Background
A life course approach to health recognizes the importance of early and later life exposures in identifying risk and protective processes operating throughout an individual’s lifetime [
1]. Exposure to low socio-economic position over the life course has been shown to influence a range of health outcomes, including cause-specific mortality and cardiovascular disease [
2,
3]. For the purpose of this review, socio-economic position (SEP) refers to the socially derived economic factors that influence the positions individuals hold within a stratified society, measured by individual or household level indicators such as education level and occupation [
4]. In chronic disease epidemiology, several conceptual models have been developed to help elucidate the mechanisms underlying life course socio-economic effects on health. These provide a foundation for investigating life course effects, however their effects are difficult to differentiate as they are empirically interlinked [
5].
The
accumulation model hypothesizes that early and later adverse socio-economic experiences have a cumulative, dose–response effect on later outcomes [
6]. The
latent model (or critical period) suggests that adverse socio-economic circumstances during childhood have an independent, detrimental effect on health, over and above current circumstances [
7].
Pathway models emphasize the importance of trajectories across the life course and are proposed if the influence of childhood SEP is attenuated after taking into account later conditions.
Social mobility models are usually divided into
intra-generational and
inter-generational. Inter-generational mobility refers to a change in social class between generations, often measured by comparing parental social class to own social class in adulthood. Intra-generational mobility is the movement between different social classes in adulthood, such as the first and last occupation. No consensus regarding the health consequences of social mobility exists. It has been proposed that downward mobility may negatively impact on mental health and wellbeing [
8], whereas others suggest that any movement between social classes will result in increased mental strain and illness [
9]. Another hypothesis states that mobility itself does not have an independent influence, but mobile individuals eventually experience levels of health and wellbeing between those of their current class and class of origin, closest to the current social class [
10,
11].
A positive association between current SEP and quality of life has been demonstrated using subjective wellbeing, life and needs satisfaction approaches [
12‐
17]. Subjective wellbeing is defined as the balance between positive and negative affect [
18]. Life satisfaction is the cognitive evaluation of one’s life, which involves the comparison between one’s aspirations and achievements [
19]. Needs satisfaction approaches derive from Maslow’s theory of human need, which proposes that once basic human needs are satisfied, such as food and safety, humans strive for higher needs such as self-happiness and esteem [
19,
20]. Most quality of life research has focused on contemporary influences and less is known regarding the effect of different life course socio-economic trajectories on later quality of life. This is of growing interest to academics and policymakers, as subjective quality of life is now considered an important indicator for the evaluation of interventions across several disciplines, and governments are increasingly adopting the measurement of national subjective wellbeing to inform policy decisions [
21,
22]. However, there is no consensus regarding the optimal measure of quality of life and it remains an ill-defined concept [
23].
Broadly, subjective quality of life involves the self-evaluation (expression of satisfaction or discontent, values and perceptions) of one’s personal circumstances in life [
19]. Subjective wellbeing (positive and negative affect), happiness, life and needs satisfaction measures are often used as key indicators of subjective quality of life. Numerous tools have been designed to capture these, such as the Satisfaction with Life Scale [
24], Ryff’s psychological wellbeing scale [
25] and CASP-19, a needs satisfaction measure comprised of control, autonomy, self-realization and pleasure domains [
23].
This systematic review aimed to assess whether evidence supported an overall relationship between life course socio-economic position and quality of life during adulthood and if so, whether there was support for one or more life course models.
Methods
PRISMA guidelines for the reporting of systematic reviews were followed [
26] and a review protocol was developed and updated as necessary throughout the review process and is available from the authors on request.
Search strategy
Articles were identified by searching (via Ovid) the electronic databases Medline (1948-present), Embase (1947-present) and PsycInfo (1987-present). Additional searches were executed in Web of Science and Cambridge Scientific Abstracts (CSA) Illumina. Web of Science covered the databases Science Citation Index Expanded (1945-present), Social Sciences Citation Index (1956-present), Arts & Humanities Citation Index (1975-present), Conference Proceedings Citation Index- Science (1990-present) and Conference Proceedings Citation Index- Social Science & Humanities (1990-present). CSA Illumina covered Applied Social Sciences Index and Abstracts (1987-present), International Bibliography of the Social Sciences (1951-present), CSA Sociological Abstracts (1952-present), and Worldwide Political Science Abstracts (1975-present). All searches were carried out on January 2nd 2012 and limited to English language articles. No restrictions were placed on the publication date of articles. Reference lists of included articles were checked for any additional articles and citations were accessed via Google Scholar and checked manually.
