Abstract

Background: Post-industrial decline is frequently cited as one of the major underlying reasons behind the poor health profile of Scotland and, especially, West Central Scotland (WCS). This begs the question: to what extent is poor health a common outcome in other post-industrial regions and how does Scotland's experience compare to these other comparable regions? Methods: Regions were identified by means of an expert-based consultation, backed up by analysis of regional industrial employment loss over the past 30 years. Mortality rates and related statistics were calculated from data obtained from national and regional statistical agencies. Results: Twenty candidate regions (in: Belgium; France; Germany; Netherlands; UK; Poland; Czech Republic) were identified, of which ten were selected for in-depth analyses. WCS mortality rates are generally higher and—crucially—appear to be improving at a slower rate than in the other post-industrial regions. This relatively poor rate of improvement is largely driven by mortality among the younger working age (especially male) and middle-aged female populations. Conclusion: WCS mortality trends compare badly with other, similar, post-industrial regions of Europe, including regions in Eastern Europe which tend to be characterized by higher levels of poverty. This finding challenges any simplistic explanation of WCS's poor health being caused by post-industrial decline alone, and begs the question as to what other factors may be at work.

Introduction

Scotland has the highest mortality rates and lowest life expectancy of any western European nation.1,2 The most frequently cited reason for Scotland's poor health is post-industrial decline with its many associated features such as socio-economic deprivation.3–6 Part of the evidence to support this theory comes from the observation that, within Scotland, the areas of poorest health are found in its West-Central belt, a region that has suffered from the effects of profound deindustrialization in recent decades. Many other parts of the UK and Western Europe have also suffered deindustrialization and are characterized by social deprivation and relative poverty. We wanted to discover whether these areas have also suffered adverse health effects and how their experiences compare to those of Scotland and, more particularly, the West Central Scotland (WCS).

Specifically, the questions we sought to answer were:

  1. Which regions of Europe are most comparable to WCS in terms of their experience of post-industrial decline?

  2. How do long-term trends in mortality in each region compare with WCS?

Methods

The principal methodologies employed in these analyses are summarized briefly below. However, a detailed description of all relevant methodological issues is available in a separate report produced by the Glasgow Centre for Population Health and NHS Health Scotland.7

Identification of areas

Two methods were used to identify post-industrial regions in Europe: expert opinion and analyses of regions’ employment trends. Nine experts in European public health and European history were asked to create a list of regions that had experienced a loss of industrial employment following a collapse of heavy industry. Next, an analysis of regional industrial employment loss over the past 30 years was carried out. The latter involved comparisons between a base year and 2005 for each region. For Western Europe, base year data were obtained for years as close to 1970 (the peak year of industrial employment in Western Europe) as possible. For Central and Eastern European regions, data availability largely determined the base year: 1980 for Katowice in Poland, 1991 for Saxony in (Eastern) Germany, and 1993 for Northern Moravia in the Czech Republic. Nonetheless, these dates are close to the peaks of industrial employment for their parent countries.8,9 The base years used in these analyses are listed in table 1. Note tat the 1960s saw the highest levels of industrial employment in WCS.7

