What’s complexity got to do with health? It’s complex…

Still defrosting from my visit to Washington DC, I’ve reflected on the conference that I’ve just attended in complexity, inequalities and health. Sound complex? Well, here’s a simple summary that’s not as snow-covered as I have been over the past few days. But why waste your coffee time reading this article? Well, this might give you some insights about the perspectives and methods emerging from leading researchers working in complex systems, health and inequalities, as well as the investments in the area from the main health policy agency in the US.

• “Complex Systems, Health Disparities & Population Health: Building bridges”

http://conferences.thehillgroup.com/UMich/complexity-disparities-populationhealth/agenda.html This conference was organised by the USA’s Network on Inequalities, Complexity and Health (NICH) and hosted by the National Institutes of Health (NIH) on campus in Bethesda, Maryland, USA. Not much tweeting throughout the two days, but I did start a hashtag that was picked up: #NICHconference

• The socio-ecological model of health lives on

As with most quality public health conferences, we saw the socio-ecological model in the opening comments. And one of the authors of papers about the socio-ecological model was present! It is a crucial framework by which we think, talk, measure, and report – important to communicate the individual, interpersonal, organisational, community and social policy impacts upon health of populations globally. It shows the complexity of health determinants, simply.

• Complex systems theory challenges our thinking about how health is constructed

To begin we heard the nuts and bolts of complex systems science as it applies to health, and a message that the “find it, fix it” approach to public health isn’t working. If traditional approaches were effective, we wouldn’t have epidemics of non-communicable disease and unfair health inequalities.  Unbalanced investment exists in most contexts – for example in the USA they know that 40% of health problems are socially determined, 50% behavioural and only 10% due to health care. However, only 3% is spent on societal and individual-level prevention strategies (complex solutions), whilst 97% is spent on health care (simple solutions).

• Complex systems science reorients our thinking about how to act to improve health

We can always interrogate the ‘why’ of health issues and inequalities. A person smokes because it’s socially acceptable, affordable, possible to do where they live, work/learn and play, and because cigarettes are available– actively marketed by for-profit companies. Food supply was given as another example. The production, marketing, acquisition, distribution, retail, purchasing and consumption of food is dynamic and depends on many factors such as market forces, housing, economics and built environment. Consider that the majority of countries in the world have McDonalds in urban areas; and, that the majority of countries have 50% of their population housed in urban areas.  What influences do these factors have on healthy food supply and access? Then how does that affect health and lifespan? As you can see, it’s complex. Check out this paper by Sandro Galea for more: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3134519/  

• Everything should be made as simple as possible, but not simpler

One speaker articulated that using simple interventions to address complex health issues is likely to fail. It’s a bit like King Canute ‘ordering back the tide’ – with health interventions and measurements, we can’t simply push against how things go naturally in a system, we need to identify multiple points and levers for interventions at different socio-ecological levels. Similarly, intervention research in this area can’t continue to be ‘linear’ and use averages for estimating effects –we need to capture heterogeneity. It’s tempting and logical to believe that if the parts get better (e.g. risk factors) then the whole will get better (e.g. populations), but change is contextually dependent. The response to multiple interventions will be very different than the totality of responses to each intervention separately. In other words, the whole is greater than the sum of its parts!

Image from: http://canute2.sealevelrise.info/slr/Story%20of%20Canute

• When it’s all ‘too complex’, or when there’s no ‘real’ data?

Try simulating or modelling data! There are often times when observed health issues are ‘too hard’ to disentangle from the modifiers and contextual factors. Modelling epidemiological associations between factor X (e.g. fast food) and factor Y (e.g. heart disease) may not reveal the nuances of what produced the issue in the first place – the causes of the causes.  The same goes for evaluating multifaceted interventions across the many socio-ecological levels – it’s hard to measure each and every factor that might have had an impact upon the observed outcomes, and then to attribute causation. Thus, we are often without empirical data that integrates the diversity of elements in a system, so it’s hard to prove what determinants to target. Also, limited quality evidence exists on processes and effectiveness of complex interventions, so we’re often ‘working in the gaps’.  Synthetic estimates can be produced by building simulation models, guided by existing data, evidence and theory. Models can control experimental conditions in a complex system, which is obviously impossible to do in ‘real world’ observational studies. Also, and rather compellingly, we heard that standard statistical approaches can’t examine feedback and adaptive mechanisms between environments and individuals/agents – whereas computational modelling can. This recent paper by Amy Auchincloss et al provides a recent example, with links between neighbourhood resources and obesity under study: http://onlinelibrary.wiley.com/doi/10.1002/oby.20255/full

