The impact of home energy efficiency interventions and winter fuel payments on winter- and cold-related mortality and morbidity in England: a natural equipment mixed-methods study

Methods

For the period 1975–2012, we based analyses on the following data sets:

1. regional (government office region – a standard administrative region) time series of counts of death by day, stratified by age group and by broad cause-of-death group

2. meteorological data, specifically including daily minimum and maximum temperatures and relative humidity

3. regional weekly data of influenza A and B cases reported to the Public Health Laboratory Service, as well as annual survey-based estimates of influenza vaccine coverage for people aged ≥ 65 years

4. national data on domestic fuel costs, adjusted to 2011 prices, as well as data on trends in home heating, including the proportion of homes with central heating and indoor temperatures, derived from the United Kingdom Housing Energy Fact File 201317

5. year indicators as markers of the periods covered by WFPs (first introduced from the winter of 1999/2000).

The association between daily mortality rates and outdoor temperature was analysed using Poisson regression methods, adjusted for long-term trend and seasonality, day of the week and reported influenza cases. Model parameterisations were informed by recent research. 1,3,19 Adjustment for trend and seasonality was based on use of cubic spline curves generated using an adaptation of Stata^®’s (StataCorp LP, College Station, TX, USA) frencurv command, specifying seven knots per year in keeping with previous studies, as a compromise between providing adequate control for unmeasured confounders and leaving sufficient information from which to estimate temperature effects. 19 The (non-linear) temperature mortality function was fitted using natural cubic spline functions of temperature, implemented using Stata’s mkspline command with specification of two internal and two boundary knots. This function was applied to the mean of the maximum daily temperature over a lag period of 0–13 days (a single term of the mean of the daily maximum temperatures over this period), a lag structure chosen to capture the typical lags of the effect of cold exposure on mortality. 1,19,20 Maximum temperature was chosen as the index providing the best fit in analyses of all-cause mortality in the UK.

To test for change in the association between low temperature and mortality we fitted a simplified model in which the (log) risk of mortality at lags 0–13 days was assumed to be a linear function of temperature below a fixed cold temperature threshold assumed to be at 10 °C, a temperature chosen following inspection of plots of the temperature-related mortality function as a convenient reference point close to the minimum mortality temperature for lags of 0–13 days.

Attributable deaths n_i on each day i were computed using the formula n_i = N_i × (RR_i− 1)/RR_i, where RR_i = exp[β × (t_i − t₀)], N_i is the total number of deaths on day i, RR is the relative risk on day i and t₀ is the ‘no-cold-effect’ baseline temperature. Daily attributable deaths were then summed over days for the year to compute the annual burden of cold-related deaths. For this, years were defined to run from July to June to capture cold-related deaths during whole winters.

Results

The regression results confirmed the well-established relationship between low outdoor temperature (over lags 0–13 days) and mortality risk.

Figure 5 plots the annual estimates of (all-cause) cold-related mortality for the London region for the period of analysis. In both plots, points indicated by black dots represent data for the years before the introduction of the government’s WFPs and the green dots data for the years after the introduction of WFPs.

FIGURE 5.

The annual estimates of (all-cause) cold-related mortality for the London region. (a) Changes over time in cold-related deaths and mean of daily maximum temperatures in winter (December, January, February, March); and (b) the relationship between cold-related deaths and mean winter temperature, 1975–2011.

It is apparent that there has been a steady but progressive reduction in the annual number of cold-related deaths over time, an observation that is broadly consistent with analyses of the trend in the Excess Winter Mortality Index15 published by the Office for National Statistics (ONS) (see Figure 3). Although it is not possible to make a robust interpretation of the exact pattern of the decline in cold-related deaths, the reduction appears to have been relatively greater over the period leading up to the year 2000 than in subsequent years.

As suggested by Figure 5b, the annual burden of cold-related deaths is directly related to mean winter temperature, with higher burdens in colder years. Thus, the overall decline in cold-related deaths over time is in part, but not wholly, explained by the gradual rise in mean winter (December, January, February, March) temperatures during the period of analysis (see Figure 5a), remembering that the computation of cold-attributable deaths was made by reference to the fixed cold temperature threshold of 10 °C.

Table 1 shows the results of analyses of the variation in the gradient of association between outdoor temperature and mortality in relation to both the introduction of WFPs and the annual domestic fuel price. We found evidence that since the introduction of WFPs in 1997 the gradient of association has been somewhat weaker than in earlier years (p = 0.02 for the test of statistical interaction). The period since 1997 has also been accompanied by higher indoor temperatures (see Figure 4).

There is also evidence that years with higher than average domestic fuel prices have a somewhat stronger relationship between low outdoor temperatures and mortality [per cent increase in mortality per degree Celsius fall in temperature of 2.49%, 95% confidence interval (CI) 2.32% to 2.66%] than years with below-average fuel prices (1.97%, 95% CI 1.77% to 2.18%). This is compatible with high fuel prices having a detrimental effect on cold-related deaths.

Discussion

The analyses presented in this section reflect year-to-year changes in cold mortality. The analyses are also dependent on a number of assumptions about model parameterisation and the temperature baseline used to define cold-attributable mortality in a given year.

These appear to indicate a slight weakening of the relationship between low outdoor temperature and mortality since the introduction of the WFPs in 1997, a result that is compatible with an econometric analysis by Iparraguirre. 21 However, the period since 1997 has also been accompanied by changes in many other factors that may have reduced the risk of cold-related death. These include the efficiency of the thermal insulation of houses, improved heating systems (which are more likely to account for the temporal increase in indoor temperatures shown in Figure 4) and the reduced incidence/fatality of important ‘temperature-sensitive’ diseases, such as ischaemic heart disease, whose rates have declined rapidly in recent years. Therefore, it is not possible to conclude with certainty that the contribution of WFPs to the cost of heating the home has been the main contributory factor in this decline, although it is likely to have had some benefit if families chose to spend at least part of the WFP on warmer indoor temperatures.

The result for fuel prices is consistent with what might be predicted if the cost of fuel is a constraint on the temperature to which people choose to heat their home, the presumption being that after years of higher fuel prices people have used less fuel than they would otherwise do. Fuel prices rose appreciably from 2004 onwards, largely as a result of changes in wholesale prices and energy suppliers recouping the cost of renewable investments, but the years of high fuel prices do not occur in one block throughout the period of analysis. As with the WFP analysis, a cautious interpretation is needed because of the possibility that there are other factors than high fuel price that have been contributory factors.

The analyses suggest that there has been a progressive decline in the annual burden of cold-related deaths since the mid-1970s, which probably reflects the continuation of a trend of reducing winter-/cold-related mortality going back many decades, a trend that has occurred despite the gradual rise in the proportion of people aged > 65 years, who are known to be more vulnerable to the effects of low temperatures. The factors contributing to this decline cannot be determined with confidence but are likely to include a wide range of factors associated with improving socioeconomic conditions, including improving population health and health care and more effective health protection measures,16 for example the introduction in recent years of the Cold Weather Plan22 for England.

Improvements in housing, specifically in the effectiveness of home heating and winter indoor temperatures, are also likely to be contributory. If ambient temperatures also increase as a consequence of climate change, this would be expected to lead to a gradual reduction in cold-related deaths over time, although there is unlikely to be an appreciable reduction over the next decade or so. The next section examines evidence that is relevant to this question.

Defining intervention-related changes to the indoor environment

Installing energy efficiency retrofits to improve the energy performance of dwellings (Figure 8) has a number of positive and negative consequences for the indoor and outdoor environment and for households more generally.

FIGURE 8.

The uptake of energy efficiency measures in England: 1997–2012. The HEED sample representative of the EHS (n = 168,998 dwellings). a, Data on glazing, boiler or heating system replacement were available only to 2007. For further details see Hamilton et al. 25

Our focus here was limited to changes to the indoor environment, specifically winter indoor temperatures and air quality, although the impacts that this has on the energy needs and cost of space heating are briefly considered in the section on health impact modelling (see Energy use).

Because of the range of different energy efficiency measures installed across the English stock [including, but not limited to, insulation of the roof space, cavity walls, solid walls, double (triple) glazing and improvements to heating system/boiler], we classified the impact of each in terms of change in winter indoor temperatures and air quality [specifically the change in concentrations of fine particulate matter (PM_2.5) of both indoor and outdoor origin, radon, STS and mould risk] using a combination of empirical data and building physics modelling.

It should be noted that physics-based energy simulation models provide evidence on the ‘technical potential’ for energy savings and temperature change of energy efficiency interventions, which may differ from actual changes that reflect the effect of complex sociotechnical interactions of the dwelling occupier, the building and responses to the external and indoor environment. Poor installation, technical limits, environmental factors, changes in user preferences or practices and allowing for unmet needs to be satisfied, such as ‘temperature take-back’,26 may all give rise to appreciable variations in the actual changes in indoor conditions from dwelling to dwelling and from those predicted by the ‘idealised’ assumptions of building physics models. Therefore, our preference has been to base estimates of change on empirical data whenever possible.

Temperature changes

It has been shown that the energy performance levels of English dwellings has a significant influence on temperatures6,27,28 and that changing the energy performance characteristics of the dwelling through retrofits affects temperatures. 29 It has also been shown that our indoor temperatures during wintertime are associated with risk of mortality, especially from cardiovascular disease. 6

To classify dwellings with regard to winter indoor temperatures, we used a modification of a method described by Oreszczyn et al. 2006. 27 It entailed two principal steps:

1. defining the empirical relationship between the energy efficiency characteristics of a dwelling and winter indoor temperatures measured under standardised conditions

2. determining the pre- and post-intervention energy efficiency characteristics of intervention dwellings using recorded data on dwelling type, age and other parameters in combination with building physics simulations.

Applying the results from step 2 to the relationships defined in step 1 provides the estimate of change in indoor temperature resulting from specific energy efficiency upgrades.

