Skip to main content

Estimating cardiovascular health gains from eradicating indoor cold in Australia



Exposure to cold indoor temperature (< 18 degrees Celsius) increases cardiovascular disease (CVD) risk and has been identified by the WHO as a source of unhealthy housing. While warming homes has the potential to reduce CVD risk, the reduction in disease burden is not known. We simulated the population health gains from reduced CVD burden if the temperature in all Australian cold homes was permanently raised from their assumed average temperature of 16 degrees Celsius to 20 degrees Celsius.


The health effect of eradicating cold housing through reductions in CVD was simulated using proportional multistate lifetable model. The model sourced CVD burden and epidemiological data from Australian and Global Burden of Disease studies. The prevalence of cold housing in Australia was estimated from the Australian Housing Conditions Survey. The effect of cold indoor temperature on blood pressure (and in turn stroke and coronary heart disease) was estimated from published research.


Eradication of exposure to indoor cold could achieve a gain of undiscounted one and a half weeks of additional health life per person alive in 2016 (base-year) in cold housing through CVD alone. This equates to 0.447 (uncertainty interval: 0.064, 1.34; 3% discount rate) HALYs per 1,000 persons over remainder of their lives through CVD reduction. Eight percent of the total health gains are achievable between 2016 and 2035. Although seemingly modest, the gains outperform currently recommended CVD interventions including persistent dietary advice for adults 5–9% 5 yr CVD risk (0.017 per 1000 people, UI: 0.01, 0.027) and persistent lifestyle program for adults 5–9% 5 yr CVD risk (0.024, UI: 0.01, 0.027).


Cardiovascular health gains alone achievable through eradication of cold housing are comparable with real-life lifestyle and dietary interventions. The potential health gains are even greater given cold housing eradication will also improve respiratory and mental health in addition to cardiovascular disease.

Peer Review reports


The World Health Organisation (WHO) Housing and Health guidelines make a strong recommendation that indoor temperatures should be above 18 degrees Celsius to protect residents from the harmful health effects of cold indoor environments [1]. Recent work suggests that many homes, even in the relatively mild or temperate climates of Australia, do not meet this standard [2, 3]. Cold indoor temperature is associated with elevated blood pressure and randomised controlled trials (RCTs) have shown that interventions increasing indoor temperatures reduce systolic blood pressure [4]. Observational studies, from the UK and 16 middle to high income countries also report consistent findings and confirm an association between low temperature and increased systolic blood pressure [5,6,7]. This means that intervening to improve the indoor temperature control should reduce cardiovascular disease incidence and prevalence.

Reducing exposure to unhealthy indoor temperature is achievable through interventions such as insulation, weatherization (draft-sealing), efficient heating/cooling appliances, cheaper fuel alternatives (solar panels), and subsidies and market-based initiatives [1]. Generating standardised estimates of the health gains from housing-focussed interventions such as these allows for direct comparison of their utility with other more commonly used public health interventions (e.g. tobacco control, pharmaceuticals). This paper aims to estimate the total potential cardiovascular health gains from a hypothetical ‘magic wand’ intervention that lifts all cold housing from an average indoor temperature during waking hours of 16 to 20 degrees Celsius in the six coldest months of the year.

Health gains from prevention occur many years into the future, requiring simulation modelling that quantifies health gain using summary measures such as health adjusted life years (HALYs). By using a measure such as a HALY, one can also compare the health impacts of otherwise disparate interventions [8, 9].

We used simulation modelling to quantify the health gain through a reduction in cardiovascular disease that could be achieved if exposure to indoor cold was hypothetically eliminated in three states of Australia [10]. Using an existing league table of preventive interventions for Australia and New Zealand [11], we examine the relative population health gains of hypothetical eradication of indoor cold with other actual prevention interventions. There has been limited simulation of future health benefits of reducing cold housing. For example, a NZ cost benefit analyses examined retrofitting insulation, but did not look at cold housing as its own entity, the total contribution (or ‘envelope’) of health gain from eradicating cold housing and did not use a structured disease/condition approach such as that used in burden of disease studies. Our paper applies a disease or condition-based approach, focusing on cardiovascular disease for which the evidence base is strongest (through changes in blood pressure) [12].


Intervention conceptualisation

We based our estimate of the effect of cold housing on cardiovascular disease burden on two sources of evidence. First, the negative effect of cold indoor temperature on blood pressure [4, 13]. Second, the effect of heightened blood pressure on cardiovascular disease risk (specifically ischemic heart disease and ischemic and haemorrhagic stroke) [14] (see supplementary file 1). We assume no time lag in response to temperature change.

