 Objectives of this guide This guide is intended to help build analytical capacity to assess the quality of mortality statistics that are This guide describes simple ways of analysing the currently being collected in order to improve their value internal validity and coherence of mortality data, in informing health policies and programs. and shows how comparisons with other external sources of mortality data can be used to assess data Countries routinely invest significant resources into consistency and plausibility. collecting mortality data from a variety of sources, including civil registration systems, health care facilities, ongoing longitudinal demographic and health surveillance, and from other data sources such as censuses or household surveys. The primary purpose is to generate critical information to guide public Ten simple steps health decision-making. However, data cannot be used appropriately or with any confidence if insufficient We describe a 10 step process for assessing the quality attention is paid to the quality. In the absence of of mortality data. The 10 steps can be applied to datasets systematic data quality assessment, and adjustment from different sources, but steps 6–10 are not relevant where necessary, the data that have been collected— for survey and census data, as these sources do not often at great expense—cannot be used to their full generate cause-of-death information using International potential to guide decision-making. Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) standards (WHO 2007). To assist countries in validating and correcting their mortality data, the World Health Organization (WHO), At each step, users are led through a process of checking in partnership with the Health Information Systems for errors, calculating key indicators, interpreting the Knowledge Hub at the University of Queensland public health significance of the indicator values and (UQ), Brisbane, has developed this mortality statistics reflecting on how to use the information to diagnose assessment guide and toolkit. The guide describes possible weaknesses in their mortality data systems. The relatively simple ways of analysing the internal validity 10 steps are: and coherence of mortality data, and shows how Step 1 Prepare basic tabulations of deaths by age, sex comparisons with other external sources of mortality and cause-of-death. data can be used to assess data consistency and plausibility. By carrying out these simple checks, data Step 2 Review crude death rates. collectors and practitioners will be able to diagnose weaknesses in their data. If this information is used in Step 3 Review age and sex-specific death rates. conjunction with an assessment of the functioning of Step 4 Review the age distribution of deaths. the civil registration and vital statistics systems using the Hub Knowledge Systems Health Information WHO/UQ guide (WHO and UQ 2010), country decision- Step 5 Review child mortality rates. makers will have all the tools necessary to develop and target strategies for improving the availability and quality Step 6 Review the distribution of major causes -of- of mortality data. The checks will also assist users in death. the interpretation of the data so that they can better Step 7 Review age patterns of major causes-of-death. understand prevailing levels, trends and patterns of mortality in their populations. Step 8 Review leading causes-of-death. Step 9 Review ratio of noncommunicable disease deaths to communicable disease deaths. Step 10 Review ill-defined causes- of-death. Working Paper Series • Number 13 • November 2010 3 Using the electronic mortality data quality assessment tool Applying the 10 steps To automate the data quality assessment process described step by step in this guide, an easy-to-use electronic tool is This 10 step process can be applied to any mortality available1 that will perform the calculations needed for the dataset. In many settings, mortality data will be the data quality review and automatically generate the associated product of the national civil registration and vital figures and tables. To use the tool, it is helpful to have basic statistics systems that routinely collect and compile computer skills and familiarity with software packages such information to produce statistics on births, deaths and as Microsoft Excel and Access. However, the tool does not causes-of-death. Data on mortality by age and sex (but require either advanced expertise in software packages, or not cause) can also be collected through the decennial advanced statistical or computing skills. census. Mortality data, including information on causes-of-death, are also generated through The tool aggregates and presents mortality data in a format longitudinal demographic surveillance in specific sites. that makes them easier to analyse. It automatically: In some settings, the most regular source of data • verifies and checks for gross data errors (eg maternal on mortality for a population is the routine health deaths ascribed to males) information system that records deaths occurring in • generates information on the reliability of certification and hospitals. Although these data cannot be considered coding practices (eg identifying invalid underlying causes- nationally representative (because they are biased of-death) towards deaths occurring in health care facilities and usually confined to the public sector), they can, • carries out basic calculations of health indicators and nonetheless, provide useful information on patterns generates figures, such as the distribution of broad causes- of hospital mortality and may be of considerable value of-death by age group, and age, sex and cause-specific for understanding mortality patterns in specific sectors death rates of the population. This is especially