- Email: [email protected]
Do area-level population change, deprivation and variations in deprivation affect individual-level self-reported limiting long-term illness?
Social Science & Medicine 53 (2001) 795–799
Short Report
Do area-level population change, deprivation and variations in deprivation affect individual-level self-reported limiting longterm illness? Paul J. Boylea,*, Anthony C. Gatrellb, Oliver Duke-Williamsc a
School of Geography and Geosciences, University of St Andrews, St Andrews, Fife KY16 9AL, UK b Institute for Health Research, Lancaster University, Lancaster LA1 4YT, UK c School of Geography, University of Leeds, Leeds LS2 9JT, UK
Abstract A previous study showed that variations in deprivation within small localities in England and Wales influenced the rates of self-reported limiting long-term illness, controlling for overall levels of deprivation. These results suggest that while morbidity is related to overall levels of material deprivation, the distribution of resources within small areas have a significant effect on health outcomes. However, it is possible that these area effects become redundant once individuallevel characteristics are accounted for. This analysis examines whether area-level deprivation and variations in deprivation are significant indicators of individual-level limiting long-term illness, once individual characteristics have been accounted for. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Limiting long-term illness; Health variations; England and Wales; Logit models; Material deprivation
Introduction Previous studies have indicated that mortality is affected by levels of inequality within areas, as well as overall measures of poverty (Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997; Kennedy, Kawachi, & Prothrow-Stith, 1996; Rodgers, 1979; Waldman, 1992; Wilkinson, 1993, 1996). Ben Shlomo, White, & Marmot, (1996) show that this is the case in England and Wales at the scale of the local authority district. We might anticipate that variations in socio-economic conditions would be even more directly associated with morbidity than with mortality (Davey, 1996). Previous work showed that ward-level standardised morbidity rates were positively associated with ward-level deprivation as well as variations in deprivation within the ward and its
*Corresponding author. Fax: +44-1334-463949. E-mail address: [email protected] (P.J. Boyle).
neighbours (the locality) measured at the enumeration district level (Boyle, Gatrell, & Duke-Williams, 1999). However, area-level results cannot be used to imply that deprived people necessarily have higher rates of self-reported limiting long-term illness, as this would be an ecological fallacy. It has been shown that area-level deprivation measures become redundant once individual characteristics are accounted for in analyses of mortality (Fiscella & Franks, 1997; Sloggett & Joshi, 1994), although this is not the case for morbidity (Gould & Jones, 1996; Gleave, Bartley, & Wiggins, 1998; Shouls, Congdon, & Curtis, 1996). No studies have examined whether variations in deprivation remain significant in explaining morbidity once individual-level characteristics are controlled for and this is redressed here. We aimed to determine whether the odds of an individual reporting limiting long-term illness was significantly related to area-level deprivation and variations in deprivation within SAR-areas, once individual-level characteristics were accounted for. In addition, we also
0277-9536/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 0 ) 0 0 3 7 3 - 7
796
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
examine whether area-level migration rates are related to individual limiting long-term illness.
