Results

COUNTY LEVEL ANALYSES

The geographical relation between climate and housing quality is shown in figure 1 (counties that did not contain any Health and Lifestyle Study subjects are blank). Most areas of Britain that experience a poor climate are also characterised by poor quality housing, including Scotland, most of Wales, most of north east England and the areas around Leeds, Manchester and Birmingham. In the other counties that experience a poor climate, the poor climate is compensated for by good quality housing. In the other counties that are characterised by poor quality housing, the poor housing is compensated for by a good climate. South east England, apart from London, enjoys a good climate and good quality housing.

• Download figure
• Open in new tab
• Download powerpoint

Figure 1

Climate-housing mismatch.  

INDIVIDUAL LEVEL ANALYSES

Table 1 reports the standardised β coefficients that were obtained from the sequential regression models. The outcome variable in each case is the deviation of the observed FEV₁/FVC ratio from the ratio expected in healthy subjects on the basis of their age and height. The basic model (M1) shows that climate has an association with this aspect of lung function; an association that is independent of smoking status and the use of bronchodilator medication. Sequentially, M2 and M3 show additional independent associations with Registrar General's social class and housing stock quality. Housing wealth also has an independent association with lung function (M4), although with its addition to the model the association with social class is reduced and loses statistical significance. The final model (M5), consequently, contains climate and the two housing variables, together with the two potential confounders. The addition to M5 of interaction terms for climate by housing stock quality and climate by housing wealth produces standardised β coefficients of, respectively, 0.045 and 0.036; both of which are statistically significant (respectively, p=0.027 and p=0.031) on a one tailed test. The exclusion from these models of bronchodilating medication status produces values that differ little from those presented in table 1.

Table 2 reports the standardised β coefficients when model M5, minus the climate variable, is re-run separately on people resident in the worst quarter and the best quarter of the climate distribution. Within the worst quarter of the climate distribution, housing stock quality but not housing wealth has an independent association with lung function. Within the best quarter of the climate distribution, the situation of the two housing variables is reversed; housing wealth but not housing stock quality has an independent association with lung function.

Discussion

British climate data have been integrated with information about housing and lung function from a large representative survey of the British population to test the idea of an inverse housing law. We have demonstrated a sizeable mismatch (fig 1) between the climate determined need for good quality housing and its income determined32supply. Thus, the first part of the argument for an inverse housing law (mismatch between need and provision) has been supported.

The results of the individual level analyses deal with the second part of the argument (effect of mismatch on health). Our use in these analyses of dichotomised housing variables and the inclusion of bronchodilator medication status as a potential confounder, which may have attenuated the association of interest, mean that our results are conservative estimates. We have shown (table 1) that climate and both housing measures (stock quality and wealth) are independent predictors of lung function. The interaction terms (climate by stock quality and climate by wealth) suggest that the specific relation between climate and housing is also important. The nature of these specific relations is suggested by the separate analyses at each of the extremes of the climate distribution (table 2). Where climate is coldest and wettest, lung function is predicted by housing stock quality, which measures the physical characteristics of the housing; plausibly, its ability to protect against damp and cold. Where climate is warmest and driest, lung function is predicted by housing wealth, which more than stock quality is likely to also index the general affluence or deprivation of the household. Arguably, the relative importance of these wider aspects of living standards is greater where climate is less of a direct hazard. (Although it is in the expected direction, the association between smoking and lung function loses statistical significance at the extremes of the climate distribution, probably because of variable dilution, attributable to the inclusion of passive smoking, and geographical overlap between smoking and climate, so that defining a sub-set of the data by climate may have over-controlled for smoking). These results from the individual level analyses are consistent with the idea that the mismatch between climate and housing is associated with differences in respiratory health; and a plausible candidate as one factor in the north-south health divide.

One alternative explanation of these results can be discounted. The inclusion of smoking status (own and passive) in the regression model means that the results are not attributable to the higher prevalence of cigarette smoking in areas with harsh climate, such as north west Britain, nor among those resident in poor quality housing. A second alternative explanation of our results might argue that they are a spurious consequence of the association between social class and residence in poor quality housing. As already discussed, this could not be attributable to social class differences in the prevalence of cigarette smoking, but it might reflect social class differences in exposure to other respiratory hazards, such as atmospheric pollution or occupational fumes and dusts. This possibility is supported by the regression model that includes smoking, climate and social class, but not the housing variables; in this model social class is independently associated with lung function at conventional levels of statistical significance. When the housing variables are introduced into the model, however, the effect of social class is reduced and loses statistical significance. We interpret this to mean that in this population a considerable proportion of the social class effect on lung function is attributable to the component of class that is transmitted via income to housing.

A third alternative explanation, selective migration, cannot be eliminated in a cross sectional survey, so the results could be because of healthy people with good lung function migrating out of, and unhealthy people with poor lung function remaining in, north-west Britain and poor quality housing. Migration is more common during the early parts of adulthood, however, so the inclusion in our analyses of all ages over 18 years will have diluted any migration effect. Nevertheless, selective migration cannot be definitely discounted by these analyses, although its effect would be additional to the direct housing effect on health.

A further weakness also follows from the cross sectional nature of the data. Our measure of lung function indexes a chronic process that develops over decades. Our measures of housing quality, smoking status and climate, in contrast, are point measures that tell us nothing about the length of exposure to these factors. Consequently in our analyses we have been forced by the nature of the data to try to explain a chronic process that develops slowly over time in terms of a series of exposures that have been measured at one point in time. This limitation applies also to the substantive details of the argument, such as the difference in the sources of residential damp pre-1918 (no wall cavities or damp proof foundations) and post-1952 (low building standards leading to condensation). Unfortunately we are not aware of a dataset that would allow us to overcome this problem. In the absence of a longitudinal dataset containing the necessary information, the reasoned conclusions of these analyses probably provide the best answer to the paper's question. There is an inverse housing law in Britain and, on the balance of probabilities, it both damages respiratory health and contributes to social and regional inequalities in health.