Central to these analyses is the concept of the standardised indoor temperature (SIT). The SIT is a marker of winter indoor temperature under standardised measurement conditions that has been used in the analysis of two data sets of indoor temperature monitoring in dwellings of known energy efficiency characteristics in the English stock. These data sets were from the previously published evaluation of the Warm Front energy efficiency scheme27 and the more recent Energy Follow-Up Survey (EFUS)30 (which is linked to the EHS).

Both data sets contain multiple serial measurements of indoor (living room and bedroom) temperatures during the heating season. For each dwelling in these surveys, regressing the indoor temperature on outdoor temperature (as a polynomial function) and adjusting for time of day allowed for the estimation of the mid-afternoon indoor temperature when the outdoor temperature was 5 °C, as illustrated in Figure 9. This is the SIT. At the same time, each dwelling was classified with respect to its energy efficiency using a measure of thermal performance known as the E-value. The E-value represents the heating power (in watts) required for the whole house to increase the temperature difference between indoor and outdoor environments by 1 kelvin (1 °C).

FIGURE 9.

The derivation of the SIT: dwelling-specific regression analyses of the relationship between indoor and outdoor temperature was used to define a living room temperature on the day with a maximum outdoor temperature of 5 °C (the SIT). Data from across multiple dwellings of different energy efficiency levels were then used to define the general relationship between SIT and energy efficiency as reflected by the dwelling E-value (see Figure 10).

Using results of SIT and E-value across multiple dwellings provided the empirical relationship shown in Figure 10. This is the core function used to estimate changes in indoor temperature of intervention dwellings in the HEED as energy efficiency interventions alter dwelling energy efficiency characteristics defined in terms of its E-value.

FIGURE 10.

The relationship between SIT and whole house E-value [the number of watts required to maintain a temperature difference of 1 Kelvin (1 °C) between indoor and outdoor environments]. Improving the energy efficiency of the dwelling from E to E – δE results in an increase in SIT from T to T + δT.

Improving the energy efficiency of the dwelling from E to E – δE results in an increase in SIT from T to T + δT.

The approach was used to estimate the potential change in indoor temperatures following retrofits that would affect the notional thermal performance of the dwelling31 and subsequent impact that it has on cold-related mortality/morbidity. 32

To estimate the likely indoor temperatures experienced during winter conditions, a detailed building physics model was used to estimate the SITs of the English dwelling stock for different energy performance levels. 33 Dwelling characteristics in the EHS were used to characterise the energy performance of the dwelling (i.e. its E-value), which was then used to predict the SIT at 5 °C in the living room and bedroom. Two model runs were conducted to estimate indoor temperatures: one run modelled the energy performance of the dwellings as described in the EHS (i.e. ‘baseline’), whereas the second run included the introduction of all available energy efficiency retrofits and the corresponding changes in E-value and SITs (i.e. ‘modified’). The retrofits included loft insulation to 250 mm, cavity wall insulation, solid wall insulation, double-glazing installation, condensing boiler installation and draught proofing.

Two metamodels were constructed from the detailed baseline and modified Health Impact of Domestic Energy Efficiency Measures (HIDEEM) model runs using the regression model of the average (living room and bedroom) SIT as a function of dwelling age, dwelling type, number of bedrooms and loft insulation level. These variables corresponded to those available within HEED. Model 1 predicts the baseline energy performance of the dwellings as described in the EHS, whereas model 2 includes the installation of all eligible retrofits for the UK housing stock. Because HEED provided only limited information on the dwelling, the meta-models included only a limited number of independent variables to predict the SIT.

The result of this classification is shown in Figure 11. A notional pre-intervention E-value is assigned on the basis of dwelling characteristics and, similarly, a post-intervention E-value is assigned reflecting the effect of the specific form of energy efficiency change. From these E-values, the empirical relationships are used to determine the corresponding SITs and hence the overall change in SIT. It is this change in temperature that is used as the basis for the calculation of the temperature-related impact on mortality and morbidity.

FIGURE 11.

The classification of dwellings with regard to energy efficiency (E-value) and SIT. ID, identifier; LR, living room.

Results

The uptake of specific forms of energy efficiency interventions by dwelling type, urban–rural status and decile of socioeconomic deprivation, as well as changes in SIT, is summarised graphically in Figure 12.

FIGURE 12.

The uptake of specific forms of HEE interventions. (a) HEE retrofits by dwelling type; (b) HEE retrofits by urban–rural area; (c) HEE retrofits (number of interventions) by neighbourhood deprivation decile group; and (d) mean baseline SIT (°C) for different dwelling archetypes.

Table 3 tabulates the change in SIT for the main categories of intervention by dwelling age, type and housing tenure. As this shows, for most forms of energy efficiency upgrade the change in temperature is modest but is dependent on property age and type in particular. The increases in temperature are generally greatest for dwellings that are older and, thus, less energy efficient. Loft insulation is associated with very modest increases in temperature, whereas much greater changes are seen for cavity and solid wall insulation.

Across all energy efficiency interventions (Figure 13), the change in the distribution of SITs is modest and averages at just 0.09 °C.

FIGURE 13.

Distribution of (a) the pre- and post-intervention SITs; and (b) the difference in post- and pre-intervention SITs.

The associated change in health impacts (deaths, life-years and YLDs) is illustrated in Figure 14. The plots show the evolution of the impact that the changes have had over time, noting that the number of overall deaths saved (around 280 deaths in year 1) eventually returns to baseline level but with a reduction in deaths at younger age and an eventual increase in the number of deaths at older age. The annual gain in life-years saved takes several decades to be fully realised and plateaus at over 4000 life-years per year. The morbidity impacts (as YLDs) are approximately 10% of the mortality impacts.

FIGURE 14.

Modelled change in deaths saved and life-years gained. Deaths saved (a) overall; and (b) by age group and life-years gained (c) overall; (d) by age group; and (e) a combination of mortality and morbidity impacts summarised as YLLs and YLDs.

A summary of the intervention-related change in quality-adjusted life-years (QALYs) per 100,000 population (averaged over a 50-year time horizon) is shown in Table 4 separately for temperature-related health impacts and those owing to change in indoor air quality. In each case, a range is shown to reflect results for different assumptions about ventilation characteristics (whether or not installations have associated purpose-provided vents in order to maintain ‘adequate’ ventilation). Note that the health impacts associated with the ventilation may be negative or positive (depending on assumptions about ventilation changes) and may be as much as an order of magnitude greater than those related to temperature change.

These air quality-related impacts reflect a balance of changes affecting pollutants with outdoor and indoor sources. Unless there is purpose-provided compensatory ventilation (such as trickle vents), energy efficiency measures tend to reduce dwelling permeability and, thus, ventilation. This is beneficial, especially in high outdoor pollution areas, as it reduces the ingress of particle pollution PM_2.5 from the outdoor environment [as shown in Figure 15, which shows a negative shift (reduction) in PM_2.5 derived from outdoor air after intervention], as well as contributing to improved energy efficiency (reduced loss of heated air). However, reduced permeability, and hence reduced air exchange, acts to increase the concentration of pollutants derived from indoor sources, including indoor PM_2.5, radon, second-hand cigarette smoke, humidity/mould, and such pollutants as volatile organic compounds. Again this is illustrated in Figures 16 and 17, which show increases in concentrations of PM_2.5 of indoor origin, and Figure 18, which shows the increase in radon concentrations from (in this case) solid wall insulation. (The effect of volatile organic compounds has not been quantified in any of the results shown here because of the lack of clear evidence of exposure–response relationships.) Note the relatively large impacts in converted flats and selected other dwelling types.

FIGURE 15.

Changes in indoor concentration (µg/m³) of PM_2.5 of outdoor origin as a result of different forms of energy efficiency intervention. (a) Loft insulation; (b) cavity wall insulation; (c) improved glazing; and (d) solid wall insulation. The dashed line indicates a reference point of a change of –1 µg/m³.

FIGURE 16.

Changes in indoor concentration (µg/m³) of PM_2.5 of indoor origin as a result of different forms of energy efficiency intervention. (a) Loft insulation; (b) cavity wall insulation; (c) improved glazing; and (d) solid wall insulation. The dashed line indicates a reference point of a change of 1 µg/m³.

FIGURE 17.

Modelled changes in the concentration of indoor PM_2.5 of outdoor (y-axis) and indoor (x-axis) origin following selected energy efficiency interventions. (a) Indoor concentrations of PM_2.5 of outdoor and indoor origin; (b) changes with loft insulation; (c) changes with improved glazing; and (d) changes with cavity wall insulation.

FIGURE 18.

Distribution of changes in indoor radon concentration consequent to the installation of solid wall insulation.

Unfortunately, the available data do not allow for the change in ventilation characteristics and, consequently, for indoor air quality to be modelled with precision and certainty. It is, for example, a matter of choice whether or not trickle vents are installed at the same time as other energy efficiency measures and whether or not householders subsequently choose, or even know how, to use them. Therefore, all quantified changes in indoor air quality are based on ‘best reasonable’ assumptions, informed by published empirical data. However, different assumptions about installed measures and household behaviour, as well as about local environment conditions (e.g. outdoor PM_2.5, radon levels), can appreciably alter the balance of positive and negative effects from alterations to the ventilation characteristics of a dwelling.

Energy use

Article extract reproduced from Hamilton et al. 25 Copyright © 2016 The Authors. Published by Elsevier B.V. This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. The text below includes minor additions and formatting changes to the original text.

The results for the 168,998 English dwellings included in the analysis are shown in Figure 19 and Table 5. Overall, 39% received a major energy efficiency measure, 36% received a fabric (i.e. insulation) measure and 9% received a heating measure. The annual average change in energy demand across the stock over the period of analysis was –810 kWh/year (with individual year changes of –740 kWh/year in 2004/5, –860 kWh/year in 2005/6 and –830 kWh/year in 2006/7). These are equivalent to percentage changes of –3.6% for 2004/5, –4.4% for 2005/6 and –4.4% for 2006/7 (see Figure 19).

