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Mixed evidence for the compression of morbidity hypothesis for smoking elimination—a systematic literature review

Abstract Background There is debate around the composition of life years gained from smoking elimination. The aim of this study was to conduct a systematic review of the literature to synthesize existing evidence on the effect of smoking status on health expectancy and to examine whether smoking elimination leads to compression of morbidity. Methods Five databases were systematically searched for peer-reviewed articles. Studies that presented quantitative estimates of health expectancy for smokers and non-/never-smokers were eligible for inclusion. Studies were searched, selected and reviewed by two reviewers who extracted the relevant data and assessed the risk of bias of the included articles independently. Results The search identified 2491 unique records, whereof 20 articles were eligible for inclusion (including 26 cohorts). The indicators used to measure health included disability/activity limitations (n=9), health-related quality of life (EQ-5D) (n=2), weighted disabilities (n=1), self-rated health (n=9), chronic diseases (n=6), cardiovascular diseases (n=4) and cognitive impairment (n=1). Available evidence showed consistently that non-/never-smokers experience more healthy life years throughout their lives than smokers. Findings were inconsistent on the effect of smoking on the absolute number of unhealthy life years. Findings concerning the time proportionally spent unhealthy were less heterogeneous: nearly all included articles reported that non-/never-smokers experience relatively less unhealthy life years (e.g. relative compression of morbidity). Conclusions Support for the relative compression of morbidity due to smoking elimination was evident. Further research is needed into the absolute compression of morbidity hypothesis since current evidence is mixed, and methodology of studies needs to be harmonized.


Introduction
W orldwide life expectancies are increasing, which is partly the result of new medical possibilities. We live longer, but the burden of non-communicable diseases (NCD) has never been higher. NCDs are currently the leading cause of deaths worldwide. 1 The majority of these NCDs may be the consequence of modifiable lifestyle risk factors, such as smoking, physical inactivity, excessive alcohol consumption and poor diet. 2 The exact influence of prevention of lifestyle risk factors on longevity and health, including the number of life years spent in good or impaired health, remains unclear. This is in particular the case for smoking.
The past decade can be described as a public health success story in terms of smoking prevalence reduction. Joint efforts have led to substantial decreases in tobacco consumption worldwide. However, smoking is still considered as a major public health threat and this is recognized by several initiatives that aim to combat this threat. A main initiative is the world's first public health treaty: the World Health Organization (WHO) Framework Convention for Tobacco Control, which is as of 2016 ratified by 180 parties. 3 Strengthening its implementation is also mentioned in the United Nations' Sustainable Development Goals. 4 Another explicit goal set by the WHO involves the 25Â25 NCDs targets, which include lowering tobacco use by 30% between 2010 and 2025. 5 There is a debate around the composition of life years gained from smoking elimination. A reduction in smoking prevalence leads to higher life expectancies because of the effect of smoking on mortality through fatal diseases. 1 However, smoking also has an effect on morbidity through a wide range of both fatal and non-fatal diseases. 6 Which effect is stronger, the morbidity or mortality effect, will determine how these life years are spent. A reduction in smoking prevalence may lead to either a compression of morbidity, 7 an expansion of morbidity, 8 or a dynamic equilibrium, 9 implying, respectively, that less smoking leads to fewer years lived with morbidity, more years lived with morbidity, or to a shift from more severe to less severe morbidity. The third theory, a dynamic equilibrium, has never been formally defined. Morbidity and disability here not binary but are more considered as processes and therefore often proposed as the 'intermediate' scenario between the other two theories. 10 Hence, in this study, we merely focus on the first two theories. Consequences on morbidity can be examined as absolute and relative effects. 11 The absolute effects reflect the change of number of years lived unhealthily. When this change is interpreted as a percentage change, we consider the consequences as relative effects. 12 On the individual level, distinguishing between the absolute and relative effects are not per se of an added value. However, the absolute and relative changes are both relevant to capture and estimate changes in population health.
The increased efforts that are undertaken to lower smoking prevalence's emphasize the need to understand the impact of smoking elimination on population health. This study aims to conduct a systematic literature review in order to synthesize existing knowledge on the effect of smoking status on health expectancy. Health expectancy is a measure that reflects the total life expectancy split in years lived in good health and years lived in poor health (in absolute terms and as proportion of total lifetime) 10,13 and thus allows us to assess whether non-smokers experience a compression of morbidity or expansion of morbidity when health expectancies are compared with smokers.

