# data[].attributes.body
Low prevalence of schizophrenia - good to take a subset of the population who have had hospital admission. May not need many numbers but don't want to rely on primary health care.

When we look at people with MI, will be diverse and may include First Nationa people and others

# data[].attributes.body
Western views of mental illness - so we need to capture this subgroup.

# data[].attributes.body
Communities are looking Queensland focused - need to keep a national focus

Regional - climate - 3 dimensions: heat profiles, at risk populations, and health outcomes of interest.

# data[].attributes.body
Many won't identify as heat vulnerable - and identify people who may be on registers already - Red Cross project

# data[].attributes.body
Fiona talking to QH about this, - they started in SA, but were coming here - unlikely now

# data[].attributes.body
Do we breakdown by population or location/heat region?

# data[].attributes.body
ABS has sustained the 2019/20 Reducing Illness and Lives Lost from Heatwave study (RILLH). 10 year study at post code level. https://www.pean.gov.au/sites/default/files/2021-07/BOM%20%282021%29%20Heatwaves%20report.pdf

Potential partner for population level study if existing work is leveraged.

# data[].attributes.body
Three case studies - mixing variables urban and rural / population and location / mental health conditions / mechanisms (evenly distributed for maximum diversity in case study application). I have mixed it in such a way to inform an "overarching aim", with each sub-study (case study) involving its own tailored, and applicable aim.

# data[].attributes.body
to identify the causal pathways of mental health impacts of heat exposure through a series of curated case studies representative of multiple at-risk populations

# data[].attributes.body
The aim of this project is to use system dynamics modelling to investigate the biological, environmental, and social mechanisms by which heat, resulting from climate change, impacts mental health across diverse populations. Through three curated case studies that mix urban and rural settings, varied populations and locations, different mental health conditions, and distinct mechanisms, we seek to achieve maximum diversity in application. By exploring the connections between these mechanisms within each case study, the project aims to identify common and unique factors affecting mental health (i.e., 'points of power' in complex systems), map the causal architecture involved, and inform effective interventions and policies to mitigate the adverse mental health outcomes associated with climate-induced heat.

# data[].attributes.headline
To explore the relationships between mental health and wellbeing (dual continuum)?

# data[].attributes.subject
To explore the relationships between mental health and wellbeing (dual continuum)?

# data[].attributes.body
I liked the dual continuum model that was shared seeing mental illness and wellbeing as continuum. Call prioritises biological, social and psychological mechanisms through which heat impacts anxiety, depression and psychosis. So should we be specific to these populations - and keep this our focus? So we can look at wellbeing of whole population, but we need to prioritise anxiety, depression, and psychosis. Must be clinically diagnosed.

# data[].attributes.headline
Qualitative and quantitative SD modelling

# data[].attributes.subject
Qualitative and quantitative SD modelling

# data[].attributes.body
Participatory GMB --> map existing system architecture --> refine --> undertake cross-impact analyses --> identify key factors --> identify loops of priority --> undertake multi-system links --> locate points of heat entry into model(s) --> possibly identify system archetypes --> transform subsystems into stock & flow

# data[].attributes.body
What is the prevalence in different subgroups - schiz 0.5%, anxiety and depression around 4-6%. Depends on severity - can have good wellbeing and be managing illness well. People with schizophrenia often have comorbid anxiety and depression. CALD populations - ? Older population - anxiety and depression goes up.

Biological mechanisms might be through blood through and thermoregulation and how medications are metabolised, but predominant mechanism is comorbidities - more physical problems due to heat, will be exacerbation of MH.

# data[].attributes.body
I also noticed the ‘1 year’ timeline here for these groups. Does it mean only 1 year research/engagement for each group? Would it make more sense to make these studies in parallel rather than 1 year each, one at a time?

