Evidence map›Paper›PMID 23304666›Full record

ArticleIranian journal of public health2012

Tuberculosis surveillance using a hidden markov model.

A Rafei, E Pasha, R Jamshidi Orak

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Iranian journal of public health, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

3 authors.

A RafeiDept. of Mathematics and Statistics, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran.
E Pasha
R Jamshidi Orak

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRoutinely collected data from tuberculosis surveillance system can be used to investigate and monitor the irregularities and abrupt changes of the disease incidence. We aimed at using a Hidden Markov Model in order to detect the abnormal states of pulmonary tuberculosis in Iran.

methodsData for this study were the weekly number of newly diagnosed cases with sputum smear-positive pulmonary tuberculosis reported between April 2005 and March 2011 throughout Iran. In order to detect the unusual states of the disease, two Hidden Markov Models were applied to the data with and without seasonal trends as baselines. Consequently, the best model was selected and compared with the results of Serfling epidemic threshold which is typically used in the surveillance of infectious diseases.

resultsBoth adjusted R-squared and Bayesian Information Criterion (BIC) reflected better goodness-of-fit for the model with seasonal trends (0.72 and -1336.66, respectively) than the model without seasonality (0.56 and -1386.75). Moreover, according to the Serfling epidemic threshold, higher values of sensitivity and specificity suggest a higher validity for the seasonal model (0.87 and 0.94, respectively) than model without seasonality (0.73 and 0.68, respectively).

conclusionA two-state Hidden Markov Model along with a seasonal trend as a function of the model parameters provides an effective warning system for the surveillance of tuberculosis.

Indexed as

Cyclic regressionEM-algorithmHidden Markov modelPulmonary tuberculosisSputum

Identifiers

PMID23304666
PMCPMC3494236

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.