Model overview

We used a proportional multistate lifetable (pMSLT) simulation model to estimate health gains achievable through interventions on exposure to cold indoor temperature within a specified population [10]. We simulated the 2016 Australian population through to 2126 (maximum potential lifetime of the living cohort in 2016 being set to 110 year of age) in annual time steps with transition probabilities for all-cause mortality, and incidence and case fatality rates of cardiovascular diseases in subsidiary lifetables. This model was applied once for Business as Usual (BAU), based on the current prevalence of cold housing (prevalence assumed unchanging into the future), and then for the intervention (hypothetical elimination of cold housing) by altering the CVD incidence rates given the shift in population average blood pressure. The two components of the model are linked by population impact fractions (PIFs), that summarise the proportion reduction in diseases associated with change in indoor temperature exposure for cohorts defined by age and sex.

We modelled relevant cardiovascular diseases (heart disease and stroke) as independent of each other in parallel lifetables. Estimated changes in disease-specific morbidity and mortality rates were summed together in an overall lifetable at each annual cycle. The ‘health adjustment’ to convert life years gained to HALYs gained was achieved by subtracting off each life year gained the proportion ‘lost’ due to morbidity, using years of life lived with disability (YLDs) from burden of disease studies divided by the population in each sex by age-group as a measure of proportionate morbidity.

Input parameters

We have presented data inputs with their sources in Table 1.

Table 1 Key data Inputs

Base year and BAU parameters

Estimates of the number of people exposed to inadequate indoor temperature by age and sex was obtained from the Australian Housing Conditions Dataset (AHCD) survey [15, 16] and assumed to be constant into the future The AHCD survey asked participants ‘Are you able to warm your house during winters’? This question was derived from the English Housing Survey [17]. Those responding ‘No’ were considered as exposed to cold housing. We stratified the prevalence of exposure by age.

Data on the age and sex distribution of the Australian population was obtained from the Australian Population Census 2016. Disease-specific incidence, prevalence and case fatality rates were obtained from IHME Global Burden of Disease for Australia. We checked for coherence between epidemiological parameters derived from this array of data sources (i.e., incidence, case fatality and prevalence) for each disease by examining plotted trends and further processed them through epidemiological tool DISMOD II to use as data inputs [18].

Intervention specification

Our intervention was to increase the average temperature in cold homes from 16 to 20 degrees Celsius. The relative risk of high blood pressure from exposure to indoor cold was estimated from a randomised controlled trial [4]. This study reported a difference of 5.8 mmHg (95% CI (-9.3, -2.4)) between an intervention group, who occupied a room heated to 22 °C, and the control group who occupied a room kept stable at 12 degrees Celsius. Both groups were given sufficient clothing and bedclothes to be warm. Both groups were exposed to the respective interventions for 11 h during the night and blood pressure was measured in 15 min intervals at night time and in the morning after rising. The 5.8 mmHg difference was estimated when awake (as there was no difference when asleep due to compensation using more bedding in the experimentally colder group). This effect estimate was converted into an absolute change achievable in systolic blood pressure per 1 degree Celsius temperature increase for the simulation model.

Data on the prevalence of people experiencing indoor cold was obtained from the AHCD that representatively sampled housing from 4,500 households' condition across three Australian States (Victoria, New South Wales and South Australia) [15]. The measure comprised a self-reported assessment of ability to keep warm indoors at home during cold winter weather. The average outdoor temperature for Victoria in 2016 was 15.0 degrees Celsius, for New South Wales was 18.4 degrees Celsius and for South Australia was 20.2 degrees Celsius [19]. To account for seasonal variations in temperature and time spent outside homes we assumed that our simulated population is exposed to indoor cold ranging from half (the awake 2/3rds of the day for the colder half of the year for those people at home most of the day and accounting for inadequate bedding) to one-sixth (same logic, but for people working or out of the home for approximately half of waking hours) of the time. A beta distribution for uncertainty in the intervention effect estimate was applied to account for variability in this exposure time (beta distribution Alpha 1: 10.5, Alpha 2: 22, median 31.9%, 2.5th percentile 17.6%, 97.5th percentile 49.0%). To simulate the effect of indoor cold on blood pressure we estimated the difference between cold houses (average temperature of 16 degrees Celsius) and adequately warmed houses (average temperature of 20 degrees Celsius). Assuming a short latency of cold to blood pressure, the change in average blood pressure across the year was calculated within each iteration of the simulation as: this proportion of the year exposed ranging from 1/6 to 1/2; multiplied by the difference in temperature (4 degrees, by lifting average cold housing from 16 to 20 degrees); multiplied by the RCT-based estimate of change in systolic blood pressure per 1 degree Celsius.