Table 1

Definition of regions and analysis of industrial employment losses

Region Country Populationa Geographical definition Deindustrialization analysis ‘base’ year Percentage of industrial employmentb lost, base year to 2005 
Katowice Poland 4.1 m Defined, using sets of counties (‘powiats’), as the old (pre-1999) Polish voivodeship of Katowice (slightly smaller than the current voivodeship of Silesia) 1980 55 
Limburg Netherlands 1.1 m ‘Province’ of Limburg (NUTS 2 NL42) 1968 16 
Merseyside England 1.4 m Metropolitan county—NUTS 2 code UKD5 1971 63 
Nord-Pas-de-Calais France 4.0 m French ‘région’ of Nord-Pas-de-Calais (NUTS 2: FR30) 1971 43 
Northern Ireland Northern Ireland 1.7 m NUTS 1 code UKN 1971 20 
Northern Moravia Czech Republic 1.9 m Made up of two of the Czech Republic's thirteen regions (‘kraje’), namely Moravskoslezský (NUTS 3 code CZ080), and Olomoucký (NUTS 3 code CZ071) 1993 19 
Ruhr area Germany 5.3 m Defined as a combination of 15 districts (‘kreise’) and urban districts: Duisburg (NUTS 3 code DEA12); Essen (DEA13); Mülheim an der Ruhr (DEA16); Oberhausen (DEA17); Wesel (DEA1F); Bottrop (DEA31); Gelsenkirchen (DEA32); Recklinghausen (DEA36); Bochum (DEA51); Dortmund (DEA52); Hagen (DEA53); Hamm (DEA54); Herne (DEA55); Ennepe-Ruhr-Kreis (DEA56); Unna (DEA5C) 1970 54 
Saxony Germany 4.3 m The federal state of Saxony (Saschen)NUTS 1 code DED 1991 47 
Swansea & South Wales Coalfields Wales 1.1 m Defined by the following NUTS 3 codes: UKL15 (Central Valleys, made up of the Merthyr Tydfil & Rhondda Cynon Taff local authorities); UKL16 (Gwent Valleys, covering the Blaenau Gwent, Caerphilly & Torfaen local authorities); UKL17 (including Bridgend and Neath Port Talbot local authorities); UKL18 (Swansea local authority) 1971 51 
Wallonia Belgium 3.4 m Belgian autonomous ‘région’ of Wallonia (NUTS 1: BE3) 1970 39 
West Central Scotland Scotland 2.1 m Defined by eleven local authority areas. These are: East Ayrshire, East Dunbartonshire, East Renfrewshire, Glasgow City, Inverclyde, North Ayrshire, North Lanarkshire, Renfrewshire, South Ayrshire, South Lanarkshire, and West Dunbartonshire 1971 62 
Region Country Populationa Geographical definition Deindustrialization analysis ‘base’ year Percentage of industrial employmentb lost, base year to 2005 
Katowice Poland 4.1 m Defined, using sets of counties (‘powiats’), as the old (pre-1999) Polish voivodeship of Katowice (slightly smaller than the current voivodeship of Silesia) 1980 55 
Limburg Netherlands 1.1 m ‘Province’ of Limburg (NUTS 2 NL42) 1968 16 
Merseyside England 1.4 m Metropolitan county—NUTS 2 code UKD5 1971 63 
Nord-Pas-de-Calais France 4.0 m French ‘région’ of Nord-Pas-de-Calais (NUTS 2: FR30) 1971 43 
Northern Ireland Northern Ireland 1.7 m NUTS 1 code UKN 1971 20 
Northern Moravia Czech Republic 1.9 m Made up of two of the Czech Republic's thirteen regions (‘kraje’), namely Moravskoslezský (NUTS 3 code CZ080), and Olomoucký (NUTS 3 code CZ071) 1993 19 
Ruhr area Germany 5.3 m Defined as a combination of 15 districts (‘kreise’) and urban districts: Duisburg (NUTS 3 code DEA12); Essen (DEA13); Mülheim an der Ruhr (DEA16); Oberhausen (DEA17); Wesel (DEA1F); Bottrop (DEA31); Gelsenkirchen (DEA32); Recklinghausen (DEA36); Bochum (DEA51); Dortmund (DEA52); Hagen (DEA53); Hamm (DEA54); Herne (DEA55); Ennepe-Ruhr-Kreis (DEA56); Unna (DEA5C) 1970 54 
Saxony Germany 4.3 m The federal state of Saxony (Saschen)NUTS 1 code DED 1991 47 
Swansea & South Wales Coalfields Wales 1.1 m Defined by the following NUTS 3 codes: UKL15 (Central Valleys, made up of the Merthyr Tydfil & Rhondda Cynon Taff local authorities); UKL16 (Gwent Valleys, covering the Blaenau Gwent, Caerphilly & Torfaen local authorities); UKL17 (including Bridgend and Neath Port Talbot local authorities); UKL18 (Swansea local authority) 1971 51 
Wallonia Belgium 3.4 m Belgian autonomous ‘région’ of Wallonia (NUTS 1: BE3) 1970 39 
West Central Scotland Scotland 2.1 m Defined by eleven local authority areas. These are: East Ayrshire, East Dunbartonshire, East Renfrewshire, Glasgow City, Inverclyde, North Ayrshire, North Lanarkshire, Renfrewshire, South Ayrshire, South Lanarkshire, and West Dunbartonshire 1971 62 

