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Introduction

Road traffic noise pollution increases risk of mortality from ischemic heart disease (IHD). Noise pollution is highly localized, so high resolution mapping of the exposure is a key concern. For health impact assessments the data on exposure and health outcomes would optimally both be available at small-area level. However, in Australia IHD deaths data are only easily accessed at low spatial resolution, aggregated to local government areas (with ~130,000 people). Our method utilizes a high spatial resolution noise model for producing health risk maps at the mesh-block level (MB, ~75 people), and downscaling health data from low spatial resolution public data.

Methods

We used a road traffic noise model for Melbourne, Australia from 2011 based on spatial predictions at a reference height of 1.8 metres above ground level to estimate population exposures at the MB level. We used the non-linear exposure–response function for traffic noise and IHD as recommended by the World Health Organization to calculate excess fractions of deaths for MBs. LGA-level IHD rates were downscaled to MBs using publicly available data to estimate excess risks.

We estimated excess fractions of deaths for meshblocks by first calculating an odds ratio (OR) of IHD deaths for every meshblock by first inserting their average noise levels into the nonlinear polynomial recommended for traffic noise and IHD by the World Health Organization (WHO 2012) using the following formula:

For $L_{day,16h}$ >= 55dB,

$OR(L_{day,16h}) = 1.63 - 0.000613 \times (L_{day,16h})^2 + 0.00000736 \times (L_{day,16h})^3$

Else $OR(L_{day,16h}) = 1$

where

$L_{day,16h}$ is the noise levels (0700-2300 hours).

Our approach then uses the meshblock level populations to calculate an excess risk at the small area level. In this approach the excess risk is computed as:

$ER_{i} = (OR(L_{day,16h,i}) - 1) \times P_{i} \times \hat{I_{k}}$

Where $ER_{i}$ is the excess risk of deaths in meshblock i, $L_{day,16h,i}$ represents the average exposure level in meshblock i. $OR(L_{day,16h,i})$ is the odds ratio at that noise level. This is estimated by inserting the meshblock estimated $L_{day,16h}$ into the polynomial function recommended by WHO (2012). $P_{i}$ is the population of meshblock i whilst the baseline mortality incidence rate is $\hat{I_{k}}$ . To estimate $\hat{I_{k}}$ we used the regional average annual IHD incidence mortality rate $I_{k}$ (for each region e.g. in $LGA_{k}$ ) and divide this by the population-weighted average OR of all meshblocks within region k:

$\hat{I_{k}} = \frac{I_{k}}{\bar{OR_{k}}}$

where $\bar{OR_{k}}$ represents the population-weighted average OR:

$\bar{OR_{k}} = \frac{\sum_{i=1}^{n}P_{i} \times OR(L_{day,16h,i})}{\sum_{i=1}^{n}P_{i}}$

This aims to represent the hypothetical underlying cause- specific mortality rate for region k and aproximate the health outcomes that would be observed in a counterfactual ‘unexposed’ population. Multiplying this by the population and OR will then yield the excess risk, given the observed level of exposure in the meshblocks.