Data sources and methods For the first time, the 1991 British Census included a question about self-reported morbidity, asking whether the respondent suffered from ‘any long-term illness, health problem or handicap which limits daily activities or the work that can be done’. This question is rather ‘catch-all’ but, even so, it is nationally comprehensive and is a good surrogate for overall morbidity (Bentham, Eimermann, Haynes, Lovett, & Brainard, 1995). This was also the first census for which individual-level data have been released for a 2% sample of individuals and a 1% sample of households in the Samples of Anonymised
Records (SARs) (Census Microdata Unit, 1993). The geographical identifier for individuals in the 2% sample relates to 278 British SAR-areas (253 in England and Wales). Those 595,236 individuals in England and Wales, excluding those in the City of London SARarea, who were aged between 18 and 64 and were not resident in institutions, were extracted. Twelve individual-level characteristics, expected to be related to limiting long-term illness, were retrieved for each individual (Table 1). The last four individual-level variables are the same as those used in the Townsend Index (at the area-level) to measure deprivation; the aim was to control for these variables before assessing the effect of the area-level Townsend score. Three area-level variables were also calculated for the 252 SAR-areas. These were the Townsend score for SAR-areas; the variation (standard deviation)
Table 1 Multivariate model results Explanatory variable Individual-level variables Age
Sex Class
Ethnic group
Migrant status Marital status Qualifications Industry Tenure Employment status Overcrowding Car ownership
Categories
Odds-ratio 95% confidence interval p-value
18–29 30–44 45–64 Male Female Service class (SEG 1,2.2,3,4,5.1) Petite bourgeoisie (SEG 2.1,12,13,14) White collar (SEG 5.2,6,7) Blue collar (SEG 8,9,20,11,15) Other White Black (Caribbean, African, Other) Indian, Pakistani, Bangladeshi Chinese, other-Asian, other-other Non/short distance migrant (550kms) Long distance migrant (>=50kms) Single, married, remarried Divorced, widowed No higher level qualifications One or more higher level qualifications Other Coal extraction, solid fuels manufacture Owner occupier Non owner occupier Other Unemployed 5 1 resident per room >=1 resident per room One or more cars No cars
1.00 1.90 5.78 1.00 0.62 1.00 0.85 1.24 1.42 4.07 1.00 0.91 1.20 0.74 1.00 0.86 1.00 1.20 1.00 0.72 1.00 2.47 1.00 1.58 1.00 0.57 1.00 0.84 1.00 1.73
Area-level variables SAR-area deprivation n.a. Within SAR-area variation in deprivation n.a. Log migrants (%) n.a.
1.015 1.029 0.533
(1.84–1.97) (5.61–5.95)
50.001 50.001
(0.60–0.63)
50.001
(0.81–0.89) (1.20–1.29) (1.37–1.47) (3.93–4.21)
50.001 50.001 50.001 50.001
(0.85–0.98) (1.14–1.27) (0.67–0.82)
0.0065 50.001 50.001
(0.78–0.95)
0.0018
(1.17–1.23)
50.001
(0.69–0.75)
50.001
(2.27–2.69)
50.001
(1.54–1.61)
50.001
(0.54–0.59)
50.001
(0.80–0.89)
50.001
(1.69–1.77)
50.001
(1.011–1.019) (1.015–1.043) (0.509–0.558)
50.001 50.001 50.001
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
797
Fig. 1. SAR-area residuals.
of the Townsend scores within SAR-areas measured for the 9,320 wards which aggregate neatly into SAR-areas; and the log of the percentage of the population who were one year in-migrants. These variables are similar to those used in the previous study and Boyle, Gatrell, and Duke-Williams (1999) justify their use at length. The data for these variables were extracted from the 1991 British Census Local Base Statistics (LBS). Each of the 12 individual variables and the four area-level variables were assessed using a logit regression model fitted as a generalised linear model. The y variable distinguished between those who were (1) and were not (0) suffering from self-reported limiting long-term illness. The odds ratio, and the 95% confidence interval, were calculated for each parameter, to assess whether they were significantly different from unity. Standardised residuals were calculated from the observed and estimated individual-level incidence of limiting long-term illness, aggregated for the 252 SARareas. Places with unusually high or low numbers of individuals with limiting long-term illness, according to the model results, were therefore identified.
Results The outcome from the multivariate model was the odds of suffering from limiting long-term illness, and Table 1 provides the odds ratios, confidence intervals and p-values for each variable. Increased risk of selfreported limiting long-term illness was particularly high for those aged over 45, those in the ‘other’ social class (which includes those who have never had an occupation, perhaps for health reasons), and those who worked in, or used to work in, the coal extraction and solid fuels manufacturing industry. The unemployed, females, those with higher qualifications, the Chinese and other Asians and long distance migrants had the lowest risks of suffering from limiting long-term illness. The three area-level variables were significant1 and the parameters were all in the hypothesised directions. Individuals living in areas with high percentages of in1
The deprivation and variation in deprivation variables reduced the null deviance by a highly significant 307.21 for the loss of two degrees of freedom, when added to a model that included all 12 individual-level variables. The log of migration reduced the deviance a further 755.7.
