Evidence map›Paper›PMID 39572592›Full record

ArticleScientific reports2024

The ecological impact of the COVID-19 pandemic on crime trends in Kerman Province of Iran.

Shiva Pouradeli, Hassan Ahmadinia, Mohsen Rezaeian, Mahdiyeh Khazaneha

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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
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

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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.

Shiva PouradeliSocial Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.ORCID 0000-0003-4943-8559
Hassan AhmadiniaDepartment of Epidemiology and Biostatistics, School of Health, Occupational Environment Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran.ORCID 0000-0002-7010-1726
Mohsen RezaeianDepartment of Epidemiology and Biostatistics, School of Health, Occupational Environment Research Center, Rafsanjan University of Medical Sciences, Rafsanjan, Iran. moeygmr2@yahoo.co.uk.ORCID 0000-0003-3070-0166
Mahdiyeh KhazanehaNeurology Research Center, Kerman University of Medical Sciences, Kerman, Iran.ORCID 0000-0002-3800-466X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has significantly impacted crime rates in Kerman province, Iran, revealing divergent patterns compared to global trends. This ecological study utilized the Poisson regression model and interrupted time-series Poisson regression model to analyze data collected from the Iranian Legal Medicine Organization (ILMO) database and the Kerman Criminal Investigation Department of Police database, focusing on theft, conflicts, and homicide rates in this region. Our findings indicate that in 2020, the incidence rate ratio of conflicts increased compared to previous years. Before the pandemic, thefts were on the rise, while homicides were decreasing. However, with the onset of the pandemic, thefts initially decreased, and homicides experienced a notable increase. Subsequently, both thefts and homicides showed a declining trend. By the end of 2020, thefts were significantly lower than expected based on pre-pandemic data, while homicides were slightly higher. Initial fluctuations in crime rates may be related to quarantine measures and the challenges faced during the pandemic, while subsequent changes might be influenced by governmental interventions. Understanding the complex relationship between public health emergencies and criminal activities is crucial for informing policymaking and crime prevention efforts.

Indexed as

COVID-19CrimeHomicidePandemicsHumansIranSARS-CoV-2ConflictCOVID-19CrimeHomicidePandemicsTheft

Identifiers

PMID39572592
PMCPMC11582603

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.