Understanding disease burden, evaluating healthcare systems, and comparing results across populations and environments requires mortality statistics. The majority of mortality data comes from death certificates gathered by vital records authorities in affluent nations (Fleming & Douglas, n.d.). Death certificates include demographics and the immediate, intermediate, and final cause of death. Note that death certificates may underreport or overreport the cause of death. AIDS-related mortality may be underreported, while stroke deaths may be overreported. Epidemiologists adjust death rates for age to compare them across time, settings, or persons. Epidemiology has long used age adjustment to level the playing field by correcting for age mix disparities that could mislead comparisons. Risk adjustment extends age adjustment to account for other factors that affect mortality. Risk adjustment standardizes mortality rates across patient-related dimensions, including diagnosis and risk variables, to allow comparisons between populations or healthcare providers with varied patient mixes (Adams et al., 2017).
The basic strategies for normalizing mortality rates are direct and indirect. Direct standardization applies age-specific death rates from a standard population to the study group’s age distribution. This approach lets you compare death rates across age groups. However, indirect standardization calculates anticipated deaths from the age-specific death rates of a standard group and compares them to observed deaths in the population being investigated. This approach may compare mortality rates across age groups and calculate standardized mortality ratios (Lecture Notes, 2021).
Risk-adjusting mortality rates account for factors other than age. Risk adjustment acknowledges that patient-related factors such as comorbidities, socioeconomic position, and lifestyle might affect mortality. The optimum risk-adjustment approach includes most mortality-affecting factors, such as patient characteristics, diagnoses, and treatment (Fleming & DouglasScutchfield, n.d.). However, gathering data on all these aspects may be impractical. Life quality and cultural or religious views may be hard to measure. Comparing mortality rates within and between organizations and providers requires risk adjustment. It offers a fair comparison by controlling for non-age-related mortality factors. Comorbidities, financial level, lifestyle, and other patient variables are examples (Iezzoni, 2012)
Risk adjustment has numerous steps. Select the study population and clinical participants first. Mortality is used as an outcome measure. To confirm that risk factors cause death, causality is considered. Patient characteristics that may increase mortality risk are identified, and data sources are analyzed (Iezzoni, 2012). Select a standard population for comparison. The projected mortality rates by age, sex, and other characteristics are shown in this standard population. The estimating method is chosen, and mortality-related risk factors are weighted. The risk model estimates each case’s adverse outcome probability (Lecture Notes, 2021). Testing the risk model ensures its validity and accuracy. This entails testing the model’s mortality prediction. Check and fix risk models and data-collecting biases. Finally, risk-adjusted death rates are derived and compared among populations or providers (Shine, 2012). Standards are also significant in mortality data analysis. Adjusting for age, sex, and race lets you compare mortality rates across populations. Two basic standardizing approaches are direct and indirect (Fleming & Douglas, n.d.). Direct standardization applies age-specific death rates from a standard population to the study group’s age distribution. This approach lets you compare death rates across age groups. However, indirect standardization calculates anticipated deaths from the age-specific death rates of a standard group and compares them to observed deaths in the population being investigated. This approach may compare mortality rates across age groups and calculate standardized mortality ratios.
References
Adams, M., Braun, J., Bucher, H. U., Puhan, M. A., Bassler, D., Von Wyl, V., Meyer, P., Anderegg, C., Schulzke, S., Nelle, M., Wagner, B., Riedel, T., Kaczala, G., Pfister, R. E., Tolsa, J. F., Roth-Kleiner, M., Stocker, M., Laubscher, B., Malzacher, A., … Bernet, V. (2017). Comparison of three different methods for risk adjustment in neonatal medicine. BMC Pediatrics, 17(1), 1–10. https://doi.org/10.1186/s12887-017-0861-5
Fleming, S. T., & Douglas Scutchfield, F. (n.d.). CHAPTER 7 – Mortality and Risk Adjustment. In S. T. Fleming, & F. DouglasScutchfield, Mortality Rate.
Lecture Notes. (2021). CHAPTER 7 Mortality and Risk Adjustment. Foundation of the American College of Healthcare Executives.
Iezzoni, L. 1994, 2012. Risk Adjustment for Measuring Healthcare Outcomes. Chicago: Health Administration Press
Shine, D. (2012). Risk-adjusted mortality: Problems and possibilities. Computational and Mathematical Methods in Medicine, 2012(January). https://doi.org/10.1155/2012/829465
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