FIGURE 19.

Distribution of annual gas demand (kWh/year) per dwelling in study sample (n = 145,885), 2004–7.

In regression analyses that allow for differences in dwelling type, age, tenure, size, region and median neighbourhood income, we estimate that the presence of an installed fabric energy efficiency retrofit, on average, was associated with a –790 kWh/year change in energy demand, or a 3.9% reduction from the stock mean gas demand in 2006. The presence of a heating energy efficiency retrofit was on average associated with a –1950 kWh/year (10.4%) change. When installed together, the average change in energy demand was –2290 kWh/dwelling/year, or a 11.7% reduction.

The proportional change in gas demand in 2005–7 associated with specific forms of energy efficiency intervention adjusted for dwelling type, age, tenure, size, region and neighbourhood income is shown in Table 5. (See Appendix 1 for further details by energy efficiency measure.)

Cavity wall insulation and condensing boiler installations were associated with reductions in demand of 4.9% and 5.5%, respectively, whereas loft insulation and double-glazing installation showed almost no associated change. Larger reductions were achieved with combinations of measures, especially those that included condensing boiler and cavity insulation installations (e.g. cavity wall and loft insulation combined with a condensing boiler was associated with an estimated –11.2% change in demand).

These findings are very similar to those of a study by Hong et al. 46 on the impact that the Warm Front energy efficiency scheme had on British houses, which found that loft and full cavity wall insulation reduced demand during a 1-year period by 10–17%. Although these changes in energy demand are lower than notional ‘savings’ or those of the Hong et al. study,46 it is important to bear in mind that they are based on estimates that control for physical, household and area factors. Controlling for physical factors, for example, means that the effect of number and area of exposed walls or the age of the dwelling is removed, whereas control of household effects adjusts for such factors as: ability to afford larger areas to heat and demand for greater thermal comfort.

It is noteworthy that in our adjusted estimates neither loft insulation nor double glazing was associated with appreciable energy savings over the 3-year period, suggesting that the effect of these interventions is, on average, fairly small and/or cannot easily be detected using annualised energy data. Glazing is one of the thermally weakest elements of the building fabric, and replacement with more thermally efficient glazing should have an effect on energy use. However, because of the way the data were reported, it was not possible to account for the area of glazing that was replaced, so the small estimate of impact may, in part, reflect the fact that, in many cases, only a small proportion of the glazed area of the dwelling was replaced. For loft insulation, many installations were top-ups of around 5–75 mm and the expected change in gas demand would be minimal.

There were variations in the savings associated with certain household/dwelling characteristics (see Appendix 1). For example, dwelling age appears to have an inconsistent effect on changes in gas demand, with the 1967–75 age group having much greater reduction in demand for all single retrofit measures than other age bands. It is not simply that older dwellings have greater energy savings than newer dwellings. Such variation may, in part, be explained by the eligibility and type of retrofits installed. Cavity wall filling is mostly relevant to dwellings built in the mid-century: earlier builds (pre-1950) tend not to have a wall cavity, and those built after 1990 tend to have higher specification. The impact that boilers among this mid-century group had was also greater (after controlling for size), perhaps reflecting a number of building design features, such as the prevalence of gas central heating. 47

Changes in energy demand were also generally lower among renting households or those living in areas of lower income, supporting the hypothesis that those on lower incomes are less likely than higher-income households to realise energy savings. This observation may in part be because lower-income households have higher levels of energy utility (i.e. greater need for the amount used). 48 Both social renting and living in lower-income neighbourhoods were associated with reduced energy demand, even after controlling for type and age of dwelling,6 which may also suggest that these households have a greater potential for increasing demand if energy efficiency retrofits are made. The differences could also be construed as ‘comfort taking’, whereby these households in areas of lower income reduce the potential ‘energy savings’ by taking the savings in the form of temperature increases, an effect that has been shown in a study of vulnerable households in England. 29

The impact of energy efficiency retrofits demonstrates a broad dose–response effect in which combined packages of retrofits are associated with greater (negative) changes in energy demand. Larger increases in reductions in gas demand were associated with boilers and cavity wall insulation, with only minor additional effects from lofts and glazing. The largest change in gas demand was associated with the combined installation of a condensing boiler, and cavity and loft insulation at –10.8%.

In some cases, the effect of combinations of measures appear ‘super-additive’, that is, to have an effect greater than the simple sum of the component measures. For example, the individual change attributable to cavity insulation (–3.8%), condensing boiler installation (–5.2%) and loft insulation (1.2%) ought to result in a change of –7.8%, but the combined estimate was a reduction of around 11%. This may reflect true advantages of combining retrofits into single package (which may have benefits in achieving energy demand reduction and potential cost savings of installation, e.g. wall scaffolding is set up only once), or that there is a ‘take-back’ threshold after which rebound related to thermal comfort is lessened (i.e. the potential rebound has been met).

Realising the potential energy savings set out in the DECC energy efficiency strategy would require a ‘whole-house’ retrofit package for every home in England. If an average energy saving of 10% (e.g. ≈2300 kWh reduction) was achieved from the average UK dwelling, it would take approximately 9,565,000 ‘whole-house’ retrofits (40% of the whole UK stock) to achieve the estimated 22 terawatt hours (TWh) of energy savings by 2020; to achieve a 10% reduction in 2006 levels by 2020 (i.e. 54 TWh) through energy efficiency alone would take the equivalent of every home in the UK being refurbished (i.e. 23,500,000 dwellings). Although further efficiencies might be gained from water heating and appliances, space-heating accounts for the bulk of residential demand. Thus, achieving such savings (to meet climate change targets and to realise the ancillary public health benefits) would require substantial acceleration of the historical rate of retrofit uptake, which is challenging but achievable using widely available technologies and insulating techniques that rely on an existing deployment system and skill base.

In summary, energy efficiency retrofits (although not clearly for loft insulation or double glazing) result in observable reductions in space-heating energy demand (with resultant cost savings for the householders). These savings are part of the gain from energy efficiency measures that are taken instead of increases in temperature. They may have indirect (but unquantified) benefits for the health of householders, especially those on low incomes, because of change in disposable income, and are additional to the health impacts relating to change in the indoor environment (changes in winter indoor temperature and air quality).

Discussion

Several features of these results are noteworthy. First is the relatively small size of the effect of energy efficiency interventions on indoor temperatures and, consequently, on health. This appears to be a result of two principal factors: (1) the modest nature of most individual energy efficiency upgrades and (2) the shape of the function relating to indoor temperature to dwelling E-value. As shown in Figure 20, not only is there a relatively shallow gradient of increasing temperature with improving energy efficiency at E-values above around 300 W per Kelvin, but that gradient flattens to close to zero (i.e. plateaus) at values to the left of this point. This point is very close to the middle of the distribution of dwelling E-values for the English stock (as shown in the bottom part of Figure 20). In other words, around half of the housing stock is already at a point where further increases in energy efficiency would not, on average, result in any increase in indoor temperatures (as represented by the SIT). It is also worth noting that this SIT versus E-value relationship is based on empirical data from two independent data sets (EFUS 2010/1130 and Warm Front 2006/527) that appear to generate very similar functional forms (Figure 21). The broad agreement of the results from these different data sets gives greater confidence that the underlying relationship is essentially correct.

FIGURE 20.

Relationship between SIT and dwelling E-value above the distribution of E-values. The dotted line indicates the approximate location of the inflexion point at which further improvement in energy efficiency (positions to the left on the x-axis) result in no additional gain in SIT.

FIGURE 21.

Relationship between SIT and dwelling E-value (living room): results from two independent studies. (a) EFUS 2010/1130; and (b) Warm Front 2004 to 05. 27 ‘Meta-analyses’ of SIT results from multiple dwellings.

A second important observation is the fact that impacts on health related to air quality may be just as great as, and probably greater than, those from improvements in winter temperatures. Even though there are uncertainties in quantifying these impacts because of limited empirical data, the broad patterns of change are predictable given reasonable assumptions about intervention types, householder behaviour and other factors. These impacts are especially important because they arise over the medium to longer term and hence are not usually observable or even measured in short-term evaluation studies. Therefore, it seems probable that they have not been given sufficient attention in the development of policies relating to energy efficiency interventions and in building regulation. This is an area of research need.

As discussed at the outset of this report, there are pathways of health impact that are additional to those operating through changes to the indoor environment (i.e. temperature and air quality). The analysis here confirms that improvements in energy efficiency as expected are accompanied by observable changes in energy demand. It is a simple fact that the benefits of energy efficiency can be taken either as reduced energy consumption or as increased temperature, or as some combination of the two. It is this balance that lies behind the shape of the relationship between SIT and dwelling E-value, with relatively more of the gain being taken as increased temperature when the energy efficiency is low and almost none being taken as increased temperature when energy efficiency is already good. What individual families choose to do will be determined by their own preferences and circumstances and is almost certainly influenced in part by their disposable income. We do not yet have a method that allows even a broad quantification of the impacts had on health of energy and hence cost savings, especially for those in or near to FP.

Methods

The study was based on deaths among London residents from all natural causes from September 1949 to September 2006 using two sources of mortality data to cover the whole study period. For 1949–75, we used digitised weekly counts of death registrations published in print by the Registrar General,64 supplementing 1950–64 data from a previous study. 16 For 1976–2006, we used daily counts of death collated for other studies65,66 and originally obtained from ONS. The data were for the London Administrative County for 1949–65, and for the larger Greater London thereafter. For all years, we retrieved deaths as a result of all natural causes, cardiovascular causes and respiratory causes. The daily counts were collapsed into weeks to create a complete series of weekly mortality for the entire 57 years of study, starting from 2 October 1949.

