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Incorporating uncertainty in aggregate burden of disease measures: an example of DALYs-averted by a smoking cessation campaign in the UK
  1. Frank de Vocht1,
  2. James Higgerson2,
  3. Kathryn Oliver1,2,
  4. Arpana Verma2
  1. 1Centre for Occupational and Environmental Health, Health Sciences Research Group, School of Community Based Medicine, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
  2. 2Manchester Urban Collaboration on Health, Clinical Epidemiology and Public Health Unit, School of Translational Medicine, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
  1. Correspondence to Dr Frank de Vocht, Centre for Occupational and Environmental Health, Health Sciences Research Group, School of Community Based Medicine, Faculty of Medical and Human Sciences, University of Manchester, Oxford Road, Manchester M13 9PL, UK; frank.devocht{at}manchester.ac.uk

Abstract

Background Summary measures of population health (SMPH) combine information about morbidity and mortality as a means of describing the health of a population and allow for the comparison between otherwise incomparable health problems. Despite the widespread use of SMPHs in global public health policy, the uncertainty in their calculation, inherent due to the variable quality and availability of data from different sources required to calculate SMPHs, is generally ignored.

Methods and results Using the example of the expected effect of a smoking cessation mass-media campaign on ischaemic heart disease in the UK expressed in DALYs (disability adjusted life years)-averted, a transparent and straightforward probabilistic methodology to incorporate uncertainty in the calculation of population impact measures of health, to better inform the public health debate, is described. In addition, a rationale on how this additional information can be utilised to further improve the use of quantitative data for SMPH is presented, and public health policy makers are provided with additional tools for prioritisation of interventions and cost-effective prioritisation of data collection campaigns for the improvement of the calculation of future SMPH.

Conclusion Systematic use of these tools will provide a stronger evidence base for public health policy in the future and will further direct a drive towards the use of quantitative tools.

  • Health impact assessment
  • measurement theory
  • public health policy
  • smoking
  • statistics

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Footnotes

  • Competing interests None.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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