Literature DB >> 12118052

Impact numbers: measures of risk factor impact on the whole population from case-control and cohort studies.

R F Heller1, A J Dobson, J Attia, J Page.   

Abstract

OBJECTIVE: To describe new measures of risk from case-control and cohort studies, which are simple to understand and relate to numbers of the population at risk.
DESIGN: Theoretical development of new measures of risk.
SETTING: Review of literature and previously described measures. MAIN
RESULTS: The new measures are: (1) the population impact number (PIN), the number of those in the whole population among whom one case is attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable risk); (2) the case impact number (CIN) the number of people with the disease or outcome for whom one case will be attributable to the exposure or risk factor (this is equivalent to the reciprocal of the population attributable fraction); (3) the exposure impact number (EIN) the number of people with the exposure among whom one excess case is attributable to the exposure (this is equivalent to the reciprocal of the attributable risk); (4) the exposed cases impact number (ECIN) the number of exposed cases among whom one case is attributable to the exposure (this is equivalent to the reciprocal of the aetiological fraction). The impact number reflects the number of people in each population (the whole population, the cases, all those exposed, and the exposed cases) among whom one case is attributable to the particular risk factor.
CONCLUSIONS: These new measures should help communicate the impact on a population, of estimates of risk derived from cohort or case-control studies.

Entities:  

Mesh:

Year:  2002        PMID: 12118052      PMCID: PMC1732217          DOI: 10.1136/jech.56.8.606

Source DB:  PubMed          Journal:  J Epidemiol Community Health        ISSN: 0143-005X            Impact factor:   3.710


  8 in total

1.  A note on the number needed to treat.

Authors:  E Lesaffre; G Pledger
Journal:  Control Clin Trials       Date:  1999-10

2.  Disease impact number and population impact number: population perspectives to measures of risk and benefit.

Authors:  R F Heller; A J Dobson
Journal:  BMJ       Date:  2000-10-14

3.  An/atomy

Authors: 
Journal:  BMJ       Date:  2000-10-14

Review 4.  Confidence intervals for the number needed to treat.

Authors:  D G Altman
Journal:  BMJ       Date:  1998-11-07

5.  Proportion of disease caused or prevented by a given exposure, trait or intervention.

Authors:  O S Miettinen
Journal:  Am J Epidemiol       Date:  1974-05       Impact factor: 4.897

6.  Mortality in relation to smoking: 20 years' observations on male British doctors.

Authors:  R Doll; R Peto
Journal:  Br Med J       Date:  1976-12-25

Review 7.  Expressing the magnitude of adverse effects in case-control studies: "the number of patients needed to be treated for one additional patient to be harmed".

Authors:  L M Bjerre; J LeLorier
Journal:  BMJ       Date:  2000-02-19

8.  Case-control study of stroke and the quality of hypertension control in north west England.

Authors:  X Du; K Cruickshank; R McNamee; M Saraee; J Sourbutts; A Summers; N Roberts; E Walton; S Holmes
Journal:  BMJ       Date:  1997-01-25
  8 in total
  14 in total

1.  Impact numbers in health policy decisions.

Authors:  J Attia; J Page; R F Heller; A J Dobson
Journal:  J Epidemiol Community Health       Date:  2002-08       Impact factor: 3.710

Review 2.  Communicating risks at the population level: application of population impact numbers.

Authors:  Richard F Heller; Iain Buchan; Richard Edwards; Georgios Lyratzopoulos; Patrick McElduff; Selwyn St Leger
Journal:  BMJ       Date:  2003-11-15

3.  Cardiovascular risk and all-cause mortality attributable to diabetes: Tehran lipid and glucose study.

Authors:  M Bozorgmanesh; F Hadaegh; F Sheikholeslami; F Azizi
Journal:  J Endocrinol Invest       Date:  2011-05-17       Impact factor: 4.256

4.  Using and Interpreting Adjusted NNT Measures in Biomedical Research.

Authors:  Ralf Bender
Journal:  Open Dent J       Date:  2010-07-16

5.  Estimating the impact of smoking cessation during pregnancy: the San Bernardino County experience.

Authors:  Michael Batech; Serena Tonstad; Jayakaran S Job; Richard Chinnock; Bryan Oshiro; T Allen Merritt; Gretchen Page; Pramil N Singh
Journal:  J Community Health       Date:  2013-10

6.  Impact of osteoporotic fracture type and subsequent fracture on mortality: the Tromsø Study.

Authors:  D Alarkawi; D Bliuc; T Tran; L A Ahmed; N Emaus; A Bjørnerem; L Jørgensen; T Christoffersen; J A Eisman; J R Center
Journal:  Osteoporos Int       Date:  2019-10-26       Impact factor: 4.507

7.  Assessment of School Anti-Bullying Interventions: A Meta-analysis of Randomized Clinical Trials.

Authors:  David Fraguas; Covadonga M Díaz-Caneja; Miriam Ayora; Manuel Durán-Cutilla; Renzo Abregú-Crespo; Iciar Ezquiaga-Bravo; Javier Martín-Babarro; Celso Arango
Journal:  JAMA Pediatr       Date:  2021-01-01       Impact factor: 16.193

8.  Effect of variable transmission rate on the dynamics of HIV in sub-Saharan Africa.

Authors:  Diego F Cuadros; Philip H Crowley; Ben Augustine; Sarah L Stewart; Gisela García-Ramos
Journal:  BMC Infect Dis       Date:  2011-08-11       Impact factor: 3.090

9.  Calculating confidence intervals for impact numbers.

Authors:  Mandy Hildebrandt; Ralf Bender; Ulrich Gehrmann; Maria Blettner
Journal:  BMC Med Res Methodol       Date:  2006-07-12       Impact factor: 4.615

10.  Implementing guidelines in primary care: can population impact measures help?

Authors:  Richard F Heller; Richard Edwards; Patrick McElduff
Journal:  BMC Public Health       Date:  2003-01-23       Impact factor: 3.295

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.