Weekly counts were aggregated into years commencing in autumn (which we defined to begin in the first week of October), rather than using conventional calendar years, so as to align temperature and mortality data for the same winter season (instead of artificially splitting winter at 1 January and noting that cold can be delayed by as long as 3 weeks2). Heat-related mortality effects are predominantly seen within 1–3 days,50 so the October boundary would mean that each year included most deaths resulting from hot weather in the warm season months up to and including September. Weeks were numbered sequentially from the start date and organised into years of 52 or 53 weeks, with each year starting in early October, but then dropping all weeks numbered as the 53rd to leave summed counts of deaths for 57 52-week years, for simplicity in the regression model.

Results

There were a total of 3,530,280 deaths from natural causes in the 57 years of our analysis: 1949–50 to 2005–6. Allowing for the sharp increase in the annual count of London deaths attributable to the change in the definition of London’s administrative boundary in January 1966, there was evidence of a gradual decline in counts of death from about 1970.

Mean daily temperature exceeded the threshold of 18 °C on 11.1% of days and was below this threshold on 88.7% of days. For each year during the study period, the mean cold-degrees over the year (degrees below 18 °C) was, on average, 7.4 °C and varied between 6.2 and 9.0 °C; the mean heat-degrees (degrees above 18 °C) was much lower (0.2 °C) and varied only from 0 to 0.6 °C.

The results for the estimated increase in mortality for each degree of cold and heat across the year, as determined by the regression model, are shown in Table 6. Cold years were associated with increased deaths from all causes. For each additional degree of cold across the year, all-cause mortality increased by 2.3% (95% CI 0.7% to 3.8%), after adjustment for influenza and secular trend. The effect of cold was greater in those aged ≥ 65 years, and for deaths from cardiovascular disease (2.9% per degree) and respiratory disease (7.6% per degree), but the CIs for each of these subgroup analyses were wide.

The estimated increase in mortality with heat (1.7% per degree) was also very imprecise (95% CI 2.9% to 6.5%), as were the age- and cause-specific heat-related mortality estimates, which makes comparisons unreliable.

A range of sensitivity analyses (reported in Appendix 2) showed that only when the spline used to allow for mortality trends over time was very inflexible (three knots) did the association of mortality with cold change substantially (losing statistical significance). This model, however, fitted the data appreciably less well by Akaike information criterion. The association of heat with mortality was more sensitive to model choices but estimates of mortality increment because of heat remained very imprecise in all models. Residual analysis of the main model did not suggest problems of poor model fit.

The comparative conventional month-stratified time-series analyses on weekly data for the same period gave all-cause estimated cold and heat effects as 2.0% (95% CI 2.0% to 2.1%) and 3.9% (95% CI 3.6% to 4.1%) per degree Celsius below and above the 18 °C threshold, respectively. CIs were much narrower than for the annual data estimates and point estimates for age- and cause-specific deaths showed clear pattern of higher RRs in the elderly and in deaths owing to cardiovascular and, in particular, respiratory disease.

Discussion

These analyses provide evidence on a central question for interpreting the public health importance of temperature-related mortality, namely the degree to which heat- and cold-related deaths can be attributed to a very limited shortening of life of perhaps just days or weeks. If the majority of temperature-related deaths were explained by such harvesting, most observers would consider temperature-related mortality to be much less concerning than if there were evidence for a reduction of life by months or years. This analysis does not provide a full answer to this question but suggests a lower limit. Specifically, we found that, over six decades in London, colder years were associated with increased mortality, with 2.3% (95% CI 0.7% to 3.8%) more deaths occurring per degree of cold during the year. This suggests that cold-related deaths were brought forward by at least around half a year. Had the excess of daily deaths associated with preceding weeks of cold weather (which typically occurs in the UK between December and March) been ‘due to occur’ within the same year as defined in this study – that is, before the following October – no association between cold and mortality would have been detected. Our findings were generally robust to variations in the modelling assumptions and regression techniques used.

To our knowledge, no other studies have looked at long-term mortality in relation to long-term temperature defined on an annual basis. The results of our analysis for heat are broadly supported by the cohort analyses that show that years of high summer temperature variability are associated with high mortality. 68

It is instructive also to compare our estimates with those from published daily time-series and related studies. Two London studies57,69 used the same cold threshold as us (18 °C). If all the excess deaths in these daily studies had been displaced from beyond the next October and there were no longer lag effects, they should reveal the same increments of risk as in the annual studies, setting aside the slightly different time periods of the studies (1988–92 and 1993–6). The daily studies both found increments of daily mortality of 1.4% per degree below 18 °C, broadly similar to the estimates based on our subsidiary weekly analysis (2.0% per degree Celsius). The fact that our estimate from analysis of annual data was positive, and also similar to our own weekly and the other published estimates, strongly suggests that (most of) the excesses in the daily and weekly studies are, indeed, displaced from beyond October (i.e. from the next year). The fact that the annually-based estimate is somewhat larger might also be interpreted as giving some suggestion that there may be adverse mortality effects operating at much longer lags than were detectable in the daily studies (lags of 3 days and 2 weeks), although the difference could be a result of chance, as 1.4% and 2% falls within the wide CI for this study (95% CI 0.7% to 3.8%).

Estimates for the impact that heat had from this study were, unfortunately, very imprecise. In particular, the CI for increase in mortality per degree above 18 °C (95% CI –2.9 to 6.5%) includes all of the estimates made in daily studies, including the one found in the one daily study57 using the same 18 °C threshold (1.3%), and also the estimate presented in the current study from weekly data (3.9%). This limit in precision precludes making any conclusion from this analysis on the presence of absence of short-term harvesting for heat-related deaths. The precision limitation is a result of the limited number and variation of hot days annually in London. Annual studies in cities with more days above their heat thresholds would probably have more power.

As in all epidemiological studies, our study was potentially subject to residual confounding by uncontrolled risk factors that change over time, including, for example, changes in the size and demographic structure of the London population, as well as declining age-specific deaths rates owing to changes in health care and risk factors (e.g. smoking). In common with other time-series regression studies,20 we relied mainly on a smooth (spline) function of time to control for these, under the assumption that changes in such time-varying confounders would probably be smooth. Although we cannot rule out residual confounding, it is reassuring that increasing the flexibility of the spline or adding further ‘step’ functions changed the estimated cold effect very little. The much lower estimate for cold when using a less flexible spline (see Appendix 2) has little credibility given the much poorer fit of this model (Akaike information criterion).

How and whether or not to control for influenza is problematic, as it is possible that it might be on the causal pathway between cold and death, although there is no direct evidence that cold is a direct cause of influenza. Our approach, using the proportion of total deaths resulting from influenza as covariate in the regression is, we believe, conservative.

Air pollution is an established risk factor and also changes over time. We were unable to control for this, as we were unable to assemble consistent data for the complete period. However, data on black smoke for the period 1976–2003 were available,70 and an analysis revealed a weak negative correlation between mean annual cold and black smoke during this time, after accounting for year (r = –0.25). This suggests that confounding from air pollution is unlikely to have been substantial, and control for it would be more likely to increase estimates of cold effects.

Despite the large population of London and the long duration of our series, power and precision was limited for some analyses, especially for heat effects, and for age- and cause-specific analyses, the results of which were too imprecise to allow much interpretation, even if point estimates followed broadly the expected pattern. 50,71,72 The more precisely estimated patterns that we found in the weekly analysis match those found by others.

Finally, our approach to summarising cold and heat in each year is not the only one possible. We chose it so as to maximise the link between this study and daily studies. However, in fact, the annual measure of cold adopted (degrees below 18 °C) was very highly negatively correlated, in temperate London, with the annual mean temperature (r = –0.97). The heat measure was also correlated, but less strongly so (r = 0.81). Thus, analyses using mean annual temperature would have resulted in very similar estimates of cold effects and broadly similar heat effects.

The importance of this evidence is that it suggests that, in the UK, most people who die as a result of cold in daily time-series studies lose at least 6 months of life expectancy, and possibly much more. Our methods do not allow us to derive an estimate of the actual degree of life shortening, but rather they help to define a lower limit for it. The fact that it appears to be no less than around 6 months strengthens the case that cold-related deaths are a serious public health problem and hence that policies aimed at reducing vulnerability to cold through such measures as home insulation have important benefits to health and specifically to life expectancy. This has a bearing on the quantification and valuation (monetisation) of the health gains from improved protection against cold-related mortality.

Methods

Analyses were based on mortality records (n = 322,840) of people aged ≥ 65 years dying in the summer months (June, July, August, September) of the years 2001–6. These records were linked to dwelling characteristics by the full unit (seven-digit) postcode of residence (212,110 postcodes) and to outdoor temperature data (daily maximum temperature in degree Celsius at lags of 0–1 days) using data from a national-scale Weather Research and Forecasting (see www.mmm.ucas.edn/weather-research-and-forcasting-model) weather model with spatial resolution of 5 × 5 km. The association between mortality and maximum daily temperature was based on a time-stratified case crossover design. There were two status analyses: conditional logistic regression to estimate region-specific heat slopes and their interaction with a dwelling overheating index, and subsequent meta-analysis of the results with random effects across regions.

Overheating index

The derivation of the dwelling-type specific overheating index was based on building physics simulation using the validated modelling software EnergyPlus version 8.3 (US Department of Energy, Washington, DC, USA). Using input data from the EHS,47 this was used to calculate a temperature anomaly for representative dwelling archetypes. The anomaly was defined as the difference between the mean indoor temperature [mean (T_indoor)] and the mean outdoor temperature [mean (T_outdoor)] on days when the 2-day average of daily maximum temperatures, T_{max0–1, outdoor} was above the 97.5th centile of the temperatures distribution (the anomaly for ‘extreme heat’) and on days when the 2-day average of daily maximum temperatures T_{max0–1, outdoor} was between the 93 and the 97.5 centile (‘moderate heat’). For each of 16 dwelling types, eight indices of anomaly were computed, reflecting 2 × 2 × 2 combinations of moderate/extreme heat, daytime/night-time and bedroom/living room temperature.

