Evidence map›Paper›PMID 25486292›Full record

ArticlePloS one2014

Descriptive epidemiology of typhoid fever during an epidemic in Harare, Zimbabwe, 2012.

Jonathan A Polonsky, Isabel Martínez-Pino, Fabienne Nackers, Prosper Chonzi, Portia Manangazira, Michel Van Herp, Peter Maes, Klaudia Porten, Francisco J Luquero

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Typhoid Outbreaks, 1989-2018: Implications for Prevention and Control.The American journal of tropical medicine and hygiene · 2020
    Review
  9. Article
  10. Spatial and Temporal Patterns of Typhoid and Paratyphoid Fever Outbreaks: A Worldwide Review, 1990-2018.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2019
    Review
  11. Outbreak analytics: a developing data science for informing the response to emerging pathogens.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Review
  12. Article
  13. Article
  14. Article
  15. 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

9 authors.

Jonathan A PolonskyEpicentre, Paris, France.
Isabel Martínez-PinoEpicentre, Paris, France; European Programme for Intervention Epidemiology Training, European Centre for Disease Prevention and Control, Stockholm, Sweden.
Fabienne NackersEpicentre, Paris, France.
Prosper ChonziMinistry of Health and Child Welfare, Harare City Health Department, Harare, Zimbabwe.
Portia ManangaziraMinistry of Health and Child Welfare, Epidemiology and Disease Control Directorate, Harare, Zimbabwe.
Michel Van HerpMédecins Sans Frontières Operational Centre Brussels, Brussels, Belgium.
Peter MaesMédecins Sans Frontières Operational Centre Brussels, Brussels, Belgium.
Klaudia PortenEpicentre, Paris, France.
Francisco J LuqueroEpicentre, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTyphoid fever remains a significant public health problem in developing countries. In October 2011, a typhoid fever epidemic was declared in Harare, Zimbabwe - the fourth enteric infection epidemic since 2008. To orient control activities, we described the epidemiology and spatiotemporal clustering of the epidemic in Dzivaresekwa and Kuwadzana, the two most affected suburbs of Harare.

methodsA typhoid fever case-patient register was analysed to describe the epidemic. To explore clustering, we constructed a dataset comprising GPS coordinates of case-patient residences and randomly sampled residential locations (spatial controls). The scale and significance of clustering was explored with Ripley K functions. Cluster locations were determined by a random labelling technique and confirmed using Kulldorff's spatial scan statistic. PRINCIPAL

findingsWe analysed data from 2570 confirmed and suspected case-patients, and found significant spatiotemporal clustering of typhoid fever in two non-overlapping areas, which appeared to be linked to environmental sources. Peak relative risk was more than six times greater than in areas lying outside the cluster ranges. Clusters were identified in similar geographical ranges by both random labelling and Kulldorff's spatial scan statistic. The spatial scale at which typhoid fever clustered was highly localised, with significant clustering at distances up to 4.5 km and peak levels at approximately 3.5 km. The epicentre of infection transmission shifted from one cluster to the other during the course of the epidemic.

conclusionsThis study demonstrated highly localised clustering of typhoid fever during an epidemic in an urban African setting, and highlights the importance of spatiotemporal analysis for making timely decisions about targetting prevention and control activities and reinforcing treatment during epidemics. This approach should be integrated into existing surveillance systems to facilitate early detection of epidemics and identify their spatial range.

Indexed as

Disease OutbreaksAdolescentAdultChildChild, PreschoolCluster AnalysisEpidemicsFemaleGeographyHumansInfantInfant, NewbornMaleMiddle AgedSalmonella typhiSpatio-Temporal Analysis

Identifiers

PMID25486292
PMCPMC4259398

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.