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The Australian Air National Environment Protection Measure (NEPM) is about protecting human health

In Australia the Air Quality NEPMS https://www.legislation.gov.au/Details/C2004H03935 are a measure "to be implemented by the laws and other arrangements participating jurisdictions consider necessary". Performance against the Air NEPM is assessed at compliance stations located at sites representative of air quality likely to be experienced by the general population. Australia has had national standards and goals for ambient air quality since 1998 (althought the harmful fine particulates (PM2.5) was added in the 2016 revisions to the Air NEPM, to be reported on annually from June 2018). The Air NEPM mandates a consistent approach to air quality monitoring, which has been applied by all states and territories, but — recognising the different legislative arrangements in each jurisdiction — does not dictate the means to be applied to achieve the goals. Performance against the standards and goals is published annually.

In a recent unsuccessful grant application of mine, a reviewer commented that I had a lack of understanding of the process of the development of the standards and asserted:

"1) the criteria are not health-based as such, and the regulatory
policy decisions are not all about minimising health impact"


"2) the NEPM for ambient air quality only provides for ambient air
quality that allows for the "adequate" protection of human health
and well-being"

This reviewer makes the assertion that "adequate" protection of health is not the same as "minimising health impact". I guess the argument is that an "adequate" minimum level of health impact should consider a balance between the costs of expenditure on continued reductions in emissions against the benefits to health.

So I think that the criteria ARE health-based, and that the NEPM IS about minimising human health impacts (with the caveat that this is in the context of an economic cost-benefit assessment). For example in the US Colorado continually strives to reduce air pollutant emissions in ways that ensure public health and environmental protections, while maintaining a vibrant economy.

Such a cost-benefit analysis was included in my grant application, and I argue that this is vital to equip state Environmental Protection Authorities (EPA) with metrics to inform interventions. Such interventions are needed as Australian governments try to achieve the National Clean Air Agreement (made between the Commonwealth and each state and territory jurisdictions) which aims to implement strengthened laws that move to even tighter standards in 2025.

Posted in  air pollution policy relevant research

Filling empty cells in columns with R

Happy new year!

I have had a bit of a break and caught up with some of the older blog posts I did not find time for earlier in 2017.

This one caught my eye: http://varianceexplained.org/r/start-blog/ especially the line 'When you’ve given the same advice 3 times, write a blog post'. So I thought I'd write a blog post about a bit of R code advice. (Disclaimer: This wasn't originally my work, but I used this advice more than 3 times, so am sharing here. This is a function I got from my colleague Phil TnT).

In some cases I get data in a table that simplifies the presentation by only labelling one row in a set of rows like this:

   person        fruit    suburb something
      Tom      oranges   Scullin       3.0
                apples                 6.0
                 pears                 9.0
              tim tams                 2.0
 Gertrude       durian Charnwood       3.7
          dragon fruit                 7.0
               lychees                 4.9
             pineapple               100.9
                apples                98.0
Pennelope      cashews   Higgins       2.0
             beer nuts                 5.6
              Pringles                 4.0

To use this like tidy data we need to fill these intervening cells.

fill.col <- function(x, col.name) { s <- which(!x[[col.name]] == "") item <- x[[col.name]][s] hold <- vector('list', length(item))
for(i in 1: length(hold)) hold[[i]] <- rep(item[i], ifelse(is.na(s[i+1]), dim(x)[1] + 1, s[i+1]) - s[i]) x[[col.name]] <- unlist(hold) x }

d <- fill.col(d, 'person') fill.col(d, 'suburb')

  person        fruit    suburb something

1 Tom oranges Scullin 3.0 2 Tom apples Scullin 6.0 3 Tom pears Scullin 9.0 4 Tom tim tams Scullin 2.0 5 Gertrude durian Charnwood 3.7 6 Gertrude dragon fruit Charnwood 7.0 7 Gertrude lychees Charnwood 4.9 8 Gertrude pineapple Charnwood 100.9 9 Gertrude apples Charnwood 98.0 10 Pennelope cashews Higgins 2.0 11 Pennelope beer nuts Higgins 5.6 12 Pennelope Pringles Higgins 4.0

Posted in  disentangle

My view of the research data analysis pipeline

I put these ideas down when I gave a talk for the Aust National Data Service (ANDS) training course for data librarians at universities.

