These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Systematic reviews of health effects of social interventions: 2. Best available evidence: how low should you go?
    Author: Ogilvie D, Egan M, Hamilton V, Petticrew M.
    Journal: J Epidemiol Community Health; 2005 Oct; 59(10):886-92. PubMed ID: 16166365.
    Abstract:
    STUDY OBJECTIVE: There is little guidance on how to select the best available evidence of health effects of social interventions. The aim of this paper was to assess the implications of setting particular inclusion criteria for evidence synthesis. DESIGN: Analysis of all relevant studies for one systematic review, followed by sensitivity analysis of the effects of selecting studies based on a two dimensional hierarchy of study design and study population. SETTING: Case study of a systematic review of the effectiveness of interventions in promoting a population shift from using cars towards walking and cycling. MAIN RESULTS: The distribution of available evidence was skewed. Population level interventions were less likely than individual level interventions to have been studied using the most rigorous study designs; nearly all of the population level evidence would have been missed if only randomised controlled trials had been included. Examining the studies that were excluded did not change the overall conclusions about effectiveness, but did identify additional categories of intervention such as health walks and parking charges that merit further research, and provided evidence to challenge assumptions about the actual effects of progressive urban transport policies. CONCLUSIONS: Unthinking adherence to a hierarchy of study design as a means of selecting studies may reduce the value of evidence synthesis and reinforce an "inverse evidence law" whereby the least is known about the effects of interventions most likely to influence whole populations. Producing generalisable estimates of effect sizes is only one possible objective of evidence synthesis. Mapping the available evidence and uncertainty about effects may also be important.
    [Abstract] [Full Text] [Related] [New Search]