ArticleHealth science reports2025
Analyzing the Centers for Disease Control and Prevention Mortality Data Using Weekly Exceedance in Mortality Count and Weekly Change in Mortality Indicator: A Time Series Study.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Cause-specific mortality (CSM) count prediction plays a vital role in the context of public health policy. In this study, we introduce a new analytical approach, which is divided into three phases to answer specific questions regarding CSM due to 14 specific causes by computing different simple, compound, and conditional probabilities. Methods: A multivariate time series forecasting model was developed using the CDC weekly mortality count data. A binary data matrix was constructed for 14 causes of death (COD) as a function of weeks by combining the observed and forecasted mortalities. We introduced two new concepts: Weekly Exceedance in Mortality Count ( Results: The results of chi-square test of independence indicated that there was no statistically significant association between COD and Conclusion: Utilizing this analytical approach, researchers and policymakers can compute the probabilities of any number of desired events related to different COD which can be helpful for public health interventions, resource allocation, informed decision-making and risk assessment, by controlling the underlying attributes responsible for the probabilities to surge and plummet.
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