Searches included terms used to describe SEP, such as ‘social class’ and ‘occupation’, combined with terms used to describe the life course and quality of life. Relevant MeSH headings were used when available. See Additional file
1 for a full example of the search strategy executed in Medline.
Eligibility criteria
Studies were included if they met the following criteria: primary studies published in a scholarly journal; based on populations within industrialized countries as defined by Organization for Economic Co-operation and Development criteria [
27]; reported subjective quality of life as an outcome (using indicators separate from physical health such as wellbeing, life satisfaction or specific quality of life scales such as CASP-19); reported outcomes in males and/or females aged 25 years or over (as this represented an adult population in which individuals were likely to have completed their education); contained individual or household measures of SEP from at least two time points (childhood and adulthood, or two time points in adulthood, regardless of the length of time between measurement points or whether they specified a particular life course model).
Articles were excluded if they contained only qualitative data, were review articles, did not specify any information regarding the age of participants, included only area-level or subjective measures of social status (as these were considered different constructs [
28]), only looked at employment status, job or income mobility (without a measure of social class), contained only measures of physical health-related quality of life or which did not separate between physical and mental components of health-related quality of life (as we were interested in outcomes capturing aspects of quality of life separate from physical health), included only individuals with specific health conditions (e.g. dementia, psychiatric illness) as their population of interest, or contained only outcomes relating to psychiatric symptoms (e.g. psychological distress or depressive symptoms).
Study selection and data extraction
Title and abstract screening for immediately irrelevant articles was performed by one reviewer (CLN). Two reviewers (CLN and SVK) independently assessed the full-texts of articles short-listed against the eligibility criteria. Disagreements were resolved by consensus. All records were stored in Endnote X4. An Excel proforma was developed to assist in the data extraction procedure and included: the publication information (authors, year, journal), study characteristics (sample size, study design, response and attrition rates, time period), participant demographics (age at recruitment, gender, country), measurement of SEP (collection method, age at measurement, missing data), outcomes (summary measure such as mean quality of life scores or odds ratio of experiencing low quality of life, collection method, age at measurement, missing data), analysis methods (statistical techniques, variables controlled for, treatment of missing data) and results. Data were extracted by CLN and checked by SVK.
Quality appraisal
Quality appraisal was performed using an adapted version of the ‘Quality Assessment Tool for Quantitative Studies’ [
29]. The following items were used to assess the quality and risk of bias within studies: sampling method, sample representativeness, study design, response rates, attrition rates and reasons (including death and loss to follow-up), whether the characteristics of those lost to attrition or non-response differed from those of responders, measurement of SEP and quality of life variables, reporting of missing data, and variables controlled for in the analysis to reduce confounding. Given the limitations associated with scoring criteria [
30], the quality of articles was initially considered during the synthesis process. Three key items, which we considered the most important quality criteria, were then selected to provide an overall indication of study quality using a rating system. These were: the response and attrition rates, measurement of SEP, and sample size. A grade of higher, average or lower quality was given to studies based on the sum of scores for these items (see Additional file
2 for full details of the rating system). The quality appraisal of studies did not differ between the two methods of assessment (synthesis based on all quality items extracted and the rating system using the three key items).
Data analysis and presentation
Studies differed in terms of the measures of SEP, outcomes, time points considered and analysis techniques implemented. For these reasons, meta-analysis was not appropriate and narrative synthesis was used. Studies were categorized based on the life course model analyzed: accumulation, latent, pathway, or social mobility (inter-generational and/or intra-generational). Studies were grouped into the relevant life course model based on their aim, analytic approach and findings, similar to the method by Pollitt et al. [
3]. We also compared our own classifications with those of the authors, if provided, but no conflicting groupings occurred. If more than one model was assessed within the same study, the results are presented under multiple categories according to the life course models investigated. It should be noted that positive results may be found for more than one model within the same study. A summary of the evidence is presented under each life course model (studies considered higher quality are described first), followed by a discussion of the key issues relating to the comparison of results.
Discussion
This systematic review used life course models derived from chronic disease epidemiology to assess the relationship between life course socio-economic position and quality of life during adulthood. An overall relationship was suggested by the evidence, but results for each life course model were mixed. Supportive evidence was found for the latent model among women only, but results were contradictory. Some studies indicated that low childhood SEP was associated with poorer adult quality of life, but others found high childhood SEP to be linked with poorer outcomes. Social mobility models were generally not supported, but some studies investigating intra-generational mobility did identify an effect. There was a suggestion that upwardly mobile individuals experienced higher quality of life, compared to those who moved downward or remained in the same position. However, one higher quality study which modeled separate mobility effects found no effect of intra-generational mobility; mobile individuals were more likely to report quality of life levels closer to their current class, rather than their prior class. High quality studies addressing inter-generational mobility were lacking. Few studies addressed accumulation and pathway effects and heterogeneity of these studies resulted in limited synthesis.
