DOI: 10.1111/ajag.12134
Research Heat-health behaviours of older people in two Australian states Alana Hansen, Peng Bi and Dino Pisaniello
Introduction
Discipline of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
In many parts of Australia, including areas in the southeastern states of South Australia (SA) and Victoria, extended periods of hot weather are not uncommon in summer. Heatwaves in these states are often linked with marked increases in local morbidity and/or mortality [1,2] occurring as a result of health outcomes associated directly or indirectly with exposure to high ambient temperatures.
Monika Nitschke and Graeme Tucker Department for Health and Ageing, Adelaide, South Australia, Australia
Jonathan Newbury Discipline of Rural Health, University of Adelaide, Adelaide, South Australia, Australia
Alison Kitson School of Nursing, The University of Adelaide, Adelaide, South Australia, Australia
Eleonora Dal Grande and Jodie Avery Population Research and Outcomes Studies, Discipline of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
Ying Zhang School of Public Health, University of Sydney, Sydney, New South Wales, Australia
Liza Kelsall Health Intelligence Unit, Department of Health Victoria, Melbourne, Victoria, Australia
Aim: A major heatwave occurred in Australia in early 2009 with considerable and varied health impacts in South Australia (SA) and Victoria. The aim of this study was to investigate the heat-adaptive behaviours of older people in these states. Methods: A computer-assisted telephone survey of 1000 residents of SA and Victoria aged 65 years or older was conducted at the end of summer 2010–2011. Results: The majority of respondents reported undertaking heat-adaptive behaviours. In SA, there was a significantly higher proportion of households with air conditioning compared to Victoria, and a higher recall of heat-health messages. In both states, self-reported morbidity during heatwaves was higher in women, persons with poorer health and those with cardiovascular conditions. Conclusion: An increase in global temperatures in conjunction with an ageing population is a concern for public health. Our findings suggest acclimatisation to hot weather may influence behaviours and health outcomes in older people. Key words: acclimatisation, adaptive behaviour, aged, Australia, extreme heat. Correspondence to: Professor Peng Bi, Discipline of Public Health, University of Adelaide. Email: [email protected] Australasian Journal on Ageing, Vol 34 No 1 March 2015, E19–E25 © 2014 ACOTA
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Causal links between heat exposure and acute health effects in susceptible people often stem from the physiological strain involved in maintaining the body’s thermal balance. Behavioural modifications that help cool the body or minimise heat exposure therefore act as an important defence against the risk of heat-related or heat-exacerbated illnesses [3]. Persons who are physically unable to undertake certain adaptations will be at higher risk as will those who are unaware of preventive measures. Evidence shows that older persons are particularly susceptible to heat-related morbidity with ageing and poor health being contributing factors to vulnerability [4,5] Furthermore, older people will be particularly vulnerable to the health impacts of climate change [6]. With this knowledge, public health authorities need to consider evidence-based avenues to minimise present and future susceptibility in this group. In late January to early February 2009, SA and Victoria experienced an exceptional and unprecedented heatwave. In the state capitals of Adelaide (1.26 million population) and Melbourne (4.17 million population) [7], respectively, temperatures exceeded 45°C, and records were set for the number of consecutive days above 43°C. The consequent burden on health services was remarkable in both states. However, the health impacts on the older people in each state differed, with Victoria reporting high mortality, whereas in SA there was a stronger association with morbidity [8,9]. This highlights the need to assess how knowledge of heatwave warnings and adaptation impact on the risk of heat-related illnesses [10]. The aim of this study was to explore heat-health behaviours, attitudes and adaptive capacities of older people in SA and Victoria, to investigate potential differences and similarities between the states, and provide an evidence base for heathealth promotion initiatives.
