1 Introduction

A major theme in demographic and epidemiological studies is the seemingly persistent effect of social class on mortality. In the present study, we challenge common notions of this by taking a long-term perspective on the development of social class inequalities in mortality in the adult and elderly population. The arguments for our statements are based on an investigation of the Skellefteå and Umeå regions in northern Sweden for the periods 1801–2013 for Skellefteå and 1901–2013 for Umeå. The main issue is analysed according to gender and age group (working age vs retired). Furthermore we place this in the context of how the inequality in mortality is associated with the development of economic inequality in society. The results are discussed in relation to the mortality transition and the social determinants of health and mortality, as well as their implications on some of the most influential hypotheses and concepts in health research. The study is unique in its long time-perspective and its utilization of historical micro-data of a sufficiently large and socially diverse population for analyses of a central issue in health research.

Based on the results, we argue that high social class is not necessarily favourable for survival. Social conditions and social position certainly have impact, but not always in the expected direction. In our case this is apparent for men during a large part of the studied period, particularly for men in working age; only at a surprisingly late date appears a male mortality class reversal, changing the relation to a substantial advantage of being in a higher social position. Mortality risks in different contexts must be understood in the intersection between class and gender. We suggest that health-related behaviour is not only important in present-day societies, but was also decisive in earlier phases of the mortality transition. The results implicate that the association between social class and health is more complex than is assumed in many of the dominant theories in demography and epidemiology.

1.1 Social class and mortality

One of the central aspects of survival is social class and access to economic and other resources. In present-day welfare societies, social position, either according to social class, education or income, is a strong determinant when it comes to health and mortality and its impact are even increasing (Kunst et al. 2004; Mackenbach et al. 2016; Fritzell and Lundberg 2007; Brønnum-Hansen and Baadsgaard 2012; Strand et al. 2010). Link and Phelan (2002) suggest that “… social conditions have been, are and will continue to be irreducible determinants of health outcomes and therefore deserve their appellation of ‘fundamental causes’ of disease and death”. The persistence of social inequality in mortality to the disadvantage of the lower classes is one of the main assumptions in their theory (Link and Phelan 1995; Link et al. 1998). The idea that socio-economic health inequalities were probably larger in historical societies is a reasonable assumption since these societies were often characterized by enormous class differences. Knowing that access to different kinds of resources, such as economic, social and cultural capital, provides advantages in all aspects of life, the health advantage of higher classes ought to be obvious.

However, recent studies investigating social inequality in health and mortality with micro-data have questioned the generality of the assumed pattern (Bengtsson and Poppel 2011), and solid empirical evidence about the process is lacking and studies focusing on the issue are few. One exception is the paper by Bengtsson, Dribe, and Helgertz (2020), where a study from southern Sweden for the time period 1813–2015 is presented. There is a need for additional reliable studies from different geographical and historical settings in order to better understand the role of class and socio-economic conditions in health and survival over time.

Social position affects health and mortality differently during the life course and from adulthood into old age. The differences either decrease in old age (status levelling), are constant (status maintenance), or increase (cumulative advantage) (Hoffmann 2008). Diminishing differences may be a consequence of the circumstance that biological factors become increasingly important during the ageing process and in old age, leaving less impact for social factors. The status maintenance hypothesis basically assumes continuity in the determinants of social health inequalities from adulthood to old age. The cumulative (dis)advantage hypothesis (Dannefer 2003), implies that advantages and disadvantages persist and accumulate during life in a negative spiral, rewarding some while disfavouring others.

Another aspect of the development of social inequality in mortality concerns its relation to economic inequality. Wilkinson and Pickett (2009) argue that income inequality has an independent effect on mortality, separate from the direct effect of actual access to economic resources. They find that unequal societies perform less well when it comes to health (as well as other social conditions) than equal ones in the todays economically developed world. This has initiated a vital scholarly debate and the topic has been extensively studied (Subramanian and Kawachi 2004; Wagstaff and Van Doorslaer 2000). In recent decades, the respective association between trends of inequalities in mortality and income is weak or non-existent according to Hoffmann et al. (2016). What the association looked like in previous periods is unknown; this is of particular interest as the levels of economic inequality differed fundamentally from those of recent decades. Even if not a necessary implication, it is reasonable to assume that poorer groups were the most disadvantaged due to large levels of inequality.