• Methods for research of complex systems, health determinants and impacts

The main methods presented in the presentations and posters included system dynamics, social network analysis (SNA), agent based modelling (ABM), and discrete event modelling. These methods, having emerged from complex systems science, are being applied to public health research. The methods were described as tools to help us make sense of the interactions within complex systems, and the impacts that interventions might have on health and inequalities.  For a primer, see the take-home messages from Nathan Osgood below, refer to a recent paper by Doug Luke and Katherine Stamatakis – these sources will be eminently better than my interpretation would be! http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3644212/pdf/nihms414057.pdf   

• What are some of the applications for simulation and modelling research?

For the most part, the presentations and posters highlighted a series of examples of modelling research studies that explored a range of factors related to health inequalities at the individual, institutional and neighbourhood level. Mostly, this provided case studies for how inequalities are produced, but some focused on estimating potential effects of interventions.

• Examples of data simulation/modelling studies

At the individual and community level, an ABM explored differential effects of alcohol outlet density restrictions and policing upon alcohol-related violence and homicide among white Americans and African-Americans. A simulation study explored potential effects of upstream policy on Healthy eating and Physical activity, finding proof of concept that it may be more effective to target neighbourhood factors, not race, in reducing disparities in some contexts. At the population level, a case study from New Zealand was described, conducted when the earthquakes in 2011 interrupted the annual census, and modelled data was used to predict ongoing trends in primary health care access among Maori and Pacific Islander populations.

• Progress and pitfalls for complex systems methods in public health

Collectively, from this conference it seems that certain systems science methods may tell us more about the nuanced factors causing health inequalities. It may also help reveal leverage points and suggest how to tailor interventions. But as with all research, challenges and limitations remain with these methods. These studies require interdisciplinary teams to ensure sufficient expertise in epidemiology, mathematics, computer programming, geography, public health and urban planning. Working together is essential –from observational research to computational modelling, the first step is a doozy!

Another challenge highlighted was that ultimately, we need to be able to link the models to ‘real’ data, to ensure their validity. Involvement of community stakeholders and decision-makers in the process was discussed only briefly, but this would appear to be a key step in verifying models. Community physician and systems scientist Kurt Stange described a great example of a participatory process of community stakeholder involvement in model planning and development. This may be a good point for us to start, to ensure that we ‘keep it real’.

• Closing thoughts from a complexity novice

From a KTE perspective, I would think that external validity would be a key challenge for the application of this research, which may be difficult to reconcile. The conference left me pondering how do we use the evidence generated for decision-making? How can we be sure that modelled data reflects what’s in the ‘real world’? A discussion on using these models to guide policy was led by Complex dynamics researcher Ross Hammond, and NIH program director Stephen Marcus, which began to raise these questions. I would imagine, as for research evidence generated through ‘traditional’ methods, that a similar approach to knowledge translation and exchange would be required for evidence generated through modelling.

So after that, a penny for your thoughts? Leave a comment if you’re using/exploring these methods!

 

Written by Dr Tahna Pettman

Research fellow: Public Health Evidence and Knolwedge translation
Evaluation fellow: CO-OPS collaboration

The Jack Brockhoff Child Health & Wellbeing Program.
The University of Melbourne
e: tpettman@unimelb.edu.au

 

Interests in conflict? Managing the head and heart of research

Research findings have the ability to influence decisions – with regard to practice, policy and funding directions. It’s what makes the work of researchers satisfying – the thought that it may actually make a difference! But with this warm fuzzy feeling comes responsibility and the need to check our good intentions at the door – not necessarily to leave them there, but to submit ourselves to an open and honest conflict scan.

My work involves managing the editorial steps leading to the publication of public health research, and includes assessing the appropriateness of the composition of research teams as well as allocation of editorial advisors and peer referees to provide feedback on the research.  In doing so I am very conscious of the conundrum that can arise, in identifying individuals with sound understanding of a topic to undertake the research (or review the research) yet free of any vested interest in the outcomes of that research.

There are rules and policies to identify, declare and manage potential conflicts of interest (COI), to “provide guidance to ensure that there is clarity and transparency in the declaration of any interests, a balance of perspectives, and guidance on disclosing and managing interests” (NHMRC 2012) around research committees and working groups developing guidelines, and for researchers and peer referees of researchers’ work. 