a: Population at 2005 for all regions except Nord-Pas-de-Calais, for which the year is 2003

b: Industrial employment defined as ‘all residents working in manufacturing, utilities, mining or construction’

With two exceptions, regions were defined in terms of NUTS (Nomenclature of Territorial Units for Statistics) geographies, the geographical system of national and sub-national geographies used by Eurostat.

Where multiple regions within the same country were identified, only one region was retained for in-depth analysis. Given that WCS has the highest mortality rates in Scotland, the region with the poorest health (highest mortality rates) in those countries was retained. An exception was Germany, where the historical divergence of the country meant that two regions were included: one in the former West Germany, and one in the former German Democratic Republic. Note, however, that detailed, comparable, cause-specific mortality data could not be obtained for the region with the highest mortality rates in Germany (Saxony-Anhalt). The alternative region of Saxony was therefore selected to represent the former East Germany.

Analysis was also undertaken to confirm whether the other post-industrial regions displayed higher levels of mortality than other regions within their parent countries, as is the case with WCS relative to the rest of Scotland. For this purpose, European age-standardized rates (EASRs) for the period 2001–03 were compared using data from Eurostat at NUTS 1 or NUTS 2 geographies. The only exceptions to this were Welsh local authority data, and rates for the Ruhr area in Germany, which were obtained from regional statistical offices cited elsewhere.7

Analysis of long-term mortality trends

Twenty to twenty-five years of mortality and population data were requested for all regions from local and national statistical agencies. With the one exception of Limburg (for reasons detailed elsewhere7), life expectancy estimates were calculated using Chiang (II) methodology10 for individual years, and presented as 3-year rolling averages. Infant (age <1 year) mortality trends were calculated as crude rates per 1,000 live births. A series of European age-standardized mortality rates were calculated for: all ages; 1–14 years; 15–44 years; 45–64 years; and 65+ years. This allowed analyses of mortality rates among infants, children, the elderly, and younger and older working age adults: the latter two groups have been shown to be particularly important in national level analyses of Scottish mortality data.11 Sixteen different causes of death were examined including: selected cancers (lung, breast, oesophageal, stomach, colorectal, and prostate); diseases of the circulatory system (including, separately, ischaemic heart disease (IHD) and stroke); chronic obstructive pulmonary disease (COPD) and related conditions; chronic liver disease and cirrhosis; all ‘external’ causes; motor vehicle traffic accidents; and suicide (the latter including deaths of undetermined intent). The ICD codes listed are cited elsewhere.7 For ease of presentation, data were presented in a summarized format borrowed from Leon et al.,11 with each graph showing directly age-standardized mortality rates in WCS compared to the maximum, minimum and mean rate across the 10 other regions (plus WCS itself). Note, however, that data were not always available for each region in each year.