Search strategy
Four databases were systematically searched for peer-reviewed studies from inception up to July 2018: Embase.com (since 1971), Medline ALL via Ovid (since 1946) Web of Science Core Collection (since 1975), Cochrane Central Register of Trials via Wiley (since 1992). An additional search was performed in Google Scholar. This search engine could help in retrieving articles that have not been published yet or had no relevant search terms in their title and abstract. The search equation combined search terms (using thesaurus terms when available combined with terms in title and/ or abstract) for 'smoking' and 'health status indicators'. We adopted a broad search strategy to cover a wide spectrum of articles. The search strategy was set up together with a librarian. The complete search strategies for all databases can be found in Supplementary file S1.

Eligibility criteria
We included articles based on the following eligibility criteria: (1) The article focuses on smoking and health expectancy.
(2) The article estimates health expectancy for smokers and nonsmokers, either prospectively or retrospectively. (3) The article focuses on a sample that aims to be representative for the general population. (4) The article is written in English. (5) The article is not a conference abstract, letter, note or editorial.
Health status indicators can range from objective measures (e.g. disease status) to subjective measures (e.g. self-perceived health). We did not limit inclusion by a certain type of health status indicator.

Selection strategy
Two researchers screened the retrieved articles. The predefined eligibility criteria guided the decision process for inclusion and exclusion. When the information provided in the titles and abstracts were insufficient for a decision, a brief screening of the full-text took place to decide whether the article was eligible for inclusion. Differences in screening results, among the two researchers, were discussed and resolved by dialogue. When no consensus could be achieved, a third researcher was consulted to judge. Study selection was conducted in Endnote X6.

Data extraction
Extraction forms were developed to assist in the harmonization of the extracted data. Extracted data included the following information: operationalization of exposure and outcome variables, study population characteristics, applied method to estimate health expectancy and the health expectancies. Two main approaches for the estimation of health expectancy exist, which can be applied either on cross-sectional or longitudinal data. 14 Prevalence-based life tables, also known as Sullivan's Method, is applied on crosssectional data. 15 The other approach uses multiple measurements and is based on incidence rather than prevalence and often relies on the multi-state life tables. 16 The risk of bias was assessed with a quality assessment tool for observational cohort and crosssectional studies (Supplementary files S2 and S3).

Data analysis
The study selection was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (figure 1). 17 For all studies it was evaluated whether a compression or expansion of morbidity (both in relative and absolute terms) occurred by comparing the findings between non-/ never-smokers and current smokers. 11 An absolute expansion of morbidity indicates an increase in the number of unhealthy life years for non-/never-smokers. This may lead to either an increase or decrease in the proportion of life spent in poor health for non-/neversmokers: relative compression or expansion of morbidity. Figure 1 shows the PRISMA flow diagram of the screening process. In total, 2488 unique records were identified and screened. We excluded 2367 articles based on title and abstract. Thus, the fulltext of 121 articles were assessed of which, we excluded 101 articles most of including no relevant outcomes. Moreover, one study reported health expectancies where the unhealthy and healthy life years did not add up to the total life expectancy. It was decided to exclude this article due to inadequate results. This resulted in 20 included articles. Some articles reported health expectancies for multiple population cohorts: in total health expectancies for 26 population cohorts were reported. The quality assessment (Newcastle-Ottawa quality assessment), independently conducted by two researchers, showed that the majority of the articles were of sufficient quality (scoring at least five out of the eight stars). One article 18 showed some reason for concern with scoring three stars because the study did not control for factors in the analysis. Table 1 provides characteristics of the included articles (N ¼ 20), listed by cohort (N ¼ 26). The first column in table 1 attaches a number to each cohort. These numbers will be used for reference in the rest of this article. Data collection of the population cohorts occurred between 1948 and 2014. Sample sizes varied from 1759 up to 42 516 respondents. Smoking status was defined in various manners, namely in two categories [(ever)smokers, non-/never-smokers]; three categories (current smokers, former smokers and neversmokers); or four categories (heavy smokers, moderate smokers, former smokers and never-smokers). The studies that applied a dichotomous definition for smoking status could still vary by group composition due to the categorization of former smokers. The indicators for health involved disability/activity of daily living limitations (n ¼ 9), health-related quality of life (EQ-5D) (n ¼ 2), disability weights (n ¼ 1), self-rated health (SRH) (n ¼ 9), chronic diseases (n ¼ 6), cardiovascular diseases (n ¼ 4) and cognitive impairment (n ¼ 1). Some studies estimated health expectancies for different indicators for health, e.g. for both SRH and chronic diseases. The majority of the studies (n ¼ 11) used a longitudinal approach by applying a multi-state (Markov) transition model. The Sullivan approach (cross-sectional data) was the other main applied method (n ¼ 6). More novel methods were applied by Van Baal et al. 19 and Mehta and Myrskylä. 20 The former adopted a dynamic population model (RIVM chronic disease model) whereas the latter applied a matrix population model (an extension of the multi-state technique).