798
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
migrants had lower risks, while individuals living in areas with high deprivation scores and high relative deprivation within the area had higher risks, controlling for individual characteristics. The observed and estimated values from the final model were used to calculate residuals and these were aggregated into SAR-areas. Fig. 1 shows that, controlling for the variables included in the multi-variate model, Wales, some coastal SAR-areas and SAR-areas in the North East and North West had higher numbers of people with limiting long-term illness than expected. South-East England, Central England and the West Midlands conurbation had fewer people than expected with limiting long-term illness.
Discussion Most of the individual-level results were in the hypothesised directions, although the lower risks for blacks was surprising, and this is different from previous results (Gould & Jones, 1996). It should be noted, however, that this study includes more explanatory variables than were considered in the Gould and Jones study. Also, the model does not include higher level interaction terms which may indicate, for example, that blacks in different age groups have different levels of risk compared to other ethnic groups (note also that those over 65 were ignored in this study). It is also possible that the rates for different black ethnic groups may vary. Finally, there might be cultural differences in the interpretation of questions about health. The result for the unemployed also appears counterintuitive, but those classed as not unemployed included both the employed and the economically inactive, and the latter may have been prevented from working because they were permanently sick. The overcrowding parameter estimate was negative which may question the use of this variable in deprivation indexes used to explain health outcomes. Few households now have very high levels of overcrowding, although some relatively healthy groups, such as students, may come into this category. Those who had worked in the coal industry had high risks and this may help explain the high levels of morbidity identified previously in the South Wales valleys and Easington district in Durham County (Boyle et al., 1999; Haynes, Bentham, Lovett, & Eimermann, 1997; Senior, 1998). Long distance one-year migrants were also shown to be relatively healthy, supporting work from the 1981 Census (Bentham, 1988) and the 1991 Census (Boyle & Duke-Williams, 1999). The area-level variables confirm that migration is an important variable in understanding the geography of health (Boyle, forthcoming). Areas with higher percentages of in-migrants had lower risks, and these will be relatively attractive areas. On the other hand, places
with low levels of in-migration are liable to be particularly depressed. The area-level deprivation and relative deprivation variables were significant, and the parameters were in the anticipated direction. The odds ratios were quite small, although they refer to continuous variables and cannot be interpreted in the same way as odds ratios for categorical variables. These results therefore provide cautious support for the hypothesis that area-level differences in deprivation and variations in deprivation influence morbidity. The variation in deprivation within SAR-areas had a higher odds ratio than the standard SAR-area measure of deprivation. Variations in deprivation may be more important than overall levels of deprivation, once individual characteristics are accounted for. The aggregated residuals show that South-East England and Central England had fewer cases than expected and these areas are relatively wealthy. Wales, parts of the North-East and North-West had high numbers of people with limiting long-term illness. These include places where traditional manufacturing industry has declined and in some areas the results may be related to the coal mining industry, even though this is controlled for at the individual level.2 It has also been suggested that cultural factors may influence the likelihood of a positive answer to the question on limiting long-term illness as levels of morbidity have been shown to be higher in Wales than would be expected from the mortality rates (Bentham et al., 1995; Senior, 1998). Obviously, including a Welsh/English dummy variable, as suggested by Senior (1998), would improve the fit considerably, although this would not aid the explanation of these differences. Morbidity was also high in some coastal SAR-areas with well-known seaside resorts (Blackpool and Bournemouth had very high residuals for example) and there is some anecdotal evidence that individuals claiming welfare benefit may drift towards these areas because of the supply of cheap bed and breakfast accommodation, much of which is vacant outside the summer months. The West Midlands conurbation also had fewer cases than expected, and these results are more surprising. Ongoing work is investigating improvements to the model that help account for the spatial clustering of these residuals. The results from this analysis appear to confirm the results presented earlier in a ward-level analysis of the effects of relative deprivation (Boyle et al., 1999). For this sample, study area and scale of analysis, area-level deprivation influences morbidity, even when individuallevel characteristics are controlled for. Importantly, the 2
Note that the Census codes the person with the most recent occupation they have had during the last ten years. Some miners will have found new jobs and are no longer coded as such, while others may have been out of work for longer than ten years.