Results

Identification of areas

From an initial selection of twenty regions, ten were ultimately selected: the Ruhr area and Saxony in West and East Germany respectively; Katowice in Poland; Northern Moravia in the Czech Republic; Nord-Pas-de-Calais in France; Wallonia in Belgium; Limburg in the Netherlands; Northern Ireland (For simplicity, we refer to Northern Ireland as one of the ‘regions’ analysed in the project. Clearly, however, in Northern Ireland's case, and in a UK context, the term ‘country’ also applies); Swansea & the South Wales Coalfields in Wales; and Merseyside in England. Table 1 includes population and geographical definitions of each area, together with results of the analyses of regional employment trends. The latter shows that in terms of jobs lost, the majority of areas lost 40% or more of industrial employment over the periods analysed. Between 1971 and 2005, WCS shed almost two-thirds (62%) of its jobs in industry and, along with Merseyside, this suggests its loss of industrial employment was especially severe. At the other end of the spectrum, Northern Ireland and Limburg saw much smaller reductions in industrial employmentdown by a fifth or less over the same period.

Analysis of the recent all-cause mortality EASRs confirmed that, as is the case for WCS relative to the rest of Scotland, each selected post-industrial region tended to have the highest, or among the highest, all-cause mortality rates in its own country. For example: in France, Nord-Pas-de-Calais had the highest standardized mortality rates of all the French régions for both males and females, and in England, Merseyside experienced higher rates of mortality than other English counties. The principal exception to this overall rule concerns Saxony in Germany, where although mortality rates among males are among the six highest of the 16 German federal states (Länder), for females they are among the four lowest. The results of this analysis are presented in full elsewhere.7

Analysis of long-term mortality trends

Data were obtained from all identified regions. However, the periods for which data were available varied: a full sequence of data was obtained for: Katowice (1980–2005); Ruhr area (1980–2005) [Note that although 25 years of mortality data were obtained for the Ruhr area, only 15 years (1990–2005) could be used for the calculation of life expectancy. This is explained fully elsewhere7]; Saxony (1983–2005); Nord-Pas-de-Calais (1983–2003); and Northern Ireland (1980–2005). Eighteen years of data (1988–2005) were obtained for the English and Welsh regions and 15 years (1991–2005) of data were obtained for both Northern Moravia and Limburg. However, no cause-specific mortality data after 1997 could be obtained for Wallonia (although published life expectancy figures for the region up to 2005, calculated in a similar manner, were obtained separately).12

Figure 1 shows male life expectancy respectively for WCS compared to the ten comparator post-industrial regions. Life expectancy trends in WCS are improving more slowly than in almost every other selected European region. WCS males currently have lower life expectancy than every region except Katowice in Poland and Northern Moravia in the Czech Republic. However, the rates of improvement of life expectancy in these regions compared to WCS suggest that these regions will overtake WCS in around 10 years’ time if current trends continue.

Figure 1

Male life expectancy at birth, West Central Scotland and 10 post-industrial regions, calculated from original source mortality and population data.

Figure 1

Male life expectancy at birth, West Central Scotland and 10 post-industrial regions, calculated from original source mortality and population data.

WCS females (data not shown) currently have lower life expectancy than every other selected region; improvement rates are again faster in the other comparator regions.

Analyses of age and cause specific mortality trends showed that infant and childhood mortality rates in WCS compare reasonably well with rates recorded in the 10 other regions. Over the 25-year period presented, WCS rates have generally been at or below the average rate of all the chosen regions (data not shown).

However, this is not the case for the working aged populations. Figure 2 showsin a summarized format borrowed from Leon et al.11—all-cause standardized mortality rates for WCS males aged 15–44 years compared to the maximum, minimum and mean rates recorded in other regions (plus WCS itself). The contrast between the rates of WCS males of this age and their counterparts in regions which have undergone similar industrial decline is clear: while rates have generally been rising in WCS since the start of the 1990s, the opposite is true of the other regions.

Figure 2

All-cause mortality: EASRs (3 year rolling averages) 1980–2005, working age 15–44, males. WCS in context of maximum, minimum and mean rates for selected European regions.

Figure 2

All-cause mortality: EASRs (3 year rolling averages) 1980–2005, working age 15–44, males. WCS in context of maximum, minimum and mean rates for selected European regions.

While not shown here, a similar pattern is seen for females, although with a less marked increase in rates from the mid-1990s onwards.