Methods Study population The study populations resided in SA and Victoria, the latter having a generally cooler climate. The average mean E19
H a n s e n
maximum temperatures for the summer months of December, January, and February as calculated from Bureau of Meteorology data [11] are 28.6°C for Adelaide, the capital city of SA, compared to a cooler 25.3°C for Melbourne, Victoria. Independent living residents in SA and Victoria aged 65 years or older with a private residential telephone number were eligible for inclusion in the study. People living in nursing homes were not included. Ethics approval for the study was obtained from the University of Adelaide and the South Australian Department of Health Human Research Ethics Committees. Survey design The survey instrument was informed by a literature review, a prior qualitative study and a focus group with older people. Survey questions explored demographics, social connectedness, self-reported health status, air conditioning use, adaptive capacity, self-reported morbidity during recent heatwaves, and heat advisory recall. As some comorbidities and medications can affect heat susceptibility [5,12], respondents were also asked if they had been prescribed medications for diabetes, high blood pressure, heart failure, ‘other heart condition’ (e.g. heart attack, stroke, angina), ‘kidney problem’, ‘respiratory problem’, ‘depression, anxiety, memory loss or other mental health condition’, Parkinson’s disease or multiple sclerosis. Subjective heat-related morbidity was assessed by asking respondents if they had experienced, during any recent heatwaves, health issues such as anxiety, loss of balance/dizziness, a fall, headache, shortness of breath, heat stress, heart condition, renal condition, ‘something else’ or none of these [13]. The questionnaire was peer reviewed and piloted in December 2010 and recommended guidelines regarding the involvement of older people in research were followed [14]. All apparent difficulties were resolved and a second pilot was conducted in January 2011. Participation rates The survey was managed by Population Research and Outcome Studies at the University of Adelaide, and administered by professional health interviewers. Data collection was undertaken at the end of the summer between 14 February and 10 March 2011. Interviews were collected using a Computer-Assisted Telephone Interviewing system and 10% of each interviewer’s work was monitored by a supervisor. Households were randomly selected from the electronic telephone directory. Calls were made between 10.00 and 20.00 hours on weekdays and 10.00 and 17.00 hours on weekends. Once a household was selected, up to 10 separate callbacks were made to busy or unanswered numbers. Where more than one person aged 65 years or older resided in the household, selection was made on the basis of the person with the most recent birthday. There was no replacement for noncontactable persons or refusals. E20
A ,
N i t s c h k e
M ,
B i
P
e t
a l .
To reach the target of 500 telephone interviews in each state, 4200 random households were initially selected in SA (with 47% of eligible persons completing the interview) and 5640 random households in Victoria (with a participation rate of 36% of the eligible sample). Statistical analysis To ensure that the sample was representative of the relevant distribution of the target population in SA and Victoria, data were weighted by the inverse of an individual’s probability of selection and then re-weighted using the Australian Bureau of Statistics ‘Estimated Resident Population’ figures for age group and gender in metropolitan and country areas of the states accordingly [15]. Differences in proportions were assessed for significance using the corrected design-based F-statistic. Bivariate analysis using simple logistic regression explored potential risk factors associated with heat-related morbidity. As a surrogate for subjective heat-related morbidity, a binary variable was created to equal ‘1’ for respondents who reported one or more heat-related symptoms and ‘0’ for respondents who reported no symptoms. Plausible influential risk variables with a P-value less than 0.2 at the bivariate level were included in multiple logistic regression models [16]. Using backward elimination, only significant variables (P-value ≤ 0.05) were retained in the final multivariate model. Data analysis was undertaken using the statistical package Stata v11.
