New analytic approaches for analyzing and presenting polio surveillance data to supplement standard performance indicators.

Data quality Disease surveillance Poliomyelitis

Journal

Vaccine: X
ISSN: 2590-1362
Titre abrégé: Vaccine X
Pays: England
ID NLM: 101748769

Informations de publication

Date de publication:
09 Apr 2020
Historique:
received: 12 09 2019
revised: 07 03 2020
accepted: 09 03 2020
entrez: 28 3 2020
pubmed: 28 3 2020
medline: 28 3 2020
Statut: epublish

Résumé

Sensitive surveillance for acute flaccid paralysis (AFP) allows for rapid detection of polio outbreaks and provides essential evidence to support certification of the eradication of polio. However, accurately assessing the sensitivity of surveillance systems can be difficult due to limitations in the reliability of available performance indicators, including the rate of detection of non-polio AFP and the proportion of adequate stool sample collection. Recent field reviews have found evidence of surveillance gaps despite indicators meeting expected targets. We propose two simple new approaches for AFP surveillance performance indicator analysis to supplement standard indicator analysis approaches commonly used by the Global Polio Eradication Initiative (GPEI): (1) using alternative groupings of low population districts in the country (spatial binning) and (2) flagging unusual patterns in surveillance data (surveillance flags analysis). Using GPEI data, we systematically compare AFP surveillance performance using standard indicator analysis and these new approaches. Applying spatial binning highlights areas meeting surveillance indicator targets that do not when analyzing performance of low population districts. Applying the surveillance flags we find several countries with unusual data patterns, in particular age groups which are not well-covered by the surveillance system, and countries with implausible rates of adequate stool specimen collection. Analyzing alternate groupings of administrative units is a simple method to find areas where traditional AFP surveillance indicator targets are not reliably met. For areas where AFP surveillance indicator targets are met, systematic assessment of unusual patterns ('flags') can be a useful prompt for further investigation and field review.

Sections du résumé

BACKGROUND BACKGROUND
Sensitive surveillance for acute flaccid paralysis (AFP) allows for rapid detection of polio outbreaks and provides essential evidence to support certification of the eradication of polio. However, accurately assessing the sensitivity of surveillance systems can be difficult due to limitations in the reliability of available performance indicators, including the rate of detection of non-polio AFP and the proportion of adequate stool sample collection. Recent field reviews have found evidence of surveillance gaps despite indicators meeting expected targets.
METHODS METHODS
We propose two simple new approaches for AFP surveillance performance indicator analysis to supplement standard indicator analysis approaches commonly used by the Global Polio Eradication Initiative (GPEI): (1) using alternative groupings of low population districts in the country (spatial binning) and (2) flagging unusual patterns in surveillance data (surveillance flags analysis). Using GPEI data, we systematically compare AFP surveillance performance using standard indicator analysis and these new approaches.
RESULTS RESULTS
Applying spatial binning highlights areas meeting surveillance indicator targets that do not when analyzing performance of low population districts. Applying the surveillance flags we find several countries with unusual data patterns, in particular age groups which are not well-covered by the surveillance system, and countries with implausible rates of adequate stool specimen collection.
CONCLUSIONS CONCLUSIONS
Analyzing alternate groupings of administrative units is a simple method to find areas where traditional AFP surveillance indicator targets are not reliably met. For areas where AFP surveillance indicator targets are met, systematic assessment of unusual patterns ('flags') can be a useful prompt for further investigation and field review.

Identifiants

pubmed: 32215368
doi: 10.1016/j.jvacx.2020.100059
pii: S2590-1362(20)30006-1
pii: 100059
pmc: PMC7090369
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100059

Subventions

Organisme : World Health Organization
ID : 001
Pays : International

Informations de copyright

© 2020 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Références

MMWR Morb Mortal Wkly Rep. 2014 Apr 25;63(16):356-61
pubmed: 24759658
BMC Med. 2017 Oct 11;15(1):180
pubmed: 29017491
MMWR Morb Mortal Wkly Rep. 2019 Apr 05;68(13):312-318
pubmed: 30946737
MMWR Suppl. 2012 Jul 27;61(3):35-40
pubmed: 22832996
Vaccine. 2016 Nov 21;34(48):5946-5952
pubmed: 27771181
MMWR Morb Mortal Wkly Rep. 2013 Apr 12;62(14):270-4
pubmed: 23575241
MMWR Morb Mortal Wkly Rep. 2014 Aug 29;63(34):756-61
pubmed: 25166927
J Infect Dis. 1997 Feb;175 Suppl 1:S146-50
pubmed: 9203707
BMC Infect Dis. 2015 Feb 18;15:66
pubmed: 25886823
Am J Epidemiol. 1996 Apr 15;143(8):816-22
pubmed: 8610692
BMC Med. 2016 Mar 30;14:60
pubmed: 27029535
Trop Med Int Health. 2003 May;8(5):386-91
pubmed: 12753631
J Infect Dis. 2017 Jul 1;216(suppl_1):S293-S298
pubmed: 28838175
Lancet. 2007 Apr 21;369(9570):1356-1362
pubmed: 17448821
PLoS One. 2015 Feb 17;10(2):e0107042
pubmed: 25689585
Biostatistics. 2007 Apr;8(2):158-83
pubmed: 16809429

Auteurs

Kristin VanderEnde (K)

Centers for Disease Control and Prevention, Atlanta 30329, USA.

Arend Voorman (A)

The Bill and Melinda Gates Foundation, Seattle 98109, USA.

Sara Khan (S)

Centers for Disease Control and Prevention, Atlanta 30329, USA.

Abhijeet Anand (A)

Centers for Disease Control and Prevention, Atlanta 30329, USA.

Cynthia J Snider (CJ)

Centers for Disease Control and Prevention, Atlanta 30329, USA.

Ajay Goel (A)

The World Health Organization, Geneva 1202, Switzerland.

Steve Wassilak (S)

Centers for Disease Control and Prevention, Atlanta 30329, USA.

Classifications MeSH