This increase in total mortality rates in the 15–44 year age group in WCS appears to be driven by a number of key causes. Rates are not, for example, higher in WCS for deaths from circulatory system diseases or all cancers. However, analysis showed that rates are higher for deaths from ‘external causes’ (in particular, suicide) and chronic liver disease and cirrhosis. With regard to the latter, figure 3 shows clearly the influence of alcohol harm among males of this age group, with a striking increase in mortality from chronic liver disease and cirrhosis having occurred over the past 25 years in WCS. The overall picture for females is very similar and, in both cases, WCS's relative position has altered from being significantly below the regional average in the earlier years of the analysis to being the highest of all the post-industrial regions analysed. For example, among males, rates increased from 4.6 per 100,000 population in 1980/82 (16% lower than the average for all the regions) to 17.3 per 100,000 population in 2003/05 (62% higher than the regional average).

Figure 3

Chronic liver disease and cirrhosis mortality: EASRs (3 year rolling averages) 1980–2005, working age 15–44, males. WCS in context of maximum, minimum and mean rates for selected European regions.

Figure 3

Chronic liver disease and cirrhosis mortality: EASRs (3 year rolling averages) 1980–2005, working age 15–44, males. WCS in context of maximum, minimum and mean rates for selected European regions.

In fact, the striking contrasts in trends in mortality from chronic liver disease and cirrhosis between WCS and other post-industrial regions are seen in both sexes and across all adult age bands: 15–44 years, 45–64 years and 65+ years. In contrast, the higher levels of suicide in WCS are seen only in the 15–44-year age group (and this is true of both males and females).

With regard to liver disease and cirrhosis, there is a clear split in the pattern of mortality trends between UK and non-UK regions, with rates rising in the UK (and most strikingly in WCS), but falling or remaining constant in the regions on the continent. For males of working age 45–64 years, WCS all-cause rates lie consistently around the average of all 11 regions. However, this contrasts markedly with the picture for females in this age group. WCS females have the highest mortality rates of all the regions analysed, and this has been the case for most of the 25 years for which we have comparable data (figure 4).

Figure 4

All-cause mortality: EASRs (3 year rolling averages) 1980–2005, working age 45–64, females. WCS in context of maximum, minimum and mean rates for selected European regions.

Figure 4

All-cause mortality: EASRs (3 year rolling averages) 1980–2005, working age 45–64, females. WCS in context of maximum, minimum and mean rates for selected European regions.

Detailed cause-specific analyses showed that this higher level of mortality among 45–64-year-old women in WCS is particularly attributable to: cancer (especially lung cancer, breast cancer and oesophageal cancer); IHD and stroke; COPD; and, as already mentioned, chronic liver disease and cirrhosis.

More detailed analyses of these cause-specific trends are reported elsewhere.7

Discussion

Which regions of Europe are most comparable to WCS in terms of their experience of post-industrial decline?

An initial selection of twenty regions (reduced to a core group of ten) was identified in terms of their broadly analogous experience of post-industrial decline in recent times. Combining this selection with detailed long-term mortality data had produced a valuable data set.

Of course, the extent to which each region has deindustrialized varies, and somesuch as Northern Moravia in the Czech Republicstill have an important industrial element to their economies. However, the fact that each region had still been affected to a considerable degree by the process of deindustrialization very much justifies their inclusion in a study of this type. Nonetheless, the different experiences of deindustrializatione.g. extent, timing, speedclearly have to be borne in mind when interpreting any associated health outcomes.

How do long-term trends in mortality in each region compare with WCS?

