Introduction

The prevalence of obesity has risen to pandemic proportions during the last decades, reaching an estimated global prevalence of around 14% in men and 18% in women in 20201 (projected from NCD-RisC),2 generally with a higher prevalence in high-income countries but with an accelerating prevalence in low-income and middle-income countries.2 The negative impact of obesity on public health is large, for example, with disability-adjusted life years, that is, years lost in full health, due to obesity projected to increase by 40% from 2020 to 2030.3 The large negative public health impact of obesity is both attributed to its high prevalence and to the established association of obesity with many common diseases, including cardiovascular diseases (CVD)4 and several cancers,5 as well as with all-cause mortality and cause-specific death due to CVD and cancer.6 Less is known about the association of obesity with rarer diseases, which, in a prospective cohort setting, requires a larger population size and/or a longer follow-up for investigation. Furthermore, body size including obesity has mostly been assessed using a one-time measure of body mass index (BMI, kg/m2) in relation to disease risk. Studies based on measures of central obesity, such as waist circumference (WC), and of changes in body size across life on the risk of morbidity and mortality have been much fewer and smaller.5 7 For example, to our knowledge, the largest study to date on BMI and mortality had 367 512 deaths during follow-up,6 whereas recent meta-analyses of studies on weight changes in middle age8 and older age9 included 34 038 and 69 255 deaths, respectively, for all studies combined. Moreover, most studies of weight changes on outcomes were based on only two weight assessments prone to measurement error. To explore whether particular ages of weight gain rather than cumulative life-time exposure to obesity poses a particularly high risk, different ages of weight assessments are needed in the studied population to enable prediction of age-specific individual weight changes.10 Such sensitive ages to obesity have been shown important, for example, for the development of hypertension and breast cancer.11 Last, weight fluctuations, that is, large variation in weight, are commonly believed to, but not concluded to, result in negative physiological changes, such as less muscle mass and a lower metabolic rate.12 The association of weight fluctuations on disease risk and mortality has been studied to some extent, but large studies with many repeated weight assessments are limited.13

The long history, high coverage and good quality of many Swedish nationwide registers, together with the unique personal identity number of inhabitants of Sweden enabling individual linkage to national healthcare registers,14 is a major advantage for epidemiological research in the country.15 For example, the Swedish Cause of Death Register16 and the Cancer Register17 18 are based on mandatory reporting of deaths and cancer diagnoses of the population since 1952 and 1958, respectively, enabling long and close to complete tracking of individuals for epidemiological studies. The personal identity number further facilitates cross-identification of individuals between different cohorts in pooled studies, which avoids duplicate contribution of an individual included in more than one cohort. It also enables the use of repeated individual assessments between cohorts to enlarge the sample size for the investigation of factors such as longitudinal changes of anthropometrics.

The pooled Obesity and Disease Development Sweden (ODDS) study was created to form a large population to investigate the association of anthropometric measures (BMI, WC and changes in weight and WC) with the risk of morbidity and mortality. The large sample size and long follow-up ensure high statistical power in investigations. Here, we describe the ODDS study population and provide an overview of the information retrieved from national registers linked to individuals in ODDS.

Cohort descriptionStudy population

Swedish cohorts for research and national registers with individual-level information on weight and height were identified for inclusion in ODDS. The included registers with weight and height information are hereafter denoted ‘cohorts’ when referring to the study population. We included individuals with a weight assessment at a minimum age of 17 years, which was the lower age limit of the largest male cohort (military conscripts). We further required a recorded date for the assessment (year as a minimum, with any missing month and/or day replaced with June and/or day 15), and a valid personal identification number in the Swedish Total Population Register. Information on date, weight, and if available, height, WC and smoking habits were included from each assessment. The availability of height was not used as an inclusion criterion for each individual assessment as it could often be derived from repeated assessments, and because other factors, such as sociodemographic factors, were regarded to be equally important as height in studies of changes in weight and WC. Hence, completeness of these factors as potential inclusion criteria was left for later decision according to relevance for individual studies.