2 The Skellefteå and Umeå regions

The Skellefteå and Umeå regions in Sweden.

Figure 2.1: The Skellefteå and Umeå regions in Sweden.

During the studied period, Sweden developed from a poor agricultural society with low urbanization to a rich welfare state. The regions studied here were for a long time remote from the central parts of the country. The Skellefteå and Umeå regions (Figure 2.1) are part of the county of Västerbotten in northern Sweden along the coast of the Gulf of Bothnia, where communication with the rest of Sweden was difficult until the late 19th century. The economy was dominated by agriculture, making it vulnerable to harvest failures; several severe famines occurred in the regions during the 1800s, for example after the harvest failure of 1867 (Edvinsson and Broström 2014). During the long winters, sea communication was hindered due to the Gulf of Bothnia being frozen, in some cases as late as June (Fahlgren 1956). Towards the end of the 19th century, the Swedish railway system reached this part of Sweden, facilitating contact with the rest of Sweden, improving the economy and making it possible to mitigate the effects of harvest failures.The regions became increasingly integrated in the same epidemiological pattern as the rest of Sweden.

In our dataset, before 1950 the Skellefteå region consists of a selection of parishes surrounding the town of Skellefteå, founded in 1845 but with a very small population during the 19th century. The data from the period after 1975 cover the Skellefteå, Norsjö and Malå municipalities, the same area as for the earlier period but with the addition of two more parishes. The majority of the 19th century population lived in rural villages and hamlets, making its livelihood from agricultural production. During the 20th century, industrialization took place. This also led to a population increase both in the town and the rural parts, resulting in a more diversified economy. The Skellefteå population size as defined in our data sets (all ages) was 6,142 on January 1, 1801, 43,212 on January 1, 1901, and 76,723 at the end of the 20th century.

The Umeå region in the dataset consists of Umeå urban and rural parishes 1901–1950, and from 1976 onwards of Umeå municipality, with another three parishes included. Umeå town had for a long time a small population, and was the administrative, educational and military centre of the county of Västerbotten. During the latter part of the 20th century, the establishment of Umeå University led to a rapid population increase. Agriculture dominated the rural part. Consequently the economy was more diversified than that of Skellefteå. The population size as defined in our data sets (all ages) was 19,138 on January 1, 1901 and 103,970 when the 20th century ended.

3 Data and variables

The data for the present study come from two large population databases at the Demographic Data Base (DDB),Umeå University (http://www.cedar.umu.se), which provide us with micro-data for the Skellefteå and Umeå regions in northern Sweden (“The Demographic Database, CEDAR, Umeå University” 2015). The period 1801–1950 is covered by the database Poplink (Westberg, Engberg, and Edvinsson 2016). Poplink is based on linked parish records, allowing us to reconstruct life biographies on people as long as they remained in the region. The records are linked within, but not between, the regions.

The other large data set is extracted from the Linnaeus database (Malmberg, Nilsson, and Weinehall 2010), which is based on different linked national population registers from 1960 to 2013 (censuses, LISA from Statistics Sweden and cause of death registers) and is used within the ageing programme at CEDAR, Umeå University. Due to data issues we choose to use Linnaeus data only from 1976 onwards.

Individuals are anonymized and as the two databases are not linked, they are treated as separate units. This prevents us from following individuals between the two databases throughout their lives. It also makes it impossible to add information on individuals in the Linnaeus database from what we could potentially extract from Poplink, for example family background or previous social class.

The sampling frame.

Figure 3.1: The sampling frame.

In the data set analysed here, all individuals aged 40 years and older ever having resided in either of the regions are included. The data file contains the variables social class, gender, urban/rural residence, birth date, death date, first and last date of observation and type of entrance/exit. The total number of person years is 1.59 millions leading to 39.13 thousand deaths in Poplink and 3.1 million person years leading to 60.65 thousand deaths in the Linnaeus database: see Figure 3.1.