# data[].attributes.headline
Explore the differential  impacts of heat on mental health from a climate, thermal and extreme heat wave lens

# data[].attributes.subject
Explore the differential  impacts of heat on mental health from a climate, thermal and extreme heat wave lens

# data[].attributes.headline
Understand the unique heat-related vulnerabilities and resilience factors across different populations

# data[].attributes.subject
Understand the unique heat-related vulnerabilities and resilience factors across different populations

# data[].attributes.headline
Understand the unique heat-related vulnerabilities and resilience factors across different levels of remoteness (urban through to very remote)

# data[].attributes.subject
Understand the unique heat-related vulnerabilities and resilience factors across different levels of remoteness (urban through to very remote)

# data[].attributes.headline
To understand what is the experience of heat in vulnerable populations? 

# data[].attributes.subject
To understand what is the experience of heat in vulnerable populations? 

# data[].attributes.headline
How do vulnerable populations perceive and respond to heat?

# data[].attributes.subject
How do vulnerable populations perceive and respond to heat?

# data[].attributes.body
MRFF grant in heat and mental health - UNE

NHRA

# data[].attributes.body
One of my PhD students is working on a Sydney-Fudan collaborative project to examine heat, greenspace and mental health in urban settings.-Ying

One point to consider here in examining the mechanism is 'protective factors' that can mediate the heat impact on mental health.

# data[].attributes.body
Why this method?

Response - to be able to capture the complexity

The qualitative process will reveal a lot - there will be other opportunities for data

# data[].attributes.body
1. Butland B, Jebb S, Kopelman P, et al. Tackling obesities: future choices - project report. 2nd ed. London: Foresight Programme of the Government Office for Science; 2007 [cited 2014 Oct 16]. Available from: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/287937/07-1184x-tackling-obesities-future-choices-report.pdf

2. Vennix J. Group model building: facilitating team learning in system dynamics. Hoboken, NJ: John Wiley and Sons Ltd; 1996.

3. Andersen DF, Vennix JAM, Richardson GP, Rouwette EAJA. Group model building: problem structuring, policy simulation and decision support. Undated [cited 2014 Sep 21]. Available from https://www.albany.edu/~gpr/JORS06.pdf

4. Hovmand PS, Andersen DF, Rouwette E, Richardson GP, Rux K, Calhoun A. Group model building "scripts" as a collaborative tool. Syst Res Behav Sci. 2012;29:179-93. CrossRef

5. Andersen DF, Richardson GP. Scripts for group model building. Syst Dyn Rev. 1997;13:107-29. CrossRef

6. Doyle JK, Ford DN. Mental models concepts for system dynamics research. Syst Dyn Rev. 1998;14(1):3-29. CrossRef

7. Fowler A. Systems modelling, simulation, and the dynamics of strategy. J Bus Res. 2003;56(2):135. CrossRef

8. Hunt J, Timoshkina Y, Baudains P, Bishop S. System dynamics applied to operations and policy decisions. Eur Rev. 2012;20(3):324-42. CrossRef

9. Rouwette EAJA, Vennix JAM, van Mullekom T. Group model building effectiveness: a review of assessment studies. Sys Dyn Rev. 2002;18(1):5-45. CrossRef

10. Berard C. Group model building using system dynamics: an analysis of methodological frameworks. EJBRM 2010;8(1):35-46.

11. Richardson GP. Problems with causal loop diagrams. Syst Dyn Rev 1986;2:158-70. CrossRef

These are some of the classic references around

# data[].attributes.body
This comes down to innovation and maybe the use of this SDM gives us an edge against the other applicants.

# data[].attributes.body
I would rather not highlighting SDM. It's a bit risky if the review panels don't appreciate. A more holistic bio-epi-eco-soical- methodological framework sounds better for me.

# data[].attributes.headline
John and Peng on BOM and exposures study

# data[].attributes.subject
John and Peng on BOM and exposures study

# data[].attributes.body
ABS data - 10 year cohort at postcode level with protections in, but we should investigate. The study was a Prime Minister funded special study - the paper talked about the methodology rather than the results. ABS and the BOM established that this is a technique they can apply to all hazards and impact of exposures to impact forecasting - which is a key objective for all advanced weather agencies now. The heatwave enviornment study project environment hasn't shut down, and is still a viable project. PENG commented - paper is underway for mortality and morbidity at SA2 level. Remarkable dataset.

We had available any data we could justify - we had MBS, PBS, social data at individual level on a daily basis - that could be analysed against a heatwave. It could always be updated - we could pitch it and talk to them about what we woudl like to run.