The tricky part is that declaration statements often rely on the objectivity of the individual closest to the work – the researcher, the research committee member, the guidelines developer, the content expert chosen to peer referee the research.  I hazard to guess that a failure to declare a potential conflict of interest associated with a particular task is usually not due to an underhanded intent of the researcher or research advisor, but due more to a lack of understanding of what might be perceived as a conflict.  Most are clear about declaring any financial interest in the subject at hand or funds received by parties with an interest in the findings of a research work. But what of other influences that might openly or inadvertently influence the judgements and decisions of the researcher or research advisor? And can these influences coexist in a team without compromising the integrity and outcomes of a research task?

In noting my area of work, as Managing editor of the Cochrane Public Health Group, I also declare an interest (conflicting?) in this topic for authors, editors and advisory group members and peer referees of systematic reviews – of public health topics specifically. A recent report, prepared for the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services, contended that whilst the importance of attention to financial conflicts of interest has been addressed, there has been little guidance on how to manage the risk of bias for systematic reviews systematic reviews from nonfinancial conflicts of interest. The paper outlines definitions and examples of non-financial COI, and how these can be managed and assessed for their potential to bias their involvement in the review. It also confirms that authors may not be identifying themselves as having potential conflicts.

Non-financial COI in the AHRQ report was defined as “a set of circumstances that creates a risk that the primary interest—the quality and integrity of the systematic review—will be unduly influenced by a secondary or competing interest that is not mainly financial.” They include interests relating to the individual (intellectual, professional, career advancement), persons with whom the individual has a close personal relationship (e.g., family members, friends, colleagues), and interests held by the employer or organization with which the person is affiliated (e.g., employer, academic institution, specialty organizations, other professional organizations, and community interests).

Getting the authorship team right on a systematic review is important – with a need to include content expertise, methods knowledge and experience, as well as statistical and searching expertise. Bringing together a systematic review team that adequately balances essential content expertise with independence of judgment can be tough and requires open and deliberate choices for the lead author.

What is important to understand is that the identification and declaration of a potential COI and the management of that COI are two very different things.  It is the latter that can ease the struggle between the need to be close to, knowledgeable, and dare we admit, passionate about a subject or content area, and the need to make objective decisions based purely on the information presented or available to the team. Once the risk of potential conflicts of interest is identified, based on the context of the topic, there are a range of options to managing the conflicts of interests within a research team.  These range from disclosure followed by no change in the research team or activities, inclusion on the team along with other members with differing viewpoints to ensure a range of perspectives, exclusion from certain research activities (such as assessment of risk of bias in individual studies in the case of a systematic review), to exclusion from the authorship team entirely.

Not all conflicts of interest, once identified and acknowledged, lead to a compromised research project.  Being upfront and declaring all potential conflicts, to the editorial team and in any associated publications, allows the reader to make an informed judgement about the trustworthiness of the research process and findings. 

Written by Jodie Doyle
Managing Editor, Cochrane Public Health Group
e: jodied@unimelb.edu.au

The ins and outs of data linkage

Data linkage can sound a bit daunting and perhaps boring for those who have not come across it in their research experience. I thought I would go over some of the basics so you can start to think more about the potential for data linkage when planning your own research.  Data linkage is slowly becoming more common and can be extremely useful to enrich the data used in your research.

What is data linkage?

Data linkage, as it sounds, is the joining of two or more data sets that have information on the same participant. This could be information from a similar time or could enable you to look at information from different time point in the life course of the participant. For example we are currently looking at data from a child’s School Entrant Health Questionnaire, when they are five years old, linked to NAPLAN results when they are nine. This has enabled a wealth of information to be available on a child’s demographics, health and development and their later education outcomes. For example we can now ask ‘which health conditions have a significant impact on education outcomes?’ or ‘does attendance at early childhood services improve education outcomes?’ These questions can have important implications on policy, service delivery and service evaluation.

A simple way to link data is when there is a unique identifier in both data sets such as a student identification number or a medical record number. It is then very easy to match the data on this identifier. Unfortunately in Victoria this is not a common occurrence. Legislation in different states and countries often dictates the possibility and ease of data linkage. Researchers, such as Fiona Stanley, have advocated for and pioneered data linkage at a population level to significantly enhance the power of research.  

When there is no unique identifier linking must rely on common variables within both data sets that help to ensure the participant in one set of data is the one being linked to in the other set. Such variables are usually name, sex, age and postcode. A Statistical Linkage Key (SLK) combines letters and number of variables to create a unique identifier and is a common system for linkage. The letters used are the 2^nd, 3^rd and 5^th letters of the family name and the 2^nd and 3^rd letters of the given name.

