Evidence map›Paper›PMID 38926444›Full record

ArticleScientific reports2024

Estimating prevalence of post-war health disorders using multiple systems data.

Prajamitra Bhuyan, Kiranmoy Chatterjee

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

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

2 authors.

Prajamitra BhuyanIndian Institute of Management Calcutta, Kolkata, 700104, India.
Kiranmoy ChatterjeeBidhannagar College, Kolkata, 700064, India. kiranmoy07@gmail.com.

Funding

Indian Institute of Management Calcutta Category-I Research Project Grant (No. 3878/RP: PSEISMC-RDA HIVS)Science and Engineering Research Board (SERB), Department of Science & Technology, Government of India Core Research Grant (No. CRG/2019/003204)
6 · The paper itself

Abstract

Effective surveillance on the long-term public health impact due to war and terrorist attacks remains limited. Such health issues are commonly under-reported, specifically for a large group of individuals. For this purpose, efficient estimation of the size or undercount of the population under the risk of physical and mental health hazards is of utmost necessity. A novel trivariate Bernoulli model is developed allowing heterogeneity among the individuals and dependence between the sources of information, and an estimation methodology using a Monte Carlo-based EM algorithm is proposed. Simulation results show the superiority of the performance of the proposed method over existing competitors and robustness under model mis-specifications. The method is applied to analyse two real case studies on monitoring amyotrophic lateral sclerosis (ALS) cases for the Gulf War veterans and the 9/11 terrorist attack survivors at the World Trade Center, USA. The average annual cumulative incidence rate for ALS disease increases by

Indexed as

Amyotrophic Lateral SclerosisAlgorithmsGulf WarHumansMonte Carlo MethodPrevalenceVeterans9/11 terrorist attacksAmyotrophic lateral sclerosisGulf warHumanitarian crisisMCEM algorithm

Identifiers

PMID38926444
PMCPMC11208173

What OpenQuestion holds

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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.