Evidence map›Paper›PMID 35647373›Full record

ArticleJournal of preventive medicine and hygiene2022

Effect of epidemic management and control plan on COVID-19 mortality in Iran: an interrupted time series analysis.

Yousef Alimohamadi, Mojtaba Sepandi

Open access · greenAbstract read
In one paragraph

Article in Journal of preventive medicine and hygiene, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 82% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

2 authors at 1 institution in 1 country.

Yousef AlimohamadiHealth Research Center, Life style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Mojtaba SepandiExercise Physiology Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Baqiyatallah University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Globally, several measures have been taken to decrease COVID-19 mortality. However, the effectiveness of preventive measures on the mortality related to COVID-19 has not been fully assessed. Thus, the present study aimed the present study aimed to assess the success of COVID-19 epidemic management and control plan on the mortality associated with COVID-19 in Iran from February 19, 2020, to February 5, 2021. Methods: In the current quasi experimental study an interrupted time series analysis of daily collected data on confirmed deaths of COVID-19 occurred in Iran and in the world, were performed using Newey ordinary least squares regression-based methods. Results: In Iran, the trend of new deaths increased significantly every day until 24 November 2020 according to pre-intervention slope of [(OR 1.14, 95% CI 0.96-1.32,); P < 0.001]. The occurrence of new deaths had a decreasing trend after November 24, 2020, with a coefficient of [(OR -5.12, 95% CI -6.04 - -4.20), P < 0.001)]. But in the global level daily new deaths was increasing before [(OR 18.66, 95% CI 14.41-2292; P < 0.001)] and after the 24 November 2020 [(OR 57.14, 95% CI 20.80-93.49); P: 0.002]. Conclusions: Iranian COVID-19 epidemic management and control plan effectively reduced the mortality associated to COVID-19. Therefore, it is essential to continue these measures to prevent the increase in the number of deaths.

Indexed as

COVID-19EpidemicsHumansInterrupted Time Series AnalysisIranRegression AnalysisControl MeasuresCOVID-19IranMortality

Identifiers

PMID35647373
PMCPMC9121681
OpenAlexW4282946294

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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.