3.1 Presence periods

Differences in available information in the two datasets as well as in the Linnaeus data make it necessary to apply different approaches when it comes to the identification of presence periods. The Poplink data provide us with exact dates, or at least year of start and exit of presence, allowing us to have full and continuous control over the population. This is not the case with the Linnaeus database, however. For the period 1976–2001 we use presence in the censuses 1975–1990 and information on deaths from the National Board of Health and Welfare. The populations in the Skellefteå and Umeå regions are those residing there according to the censuses. Each census constitute the baseline for which persons are followed up during the five years until the next census (e.g. the 1975 census has a follow-up from 1 November 1975 until 31 October 1980). The exception of this is the 1990 census for which the follow-up is until 31 December 2001. Thus, the deaths do not necessarily take place in our region for the Linnaeus analyses, although this is usually the case.

For the last period, from 1 January 2002 until 31 December 2013, we use the yearly population registers (LISA data) together with information on deaths from the National Board of Health and Welfare. The LISA data depict the situation at the end of the given year, so the follow-up period is from 1 January to 31 December the next year. Deaths may still occur outside the region, but less frequently than when exposure is restricted to presence in the censuses.

3.2 Social class

Mortality differences are analysed according to social class, based on a modified form of the classification scheme Hisclass (Van Leeuwen and Maas 2011). Social position during working age is defined at around age 40 but defined from the last occupation in working age for the elderly and retired population; i.e. from age 65 until death or last observation.

The availability of information on occupations varies over time and between sources. In Poplink, occupations are coded according to the original DDB coding system that has then been adapted to Hisclass. For the Linnaeus data, the occupations are available in the coding scheme HISCO (Van Leeuwen, Maas, and Miles 2002) who then are recoded into Hisclass. We consider the observed social class in a census to be valid for the full period until the next census, corresponding to our definition of presence periods. For the period 1991–2000 the social class in the 1990 census is considered to be maintained until 2001.

The two data sets represent different ways of reporting occupations. Poplink usually only provides the occupation of the head of household, thus underestimating female labour force participation as well as that of adult children residing with their parents (Vikström 2010). For the Poplink period, we have chosen to categorize wives according to the position of the head of household, usually the husband/father, assuming that the family shares the same socio-economic position. We consider this as a reasonable approach for this period.

Female labour is much better covered in the Linnaeus database, and it is difficult to define households in the same way as in Poplink. Extramarital cohabitation became common and female labour force participation developed as the norm in Sweden during this period. The husband’s occupation as signifier of social class became, if not obsolete, at least less relevant. All included persons are signified by their individual occupation for this period. Although the results for the different periods are not completely comparable, the difference in approach reflects an actual change in how social class is structured.

Hisclass, the classification system used as a basis for our categorization, is a “… HISCO-based historical international social class scheme” (Van Leeuwen and Maas 2011; Van Leeuwen, Maas, and Miles 2002). The different classes in Hisclass represent distinct categories, based on whether the work is manual or non–manual, skill level, supervision, and sector. Implicitly it reflects large differences in access to economic resources, status, social power, etc. We have chosen to work with a broad definition of classes, merging the original 12 social classes in Hisclass into four, representing different levels of control and access to resources vital for life chances, and an additional fifth category of unknown class:

Figure 3.2 shows the distribution (per cent) of exposure times according to class for women and men (see also Figures 7.1, 7.2, and 7.3 for aggregated totals during the different periods in the Appendix). The farmer category was dominant until the middle of the 20th centuries but have become marginal in the latest periods, particularly among women. The category of workers is fairly stable over time. The highest class has increased substantially, albeit from a very low level in the 19th century. The increase in academics in connection to the establishment of Umeå University explains much of this increase in later time. Furthermore, we observe a rather high percentage of missing social class in the early 19th century. For men the proportion is somewhat higher in the age group 65 and above, those who were retired, but still low enough to be used in our analyses (results not shown). If we disregard the very first period, however, there were very few with occupation missing until 1950.