Results A total of 997 completed interviews were suitable for inclusion in the analysis (499 from SA and 498 from Victoria). The demographic characteristics of the study population are displayed in Table 1, showing a few differences between the states. Close to 70% of respondents in each state resided in the metropolitan area, with the remainder living in rural areas of the respective states. The median age of respondents was 74 years (weighted); 74 and 73 years for SA and Victoria, respectively (unweighted). More than half of the respondents in each state were female. In SA compared to Victoria, there was a significantly greater ownership of household air conditioning (95.2 and 86.2%, respectively) and more respondents had their air conditioners serviced regularly. There was also a higher proportion of low-income households in SA where there was a lower proportion of older persons living in units, flats or apartments, but more living in houses or retirement villages. More than 30% in each state reported living alone and around 15% had restricted mobility requiring the use of a frame or walking stick. Around 75% of each study population reported being in excellent, very good or good health; more than half were taking medication for hypertension, and more than one in four were taking medication for heart failure or another heart condition. Australasian Journal on Ageing, Vol 34 No 1 March 2015, E19–E25 © 2014 ACOTA
H e a t - h e a l t h
b e h a v i o u r s
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Table 1: Participants by sociodemographics in South Australia (n = 499) and Victoria (n = 498) showing percentages and 95% confidence intervals (95% CI)
Age groups (years) 65–69 70–74 75–79 80–84 Over 85 Gender (female) Location Metropolitan Rural Annual household income $60 000 Refused/not stated/don't know Health status (self-rated) Excellent, very good or good Fair or poor Don't know Medication taken for†: Diabetes High blood pressure Heart failure Other heart condition Renal condition Respiratory condition Depression, anxiety, memory loss or other mental health condition Type of housing House Unit, flat or apartment Retirement village Other Lives alone Has in the home environment Air conditioning Air conditioner serviced regularly Insulation Outdoor blinds/awnings Independence/mobility Uses a walking aid Help with personal/household tasks
South Australia % (95% CI)
Victoria % (95% CI)
P-value
– 29.8 (25.8, 34.3) 22.0 (18.3, 26.1) 19.0 (15.5, 23.1) 15.6 (12.4, 19.5) 13.6 (10.7, 17.1) 53.9 (49.1, 58.6)
– 29.0 (24.8, 33.5) 23.4 (19.7, 27.6) 19.2 (15.7, 23.2) 15.1 (12.0, 19.0) 13.3 (9.6, 18.1) 54.9 (49.8, 59.8) – 68.3 (63.9, 72.4) 31.7 (27.6, 36.1) – 23.9 (20.2, 28.0) 35.6 (31.0, 40.5) 9.7 (7.2, 13.0) 30.8 (26.2, 35.7) – 75.3 (70.8, 79.3) 24.3 (20.3, 28.8) 0.4 (0.1, 1.5) – 11.5 (8.9, 14.8) 55.2 (50.2, 60.1) 6.8 (4.7, 9.6) 20.8 (16.9, 25.3) 3.1 (1.8, 5.2) 12.0 (9.0, 15.7) 8.8 (6.5, 11.9) – 78.5 (74.2, 82.3) 18.4 (15.0, 22.4) 2.9 (1.3, 6.1) 0.1 (0.0, 1.0) 30.3 (26.3, 34.6) – 86.2 (82.5, 89.2) 20.9 (16.8, 25.7) 88.5 (85.0, 91.2) 58.5 (53.5, 63.3) – 15.4 (12.0, 19.5) 36.9 (32.2, 41.9)
0.990 – – – – – 0.781 0.987 – – – 0.004 0.254 0.197 0.456 0.826 – – – – 0.060 0.866 0.359 0.874 0.938 0.309 0.486 0.002 – – – – 0.479 –
Research Heat-health behaviours of older people in two Australian states Alana Hansen, Peng Bi and Dino Pisaniello
Introduction
Discipline of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
In many parts of Australia, including areas in the southeastern states of South Australia (SA) and Victoria, extended periods of hot weather are not uncommon in summer. Heatwaves in these states are often linked with marked increases in local morbidity and/or mortality [1,2] occurring as a result of health outcomes associated directly or indirectly with exposure to high ambient temperatures.
Monika Nitschke and Graeme Tucker Department for Health and Ageing, Adelaide, South Australia, Australia
Jonathan Newbury Discipline of Rural Health, University of Adelaide, Adelaide, South Australia, Australia
Alison Kitson School of Nursing, The University of Adelaide, Adelaide, South Australia, Australia
Eleonora Dal Grande and Jodie Avery Population Research and Outcomes Studies, Discipline of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
Ying Zhang School of Public Health, University of Sydney, Sydney, New South Wales, Australia
Liza Kelsall Health Intelligence Unit, Department of Health Victoria, Melbourne, Victoria, Australia
Aim: A major heatwave occurred in Australia in early 2009 with considerable and varied health impacts in South Australia (SA) and Victoria. The aim of this study was to investigate the heat-adaptive behaviours of older people in these states. Methods: A computer-assisted telephone survey of 1000 residents of SA and Victoria aged 65 years or older was conducted at the end of summer 2010–2011. Results: The majority of respondents reported undertaking heat-adaptive behaviours. In SA, there was a significantly higher proportion of households with air conditioning compared to Victoria, and a higher recall of heat-health messages. In both states, self-reported morbidity during heatwaves was higher in women, persons with poorer health and those with cardiovascular conditions. Conclusion: An increase in global temperatures in conjunction with an ageing population is a concern for public health. Our findings suggest acclimatisation to hot weather may influence behaviours and health outcomes in older people. Key words: acclimatisation, adaptive behaviour, aged, Australia, extreme heat. Correspondence to: Professor Peng Bi, Discipline of Public Health, University of Adelaide. Email: [email protected] Australasian Journal on Ageing, Vol 34 No 1 March 2015, E19–E25 © 2014 ACOTA