  1. Finding out about data
  2. Getting data
  3. Putting data somewhere
  4. Doing stuff with data
  5. Finding out what has been done with data
  6. Sharing data with others

I suggest that the future needs actions that support each of these activities at the following levels:

  1. Individual researcher
  2. Research group
  3. Research centre
  4. Faculty/Institute
  5. University
  6. Multi-university collaborative groups (e.g. CRC or CRE)

The government and private sectors may have some similarities, but I don't know.

Posted in  swish

The impact of scale on associations between disadvantage and hospitalization for heart diseases

Disadvantage and disease hidden in plain sight: University of Canberra study

Disease mapping has been used as a modern tool for identifying risks and informing public health policy, but researchers at the University of Canberra say the real story of socio-economic disadvantage and disease is often hidden.

University of Canberra Health Research Institute (HRI) research shows that often the wrong geographical scale is being used for disease mapping which obscures the statistical associations of risks and health outcomes.

These findings are part of HRI’s Impact of scale of aggregation on associations of cardiovascular hospitalization and socio-economic disadvantage paper which has been published on 29 November 2017 in the journal PLOS One http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188161.

HRI’s data scientist and lead author of the report, Dr Ivan Hanigan, said that “in order to protect the anonymity of health data, government authorities prefer large scale mapping, which impacts the evidence they need for policy responses. It’s left them with little detail and a picture that doesn’t reflect reality,” Dr Hanigan said.

“The salt-and-pepper approach to Canberra’s public housing and high-density living, which means low socio-economic groups are spread out throughout the city’s suburbs, hides the disadvantage when studied at certain scales. Current disease mapping often shows a uniform level of health problems at suburb level or above, but by drilling it down to a smaller, neighbourhood level you begin to see a very different story," Dr Hanigan said.

He said that, for example, it is globally well known that socio-economic disadvantage is associated with more heart disease problems.

“Being able to peer down to street-level maps of disadvantage can reveal these pockets of very high risk, right in the middle of some of our more affluent suburbs.

“Health data administrators are restricted by certain interpretations of the privacy laws and this has unintentionally led to the suppression of data access by researchers. Our ability to make adequate disease maps and do correlation analysis is then hampered,” Dr Hanigan said.

“A lot of money is being spent on data collection and this needs to be better used to improve public health, especially risk identification and prevention. There is a trade-off between the public health benefits of geographical research on health versus the protection of individual privacy.”

The study examined low scale statistical areas and compared them with larger scale areas to uncover the differences in risk factors and health problems. Dr Hanigan said they found that rates of heart attack hospitalisation had distinct peaks and troughs across most suburbs when using low scale analysis.

“When we examined the same areas at larger scale, we found those peaks and troughs largely disappeared becoming far smoother across these suburbs. In some cases, these suburb-level results were four times lower than the rates seen in low scale analysis.

“It’s clear that the scale of analysis can change the understanding of geographical patterns of risk factors and diseases. This needs to be taken into account in planning future health data analysis, and when health authorities and the government review the evidence and work on population health policies,” Dr Hanigan said.

The report is co-authored by University of Canbera Professor of Public Health Tom Cochrane and HRI Director Professor Rachel Davey.

Posted in  swish

New paper on neighbourhood level air pollution for health research

This post is to announce an recent paper we got published:

Blending multiple nitrogen dioxide data sources for neighborhood estimates of long-term exposure for health research. Ivan Charles Hanigan, Grant J Williamson, Luke David Knibbs, Joshua Horsley, Margaret Rolfe, Martin Cope, Adrian Barnett, Christine Cowie, Jane S Heyworth, Marc L Serre, Bin Jalaludin, Geoffrey G Morgan. 2017, Environmental Science & Technology, http://dx.doi.org/10.1021/acs.est.7b03035

Exposure to nitrogen dioxide (NO2) pollution has been associated with a range of adverse health outcomes for both the respiratory and cardiovascular systems. This pollutant is primarily emitted by traffic and can reach very high levels next to roads, and diminish quickly away from the source. Spatial models of pollution concentrations are often used to estimate exposure levels that are then fed into models that estimate health impacts. However, these estimates can be imprecise due to difficulty modelling spatial patterns at the resolution of neighbourhoods (e.g. a scale of tens of metres) rather than at a coarse scale (around several kilometres). This is especially challenging at low concentrations, such as the level found in Sydney Australia. The Sydney region has globally low levels of air pollution compared with similar economically developed cities. Rome for example is a similar size yet mean NO2 was three times higher than the average in Sydney.

