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
]. 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
]. 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
]. 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
]. 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.