Compression of morbidity hypothesis
In order to assess the occurrence of compression of morbidity, we compared the unhealthy life years between non-/never-smokers and current smokers. Supplementary file S4 shows the health expectancies (unhealthy life years þ healthy life years ¼ life expectancy) of the included articles. Table 2 shows the answers on the question whether a compression of morbidity was observed in the health expectancies. The different starting ages for the health expectancy estimations are also included in the table. The heterogeneity in the data (different health indicators, different starting ages, different smoking definitions and further stratification) hampered direct comparisons between studies. Nonetheless, we grouped the health indicators into three categories (disability, SRH and longstanding illness) in order to structure the findings and gain insights.
The results for the absolute compression of morbidity hypothesis are diffuse: approximately half of the studies reported an absolute compression of morbidity. This means that non-/never-smokers spent less time in poor health. Yet, this also indicates that approximately half of the studies found that non-/never-smokers spent a longer time in poor health than current, moderate and heavy smokers (absolute expansion of morbidity). This last finding was mostly found in studies that applied disability as an indicator for health. Most of the studies reported a relative compression of morbidity [except cohorts #7, #8, #26 (women), #11 (men), #19 (only from 80 years), #1]. The studies that did not report a relative compression of morbidity (and thus found a relative expansion of morbidity) were prevalent in all three health categories. Further, a finding was that all studies reported that non-/never-smokers have more healthy life years compared to smokers. For men, the differences in unhealthy life years between non-/ never-smokers and current smokers varied between À0.3 years and þ1.3 years. Most studies (n ¼ 8) found more unhealthy life years for non-/never-smokers, indicating an absolute expansion of morbidity. For example, in cohort #12 it was found that non-/never-smokers would have 4.6 unhealthy life years, while current smokers would only have 3.8 unhealthy life years. The relative differences are hard to observe from this figure, but these can be found in Supplementary file S4. Most studies show a relative compression of morbidity, except cohorts #7 and #11.

Health expectancies-disability
For women, the same pattern was observed. However, the differences in the number of unhealthy life years between non-/neversmokers and current smokers were larger, ranging from -1.1 years to 2.4 years. For instance, in cohort #2, it was estimated that smokers would have 13.4 unhealthy life years while non-/never-smokers would have 15.8 unhealthy life years. A relative compression of morbidity was also found for women in most studies, except in the cohorts #7 and #26. Moreover, figure 2 shows that women have on average a higher total life expectancy and more unhealthy life years compared to men.