The objective of our research was to derive improved estimates of neighbourhood level pollutant concentrations for health studies by blending air pollutant measurements with modelled predictions using the Bayesian statistical philosophy. The improved estimates of exposure will theoretically reduce bias when used in second stage analyses of the impacts on health. This improved evidence will guide decision makers in the delicate balance between the costs of reducing air pollution emissions, while minimising health impacts.

In our paper that has just been accepted for publication in the journal Environmental Science & Technology we implemented a high-tech method called the Bayesian Maximum Entropy (BME) model to blend data based on our prior knowledge of the probabilities and uncertainty surrounding the information sources. We brought together all the different NO2 data from measuring stations (monitors), chemical transport models (physical models that mimic the dispersion of emissions and weather patterns), and statistical ‘land use regression’ models (which incorporated satellite-based data) to estimate neighbourhood level annual average NO2 concentrations in Sydney. Our validation assessment using independent data from a separate set of samples showed an improvement compared to either the land use regression and chemical transport model used alone.

How low should we go? Is there a ‘safe’ low threshold of air pollution for Australians?

Future outputs of our work will seek to enable the policy and management communities to develop further improvements to air pollution maps and to explore health cost-benefit estimates under various emission reduction scenarios. For example, the Australian National Environment Protection Council decides on the National Environment Protection Measures (NEPMs) which have the goal of achieving a safe threshold of exposure in our cities. The results of our research will inform these stakeholders as they revise the regulations, and try to achieve the National Clean Air Agreement (made between the Commonwealth and each state and territory jurisdictions) which aims to implement strengthened laws that move to even tighter standards on air pollution emissions by 2025.

Key talking points:

  • Air pollution health impacts are well known from studies in high concentration cities (e.g. Rome) but there is a lack of knowledge about how low we need to go to minimise health impacts.
  • Sydney has globally low levels of NO2 (a traffic related air pollutant) and this makes Australia one of the best places in the world to study this low end of the exposure spectrum.
  • Our study produced an air pollution map with the best validation statistics for NO2 made so far for Sydney, at a scale of hundreds of metres.
  • These more precise exposure estimates will produce better knowledge about the health impacts.
  • With this evidence we can make better choices on regulatory interventions that seek to maximise the health benefits of reducing air pollution emissions while also delivering the lifestyle and economic prosperity afforded by burning fossil fuel for energy.

Media release

A breath of fresh air – new pollution research from Australia

Calculating whether traffic-related air pollution exposure can make us sick even at low levels has stumped experts around the world, but new research from a collaboration among eight universities and the CSIRO is now filling in the blanks. Nitrogen dioxide (NO2) is a pollutant mostly emitted by traffic and has been associated with respiratory and cardiovascular health problems.

The research, Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research has been published in the journal Environmental Science and Technology.

Lead author Dr Ivan Hanigan from the University of Canberra’s Health Research Institute and the Centre for air quality and health Research and evaluation (CAR) based at University of Sydney says past studies of NO2 pollution have focused on major cities around the world, mostly with severe problems.

“We know about the health impacts because of research which examined cities like Rome, which have very high concentrations of pollution,” Dr Hanigan said. “While, Sydney is about the same size as Rome; the Italian capital has three times higher levels of NO­2 pollution. It is still a big unknown if there might be a safe lower threshold where health impacts are minimal. If there is, then emission reduction policies can use this as a target, but if not then continual pollution reduction measures may be justified. Sydney is therefore one of the best places in the world to study this, and we can help answer this globally significant question.”

“Using the very precise air quality monitors installed around Sydney, along with sophisticated modelling by my collaborators, we are gaining insights that other studies have missed about the lower levels of NO2 exposure. We’ve been able to produce the best maps so far of air pollution for Sydney, and the scale is down to a hundred metres or so.”

Previous analysis used scales of several kilometres, Dr Hanigan says his new maps are much closer to the level of detail needed to accurately determine health impacts and plan for the future. “I expect health policy experts, infrastructure planners and even environmental managers will be keenly interested in this analysis,” he said. “This work can help to develop improvements to Australia’s air pollution regulations and lead to better health cost-benefit estimates for emission reduction scenarios.” “With this evidence we can make better choices on interventions to maximise the health benefits of reducing air pollution emissions while also delivering the lifestyle and economic prosperity afforded by burning fossil fuels for energy.”

The work is a collaboration between Dr Hanigan and colleagues from around Australia and the United States, including from University of Tasmania, University of Queensland, University of Sydney, CSIRO, Queensland University of Technology, University of New South Wales, University of Western Australia and the University of North Carolina.

Posted in  swish