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Analyzing the Centers for Disease Control and Prevention Mortality Data Using Weekly Exceedance in Mortality Count
Aditya Chakrabarty1, Mohan D Pant1
1Department of Epidemiology, Biostatistics, & Environmental Health, Joint School of Public Health Old Dominion University Norfolk Virginia USA.
Health Science Reports
|September 17, 2025
Summary
This study introduces a new method for predicting cause-specific mortality (CSM) counts. The approach uses a multivariate time series model and introduces Weekly Exceedance in Mortality Count (WEMC) and Weekly Change in Mortality Indicator (WCMI) to calculate probabilities for public health policy.
Area of Science:
- Public Health
- Biostatistics
- Epidemiology
Background:
- Cause-specific mortality (CSM) prediction is crucial for public health policy.
- Existing methods may lack the granularity needed for specific cause-based interventions.
Purpose of the Study:
- To develop and validate a novel analytical approach for CSM count prediction.
- To compute simple, compound, and conditional probabilities for 14 specific causes of death.
- To introduce and apply new metrics: Weekly Exceedance in Mortality Count (WEMC) and Weekly Change in Mortality Indicator (WCMI).
Main Methods:
- A multivariate time series forecasting model was applied to CDC weekly mortality data.
- A binary data matrix was created for 14 causes of death (COD) incorporating observed and forecasted mortalities.
- Statistical tests (chi-square, Cramer's V, Wilcoxon rank sum) were used for validation.
Main Results:
- No statistically significant association was found between COD and WEMC (p=0.79, Cramer's V=0.055).
- The forecasting model demonstrated consistency, with no significant difference between observed and forecasted counts (p=0.11).
- Probabilities associated with WCMIs were computed, illustrating the method's utility.
Conclusions:
- The developed analytical approach enables computation of probabilities for various CSM events.
- This method supports public health interventions, resource allocation, and risk assessment.
- Policymakers can utilize this approach for informed decision-making by monitoring factors influencing mortality trends.
Keywords:
CDC provisional death countscause of death (COD) data matrixmultivariate time‐series forecasting modelingweekly change in mortality indicator (WCMI)weekly exceedance in mortality count (WEMC)More Related Videos
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