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Mortality odds ratio, proportionate mortality ratio, and healthy worker effect
1Department of Epidemiology, School of Hygiene and Public Health, Johns Hopkins University, Baltimore 21205.
Abstract:
The standardized proportionate mortality ratio (PMRi) and the mortality odds ratio (MORi) are two statistics used to approximate the cause specific standardized mortality ratio (SMRi) when death data are available but the population at risk is not known. When there is a healthy worker effect, the MORi will always overestimate the SMRi and will always be greater than the PMRi. The PMRi is influenced by the relative frequency of the cause of death. For rare causes, such as brain cancer or leukemia, the PMRi will overestimate the SMRi to essentially the same degree as the MORi. For more common conditions, such as lung cancer, the PMRi will overestimate or underestimate the SMRi depending on the magnitude of the healthy worker effect. When the SMRi = 1 and there is a healthy worker effect, both the PMRi and MORi are in excess of one (1) regardless of the disease rate. As the SMRi increases it is more likely to be bounded by the PMRi (lower) and the MORi (upper). We therefore recommend that each statistic be derived when death certificates are the only source of data used to assess risk due to occupational exposures.
Insights
The mortality odds ratio (MORi) and standardized proportionate mortality ratio (PMRi) approximate cause-specific mortality when population data is missing. MORi consistently overestimates, while PMRi
Area of Science:
- Occupational health
- Epidemiology
- Biostatistics
Background:
- Assessing occupational exposure risks often lacks complete population data.
- Standardized proportionate mortality ratio (PMRi) and mortality odds ratio (MORi) are used to estimate cause-specific standardized mortality ratio (SMRi) in such scenarios.
- The healthy worker effect can influence the accuracy of these estimations.
Purpose of the Study:
- To evaluate the performance of PMRi and MORi in approximating SMRi under conditions of unknown population risk.
- To understand the impact of the healthy worker effect on these mortality ratios.
- To provide recommendations for using these statistics in occupational risk assessment.
Main Methods:
- Comparative analysis of PMRi and MORi against SMRi.
- Examination of statistical behavior under varying disease prevalence and healthy worker effect magnitudes.
- Utilizing death certificate data as the primary data source.
Main Results:
- MORi consistently overestimates SMRi when a healthy worker effect is present and is always greater than PMRi.
- PMRi's accuracy depends on the cause of death's frequency; it overestimates for rare diseases (e.g., brain cancer, leukemia) similarly to MORi.
- For common conditions (e.g., lung cancer), PMRi may overestimate or underestimate SMRi based on the healthy worker effect's strength.
- When SMRi = 1 and a healthy worker effect exists, both PMRi and MORi exceed 1.
- As SMRi increases, PMRi tends to be the lower bound and MORi the upper bound.
Conclusions:
- Both PMRi and MORi are valuable when only death data is available for occupational risk assessment.
- MORi provides an upper bound and PMRi a lower bound for SMRi, especially for common diseases.
- Deriving both statistics is recommended for a comprehensive risk assessment using death certificates.