# data[].attributes.body
Sticking point will be mental health data - we have three or four national surveys - first done in 1997, and then every four years. The K10 (one of the measures in it), has also been used in other surveys - it is favoured here in Australia and other countries, but it may not be as diagnostic as what we need. Getting MH data is very difficult. After meeting with the Matilda Centre at USYD is working on harmonising on the disparate and heterogeneous MH datasets into one dataset - which goes back about 40 years and opens up opportunities when we talk about linking data. We also have administrative data from health services etc. See the Qld Health data pages - from Neeraj in chat. Peng said the ABS data set goes back 20 years and daily hospital admissions and includes MH admissions nationally, across all jurisdications.

# data[].attributes.body
ABS - mortality data (though they also have morbidity - from national surveys)

AIHW - morbidity data (health service data goes here)

Could the social side be looked at in detail if we have SA2 - also Nexus data - Geosciences Australia - shows you greenspace, density, quality of housing - all by postcode.

# data[].attributes.body
Challenging - we need to indicate teh anticipated data types we require for specific purposes eg. calibrating the model against specific data sets so you have some validity that the model is accurate. Typically we would have anticipated data sets for the three case studies - but difficult to know what data we need until this is further developed. It could be things like heat (a heat module that links to all three case studies), the model would need to trace the historical heat data, and model it so it can capture the trend. Start with what we know will definitely be needed. Eg. ABS, heat, social security level data, loneliness scales and other metrics that can be converted.

# data[].attributes.body
three ways

1. Could do robust epi work on identifying vulnerable people or populations not previously identified to generate hypotheses - we do it right up the front end of the project,

2. It will help us ensure we draw it out adequately in the SDM process and inform the qualitative interviews - tell us questions we hadn't thought of asking

3. It will also give us data to fit into the simulation

# data[].attributes.body
TEMPERATURE Historical temperature records (daily max/min temperatures) / Frequency and duration of heatwaves / Humidity levels / HEALTH & MENTAL HEALTH Prevalence and incidence rates of mental health conditions (e.g., anxiety, depression) / Hospital admissions and emergency visits related to mental health / Medication prescription rates / DEMOGRAPHIC Population density and distribution (urban vs. rural) / Age, gender, ethnicity, and cultural background statistics / Income levels, employment rates, education levels

# data[].attributes.body
Good to have a lived experience researcher - or employ lived experience organisations/contributions

The budget should include peer support workers, and lived experience contributions

# data[].attributes.headline
Neeraj - including too many things is a risk?

# data[].attributes.subject
Neeraj - including too many things is a risk?

# data[].attributes.body
We need to keep the scope manageable - as long as we have metropolitan, rural and remote - it will be okay even if only one state. It may not matter elsewhere that several states are covered - more that metro, rural and remote breakdown

# data[].attributes.body
Adam - can be seen to be rough in group model building workshops to develop causal loop diagrams and abstracted heiarchies of systems from national level and down. The approach that has been taken to them has been similar across projects. In practice it looks like a good facilitator in a room with post-it notes - ideas factory. Social science approach. When you write up the methodology - it's called a conceptual basis, and there is a rigid methodology and script. Versus community based systems modelling.

# data[].attributes.body
GMB and participatory approaches operate under a lens of constructivism - people make sense of their world and offer rich, lived experiences around the problem of interest. The researcher is a facilitator, impartial agent that guides the process but does not impose pre-conceived ideas etc. I think WT would like to see us acknowledge this thinking

# data[].attributes.body
so we might have idea of heat and particular disorders but how do we capture the impact of deterioration of MH. eg - they get aggressive, etc

ie. how do we capture people's experiences when we are not interviewing during a heatwave - is there a particular scale/survey etc?

Tara - don't necessarily ask while a heatwave is happening - you could ask them qualitatively after the event - eg. How they perceived the event and how they cooled down, etc. - Could do it after the event.

Also ask about their past experiences with heat as well across a period of time.

John - conscious that Airator?app (Menzies Institute - Fay Johnston at UTas) included data on heatwave so people could see additional data in terms of how they might be impacted by events in terms of heat. It depends how you recruit people, but it could inform how peope are impacted during a heatwave.

If we look at hospital admissions and qualitative research - average LOS is about 11 days - semistrutured interviews coudl be done if a peer support worker gets in touch with patients and if well enough could share their experiences, it coudl be the reason or impacted their admission.

Not everyone is a psych ward is so unwell that they can't talk to a researcher.

SHANNON - could we also tie to medication use, social isolation, housing, etc.