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
study also shows that relative deprivation within areas has a positive and significant effect on morbidity. Finally, the importance of population change is also identified. Places experiencing higher levels of inmigration tend to be economically buoyant and they are likely to be ‘healthy’ places as a result. Within England and Wales, places with the lowest levels of inmigration are generally depressed areas. The psychological effect of living in these areas may impact upon health outcomes. Widening disparities in economic and social status will increase the level of morbidity. Measures used in the geographical allocation of resources should acknowledge the importance of within area variations in deprivation and population change.
Acknowledgements The census data are Crown Copyright, were bought for academic use by the ESRC/JISC and are held at the Manchester Computer Centre.
References Ben-Shlomo, Y., White, I. R., & Marmot, M. G. (1996). Does the variation in the socioeconomic characteristics of an area affect mortality?. British Medical Journal, 312, 1013–1014. Bentham, G. (1988). Migration and morbidity: Implications for geographical studies of disease. Social Science and Medicine, 26, 49–54. Bentham, G., Eimermann, J., Haynes, R., Lovett, A., & Brainard, J. (1995). Limiting long-term illness and its associations with mortality and indicators of social deprivation. Journal of Epidemiology and Community Health, 49(Suppl 2), S57–S64. Boyle, P. J. Migration and Health. London: Routledge (forthcoming). Boyle, P. J. & Duke-Williams, O. (1999). Does migration exaggerate the relationship between deprivation and morbidity? Association of American Geographers, University of Hawaii, Hawaii, March 21–25. Boyle, P. J., Gatrell, A. C., & Duke-Williams, O. (1999). Selfreported limiting long-term illness and variations in
799
population change and deprivation within and in neighbouring small areas. Social Science and Medicine, 49, 791– 799. Census Microdata Unit (1993). A User Guide to the SARs. University of Manchester: Census Microdata Unit. Davey, S. G. (1996). Income inequality and mortality: Why are they related? British Medical Journal, 312, 987–988. Fiscella, K., & Franks, P. (1997). Poverty or income inequality as predictor of mortality: Longitudinal cohort study. British Medical Journal, 314, 1724–1728. Gleave, S., Bartley, M., & Wiggins, R. (1998). Limiting longterm illness: A question of where you live or who you are? 1998 LS Working Paper 77, Social Statistics Research Unit. Gould, M. I., & Jones, K. (1996). Analysing perceived limiting long-term illness using UK census microdata. Social Science and Medicine, 42, 857–869. Haynes, R., Bentham, G., Lovett, A., & Eimermann, J. (1997). Effect of labour market conditions on reporting of limiting long-term illness and permanent sickness in England and Wales. Journal of Epidemiology and Community Health, 51, 283–288. Kawachi, I., Kennedy, B. P., Lochner, K., & Prothrow-Stith, D. (1997). Social capital, income inequality and mortality. American Journal of Public Health, 87, 1491–1498. Kennedy, B. P., Kawachi, I., & Prothrow-Stith, D. (1996). Income distribution and mortality: cross sectional ecological study of the Robin Hood index in the United States. British Medical Journal, 312, 1004–1007. Rodgers, G. B. (1979). Income and inequality as determinants of mortality: an international cross-sectional analysis. Population Studies, 33, 343–351. Senior, M. (1998). Area variations in self-perceived limiting long-term illness in Britain, 1991, Is the Welsh experience exceptional?. Regional Studies, 32, 265–280. Shouls, S., Congdon, P., & Curtis, S. (1996). Modelling inequality in reported long term illness in the UK: combining individual and area characteristics. Journal of Epidemiology and Community Health, 50, 366–376. Sloggett, S., & Joshi, H. (1994). Higher mortality in deprived areas: community or personal disadvantage? British Medical Journal, 309, 1471–1474. Waldman, R. J. (1992). Income distribution and infant mortality. Quarterly Journal of Economics, 107, 1283–1302. Wilkinson, R. G. (1993). Income distribution and life expectancy. British Medical Journal, 304, 165–168. Wilkinson, R. G. (1996). Unhealthy Societies. London: Routledge.