The characteristics of the included cohorts forming the ODDS Study population of 4 295 859 unique individuals with 7 733 901 weight assessments are described in table 1. Each cohort is presented separately in the table so individuals participating in more than one cohort are counted in each of these cohorts. Weight in the cohorts was assessed between 1963 and 2020, at an age of 17–103 years. The two largest cohorts are the Swedish Military Conscription Register and the Medical Birth Register, both nationwide and further described below. Other cohorts have local, regional or nationwide coverage of included individuals (table 1). Some of them were designed for research purposes and others were designed as general or disease-specific health check-ups, from which the information was later made available for research.19–42

Table 1

Characteristics of cohorts of the Obesity and Disease Development Sweden (ODDS) Study*

Weight assessments, completeness and population coverage of the two largest cohorts

Data from military conscripts make up the majority of the male population (1 771 429 men with 1 779 681 weight assessments) and the Medical Birth Register makes up the majority of the female population (1 855 606 women with 3 208 127 weight assessments). In Sweden, conscription, that is, enrolment for military service, was mandatory for men at around 18 years of age until 30 June 2010, and was thereafter voluntary until 2017.20 For young women, conscription has been voluntary since the 1980s. The coverage in the Military Conscription Register of the Swedish male birth cohorts of 1951–1988 (corresponding to conscription between 1969 and 2006) is 90%. Weight is missing for at most 5% of conscript examinations before year 2000. No information on WC is available in the Military Conscription Register.

The Medical Birth Register includes information on around 98% of all births in Sweden. Since 1982, information is recorded at the first antenatal visit of pregnancy, generally taking place between 8 and 10 weeks of gestation.19 A large population-based study in Sweden showed that weight gain in pregnancy was minimal during the first 15 weeks of gestation, for example, with a median weight gain in normal weight women of 0.7 kg between gestational week 6 and 10.43 Weight of pregnant women were recorded in the Medical Birth Register in 1982–1989 and from 1992 onwards and has a coverage of more than 85% since 1992. In 1982–1989, early pregnancy weight was estimated by subtracting pregnancy weight gain from weight at delivery, and since 1992, weight was measured at the first antenatal visit. Due to this change in weight recording, weight in 1982–1989 was much lower than expected compared with measurements from 1992 onwards, as also observed in other studies.44 45 Therefore, we corrected weight assessments in 1982–1989 in the Medical Birth Register using a linear regression model of weight and calendar year from 1992 onwards. For illustration purposes, we let j represent the calendar years 1982–1989 for an individual i. We first regressed weight (Wi) on the year (Yi) at baseline examination (ie, first antenatal care visit) to obtain the average weight change over time (constant, b0) and slope (b1). Using these coefficients, we predicted the average weight for each calendar year j (1982–1989) as Wj=b0 + b1 × Yj. We then obtained the difference of the observed average weight from the predicted average weight between 1982–1989, then added this factor to the original weight value and obtained a more linear weight change, as also reported elsewhere.44 These corrected weights were used in the pooled analysis presented here and will be used in original ODDS studies. Additionally, pregnancy will be accounted for in all pooled analyses by inclusion of a binary variable (Medical Birth Register, yes/no) in the statistical regression or other models. No information on WC is available in the Medical Birth Register.

Weight and WC assessments in other cohorts

Other cohorts than the two largest include 972 974 individuals with 2 225 946 objectively measured or self-reported current weight assessments, of which objectively measured or self-reported WC is available in 364 747 individuals with 512 273 assessments. Self-reported current weight has shown very high validity in western populations with a correlation coefficient with objectively measured weight of over 0.95.46 47 High validity has also been shown for self-reported WC in western populations, showing correlation coefficients with objectively measured WC of over 0.85 when instructions were provided on how to measure WC,46 47 and only slightly lower when no instructions were provided.48 In ODDS, the SIMPLER cohorts and the National March Cohort collected self-reported WC information from questionnaires without further instructions, and in Women’s Lifestyle and Health, a picture indicated where to place the tape measure. WC in the other cohorts were objectively measured. In several cohorts, information on recalled weight was collected simultaneously with that of current weight (see footnote of table 1). A total of 225 331 individuals reported 500 219 recalled weights, mostly for age 20 years (139 936 (28%) of assessments), but also for 18, 25, 30, 40, 50, 60, 70 and 80 years of age, from the year 1911 or later. These assessments will primarily be used in ODDS studies of weight changes, and a sensitivity analysis excluding these measures will always be performed. In the pooled ODDS study population, recalled weight is counted as a distinct weight assessment with the date set to that of the age for the recalled weight.