Cause-specific mortality analyses

Prior to discussing the headline findings of these mortality analyses, some of the detail of the cause-specific mortality analyses warrants further consideration. For example, the increase in mortality rates among WCS males (and to a lesser extent females) in the 15–44-year age group is striking. This increase in younger male mortality has been seen at the national (Scotland) level and has been highlighted by other commentators.13,14 The rise results from increased numbers of Scottish male deaths involving suicide, alcohol, drugs and violence. What is of particular interest, however, is the fact that this phenomenon (rising rates of death in younger males) is not being repeated in the majority of other, socio-historically comparable, European regions. In the continental (i.e. non-UK) areas, mortality has been falling consistently in younger males since the start of the 1990s. Increases were recorded in Merseyside and Swansea & S. Wales Coalfields but not to the same extent as was seen in Scotland or WCS. In Northern Ireland rates decreased. These analyses are shown fully elsewhere.7

Second, the dramatic upward trend in mortality in WCS from chronic liver disease and cirrhosis, as highlighted above in all three of the adult age groups, is also important. The increase in alcohol related deaths generally in Scotland has been the focus of a great deal of attention.15–19 However, what is of particular interest here is the contrast with other regions. Broadening the analysis to all ages over the whole 20–25-year period, mortality rates from alcohol have either fallen, or remained unchanged, in virtually every continental (i.e. non-UK) region for which we have comparable data. The exception is Saxony where, having risen both pre andmore rapidlypost German reunification, rates have fallen consistently since the mid-1990s. This is in sharp contrast to the UK, where an increase in rates has been seen in Northern Ireland and in the English and Welsh regionsand to the greatest extent in WCS.

Finally, WCS's higher mortality is also contributed to by consistently high rates among older working aged females. In short, WCS has higher rates and slower improvement in mortality primarily due to adverse outcomes in younger working aged men and older working aged women. From these data and other sources13,14 we know that alcohol, drugs, suicide and violence are the rising problems.

Overall findings

The main findings of this investigation are that mortality rates in WCS are generally higher andcruciallyare improving at a slower rate than those of other, comparable, post-industrial regions in Europe. In Western European regions (including those in other parts of the UK) rates tended to be lower to start with, but the gap relative to WCS has widened; rates in Eastern European regions, starting from a higher base, have now overtaken, or are about to overtake WCS in these health terms.

How do we explain this? At one level the pathways between loss of employment (industrial or otherwise) and poor health and mortality are relatively well known.20–25 Thus, poverty underpins post-industrial decline, and manifests itselfvia interactions with other structural, behavioural, psychosocial and cultural factorsin illness and disease. It is tempting, therefore, to explain WCS's relative poor heath status on its generally higher levels of deindustrialization (as seen in table 1), resulting in greater levels of socio-economic deprivation (and associated factors) than that of the other regions studied. Indeed, in a UK context, this is the explanation that has traditionally been put forward to explain Scotland's overall levels of poor health relative to England.3–6 However, in terms of the socio-economic aspect, available data do not entirely support this hypothesis. For example, analysis of comparable (small area based) measures of deprivation and mortality between WCS and the main English post-industrial region, Merseyside, shows that excess mortality levels remain even when controlling for respective levels of deprivation.7 Furthermore, analysis of a range of socio-economic indicators (GDP, economic activity, unemployment, long-term unemployment, male worklessness, educational attainment) has shown that at the regional level, WCS does not appear to be any worse off than the ten comparator post-industrial regions, and indeed compares favourably to many, especially those in Eastern Europe wherefor exampleunemployment rates are several times higher.7 Taken as a whole, therefore, such data suggest that it is unlikely that poverty and material deprivation alone are the cause of WCS's poor health profile relative to other European post-industrial regions.

Of course one must exercise caution in interpreting differences in these types of cross-national, routine administrative and survey-based measures cited above, given the potential issues around compatibility and robustness of the data. In addition, comparisons of any current socio-economic conditions may mask important differences in earlier decades. Another limitation is the fact that such data are reported at regional level: another possible hypothesis may lie with the variations in socio-economic circumstances seen within the regions. In other words, inequalities within WCS may be greater than those of the other regions. Other factors may also be important. For example, migration and other demographic forces may play a part, while the health of any population or country will be shaped to a degree by political, economic, historical, institutional and other relevant influences. However, although these questions are under active investigation, we currently lack the data to be able prove or disprove any of these hypotheses.