Short Report
Do area-level population change, deprivation and variations in deprivation affect individual-level self-reported limiting longterm illness? Paul J. Boylea,*, Anthony C. Gatrellb, Oliver Duke-Williamsc a
School of Geography and Geosciences, University of St Andrews, St Andrews, Fife KY16 9AL, UK b Institute for Health Research, Lancaster University, Lancaster LA1 4YT, UK c School of Geography, University of Leeds, Leeds LS2 9JT, UK
Abstract A previous study showed that variations in deprivation within small localities in England and Wales influenced the rates of self-reported limiting long-term illness, controlling for overall levels of deprivation. These results suggest that while morbidity is related to overall levels of material deprivation, the distribution of resources within small areas have a significant effect on health outcomes. However, it is possible that these area effects become redundant once individuallevel characteristics are accounted for. This analysis examines whether area-level deprivation and variations in deprivation are significant indicators of individual-level limiting long-term illness, once individual characteristics have been accounted for. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Limiting long-term illness; Health variations; England and Wales; Logit models; Material deprivation
Introduction Previous studies have indicated that mortality is affected by levels of inequality within areas, as well as overall measures of poverty (Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997; Kennedy, Kawachi, & Prothrow-Stith, 1996; Rodgers, 1979; Waldman, 1992; Wilkinson, 1993, 1996). Ben Shlomo, White, & Marmot, (1996) show that this is the case in England and Wales at the scale of the local authority district. We might anticipate that variations in socio-economic conditions would be even more directly associated with morbidity than with mortality (Davey, 1996). Previous work showed that ward-level standardised morbidity rates were positively associated with ward-level deprivation as well as variations in deprivation within the ward and its
*Corresponding author. Fax: +44-1334-463949. E-mail address: [email protected] (P.J. Boyle).
neighbours (the locality) measured at the enumeration district level (Boyle, Gatrell, & Duke-Williams, 1999). However, area-level results cannot be used to imply that deprived people necessarily have higher rates of self-reported limiting long-term illness, as this would be an ecological fallacy. It has been shown that area-level deprivation measures become redundant once individual characteristics are accounted for in analyses of mortality (Fiscella & Franks, 1997; Sloggett & Joshi, 1994), although this is not the case for morbidity (Gould & Jones, 1996; Gleave, Bartley, & Wiggins, 1998; Shouls, Congdon, & Curtis, 1996). No studies have examined whether variations in deprivation remain significant in explaining morbidity once individual-level characteristics are controlled for and this is redressed here. We aimed to determine whether the odds of an individual reporting limiting long-term illness was significantly related to area-level deprivation and variations in deprivation within SAR-areas, once individual-level characteristics were accounted for. In addition, we also
0277-9536/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 0 ) 0 0 3 7 3 - 7
796
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
examine whether area-level migration rates are related to individual limiting long-term illness.
Data sources and methods For the first time, the 1991 British Census included a question about self-reported morbidity, asking whether the respondent suffered from ‘any long-term illness, health problem or handicap which limits daily activities or the work that can be done’. This question is rather ‘catch-all’ but, even so, it is nationally comprehensive and is a good surrogate for overall morbidity (Bentham, Eimermann, Haynes, Lovett, & Brainard, 1995). This was also the first census for which individual-level data have been released for a 2% sample of individuals and a 1% sample of households in the Samples of Anonymised
Records (SARs) (Census Microdata Unit, 1993). The geographical identifier for individuals in the 2% sample relates to 278 British SAR-areas (253 in England and Wales). Those 595,236 individuals in England and Wales, excluding those in the City of London SARarea, who were aged between 18 and 64 and were not resident in institutions, were extracted. Twelve individual-level characteristics, expected to be related to limiting long-term illness, were retrieved for each individual (Table 1). The last four individual-level variables are the same as those used in the Townsend Index (at the area-level) to measure deprivation; the aim was to control for these variables before assessing the effect of the area-level Townsend score. Three area-level variables were also calculated for the 252 SAR-areas. These were the Townsend score for SAR-areas; the variation (standard deviation)
Table 1 Multivariate model results Explanatory variable Individual-level variables Age
Sex Class
Ethnic group
Migrant status Marital status Qualifications Industry Tenure Employment status Overcrowding Car ownership
Categories
Odds-ratio 95% confidence interval p-value
18–29 30–44 45–64 Male Female Service class (SEG 1,2.2,3,4,5.1) Petite bourgeoisie (SEG 2.1,12,13,14) White collar (SEG 5.2,6,7) Blue collar (SEG 8,9,20,11,15) Other White Black (Caribbean, African, Other) Indian, Pakistani, Bangladeshi Chinese, other-Asian, other-other Non/short distance migrant (550kms) Long distance migrant (>=50kms) Single, married, remarried Divorced, widowed No higher level qualifications One or more higher level qualifications Other Coal extraction, solid fuels manufacture Owner occupier Non owner occupier Other Unemployed 5 1 resident per room >=1 resident per room One or more cars No cars
1.00 1.90 5.78 1.00 0.62 1.00 0.85 1.24 1.42 4.07 1.00 0.91 1.20 0.74 1.00 0.86 1.00 1.20 1.00 0.72 1.00 2.47 1.00 1.58 1.00 0.57 1.00 0.84 1.00 1.73
Area-level variables SAR-area deprivation n.a. Within SAR-area variation in deprivation n.a. Log migrants (%) n.a.