# data[].attributes.headline
What is benefit of using statewide data over what Peng has an agreement with national datasets

# data[].attributes.subject
What is benefit of using statewide data over what Peng has an agreement with national datasets

# data[].attributes.body
Neeraj - we should use both. Good idea to look at what variables are available in statewide data versus national data. The statewide will have clinical/diagnostic information - national data based on self report and survey data.

Australian research Data council (ARDC) better datasets then AIHW - don't capture the community controlled sector, but good primary healthcare data. Haven't reached NT yet. Collecting through PHNs?

Neeraj - data on higher prevalence of severe mental illness in First Nations communities has been based on self-reported data - we conducted NHMRC study which was the first objective study.

# data[].attributes.headline
Weather data requirements must be stipulated

# data[].attributes.subject
Weather data requirements must be stipulated

# data[].attributes.body
Need to put in the weather data requirements - we have to be clear about this - needs to be included

need link into national weather authority data (engage the national metereological agency) - will be a pipeline to the future and they build it into their plans about what they supply you into the future - and this will build the health needs in to make it happen. Also building comms systems that are truly national - describe your requirement to the national meteorological authority and they will build it. Good to have a meteorologist onside to guide what you have in your model - they will give you what you ask for, and sometimes it doesn't inform you properly, so get guidance and input from the beginning.

# data[].attributes.body
Climate Data

Heatwave data - severity etc has become routine - John can help to ensure they give you what is sensible

Thermal data - temp, humidity, windspeed and radiation - look at how you combine into your thermal measure of choice to ensure it is applicable to your study

What's the resolution? 5km resolution - and also reanalysis datasets that are hourly. Best way to get humidity data for the thermal measurements you want to do at hourly time steps.

# data[].attributes.body
John thinks there are lit reviews on different thermal comfort measures - but difficult to work out how people experience the thermal discomfort. Shannon - what drives the thermal comfort and related behaviours.

Fergus - could we use an in-home monitoring system with questionnaires throughout the day - and link to the Canadian study using in-home monitoring funded by WMO from the Joint WHO/WMO Office for climate and Health, on heat and health

# data[].attributes.body
is this population group more sensitive to fluctuations in temperature which translates to thermal discomfort, and how are they perceiving the stress?

Eg. older people sense heat discomfort later - which leads to delayed response to action

Jonathan - at what point do they perceive they are too hot, and at what point do they take action to mitigate that

Can we link secondary data sets with health, with thermal meaures (eg. wet bulb)? You can have two completely different weather conditions that elicit the same wet bulb (UTCI - Universal Thermal comfort INdex), or Feels like (iheat index) - I would use wet bulb globe and another one of the other measures. The wet bulb globe is not universal - eg. Japan different from Hong Kong - designed for local temperature. Wet bulb was designed for elite athletes - commandos in uniform in 1950s. Has been used for racehorses.

# data[].attributes.body
Number of participants - how would we recruit?

Would it be convenience sampling?

PSWMI - clinical samples, convenience sampling (snowball sampling)

First Nations - stratified sampling in remote settings - often stratify by gender - often get more female or elderly people participating so deliberately choose so we don't miss key groups.

Group model building - what's a ballpark - 30 in current grant - convenience sampling used to recruit approx 30 participants who will contribute to development of causal loop diagrams - making sure they are diverse stakeholders from across the system who are representative of the system. Includes 30 with lived experience.

# data[].attributes.body
We measure 10 minute air temp and humidity - we have a system that could be used to answer other questions

We can't meausre individual temp risk

Surveys done through the app

We could tag on a visual analogue scale - validated scale available

Get alerts from the device - based off a core temperature prediction based on older adults in a lab study and in the field. We would need to set the alert on a different value - eg time. Or link to national heatwave.

Menzies app uses the EHF. Peng is using bureau EHF. It was scraping data or calculating itself rather than using official gridded data - this is something to chase.

John - Could they apply a feels-like?

# data[].attributes.body
There is a mental health app that MSHR developed for INdigenous populations given K10 questionairres aren't really relevant. The team is within MSHR so we can get advice and assistance. It would be good to know what would be the best screening to use.

