ArticleJournal of epidemiology and community health2002
Impact numbers in health policy decisions.
Article in Journal of epidemiology and community health, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Number needed to treat (NNT) in clinical literature: an appraisal.BMC medicine · 2017Article
- Using and Interpreting Adjusted NNT Measures in Biomedical Research.The open dentistry journal · 2010Article
- What are the benefits of cognitive enhancers for Alzheimer's Disease: use of Population Impact Measures.BMC geriatrics · 2007Article
- Helping to prioritise interventions for depression and schizophrenia: use of Population Impact Measures.Clinical practice and epidemiology in mental health : CP & EMH · 2006Article
- Assessing the impact of heart failure specialist services on patient populations.BMC health services research · 2004Article
- Implementing guidelines in primary care: can population impact measures help?BMC public health · 2003Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo outline the major methodological issues appropriate to the use of the population impact number (PIN) and the disease impact number (DIN) in health policy decision making.
designReview of literature and calculation of PIN and DIN statistics in different settings.
settingPreviously proposed extensions to the number needed to treat (NNT): the DIN and the PIN, which give a population perspective to this measure. MAIN
resultsThe PIN and DIN allow us to compare the population impact of different interventions either within the same disease or in different diseases or conditions. The primary studies used for relative risk estimates should have outcomes, time periods and comparison groups that are congruent and relevant to the local setting. These need to be combined with local data on disease rates and population size. Depending on the particular problem, the target may be disease incidence or prevalence and the effects of interest may be either the incremental impact or the total impact of each intervention. For practical application, it will be important to use sensitivity analyses to determine plausible intervals for the impact numbers.
conclusionsAttention to various methodological issues will permit the DIN and PIN to be used to assist health policy makers assign a population perspective to measures of risk.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.