The thesis conceptual framework (outlined in Chapter 2) demonstrates that area regeneration programmes attempt to induce change in health and well-being outcomes by developing economic, social and physical initiatives that are intended to impact positively on the determinants of health and well-being at both the individual and community levels.
With this in mind, a range of variables available from the SLS that represented direct or indirect indicators of the determinants of health and well-being (outlined in the conceptual framework) were chosen to be employed in the statistical analyses as independent variables. These variables therefore represented characteristics thought to be important in explaining changes on the outcome variables over the study period.
69 For example, at the individual level, educational qualifications and employment status were identified in the conceptual framework as key determinants of health that (primarily) economic initiatives attempts to target through supply side initiatives to improve the employability of residents in disadvantaged areas. Individuals with low educational qualifications have been found to be more likely to suffer from ill health than those with good qualifications (e.g. Stafford et al. 2008), whilst those from higher social classes and who have greater incomes have routinely been found to be more likely to be in better health than those from lower social classes who have small incomes (BMA 2011). Variables on qualifications, social class and economic status were available from the SLS and were therefore chosen for use in this study as independent variables which can influence health and well-being outcomes.
Issues around housing may also affect the likelihood of improvement in health and well-being outcomes. The conceptual framework identified housing as a determinant of health that physical regeneration initiatives can have an impact on by improving existing housing stock and building new better quality housing. With that said, the SLS allowed the inclusion of four independent variables pertaining to housing that were included in the statistical analyses. For example, whether individuals have central heating or not was included as an independent variable. Poor housing is associated with adverse health outcomes such as respiratory problems (Thomson 2006). Thus, central heating is a key housing issue as maintaining warmth during the winter months is more difficult and expensive if households lack central heating.
A variable on housing tenure was also available from the SLS and included as an independent variable. Previous research (e.g. Macintyre et al. 2000) has found that those who rent social housing are more likely to be in worse health than private renters due to exposure to stressors such as perceptions of stigma and low prestige, whilst exposure to undesirable neighbours, poor quality of dwelling and remoteness of landlords further compound these ill effects for residents of social rented property compared to those in privately rented accommodation.
Furthermore, a variable on household type was also available from the SLS and included as an independent variable. This variable was included as for example, lone parents to be more likely to experience poor health than other household types such as couples without dependent children by virtue of a higher likelihood of being unemployed (Begum 2004). Thus in this way household type may be considered a determinant of health and well-being that relates to housing as a general health determinant.
70 An SLS variable detailing whether residents live alone or not was also included as an independent variable related to housing. Social isolation is known to damage health and well-being and is experienced more by those who live alone, particularly older people (Blane 1985). Thus this variable was chosen for inclusion as an independent variable as it can have a key impact on the likelihood of an individual experiencing improved health. For example, housing regeneration initiatives such as the demolishing of flats and the building of low rise accommodation may facilitate increased social networks for isolated residents. However, conversely housing renewal may compound or cause social isolation if it involves the decanting of residents to areas for long periods of time and thus disrupting social networks.
The other individual variables employed in the statistical analyses (e.g. age, sex, marital status, ethnicity and car ownership) are characteristics that regeneration programmes cannot attempt to directly influence. Thus their inclusion is not influenced by the thesis conceptual framework; rather they are included as each of these characteristics has an influence on the health and well-being of an individual. For example, we know that older people are more likely to be ill compared with younger individuals, whilst in addition women generally live longer than men. Marital status can also predict health status as married individuals have routinely been found to be more likely to be healthy than non-married people (e.g. Asthana and Halliday 2006). Furthermore those individuals belonging to ethnic minorities have also been found to be more likely to be in poorer health than non-ethnic minorities in the UK (e.g. Nazroo 1997). Lastly car ownership is also a known predictor of physical ill health. For example Wiggins et al. (2002) found that car ownership were useful markers of social and material advantage that protected against the risk of reporting long term illness. Thus each of these individual-level variables were available from the SLS and included in the statistical analyses as independent variables.
The bearing that these variables may have in relation to each outcome variable is discussed in more detail in the results sections of the following empirical analysis chapters.
Other determinants of health and well-being that were included in the conceptual framework were not captured (directly or indirectly) by SLS variables for use as independent variables in the statistical analyses. For example, there were no SLS variables available that could act as direct or indirect indicators of physical activity and diet as a determinant of health. Nevertheless, the qualitative phase of this thesis aimed to ascertain whether the SARP programmes improved these aspects of resident’s lives.
71 In addition, no variables available from the SLS could act as direct indicators of community level determinants of health and well-being such as access to health services, leisure facilities and the general appearance of the area. These determinants of health and well-being are nevertheless focused on in the qualitative phase of this thesis. However, the other community level determinant of health outlined in the conceptual framework – population retention- was an aspect that could be focused on with SLS data that follows the movements of individuals over time. SLS migration data can also be viewed as a proxy for understanding how regeneration impacted on residents’ perceptions of the appearance of the regeneration area. For example if those who remain in the areas over time are more likely to be healthy than those who move out then this can perhaps be attributed to improvements in the physical appeal of the area. Furthermore, if those who move in to regeneration areas are more likely to be healthy than those who move out, then similar inferences could be drawn.
Thus, migration was included as an independent variable that can be viewed as an indirect indicator of population retention. However unlike the other independent variables outlined above, this independent variable forms a central part of the quantitative analysis in this thesis, which investigates the ‘moving escalator’ effect described in Chapter 2, that asks whether those who have their health and socio-economic outcomes improved through regeneration initiatives, move out to areas perceived as being ‘better off’. A full description of how this independent variable was used in a selective migration analysis is found in Chapter 4.