# data[].attributes.body
Depends on data available - we have looked at sweat samples - easy and non-invasive to get and biological mechanisms there - could be difficult but we could look at thermal sensitivity testing. There is a research gap looking at sweat rate production and blood flow - could build in non-invasive measurements into the model - can see if they are altered in this population compared with healthy population. These could be useful at a biological level that are not too invasive. Ideally you would get people in a heat chamber - but this would be a barrier - so sweat rate and blood flow are probably the best. Skin temperature also.

Sweat rate, blood flow, and thermal sensitivity is a device that can be used - gaps in the knowledge in these areas. Sweat rate is a device - ventilated capsule - need to stimulate sweating and skin blood flow. Problems that local sites don't necessarily reflect whole body.

Supriya - sensors that need to be carried or stickers on skin surfaces, bio capsules are out. Everyone uncomfortable.

how do we link to the MH outcomes - are there too many confounding variables to be considered?

Is there a cell signalling pathway leading to an exacerbation - at baseline there is literature and evidence to suggest that they have dysregulation of HSB70 and could disregulate dopamine and lead to an exacerbation.

Is it possible to look at people with high anxiety or is this not fit in with the scheme?

Neeraj - bloods aren't necessarily routine - maybe at first admission, sometimes once a year bloods. Could we look at biomarkers? Could we interview individuals (or less if saturation is achieved early), what is the power needed to achieve statistical significance? Can alter blood temperature after taking blood.

What would the specific research question be? Assuming any of the samples being available....

# data[].attributes.headline
Dr Hagham Ailabouni - UQm- CALD at UQ - just got her EL1 award

# data[].attributes.subject
Dr Hagham Ailabouni - UQm- CALD at UQ - just got her EL1 award

# data[].attributes.body
From Adam

# data[].attributes.body
or reference following the discussion.  If the grant is building in observation systems - there are some useful principles to consider - does the data meet the WMO standard such that it can be ingested by the national weather provider - who will then incorporate it into improved weather and warning products. Also consider existing private weather networks that meet the Bureau's standards - such as https://mesonet.com.au/ which are operate with different overheads to the Bureau - particularly if the grant is building in an observation system.  These automated weather stations are different to the in-house systems which are dealing with microclimate exposures.

# data[].attributes.body
A ‘nationally represented’ team will collaborate on this project is a strength… e.g. CALD ad Farmers in SA/NSW/VIC

# data[].attributes.body
My suggestion is that we still focus on these high-risk populations but they are from different locations, e.g. group 1 from A, group 2 from B, etc.

# data[].attributes.body
QHAPDC is a great resource for all admitted patients in Queensland. Available free of cost after HREC and PHA ethics approvals
https://www.health.qld.gov.au/__data/assets/pdf_file/0019/1363105/2425-qhapdc-manual-v1.0.pdf

# data[].attributes.body
Anxiety and Depression - not severe mental illness- be careful with terminology

# data[].attributes.body
Being able to identify those normal management mechanisms to manage mental health - that we might find exacerbate heat vulnerability/impacts - eg. going for a walk or outside to mitigate anxiety

Adam says this become a Limits to Growth issue - where one loop amplifies, and then the other moderates and cancel each other out. Eg. today's solutions are tomorrow's problems, cause and affect are distant in time and space. The complexity and counterintuitive nature of systems makes the modelling important.

A heatwave as a hazard doesn't occur alone - bushfires exacerbated by the heat, then other elements contribute to the mental state of people - other stressors out there at the same time. More than one hazard is part of the issue. Loop dominance analysis - can look at cascading or cumulative or compounding effects.

When these events occur, they can occur irregularly, and behaviours protecting against one hazard can make people more vulnerable to another hazard. Eg. heat wave, influenced people to stay inside and then not avoid bushfires.

# data[].attributes.body
Ability to anticipate events before they happen and prevent protective loops

Who is our target who will deliver the interventiosn - eg. state agencies, health agencies,

Talked about the bank staff example in Stanthorpe and value of reference groups - we want to talk to end users about what they want. ARAFMI

Shannon - would their be an overarching reference group or one for every study? Pathways to impact are at the local, state and national level

Neeraj suggested - could be focus group interviews with experts eg. PHNs, reps from councils, community organisations, etc - it's considering a governance group or steering committee - they arbitrate and have input across the process. Understanding the drivers is critical to make sure we are making the changes on the ground. End users.

