Evidence map›Paper›PMID 42104049›Full record

ReviewJournal of epidemiology and global health2026

Understanding Mortality Data: A Step-by-Step Guide to CDC WONDER, Joinpoint Analysis, and Forecasting Models.

Abdalhakim Shubietah, Mohammed Ruzieh, Belal Mohamed Hamed, Sarah Saife, Mohamed Abuelazm, Mohamed S Elgendy, Ahmed Emara, Ahmed Ibrahim, Ameer Awashra, Ibrahim Gowaily and 5 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of epidemiology and global health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Abdalhakim ShubietahDepartment of Medicine, Advocate Illinois Masonic Medical Center, Chicago, IL, USA.
Mohammed RuziehBluhm Cardiovascular Institute, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Belal Mohamed HamedFaculty of Medicine, Al-Azhar University, Cairo, Egypt.
Sarah SaifeDepartment of Medicine, An-Najah National University, Old Campus Street 7, P.O. Box 7, Nablus, Palestine.
Mohamed AbuelazmFaculty of Medicine, Tanta University, Tanta, Egypt.
Mohamed S ElgendyFaculty of Medicine, Tanta University, Tanta, Egypt.
Ahmed EmaraFaculty of Medicine, Al-Azhar University, Cairo, Egypt.
Ahmed IbrahimFaculty of Medicine, Alexandria University, Alexandria, Egypt.
Ameer AwashraDepartment of Medicine, An-Najah National University, Old Campus Street 7, P.O. Box 7, Nablus, Palestine. ameer.awashra7@gmail.com.ORCID http://orcid.org/0009-0002-5122-8200
Ibrahim GowailyDepartment of Medicine, Creighton University School of Medicine, Omaha, NE, USA.
Mohammed AbuBahaDepartment of Medicine, An-Najah National University, Old Campus Street 7, P.O. Box 7, Nablus, Palestine.
Ghada ShbeitahLanguage Center, Faculty of Humanities, An-Najah National University, Nablus, West Bank, Palestine.
Elsayed BalbaaFaculty of Medicine, Alexandria University, Alexandria, Egypt.
Mohamed Saad RakabFaculty of Medicine, Mansoura University, Mansoura, Egypt.
Mohammed MhannaDivision of Cardiovascular Medicine, University of Iowa, Iowa, IA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of mortality data in public health research has surged with the rise of open-access databases such as CDC WONDER. However, caution is needed when defining the relationship between ICD codes and when transitioning from older to newer versions of the data. This review provides a practical, step-by-step guide to using the CDC WONDER mortality database.

methodsWe outline key functionalities of the CDC WONDER interface, explain mortality rate calculations, and describe best practices for configuring queries using underlying and multiple causes of death. The review further introduces Joinpoint regression to identify temporal trend changes and compares forecasting approaches using traditional ARIMA models and modern deep learning architectures.

resultsUsing illustrative examples and visual guides, we demonstrate how data interpretations can vary significantly depending on query configuration, Boolean logic (AND vs. OR), and coding practices. We highlight the strengths and limitations of different analytical strategies and show how misinterpretation can arise from common errors, such as misunderstanding age adjustment or combining ICD codes without appropriate logic.

conclusionCDC WONDER is a powerful tool for mortality analysis, but its effective use requires a clear understanding of its data structure, coding logic, and statistical tools. Joinpoint regression and forecasting models complement WONDER data by enabling trend segmentation and future projections. This guide empowers researchers to use these tools accurately, improving the rigor and reproducibility of public health research.

Indexed as

Centers for Disease Control and Prevention, U.S.Databases, FactualMortalityForecastingHumansInternational Classification of DiseasesPrediction AlgorithmsUnited StatesARIMA forecastingCDC WONDER guideDeep learning forecastingJoinpoint analysis

Identifiers

PMID42104049
PMCPMC13168393

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.