These archetype-specific temperature anomalies were then linked to (geographically-referenced) Address Point Data Ordnance Survey, Southampton, UK; see www.ordnancesurvey.co.uk/business-and-support/productsaddress-point.html accessed 3 August 2018) to classify dwellings by postcode location, to which mortality data were also linked. This enabled postcode locations and, thus, mortality data to be classified with respect to the overheating potential (temperature anomaly indices) for their place of residence (Figure 22). The mortality data so classified were analysed for the association between mortality risk and outdoor temperature and the statistical interaction with the overheating index.

FIGURE 22.

Illustration of the classification of postcode locations with respect to dwelling type and overheating indices (temperature anomalies). Orange and red dwellings have positive temperature anomalies. Based on data from OS MasterMap^® Building Heights [FileGeoDatabase geospatial data], Scale 1 : 2500, Updated: 29 November 2014, Ordnance Survey (GB), Using: EDINA Digimap Ordnance Survey Service, <http://digimap.edina.ac.uk>, Downloaded: 2016–09–15 12:27:53.213 and Versik Analytics^® UK Buildings [FileGeoDatabase geospatial data], Updated: 1 November 2014.

Results

The overall (regionally ‘averaged’) slope for the association between high temperature and mortality was a 3.3% (95% CI 2.5% to 4.0%) increase in mortality risk for each 1 °C rise in temperature (lags of 0,1 days).

Table 7 shows the summary results of the modification of this relationship with each of the eight temperature anomaly indices. The pattern of results is mixed. Only one coefficient for effect modification was nominally statistically significant, namely that relating to the temperature anomaly for the daytime bedroom temperature on very hot days. This indicates a 1.34% (95% CI of 0.37% to 2.32%) increase in the temperature–mortality slope for each degree Celsius increase in temperature anomaly. Therefore, this suggests that people living in dwellings that are likely to develop higher bedroom temperatures during the daytime in very hot weather are more vulnerable to heat-related death.

However, it should be noted that across all eight coefficients the pattern of results does not seem consistent. The point estimates for five out of the eight effect modification coefficients are negative (although with CIs including zero).

Discussion

This analysis was an attempt to obtain direct empirical evidence that people living in dwellings that are more likely to overheat during hot weather would have greater risk of heat-related death. Overall, its results provide only weak, although plausible, evidence that such modification of risk does occur, and specifically that those living in houses in which the bedroom temperature increases more than in most other houses during periods of very hot weather are more vulnerable to heat-related death.

It is not possible to draw a very robust conclusion from this result, especially given that only one out of eight different coefficients examined showed evidence of effect that was clearly different from the null. The overall pattern of results suggests that the evidence for modification of heat risk by dwelling characteristics is greater on very hot days and on hot days and for markers relating to bedroom rather than living room temperatures and for daytime rather than night-time anomalies. Arguably this is a mismatch with expected time–activity patterns. Given the rooms that people are most likely to occupy different times of day, a priori prediction would suggest that the most sensitive markers would be those reflecting daytime living room temperatures or night-time bedroom temperatures.

Nonetheless, it seems very probable on physiological grounds that the risk of heat-related mortality would be greater in dwellings that are likely to exhibit higher indoor temperatures during hot weather. It is also worth remembering that this analysis depends on indirect methods of classification for heat exposure, specifically using building physics models to classify the overheating propensity of different dwelling types and then to assign dwelling types to postcode locations to which mortality data were linked using the postcode of residence. All of the steps entail an imperfect link and hence there is likely to be a degree of misclassification, which is likely to introduce a conservative bias (i.e. against finding a modification effect using such indirect markers). This, and the plausibility of genuine effect modification, adds some weight to the interpretation that the observed association reflects a true causal mechanism, even if it is imprecise.

Therefore, the evidence is far from conclusive but does offer a weak indication of effects that would be entirely compatible with reasonable expectation. The evidence needs to be further strengthened and made more precise if it is to be truly useful in helping to inform assessments of the role of building characteristics as determinants of heat-related health risk. Its importance is perhaps more qualitative. Energy efficiency measures more often act to increase, rather than decrease, the propensity to overheating and, thus, could represent a potentially important unintended adverse consequence of retrofit energy efficiency upgrades that may come to have increasing significance in the context of increasing temperatures under climate change. At present, our best judgement is that level of increased risk is relatively modest, at least while summer temperature distributions are not much more extreme than the present-day average. Helping to improve the empirical evidence on this risk would, however, be an important goal for future research, and an immediate precautionary practical step would be to consider the additional measures that might be included alongside energy efficiency upgrades to help protect the indoor environment against high temperatures in summer. This would be an important adaptation in any case, given the likely increase in summer temperatures as this century progresses.

Methods

We compared two specific forms of policy:

1. A policy of fuel subsidy based on the 2014/15 entitlement of the UK government’s Winter Fuel Allowance scheme: £200 per household per year for households containing someone born on or before 5 January 1952 or £300 per household per year for households containing someone over the age of 80 years. The £200 WFP is equivalent of 4000 kWh of heating energy at our assumed 2015 price of £0.05 per unit,78 if all were spent on improving indoor temperatures.

2. An expanded programme of HEE improvement, installing in each qualifying dwelling all energy efficiency interventions (including loft insulation, double glazing, solid and cavity wall insulation, boiler replacement and gas central heating) when such measures are currently (2011 data) absent or suboptimal. We assumed that ‘qualifying households’ were the same as those entitled to receive the Winter Fuel Allowance (policy 1), ensuring the same target households and, thus, direct comparability of the two strategies.

We considered timetables for the HEE improvements based on reinvesting savings from WFPs, entitlement to which was assumed to lapse once a dwelling had undergone a full energy efficiency upgrade. However, even with substantial seed money in year 1 of £0.7B, half of the current WFP annual budget for England, simple calculations showed that the investment programme would not be complete until after 2090. Therefore, we assumed a substantial upfront capital investment of £20B to allow the upgrade to all 4.5 million target dwellings over 5 years. This equates to a rate of upgrades of around one dwelling per minute over 5 years, which is the rate required for the whole stock to 2050 to meet climate change abatement targets. The annualised cost of this capital investment over the lifetime of the upgrades is very similar to that of current WFPs, assuming continuing very low rates of interest.

The impact had on the indoor environment and health

The impact that the outlined policies had on health and other outcomes was estimated by refinement of a model, HIDEEM,31 originally developed for use by the DECC to assess the impact had on health of energy efficiency investments in the housing sector. Core to the model is the 2010/11 EHS,79 which contains data on 16,000 dwellings in England and their 31,000 occupants. The indoor environment (temperatures, indoor pollutants) and energy performance [heating energy and carbon dioxide (CO₂) emissions] of EHS dwellings were modelled by linking their data with that of simulations carried out in a validated building physics and airflow model, CONTAM, developed at the US National Institute of Standards and Technology. 33 Simulations were carried out for 16 representative archetypes of dwelling forms (seven types of houses, nine types of flats), under a range of assumptions about air permeability (‘building leakiness’) and ventilation regimens, and the results were matched to the 2010/11 EHS on the basis of dwelling type, floor area and notional permeability. The result was a (modelled) distribution of indoor environmental conditions (indoor temperature and air pollutants) for a representative sample of English dwellings, which was then ‘perturbed’ to estimate changes in personal exposures following policy interventions.

Changes in winter indoor temperature and fuel use were determined by a method based on the empirical relationship between indoor temperature and the E-value of the home, as described in Hamilton et al. 26 and previously applied in modelling for the National Institute for Health and Care Excellence. 80 The E-value is the heating power input (watts) required to maintain a 1-kelvin temperature difference between the indoor and outdoor environments and is, thus, an indication of whole-house energy efficiency. This empirical relationship, shown in Figure 2, was derived from temperature measurements of dwellings that underwent energy efficiency upgrades as part of the Warm Front scheme. 26 A very similar functional form has more recently been obtained by the authors from data in the 2011 EFUS,30 as yet unpublished. The gradient of the relationship between the SIT and E-value indicates the degree to which the benefits of energy efficiency are taken as having higher indoor temperatures and how much as energy/cost saving. With an assumed average cost for heating energy of £0.05 per kWh, the relationship provides a basis for calculating the change in (winter) indoor temperature associated with WFPs, depending on the E-value of the dwelling (but see also Beatty et al. 81).

Quantification of health impact

The health impact that changes in indoor air quality and temperature had was modelled using life table methods similar to those of the IOMLIFET model,45 which estimates patterns of survival in the population over time using age- and cause-specific mortality rates. Life tables were set up using 2010 age-specific population and (disease-specific and all-cause) mortality data for England and Wales from ONS, with separate life tables for men and women. We modelled changes in five exposures (indoor temperature, STS, indoor and outdoor sources of PM_2.5, radon and mould) and the selected mortality outcomes listed in Table 2. Morbidity impacts for these same outcomes were estimated from the mortality estimates by applying age- and cause-specific ratios of years of healthy life lost owing to disability (YLD) to the overall YLL derived from the World Health Organization’s Global Burden of Disease data.

Capital costs for HEE improvements and the costs of energy for home heating were based on data published by the Department for Communities and Local Government (now the Ministry of Housing, Communities and Local Government). 82 We did not include costs associated with modelled changes in contacts with the NHS.

From the models we determined four parameters of impact that are associated with the WFP and HEE policies: health (the sum of QALYs for all modelled health effects relating to changes in temperature and indoor air quality), CO₂ emissions (based on assumed trends in the carbon intensity of domestic fuel use), energy costs adjusted for projected trends in domestic fuel mix and price and a measure of socioeconomic differentials in impact. These were assessed over time horizons of 5 years and 20 years, with discounting of costs and benefits at 3% per annum.

Results

Across England as whole, we estimated that a total of 4.5 million dwellings were occupied by households qualifying for WFPs. The comparison of the effects of the two policies is summarised in Table 8. For all of the parameters listed, the HEE programme appears more beneficial than using equivalent funding to perpetuate the WFPs.