They could be part of governance, translation and data collection as well. Good to engage early.

Need for round tables and think tanks.

Codesign process - feedback loop that gives back.

Different purposes for bringing people together - identify pathways to impact - then we have stakeholders who are local and part of local group - then people with lived experience, and help us work with lived experience cohort.

SHannon - outcome - heat to the health outcome - exacerbation of the condition - what does heat do before it gets serious before people end up in hospital. Fergus - gets to heart of whether this is physiological or exacerbation of symptoms.

Who is ending up in hospital and why? - that's the beauty of the SDM model

Patient wellbeing focused outcomes - reslience strategies, improving life quality, ability to be resilient - identifying adaptation measures for individuals, workplaces, access to air conditioning subsidies for certain conditions - infomring advocacy and support organisations about how to improve support. Surge capacity around heatwave.

John - ambos in early stages of heatwaves, predict higher violence due to increased alcohol consumption

HEat stuff - better understanding of heat and what type of heat is most concerning.

John - consider structure of UN's early warning as a template to consider - hazard awareness, observation of customer warnings, international communication union, red cross communities responsible for how communities interact at local level.

Neeraj - could recruit through community MH team - how is heat impacting people not hospitalised and in cty metnal health services

# data[].attributes.body
Go back to initial stakeholder groups and present with a beautiful simulator they can play around with themselves to understand impacts of different pathways and impact on teh community - a bit like EN-ROADS - looks at greenhouse gas emissions - developed a website, interface, etc outcomes would be a tangible. Lots of models stellar's online library of models.

User friendly interface that appeals to end users - consider an evolving SDM model that uses AI to feed it real time data so it's a self learning model. Need to do this.

# data[].attributes.body
Make explicit that the project will explore the relationship between heat, wellbeing and MH across different climate zones.

# data[].attributes.body
are climate zones a proxy for social divergence?

The social response to an extreme heat event in remote NQ will be very different to SA

Also need to remember that climate zones can experience atypical events eg. wet tropics are a massive problem when they get hot and dry. Eg. burning of wet tropics.

# data[].attributes.body
PENG mentioned a paper - by 2070 it projected an increase in MH issues in NT

# data[].attributes.body
Biological measures - sweat rate/blood flow - are these variables we can measure and how? Should this be part of one of the studies?

# data[].attributes.body
We need to look at each location through three lenses - climate, thermal, and extreme heat wave.

Noting that exposures will differ by location and that climate and exposures will change over time - we need to be future-ready with models/interventions that can account for this change.

# data[].attributes.body
The studies should cover Rural, remote and urban sites, though Adam said from a modelling standpoint it is a big undertaking, very big in terms of engagement, and we will need to pare it down.

# data[].attributes.body
NSW heat study breakdowns worked well (John)

# data[].attributes.body
Ying proposed high risk poulations in different locations - taking a case studies approach to understand the uniqueness of a population in different areas

Benefit of the case study approach is the transferability.

# data[].attributes.body
In any given community the social and psychological factors are likely to change but the biological factors are more likely to remain the same over time

# data[].attributes.body
Is it useful to choose rural communities in particular climate zones?

# data[].attributes.body
Just considering social factors alone - such as stigma, income inequality, etc the system maps could be enormous.....

# data[].attributes.body
The system maps will be very different by the population - how do we make groups distinct in a logical way?

# data[].attributes.body
First nations people will tell us very different things then people with severe mental illness

# data[].attributes.body
risk assessment, beliefs, cultural beliefs, tendency to cope - form under psych mechanisms

# data[].attributes.body
Adam spoke about a Matrix - down one side you have the groups, and across the top, you have the mechanisms. Each cell represents indficival submpaps - use a link analysis to combine B, P and S together.

This will make it possible to see if any possible interventions might work across populations, no matter who they are or where they live, and also make it obvious where tailored solutions will be needed.

WE may need substudies under each population by climate zone/location?

# data[].attributes.body
A national study is needed to make it clear why we are doing this in a high income country. It is important to look at different climate zones - marks go to research location so this would be a rationale.

# data[].attributes.body
We need to look at mental wellbeing rather than mental disorders - MH and wellbeing is a more inclusive term

# data[].attributes.body
Maybe we need to target for general populations across different climate zones and demographics

# data[].attributes.body
need to be more representative and inclusive