Evidence map›Paper›PMID 34447495›Full record

ArticleInternational journal of preventive medicine2021

Mapping the Relative Risk of Congenital Hypothyroidism Incidence via Spatial Zero-Inflated Poisson Model in Guilan Province, Iran.

Bahare Gholami Chaboki, Manijeh Tabrizi, Maryam Heydarpour Meymeh, Hojjat Alaei, Alireza Akbarzadeh Baghban

Open access · goldAbstract read
In one paragraph

Article in International journal of preventive medicine, 2021. 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
0.5field-weighted citation impact, top 34% 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Bahare Gholami ChabokiDepartment of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Manijeh TabriziPediatric Diseases Research Center, Guilan University of Medical Sciences, Rasht, Iran.
Maryam Heydarpour MeymehEnglish Language Department, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hojjat AlaeiDepartment of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Alireza Akbarzadeh BaghbanDepartment of Biostatistics, Proteomics Research Center, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Shahid Beheshti University of Medical Sciences · IRGuilan University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCongenital hypothyroidism (CH) is one of the most prevalent preventable causes of mental retardation. Studies show that the incidence rate of CH is very high in Iran. Disease mapping is a tool for visually expressing the frequency, incidence, or relative risk of illness. The present study aimed to model CH counts considering the effects of the neighborhood in towns and perform mapping based on the relative risk.

methodsIn this historical cohort study, data of all neonates diagnosed with CH with TSH level ≥5 mIU/L between March 21, 2017, and March 20, 2018, in health centers in Guilan, Iran were used. The number of neonates with CH was zero in most towns of Guilan Province. The Bayesian spatial zero-inflated Poisson (ZIP) regression model was employed to investigate the effect of the town's neighborhood on the relative risk of CH incidence. Then, the map of the posterior mean of the relative risk for CH incidence was provided. The analysis was performed using OpenBUGS and Arc GIS software programs.

resultsThe relative risk of CH incidence was high in the West of Guilan. Moreover, the goodness-of-fit criterion indicated that it is more appropriate to fit the Bayesian spatial ZIP model to these data than the common model.

conclusionsConsidering the high relative risk of CH in the Western towns of Guilan Province, it is better to check important risk factors in this region.

Indexed as

Bayes theoremcongenital hypothyroidismneonatesPoisson distributionspatial analysis

Identifiers

PMID34447495
PMCPMC8356956
OpenAlexW3175337493

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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.