bs_bs_banner
Causal links between heat exposure and acute health effects in susceptible people often stem from the physiological strain involved in maintaining the body’s thermal balance. Behavioural modifications that help cool the body or minimise heat exposure therefore act as an important defence against the risk of heat-related or heat-exacerbated illnesses [3]. Persons who are physically unable to undertake certain adaptations will be at higher risk as will those who are unaware of preventive measures. Evidence shows that older persons are particularly susceptible to heat-related morbidity with ageing and poor health being contributing factors to vulnerability [4,5] Furthermore, older people will be particularly vulnerable to the health impacts of climate change [6]. With this knowledge, public health authorities need to consider evidence-based avenues to minimise present and future susceptibility in this group. In late January to early February 2009, SA and Victoria experienced an exceptional and unprecedented heatwave. In the state capitals of Adelaide (1.26 million population) and Melbourne (4.17 million population) [7], respectively, temperatures exceeded 45°C, and records were set for the number of consecutive days above 43°C. The consequent burden on health services was remarkable in both states. However, the health impacts on the older people in each state differed, with Victoria reporting high mortality, whereas in SA there was a stronger association with morbidity [8,9]. This highlights the need to assess how knowledge of heatwave warnings and adaptation impact on the risk of heat-related illnesses [10]. The aim of this study was to explore heat-health behaviours, attitudes and adaptive capacities of older people in SA and Victoria, to investigate potential differences and similarities between the states, and provide an evidence base for heathealth promotion initiatives.
Methods Study population The study populations resided in SA and Victoria, the latter having a generally cooler climate. The average mean E19
H a n s e n
maximum temperatures for the summer months of December, January, and February as calculated from Bureau of Meteorology data [11] are 28.6°C for Adelaide, the capital city of SA, compared to a cooler 25.3°C for Melbourne, Victoria. Independent living residents in SA and Victoria aged 65 years or older with a private residential telephone number were eligible for inclusion in the study. People living in nursing homes were not included. Ethics approval for the study was obtained from the University of Adelaide and the South Australian Department of Health Human Research Ethics Committees. Survey design The survey instrument was informed by a literature review, a prior qualitative study and a focus group with older people. Survey questions explored demographics, social connectedness, self-reported health status, air conditioning use, adaptive capacity, self-reported morbidity during recent heatwaves, and heat advisory recall. As some comorbidities and medications can affect heat susceptibility [5,12], respondents were also asked if they had been prescribed medications for diabetes, high blood pressure, heart failure, ‘other heart condition’ (e.g. heart attack, stroke, angina), ‘kidney problem’, ‘respiratory problem’, ‘depression, anxiety, memory loss or other mental health condition’, Parkinson’s disease or multiple sclerosis. Subjective heat-related morbidity was assessed by asking respondents if they had experienced, during any recent heatwaves, health issues such as anxiety, loss of balance/dizziness, a fall, headache, shortness of breath, heat stress, heart condition, renal condition, ‘something else’ or none of these [13]. The questionnaire was peer reviewed and piloted in December 2010 and recommended guidelines regarding the involvement of older people in research were followed [14]. All apparent difficulties were resolved and a second pilot was conducted in January 2011. Participation rates The survey was managed by Population Research and Outcome Studies at the University of Adelaide, and administered by professional health interviewers. Data collection was undertaken at the end of the summer between 14 February and 10 March 2011. Interviews were collected using a Computer-Assisted Telephone Interviewing system and 10% of each interviewer’s work was monitored by a supervisor. Households were randomly selected from the electronic telephone directory. Calls were made between 10.00 and 20.00 hours on weekdays and 10.00 and 17.00 hours on weekends. Once a household was selected, up to 10 separate callbacks were made to busy or unanswered numbers. Where more than one person aged 65 years or older resided in the household, selection was made on the basis of the person with the most recent birthday. There was no replacement for noncontactable persons or refusals. E20
A ,
N i t s c h k e
M ,
B i
P
e t
a l .