1.015 1.029 0.533
(1.84–1.97) (5.61–5.95)
50.001 50.001
(0.60–0.63)
50.001
(0.81–0.89) (1.20–1.29) (1.37–1.47) (3.93–4.21)
50.001 50.001 50.001 50.001
(0.85–0.98) (1.14–1.27) (0.67–0.82)
0.0065 50.001 50.001
(0.78–0.95)
0.0018
(1.17–1.23)
50.001
(0.69–0.75)
50.001
(2.27–2.69)
50.001
(1.54–1.61)
50.001
(0.54–0.59)
50.001
(0.80–0.89)
50.001
(1.69–1.77)
50.001
(1.011–1.019) (1.015–1.043) (0.509–0.558)
50.001 50.001 50.001
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
797
Fig. 1. SAR-area residuals.
of the Townsend scores within SAR-areas measured for the 9,320 wards which aggregate neatly into SAR-areas; and the log of the percentage of the population who were one year in-migrants. These variables are similar to those used in the previous study and Boyle, Gatrell, and Duke-Williams (1999) justify their use at length. The data for these variables were extracted from the 1991 British Census Local Base Statistics (LBS). Each of the 12 individual variables and the four area-level variables were assessed using a logit regression model fitted as a generalised linear model. The y variable distinguished between those who were (1) and were not (0) suffering from self-reported limiting long-term illness. The odds ratio, and the 95% confidence interval, were calculated for each parameter, to assess whether they were significantly different from unity. Standardised residuals were calculated from the observed and estimated individual-level incidence of limiting long-term illness, aggregated for the 252 SARareas. Places with unusually high or low numbers of individuals with limiting long-term illness, according to the model results, were therefore identified.
Results The outcome from the multivariate model was the odds of suffering from limiting long-term illness, and Table 1 provides the odds ratios, confidence intervals and p-values for each variable. Increased risk of selfreported limiting long-term illness was particularly high for those aged over 45, those in the ‘other’ social class (which includes those who have never had an occupation, perhaps for health reasons), and those who worked in, or used to work in, the coal extraction and solid fuels manufacturing industry. The unemployed, females, those with higher qualifications, the Chinese and other Asians and long distance migrants had the lowest risks of suffering from limiting long-term illness. The three area-level variables were significant1 and the parameters were all in the hypothesised directions. Individuals living in areas with high percentages of in1
The deprivation and variation in deprivation variables reduced the null deviance by a highly significant 307.21 for the loss of two degrees of freedom, when added to a model that included all 12 individual-level variables. The log of migration reduced the deviance a further 755.7.
798
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
migrants had lower risks, while individuals living in areas with high deprivation scores and high relative deprivation within the area had higher risks, controlling for individual characteristics. The observed and estimated values from the final model were used to calculate residuals and these were aggregated into SAR-areas. Fig. 1 shows that, controlling for the variables included in the multi-variate model, Wales, some coastal SAR-areas and SAR-areas in the North East and North West had higher numbers of people with limiting long-term illness than expected. South-East England, Central England and the West Midlands conurbation had fewer people than expected with limiting long-term illness.