Discussion
While health expectancies are central in the debate related to quantity vs. quality of life, a relatively small number of studies have investigated the health expectancies for smokers and non-/neversmokers. This systematic review extracted 20 articles from the 2488 unique retrieved records. Within these 20 articles, a total of 26 cohorts were studied. Our collected evidence showed consistently that non-/never-smokers spent more years in good health throughout their lives than smokers. In contrast, findings were inconsistent regarding the effect smoking has on the absolute number of unhealthy life years. Estimates for unhealthy life years were diffuse for all applied health indicators. Approximately half of the studies found an absolute compression of morbidity for non-/never-smokers compared to smokers. As such, the other half of the studies reported an absolute expansion of morbidity, which is a finding that deserves attention in this discussion. Findings concerning the time proportionally spent unhealthy were less heterogeneous: nearly all included articles reported relative compression of unhealthy life years for non-/never-smokers compared to smokers. Caution in drawing universal conclusions of our findings is required because of the heterogeneity in the studies.  Chronic diseases mostly have an onset earlier in life than disabilities, however, the mortality effect of chronic diseases is reduced nowadays. Hence, life years after the onset of chronic diseases are likely to exceed life years after the onset of disabilities. Smokers may not reach the average age at which severe disabilities [e.g. Activities of daily living (ADL)] become relevant, which might explain the reported relative small differences in unhealthy life years between non-/never-smokers and smokers. Some included articles analyzed  SRH and chronic diseases for the same study population (cohorts #3, #21, #22, #23 and #24). In the majority of these cohorts the largest differences between smokers and non-/never-smokers were found for health expectancies estimated with SRH. Three of the studies reported that male smokers had less years with chronic diseases, while none of these studies reported that male smokers had less years in poor SRH. Put differently, health expectancy measured with SRH resulted in fewer healthy life years and more unhealthy life years for smokers compared to health expectancy estimated with chronic diseases. This could be because chronic diseases are stronger associated with mortality than SRH. Further, one study in our review had cognitive impairments as an indicator for health and reported absolute and relative expansion of morbidity for nonsmokers. This health measure deviates from the other included health indicators but is an important health indicator in an aging population. Hence, the effect of smoking elimination on this health indicator is worth further exploring. From a methodological perspective, studies differed considerably. Firstly, remaining health expectancies were estimated from a variety of ages. Health expectancy estimations from an older age (e.g. 65 years) are likely to underestimate the effect of smoking since premature deaths and early onset of reduced health are not captured. In total, eleven cohorts estimated health expectancy with a start age of 65 years and higher of which, seven cohorts reported absolute expansion of morbidity and five cohorts a relative expansion of morbidity. Secondly, various categorizations for smoking were applied. Respondents belonging to a never/non-smoking group could differ from studies with a category for former smokers. Thirdly, estimation methods for health expectancy were characterized by the Sullivan's method or the multi-state life table method. The multi-state life table method provides a richer analysis, but for health expectancy estimations, it is suggested that Sullivan's method is easier to implement and needs less assumptions. 38 Fourthly, studies used different methods to measure health status. Questionnaires, interviews, health care professionals and other sources were used.

Strengths and limitations
The discussed variety of methods hampered comparability and interpretation of the results. A meta-analysis was therefore not possible. Health expectancy as a measure for population health has become a standard. A call for adopting a homogeneous measure of health expectancy has frequently been made. 39 A strength of this systematic review was the applied search strategy, which was comprehensive and covered a variety of databases and the consultation of experts. This creates confidence that we reached the most relevant published articles. All retrieved articles were independently double screened, and the data were extracted independently.

Conclusion
When a reduction in smoking prevalence delays both death and ill health, relative and absolute time spent unhealthily may still increase. 40 Relative compression of morbidity due to smoking elimination was found in most of the studies. Further research is needed to examine the effect of smoking elimination on the absolute number of years spent unhealthily since findings were mixed in all health categories. The health indicators for health expectancy should be more streamlined in future research to synthesize and understand the implications of smoking elimination on population health better. The support found in approximately half of the studies for absolute expansion of morbidity for non-/never-smokers raises questions concerning the related consequences for public health. When society will be smoke-free, health expectancies change and thus the environment need to adapt accordingly to cope with this change. Health systems might be in need for specific adaptations in order to deal with people spending more years unhealthily. For instance, disease management programs with a particular focus on the most common NCDs will become more important. 41

Supplementary data
Supplementary data are available at EURPUB online.

Key points
• While health expectancies are central in the debate related to quantity versus quality of life, a relatively small number of studies have investigated the health expectancies for smokers and non-/never-smokers. • Relative compression of morbidity was found for non-/neversmokers in most of the studies, absolute expansion of morbidity was found in approximately half of the studies. • In order to estimate the effect of smoking elimination on population health more precisely it is important that research on this subject is harmonized in terms of methodology. • When society will be smoke-free, health expectancies change and thus health systems need to adapt accordingly to cope with this change.