Our scenario proposes fairly full implementation of all reasonably straightforward retrofit HEE measures, which would probably achieve close to 0.4 °C increase in SIT, which is nearly twice the temperature increase achieved even if WFP recipients spend that money on additional heating.

In terms of health benefits (measured as QALYs), again the HEE programme appears to offer greater gains than those attributable to WFPs. However, one of the clearest and very obvious differences is that the HEE programme reduces the stock energy demand and associated CO₂ emissions, whereas the WFPs act to increase them by encouraging householders to burn more (or effectively subsidise) fuel.

Not everyone would win by comparison with regard to status quo, as illustrated by the plot of the expected distribution of temperature changes comparing HEE and WFP programmes (Figure 23). The premise here is that the universal WFPs would be withdrawn as dwellings receive full energy efficiency upgrades.

FIGURE 23.

Distribution of change in SIT with the HEE relative to WFPs on the assumption that currently universal WFPs would be withdrawn following the installation of HEE measures.

Discussion

Comparing the effect on health and GHG emissions of WFPs and HEE investments is seemingly very specific, but it illustrates an important point of principle. Burdens of winter- and cold-related mortality and morbidity remain a major problem for the UK. At the same time, by almost any objective measure, the collective global response to climate change, and that of the UK specifically, has been inadequate. No government has yet succeeded in implementing policies of the scale and ambition needed to alter this, which is doubtless in part because of the perceived economic cost to the Treasury, or to society as a whole, of doing so.

However, the analyses that we present here suggest that, for one major section of the UK population, namely elderly people, the UK Treasury already commits expenditure to combat FP at a very similar annualised cost to that needed to transform the energy efficiency of all homes currently occupied by the same population. Moreover, our models indicate that energy efficiency upgrades would have a greater impact on health than WFPs and would decrease rather than increase CO₂ emissions. In other words, redirecting the WFP budget to HEE interventions would achieve all of the avowed aims for which the WFP is provided and also meet the UK’s HEE investment targets for GHG reduction in this part of the housing sector. Admittedly, such a policy would entail an upfront capital investment, but the debt repayments would be almost the same as the current WFP budget if (possibly appreciable) additional administrative costs are ignored. Moreover, as already noted, the accelerated speed of retrofit installations that this would achieve is needed for the whole period up to 2050 to meet climate change mitigation targets. Therefore, in effect, it represents no more than a prioritisation of the elderly in a programme of investment that should already be occurring and continued for 35 years.

Our comparison of the relative benefits of HEE investment is based on the results of a model, and all models are uncertain. In our judgement, however, it is unlikely that those uncertainties materially affect the general conclusion. The need/opportunity for HEE investment is based on analysis of data from a recent EHS. The current cost of WFPs is known, and although estimates of the capital costs of HEE investment vary somewhat, we used reasonably authoritative figures published by DECC. The estimates of the change in winter indoor temperature for both policies were at least in part based on the same empirical relationship and appear consistent with published before-and-after measurements. 27 There is also increasing evidence about the health benefits of HEE interventions,9,83,84 even if our estimated changes to the indoor environment are imprecise. It is unlikely that we have so overestimated the relative advantage of HEE interventions in terms of health or energy costs as to reverse the sign, and it seems quite consistent with expectation that HEE investments decrease CO₂ emissions whereas WFPs do the opposite.

Redeploying WFPs would raise various practical and ethical issues. Even if there are more winners than losers, safeguarding some categories of loser (e.g. the poorest households) would seem important. It is likely that there would be additional administrative costs with HEE investments, which we have not attempted to estimate; neither have we taken account of the fact that a proportion of elderly people will move home or suggested what approach to use for subsequent cohorts of people who reach pension age after the 5-year programme. In addition, of course, there are questions of equity and fairness, including the fact that larger homes, generally occupied by wealthier families, would need, and thus receive, more investment (unless capped) and the fact that private landlords would be potential beneficiaries unless the government prescribed this as a mandatory investment cost that private landlords should bear themselves.

The views of potential target householders show that the policy choice would have different importance for and impact on different households. Climate change objectives are not a priority, and poorer families in particular value, and even depend on, the WFP as a universal entitlement. However, there appears to be a degree of acceptance that HEE improvements could offer relative advantages for health, well-being, security and even pride in one’s home.

Whether the intention is to protect health, to ease household budgets or to limit CO₂ emissions, our estimates suggest that more can be achieved through HEE. Even though the argument is more complex with regard to equity, there is a case to favour HEE over WFP if consideration is given to the responsibility of current to future generations.

In conclusion, for most households, HEE improvements of similar annualised cost to current WFPs achieve greater improvements in indoor temperatures and health, and reduce rather than increase CO₂ emissions. Replacing policies that incentivise additional fuel consumption for home heating with a rapid full-scale programme of energy efficiency could help transform the UK stock to achieve important benefits for health and crucial climate change abatement targets without imposing undue additional financial burdens.

Workshop

We elicited inputs to the MCDA work through three workshops: two (one small and one larger) held at the London School of Hygiene & Tropical Medicine (LSHTM) and one hosted by Age UK. In each case, we invited a range of stakeholders from non-governmental organisations, local and national government departments, academia and others. In the first workshops (10 participants: four academic, four from charities and two housing organisations), we discussed plans for applying the MCDA method and asked for inputs on the policy comparisons and the proposed criteria for their assessments, which were further elaborated in a more general stakeholder workshop (26 participants: 11 academic, five from charities, two from housing organisations, four from national or local government, five from Public Health England/National Institute for Health and Care Excellence and one from an international organisation). In the last workshop (formed of 24 participants: 11 academic, three from charities, three from housing organisations/consultancies, three from national or local government and three from Public Health England), we showed the use of the MCDA tool in a ‘live’ demonstration and sought inputs on the interpretation of its results. We initially showed results using analyses performed ‘offline’ ahead of the workshop using equal relative weightings for the selected criteria but then used it live to explore the results interactively. This showed how the results (in terms of the overall intervention scores and, hence, the choice of which intervention performs best) are sensitive to the values of the relative weights and the normalised impacts.

The stakeholders were broadly in agreement with the choice of intervention options to compare, but the choice of the criteria and how they altered the option comparisons generated considerable debate. The main issue of discussion was the extent to which the overall performance scores changed with different choice of criteria weightings and hence the potential variation that could arise given the different perspectives/priorities of different stakeholders. Our criteria reflected several important perspectives, including public health (reduction in mortality and morbidity), NHS (increasing cost savings associated with diseases averted), climate change mitigation (reduction in energy use) and inequality (reduction in health inequality between FP vs. non-FP, least deprived vs. most deprived and pensioners vs. non-pensioners). However, we discussed that an alternative set or a subset of criteria could have been chosen, would have been equally valid and would have produced different performance scores for the interventions. There was discussion that the value of the MCDA approach is, therefore, not so much the single headline ‘answer’ that any one analysis generates but that it may yield understanding (if used as an interactive tool) about the importance of different factors in reaching particular results.

We showed online how the performance of the interventions in terms of their ranking depend on the weights assigned to the criteria and the values of the normalised impacts. The participants appreciated the transparency that the MCDA tool brings, particularly in relation to the influence of the relative weights attached to different criteria (which reflects the clear meaning attached to the changes in the weights). However, they were more concerned with the sensitivity of the results to the estimates of normalised impacts, the calculations of which are model-dependent and more opaque. This emphasised the potential value to decision-makers not just of the results but of understanding how different exposures and outcomes are affected by different interventions.

In summary, participants engaged actively with the MCDA tool because the evidence of impacts, the importance attached to the specific criteria (relative weights) and the overall performance scores were clearly displayed and the changes in performance scores could be tested against changes to the criteria weightings. This provided insight into how the results of the optimal choice depend on the priority given to different criteria. There was some desire also to have further detail on how interventions result in the specific exposure changes and outcomes.

Information needs for improved decision support

The analyses suggest areas in which better evidence would be helpful to improved decisions on HEE interventions. One of those is evidence on the consequences of interventions for air exchange. For example, as Figures 25 and 26 show, in circumstances where there is compensatory ventilation, the six insulation measures [cavity wall insulation, draught proofing, (double) glazing, loft insulation, solid wall insulation] were broadly similar with regard to the impact that they had on mortality, morbidity and NHS cost savings and were appreciably better than that of boiler replacement for these criteria, although the impact had on inequalities was generally worse. However, the situation is almost reversed when there is no compensatory ventilation, with boiler replacement having a mostly greater impact with respect to mortality, morbidity and NHS cost savings.

Therefore, the accuracy and validity of evidence on ventilation changes appear central to the assessment of the relative position of intervention options, and indeed to the overall benefit of intervention measures. However, there remains uncertainty over the quantification of ventilation changes associated with each form of intervention. Hence, obtaining improved empirical evidence on the consequences of interventions for ventilation, and how those consequences may vary with occupant behaviour, is important and merits further research, especially as adverse effects on ventilation may be detrimental to health.

A second area relates to criteria weightings. The outputs of the MCDA are sensitive to the weights given to each criterion (and also to the criteria chosen), as illustrated by altered patterns when alternative weights were used. For example, in analyses that assume compensatory ventilation, the optimal choice was double glazing when equal weight was assigned to each criterion, but replacement boilers were favoured when there was heavier weighting on energy savings (e.g. as might be appropriate for stakeholders with greater concern with carbon impact or FP). As the workshop discussions emphasised, the question of perspective (i.e. whose views) is potentially very important. But there are also complexities here, because the benefits and costs of interventions potentially accrue for different sections of society and government, and so the framing of the options is also important. If the MCDA is used to support ‘real-life’ decision-making, it would be valuable to develop through participatory processes, greater shared understanding of the ultimate objectives of HEE programmes (e.g. in relation to such outcomes such as health, FP, climate change and inequalities) and to elicit criteria weights appropriate for balancing those objectives. In the analyses presented in this report, we focused on the health, environment and inequality impacts from a broadly societal perspective.

