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Comparison of event rates among three MONICA centres
R Beaglehole1, A Dobson, M Hobbs
1Department of Community Health, School of Medicine, University of Auckland, New Zealand.
Insights
Comparing coronary heart disease event rates across MONICA centres is challenging due to differing data collection methods. Non-fatal definite myocardial infarction and all coronary heart disease deaths offer the most reliable comparison metrics.
Area of Science:
- Cardiology
- Epidemiology
- Public Health
Background:
- International variations in diagnostic criteria and data collection methods complicate comparisons of cardiovascular event rates.
- The WHO MONICA (Multinational Monitoring of Trends and Determinants in Cardiovascular Disease) project aims to standardize data collection, but methodological differences persist.
- Comparing event rates and case fatality across different MONICA centres requires careful consideration of ascertainment biases.
Purpose of the Study:
- To evaluate the impact of different event ascertainment and data collection methods on comparing cardiovascular event rates and case fatality across three MONICA centres.
- To identify the most reliable diagnostic categories for inter-centre comparisons within the MONICA framework.
- To inform future strategies for harmonizing data collection in multinational cardiovascular disease monitoring programs.
Main Methods:
- Retrospective analysis of data from three MONICA centres: Auckland (New Zealand), Newcastle (Australia), and Perth (Australia).
- Comparison of two primary event ascertainment methods: 'hot pursuit' (in-hospital interviews) used in Auckland and Newcastle, versus 'cold pursuit' (record-based abstraction) used in Perth.
- Examination of MONICA diagnostic classifications for definite, possible, and insufficient data myocardial infarction events and coronary heart disease deaths.
Main Results:
- Significant variations were observed in the distribution of MONICA diagnostic classifications across the centres.
- Perth exhibited a higher proportion of definite myocardial infarction events and a lower proportion of possible myocardial infarction events compared to Auckland and Newcastle.
- Differences in event ascertainment, data collection, and post-mortem rates were identified as key factors influencing observed variations.
Conclusions:
- Methodological differences in event ascertainment and data collection significantly impact the comparability of cardiovascular event rates across MONICA centres.
- For robust inter-centre comparisons, the categories of non-fatal definite myocardial infarction and all coronary heart disease deaths are recommended.
- Standardization of data collection protocols and careful consideration of ascertainment biases are crucial for accurate multinational cardiovascular disease surveillance.
Abstract:
Data from three MONICA centres in Auckland (New Zealand) and Newcastle and Perth (Australia) are used to explore some of the issues involved in comparing event rates and case fatality among MONICA centres. Auckland and Newcastle follow the "hot pursuit" method of identifying and interviewing patients while they are still in hospital. Perth follows the "cold pursuit" method, in which patients are identified by search of computerized hospital records after discharge and all data are abstracted retrospectively from case notes. Fatal cases are identified by the same method in the three centres. The distribution of events by MONICA diagnostic classification varied among centres, with Perth having the highest proportion of definite myocardial infarction events and the lowest proportion of possible myocardial infarction events. These differences appear to be due to the different methods of event ascertainment and data collection, and to variations in post mortem rates between centres. For comparisons among these three centres, the categories of non-fatal definite myocardial infarction and of all coronary heart disease deaths (that is those in the MONICA categories fatal definite myocardial infarction, fatal possible myocardial infarction, and fatal cases with insufficient data) appear to be the most useful.