Smoking information

The questions asked regarding smoking habits and the completeness of smoking information varies considerably between ODDS cohorts. To increase the completeness of smoking status in individuals with repeated weight assessments with missing and non-missing smoking information, we performed single imputation by carrying information forwards or backwards within and between cohorts, prioritising previous records over later records for never/former/current smoking status, but not for current smoking (yes/no). After imputation, never/former/current smoking information is available in 2 856 160 (37%) assessments, and current smoking (yes/no) information in 5 061 903 (65%) assessments. In total 828 763 individuals (19%) have at least one weight assessment with never/former/current smoking information available, and the proportion is larger for individuals with WC assessments (330 775 individuals, 91%). To increase the coverage of smoking information, multiple imputation49 based on all available data may be considered in single ODDS studies. Furthermore, studies dependent on the coverage of registers with a later start, such as the Patient Register (for information on CVD and Charlson comorbidity index50) or the Swedish Social Insurance Agency for studies on sick leave, will include only later years’ measurements of weight, which have a markedly higher coverage of smoking information.

Register linkages

By use of the unique personal identity number assigned to each resident of Sweden, individuals in ODDS were linked to a range of Swedish national registers from inception to the last possible follow-up date, varying between 2019 and 2021 depending on the specific register. Birth date was retrieved from the Total Population Register; however, due to Swedish legislation protecting individual integrity, date of birth was obtained only for the year and quarter of the year, so we imputed the missing date by the mean value of possible dates, for example, 15 February for the first quarter of the year.

Table 2 gives an overview of the registers and the type of information retrieved.16–18 ,51–60 This includes information on outcomes: all-cause and cause-specific mortality by use of the Cause of Death Register; specific cancers in the Cancer Register; prostate cancer in detailed diagnostic risk categories by use of information from the National Prostate Cancer Register; CVD, hypertension and type 2 diabetes by use of the Patient Register and National Diabetes Register; and sick leave by use of information from the Swedish Social Insurance Agency. Sociodemographic information of relevance for all ODDS studies, such as education, income, marital status and birth country, was retrieved from several national registers.

Table 2

Information on Swedish national registers with linkage to the Obesity and Disease Development Sweden (ODDS) study population

Patient and public involvement

Study participants and/or the public were not involved in the planning, design or conduct of the study.

Findings to dateCharacteristics of all weight and WC assessments

The characteristics of all 7 733 901 weight assessments in the 4 295 859 individuals in ODDS is shown in table 3 and the cumulative number of ODDS participants by year of assessment of the first recorded current weight and WC, respectively, is shown in figure 1. Most weight assessments were objectively measured (85%), and 4 066 876 individuals (95%) have information from at least one objectively measured weight assessment. The median (IQR) year and age of weight assessment is lower in men than in women; year 1985 (1977–1994) vs 2001 (1991–2010) and 19 (18–40) vs 30 (26–36) years of age. Normal weight (BMI 18.5–24.9 kg/m2) is present in 67% of assessments in men and 64% in women, and obesity (BMI≥30 kg/m2) in 5% of assessments in men and 10% in women. The low proportion of obesity in men is likely due to the, on average, young age and early calendar year at weight assessment. Information on WC is available in 152 089 men with 209473 WC assessments and in 212 658 women with 302 800 WC assessments; 52% of these were objectively measured and 63% of individuals have at least one objectively measured WC assessment. The median (IQR) year of the 512 273 WC assessments is later than that of weight, year 2007 (1997–2012) for men and 2003 (1997–2010) for women and the median age is higher, 57 (49–66) years in men and 51 (43–62) years in women.61 62

Figure 1

Cumulative number (n) of men (solid line) and women (dotted line) per calendar year, at the first objectively measured or self-reported current weight (1963–2020) or waist circumference (1981–2020), in the Obesity and Diseases Development Sweden (ODDS) study.

Table 3

Characteristics of all assessments in the Obesity and Disease Development Sweden (ODDS) Study, in a total of 4 295 859 unique individuals and separately in 2 165 048 men and 2 130 811 women

Characteristics of repeated weight and WC assessments

Individuals with two or more assessments of weight or WC, respectively, will be eligible for inclusion in studies of change of these factors, on outcomes. In these studies, we will use all assessments in an individual, that is, allowing assessments to take place in different cohorts. For each assessment, age, form of assessment (objectively measured/self-reported current/self-reported recalled) and, in women, a pregnancy indicator (Medical Birth Register, yes/no) will be included as predictor variables to model weight and WC trajectories in mixed effects models.10