72 Table 3-4Summary of all variables included in the research
Variable Category SARP Areas Comparator Areas
1991 2001 1991 2001
Frequency % Frequency % Frequency % Frequency %
LLTI Yes 4,286 15.73 5,594 23.13 2,879 16.85 3,566 25.28
No 22,960 84.27 18,591 76.87 14,212 83.15 10,541 74.72
Hospital Admission Yes 7,644 28.31 7,379 28.89 4,827 28.5 4,515 30.18
No 19,360 71.69 18,160 71 12,112 71.5 10,446 69.82 Employed Yes 10,536 83.89 9,465 90.96 6,353 83.2 5,215 90.43 No 2,024 16.11 941 9.04 1,283 16.8 552 9.57 Mortality Yes 888 3.26 894 3.23 559 96.73 568 2.97 No 26,358 96.74 26,745 96.77 16,532 3.27 18,581 97.03 Age Mean 46.3 47.8 48.6 45.7
Sex Male (reference) 12,898 47.34 12,084 47.2 8,115 47.48 7,029 7,963
Female 14,348 52.66 13,515 52.8 8,976 52.52 46.89 53.11
Marital Status Single (reference) 11,882 43.61 12,026 47.62 7,438 43.52 6,922 46.85
Married 11,773 43.21 9,728 38.52 7,351 43.01 5,598 37.89
Widowed 1,481 5.44 1,776 7.03 972 5.69 1,109 7.51
Divorced 2,110 7.74 1,723 6.82 1,330 7.78 1,145 7.75
Social Class Professional (reference) 331 1.21 474 1.85 155 0.91 221 1.47
Managerial 2,715 9.96 3,483 13.61 1,308 7.65 1,645 10.97
Skilled and non-manual 3,222 11.83 3,816 14.91 2,041 11.94 2,149 14.33
Skilled-manual 3,733 13.7 3,281 12.82 2,453 14.35 2,047 13.65
Partly-skilled 3,183 11.68 3,591 14.03 2,161 12.64 2,148 14.33
Unskilled 1,603 5.88 1,528 5.97 1,082 6.33 1,090 7.27
Never worked 12,459 45.73 9,426 36.82 7,891 46.17 5,692 37.97
Economic Status In full-time employment (reference) 7,877 28.91 6,918 27.02 4,679 27.38 3,791 25.29 In part-time employment 2,031 7.45 1,872 7.31 1,330 7.78 1,087 7.25 Self-employed 628 2.3 675 2.64 344 2.01 337 2.25 Unemployed 2,024 7.43 941 3.68 1,283 7.51 552 3.68 Student 6,483 23.79 6,456 25.22 4,034 23.6 3,636 24.25 Permanently sick 1,607 5.9 1,876 7.33 1,104 6.46 1,207 8.05 Retired 3,954 14.51 3,691 14.42 2,526 14.78 2,441 16.28 Other inactive 2,642 9.7 3,170 12.38 1,791 10.48 1,941 12.95
73
Qualifications No qualification and NCR Persons under 18 (reference)
23,123 84.87 19,207 75.03 14,813 86.67 11,504 76.73
Sub-degree 947 3.48 1,149 4.49 456 2.67 625 4.17
Degree and higher degree 687 2.52 2,369 9.25 310 1.81 1,035 6.9
Not stated 923 3.39 1,338 5.23 589 3.45 858 5.72
Over 75 with qualification 1,566 5.75 1,536 6 923 5.4 970 6.47
Ethnicity White (reference) 26,943 98.89 24,541 98.15 16,938 99.1 14,391 98.19
Non-white 303 1.11 462 1.85 153 0.9 266 1.81
House Tenure Owner occupied (reference) 10,583 38.84 13,552 54.78 5,561 32.54 7,031 48.7
Social renting 15,525 56.98 8,947 36.16 10,961 64.13 6,348 43.97
Private renting 1,138 4.18 2,241 9.06 569 3.33 1,059 7.33
Central Heating Central heating (reference) 20,538 75.38 23,399 93.28 12,513 73.21 13,619 92.76
No central heating 6,708 24.62 1,686 6.72 4,578 26.79 1,063 7.24
Persons living in the dwelling
Living alone 3,579 13.14 4,398 17.18 2,169 12.69 2,665 17.78
Not living alone (reference) 23,667 86.86 21,201 82.82 14,922 87.31 12,327 82.22
Car ownership 0 cars (reference) 13,589 49.88 9,531 37.23 8,940 52.31 5,953 39.71
1 cars 10,425 38.26 10,552 41.22 6,591 38.56 6,415 42.79
2 cars 2,744 10.07 4,076 15.92 1,326 7.76 1,903 12.69
3 cars 488 1.79 800 3.13 234 1.37 336 2.24
Household type Married and unmarried couples with no dependent children (reference)
2,348 19.47 4,270 29.37 1,392 18.9 2,429 29.73
Unmarried adult 3,345 27.74 5,295 36.42 2,051 27.85 2,977 36.44
One parent families with
dependent children
756 6.27 1,123 7.72 485 6.59 684 8.37
Married and unmarried
couples with dependent children
3,628 30.09 3,758 25.85 2,236 30.36 2,029 24.83
Having outlined the data sources and variables that I have used, I will now move on to discuss the specific methods employed in the analysis.