A similar systematic review focusing on cardiovascular outcomes found consistent support for an accumulation effect of socio-economic adversity on cardiovascular disease risk and moderate support for a latent effect of low childhood SEP on increased cardiovascular disease risk factors, morbidity and mortality [
3]. Little support was found for a unique influence of social mobility, although they did not distinguish between inter- and intra-generational effects. Our review particularly lacked studies investigating accumulation effects, thus additional research is required to fully assess this hypothesis for quality of life outcomes. Regarding social mobility effects, the study by Houle (2011) is consistent with the literature which demonstrates that mobile individuals tend to have health outcomes between their social class of origin and destination, so social mobility has a constraining effect on health inequalities [
11,
43‐
45]. Further research would be useful to investigate whether this applies to other quality of life outcomes and in other countries with differing levels of social mobility.
A particular strength of the systematic review was the number of databases searched. However, the grey literature was not explored and only quantitative English language articles were included. Important unpublished articles and foreign language studies may exist which were not considered. It is also possible that key insights into the individual experience of different life course socio-economic trajectories may be provided by qualitative studies. Quality assessment was performed by describing all quality items relevant to a study and by ranking studies based on key quality appraisal items. The latter system may be crude and opinions are likely to differ regarding the key criteria. However, compared to the pure description of studies, we feel the criteria enable the reader to better discern between higher and lower quality studies.
A number of limitations relating to quality of life research require highlighting. Due to the ambiguous nature of quality of life, it is possible that studies measuring outcomes such as life satisfaction and wellbeing are capturing different concepts, or various domains of the same concept [
46]. It is therefore suggested that researchers try to include a variety quality of life measures in their studies, to investigate whether associations differ between indicators. Cohort studies which record quality of life at several time points throughout adulthood would also be an improvement, especially when investigating intra-generational mobility effects.
Cultural differences may also exist in the understanding of survey questions and the degree to which expressing satisfaction is believed desirable [
24,
47]. Populations from a range of countries were included in the review and the results between countries became difficult to interpret. A number of macro-level factors, such as welfare state arrangements and educational policies, may influence the degree to which life course socio-economic conditions shape later quality of life. To enable investigation of between-country differences, there is a need for the increased collection, harmonization and utilization of comparable cross-national data. Studies that take into account potential cultural differences in reporting styles, as well as local and national context are required [
48].
Although methodological limitations in life course models have been noted [
49], they provide a useful, albeit simplified, foundation to investigate potential life course effects. As previously suggested, it is recommended that all life course models are considered within individual studies, to prevent patterns of association being overlooked [
3]. Separating the effects of different models is difficult as they are empirically interlinked; however this need not be the key objective and perhaps risks their reification. Examining the evidence for each model together can help to obtain a more complete understanding of any relationship, refine the concepts and generate new hypotheses [
50]. Studies which include multiple measures of SEP are also recommended, as results may vary depending on the specific indicator used [
3,
51]. It is important that when including multiple measures, such as social class and education, the sociological meaning of these is considered and related to the specific hypotheses and causal pathways under study [
52]. It may also be useful to consider the length of time spent in each social class to better quantify accumulation and mobility effects. In addition, future empirical work may benefit from considering the life course principles of biological and social plausibility [
5]. Cross-sectional research often relies on the retrospective recall of socio-economic variables. However, studies have shown that this may not be a major issue especially when using methods to facilitate recall of events, such as the life-grid method, and may only lead to the under-estimation of associations [
53‐
55]. Cohort studies which record socio-economic information from birth to old age would be ideal, although time-consuming and expensive. These are not without problems, however, often suffering from increasing attrition over time. This may lead to selection bias or a ‘healthy survivor effect’ where individuals with poorer outcomes are selected out of studies. However, better understanding of micro- and macro-level factors which nurture high quality of life in those exposed to adversity across the life course has key policy relevance.
Competing interests
The authors declare that they have no competing interests
Authors’ contributions
CLN, JPP and RM conceived the review and contributed to its methodology. CLN conducted the literature searches, selected and categorized studies, extracted and analyzed the data and wrote the manuscript. SVK acted as second reviewer and was involved in the short-listing of articles and checked extracted data. All authors contributed comments on several manuscript drafts and read and approved the final manuscript.