Currently data linkage for research is commonly carried out by an independent data linkage agency that takes the data from the data custodians, links the data and then gives the de-identified data to the researchers. This is an extremely simplified version of a costly and lengthy process.

 

 

When looking at a linked dataset it is important to account for any bias that may have occurred. For example one set of the data may be missing some of the cohort. Also depending on which variables were used to link there may be additional bias introduced. In the SEHQ-NAPLAN data, one set only has information on children attending government school. Additionally the data was linked on where the children lived so any child that moved may not be included. It is important to look at who within both dataset were and were not linked and how these children may differ. To note, a data linkage rate of around 60% is quite good.

Lastly I will touch on the ethics of data linkage. Participants may have agreed to research being carried out on the initial study you have undertaken but it is likely that they have not agreed for that data to be linked to other information about them. If it is possible to include the potential for data linkage in the initial participant consent headaches along the way can be avoided.

I hope this brief introduction to the ins and outs of data linkage may be of use in your future research. I imagine the more researchers advocate for and use data linkage the more accessible it becomes.

 

Written by Shae Johnson

Cohort and Platform Data Programs Coordinator

Jack Brockhoff Child Health & Wellbeing Program

e: shae.johnson@unimelb.edu.au

Obesogenic Australia: Would you like any chocolates with that?

Guest blog by Alexandria Hoare, Dietitian (APD, AN)

On my half hour commute home from work each day I directly pass 4 McDonalds, 2 KFC’s and 1 Hungry Jacks. Recently, I had to stop off to fill up with petrol on my way home. The service station was packed wall to wall with chips, chocolates, icecreams and sugar filled drinks. As I went to pay I was not greeted with ‘hello, how are you?’ but ‘would you like any chocolates, 2 for $3?’ I got back into my car (chocolate free) and as I drove I listened to the ads on the radio telling me that I had to get in quick to try the limited addition burger from McDonalds, how Coca-Cola brings happiness to summer and how I can win tickets to the tennis if I eat Drumsticks. I decided to go out of my way to go to the supermarket (as I do not directly pass any, unlike the fast food restaurants).  As I entered I was faced with front end advertising promoting sales on soft drink, chips and sugary lunch box fillers. I decided not to purchase these products but just in case I changed my mind I was greeted with more calorie dense, nutrient poor food at the checkout.

This is what you call an obesogenic environment. Where the environment we live in makes unhealthy choices the easier option. And that’s just looking at half the picture. Don’t forget the endless amounts of labour saving devices which allow you to stay in the comfort of your own home for days, months, even years on end if you wish. People are told to eat healthy and to be physically active but how can they achieve that when the environment is encouraging them to do the exact opposite? If we are serious about tackling the obesity epidemic we need to create supportive environments. Environments where the healthy choice is the easy choice. 

Many countries worldwide have started to step up their efforts to tackle their obesogenic environments. In the UK and Korea national programs have been launched to reduce trans fats in food, in Japan employees have mandatory waist measurements enforced by their insurance company, Hungary has a tax of high sugar foods, Finland a tax on confectionary, France a tax on soft drinks and Denmark introduced a tax on foods that contain more than 2.3% saturated fat. Some of these strategies are showing some promising results, others are not, but at least they’re trying.

What can we do about obesogenic Australia? I don’t have the answer but let’s at least try to do something. What about restrictions on the marketing and promotion of high calorie, sugary foods or food policies to reduce the price of fresh produce, introducing a traffic light system on food packaging, menu labelling at restaurants, limitations on product placement in supermarkets,  working with food industries to make healthier more affordable foods, mandatory nutrition and physical education in schools, healthier foods to be available at school canteens, sporting venues and service stations, better urban planning to promote physical activity, accessible healthy food and active commuting?

Let’s put more funding into research, collecting evidence and determining what interventions are more successful. What lessons can we learn from countries like Japan, Switzerland and Norway where they have some of the lowest rates of obesity in the developed world?

For these changes to occur the whole of society will need to get on board. Federal, state and local governments, non-government organisations, schools, communities, health and food industries, sporting clubs and the media all have the potential to make some changes for the better, to promote a healthy lifestyle and to help reduce our obesogenic environment.  

If we are serious about tackling obesity our environment needs to change. We need to create supportive environments so the healthy choice is the easy choice. 