To reach the target of 500 telephone interviews in each state, 4200 random households were initially selected in SA (with 47% of eligible persons completing the interview) and 5640 random households in Victoria (with a participation rate of 36% of the eligible sample). Statistical analysis To ensure that the sample was representative of the relevant distribution of the target population in SA and Victoria, data were weighted by the inverse of an individual’s probability of selection and then re-weighted using the Australian Bureau of Statistics ‘Estimated Resident Population’ figures for age group and gender in metropolitan and country areas of the states accordingly [15]. Differences in proportions were assessed for significance using the corrected design-based F-statistic. Bivariate analysis using simple logistic regression explored potential risk factors associated with heat-related morbidity. As a surrogate for subjective heat-related morbidity, a binary variable was created to equal ‘1’ for respondents who reported one or more heat-related symptoms and ‘0’ for respondents who reported no symptoms. Plausible influential risk variables with a P-value less than 0.2 at the bivariate level were included in multiple logistic regression models [16]. Using backward elimination, only significant variables (P-value ≤ 0.05) were retained in the final multivariate model. Data analysis was undertaken using the statistical package Stata v11.
Results A total of 997 completed interviews were suitable for inclusion in the analysis (499 from SA and 498 from Victoria). The demographic characteristics of the study population are displayed in Table 1, showing a few differences between the states. Close to 70% of respondents in each state resided in the metropolitan area, with the remainder living in rural areas of the respective states. The median age of respondents was 74 years (weighted); 74 and 73 years for SA and Victoria, respectively (unweighted). More than half of the respondents in each state were female. In SA compared to Victoria, there was a significantly greater ownership of household air conditioning (95.2 and 86.2%, respectively) and more respondents had their air conditioners serviced regularly. There was also a higher proportion of low-income households in SA where there was a lower proportion of older persons living in units, flats or apartments, but more living in houses or retirement villages. More than 30% in each state reported living alone and around 15% had restricted mobility requiring the use of a frame or walking stick. Around 75% of each study population reported being in excellent, very good or good health; more than half were taking medication for hypertension, and more than one in four were taking medication for heart failure or another heart condition. Australasian Journal on Ageing, Vol 34 No 1 March 2015, E19–E25 © 2014 ACOTA
H e a t - h e a l t h
b e h a v i o u r s
o f
o l d e r
p e o p l e
Table 1: Participants by sociodemographics in South Australia (n = 499) and Victoria (n = 498) showing percentages and 95% confidence intervals (95% CI)
Age groups (years) 65–69 70–74 75–79 80–84 Over 85 Gender (female) Location Metropolitan Rural Annual household income $60 000 Refused/not stated/don't know Health status (self-rated) Excellent, very good or good Fair or poor Don't know Medication taken for†: Diabetes High blood pressure Heart failure Other heart condition Renal condition Respiratory condition Depression, anxiety, memory loss or other mental health condition Type of housing House Unit, flat or apartment Retirement village Other Lives alone Has in the home environment Air conditioning Air conditioner serviced regularly Insulation Outdoor blinds/awnings Independence/mobility Uses a walking aid Help with personal/household tasks
South Australia % (95% CI)
Victoria % (95% CI)
P-value
– 29.8 (25.8, 34.3) 22.0 (18.3, 26.1) 19.0 (15.5, 23.1) 15.6 (12.4, 19.5) 13.6 (10.7, 17.1) 53.9 (49.1, 58.6)
– 29.0 (24.8, 33.5) 23.4 (19.7, 27.6) 19.2 (15.7, 23.2) 15.1 (12.0, 19.0) 13.3 (9.6, 18.1) 54.9 (49.8, 59.8) – 68.3 (63.9, 72.4) 31.7 (27.6, 36.1) – 23.9 (20.2, 28.0) 35.6 (31.0, 40.5) 9.7 (7.2, 13.0) 30.8 (26.2, 35.7) – 75.3 (70.8, 79.3) 24.3 (20.3, 28.8) 0.4 (0.1, 1.5) – 11.5 (8.9, 14.8) 55.2 (50.2, 60.1) 6.8 (4.7, 9.6) 20.8 (16.9, 25.3) 3.1 (1.8, 5.2) 12.0 (9.0, 15.7) 8.8 (6.5, 11.9) – 78.5 (74.2, 82.3) 18.4 (15.0, 22.4) 2.9 (1.3, 6.1) 0.1 (0.0, 1.0) 30.3 (26.3, 34.6) – 86.2 (82.5, 89.2) 20.9 (16.8, 25.7) 88.5 (85.0, 91.2) 58.5 (53.5, 63.3) – 15.4 (12.0, 19.5) 36.9 (32.2, 41.9)
0.990 – – – – – 0.781 0.987 – – – 0.004 0.254 0.197 0.456 0.826 – – – – 0.060 0.866 0.359 0.874 0.938 0.309 0.486 0.002 – – – – 0.479 –