Out of 477 705 men and 1 263 056 women with 2 or more weight assessments, totalling 1 843 609 assessments in men and 3 335 194 in women, the median number of assessments is 3 (IQR 2–5) in men and 2 (IQR 2–3) in women. The much smaller number of assessments in men than in women is due to the lack of repeated weight assessments in the Military Conscription Register as opposed to the many assessments in the Medical Birth Register. In individuals with two or more weight assessments, the by far largest number of assessments in men originate from the Construction Workers Cohort (n=1 036 968) followed by the Cohort of Swedish Men (n=268 408) and the Military Conscription Register (n=216 585), and the by far most assessments in women are from the Medical Birth Register (n=2 435 226) followed by the Swedish Mammography Cohort (n=273 717) and Women’s Lifestyle and Health (n=127 384). Out of 41 669 men and 67 126 women with 2 or more WC assessments, totalling 99 053 assessments in men and 157 268 in women, the median number of assessments is 2 (IQR 2–3) in men and 2 (IQR 2–2) in women. Of these, the largest number of WC assessments in men originate from the Cohort of Swedish Men (n=59 863) and the Västerbotten Intervention Programme (n=19 549) and in women from the Swedish Mammography Cohort (n=56 222) and Women’s Lifestyle and Health (n=51 696).

Follow-up time and events

The follow-up time from the first recorded current weight until emigration, death or end of follow-up for death (31 December 2020) is up to 58 years, with a median (IQR) of 31.4 (21.8–40.8) years in men and 19.6 (9.3–29.0) years in women, and a median (IQR) from the first WC assessment of 13.4 (8.0–23.2) years in men and 17.1 (9.6–25.0) years in women. The total time at risk for mortality is 66.3 million person-years for men and 42.0 million person-years for women, during which 280 537 men and 121 635 women died. Among morbidity outcomes, cancer is of main focus in the ODDS working group. Information on the number of deaths (all-cause and cause-specific) and incident cancers (all and some site-specific) during follow-up in the full ODDS population, in individuals with two or more weight assessments for studies of weight change, and in individuals with one and two or more WC assessments, respectively, is shown in table 4.

Table 4

Number of individuals, deaths and incident cancers during follow-up according to anthropometric measure in the Obesity and Disease Development Sweden (ODDS) study

Ongoing and planned studies

Several ODDS studies are ongoing or planned to start shortly on BMI, WC and changes in weight and WC, in relation to the risk of outcomes (currently mortality and cancer). Changes in weight and WC include the investigation of changes across adulthood and in age spans of young, middle and late adulthood. We further plan to investigate: (1) weight fluctuations in individuals with many repeated weight assessments in relation to outcomes, (2) anthropometric measures in interaction with, and mediated through, cardiometabolic diseases (CVD, hypertension and type 2 diabetes) on non-CVD outcomes, (3) anthropometric measures jointly with prescribed drugs (using retrieved data and after additional register linkages) on outcomes and (4) anthropometric measures in relation to specific cancers with detailed diagnostic information, after additional linkages to national quality registers of cancer. ODDS enables a range of further possible investigations to be done in the obesity field, based on already collected data or after additional register linkages. Further information about ODDS can be found at the https://odds.blogg.lu.se webpage.

Strengths and limitations

The ODDS study has several strengths. First, the population is very large and has a nationwide coverage of Swedish cohorts, which, although not completely population-based, generally have a high representativeness of their background population. Furthermore, the unique personal identity number of Swedish inhabitants enables cross identification of individuals between ODDS cohorts as well as tracking of individuals in Swedish nationwide registers. Loss to follow-up is negligible in those registers, and the objective, harmonised information on outcomes as well as on sociodemographic factors by use of national registers, is an additional major strength. The combination of anthropometric measures in the pooled population with, for example, cardiometabolic diseases and medications retrieved from national registers, enables the investigation of various risk factors jointly with anthropometrics. At last, the long follow-up time together with the large sample size has accumulated many events, resulting in overall high statistical power and the possibility to investigate subgroups and rarer outcomes.

The study also has some limitations. Because repeated weight measurements were not performed in military conscripts, which make up the largest male cohort in ODDS, only 22% of men have repeated measurements on weight. By contrast, almost 60% of women have two or more weight measurements. Furthermore, the large heterogeneity of the study population in terms of geographical region, age and year of weight assessment, enforces subgroup investigation, to examine whether associations differ between subgroups. The study is further limited in that information on smoking habits, an important confounder for mortality and many diseases, is missing in a large part of the population and, apart from sociodemographic information, the ODDS database has no information on potential confounders such as diet, physical activity and other disease-specific confounders.