A third area needing improved evidence is the transparency and empirical basis of evidence on the normalised impacts (on health and other criteria). This was an issue of some discussion among workshop participants because many of the relevant data were generated by models. Discussants generally wanted to understand more about how model results were derived and the underlying assumptions so that they could judge the reasonableness of suggested impacts. To some extent this can be addressed by showing intermediate model outputs and underlying empirical relationships, but there is a wider issue of needing more empirical evidence on the relationship between housing characteristics and dwelling performance, to provide both further scientific validation and greater transparency for non-expert stakeholders.

A further issue is the characterisation and presentation of uncertainty. For this work, we used only simple sensitivity analyses to explore the effect of variation in preferences. Methods to represent uncertainty in model and other outputs is desirable but also complex because of the many factors (both structural and parametric) that potentially contribute to the uncertainties in these MCDAs. Conventional Monte Carlo methods are difficult to implement with the fairly complex sets of model-based outputs needed for assessing HEE interventions against multiple criteria. Again, this is an area that would benefit from further research.

In conclusion, the MCDA provides an analytical framework of potential value as a tool to support decisions on HEE interventions, especially if used in iterative fashion to explore options with relevant stakeholder groups. The robustness of those comparisons would be improved by strengthened empirical evidence, specifically on the effect of interventions on ventilation changes, but also by participatory engagements with stakeholders on the framing of the objectives of intervention programmes and the criteria by which they should be judged.

Abstract

Increasing the uptake of HEE technologies is key to multiple policy objectives, including meeting carbon reduction targets, ensuring energy security, reducing excess winter mortality and morbidity and eliminating FP. However, framings focusing on ecology and health improvement do not dovetail neatly with how householders think about HEE installations. The qualitative component of this project drew on interviews with households in three areas of England to better understand why and what HEEs were adopted, how decisions about uptake were made, how HEEs were managed post installation and what impacts HEE-related practices had on health. In analysing the data, we identify four distinct framings of HEE, which have different implications for future uptake rates and for potential health gains: home improvement, home maintenance, subsidised public goods and contributions to sustainability. Although policy framing of HEE more explicitly in consumerist terms might improve rates of uptake (and public health), at least in the short term, this might have costs in terms of eroding the ‘common good’ of commitment to environmental sustainability.

Policy shifts in energy problem framing in the UK

Different ways of framing policy have implications for ‘setting agendas, defining goals; characterizing options; posing questions; prioritising issues; deciding context; setting baselines; drawing boundaries; discounting time; choosing methods; including disciplines, expertise or informal knowledge, and handling uncertainties’. 94 Which solutions are considered legitimate, and which actors are brought into the policy process, depends on how those policies are framed. Thus, framing is key to how particular actions, strategies and policies are likely to be selected, prioritised and implemented.

Current policy context

The 2010–15 Conservative government fundamentally reoriented domestic energy policy by advancing the Green Deal, a market-based policy launched in 2013 targeted to improve HEE by providing loans to homeowners for energy efficient installations, repaid via energy savings. The Energy Act 2011106 provided the legal basis for the Green Deal and Energy Company Obligation107 (provision for poorer communities and higher cost installations). It also set out the ban of ‘inefficient’ rental properties, which in 2015 were defined as those with an energy performance certificate rating of F or below. The rhetoric of ‘energy efficiency’ remains, with a UK National Energy Efficiency Action Plan linking UK policy to a European Energy Efficiency Directive. Energy efficiency is framed as central to low carbon growth, reducing CO₂, reducing bills for consumers, increasing productivity through ‘energy efficiency’ investment and securing energy supply. 108

Current political framings of HEE propose solutions in terms of aggregate household installations. Improving the ‘installation per year’ rate is key to meeting internationally agreed targets, and removing impediments to these installations is crucial to its success. Numerical targets and aggregate solutions go hand in hand. Such policies are utilitarian, acting in the service of liberal political philosophy and market dynamics; they aim towards a ‘total good’; that is, benefits at a population level are seen to accrue from scaling individual decisions, made in the interests of those who can benefit from either the purchase of HEE installations, or those who live in homes where they have the direct decision-making authority (i.e. owner-occupiers) to take part in ‘free’ HEE installation schemes. Other groups, especially those in privately rented accommodation who cannot make decisions on behalf of their landlords, those not meeting criteria/outside catchment areas for free installation despite HEE problems, and those for whom installation would involve high upfront costs, potentially miss out because of this framing and its market-oriented solution. This ‘total good’ approach sidesteps the fostering of a ‘common good’: in this case, an orientation to improvement of the housing stock as something that benefits the whole population, irreducible to the aggregation of individual benefits. Common goods are antiutilitarian and their effect is to produce widespread advantages for a community, for example a sustainable environment or trust. By understanding householders’ own narratives about HEE technologies and by exploring HEE installation as part of everyday household practices, we can begin to understand connections to ‘total’ and ‘public’ goods and infer implications for political and social framings. 109

Home energy efficiency and health

Home energy efficiency has become a public health issue in terms of direct health outcomes for households from inefficiently heated homes, indirect outcomes arising from resulting FP and also the public health impacts of how such homes mediate the impacts of cold weather. HEE is, then, a fundamental element of policies aiming to reduce cold-related mortality and morbidity. 92

To date, the evaluative literature on how housing improvements have an impact on public health is limited. A recent systematic review indicated that few studies have reported statistically significant improvements in health following housing improvement,84 given that most studies have not been powered to detect clinical changes110 and are often focused on short-term evaluations. 111 However, the review concluded that HEE interventions that improve thermal comfort can lead to health gains, especially when houses were previously inadequately warm or when residents have chronic respiratory disease. A recent meta-analysis also concluded that HEE had a small positive impact on health. 83 The pathways through which inefficient housing relates to health, however, are little understood, representing a ‘black box’ of mediating factors between intervention and impact. 112 Reduced fuel bills113 may lead to increased disposable income, which could be used to improve diet. 114 Increased thermal comfort and control over heating/hot water114–116 may lead to both increased use of indoor space and emotional security,113–115 potentially benefiting privacy and interpersonal relationships. 114,116 HEE items may give householders more pride in and positive feelings about their homes, serving a positive well-being effect. 114 Health benefits from HEE installations may also reduce time taken off work or school117 and reduce sleep disturbances in children caused by wheeze and dry cough. 118 Yet complex mechanisms remain unclear. There are gaps in the literature about how salient these pathways are across different population groups (e.g. through the lifecourse) and how far preferences for open windows, indoor temperatures or reducing fuel use are rooted in personal circumstances or more deeply seated beliefs relating to health, climate change or other framings. Day and Hitchings119 found that heating was the last thing that older householders would compromise on in terms of sustainability, but it is not known how far such trade-offs between sustainability and comfort might be affected by future changes in householders’ attitudes to sustainability or in younger cohorts.

The decision about whether or not to prioritise fuel use reduction or temperature increases is one example of the variable responses possible to HEE improvements in the household. Previous research identifies a range of responses to HEE interventions, and to advice on cold-weather related health in general, but has not gone much beyond noting that there are differences. Harrington et al. ,113 for instance, identified a range of views in households in the Warm Homes Project and a range of coping strategies used by older citizens but only suggested that these were likely to be cultural and historically specific. Psychological factors are also likely to modify pathways. Critchley et al. 120 looked specifically at those households that maintained cold temperatures post installation of heating improvement interventions, distinguishing those who preferred such temperatures and those for whom constraints had limited temperature increases; cold, they suggest, may well have different health effects for those who psychologically adapt to or who prefer colder temperatures and for those who perceive little choice over their indoor temperatures. If we take the issue of comfortable temperature being a subjective measure seriously, then feelings of control over it are likely to be as important as objective temperature in the pathways linking HEE and health. This suggests potentially complex pathways linking HEE improvements and health, with possible feedback mechanisms such as cultural norms relating to ‘normal’ indoor temperature rise.

Aims

This chapter has three aims. First, we aim to understand which different framings intersect with and are coproduced by householders as they install, or forgo the installation of, HEE technologies. Explication of these helps foreground our second aim, which is to shed light on the (blocked) pathways and mechanisms between HEE installations and health improvements, which have hitherto been ‘black boxed’. Third, we consider the consequences for health improvement by showing the summative and relational conceptions of the social framing that are invoked in policy and householder discourses.

Methods

To meet these aims, we draw on qualitative accounts of how home energy practices are integrated into everyday household decisions across a range of household types. These are derived from 12 household interviews, each comprising 2–4 participants, and 41 individual interviews, which were purposively sampled across three geographical regions in the UK. Household interviews were included to provide insights into the ways in which decisions might be taken collaboratively, and to observe, when possible, HEE interventions in situ. 134 When practical, householders demonstrated their use of HEE artefacts and sometimes led a tour of the house to allow observations to be made. The sampling strategy aimed to generate enough data to provide analytical comparisons across the following factors: local norms about HEE interventions (indicated by high/low density of take-up), type of HEE adopted and length of time since adoption (no installation/1 year post/3 years post), household type (working age, no children/family with children/older citizens), area deprivation, geographical region and those with the more extensive HEE upgrades likely to be necessary in the future.

Households were recruited at local festivals and by invitations to participate mailed with a stamped addressed envelope to addresses within three UK postcode areas (north, south east and south west) identified as having high levels of HEE uptake in the last 3 years by the HEED. Within these areas, households were sampled purposively to include a range of housing and household type, tenure, age and income level.

Interviews used a topic guide to explore the experience of applying for and organising the interventions (or decisions about not to install); narratives of how life in the home was before, immediately after and now in relation to the (considered) intervention; the impact had on physical and mental health; the impact on fuel costs; comparisons with neighbours/family members in similar homes without energy interventions; views on the importance of energy efficiency interventions compared with other potential benefits to improve health and well-being; and underlying values and beliefs relating to domains such as indoor temperature, ventilation, fuel use and responsibilities for climate change.