Relative risks for the causal relationship between systolic blood pressure and ischemic heart disease, ischemic stroke and haemorrhagic stroke were obtained from the Global Burden of Disease (GBD) study [20] (see supplementary Table 5). The intervention was simulated on the 2016 population for the same jurisdictions covered by the Australian Housing Conditions datasets (i.e., Victoria, New South Wales and South Australia). The intervention (eradication of cold housing) was modelled as lifelong. BAU exposure to cold housing in the future was based on exposures in 2016 for cohorts defined by age-group and sex.


Probabilistic uncertainty analyses using a Monte Carlo simulation method was conducted on input parameters (see Table 1) [10]. More generous uncertainty was applied where we were less confident on input parameters (for example the subjective measurement of indoor cold in Australia). Simulations were run using the ERSATZ add in to Excel with 2000 iterations used to generate 95% uncertainty intervals (UI) for the HALY estimates.

Our outputs included HALYs gained by indoor cold eradication within a life-time and ten- and -twenty-year time horizons. Outcomes were reported both with 0% and 3% discount rates and also per 1000 persons alive in 2016.

We compared the estimated HALYs gained from cold housing eradication with other CVD-related interventions described in interactive league tables [11].


Nearly 6% of the population were estimated to experience cold housing. Younger people reported a higher prevalence of indoor cold exposure (9% under the age of 30 years) compared to older ages (3.4% over the age of 75 years) (Table 2).

Table 2 Input data for starting cohort in 2016

We estimated that cold eradication generated an additional 1.64 (95% uncertainty interval (UI): 0.232, 4.90) undiscounted HALYs per 1000 persons compared with BAU across the lifespan and 0.447 (95% UI: 0.064, 1.34) with a 3 per cent discount applied (Table 3). From the perspective of the ‘target population’ (i.e. the 5.74% in cold housing) this equates to 29 undiscounted HALYs per 1000 people (i.e. 1.64/0.0574), or an average (across ages) of one and a half weeks of additional health life per person in cold housing through CVD alone. Eight per cent of this gain is achieved within the first 20 years: we estimated 0.135 (UI: 0.019, 0.442) undiscounted HALYs per 1000 persons between 2016 and 2036. We note that at this point, most cohort members have not reached the ages of high CVD risk.

Table 3 Future HALYs per people under BAU and HALYs gained by eradication of unhealthy indoor temperature

In Fig. 1 we have compared the quantified discounted HALYs gained per 1000 persons with actual CVD interventions in the Australia New Zealand Health Intervention League Table. The hypothetical eradication of indoor cold (HALYs gained during lifetime: 0.447 (UI: 0.064, 1.34) outperformed many CVD interventions with a lifetime horizon for accrual of health gains. For example, a persistent dietary advice intervention for adults with 5–9% 5 yr CVD risk (0.017, UI: 0.01, 0.027), a persistent lifestyle program taken up by adults with 5–9% 5 yr CVD risk (0.024, UI: 0.01, 0.027) and persistent Community Heart Health Program (0.141, UI: 0.071, 0.221).

Fig. 1
figure 1

Ranking of eradicating exposure to indoor cold compared to actual preventive interventions. Note: All interventions in adults aged 35–84 years old with lifetime duration unless otherwise stated in the footnotes. Standard dose of statin for adults with 5–9% five-year risk of CVD through primary care practice. Mandataory ‘Tick’ program to reduce salt in bread, margarine and breakfast cereal by 10.65 mgNa day for men and 7.3 mgNa for women with at least 5% five-year risk of CVD. Standard dose of diuretic for adults with 5–9% five-year risk of CVD through primary care practice. Standard dose of calcium channel blocker for adults with 5–9% five-year risk of CVD through primary care practice. Standard dose of ACE inhibitor for adults with 10–14% five-year of CVD risk through primary care practice. Standard dose of beta-blocker for adults with 5–9% five-year CVD risk through primary care practice. Standard dose of aspirin for adults with 5–9% five-year risk of CVD through a combination of primary care practice prescription (50%) and over the counter (50%) use. Community heart health program to promote dietary change, physical activity and smoking cessation for the whole population. Tobacco, Exercise and Diet Messages (TEXT ME) providing advice, motivation, information and support to improve health related behaviours plus usual care, for individuals with documented CHD over a lifetime. Lifestyle program providing professional advice in diet and physical activity for adults with 5–9% five-year risk of CVD. Dietary advice for adults with 5–9% five-year CVD risk. Phytosterol-enriched margarine (92 g per kg of margarine) for adults with 5–9% five-year risk of CVD [31]


