BackgroundPrice and affordability of foods are important determinants of health. Targeted food pricing policies may help improve population diets. However, methods producing comparable data to inform relevant policy decisions are lacking in Australia and globally. The objective was to develop and pilot standardised methods to assess the price, relative price and affordability of healthy (recommended) and current (unhealthy) diets and test impacts of a potential policy change.MethodsMethods followed the optimal approach proposed by INFORMAS using recent Australian dietary intake data and guidelines. Draft healthy and current (unhealthy) diet baskets were developed for five household structures. Food prices were collected in stores in a high and low SES location in Brisbane, Australia. Diet prices were calculated and compared with household incomes, and with potential changes to the Australian Taxation System. Wilcoxen-signed rank tests were used to compare differences in price.ResultsThe draft tools and protocols were deemed acceptable at household level, but methods could be refined. All households spend more on current (unhealthy) diets than required to purchase healthy (recommended) diets, with the majority (53–64 %) of the food budget being spent on ‘discretionary’ choices, including take-away foods and alcohol. A healthy diet presently costs between 20–31 % of disposable income of low income households, but would become unaffordable for these families under proposed changes to expand the GST to apply to all foods in Australia.ConclusionsResults confirmed that diet pricing methods providing meaningful, comparable data to inform potential fiscal and health policy actions can be developed, but draft tools should be refined. Results suggest that healthy diets can be more affordable than current (unhealthy) diets in Australia, but other factors may be as important as price in determining food choices.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-2996-y) contains supplementary material, which is available to authorized users.

BackgroundThis paper describes the rationale, development and final protocol of the Healthy Diets Australian Standardised Affordability and Pricing (ASAP) method which aims to assess, compare and monitor the price, price differential and affordability of healthy (recommended) and current (unhealthy) diets in Australia. The protocol is consistent with the International Network for Food and Obesity / non-communicable Diseases Research, Monitoring and Action Support’s (INFORMAS) optimal approach to monitor food price and affordability globally.MethodsThe Healthy Diets ASAP protocol was developed based on literature review, drafting, piloting and revising, with key stakeholder consultation at all stages, including at a national forum.DiscussionThe protocol was developed in five parts. Firstly, for the healthy (recommended) and current (unhealthy) diet pricing tools; secondly for calculation of median and low-income household incomes; thirdly for store location and sampling; fourthly for price data collection, and; finally for analysis and reporting. The Healthy Diets ASAP protocol constitutes a standardised approach to assess diet price and affordability to inform development of nutrition policy actions to reduce rates of diet-related chronic disease in Australia. It demonstrates application of the INFORMAS optimum food price and affordability methods at country level. Its wide application would enhance monitoring and utility of dietary price and affordability data from a health perspective in Australia. The protocol could be adapted in other countries to monitor the price, price differential and affordability of current and healthy diets.Electronic supplementary materialThe online version of this article (10.1186/s12937-018-0396-0) contains supplementary material, which is available to authorized users.

This paper presents an analysis of the relationship between land surface temperatures (LST) and screen‐level air temperatures (T2m) using in situ observations from 19 Atmospheric Radiation Measurement (ARM) deployments located in a range of geographical regimes. The diurnal cycle is resolved using 1 min observations: a particular focus of the study is on the relationship between daily extremes of LST (LSTmax, LSTmin) and T2m (Tmax, Tmin). Temperature differences are analyzed with respect to cloud, wind speed, and snow cover. Under cloud‐free, low wind speed conditions, daytime LST is often several degrees Celsius (°C) higher than T2m at low‐to‐middle latitudes and at high latitudes during the summer months. In contrast, LST and T2m are often close (e.g., within 2°C) under cloudy and/or moderate‐to‐high wind speed conditions or when solar insolation is low or absent. LSTmin and Tmin are generally well correlated (r > 0.8, often r > 0.9), while seasonal correlations between LSTmax and Tmax are weaker (r > 0.6, often r > 0.8). At high latitudes, LST and T2m are well coupled in spring/autumn/winter; the relationship between LST and T2m tends to weaken with decreasing latitude. The timing of daily extremes is also investigated and it is found that LSTmin and Tmin typically occur close to sunrise, with Tmin occurring slightly after LSTmin. LSTmax occurs close to solar noon, with Tmax typically occurring 1–3 hours later. This study will inform temperature data users on differences between LST and T2m and aid development of methods to estimate T2m using satellite LSTs.

The relationship between satellite land surface temperature (LST) and ground‐based observations of 2 m air temperature (T2m) is characterized in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along‐Track Scanning Radiometer series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST‐T2m differences are analyzed with respect to location, land cover, vegetation fraction, and elevation, all of which are found to be important influencing factors. LSTnight (~10 P.M. local solar time, clear‐sky only) is found to be closely coupled with minimum T2m (Tmin, all‐sky) and the two temperatures generally consistent to within ±5°C (global median LSTnight‐Tmin = 1.8°C, interquartile range = 3.8°C). The LSTday (~10 A.M. local solar time, clear‐sky only)‐maximum T2m (Tmax, all‐sky) variability is higher (global median LSTday‐Tmax = −0.1°C, interquartile range = 8.1°C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m—which are completely independent data sets—compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations.