Evidence map›Paper›PMID 35715830›Full record

ArticleImplementation science communications2022

Planning for resilience in screening operations using discrete event simulation modeling: example of HPV testing in Peru.

Anne F Rositch, Aditya Singh, Nadia Lahrichi, Valerie A Paz-Soldan, Anna Kohler-Smith, Patti Gravitt, Erica Gralla

Abstract read
In one paragraph

Article in Implementation science communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Anne F RositchDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St, Room E6150, Baltimore, MD, 21205, USA. arositch@jhu.edu.ORCID http://orcid.org/0000-0002-0403-8482
Aditya SinghEngineering Management and Systems Engineering, The George Washington University, 800 22nd Street NW, Suite 2680, Washington, D.C., 20052, USA.
Nadia LahrichiMathematics and Industrial Engineering, Polytechnique Montreal, CP6079 Succ. centre-ville, Montreal, H3C 3A7, Canada.
Valerie A Paz-SoldanTropical Medicine, Tulane School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2210, New Orleans, LA, 70112, USA.
Anna Kohler-SmithAsociación Benéfica Prisma, Av. Santo Toribio 115. Oficina 701. Urb. El Rosario, San Isidro, Peru.
Patti GravittDepartment of Epidemiology and Public Health, University of Maryland School of Medicine, 655 W. Baltimore Street, Baltimore, MD, 21201, USA.
Erica GrallaEngineering Management and Systems Engineering, The George Washington University, 800 22nd Street NW, Suite 2680, Washington, D.C., 20052, USA.

Funding

Clinical and Translational Science Institute at Childrens NationalUL1TR001876 · NCATS · CHILDREN'S RESEARCH INSTITUTE · PI BELL, MICHAEL J · 2016 to 2021
$25.6M
Translating Molecular Diagnostics for Cervical Cancer Prevention into PracticeR01CA190366 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI PAZ-SOLDAN, VALERIE ANDREA, TRACY, J. KATHLEEN · 2016 to 2020
$2.7M
NCATS NIH HHS UL1 TR001876NCATS NIH HHS UL1TR001876NCI NIH HHS R01 CA190366NCI NIH HHS R01-CA190366
6 · The paper itself

Abstract

backgroundThe World Health Organization (WHO) has called for the elimination of cervical cancer. Unfortunately, the implementation of cost-effective prevention and control strategies has faced significant barriers, such as insufficient guidance on best practices for resource and operations planning. Therefore, we demonstrate the value of discrete event simulation (DES) in implementation science research and practice, particularly to support the programmatic and operational planning for sustainable and resilient delivery of healthcare interventions. Our specific example shows how DES models can inform planning for scale-up and resilient operations of a new HPV-based screen and treat program in Iquitos, an Amazonian city of Peru.

methodsUsing data from a time and motion study and cervical cancer screening registry from Iquitos, Peru, we developed a DES model to conduct virtual experimentation with "what-if" scenarios that compare different workflow and processing strategies under resource constraints and disruptions to the screening system.

resultsOur simulations show how much the screening system's capacity can be increased at current resource levels, how much variability in service times can be tolerated, and the extent of resilience to disruptions such as curtailed resources. The simulations also identify the resources that would be required to scale up for larger target populations or increased resilience to disruptions, illustrating the key tradeoff between resilience and efficiency. Thus, our results demonstrate how DES models can inform specific resourcing decisions but can also highlight important tradeoffs and suggest general "rules" for resource and operational planning.

conclusionsMultilevel planning and implementation challenges are not unique to sustainable adoption of cervical cancer screening programs but represent common barriers to the successful scale-up of many preventative health interventions worldwide. DES represents a broadly applicable tool to address complex implementation challenges identified at the national, regional, and local levels across settings and health interventions-how to make effective and efficient operational and resourcing decisions to support program adaptation to local constraints and demands so that they are resilient to changing demands and more likely to be maintained with fidelity over time.

Indexed as

Cervical cancer eliminationDES modelingDiscrete event simulationDisruptionImplementation planningLMICsOperations researchPeruResilience

Identifiers

PMID35715830
PMCPMC9204370

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LicenceCC BY
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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.