This is the first study that quantifies cardiovascular disease related health gains from raising the temperature in Australian cold housing by 4 degrees Celsius. We estimate these health gains are on par with currently recommended lifestyle interventions to reduce CVD risk including dietary advice and lifestyle and community targeted programs. Further, we note that this is only part of the intervention’s total health benefit with gains from reductions in respiratory and mental ill-health also likely to follow interventions that warm the indoor temperature of cold homes. Our study should be viewed as a departure point for estimating actual interventions (e.g., retrofitting houses) and our findings support and extend previous research on the cost effectiveness of installing insulation, for example, to improve occupant’s health in New Zealand [12].

Our study has several limitations that should be acknowledged. First, there is ‘structural’ or mechanistic uncertainty in how changes in indoor temperature flow to changes in blood pressure and then onto CVD incidence. We have used the effect size of cold housing onto blood pressure from a well-conducted randomised controlled trial that examined a short-term effect of temperature change on blood pressure. However, this is essentially the effect of temperature on labile hypertension (immediate fluctuations in blood pressure) rather than longer-term average blood pressure. By using this effect size in our simulation models, we are assuming that the cumulative impact of labile hypertension has the same impact as a constant but lesser increased in blood pressure (i.e. a cold housing impact of raising systolic blood pressure by 2 mm Hg during waking hours whilst in your house in winter is assumed to be similar to a 0.33 (1/6th) to 1 (1/2) mmHg increase in blood pressure over the whole year). There is some literature suggesting poor cardiovascular effects of labile hypertension [21, 22]. Second, a simulation study forces one to pull together all the data inputs necessary to quantify impacts, and often discloses data weaknesses. In conducting this study, we found that the underlying evidence on the quantitative association of indoor temperature with health outcomes is lacking (e.g. we could not find robust estimates of cold housing impacts on respiratory disease incidence or severity, restricting us to a focus on blood pressure as a mediating factor to CVD). Third, as highlighted at the beginning of this discussion, our estimates for health gains are conservative because they only include cardiovascular disease related health impacts. Future modelling may quantify population health gains achievable through reduction of morbidity and mortality across a greater range of diseases as well as changes in disease-specific incidence rates. Fourth, there is no quality objective data (to our knowledge) on the exact proportion of houses in Australia that have an indoor air temperature in living areas less than the recommended threshold of 18 degrees Celsius (and for what duration of the year, and other aspects of exposure characterisation). Instead, we had to use subjective measures of indoor temperature from the AHCD and ‘crosswalk’ this to being equivalent to the proportion of houses that are cold. Finally, we assumed no time lags between indoor temperature change and change in incidence in cardiovascular diseases as blood pressure responds immediately to temperature, and CVD rates change quickly (within a couple of years) to changes in blood pressure.

Inadequate housing is strongly patterned by socioeconomic position [23,24,25,26], and provides a domain for intervening to both improve health and to reduce inequalities in health. Next steps in research in this field include equity-informed estimation of cost effectiveness of remedying cold housing through actual interventions, from both a health system and a wider societal perspective given that housing interventions also usually lead to other social impacts such as less energy consumption (see Chapman, Howden-Chapman [27] for an early example). Specific interventions to evaluate include insulation, weatherization (draft-sealing), efficient heating/cooling appliances, cheaper fuel alternatives (solar panels), and subsidy and market-based initiatives to achieve reduction of indoor cold. From the point of view of CVD-reduction specifically, we note that existing interventions mainly involve behavioural or pharmaceutical treatments. Through this modelling we were able to compare health gains due to the eradication of indoor cold with existing cardiovascular interventions and found that the benefits are on par, which creates opportunities to compare population health gains from housing interventions to more traditional medicalised or health behaviour interventions.


This simulation modelling extends the current knowledge on ill health effects of indoor cold [1], but also reveals profound limitations in current knowledge of cold housing’s impact on health. Existing interventional studies from New Zealand also point to cost-effectiveness and equitable health benefits of retrofitting insulation to reduce indoor cold [12]. Our study straddles comparative risk assessment and intervention modelling, to create a strong policy argument for addressing indoor temperature. Our finding that substantial prospective population health gains are achievable by eliminating indoor cold in Australia is an important first step in estimating which interventions are the most cost-effective to deliver this. Moreover, this may be as effective in preventing and treating cardiovascular disease as some more medically focussed current approaches.

Availability of data and materials

Data generated or analysed during this study are included in this published article and in its supplementary files. Unit record data from the Australian Housing Conditions Dataset is available from



Business as Usual


Cardiovascular disease


Global Burden of Disease


Health adjusted life years


Population impact fractions


Proportional multistate lifetable


Randomised controlled trials


Uncertainty intervals


World Health Organisation


Years of life lived with disability


  1. WHO. Housing and health guidelines. Geneva: World Health Organization; 2018. Report No.: CC BY-NC-SA 3.0 IGO.

    Google Scholar 

  2. Daniel L, Baker E, Beer A, Pham NTA. Cold housing: evidence, risk and vulnerability. Hous Stud. 2021;36(1):110–30.

    Article  Google Scholar 

  3. Daniel L, Baker E, Williamson T. Cold housing in mild-climate countries: a study of indoor environmental quality and comfort preferences in homes, Adelaide Australia. Build Environ. 2019;151:207.

    Article  Google Scholar 

  4. Saeki K, Obayashi K, Iwamoto J, Tanaka Y, Tanaka N, Takata S, et al. Influence of room heating on ambulatory blood pressure in winter: a randomised controlled study. J Epidemiol Community Health. 2013;67(6):484–90.

    Article  Google Scholar 

  5. Barnett AG, Sans S, Salomaa V, Kuulasmaa K, Dobson AJ, Project ftWM. The effect of temperature on systolic blood pressure. Blood Press Monit. 2007;12(3):195–203.

    Article  Google Scholar 

  6. Saeki K, Obayashi K, Iwamoto J, Tone N, Okamoto N, Tomioka K, et al. The relationship between indoor, outdoor and ambient temperatures and morning BP surges from inter-seasonally repeated measurements. J Hum Hypertens. 2014;28(8):482–8.

    Article  CAS  Google Scholar 

  7. Shiue I, Shiue M. Indoor temperature below 18 C accounts for 9% population attributable risk for high blood pressure in Scotland. Int J Cardiol. 2014;171(1):e1–2.

    Article  Google Scholar 

  8. Blakely T, Moss R, Collins J, Mizdrak A, Singh A, Carvalho N, et al. Proportional multistate lifetable modelling of preventive interventions: concepts, code and worked examples. Int J Epidemiol. 2020;49(5):16.

    Article  Google Scholar 

  9. Briggs AD, Wolstenholme J, Blakely T, Scarborough P. Choosing an epidemiological model structure for the economic evaluation of non-communicable disease public health interventions. Popul Health Metrics. 2016;14(1):17.

    Article  Google Scholar 

  10. Blakely T, Moss R, Collins J, Mizdrak A, Singh A, Carvalho N, et al. Proportional multistate lifetable modelling of preventive interventions: concepts, code and worked examples. Int J Epidemiol. 2020;49(5):1624–36.

    Article  Google Scholar 

  11. University of Otago UoM. ANZ-HILT: Australia and New Zealand Health Intervention League Table (Version 2.0). 2019. Available from:

  12. Howden-Chapman P, Crane J, Matheson A, Viggers H, Cunningham M, Blakely T, et al. Retrofitting houses with insulation to reduce health inequalities: aims and methods of a clustered, randomised community-based trial. Soc Sci Med. 2005;61(12):2600–10.

    Article  CAS  Google Scholar 

  13. Saeki K, Obayashi K, Iwamoto J, Tanaka Y, Tanaka N, Takata S, et al. Influence of room heating on ambulatory blood pressure in winter: a randomised controlled study. J Epidemiol Community Health. 2013;67(6):484–90.

    Article  Google Scholar 

  14. Stevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;354: i4098.

    Article  Google Scholar 

  15. Baker E, Daniel L, Bentley R, Pawson H, Stone W, Rajagopalan P. The Australian housing conditions dataset-technical report. 2018.

