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Background: This paper proposes that Population Impact Measures (PIMs), the Population Impact Number of Eliminating a Risk Factor over a time period (PIN-ER-t) and the number of events prevented in your population (NEPP), can assist in policy making as they include relevant information which describes the impact or benefits to the population of risk factors and interventions. In this study, we explore the utilization of the indicators from European System of Urban Health Indicators System to produce the two PIMs. Methods: We identified from the indicators list the health determinants, health status and health interventions which can be linked, and searched Medline for evidence of association. We then investigated whether the type of frequency measure available for the indicator match with the measure used in PIMs, and explored data availability for the City of Manchester (UK) as an urban area. Results: Of the 39 indicators relevant to socio-economic factors, health determinants and health status, it was possible to calculate the population impact of a risk factor, i.e. the PIN-ER-t, for only six associations, and the population impact of health interventions, i.e. NEPP, for only one out of the three listed indicators, as the relevant health conditions were not included. Conclusions: The results of this study suggest that if an indicator system is intended to play a part in the policy making process, then the method of presentation to policy-makers should be decided before setting up the system, as it is likely that some indicators which would be essential might not be available. [ABSTRACT FROM AUTHOR]

Background: Research suggests that policymakers often use personal contacts to find information and advice. However, the main sources of information for public health policymakers are not known. This study aims to describe policymakers' sources of information. Methods: A questionnaire survey of public health policymakers across Greater Manchester (GM) was carried out (response rate 48%). All policy actors above Director level involved in public health policy (finding, analyzing or producing information, producing or implementing policy) in GM were included in the sampling frame. Respondents were provided with a list of sources of information and asked which they used (categorical data) and to name specific individuals who acted as sources of information (network data). Data were analyzed using frequencies and network analysis. Results: The most frequently chosen sources of information from the categorical data were NICE, government websites and Directors of Public Health. However, the network data showed that the main sources of information in the network were actually mid-level managers in the NHS, who had no direct expertise in public health. Academics and researchers did not feature in the network. Conclusions: Both survey and network analyses provide useful insights into how policymakers access information. Network analysis offers practical and theoretical contributions to the evidence-based policy debate. Identifying individuals who act as key users and producers of evidence allows academics to target actors likely to use and disseminate their work. [ABSTRACT FROM AUTHOR]