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Record W2965579308 · doi:10.1093/heapro/daz052

Health impact assessment and the sustainable development goals

2019· editorial· en· W2965579308 on OpenAlexaff
Gabriel Guliš

Bibliographic record

VenueHealth Promotion International · 2019
Typeeditorial
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsSustainable developmentEnvironmental healthPsychologyEnvironmental planningPolitical scienceMedicineGeography

Abstract

fetched live from OpenAlex

I had the pleasure to enjoy time with both of my grandfathers. They taught me to work hard, achieve goals and not to waste time dealing with adversaries (people, beliefs, apologies) that can stand in your way. Over the last few decades of work in public health practice, policy, education and research I am often reminded of that wisdom. I entered the public health arena after working in environmental health within a local Public Health Authority. I soon moved onto the analysis of population health status. Being dissatisfied with the minimal use of epidemiology to change health status, I found myself in health promotion, as it allowed for the co-creation of knowledge with people—allowing them to control their own lives. I started working in the area of Health Impact Assessment (HIA)—which aims to identify and control the determinants of health of populations in a prospective manner. This combined experience moved me into the policy arena and then academia, engaging me fully in teaching and research. HIA has become my main area of research and has allowed me to learn about different countries’ habits, practices, cultures and contexts. My grandfathers’ encouragements were essential at every stage and aspect of these developments. In HIA there are differences in the health promotion and environmental health views. Or not? Although I agree there are differences in focus, priorities and methods involved, HIA is one coherent body of concepts, albeit with different flavours. The health promotion approach focuses on social determinants such as social cohesion, education and income in line with the ‘new public health’ approach of the Ottawa Charter (WHO, 1986). Yet the environmental health approach focuses on classical risk assessment-based methods related to expected changes of a range of environmental pollutants—more in line with the ‘old’ public health approach. The aim is the same though, to minimize the risk of damage to human health and the ecosystem, and ultimately to maximize potential. I have observed attempts to add additional letters to the HIA acronym in an effort to strengthen the focus on equity (e.g. Equity-Focused HIA) in commercial sectors, private industry and through human rights. No one questions that equity is a major issue. Yet, we need to consider whether by analysing differences in health by increasingly smaller and smaller population groups, and in shorter time periods, are we running into a Pyrrhic victory of ‘not seeing the forest for the trees?’ The key to a well-rounded HIA is to remain grounded and explicit in one’s foundations and choices about what ‘health’ is. For some, a healthy population is defined by statistical measures on the absence or presence of an aetiology that limits the populations’ function, within a certain range, with means and standard deviations. For others, healthy populations are inclusive, community minded, proactive, ecologically balanced groups rich in social capital. Depending on the gaze then, letters could be added to HIA. But if screening and scoping—key steps in the development and implementation of quality impact assessments—are done well (WHO, 1999), then equity is always part of any assessment. If a policy being assessed deals with a corporate entity, then corporate identity and challenges will be key elements of the assessment. I have learnt that HIA needs to consider the impacts in different phases of a strategy, policy, project or proposal. Yes—time is a crucial factor and HIA, by its core values, is a dynamic exercise. Time, however, is not an easily framed variable, as it is not only about timing of the potential impacts, but also the changing demography and social characteristics of the target population. If we forecast an impact 15 years from inception of a policy, we also need to consider how will the demographic and social characteristics of the target population look in 15 years. To see HIAs as just a snapshot of health impacts from a particular vantage point in time may indeed limit the potential of the toolkit; good HIAs can and should be dynamic and responsive to changing circumstances. A view I often encounter is that HIA is the ‘perfect’ method, and that politicians, decision makers, and economists are unconscientious and unpredictable HIA clients who do not want to follow the HIA recommendations. These kinds of arguments lead me back to my grandfathers. Are not we only looking for excuses for our weaknesses? Should not we focus on integration instead of looking for more and more particular and specific ‘adversaries’? HIA aims to assess the future impacts of recent or projected decisions on determinants of health and population health. As HIA focuses on determinants of health often outside of the reach of traditional health care service delivery sectors, the HIA community implemented the Sustainable Development Goals (SDGs)—before they were a major policy framework of the United Nations (United Nations, 2015). The SDGs have the potential to provide a framework for bringing all stakeholders together, integrating those ‘unreliable’ politicians and economists into a public health professional and community inclusive gaze. The SDGs provide an opportunity to introduce HIA in countries where it is missing. Developmental processes that target SDGs guided by international investment schemes, bank loans or investments of international corporations should include HIA to ensure no harm to human health will occur. Low- and middle-income countries, where the risk of causing further harm to the health of a population (due to an exhaustive focus on economic growth), can only lead to the spread of environmental and social hazards. Abah (Abah, 2012) reviewed HIA cases in Nigeria and called for active involvement of the health sector in environmental and HIAs. HIA, when used with banks and governments, can make a major contribution such as in sand mining in Vietnam (National Geographic, 2018), mining in Armenia (Bankwatch Network, 2019) or mining in Ghana (Emmanuel et al., 2018). A recent HIA completed by colleagues in Wales (UK) on Brexit illustrates the opportunities embedded in this methodology to link major political decisions to human health (Green et al., 2019). Looking at the line-up of papers in this volume of Health Promotion International then why an editorial about HIA? Ask yourself: Are leisure centre entrance charges based on policies? Could their impact on health be assessed in prospective ways? Ward et al. (Ward et al., 2019) and Hanlon et al. (Hanlon et al., 2019) Should potential health impacts of food and beverage marketing policies be assessed in prospective way? Granheim et al. (Granheim et al., 2019) Should potential impacts of mental health programmes and services be assessed in prospective ways? Lyssenko et al. (Lyssenko et al., 2019) and Mjøsund et al. (Mjøsund et al., 2019). My answer to all three is ‘YES’. It is time to see HIA as ‘a way of thinking and acting’ and not merely a scientific methodology. It should be utilized around the globe by governments, corporations, banks and other stakeholders. The Ottawa Charter (WHO, 1986) defined healthy public policy as one of key areas to act within health promotion; HIA is one of the most transparent and sound methods to develop healthy public policy. It is time to think positive and look at the positive impacts of future development strategies, policies, programmes and projects, and aim for integration under a unifying framework of the SDGs, assuming the principles of planetary health (Whitmee et al., 2015). Gabriel Gulis

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.042
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.059
Meta-epidemiology (narrow)0.0060.002
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0080.004
Science and technology studies0.0050.007
Scholarly communication0.0150.009
Open science0.0060.004
Research integrity0.0420.046
Insufficient payload (model declined to judge)0.0120.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.385
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations10
Published2019
Admission routes1
Has abstractno

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