 

Written by Alexandria Hoare
Dietitian (APD, AN)
BHSc, MDiet

https://www.facebook.com/TheDietitiansPantry

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Highlights from a rookie researcher’s first conference.

A few weeks ago I attended the conference Progress 2013 (http://progress2013.org.au/). The first of its kind in Australia, it brought together progressive left thinkers with not-for profit organizations, unions, private industry and experts in the health and environment sector. Its aim was to talk about the issues that will define Australia’s not-for profits and social movements for the years to come. As a recent graduate, this was my first ever conference and a chance to understand how people from all over the workforce come together to share skills, nut out ideas and most importantly – network. This blog post will cover some of the major highlights from the conference and touch on some of the lessons I learnt, from the perspective of a budding young researcher.

Highlight no 1.
To begin, one of the major highlights for me was listening to rock star academic and expert on the social determinants of health, Richard Wilkinson (http://www.ted.com/talks/richard_wilkinson.html), speak about the importance of understanding how income inequality affects health and wellbeing. Wilkinson, author of ‘The Spirit level’ and co-founder of The Equality Trust (http://www.equalitytrust.org.uk), researches the problems of inequality in society and produces evidence-based arguments to support social movements for change. In particular, Wilkinson drew attention to the problem that health and wellbeing in high and middle income countries is worse for all when the gap between the rich and poor is greater. Data was collated to demonstrate that even in high income countries as measured by Gross Domestic Product (GDP), population levels of health and wellbeing are influenced by income inequality. Therefore, the average wellbeing of societies is not dependent on gross national income and the rhetoric of economic growth but rather the relation between each other within society itself. This trend also occurs in child health and wellbeing, mental health, drug abuse and obesity – proving the tangible effect that inequality has in society. Some factors Wilkinson attributed as the drivers of negative health in unequal societies include status anxiety, stress, mistrust and dominance caused by a competitive consumer based economy. Wilkinson therefore advocated for a more inclusive society where value is placed on the way we relate to one another and where possible to harness positive social relations, such as friendship. Although these insights seem somewhat intuitive, I was taken aback by how relevant it is to continue to produce evidence that highlights this problem. When considering health and wellbeing, Wilkinson makes us think about the less visible effects of how we relate to one another and re-establishes the importance of family, friendship and positive social interaction to maintaining a sustainable quality of life.

Highlight no 2.

Another highlight was the talk given by Anat Shenker-Osorio, a communications expert and researcher who authored the book ‘Don’t Buy It: The Trouble with Talking Nonsense about the Economy’. Her work looks at how people understand issues, such as the economy or climate change, through the words used to narrate them. Without getting too bogged down in detail, the gist of her research suggests that you can reliably persuade or change a person’s thinking about an issue depending on the words used to describe it. For example, immigration. A study was conducted to measure how people responded to immigrants depending on whether they were framed as either a burden or a resource. The findings showed that when immigrants were talked about in a positive framing, by expressing what they bring to society and not what they lack, people’s acceptance of immigrants were overall more favourable. Shenker-Osorio argues that by literally changing the words we use to speak about an issue, we can also influence how people think about it, having repercussions for politics and policy. Something to think about when writing the next report or talking at a conference about a sticky issue. Frame it positively and you will have people receive it much more favorably.

Highlight no 3.

Arguably one of the best parts of Progress 2013 however, was the chance to mingle with those I consider some of my professional role models. As I mentioned above, this was my first ever conference, so the task of introducing yourself to those you admire is quite daunting. However, after a few awkward first conversations I learnt the following things;

1. Go with a plan. Since time is scarce at these events and the professionals you meet talk to so many different individuals every day, working out a plan of who you want to speak to and what you want to speak to them about prior to the meeting is essential. This way, you won’t get caught in a conversation about the weather and how good the muffins are, but instead get to use your limited time to your best advantage.
2. Don’t be scared to introduce yourself. As daunted as you might be about shaking hands with someone you find just a huge bit intimidating because of their greatness, it never hurts to just introduce yourself and say you are a huge admirer of their work. A few times I saw rock star academics on their lonesome at the coffee table, probably because everyone was too in awe to say hi.
3. When in doubt ask questions. When you have reached your small talk capacity and feel like the conversation is drying out, ask questions of them. People love to talk about themselves and asking them questions about themselves shows that a) you have a strong interest and b) that you are engaged in what they do.

Written by Hannah Morrice
Research Assistant, Jack Brockhoff Child Health & Wellbeing Program
The University of Melbourne
e: hannah.morrice@unimelb.edu.au