The key point to make is that structural factors are likely to play an important role in the population health of all the post-industrial areas examined. Do variations in these structural determinants of health explain the current ranking and trends in mortality that we have reported, or do we need to look for other explanations? The wisest conclusion might be that the reasons remain unclear. However, one of our motivations for this work is to encourage policy makers to move beyond the point where the relatively poor health of a large population like WCS can be explained as being simply due to ‘post-industrial decline’ and ‘deprivation’. The phenomenon of post-industrial decline is impacting on populations all over Europe and will soon manifest itself in other parts of the world. The data presented here raise more questions than they answer, but suggest that an enquiry into the more general impact of deindustrialization on health and the factors that accentuate or ameliorate this impact is needed.

Acknowledgements

This project would not have been possible without the cooperation and assistance of a number of individuals and organizations. In particular, we would like to thank the following for all their help in providing the required data: Dr Wolfgang Hellmeier and Dr Helmut Brand at the Landesinstitut für Gesundheit und Arbeit (LIGA), Bielefeld, North Rhine-Westphalia, Germany; Frau Renate Recknagel of the Statistisches Landesamt des Freistaates Sachsen, Saxony, Germany; Dr Goetz Wahl, Landesamt für Verbraucherschutz Sachsen-Anhalt, Saxony-Anhalt, Germany; Prof. Witold Zatonski and Mr Wojciech Tarkowski, Cancer Centre and Institute of Oncology, Warsaw, Poland; Mme. Martine Bovet, CépiDc, Institut National de la Santé et de la Recherche Médicale (INSERM), France; Dr Zuzana Kamberska, Institute of Health Information and Statistics of the Czech Republic, Prague, Czech Republic; Drs Aart Lodder and Dr Daan Uitenbroek, GGD Nederland, Utrecht, Netherlands; Ms Sabine Drieskens, Scientific Institute of Public Health, Centre of Operational Research of Public Health, Brussels, Belgium; Dr Emma Gordon, Office for National Statistics, Newport, Wales; Miss Naomi O’Neil, Northern Ireland Statistics and Research Agency, Belfast, Northern Ireland; the General Register Office for Scotland. We are also extremely grateful to all those who helped in the process of identifying the various regions. Thanks are due in particular to: Prof. Martin McKee, London School of Hygiene and Tropical Medicine; Prof. Ray Stokes, Department of Economic and Social History, University of Glasgow; Prof. Conan Fischer, Department of History, University of Strathclyde; Dr Murray Frame, Department of History, University of Dundee; Dr Anja Johansen, Department of History, University of Dundee; Dr Riccardo Bavaj Department of Modern HistorySt. Andrews University; Dr John Bates, Department of Slavonic Studies, University of Glasgow; Prof. Ray Hudson, Wolfson Research Institute, University of Durham; John Hacking, Manchester Joint Health Unit. The work has been presented orally at: The Society for Social Medicine 52nd Annual Scientific Meeting, 17–19 September 2008, Southampton, England and 2008 European Public Health Association (EUPHA) conference, 6–8 November, Lisbon, Portugal.

Conflicts of interest: None declared.

Key points

  • Scotland has the highest mortality rates and lowest life expectancy of any western European nation.

  • This poor health profile is driven by high rates of mortality in the post-industrial West-Central belt of the country, and has traditionally been blamed on socio-economic deprivation.

  • Mortality rates are generally lower andcruciallyappear to be improving at a slower rate in the West Central Scotland (WCS) compared to other, similar, post-industrial regions of Europe.

  • While socio-economic factors have a detrimental impact on health in all post-industrial regions, it appears unlikely that the very high mortality rates and the slow rate of improvement in WCS can be attributed, exclusively, to current levels of deprivation. More nuanced or complex explanations are needed.