    Google Scholar 

  16. The Australian Housing Conditions Dataset. ADA Dataverse. 2019. Available from:

  17. Ministry of Housing Communities and Local Government. English Housing Survey Questionnaire Documentation. London, UK: Ministry of Housing, Communities and Local Government; 2013. Available from:

  18. Barendregt JJ, Van Oortmarssen GJ, Vos T, Murray CJ. A generic model for the assessment of disease epidemiology: the computational basis of DisMod II. Popul Health Metr. 2003;1(1):4.

    Article  Google Scholar 

  19. Australian Bureau of Meteorology. Australian climate variability & change - Time series graphs. Canberra: Australian Bureau of Meteorology; 2016. Available from: Updated c2021; cited 2021 Sept 16.

  20. Forouzanfar MH, Liu P, Roth GA, Ng M, Biryukov S, Marczak L, et al. Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015. JAMA. 2017;317(2):165–82.

    Article  Google Scholar 

  21. Sanidas E, Grassos C, Papadopoulos DP, Velliou M, Tsioufis K, Mantzourani M, et al. Labile hypertension: a new disease or a variability phenomenon? J Hum Hypertens. 2019;33(6):436–43.

    Article  Google Scholar 

  22. Mann SJ. The clinical spectrum of labile hypertension: a management dilemma. J Clin Hypertens. 2009;11(9):491–7.

    Article  Google Scholar 

  23. Baker E, Bentley R, Lester L, Beer A. Housing affordability and residential mobility as drivers of locational inequality. Appl Geogr. 2016;72:65–75.

    Article  Google Scholar 

  24. Bentley RJ, Pevalin D, Baker E, Mason K, Reeves A, Beer A. Housing affordability, tenure and mental health in Australia and the United Kingdom: a comparative panel analysis. Hous Stud. 2016;31(2):208–22.

    Article  Google Scholar 

  25. Kavanagh AM, Aitken Z, Baker E, LaMontagne AD, Milner A, Bentley R. Housing tenure and affordability and mental health following disability acquisition in adulthood. Soc Sci Med. 2016;151:225–32.

    Article  Google Scholar 

  26. Pevalin DJ, Reeves A, Baker E, Bentley R. The impact of persistent poor housing conditions on mental health: a longitudinal population-based study. Prev Med. 2017;105:304–10.

    Article  Google Scholar 

  27. Chapman R, Howden-Chapman P, Viggers H, O’Dea D, Kennedy M. Retrofitting houses with insulation: a cost-benefit analysis of a randomised community trial. J Epidemiol Community Health. 2009;63(4):271–7.

  28. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME). 2018. Available from:

  29. Barendregt JJ, Van Oortmarssen GJ, Vos T, Murray CJ. A generic model for the assessment of disease epidemiology: the computational basis of DisMod II. Popul Health Metrics. 2003;1(1):1–8.

    Article  Google Scholar 

  30. Australian Bureau of Statistics. National Health Survey: First results, 2017–18. 2018.

    Google Scholar 

  31. Vos T, Carter R, Barendregt J, Mihalopoulis C, Veerman L, Magnus A. Assessing cost-effectiveness in the prevention (Ace-Prevention): final report. Brisbane and Melbourne: University of Queensland and Deakin University; 2010. 2010 September 2010.

    Google Scholar 

Download references


We are grateful to Shiva Raj Mishra for his comments on our manuscript


Project funded by Affordable Housing Hallmark Initiative seed funding by University of Melbourne.

Author information

Authors and Affiliations



AS conceptualised the study, contributed to acquisition of data, analysis, interpretation of data and drafting of the manuscript. AM to analysis, interpretation of data and providing critical feedback on the manuscript. LD contributed to interpretation of data and providing critical feedback on the manuscript. TB contributed to analysis, interpretation of data and providing critical feedback on the manuscript. EB contributed to interpretation of data and providing critical feedback on the manuscript. LFA contributed to acquisition of data, interpretation of data and providing critical feedback on the manuscript RB contributed to conceptualisation of study, interpretation of data and drafting of the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Ankur Singh.

Ethics declarations

Ethics approval and consent to participate

Ethics approval for this project not sought as it uses publicly available aggregated data which poses minimal risk for participants.

Consent for publication

Not applicable.

Competing interests

None to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, A., Mizdrak, A., Daniel, L. et al. Estimating cardiovascular health gains from eradicating indoor cold in Australia. Environ Health 21, 54 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: