Supporting health impact assessment in practice

Health impact assessment (HIA) is a process that aims to predict potential positive and negative effects of project, programme or policy proposals on health and health inequalities. It is recommended by national government and internationally. Supporting health impact assessment is one of the roles of English Public Health Observatories.The few centres in England with accredited health impact training centres have inadequate resources to meet demand. Currently, the London Health Observatory is providing the bulk of the training nationally. Some Public Health Observatories are currently investigating the preferences for support of those commissioning or conducting health impact assessment within their regions.The availability of published guidance on how to conduct health impact assessments has increased substantially over the past few years. The Department of Health has funded a research project led by the London Health Observatory to develop advice for reviewing evidence for use in health impact assessment. Completed health impact assessments can be useful resources. Evaluation of the process and impact of health impact assessment is important in order to demonstrate its usefulness and to learn lessons for the future.The focus for Public Health Observatories is to train and support others to conduct health impact assessment according to good practice, rather than undertaking health impact assessments themselves. The aim is to create sufficient skilled capacity around the country to undertake health impact assessments. The London Health Observatory plans to share its support models and to roll out a train the trainer programme nationally to enable effective local delivery of their national health impact assessment programme. (c) 2005 The Royal Institue of Public Health. Published by Elsevier Ltd. All rights reserved

Bodies, Ideas, and Dynamics: Historical Perspectives on Systems Thinking in Engineering

Today, the idea that technology consists not simply of individual machines but of systems of components and interconnections underlies much of engineering theory and practice. Yet this idea is relatively new in the history of technology; it evolved over a long period, spanning more than a century, as engineers grappled with the implications of machinery and collections of apparatus that spread over broad geographical areas. A historical perspective on systems thinking provides a critical background for contemplating new directions in “engineering systems,” by highlighting the problems that have constantly challenged engineers, as well as the new puzzles posed by today’s world

What do we need for robust and quantitative health impact assessment?

Health impact assessment (HIA) aims to make the health consequences of decisions explicit. Decision-makers need to know that the conclusions of HIA are robust. Quantified estimates of potential health impacts may be more influential but there are a number of concerns. First, not everything that can be quantified is important. Second, not everything that is being quantified at present should be, if this cannot be done robustly. Finally, not everything that is important can be quantified; rigorous qualitative HIA will still be needed for a thorough assessment. This paper presents the first published attempt to provide practical guidance on what is required to perform robust, quantitative HIA. Initial steps include profiling the affected populations, obtaining evidence from for postulated impacts, and determining how differences in subgoups' exposures and suscepibilities affect impacts. Using epidemiological evidence for HIA is different from carrying out a new study. Key steps in quantifying impacts are mapping the causal pathway, selecting appropriate outcome measures and selecting or developing a statistical model. Evidence from different sources is needed. For many health impacts, evidence of an effect may be scarce and estimates of the size and nature of the relationship may be inadequate. Assumptions and uncertainties must therefore be explicit. Modelled data can sometimes be tested against empirical data but sensitivity analyses are crucial. When scientific problems occur, discontinuing the study is not an option, as HIA is usually intended to inform real decisions. Both qualitative and quantitative elements of HIA must be performed robustly to be of value

Impaired Glucose Metabolism among Those with and without Diagnosed Diabetes and Mortality: A Cohort Study Using Health Survey for England Data.

The extent that controlled diabetes impacts upon mortality, compared with uncontrolled diabetes, and how pre-diabetes alters mortality risk remain issues requiring clarification

The effect of mode and context on survey results: analysis of data from the Health Survey for England 2006 and the Boost Survey for London.

BACKGROUND: Health-related data at local level could be provided by supplementing national health surveys with local boosts. Self-completion surveys are less costly than interviews, enabling larger samples to be achieved for a given cost. However, even when the same questions are asked with the same wording, responses to survey questions may vary by mode of data collection. These measurement differences need to be investigated further. METHODS: The Health Survey for England in London ('Core') and a London Boost survey ('Boost') used identical sampling strategies but different modes of data collection. Some data were collected by face-to-face interview in the Core and by self-completion in the Boost; other data were collected by self-completion questionnaire in both, but the context differed. Results were compared by mode of data collection using two approaches. The first examined differences in results that remained after adjusting the samples for differences in response. The second compared results after using propensity score matching to reduce any differences in sample composition. RESULTS: There were no significant differences between the two samples for prevalence of some variables including long-term illness, limiting long-term illness, current rates of smoking, whether participants drank alcohol, and how often they usually drank. However, there were a number of differences, some quite large, between some key measures including: general health, GHQ12 score, portions of fruit and vegetables consumed, levels of physical activity, and, to a lesser extent, smoking consumption, the number of alcohol units reported consumed on the heaviest day of drinking in the last week and perceived social support (among women only). CONCLUSION: Survey mode and context can both affect the responses given. The effect is largest for complex question modules but was also seen for identical self-completion questions. Some data collected by interview and self-completion can be safely combined

Income-based inequalities in hypertension and in undiagnosed hypertension: analysis of Health Survey for England data

Objective: To quantify income-based inequalities in hypertension and in undiagnosed hypertension. Methods: We used nationally representative data from 28 002 adults (aged 16 years and older) living in private households who participated in the cross-sectional Health Survey for England 2011–2016. Using bivariate probit regression modelling, we jointly modelled hypertension and self-reported previous diagnosis of hypertension by a doctor or nurse. We then used the model estimates to quantify inequalities in undiagnosed hypertension. Inequalities, using household income tertiles as an indicator of socioeconomic status, were quantified using average marginal effects (AMEs) after adjustment for confounding variables. Results: Overall, 32% of men and 27% of women had survey-defined hypertension (measured blood pressure ≥140/90 mmHg and/or currently using medicine to treat high blood pressure). Higher proportions (38% of men and 32% of women) either self-reported previous diagnosis or had survey-defined hypertension. Of these, 65% of men and 70% of women had diagnosed hypertension. Among all adults, participants in low-income versus high-income households had a higher probability of being hypertensive [AMEs: men 2.1%; 95% confidence interval (CI): −0.2, 4.4%; women 3.7%; 95% CI: 1.8, 5.5%] and of being diagnosed as hypertensive (AMEs: men 2.0%; 95% CI: 0.4, 3.7%; women 2.5%; 95% CI: 1.1, 3.9%). Among those classed as hypertensive, men in low-income households had a marginally lower probability of being undiagnosed than men in high-income households (AME: −5.2%; 95% CI: −10.5, 0.1%), whereas no difference was found among women. Conclusion: Our findings suggest that income-based inequalities in hypertension coexist with equity in undiagnosed hypertension

A review of health impact assessment frameworks

Background: Consideration of health impacts of non-health sector policies has been encouraged in many countries, with health impact assessment (HIA) increasingly used worldwide for this purpose. HIA aims to assess the potential impacts of a proposal and make recommendations to improve the potential health outcomes and minimize inequalities. Although many of the same techniques can be used, such as community consultation, engagement or profiling, HIA differs from other community health approaches in its starting point, purpose and relationship to interventions. Many frameworks have been produced to aid practitioners in conducting HIA. Objective: To review the many HIA frameworks in a systematic and comparative way. Study design: Systematic review. Method: The literature was searched to identify published frameworks giving sufficient guidance for those with the necessary skills to be able to undertake an HIA. Results: Approaches to HIA reflect their origins, particularly those derived from Environmental Impact Assessment (EIA). Early HIA resources tended to use a biomedical model of health and examine projects. Later developmentswere designed for usewith policy proposals, and tended to use a socio-economic or environmental model of health. There aremore similarities than differences in approaches to HIA, with convergence over time, such as the distinction between ‘narrow’ and ‘broad’ focus HIA disappearing. Consideration of health disparities is integral to most HIA frameworks but not universal. A few resources focus solely on inequalities. The extent of community participation advocated varies considerably. Conclusion: It is important to select an HIA framework designed for a comparable context, level of proposal and available resources