Evidence map›Paper›PMID 41729422›Full record

ArticleThe Journal of the Egyptian Public Health Association2026

Mortality surveillance as an early warning system for respiratory infection outbreaks: lessons from the COVID-19 pandemic in Alexandria, Egypt.

Rehab Meckawy, Heba M T El Weshahi, Eman Foda, Eman A Sultan

Abstract read
In one paragraph

Article in The Journal of the Egyptian Public Health Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Rehab MeckawyCommunity Medicine Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt. R_Mekawy2120396@alexmed.edu.eg.
Heba M T El WeshahiCommunity Medicine Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Eman FodaCommunity Medicine Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Eman A SultanCommunity Medicine Department, Faculty of Medicine, Alexandria University, Alexandria, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic highlighted critical gaps in mortality surveillance, particularly in low- and middle-income countries (LMICs). This study evaluated the feasibility of using routine mortality data as an early warning system for respiratory outbreaks in Alexandria, Egypt.

methodsA retrospective time-series analysis of 61,378 deaths (2017-2022) was conducted. Respiratory failure (ICD-10: J96.90) was used as a proxy for COVID-19-related mortality. As population denominators were unavailable at subdistrict level, analyses relied on proportionate mortality, reflecting the pragmatic data scope of the current registry system. Expected deaths were estimated using an exponential smoothing model, and excess mortality was identified when observed deaths exceeded expectations for three or more consecutive months.

resultsDeaths attributed to respiratory failure increased notably during 2020-2021, with sustained excess mortality signals corresponding to pandemic peaks. Mortality among adults aged 70-75 years rose markedly, and total years of life lost during the pandemic reached 41,819 across high-risk age groups (40-70 years).

conclusionsRoutine mortality data can serve as a practical foundation for early detection of respiratory disease outbreaks in resource-limited settings. Future investments should focus on strengthening human resources, expanding digital infrastructure, and improving data standardization to ensure the long-term sustainability and scalability of mortality surveillance systems in LMICs.

Indexed as

COVID-19Early warning systemLMICsMortality surveillanceRespiratory failure

Identifiers

PMID41729422
PMCPMC12929754

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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 OpenQuestion graph.