Evidence map›Paper›PMID 26167424›Full record

ArticleFrontiers in public health services & systems research

A Cost Analysis of the 1-2-3 Pap Intervention.

Kristina M Rabarison, Rui Li, Connie L Bish, Robin C Vanderpool, Richard A Crosby, Mehran S Massoudi

Abstract read
In one paragraph

Article in Frontiers in public health services & systems research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Kristina M RabarisonCenters for Disease Control and Prevention, krabarison@cdc.gov.
Rui LiDivision of Diabetes Translation, Centers for Disease Control and Prevention, RuiLi@cdc.gov.
Connie L BishDivision of Population Health, Centers for Disease Control and Prevention, aez2@cdc.gov.
Robin C VanderpoolUniversity of Kentucky, robin@kcr.uky.edu.
Richard A CrosbyUniversity of Kentucky, richard.crosby@uky.edu.
Mehran S MassoudiDivision of Population Health, Centers for Disease Control and Prevention, mrm8@cdc.gov.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

backgroundCervical cancer places a substantial economic burden on our healthcare system. The three-dose human papillomavirus (HPV) vaccine series is a cost-effective intervention to prevent HPV infection and resultant cervical cancer. Despite its efficacy, completion rates are low in young women aged 18 through 26 years. 1-2-3 Pap is a video intervention tested and proven to increase HPV vaccination completion rates. PURPOSE: To provide the full scope of available evidence for 1-2-3 Pap, this study adds economic evidence to the intervention's efficacy. This study tested the economies of scale hypothesis that the cost of 1-2-3 Pap intervention per number of completed HPV vaccine series would decrease when offered to more women in the target population.

methodsUsing cost and efficacy data from the Rural Cancer Prevention Center, a cost analysis was done through a hypothetical adaptation scenario in rural Kentucky.

resultsAssuming the same success rate as in the efficacy study, the 1-2-3 Pap adaptation scenario would cover 1000 additional women aged 18 through 26 years (344 in efficacy study; 1346 in adaptation scenario), and almost three times as many completed series (130 in efficacy study; 412 in adaptation scenario) as in the original 1-2-3 Pap efficacy study. IMPLICATIONS: Determination of the costs of implementing 1-2-3 Pap is vital for program expansion. This study provides practitioners and decision makers with objective measures for scalability.

Indexed as

cervical cancercost analysiseconomic evaluationHPV vaccineprevention research center

Identifiers

PMID26167424
PMCPMC4497949

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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.