Evidence map›Paper›PMID 34469550›Full record

ArticleThe Journal of infectious diseases2021

Invasive Bacterial Vaccine-Preventable Disease Surveillance: Successes and Lessons Learned in Bangladesh for a Sustainable Path Forward.

Senjuti Saha, Samir K Saha

Abstract read
In one paragraph

Article in The Journal of infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Observational
  4. Article
  5. 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

2 authors.

Senjuti SahaChild Health Research Foundation, Dhaka, Bangladesh.ORCID 0000-0001-6087-6766
Samir K SahaChild Health Research Foundation, Dhaka, Bangladesh.ORCID 0000-0003-3820-0748

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have made considerable progress in setting and scaling up surveillance systems to drive evidence-based policy decisions, but the recent epidemics highlight that current systems are not optimally designed. Good surveillance systems should be coordinated, comprehensive, and adaptive. They should generate data in real time for immediate analysis and intervention, whether for endemic diseases or potential epidemics. Such systems are especially needed in low-resource settings where disease burden is the highest, but tracking systems are the weakest here due to competing priorities and constraints on available resources. In this article, using the examples of 3 large, and mostly successful, infectious disease surveillance studies in Bangladesh, we identify 2 core limitations-the pathogen bias and the vaccine bias-in the way current surveillance programs are designed for low-resource settings. We highlight the strengths of the current Global Invasive Bacterial Vaccine Preventable Disease Surveillance Network of the World Health Organization and present case studies from Bangladesh to illustrate how this surveillance platform can be leveraged to overcome its limitations. Finally, we propose a set of criteria for building a comprehensive infectious disease surveillance system with the hope of encouraging current systems to use the limited resources as optimally as possible to generate the maximum amount of knowledge.

Indexed as

Communicable Disease ControlCommunicable DiseasesPublic Health SurveillanceVaccine-Preventable DiseasesVaccinesBangladeshChild, PreschoolCost of IllnessFemaleHumansInfantMaleSentinel SurveillanceWorld Health OrganizationVaccinesBangladeshdisease burdenepidemic controlinfection controlLMICssurveillance

Identifiers

PMID34469550
PMCPMC8409528

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.