Evidence map›Paper›PMID 16704669›Full record

ArticleHealth services research2006

Evaluating medical effectiveness for the california health benefits review program.

Harold S Luft, Karen M Rappaport, Edward H Yelin, Wade M Aubry

Abstract read
In one paragraph

Article in Health services research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

4 authors.

Harold S LuftInstitute for Health Policy Studies, 3333 California Street, Suite 265, San Francisco, CA, USA.
Karen M Rappaport
Edward H Yelin
Wade M Aubry

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An important aspect of the mandate assessments requested by the California legislature is a review of the scientific and medical literature on the medical effectiveness of the proposed health insurance benefit mandate. Although such a review bears many similarities to effectiveness reviews that might be undertaken for publication as research studies, several important differences arise from the requirements of the California legislation. Our reviews are intended to assist the legislators in deciding whether to support a specific mandate to modify health insurance benefits in a particular way. Thus, our assessments focus on how the scientific literature bears on the proposed mandate, which may involve a complicated chain of potential effects leading from altered coverage to ultimate impact on health. Evidence may be available for only some of the links in the chain. Furthermore, not all the evidence may be directly applicable to the diverse population of California or the subpopulation affected by the mandate. The mandate reviews, including the medical effectiveness analyses, may be used in a potentially contentious decision making setting. The legislative calendar requires that they need to be timely, yet they must be as valid, credible, and based on the best information available as possible. The focus on applicability also implies the need for informed, technical decisions concerning the relevance of the articles for the report, and these decisions need to be made as transparent as possible. These goals and constraints yield an approach that differs somewhat from an investigator-initiated review of the literature.

Indexed as

Evaluation Studies as TopicInsurance, HealthTreatment OutcomeCaliforniaHumansInsurance BenefitsMandatory ProgramsMeta-Analysis as TopicRandomized Controlled Trials as Topic

Identifiers

PMID16704669
PMCPMC1713216

What OpenQuestion holds

Textmetadata
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.