Study population

We used data from four prospective cohort studies from England, Finland, France and Sweden to calculate partial life expectancy (LE) and health expectancies between the ages of 50 and 75. In all cohorts, people aged 50 years or older with valid data on health and BMI were included from the first observation. We limited our calculation of partial LE to an upper age of 75 as not all cohorts had participants aged 75 and older and this choice allowed us to have comparable time frames for each cohort.

The English data are from the first six waves (2002–2003 to 2012–2013) of the English Longitudinal Study of Ageing (ELSA), an open-access, nationally representative biennial longitudinal survey of those aged 50 and over living in private households in England who had previously taken part in the Health Survey for England in 1998, 1999 or 2001 (wave 0).27 We included 7225 participants aged 50–75 years at baseline who had valid information on objectively measured BMI collected during the home visit at either wave 0 or wave 2.

The Finnish data are from five waves of the Finnish Public Sector study (FPS). The FPS, established in 1997/1998, comprises all 151 901 employees with ⩾6 month job contract in any year from 1991/2000 to 2005 in 10 towns and 5 hospital districts in Finland. Survey data has been collected by repeated surveys at 4-year intervals on all 103 866 cohort members, who were working in the participating organizations during the surveys in the years 1997/1998, 2000/2001, 2004/2005, 2008/2009 and/or 2012/2013 or had retired or left the organizations after the 2000/2001 survey. For the analysis we used data from 42 702 participants aged 50–75 years at the first wave for which valid data on BMI was available.

The French data are from the GAZEL Cohort Study, established in 1989 among Électricité de France-Gaz de France (EDF-GDF) workers, the French national utility company, with annual waves of data collection up to 2014. At inception in 1989, the GAZEL Cohort Study contained 20 625 volunteers (15 011 men and 5614 women) working at EDF-GDF who were then aged from 35 to 50 years.28 We included 14 967 participants who had valid information on BMI measured in 1996 and who were aged 50–57 years at the first wave.

Data for Sweden came from five waves of the Swedish Longitudinal Occupational Survey of Health (SLOSH).29 The first wave of SLOSH in 2006 was a postal questionnaire follow-up of all respondents to the 2003 Swedish Work Environment Survey (SWES), a cross-sectional, biennial survey of a random stratified sample of those gainfully employed people aged 16–64 years. At wave 2 in 2008, the sample was increased by adding the respondents from the 2005 SWES yielding an overall sample of n=18 915 women and men originally representative of the working population in Sweden in 2003 and 2005. These people were then re-surveyed in 2010, 2012 and 2014. In total, 77% responded at least once. The analytic sample in the present study comprised the 8048 participants who had responded to at least one SLOSH wave and who were aged 50–75 years at the first wave for which valid data on BMI was recorded.

In all cohorts, participants gave their informed consent to take part. Ethical approval was given in each of the countries from relevant ethical committees/boards.

Measurement of BMI

BMI was calculated using self-reported body weight and height in FPS, GAZEL and SLOSH. In ELSA, body weight and height were measured by a study nurse in the participants’ homes. Obesity was categorized according to World Health Organization cutoff criteria as (1) underweight (BMI <18.5 kg m−2), (2) normal weight (18.5–24.9 kg m−2), (3) overweight (25–29.9 kg m−2), (4) obese class I (30–34.9 kg m−2) and (5) obese class II (⩾35 kg m−2).30 Because the proportion of underweight men and women in each cohort was less than 1% with the exception of GAZEL women (3%), the underweight people were excluded from the analyses.

Outcome measures

In each study cohort, we defined two health expectancy outcomes: (1) healthy LE using self-rated health and (2) chronic disease-free LE based on the occurrence of chronic diseases. In addition, we took mortality into account.

Self-rated health

All participants were asked about their health status at each wave. Responses were categorized into good and sub-optimal health. In ELSA, FPS and SLOSH participants were asked to rate their general health on a five-point Likert scale, which was dichotomized by categorizing response scores 1–2 as good health and scores 3–5 as sub-optimal health. GAZEL used an 8-point Likert scale (1=very good, 8=very poor), which was dichotomized by categorizing response scores 1–4 as good health and scores 5–8 as sub-optimal health, as previously validated.31 Health expectancy based on self-rated health is labeled hereafter as healthy LE.

Chronic diseases

Presence of the following chronic diseases was ascertained in each study by asking ‘has a doctor ever told you that you have …’:1 heart disease (heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems),2 stroke (stroke or transient ischemic attack),3 chronic lung disease (chronic bronchitis or emphysema or asthma),4 cancer (cancer or a malignant tumor of any kind except skin cancer) and5 diabetes (diabetes or high blood sugar). Individuals were defined as having a chronic disease if they reported one or more of these conditions. The presence of chronic diseases at baseline (first observation included in analysis) included any chronic diseases reported before the age of 50 from available information on respondents. Health expectancy based on chronic diseases is hereafter labeled as chronic disease-free LE.

Mortality was ascertained from linked register data for each study cohort with follow-up censored on 31 December of the year in which data collection last took place for each study cohort.

Statistical analyses

Characteristics of the participating cohorts are presented at the first observation point, which refers to the date each participant was included in the dataset.

We applied multistate models to longitudinal data to obtain transition probabilities between health states. Discrete-time multistate life table models were used to estimate partial LE and healthy LE and chronic disease-free LE between the ages of 50 and 75 years (in total 26 years). For both outcome measures, three health states were defined: healthy, unhealthy and dead. For healthy LE, there were four possible transitions between the health states, namely: healthy to sub-optimal health (onset), sub-optimal health to healthy (recovery), healthy to dead, sub-optimal health to dead. For chronic disease-free LE, there were only three possible transitions as, by definition, recovery was not possible.

For each study cohort, age-specific transition probabilities by sex and BMI were estimated from multinomial logistic models with age (in years), sex and socioeconomic position as covariates. Partial LE, healthy LE and chronic disease-free LE from ages 50–75 years were then calculated based on these estimated transition probabilities using a stochastic (microsimulation) approach.32 For each study, individual trajectories for a simulated cohort of 100 000 persons were generated with distributions of covariates at the starting point based on the observed study-specific prevalence by five year age group, sex, socioeconomic position and BMI. Partial LE, healthy LE and chronic disease-free LE from age 50 to 75 were then calculated as the average from these trajectories for BMI and sex. Computation of 95% confidence intervals (CI) (from 2.5th and 97.5th percentiles) for these multistate life table estimates was performed using a bootstrap method with 500 replicates for the whole analysis process (multinomial analysis and simulation steps). As BMI-related transitions to poor health and death may differ by sex, we repeated analyses including interactions between sex and BMI in the multinomial logistic models. Finally, since tobacco smoking is associated with increased risk of mortality and morbidity33, 34 and it is also typically associated with lower weight,35 we conducted sensitivity analyses among never smokers using a bootstrap method with 50 replicates.

All analyses were conducted in SAS 9.2 using the SPACE (Stochastic Population Analysis of Complex Events) program.36, 37 This program uses the stochastic (that is, microsimulation) approach to estimate the healthy LE as opposed to another well-known program, IMaCh (Interpolation of Markov Chains) which uses a deterministic approach.38