Discussion Most of the individual-level results were in the hypothesised directions, although the lower risks for blacks was surprising, and this is different from previous results (Gould & Jones, 1996). It should be noted, however, that this study includes more explanatory variables than were considered in the Gould and Jones study. Also, the model does not include higher level interaction terms which may indicate, for example, that blacks in different age groups have different levels of risk compared to other ethnic groups (note also that those over 65 were ignored in this study). It is also possible that the rates for different black ethnic groups may vary. Finally, there might be cultural differences in the interpretation of questions about health. The result for the unemployed also appears counterintuitive, but those classed as not unemployed included both the employed and the economically inactive, and the latter may have been prevented from working because they were permanently sick. The overcrowding parameter estimate was negative which may question the use of this variable in deprivation indexes used to explain health outcomes. Few households now have very high levels of overcrowding, although some relatively healthy groups, such as students, may come into this category. Those who had worked in the coal industry had high risks and this may help explain the high levels of morbidity identified previously in the South Wales valleys and Easington district in Durham County (Boyle et al., 1999; Haynes, Bentham, Lovett, & Eimermann, 1997; Senior, 1998). Long distance one-year migrants were also shown to be relatively healthy, supporting work from the 1981 Census (Bentham, 1988) and the 1991 Census (Boyle & Duke-Williams, 1999). The area-level variables confirm that migration is an important variable in understanding the geography of health (Boyle, forthcoming). Areas with higher percentages of in-migrants had lower risks, and these will be relatively attractive areas. On the other hand, places
with low levels of in-migration are liable to be particularly depressed. The area-level deprivation and relative deprivation variables were significant, and the parameters were in the anticipated direction. The odds ratios were quite small, although they refer to continuous variables and cannot be interpreted in the same way as odds ratios for categorical variables. These results therefore provide cautious support for the hypothesis that area-level differences in deprivation and variations in deprivation influence morbidity. The variation in deprivation within SAR-areas had a higher odds ratio than the standard SAR-area measure of deprivation. Variations in deprivation may be more important than overall levels of deprivation, once individual characteristics are accounted for. The aggregated residuals show that South-East England and Central England had fewer cases than expected and these areas are relatively wealthy. Wales, parts of the North-East and North-West had high numbers of people with limiting long-term illness. These include places where traditional manufacturing industry has declined and in some areas the results may be related to the coal mining industry, even though this is controlled for at the individual level.2 It has also been suggested that cultural factors may influence the likelihood of a positive answer to the question on limiting long-term illness as levels of morbidity have been shown to be higher in Wales than would be expected from the mortality rates (Bentham et al., 1995; Senior, 1998). Obviously, including a Welsh/English dummy variable, as suggested by Senior (1998), would improve the fit considerably, although this would not aid the explanation of these differences. Morbidity was also high in some coastal SAR-areas with well-known seaside resorts (Blackpool and Bournemouth had very high residuals for example) and there is some anecdotal evidence that individuals claiming welfare benefit may drift towards these areas because of the supply of cheap bed and breakfast accommodation, much of which is vacant outside the summer months. The West Midlands conurbation also had fewer cases than expected, and these results are more surprising. Ongoing work is investigating improvements to the model that help account for the spatial clustering of these residuals. The results from this analysis appear to confirm the results presented earlier in a ward-level analysis of the effects of relative deprivation (Boyle et al., 1999). For this sample, study area and scale of analysis, area-level deprivation influences morbidity, even when individuallevel characteristics are controlled for. Importantly, the 2
Note that the Census codes the person with the most recent occupation they have had during the last ten years. Some miners will have found new jobs and are no longer coded as such, while others may have been out of work for longer than ten years.
P.J. Boyle et al. / Social Science & Medicine 53 (2001) 795–799
study also shows that relative deprivation within areas has a positive and significant effect on morbidity. Finally, the importance of population change is also identified. Places experiencing higher levels of inmigration tend to be economically buoyant and they are likely to be ‘healthy’ places as a result. Within England and Wales, places with the lowest levels of inmigration are generally depressed areas. The psychological effect of living in these areas may impact upon health outcomes. Widening disparities in economic and social status will increase the level of morbidity. Measures used in the geographical allocation of resources should acknowledge the importance of within area variations in deprivation and population change.
Acknowledgements The census data are Crown Copyright, were bought for academic use by the ESRC/JISC and are held at the Manchester Computer Centre.
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