References

1
Scotland and European Health for All (HfA) Database 2007
ScotPHO
 , 
2007
 
2
Whyte
B
Scottish mortality in a European context, 1950–2000. An analysis of comparative mortality trends.
ScotPHO
 , 
2007
 
3
Carstairs
V
Morris
R
Deprivation: explaining differences in mortality between Scotland and England and Wales
Br Med J
 , 
1989
, vol. 
299
 (pg. 
886
-
889
)
4
Scottish Office
Towards a healthier Scotland – a white paper on health
The Stationary Office
 , 
1999
5
Scottish Executive
Social justice … a Scotland where everyone matters
Annual Report 2000
 , 
2000
Edinburgh
Scottish Executive
6
Scottish Council Foundation
The Scottish Effect? Edinburgh: Scottish Council Foundation
Healthy Public Policy Network
 , 
1998
 
7
David
W
Martin
T
Phil
H
The Aftershock of Deindustrialisation – trends in mortality in Scotland and other parts of post-industrial Europe.
Glasgow Centre for Population Health, April 2008
  
8
United Nations Industrial Development Organization (UNIDO)
Industrial Statistics Database
2004
 
ESDS International, (Mimas) University of Manchester
9
Cumbers
A
Birch
K
MacKinnon
D
Revisiting the old industrial region: adaptation and adjustment in an integrating Europe
2006
University of Glasgow
CPPR
 
Working Paper 1
10
Chiang
CL
Life table and mortality analysis
 , 
1978
World Health Organisation
11
Leon
D
Scotland's health in an international context
 , 
2003
Public Health Institute of Scotland
12
Statistics Belgium.
 
Available from: http://statbel.fgov.be
13
McLoone
P
Increasing mortality among adults in Scotland 1981–1999
Eur J Public Health
 , 
2003
14
Leyland
AH
Dundas
R
McLoone
P
Boddy
FA
Inequalities in mortality in Scotland 1981–2001
MRC Social and Public Health Sciences Unit
 , 
2007
15
Leon
DA
McCambridge
J
Liver cirrhosis mortality rates in Britain from 1950 to 2002: an analysis of routine data
Lancet
 , 
2006
, vol. 
367
 (pg. 
52
-
56
)
16
ONS Trends and geographical variations in alcohol-related deaths in the United Kingdom, 1991–2004.
Health Stat Quart
 , 
2007
, vol. 
33
 
17
Information Services Division
Alcohol Statistics Scotland 2007
 , 
2007
Edinburgh
NHS National Services Scotland
18
Hanlon
P
Walsh
D
Whyte
B
Let Glasgow flourish
 , 
2006
Glasgow Centre for Population Health
19
Scottish Executive
Cost to society of alcohol misuse in Scotland: an update to alcohol misuse in Scotland trends and costs
 , 
2005
Edinburgh
Scottish Executive
20
Charlesworth
SJ
Gilfillan
P
Wilkinson
R
Living inferiority
Br Med Bull
 , 
2004
, vol. 
69
 (pg. 
49
-
60
)
21
Mitchell
R
Gleave
S
Bartley
M
, et al.  . 
Do attitude and area influence health? A multilevel approach to health inequalities
Health Place
 , 
2000
, vol. 
6
 (pg. 
67
-
79
)
22
Martikainen
P
Valkonen
T
Excess mortality of unemployed men and women during a period of rapidly increasing unemployment
Lancet
 , 
1996
, vol. 
348
 (pg. 
909
-
12
)
23
Bartley
M
Unemployment and ill health: understanding the relationship
J Epidemiol Commun Health
 , 
1994
, vol. 
48
 (pg. 
333
-
7
)
24
Morris
JK
Cook
DG
Shaper
AG
Loss of employment and mortality
Br Med J
 , 
1994
, vol. 
308
 (pg. 
1135
-
9
)
25
Bartley
M
Ferrie
J
Montgomery
SM
Marmot
M
Wilkinson
RG
Health and labour market disadvantage: unemployment, non-employment, and job insecurity
Social Determinants of Health
 , 
2006
Oxford
Oxford University Press
(pg. 
78
-
96
)

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