All interviews except one were recorded and part-transcribed after participants’ consent was obtained. All early interviews and selected later ones were transcribed in full, and analysed qualitatively using methods from the constant comparative method for inductive analysis as well as a more deductive content analysis around (1) the key themes of interest related to well-being and (2) the relative importance users placed on these interventions in the context of health, well-being, costs and climate change. All data have been anonymised, with pseudonyms assigned to participants and places.

Results

Do home improvements lead to health improvements?

In general, participants recounted experiences of increased warmth following home improvements, with some older participants happily reporting a reduced need to wear thermal clothing indoors. However, health pathways within households installing HEE technologies via home improvement framings were not straightforward, and the mixed effects on thermal comfort and ventilation within any house make generalisation difficult:

It’s hard to isolate what’s related to the property and what’s related to just being a bit more sorted in general. The upheaval, again as I say, of doing up a house you end up feeling quite stressed and run down but you don’t know if that’s because of your job or because of your house or because of your health or because of any other number of underlying factors. So it’s hard to say whether it’s improved directly because of the house and the health of the house.

Dani, Area 3

Given that multiple installations are often installed in a relatively short space of time in the context of wider, sometimes snowballing, structural changes to the house, a number of problems can occur. First, protracted installation processes can worsen health pathways in the short term. For example, dust can accumulate – often as an indirect consequence of HEE adoption – presenting respiratory health risks. Similarly, installations may be delayed, for example by renovation errors or prioritising the completion of communal living spaces before bedrooms. Indeed, inadequate installation of heating systems and insulation during extension works can increase fuel costs, diverting funds from things such as nutrition:

It was just a complete shambles. At that stage [after the initial installation of a new heating system and insulation] our energy bills were massive. Our direct debit was very high. It was going up relentlessly and I thought ‘this isn’t right’.

Colin, area 1

Second, householders often have to develop new practical relationships with technomaterial artefacts to benefit from any savings on bills. However, this can be difficult to achieve or can be seen as unimportant, even a few years after home improvements have taken place:

I have to admit that I haven’t got round to looking – there’s an option of keeping a reservoir of water warm and it might be advantageous, I don’t know. Because, if you don’t do that, a lot of water is wasted every time you switch on the hot tap to get it up to temperature.

John, area 2

Third, home improvement framings can also influence the installation of HEE technologies that subjectively serve cultural rather than efficiency aims, leading to greater expenditure on energy than is entirely necessary, which may have indirect effects on health through reduction in disposable income. Fourth, prioritising aesthetic, decorative and functional changes to internal layouts in the course of home improvements, and conducting improvements in stages, can potentially lead to negative pathways in terms of thermal discomfort, decreased ventilation, worsening draughts and higher bills than might be necessary:

Once it started getting cold around Christmas, I did feel that the warmth from in [the kitchen] was being, because it’s all open plan, being sucked up the stairs and so I was tending to be a little bit chilly, and I also don’t think I’ve got the thermostat in the right place, because I think it’s a bit too sheltered there, and so then the heating goes off but the heat’s all gone upstairs and then I’m cold. So . . . I’ve got a blanket [pinned up] at the top of the stairs.

Rachel, area 1

Peter decided against getting external wall insulation partly because he was prioritising improvement costs elsewhere in the home and partly owing to aesthetic reasons, but this meant that his daughter’s bedroom remained damp:

It does make you feel awful as a parent, that your daughter is sleeping in a room that has mould growing in it. You don’t really want that, necessarily. But it’s, I think the problem, and it’s been a particularly wet autumn and winter, which has been, it hasn’t happened before in her room, which makes me think it won’t happen again if we’re, with a combination of the radiator and the weather, I think, proper weather, hopefully it will sort it.

Peter, area 2

Negative health pathways, of course, may be passed on to future owners or tenants. When local authorities have more effective building regulation enforcement methods, this can be somewhat tempered.

When home improvement frames are entirely enacted by homeowners through their do-it-yourself activities, limitations on potential health improvements can follow when a householder does not consider a potential HEE issue to be within their limited expertise or because of the amount of disruption envisaged and, hence, it is something that will not be done.

However, well-being may improve, as HEE technology installed during the course of home improvements can enable relaxation, foster a sense of social identity and home-making, and provide ontological security in respect of managing and enhancing the foundational relation and improve social relations in the home. Moreover, satisfaction and pride can follow on from realising ambitious improvements that incorporate HEE:

I can see that it gives him [husband] pride and I’m very proud of him having achieved this and giving us a home like this.

Tracy, area 2

I had the bi-fold doors put in and I can now sit on my settee and see my garden and before [when an inefficient window was in its place] all you could see was sky. That is a big plus in my life.

Rachel, area 1

Furthermore, significantly reduced bills can provide a well-being effect because of a sense of victory against the perceived ‘oligopolistic’ energy market.

Unintended positive consequences for health pathways may also occur. For instance, internal wall insulation installed for thermal comfort reasons or double glazing installed to eliminate draughts may also provide sound insulation from traffic and pedestrians outside the home, thereby aiding sleep and relaxation:

Like, the windows, because we could hear noise as well from outside, so it [the window replacement] stopped the noise.

Kirsty, area 1

Lower bills can also be a positive unintended consequence, alongside contributions to broader public health outcomes by lowering one’s carbon footprint.

Health pathways from home maintenance

The framing of HEE as necessary for home maintenance was often related to warmth, ventilation and hygiene to a much greater extent than the home improvement framings. In this way, specific aspects of health pathways could be affected leading to greater thermal comfort, improved mental health (e.g. peace of mind at having more reliable technology, fewer interpersonal grumbles and not having to worry about basic needs of warmth and sanitation, which could otherwise niggle at them), the increased use of rooms and fewer worries about specific health hazards such as carbon monoxide poisoning.

Compared with installations carried out during home improvements, there was the potential for greater generation of relational goods pertaining to shared well-being, such as reciprocity, neighbourliness and interpersonal care. For instance, some householders co-ordinated their maintenance with neighbours, sharing the cost of scaffolding and external insulation installations, thereby enhancing interhousehold relations.

However, the specific rather than more holistic action on the foundational relation can leave other health pathways unattended. The slower processes of HEE domestication in cases of home maintenance framing were a result of lack of capital, cluttered lofts or simply not seeing it as a pressing priority. There were few negative health pathways created by this framings of HEE; however, pre-existing negative pathways within a household could continue unabated, for example lingering dampness or draughts in certain rooms:

It’s damp and dampness that used to be there.

It’s like damp in the air and getting, like, condensation.

Has that ceased to be a problem?

It’s still a little bit, still there sometimes.

Area 1

Furthermore, the socioeconomic context of increasing fuel prices meant that the few HEE technologies installed during maintenance seemed to have little effect on reducing bills compared with holistic changes carried out in home improvement framings.

Health pathways from public good framings

Framings that led to installations within public good framings could have similar health pathways to other framings; ‘free’ loft insulation could improve thermal comfort and aid ontological security, and subsidised windows could improve air quality and warmth and reduce damp:

I think it was warmer [during the winter after window installation], because we also used to put the heating on but we didn’t need to put it on as much, I don’t think.

Ellen, area 1

Cavity wall insulation often had an imperceptible effect on thermal comfort. Indeed, occasionally householders said that, before the interview, they had not thought to connect the increase in thermal comfort they experienced with the HEE installation.

Publicly funded schemes also offer mechanisms that bypass the delay in HEE technology adoption forced by financing issues, thereby tackling any underlying health problems or risks posed by the foundational relation more quickly:

We would have done it inevitably anyway.

Oh yeah, it was on, we were getting close anyway.

It was on our hit list, it just made it happen a lot quicker.

Made it much more affordable.

Area 2

Some households found that they needed the heating on less after they had loft insulation installed. However, it was suspected that some older people failed to change long-term thermostatic regulation practices after the loft installation, with the result that they found conditions too hot. Although persisting dispositions may partly account for this, other reasons can include the way that householders are often removed from the installation process and, therefore, lack knowledge about the nature and function of the installed measure while simultaneously having little interest in the technology itself.

When HEE was framed as a matter of public intervention but such intervention was not forthcoming, health pathways could be exacerbated by a lack of funds to pay for a higher-cost HEE intervention. An elderly couple, for instance, hoped for a subsidised solution to connect them to mains gas or provide them with more efficient electric night storage heaters. This was needed because one of them was experiencing the early stages of dementia and beginning to feel increasingly cold at home during the evenings. As a solution could not be found, the consequence for them was persisting discomfort and increased fuel expenditure.

Indeed, when HEE was installed through this framing, energy bills were reported as showing only marginal reductions, if at all. Hence, there were no significant increases in disposable income that could be redirected to improve things such as nutrition, even after long-term adoption. Similar to participants framing HEE in other ways, these participants were also uncertain about effects on energy bills from general increases in fuel prices. Hence, there may be more potential for dissatisfaction with the financial effects of HEE post installation among people framing HEE in this way than among others.

Scenario 1

In this scenario, all of the calculated impacts are gains (i.e. the impacts have positive values). Say that we are evaluating five interventions (for one of the criteria) that result in the following gains (nominal values):

where 2 is the gain corresponding to the first intervention, 4 is the gain corresponding to the second intervention, etc. The highest impact in absolute term is 6 and so the normalised impacts are:

Scenario 2

In this scenario all the interventions result in losses for the criterion (i.e. the impacts have negative values), for example:

Here the highest impact is ‘–1’. We first employ a linear transformation so that this number becomes 1. This is done in two steps: first shift all the numbers by +6 so that –6 represents the lowest impact (i.e. 0):

and then normalise in the usual way:

Therefore, ‘–1’, which represents the highest impact, has the normalised value of 1. Other impacts are transformed accordingly.

Scenario 3

In the third scenario there is a mixture of positive impacts (i.e. gains) and negative impacts (i.e. losses):

The highest impact here is 6. We should apply a transformation to make it 1. Here, we shift first the values by the absolute value of the lowest negative impact (i.e. 4):

and then normalise in the usual way: